ArticlePDF Available

Preoperative Risk Stratification: Identifying Modifiable Risks for Optimization


Abstract and Figures

Purpose of Review This chapter aims to examine current strategies for risk stratification in the preoperative setting. Risk stratification tools may include commercially available calculators, laboratory assessments, or screening tests. Risk stratification informs the surgical team on the need for preoperative optimization of modifiable risks. Optimization aims to improve clinical, hospital, and patient-centered outcomes across all phases of perioperative care. Preoperative optimization should be a collaborative, multidisciplinary effort that balances the patient’s needs and values with the timing and urgency of surgery. Recent Findings Preoperative patient optimization is feasible, improves patient satisfaction, reduces clinical complications, and lowers hospital costs. Summary Preoperative physicians are encouraged to use risk stratification tools to identify patients with modifiable risks and implement optimization plans to mitigate postoperative complications.
This content is subject to copyright. Terms and conditions apply.
1 3
Current Anesthesiology Reports
Preoperative Risk Stratification: Identifying Modifiable Risks
MatthewSherrer1· JereyW.Simmons1 · JereyB.Dobyns1
Accepted: 4 February 2022
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022
Purpose of Review This chapter aims to examine current strategies for risk stratification in the preoperative setting. Risk
stratification tools may include commercially available calculators, laboratory assessments, or screening tests. Risk stratifica-
tion informs the surgical team on the need for preoperative optimization of modifiable risks. Optimization aims to improve
clinical, hospital, and patient-centered outcomes across all phases of perioperative care. Preoperative optimization should be
a collaborative, multidisciplinary effort that balances the patient’s needs and values with the timing and urgency of surgery.
Recent Findings Preoperative patient optimization is feasible, improves patient satisfaction, reduces clinical complications,
and lowers hospital costs.
Summary Preoperative physicians are encouraged to use risk stratification tools to identify patients with modifiable risks
and implement optimization plans to mitigate postoperative complications.
Keywords Modifiable risk· Optimization· Risk calculation· Perioperative· Risk stratification
Approximately 27 million patients undergo noncardiac sur-
gery yearly in the USA. Of these patients, more than 50,000
will sustain a postoperative myocardial infarction, and over
20,000 of these patients die from cardiovascular causes in
the perioperative period [1]. Poor surgical outcomes are mul-
tifactorial, with medical issues rather than surgical being the
most common cause of complications following anesthesia
and surgery. Following the publication of To Err is Human
and Crossing the Quality Chasm in 1999 and 2001, there has
been a tremendous push to improve the quality of healthcare
delivered in the USA. In response to these reports, the Insti-
tute for Healthcare Improvement launched the Triple Aim
Initiative, a drive designed to improve patients’ healthcare
experience, including quality, safety, and satisfaction, while
improving the health of the patient population and reduc-
ing the per capita cost of healthcare [2]. Preoperative risk
This article is part of the Topical Collection on Prehabilitation
Search Strategy
The authors of this chapter currently lead the preoperative
optimization effort at the University of Alabama at Birmingham
(UAB Medicine). The Preoperative Assessment, Consultation,
and Treatment Clinic (PACT) focuses on identifying modifiable
conditions in surgical patients, and we have developed
optimization strategies for these. Modifiable conditions include
smoking cessation, hyperglycemia, hypertension, malnutrition,
poor dentition, elder care and frailty, obstructive sleep apnea,
anemia, opioid use, and cardiac risk assessment. The authors
examined current topics discussed at the American Society
of Enhanced Recovery (ASER) and Society for Perioperative
Assessment and Quality Improvement (SPAQI) conferences for
inclusion in this chapter. Both societies focus on perioperative
optimization for modifiable risks and improvement of patient
outcomes. The most useful risk stratification tools were selected
based on the authors’ experience and examination of “hot topics”
at recent annual meetings.
* Jeffrey B. Dobyns
Matthew Sherrer
Jeffrey W. Simmons
1 Department ofAnesthesiology andPerioperative Medicine,
University ofAlabama atBirmingham (UAB Medicine), JT
845, 619 South 19th Street, AL35249-6810Birmingham,
Current Anesthesiology Reports
1 3
stratification identifies patients at risk for adverse outcomes
following surgery. The risk stratification results drive pre-
habilitation programs, such as nutritional supplementation,
smoking cessation, and anemia correction, with the intent
of reducing morbidity and mortality, improving postopera-
tive outcomes, and longitudinal population health (Table1).
What Are Preoperative Modifiable Risks andHow Do
They Impact Postoperative Outcomes?
Modifiable risks are patient comorbidities with specific
therapies available to them such that the disease can be opti-
mized in the preoperative setting. The time from the first
surgical clinic encounter until the day of surgery defines
the preoperative setting. Perioperative physicians should
consider optimization for all surgical patients, but the high-
est value may be seen in certain high-risk surgeries (total
joint replacements or abdominal surgery or major cancer
surgery) and frail patients. Optimization aims to improve
patient outcomes throughout all phases of care (preoperative
to discharge after surgery) and to improve all aspects of care
(clinical, hospital, and patient-centered) (Table2).
Identifying Patients atHigh Risk ofCardiac
Complications Before Surgery
The preoperative identification of patients at increased risk
of adverse surgical outcomes guides the process of shared
decision-making between patients, caregivers, and pro-
viders. Additionally, preoperative risk stratification iden-
tifies the need for specialty referral, ordering additional
diagnostic testing, and initiating prehabilitative efforts to
reduce perioperative risk. In the setting of the very-high
risk patient, preoperative risk stratification sometimes
prompts delay of the surgical procedure for optimization
or cancellation of the surgical procedure in favor of less
invasive treatment options.
There are numerous preoperative cardiac risk stratifica-
tion models available for use. The most used risk stratifica-
tion models intend to predict the incidence of perioperative
major adverse cardiac events (MACEs), defined as a post-
operative myocardial ischemic event such as myocardial
infarction, heart failure, stroke, or sudden death. MACE is
relatively common following noncardiac surgery, with an
Table 1 Risk calculators discussed in this chapter
Cardiac • Metabolic equivalent (METS) assessment
• Duke Activity Status Index (DASI)
• N-terminal pro-B-type natriuretic peptide (NT pro-BNP) biomarker
• Revised Cardiac Risk Index (RCRI)
• Gupta risk score for perioperative major adverse cardiac events (MACEs)-myo-
cardial infarction or cardiac arrest (MICA)
• American College of Surgeons National Surgical Quality Improvement Program
• Cardiovascular risk index (CVRI)
• ACS NSQIP Surgical Risk Calculator (SRC)
Global patient indexes • American Society of Anesthesiologists (ASA) Physical Status Score
• ACS NSQIP Surgical Risk Calculator (SRC)
Social determinants of health No specific calculator. Based on individual factors
Anemia No specific calculator. Based on laboratory assessment
Pulmonary • Assess Respiratory Risk in Surgical Patients in Catalonia (ARISCAT) risk score
• Gupta Postoperative Pneumonia Risk
• Arozullah Respiratory Failure Index
• STOP-BANG score for obstructive sleep apnea
Nutrition • Nutritional Risk Index (NRI)
• Nestle Mini Nutritional Assessment-Short Form (MNA)
• Malnutrition Universal Screening Tool (MUST)
• Perioperative Nutrition Screen (PONS)
• Sarcopenia Ultrasound Imaging
Cognition • Six-item Screener (SIS)
• MINI-COG cognitive assessment
Frailty • Edmonton Frail Scale
• Clinical Frailty Scale (CFS)
Postoperative and post discharge nausea and vomiting • Apfel Score
• ASA Risk Factor Identification
Opioid abuse Opioid Risk Tool (ORT)
Risk calculator commercial applications MDCalc: www. mdcalc. com
QxMD: www. qxmd. com
Current Anesthesiology Reports
1 3
incidence of postoperative myocardial infarction of 0.9%
[3]. Perioperative MACE results in increased hospitaliza-
tion, ICU admission, and mortality.
A larger percentage of surgical patients experience a
myocardial injury after noncardiac surgery (MINS). MINS
is characterized by a perioperative increase in serum cardiac
troponin concentration without other diagnostic criteria for
myocardial infarction. MINS occurs during the hospital stay
or within 30days of noncardiac surgery and is associated
with significant mortality. Risk stratification models are
not predictive for MINS, but a 2014 study by the VISION
writing group identified several risk factors independently
predictive of MINS [4]. Despite the appreciation that MINS
contributes significantly to mortality following noncardiac
surgery, there is no consensus on treatment or prevention
strategies (Table3).
Assessment ofFunctional Capacity
Clarifying a patient’s functional capacity is inherent to pre-
operative risk assessment and is a primary determinant of the
need for further cardiovascular testing. Patients who achieve
four metabolic equivalents (METS) or higher functional
capacity without developing cardiopulmonary symptoms
are deemed low risk and may proceed to surgery without
additional cardiac testing. The majority of METS assess-
ment is based on subjective patient responses when asked
about the ability to walk a given distance on level ground or
climb two flights of stairs without becoming short of breath.
Wijeysundera and colleagues compared a patient-reported
subjective METS assessment with more objective meas-
ures, such as cardiopulmonary exercise testing (CPET), the
Duke Activity Status Index (DASI) functional assessment,
and biomarkers such as serum concentrations of N-terminal
pro-B-type natriuretic peptide (NT pro-BNP) for the ability
to predict death or major cardiac complications following
noncardiac surgery. Subjective functional capacity assess-
ment is only 19.2% sensitive and 94.7% specific for predict-
ing a patient’s ability to obtain 4 METS on CPET testing
[5••]. Only objective METS evaluation by DASI accurately
predicted death or myocardial infarction within 30days
of surgery, suggesting that perioperative clinicians should
abandon subjective assessment of functional capacity.
Models ofPreoperative Risk Stratification
andClinical Risk Indices
The American College of Cardiology/American Heart Asso-
ciation (ACC/AHA) guidelines on perioperative evaluation
for noncardiac surgery categorizes cardiac risk factors as
active (previously termed major risk factors) or clinical (pre-
viously termed intermediate-risk factors) risk factors [6].
The stepwise approach to preoperative cardiac risk strati-
fication considers active and clinical risk factors with pro-
cedural risk and patient functional capacity. Patients with
active cardiac conditions or vascular surgery patients with
three or more clinical risk factors and low functional capac-
ity warrant further preoperative cardiac evaluation (Table4).
There are numerous preoperative cardiac risk stratifica-
tion models available for use. Three commonly used models
include the Revised Cardiac Risk Index (RCRI), the Ameri-
can College of Surgeons National Surgical Quality Improve-
ment Program (ACS NSQIP) database myocardial infarc-
tion/cardiac arrest (MICA) risk model, and the American
College of Surgeons Surgical Risk Calculator (ACS SRC).
The preoperative cardiovascular risk index (CVRI) is a
newer model that estimates 30-day all-cause mortality risk.
Validation of the CVRI against the NSQIP database demon-
strates greater discriminatory power over the RCRI, which
includes only inpatient complications, but needs further
Table 2 Preoperative optimization to improve aspects of care
Clinical outcomes Hospital outcomes Patient-centered outcomes
Reduction in
• Stroke risk
• Major adverse cardiac events (MACEs)
• Pulmonary complications
• Renal failure
• Surgical site infections (SSI)
• Venous thromboembolic disease (VTE)
• Transfusion-related complications
Reduction in
• Same-day surgery delays
• Surgery cancellations
• Blood transfusions
• ICU admissions
• Hospital length of stay
• Discharge to nursing home
• Readmissions
• Overall costs
Improvement in
• Energy levels for rehabilitation
• Vitality
• Mental health
• Satisfaction of care
• Return to work
Table 3 Independent preoperative predictors of MINS
Age ≥ 75years Female Current atrial
History of coronary
artery disease
History of hypertension History of
heart failure
History of diabetes Peripheral vascular disease Stroke
Glomerular filtration
rate (GFR) < 60mL/
Urgent/emergent surgery
Current Anesthesiology Reports
1 3
validation against specific patient subpopulations [7]. A
benefit of the CVRI is that it identifies several preopera-
tive patient risk factors that are amenable to preoperative
intervention. Other modifiable factors not included in these
models that contribute to adverse outcomes are anemia, mal-
nutrition, chronic pain, obstructive sleep apnea, and obesity.
Certain cardiac conditions amenable to optimization also
increase perioperative risks, such as atrial fibrillation and
pulmonary hypertension.
Practical Considerations andLimitations oftheRCRI
andtheACS MICA Risk Indices
The RCRI, introduced in 1999 by Lee etal. [8] and based on
the original Goldman Cardiac Risk Index, is still a widely
used risk stratification tool (Table5). Compared with the
Goldman Cardiac Risk Index, the RCRI is more accurate and
easier to use. However, the RCRI is based on a single institu-
tion’s database and is not accurately predictive in patients
with long-standing vascular disease or those scheduled for
low-risk surgery. For patients having more extensive surgery,
the RCRI discriminates moderately well between observed
and expected MACE risk. The RCRI is a useful tool for
screening for additional testing [9].
The ACS MICA has a potential bias since only hospitals
participating in the ACS NSQIP are included in the data-
base. However, this is offset by the database exceeding one
million patients. While the RCRI is validated in several stud-
ies, the ACS MICA is only retrospectively validated [10••].
Consequently, it corresponds very well with ACS SRC, the
database from which it is derived. Compared to the RCRI,
the MICA more accurately predicts MACE risk in low-risk
surgical procedures, and those patients expected to have less
than 2-day hospital admission. It underestimates MACE in
high-risk patients. Additionally, the MICA provides excel-
lent risk prediction for perioperative stroke in patients
undergoing noncardiac surgery, although its discriminatory
power is reduced in patients undergoing vascular surgery
[11]. Incorporation of biomarkers into the MICA and RCRI
will enhance the predictive capabilities of both (Table5).
A notable limitation of risk stratification tools is the
exclusion of low-prevalence, high-risk conditions that
contribute significantly to adverse perioperative out-
comes, such as decompensated heart failure, severe pul-
monary hypertension, and severe valvular heart disease.
Table 4 Active cardiac conditions
Unstable coronary syndromes • Unstable or severe angina
• Myocardial infarction (MI) within previous 30days
Decompensated heart failure • New York Heart Association (NYHA) functional classes III–IV
• Worsening or new-onset heart failure
Significant arrhythmias • High-grade atrioventricular (AV) conduction block
• Symptomatic ventricular arrhythmias
• Supraventricular arrhythmias with uncontrolled ventricular rate (> 100bpm at rest)
• Symptomatic bradycardia
Severe valvular disease • Severe aortic stenosis (mean gradient > 40mmHg, valve area < 1 cm2, or symptomatic)
• Symptomatic mitral stenosis
Table 5 The Revised Cardiac Risk Index (RCRI): independent predictors of perioperative MACE [8]
High-risk surgery (including major vascular, open intra-abdominal, intrathoracic)
History of ischemic heart disease (history of MI, positive stress test, active cardiac conditions, use of nitrate therapy, pathologic Q-waves on
EKG). Note: this does not include prior PCI unless other criteria are met
History of heart failure
History of cerebrovascular disease
Diabetes mellitus requiring insulin management
Serum creatinine > 2.0mg/dL
Rate of pulmonary edema, ventricular fibrillation, primary cardiac
arrest, and complete heart block [8]Rate of cardiac death, non-fatal myocardial infarction, and non-
fatal cardiac arrest by number of predictors [12]
No. predictors Rate No. predictors Rate
0 0.5% 0 0.4%
1 1.3% 1 1%
2 3.6% 2 2.4%
3 or more 9.1% 3 or more 5.4%
Current Anesthesiology Reports
1 3
The ACS MICA estimation does not include congestive
heart failure or coronary artery disease history despite
numerous studies establishing the predictive risk of both
conditions. The exclusion of coronary artery disease from
the ACS MICA and the ACS SRC likely reflects a dif-
ference in coronary artery disease definition, where both
define myocardial infarction as troponin concentrations 3
times the upper limit of normal [13]. The differences in
input variables, definitions of MACE, and the validation
population create variability in calculated risk estimations.
Further increasing variation is the multiple versions of the
RCRI, which undergoes periodic revision and updates as
diagnostic modalities, medical therapy, and surgical and
anesthetic techniques evolve. These revisions reflecting
current management and diagnostic criteria render the
older calculators such as the Detsky modified risk index,
the Eagle criteria, and the original Goldman Cardiac Risk
Index obsolete.
Noncardiac Determinants ofPerioperative
While most preoperative risk models focus on risk assess-
ment for MACE, other medical and non-medical factors
contribute significantly to poor surgical outcomes.
ASA Physical Status Score
The ASA-PS score is a subjective preoperative assessment
of patient health. Several studies demonstrate an asso-
ciation with perioperative risk. Its main limitation is the
inherent inter-rater variability in its application between
the preoperative clinic and operating rooms where the
assigned ASA-PS scores differed significantly [14]. Simi-
larly, it lacks objective assessment of functional status of
the patient. While the ASA-PS score is an integral part
of the ACS MICA and the ACS SRC, its use introduces
an element of subjectivity and may over- or underesti-
mate the risk depending on the provider. The assignment
of inappropriate ASA-PS scores has significant impact
on pre-surgical patients and their caregivers. Since the
ASA-PS score drives the decision to pursue additional
preoperative cardiac and laboratory testing, misclassifi-
cation of patients results in unnecessary surgical delays
and cancellations and unnecessarily increased healthcare
expenditures. In addition to inter-rater variability (Fig.1),
the ASA-PS score omits the systemic effect of acute sur-
gical pathology, a consideration included in many surgical
risk calculators.
Socioeconomic Determinants ofHealthcare
Socioeconomic status directly affects surgical outcomes
yet is not reflected in risk assessment models. These factors
include six broad categories of health determinants: eco-
nomic stability, physical environment, education, food, com-
munity, social context, and healthcare system access [15].
These six classes synergistically act with medical and sur-
gical risk factors in determining a patient’s response to the
surgical outcome (Table6). Patients from lower socioeco-
nomic status communities have higher rates of many medical
comorbidities and higher predicted rates of postoperative
complications determined by ACS SRC. They also have
increased 30-day mortality, postoperative complications,
readmission rates, and higher healthcare resource utiliza-
tion [16].
Anemia is a known predictor of poor postoperative out-
comes and is therefore an important preoperative optimi-
zation target. Anemia may be one of the most prevalent
modifiable conditions in the preoperative setting, affecting
approximately 40% of patients [17]. Preoperative anemia is
linked to renal failure, infection, pulmonary complications,
increased transfusion rates, and mortality [18••]. Trans-
fusion has its own risks and is associated with increased
mortality, increased length of hospital stays, and increased
surgical site infections [19]. Identifying preoperative anemia
informs providers on the need for certain medications such
as preoperative tranexamic acid to reduce bleeding, oral or
Fig. 1 Inter-rater variability of American Society of Anesthesiolo-
gists Physical Status (ASA-PS) scoring. The
-labels show the rating
in the preoperative assessment clinic, while the columns in each class
represent subsequent assigned rating in the operating room (used with
permission [14])
Current Anesthesiology Reports
1 3
intravenous iron therapy, erythropoietin stimulation, coor-
dination of pre- and postoperative consults, and the need for
appropriate blood ordering.
Preoperative risk stratification for anemia includes
obtaining a preoperative complete/full blood count (CBC
and FBC). A complete blood count should be ordered based
on the type of surgery, patient comorbidities, or known his-
tory of anemia. Obtaining a complete blood count with an
anemia panel (including iron, B12, and folate studies) clari-
fies the reason for anemia and avoids the patient returning
to the preoperative clinic for additional studies (Table7).
Once a complete blood count with an anemia panel is
obtained, a simple algorithmic approach towards preop-
erative treatment can be activated (Fig.2). The algorithm
should incorporate hemoglobin level, mean corpuscular vol-
ume, and transferrin saturation. For example, patients with
a hemoglobin < 120g/L (12g/dL) and transferrin saturation
of less than 20% can be preoperatively treated with oral or
preferably intravenous iron. Further research is required to
elucidate if this threshold should be > 130g/L for both males
and females and in fact if non-anemic iron deficient patients
require preoperative iron therapy. Guidelines can also be
found on software application platforms (e.g., CLOTS app—
https:// apps. apple. com/ us/ app/ clots/ id143 66724 91) which
provide hematinic optimization guidelines (as well as guid-
ance for surgical thromboembolism prevention, management
of antithrombotic drugs, and fast track warfarin reversal).
The goals of preoperative anemia treatment are to
improve hemoglobin and reduce the patient’s risk of trans-
fusion. A minimum of 2weeks is needed for treatment to
improve hemoglobin, by approximately 2g/dL. Preoperative
intravenous iron infusions have recently been called into
question as they were not shown to reduce blood transfu-
sions [20]. Secondary analysis of this study, however, sug-
gested iron infusion was associated with reduced postop-
erative readmission to hospital. Other smaller studies have
demonstrated that preoperative iron treatment improves
hemoglobin and reduces blood transfusion [21, 22]. Treat-
ment of anemia postoperatively may improve a patient’s
ability to ambulate owing to increased energy levels. Ane-
mia optimization is ideally a collaborative, multidisciplinary
effort that may include anesthesia, surgery, and hematology.
Postoperative pulmonary complications (PPCs) are one of
the most common adverse events following anesthesia and
surgery. Patients experiencing PPC have higher morbidity
and mortality rates, increased hospitalization, and increased
healthcare costs [23]. The Assess Respiratory Risk in Surgi-
cal Patients in Catalonia (ARISCAT) score determines PPC
risk based on seven objectives: categorized as low, interme-
diate, or high (1.6%, 13.3%, and 42.2%) risk of postoperative
pulmonary complications, respectively (Table8) [24].
Practical Considerations inImplementing
theARISCAT Tool
The ARISCAT score has been validated in multiple surgical
patient populations for predicting patients at risk of postop-
erative pulmonary complications. In particular, the three fac-
tors with the highest correlation were preoperative anemia,
emergency surgery, and surgery exceeding 2h in duration
[25]. While many of the factors contributing to the increased
risk are beyond the perioperative specialist’s control, this
tool calls attention to the significant effect that anemia has
on PPC development. Intermediate- and high-risk patients
benefit from prehabilitation efforts, such as anemia correc-
tion, preoperative incentive spirometry or breathing exer-
cises using the active cycle of breathing technique (ACBT),
smoking cessation, and increased physical activity. Interest-
ingly, patients who received a preoperative influenza vac-
cine had lower postoperative pneumonia rates and reduced
inhospital mortality (Table9) [26].
Table 6 Socioeconomic determinants of healthcare [15]
Economic stability Employment, income, debt, support
Physical environment Housing, transportation, safety, parks, geography (zip code)
Education Literacy, education level, vocational training
Food Hunger, malnutrition, access to healthy food options
Community and social context Social integration, support systems, community engagement, discrimination, social stressors
Healthcare system access Healthcare coverage, provider availability, provider linguistic and cultural competency,
quality of care delivered
Table 7 Components of preoperative complete blood count (CBC)
with anemia panel
Complete blood count Anemia panel
White blood cell count, without
Hemoglobin Total iron binding capacity
Hematocrit Transferrin saturation percentage
Platelets Vitamin B12
Mean corpuscular volume Folate
Current Anesthesiology Reports
1 3
Other pulmonary risk calculators determine the risk of
developing specific postoperative complications, such as
the Gupta calculator for postoperative respiratory failure,
which predicts the risk of failure to wean from mechanical
ventilation in the 48h following surgery, and the Gupta
calculator for postoperative pneumonia. The Arozullah
Respiratory Failure Index predicts postoperative respira-
tory failure risk, defined as the need for mechanical venti-
lation for longer than 48h after surgery. It is complicated
to use but incorporates additional factors relevant to pul-
monary function such as nutrition and renal status and
functional capacity.
Obstructive sleep apnea (OSA) predisposes surgical
patients to postoperative pulmonary complications, includ-
ing hypoxemia, pneumonia, myocardial infarction, pulmo-
nary embolism, atelectasis, and unplanned ICU admission
[27, 28]. Preoperative screening and recognition of OSA
allows for perioperative risk mitigation with strategies such
as those in Table8. While the gold standard for diagnosing
OSA is polysomnography, the STOP-BANG questionnaire
Fig. 2 Sample of a preoperative anemia screening guideline. Abbre-
viations: B12, vitamin B12; DOS, day of surgery; Hem/onc, hemato-
logic or oncologic; HgB, hemoglobin; MCV, mean corpuscular vol-
ume; PACT, Preoperative Assessment, Consultation, and Treatment
Clinic; PACU, post anesthesia care unit; TSAT, transferrin saturation
percentage; TXA, tranexamic acid
Table 8 ARISCAT score risk factors for postoperative pulmonary
complications (PPCs)
Abbreviations: HgB hemoglobin, SpO2 peripheral oxygen saturation,
URI upper respiratory tract infection
Age, in years
Preoperative SpO2
Respiratory infection (pneumonia, URI, bronchitis) within the past
Preoperative anemia (HgB ≤ 100g/L)
Surgical incision (upper abdominal and thoracic having highest risk)
Duration of surgery (risk increases for duration 2h)
Emergency procedure
Current Anesthesiology Reports
1 3
is highly sensitive for OSA identification and easy to imple-
ment preoperatively [29].
Suboptimal nutrition in the perioperative period is
an independent predictor of poor surgical outcomes.
Specifically, malnutrition is associated with increased
postoperative morbidity and mortality, hospital read-
mission, length of stay, and cost. Malnourished
colorectal surgery patients are twice as likely to be
readmitted to the hospital within 30days of elective
surgery [30]. Logically, perioperative nutritional ther-
apeutic interventions improve outcomes in gastroin-
testinal cancer surgery. Despite the long history of
randomized controlled trials and meta-analyses, dem-
onstrating perioperative benefit from nutritional inter-
vention, acceptance, and implementation of evidence-
based nutrition practices remains poor, with only 20%
of US gastrointestinal cancer patients receiving nutri-
tional supplementations perioperatively, and only 1
in 5 hospitals utilize formal nutrition screening tools
Various screening tools exist to identify preoperative mal-
nutrition. The Nutritional Risk Index (NRI) is a classic formula
used to identify malnutrition risk upon hospital admission (not
specific to surgery). The formula for NRI is as follows:
An NRI score of 100 indicates no risk, 97.5 to 100
mild risk, 83.5 to 97.5 moderate risk, and less than
83.5 severe malnutrition risk. Severe risk of malnutri-
tion using NRI is correlated with an extended hospital
stay. Difficulty in determining usual body weight, par-
ticularly among elderly patients, may limit the practi-
cality of this tool [32].
The Nestlé Mini Nutritional Assessment Short
Form (MNA®-SF) is a validated screening tool that
uses the quantitative parameters of body mass index
(BMI) and recent oral intake, weight loss, mobil-
ity, psychological stress, and neuropsychological
NRI =(1.519 ×serum albumin, gL)+(41.7 ×presentusual body weight)
problems. A numerical score identifies patients as
malnourished, at risk of malnutrition, or of normal
nutritional status. The MNA®-SF, when explicitly
used to assess nutritional risk in patients before
elective surgery, has 97.9% sensitivity and 100%
specificity, with a diagnostic accuracy of 99% for
predicting undernutrition. The MNA®-SF is easy to
use and efficient and minimally impacts workflow
The Perioperative Nutrition Screen (PONS) is a
modified version of the Malnutrition Universal Screen-
ing Tool (MUST) that has been adapted to surgical
patients. PONS is promoted by the American College
of Surgeons “Strong for Surgery” initiative. PONS
identifies nutritional risk based on four criteria: BMI
(less than 18.5 for patients under 65years or less than
20 for patients over 65years), unintentional weight loss
(greater than 10% weight loss in 6months), decreased
oral intake (less than 50% of average intake in the last
week), and low serum albumin (less than 3g/L). Any
positive response identifies patients at high risk for
malnutrition and warrants nutritional intervention [34].
Recently, the concept of sarcopenia as a surrogate for frailty,
malnutrition, and worsened surgical outcomes has become a
target of study. The gold standard for sarcopenia diagnosis is
quantifying skeletal muscle mass by computed tomography
(CT). This method carries the risk of radiation exposure and the
expense of specialized equipment and specially trained person-
nel. Portability, ease of use, and lower cost make bedside ultra-
sound an attractive alternative for sarcopenia assessment. Recent
data involving elderly patients undergoing emergency abdominal
surgery shows that thigh muscle thickness ultrasound correlates
with CT estimates of skeletal mass index. This data can be of use
by providing a rapid, low-cost option for objectively assessing
sarcopenia and frailty and associated risk of developing major
postoperative complications [35].
While some clinics have implemented programs for dieti-
cian referral, other programs utilize simple nutrition sup-
plementation algorithms using high-protein oral nutritional
supplements (ONS) and immunonutrition (IMN) supple-
ments [34]. A regimen of three high-protein ONS servings
Table 9 Perioperative maneuvers to mitigate the risk of postoperative pulmonary complications (PPCs) (used with permission from Pfeifer, K.
Guide to Preoperative Evaluation, www. preop evalg uide. com, 2020)
Low risk of PPC Intermediate risk of PPC High risk of PPC
• Early mobilization
• Good oral hygiene
• Optimization of chronic lung disease
• Smoking cessation counseling and resources
All of the low risk maneuvers, plus
• Postoperative incentive spirometry
• Identification and communication of
“increased risk” status
• Regional anesthesia/analgesia, if applicable
• Lung-protective ventilation
All of the low and intermediate risk maneuvers,
• 1–2weeks preoperative incentive spirometry
• Increased postoperative surveillance
Current Anesthesiology Reports
1 3
a day achieves the goal of 1.2g/kg/day of protein. Arginine,
omega-3 fatty acid, and antioxidant containing immunonu-
trition supplements are recommended over supplements
containing a single immunonutrient alone. Arginine sup-
ports immune function through activation and promotion
of T lymphocytes and T-helper cells, respectively. Arginine
also promotes wound and anastomotic healing by serving
as a precursor to proline and nitric oxide, and omega-3 fatty
acids play a wide range of anti-inflammatory roles. Infec-
tious complications can be reduced by up to 40% by immu-
nonutrition supplementation regimens [36]. The guidelines
for duration and composition of perioperative nutritional
support vary widely, but data indicates that even 5–7days
of preoperative nutrition therapy can decrease morbidity by
50%. Other perioperative nutritional interventions include
minimizing preoperative fasting, providing preoperative oral
carbohydrate loading, and early resumption of oral intake
postoperatively [31••].
The incidence of cognitive dysfunction is markedly associ-
ated with advanced age [37]. One in three patients presenting
for surgery is now over the age of 65years [38]. Cognitive
dysfunction has been associated with an increased odds ratio
of discharge to nursing homes and increased hospital length
of stay [39]. Consequently, risk stratification and identifica-
tion of preoperative cognitive dysfunction offer the surgical
team the ability to preplan hospital management. The MINI-
COG and Six-Item Screener are two commercially available
screening tools for preoperative assessment. The MINI-COG
test is a simple screening tool with external validation incor-
porating a three-item recall challenge with a clock-drawing
challenge. A 2019 study from Brigham and Women’s Hos-
pital demonstrated that the entire test could be completed
in less than 5min when performed in patients over 65years
[40]. Trained nurse practitioners and residents administered
these tests. Similarly, the Six-Item Screener uses a three-
word recall challenge coupled with three questions on orien-
tation. This screening can also be accomplished in less than
5min. Currently, the MINI-COG test is the most validated
test for preoperative screening. Comparing the two tests,
the MINI-COG has 99% sensitivity and 93% specificity for
dementia, while the Six-Item Screener has 88.7% sensitivity
and 88% specificity [41].
Practical considerations in cognitive function screening
should include appropriate training for staff and workflow
considerations. All patients over the age of 65years should
be screened. Screening should be accomplished early in the
preoperative assessment to allow for teaching adjustment
if the patient has dysfunction. The patient with cognitive
dysfunction may not fully engage in the decision-making
process or comprehend preoperative instructions. Patients
with cognitive dysfunction will also benefit from specialized
pathways in the perioperative setting, such as family pres-
ence in recovery, cognitive stimulation postoperatively, and
medication management to avoid potentially inappropriate
medication use in older adults as listed by the American
Geriatrics Society Beers Criteria. Optimal perioperative
management of the geriatric patient has been described by
the American College of Surgeons in 2016 [42].
Preoperative frailty is a common problem with a significant
association with postoperative mortality owing to increased
vulnerability to surgical stressors [43]. Frailty in the general
population of patients over the age of 65years has been
estimated between 15 and 20%. The prevalence increases
by up to 43% in older patients with cancer [44]. Multiple
modifiable comorbidities often burden the frail patient.
Frail patients often have deficits in nutrition, mobility, and
cognition. These modifiable risks are exacerbated by social
determinants of health and availability of assistance in the
home. Frailty is a specific concern for geriatric patients but
can exist at any age. In 2016, a Johns Hopkins study revealed
over 67 instruments that have been used to define frailty
[45]. A subsequent study in 2017 demonstrated significant
heterogeneity and a lack of consensus among many of these
tools [46].
As there is no single best tool for evaluating frailty, mul-
tiple techniques should be employed to create a frailty index
[47]. When evaluating patients with this method, the accu-
mulation of deficits is viewed and individually treated. For
example, a patient may have deficits in cognition without
malnutrition and be treated accordingly. The Edmonton Frail
Scale (EFS) is one such index that evaluates multiple param-
eters for frailty [48]. Another useful tool with high inter-rater
reliability and association with postoperative outcomes is
the Clinical Frailty Scale (CFS) [49]. The Clinical Frailty
Scale uses a phenotypic description of frailty and includes
pictures to increase the inter-rater reliability of preoperative
assessment and is strongly correlated to the Edmonton Frail
Scale in a single-center prospective cohort study [50]. In
practice, frailty is best identified with a thorough screening
of all preoperative patients and then a treatment based upon
the deficits identified.
Postoperative Nausea andVomiting (PONV)
andPost Discharge Nausea andVomiting (PDNV)
Postoperative nausea and vomiting (PONV) is a significant
patient dissatisfier following surgery. Vomiting and retching
may cause wound dehiscence, aspiration, esophageal rup-
ture, increased intracranial pressure, and dehydration. PONV
contributes to delayed recovery and discharge from the post
Current Anesthesiology Reports
1 3
anesthesia care unit (PACU) and may result in unplanned
hospital admission and increased healthcare costs.
There are several PONV risk assessment models available
for preoperative use. The most used tool is the model for
adult patients developed by Apfel and colleagues with vali-
dation across several institutions without losing discrimina-
tory power [51]. The Apfel PONV prediction models evalu-
ate four risk factors as shown in Table10 [52], with the
corresponding incidence of PONV shown in Fig.3. PONV is
easily predicted in the preoperative setting. Communication
to the day-of-surgery anesthesia team allows for modifica-
tion of the anesthetic plan to reduce the risk, such as regional
anesthesia/analgesia, opioid-sparing multimodal analgesia,
prophylactic anti-emetic administration, or techniques such
as P6 acupressure point injection. Current guidelines recom-
mend multimodal prophylaxis in patients with one or more
risk factors for PONV and PDNV [52].
Post discharge nausea and vomiting (PDNV) significantly
impacts a patient’s quality of recovery and functional sta-
tus. A study from 2014 noted a high correlation (Pearson
correlation coefficient of 0.77) between PDNV and adverse
effects on the patient’s quality of life and recovery [53].
The PDNV risk factors are similar to PONV, and identifica-
tion and mitigation improve patient outcomes and reduce
healthcare costs and indirect societal costs related to pro-
longed recovery (Fig.3).
Future PONV and PDNV risk prediction models
will likely be strengthened by emerging data in the field
of genomic medicine. Identifying multiple alleles of
cytochrome P450 coupled with a patient’s current medica-
tion regimen allows for more precise anesthetic planning and
PONV/PDNV risk reduction strategies.
Opioid Abuse andPersistent Postoperative Use Risk
Appropriate treatment of pre- and postoperative pain is para-
mount. Likewise, screening for potential postoperative opi-
oid abuse and persistent postoperative opioid use is critically
important to all patients’ well-being and function. Persistent
use after surgery depends on the type of surgery performed
and the time after surgery. Estimates range from 0.7 to 14%
of previously opioid-naive surgical patients still using opi-
oids 1year after surgery [54]. Risk tools exist for opioid
abuse, such as the Opioid Risk Tool (ORT). The ORT is
well validated in both men and women with pain conditions
and can be administered in under a few minutes. The ORT
assesses personal and family history of substance abuse,
sexual abuse, psychological illness, and age [55].
Practical applications of the ORT include identifying
patients at high risk of persistent postoperative use or opi-
oid abuse and planning perioperative interventions to reduce
the overall need for opioids. These measures may include
planning for non-opioid preoperative medications (NSAIDs,
acetaminophen/paracetamol), preoperative regional nerve
blockade, intraoperative non-opioid adjuncts (lidocaine
infusions, ketamine, and magnesium), and postoperative
physical and psychological therapy. Preoperative clinics
may consider prescribing naloxone nasal spray for patients
Table 10 PONV and PDNV risk factors
Abbreviation: PACU post anesthesia care unit
• Female gender
• History of PONV or motion sickness
• Intended use of postoperative opioids
• Non-smoker
• Female gender
• History of PONV
• Use of opioids in PACU
Age < 50years
• Nausea in PACU
Fig. 3 Incidence of PONV and
PDNV by number of risk fac-
tors listed in Table10
Current Anesthesiology Reports
1 3
at high risk for opioid abuse, understanding that they will
likely have increased need for pain medications after sur-
gery and would be at higher risk for adverse respiratory
Global Risk Calculators
The American College of Surgeons (ACS) National Surgi-
cal Quality Improvement Program (NSQIP) Surgical Risk
Calculator (SRC) is a global risk calculator tool. The NSQIP
calculator uses 20 patient predictors and the planned surgery
(based on current surgical procedural terminology (CPT)
code) to predict 18 different outcomes within 30days of
surgery [56]. The calculator was built on data from over 4.3
million operations performed at 780 participating hospitals
from 2013 to 2017. Since August 2019, the ACS NSQIP
Surgical Risk Calculator also has the option to view geriatric
outcomes for patients 65 + years of age.
Practical considerations for using the SRC include staff
training and workflow adjustments. We recommend using
this tool at the end of the preoperative visit to allow for
patient discussions in real time. The tool provides a color-
coded estimate of risk compared to a cohort of similar
patients having the same procedure. Demonstration of cer-
tain risks may motivate patients to adhere to optimization
programs. An example here is provided using a fictitious
patient (Fig.4).
Future Models
Two extensively studied biomarkers for perioperative
MACE prediction are brain natriuretic peptide (BNP) and
N-terminal-pro-BNP (NT pro-BNP). These peptides leak
from the myocardium in response to increased cardiac wall
stress resulting from ischemia, pressure overload, or volume
expansion. BNP values above established thresholds support
a diagnosis of heart failure. Still, they are not conclusive, as
other medical conditions such as glomerular filtration rates
(GFRs) below 60 are also associated with increased serum
BNP concentrations. Several studies have demonstrated an
independent association between a preoperative BNP or NT
pro-BNP level exceeding established thresholds (372pg/mL)
and an increased risk of MACE or MINS within 30days
of noncardiac surgery [57]. The Canadian Cardiovascular
Society Guidelines on Perioperative Cardiac Risk Assess-
ment and Management for Patients undergoing noncardiac
surgery recommend obtaining a preoperative BNP level (or
NT pro-BNP) to increase the predictive accuracy of MACE
risk in patients age 65years or older, those aged 45–64years
with significant cardiovascular disease, or patients with an
RCRI score ≥ 1 [10••].
Asymmetric dimethylarginine (ADMA) is a naturally
occurring inhibitor of nitric oxide synthesis and closely
related to L-arginine. High serum levels of ADMA inde-
pendently predict mortality irrespective of other cardiovas-
cular risk factors [58]. The use of ADMA levels in conjunc-
tion with other risk stratification models provides a more
accurate MACE risk assessment than either alone. Patients
treated preoperatively with supplementation of L-arginine
and L-citrulline had higher arginine and lower ADMA lev-
els on the day of surgery and decreased MACE and 30-day
mortality [58]. While this study’s results were not statisti-
cally significant, it suggests clinical significance since high
ADMA levels are associated with increased length of hospi-
tal and ICU stay and prolonged mechanical ventilation [59].
Along with biomarkers, genetic polymorphisms may provide
unique and precise information on perioperative risk. Cur-
rently, genetic variants have promise in predicting multiple
perioperative complications (Table11). Obtaining genetic
profiles and tailoring therapy will require further investi-
gations towards outcome improvement, creating electronic
medical record (EMR) displays to flag relevant variants, and
developing workflows to obtain genetic profiles in a timely
manner [60]. The All of Us National Institute of Health study
is one such study that aims to collect a diverse genetic data-
base to advance precision medicine [61].
The “Big Data” Era andRisk Stratication
The overwhelming amount of data stored in the electronic
medical record must be transformed into useful information
for clinical decision making. Data is stored as file images,
numerical laboratory data, and narrative in both discrete
fields and natural language. Harnessing this data will require
the power of computing, using predictive analytics and artifi-
cial intelligence (AI). Predictive analytics (PA) encompasses
various statistical techniques from data mining, predictive
modeling, and machine learning that analyze current and
historical facts to make predictions about future or otherwise
unknown events. This topic’s scope is well beyond what can
be described in this chapter; however, clinical applications
are developing rapidly [66]. The challenge to today’s phy-
sicians will be to become knowledgeable with and inter-
act with deep neural network capabilities and to trust the
computer-generated suggestions that impact patient care
Current Anesthesiology Reports
1 3
Fig. 4 Sample Output from
the ACS NSQIP Surgical Risk
Table 11 Sample of genetic polymorphisms related to postoperative outcomes
Polymorphism/variant Disease Clinical use
Cytochrome P450 2D6 [62] Pharmacologic opioid response Tailors opioid prescribing based on metabolism
Factor V, chromosome 1q23 [63] Factor V Leiden mutation Guides anticoagulant therapy
Butyrylcholinesterase variants [63] Delayed recovery from succinylcholine Guides medication use
RYR1, CACNA1S [63] Malignant hyperthermia Guides anesthetic choice
Polygenic analysis [63] MINS (myocardial injury after noncardiac surgery) Guides postoperative ischemia surveillance
Allele at the chromosome 4q25 locus [64] Postoperative atrial fibrillation Guides postoperative surveillance
APOE4 allele [65] Postoperative cognitive dysfunction Guides medication management and need for
specialized consults
Current Anesthesiology Reports
1 3
The Art andPractice ofRisk Calculation
Risk calculators are useful for guiding appropriate preop-
erative testing, shared-decision making, and preoperative
patient motivation. However, a false sense of security is
created when we clear patients for surgery based on risk
calculation alone. Despite multiple risk factor calcula-
tions, the art of medicine must be instilled in preoperative
evaluation. The art and practice of medicine rely on care-
ful examination of all the risks followed by coordinated
communication and precision planning for optimization.
Applying the data obtained from risk calculation equals
balancing the art and science of risk optimization [67••].
Coordination starts as a collaborative effort between the
preoperative physician, the patient, and the surgical team
(“alert phase”). Alerting the team to increased risk for
potential complications and the plan to prevent such com-
plications is the first step. Alerts can be accomplished via
internal electronic medical record communication and/
or telephone conversations. Secondly, defined modifiable
risks must be coupled with defined and agreed upon opti-
mization plans (“action phase”). Action plans or preop-
erative guidelines for optimizing modifiable risks should
include the modifiable risk, how it is defined, markers
for preoperative measurement, the timeline for optimiza-
tion, and markers for improvement that allow the patient
to progress. Finally, the patient will need careful man-
agement of the comorbidity or modifiable risk during
and after the hospital stay (“follow-up phase”). In this
phase, a global perspective is used to view the patient,
and appropriate consultation is sought after the intended
procedure (Table13).
Summary andConclusion
Numerous simple, readily available, and useful risk stratifica-
tion tools exist. While risk stratification tools are a useful means
of guiding preoperative testing and optimization, they are not
designed to take the place of the preoperative history and physi-
cal, as doing so could increase unnecessary testing. Although
the sheer number of available tools and data can be overwhelm-
ing, it is incumbent upon perioperative physicians to research
and implement systems and protocols that work within their
hospital or ambulatory surgery center settings. While it is true
that the level of infrastructure and financial support for such
programs varies widely, this chapter has provided numerous
tools and recommendations that can be applied effectively, even
in facilities with significantly constrained resources.
Compliance with Ethical Standards
Conflict of Interest The authors declare no competing interests.
Human and Animal Rights and Informed Consent This article does not
contain any studies with human or animal subjects performed by any
of the authors.
Papers of particular interest, published recently, have
been highlighted as:
• Of importance
•• Of major importance
1. Pannell LM, Reyes EM, Underwood SR. Cardiac risk assess-
ment before noncardiac surgery. Eur Heart J Cardiovasc Imag-
ing. 2013;14(4):316–22.
Table 12 Applications for predictive analytics, artificial intelligence,
and machine learning
Predicting clinical deterioration
Predicting hospital readmission
Recognizing obscure disease patterns
Predicting failure to follow-up
Identifying image pathology
Suggesting optimal workforce and supply chain
Predicting a disease outbreak
Table 13 Practical application of risk calculator data
Alert Action Follow-up
• Identification of modifiable risk and commu-
nicated to all members of the team
• Clear documentation of risk in preoperative
• Risk defined by laboratory tests or risk
• Timeline established for treatment
• Risk reassessed to allow the patient to pro-
gress to procedure
• Modifiable risk is managed by the team
• Consultation for longitudinal management
postoperatively is established
Current Anesthesiology Reports
1 3
2. Berwick DM, Nolan TW, Whittington J. The triple aim: care,
health, and cost. Health Aff. 2008;27(3):759–69.
3. Pisano A, Oppizzi M, Turi S, Landoni G. Reducing major
adverse cardiac events and all-cause mortality in noncardiac sur-
gery: perioperative strategies. Essentials of Cardiac Anesthesia
for Noncardiac Surgery. Elsevier; 2019. p. 538–77.
4. Botto F, Alonso-Coello P, Chan MT, Villar JC, Xavier D, Srina-
than S, etal. Myocardial injury after noncardiac surgery: a large,
international, prospective cohort study establishing diagnostic
criteria, characteristics, predictors, and 30-day outcomes. Anes-
thesiology. 2014;120(3):564–78.
5.•• Wijeysundera DN, Pearse RM, Shulman MA, Abbott TE, Tor-
res E, Ambosta A, etal. Assessment of functional capacity
before major noncardiac surgery: an international, prospective
cohort study. Lancet. 2018;391(10140):2631–40. This study
established that objective assessment of functional capacity
is superior to subjective assessment and is associated with
adverse postoperative outcomes.
6. Fleisher LA, Fleischmann KE, Auerbach AD, Barnason SA,
Beckman JA, Bozkurt B, etal. 2014 ACC/AHA guideline on
perioperative cardiovascular evaluation and management of
patients undergoing noncardiac surgery: a report of the American
College of Cardiology/American Heart Association Task Force
on practice guidelines. Circulation. 2014;130(24):e278–333.
7. Dakik HA, Chehab O, Eldirani M, Sbeity E, Karam C, Abou
Hassan O, etal. A new index for preoperative cardiovascular
evaluation. J Am Coll Cardiol. 2019;73(24):3067–78.
8. Lee TH, Marcantonio ER, Mangione CM, Thomas EJ, Polanczyk
CA, Cook EF, etal. Derivation and prospective validation of a
simple index for prediction of cardiac risk of major noncardiac
surgery. Circulation. 1999;100(10):1043–9.
9.• Glance LG, Faden E, Dutton RP, Lustik SJ, Li Y, Eaton MP,
etal. Impact of the choice of risk model for identifying low-
risk patients using the 2014 American College of Cardiology/
American Heart Association Perioperative Guidelines. Anesthe-
siology. 2018;129(5):889–900. This study demonstrated wide
variability in the different risk prediction models especially
in low-risk patients.
10.•• Duceppe E, Parlow J, MacDonald P, Lyons K, McMullen M,
Srinathan S, etal. Canadian Cardiovascular Society guidelines
on perioperative cardiac risk assessment and management
for patients who undergo noncardiac surgery. Can J Cardiol.
2017;33(1):17–32. https:// doi. org/ 10. 1016/j. cjca. 2016. 09. 008.
These guidelines explain the use of biomarkers for preop-
erative cardiovascular risk assessment and provide helpful
preoperative management strategies.
11. Wilcox T, Smilowitz NR, Xia Y, Berger JS. Cardiovascular risk
scores to predict perioperative stroke in noncardiac surgery.
Stroke. 2019;50(8):2002–6.
12. Devereaux P, Goldman L, Cook DJ, Gilbert K, Leslie K, Guyatt
GH. Perioperative cardiac events in patients undergoing non-
cardiac surgery: a review of the magnitude of the problem, the
pathophysiology of the events and methods to estimate and com-
municate risk. CMAJ. 2005;173(6):627–34.
13. Alrezk R, Jackson N, Al Rezk M, Elashoff R, Weintraub N,
Elashoff D, etal. Derivation and validation of a geriatric-
sensitive perioperative cardiac risk index. J Am Heart Assoc.
14. Sankar A, Johnson S, Beattie W, Tait G, Wijeysundera D. Relia-
bility of the American Society of Anesthesiologists physical sta-
tus scale in clinical practice. Br J Anaesth. 2014;113(3):424–32.
15. Artiga S, Hinton E. Beyond health care: the role of social
determinants in promoting health and health equity. Health.
16. Mehaffey JH, Hawkins RB, Charles EJ, Turrentine FE, Kaplan
B, Fogel S, etal. Community level socioeconomic status
association with surgical outcomes and resource utilisation in a
regional cohort: a prospective registry analysis. BMJ Qual Saf.
17. Beattie WS, Karkouti K, Wijeysundera DN, Tait G. Risk associ-
ated with preoperative anemia in noncardiac surgerya single-
center cohort study. Anesthesiology. 2009;110(3):574–81.
18.•• Warner MA, Shore-Lesserson L, Shander A, Patel SY, Perel-
man SI, Guinn NR. Perioperative anemia: prevention, diagno-
sis, and management throughout the spectrum of periopera-
tive care. Anesth Analg. 2020;130(5):1364–80. This review
describes the negative impacts of preoperative anemia and
provides helpful preoperative treatment strategies.
19. Glance LG, Dick AW, Mukamel DB, Fleming FJ, Zollo RA,
Wissler R, etal. Association between intraoperative blood
transfusion and mortality and morbidity in patients undergo-
ing noncardiac surgery. Anesthesiology. 2011;114(2):283–92.
20. Richards T, Baikady RR, Clevenger B, Butcher A, Abeysiri S,
Chau M, etal. Preoperative intravenous iron to treat anaemia before
major abdominal surgery (PREVENTT): a randomised, double-
blind, controlled trial.The Lancet. 2020;396(10259):1353–61.
This study demonstrated that preoperative intravenous iron
infusion was not superior to placebo in reducing perioperative
blood transfusion.
21. Triphaus C, Judd L, Glaser P, Goehring MH, Schmitt E, West-
phal S, etal. Effectiveness of preoperative iron supplementa-
tion in major surgical patients with iron deficiency: a prospec-
tive observational study. Ann Surg. 2021;274(3):e212–9.
22. Froessler B, Palm P, Weber I, Hodyl NA, Singh R, Murphy
EM. The important role for intravenous iron in perioperative
patient blood management in major abdominal surgery: a
randomized controlled trial. Ann Surg. 2018;267(2):e39–40.
https:// doi. org/ 10. 1097/ SLA. 00000 00000 002055.
23. Gupta S, Fernandes RJ, Rao JS, Dhanpal R. Perioperative risk
factors for pulmonary complications after noncardiac surgery.
J Anaesthesiol Clin Pharmacol. 2020;36(1):88.
24. Canet J, Gallart L, Gomar C, Paluzie G, Vallès J, Castillo J,
etal. Prediction of postoperative pulmonary complications in
a population-based surgical cohort. J Am Soc Anesthesiol.
25. Tilak KM, Litake MM, Shingada KV. Study of risk, incidence
and mortality associated with postoperative pulmonary com-
plications using Assess Respiratory Risk in Surgical Patients
in Catalonia score. Int Surg J. 2019;6(9):3215–22.
26. Liu W-C, Lin C-S, Yeh C-C, Wu H-Y, Lee Y-J, Chung C-L,
etal. Effect of influenza vaccination against postoperative
pneumonia and mortality for geriatric patients receiving
major surgery: a nationwide matched study. J Infect Dis.
27. Vasu TS, Grewal R, Doghramji K. Obstructive sleep apnea syn-
drome and perioperative complications: a systematic review of
the literature. J Clin Sleep Med. 2012;8(2):199–207.
28. Weingarten TN, Kor DJ, Gali B, Sprung J. Predicting postop-
erative pulmonary complications in high-risk populations. Curr
Opin Anaesthesiol. 2013;26(2):116.
29. Mergen H, Altındağ B, Uçar ZZ, Kılıçaslan IK. The predictive
performance of the STOP-Bang questionnaire in obstructive
sleep apnea screening of obese population at sleep clinical set-
ting. Cureus. 2019;11(12).
30. Bliss LA, Maguire LH, Chau Z, Yang CJ, Nagle DA, Chan AT,
etal. Readmission after resections of the colon and rectum: pre-
dictors of a costly and common outcome. Dis Colon Rectum.
31.•• Wischmeyer PE, Carli F, Evans DC, Guilbert S, Kozar R, Pryor
A, etal. American Society for Enhanced Recovery and Perio-
perative Quality Initiative Joint Consensus Statement on nutri-
tion screening and therapy within a surgical enhanced recovery
Current Anesthesiology Reports
1 3
pathway. Anesth Analg. 2018;126(6):1883–95. These guidelines
provide strategies for preoperative nutrition optimization.
32. Kyle UG, Pirlich M, Schuetz T, Lochs H, Pichard C. Is nutri-
tional depletion by Nutritional Risk Index associated with
increased length of hospital stay? A population-based study. J
Parenter Enter Nutr. 2004;28(2):99–104.
33. Mays LC, Drummonds JW, Powers S, Buys DR, Watts
PI. Identifying geriatric patients at risk for malnutrition:
a quality improvement project. J Nutr Gerontol Geriatr.
34. Williams DG, Villalta E, Aronson S, Murray S, Blitz J, Kosmos V,
etal. Tutorial: development and implementation of a multidiscipli-
nary preoperative nutrition optimization clinic. J Parenter Enteral Nutr.
35. Salim SY, Al-Khathiri O, Tandon P, Baracos VE, Churchill TA,
Warkentin LM, etal. Thigh ultrasound used to identify frail elderly
patients with sarcopenia undergoing surgery: a pilot study. J Surg
Res. 2020;256:422–32.
36 Drover JW, Dhaliwal R, Weitzel L, Wischmeyer PE, Ochoa
JB, Heyland DK. Perioperative use of arginine-supplemented
diets: a systematic review of the evidence. J Am Coll Surg.
2011;212(3):385-99. e1.
37. Plassman BL, Langa KM, Fisher GG, Heeringa SG, Weir DR,
Ofstedal MB, etal. Prevalence of dementia in the United States:
the aging, demographics, and memory study. Neuroepidemiology.
38. Gajdos C, Kile D, Hawn MT, Finlayson E, Henderson WG,
Robinson TN. Advancing age and 30-day adverse outcomes
after nonemergent general surgeries. J Am Geriatr Soc.
39.• Culley DJ, Flaherty D, Fahey MC, Rudolph JL, Javedan H, Huang
C-C, etal. Poor performance on a preoperative cognitive screen-
ing test predicts postoperative complications in older orthopedic
surgical patients. Anesthesiology. 2017;127(5):765–74. This study
demonstrated the association between preoperative cognitive
dysfunction and postoperative delirium, longer hospital stay,
and discharge to nursing facility.
40. Sherman JB, Chatterjee A, Urman RD, Culley DJ, Crosby GJ,
Cooper Z, etal. Implementation of routine cognitive screen-
ing in the preoperative assessment clinic. A&A Practice.
41. Long LS, Shapiro WA, Leung JM. A brief review of practi-
cal preoperative cognitive screening tools. Can J Anesth.
42. Mohanty S, Rosenthal RA, Russell MM, Neuman MD, Ko CY,
Esnaola NF. Optimal perioperative management of the geriatric
patient: a best practices guideline from the American College of
Surgeons NSQIP and the American Geriatrics Society. J Am Coll
Surg. 2016;222(5):930–47.
43 Shinall MC, Arya S, Youk A, Varley P, Shah R, Massar-
weh NN, etal. Association of preoperative patient frailty and
operative stress with postoperative mortality. JAMA Surg.
44. Handforth C, Clegg A, Young C, Simpkins S, Seymour
M, Selby P, et al. The prevalence and outcomes of frailty
in older cancer patients: a systematic review. Ann Oncol.
45. Buta BJ, Walston JD, Godino JG, Park M, Kalyani RR, Xue Q-L,
etal. Frailty assessment instruments: systematic characterization
of the uses and contexts of highly-cited instruments. Ageing Res
Rev. 2016;26:53–61.
46. Aguayo GA, Donneau A-F, Vaillant MT, Schritz A, Franco
OH, Stranges S, etal. Agreement between 35 published
frailty scores in the general population. Am J Epidemiol.
47. Rockwood K, Mitnitski A. Frailty in relation to the accumulation
of deficits. J Gerontol A Biol Sci Med Sci. 2007;62(7):722–7.
48. Rolfson DB, Majumdar SR, Tsuyuki RT, Tahir A, Rockwood K.
Validity and reliability of the Edmonton Frail Scale. Age Ageing.
49. Rockwood K, Song X, MacKnight C, Bergman H, Hogan DB,
McDowell I, etal. A global clinical measure of fitness and frailty
in elderly people. CMAJ. 2005;173(5):489–95.
50. Darvall JN, Loth J, Bose T, Braat S, De Silva A, Story DA, etal.
Accuracy of the Clinical Frailty Scale for perioperative frailty
screening: a prospective observational study. Can J Anesth.
51. Apfel CC, Läärä E, Koivuranta M, Greim C-A, Roewer N. A sim-
plified risk score for predicting postoperative nausea and vomiting
conclusions from cross-validations between two centers. Anesthe-
siology. 1999;91(3):693.
52.• Nathan N. Management of postoperative nausea and vomiting:
the 4th consensus guidelines. Anesth Analg. 2020;131(2):410.
These guidelines update best practices for predicing
postoperative nausea and vomitting and perioperative
53. Odom-Forren J, Hooper V, Moser DK, Hall LA, Lennie TA,
Holtman J, etal. Postdischarge nausea and vomiting: manage-
ment strategies and outcomes over 7 days. J Perianesth Nurs.
54. Jivraj NK, Raghavji F, Bethell J, Wijeysundera DN, Ladha KS,
Bateman BT, etal. Persistent postoperative opioid usea systematic
literature search of definitions and population-based cohort study.
Anesthesiology. 2020;132(6):1528–39.
55. Webster LR, Webster RM. Predicting aberrant behaviors in opioid-
treated patients: preliminary validation of the Opioid Risk Tool.
Pain Med. 2005;6(6):432–42.
56. About the ACS Risk Calculator. American College of Surgeons.
2020. https:// riskc alcul ator. facs. org/ RiskC alcul ator/ about. html.
Accessed October 21, 2020.
57. Duceppe E, Patel A, Chan MT, Berwanger O, Ackland G, Kavsak
PA, etal. Preoperative N-terminal pro–B-type natriuretic peptide
and cardiovascular events after noncardiac surgery: a cohort study.
Ann Intern Med. 2020;172(2):96–104.
58. Appel D, Böger R, Windolph J, Heinze G, Goetz AE, Hanne-
mann J. Asymmetric dimethylarginine predicts periop-
erative cardiovascular complications in patients undergo-
ing medium-to-high risk noncardiac surgery. J Int Med Res.
59. Hassinger AB, Wainwright MS, Lane JC, Haymond S,
Backer CL, Wald E. Elevated preoperative serum asymmetri-
cal dimethylarginine (ADMA) is associated with poor out-
comes after pediatric cardiac surgery. Intensive Care Med.
60. Gabriel RA, Ehrenfeld JM, Urman RD. Preoperative genetic test-
ing and personalized medicine: changing the care paradigm. J Med
Syst. 2017;41(12):185.
61. NIH. All of Us. 2020.https:// allof us. nih. gov/ about/ all- us- resea
rch- progr am- overv iew. Accessed 13 Feb 2022.
62. Saba R, Kaye AD, Urman RD. Pharmacogenomics in pain man-
agement. Anesthesiol Clin. 2017;35(2):295–304.
63. Douville NJ, Kheterpal S, Engoren M, Mathis M, Mashour GA,
Hornsby WE, etal. Genetic mutations associated with susceptibil-
ity to perioperative complications in a longitudinal biorepository
with integrated genomic and electronic health records. Br J Anaesth.
64. Kolek MJ, Muehlschlegel JD, Bush WS, Parvez B, Murray KT,
Stein CM, etal. Genetic and clinical risk prediction model for
postoperative atrial fibrillation. Circ Arrhythm Electrophysiol.
Current Anesthesiology Reports
1 3
65. Schenning KJ, Murchison CF, Mattek NC, Kaye JA, Quinn
JF. Sex and genetic differences in postoperative cognitive
dysfunction: a longitudinal cohort analysis. Biol Sex Differ.
66 Hashimoto DA, Witkowski E, Gao L, Meireles O, Ros-
man G. Artificial intelligence in anesthesiologycurrent tech-
niques, clinical applications, and limitations. Anesthesiology.
67.•• Rohatgi N. Perioperative risk calculators and the art of medicine.
JAMA Intern Med. 2019;179(12):1619–20. This opinion piece
emphasizes the importance of balancing the art of medicine
with preoperative risk calculators. Risk calculators should be
a decision aide.
Publisher's Note Springer Nature remains neutral with regard to
jurisdictional claims in published maps and institutional affiliations.
With the rapid development of human society, the traditional fossil energy, which is necessary for development, is facing the threat of exhaustion and environmental pollution. As a clean and efficient secondary energy, electric energy is mainly converted from fossil energy to cope with the threat of resource depletion and environmental pollution brought by fossil energy. Faced with the problem of power integrated energy cooperative optimization system, we decide to use quantitative recursive analysis and robustness test of power statistical method. The experimental results show that the number of iterations of this clustering is 11, and the objective function is reduced from the initial 22346.3 to 6061.4. The results show that the comprehensive evaluation of hybrid electric energy utilization effect by using this method is globally optimal with good accuracy and high confidence, and the use efficiency of electric energy is improved.KeywordsPSO-PFCM Clustering algorithmBlock Chain TechnologyPower Integrated EnergyCollaborative Optimization System
Objectives: To review the recent literature around patient-centric prehabilitation in oncology patients and propose a conceptual framework to inform development of interdisciplinary prehabilitation services leading to focused, individualized prehabilitation interventions. Data sources: A review of recent peer-reviewed literature, national guidance, and government strategy on prehabilitation in oncology patients. Conclusion: Patient- centric prehabilitation is key to improving patient's experiences of cancer throughout the cancer journey while improving population health and reducing financial costs. Successful personalized prehabilitation interventions are comprised of an interplay between individual interdisciplinary roles, as illustrated in the conceptual framework. The role of the nurse underpins this whole process in patient screening, assessment, implementation of the intervention, and patient reassessment, ensuring care is dynamic and tailored to patient need. Implications for nursing practice: The review has discussed the key role that nurses play in the process but warrants more research in the area. The conceptual framework provides a basis to develop interdisciplinary prehabilitation services underpinned by the nurse's role. The review advocates the use of educational interventions to equip all health professionals with prehabilitation knowledge to enable interdisciplinary prehabilitation services to be developed.
Full-text available
Background: Preoperative anaemia affects a high proportion of patients undergoing major elective surgery and is associated with poor outcomes. We aimed to test the hypothesis that intravenous iron given to anaemic patients before major open elective abdominal surgery would correct anaemia, reduce the need for blood transfusions, and improve patient outcomes. Methods: In a double-blind, parallel-group randomised trial, we recruited adult participants identified with anaemia at preoperative hospital visits before elective major open abdominal surgery at 46 UK tertiary care centres. Anaemia was defined as haemoglobin less than 130 g/L for men and 120 g/L for women. We randomly allocated participants (1:1) via a secure web-based service to receive intravenous iron or placebo 10-42 days before surgery. Intravenous iron was administered as a single 1000 mg dose of ferric carboxymaltose in 100 mL normal saline, and placebo was 100 mL normal saline, both given as an infusion over 15 min. Unblinded study personnel prepared and administered the study drug; participants and other clinical and research staff were blinded to treatment allocation. Coprimary endpoints were risk of the composite outcome of blood transfusion or death, and number of blood transfusions from randomisation to 30 days postoperatively. The primary analysis included all randomly assigned patients with data available for the primary endpoints; safety analysis included all randomly assigned patients according to the treatment received. This study is registered, ISRCTN67322816, and is closed to new participants. Findings: Of 487 participants randomly assigned to placebo (n=243) or intravenous iron (n=244) between Jan 6, 2014, and Sept 28, 2018, complete data for the primary endpoints were available for 474 (97%) individuals. Death or blood transfusion occurred in 67 (28%) of the 237 patients in the placebo group and 69 (29%) of the 237 patients in the intravenous iron group (risk ratio 1·03, 95% CI 0·78-1·37; p=0·84). There were 111 blood transfusions in the placebo group and 105 in the intravenous iron group (rate ratio 0·98, 95% CI 0·68-1·43; p=0·93). There were no significant differences between the two groups for any of the prespecified safety endpoints. Interpretation: Preoperative intravenous iron was not superior to placebo to reduce need for blood transfusion when administered to patients with anaemia 10-42 days before elective major abdominal surgery. Funding: UK National Institute of Health Research Health Technology Assessment Program.
Full-text available
Objectives Perioperative cardiovascular events remain an important factor that affects surgery outcome. We assessed if asymmetric dimethylarginine (ADMA), an endogenous inhibitor of nitric oxide synthesis, predicts perioperative risk, and if pre-operative supplementation with L-arginine/L-citrulline improves the plasma L-arginine/ADMA ratio. Methods In this prospective study, planned thoracic and/or abdominal surgery patients were randomized to receive L-arginine/L-citrulline (5 g/day) or placebo 1 to 5 days before surgery. We measured perioperative plasma ADMA and L-arginine levels. The primary outcome was a 30-day combined cardiovascular endpoint. Results Among 269 patients, 23 (8.6%) experienced a major adverse cardiovascular event. ADMA and C-reactive protein were significantly associated with the incidence of cardiovascular complications in the multivariable-adjusted analysis. The L-arginine plasma concentration was significantly higher on the day of surgery with L-arginine/L-citrulline supplementation compared with placebo. In patients with high pre-operative ADMA, there was a non-significant trend towards reduced incidence of the primary endpoint with L-arginine/L-citrulline supplementation (six vs. nine events). Conclusions ADMA is a predictor of major adverse cardiovascular complications in the perioperative period for patients who are undergoing major abdominal and/or thoracic surgery. Supplementation with L-arginine/L-citrulline increased the L-arginine plasma concentration, enhanced the L-arginine/ADMA ratio, and induced a trend towards fewer perioperative events.
Full-text available
Although much is known about surgical risk, little evidence exists regarding how best to proactively address preoperative risk factors to improve surgical outcomes. Preoperative malnutrition is a widely prevalent and modifiable risk factor in patients undergoing surgery. Malnutrition prior to surgery portends significantly higher postoperative mortality, morbidity, length of stay, readmission rates, and hospital costs. Unfortunately, perioperative malnutrition is poorly screened for and remains largely unrecognized and undertreated—a true “silent epidemic” in surgical care. To better address this silent epidemic of surgical nutrition risk, here we describe the rationalization, development, and implementation of a multidisciplinary, registered dietitian–driven, preoperative nutrition optimization clinic program designed to improve perioperative outcomes and reduce cost. Implementation of this novel Perioperative Enhancement Team (POET) Nutrition Clinic required a collaboration among many disciplines, as well as an identified need for multidimensional scheduling template development, data tracking systems, dashboard development, and integration of electronic health records. A structured malnutrition risk score (Perioperative Nutrition Screen score) was developed and is being validated. A structured malnutrition pathway was developed and is under study. Finally, the POET Nutrition Clinic has established a novel role for a perioperative registered dietitian as the integral point person to deliver perioperative nutrition care. We hope this structured model of perioperative nutrition assessment and optimization will allow for wide implementation and generalizability in other centers worldwide to improve recognition and treatment of perioperative nutrition risk.
Full-text available
Background and Aims: Postoperative pulmonary complications (PPCs) lead to increased morbidity, mortality, length of hospital stay, and cost to the patient. This study was conducted to determine the risk factors and assess the incidence of PPC after non-cardiac surgery. Material and Methods: This prospective, observational study was conducted on 1,170 patients undergoing non-cardiac surgery. Details of patient, surgical, and anesthetic factors were collected and patients were followed up for the entire duration of hospital stay for the occurrence of PPC. Assess Respiratory Risk in Surgical Patients in Catalonia (ARISCAT) score and the length of hospital stay was noted for all the patients. Regression analysis was used to find the risk factors associated with development of respiratory complications. Results: The incidence of PPC was found to be 59 in 1,170 patients (5%) in our hospital. Multivariate analysis revealed that patients with intermediate and high risk ARISCAT scoring had higher odds of developing PPC. Higher age (>50 years), positive cough test, presence of nasogastric tube, and intraoperative pulmonary complications were identified as independent risk factors associated with the occurrence of PPC. Conclusion: We found 5% incidence of PPC in our study. Recognition of the delineated risk factors and routine use of ARISCAT score for preoperative assessment may help identify patients at a higher risk of developing postoperative pulmonary complications.
Background: Existing genetic information can be leveraged to identify patients with susceptibilities to conditions that might impact their perioperative care, but clinicians generally have limited exposure and are not trained to contextualise this information. We identified patients with genetic susceptibilities to anaesthetic complications using a perioperative biorepository and characterised the concordance with existing diagnoses. Methods: Adult patients undergoing surgery within Michigan Medicine from 2012 to 2017 were consented for genotyping. Genotypes were integrated with the electronic health record (EHR). We retrospectively characterised frequencies of variants associated with butyrylcholinesterase deficiency, factor V Leiden, and malignant hyperthermia, three pharmacogenetic factors with perioperative implications. We calculated the percentage homozygous and heterozygous for each that had been diagnosed previously and searched for EHR findings consistent with a predisposition. Results: Analysis of genetic data revealed that 25 out of 40 769 (0.1%) patients were homozygous and 1918 (4.7%) were heterozygous for mutations associated with butyrylcholinesterase deficiency. Of the homozygous individuals, 14 (56%) carried a pre-existing diagnosis. For factor V Leiden, 29 (0.1%) were homozygous and 2153 (5.3%) heterozygous. Of the homozygous individuals, three (10%) were diagnosed by EHR-derived phenotype and six (21%) by clinician review. Malignant hyperthermia was assessed in a subset of patients. We detected two patients with associated mutations. Neither carried clinical diagnoses. Conclusions: We identified patients with genetic susceptibility to perioperative complications using an open source script designed for clinician use. We validated this application in a retrospective analysis for three conditions with well-characterised inheritance, and showed that not all genetic susceptibilities were documented in the EHR.
Background Computed tomography (CT) scan quantifying skeletal muscle mass is the gold standard tool to identify sarcopenia. Unfortunately, high cost, limited availability, and radiation exposure limit its use. We suggest that ultrasound of the thigh muscle could be an objective, reproducible, portable, and risk-free tool, used as a surrogate to a CT scan, to help identify frail patients with sarcopenia. Materials and methods We included 49 patients over 64 y old, referred to the acute care surgery service. An ultrasound of thigh muscle thickness was standardized to patient thigh length (U/Swhole/L). CT skeletal muscle index (SMI) was calculated using skeletal muscle surface area of the L3 region divided by height². Frailty status was assessed using the Canadian Study of Healthy Aging Clinical Frailty Scale. Results The mean (SD) age was 76 (8) y, and 34% (n = 17) were men. CT-defined sarcopenia was identified in 65% (n = 11) of men and 75% (n = 24) of women. In general, women had longer stay in hospital than men (mean + SD 14 ± 9 versus 7 ± 3 d, P = 0.003). There was a significant positive correlation between thigh U/Swhole/L and CT SMI. There was an inverse correlation between thigh U/Swhole/L and frailty score; a similar relationship was observed between CT SMI and frailty. There was an association between U/Swhole/L and postoperative major complications. Conclusions This prospective observational study illustrates that the U/Swhole/L index can be used as a surrogate to CT scan, whereby it can identify elderly frail patients with sarcopenia. Thigh ultrasound should be further tested as an objective tool to assess for stratifying frailty.
Background: While persistent opioid use after surgery has been the subject of a large number of studies, it is unknown how much variability in the definition of persistent use impacts the reported incidence across studies. The objective was to evaluate the incidence of persistent use estimated with different definitions using a single cohort of postoperative patients, as well as the ability of each definition to identify patients with opioid-related adverse events. Methods: The literature was reviewed to identify observational studies that evaluated persistent opioid use among opioid-naive patients requiring surgery, and any definitions of persistent opioid use were extracted. Next, the authors performed a population-based cohort study of opioid-naive adults undergoing 1 of 18 surgical procedures from 2013 to 2017 in Ontario, Canada. The primary outcome was the incidence of persistent opioid use, defined by each extracted definition of persistent opioid use. The authors also assessed the sensitivity and specificity of each definition to identify patients with an opioid-related adverse event in the year after surgery. Results: Twenty-nine different definitions of persistent opioid use were identified from 39 studies. Applying the different definitions to a cohort of 162,830 opioid-naive surgical patients, the incidence of persistent opioid use in the year after surgery ranged from 0.01% (n = 10) to 14.7% (n = 23,442), with a median of 0.7% (n = 1,061). Opioid-related overdose or diagnosis associated with opioid use disorder in the year of follow-up occurred in 164 patients (1 per 1,000 operations). The sensitivity of each definition to identify patients with the composite measure of opioid use disorder or opioid-related toxicity ranged from 0.01 to 0.36, while specificity ranged from 0.86 to 1.00. Conclusions: The incidence of persistent opioid use reported after surgery varies more than 100-fold depending on the definition used. Definitions varied markedly in their sensitivity for identifying adverse opioid-related event, with low sensitivity overall across measures. : WHAT WE ALREADY KNOW ABOUT THIS TOPIC: Persistent opioid use after surgery is a matter of great concernDefining appropriate opioid prescribing practices and policies depends critically on understanding the rate of and reasons for persistent postoperative opioid use WHAT THIS ARTICLE TELLS US THAT IS NEW: A systematic search of the literature revealed 29 distinct definitions of persistent opioid use employed in 39 different studiesApplying the definitions to a separate study cohort of more than 162,000 surgical patients identified persistent opioid use rates varying more than 100-fold with low sensitivity for the identification of opioid use disorder.
Anemia is common in the perioperative period and is associated with poor patient outcomes. Remarkably, anemia is frequently ignored until hemoglobin levels drop low enough to warrant a red blood cell transfusion. This simplified transfusion-based approach has unfortunately shifted clinical focus away from strategies to adequately prevent, diagnose, and treat anemia through direct management of the underlying cause(s). While recommendations have been published for the treatment of anemia before elective surgery, information regarding the design and implementation of evidence-based anemia management strategies is sparse. Moreover, anemia is not solely a concern of the preoperative encounter. Rather, anemia must be actively addressed throughout the perioperative spectrum of patient care. This article provides practical information regarding the implementation of anemia management strategies in surgical patients throughout the perioperative period. This includes evidence-based recommendations for the prevention, diagnosis, and treatment of anemia, including the utility of iron supplementation and erythropoiesis-stimulating agents (ESAs).