The Relationship Between Body Mass Index and 30-Day Mortality Risk, by Principal Surgical Procedure
ABSTRACT To examine the relationship between body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) and 30-day mortality risk among patients in the participant use data file database of the American College of Surgeons National Surgical Quality Improvement Program. Obesity is a prevalent chronic disease in the United States, and general and vascular surgeons are caring for an increasing population of obese patients.
Multivariable logistic regression analysis was used to assess the statistical significance of the relationship between BMI and mortality, with adjustments for patient-level differences in overall mortality risk and principal operating procedures. Odds ratios with 95% CIs were calculated to measure the relative difference in mortality by BMI quintile, with reference to the middle quintile of the BMI. The overall significance of the BMI and of the other covariates was measured using the Wald χ(2) test statistic. A separate multivariable logistic regression model was developed to assess the significance of the interaction between BMI and primary procedure.
A total of 183 sites.
Patients with major surgical procedures reported in the participant use data file database of the American College of Surgeons National Surgical Quality Improvement Program.
The data included 189 533 cases of general and vascular surgical procedures reported in 2005 and 2006 for patients with known overall probabilities of death. Among these, 3245 patients died within 30 days of their surgery (1.7%). Patients with a BMI of less than 23.1 demonstrated a significant increased risk of death, with 40% higher odds compared with patients in the middle range for BMI (26.3 to <29.7). Important differences in the association between BMI and mortality risk occur by type of primary procedure.
Body mass index is a significant predictor of mortality within 30 days of surgery, even after adjusting for the contribution to mortality risk made by type of surgery and for a specific patient's overall expected risk of death.
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ABSTRACT: Periprosthetic joint infection continues to potentially complicate an otherwise successful joint replacement. The treatment of this infection often requires multiple surgical procedures associated with increased complications and morbidity. This study examined the relationship between periprosthetic joint infection and mortality and aimed to determine the effect of periprosthetic joint infection on mortality and any predictors of mortality in patients with periprosthetic joint infection. Four hundred and thirty-six patients with at least one surgical intervention secondary to confirmed periprosthetic joint infection were compared with 2342 patients undergoing revision arthroplasty for aseptic failure. The incidence of mortality at thirty days, ninety days, one year, two years, and five years after surgery was assessed. Multivariate analysis was used to assess periprosthetic joint infection as an independent predictor of mortality. In the periprosthetic joint infection population, variables investigated as potential risk factors for mortality were evaluated. Mortality was significantly greater (p < 0.001) in patients with periprosthetic joint infection compared with those undergoing aseptic revision arthroplasty at ninety days (3.7% versus 0.8%), one year (10.6% versus 2.0%), two years (13.6% versus 3.9%), and five years (25.9% versus 12.9%). After controlling for age, sex, ethnicity, number of procedures, involved joint, body mass index, and Charlson Comorbidity Index, revision arthroplasty for periprosthetic joint infection was associated with a fivefold increase in mortality compared with revision arthroplasty for aseptic failures. In the periprosthetic joint infection population, independent predictors of mortality included increasing age, higher Charlson Comorbidity Index, history of stroke, polymicrobial infections, and cardiac disease. Although it is well known that periprosthetic joint infection is a devastating complication that severely limits joint function and is consistently difficult to eradicate, surgeons must also be cognizant of the systemic impact of periprosthetic joint infection and its major influence on fatal outcome in patients. Prognostic Level II. See Instructions for Authors for a complete description of levels of evidence.The Journal of Bone and Joint Surgery 12/2013; 95(24):2177-84. DOI:10.2106/JBJS.L.00789 · 4.31 Impact Factor
Anaesthesia 03/2014; 69(3):203-7. DOI:10.1111/anae.12599 · 3.85 Impact Factor
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ABSTRACT: Background Existing risk stratification tools have limitations and clinical experience suggests they are not used routinely. The aim of this study was to develop and validate a preoperative risk stratification tool to predict 30-day mortality after non-cardiac surgery in adults by analysis of data from the observational National Confidential Enquiry into Patient Outcome and Death (NCEPOD) Knowing the Risk study. Methods The data set was split into derivation and validation cohorts. Logistic regression was used to construct a model in the derivation cohort to create the Surgical Outcome Risk Tool (SORT), which was tested in the validation cohort. Results Prospective data for 19 097 cases in 326 hospitals were obtained from the NCEPOD study. Following exclusion of 2309, details of 16 788 patients were analysed (derivation cohort 11 219, validation cohort 5569). A model of 45 risk factors was refined on repeated regression analyses to develop a model comprising six variables: American Society of Anesthesiologists Physical Status (ASA-PS) grade, urgency of surgery (expedited, urgent, immediate), high-risk surgical specialty (gastrointestinal, thoracic, vascular), surgical severity (from minor to complex major), cancer and age 65 years or over. In the validation cohort, the SORT was well calibrated and demonstrated better discrimination than the ASA-PS and Surgical Risk Scale; areas under the receiver operating characteristic (ROC) curve were 0·91 (95 per cent c.i. 0·88 to 0·94), 0·87 (0·84 to 0·91) and 0·88 (0·84 to 0·92) respectively (P < 0·001). Conclusion The SORT allows rapid and simple data entry of six preoperative variables, and provides a percentage mortality risk for individuals undergoing surgery.British Journal of Surgery 12/2014; 101(13):1774-83. DOI:10.1002/bjs.9638 · 5.21 Impact Factor