Gabriel J Escobar

Kaiser Permanente, Oakland, California, United States

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Publications (179)765.09 Total impact

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    ABSTRACT: Objectives: To evaluate process metrics and outcomes after implementation of the "Rethinking Critical Care" ICU care bundle in a community setting. Design: Retrospective interrupted time-series analysis. Setting: Three hospitals in the Kaiser Permanente Northern California integrated healthcare delivery system. Patients: ICU patients admitted between January 1, 2009, and August 30, 2013. Interventions: Implementation of the Rethinking Critical Care ICU care bundle which is designed to reduce potentially preventable complications by focusing on the management of delirium, sedation, mechanical ventilation, mobility, ambulation, and coordinated care. Rethinking Critical Care implementation occurred in a staggered fashion between October 2011 and November 2012. Measurements and main results: We measured implementation metrics based on electronic medical record data and evaluated the impact of implementation on mortality with multivariable regression models for 24,886 first ICU episodes in 19,872 patients. After implementation, some process metrics (e.g., ventilation start and stop times) were achieved at high rates, whereas others (e.g., ambulation distance), available late in the study period, showed steep increases in compliance. Unadjusted mortality decreased from 12.3% to 10.9% (p < 0.01) before and after implementation, respectively. The adjusted odds ratio for hospital mortality after implementation was 0.85 (95% CI, 0.73-0.99) and for 30-day mortality was 0.88 (95% CI, 0.80-0.97) compared with before implementation. However, the mortality rate trends were not significantly different before and after Rethinking Critical Care implementation. The mean duration of mechanical ventilation and hospital stay also did not demonstrate incrementally greater declines after implementation. Conclusions: Rethinking Critical Care implementation was associated with changes in practice and a 12-15% reduction in the odds of short-term mortality. However, these findings may represent an evaluation of changes in practices and outcomes still in the midimplementation phase and cannot be directly attributed to the elements of bundle implementation.
    Critical care medicine 11/2015; DOI:10.1097/CCM.0000000000001462 · 6.31 Impact Factor
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    Gabriel J. Escobar · Arona Ragins · Peter Scheirer · Vincent Liu · Jay Robles · Patricia Kipnis ·
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    ABSTRACT: Background: Hospital discharge planning has been hampered by the lack of predictive models. Objective: To develop predictive models for nonelective rehospitalization and postdischarge mortality suitable for use in commercially available electronic medical records (EMRs). Design: Retrospective cohort study using split validation. Setting: Integrated health care delivery system serving 3.9 million members. Participants: A total of 360,036 surviving adults who experienced 609,393 overnight hospitalizations at 21 hospitals between June 1, 2010 and December 31, 2013. Main Outcome Measure: A composite outcome (nonelective rehospitalization and/or death within 7 or 30 days of discharge). Results: Nonelective rehospitalization rates at 7 and 30 days were 5.8% and 12.4%; mortality rates were 1.3% and 3.7%; and composite outcome rates were 6.3% and 14.9%, respectively. Using data from a comprehensive EMR, we developed 4 models that can generate risk estimates for risk of the combined outcome within 7 or 30 days, either at the time of admission or at 8 AM on the day of discharge. The best was the 30-day discharge day model, which had a c-statistic of 0.756 (95% confidence interval, 0.754-0.756) and a Nagelkerke pseudo-R2 of 0.174 (0.171-0.178) in the validation dataset. The most important predictors-a composite acute physiology score and end of life care directives-accounted for 54% of the predictive ability of the 30-day model. Incorporation of diagnoses (not reliably available for real-time use) did not improve model performance. Conclusions: It is possible to develop robust predictive models, suitable for use in real time with commercially available EMRs, for nonelective rehospitalization and postdischarge mortality.
    Medical Care 11/2015; 53(11):916-923. DOI:10.1097/MLR.0000000000000435 · 3.23 Impact Factor
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    ABSTRACT: Background Blood conservation strategies have been shown to be effective in decreasing red blood cell (RBC) utilization in specific patient groups. However, few data exist describing the extent of RBC transfusion reduction or their impact on transfusion practice and mortality in a diverse inpatient population.Study Design and Methods We conducted a retrospective cohort study using comprehensive electronic medical record data from 21 medical facilities in Kaiser Permanente Northern California. We examined unadjusted and risk-adjusted RBC transfusion and 30-day mortality coincident with implementation of RBC conservation strategies.ResultsThe inpatient study cohort included 391,958 patients who experienced 685,753 hospitalizations. From 2009 to 2013, the incidence of RBC transfusion decreased from 14.0% to 10.8% of hospitalizations; this change coincided with a decline in pretransfusion hemoglobin (Hb) levels from 8.1 to 7.6 g/dL. Decreased RBC utilization affected broad groups of admission diagnoses and was most pronounced in patients with a nadir Hb level between 8 and 9 g/dL (n = 73,057; 50.8% to 19.3%). During the study period, the standard deviation of risk-adjusted RBC transfusion incidence across hospitals decreased by 44% (p < 0.001). Thirty-day mortality did not change significantly with declines in RBC utilization in patient groups previously studied in clinical trials nor in other subgroups.Conclusions After the implementation of blood conservation strategies, RBC transfusion incidence and pretransfusion Hb levels decreased broadly across medical and surgical patients. Variation in RBC transfusion incidence across hospitals decreased from 2010 to 2013. Consistent with clinical trial data, more restrictive transfusion practice did not appear to impact 30-day mortality.
    Transfusion 09/2014; 54(10). DOI:10.1111/trf.12825 · 3.23 Impact Factor
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    ABSTRACT: Study aims: To assess whether increased use of targeted temperature management (TTM) within an integrated healthcare delivery system resulted in improved rates of good neurologic outcome at hospital discharge (Cerebral Performance Category score of 1 or 2). Methods: Retrospective cohort study of patients with OHCA admitted to 21 medical centers between January 2007 and December 2012. A standardized TTM protocol and educational program were introduced throughout the system in early 2009. Comatose patients eligible for treatment with TTM were included. Adjusted odds of good neurologic outcome at hospital discharge and survival to hospital discharge were assessed using multivariate logistic regression. Results: A total of 1119 patients were admitted post-OHCA with coma, 59.1% (661 of 1119) of which were eligible for TTM. The percentage of patients treated with TTM markedly increased during the study period: 10.5% in the years preceding (2007-2008) vs. 85.1% in the years following (2011-2012) implementation of the practice improvement initiative. However, unadjusted in-hospital survival (37.3% vs. 39.0%, p=0.77) and good neurologic outcome at hospital discharge (26.3% vs. 26.6%, p=1.0) did not change. The adjusted odds of survival to hospital discharge (AOR 1.0, 95% CI 0.85-1.17) or a good neurologic outcome (AOR 0.94, 95% CI 0.79-1.11) were likewise non-significant. Interpretation: Despite a marked increase in TTM rates across hospitals in an integrated delivery system, there was no appreciable change in the crude or adjusted odds of in-hospital survival or good neurologic outcomes at hospital discharge among eligible post-arrest patients.
    Resuscitation 08/2014; 85(11). DOI:10.1016/j.resuscitation.2014.08.014 · 4.17 Impact Factor
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    ABSTRACT: BACKGROUND Sepsis, the most expensive cause of hospitalization in the United States, is associated with high morbidity and mortality. However, healthcare utilization patterns following sepsis are poorly understood.OBJECTIVE To identify patient-level factors that contribute to postsepsis mortality and healthcare utilization.DESIGN, SETTING, PATIENTSA retrospective study of sepsis patients drawn from 21 community-based hospitals in Kaiser Permanente Northern California in 2010.MEASUREMENTSWe determined 1-year survival and use of outpatient and facility-based healthcare before and after sepsis and used logistic regression to identify the factors that contributed to early readmission (within 30 days) and high utilization (≥15% of living days spent in facility-based care).RESULTSAmong 6344 sepsis patients, 5479 (86.4%) survived to hospital discharge. Mean age was 72 years with 28.9% of patients aged <65 years. Postsepsis survival was strongly modified by age; 1-year survival was 94.1% for <45 year olds and 54.4% for ≥85 year olds. A total of 978 (17.9%) patients were readmitted within 30 days; only a minority of all rehospitalizations were for infection. After sepsis, adjusted healthcare utilization increased nearly 3-fold compared with presepsis levels and was strongly modified by age. Patient factors including acute severity of illness, hospital length of stay, and the need for intensive care were associated with early readmission and high healthcare utilization; however, the dominant factors explaining variability—comorbid disease burden and high presepsis utilization—were present prior to sepsis admission.CONCLUSION Postsepsis survival and healthcare utilization were most strongly influenced by patient factors already present prior to sepsis hospitalization. Journal of Hospital Medicine 2014;. © 2014 Society of Hospital Medicine
    Journal of Hospital Medicine 08/2014; 9(8). DOI:10.1002/jhm.2197 · 2.30 Impact Factor

  • JAMA Internal Medicine 08/2014; 174(8):1405. DOI:10.1001/jamainternmed.2014.2889 · 13.12 Impact Factor
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    David W Bates · Suchi Saria · Lucila Ohno-Machado · Anand Shah · Gabriel Escobar ·
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    ABSTRACT: The US health care system is rapidly adopting electronic health records, which will dramatically increase the quantity of clinical data that are available electronically. Simultaneously, rapid progress has been made in clinical analytics-techniques for analyzing large quantities of data and gleaning new insights from that analysis-which is part of what is known as big data. As a result, there are unprecedented opportunities to use big data to reduce the costs of health care in the United States. We present six use cases-that is, key examples-where some of the clearest opportunities exist to reduce costs through the use of big data: high-cost patients, readmissions, triage, decompensation (when a patient's condition worsens), adverse events, and treatment optimization for diseases affecting multiple organ systems. We discuss the types of insights that are likely to emerge from clinical analytics, the types of data needed to obtain such insights, and the infrastructure-analytics, algorithms, registries, assessment scores, monitoring devices, and so forth-that organizations will need to perform the necessary analyses and to implement changes that will improve care while reducing costs. Our findings have policy implications for regulatory oversight, ways to address privacy concerns, and the support of research on analytics.
    Health Affairs 07/2014; 33(7):1123-31. DOI:10.1377/hlthaff.2014.0041 · 4.97 Impact Factor
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    ABSTRACT: Background: Acculturation may influence women's perceptions of health care experiences and may explain the epidemiologic paradox, whereby foreign-born women have lower rates of adverse birth outcomes than United States (US)-born women. We evaluated the relationship between maternal acculturation and specific dimensions of prenatal interpersonal processes of care (IPC) in ethnically diverse women. Methods: Cross-sectional analysis of 1243 multiethnic, postpartum women who delivered at Kaiser Permanente Medical Center in Walnut Creek or San Francisco General Hospital. Women retrospectively reported on their experiences in seven domains of IPC during their pregnancy pertaining to communication, decision making, and interpersonal style. The primary independent variables were four measures of maternal acculturation: birthplace, English language proficiency, the number of years residing in the US, and age at immigration to the US. Generalized linear models, stratified by infant outcome, measured the association between each maternal acculturation measure and specific IPC domains while adjusting for type of health insurance, demographic, and reproductive factors. Results: Approximately 60% of the sample was foreign-born, 36% reported low English proficiency, 43% had resided in the US <10 years, and 35% were age 20 years or older when they immigrated to the US. Over 64% of the women reported having public insurance during pregnancy. In adjusted analyses among women who delivered term and normal birth weight infants, less acculturated women and women with non-private health insurance were more likely to have higher mean IPC scores when compared to more acculturated or US-born women and women with private health insurance, respectively. Conclusion: In a large and ethnically diverse sample of childbearing women in Northern California, less acculturated pregnant women reported better prenatal care experiences than more acculturated and US-born women, another dimension of the "epidemiologic paradox." However, the relationship between acculturation and IPC, as reported during the postpartum period, differed according to infant outcomes.
    Journal of Women's Health 06/2014; 23(8). DOI:10.1089/jwh.2013.4585 · 2.05 Impact Factor
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    ABSTRACT: Rationale: Hospitalizations for severe sepsis are common, and a growing number of patients survive to hospital discharge. Nonetheless, little is known about survivors' post-discharge health care utilization. Objectives: To measure inpatient health care use of severe sepsis survivors compared to (1) patients' own pre-sepsis resource use and (2) the resource use of survivors of otherwise similar non-sepsis hospitalizations. Methods: This is an observational cohort study of survivors of severe sepsis and non-sepsis hospitalizations identified from participants in the Health & Retirement Study with linked Medicare claims, 1998-2005. We matched severe sepsis and non-sepsis hospitalizations by demographics, comorbidity burden, pre-morbid disability, hospitalization length, and intensive care use. Measurements and Main Results: Using Medicare claims, we measured patients' utilization of inpatient facilities-hospitals, long-term acute care hospitals, and skilled nursing facilities-in the two years surrounding hospitalization. Severe sepsis survivors spent more days (median 16 (IQR 3-45) vs. 7(0-29), p<0.001) and a higher proportion of days alive (median 9.6% (IQR 1.4%-33.8%) vs. 1.9%(0.0%-7.9%) p<0.001) admitted to facilities in the year after hospitalization, compared to the year prior. The increase in facility-days was similar for non-sepsis hospitalizations. However, the severe sepsis cohort experienced greater post-discharge mortality (44.2% (95%CI:41.3-47.2%) vs. 31.4%(95%CI:28.6%-34.2%) at one year), a steeper decline in days spent at home (difference-in-differences:-38.6 days (95%CI:-50.9--26.3), p<0.001), and a greater increase in the proportion of days alive spent in a facility (difference-in-differences:5.4%(95%CI:2.8%-8.1%), p<0.001). Conclusion: Health care use is markedly elevated after severe sepsis, and post-discharge management may be an opportunity to reduce resource utilization.
    American Journal of Respiratory and Critical Care Medicine 05/2014; 190(1). DOI:10.1164/rccm.201403-0471OC · 13.00 Impact Factor

  • JAMA The Journal of the American Medical Association 05/2014; 312(1). DOI:10.1001/jama.2014.5804 · 35.29 Impact Factor
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    ABSTRACT: Background Randomized controlled trial evidence supports a restrictive strategy of red blood cell (RBC) transfusion, but significant variation in clinical transfusion practice persists. Patient characteristics other than hemoglobin levels may influence the decision to transfuse RBCs and explain some of this variation. Our objective was to evaluate the role of patient comorbidities and severity of illness in predicting inpatient red blood cell transfusion events. Methods We developed a predictive model of inpatient RBC transfusion using comprehensive electronic medical record (EMR) data from 21 hospitals over a four year period (2008-2011). Using a retrospective cohort study design, we modeled predictors of transfusion events within 24 hours of hospital admission and throughout the entire hospitalization. Model predictors included administrative data (age, sex, comorbid conditions, admission type, and admission diagnosis), admission hemoglobin, severity of illness, prior inpatient RBC transfusion, admission ward, and hospital. Results The study cohort included 275,874 patients who experienced 444,969 hospitalizations. The 24 hour and overall inpatient RBC transfusion rates were 7.2% and 13.9%, respectively. A predictive model for transfusion within 24 hours of hospital admission had a C-statistic of 0.928 and pseudo-R2 of 0.542; corresponding values for the model examining transfusion through the entire hospitalization were 0.872 and 0.437. Inclusion of the admission hemoglobin resulted in the greatest improvement in model performance relative to patient comorbidities and severity of illness. Conclusions Data from electronic medical records at the time of admission predicts with very high likelihood the incidence of red blood transfusion events in the first 24 hours and throughout hospitalization. Patient comorbidities and severity of illness on admission play a small role in predicting the likelihood of RBC transfusion relative to the admission hemoglobin.
    BMC Health Services Research 05/2014; 14(1):213. DOI:10.1186/1472-6963-14-213 · 1.71 Impact Factor
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    Carri W. Chan · Galit Yom-Tov · Gabriel Escobar ·
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    ABSTRACT: In a number of service systems, there can be substantial latitude to vary service rates. However, although speeding up service rate during periods of congestion may address a present congestion issue, it may actually exacerbate the problem by increasing the need for rework. We introduce a state-dependent queuing network where service times and return probabilities depend on the “overloaded” and “underloaded” state of the system. We use a fluid model to examine how different definitions of “overload” affect the long-term behavior of the system and provide insight into the impact of using speedup. We identify scenarios where speedup can be helpful to temporarily alleviate congestion and increase access to service. For such scenarios, we provide approximations for the likelihood of speedup to service. We also identify scenarios where speedup should never be used; moreover, in such a situation, an interesting bi-stability arises, such that the system shifts randomly between two equilibria states. Hence, our analysis sheds light on the potential benefits and pitfalls of using speedup when the subsequent returns may be unavoidable.
    Operations Research 04/2014; 62(2). DOI:10.1287/opre.2014.1258 · 1.74 Impact Factor
  • Patricia Kipnis · Vincent Liu · Gabriel J Escobar ·
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    ABSTRACT: Background: Risk-adjusted mortality rates are commonly used in quality report cards to compare hospital performance. The risk adjustment depends on models that are assessed for goodness-of-fit using various discrimination and calibration measures. However, the relationship between model fit and the accuracy of hospital comparisons is not well characterized. Objectives: To evaluate the impact of imperfect model calibration (miscalibration) on the accuracy of hospital comparisons. Methods: We constructed Monte Carlo simulations where a risk-adjustment model is used in a population with a different mortality distribution than in the original model. We estimated the power of calibration metrics to detect miscalibration. We estimated the sensitivity and specificity of a hospital comparisons method under different imperfect model calibration scenarios using an empirical method. Results: The U-statistics showed the highest power to detect intercept and slope deviations in the calibration curve, followed by the Hosmer-Lemeshow, and the calibration intercept and slope tests. The specificity decreased with increased intercept and slope deviations and with hospital size. The effect of an imperfect model fit on sensitivity is a function of the true standardized mortality ratio, the underlying mortality rate, sample size, and observed intercept and slope deviations. Poorly performing hospitals can appear as good performers and vice versa, depending on the deviation magnitude and direction. Conclusions: Deviations from perfect model calibration have a direct impact on the accuracy of hospital comparisons. Publishing the calibration intercept and slope of risk-adjustment models would allow the users to monitor their performance against the true standard population.
    Medical care 04/2014; 52(4):378-84. DOI:10.1097/MLR.0000000000000111 · 3.23 Impact Factor
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    ABSTRACT: Adherence to evidence-based recommendations for acute myocardial infarction (AMI) remains unsatisfactory. Quantifying association between using an electronic AMI order set (AMI-OS) and hospital processes and outcomes. Retrospective cohort study. Twenty-one community hospitals. A total of 5879 AMI patients were hospitalized between September 28, 2008 and December 31, 2010. We ascertained whether patients were treated using the AMI-OS or individual orders (a la carte). Dependent process variables were use of evidence-based care; outcome variables were mortality and rehospitalization. Use of individual and combined therapies improved outcomes (eg, 50% lower odds of 30-day mortality for patients with ≥3 therapies). The 3531 patients treated using the AMI-OS were more likely to receive evidence-based therapies (eg, 50% received 5 different therapies vs 36% a la carte). These patients had lower 30-day mortality (5.7% vs 8.5%) than the 2348 treated using a la carte orders. Although AMI-OS patients' predicted mortality risk was lower (3.2%) than that of a la carte patients (4.8%), the association of improved processes and outcomes with the use of the AMI-OS persisted after risk adjustment. For example, after inverse probability weighting, the relative risk for inpatient mortality in the AMI-OS group was 0.67 (95% confidence interval: 0.52-0.86). Inclusion of use of recommended therapies in risk adjustment eliminated the benefit of the AMI-OS, highlighting its mediating effect on adherence to evidence-based treatment. Use of an electronic order set is associated with increased adherence to evidence-based care and better AMI outcomes. Journal of Hospital Medicine 2014. © 2014 Society of Hospital Medicine.
    Journal of Hospital Medicine 03/2014; 9(3). DOI:10.1002/jhm.2149 · 2.30 Impact Factor
  • Thomas B Newman · David Draper · Karen M Puopolo · Soora Wi · Gabriel J Escobar ·
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    ABSTRACT: The absolute neutrophil count and the immature/total neutrophil ratio (I/T) provide information about the risk of early-onset sepsis in newborns. However, it is not clear how to combine their potentially overlapping information into a single likelihood ratio. We obtained electronic records of blood cultures and of complete blood counts with manual differentials drawn <1 hour apart on 66,846 infants ≥34 weeks gestation and <72 hours old born at Kaiser Permanente Northern California and Brigham and Women's Hospitals. We hypothesized that dividing the immature neutrophil count (I) by the total neutrophil count (T) squared (=I/T) would provide a useful summary of the risk of infection. We evaluated the ability of the I/T to discriminate newborns with pathogenic bacteremia from other newborns tested using the area under the receiver operating characteristic curve (c). Discrimination of the I/T (c=0.79; 95% CI: 0.76, 0.82) was similar to that of logistic models with indicator variables for each of 24 combinations of the absolute neutrophil count and the proportion of immature neutrophils (c =0.80, 95% CI: 0.77, 0.83). Discrimination of the I/T improved with age, from 0.70 at <1 hour to 0.87 at ≥4 hours. However, 60% of I/T had likelihood ratios of 0.44 to 1.3, thus only minimally altering the pretest odds of disease. Calculating the I/T could enhance prediction of early onset sepsiss, but the CBC will remain helpful mainly when done at >4 hours of age and when the pretest probability of infection is close to the treatment threshold.
    The Pediatric Infectious Disease Journal 02/2014; 33(8). DOI:10.1097/INF.0000000000000297 · 2.72 Impact Factor

  • Journal of Allergy and Clinical Immunology 02/2014; 133(2):AB282. DOI:10.1016/j.jaci.2013.12.998 · 11.48 Impact Factor
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    ABSTRACT: Objective: To define a quantitative stratification algorithm for the risk of early-onset sepsis (EOS) in newborns ≥ 34 weeks' gestation. Methods: We conducted a retrospective nested case-control study that used split validation. Data collected on each infant included sepsis risk at birth based on objective maternal factors, demographics, specific clinical milestones, and vital signs during the first 24 hours after birth. Using a combination of recursive partitioning and logistic regression, we developed a risk classification scheme for EOS on the derivation dataset. This scheme was then applied to the validation dataset. Results: Using a base population of 608,014 live births ≥ 34 weeks' gestation at 14 hospitals between 1993 and 2007, we identified all 350 EOS cases <72 hours of age and frequency matched them by hospital and year of birth to 1063 controls. Using maternal and neonatal data, we defined a risk stratification scheme that divided the neonatal population into 3 groups: treat empirically (4.1% of all live births, 60.8% of all EOS cases, sepsis incidence of 8.4/1000 live births), observe and evaluate (11.1% of births, 23.4% of cases, 1.2/1000), and continued observation (84.8% of births, 15.7% of cases, incidence 0.11/1000). Conclusions: It is possible to combine objective maternal data with evolving objective neonatal clinical findings to define more efficient strategies for the evaluation and treatment of EOS in term and late preterm infants. Judicious application of our scheme could result in decreased antibiotic treatment in 80,000 to 240,000 US newborns each year.
    PEDIATRICS 12/2013; 133(1). DOI:10.1542/peds.2013-1689 · 5.47 Impact Factor
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    ABSTRACT: Introduction: Quantitative resuscitation (QR), or early goal-directed therapy, is widely used in sepsis, however, the optimal timing for initiating therapy is not well understood. The goal of this retrospective study was to quantify the effects of the timing of QR initiation on hospital mortality. Methods: We retrospectively analyzed 4,294 QR-eligible adult sepsis patients from 21 community hospitals within the Kaiser Permanente Northern California integrated healthcare delivery system. We used logistic and proportional hazards regressions as well as propensity score matching and Monte Carlo simulation to estimate the effects associated with earlier initiation of QR in the Emergency Department (ED). Patients were grouped based on the timing of QR (as measured by the first documented physiologic target reading recorded in the electronic medical record after central venous catheter insertion) including 'early' (within 2 hours of ED presentation), 'middle' (2 to 6 hours), and 'late' (after 6 hours) cohorts. Results: Of 4,294 QR-eligible patients, 2,736 (63.7%) consented to QR while 1,558 refused. Patients initially refusing QR were older than patients consenting to QR (74.3 +/- 16.2 versus 68.4 +/- 15.5 years) but had similar comorbid disease burden and acute severity of illness. Among patients who received QR, 1,112 (45.2%) were in the 'early', 1,230 (50.0%) in the 'middle', and 120 (4.9%) in the 'late' groups. Patients in the 'early' and 'middle' QR cohorts had similar age (median, 70.0 years), first lactate values (4.3 mmol/L), and Charlson comorbidity scores (1.0). However, 'early' QR patients had higher severity of illness (based on a comprehensive physiologic severity score, LAPS2; 136 +/- 35 versus 124 +/- 29) and higher predicted mortality (16.8% +/- 14.9% versus 16.1% +/- 14.4%). All individual targets were achieved with >80% frequency among 'early' QR patients (most >94%); in contrast, 'middle' QR patients achieved ScVO2 targets only 68.1% of the time. Unadjusted hospital mortality was 24.0% for 'early', 18.5% for 'middle', and 26.7% for 'late' QR patients. After adjustment with multiple techniques, hospital mortality was not significantly different between 'early' (16.2%; 95% CI: 14.0-18.7%) and 'middle' (14.5%; 95% CI: 12.5-16.7%) patients; both had lower mortality compared with patients receiving 'late' QR. Conclusions: In a community-based cohort of patients receiving QR for sepsis, outcomes were similar between patients receiving 'early' and 'late' QR initiation. However, even with advanced causal inference methods, the potential for confounding by indication remains.
    43rd Annual Critical Care Congress; 12/2013
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    ABSTRACT: The increased vulnerability of late preterm infants is no longer a novel concept in neonatology, with many studies documenting excess morbidity and mortality in these infants during the birth hospitalization. Because outcomes related to gestational age constitute a continuum, it is important to analyze data from the gestational age groups that bookend late preterm infants infants-moderate preterm infants (31-32 weeks) and early term infants (37-38 weeks). This article evaluates hospital readmissions and emergency department visits in the first 30 days after discharge from birth hospitalization in a large cohort of infants greater than or equal to 31 weeks' gestation.
    Clinics in perinatology 12/2013; 40(4):753-75. DOI:10.1016/j.clp.2013.07.008 · 2.44 Impact Factor
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    ABSTRACT: Introduction: Sepsis is a morbid cause of hospitalization, however, little is known about the relative proportion of hospital deaths that result from sepsis. We evaluated a multi-center sample of hospitalizations at 21 community medical centers in the Kaiser Permanente Northern California integrated healthcare delivery system between September 2010 and September 2012 to determine the proportion of all hospital deaths associated with sepsis. Methods: We retrospectively analyzed all hospitalizations that included non-obstetrical patients aged >=18 years with overnight stays (or death at any time). We identified sepsis patients based on diagnosis coding and manually-collected data from regional sepsis quality improvement (QI) initiatives. We used QI data-available for 83.9% of sepsis patients-to categorize patients based on sepsis severity and their eligibility for, or use of, early goal-directed therapy (EGDT). Results: Over two years, a total of 345,264 patients met the study criteria including 42,457 (12.3%) hospitalized for sepsis-85.7% with the 038 ICD-9 diagnosis code. Mean age was 69.5 +/- 16.9 for sepsis patients and 64.1 +/- 17.5 years for others. Sepsis patients had higher comorbid disease burden, acute severity of illness, and predicted mortality (9.3% +/- 12.6% versus 2.4% +/- 5.5%, p<0.001) when compared with non-sepsis patients. They also had higher ICU utilization (31.8% versus 13.5%) and length of stay (6.7 versus 3.8 days) than other inpatients. Sepsis hospital mortality was 10.5%; 30-day mortality was 15.2%. Hospital mortality otherwise was 1.9%. More than 43% (n = 4,463) of all inpatient deaths were among sepsis patients. Most had sepsis present on admission (93.8%) and nearly 40% had severe sepsis or septic shock. Hospital mortality varied by severity strata; for example, 6.6% of patients with non-severe sepsis died compared with 17.8% of patients receiving EGDT. Hospital mortality was 26.2% to 33.6% among EGDT-eligible patients who did not receive EGDT (either based on clinician judgment, contraindications, patient refusal, or comfort care status). In terms of all sepsis deaths, each sepsis strata (non-severe sepsis, sepsis with intermediate lactate, and EGDT-eligible sepsis) contributed roughly equal numbers of deaths. This included 1,225 (31.9%) deaths among non-severe, 1,213 (31.6%) among intermediate lactate, and 1,397 (36.4%) among EGDT-eligible sepsis patients. Conclusions: In a multi-center study of community hospitals, >43% of patients dying in the hospital had sepsis. These sepsis deaths were roughly equally distributed across severity strata.
    43rd Annual Critical Care Congress; 12/2013

Publication Stats

5k Citations
765.09 Total Impact Points


  • 1994-2014
    • Kaiser Permanente
      • Department of Obstetrics and Gynecology
      Oakland, California, United States
  • 2011
    • Children's Hospital & Research Center Oakland
      Oakland, California, United States
  • 2010
    • The Children's Hospital of Philadelphia
      • Department of Pediatrics
      Filadelfia, Pennsylvania, United States
  • 1999-2010
    • Permanente Medical Group
      Pasadena, California, United States
  • 2004-2007
    • Harvard University
      • Department of Society, Human Development, and Health
      Cambridge, Massachusetts, United States
  • 2005
    • University of California, Berkeley
      • Department of Health Services and Policy Analysis
      Berkeley, California, United States
    • Harvard Medical School
      • Department of Medicine
      Boston, Massachusetts, United States
  • 2003-2005
    • University of California, San Francisco
      • • Division of Hospital Medicine
      • • Department of Pediatrics
      San Francisco, CA, United States
    • Stanford University
      • Department of Pediatrics
      Stanford, CA, United States
  • 1999-2000
    • Boston Children's Hospital
      Boston, Massachusetts, United States