Dataset Information

A Severe Sepsis Mortality Prediction Model and Score for Use With Administrative Data.

ABSTRACT: Administrative data are used for research, quality improvement, and health policy in severe sepsis. However, there is not a sepsis-specific tool applicable to administrative data with which to adjust for illness severity. Our objective was to develop, internally validate, and externally validate a severe sepsis mortality prediction model and associated mortality prediction score.Retrospective cohort study using 2012 administrative data from five U.S. states. Three cohorts of patients with severe sepsis were created: 1) International Classification of Diseases, 9th Revision, Clinical Modification codes for severe sepsis/septic shock, 2) Martin approach, and 3) Angus approach. The model was developed and internally validated in International Classification of Diseases, 9th Revision, Clinical Modification, cohort and externally validated in other cohorts. Integer point values for each predictor variable were generated to create a sepsis severity score.Acute care, nonfederal hospitals in New York, Maryland, Florida, Michigan, and Washington.Patients in one of three severe sepsis cohorts: 1) explicitly coded (n = 108,448), 2) Martin cohort (n = 139,094), and 3) Angus cohort (n = 523,637) INTERVENTIONS: None.Maximum likelihood estimation logistic regression to develop a predictive model for in-hospital mortality. Model calibration and discrimination assessed via Hosmer-Lemeshow goodness-of-fit and C-statistics, respectively. Primary cohort subset into risk deciles and observed versus predicted mortality plotted. Goodness-of-fit demonstrated p value of more than 0.05 for each cohort demonstrating sound calibration. C-statistic ranged from low of 0.709 (sepsis severity score) to high of 0.838 (Angus cohort), suggesting good to excellent model discrimination. Comparison of observed versus expected mortality was robust although accuracy decreased in highest risk decile.Our sepsis severity model and score is a tool that provides reliable risk adjustment for administrative data.

SUBMITTER: Ford DW

PROVIDER: S-EPMC4724863 | biostudies-other | 2016 Feb

REPOSITORIES: biostudies-other

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A Severe Sepsis Mortality Prediction Model and Score for Use With Administrative Data.

Ford Dee W DW Goodwin Andrew J AJ Simpson Annie N AN Johnson Emily E Nadig Nandita N Simpson Kit N KN

Critical care medicine 20160201 2

<h4>Objective</h4>Administrative data are used for research, quality improvement, and health policy in severe sepsis. However, there is not a sepsis-specific tool applicable to administrative data with which to adjust for illness severity. Our objective was to develop, internally validate, and externally validate a severe sepsis mortality prediction model and associated mortality prediction score.<h4>Design</h4>Retrospective cohort study using 2012 administrative data from five U.S. states. Thre ...[more]

PMID: 26496452

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Project description:BackgroundGiven the increased attention to sepsis at the population level there is a need to assess hospital performance in the care of sepsis patients using widely-available administrative data. The goal of this study was to develop an administrative risk-adjustment model suitable for profiling hospitals on their 30-day mortality rates for patients with sepsis.MethodsWe conducted a retrospective cohort study using hospital discharge data from general acute care hospitals in Pennsylvania in 2012 and 2013. We identified adult patients with sepsis as determined by validated diagnosis and procedure codes. We developed an administrative risk-adjustment model in 2012 data. We then validated this model in two ways: by examining the stability of performance assessments over time between 2012 and 2013, and by examining the stability of performance assessments in 2012 after the addition of laboratory variables measured on day one of hospital admission.ResultsIn 2012 there were 115,213 sepsis encounters in 152 hospitals. The overall unadjusted mortality rate was 18.5%. The final risk-adjustment model had good discrimination (C-statistic = 0.78) and calibration (slope and intercept of the calibration curve = 0.960 and 0.007, respectively). Based on this model, hospital-specific risk-standardized mortality rates ranged from 12.2 to 24.5%. Comparing performance assessments between years, correlation in risk-adjusted mortality rates was good (Pearson's correlation = 0.53) and only 19.7% of hospitals changed by more than one quintile in performance rankings. Comparing performance assessments after the addition of laboratory variables, correlation in risk-adjusted mortality rates was excellent (Pearson's correlation = 0.93) and only 2.6% of hospitals changed by more than one quintile in performance rankings.ConclusionsA novel claims-based risk-adjustment model demonstrated wide variation in risk-standardized 30-day sepsis mortality rates across hospitals. Individual hospitals' performance rankings were stable across years and after the addition of laboratory data. This model provides a robust way to rank hospitals on sepsis mortality while adjusting for patient risk.

Project description:PURPOSE:Sepsis is a common acute life-threatening condition that emergency physicians routinely face. Diagnostic options within the Emergency Department (ED) are limited due to lack of infrastructure, consequently limiting the use of invasive hemodynamic monitoring or imaging tests. The mortality rate due to sepsis can be assessed via multiple scoring systems, for example, mortality in emergency department sepsis (MEDS) score and sepsis patient evaluation in the emergency department (SPEED) score, both of which quantify the variation of mortality rates according to clinical findings, laboratory data, or therapeutic interventions. This study aims to improve the management processes of sepsis patients by comparing SPEED score and MEDS score for predicting the 28-day mortality in cases of emergency sepsis. METHODS:The study is a cross-sectional, prospective study including 61 sepsis patients in ED in Suez Canal University Hospital, Egypt, from August 2017 to June 2018. Patients were selected by two steps: (1) suspected septic patients presenting with at least one of the following abnormal clinical findings: (a) body temperature higher than 38 °C or lower than 36 °C, (b) heart rate higher than 90 beats/min, (c) hyperventilation evidenced by respiratory rate higher than 20 breaths/min or PaCO2 lower than 32 mmHg, and (d) white blood cell count higher than 12,000/?L or lower than 4000/?L; (2) confirmed septic patients with at least a 2-point increase from the baseline total sequential organ failure assessment (SOFA) score following infection. Other inclusion criteria included adult patients with an age ?18 years regardless of gender and those who had either systemic inflammatory response syndrome or suspected/confirmed infection. Patients were shortly follow-up for the 28-day mortality. Each patient was subject to SPEED score and MEDS score and then the results were compared to detect which of them was more effective in predicting outcome. The receiver operating characteristic curves were also done for MEDS and SPEED scores. RESULTS:Among the 61 patients, 41 died with the mortality rate of 67.2%. The mortality rate increased with a higher SPEED and MEDS scores. Both SPEED and MEDS scores revealed significant difference between the survivors and nonsurvivors (p = 0.004 and p < 0.001, respectively), indicating that both the two systems are effective in predicting the 28-day mortality of sepsis patients. Thereafter, the receiver operating characteristic curves were plotted, which showed that SPEED was better than the MEDS score when applied to the complete study population with an area under the curve being 0.87 (0.788-0.963) as compared with 0.75 (0.634-0.876) for MEDS. Logistic regression analysis revealed that the best fitting predictor of 28-day mortality for sepsis patients was the SPEED scoring system. For every one unit increase in SPEED score, the odds of 28-day mortality increased by 37%. CONCLUSION:SPEED score is more useful and accurate than MEDS score in predicting the 28-day mortality among sepsis patients. Therefore SPEED rather than MEDS should be more widely used in the ED for sepsis patients.

Dataset Information

A Severe Sepsis Mortality Prediction Model and Score for Use With Administrative Data.

Publications

A Severe Sepsis Mortality Prediction Model and Score for Use With Administrative Data.

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