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

Critical Care Medicine

Volumn 44, Issue 2, 2016, Pages 319-327

  Ford, Dee W ^a   Goodwin, Andrew J ^a   Simpson, Annie N ^b   Johnson, Emily ^b   Nadig, Nandita ^a   Simpson, Kit N ^b  

^a MEDICAL UNIVERSITY OF SOUTH CAROLINA   (United States)

^b MEDICAL UNIVERSITY OF SOUTH CAROLINA   (United States)

Author keywords

administrative data; critical care; health service research; intensive care; mortality prediction model; risk adjustment; sepsis; septic shock; severe sepsis; severity index; severity of illness; severity score

Indexed keywords

ACCURACY; ADULT; AGED; ARTICLE; ARTIFICIAL VENTILATION; CALIBRATION; COHORT ANALYSIS; EXTERNAL VALIDITY; FEMALE; HUMAN; ICD-9-CM; INTENSIVE CARE UNIT; INTERNAL VALIDITY; MALE; MIDDLE AGED; MORTALITY; PREDICTION; PREDICTOR VARIABLE; PRIORITY JOURNAL; RETROSPECTIVE STUDY; RISK; SCORING SYSTEM; SEPSIS; SEPTIC SHOCK; SYSTEMIC INFLAMMATORY RESPONSE SYNDROME; VERY ELDERLY; AGE; ANCESTRY GROUP; CLINICAL TRIAL; COMORBIDITY; HEALTH INSURANCE; INTENSIVE CARE; INTERNATIONAL CLASSIFICATION OF DISEASES; MULTICENTER STUDY; REPRODUCIBILITY; RISK ASSESSMENT; SEVERITY OF ILLNESS INDEX; SEX DIFFERENCE; STATISTICAL MODEL; THEORETICAL MODEL; UNITED STATES;

AGE FACTORS; AGED; AGED, 80 AND OVER; COMORBIDITY; CONTINENTAL POPULATION GROUPS; CRITICAL CARE; FEMALE; HUMANS; INSURANCE, HEALTH; INTERNATIONAL CLASSIFICATION OF DISEASES; LIKELIHOOD FUNCTIONS; LOGISTIC MODELS; MALE; MIDDLE AGED; MODELS, THEORETICAL; REPRODUCIBILITY OF RESULTS; RETROSPECTIVE STUDIES; RISK ASSESSMENT; SEPSIS; SEVERITY OF ILLNESS INDEX; SEX FACTORS; UNITED STATES;

EID: 84954381373     PISSN: 00903493     EISSN: 15300293     Source Type: Journal    
DOI: 10.1097/CCM.0000000000001392     Document Type: Article

References (38)

1. Smith EE, Shobha N, Dai D, A risk score for in-hospital death in patients admitted with ischemic or hemorrhagic stroke. J Am Heart Assoc 2013 2 e005207
2. Kansagara D, Englander H, Salanitro A, Risk prediction models for hospital readmission: A systematic review. JAMA 2011 306 1688 1698
3. Uematsu H, Kunisawa S, Sasaki N, Development of a risk-adjusted in-hospital mortality prediction model for community-acquired pneumonia: A retrospective analysis using a Japanese administrative database. BMC Pulm Med 2014 14 203
4. Chang HY, Weiner JP, Richards TM, Validating the adapted Diabetes Complications Severity Index in claims data. Am J Manag Care 2012 18 721 726
5. Knaus WA, APACHE 1978-2001: The development of a quality assurance system based on prognosis: Milestones and personal reflections. Arch Surg 2002 137 37 41
6. Ferreira FL, Bota DP, Bross A, Serial evaluation of the SOFA score to predict outcome in critically ill patients. JAMA 2001 286 1754 1758
7. Le Gall JR, Lemeshow S, Saulnier F, A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study. JAMA 1993 270 2957 2963
8. Lemeshow S, Teres D, Klar J, Mortality Probability Models (MPM II) based on an international cohort of intensive care unit patients. JAMA 1993 270 2478 2486
9. Zimmerman JE, Kramer AA, McNair DS, Acute Physiology and Chronic Health Evaluation (APACHE) IV: Hospital mortality assessment for today's critically ill patients. Crit Care Med 2006 34 1297 1310
10. Osborn TM, Phillips G, Lemeshow S, Sepsis severity score: An internationally derived scoring system from the surviving sepsis campaign database. Crit Care Med 2014 42 1969 1976
11. Dellinger RP, Levy MM, Rhodes A, Surviving Sepsis Campaign Guidelines Committee including the Pediatric Subgroup Surviving sepsis campaign: International guidelines for management of severe sepsis and septic shock: 2012. Crit Care Med 2013 41 580 637
12. Ting G, Schneeweiss S, Scranton R, Development of a health care utilisation data-based index for rheumatoid arthritis severity: A preliminary study. Arthritis Res Ther 2008 10 R95
13. Ananthakrishnan AN, McGinley EL, Binion DG, A novel risk score to stratify severity of Crohn's disease hospitalizations. Am J Gastroenterol 2010 105 1799 1807
14. Pavlović I, Kern T, Miklavcic D, Comparison of paper-based and electronic data collection process in clinical trials: Costs simulation study. Contemp Clin Trials 2009 30 300 316
15. Newgard CD, Zive D, Jui J, Electronic versus manual data processing: Evaluating the use of electronic health records in out-of-hospital clinical research. Acad Emerg Med 2012 19 217 227
16. Higgins TL, Quantifying risk and benchmarking performance in the adult intensive care unit. J Intensive Care Med 2007 22 141 156
17. Greenberg MD, Haviland AM, Yu H, Safety outcomes in the United States: Trends and challenges in measurement. Health Serv Res 2009 44 739 755
18. Aylin P, Bottle A, Majeed A, Use of administrative data or clinical databases as predictors of risk of death in hospital: Comparison of models. BMJ 2007 334 1044
19. Cooke CR, Iwashyna TJ, Using existing data to address important clinical questions in critical care. Crit Care Med 2013 41 886 896
20. Lagu T, Lindenauer PK, Rothberg MB, Development and validation of a model that uses enhanced administrative data to predict mortality in patients with sepsis. Crit Care Med 2011 39 2425 2430
21. Iwashyna TJ, Odden A, Rohde J, Identifying patients with severe sepsis using administrative claims: Patient-level validation of the angus implementation of the international consensus conference definition of severe sepsis. Med Care 2014 52 e39 e43
22. Martin GS, Mannino DM, Eaton S, The epidemiology of sepsis in the United States from 1979 through 2000. N Engl J Med 2003 348 1546 1554
23. Angus DC, Linde-Zwirble WT, Lidicker J, Epidemiology of severe sepsis in the United States: Analysis of incidence, outcome, and associated costs of care. Crit Care Med 2001 29 1303 1310
24. Centers for Medicare and Medicaid Services 2014. Available at: http://www.cms.gov/Newsroom/MediaReleaseDatabase/Fact-sheets/2014-Fact-sheets-items/2014-08-04-2.html. Accessed March 13, 2015
25. Poulose JT, Cartin-Ceba R, Shoja A, Comparison of International Classification of Disease Ninth Revision (ICD-9) coding with retrospective case review for the diagnosis of septic shock. Am J Respir Crit Care Med 2009 179 A4361
26. Whittaker SA, Mikkelsen ME, Gaieski DF, Severe sepsis cohorts derived from claims-based strategies appear to be biased toward a more severely ill patient population. Crit Care Med 2013 41 945 953
27. Elias KM, Moromizato T, Gibbons FK, Derivation and validation of the acute organ failure score to predict outcome in critically ill patients: A cohort study. Crit Care Med 2015 43 856 864
28. Oppert M, Engel C, Brunkhorst FM, German Competence Network Sepsis (Sepnet) Acute renal failure in patients with severe sepsis and septic shockA significant independent risk factor for mortality: Results from the German Prevalence Study. Nephrol Dial Transplant 2008 23 904 909
29. Seymour CW, Iwashyna TJ, Ehlenbach WJ, Hospital-level variation in the use of intensive care. Health Serv Res 2012 47 2060 2080
30. Oltean S, Ţəţulescu D, Bondor C, Charlson's weighted index of comorbidities is useful in assessing the risk of death in septic patients. J Crit Care 2012 27 370 375
31. Elixhauser A, Steiner C, Harris DR, Comorbidity measures for use with administrative data. Med Care 1998 36 8 27
32. van Walraven C, Austin PC, Jennings A, A modification of the Elixhauser comorbidity measures into a point system for hospital death using administrative data. Med Care 2009 47 626 633
33. Paul P, Pennell ML, Lemeshow S, Standardizing the power of the Hosmer-Lemeshow goodness of fit test in large data sets. Stat Med 2013 32 67 80
34. Goodwin AJ, Rice DA, Simpson KN, Frequency, cost, and risk factors of readmissions among severe sepsis survivors. Crit Care Med 2015 43 738 746
35. Knaus WA, Zimmerman JE, Wagner DP, APACHE-acute physiology and chronic health evaluation: A physiologically based classification system. Crit Care Med 1981 9 591 597
36. Knaus WA, Draper EA, Wagner DP, APACHE II: A severity of disease classification system. Crit Care Med 1985 13 818 829
37. Knaus WA, Wagner DP, Draper EA, The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults. Chest 1991 100 1619 1636
38. Norton SA, Hogan LA, Holloway RG, Proactive palliative care in the medical intensive care unit: Effects on length of stay for selected high-risk patients. Crit Care Med 2007 35 1530 1535