title: Predicting mortality rates: Comparison of an administrative predictive model (hospital standardized mortality ratio) with a physiological predictive model (Acute Physiology and Chronic Health Evaluation IV)--A cross-sectional study.
abstract: BACKGROUND
  Direct comparison of mortality rates has limited value because most deaths are due to the disease process. Predicting the risk of death accurately remains a challenge.
  
  
  METHODS
  A cross-sectional study compared the expected mortality rate as calculated with an administrative model to a physiological model, Acute Physiology and Chronic Health Evaluation IV. The combined cohort and stratified samples (<0.1, 0.1-0.5, or >0.5 predicted mortality) were considered. A total of 47,982 patients were scored from 1 July 2013 to 30 June 2014, and 46,061 records were included in the analysis.
  
  
  RESULTS
  A moderate correlation was shown for the combined cohort (Pearson correlation index, 0.618; 95% confidence interval [CI], 0.380-0.779; R(2) = 0.38). A very good correlation for the less than 10% stratum (Pearson correlation index, 0.884; R(2) = 0.78; 95% CI, 0.79-0.937) and a moderate correlation for 0.1 to 0.5 predicted mortality rates (Pearson correlation index, 0.782; R(2) = 0.61; 95% CI, 0.623-0.879). There was no significant positive correlation for the greater than 50% predicted mortality stratum (Pearson correlation index, 0.087; R(2) = 0.007; 95% CI, -0.23 to 0.387).
  
  
  CONCLUSIONS
  At less than 0.1, the models are interchangeable, but in spite of a moderate correlation, greater than 0.1 hospital standardized mortality ratio cannot be used to predict mortality.
authors.affiliation: Division of Emergency Medicine, University of Cape Town, 129 Main Rd, Observatory, Cape Town, South Africa; Mediclinic Pty Ltd, Trumali Bldg, Strand St, Stellenbosch, Cape Town, South Africa. Electronic address: rene.toua@mediclinic.co.za.
authors.forename: Rene Elaine
authors.initials: RE
authors.lastname: Toua
authors.affiliation: Division of Emergency Medicine, University of Cape Town, 129 Main Rd, Observatory, Cape Town, South Africa; Mediclinic Pty Ltd, Trumali Bldg, Strand St, Stellenbosch, Cape Town, South Africa. Electronic address: jacques.dekock@mediclinic.com.
authors.forename: Jacques Erasmus
authors.initials: JE
authors.lastname: de Kock
authors.affiliation: Division of Emergency Medicine, University of Cape Town, 129 Main Rd, Observatory, Cape Town, South Africa; Mediclinic Pty Ltd, Trumali Bldg, Strand St, Stellenbosch, Cape Town, South Africa. Electronic address: tyson.welzel@uct.ac.za.
authors.forename: Tyson
authors.initials: T
authors.lastname: Welzel
mesh_terms: D018806:APACHE; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D015331:Cohort Studies; D016638:Critical Illness / Q000401:mortality*; D003430:Cross-Sectional Studies; D003661:Decision Support Techniques; D005260:Female; D017052:Hospital Mortality; D006761:Hospitals; D006801:Humans; D007362:Intensive Care Units; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D018570:Risk Assessment; D055815:Young Adult
keywords: APACHE IV; Administrative model; Standardized mortality rate