title: Placing clinical variables on a common linear scale of empirically based risk as a step towards construction of a general patient acuity score from the electronic health record: a modelling study.
abstract: OBJECTIVE
  To explore the hypothesis that placing clinical variables of differing metrics on a common linear scale of all-cause postdischarge mortality provides risk functions that are directly correlated with in-hospital mortality risk.
  
  
  METHODS
  Modelling study.
  
  
  METHODS
  An 805-bed community hospital in the southeastern USA.
  
  
  METHODS
  42302 inpatients admitted for any reason, excluding obstetrics, paediatric and psychiatric patients.
  
  
  METHODS
  All-cause in-hospital and postdischarge mortalities, and associated correlations.
  
  
  RESULTS
  Pearson correlation coefficients comparing in-hospital risks with postdischarge risks for creatinine, heart rate and a set of 12 nursing assessments are 0.920, 0.922 and 0.892, respectively. Correlation between postdischarge risk heart rate and the Modified Early Warning System (MEWS) component for heart rate is 0.855. The minimal excess risk values for creatinine and heart rate roughly correspond to the normal reference ranges. We also provide the risks for values outside that range, independent of expert opinion or a regression model. By summing risk functions, a first-approximation patient risk score is created, which correctly ranks 6 discharge categories by average mortality with p<0.001 for differences in category means, and Tukey's Honestly Significant Difference Test confirmed that the means were all different at the 95% confidence level.
  
  
  CONCLUSIONS
  Quantitative or categorical clinical variables can be transformed into risk functions that correlate well with in-hospital risk. This methodology provides an empirical way to assess inpatient risk from data available in the Electronic Health Record. With just the variables in this paper, we achieve a risk score that correlates with discharge disposition. This is the first step towards creation of a universal measure of patient condition that reflects a generally applicable set of health-related risks. More importantly, we believe that our approach opens the door to a way of exploring and resolving many issues in patient assessment.
authors.affiliation: PeraHealth, Inc, Sarasota, Florida, USA.
authors.forename: Steven I
authors.initials: SI
authors.lastname: Rothman
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Rothman
authors.forename: Alan B
authors.initials: AB
authors.lastname: Solinger
keywords: Biotechnology & Bioinformatics; Performance measures; Quality measurement