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.

Design: Modelling study.

Setting: An 805-bed community hospital in the southeastern USA.

Participants: 42302 inpatients admitted for any reason, excluding obstetrics, paediatric and psychiatric patients.

Outcome measures: 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.

Keywords: Biotechnology & Bioinformatics; Performance measures; Quality measurement.

Figure 1

Creatinine level versus excess risk: (A) 1-Year postdischarge; (B) in-hospital; (C) correlation=0.920. The reference range for creatinine at Sarasota Memorial Hospital is 0.5–1.2 mg/dl.

Figure 2

Heart rate versus excess risk: (A) 1-Year postdischarge; (B) in-hospital; (C) correlation=0.922. Displayed for comparison is the Modified Early Warning System (MEWS) heart risk score (in grey dots), scaled to correspond roughly with our results (MEWS correlation=0.855).

Figure 3

Nursing assessments versus excess risk: (A) 1-Year postdischarge; (B) in-hospital; (C) correlation=0.892 for the set of 12 nursing assessments—in-hospital versus postdischarge.