min-SIA: a Lightweight Algorithm to Predict the Risk of 6-Month Mortality at the Time of Hospital Admission

Abstract

Background: Predicting death in a cohort of clinically diverse, multi-condition hospitalized patients is difficult. This frequently hinders timely serious illness care conversations. Prognostic models that can determine 6-month death risk at the time of hospital admission can improve access to serious illness care conversations.

Objective: The objective is to determine if the demographic, vital sign, and laboratory data from the first 48 h of a hospitalization can be used to accurately quantify 6-month mortality risk.

Design: This is a retrospective study using electronic medical record data linked with the state death registry.

Participants: Participants were 158,323 hospitalized patients within a 6-hospital network over a 6-year period.

Main measures: Main measures are the following: the first set of vital signs, complete blood count, basic and complete metabolic panel, serum lactate, pro-BNP, troponin-I, INR, aPTT, demographic information, and associated ICD codes. The outcome of interest was death within 6 months.

Key results: Model performance was measured on the validation dataset. A random forest model-mini serious illness algorithm-used 8 variables from the initial 48 h of hospitalization and predicted death within 6 months with an AUC of 0.92 (0.91-0.93). Red cell distribution width was the most important prognostic variable. min-SIA (mini serious illness algorithm) was very well calibrated and estimated the probability of death to within 10% of the actual value. The discriminative ability of the min-SIA was significantly better than historical estimates of clinician performance.

Conclusion: min-SIA algorithm can identify patients at high risk of 6-month mortality at the time of hospital admission. It can be used to improved access to timely, serious illness care conversations in high-risk patients.

Keywords: data mining; hospital outcomes; palliative care; predictive models.

Fig. 1

Feature importance in the random forest models. The 25 highest-ranking features in the SIA model ranked by importance (as measured by the Mean Decrease in Gini Index). f48_ prefix refers to values obtained within the first 48 h of a hospitalization.

Fig. 2

The receiver operator curve for the minSIA8 model.

Fig. 3

Left panel: The recall plot shows the percentage of the overall number of cases in a given category “gained” (y-axis) when we apply the min-SIA8 and select the highest k-deciles (x-axis). For example, if the positivity threshold is set to be the highest-ranking 20% cases (by predicted probability), then 83% of true positives would be selected. Right panel: The accuracy plot plots the accuracy (y-axis) of the model at each decile threshold of predicted probability (x-axis).