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

Nishant Sahni, Roshan Tourani, Donald Sullivan, Gyorgy Simon

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)1413-1418
Number of pages6
JournalJournal of general internal medicine
Volume35
Issue number5
DOIs
StatePublished - May 1 2020

Keywords

  • data mining
  • hospital outcomes
  • palliative care
  • predictive models

PubMed: MeSH publication types

  • Journal Article

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