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A simplified critical illness severity scoring system (CISSS): Development and internal validation.

Spyridon Fortis1, Amy M J O'Shea2, Brice F Beck Mae3

  • 1Center for Access & Delivery Research & Evaluation (CADRE), Iowa City VA Health Care System, Iowa City, IA, USA; Department of Internal Medicine, Division of Pulmonary, Critical Care and Occupational Medicine, University of Iowa Roy J. and Lucille A. Carver College of Medicine, Iowa City, IA, USA.

Journal of Critical Care
|October 13, 2020
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Summary

A new scoring system, the Critical Illness Severity Scoring System (CISSS), accurately predicts 30-day mortality in ICU patients. It uses readily available electronic health record data for simplified clinical assessment.

Keywords:
APACHEAutomatedComputerizedIllness severity scoreIntensive care unitsMortality

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Area of Science:

  • Critical care medicine
  • Health informatics
  • Biostatistics

Background:

  • Accurate prediction of mortality in intensive care unit (ICU) patients is crucial for clinical decision-making and resource allocation.
  • Existing severity scoring systems often rely on numerous variables, some of which may not be readily available in electronic health records (EHRs).
  • There is a need for simplified, yet accurate, scoring systems for critical illness severity.

Purpose of the Study:

  • To develop and validate a simplified Critical Illness Severity Scoring System (CISSS).
  • To achieve high prediction accuracy for 30-day mortality using only commonly available variables.
  • To facilitate easier implementation in clinical practice through EHR data extraction.

Main Methods:

  • A retrospective cohort study involving 534,001 ICU admissions from 306 ICUs in 117 Veterans Affairs hospitals (2010-2015).
  • The cohort was randomly divided into a training (75%) and a validation (25%) dataset.
  • The CISSS was developed using variables such as age, comorbidities, vital signs, laboratory results, and admission type (surgical vs. non-surgical).

Main Results:

  • The CISSS demonstrated high predictive performance for 30-day mortality in both training and validation datasets.
  • In the validation dataset, the area under the curve (AUC) was 0.848 (95% CI: 0.844-0.852).
  • The standardized mortality ratio (SMR) was 1.00 (95% CI: 0.98-1.02) and Brier's score was 0.058 (95% CI: 0.057-0.059), indicating acceptable calibration.

Conclusions:

  • The developed CISSS is a simplified scoring system with high accuracy for predicting 30-day mortality.
  • The system utilizes commonly available variables easily extractable from EHRs.
  • CISSS offers a practical tool for assessing critical illness severity and patient outcomes.