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Related Experiment Videos

Risk-Adjusting Mortality in the Nationwide Veterans Affairs Healthcare System.

Hallie C Prescott1,2, Rajendra P Kadel3, Julie R Eyman3

  • 1VA Center for Clinical Management Research, Ann Arbor, MI, USA. hprescot@med.umich.edu.

Journal of General Internal Medicine
|January 14, 2022
PubMed
Summary

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This summary is machine-generated.

The US Veterans Affairs

Area of Science:

  • Healthcare analytics
  • Medical informatics
  • Public health surveillance

Background:

  • The US Veterans Affairs (VA) healthcare system introduced risk-adjusted mortality reporting for intensive care unit (ICU) admissions in 2005.
  • While VA's mortality models have been updated for all inpatient hospitalizations, recent performance data remains unpublished.
  • This study assesses the current performance of four standardized mortality ratio (SMR) models within the VA system.

Purpose of the Study:

  • To evaluate the predictive accuracy and calibration of the VA's four standardized mortality ratio (SMR) models.
  • To determine if the current SMR models, adapted for broader use, maintain performance levels.
  • To provide updated performance metrics for VA mortality risk-adjustment tools.

Main Methods:

Keywords:
hospital mortalitylogistic modelsrisk adjustment

Related Experiment Videos

  • A retrospective cohort study utilizing split derivation and validation datasets from nationwide VA hospitalizations (FY 2017-2018 for derivation, FY 2019 for validation).
  • Standardized mortality ratio (SMR) models were developed using derivation data and coefficients applied to the validation sample.
  • Model performance was assessed using c-statistics for discrimination and observed versus predicted deaths for calibration.

Main Results:

  • C-statistics in the validation data ranged from 0.864 (ICU SMR-30) to 0.914 (acute care SMR), indicating strong discrimination.
  • Observed 30-day mortality was 4.29% versus 4.67% predicted, showing minimal over-prediction (0.38%).
  • Calibration was good across risk deciles, with a maximum error of 1.81% in the highest-risk group.

Conclusions:

  • The VA's current SMR models are highly predictive and well-calibrated, performing effectively across different risk levels.
  • These models demonstrate appropriate adaptation and re-calibration, maintaining performance similar to earlier versions.
  • The findings support the continued use and reliability of VA SMR models for risk-adjustment in healthcare quality assessment.