Multidimensional prognostic index (MPI) predicts non-invasive ventilation failure in older adults with acute

Carlo Custodero1, Federica Gandolfo2, Alberto Cella2

  • 1Department of Interdisciplinary Medicine, University of Bari, Italy.

Abstract

Insights

The Multidimensional Prognostic Index (MPI) helps identify older adults with acute respiratory failure (ARF) at higher risk for in-hospital death and non-invasive ventilation (NIV) failure. This tool aids in better patient selection for NIV.

Area of Science:

  • Geriatrics
  • Pulmonology
  • Internal Medicine

Background:

  • Acute respiratory failure (ARF) is a common complication in hospitalized older adults.
  • Non-invasive ventilation (NIV) can prevent intensive care unit admission but lacks specific indications for this population.
  • The Multidimensional Prognostic Index (MPI), derived from comprehensive geriatric assessment (CGA), may help define prognosis and guide NIV use in older adults with ARF.

Purpose of the Study:

  • To evaluate the role of MPI in predicting short-term prognosis in older adults with ARF.
  • To determine if MPI can identify patients who are best candidates for NIV.
  • To assess MPI's ability to predict in-hospital mortality and NIV failure.

Main Methods:

  • Retrospective observational study of patients over 70 years admitted with ARF.
  • Standardized CGA used to calculate MPI at admission.
  • Multivariate Cox regression and ROC analysis to assess MPI's predictive power for mortality and NIV failure.

Main Results:

  • 231 patients (mean age 88.2 years) were enrolled. Mean MPI was 0.76.
  • In-hospital mortality was 33.8%. NIV failure occurred in 39.3% of NIV users.
  • Higher MPI scores predicted in-hospital mortality and NIV failure. MPI demonstrated good discriminatory power for NIV failure (AUC=0.72).

Conclusions:

  • MPI at admission is a useful tool for early risk detection in older adults with ARF.
  • MPI can help identify patients at higher risk of in-hospital death and NIV failure.
  • This supports using MPI to guide NIV decisions in this patient group.

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