Predictors of In-Hospital Cardiac Arrest Outcomes: A Single-Center Observational Study.

Area of Science:

• Cardiology
• Critical Care Medicine
• Health Services Research

Background:

• In-hospital cardiac arrest (IHCA) has high mortality and significant neurological impairment risk.
• Existing risk models are limited, particularly in Southern European healthcare systems.
• There is a need for effective bedside risk stratification tools for IHCA patients.

Purpose of the Study:

• To develop and validate a process-aware model for bedside risk stratification of IHCA patients.
• To identify key predictors of in-hospital mortality following IHCA.
• To improve clinical decision-making and patient outcomes.

Main Methods:

• Retrospective analysis of a single-center cohort from a resuscitation registry (2017-2019).
• Inclusion of adult patients (≥18 years) experiencing index IHCA.
• Utilized Utstein variables, LASSO selection, and multivariable logistic regression for mortality prediction, assessing discrimination (AUC) and calibration.

Main Results:

• Higher mortality associated with longer CPR, older age, and CCU location.
• Protective factors included operating room/ICU/HDU location and initial shockable rhythm.
• Longer time to CPR initiation predicted mortality; model showed strong performance (AUC=0.897) and good calibration.

Conclusions:

• A process-aware model integrating patient factors, intra-arrest metrics, and location effectively predicts IHCA mortality.
• Age, rhythm, and resuscitation timeliness/intensity are crucial prognostic indicators.
• Future research should extend prediction to neurological/functional outcomes and test targeted care bundles.