• Resuscitation · Jul 2004

    A predictive model for survival after in-hospital cardiopulmonary arrest.

    • Sorin C Danciu, Liviu Klein, Maziyar Mir Hosseini, Lamia Ibrahim, Bryan W Coyle, and Richard F Kehoe.
    • Advocate Illinois Masonic Medical Center, 836 W. Wellington Avenue, Chicago, IL 60657, USA.
    • Resuscitation. 2004 Jul 1;62(1):35-42.

    BackgroundIn-hospital cardiopulmonary resuscitation (CPR) has seen a steady increase in the application of technology and techniques since the introduction of closed cardiac massage in 1960. Despite this progress, there has not been a demonstrated improvement in survival rates after in-hospital cardiac arrest over the last 40 years. Identification of prognostic factors associated with survival after a resuscitation attempt can help physician decisions and patients' end-of-life choices in a pre-arrest situation.MethodsUsing an Utstein-based template we analyzed 219 consecutive adult attempted resuscitations in a large urban teaching hospital over a 3-year period. The main outcome measures were survival to discharge, 1 and 3 months. Backwards stepwise logistic regression was used to select baseline variables that predict survival at discharge, 1 and 3 months.ResultsSurvival rates at discharge, 1 and 3 months were 15.1, 13.3, and 11.5%. Meaningful neurological status (cerebral performance score of 1) at discharge was achieved in 61% of survivors. Independent predictors of survival were: higher body-mass index (BMI), presence of chronic renal insufficiency (CRI), respiratory arrest, ventricular tachycardia/fibrillation (VT/VF) as initial rhythm and arrest early during the hospital stay. A risk model based on these variables demonstrated a significant fit between predicted and observed survival at discharge with goodness of fit test P-value of 0.87.ConclusionsSurvival after in-hospital cardiopulmonary arrest is poor and can be estimated by using clinical variables. If validated in a large prospective trial, this score could help physicians in attempting resuscitation, patients and families in making end-of-life decisions and hospitals in resource allocation.

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