BACKGROUND: Considerations about the application of cardiopulmonary resuscitation (CPR) should include the expected probability of survival. The survival probability after CPR may be more accurately estimated by the occurrence in time of the prearrest morbidity of patients. OBJECTIVE: To identify risk factors for poor survival after CPR in relation to the dynamics of prearrest morbidity. METHODS: Medical records of CPR patients were reviewed. Prearrest morbidity was established by categorizing the medical diagnoses according to 3 functional time frames: before hospital admission, on hospital admission, and during hospital admission. Indicators of poor survival after CPR were identified through a logistic regression model. RESULTS: Included in the study were 553 CPR patients with a median age of 68 years (age range, 18-98 years); 21.7% survived to hospital discharge. Independent indicators of poor outcome were an age of 70 years or older (odds ratio [OR]=0.6, 95% confidence interval [CI]=0.4-0.9), stroke (OR=0.3, 95% CI=0.1-0.7) or renal failure (OR=0.3, 95% CI=0.1-0.8) before hospital admission, and congestive heart failure during hospital admission (OR=0.4, 95% CI=0.2-0.9). Indicators of good survival were angina pectoris before hospital admission (OR=2.1, 95% CI=1.3-.3.3) or ventricular dysrhythmia as the diagnosis on hospital admission (OR=11.0, 95% CI=4.1-33.7). Based on a logistic regression model, 17.4% of our CPR patients (n= 96) were identified as having a high risk for a poor outcome (< 10% survival). CONCLUSIONS: Time of prearrest morbidity has a prognostic value for survival after CPR. Patients at risk for poor survival can be identified on or during hospital admission, but the reliability and validity of the model needs further research. Although decisions will not be made by the model, its information can be useful for physicians in discussions about patient prognoses and to make decisions about CPR with more confidence.
BACKGROUND: Considerations about the application of cardiopulmonary resuscitation (CPR) should include the expected probability of survival. The survival probability after CPR may be more accurately estimated by the occurrence in time of the prearrest morbidity of patients. OBJECTIVE: To identify risk factors for poor survival after CPR in relation to the dynamics of prearrest morbidity. METHODS: Medical records of CPR patients were reviewed. Prearrest morbidity was established by categorizing the medical diagnoses according to 3 functional time frames: before hospital admission, on hospital admission, and during hospital admission. Indicators of poor survival after CPR were identified through a logistic regression model. RESULTS: Included in the study were 553 CPR patients with a median age of 68 years (age range, 18-98 years); 21.7% survived to hospital discharge. Independent indicators of poor outcome were an age of 70 years or older (odds ratio [OR]=0.6, 95% confidence interval [CI]=0.4-0.9), stroke (OR=0.3, 95% CI=0.1-0.7) or renal failure (OR=0.3, 95% CI=0.1-0.8) before hospital admission, and congestive heart failure during hospital admission (OR=0.4, 95% CI=0.2-0.9). Indicators of good survival were angina pectoris before hospital admission (OR=2.1, 95% CI=1.3-.3.3) or ventricular dysrhythmia as the diagnosis on hospital admission (OR=11.0, 95% CI=4.1-33.7). Based on a logistic regression model, 17.4% of our CPR patients (n= 96) were identified as having a high risk for a poor outcome (< 10% survival). CONCLUSIONS: Time of prearrest morbidity has a prognostic value for survival after CPR. Patients at risk for poor survival can be identified on or during hospital admission, but the reliability and validity of the model needs further research. Although decisions will not be made by the model, its information can be useful for physicians in discussions about patient prognoses and to make decisions about CPR with more confidence.
Authors: Arash Nayeri; Nirmanmoh Bhatia; Benjamin Holmes; Nyal Borges; Michael N Young; Quinn S Wells; John A McPherson Journal: Heart Vessels Date: 2017-06-06 Impact factor: 2.037
Authors: Alexis A Topjian; A Russell Localio; Robert A Berg; Evaline A Alessandrini; Peter A Meaney; Paul E Pepe; G Luke Larkin; Mary Ann Peberdy; Lance B Becker; Vinay M Nadkarni Journal: Crit Care Med Date: 2010-05 Impact factor: 7.598