Murray M Pollack1, Richard Holubkov2, Robert A Berg3, Christopher J L Newth4, Kathleen L Meert5, Rick E Harrison6, Joseph Carcillo7, Heidi Dalton8, David L Wessel9, J Michael Dean2. 1. Department of Pediatrics, Children's National Health System and the George Washington University School of Medicine and Health Sciences, Washington DC, United States. Electronic address: mpollack@childrensnational.org. 2. Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, UT, United States. 3. Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, PA, United States. 4. Department of Anesthesiology and Critical Care Medicine, Children's Hospital Los Angeles, University of Southern California Keck School of Medicine, Los Angeles, CA, United States. 5. Department of Pediatrics, Children's Hospital of Michigan, Detroit, MI, United States. 6. Department of Pediatrics, University of California at Los Angeles, Los Angeles, CA, United States. 7. Department of Critical Care Medicine, Children's Hospital of Pittsburgh, Pittsburgh, PA, United States. 8. Department of Child Health, Phoenix Children's Hospital and University of Arizona College of Medicine-Phoenix, Phoenix, AZ, United States(1). 9. Department of Pediatrics, Children's National Medical Center, Washington DC, United States.
Abstract
BACKGROUND: Early identification of children at risk for cardiac arrest would allow for skill training associated with improved outcomes and provides a prevention opportunity. OBJECTIVE: Develop and assess a predictive model for cardiopulmonary arrest using data available in the first 4 h. METHODS: Data from PICU patients from 8 institutions included descriptive, severity of illness, cardiac arrest, and outcomes. RESULTS: Of the 10074 patients, 120 satisfying inclusion criteria sustained a cardiac arrest and 67 (55.9%) died. In univariate analysis, patients with cardiac arrest prior to admission were over 6 times and those with cardiac arrests during the first 4 h were over 50 times more likely to have a subsequent arrest. The multivariate logistic regression model performance was excellent (area under the ROC curve = 0.85 and Hosmer-Lemeshow statistic, p = 0.35). The variables with the highest odds ratio's for sustaining a cardiac arrest in the multivariable model were admission from an inpatient unit (8.23 (CI: 4.35-15.54)), and cardiac arrest in the first 4 h (6.48 (CI: 2.07-20.36). The average risk predicted by the model was highest (11.6%) among children sustaining an arrest during hours >4-12 and continued to be high even for days after the risk assessment period; the average predicted risk was 9.5% for arrests that occurred after 8 PICU days. CONCLUSIONS: Patients at high risk of cardiac arrest can be identified with routinely available data after 4 h. The cardiac arrest may occur relatively close to the risk assessment period or days later.
BACKGROUND: Early identification of children at risk for cardiac arrest would allow for skill training associated with improved outcomes and provides a prevention opportunity. OBJECTIVE: Develop and assess a predictive model for cardiopulmonary arrest using data available in the first 4 h. METHODS: Data from PICU patients from 8 institutions included descriptive, severity of illness, cardiac arrest, and outcomes. RESULTS: Of the 10074 patients, 120 satisfying inclusion criteria sustained a cardiac arrest and 67 (55.9%) died. In univariate analysis, patients with cardiac arrest prior to admission were over 6 times and those with cardiac arrests during the first 4 h were over 50 times more likely to have a subsequent arrest. The multivariate logistic regression model performance was excellent (area under the ROC curve = 0.85 and Hosmer-Lemeshow statistic, p = 0.35). The variables with the highest odds ratio's for sustaining a cardiac arrest in the multivariable model were admission from an inpatient unit (8.23 (CI: 4.35-15.54)), and cardiac arrest in the first 4 h (6.48 (CI: 2.07-20.36). The average risk predicted by the model was highest (11.6%) among children sustaining an arrest during hours >4-12 and continued to be high even for days after the risk assessment period; the average predicted risk was 9.5% for arrests that occurred after 8 PICU days. CONCLUSIONS:Patients at high risk of cardiac arrest can be identified with routinely available data after 4 h. The cardiac arrest may occur relatively close to the risk assessment period or days later.
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