Prediction model for length of intubation with assisted mechanical ventilation in pediatric heart surgery

Eva M. Marwali, Novik Budiwardhana, Sudigdo Sastroasmoro, Antonius H. Pudjiadi, Nikolaus A. Haas

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


Objective: To establish a prediction model for intubation time in children undergoing heart surgery. Design: Retrospective chart review. Setting: A tertiary care pediatric cardiac intensive care. Intervention: None Measurements and Main Results: This retrospective cohort study included all patients undergoing cardiac surgery with cardiopulmonary bypass (CPB) and age of less than 18 years in the period of October 2011 through November 2012. Multivariate linear regression was used to determine the predictive model of intubation time. A total of 401 pediatric patients admitted to cardiac intensive care unit after cardiac surgery with cardiopulmonary bypass and survived to hospital discharge. Mean age was 57.8 months (95% confidence interval of mean 52.0 - 63.7). Mean duration of mechanical ventilation was 23.7 (18.6 - 28.9) hours. Among 126 (31.4%) and 323 (80.5%) patients were respectively extubated within 6 and 24 hours after surgery. Age less than 11.5 months (sentitivity and specificity of 83.3% and 54.5%, AUC 0.81 (0.75-0.87)), CPB time longer than 72.5 minutes (sentitivity and specificity of 54% and 29.1%, AUC 0.38 (0.30-0.46)), Pediatric Risk of Mortality (PRISM) III score higher than 3.5 (sentitivity and specificity of 46.3% and 43.6%, AUC 0.41 (0.33-0.50)) were all significant factors to predict intubation time longer than 24 hours after cardiac surgery. Conclusions: Younger age, longer CPB time, greater severity of illness at post-operative admission are predictive factors for longer intubation time in pediatric cardiac surgery.

Original languageEnglish
Pages (from-to)74-82
Number of pages9
JournalCritical Care and Shock
Issue number3
Publication statusPublished - 2013


  • Cardiac surgical procedures
  • Intubation time
  • Pediatric cardiac surgery
  • Predictive model


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