A validated rule for predicting patients who require prolonged ventilation post cardiac surgery

Objective: Prolonged ventilation post surgery causes logistic problems on cardiac surgical intensive care units (CSU). We thus sought to derive and validate a clinical decision rule to predict patients at high risk of prolonged ventilation, so that the timing of operations on high risk patients can be optimised in the context of the workload of the CSU. Methods: The North Staffordshire Royal Infirmary (NSRI) Open Heart Registry was analysed from April 1998 to May 2002. Prolonged ventilation was defined as that which was longer than 24 It. The Parsonnet score was assessed for its ability to predict these patients. Univariate analysis was first performed to identify predictive variables. Recursive partitioning and logistic regression was then performed to identify the optimal decision rule. This rule was then validated on the Blackpool Victoria Hospital (BVH) Open Heart Registry. Results: A total of 3070 patients were analysed of whom 201 were ventilated for more than 24 h. A Parsonnet score of 10 predicted 49% of high risk patients but 618 low risk patients are misclassified. Our rule that uses Parsonnet score over 7, ejection fraction, operation status, PA pressure and age, to identify high risk patients identifies 50% of those needing prolonged ventilation and only incorrectly identifies 282 of the 2869 patients with normal ventilation times giving a specificity of over 90%. Validation in the BVH database demonstrated similar findings. Conclusion: Our rule identifies 14% of all our patients as high risk and 50% of these required prolonged ventilation. Such a rule allows more efficient use of scarce CSU resources by appropriate surgical scheduling. (C) 2003 Elsevier Science B.V. All rights reserved.