Accurate Prediction of Congenital Heart Surgical Length of Stay Incorporating a Procedure-Based Categorical Variable*

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Objectives:There is increasing demand for the limited resource of Cardiac ICU care. In this setting, there is an expectation to optimize hospital resource use without restricting care delivery. We developed methodology to predict extended cardiac ICU length of stay following surgery for congenital heart disease.Design:Retrospective analysis by multivariable logistic regression of important predictive factors for outcome of postoperative ICU length of stay greater than 7 days.Setting:Cardiac ICU at Boston Children’s Hospital, a large, pediatric cardiac surgical referral center.Patients:All patients undergoing congenital heart surgery at Boston Children’s Hospital from January 1, 2010, to December 31, 2015.Interventions:No study interventions.Measurements and Main Results:The patient population was identified. Clinical variables and Congenital Heart Surgical Stay categories were recorded based on surgical intervention performed. A model was built to predict the outcome postoperative ICU length of stay greater than 7 days at the time of surgical intervention. The development cohort included 4,029 cases categorized into five Congenital Heart Surgical Stay categories with a C statistic of 0.78 for the outcome ICU length of stay greater than 7 days. Explanatory value increased with inclusion of patient preoperative status as determined by age, ventilator dependence, and admission status (C statistic = 0.84). A second model was optimized with inclusion of intraoperative factors available at the time of postoperative ICU admission, including cardiopulmonary bypass time and chest left open (C statistic 0.87). Each model was tested in a validation cohort (n = 1,008) with equivalent C statistics.Conclusions:Using a model comprised of basic patient characteristics, we developed a robust prediction tool for patients who will remain in the ICU longer than 7 days after cardiac surgery, at the time of postoperative ICU admission. This model may assist in patient counseling, case scheduling, and capacity management. Further examination in external settings is needed to establish generalizability.

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