Development and Validation of an Intraoperative Predictive Model for Unplanned Postoperative Intensive Care
ABSTRACT The allocation of intensive care unit (ICU) beds for postoperative patients is a challenging daily task that could be assisted by the real-time detection of ICU needs. The goal of this study was to develop and validate an intraoperative predictive model for unplanned postoperative ICU use.
With the use of anesthesia information management system, postanesthesia care unit, and scheduling data, a data set was derived from adult in-patient noncardiac surgeries. Unplanned ICU admissions were identified (4,847 of 71,996; 6.7%), and a logistic regression model was developed for predicting unplanned ICU admission. The model performance was tested using bootstrap validation and compared with the Surgical Apgar Score using area under the curve for the receiver operating characteristic.
The logistic regression model included 16 variables: age, American Society of Anesthesiologists physical status, emergency case, surgical service, and 12 intraoperative variables. The area under the curve was 0.905 (95% CI, 0.900-0.909). The bootstrap validation model area under the curves were 0.513 at booking, 0.688 at 3 h before case end, 0.738 at 2 h, 0.791 at 1 h, and 0.809 at case end. The Surgical Apgar Score area under the curve was 0.692. Unplanned ICU admissions had more ICU-free days than planned ICU admissions (5 vs. 4; P < 0.001) and similar mortality (5.6 vs.6.0%; P = 0.248).
The authors have developed and internally validated an intraoperative predictive model for unplanned postoperative ICU use. Incorporation of this model into a real-time data sniffer may improve the process of allocating ICU beds for postoperative patients.
- Anesthesiology 09/2013; 119(3):498-500. DOI:10.1097/ALN.0b013e31829ce927 · 6.17 Impact Factor
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ABSTRACT: This review discusses our present understanding of postoperative respiratory failure (PRF) pathogenesis, risk factors, and perioperative-risk reduction strategies. PRF, the most frequent postoperative pulmonary complication, is defined by impaired blood gas exchange appearing after surgery. PRF leads to longer hospital stays and higher mortality. The time frame for recognizing when respiratory failure is related to the surgical-anesthetic insult remains imprecise, however, and researchers have used different clinical events instead of blood gas measures to define the outcome. Still, studies in specific surgical populations or large patient samples have identified a range of predictors of PRF risk: type of surgery and comorbidity, mechanical ventilation, and multiple hits to the lung have been found to be relevant in most of these studies. Recently, risk-scoring systems for PRF have been developed and are being applied in new controlled trials of PRF-risk reduction measures. Current evidence favors carefully managing intraoperative ventilator use and fluids, reducing surgical aggression, and preventing wound infection and pain. PRF is a life-threatening event that is challenging for the surgical team. Risk prediction scales based on large population studies are being developed and validated. We need high-quality trials of preventive measures, particularly those related to ventilator use in both high risk and general populations.Current opinion in critical care 11/2013; 20(1). DOI:10.1097/MCC.0000000000000045 · 3.18 Impact Factor
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