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|Title:||Validation of a delirium predictive model in patients admitted to surgical intensive care units: A multicentre prospective observational cohort study|
|Abstract:||Objective To internally and externally validate a delirium predictive model for adult patients admitted to intensive care units (ICUs) following surgery. Design A prospective, observational, multicentre study. Setting Three university-affiliated teaching hospitals in Thailand. Participants Adults aged over 18 years were enrolled if they were admitted to a surgical ICU (SICU) and had the surgery within 7 days before SICU admission. Main outcome measures Postoperative delirium was assessed using the Thai version of the Confusion Assessment Method for the ICU. The assessments commenced on the first day after the patient's operation and continued for 7 days, or until either discharge from the ICU or the death of the patient. Validation was performed of the previously developed delirium predictive model: age+(5×SOFA)+(15×benzodiazepine use)+(20×DM)+(20×mechanical ventilation)+(20×modified IQCODE>3.42). Results In all, 380 SICU patients were recruited. Internal validation on 150 patients with the mean age of 75±7.5 years resulted in an area under a receiver operating characteristic curve (AUROC) of 0.76 (0.683 to 0.837). External validation on 230 patients with the mean age of 57±17.3 years resulted in an AUROC of 0.85 (0.789 to 0.906). The AUROC of all validation cohorts was 0.83 (0.785 to 0.872). The optimum cut-off value to discriminate between a high and low probability of postoperative delirium in SICU patients was 115. This cut-off offered the highest value for Youden's index (0.50), the best AUROC, and the optimum values for sensitivity (78.9%) and specificity (70.9%). Conclusions The model developed by the previous study was able to predict the occurrence of postoperative delirium in critically ill surgical patients admitted to SICUs. Trial registration number Thai Clinical Trail Registry (TCTR20180105001).|
|Appears in Collections:||CMUL: Journal Articles|
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