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BackgroundAccurate, pragmatic risk stratification for postoperative delirium (POD) is necessary to target preventative resources toward high-risk patients. Machine learning (ML) offers a novel approach to leveraging electronic health record (EHR) data for POD prediction. We sought to develop and internally validate a ML-derived POD risk prediction model using preoperative risk features, and to compare its performance to models developed with traditional logistic regression.MethodsThis was a retrospective analysis of preoperative EHR data from 24,885 adults undergoing a procedure requiring anesthesia care, recovering in the main post-anesthesia care unit, and staying in the hospital at least overnight between December 2016 and December 2019 at either of two hospitals in a tertiary care health system. One hundred fifteen preoperative risk features including demographics, comorbidities, nursing assessments, surgery type, and other preoperative EHR data were used to predict postoperative delirium (POD), defined as any instance of Nursing Delirium Screening Scale ≥2 or positive Confusion Assessment Method for the Intensive Care Unit within the first 7 postoperative days. Two ML models (Neural Network and XGBoost), two traditional logistic regression models ("clinician-guided" and "ML hybrid"), and a previously described delirium risk stratification tool (AWOL-S) were evaluated using the area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, positive likelihood ratio, and positive predictive value. Model calibration was assessed with a calibration curve. Patients with no POD assessments charted or at least 20% of input variables missing were excluded.ResultsPOD incidence was 5.3%. The AUC-ROC for Neural Net was 0.841 [95% CI 0. 816-0.863] and for XGBoost was 0.851 [95% CI 0.827-0.874], which was significantly better than the clinician-guided (AUC-ROC 0.763 [0.734-0.793], p

Abstract Background The effect of peripheral nerve blocks on postoperative delirium in older patients has not been studied. Peripheral nerve blocks may reduce the incidence of postoperative opioid use and its side effects such as delirium via opioid-sparing effect. Methods A prospective cohort study was conducted in patients who underwent total knee replacement. Baseline cognitive function was assessed using the Telephone Interview for Cognitive Status. Postoperative delirium was measured using the Confusion Assessment Method postoperatively. Incidence of postoperative delirium was compared in two postoperative management groups: femoral nerve block ± patient-controlled analgesia and patient-controlled analgesia only. In addition, pain levels (using numeric rating scales) and opioid use were compared in two groups. Results 85 patients were studied. The overall incidence of postoperative delirium either on postoperative day one or day two was 48.1%. Incidence of postoperative delirium in the femoral nerve block group was lower than patient controlled analgesia only group (25% vs. 61%, P = 0.002). However, there was no significant difference between the groups with respect to postoperative pain level or the amount of intravenous opioid use. Conclusions Femoral nerve block reduces the incidence of postoperative delirium. These results suggest that a larger randomized control trial is necessary to confirm these preliminary findings.