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OBJECTIVE Postoperative urinary retention (POUR) is a common complication after spine surgery and is associated with prolongation of hospital stay, increased hospital cost, increased rate of urinary tract infection, bladder overdistention, and autonomic dysregulation. POUR incidence following spine surgery ranges between 5.6% and 38%; no reliable prediction tool to identify those at higher risk is available, and that constitutes an important gap in the literature. The objective of this study was to develop and validate a preoperative risk model to predict the occurrence of POUR following routine elective spine surgery. METHODS The authors conducted a retrospective chart review of consecutive adults who underwent lumbar spine surgery between June 1, 2017, and June 1, 2019. Patient characteristics, preexisting ICD-10 codes, preoperative pain and opioid use, preoperative alpha-1 blocker use, details of surgical planning, development of POUR, and management strategies were abstracted from electronic medical records. A binomial logistic model and a multilayer perceptron (MLP) were optimized using training and validation sets. The models’ performance was then evaluated on model-naïve patients (not a part of either cohort). The models were then stacked to take advantage of each model’s strengths and to avoid their weaknesses. Four additional models were developed from previously published models adjusted to include only relevant factors (i.e., factors known preoperatively and applied to the lumbar spine). RESULTS Overall, 891 patients were included in the cohort, with a mean of 59.6 ± 15.5 years of age, 52.7% male, BMI 30.4 ± 6.4, American Society of Anesthesiologists class 2.8 ± 0.6, and a mean of 5.6 ± 5.7 comorbidities. The rate of POUR was found to be 25.9%. The two models were comparable, with an area under the curve (AUC) of 0.737 for the regression model and 0.735 for the neural network. By combining the two models, an AUC of 0.753 was achieved. With a regression model probability cutoff of 0.24 and a neural network cutoff of 0.23, maximal sensitivity and specificity were achieved, with specificity 68.2%, sensitivity 72.9%, negative predictive value 88.2%, and positive predictive value 43.4%. Both models individually outperformed previously published models (AUC 0.516–0.645) when applied to the current data set. CONCLUSIONS This predictive model can be a powerful preoperative tool in predicting patients who will be likely to develop POUR. By using a combination of regression and neural network modeling, good sensitivity, specificity, and NPV are achieved.

OBJECTIVEInhibition of platelet aggregation is universally used to prevent thromboembolic complications related to stent placement in endovascular neurosurgery, but excessive inhibition potentiates hemorrhagic complications. Previously, the authors demonstrated that two different commercially available measures of adenosine diphosphate (ADP)–dependent platelet inhibition—the VerifyNow P2Y12 clopidogrel assay (measured in platelet reactivity units [PRU]) and maximal amplitude (MA) attributable to ADP activity (MA-ADP) derived from thromboelastography (TEG) with platelet mapping (PM)—yielded wildly different results. This study sought to analyze observed complications to quantify the ideal therapeutic windows for both tests.METHODSNinety-one patients with simultaneous or near-simultaneous PRU and TEG-PM results who underwent craniocervical endovascular stenting at the authors’ institution between September 2015 and November 2017 were identified and retrospectively enrolled. From November 2017 until June 2019, 109 additional patients were prospectively enrolled. For this study, in-hospital thrombotic and hemorrhagic complications (both CNS and non-CNS) were tabulated, and receiver operating characteristic (ROC) curve analysis was used to identify threshold values of the PRU and MA-ADP for predicting each type of complication.RESULTSOf the 200 patients enrolled, 7 were excluded because of anemia or thrombocytopenia outside of the test manufacturer’s specified ranges and 1 was excluded because they did not have a TEG-PM result. Including complications of all severities, there were a total of 15 CNS thrombotic complications, 1 access-site thrombotic complication, 3 CNS hemorrhages, 8 access-site hemorrhagic complications, and 3 hemorrhagic complications not affecting either the CNS or the access site. ROC curve analysis yielded therapeutic threshold values of 118–144 PRU. The results demonstrated PRU has a significant dose-dependent effect on the rates of thrombosis and hemorrhage. Logistic regression models did not demonstrate statistically significant relationships between the MA-ADP and either thrombosis or hemorrhage. ROC analysis based on these models is of little value and did not identify significant threshold values for MA-ADP.CONCLUSIONSThere continues to be poor correlation between the results of TEG-PM and PRU. PRU accurately predicted complications, with a relatively narrow ideal value range of 118–144. The MA-ADP alone does not appear able to accurately predict either hemorrhagic or thrombotic complications in this group.