Preoperative prediction of postoperative urinary retention in lumbar surgery: a comparison of regression to multilayer neural network.
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...
| Published in: | Journal of Neurosurgery: Spine pp. 1 - 11 |
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| Main Authors: | , , , , , , |
| Format: | research tables/charts Journal Article |
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American Association of Neurological Surgeons & the Journal of Neurosurgical Publishing Group
Sep 10, 2021
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=152363564&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 152363564 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15475654 3ANO jtl: Journal of Neurosurgery: Spine issn: 15475654 maglogo: N pubinfo: dt: Sep 10, 2021 pid: 13926 pub: American Association of Neurological Surgeons & the Journal of Neurosurgical Publishing Group place: Rolling Meadows, Illinois artinfo: ui: 152363564 152363564 NLM34507288 152363564 10.3171/2021.3.SPINE21189 NLM34507288 152363564 ppf: 1 ppct: 10 formats: tig: atl: Preoperative prediction of postoperative urinary retention in lumbar surgery: a comparison of regression to multilayer neural network. aug: au: Porche, Ken Maciel, Carolina B Lucke-Wold, Brandon Robicsek, Steven A Chalouhi, Nohra Brennan, Meghan Busl, Katharina M affil: 1Lillian S. Wells Department of Neurosurgery sug: subj: Spinal Diseases Surgery Postoperative Complications Etiology Regression Lumbar Vertebrae Urinary Retention Etiology ROC Curve Middle Age Predictive Value of Tests Adult Aged Human Female Male Risk Factors Reproducibility of Results Retrospective Design Comparative Studies Multicenter Studies Evaluation Research Validation Studies Funding Source Middle Aged: 45-64 years Adult: 19-44 years Aged: 65+ years Female Male ab: 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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