Preoperative prediction of postoperative urinary retention in lumbar surgery: a comparison of regression to multilayer neural network

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  • 1 Lillian S. Wells Department of Neurosurgery,
  • | 2 Departments of Neurology and Neurosurgery, and
  • | 3 Department of Anesthesiology, University of Florida, Gainesville, Florida
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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.

ABBREVIATIONS

ASA = American Society of Anesthesiologists; AUC = area under the curve; BMI = body mass index; LOS = length of stay; MLP = multilayer perceptron; NPV = negative predictive value; POUR = postoperative urinary retention; PPV = positive predictive value; UTI = urinary tract infection.

Supplementary Materials

    • Supplementary Materials (ZIP 4.96 MB)

Illustrations from Hubbe et al. (pp 160–163). Copyright Ioannis Vasilikos and Roberto Ferrarese. Published with permission.

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  • 1

    Swann MC, Hoes KS, Aoun SG, McDonagh DL. Postoperative complications of spine surgery. Best Pract Res Clin Anaesthesiol. 2016;30(1):103120.

  • 2

    How many spinal fusions are performed each year in the United States? iData Research. Accessed April 23, 2021.https://idataresearch.com/how-many-instrumented-spinal-fusions-are-performed-each-year-in-the-united-states/

    • Search Google Scholar
    • Export Citation
  • 3

    Altschul D, Kobets A, Nakhla J, et al. Postoperative urinary retention in patients undergoing elective spinal surgery. J Neurosurg Spine. 2017;26(2):229234.

  • 4

    Baldini G, Bagry H, Aprikian A, Carli F. Postoperative urinary retention: anesthetic and perioperative considerations. Anesthesiology. 2009;110(5):11391157.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 5

    Strickland AR, Usmani MF, Camacho JE, et al. Evaluation of risk factors for postoperative urinary retention in elective thoracolumbar spinal fusion patients. Global Spine J. 2021;11(3):338344.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 6

    Grass F, Slieker J, Frauche P, et al. Postoperative urinary retention in colorectal surgery within an enhanced recovery pathway. J Surg Res. 2017;207:7076.

  • 7

    Boulis NM, Mian FS, Rodriguez D, et al. Urinary retention following routine neurosurgical spine procedures. Surg Neurol. 2001;55(1):2328.

  • 8

    Balderi T, Mistraletti G, D’Angelo E, Carli F. Incidence of postoperative urinary retention (POUR) after joint arthroplasty and management using ultrasound-guided bladder catheterization. Minerva Anestesiol. 2011;77(11):10501057.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 9

    Cremins M, Vellanky S, McCann G, et al. Considering healthcare value and associated risk factors with postoperative urinary retention after elective laminectomy. Spine J. 2020;20(5):701707.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 10

    Garg D, Agarwal A. Comment on “Early presentation of urinary retention in multiple system atrophy: can the disease begin in the sacral spinal cord?”. J Neurol. 2020;267(3):665.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 11

    Agrawal K, Majhi S, Garg R. Post-operative urinary retention: review of literature. World J Anesthesiol. 2019;8(1):112.

  • 12

    Mouchtouris N, Hines K, Fitchett EM, et al. Cost of postoperative urinary retention after elective spine surgery: significant variation by surgeon and department. Neurosurgery. 2020;67(suppl1):nyaa447_115.

    • Search Google Scholar
    • Export Citation
  • 13

    Golubovsky JL, Ilyas H, Chen J, et al. Risk factors and associated complications for postoperative urinary retention after lumbar surgery for lumbar spinal stenosis. Spine J. 2018;18(9):15331539.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 14

    Hospital adjusted expenses per inpatient day by ownership. KFF. Accessed April 23, 2021. https://www.kff.org/health-costs/state-indicator/expenses-per-inpatient-day-by-ownership/

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 15

    Sullivan NM, Sutter VL, Mims MM, et al. Clinical aspects of bacteremia after manipulation of the genitourinary tract. J Infect Dis. 1973;127(1):4955.

  • 16

    Estimating the additional hospital inpatient cost and mortality associated with selected hospital-acquired conditions. Agency for Health Research and Quality. Accessed April 23, 2021. https://www.ahrq.gov/hai/pfp/haccost2017-results.html

    • Search Google Scholar
    • Export Citation
  • 17

    Mormol JD, Basques BA, Harada GK, et al. Risk factors associated with development of urinary retention following posterior lumbar spinal fusion: special attention to the use of glycopyrrolate in anesthesia reversal. Spine (Phila Pa 1976). 2021;46(2):E133E138.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 18

    Aiyer SN, Kumar A, Shetty AP, et al. Factors influencing postoperative urinary retention following elective posterior lumbar spine surgery: a prospective study. Asian Spine J. 2018;12(6):11001105.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 19

    Nickerson P, Tighe P, Shickel B, Rashidi P. Deep neural network architectures for forecasting analgesic response. Annu Int Conf IEEE Eng Med Biol Soc. 2016;2016:29662969.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 20

    Balabaud L, Pitel S, Caux I, et al. Lumbar spine surgery in patients 80 years of age or older: morbidity and mortality. Eur J Orthop Surg Traumatol. 2015;25(suppl 1):S205S212.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 21

    Knight BA, Bayne AP, Zusman N, et al. Postoperative management factors affect urinary retention following posterior spinal fusion for adolescent idiopathic scoliosis. Spine Deform. 2020;8(4):703709.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 22

    Petros JG, Bradley TM. Factors influencing postoperative urinary retention in patients undergoing surgery for benign anorectal disease. Am J Surg. 1990;159(4):374376.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 23

    Petros JG, Rimm EB, Robillard RJ, Argy O. Factors influencing postoperative urinary retention in patients undergoing elective inguinal herniorrhaphy. Am J Surg. 1991;161(4):431434.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 24

    Petros JG, Rimm EB, Robillard RJ. Factors influencing urinary tract retention after elective open cholecystectomy. Surg Gynecol Obstet. 1992;174(6):497500.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 25

    Petros JG, Mallen JK, Howe K, et al. Patient-controlled analgesia and postoperative urinary retention after open appendectomy. Surg Gynecol Obstet. 1993;177(2):172175.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 26

    Petros JG, Alameddine F, Testa E, et al. Patient-controlled analgesia and postoperative urinary retention after hysterectomy for benign disease. J Am Coll Surg. 1994;179(6):663667.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 27

    Faas CL, Acosta FJ, Campbell MDR, et al. The effects of spinal anesthesia vs epidural anesthesia on 3 potential postoperative complications: pain, urinary retention, and mobility following inguinal herniorrhaphy. AANA J. 2002;70(6):441447.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 28

    Larsen J, Goutte C. On optimal data split for generalization estimation and model selection. In: Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop. IEEE;1999:225234.

    • Search Google Scholar
    • Export Citation
  • 29

    Draelos R. Best use of train/val/test splits, with tips for medical data. Glass Box. Published September 15, 2019.Accessed April 23, 2021. https://glassboxmedicine.com/2019/09/15/best-use-of-train-val-test-splits-with-tips-for-medical-data/

    • Search Google Scholar
    • Export Citation
  • 30

    Bursac Z, Gauss CH, Williams DK, Hosmer DW. Purposeful selection of variables in logistic regression. Source Code Biol Med. 2008;3:17.

  • 31

    Harrell FE Jr. Binary logistic regression. In: Harrell FE Jr, ed.Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis. 2nd ed. Springer Series in Statistics.Springer International Publishing;2015:219274.

    • Search Google Scholar
    • Export Citation
  • 32

    Bisong E. Ensemble methods. In: Bisong E, ed.Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners. Apress; 2019:269286.

    • Search Google Scholar
    • Export Citation
  • 33

    Bisong E. Principles of learning. In: Bisong E, ed.Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners. Apress; 2019:171197.

    • Search Google Scholar
    • Export Citation
  • 34

    Meddings J, Skolarus TA, Fowler KE, et al. Michigan Appropriate Perioperative (MAP) criteria for urinary catheter use in common general and orthopaedic surgeries: results obtained using the RAND/UCLA Appropriateness Method. BMJ Qual Saf. 2019;28(1):5666.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 35

    Hoke N, Bradway C. A clinical nurse specialist-directed initiative to reduce postoperative urinary retention in spinal surgery patients. Am J Nurs. 2016;116(8):4752.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 36

    Turan A, Karamanlioğlu B, Memiş D, et al. Analgesic effects of gabapentin after spinal surgery. Anesthesiology. 2004;100(4):935938.

  • 37

    Madani AH, Aval HB, Mokhtari G, et al. Effectiveness of tamsulosin in prevention of post-operative urinary retention: a randomized double-blind placebo-controlled study. Int Braz J Urol. 2014;40(1):3036.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 38

    Fan F, Xiong J, Li M, Wang G. On interpretability of artificial neural networks: a survey. ArXiv. Preprint posted online November 30, 2020. http://arxiv.org/abs/2001.02522

    • Search Google Scholar
    • Export Citation
  • 39

    Pavlyshenko B. Using stacking approaches for machine learning models. In: 2018 IEEE Second International Conference on Data Stream Mining Processing (DSMP). IEEE; 2018:255258.

    • Search Google Scholar
    • Export Citation
  • 40

    Mandrekar JN. Receiver operating characteristic curve in diagnostic test assessment. J Thorac Oncol. 2010;5(9):13151316.

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