Supervised machine learning to predict non-home discharge following surgical treatment of pelvic fractures
DOI:
https://doi.org/10.18203/issn.2455-4510.IntJResOrthop20241103Keywords:
Machine learning, Artificial intelligence, Discharge destination, Surgical outcomes, Random forestAbstract
Background: Decision-tree-based machine learning (ML) algorithms such as random forest (RF) are useful for their ability to predict outcomes and rank variables according to their utility in the decision-making process. This study utilizes RF to identify important predictors of discharge to facility following surgical stabilization of pelvis fractures, a traumatic injury that often precludes mortality and diminished quality of life.
Methods: The American College of Surgeons national surgical quality improvement program (ACS-NSQIP) database was queried for patients aged 16 to 70 undergoing surgical fixation of pelvis fractures between 2008 and 2018. Outcome of interest was discharge home versus to facility. RF was trained with surgical variables, comorbidities, and other patient factors and tasked with predicting discharge location. Permutation feature importance (PFI) was then generated to identify important variables.
Results: Out of 492 patients, 184 patients were discharged to facility, and 308 patients were discharged home. RF identified age, American Society of Anesthesiologists (ASA) classification, and preoperative hematocrit as top predictors for discharge to facility. Patients being discharged home were younger, had lower ASA scores, and had higher preoperative hematocrit.
Conclusions: RF identified age, ASA classification, and preoperative hematocrit as top predictors for discharge destination following pelvic surgery. Knowledge of the impact of these variables can inform preoperative planning for both patients and their care team, while highlighting the opportunity to address preoperative hematocrit to both reduce cost and improve quality of care.
References
Centers for Medicare & Medicaid Services. NHE fact sheet. 2015. Available at: https://www.cms.gov/data-research/statistics-trends-and-reports/national-health-expenditure-data/nhe-fact-sheet. Accessed on 09 December 2023.
Kim JS, Iorio R. The 5 clinical pillars of value for total joint arthroplasty in a bundled payment paradigm. J Arthroplasty. 2017;32:1712-6.
Gelbard RB, Hensman H, Schobel S, Khatri V, Tracy BM, Dente CJ, et al. Random forest modeling can predict infectious complications following trauma laparotomy. J Trauma Acute Care Surg. 2019;87(5):1125-32.
Tseng P-Y, Chen Y-T, Wang C-H, Chiu K-M, Peng Y-S, Hsu S-P, et al. Prediction of the development of acute kidney injury following cardiac surgery by machine learning. Critical Care. 2020;24(1):478.
Kaneko H. Cross-validated permutation feature importance considering correlation between features. Analytical Sci Advances. 2022;3(9-10):278-87.
Hossain A, Islam S, Haque Qasem MF, Faisal Eskander SM, Hasan MT, Nahar M. Epidemiology of pelvic fractures in adult: Our experience at two tertiary care hospital in Dhaka, Bangladesh. J Clin Orthop Trauma. 2020;11(6):1162-7.
Rosman M, Rachminov O, Segal O, Segal G. Prolonged patients’ In-Hospital Waiting Period after discharge eligibility is associated with increased risk of infection, morbidity and mortality: a retrospective cohort analysis. BMC Health Serv Res. 2015;15(1):246.
Lavoie-Gagne O, Lu Y, MacLean I, Forlenza E, Forsythe B. Discharge Destination After Shoulder Arthroplasty: An Analysis of Discharge Outcomes, Placement Risk Factors, and Recent Trends. J Am Acad Orthop Surg. 2021;29(19):e969-78.
Eastlack RK, Ledesma JB, Tran S, Khalsa A, Park P, Mummaneni PV, et al. Home Versus Rehabilitation: Factors that Influence Disposition After Minimally Invasive Surgery in Adult Spinal Deformity Surgery. World Neurosurg. 2018;118:e610-5.
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion V. Scikit-learn: Machine Learning in Python. J Machine Learn Res. 2011;12:2825-30.
Gholamy A, Kreinovich V, Kosheleva O. Why 70/30 or 80/20 Relation Between Training and Testing Sets: A Pedagogical Explanation. 2018;1209.
David Hosmer SL, Rodney Sturdivant. Applied Logistic Regression. Third edition. John Wiley & Sons, Inc. 2013.
Harris AHS, Kuo AC, Weng Y, Trickey AW, Bowe T, Giori NJ. Can Machine Learning Methods Produce Accurate and Easy-to-use Prediction Models of 30-day Complications and Mortality After Knee or Hip Arthroplasty? Clin Orthop Relat Res. 2019;477(2):452-60.
Kunze KN, Polce EM, Ranawat AS, Randsborg PH, Williams RJ, Allen AA, et al. Application of Machine Learning Algorithms to Predict Clinically Meaningful Improvement After Arthroscopic Anterior Cruciate Ligament Reconstruction. Orthop J Sports Med. 2021;9(10):23259671211046575.
Merali ZG, Witiw CD, Badhiwala JH, Wilson JR, Fehlings MG. Using a machine learning approach to predict outcome after surgery for degenerative cervical myelopathy. PLoS One. 2019;14(4):e0215133.
Chen Y-C, Chuang C-H, Hsieh M-H, Yeh H-W, Yang S-F, Lin C-W, et al. Risk of Mortality and Readmission among Patients with Pelvic Fracture and Urinary Tract Infection: A Population-Based Cohort Study. Int J Env Res Public Health. 2021;18(9):4868.
Malik AT, Quatman CE, Phieffer LS, Jain N, Khan SN, Ly TV. 30-day adverse events, length of stay and re-admissions following surgical management of pelvic/acetabular fractures. J Clin Orthop Trauma. 2019;10(5):890-5.
Ogink PT, Groot OQ, Karhade AV, Bongers MER, Oner FC, Verlaan JJ, et al. Wide range of applications for machine-learning prediction models in orthopedic surgical outcome: a systematic review. Acta Orthop. 2021;92(5):526-31.
Jurgensmeier K, Till SE, Lu Y, Arguello AM, Stuart MJ, Saris DBF, et al. Risk factors for secondary meniscus tears can be accurately predicted through machine learning, creating a resource for patient education and intervention. Knee Surg Sports Traumatol Arthrosc. 2023;31(2):518-29.
Gordon AM, Malik AT, Khan SN. Risk Factors for Discharge to a Non-Home Destination and Reoperation Following Outpatient Total Hip Arthroplasty (THA) in Medicare-Eligible Patients. Geriatr Orthop Surg Rehab. 2021;12:2151459321991500.
Sokas CM, Cowan J, Dalton MK, Coogan K, Bader A, Bernacki R, et al. Association between patient‐reported frailty and non‐home discharge among older adults undergoing surgery. J Am Geriatrics Soc. 2020;68(12):2909-13.
Malik AT, Barlow JD, Jain N, Khan SN. Incidence, risk factors, and clinical impact of non-home discharge following surgical management of proximal humerus fractures. Shoulder Elbow. 2018;11(6):430-9.
Murphy ME, Maloney PR, McCutcheon BA, Rinaldo L, Shepherd D, Kerezoudis P, et al. Predictors of Discharge to a Nonhome Facility in Patients Undergoing Lumbar Decompression Without Fusion for Degenerative Spine Disease. Neurosurgery. 2017;81(4):638-49.
Samuel AM, Lukasiewicz AM, Webb ML, Bohl DD, Basques BA, Varthi AG, et al. Do we really know our patient population in database research? A comparison of the femoral shaft fracture patient populations in three commonly used national databases. Bone Joint J. 2016;98-b(3):425-32.
