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Utilizing a comprehensive machine learning approach to identify patients at high risk for extended length of stay following spinal deformity surgery in pediatric patients with early onset scoliosis

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Abstract

Purpose

Early onset scoliosis (EOS) patient diversity makes outcome prediction challenging. Machine learning offers an innovative approach to analyze patient data and predict results, including LOS in pediatric spinal deformity surgery.

Methods

Children under 10 with EOS were chosen from the American College of Surgeon’s NSQIP database. Extended LOS, defined as over 5 days, was predicted using feature selection and machine learning in Python. The best model, determined by the area under the curve (AUC), was optimized and used to create a risk calculator for prolonged LOS.

Results

The study included 1587 patients, mostly young (average age: 6.94 ± 2.58 years), with 33.1% experiencing prolonged LOS (n = 526). Most patients were female (59.2%, n = 940), with an average BMI of 17.0 ± 8.7. Factors influencing LOS were operative time, age, BMI, ASA class, levels operated on, etiology, nutritional support, pulmonary and neurologic comorbidities. The gradient boosting model performed best with a test accuracy of 0.723, AUC of 0.630, and a Brier score of 0.189, leading to a patient-specific risk calculator for prolonged LOS.

Conclusions

Machine learning algorithms accurately predict extended LOS across a national patient cohort and characterize key preoperative drivers of increased LOS after PSIF in pediatric patients with EOS.

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No extra funding for this project.

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Authors and Affiliations

  1. Department of Orthopaedic Surgery, Columbia University, New York, NY, USA

    Michael W. Fields, Matan S. Malka, Nathan J. Lee, Christina C. Rymond, Matthew E. Simhon, Theodore Quan, Benjamin D. Roye & Michael G. Vitale

  2. Department of Orthopaedic Surgery, New York University Langone Health, New York, NY, USA

    Jay Zaifman

  3. Department of Orthopaedic Surgery, Morgan Stanley Children’s Hospital of New York Presbyterian, Columbia University Medical Center, 3959 Broadway, CHONY 8-N, New York, NY, 10032-3784, USA

    Matan S. Malka, Benjamin D. Roye & Michael G. Vitale

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Contributions

Michael W. Fields: made substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data, or the creation of new software used in the work, drafted the work or revised it critically for important intellectual content, approved of the version to be published, agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. Jay Zaifman: made substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data, or the creation of new software used in the work, drafted the work or revised it critically for important intellectual content, approved of the version to be published, agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. Matan S. Malka: made substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data, or the creation of new software used in the work, drafted the work or revised it critically for important intellectual content, approved of the version to be published, agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. Nathan J. Lee: made substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data, or the creation of new software used in the work, drafted the work or revised it critically for important intellectual content, approved of the version to be published, agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. Christina C. Rymond: made substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data, or the creation of new software used in the work, drafted the work or revised it critically for important intellectual content, approved of the version to be published, agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. Matthew E. Simhon: made substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data, or the creation of new software used in the work, drafted the work or revised it critically for important intellectual content, approved of the version to be published, agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. Theodore Quan: made substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data, or the creation of new software used in the work, drafted the work or revised it critically for important intellectual content, approved of the version to be published, agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. Benjamin D. Roye: made substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data, or the creation of new software used in the work, drafted the work or revised it critically for important intellectual content, approved of the version to be published, agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. Michael G. Vitale: made substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data, or the creation of new software used in the work, drafted the work or revised it critically for important intellectual content, approved of the version to be published, agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Corresponding author

Correspondence to Matan S. Malka.

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Conflict of interest

MWF has no conflicts of interest to disclose. JZ has no conflicts of interest to disclose. NJL has no conflicts of interest to disclose. CCR has no conflicts of interest to disclose. MSM has no conflicts of interest to disclose. MES has no conflicts of interest to disclose. TQ has no conflicts of interest to disclose. BDR has received grants from the Pediatric Orthopaedic Society of North America and the Orthopedic Science Research Foundation. MGV has received grants from the Pediatric Orthopaedic Society of North America, Orthopedic Science Research Foundation, Pediatric Spine Foundation, and Setting Scoliosis Straight Foundation and royalties from Biomet. He is a paid consultant for Stryker, Biomet, and NuVasive. MGV is on the Board of Directors of the Pediatric Spine Foundation, Pediatric Spine Study Group, and C4K. He is a former president of the Pediatric Orthopaedic Society of North America and is a Board Member, and Chair Emeritus of the International Pediatric Orthopaedic Symposium.

Ethical approval

This study did not require Columbia University Institutional Review Board review since our study used a public database. The database was created in accordance with the ethical standards of the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.

Informed consent

This study qualifies for a waiver of consent because it is a database study. It does not require patient participation, as all data has already been collected during routine clinical care. There is no potential to adversely affect the rights or welfare of subjects since this is a database study.

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Fields, M.W., Zaifman, J., Malka, M.S. et al. Utilizing a comprehensive machine learning approach to identify patients at high risk for extended length of stay following spinal deformity surgery in pediatric patients with early onset scoliosis. Spine Deform (2024). https://doi.org/10.1007/s43390-024-00889-w

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