Evidence map›Paper›PMID 38702550›Full record

ArticleSpine deformity2024

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.

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

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Article in Spine deformity, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Michael W FieldsDepartment of Orthopaedic Surgery, Columbia University, New York, NY, USA.
Jay ZaifmanDepartment of Orthopaedic Surgery, New York University Langone Health, New York, NY, USA.
Matan S MalkaDepartment of Orthopaedic Surgery, Columbia University, New York, NY, USA. msm2244@cumc.columbia.edu.ORCID 0009-0001-1470-3335
Nathan J LeeDepartment of Orthopaedic Surgery, Columbia University, New York, NY, USA.
Christina C RymondDepartment of Orthopaedic Surgery, Columbia University, New York, NY, USA.
Matthew E SimhonDepartment of Orthopaedic Surgery, Columbia University, New York, NY, USA.
Theodore QuanDepartment of Orthopaedic Surgery, Columbia University, New York, NY, USA.
Benjamin D RoyeDepartment of Orthopaedic Surgery, Columbia University, New York, NY, USA.
Michael G VitaleDepartment of Orthopaedic Surgery, Columbia University, New York, NY, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeEarly 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.

methodsChildren 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.

resultsThe 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.

conclusionsMachine 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.

Indexed as

Length of StayMachine LearningScoliosisAge of OnsetChildChild, PreschoolFemaleHumansMaleRisk AssessmentRisk FactorsEarly onset scoliosisMachine learningProlonged length of stay

Identifiers

PMID38702550

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.