Evidence map›Paper›PMID 40864313›Full record

ArticleEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2026

Age-based prediction of non-routine discharge after anterior cervical discectomy and fusion using machine learning.

Paul G Mastrokostas, Leonidas E Mastrokostas, Aaron B Lavi, Abigail Razi, Ahmed K Emara, Jonathan Dalton, Christopher K Kepler, Jad Bou Monsef, Afshin E Razi, Mitchell K Ng

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Article in European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

The trial behind it

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

1 citing paper in PubMed.

  1. Article
4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Paul G MastrokostasSUNY Downstate Health Sciences University, Brooklyn, USA. Pmastrokostas06@gmail.com.
Leonidas E MastrokostasSUNY Downstate Health Sciences University, Brooklyn, USA.
Aaron B LaviSUNY Downstate Health Sciences University, Brooklyn, USA.
Abigail RaziMaimonides Medical Center, New York, USA.
Ahmed K EmaraCleveland Clinic, Cleveland, USA.
Jonathan DaltonRothman Orthopaedic Institute, Thomas Jefferson University Hospital, Philadelphia, USA.
Christopher K KeplerRothman Orthopaedic Institute, Thomas Jefferson University Hospital, Philadelphia, USA.
Jad Bou MonsefSUNY Downstate Health Sciences University, Brooklyn, USA.
Afshin E RaziMaimonides Medical Center, New York, USA.
Mitchell K NgMaimonides Medical Center, New York, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo examine factors influencing non-routine discharge in ACDF patients stratified by age utilizing machine learning.

methodsA cohort of 219,380 weighted ACDF cases from the National Inpatient Sample (NIS) database spanning 2016-2020 was divided into three age groups: 50-64, 65-79, and 80 + years. Eight supervised machine learning models predicted non-routine discharge based on patient characteristics, including age, length of stay (LOS), and comorbidities. Chi-square and t-tests compared outcomes. After Bonferroni correction, significance was set at P < 0.004.

resultsAcross all age groups, several patient-specific factors were associated with non-routine discharge. In the 50-64 group, deficiency anemias (1.1% vs. 0.6%, P < 0.001), paralysis (1.2% vs. 0.1%, P < 0.001), and race (Black: 15.4% vs. 10.0%, P < 0.001) were significant predictors. For 65-79, heart failure (1.2% vs. 0.5%, P < 0.001) and dementia (0.5% vs. 0.1%, P < 0.001) increased risk. In the 80 + group, racial disparities persisted. Machine learning models-especially AdaBoost and Gradient Boosting-demonstrated strong predictive performance, with AUCs exceeding 80% for the 65-79 and 80 + cohorts. Prolonged LOS was also significantly associated with non-routine discharge across all age groups, with patients staying over twice as long on average (all P < 0.001).

conclusionNon-routine discharge after ACDF is influenced by patient-specific factors. Strategies targeting older patients with complex comorbidities could help reduce adverse outcomes.

Indexed as

Anterior cervical discectomy and fusionComorbiditiesDischarge dispositionMachine learningNational inpatient sample

Identifiers

PMID40864313

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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.