Mastrokostas Paul G, Mastrokostas Leonidas E, Lavi Aaron B, Razi Abigail, Emara Ahmed K, Dalton Jonathan, Kepler Christopher K, Monsef Jad Bou, Razi Afshin E, Ng Mitchell K
SUNY Downstate Health Sciences University, Brooklyn, USA.
Maimonides Medical Center, New York, USA.
Eur Spine J. 2025 Aug 27. doi: 10.1007/s00586-025-09303-z.
To examine factors influencing non-routine discharge in ACDF patients stratified by age utilizing machine learning.
A 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.
Across 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).
Non-routine discharge after ACDF is influenced by patient-specific factors. Strategies targeting older patients with complex comorbidities could help reduce adverse outcomes.
利用机器学习研究按年龄分层的ACDF患者非常规出院的影响因素。
将2016 - 2020年国家住院患者样本（NIS）数据库中的219380例加权ACDF病例分为三个年龄组：50 - 64岁、65 - 79岁和80岁及以上。八个监督机器学习模型根据患者特征（包括年龄、住院时间（LOS）和合并症）预测非常规出院。卡方检验和t检验比较结果。经过Bonferroni校正后，显著性设定为P < 0.004。
在所有年龄组中，几个患者特定因素与非常规出院相关。在50 - 64岁组中，缺铁性贫血（1.1%对0.6%，P < 0.001）、瘫痪（1.2%对0.1%，P < 0.001）和种族（黑人：15.4%对10.0%，P < 0.001）是显著的预测因素。对于65 - 79岁组，心力衰竭（1.2%对0.5%，P < 0.001）和痴呆（0.5%对0.1%，P < 0.001）增加了风险。在80岁及以上组中，种族差异仍然存在。机器学习模型——尤其是AdaBoost和梯度提升——表现出很强的预测性能，65 - 79岁和80岁及以上队列的曲线下面积（AUC）超过80%。在所有年龄组中，住院时间延长也与非常规出院显著相关，患者平均住院时间延长两倍多（所有P < 0.001）。
ACDF后的非常规出院受患者特定因素影响。针对患有复杂合并症的老年患者的策略可能有助于减少不良结局。
