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Development and external validation of a predictive model for prolonged length of hospital stay in elderly patients undergoing lumbar fusion surgery: comparison of three predictive models – Lumbar Fusion

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The article discusses the development of predictive models for prolonged length of hospital stay (pLOS) in elderly patients undergoing lumbar fusion surgery. The study utilized multivariate logistic regression, single classification and regression tree, and random forest machine-learning algorithms to develop three predictive models. The models were developed using a retrospective review of a prospective Geriatric Lumbar Disease Database and were validated using testing datasets. The results showed that older age, higher BMI, number of fused segments, longer operative time, and diabetes were independent risk factors for pLOS. The models had comparable predictive abilities, but the logistic regression model had a higher net benefit for clinical intervention. The article concludes that the predictive models could help inform physicians about elderly patients with a high risk of pLOS after surgery

Summarised by Mr Mo Akmal – Lead Spinal Surgeon
The London Spine Unit : best rated sugical centre in UK

Published article

: This investigation produced three predictive models for pLOS in elderly patients undergoing lumbar fusion surgery. The predictive ability of our three models was comparable. Logistic regression model had a higher net benefit for clinical intervention than the other models. Our predictive model could inform physicians about elderly patients with a high risk of pLOS after surgery.

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Eur Spine J. 2024 Jan 30. doi: 10.1007/s00586-024-08132-w. Online ahead of print.ABSTRACTPURPOSE: This study aimed to develop a predictive model for prolonged length of hospital stay (pLOS) in elderly patients undergoing lumbar fusion surgery, utilizing multivariate logistic regression, single classification and regression tree (hereafter, “classification tree”) and random forest machine-learning algorithms.METHODS: This study was a,

Eur Spine J. 2024 Jan 30. doi: 10.1007/s00586-024-08132-w. Online ahead of print.

ABSTRACT

PURPOSE: This study aimed to develop a predictive model for prolonged length of hospital stay (pLOS) in elderly patients undergoing lumbar fusion surgery, utilizing multivariate logistic regression, single classification and regression tree (hereafter, “classification tree”) and random forest machine-learning algorithms.

METHODS: This study was a retrospective review of a prospective Geriatric Lumbar Disease Database. The primary outcome measure was pLOS, which was defined as the LOS greater than the 75th percentile. All patients were grouped as pLOS group and non-pLOS. Three models (including logistic regression, single-classification tree and random forest algorithms) for predicting pLOS were developed using training dataset and internal validation using testing dataset. Finally, online tool based on our model was developed to assess its validity in the clinical setting (external validation).

RESULTS: The development set included 1025 patients (mean [SD] age, 72.8 [5.6] years; 632 [61.7%] female), and the external validation set included 175 patients (73.2 [5.9] years; 97[55.4%] female). Multivariate logistic analyses revealed that older age (odds ratio [OR] 1.06, p < 0.001), higher BMI (OR 1.08, p = 0.002), number of fused segments (OR 1.41, p < 0.001), longer operative time (OR 1.02, p < 0.001), and diabetes (OR 1.05, p = 0.046) were independent risk factors for pLOS in elderly patients undergoing lumbar fusion surgery. The single-classification tree revealed that operative time ≥ 232 min, delayed ambulation, and BMI ≥ 30 kg/m2 as particularly influential predictors for pLOS. A random forest model was developed using the remaining 14 variables. Intraoperative EBL, operative time, delayed ambulation, age, number of fused segments, BMI, and RBC count were the most significant variables in the final model. The predictive ability of our three models was comparable, with no significant differences in AUC (0.73 vs. 0.71 vs. 0.70, respectively). The logistic regression model had a higher net benefit for clinical intervention than the other models. The nomogram was developed, and the C-index of external validation for PLOS was 0.69 (95% CI, 0.65-0.76).

: This investigation produced three predictive models for pLOS in elderly patients undergoing lumbar fusion surgery. The predictive ability of our three models was comparable. Logistic regression model had a higher net benefit for clinical intervention than the other models. Our predictive model could inform physicians about elderly patients with a high risk of pLOS after surgery.

PMID:38291294 | DOI:10.1007/s00586-024-08132-w

The London Spine Unit : best rated sugical centre in UK

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Development and external validation of a predictive model for prolonged length of hospital stay in elderly patients undergoing lumbar fusion surgery: comparison of three predictive models

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Eur Spine J. 2024 Jan 30. doi: 10.1007/s00586-024-08132-w. Online ahead of print.ABSTRACTPURPOSE: This study aimed to develop a predictive model for prolonged length of hospital stay (pLOS) in elderly patients undergoing lumbar fusion surgery, utilizing multivariate logistic regression, single classification and regression tree (hereafter, "classification tree") and random forest machine-learning algorithms.METHODS: This study was a
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