Predicting prolonged length of stay in patients undergoing transforaminal lumbar interbody fusion

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

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).

CONCLUSION

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.

Risk factors for prolonged length of stay after first single-level lumbar microdiscectomy

OBJECTIVE

The objective is to identify risk factors that potentially prolong the hospital stay in patients after undergoing first single-level open lumbar microdiscectomy.

METHODS

A retrospective single-centre study was conducted. Demographic data, medical records, intraoperative course, and imaging studies were analysed. The outcome measure was defined by the number of days stayed after the operation. A prolonged length of stay (LOS) stay was defined as a minimum of one additional day beyond the median hospital stay in our patient collective. Bivariate analysis and multiple stepwise regression were used to identify independent factors related to the prolonged hospital stay.

RESULTS

Two hundred consecutive patients who underwent first lumbar microdiscectomy between 2018 and 2022 at our clinic were included in this study. Statistical analysis of factors potentially prolonging postoperative hospital stay was done for a total of 24 factors, seven of them were significantly related to prolonged LOS in bivariate analysis. Sex (p = 0.002, median 5 vs. 4 days for females vs. males) and age (rs = 0.35, p ≤ 0.001, N = 200) were identified among the examined demographic factors. Regarding preoperative physical status, preoperative immobility reached statistical significance (p ≤ 0.001, median 5 vs. 4 days). Diabetes mellitus (p = 0.043, median 5 vs. 4 days), anticoagulation and/or antiplatelet agents (p = 0.045, median 5 vs. 4 days), and postoperative narcotic consumption (p ≤ 0.001, median 5 vs. 4 days) as comorbidities were associated with a prolonged hospital stay. Performance of nucleotomy (p = 0.023, median 5 vs. 4 days) was a significant intraoperative factor. After linear stepwise multivariable regression, only preoperative immobility (p ≤ 0.001) was identified as independent risk factors for prolonged length of postoperative hospital stay.

CONCLUSION

Our study identified preoperative immobility as a significant predictor of prolonged hospital stay, highlighting its value in preoperative assessments and as a tool to pinpoint at-risk patients. Prospective clinical trials with detailed assessment of mobility, including grading, need to be done to verify our results.

Development and external validation of a nomogram for predicting postoperative adverse events in elderly patients undergoing lumbar fusion surgery: comparison of three predictive models

BACKGROUND

The burden of lumbar degenerative diseases (LDD) has increased substantially with the unprecedented aging population. Identifying elderly patients with high risk of postoperative adverse events (AEs) and establishing individualized perioperative management is critical to mitigate added costs and optimize cost-effectiveness to the healthcare system. We aimed to develop a predictive tool for AEs in elderly patients with transforaminal lumbar interbody fusion (TLIF), 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 (age ≥ 65). Our outcome measure was postoperative AEs, including prolonged hospital stays, postoperative complications, readmission, and reoperation within 90 days. Patients were grouped as either having at least one adverse event (AEs group) or not (No-AEs group). Three models for predicting postoperative AEs were developed using training dataset and internal validation using testing dataset. Finally, online tool 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). The predictive ability of our three models was comparable, with no significant differences in AUC (0.73 vs. 0.72 vs. 0.70, respectively). The logistic regression model had a higher net benefit for clinical intervention than the other models. A nomogram based on logistic regression was developed, and the C-index of external validation for AEs was 0.69 (95% CI 0.65-0.76).

CONCLUSION

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 nomogram and online tool ( https://xuanwumodel.shinyapps.io/Model_for_AEs/ ) could inform physicians about elderly patients with a high risk of AEs within the 90 days after TLIF surgery.