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

According to the United Nations 2022 Revision of World Population Prospects, the proportion of people over 65 years of age is expected to increase from approximately 9.7% in 2022 to 16.4% in 2050 [1]. The burden of lumbar degenerative diseases (LDD) has increased substantially with the unprecedented aging population. From 2004 to 2015, the volume of elective lumbar fusion procedures for LDD among those over age 65 in the USA increased by 138%, and the costs for elective lumbar fusion increased from $3.7 billion dollars in 2004 to $10.2 billion dollars in 2015 [2]. Transforaminal lumbar interbody fusion (TLIF) was first reported in the early 1980s as a modification of posterior LIF and has become a commonly used surgical procedure for nerve decompression and bone stabilization with excellent and reliable outcomes [3, 4]. Postoperative adverse events (AEs) following lumbar fusion surgery include complications, prolonged hospital stay, and readmission, which increase hospitalization-related expenditures and postoperative dissatisfaction [5, 6]. In previous studies, elderly patients (aged 65 years and older) had more extended hospital stays and about twice the complication rate of younger patients [7, 8]. Identifying elderly patients with high risk of AEs and establishing individualized perioperative management is critical to mitigate added costs and optimize cost-effectiveness to the healthcare system.

Many independent variables are associated with postoperative AEs following lumbar fusion surgery. Variables associated with increased length of stay (LOS) include increased age, morbid obesity, diabetes, opioid use, greater number of comorbid conditions, unemployment, drain use, and blood transfusion [9‐11]. Older and non-married patients, those with obesity, positive smoking history, longer procedure times, and emergent cases were significantly more likely to be readmitted for complications or physical rehabilitation [9, 10, 12]. Variables associated with complications include cerebrovascular disease, electrolyte disorders, hemi/paraplegia, mass blood loss, and postoperative delayed ambulation [13‐16]. Predictive models are more likely to provide individualized expectations for postoperative outcomes during preoperative consultation than risk factor analysis alone. Furthermore, although postoperative AEs are more common in the elderly population, research regarding developing predictive models for AEs in elderly patients is lacking.

Multivariate logistic regression and machine learning algorithms are now applied widely across medical field to develop predictive models. Compared with models based on machine learning algorithms, logistic regression models have better stability and model interpretability, while machine learning algorithms have advantages in data processing and nonlinear and multivariate forecasting [17]. Here, we sought to develop a predictive tool for postoperative AEs in elderly patients, utilizing multivariate logistic regression, single classification and regression tree (hereafter, “classification tree”), and random forest machine learning algorithms.

The hospitalized patient experience has become an area of increased focus for hospitals given the recent coupling of patient satisfaction to reimbursement rates for inpatients [19]. Although previous studies focused on identifying risk factors for complications, reducing LOS and readmission rates are equally important to improve the patient experience and reduce costs [6, 20]. Therefore, postoperative AEs should include complications, readmission, and prolonged LOS after spine surgery. In this study, we developed and validated three models (logistic regression, single classification tree and random forest algorithms) for predicting postoperative AEs following lumbar fusion surgery in elderly patients using a prospective Geriatric Lumbar Disease Database. To our knowledge, this is the first study to develop and externally validate a prediction model for postoperative AEs in elderly patients with high predictive accuracy.

The predictive ability of our three models was comparable, with no significant differences in AUC. Then, we performed a decision curve analysis to determine the clinical usefulness of the three risk stratification models. The logistic regression model had a higher net benefit for clinical intervention than the other models. Our final models (logistic regression model) had good calibration and predictive performance in the internal validation cohort (C-statistics, 0.69–0.76), demonstrating that it can accurately predict potential outcomes in new populations with similar characteristics. Finally, we performed an external validation of the most appropriate model and provided an online user-friendly risk prediction tool at https://​xuanwumodel.​shinyapps.​io/​Model_​for_​AEs/​.

Multivariable regression analysis revealed that age, BMI, operative time, intraoperative blood loss, and delayed ambulation were independently associated with postoperative AEs. This finding was in line with other spine surgery literature, which suggested that older age, obesity, and surgical trauma were risk factors for postoperative complications [8, 14, 21]. Intraoperative blood loss was also associated with readmission and prolonged length of hospital stay in previous studies [12, 22]. Our findings suggest that operative time and blood loss were more critical risk factors than the number of surgical segments for AEs in lumbar fusion for degenerative disorders. Preoperative risk of AEs may be modified by intraoperative events, most notably blood loss, which was the most relevant single parameter determining the risk of AEs in elderly patients. For older patients with higher BMI, reducing intraoperative blood loss and operative time can decrease the occurrence rate of adverse events. As an important intervention of enhanced recovery after surgery pathway (ERAS), early ambulation had been demonstrated to be associated with better clinical outcomes [5, 23]. Our study also revealed that delayed ambulation (> 48 h) after surgery was a predictor for postoperative AEs when patients with intraoperative complications were excluded. Therefore, how to improve ERAS compliance of elderly patients is an urgent problem to be solved.

Tree-based machine learning algorithms (including the single classification tree and the random forest approach) were chosen based on many desirable properties for the given binary outcome of having an adverse event or not, including the ability to handle hundreds of variables (both categorical and continuous) and ease of construction [14]. The single classification tree algorithm showed that the number of fused segments was the first discriminator for predicting postoperative AEs. Our findings supported that > 3 fusion segments in lumbar surgery was considered to be long-segment fusion that can cause more extensive surgical trauma. Other discriminators of the present classification tree included age, intraoperative blood loss, delayed ambulation, and weight, similar to our regression analysis and some prior studies [13, 18, 24, 25]. In a retrospective multicenter database analysis, Arora et al. [11] performed a decision tree analysis and found that advanced age, obesity, and greater surgical invasiveness were significant variables increasing the likelihood of readmission and prolonged LOS. To reduce the incidence of postoperative AEs, in older patients (aged 79 years or older) who undergo long-segment fusion, hemostatic agents and minimally invasive surgery should be used to reduce intraoperative bleeding.

Random forest is an algorithm that, in many situations, improves on single classification trees. However, the random forest model exhibited a nonsignificant trend to superior discrimination compared to the classification tree model (classification tree AUC = 0.70 vs. random forest AUC = 0.72). This lack of significant difference is likely due to the small size of our testing dataset, as the method utilized to calculate AUC confidence intervals dependent on sample size. Similar to the other two models, the most important variables in the random forest model were operative time, intraoperative blood loss, BMI, age, and delayed ambulation. Preoperative RBC count and drainage volume on POD0 were relatively important predictors for postoperative AEs within 90 days of surgery. These two variables correlate highly with postoperative RBC count and hemoglobin, which could affect discharge planning. This conjecture, however, will require further study.

Previous spinal surgery studies focused solely on comparing different machine learning and regression approaches for postoperative AEs in patients with spine deformity and involved only internal validation of the developed prediction models [13, 25]. Yagi et al. and Passias et al. demonstrated the efficacy of classification trees in preoperative screening and risk stratification of patients likely to have major complications following corrective spine surgery and cervical deformity surgery, respectively [13, 26]. In a retrospective study of 37,852 patients, Jain et al. developed nine models to assess risks of discharge-to-facility, 90-day readmissions, and major medical complications after long-segment lumbar spine fusion with moderate sensitivity and specificity. The authors found that logistic regression models modestly outperformed random forest and elastic net models [25]. Although many predictive models for spinal disorders had been developed, that as yet we have to pay careful attention in deciding which tools to use depending on the outcomes and the setting of interest. Despite the considerable rate of AEs in elderly patients undergoing lumbar fusion surgery, there are few quantitative tools to predict AEs in elderly patients. The present study offers a novel contribution to the field by assessing and comparing the performance of logistic regression models versus tree-based algorithms and validating them using external data.

This study had several limitations. First, our models were created using retrospective data and are relevant to context of the current standard of care. Prospective multicenter studies can guarantee the sustained effectiveness of the model while ensuring a large sample size. However, our retrospective and uncontrolled data theoretically lends to greater generalizability across institutions and surgical teams. Second, there might be significant predictors of outcomes that are not available in our data set, such as insurance status, dependency status, income levels, and details of spinal pathology. In the present study, we found many non-modifiable preoperative characteristics and intraoperative variables were significantly risk factors for postoperative AEs. Of note, our primary goal was to predict the absolute risk of AEs following surgery and not to identify modifiable factors. Third, given that postoperative complications mainly occurred within the first three months after surgery, we included only patients who were followed up for three months or more [27, 28]. Finally, as we move toward value-based care and shared decision-making, there is an increasing need to collect and use patients-reported outcomes not just in research settings, but also in routine clinical care or quality improvement activities. Thus, our future research will develop models for postoperative minimal clinically important difference and satisfaction in elderly patients undergoing lumbar fusion surgery with a long-term follow-up.

This investigation produced three predictive models for postoperative adverse events 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. An online dynamic nomogram calculator was established based on the final logistic regression model. Our predictive tool could inform physicians about elderly patients with a high risk of AEs within the 90 days after surgery.