Evaluating the predictive value of comorbidity indices in pituitary surgery: a mixed-effects modeling study using the Nationwide Readmissions Database

Hospital frailty risk score predicts postoperative outcomes after endoscopic endonasal resection of non-functioning pituitary adenomas

PURPOSE

Frailty indices are invaluable resources in risk stratification and predicting high-value care outcomes for neurosurgical patients. The Hospital Frailty Risk Score (HFRS) is a recently developed and validated method for evaluating frailty; however, its implementation has yet to be assessed in patients with non-functioning pituitary adenomas undergoing endoscopic endonasal resection. In this study, we aimed to evaluate HFRS's predictive ability for high-value care outcomes, namely postoperative complications, length of stay (LOS), and hospital charges, and to compare it to other traditionally used frailty indices.

METHODS

A retrospective review of electronic medical records from 2017 to 2020. A total of 109 ICD-10 codes corresponding to various frailty-related conditions were used to identify the components of HFRS. These components were then used to calculate the HFRS for each patient, with higher scores indicative of elevated frailty. Standard multivariate logistic regression models were employed to explore the association between HFRS and high-value care outcomes. Model discrimination was assessed using the area under the ROC curves, and the DeLong test was used to compare AUCs.

RESULTS

A total of 172 patients were included, with a mean age of 57.27 ± 12.95 years and an average HFRS score of 3.65 ± 3.27. Among patients, 56% were male, 5.2% experience postoperative complications, 23.3% endured extended LOS, 25.0% incurred high hospital charges. In multivariate regression models, greater HFRS was significantly and independently associated with postoperative complications (OR = 1.51, P < 0.001), extended LOS (OR = 1.17, P = 0.006) and high hospital charges (OR = 1.18, P = 0.004). HFRS had the highest AUC compared to other frailty indices and was the most parsimonious model, with AUC values of 0.82, 0.64, and 0.63 for predicting complications, extended LOS, and higher charges, respectively.

CONCLUSION

Higher HFRS scores are significantly associated with postoperative complications, prolonged LOS, and high hospital charges for patients undergoing pituitary surgery.

Integration of Chronological Age Does Not Improve the Performance of a Mixed-Effect Model Using Comorbidity Burden and Frailty to Predict 90-Day Readmission After Surgery for Degenerative Scoliosis

OBJECTIVE

We evaluated the contributions of chronological age, comorbidity burden, and/or frailty in predicting 90-day readmission in patients undergoing degenerative scoliosis surgery.

METHODS

Patients were identified through the Healthcare Cost and Utilization Project Nationwide Readmissions Database. Frailty was assessed using the Johns Hopkins Adjusted Clinical Groups frailty-defining indicator. Comorbidity was assessed using the Elixhauser Comorbidity Index (ECI). Generalized linear mixed-effects models were created to predict readmission using age, frailty, and/or ECI. Area under the curve (AUC) was compared using DeLong's test.

RESULTS

A total of 8104 patients were identified. Readmission rate was 9.8%, with infection representing the most common cause (3.5%). Our first model utilized chronological age, ECI, and/or frailty as primary predictors. The combination of ECI + frailty + age performed best, but the inclusion of chronological age did not significantly improve performance compared to ECI + frailty alone (AUC 0.603 vs. 0.599, P = 0.290). A second model using only chronological age and frailty as primary predictors performed better, however the inclusion of chronological age worsened performance when compared to frailty alone (AUC 0.747 vs. 0.743, P = 0.043).

CONCLUSIONS

These data support frailty as a predictor of 90-day readmission within a nationally representative sample. Frailty alone performed better than combinations of ECI and age. Interestingly, the integration of chronological age did not dramatically improve the model's performance. Limitations include the use of a national registry and a single frailty index. This provides impetus to explore biological age, rather than chronological age, as a potential tool for surgical risk assessment.

Developing Mixed-Effects Models to Compare the Predictive Ability of Various Comorbidity Indices in a Contemporary Cohort of Patients Undergoing Lumbar Fusion

BACKGROUND AND OBJECTIVE

As incidence of operative spinal pathology continues to grow, so do the rates of lumbar spinal fusion procedures. Comorbidity indices can be used preoperatively to predict potential complications. However, there is a paucity of research defining the optimal comorbidity indices in patients undergoing spinal fusion surgery. We aimed to use modeling strategies to evaluate the predictive validity of various comorbidity indices and combinations thereof.

METHODS

Patients who underwent spinal fusion were queried using data from the Nationwide Readmissions Database for the years 2016 through 2019. Using comorbidity indices as predictor variables, receiver operating characteristic curves were developed for pertinent complications such as mortality, nonroutine discharge, top-quartile cost, top-quartile length of stay, and 30-day readmission.

RESULTS

A total of 750 183 patients were included. Nonroutine discharges occurred in 161 077 (21.5%) patients. The adjusted all-payer cost for the procedure was $37 616.97 ± $27 408.86 (top quartile: $45 409.20), and the length of stay was 4.1 ± 4.4 days (top quartile: 8.1 days). By comparing receiver operating characteristics of various models, it was found that models using Frailty + Elixhauser Comorbidity Index (ECI) as the primary predictor performed better than other models with statistically significant P -values on post hoc testing. However, for prediction of mortality, the model using Frailty + ECI was not better than the model using ECI alone ( P = .23), and for prediction of all-payer cost, the ECI model outperformed the models using frailty alone ( P < .0001) and the model using Frailty + ECI ( P < .0001).

CONCLUSION

This investigation is the first to use big data and modeling strategies to delineate the relative predictive utility of the ECI and Johns Hopkins Adjusted Clinical Groups comorbidity indices for the prognostication of patients undergoing lumbar fusion surgery. With the knowledge gained from our models, spine surgeons, payers, and hospitals may be able to identify vulnerable patients more effectively within their practice who may require a higher degree of resource utilization.

The risk analysis index demonstrates superior discriminative ability in predicting extended length of stay in pituitary adenoma resection patients when compared to the 5-point modified frailty index

Objective

To compare the predictive abilities of two frailty indices on post-operative morbidity and mortality in patients undergoing pituitary adenoma resection.

Methods

The National Surgical Quality Improvement Program (NSQIP) database was used to retrospectively collect data for patients undergoing pituitary adenoma resection between 2015-2019. To compare the predictive abilities of two of the most common frailty indices, the 5-point modified frailty index (mFI-5) and the risk analysis index (RAI), receiver operating curve analysis (ROC) and area under the curve (AUC)/Cstatistic were used.

Results

In our cohort of 1,454 patients, the RAI demonstrated superior discriminative ability to the mFI-5 in predicting extended length of stay (C-statistic 0.59, 95% CI 0.56-0.62 vs. C-statistic 0.51, 95% CI: 0.48-0.54, p = 0.0002). The RAI only descriptively appeared superior to mFI-5 in determining mortality (C-statistic 0.89, 95% CI 0.74-0.99 vs. Cstatistic 0.63, 95% CI 0.61-0.66, p=0.11), and NHD (C-statistic 0.68, 95% CI 0.60-0.76 vs. C-statistic 0.60, 95% CI: 0.57-0.62, p=0.15).

Conclusions

Pituitary adenomas account for one of the most common brain tumors in the general population, with resection being the preferred treatment for patients with most hormone producing tumors or those causing compressive symptoms. Although pituitary adenoma resection is generally safe, patients who experience post-operative complications frequently share similar pre-operative characteristics and comorbidities. Therefore, appropriate pre-operative risk stratification is imperative for adequate patient counseling and informed consent in these patients. Here we present the first known report showing the superior discriminatory ability of the RAI in predicting eLOS when compared to the mFI-5.

Artificial Intelligence, Radiomics, and Computational Modeling in Skull Base Surgery

This chapter explores current artificial intelligence (AI), radiomics, and computational modeling applications in skull base surgery. AI advancements are providing opportunities to improve diagnostic accuracy, surgical planning, and postoperative care. Currently, computational models can assist in diagnosis, simulate surgical scenarios, and improve safety during surgical procedures by identifying critical structures. AI-powered technologies, such as liquid biopsy, machine learning, radiomic analysis, computer vision, and label-free optical imaging, aim to revolutionize skull base surgery. AI-driven advancements promise safer, more precise, and effective surgeries, improving patient outcomes and preoperative assessment.