Worse cranial neurosurgical outcomes predicted by increasing frailty in patients with interhospital transfer status: Analysis of 47,736 patients from the National Surgical Quality Improvement Program (NSQIP) 2015-2019

Frailty as a Predictor of Post-Surgical Outcomes in Patients With Cutaneous Malignancies of the Scalp and Neck Requiring Flap Reconstruction

BACKGROUND

Investigate the ability of frailty status to predict post-surgical outcomes in patients with cutaneous malignancies of the scalp and neck undergoing flap reconstruction.

METHODS

National Surgical Quality Improvement Program database was used to isolate patients with cutaneous malignancies of the scalp and neck who underwent surgical resection between 2015 to 2019. Univariate and multivariate analyses were performed to determine if frailty score correlated with negative post-operative outcomes. Receiver operating characteristic (ROC) curves allowed testing of the discriminative performance of age versus frailty.

RESULTS

This study demonstrated an independent correlation between frailty and major complications as well as non-home discharge. In ROC curve analysis, frailty demonstrated superior discrimination compared to age for predicting major complications.

CONCLUSION

Our study demonstrated an association between increasing frailty and major complications as well as the likelihood of a non-home discharge. When compared to age, frailty was also shown to be a better predictor of major complications.

Baseline Frailty and Discharge to Advanced Care Facilities in Patients Undergoing Lumbar Interbody Fusion for Lumbar Degenerative Disease: A Multicenter Registry Analysis of 7153 Patient Cases Comparing the Risk Analysis Index to the 5-Factor Modified Frailty Index

OBJECTIVE

To evaluate the impact of frailty, as measured by the 5-factor modified Frailty Index (mFI-5) and the Risk Analysis Index (RAI), on advanced care facility discharge (FD) in patients who underwent lumbar fusion for lumbar degenerative spine disease.

METHODS

The American College of Surgeons National Surgical Quality Improvement Program (2012-2020) was queried for adults (≥18 years) undergoing lumbar fusion for lumbar degenerative disease. Descriptive statistics and univariate crosstabulation were used to assess baseline demographics, preoperative comorbidities, and postoperative outcomes. Receiver operating characteristic curve analysis was used to assess the discriminative threshold of the mFI-5 and RAI on FD within this population.

RESULTS

The median patient age in this study cohort (N = 7153) was 56 years and FD occurred in 7.3% of cases. Receiver operating characteristic curve analysis demonstrated that both the mFI-5 and the RAI accurately predicted FD (C-statistics: mFI-5: 0.627; RAI: 0.746). DeLong's test found that the RAI had superior discrimination when compared to the mFI-5 (P < 0.0001).

CONCLUSIONS

RAI is a reliable predictor of FD in lumbar degenerative disease patients who underwent lumbar interbody fusion and demonstrated superior discrimination compared to the mFI-5. Identification of patients at risk for FD may facilitate more precise risk stratification to enable better preoperative decision-making and help set more realistic expectations of care.

Benchmarking short-term postoperative mortality across neurosurgery units: is hospital administrative data good enough for risk-adjustment?

BACKGROUND

Surgical mortality indicators should be risk-adjusted when evaluating the performance of organisations. This study evaluated the performance of risk-adjustment models that used English hospital administrative data for 30-day mortality after neurosurgery.

METHODS

This retrospective cohort study used Hospital Episode Statistics (HES) data from 1 April 2013 to 31 March 2018. Organisational-level 30-day mortality was calculated for selected subspecialties (neuro-oncology, neurovascular and trauma neurosurgery) and the overall cohort. Risk adjustment models were developed using multivariable logistic regression and incorporated various patient variables: age, sex, admission method, social deprivation, comorbidity and frailty indices. Performance was assessed in terms of discrimination and calibration.

RESULTS

The cohort included 49,044 patients. Overall, 30-day mortality rate was 4.9%, with unadjusted organisational rates ranging from 3.2 to 9.3%. The variables in the best performing models varied for the subspecialties; for trauma neurosurgery, a model that included deprivation and frailty had the best calibration, while for neuro-oncology a model with these variables plus comorbidity performed best. For neurovascular surgery, a simple model of age, sex and admission method performed best. Levels of discrimination varied for the subspecialties (range: 0.583 for trauma and 0.740 for neurovascular). The models were generally well calibrated. Application of the models to the organisation figures produced an average (median) absolute change in mortality of 0.33% (interquartile range (IQR) 0.15-0.72) for the overall cohort model. Median changes for the subspecialty models were 0.29% (neuro-oncology, IQR 0.15-0.42), 0.40% (neurovascular, IQR 0.24-0.78) and 0.49% (trauma neurosurgery, IQR 0.23-1.68).

CONCLUSIONS

Reasonable risk-adjustment models for 30-day mortality after neurosurgery procedures were possible using variables from HES, although the models for trauma neurosurgery performed less well. Including a measure of frailty often improved model performance.

Frailty predicts hospital acquired infections after brain tumor resection: Analysis of 27,947 patients' data from a prospective multicenter surgical registry

BACKGROUND

Hospital acquired infections (HAIs) present a significant source of economic burden in the United States. The role of frailty as a predictor of HAIs has not been illustrated among patients undergoing craniotomy for brain tumor resection (BTR).

METHODS

The American College of Surgery National Surgical Quality Improvement Program (ACS-NSQIP) database was queried from 2015 to 2019 to identify patients who underwent craniotomy for BTR. Patients were categorized as pre-frail, frail and severely frail using the 5-factor Modified Frailty Index (mFI-5). Demographics, clinical and laboratory parameters, and HAIs were assessed. A multivariate logistic regression model was created to predict the occurrence of HAIs using these variables.

RESULTS

A total of 27,947 patients were assessed. 1772 (6.3 %) of these patients developed an HAI after surgery. Severely frail patients were more likely to develop an HAI in comparison to pre-frail patients (OR = 2.48, 95 % CI = 1.65-3.74, p < 0.001 vs. OR = 1.43, 95 % CI = 1.18-1.72, p < 0.001). Ventilator dependence was the strongest predictor of developing an HAI (OR = 2.96, 95 % CI = 1.86-4.71, p < 0.001).

CONCLUSION

Baseline frailty, by virtue of its ability to predict HAIs, should be utilized in adopting measures to reduce the incidence of HAIs.

Endoscopic and Nonendoscopic Approaches to Single-Level Lumbar Spine Decompression: Propensity Score-Matched Comparative Analysis and Frailty-Driven Predictive Model

Objective: The endoscopic spine surgery (ESS) approach is associated with high levels of patient satisfaction, shorter recovery time, and reduced complications. The present study reports multicenter, international data, comparing ESS and non-ESS approaches for singlelevel lumbar decompression, and proposes a frailty-driven predictive model for nonhome discharge (NHD) disposition.Methods: Cases of ESS and non-ESS lumbar spine decompression were queried from the American College of Surgeons National Surgical Quality Improvement Program database (2017–2020). Propensity score matching was performed on baseline characteristics frailty score (measured by risk analysis index [RAI] and modified frailty index-5 [mFI-5]). The primary outcome of interest was NHD disposition. A predictive model was built using logistic regression with RAI as the primary driver.Results: Single-level nonfusion spine lumbar decompression surgery was performed in 38,686 patients. Frailty, as measured by RAI, was a reliable predictor of NHD with excellent discriminatory accuracy in receiver operating characteristic (ROC) curve analysis: C-statistic: 0.80 (95% confidence interval [CI], 0.65–0.94) in ESS cohort, C-statistic: 0.75 (95% CI, 0.73–0.76) overall cohort. After propensity score matching, there was a reduction in total operative time (89 minutes vs. 103 minutes, p = 0.049) and hospital length of stay (LOS) (0.82 days vs. 1.37 days, p < 0.001) in patients treated endoscopically. In ROC curve analysis, the frailty-driven predictive model performed with excellent diagnostic accuracy for the primary outcome of NHD (C-statistic: 0.87; 95% CI, 0.85–0.88).Conclusion: After frailty-based propensity matching, ESS is associated with reduced operative time, shorter hospital LOS, and decreased NHD. The RAI frailty-driven model predicts NHD with excellent diagnostic accuracy and may be applied to preoperative decisionmaking with a user-friendly calculator: nsgyfrailtyoutcomeslab.shinyapps.io/lumbar_decompression_dischargedispo.

Preoperative frailty risk in deep brain stimulation patients: Risk analysis index predicts Clavien-Dindo IV complications

OBJECTIVE

Deep brain stimulation (DBS) improves patients' quality of life in multiple movement disorders and chronic neurodegenerative diseases. There are no published studies assessing frailty's impact on DBS outcomes. We evaluated frailty's impacts on DBS outcomes, comparing discriminative thresholds of the risk analysis index (RAI) to modified frailty index-5 (mFI-5) for predicting Clavien-Dindo complications (CDIV).

METHODS

Patients who underwent DBS between 2015 and 2019 in the ACS-NSQIP registry were included. We employed receiver operating characteristic (ROC) curve to examine the discriminative thresholds of RAI and mFI-5 and multivariable analyses for postoperative outcomes. Our primary outcome was CDIV, and secondary outcomes were discharge to higher-level care facility, unplanned reoperation within 30 days, in any hospital, for any procedure related to the index procedure, and extended length of stay.

RESULTS

A total of 3795 patients were included. In the ROC analysis for CDIV, RAI showed superior discriminative threshold (C-statistic = 0.70, 95% CI 0.61-0.80, <0.001) than mFI-5 (C-statistic = 0.60, 95% CI 0.49-0.70, P = 0.08). On multivariable analyses, frailty stratified by RAI, had independent associations with CDIV, i.e., pre-frail 2-fold increase OR 2.04 (95% CI: 1.94-2.14) p < 0.001, and frail 39% increase OR 1.39 (95% CI: 1.27-1.53), p < 0.001.

CONCLUSION

Frailty was an independent risk-factor for CDIV. The RAI had superior discriminative thresholds than mFI-5 in predicting CDIV after DBS. Our ability to identify frail patients prior to DBS presents a novel clinical opportunity for quality improvement strategies to target this specific patient population. RAI may be a useful primary frailty screening modality for potential DBS candidates.