Risk Analysis Index and Its Recalibrated Version Predict Postoperative Outcomes Better Than 5-Factor Modified Frailty Index in Traumatic Spinal Injury

Frailty in intracranial meningioma resection: the risk analysis index demonstrates strong discrimination for predicting non-home discharge and in-hospital mortality

PURPOSE

Frailty is an independent risk factor for adverse postoperative outcomes following intracranial meningioma resection (IMR). The role of the Risk Analysis Index (RAI) in predicting postoperative outcomes following IMR is nascent but may inform preoperative patient selection and surgical planning.

METHODS

IMR patients from the Nationwide Inpatient Sample were identified using diagnostic and procedural codes (2019-2020). The relationship between preoperative RAI-measured frailty and primary outcomes (non-home discharge (NHD), in-hospital mortality) and secondary outcomes (extended length of stay (eLOS), complication rates) was assessed via multivariate analyses. The discriminatory accuracy of the RAI for primary outcomes was measured in area under the receiver operating characteristic (AUROC) curve analysis.

RESULTS

A total of 23,230 IMR patients (mean age = 59) were identified, with frailty statuses stratified by RAI score: 0-20 "robust" (R)(N = 10,665, 45.9%), 21-30 "normal" (N)(N = 8,895, 38.3%), 31-40 "frail" (F)(N = 2,605, 11.2%), and 41+ "very frail" (VF)(N = 1,065, 4.6%). Rates of NHD (R 11.5%, N 29.7%, F 60.8%, VF 61.5%), in-hospital mortality (R 0.5%, N 1.8%, F 3.8%, VF 7.0%), eLOS (R 13.2%, N 21.5%, F 40.9%, VF 46.0%), and complications (R 7.5%, N 11.6%, F 15.7%, VF 16.0%) significantly increased with increasing frailty thresholds (p < 0.001). The RAI demonstrated strong discrimination for NHD (C-statistic: 0.755) and in-hospital mortality (C-statistic: 0.754) in AUROC curve analysis.

CONCLUSION

Increasing RAI-measured frailty is significantly associated with increased complication rates, eLOS, NHD, and in-hospital mortality following IMR. The RAI demonstrates strong discrimination for predicting NHD and in-hospital mortality following IMR, and may aid in preoperative risk stratification.

Frailty as a predictor of postoperative outcomes in neurosurgery: a systematic review

INTRODUCTION

Baseline frailty status has been utilized to predict a wide range of outcomes and guide preoperative decision making in neurosurgery. This systematic review aims to analyze existing literature on the utilization of frailty as a predictor of neurosurgical outcomes.

EVIDENCE ACQUISITION

We conducted a systematic review following PRISMA guidelines. Studies that utilized baseline frailty status to predict outcomes after a neurosurgical intervention were included in this systematic review. Studies that utilized sarcopenia as the sole measure of frailty were excluded. PubMed, EMBASE, and Cochrane library was searched from inception to March 1st, 2023, to identify relevant articles.

EVIDENCE SYNTHESIS

Overall, 244 studies met the inclusion criteria. The 11-factor modified frailty index (mFI-11) was the most utilized frailty measure (N.=91, 37.2%) followed by the five-factor modified Frailty Index (mFI-5) (N.=80, 32.7%). Spine surgery was the most common subspecialty (N.=131, 53.7%), followed by intracranial tumor resection (N.=57, 23.3%), and post-operative complications were the most reported outcome (N.=130, 53.2%) in neurosurgical frailty studies. The USA and the Bowers author group published the greatest number of articles within the study period (N.=176, 72.1% and N.=37, 15.2%, respectively).

CONCLUSIONS

Frailty literature has grown exponentially over the years and has been incorporated into neurosurgical decision making. Although a wide range of frailty indices exist, their utility may vary according to their ability to be incorporated in the outpatient clinical setting.

A Predictive Model of Failure to Rescue After Thoracolumbar Fusion

OBJECTIVE

Although failure to rescue (FTR) has been utilized as a quality-improvement metric in several surgical specialties, its current utilization in spine surgery is limited. Our study aims to identify the patient characteristics that are independent predictors of FTR among thoracolumbar fusion (TLF) patients.

METHODS

Patients who underwent TLF were identified using relevant diagnostic and procedural codes from the National Surgical Quality Improvement Program (NSQIP) database from 2011-2020. Frailty was assessed using the risk analysis index (RAI). FTR was defined as death, within 30 days, following a major complication. Univariate and multivariable analyses were used to compare baseline characteristics and early postoperative sequelae across FTR and non-FTR cohorts. Receiver operating characteristic (ROC) curve analysis was used to assess the discriminatory accuracy of the frailty-driven predictive model for FTR.

RESULTS

The study cohort (N = 15,749) had a median age of 66 years (interquartile range, 15 years). Increasing frailty, as measured by the RAI, was associated with an increased likelihood of FTR: odds ratio (95% confidence interval [CI]) is RAI 21-25, 1.3 [0.8-2.2]; RAI 26-30, 4.0 [2.4-6.6]; RAI 31-35, 7.0 [3.8-12.7]; RAI 36-40, 10.0 [4.9-20.2]; RAI 41- 45, 21.5 [9.1-50.6]; RAI ≥ 46, 45.8 [14.8-141.5]. The frailty-driven predictive model for FTR demonstrated outstanding discriminatory accuracy (C-statistic = 0.92; CI, 0.89-0.95).

CONCLUSION

Baseline frailty, as stratified by type of postoperative complication, predicts FTR with outstanding discriminatory accuracy in TLF patients. This frailty-driven model may inform patients and clinicians of FTR risk following TLF and help guide postoperative care after a major complication.

Causes and Predictors of Unplanned Readmission in Patients Undergoing Intracranial Tumor Resection: A Multicenter Analysis of 31,776 Patients

BACKGROUND

Although unplanned readmission is a postoperative outcome metric associated with significant morbidity and financial burden, precise assessment tools for its prediction have not yet been developed. The Risk Analysis Index (RAI) could potentially be used to help improve the prediction of unplanned readmissions for patients undergoing intracranial tumor resection (ITR). In the present study, we evaluate the predictive accuracy of frailty on 30-day unplanned readmission after ITR using the RAI.

METHODS

Data were obtained from the American College of Surgeons National Surgical Quality Improvement Program database. The baseline characteristics, preoperative clinical status, and outcomes were compared between patients with and without unplanned readmission. Frailty was calculated using the RAI. Univariate and multivariate logistic regression analyses were performed to identify independent associations between unplanned readmissions and patient characteristics.

RESULTS

The unplanned readmission rate for this cohort (n = 31,776) was 10.8% (n = 3420). Of the 3420 readmitted patients, 958 required unplanned reoperation. Multiple characteristics were significantly different between the 2 groups, including age, body mass index, comorbidities, and RAI groups (P < 0.05). The common causes of unplanned readmission included infection (9.4%), seizures (6%), and pulmonary embolism (4%). The patient characteristics identified as reliable predictors of unplanned readmission included age, body mass index, functional status, diabetes, hypertension, hyponatremia, and the patient's RAI score (P < 0.05). Frail status, hyponatremia, leukocytosis, hypertension, and thrombocytosis were significant predictors of unplanned readmissions.

CONCLUSIONS

The RAI is a reliable preoperative frailty index for predicting unplanned readmissions after ITR. Using the RAI could decrease unplanned readmissions by identifying high-risk patients and enabling future implementation of appropriate management guidelines.

A systematic review of predictive accuracy via c-statistic of preoperative frailty tests for extended length of stay, post-operative complications, and mortality

Frailty, as an age-related syndrome of reduced physiological reserve, contributes significantly to post-operative outcomes. With the aging population, frailty poses a significant threat to patients and health systems. Since 2012, preoperative frailty assessment has been recommended, yet its implementation has been inhibited by the vast number of frailty tests and lack of consensus. Since the anesthesiologist is the best placed for perioperative care, an anesthesia-tailored preoperative frailty test must be simple, quick, universally applicable to all surgeries, accurate, and ideally available in an app or online form. This systematic review attempted to rank frailty tests by predictive accuracy using the c-statistic in the outcomes of extended length of stay, 3-month post-operative complications, and 3-month mortality, as well as feasibility outcomes including time to completion, equipment and training requirements, cost, and database compatibility. Presenting findings of all frailty tests as a future reference for anesthesiologists, Clinical Frailty Scale was found to have the best combination of accuracy and feasibility for mortality with speed of completion and phone app availability; Edmonton Frailty Scale had the best accuracy for post-operative complications with opportunity for self-reporting. Finally, extended length of stay had too little data for recommendation of a frailty test. This review also demonstrated the need for changing research emphasis from odds ratios to metrics that measure the accuracy of a test itself, such as the c-statistic.

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.

The modified 5-item frailty index as a predictor of complications in burn patients

The modified 5-item frailty index (mFI-5), as a measure of frailty and biological age, has been shown to be a reliable predictor of complications and mortality in a variety of surgical specialties. However, its role in burn care remains to be fully elucidated. We, therefore, correlated frailty with in-hospital mortality and complications after burn injury. The medical charts of all burn patients admitted between 2007 and 2020 who had ≥ 10 % of their total body surface area affected were retrospectively reviewed. Data on clinical, demographic, and outcome parameters were collected and evaluated, and mFI-5 was calculated on the basis of the data obtained. Univariate and multivariate regression analyses were used to investigate the association between mFI-5 and medical complications and in-hospital mortality. A total of 617 burn patients were included in this study. Increasing mFI-5 scores were significantly associated with increased in-hospital mortality (p < 0.0001), myocardial infarction (p = 0.03), sepsis (p = 0.005), urinary tract infections (p = 0.006), and perioperative blood transfusions (p = 0.0004). They were also associated with an increase in the length of hospital stay and the number of surgical procedures, albeit without statistical significance. An mFI-5 score of ≥ 2 was a significant predictor of sepsis (odds ratio [OR] = 2.08; 95% confidence interval [CI]: 1.03 to 3.95; p = 0.04), urinary tract infection (OR = 2.82; 95% CI: 1.47 to 5.19; p = 0.002), and perioperative blood transfusions (OR = 2.61; 95% CI: 1.61 to 4.25; p = 0.0001). Multivariate logistic regression analysis revealed that an mFI-5 score of ≥ 2 was not an independent risk factor for in-hospital mortality (OR = 1.44; 95% CI: 0.61 to 3.37; p = 0.40). mFI-5 is a significant risk factor for only a few select complications in the burn population. It is not a reliable predictor of in-hospital mortality. Therefore, its utility as a risk stratification tool in the burn unit may be limited.