Frailty Measured by the Risk Analysis Index Predicts Nonhome Discharge and Mortality After Resection in Refractory Epilepsy: Analysis of 1236 Patients From a Prospective Surgical Registry, 2012 to 2020

The frailty-driven predictive model for failure to rescue among patients who experienced a major complication following cervical decompression and fusion: an ACS-NSQIP analysis of 3,632 cases (2011-2020)

BACKGROUND CONTEXT

Preoperative risk stratification for patients considering cervical decompression and fusion (CDF) relies on established independent risk factors to predict the probability of complications and outcomes in order to help guide pre and perioperative decision-making.

PURPOSE

This study aims to determine frailty's impact on failure to rescue (FTR), or when a mortality occurs within 30 days following a major complication.

STUDY DESIGN/SETTING

Cross-sectional retrospective analysis of retrospective and nationally-representative data.

PATIENT SAMPLE

The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database was queried for all CDF cases from 2011-2020.

OUTCOME MEASURES

CDF patients who experienced a major complication were identified and FTR was calculated as death or hospice disposition within 30 days of a major complication.

METHODS

Frailty was measured by the Risk Analysis Index-Revised (RAI-Rev). Baseline patient demographics and characteristics were compared for all FTR patients. Significant factors were assessed by univariate and multivariable regression for the development of a frailty-driven predictive model for FTR. The discriminative ability of the predictive model was assessed using a receiving operating characteristic (ROC) curve analysis.

RESULTS

There were 3632 CDF patients who suffered a major complication and 7.6% (277 patients) subsequently expired or dispositioned to hospice, the definition of FTR. Independent predictors of FTR were nonelective surgery, frailty, preoperative intubation, thrombosis or embolic complication, unplanned intubation, on ventilator for >48 hours, cardiac arrest, and septic shock. Frailty, and a combination of preoperative and postoperative risk factors in a predictive model for FTR, achieved outstanding discriminatory accuracy (C-statistic = 0.901, CI: 0.883-0.919).

CONCLUSION

Preoperative and postoperative risk factors, combined with frailty, yield a highly accurate predictive model for FTR in CDF patients. Our model may guide surgical management and/or prognostication regarding the likelihood of FTR after a major complication postoperatively with CDF patients. Future studies may determine the predictive ability of this model in other neurosurgical patient populations.

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.

RAI-measured frailty predicts non-home discharge following metastatic brain tumor resection: national inpatient sample analysis of 20,185 patients

PURPOSE

Preoperative risk stratification for patients undergoing metastatic brain tumor resection (MBTR) is based on established tumor-, patient-, and disease-specific risk factors for outcome prognostication. Frailty, or decreased baseline physiologic reserve, is a demonstrated independent risk factor for adverse outcomes following MBTR. The present study sought to assess the impact of frailty, measured by the Risk Analysis Index (RAI), on MBTR outcomes.

METHODS

All MBTR were queried from the National Inpatient Sample (NIS) from 2019 to 2020 using diagnosis and procedural codes. The relationship between preoperative RAI frailty score and our primary outcome - non-home discharge (NHD) - and secondary outcomes - complication rates, extended length of stay (eLOS), and mortality - were analyzed via univariate and multivariable analyses. Discriminatory accuracy was tested by computation of concordance statistics in area under the receiver operating characteristic (AUROC) curve analysis.

RESULTS

There were 20,185 MBTR patients from the NIS database from 2019 to 2020. Each patient's frailty status was stratified by RAI score: 0-20 (robust): 34%, 21-30 (normal): 35.1%, 31-40 (very frail): 13.9%, 41+ (severely frail): 16.8%. Compared to robust patients, severely frail patients demonstrated increased complication rates (12.2% vs. 6.8%, p < 0.001), eLOS (37.6% vs. 13.2%, p < 0.001), NHD (52.0% vs. 20.6%, p < 0.001), and mortality (9.9% vs. 4.1%, p < 0.001). AUROC curve analysis demonstrated good discriminatory accuracy of RAI-measured frailty in predicting NHD after MBTR (C-statistic = 0.67).

CONCLUSION

Increasing RAI-measured frailty status is significantly associated with increased complication rates, eLOS, NHD, and mortality following MBTR. Preoperative frailty assessment using the RAI may aid in preoperative surgical planning and risk stratification for patient selection.

Start Low and Go Slow: The General Principle of Managing Older Adults With Epilepsy Finds Robust Support From Frailty

Association Between Frailty and Antiseizure Medication Tolerability in Older Adults With Epilepsy Vary-O’Neal A, Miranzadeh S, Husein N, Holroyd-Leduc J, Sajobi TT, Wiebe S, Deacon C, Tellez-Zenteno JF, Josephson CB, Keezer MR. Neurology . 2023;100(11):e1135-e1147. doi:10.1212/WNL.0000000000201701 . PMID: 36535780. Background and Objective: Frailty is an important aspect of biological aging, referring to the increased vulnerability of individuals with frailty to physical and psychological stressors. While older adults with epilepsy are an important and distinct clinical group, there are no data on frailty in this population. We hypothesize that frailty will correlate with the seizure frequency and especially the tolerability of anti-seizure medications (ASMs) in older adults with epilepsy. Methods: We recruited individuals aged 60 years or older with active epilepsy from 4 Canadian hospital centers. We reported the seizure frequency in the 3 months preceding the interview, while ASM tolerability was quantified using the Liverpool Adverse Events Profile (LAEP). We applied 3 measures of frailty: grip strength as a measure of physical frailty, 1 self-reported score (Edmonton frail score [EFS]), and 1 scale completed by a healthcare professional (clinical frailty scale [CFS]). We also administered standardized questionnaires measuring levels of anxiety, depression, functional disability, and quality of life and obtained relevant clinical and demographic data. Results: Forty-three women and 43 men aged 60-93 years were recruited, 87% of whom had focal epilepsy, with an average frequency of 3.4 seizures per month. Multiple linear regression and zero-inflated negative binomial regression models showed that EFS and CFS scores were associated with decreased ASM tolerability, each point increase leading to 1.83 (95% CI: 0.67-4.30) and 2.49 (95% CI: 1.27-2.39) point increases on the LAEP scale, respectively. Neither the EFS and CFS scores nor grip strength were significantly associated with seizure frequency. The EFS was moderately correlated with depression, anxiety, quality of life, and functional disability, demonstrating the best construct validity among the 3 tested measures of frailty. Discussion: The EFS was significantly, both statistically and clinically, associated with ASM tolerability. It also showed multiple advantages in performance while assessing for frailty in older adults with epilepsy, when compared with the 2 other measures of frailty that we tested. Future studies must focus on what role the EFS during epilepsy diagnosis may play in ASM selection among older adults with epilepsy.