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OBJECTIVE When indicated, patients with symptomatic Chiari malformation type I (CM-I) may benefit from suboccipital decompression (SOD). Although SOD is considered a lower-risk neurosurgical procedure, preoperative risk assessment and careful surgical patient selection remain critical. The objectives of the present study were twofold: 1) describe 30-day SOD outcomes for CM patients with attention to the impact of preoperative frailty and 2) design a predictive model for the primary endpoint of nonhome discharge (NHD). METHODS There were 1015 CM-I patients who underwent SOD in the 2011–2020 American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) database, as specified by diagnostic and procedural codes (Current Procedural Terminology code 61343). Descriptive statistics were used to analyze total cohort baseline demographics, preoperative comorbidities, and postoperative outcomes within 30 days of surgery. Univariate cross-tabulation was used to compare baseline demographics and preoperative characteristics across the NHD and home discharge (HD) cohorts. Receiver operating characteristic (ROC) curve analysis was used to assess the discriminative ability of the revised Risk Analysis Index (RAI-rev) on NHD. RESULTS The study cohort had a median age of 36 years, and 80.6% of patients were female. Race distribution was categorized as White (69.9%), Black (16.6%), and other groups (13.6%). The most common preoperative comorbidities were active smoking (24.4%), hypertension (19.2%), and diabetes mellitus (4.7%). The primary outcome of NHD occurred in 4.6% of patients (n = 47). Increasing frailty (measured by the RAI-rev) was associated with a stepwise increase in the rate of NHD: 2.3% for RAI-rev scores 0–10, 5.8% for RAI-rev scores 11–15, 7.6% for RAI-rev scores 16–20, 18.2% for RAI-rev scores 21–25, and 77.8% for RAI-rev scores ≥ 26 (p < 0.001). Other preoperative factors associated with NHD included older age, nonelective surgery, diabetes, hypertension, and elevated creatinine (all p < 0.01). The other most common 30-day complications included unplanned readmission (9.3%), unplanned reoperation (5.3%), return to the operating room (5.8%), Clavien-Dindo grade IV (life-threatening) (1.5%), organ space surgical site infection (SSI) (1.5%), superficial SSI (1.4%), and reoperation for a CSF leak (1.1%). Surgical mortality (within 30 days) was extremely rare (1/1015, 0.1%). ROC curve analysis demonstrated that RAI-rev predicted NHD with significant discriminatory accuracy among CM-I patients who received SOD treatment (C-statistic 0.731, 95% CI 0.648–0.814). CONCLUSIONS This decade-long analysis of a multicenter surgical registry provides internationally representative, modern rates of 30-day outcomes after suboccipital decompression (with or without duraplasty) for adult CM-I patients. Preoperative frailty assessment with the RAI-rev may help identify higher-risk surgical candidates.

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BACKGROUND: Risk stratification of epilepsy surgery patients remains difficult. The Risk Analysis Index (RAI) is a frailty measurement that augments preoperative risk stratification. OBJECTIVE: To evaluate RAI's discriminative threshold for nonhome discharge disposition (NHD) and mortality (or discharge to hospice within 30 days of operation) in epilepsy surgery patients. METHODS: Patients were queried from the American College of Surgeons-National Surgical Quality Improvement Program database (2012-2020) using diagnosis/procedure codes. Linear-by-linear trend tests assessed RAI's relationship with NHD and mortality. Discriminatory accuracy was assessed by C-statistics (95% CI) in receiver operating characteristic curve analysis. RESULTS: Epilepsy resections (N = 1236) were grouped into temporal lobe (60.4%, N = 747) and nontemporal lobe (39.6%, N = 489) procedures. Patients were stratified by RAI tier: 76.5% robust (RAI 0-20), 16.2% normal (RAI 21-30), 6.6% frail (RAI 31-40), and 0.8% severely frail (RAI 41 and above). The NHD rate was 18.0% (N = 222) and positively associated with increasing RAI tier: 12.5% robust, 34.0% normal, 38.3% frail, and 50.0% severely frail (P < .001). RAI had robust predictive discrimination for NHD in overall cohort (C-statistic 0.71), temporal lobe (C-statistic 0.70), and nontemporal lobe (C-statistic 0.71) cohorts. The mortality rate was 2.7% (N = 33) and significantly associated with RAI frailty: 1.1% robust, 8.0% normal, 6.2% frail, and 20.0% severely frail (P < .001). RAI had excellent predictive discrimination for mortality in overall cohort (C-statistic 0.78), temporal lobe (C-statistic 0.80), and nontemporal lobe (C-statistic 0.74) cohorts. CONCLUSION: The RAI frailty score predicts mortality and NHD after epilepsy surgery. This is accomplished with a user-friendly calculator: https://nsgyfrailtyoutcomeslab.shinyapps.io/epilepsy/.

OBJECTIVE Perioperative and/or postoperative cerebrovascular accidents (PCVAs) after intracranial tumor resection (ITR) are serious complications with devastating effects on quality of life and survival. Here, the authors retrospectively analyzed a prospectively maintained, multicenter surgical registry to design a risk model for PCVA after ITR to support efforts in neurosurgical personalized medicine to risk stratify patients and potentially mitigate poor outcomes. METHODS The National Surgical Quality Improvement Program database was queried for ITR cases (2015–2019, n = 30,951). Patients with and without PCVAs were compared on baseline demographics, preoperative clinical characteristics, and outcomes. Frailty (physiological reserve for surgery) was measured by the Revised Risk Analysis Index (RAI-rev). Logistic regression analysis was performed to identify independent associations between preoperative covariates and PCVA occurrence. The ITR-PCVA risk model was generated based on logit effect sizes and assessed in area under the receiver operating characteristic curve (AUROC) analysis. RESULTS The rate of PCVA was 1.7% (n = 532). Patients with PCVAs, on average, were older and frailer, and had increased rates of nonelective surgery, interhospital transfer status, diabetes, hypertension, unintentional weight loss, and elevated BUN. PCVA was associated with higher rates of postoperative reintubation, infection, thromboembolic events, prolonged length of stay, readmission, reoperation, nonhome discharge destination, and 30-day mortality (all p < 0.001). In multivariable analysis, predictors of PCVAs included RAI “frail” category (OR 1.7, 95% CI 1.2–2.4; p = 0.006), Black (vs White) race (OR 1.5, 95% CI 1.1–2.1; p = 0.009), nonelective surgery (OR 1.4, 95% CI 1.1–1.7; p = 0.003), diabetes mellitus (OR 1.5, 95% CI 1.1–1.9; p = 0.002), hypertension (OR 1.4, 95% CI 1.1–1.7; p = 0.006), and preoperative elevated blood urea nitrogen (OR 1.4, 95% CI 1.1–1.8; p = 0.014). The ITR-PCVA predictive model was proposed from the resultant multivariable analysis and performed with a modest C-statistic in AUROC analysis of 0.64 (95% CI 0.61–0.66). Multicollinearity diagnostics did not detect any correlation between RAI-rev parameters and other covariates (variance inflation factor = 1). CONCLUSIONS The current study proposes a novel preoperative risk model for PCVA in patients undergoing ITR. Patients with poor physiological reserve (measured by frailty), multiple comorbidities, abnormal preoperative laboratory values, and those admitted under high acuity were at highest risk. The ITR-PCVA risk model may support patient-centered counseling striving to respect goals of care and maximize quality of life. Future prospective studies are warranted to validate the ITR-PCVA risk model and evaluate its utility as a bedside clinical tool.