Predictors and Impact of Postoperative 30-Day Readmission in Glioblastoma.

BACKGROUND: Postoperative 30-day readmissions have been shown to negatively affect survival and other important outcomes in patients with glioblastoma (GBM). OBJECTIVE: To further investigate patient readmission risk factors of primary and recurrent patients with GBM. METHODS: The authors retrospectively reviewed records of 418 adult patients undergoing 575 craniotomies for histologically confirmed GBM at an academic medical center. Patient demographics, comorbidities, and clinical characteristics were collected and compared by patient readmission status using chi-square and Mann-Whitney U testing. Multivariable logistic regression was performed to identify risk factors that predicted 30-day readmissions. RESULTS: The cohort included 69 (12%) 30-day readmissions after 575 operations. Readmitted patients experienced significantly lower median overall survival (11.3 vs 16.4 months, P = .014), had a lower mean Karnofsky Performance Scale score (66.9 vs 74.2, P = .005), and had a longer initial length of stay (6.1 vs 5.3 days, P = .007) relative to their nonreadmitted counterparts. Readmitted patients experienced more postoperative deep vein thromboses or pulmonary embolisms (12% vs 4%, P = .006), new motor deficits (29% vs 14%, P = .002), and nonhome discharges (39% vs 22%, P = .005) relative to their nonreadmitted counterparts. Multivariable analysis demonstrated increased odds of 30-day readmission with each 10-point decrease in Karnofsky Performance Scale score (odds ratio [OR] 1.32, P = .002), each single-point increase in 5-factor modified frailty index (OR 1.51, P = .016), and initial presentation with cognitive deficits (OR 2.11, P = .013). CONCLUSION: Preoperatively available clinical characteristics strongly predicted 30-day readmissions in patients undergoing surgery for GBM. Opportunities may exist to optimize preoperative and postoperative management of at-risk patients with GBM, with downstream improvements in clinical outcomes.

A novel radiographic marker of sarcopenia with prognostic value in glioblastoma.

OBJECTIVE: Sarcopenia is an important prognostic consideration in surgical oncology that has received relatively little attention in brain tumor patients. Temporal muscle thickness (TMT) has recently been proposed as a novel radiographic marker of sarcopenia that can be efficiently obtained within existing workflows. We investigated the prognostic value of TMT in primary and progressive glioblastoma. METHODS: TMT measurements were performed on magnetic resonance images of 384 patients undergoing 541 surgeries for glioblastoma. Relationships between TMT and clinical characteristics were examined on bivariate analysis. Optimal TMT cutpoints were established using maximally selected rank statistics. Predictive value of TMT upon postoperative survival (PS) was assessed using Cox proportional hazards regression adjusted for age, sex, Karnofsky performance status (KPS), Stupp protocol completion, extent of resection, and tumor molecular markers. RESULTS: Average TMT for the primary and progressive glioblastoma cohorts was 9.55 mm and 9.40 mm, respectively. TMT was associated with age (r = -0.14, p = 0.0008), BMI (r = 0.29, p < 0.0001), albumin (r = 0.11, p = 0.0239), and KPS (r = 0.11, p = 0.0101). Optimal TMT cutpoints for the primary and progressive cohorts were ≤ 7.15 mm and ≤ 7.10 mm, respectively. High TMT was associated with increased Stupp protocol completion (p = 0.001). On Cox proportional hazards regression, high TMT predicted increased PS in progressive [HR 0.47 (95% confidence interval (CI)) 0.25-0.90), p = 0.023] but not primary [HR 0.99 (95% CI 0.64-1.51), p = 0.949] glioblastoma. CONCLUSIONS: TMT correlates with important prognostic variables in glioblastoma and predicts PS in patients with progressive, but not primary, disease. TMT may represent a pragmatic neurosurgical biomarker in glioblastoma that could inform treatment planning and perioperative optimization.

Assessing the efficacy of repeat resections in recurrent glioblastoma: a systematic review.

BACKGROUND: The inevitable recurrence of glioblastoma (GBM) results in patients often undergoing multiple resections with questionable benefit to overall survival (OS). OBJECTIVE: To systematically review and analyze prior studies examining the potential added benefit of repeat resection (RR) in recurrent GBM. METHODS: We performed a PRISMA-compliant systematic review of literature published between 1969 to 2019 involving patients undergoing RR at GBM recurrence. RESULTS: The search yielded 3994 non-duplicate citations. Final abstraction included 43 articles, with 2 level II and 41 level III studies. The earliest paper we included was published in 1987 [1], and 35 identified papers (81.4%) were published within the last 10 years. The survival data of 9236 patients (55% male) were analyzed, with a median age of 56; 3726 patients underwent RR. In 31 studies with a comparable single-surgery-only cohort, 20 articles reported a statistically significant increase in OS with RR, 7 reported nonsignificant trends toward increased OS with RR, and 4 reported no significant increase in OS with RR. Twenty-two articles with multivariate analyses of Karnofsky performance scores and 17 articles with extent-of-resection reported these as significant prognostic factors of OS. In 26 studies, median OS among all patients was 17.85 months inclusive of median OS following RR totaling 9.6 months. Notably, in 10 studies with data on subsequent progressions (2+ recurrences), 6 studies reported significant increases in OS with subsequent repeat resection (sRR) compared to those not undergoing sRR. CONCLUSIONS: Recurrent GBM presents a treatment challenge. There appears to be an OS benefit for RR upon first recurrence as well as sRR. Such findings warrant further investigation of the potential benefits of continued surgical intervention after subsequent progressions of GBM.

Predictive Model and Online Calculator for Discharge Disposition in Brain Tumor Patients.

BACKGROUND: In the era of value-based payment models, it is imperative for neurosurgeons to eliminate inefficiencies and provide high-quality care. Discharge disposition is a relevant consideration with clinical and economic ramifications in brain tumor patients. We developed a predictive model and online calculator for postoperative non-home discharge disposition in brain tumor patients that can be incorporated into preoperative workflows. METHODS: We reviewed all brain tumor patients at our institution from 2017 to 2019. A predictive model of discharge disposition containing preoperatively available variables was developed using stepwise multivariable logistic regression. Model performance was assessed using receiver operating characteristic curves and calibration curves. Internal validation was performed using bootstrapping with 2000 samples. RESULTS: Our cohort included 2335 patients who underwent 2586 surgeries with a 16% non-home discharge rate. Significant predictors of non-home discharge were age >60 years (odds ratio [OR], 2.02), African American (OR, 1.73) or Asian (OR, 2.05) race, unmarried status (OR, 1.48), Medicaid insurance (OR, 1.90), admission from another health care facility (OR, 2.30), higher 5-factor modified frailty index (OR, 1.61 for 5-factor modified frailty index ≥2), and lower Karnofsky Performance Status (increasing OR with each 10-point decrease in Karnofsky Performance Status). The model was well calibrated and had excellent discrimination (optimism-corrected C-statistic, 0.82). An open-access calculator was deployed (https://neurooncsurgery.shinyapps.io/discharge_calc/). CONCLUSIONS: A strongly performing predictive model and online calculator for non-home discharge disposition in brain tumor patients was developed. With further validation, this tool may facilitate more efficient discharge planning, with consequent improvements in quality and value of care for brain tumor patients.

The Prognostic Impact of Nutritional Status on Postoperative Outcomes in Glioblastoma.

OBJECTIVE: The clinical impact and optimal method of assessing nutritional status (NS) have not been rigorously examined in glioblastoma. We investigated the relationship between NS and postoperative survival (PS) in glioblastoma using 4 nutritional indices and identified which index best modeled PS. METHODS: NS was retrospectively assessed for patients with glioblastoma undergoing surgery at our institution from 2007 to 2019 using the albumin level, albumin/globulin ratio (AGR), nutritional risk index (NRI), and prognostic nutritional index (PNI). Optimal cut points for each index were identified using maximally selected rank statistics and previously established criteria. The predictive value of each index on PS was determined using Cox proportional hazards models adjusted for prognostic variables. The best-performing model was identified using the Akaike Information Criterion. RESULTS: Our analysis included 242 patients (64% male) with a mean age of 57.6 years, Karnofsky Performance Status of 77.6, 5-factor modified frailty index of 0.59, albumin level of 4.2 g/dL, AGR of 1.9, NRI of 105.6, and PNI of 47.4. Median PS after index and repeat surgery was 12.7 and 7.8 months, respectively. On multivariable analysis, low albumin level (hazard ratio [HR], 2.09; 95% confidence interval [CI], 1.52-2.89; P < 0.001), mild NRI (HR, 1.61; 95% CI, 1.04-2.49; P = 0.032), moderate/severe NRI (HR, 2.51; 95% CI, 1.64-3.85; P < 0.001), and low PNI (HR, 2.51; 95% CI, 1.78-3.53; P < 0.001), but not low AGR (HR, 1.17; 95% CI, 0.89-1.54; P = 0.270), predicted decreased PS. PNI had the lowest Akaike Information Criterion. CONCLUSIONS: NS predicts PS in glioblastoma. PNI may provide the best model for assessing NS. NS is an important modifiable aspect of brain tumor management that warrants increased attention.