Analysis of modifiable and nonmodifiable risk factors in patients undergoing pituitary surgery

Abstract

OBJECTIVEPituitary adenomas (PAs) are among the most common intracranial tumors. Understanding the clinical effects of various modifiable risk factors (MRFs) and nonmodifiable risk factors (NMRFs) is important in guiding proper treatment, yet there is limited evidence outlining the influence of MRFs and NMRFs on outcomes of PA resection. The aim of this study was to analyze MRFs and NMRFs in patients undergoing resection for PAs.METHODSUsing the 2016 and 2017 National Readmission Database, the authors identified a cohort of 9472 patients undergoing microscopic or endoscopic resection of a PA. Patients with nonoverlapping MRFs and NMRFs were analyzed for length of stay (LOS), hospital cost, readmission rates, and postoperative complications. From the original cohort, a subset of 373 frail patients (as defined by the Johns Hopkins Frailty Index) were identified and propensity matched to nonfrail patients. Statistical analysis included 1-way ANOVA, Tukey multiple comparisons of means, odds ratios, Wald testing, and unpaired Welch 2-sample t-tests to compare complications, outcomes, and costs between each cohort. Perioperative outcomes and hospital readmission rates were tracked, and predictive algorithms were developed to establish precise relationships between relevant risk factors and neurosurgical outcomes.RESULTSMalnourished patients had significantly longer LOSs when compared to nonmalnourished patients (p < 0.001). There was a significant positive correlation between the number of MRFs and readmission at 90 days (p = 0.012) and 180 days (p = 0.020). Obese patients had higher rates of postoperative neurological injury at the 30-day follow-up (p = 0.048) compared to patients with normal BMI. Within this NMRF cohort, frail patients were found to have significantly increased hospital LOS (p < 0.001) and total inpatient costs compared to nonfrail patients (p < 0.001). Predictive analytics showed that frail patients had significantly higher readmission rates at both 90-day (p < 0.001) and 180-day follow-ups (p < 0.001). Lastly, rates of acute postsurgical infection were higher in frail patients compared to nonfrail patients (p < 0.001).CONCLUSIONSThese findings suggest that both MRFs and NMRFs negatively affect the perioperative outcomes following PA resection. Notable risk factors including malnutrition, obesity, elevated lipid panels, and frailty make patients more prone to prolonged LOS, higher inpatient costs, and readmission. Further prospective research with longitudinal data is required to precisely pinpoint the effects of various risk factors on the outcomes of pituitary surgery.

Neural network modeling for prediction of recurrence, progression, and hormonal non-remission in patients following resection of functional pituitary adenomas

“…This study has the limitations associated with the retrospective character of this cohort study, in which our data is limited by the quality and depth of the records kept for patients included in this series. While retrospective chart review allowed for the collection of a great amount of data for each patient, we lack data on patient-specific risk factors such as nutritional status, drug use, and frailty [40]. Furthermore, patients developing suboptimal outcomes outside of our follow-up interval may not be included in this analysis, and as such, our analysis may underreport suboptimal outcomes.…”

Frailty as a Predictor of Neurosurgical Outcomes in Brain Tumor Patients: A Systematic Review and Meta-Analysis

“…6). Frailty is signi cantly increased the LOS in the studies by Asemota [19] [20] (incident rate ratio, 1.92; CI, 1.87-1.98; P < 0.0001), Cloney [21] (6 days vs 4 days), Shahrestani1 [27] (13.79 ± 19.10 days vs 4.37 ± 5.22 days, p < 0.001). Shahrestani2[28] (16.1 ± 13.9 days vs 9.0 ± 8.1 days, P < 0.0001).…”

“…And frailty has a signi cant potential role in risk strati cation, preoperative shared decision-making and perioperative management. Further study Shahrestani1 [27] Quality assessment of studies using the Newcastle-Ottawa Scale 0 1 8 Forest plots presenting frailty is the risk of mortality in brain tumor patients Figure 3 Forest plots presenting frailty is the risk of complications in brain tumor patients Figure 4 Forest plots presenting frailty is the risk of non-routine discharge disposition in brain tumor patients…”

“…Publication location come from the United States [18 -20, 22-30] and Columbia [21] between 2013 and 2021. Frailty was assessed use mFI [18,21,25,[29][30], JHACG [19][20][27][28], HFS [22], mFI-5 [23][24]26]. Prevalence of frailty ranged from 1.48% to 57%.…”

“…12 studies [18-24,26-30] assessed using the NOS [15], the overall studies were high-quality with 9 scores of 7 studies [20,[23][24][25][26][27][28][29], 8 scores of 3 studies [18,22,30] and 7 scores of 2 studies [19,21] (Tables 2). According to the JBI critical appraisal checklist, the methodological quality one study [25] was strong with the score of 8 (Tables 3).…”