Predictors of Prolonged Length of Stay After Pituitary Adenoma Resection: A Large Cohort Analysis Using the National Inpatient Sample (2016-2019).

OBJECTIVE: In this nationwide retrospective study, the authors aimed to identify demographic, clinical, and baseline health risk factors predictive of a prolonged length of stay (PLOS) for patients with pituitary adenomas (PAs). METHODS: The National Inpatient Sample dataset from 2016 to 2019 was utilized to identify all included hospitalizations for PA resection as identified by the appropriate diagnosis-related group code. Comorbidities were classified based on the Charlson Comorbidity Index mapping of ICD-10 codes, and PLOS was identified as any stay longer than 3 days. Univariable and multivariable logistic regression models, accounting for the sample design, were built to determine factors associated with PLOS and emergent surgery. RESULTS: Overall, 30 945 patients were included in this study with 10 535 patients having PLOS. Female patients experienced an increased odds of PLOS (odds ratio [OR]: 1.29; P < .001). Black patients (OR: 1.49; P < .001) and Hispanic patients (OR: 1.30; P = .003) had 1.49 times and 1.30 times the odds of PLOS compared to White patients, respectively. Compared to patients insured by Medicare, patients insured by Medicaid had an increased odds of PLOS (OR: 1.36; P = .007) as well as emergent surgery (OR: 5.40; P < .001). When stratified by emergent surgeries, Black patients (OR: 1.89; P < .001), Hispanic patients, (OR: 2.14; P < .001), and patients on Medicaid insurance (OR: 1.71; P < .001) were at an increased risk of emergent procedures. However, female sex (OR: 0.65; P < .001), upper third quartile (OR: 0.73; P = .017), and fourth quartile (OR: 0.69; P = .014) of patients categorized by zip code income were at decreased odds of an emergent procedure. CONCLUSIONS: Black and Hispanic patients, patients with Medicaid insurance, and patients of low socioeconomic status patients are at significantly higher risk of emergent PA resection and PLOS. Efforts to prevent emergent surgeries and shorten hospitalization after pituitary surgery may need to primarily focus on patient groups with select sociodemographic characteristics.

Utilization of Palliative Care Services Among Patients With Malignant Brain Tumors: An Analysis of the National Inpatient Sample (2016-2019).

BACKGROUND: Recent advances in treatment of malignant brain tumors have improved outcomes. However, patients continue to experience significant disability. Palliative care helps patients with advanced illnesses improve their quality of life. There is a paucity of clinical studies examining palliative care usage among patients with malignant brain tumors. OBJECTIVE: To assess if there were any patterns in palliative care utilization among patients hospitalized with malignant brain tumors. METHODS: A retrospective cohort representing hospitalizations for malignant brain tumors was created from The National Inpatient Sample (2016-2019). Palliative care utilization was identified by ICD-10 code. Univariable and multivariable logistic regression models, accounting for the sample design, were built to evaluate the demographic variables associated with palliative care consultation in all patients and fatal hospitalizations. RESULTS: 375 010 patients admitted with a malignant brain tumor were included in this study. Over the whole cohort, 15.0% of patients used palliative care. In fatal hospitalizations, Black and Hispanic patients had 28% lower odds of receiving a palliative care consultation compared with White patients (odds ratio for both = 0.72; P = .02). For fatal hospitalizations, patients insured privately were 34% more likely to use palliative care services compared with patients insured with Medicare (odds ratio = 1.34, P = .006). CONCLUSION: Palliative care is underutilized among all patients with malignant brain tumors. Within this population, disparities in utilization are exacerbated by sociodemographic factors. Prospective studies investigating utilization disparities across race and insurance status are necessary to improve access to palliative care services for this population.

PRSet: Pathway-based polygenic risk score analyses and software.

Polygenic risk scores (PRSs) have been among the leading advances in biomedicine in recent years. As a proxy of genetic liability, PRSs are utilised across multiple fields and applications. While numerous statistical and machine learning methods have been developed to optimise their predictive accuracy, these typically distil genetic liability to a single number based on aggregation of an individual's genome-wide risk alleles. This results in a key loss of information about an individual's genetic profile, which could be critical given the functional sub-structure of the genome and the heterogeneity of complex disease. In this manuscript, we introduce a 'pathway polygenic' paradigm of disease risk, in which multiple genetic liabilities underlie complex diseases, rather than a single genome-wide liability. We describe a method and accompanying software, PRSet, for computing and analysing pathway-based PRSs, in which polygenic scores are calculated across genomic pathways for each individual. We evaluate the potential of pathway PRSs in two distinct ways, creating two major sections: (1) In the first section, we benchmark PRSet as a pathway enrichment tool, evaluating its capacity to capture GWAS signal in pathways. We find that for target sample sizes of >10,000 individuals, pathway PRSs have similar power for evaluating pathway enrichment as leading methods MAGMA and LD score regression, with the distinct advantage of providing individual-level estimates of genetic liability for each pathway -opening up a range of pathway-based PRS applications, (2) In the second section, we evaluate the performance of pathway PRSs for disease stratification. We show that using a supervised disease stratification approach, pathway PRSs (computed by PRSet) outperform two standard genome-wide PRSs (computed by C+T and lassosum) for classifying disease subtypes in 20 of 21 scenarios tested. As the definition and functional annotation of pathways becomes increasingly refined, we expect pathway PRSs to offer key insights into the heterogeneity of complex disease and treatment response, to generate biologically tractable therapeutic targets from polygenic signal, and, ultimately, to provide a powerful path to precision medicine.