Background Disseminated pediatric low-grade gliomas and glioneuronal tumors (dpLGG/GNTs) are associated with a poorer prognosis than non-disseminated pLGG/GNTs. To date there is no comprehensive report characterizing the genome profile of dpLGG/GNTs and their relative survival. This systematic review aims to identify the pattern of genetic alterations and long-term outcomes described for dpLGG/GNT. Methods A systematic review of the literature was performed to identify relevant articles. A quality and risk of bias assessment of articles was done using the GRADE framework and ROBINS-I tool, respectively. Results Fifty studies published from 1994 to 2020 were included in this review with 366 cases reported. There was sporadic reporting of genetic alterations. The most common molecular alterations observed among subjects were 1p deletion (75%) and BRAF-KIAA1549 fusion (55%). BRAF p.V600E mutation was found in 7% of subjects. A higher proportion of subjects demonstrated primary dissemination compared to secondary dissemination (65% vs 25%). First-line chemotherapy consisted of an alkylation-based regimen and vinca alkaloids. Surgical intervention ranged from biopsy alone (59%) to surgical resection (41%) and CSF diversion (28%). Overall, 73% of cases were alive at last follow-up. Survival did not vary by tumor type or timing of dissemination. All studies reviewed either ranked low or moderate for both quality and risk of bias assessments. Conclusion Chromosome 1p deletion and BRAF-KIAA1549 fusion were the most common alterations identified in dpLGG/GNT cases reviewed. The relative molecular heterogeneity between DLGG and DLGNT however deserves further exploration and ultimately correlation with their biologic behavior to better understand the pathogenesis of dpLGG/GNT.

BackgroundDespite significant advancements in our understanding of Alzheimer’s disease (AD) over the last decade, no disease‐modifying treatments exist for it. Rich clinical information on patients with AD is currently available in electronic health records (EHR) worldwide. Our aim is to develop a portable phenotyping algorithm to mine EHRs and identify patients with clinical and probable preclinical AD. Identified patients can subsequently be used as cohorts in future biomedical research studies.MethodWe developed an algorithm using the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). This enables other institutes using this standardized EHR data representation to avail the algorithm with minimum effort for local implementation. The algorithm’s backbone is based on ubiquitous information readily derived from EHRs such as ICD‐10 billing codes, medication history, cognitive test scores (Mini‐Mental State Exam), imaging reports and lab results. The model classifies identified patients into three categories based on high (category 1), intermediate (category 2) and low (category 3) probability with AD. The algorithm was applied to a deidentified EHR database at the Vanderbilt University Medical Center (VUMC) with clinical information from over 3 million individuals. We performed an manual chart review on 100 randomly selected samples of each category’s AD cases to assess its predictive value.ResultImplementation of the phenotyping algorithm yielded 274,000 patients with probable Alzheimer’s disease. 4169 patients were identified based on category 1 criteria, 13270 patients were identified using category 2 and 256,302 patients were identified in category 3. Independent chart review resulted in a PPV of 94% for category 1, 86% for category 2 and 8% for category 3 patients. Majority of false positive cases occurred because of mild cognitive impairment (MCI) patients in category 3 who were selected through broad billing codes and medication history.ConclusionA portable phenotyping algorithm can help identify large cohorts of patients with diagnosed and probable Alzheimer’s disease for research use by mining EHRs. Based on preliminary results, our algorithm is a promising attempt to achieve this goal. We aim to improve on it till it selects AD patients with an even higher predictive value (>95%).

BACKGROUND:Loss to follow-up (LTF) and unplanned readmission are barriers to recovery after acute subdural hematoma evacuation. The variables associated with these postdischarge events are not fully understood.OBJECTIVE:To determine factors associated with LTF and unplanned readmission, emphasizing socioeconomic status (SES).METHODS:A retrospective analysis was conducted of surgical patients with acute subdural hematoma managed operatively from 2009 to 2019 at a level 1 regional trauma center. Area Deprivation Index (ADI), which is a neighborhood-level composite socioeconomic score, was used to measure SES. Higher ADI corresponds to lower SES. To decrease the number of covariates in the model, principal components (PCs) analysis was used. Multivariable logistic regression analyses of PCs were performed for LTF and unplanned readmission.RESULTS:A total of 172 patients were included in this study. Thirty-six patients (21%) were LTF, and 49 (28%) patients were readmitted; 11 (6%) patients were both LTF and readmitted (P = .9). The median time to readmission was 10 days (Q1: 4.5, Q3: 35). In multivariable logistic regression analyses for LTF, increased ADI and distance to hospital through PC2 (odds ratio [OR] 1.49; P = .009) and uninsured/Medicaid status and increased length of stay through PC4 (OR 1.73; P = .015) significantly contributed to the risk of LTF. Unfavorable discharge functional status and nonhome disposition through PC3 were associated with decreased odds of unplanned readmission (OR = 0.69; P = .028).CONCLUSION:Patients at high risk for LTF and unplanned readmissions, as identified in this study, may benefit from targeted resources individualized to their needs to address barrier to follow-up and to ensure continuity of care.

No abstract