Phenome-wide Mendelian-randomization study of genetically determined vitamin D on multiple health outcomes using the UK Biobank study.

BACKGROUND: Vitamin D deficiency is highly prevalent across the globe. Existing studies suggest that a low vitamin D level is associated with more than 130 outcomes. Exploring the causal role of vitamin D in health outcomes could support or question vitamin D supplementation. METHODS: We carried out a systematic literature review of previous Mendelian-randomization studies on vitamin D. We then implemented a Mendelian Randomization-Phenome Wide Association Study (MR-PheWAS) analysis on data from 339 256 individuals of White British origin from UK Biobank. We first ran a PheWAS analysis to test the associations between a 25(OH)D polygenic risk score and 920 disease outcomes, and then nine phenotypes (i.e. systolic blood pressure, diastolic blood pressure, risk of hypertension, T2D, ischaemic heart disease, body mass index, depression, non-vertebral fracture and all-cause mortality) that met the pre-defined inclusion criteria for further analysis were examined by multiple MR analytical approaches to explore causality. RESULTS: The PheWAS analysis did not identify any health outcome associated with the 25(OH)D polygenic risk score. Although a selection of nine outcomes were reported in previous Mendelian-randomization studies or umbrella reviews to be associated with vitamin D, our MR analysis, with substantial study power (>80% power to detect an association with an odds ratio >1.2 for per standard deviation increase of log-transformed 25[OH]D), was unable to support an interpretation of causal association. CONCLUSIONS: We investigated the putative causal effects of vitamin D on multiple health outcomes in a White population. We did not support a causal effect on any of the disease outcomes tested. However, we cannot exclude small causal effects or effects on outcomes that we did not have enough power to explore due to the small number of cases.

Discovery of Noncancer Drug Effects on Survival in Electronic Health Records of Patients With Cancer: A New Paradigm for Drug Repurposing.

PURPOSE: Drug development is becoming increasingly expensive and time consuming. Drug repurposing is one potential solution to accelerate drug discovery. However, limited research exists on the use of electronic health record (EHR) data for drug repurposing, and most published studies have been conducted in a hypothesis-driven manner that requires a predefined hypothesis about drugs and new indications. Whether EHRs can be used to detect drug repurposing signals is not clear. We want to demonstrate the feasibility of mining large, longitudinal EHRs for drug repurposing by detecting candidate noncancer drugs that can potentially be used for the treatment of cancer. PATIENTS AND METHODS: By linking cancer registry data to EHRs, we identified 43,310 patients with cancer treated at Vanderbilt University Medical Center (VUMC) and 98,366 treated at the Mayo Clinic. We assessed the effect of 146 noncancer drugs on cancer survival using VUMC EHR data and sought to replicate significant associations (false discovery rate < .1) using the identical approach with Mayo Clinic EHR data. To evaluate replicated signals further, we reviewed the biomedical literature and clinical trials on cancers for corroborating evidence. RESULTS: We identified 22 drugs from six drug classes (statins, proton pump inhibitors, angiotensin-converting enzyme inhibitors, ß-blockers, nonsteroidal anti-inflammatory drugs, and α-1 blockers) associated with improved overall cancer survival (false discovery rate < .1) from VUMC; nine of the 22 drug associations were replicated at the Mayo Clinic. Literature and cancer clinical trial evaluations also showed very strong evidence to support the repurposing signals from EHRs. CONCLUSION: Mining of EHRs for drug exposure-mediated survival signals is feasible and identifies potential candidates for antineoplastic repurposing. This study sets up a new model of mining EHRs for drug repurposing signals.

Natural language processing improves identification of colorectal cancer testing in the electronic medical record.

BACKGROUND: Difficulty identifying patients in need of colorectal cancer (CRC) screening contributes to low screening rates. OBJECTIVE: To use Electronic Health Record (EHR) data to identify patients with prior CRC testing. DESIGN: A clinical natural language processing (NLP) system was modified to identify 4 CRC tests (colonoscopy, flexible sigmoidoscopy, fecal occult blood testing, and double contrast barium enema) within electronic clinical documentation. Text phrases in clinical notes referencing CRC tests were interpreted by the system to determine whether testing was planned or completed and to estimate the date of completed tests. SETTING: Large academic medical center. PATIENTS: 200 patients ≥ 50 years old who had completed ≥ 2 non-acute primary care visits within a 1-year period. MEASURES: Recall and precision of the NLP system, billing records, and human chart review were compared to a reference standard of human review of all available information sources. RESULTS: For identification of all CRC tests, recall and precision were as follows: NLP system (recall 93%, precision 94%), chart review (74%, 98%), and billing records review (44%, 83%). Recall and precision for identification of patients in need of screening were: NLP system (recall 95%, precision 88%), chart review (99%, 82%), and billing records (99%, 67%). LIMITATIONS: Small sample size and requirement for a robust EHR. CONCLUSIONS: Applying NLP to EHR records detected more CRC tests than either manual chart review or billing records review alone. NLP had better precision but marginally lower recall to identify patients who were due for CRC screening than billing record review.

Increased hospital mortality in patients with bedside hippus.

BACKGROUND: Hippus is a prominent, repetitive oscillation of the pupils. Although regarded by some as a normal variant of pupillary unrest, the clinical importance of hippus has not been investigated systematically in hospitalized patients. METHODS: We conducted a retrospective cohort study of 117 hospitalized patients demonstrating hippus. To mitigate observer bias, 486 control patients were selected using 2 adjacent admissions by the same attending physician before and after each index case. The primary outcomes were mortality during the admission and within 30 days of discharge. RESULTS: Patients with bedside hippus were more likely to die within 30 days of observation (P <.00005). Independent risk factors for death by 30 days were altered mental status (odds ratio [OR] 4.11; 95% confidence interval [CI], 2.05-8.25, P <.001), hippus (OR 2.99; 95% CI, 1.46-6.11, P = .003), cirrhosis (P = .029), and renal disease (P = .054); angiotensin-system inhibitors were protective (P = .012). Patients with hippus were more likely to have altered mental status (OR 11.23; 95% CI, 6.27-20.09, P <.001), a history of trauma (OR 3.76; 95% CI, 1.65-8.59, P = .002), cirrhosis (P = .038), renal disease (P = .051), and a history of using iron supplements (P = .016). CONCLUSION: The recognition of hippus in hospitalized patients is a clinically important predictor of early mortality.