Visit-to-visit variability of lipid measurements as predictors of cardiovascular events.

BACKGROUND:Higher visit-to-visit variability in risk factors such as blood pressure and low-density lipoprotein (LDL)-cholesterol are associated with an increase in cardiovascular (CV) events. OBJECTIVE:The purpose of this study was to determine whether variability in high-density lipoprotein cholesterol (HDL-C) and triglyceride levels predicted coronary and CV events in a clinical trial population with known coronary disease. METHODS:We assessed intraindividual variability in fasting high-density lipoprotein (HDL)-cholesterol, triglyceride, and LDL-cholesterol measurements among 9572 patients in the Treating to New Targets trial and correlated the results with coronary events over a median follow-up of 4.9 years. RESULTS:In the fully adjusted Cox model, 1 standard deviation of average successive variability, defined as the average absolute difference between successive values, was associated with an increased risk of a coronary event for HDL-cholesterol (hazard ratio [HR] 1.16, 95% confidence interval [CI] 1.11-1.21, P < .0001), for triglycerides (HR 1.09, 95% CI 1.04-1.15, P = .0005), and for LDL-cholesterol (HR 1.14, 95% CI 1.09-1.19, P < .0001). Similar results were found for the 3 other measures of variability, standard deviation, coefficient of variability, and variability independent of the mean. Similar results were seen for CV events, stroke, and nonfatal myocardial infarction. Higher variability in triglyceride and LDL-cholesterol, but not HDL-cholesterol, was predictive of incident diabetes. The correlation among the variability of the 3 lipid measurements was weak. CONCLUSION:Visit-to-visit variability in fasting measurements of HDL-cholesterol, triglycerides, and LDL-cholesterol are predictive of coronary events, CV events, and for triglyceride and low-density lipoprotein cholesterol variability, incident diabetes. The mechanisms accounting for these associations remain to be determined

Associations Between Natural Language Processing (NLP) Enriched Social Determinants of Health and Suicide Death among US Veterans

Importance: Social determinants of health (SDOH) are known to be associated with increased risk of suicidal behaviors, but few studies utilized SDOH from unstructured electronic health record (EHR) notes. Objective: To investigate associations between suicide and recent SDOH, identified using structured and unstructured data. Design: Nested case-control study. Setting: EHR data from the US Veterans Health Administration (VHA). Participants: 6,122,785 Veterans who received care in the US VHA between October 1, 2010, and September 30, 2015. Exposures: Occurrence of SDOH over a maximum span of two years compared with no occurrence of SDOH. Main Outcomes and Measures: Cases of suicide deaths were matched with 4 controls on birth year, cohort entry date, sex, and duration of follow-up. We developed an NLP system to extract SDOH from unstructured notes. Structured data, NLP on unstructured data, and combining them yielded six, eight and nine SDOH respectively. Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) were estimated using conditional logistic regression. Results: In our cohort, 8,821 Veterans committed suicide during 23,725,382 person-years of follow-up (incidence rate 37.18/100,000 person-years). Our cohort was mostly male (92.23%) and white (76.99%). Across the five common SDOH as covariates, NLP-extracted SDOH, on average, covered 80.03% of all SDOH occurrences. All SDOH, measured by structured data and NLP, were significantly associated with increased risk of suicide. The SDOH with the largest effects was legal problems (aOR=2.66, 95% CI=.46-2.89), followed by violence (aOR=2.12, 95% CI=1.98-2.27). NLP-extracted and structured SDOH were also associated with suicide. Conclusions and Relevance: NLP-extracted SDOH were always significantly associated with increased risk of suicide among Veterans, suggesting the potential of NLP in public health studies.Comment: Submitted to JAMA Network Ope

Thrombosis, Bleeding, and the Observational Effect of Early Therapeutic Anticoagulation on Survival in Critically Ill Patients With COVID-19

This article is made available for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.Background: Hypercoagulability may be a key mechanism of death in patients with coronavirus disease 2019 (COVID-19). Objective: To evaluate the incidence of venous thromboembolism (VTE) and major bleeding in critically ill patients with COVID-19 and examine the observational effect of early therapeutic anticoagulation on survival. Design: In a multicenter cohort study of 3239 critically ill adults with COVID-19, the incidence of VTE and major bleeding within 14 days after intensive care unit (ICU) admission was evaluated. A target trial emulation in which patients were categorized according to receipt or no receipt of therapeutic anticoagulation in the first 2 days of ICU admission was done to examine the observational effect of early therapeutic anticoagulation on survival. A Cox model with inverse probability weighting to adjust for confounding was used. Setting: 67 hospitals in the United States. Participants: Adults with COVID-19 admitted to a participating ICU. Measurements: Time to death, censored at hospital discharge, or date of last follow-up. Results: Among the 3239 patients included, the median age was 61 years (interquartile range, 53 to 71 years), and 2088 (64.5%) were men. A total of 204 patients (6.3%) developed VTE, and 90 patients (2.8%) developed a major bleeding event. Independent predictors of VTE were male sex and higher D-dimer level on ICU admission. Among the 2809 patients included in the target trial emulation, 384 (11.9%) received early therapeutic anticoagulation. In the primary analysis, during a median follow-up of 27 days, patients who received early therapeutic anticoagulation had a similar risk for death as those who did not (hazard ratio, 1.12 [95% CI, 0.92 to 1.35]). Limitation: Observational design. Conclusion: Among critically ill adults with COVID-19, early therapeutic anticoagulation did not affect survival in the target trial emulation

Mining patterns in genomic and clinical cancer data to characterize novel driver genes

Cancer research, like many areas of science, is adapting to a new era characterized by increasing quantity, quality, and diversity of observational data. An example of the advances, and the resulting challenges, is represented by The Cancer Genome Atlas, an enormous public effort that has provided genomic profiles of hundreds of tumors of each of the most common solid cancer types. Alongside this resource is a host of other data and knowledge, including gene interaction databases, Mendelian disease causal variants, and electronic health records spanning many millions of patients. Thus, a current challenge is how best to integrate these data to discover mechanisms of oncogenesis and cancer progression. Ultimately, this could enable genomics-based prediction of an individual patient's outcome and targeted therapies, a goal termed precision medicine. In this thesis, I develop novel approaches that examine patterns in populations of cancer patients to identify key genetic changes and suggest likely roles of these driver genes in the diseases. In the first section I show how genomics can lead to the identification of driver alterations in melanoma. The most recurrent genetic mutations are often in important cancer driver genes: in a newly sequenced melanoma cohort, recurrent inactivating mutations point to an exciting new melanoma candidate tumor suppressor, FBXW7, with therapeutic implications. But each tumor is unique, underlining the fact that recurrence will never capture all relevant mutations responsible for the disease. Tumors are a result of random events that must collaborate to endow a cell with all of the invasive and immortal properties of a cancer. Some combinations of events are lethal to a developing tumor, while other combinations are simply not preferentially selected. In order to discover these complex patterns, I develop a method based on the joint entropy of a set of genes, called GAMToC. Using GAMToC, I identify sets of recurrently altered genes with a strongly non-random joint pattern of co-occurrence and mutual exclusivity. Then, I extend this method as a means of identifying novel genes with a role in cancer, by virtue of their non-random pattern of alteration. Insights into the roles of these novel drivers can come from their most strongly co-selected partners. In the final section of the main text, I develop the use of cancer comorbidity, or increased cancer risk, as a novel data source for understanding cancer. The recent availability of clinical records spanning a large percentage of the American population has enabled discovery of many cancer comorbidities. Although most cancers arise as a result of somatic mutations accumulating over a patient's lifespan, mutations present at birth could predispose some rare populations to increased cancer risk. Mendelian disease phenotype provides strong insight into the genotype of an afflicted individual. Thus, if Mendelian diseases with cancer comorbidity can be shown to have specific defects in processes that are important in the development of that cancer, statistical comorbidity could provide a new a resource for prioritizing Mendelian disease genes as novel cancer related genes. For this purpose, I integrate clinical comorbidity, Mendelian disease causal variants, and somatic genomic profiles of thousands of cancers. I demonstrate that comorbidity indeed is associated with significant genetic similarity between Mendelian diseases and the cancers these patients are predisposed to, suggesting highly interesting and plausible new candidate cancer genes. While cancer may be the result of a series of selected random events, patterns of incidence across large populations, as measured by genomics or by other phenotypes, contain much non-random signal yet to be mined

Visit-to-visit variability of lipid measurements as predictors of cardiovascular events.

BACKGROUND:Higher visit-to-visit variability in risk factors such as blood pressure and low-density lipoprotein (LDL)-cholesterol are associated with an increase in cardiovascular (CV) events. OBJECTIVE:The purpose of this study was to determine whether variability in high-density lipoprotein cholesterol (HDL-C) and triglyceride levels predicted coronary and CV events in a clinical trial population with known coronary disease. METHODS:We assessed intraindividual variability in fasting high-density lipoprotein (HDL)-cholesterol, triglyceride, and LDL-cholesterol measurements among 9572 patients in the Treating to New Targets trial and correlated the results with coronary events over a median follow-up of 4.9 years. RESULTS:In the fully adjusted Cox model, 1 standard deviation of average successive variability, defined as the average absolute difference between successive values, was associated with an increased risk of a coronary event for HDL-cholesterol (hazard ratio [HR] 1.16, 95% confidence interval [CI] 1.11-1.21, P < .0001), for triglycerides (HR 1.09, 95% CI 1.04-1.15, P = .0005), and for LDL-cholesterol (HR 1.14, 95% CI 1.09-1.19, P < .0001). Similar results were found for the 3 other measures of variability, standard deviation, coefficient of variability, and variability independent of the mean. Similar results were seen for CV events, stroke, and nonfatal myocardial infarction. Higher variability in triglyceride and LDL-cholesterol, but not HDL-cholesterol, was predictive of incident diabetes. The correlation among the variability of the 3 lipid measurements was weak. CONCLUSION:Visit-to-visit variability in fasting measurements of HDL-cholesterol, triglycerides, and LDL-cholesterol are predictive of coronary events, CV events, and for triglyceride and low-density lipoprotein cholesterol variability, incident diabetes. The mechanisms accounting for these associations remain to be determined