Characteristics and Symptoms of App Users Seeking COVID-19-Related Digital Health Information and Remote Services: Retrospective Cohort Study.

BACKGROUND: Patient-facing digital health tools have been promoted to help patients manage concerns related to COVID-19 and to enable remote care and self-care during the COVID-19 pandemic. It has also been suggested that these tools can help further our understanding of the clinical characteristics of this new disease. However, there is limited information on the characteristics and use patterns of these tools in practice. OBJECTIVE: The aims of this study are to describe the characteristics of people who use digital health tools to address COVID-19-related concerns; explore their self-reported symptoms and characterize the association of these symptoms with COVID-19; and characterize the recommendations provided by digital health tools. METHODS: This study used data from three digital health tools on the K Health app: a protocol-based COVID-19 self-assessment, an artificial intelligence (AI)-driven symptom checker, and communication with remote physicians. Deidentified data were extracted on the demographic and clinical characteristics of adults seeking COVID-19-related health information between April 8 and June 20, 2020. Analyses included exploring features associated with COVID-19 positivity and features associated with the choice to communicate with a remote physician. RESULTS: During the period assessed, 71,619 individuals completed the COVID-19 self-assessment, 41,425 also used the AI-driven symptom checker, and 2523 consulted with remote physicians. Individuals who used the COVID-19 self-assessment were predominantly female (51,845/71,619, 72.4%), with a mean age of 34.5 years (SD 13.9). Testing for COVID-19 was reported by 2901 users, of whom 433 (14.9%) reported testing positive. Users who tested positive for COVID-19 were more likely to have reported loss of smell or taste (relative rate [RR] 6.66, 95% CI 5.53-7.94) and other established COVID-19 symptoms as well as ocular symptoms. Users communicating with a remote physician were more likely to have been recommended by the self-assessment to undergo immediate medical evaluation due to the presence of severe symptoms (RR 1.19, 95% CI 1.02-1.32). Most consultations with remote physicians (1940/2523, 76.9%) were resolved without need for referral to an in-person visit or to the emergency department. CONCLUSIONS: Our results suggest that digital health tools can help support remote care and self-management of COVID-19 and that self-reported symptoms from digital interactions can extend our understanding of the symptoms associated with COVID-19.

Predicting Breast Cancer by Applying Deep Learning to Linked Health Records and Mammograms.

Background Computational models on the basis of deep neural networks are increasingly used to analyze health care data. However, the efficacy of traditional computational models in radiology is a matter of debate. Purpose To evaluate the accuracy and efficiency of a combined machine and deep learning approach for early breast cancer detection applied to a linked set of digital mammography images and electronic health records. Materials and Methods In this retrospective study, 52 936 images were collected in 13 234 women who underwent at least one mammogram between 2013 and 2017, and who had health records for at least 1 year before undergoing mammography. The algorithm was trained on 9611 mammograms and health records of women to make two breast cancer predictions: to predict biopsy malignancy and to differentiate normal from abnormal screening examinations. The study estimated the association of features with outcomes by using t test and Fisher exact test. The model comparisons were performed with a 95% confidence interval (CI) or by using the DeLong test. Results The resulting algorithm was validated in 1055 women and tested in 2548 women (mean age, 55 years ± 10 [standard deviation]). In the test set, the algorithm identified 34 of 71 (48%) false-negative findings on mammograms. For the malignancy prediction objective, the algorithm obtained an area under the receiver operating characteristic curve (AUC) of 0.91 (95% CI: 0.89, 0.93), with specificity of 77.3% (95% CI: 69.2%, 85.4%) at a sensitivity of 87%. When trained on clinical data alone, the model performed significantly better than the Gail model (AUC, 0.78 vs 0.54, respectively; P < .004). Conclusion The algorithm, which combined machine-learning and deep-learning approaches, can be applied to assess breast cancer at a level comparable to radiologists and has the potential to substantially reduce missed diagnoses of breast cancer. © RSNA, 2019 Online supplemental material is available for this article.

Changing the approach to treatment choice in epilepsy using big data.

PURPOSE: A UCB-IBM collaboration explored the application of machine learning to large claims databases to construct an algorithm for antiepileptic drug (AED) choice for individual patients. METHODS: Claims data were collected between January 2006 and September 2011 for patients with epilepsy > 16 years of age. A subset of patient claims with a valid index date of AED treatment change (new, add, or switch) were used to train the AED prediction model by retrospectively evaluating an index date treatment for subsequent treatment change. Based on the trained model, a model-predicted AED regimen with the lowest likelihood of treatment change was assigned to each patient in the group of test claims, and outcomes were evaluated to test model validity. RESULTS: The model had 72% area under receiver operator characteristic curve, indicating good predictive power. Patients who were given the model-predicted AED regimen had significantly longer survival rates (time until a treatment change event) and lower expected health resource utilization on average than those who received another treatment. The actual prescribed AED regimen at the index date matched the model-predicted AED regimen in only 13% of cases; there were large discrepancies in the frequency of use of certain AEDs/combinations between model-predicted AED regimens and those actually prescribed. CONCLUSIONS: Chances of treatment success were improved if patients received the model-predicted treatment. Using the model's prediction system may enable personalized, evidence-based epilepsy care, accelerating the match between patients and their ideal therapy, thereby delivering significantly better health outcomes for patients and providing health-care savings by applying resources more efficiently. Our goal will be to strengthen the predictive power of the model by integrating diverse data sets and potentially moving to prospective data collection.

A standard based approach for biomedical knowledge representation.

The new generation of health information standards, where the syntax and semantics of the content is explicitly formalized, allows for interoperability in healthcare scenarios and analysis in clinical research settings. Studies involving clinical and genomic data include accumulating knowledge as relationships between genotypic and phenotypic information as well as associations within the genomic and clinical worlds. Some involve analysis results targeted at a specific disease; others are of a predictive nature specific to a patient and may be used by decision support applications. Representing knowledge is as important as representing data since data is more useful when coupled with relevant knowledge. Any further analysis and cross-research collaboration would benefit from persisting knowledge and data in a unified way. This paper describes a methodology used in Hypergenes, an EC FP7 project targeting Essential Hypertension, which captures data and knowledge using standards such as HL7 CDA and Clinical Genomics, aligned with the CEN EHR 13606 specification. We demonstrate the benefits of such an approach for clinical research as well as in healthcare oriented scenarios.

Emulated Clinical Trials from Longitudinal Real-World Data Efficiently Identify Candidates for Neurological Disease Modification: Examples from Parkinson's Disease.

Real-world healthcare data hold the potential to identify therapeutic solutions for progressive diseases by efficiently pinpointing safe and efficacious repurposing drug candidates. This approach circumvents key early clinical development challenges, particularly relevant for neurological diseases, concordant with the vision of the 21st Century Cures Act. However, to-date, these data have been utilized mainly for confirmatory purposes rather than as drug discovery engines. Here, we demonstrate the usefulness of real-world data in identifying drug repurposing candidates for disease-modifying effects, specifically candidate marketed drugs that exhibit beneficial effects on Parkinson's disease (PD) progression. We performed an observational study in cohorts of ascertained PD patients extracted from two large medical databases, Explorys SuperMart (N = 88,867) and IBM MarketScan Research Databases (N = 106,395); and applied two conceptually different, well-established causal inference methods to estimate the effect of hundreds of drugs on delaying dementia onset as a proxy for slowing PD progression. Using this approach, we identified two drugs that manifested significant beneficial effects on PD progression in both datasets: rasagiline, narrowly indicated for PD motor symptoms; and zolpidem, a psycholeptic. Each confers its effects through distinct mechanisms, which we explored via a comparison of estimated effects within the drug classification ontology. We conclude that analysis of observational healthcare data, emulating otherwise costly, large, and lengthy clinical trials, can highlight promising repurposing candidates, to be validated in prospective registration trials, beneficial against common, late-onset progressive diseases for which disease-modifying therapeutic solutions are scarce.

Framework for identifying drug repurposing candidates from observational healthcare data.

OBJECTIVE: Observational medical databases, such as electronic health records and insurance claims, track the healthcare trajectory of millions of individuals. These databases provide real-world longitudinal information on large cohorts of patients and their medication prescription history. We present an easy-to-customize framework that systematically analyzes such databases to identify new indications for on-market prescription drugs. MATERIALS AND METHODS: Our framework provides an interface for defining study design parameters and extracting patient cohorts, disease-related outcomes, and potential confounders in observational databases. It then applies causal inference methodology to emulate hundreds of randomized controlled trials (RCTs) for prescribed drugs, while adjusting for confounding and selection biases. After correcting for multiple testing, it outputs the estimated effects and their statistical significance in each database. RESULTS: We demonstrate the utility of the framework in a case study of Parkinson's disease (PD) and evaluate the effect of 259 drugs on various PD progression measures in two observational medical databases, covering more than 150 million patients. The results of these emulated trials reveal remarkable agreement between the two databases for the most promising candidates. DISCUSSION: Estimating drug effects from observational data is challenging due to data biases and noise. To tackle this challenge, we integrate causal inference methodology with domain knowledge and compare the estimated effects in two separate databases. CONCLUSION: Our framework enables systematic search for drug repurposing candidates by emulating RCTs using observational data. The high level of agreement between separate databases strongly supports the identified effects.

Estimating the effects of second-line therapy for type 2 diabetes mellitus: retrospective cohort study.

OBJECTIVE: Metformin is the recommended initial drug treatment in type 2 diabetes mellitus, but there is no clearly preferred choice for an additional drug when indicated. We compare the counterfactual drug effectiveness in lowering glycated hemoglobin (HbA1c) levels and effect on body mass index (BMI) of four diabetes second-line drug classes using electronic health records. STUDY DESIGN AND SETTING: Retrospective analysis of electronic health records of US-based patients in the Explorys database using causal inference methodology to adjust for patient censoring and confounders. PARTICIPANTS AND EXPOSURES: Our cohort consisted of more than 40 000 patients with type 2 diabetes, prescribed metformin along with a drug out of four second-line drug classes-sulfonylureas, thiazolidinediones, dipeptidyl peptidase 4 (DPP-4) inhibitors and glucagon-like peptide-1 agonists-during the years 2000-2015. Roughly, 17 000 of these patients were followed for 12 months after being prescribed a second-line drug. MAIN OUTCOME MEASURES: HbA1c and BMI of these patients after 6 and 12 months following treatment. RESULTS: We demonstrate that all four drug classes reduce HbA1c levels, but the effect of sulfonylureas after 6 and 12 months of treatment is less pronounced compared with other classes. We also estimate that DPP-4 inhibitors decrease body weight significantly more than sulfonylureas and thiazolidinediones. CONCLUSION: Our results are in line with current knowledge on second-line drug effectiveness and effect on BMI. They demonstrate that causal inference from electronic health records is an effective way for conducting multitreatment causal inference studies.

Epidemiological Models Lacking Process Noise Can Be Overconfident.

Mathematic models of epidemics are the key tool for predicting future course of disease in a population and analyzing the effects of possible intervention policies. Typically, models that produce deterministic are applied for making predictions and reaching decisions. Stochastic modeling methods present an alternative. Here, we demonstrate by example why it is important that stochastic modeling be used in population health decision support systems.

Fast and Efficient Feature Engineering for Multi-Cohort Analysis of EHR Data.

We present a framework for feature engineering, tailored for longitudinal structured data, such as electronic health records (EHRs). To fast-track feature engineering and extraction, the framework combines general-use plug-in extractors, a multi-cohort management mechanism, and modular memoization. Using this framework, we rapidly extracted thousands of features from diverse and large healthcare data sources in multiple projects.

A Data-Driven Decision-Support Tool for Population Health Policies.

Epidemiological models are key tools in assessing intervention policies for population health management. Statistical models, fitted with survey or health system data, can be combined with lab and field studies to provide reliable predictions of future population-level disease dynamics distributions and the effects of interventions. All too often, however, the end result of epidemiological modeling and cost-effectiveness studies is in the form of a report or journal paper. These are inherently limited in their coverage of locations, policy options, and derived outcome measures. Here, we describe a tool to support population health policy planning. The tool allows users to explore simulations of various policies, to view and compare interventions spanning multiple variables, time points, and locations. The design's modular architecture, and data representation separate the modeling methods, the outcome measures calculations, and the visualizations, making each component easily replaceable. These advantages make it extremely versatile and suitable for multiple uses.

Integrated multisystem analysis in a mental health and criminal justice ecosystem.

BACKGROUND: Patients with a serious mental illness often receive care that is fragmented due to reduced availability of or access to resources, and inadequate, discontinuous, and uncoordinated care across health, social services, and criminal justice organizations. This article describes the creation of a multisystem analysis that derives insights from an integrated dataset including patient access to case management services, medical services, and interactions with the criminal justice system. METHODS: Data were combined from electronic systems within a US mental health ecosystem that included mental health and substance abuse services, as well as data from the criminal justice system. Cox models were applied to test the associations between delivery of services and re-incarceration. Additionally, machine learning was used to train and validate a predictive model to examine effects of non-modifiable risk factors (age, past arrests, mental health diagnosis) and modifiable risk factors (outpatient, medical and case management services, and use of a jail diversion program) on re-arrest outcome. RESULTS: An association was found between past arrests and admission to crisis stabilization services in this population (N = 10,307). Delivery of case management or medical services provided after release from jail was associated with a reduced risk for re-arrest. Predictive models linked non-modifiable and modifiable risk factors and outcomes and predicted the probability of re-arrests with fair accuracy (area under the receiver operating characteristic curve of 0.67). CONCLUSIONS: By modeling the complex interactions between risk factors, service delivery, and outcomes, systems of care might be better enabled to meet patient needs and improve outcomes.

Driver gene classification reveals a substantial overrepresentation of tumor suppressors among very large chromatin-regulating proteins.

Compiling a comprehensive list of cancer driver genes is imperative for oncology diagnostics and drug development. While driver genes are typically discovered by analysis of tumor genomes, infrequently mutated driver genes often evade detection due to limited sample sizes. Here, we address sample size limitations by integrating tumor genomics data with a wide spectrum of gene-specific properties to search for rare drivers, functionally classify them, and detect features characteristic of driver genes. We show that our approach, CAnceR geNe similarity-based Annotator and Finder (CARNAF), enables detection of potentially novel drivers that eluded over a dozen pan-cancer/multi-tumor type studies. In particular, feature analysis reveals a highly concentrated pool of known and putative tumor suppressors among the <1% of genes that encode very large, chromatin-regulating proteins. Thus, our study highlights the need for deeper characterization of very large, epigenetic regulators in the context of cancer causality.

Paradoxical Hypersusceptibility of Drug-resistant Mycobacteriumtuberculosis to ß-lactam Antibiotics.

Mycobacterium tuberculosis (M. tuberculosis) is considered innately resistant to ß-lactam antibiotics. However, there is evidence that susceptibility to ß-lactam antibiotics in combination with ß-lactamase inhibitors is variable among clinical isolates, and these may present therapeutic options for drug-resistant cases. Here we report our investigation of susceptibility to ß-lactam/ß-lactamase inhibitor combinations among clinical isolates of M. tuberculosis, and the use of comparative genomics to understand the observed heterogeneity in susceptibility. Eighty-nine South African clinical isolates of varying first and second-line drug susceptibility patterns and two reference strains of M. tuberculosis underwent minimum inhibitory concentration (MIC) determination to two ß-lactams: amoxicillin and meropenem, both alone and in combination with clavulanate, a ß-lactamase inhibitor. 41/91 (45%) of tested isolates were found to be hypersusceptible to amoxicillin/clavulanate relative to reference strains, including 14/24 (58%) of multiple drug-resistant (MDR) and 22/38 (58%) of extensively drug-resistant (XDR) isolates. Genome-wide polymorphisms identified using whole-genome sequencing were used in a phylogenetically-aware linear mixed model to identify polymorphisms associated with amoxicillin/clavulanate susceptibility. Susceptibility to amoxicillin/clavulanate was over-represented among isolates within a specific clade (LAM4), in particular among XDR strains. Twelve sets of polymorphisms were identified as putative markers of amoxicillin/clavulanate susceptibility, five of which were confined solely to LAM4. Within the LAM4 clade, 'paradoxical hypersusceptibility' to amoxicillin/clavulanate has evolved in parallel to first and second-line drug resistance. Given the high prevalence of LAM4 among XDR TB in South Africa, our data support an expanded role for ß-lactam/ß-lactamase inhibitor combinations for treatment of drug-resistant M. tuberculosis.

Integrated multisystem analysis in a mental health and criminal justice ecosystem.

Patients with a serious mental illness often receive care that is fragmented due to reduced availability of or access to resources, and inadequate, discontinuous, and uncoordinated care across health, social services, and criminal justice organizations. These gaps in care may lead to increased mental health disease burden and relapse, as well as repeated incarcerations. Further, the complex health, social service, and criminal justice ecosystem within which the patient may be embedded makes it difficult to examine the role of modifiable risk factors and delivered services on patient outcomes, particularly given that agencies often maintain isolated sets of relevant data. Here we describe an approach to creating a multisystem analysis that derives insights from an integrated data set including patient access to case management services, medical services, and interactions with the criminal justice system. We combined data from electronic systems within a US mental health ecosystem that included mental health and substance abuse services, as well as data from the criminal justice system. We applied Cox models to test the associations between delivery of services and re-incarceration. Using this approach, we found an association between arrests and crisis stabilization services in this population. We also found that delivery of case management or medical services provided after release from jail was associated with a reduced risk for re-arrest. Additionally, we used machine learning to train and validate a predictive model linking non-modifiable and modifiable risk factors and outcomes. A predictive model, constructed using elastic net regularized logistic regression, and considering age, past arrests, mental health diagnosis, as well as use of a jail diversion program, outpatient, medical and case management services predicted the probability of re-arrests with fair accuracy (AUC=.67). By modeling the complex interactions between risk factors, service delivery and outcomes, we may better enable systems of care to meet patient needs and improve outcomes.