Association of Reduced Hospitalizations and Mortality Among COVID-19 Vaccinated Patients with Heart Failure

BACKGROUND

Patients with Heart Failure (HF) are at high risk for adverse outcomes with COVID-19. Reports of COVID-19 vaccine-related cardiac complications may contribute to vaccine hesitancy in patients with heart failure (HF).

METHODS

To analyze the impact of COVID-19 vaccine status on clinical outcomes in patients with HF, we conducted a retrospective cohort study on the association of COVID-19 vaccination status with hospitalizations, ICU admission, and mortality after adjustment for covariates. Inverse probability treatment weighted (IPTW) models were used to adjust for potential confounding.

RESULTS

Among 7094 patients with HF, 645 (9.1%) were partially vaccinated, 2,200 (31.0%) fully vaccinated, 1,053 vaccine-boosted (14.8%), and 3,196 remained unvaccinated (45.1%) by January 2022. The mean age was 73.3 ± 14.5 years, with 48% female. Lower mortality was observed among patients who were vaccine-boosted followed by those who were fully vaccinated experienced lower mortality (HRs 0.33 (CI 0.23, 0.48) and 0.36 (CI 0.30, 0.43), respectively, compared to unvaccinated individuals, p<0.001) over the mean follow up time of 276.5 ± 104.9 days, while no difference was observed between those who were unvaccinated or only partially vaccinated.

CONCLUSION/RELEVANCE

COVID-19 vaccination was associated with significant reduction in all-cause hospitalization rates and mortality, lending further evidence to support the importance of its implementation in the high-risk population of patients living with HF.

Predictive Modelling of Susceptibility to Substance Abuse, Mortality and Drug-Drug Interactions in Opioid Patients

Objective: Opioids are a class of drugs that are known for their use as pain relievers. They bind to opioid receptors on nerve cells in the brain and the nervous system to mitigate pain. Addiction is one of the chronic and primary adverse events of prolonged usage of opioids. They may also cause psychological disorders, muscle pain, depression, anxiety attacks etc. In this study, we present a collection of predictive models to identify patients at risk of opioid abuse and mortality by using their prescription histories. Also, we discover particularly threatening drug-drug interactions in the context of opioid usage.

Methods and Materials: Using a publicly available dataset from MIMIC-III, two models were trained, Logistic Regression with L2 regularization (baseline) and Extreme Gradient Boosting (enhanced model), to classify the patients of interest into two categories based on their susceptibility to opioid abuse. We’ve also used K-Means clustering, an unsupervised algorithm, to explore drug-drug interactions that might be of concern.

Results: The baseline model for classifying patients susceptible to opioid abuse has an F1 score of 76.64% (accuracy 77.16%) while the enhanced model has an F1 score of 94.45% (accuracy 94.35%). These models can be used as a preliminary step towards inferring the causal effect of opioid usage and can help monitor the prescription practices to minimize the opioid abuse.

Discussion and Conclusion: Results suggest that the enhanced model provides a promising approach in preemptive identification of patients at risk for opioid abuse. By discovering and correlating the patterns contributing to opioid overdose or abuse among a variety of patients, machine learning models can be used as an efficient tool to help uncover the existing gaps and/or fraudulent practices in prescription writing. To quote an example of one such incidental finding, our study discovered that insulin might possibly be interacting with opioids in an unfavourable way leading to complications in diabetic patients. This indicates that diabetic patients under long term opioid usage might need to take increased amounts of insulin to make it more effective. This observation backs up prior research studies done on a similar aspect. To increase the translational value of our work, the predictive models and the associated software code are made available under the MIT License.

Artificial intelligence in gastroenterology: A state-of-the-art review

The development of artificial intelligence (AI) has increased dramatically in the last 20 years, with clinical applications progressively being explored for most of the medical specialties. The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception. The clinical applications of AI systems in this field include the identification of premalignant or malignant lesions (e.g., identification of dysplasia or esophageal adenocarcinoma in Barrett’s esophagus, pancreatic malignancies), detection of lesions (e.g., polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response [e.g., determining survival in patients post-resection of hepatocellular carcinoma), determining which patients with inflammatory bowel disease (IBD) will benefit from biologic therapy], or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this comprehensive review is to analyze the available AI-related studies pertaining to the entirety of the gastrointestinal tract, including the upper, middle and lower tracts; IBD; the hepatobiliary system; and the pancreas, discussing the findings and clinical applications, as well as outlining the current limitations and future directions in this field.

Using Deep-Learning Algorithms to Simultaneously Identify Right and Left Ventricular Dysfunction From the Electrocardiogram

OBJECTIVES

This study sought to develop DL models capable of comprehensively quantifying left and right ventricular dysfunction from ECG data in a large, diverse population.

BACKGROUND

Rapid evaluation of left and right ventricular function using deep learning (DL) on electrocardiograms (ECGs) can assist diagnostic workflow. However, DL tools to estimate right- ventricular (RV) function do not exist, whereas those to estimate left ventricular (LV) function are restricted to quantification of very low LV function only.

METHODS

A multicenter study was conducted with data from 5 New York City hospitals: 4 for internal testing and 1 serving as external validation. We created novel DL models to classify left ventricular ejection fraction (LVEF) into categories derived from the latest universal definition of heart failure, estimate LVEF through regression, and predict a composite outcome of either RV systolic dysfunction or RV dilation.

RESULTS

We obtained echocardiogram LVEF estimates for 147,636 patients paired to 715,890 ECGs. We used natural language processing (NLP) to extract RV size and systolic function information from 404,502 echocardiogram reports paired to 761,510 ECGs for 148,227 patients. For LVEF classification in internal testing, area under curve (AUC) at detection of LVEF ≤40%, 40% < LVEF ≤50%, and LVEF >50% was 0.94 (95% CI: 0.94-0.94), 0.82 (95% CI: 0.81-0.83), and 0.89 (95% CI: 0.89-0.89), respectively. For external validation, these results were 0.94 (95% CI: 0.94-0.95), 0.73 (95% CI: 0.72-0.74), and 0.87 (95% CI: 0.87-0.88). For regression, the mean absolute error was 5.84% (95% CI: 5.82%-5.85%) for internal testing and 6.14% (95% CI: 6.13%-6.16%) in external validation. For prediction of the composite RV outcome, AUC was 0.84 (95% CI: 0.84-0.84) in both internal testing and external validation.

CONCLUSIONS

DL on ECG data can be used to create inexpensive screening, diagnostic, and predictive tools for both LV and RV dysfunction. Such tools may bridge the applicability of ECGs and echocardiography and enable prioritization of patients for further interventions for either sided failure progressing to biventricular disease.

Phe2vec: Automated disease phenotyping based on unsupervised embeddings from electronic health records

Robust phenotyping of patients from electronic health records (EHRs) at scale is a challenge in clinical informatics. Here, we introduce Phe2vec, an automated framework for disease phenotyping from EHRs based on unsupervised learning and assess its effectiveness against standard rule-based algorithms from Phenotype KnowledgeBase (PheKB). Phe2vec is based on pre-computing embeddings of medical concepts and patients' clinical history. Disease phenotypes are then derived from a seed concept and its neighbors in the embedding space. Patients are linked to a disease if their embedded representation is close to the disease phenotype. Comparing Phe2vec and PheKB cohorts head-to-head using chart review, Phe2vec performed on par or better in nine out of ten diseases. Differently from other approaches, it can scale to any condition and was validated against widely adopted expert-based standards. Phe2vec aims to optimize clinical informatics research by augmenting current frameworks to characterize patients by condition and derive reliable disease cohorts.

Opportunities and challenges for artificial intelligence in clinical cardiovascular genetics

A combination of emerging genomic and artificial intelligence (AI) techniques may ultimately unlock a deeper understanding of heterogeneity and biological complexities in cardiovascular diseases (CVDs), leading to advances in prognostic guidance and personalized therapies. We discuss the state of AI in cardiovascular genetics, current applications, limitations, and future directions of the field.

Abstract P405: A Time-Series Forecast Model to Assess Vital Sign Waveform Variability Prior to Vasospasm

Introduction: Symptomatic vasospasm (SV) is a complication of aneurysmal subarachnoid hemorrhage (aSAH) and can lead to cerebral infarction. Changes in vital trends, such as heart rate (HR) and mean arterial blood pressure (MAP), have been associated with SV in aSAH. Real-time assessment of instantaneous vital sign waveform data could improve detection of vital sign variability associated with vasospasm. However, no model using instantaneous waveform data exists to predict SV. We hypothesize that autoregressive integrated moving average (ARIMA) analysis, a time-series forecast model, is a useful approach to assess the variability of vital sign waveforms associated with SV.

Methods: In this small case-control study, vital signs of patients admitted to the neuroICU with aSAH were obtained using a software-based analytics platform, Sickbay. HR and MAP from 15 aSAH patients were continuously obtained from ECG and arterial line waveforms. Ten patients developed neurologic deficits attributed to angiographically-confirmed SV (Det). Five controls (Con) without SV were matched based on age. 3 Det and 3 Con were randomly selected for further analysis. For Det, waveforms were analyzed at 5-second intervals for 48 hours prior to clinical deterioration. For Con, waveforms were analyzed at a random 48-hour interval.

Results: Visually, MAP and not HR was more variable in Det than in Con patients (Figure). The ARIMA model plotted the forecasted-fit for each delta-variable waveform. The MAP confidence interval margins were significantly larger for Det patients compared to the Con patient. This trend was consistent across all other patients.

Conclusion: ARIMA is a useful tool to assess HR and MAP waveform variations prior to SV in aSAH. Larger studies are required to solidify this concept and further explore the combination of data analytics platform and ARIMA to predict neurological deterioration in SV.

Deep Neural Networks Can Predict New-Onset Atrial Fibrillation From the 12-Lead ECG and Help Identify Those at Risk of Atrial Fibrillation-Related Stroke

Supplemental Digital Content is available in the text.

Atrial fibrillation (AF) is associated with substantial morbidity, especially when it goes undetected. If new-onset AF could be predicted, targeted screening could be used to find it early. We hypothesized that a deep neural network could predict new-onset AF from the resting 12-lead ECG and that this prediction may help identify those at risk of AF-related stroke.

Molecular Imaging of Apoptosis in Atherosclerosis by Targeting Cell Membrane Phospholipid Asymmetry

BACKGROUND

Apoptosis in atherosclerotic lesions contributes to plaque vulnerability by lipid core enlargement and fibrous cap attenuation. Apoptosis is associated with exteriorization of phosphatidylserine (PS) and phosphatidylethanolamine (PE) on the cell membrane. Although PS-avid radiolabeled annexin-V has been employed for molecular imaging of high-risk plaques, PE-targeted imaging in atherosclerosis has not been studied.

OBJECTIVES

This study sought to evaluate the feasibility of molecular imaging with PE-avid radiolabeled duramycin in experimental atherosclerotic lesions in a rabbit model and compare duramycin targeting with radiolabeled annexin-V.

METHODS

Of the 27 rabbits, 21 were fed high-cholesterol, high-fat diet for 16 weeks. Nine of the 21 rabbits received ^99mTc-duramycin (test group), 6 received ^99mTc-linear duramycin (duramycin without PE-binding capability, negative radiotracer control group), and 6 received ^99mTc-annexin-V for radionuclide imaging. The remaining normal chow-fed 6 animals (disease control group) received ^99mTc-duramycin. In vivo microSPECT/microCT imaging was performed, and the aortas were explanted for ex vivo imaging and for histological characterization of atherosclerosis.

RESULTS

A significantly higher duramycin uptake was observed in the test group compared with that of disease control and negative radiotracer control animals; duramycin uptake was also significantly higher than the annexin-V uptake. Quantitative duramycin uptake, represented as the square root of percent injected dose per cm (√ID/cm) of abdominal aorta was >2-fold higher in atherosclerotic lesions in test group (0.08 ± 0.01%) than in comparable regions of disease control animals (0.039 ± 0.0061%, p = 3.70·10^-8). Mean annexin uptake (0.060 ± 0.010%) was significantly lower than duramycin (p = 0.001). Duramycin uptake corresponded to the lesion severity and macrophage burden. The radiation burden to the kidneys was substantially lower with duramycin (0.49% ID/g) than annexin (5.48% ID/g; p = 4.00·10^-4).

CONCLUSIONS

Radiolabeled duramycin localizes in lipid-rich areas with high concentration of apoptotic macrophages in the experimental atherosclerosis model. Duramycin uptake in atherosclerotic lesions was significantly greater than annexin-V uptake and produced significantly lower radiation burden to nontarget organs.

Federated Learning of Electronic Health Records to Improve Mortality Prediction in Hospitalized Patients With COVID-19: Machine Learning Approach

BACKGROUND

Machine learning models require large datasets that may be siloed across different health care institutions. Machine learning studies that focus on COVID-19 have been limited to single-hospital data, which limits model generalizability.

OBJECTIVE

We aimed to use federated learning, a machine learning technique that avoids locally aggregating raw clinical data across multiple institutions, to predict mortality in hospitalized patients with COVID-19 within 7 days.

METHODS

Patient data were collected from the electronic health records of 5 hospitals within the Mount Sinai Health System. Logistic regression with L1 regularization/least absolute shrinkage and selection operator (LASSO) and multilayer perceptron (MLP) models were trained by using local data at each site. We developed a pooled model with combined data from all 5 sites, and a federated model that only shared parameters with a central aggregator.

RESULTS

The LASSOfederated model outperformed the LASSOlocal model at 3 hospitals, and the MLPfederated model performed better than the MLPlocal model at all 5 hospitals, as determined by the area under the receiver operating characteristic curve. The LASSOpooled model outperformed the LASSOfederated model at all hospitals, and the MLPfederated model outperformed the MLPpooled model at 2 hospitals.

CONCLUSIONS

The federated learning of COVID-19 electronic health record data shows promise in developing robust predictive models without compromising patient privacy.

Retrospective cohort study of clinical characteristics of 2199 hospitalised patients with COVID-19 in New York City

OBJECTIVE

The COVID-19 pandemic is a global public health crisis, with over 33 million cases and 999 000 deaths worldwide. Data are needed regarding the clinical course of hospitalised patients, particularly in the USA. We aimed to compare clinical characteristic of patients with COVID-19 who had in-hospital mortality with those who were discharged alive.

DESIGN

Demographic, clinical and outcomes data for patients admitted to five Mount Sinai Health System hospitals with confirmed COVID-19 between 27 February and 2 April 2020 were identified through institutional electronic health records. We performed a retrospective comparative analysis of patients who had in-hospital mortality or were discharged alive.

SETTING

All patients were admitted to the Mount Sinai Health System, a large quaternary care urban hospital system.

PARTICIPANTS

Participants over the age of 18 years were included.

PRIMARY OUTCOMES

We investigated in-hospital mortality during the study period.

RESULTS

A total of 2199 patients with COVID-19 were hospitalised during the study period. As of 2 April, 1121 (51%) patients remained hospitalised, and 1078 (49%) completed their hospital course. Of the latter, the overall mortality was 29%, and 36% required intensive care. The median age was 65 years overall and 75 years in those who died. Pre-existing conditions were present in 65% of those who died and 46% of those discharged. In those who died, the admission median lymphocyte percentage was 11.7%, D-dimer was 2.4 μg/mL, C reactive protein was 162 mg/L and procalcitonin was 0.44 ng/mL. In those discharged, the admission median lymphocyte percentage was 16.6%, D-dimer was 0.93 μg/mL, C reactive protein was 79 mg/L and procalcitonin was 0.09 ng/mL.

CONCLUSIONS

In our cohort of hospitalised patients, requirement of intensive care and mortality were high. Patients who died typically had more pre-existing conditions and greater perturbations in inflammatory markers as compared with those who were discharged.

Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation

BACKGROUND

COVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods to meet these needs are lacking.

OBJECTIVE

The aims of this study were to analyze the electronic health records (EHRs) of patients who tested positive for COVID-19 and were admitted to hospitals in the Mount Sinai Health System in New York City; to develop machine learning models for making predictions about the hospital course of the patients over clinically meaningful time horizons based on patient characteristics at admission; and to assess the performance of these models at multiple hospitals and time points.

METHODS

We used Extreme Gradient Boosting (XGBoost) and baseline comparator models to predict in-hospital mortality and critical events at time windows of 3, 5, 7, and 10 days from admission. Our study population included harmonized EHR data from five hospitals in New York City for 4098 COVID-19-positive patients admitted from March 15 to May 22, 2020. The models were first trained on patients from a single hospital (n=1514) before or on May 1, externally validated on patients from four other hospitals (n=2201) before or on May 1, and prospectively validated on all patients after May 1 (n=383). Finally, we established model interpretability to identify and rank variables that drive model predictions.

RESULTS

Upon cross-validation, the XGBoost classifier outperformed baseline models, with an area under the receiver operating characteristic curve (AUC-ROC) for mortality of 0.89 at 3 days, 0.85 at 5 and 7 days, and 0.84 at 10 days. XGBoost also performed well for critical event prediction, with an AUC-ROC of 0.80 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. In external validation, XGBoost achieved an AUC-ROC of 0.88 at 3 days, 0.86 at 5 days, 0.86 at 7 days, and 0.84 at 10 days for mortality prediction. Similarly, the unimputed XGBoost model achieved an AUC-ROC of 0.78 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. Trends in performance on prospective validation sets were similar. At 7 days, acute kidney injury on admission, elevated LDH, tachypnea, and hyperglycemia were the strongest drivers of critical event prediction, while higher age, anion gap, and C-reactive protein were the strongest drivers of mortality prediction.

CONCLUSIONS

We externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons. These models identified at-risk patients and uncovered underlying relationships that predicted outcomes.

Machine Learning Applications in the Neuro ICU: A Solution to Big Data Mayhem?

The neurological ICU (neuro ICU) often suffers from significant limitations due to scarce resource availability for their neurocritical care patients. Neuro ICU patients require frequent neurological evaluations, continuous monitoring of various physiological parameters, frequent imaging, and routine lab testing. This amasses large amounts of data specific to each patient. Neuro ICU teams are often overburdened by the resulting complexity of data for each patient. Machine Learning algorithms (ML), are uniquely capable of interpreting high-dimensional datasets that are too difficult for humans to comprehend. Therefore, the application of ML in the neuro ICU could alleviate the burden of analyzing big datasets for each patient. This review serves to (1) briefly summarize ML and compare the different types of MLs, (2) review recent ML applications to improve neuro ICU management and (3) describe the future implications of ML to neuro ICU management.

Deep learning for cardiovascular medicine: a practical primer

Deep learning (DL) is a branch of machine learning (ML) showing increasing promise in medicine, to assist in data classification, novel disease phenotyping and complex decision making. Deep learning is a form of ML typically implemented via multi-layered neural networks. Deep learning has accelerated by recent advances in computer hardware and algorithms and is increasingly applied in e-commerce, finance, and voice and image recognition to learn and classify complex datasets. The current medical literature shows both strengths and limitations of DL. Strengths of DL include its ability to automate medical image interpretation, enhance clinical decision-making, identify novel phenotypes, and select better treatment pathways in complex diseases. Deep learning may be well-suited to cardiovascular medicine in which haemodynamic and electrophysiological indices are increasingly captured on a continuous basis by wearable devices as well as image segmentation in cardiac imaging. However, DL also has significant weaknesses including difficulties in interpreting its models (the 'black-box' criticism), its need for extensive adjudicated ('labelled') data in training, lack of standardization in design, lack of data-efficiency in training, limited applicability to clinical trials, and other factors. Thus, the optimal clinical application of DL requires careful formulation of solvable problems, selection of most appropriate DL algorithms and data, and balanced interpretation of results. This review synthesizes the current state of DL for cardiovascular clinicians and investigators, and provides technical context to appreciate the promise, pitfalls, near-term challenges, and opportunities for this exciting new area.

Machine learning prediction in cardiovascular diseases: a meta-analysis

Several machine learning (ML) algorithms have been increasingly utilized for cardiovascular disease prediction. We aim to assess and summarize the overall predictive ability of ML algorithms in cardiovascular diseases. A comprehensive search strategy was designed and executed within the MEDLINE, Embase, and Scopus databases from database inception through March 15, 2019. The primary outcome was a composite of the predictive ability of ML algorithms of coronary artery disease, heart failure, stroke, and cardiac arrhythmias. Of 344 total studies identified, 103 cohorts, with a total of 3,377,318 individuals, met our inclusion criteria. For the prediction of coronary artery disease, boosting algorithms had a pooled area under the curve (AUC) of 0.88 (95% CI 0.84-0.91), and custom-built algorithms had a pooled AUC of 0.93 (95% CI 0.85-0.97). For the prediction of stroke, support vector machine (SVM) algorithms had a pooled AUC of 0.92 (95% CI 0.81-0.97), boosting algorithms had a pooled AUC of 0.91 (95% CI 0.81-0.96), and convolutional neural network (CNN) algorithms had a pooled AUC of 0.90 (95% CI 0.83-0.95). Although inadequate studies for each algorithm for meta-analytic methodology for both heart failure and cardiac arrhythmias because the confidence intervals overlap between different methods, showing no difference, SVM may outperform other algorithms in these areas. The predictive ability of ML algorithms in cardiovascular diseases is promising, particularly SVM and boosting algorithms. However, there is heterogeneity among ML algorithms in terms of multiple parameters. This information may assist clinicians in how to interpret data and implement optimal algorithms for their dataset.

Federated Learning of Electronic Health Records Improves Mortality Prediction in Patients Hospitalized with COVID-19

Machine learning (ML) models require large datasets which may be siloed across different healthcare institutions. Using federated learning, a ML technique that avoids locally aggregating raw clinical data across multiple institutions, we predict mortality within seven days in hospitalized COVID-19 patients. Patient data was collected from Electronic Health Records (EHRs) from five hospitals within the Mount Sinai Health System (MSHS). Logistic Regression with L1 regularization (LASSO) and Multilayer Perceptron (MLP) models were trained using local data at each site, a pooled model with combined data from all five sites, and a federated model that only shared parameters with a central aggregator. Both the federated LASSO and federated MLP models performed better than their local model counterparts at four hospitals. The federated MLP model also outperformed the federated LASSO model at all hospitals. Federated learning shows promise in COVID-19 EHR data to develop robust predictive models without compromising patient privacy.

Prevalence and Impact of Myocardial Injury in Patients Hospitalized With COVID-19 Infection

BACKGROUND

The degree of myocardial injury, as reflected by troponin elevation, and associated outcomes among U.S. hospitalized patients with coronavirus disease-2019 (COVID-19) are unknown.

OBJECTIVES

The purpose of this study was to describe the degree of myocardial injury and associated outcomes in a large hospitalized cohort with laboratory-confirmed COVID-19.

METHODS

Patients with COVID-19 admitted to 1 of 5 Mount Sinai Health System hospitals in New York City between February 27, 2020, and April 12, 2020, with troponin-I (normal value <0.03 ng/ml) measured within 24 h of admission were included (n = 2,736). Demographics, medical histories, admission laboratory results, and outcomes were captured from the hospitals' electronic health records.

RESULTS

The median age was 66.4 years, with 59.6% men. Cardiovascular disease (CVD), including coronary artery disease, atrial fibrillation, and heart failure, was more prevalent in patients with higher troponin concentrations, as were hypertension and diabetes. A total of 506 (18.5%) patients died during hospitalization. In all, 985 (36%) patients had elevated troponin concentrations. After adjusting for disease severity and relevant clinical factors, even small amounts of myocardial injury (e.g., troponin I >0.03 to 0.09 ng/ml; n = 455; 16.6%) were significantly associated with death (adjusted hazard ratio: 1.75; 95% CI: 1.37 to 2.24; p < 0.001) while greater amounts (e.g., troponin I >0.09 ng/dl; n = 530; 19.4%) were significantly associated with higher risk (adjusted HR: 3.03; 95% CI: 2.42 to 3.80; p < 0.001).

CONCLUSIONS

Myocardial injury is prevalent among patients hospitalized with COVID-19; however, troponin concentrations were generally present at low levels. Patients with CVD are more likely to have myocardial injury than patients without CVD. Troponin elevation among patients hospitalized with COVID-19 is associated with higher risk of mortality.

Sepsis in the era of data-driven medicine: personalizing risks, diagnoses, treatments and prognoses

Sepsis is a series of clinical syndromes caused by the immunological response to infection. The clinical evidence for sepsis could typically attribute to bacterial infection or bacterial endotoxins, but infections due to viruses, fungi or parasites could also lead to sepsis. Regardless of the etiology, rapid clinical deterioration, prolonged stay in intensive care units and high risk for mortality correlate with the incidence of sepsis. Despite its prevalence and morbidity, improvement in sepsis outcomes has remained limited. In this comprehensive review, we summarize the current landscape of risk estimation, diagnosis, treatment and prognosis strategies in the setting of sepsis and discuss future challenges. We argue that the advent of modern technologies such as in-depth molecular profiling, biomedical big data and machine intelligence methods will augment the treatment and prevention of sepsis. The volume, variety, veracity and velocity of heterogeneous data generated as part of healthcare delivery and recent advances in biotechnology-driven therapeutics and companion diagnostics may provide a new wave of approaches to identify the most at-risk sepsis patients and reduce the symptom burden in patients within shorter turnaround times. Developing novel therapies by leveraging modern drug discovery strategies including computational drug repositioning, cell and gene-therapy, clustered regularly interspaced short palindromic repeats -based genetic editing systems, immunotherapy, microbiome restoration, nanomaterial-based therapy and phage therapy may help to develop treatments to target sepsis. We also provide empirical evidence for potential new sepsis targets including FER and STARD3NL. Implementing data-driven methods that use real-time collection and analysis of clinical variables to trace, track and treat sepsis-related adverse outcomes will be key. Understanding the root and route of sepsis and its comorbid conditions that complicate treatment outcomes and lead to organ dysfunction may help to facilitate identification of most at-risk patients and prevent further deterioration. To conclude, leveraging the advances in precision medicine, biomedical data science and translational bioinformatics approaches may help to develop better strategies to diagnose and treat sepsis in the next decade.

Machine Learning in Cardiology-Ensuring Clinical Impact Lives Up to the Hype

Despite substantial advances in the study, treatment, and prevention of cardiovascular disease, numerous challenges relating to optimally screening, diagnosing, and managing patients remain. Simultaneous improvements in computing power, data storage, and data analytics have led to the development of new techniques to address these challenges. One powerful tool to this end is machine learning (ML), which aims to algorithmically identify and represent structure within data. Machine learning's ability to efficiently analyze large and highly complex data sets make it a desirable investigative approach in modern biomedical research. Despite this potential and enormous public and private sector investment, few prospective studies have demonstrated improved clinical outcomes from this technology. This is particularly true in cardiology, despite its emphasis on objective, data-driven results. This threatens to stifle ML's growth and use in mainstream medicine. We outline the current state of ML in cardiology and outline methods through which impactful and sustainable ML research can occur. Following these steps can ensure ML reaches its potential as a transformative technology in medicine.

Clinical Characteristics of Hospitalized Covid-19 Patients in New York City

Background

The coronavirus 2019 (Covid-19) pandemic is a global public health crisis, with over 1.6 million cases and 95,000 deaths worldwide. Data are needed regarding the clinical course of hospitalized patients, particularly in the United States.

Methods

Demographic, clinical, and outcomes data for patients admitted to five Mount Sinai Health System hospitals with confirmed Covid-19 between February 27 and April 2, 2020 were identified through institutional electronic health records. We conducted a descriptive study of patients who had in-hospital mortality or were discharged alive.

Results

A total of 2,199 patients with Covid-19 were hospitalized during the study period. As of April 2 ^nd , 1,121 (51%) patients remained hospitalized, and 1,078 (49%) completed their hospital course. Of the latter, the overall mortality was 29%, and 36% required intensive care. The median age was 65 years overall and 75 years in those who died. Pre-existing conditions were present in 65% of those who died and 46% of those discharged. In those who died, the admission median lymphocyte percentage was 11.7%, D-dimer was 2.4 ug/ml, C-reactive protein was 162 mg/L, and procalcitonin was 0.44 ng/mL. In those discharged, the admission median lymphocyte percentage was 16.6%, D-dimer was 0.93 ug/ml, C-reactive protein was 79 mg/L, and procalcitonin was 0.09 ng/mL.

Conclusions

This is the largest and most diverse case series of hospitalized patients with Covid-19 in the United States to date. Requirement of intensive care and mortality were high. Patients who died typically had pre-existing conditions and severe perturbations in inflammatory markers.

Prevalence and Impact of Myocardial Injury in Patients Hospitalized with COVID-19 Infection

BACKGROUND

The degree of myocardial injury, reflected by troponin elevation, and associated outcomes among hospitalized patients with Coronavirus Disease (COVID-19) in the US are unknown.

OBJECTIVES

To describe the degree of myocardial injury and associated outcomes in a large hospitalized cohort with laboratory-confirmed COVID-19.

METHODS

Patients with COVID-19 admitted to one of five Mount Sinai Health System hospitals in New York City between February 27th and April 12th, 2020 with troponin-I (normal value <0.03ng/mL) measured within 24 hours of admission were included (n=2,736). Demographics, medical history, admission labs, and outcomes were captured from the hospital EHR.

RESULTS

The median age was 66.4 years, with 59.6% men. Cardiovascular disease (CVD) including coronary artery disease, atrial fibrillation, and heart failure, was more prevalent in patients with higher troponin concentrations, as were hypertension and diabetes. A total of 506 (18.5%) patients died during hospitalization. Even small amounts of myocardial injury (e.g. troponin I 0.03-0.09ng/mL, n=455, 16.6%) were associated with death (adjusted HR: 1.77, 95% CI 1.39-2.26; P<0.001) while greater amounts (e.g. troponin I>0.09 ng/dL, n=530, 19.4%) were associated with more pronounced risk (adjusted HR 3.23, 95% CI 2.59-4.02).

CONCLUSIONS

Myocardial injury is prevalent among patients hospitalized with COVID-19, and is associated with higher risk of mortality. Patients with CVD are more likely to have myocardial injury than patients without CVD. Troponin elevation likely reflects non-ischemic or secondary myocardial injury.

Predictors of In-Hospital Mortality after Transcatheter Aortic Valve Implantation

The development of aortic valve stenosis is strongly associated with older adults. Patients who undergo transcatheter aortic valve implantation (TAVI) for severe aortic stenosis frequently have heart failure (HF). We investigated the predictors of mortality after TAVI according to the presence of HF, and specifically HF with preserved ejection fraction (HFpEF) versus HF with reduced ejection fraction (HFrEF). Patients were identified from the Nationwide Inpatient Sample registry from January 2011 to September 2015 using the ICD-9 codes. Patients with HF who underwent TAVI were classified according to whether they were diagnosed with HFrEF or HFpEF. The principal outcome of interest was in-hospital mortality. Multivariable analysis was used to adjust for potential baseline confounders. Among 11,609 patients undergoing TAVI, 6,368 (54.9%) had baseline HF, including 4,290 (67.4%) with HFpEF and 2,078 (32.6%) with HFrEF. In TAVI patients with HF, in-hospital mortality was also not significantly different in those with HFrEF compared with HFpEF (3.66% vs 3.17%, respectively; adjusted odds ratio 1.14, 95% confidence interval 0.84 to 1.53; p = 0.38). Polyvalvular heart disease was an additional independent predictor of in-hospital mortality in HFrEF, whereas age, liver disease, and the absence of depression and anemia were additional independent predictors of mortality in HFpEF. In conclusion, baseline HF in patients undergoing TAVI is prevalent and is more commonly due to HFpEF than HFrEF. Mortality is similar in those with HFrEF and HFpEF. Knowledge of the specific predictors of mortality after TAVI in HF patients may be useful for patient selection and prognostic guidance.

PatientExploreR: an extensible application for dynamic visualization of patient clinical history from electronic health records in the OMOP common data model

MOTIVATION

Electronic health records (EHRs) are quickly becoming omnipresent in healthcare, but interoperability issues and technical demands limit their use for biomedical and clinical research. Interactive and flexible software that interfaces directly with EHR data structured around a common data model (CDM) could accelerate more EHR-based research by making the data more accessible to researchers who lack computational expertise and/or domain knowledge.

RESULTS

We present PatientExploreR, an extensible application built on the R/Shiny framework that interfaces with a relational database of EHR data in the Observational Medical Outcomes Partnership CDM format. PatientExploreR produces patient-level interactive and dynamic reports and facilitates visualization of clinical data without any programming required. It allows researchers to easily construct and export patient cohorts from the EHR for analysis with other software. This application could enable easier exploration of patient-level data for physicians and researchers. PatientExploreR can incorporate EHR data from any institution that employs the CDM for users with approved access. The software code is free and open source under the MIT license, enabling institutions to install and users to expand and modify the application for their own purposes.

AVAILABILITY AND IMPLEMENTATION

PatientExploreR can be freely obtained from GitHub: https://github.com/BenGlicksberg/PatientExploreR. We provide instructions for how researchers with approved access to their institutional EHR can use this package. We also release an open sandbox server of synthesized patient data for users without EHR access to explore: http://patientexplorer.ucsf.edu.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Artificial Intelligence in Cardiology

Artificial intelligence and machine learning are poised to influence nearly every aspect of the human condition, and cardiology is not an exception to this trend. This paper provides a guide for clinicians on relevant aspects of artificial intelligence and machine learning, reviews selected applications of these methods in cardiology to date, and identifies how cardiovascular medicine could incorporate artificial intelligence in the future. In particular, the paper first reviews predictive modeling concepts relevant to cardiology such as feature selection and frequent pitfalls such as improper dichotomization. Second, it discusses common algorithms used in supervised learning and reviews selected applications in cardiology and related disciplines. Third, it describes the advent of deep learning and related methods collectively called unsupervised learning, provides contextual examples both in general medicine and in cardiovascular medicine, and then explains how these methods could be applied to enable precision cardiology and improve patient outcomes.

Integrative analysis of loss-of-function variants in clinical and genomic data reveals novel genes associated with cardiovascular traits

Background

Genetic loss-of-function variants (LoFs) associated with disease traits are increasingly recognized as critical evidence for the selection of therapeutic targets. We integrated the analysis of genetic and clinical data from 10,511 individuals in the Mount Sinai BioMe Biobank to identify genes with loss-of-function variants (LoFs) significantly associated with cardiovascular disease (CVD) traits, and used RNA-sequence data of seven metabolic and vascular tissues isolated from 600 CVD patients in the Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task (STARNET) study for validation. We also carried out in vitro functional studies of several candidate genes, and in vivo studies of one gene.

Results

We identified LoFs in 433 genes significantly associated with at least one of 10 major CVD traits. Next, we used RNA-sequence data from the STARNET study to validate 115 of the 433 LoF harboring-genes in that their expression levels were concordantly associated with corresponding CVD traits. Together with the documented hepatic lipid-lowering gene, APOC3, the expression levels of six additional liver LoF-genes were positively associated with levels of plasma lipids in STARNET. Candidate LoF-genes were subjected to gene silencing in HepG2 cells with marked overall effects on cellular LDLR, levels of triglycerides and on secreted APOB100 and PCSK9. In addition, we identified novel LoFs in DGAT2 associated with lower plasma cholesterol and glucose levels in BioMe that were also confirmed in STARNET, and showed a selective DGAT2-inhibitor in C57BL/6 mice not only significantly lowered fasting glucose levels but also affected body weight.

Conclusion

In sum, by integrating genetic and electronic medical record data, and leveraging one of the world’s largest human RNA-sequence datasets (STARNET), we identified known and novel CVD-trait related genes that may serve as targets for CVD therapeutics and as such merit further investigation.

Electronic supplementary material

The online version of this article (10.1186/s12920-019-0542-3) contains supplementary material, which is available to authorized users.

A transcriptomic model to predict increase in fibrous cap thickness in response to high-dose statin treatment: Validation by serial intracoronary OCT imaging

Background

Fibrous cap thickness (FCT), best measured by intravascular optical coherence tomography (OCT), is the most important determinant of plaque rupture in the coronary arteries. Statin treatment increases FCT and thus reduces the likelihood of acute coronary events. However, substantial statin-related FCT increase occurs in only a subset of patients. Currently, there are no methods to predict which patients will benefit. We use transcriptomic data from a clinical trial of rosuvastatin to predict if a patient's FCT will increase in response to statin therapy.

Methods

FCT was measured using OCT in 69 patients at (1) baseline and (2) after 8–10 weeks of 40  mg rosuvastatin. Peripheral blood mononuclear cells were assayed via microarray. We constructed machine learning models with baseline gene expression data to predict change in FCT. Finally, we ascertained the biological functions of the most predictive transcriptomic markers.

Findings

Machine learning models were able to predict FCT responders using baseline gene expression with high fidelity (Classification AUC = 0.969 and 0.972). The first model (elastic net) using 73 genes had an accuracy of 92.8%, sensitivity of 94.1%, and specificity of 91.4%. The second model (KTSP) using 18 genes has an accuracy of 95.7%, sensitivity of 94.3%, and specificity of 97.1%. We found 58 enriched gene ontology terms, including many involved with immune cell function and cholesterol biometabolism.

Interpretation

In this pilot study, transcriptomic models could predict if FCT increased following 8–10 weeks of rosuvastatin. These findings may have significance for therapy selection and could supplement invasive imaging modalities.

The next generation of precision medicine: observational studies, electronic health records, biobanks and continuous monitoring

Precision medicine can utilize new techniques in order to more effectively translate research findings into clinical practice. In this article, we first explore the limitations of traditional study designs, which stem from (to name a few): massive cost for the assembly of large patient cohorts; non-representative patient data; and the astounding complexity of human biology. Second, we propose that harnessing electronic health records and mobile device biometrics coupled to longitudinal data may prove to be a solution to many of these problems by capturing a 'real world' phenotype. We envision that future biomedical research utilizing more precise approaches to patient care will utilize continuous and longitudinal data sources.

Prevalence, Presentation, and Associated Conditions of Patients With Fibromuscular Dysplasia

Fibromuscular dysplasia (FMD) is defined by focal narrowing of small and medium-sized arteries due to an idiopathic, noninflammatory, nonatherosclerotic vascular disease. The population-based prevalence of FMD remains unknown. Using the National Inpatient Sample database, we evaluated the prevalence, clinical presentation, mortality, and associated conditions of FMD from January 1, 2004, to September 30, 2015. Among 2,420 patients who presented with FMD, 2,086 (86.20%) of patients were female. The mean age was 55.18 ± 18.99 years in men and 63.37 ± 17.10 years in women. FMD patients most commonly presented with hypertension (67.3%), transient ischemic attack (3.7%), headache (2.1%), dizziness (1.1%), abdominal pain (0.6%), or hematuria (0.3%). In-hospital mortality of FMD patients was 0.74%. In conclusion, FMD is a rare condition with low in-hospital mortality that may be considered among female patients presenting with hypertension.

Conditions and Factors Associated With Spontaneous Coronary Artery Dissection (from a National Population-Based Cohort Study)

The pathophysiology of spontaneous coronary artery dissection (SCAD) is heterogeneous, associated with systemic arteriopathies and inflammatory diseases, and often compounded by environmental precipitants, genetics, or stressors. However, the frequency of these associated conditions with SCAD on a population level remains unknown. Therefore, the objective of this analysis was to evaluate heterogeneous phenotypes of SCAD in the United States using data from the Nationwide Inpatient Sample collected from January 1, 2004, to September 31, 2015. Among 66,360 patients diagnosed with SCAD, the mean age was 63.1 ± 13.2 years and 44.2% were women. A total of 3,415 (5.14%) had depression, 670 (1.0%) had rheumatoid arthritis, 640 (0.96%) had anxiety, 545 (0.82%) had a migraine disorder, 440 (0.66%) used steroids, 385 (0.58%) had malignant hypertension, 280 (0.42%) had systemic lupus erythematosus, 250 (0.38%) had cocaine abuse, 215 (0.32%) had hypertensive heart or renal disease, 130 (0.19%) had coronary spasm, 105 (0.16%) had fibromuscular dysplasia, 85 (0.13%) had Crohn's disease, 75 (0.11%) had celiac disease, 60 (0.09%) had adult autosomal dominant polycystic kidney disease, 60 (0.09%) had hormone replacement therapy, 55 (0.08%) had sarcoidosis, 55 (0.08%) had amphetamine abuse, 15 (0.02%) had granulomatosis polyangiitis, 10 (0.02%) had α1-antitrypsin deficiency, 10 (0.02%) had Marfan syndrome, 10 (0.02%) had Ehlers-Danlos syndrome, 10 (0.02%) had Kawasaki disease, 10 (0.02%) had polyarteritis nodosa, and 5 (0.01%) had multiparity. In conclusion, most cases of SCAD had no apparent concomitant arteriopathy, inflammatory disorder, or evident risk factor.

Evaluation of patient re-identification using laboratory test orders and mitigation via latent space variables

Anonymized electronic health records (EHR) are often used for biomedical research. One persistent concern with this type of research is the risk for re-identification of patients from their purportedly anonymized data. Here, we use the EHR of 731,850 de-identified patients to demonstrate that the average patient is unique from all others 98.4% of the time simply by examining what laboratory tests have been ordered for them. By the time a patient has visited the hospital on two separate days, they are unique in 72.3% of cases. We further present a computational study to identify how accurately the records from a single day of care can be used to re-identify patients from a set of 99 other patients. We show that, given a single visit's laboratory orders (even without result values) for a patient, we can re-identify the patient at least 25% of the time. Furthermore, we can place this patient among the top 10 most similar patients 47% of the time. Finally, we present a proof-of-concept technique using a variational autoencoder to encode laboratory results into a lower-dimensional latent space. We demonstrate that releasing latentspace encoded laboratory orders significantly improves privacy compared to releasing raw laboratory orders (<5% re-identification), while preserving information contained within the laboratory orders (AUC of >0.9 for recreating encoded values). Our findings have potential consequences for the public release of anonymized laboratory tests to the biomedical research community. We note that our findings do not imply that laboratory tests alone are personally identifiable. In the attack scenario presented here, reidentification would require a threat actor to possess an external source of laboratory values which are linked to personal identifiers at the start.

Pharmacological risk factors associated with hospital readmission rates in a psychiatric cohort identified using prescriptome data mining

BACKGROUND

Worldwide, over 14% of individuals hospitalized for psychiatric reasons have readmissions to hospitals within 30 days after discharge. Predicting patients at risk and leveraging accelerated interventions can reduce the rates of early readmission, a negative clinical outcome (i.e., a treatment failure) that affects the quality of life of patient. To implement individualized interventions, it is necessary to predict those individuals at highest risk for 30-day readmission. In this study, our aim was to conduct a data-driven investigation to find the pharmacological factors influencing 30-day all-cause, intra- and interdepartmental readmissions after an index psychiatric admission, using the compendium of prescription data (prescriptome) from electronic medical records (EMR).

METHODS

The data scientists in the project received a deidentified database from the Mount Sinai Data Warehouse, which was used to perform all analyses. Data was stored in a secured MySQL database, normalized and indexed using a unique hexadecimal identifier associated with the data for psychiatric illness visits. We used Bayesian logistic regression models to evaluate the association of prescription data with 30-day readmission risk. We constructed individual models and compiled results after adjusting for covariates, including drug exposure, age, and gender. We also performed digital comorbidity survey using EMR data combined with the estimation of shared genetic architecture using genomic annotations to disease phenotypes.

RESULTS

Using an automated, data-driven approach, we identified prescription medications, side effects (primary side effects), and drug-drug interaction-induced side effects (secondary side effects) associated with readmission risk in a cohort of 1275 patients using prescriptome analytics. In our study, we identified 28 drugs associated with risk for readmission among psychiatric patients. Based on prescription data, Pravastatin had the highest risk of readmission (OR = 13.10; 95% CI (2.82, 60.8)). We also identified enrichment of primary side effects (n = 4006) and secondary side effects (n = 36) induced by prescription drugs in the subset of readmitted patients (n = 89) compared to the non-readmitted subgroup (n = 1186). Digital comorbidity analyses and shared genetic analyses further reveals that cardiovascular disease and psychiatric conditions are comorbid and share functional gene modules (cardiomyopathy and anxiety disorder: shared genes (n = 37; P = 1.06815E-06)).

CONCLUSIONS

Large scale prescriptome data is now available from EMRs and accessible for analytics that could improve healthcare outcomes. Such analyses could also drive hypothesis and data-driven research. In this study, we explored the utility of prescriptome data to identify factors driving readmission in a psychiatric cohort. Converging digital health data from EMRs and systems biology investigations reveal a subset of patient populations that have significant comorbidities with cardiovascular diseases are more likely to be readmitted. Further, the genetic architecture of psychiatric illness also suggests overlap with cardiovascular diseases. In summary, assessment of medications, side effects, and drug-drug interactions in a clinical setting as well as genomic information using a data mining approach could help to find factors that could help to lower readmission rates in patients with mental illness.

Pathology of Peripheral Artery Disease in Patients With Critical Limb Ischemia

BACKGROUND

Critical limb ischemia (CLI) is the most serious complication of peripheral artery disease (PAD).

OBJECTIVES

The purpose of this study was to characterize pathology of PAD in below- and above-knee amputation specimens in patients presenting with CLI.

METHODS

Peripheral arteries from 95 patients (121 amputation specimens) were examined; 75 patients had presented with CLI, and the remaining 20 had amputations performed for other reasons. The pathological characteristics were separately recorded for femoral and popliteal arteries (FEM-POP), and infrapopliteal arteries (INFRA-POP).

RESULTS

A total of 299 arteries were examined. In the 239 arteries from CLI patients, atherosclerotic plaques were more frequent in FEM-POP (23 of 34, 67.6%) compared with INFRA-POP (79 of 205, 38.5%) arteries. Of these 239 arteries, 165 (69%) showed ≥70% stenosis, which was due to significant pathological intimal thickening, fibroatheroma, fibrocalcific lesions, or restenosis in 45 of 165 (27.3%), or was due to luminal thrombi with (39 of 165, 23.6%) or without (81 of 165, 49.1%) significant atherosclerotic lesions. Presence of chronic luminal thrombi was more frequently observed in arteries with insignificant atherosclerosis (OR: 16.7; p = 0.0002), more so in INFRA-POP compared with FEM-POP (OR: 2.14; p = 0.0041) arteries. Acute thrombotic occlusion was less frequently encountered in INFRA-POP than FEM-POP arteries (OR: 0.27; p = 0.0067). Medial calcification was present in 170 of 239 (71.1%) large arteries.

CONCLUSIONS

Thrombotic luminal occlusion associated with insignificant atherosclerosis is commonly observed in CLI and suggests the possibility of atherothromboembolic disease. The pathological characteristics of arteries in CLI suggest possible mechanisms of progression of PAD to CLI, especially in INFRA-POP arteries, and may support the preventive role of antithrombotic agents.

Incidence and aetiology of bacterial meningitis among children aged 1-59 months in South Asia: systematic review and meta-analysis

BACKGROUND

Bacterial meningitis is a significant cause of morbidity and mortality worldwide among children aged 1-59 months. We aimed to describe its burden in South Asia, focusing on vaccine-preventable aetiologies.

METHODS

We searched five databases for studies published from January 1, 1990, to April 25, 2017. We estimated incidence and aetiology-specific proportions using random-effects meta-analysis. In secondary analyses, we described vaccine impact and pneumococcal meningitis serotypes.

RESULTS

We included 48 articles cumulatively reporting 20,707 cases from 1987 to 2013. Mean annual incidence was 105 (95% confidence interval [CI], 53-173) cases per 100,000 children. On average, Haemophilus influenzae type b (Hib) accounted for 13% (95% CI, 8-19%) of cases, pneumococcus for 10% (95% CI, 6-15%), and meningococcus for 1% (95% CI, 0-2%). These meta-analyses had substantial between-study heterogeneity (I² > 78%, P < 0.0001). Among studies reporting only confirmed cases, these three bacteria caused a median of 78% cases (IQR, 50-87%). Hib meningitis incidence declined by 72-83% at sentinel hospitals in Pakistan and Bangladesh, respectively, within two years of implementing nationwide vaccination. On average, PCV10 covered 49% (95% CI, 39-58%), PCV13 covered 51% (95% CI, 40-61%), and PPSV23 covered 74% (95% CI, 67-80%) of pneumococcal meningitis serotypes. Lower PCV10 and PCV13 serotype coverage in Bangladesh was associated with higher prevalence of serotype 2, compared to India and Pakistan.

CONCLUSIONS

South Asia has relatively high incidence of bacterial meningitis among children aged 1-59 months, with vaccine-preventable bacteria causing a substantial proportion. These estimates are likely underestimates due to multiple epidemiological and microbiological factors. Further research on vaccine impact and distribution of pneumococcal serotypes will inform vaccine policymaking and implementation.

Association of Hemoglobin A1c Levels With Use of Sulfonylureas, Dipeptidyl Peptidase 4 Inhibitors, and Thiazolidinediones in Patients With Type 2 Diabetes Treated With Metformin: Analysis From the Observational Health Data Sciences and Informatics Initiative

IMPORTANCE

Consensus around an efficient second-line treatment option for type 2 diabetes (T2D) remains ambiguous. The availability of electronic medical records and insurance claims data, which capture routine medical practice, accessed via the Observational Health Data Sciences and Informatics network presents an opportunity to generate evidence for the effectiveness of second-line treatments.

OBJECTIVE

To identify which drug classes among sulfonylureas, dipeptidyl peptidase 4 (DPP-4) inhibitors, and thiazolidinediones are associated with reduced hemoglobin A1c (HbA1c) levels and lower risk of myocardial infarction, kidney disorders, and eye disorders in patients with T2D treated with metformin as a first-line therapy.

DESIGN, SETTING, AND PARTICIPANTS

Three retrospective, propensity-matched, new-user cohort studies with replication across 8 sites were performed from 1975 to 2017. Medical data of 246 558 805 patients from multiple countries from the Observational Health Data Sciences and Informatics (OHDSI) initiative were included and medical data sets were transformed into a unified common data model, with analysis done using open-source analytical tools. Participants included patients with T2D receiving metformin with at least 1 prior HbA1c laboratory test who were then prescribed either sulfonylureas, DPP-4 inhibitors, or thiazolidinediones. Data analysis was conducted from 2015 to 2018.

EXPOSURES

Treatment with sulfonylureas, DPP-4 inhibitors, or thiazolidinediones starting at least 90 days after the initial prescription of metformin.

MAIN OUTCOMES AND MEASURES

The primary outcome is the first observation of the reduction of HbA1c level to 7% of total hemoglobin or less after prescription of a second-line drug. Secondary outcomes are myocardial infarction, kidney disorder, and eye disorder after prescription of a second-line drug.

RESULTS

A total of 246 558 805 patients (126 977 785 women [51.5%]) were analyzed. Effectiveness of sulfonylureas, DPP-4 inhibitors, and thiazolidinediones prescribed after metformin to lower HbA1c level to 7% or less of total hemoglobin remained indistinguishable in patients with T2D. Patients treated with sulfonylureas compared with DPP-4 inhibitors had a small increased consensus hazard ratio of myocardial infarction (1.12; 95% CI, 1.02-1.24) and eye disorders (1.15; 95% CI, 1.11-1.19) in the meta-analysis. Hazard of observing kidney disorders after treatment with sulfonylureas, DPP-4 inhibitors, or thiazolidinediones was equally likely.

CONCLUSIONS AND RELEVANCE

The examined drug classes did not differ in lowering HbA1c and in hazards of kidney disorders in patients with T2D treated with metformin as a first-line therapy. Sulfonylureas had a small, higher observed hazard of myocardial infarction and eye disorders compared with DPP-4 inhibitors in the meta-analysis. The OHDSI collaborative network can be used to conduct a large international study examining the effectiveness of second-line treatment choices made in clinical management of T2D.

Systematic analyses of drugs and disease indications in RepurposeDB reveal pharmacological, biological and epidemiological factors influencing drug repositioning

Increase in global population and growing disease burden due to the emergence of infectious diseases (Zika virus), multidrug-resistant pathogens, drug-resistant cancers (cisplatin-resistant ovarian cancer) and chronic diseases (arterial hypertension) necessitate effective therapies to improve health outcomes. However, the rapid increase in drug development cost demands innovative and sustainable drug discovery approaches. Drug repositioning, the discovery of new or improved therapies by reevaluation of approved or investigational compounds, solves a significant gap in the public health setting and improves the productivity of drug development. As the number of drug repurposing investigations increases, a new opportunity has emerged to understand factors driving drug repositioning through systematic analyses of drugs, drug targets and associated disease indications. However, such analyses have so far been hampered by the lack of a centralized knowledgebase, benchmarking data sets and reporting standards. To address these knowledge and clinical needs, here, we present RepurposeDB, a collection of repurposed drugs, drug targets and diseases, which was assembled, indexed and annotated from public data. RepurposeDB combines information on 253 drugs [small molecules (74.30%) and protein drugs (25.29%)] and 1125 diseases. Using RepurposeDB data, we identified pharmacological (chemical descriptors, physicochemical features and absorption, distribution, metabolism, excretion and toxicity properties), biological (protein domains, functional process, molecular mechanisms and pathway cross talks) and epidemiological (shared genetic architectures, disease comorbidities and clinical phenotype similarities) factors mediating drug repositioning. Collectively, RepurposeDB is developed as the reference database for drug repositioning investigations. The pharmacological, biological and epidemiological principles of drug repositioning identified from the meta-analyses could augment therapeutic development.

A 72-Year-Old Patient with Longstanding, Untreated Familial Hypercholesterolemia but no Coronary Artery Calcification: A Case Report

Familial hypercholesterolemia (FH) is a genetic disease associated with persistently elevated levels of low-density lipoprotein cholesterol (LDL-C), which ultimately leads to greatly increased rates of atherosclerosis and cardiovascular disease. Atherosclerosis progression can be clinically approximated through measurement of coronary artery calcification (CAC). CAC can be measured via electron beam computed tomography (EBCT), multi-slice computed tomography (MSCT), or contrast-enhanced CT coronary angiography (CTCA). Here, we present the case of a 72-year-old man with known FH and established hypercholesterolemia who has consistently tested negative for any significant CAC.

Machine learning in cardiovascular medicine: are we there yet?

Artificial intelligence (AI) broadly refers to analytical algorithms that iteratively learn from data, allowing computers to find hidden insights without being explicitly programmed where to look. These include a family of operations encompassing several terms like machine learning, cognitive learning, deep learning and reinforcement learning-based methods that can be used to integrate and interpret complex biomedical and healthcare data in scenarios where traditional statistical methods may not be able to perform. In this review article, we discuss the basics of machine learning algorithms and what potential data sources exist; evaluate the need for machine learning; and examine the potential limitations and challenges of implementing machine in the context of cardiovascular medicine. The most promising avenues for AI in medicine are the development of automated risk prediction algorithms which can be used to guide clinical care; use of unsupervised learning techniques to more precisely phenotype complex disease; and the implementation of reinforcement learning algorithms to intelligently augment healthcare providers. The utility of a machine learning-based predictive model will depend on factors including data heterogeneity, data depth, data breadth, nature of modelling task, choice of machine learning and feature selection algorithms, and orthogonal evidence. A critical understanding of the strength and limitations of various methods and tasks amenable to machine learning is vital. By leveraging the growing corpus of big data in medicine, we detail pathways by which machine learning may facilitate optimal development of patient-specific models for improving diagnoses, intervention and outcome in cardiovascular medicine.

Automated disease cohort selection using word embeddings from Electronic Health Records

Accurate and robust cohort definition is critical to biomedical discovery using Electronic Health Records (EHR). Similar to prospective study designs, high quality EHR-based research requires rigorous selection criteria to designate case/control status particular to each disease. Electronic phenotyping algorithms, which are manually built and validated per disease, have been successful in filling this need. However, these approaches are time-consuming, leading to only a relatively small amount of algorithms for diseases developed. Methodologies that automatically learn features from EHRs have been used for cohort selection as well. To date, however, there has been no systematic analysis of how these methods perform against current gold standards. Accordingly, this paper compares the performance of a state-of-the-art automated feature learning method to extracting research-grade cohorts for five diseases against their established electronic phenotyping algorithms. In particular, we use word2vec to create unsupervised embeddings of the phenotype space within an EHR system. Using medical concepts as a query, we then rank patients by their proximity in the embedding space and automatically extract putative disease cohorts via a distance threshold. Experimental evaluation shows promising results with average F-score of 0.57 and AUC-ROC of 0.98. However, we noticed that results varied considerably between diseases, thus necessitating further investigation and/or phenotype-specific refinement of the approach before being readily deployed across all diseases.

Causal inference on electronic health records to assess blood pressure treatment targets: an application of the parametric g formula

Hypertension is a major risk factor for ischemic cardiovascular disease and cerebrovascular disease, which are respectively the primary and secondary most common causes of morbidity and mortality across the globe. To alleviate the risks of hypertension, there are a number of effective antihypertensive drugs available. However, the optimal treatment blood pressure goal for antihypertensive therapy remains an area of controversy. The results of the recent Systolic Blood Pressure Intervention Trial (SPRINT) trial, which found benefits for intensive lowering of systolic blood pressure, have been debated for several reasons. We aimed to assess the benefits of treating to four different blood pressure targets and to compare our results to those of SPRINT using a method for causal inference called the parametric g formula. We applied this method to blood pressure measurements obtained from the electronic health records of approximately 200,000 patients who visited the Mount Sinai Hospital in New York, NY. We simulated the effect of four clinically relevant dynamic treatment regimes, assessing the effectiveness of treating to four different blood pressure targets: 150 mmHg, 140 mmHg, 130 mmHg, and 120 mmHg. In contrast to current American Heart Association guidelines and in concordance with SPRINT, we find that targeting 120 mmHg systolic blood pressure is significantly associated with decreased incidence of major adverse cardiovascular events. Causal inference methods applied to electronic methods are a powerful and flexible technique and medicine may benefit from their increased usage.

Effects of ibudilast on oxycodone-induced analgesia and subjective effects in opioid-dependent volunteers

Opioid-induced glial activation is hypothesized to contribute to the development of tolerance to opioid-induced analgesia. This inpatient, double-blind, placebo-controlled, within-subject and between-groups pilot study investigated the dose-dependent effects of ibudilast, a glial cell modulator, on oxycodone-induced analgesia. Opioid-dependent volunteers were maintained on morphine (30mg, PO, QID) for two weeks and received placebo ibudilast (0mg, PO, BID) during the 1st week (days 1-7). On day 8, participants (N=10/group) were randomized to receive ibudilast (20 or 40mg, PO, BID) or placebo for the remainder of the study. On days 4 (week 1) and 11 (week 2), the analgesic, subjective, and physiological effects of oxycodone (0, 25, 50mg/70kg, PO) were determined. Analgesia was measured using the cold pressor test; participants immersed their hand in cold water (4°C) and pain threshold and pain tolerability were recorded. Oxycodone decreased pain threshold and tolerability in all groups during week 1. During week 2, the placebo group exhibited a blunted analgesic response to oxycodone for pain threshold and subjective pain ratings, whereas the 40mg BID ibudilast group exhibited greater analgesia as measured by subjective pain ratings (p≤0.05). Oxycodone also increased subjective drug effect ratings associated with abuse liability in all groups during week 1 (p≤0.05); ibudilast did not consistently affect these ratings. These findings suggest that ibudilast may enhance opioid-induced analgesia. Investigating higher ibudilast doses may establish the utility of pharmacological modulation of glial activity to maximize the clinical use of opioids.

Intracoronary Imaging, Cholesterol Efflux, and Transcriptomics after Intensive Statin Treatment in Diabetes

Residual atherothrombotic risk remains higher in patients with versus without diabetes mellitus (DM) despite statin therapy. The underlying mechanisms are unclear. This is a retrospective post-hoc analysis of the YELLOW II trial, comparing patients with and without DM (non-DM) who received rosuvastatin 40 mg for 8–12 weeks and underwent intracoronary multimodality imaging of an obstructive nonculprit lesion, before and after therapy. In addition, blood samples were drawn to assess cholesterol efflux capacity (CEC) and changes in gene expression in peripheral blood mononuclear cells (PBMC). There was a significant reduction in low density lipoprotein-cholesterol (LDL-C), an increase in CEC and beneficial changes in plaque morphology including increase in fibrous cap thickness and decrease in the prevalence of thin cap fibro-atheroma by optical coherence tomography in DM and non-DM patients. While differential gene expression analysis did not demonstrate differences in PBMC transcriptome between the two groups on the single-gene level, weighted gene coexpression network analysis revealed two modules of coexpressed genes associated with DM, Collagen Module and Platelet Module, related to collagen catabolism and platelet function respectively. Bayesian network analysis revealed key driver genes within these modules. These transcriptomic findings might provide potential mechanisms responsible for the higher cardiovascular risk in DM patients.

Abstract 3250: Computational drug repositioning and biochemical validation of piperlongumine as a potent therapeutic agent for neuroendocrine prostate cancer

Introduction and Objectives: Neuroendocrine prostate cancer (NEPC) is a highly lethal and drug-resistant variant of prostate cancer (PCa). We have used a genomics-based drug-repositioning approach and identified compounds with therapeutic activity against NEPC. One of the compounds- piperlongumine (PL) is a natural product constituent of the fruit of the Long pepper (Piper longum). The efficacy of this drug was tested in the NEPC cell line model NCI-H660 and compared to several other PCa cell lines in a modified WST-1 assay. Pre-clinical testing in mouse xenograft models of NEPC was also undertaken. Finally, the ability of piperlongumine to inhibit p-STAT3 signaling and promote apoptosis was measured by western blot analyses.

Methods: PCa cell lines (LNCaP, 22Rv1, Du145, PC3, H660 and RWPE) were grown in complete media (RPM1 10%FBS, Advanced DMEM 5%FBS or Keratinocyte-SFM) and treated with piperlongumine for 3 or 7 days. IC50 values were generated from WST assay data using Prism6. Nude mice (n=5) were injected with 1.5x106 H660 cells on each flank, and intraperitoneally administered with either 2.5mg/kg PL (n=3) or DMSO (n=2) daily for 3 weeks after tumors formed 200mm3 in volume. Tumor volume was measured daily with calipers. Western blot analyses were performed on protein lysates extracted from tumors and PCa cells treated with 0-10 μM of drug.

Results: PL was highly effective in inhibiting the growth of H660 cells (IC50 = 0.4 μM) compared to LNCaP, 22Rv1, Du145, PC3, and RWPE cells. This was consistent with an increased level of cPARP1 in H660 cells when compared to other prostate cancer cell lines. On average, the growth rate of untreated tumors was 2.7 times greater than those treated with PL. Similar to previous reports suggesting PL to inhibit STAT3 activity, treated H660 cells exhibited inhibition of STAT3 phosphorylation.

Conclusions: Using computational drug repositioning approaches we have identified several lead compounds for NEPC. One of the compound piperlongumine, a water-soluble plant product with no known side effects inhibits the growth of the highly drug-resistant NEPC cell line H660.

Source of Funding: Deane Prostate Health, Icahn School of Medicine at Mount Sinai. Both Shalini S Yadav and Kamlesh K Yadav are supported by the Prostate Cancer Foundation Young Investigator Awards. Research in Dudley's lab is supported by grants from National Institutes of Health R01 DK098242 and U54 CA189201.

Note: This abstract was not presented at the meeting.

Citation Format: Kamlesh Kumar Yadav, Khader Shameer, Ben Readhead, Jennifer A. Stockert, Cordelia Elaiho, Shalini S. Yadav, Benjamin S. Glicksberg, Kipp W. Johnson, Christine Becker, Andrew Kasarskis, Ashutosh K. Tewari, Joel T. Dudley. Computational drug repositioning and biochemical validation of piperlongumine as a potent therapeutic agent for neuroendocrine prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3250. doi:10.1158/1538-7445.AM2017-3250