Opportunistic Screening of Chronic Liver Disease with Deep Learning Enhanced Echocardiography.

Importance: Chronic liver disease affects more than 1.5 billion adults worldwide, however the majority of cases are asymptomatic and undiagnosed. Echocardiography is broadly performed and visualizes the liver; but this information is not leveraged. Objective: To develop and evaluate a deep learning algorithm on echocardiography videos to enable opportunistic screening for chronic liver disease. Design: Retrospective observational cohorts. Setting: Two large urban academic medical centers. Participants: Adult patients who received echocardiography and abdominal imaging (either abdominal ultrasound or abdominal magnetic resonance imaging) with ≤30 days between tests, between July 4, 2012, to June 4, 2022. Exposure: Deep learning model predictions from a deep-learning computer vision pipeline that identifies subcostal view echocardiogram videos and detects the presence of cirrhosis or steatotic liver disease (SLD). Main Outcome and Measures: Clinical diagnosis by paired abdominal ultrasound or magnetic resonance imaging (MRI). Results: A total of 1,596,640 echocardiogram videos (66,922 studies from 24,276 patients) from Cedars-Sinai Medical Center (CSMC) were used to develop EchoNet-Liver, an automated pipeline that identifies high quality subcostal images from echocardiogram studies and detects the presence of cirrhosis or SLD. In the held-out CSMC test cohort, EchoNet-Liver was able to detect the presence of cirrhosis with an AUC of 0.837 (0.789 - 0.880) and SLD with an AUC of 0.799 (0.758 - 0.837). In a separate test cohort with paired abdominal MRIs, cirrhosis was detected with an AUC of 0.704 (0.689-0.718) and SLD was detected with an AUC of 0.726 (0.659-0.790). In an external test cohort of 106 patients (n = 5,280 videos), the model detected cirrhosis with an AUC of 0.830 (0.738 - 0.909) and SLD with an AUC of 0.768 (0.652 - 0.875). Conclusions and Relevance: Deep learning assessment of clinical echocardiography enables opportunistic screening of SLD and cirrhosis. Application of this algorithm may identify patients who may benefit from further diagnostic testing and treatment for chronic liver disease.

Prediction of Readmission Following Sepsis Using Social Determinants of Health.

OBJECTIVES: To determine the predictive value of social determinants of health (SDoH) variables on 30-day readmission following a sepsis hospitalization as compared with traditional clinical variables. DESIGN: Multicenter retrospective cohort study using patient-level data, including demographic, clinical, and survey data. SETTINGS: Thirty-five hospitals across the United States from 2017 to 2021. PATIENTS: Two hundred seventy-one thousand four hundred twenty-eight individuals in the AllofUs initiative, of which 8909 had an index sepsis hospitalization. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Unplanned 30-day readmission to the hospital. Multinomial logistic regression models were constructed to account for survival in determination of variables associate with 30-day readmission and are presented as adjusted odds rations (aORs). Of the 8909 sepsis patients in our cohort, 21% had an unplanned hospital readmission within 30 days. Median age (interquartile range) was 54 years (41-65 yr), 4762 (53.4%) were female, and there were self-reported 1612 (18.09%) Black, 2271 (25.49%) Hispanic, and 4642 (52.1%) White individuals. In multinomial logistic regression models accounting for survival, we identified that change to nonphysician provider type due to economic reasons (aOR, 2.55 [2.35-2.74]), delay of receiving medical care due to lack of transportation (aOR, 1.68 [1.62-1.74]), and inability to afford flow-up care (aOR, 1.59 [1.52-1.66]) were strongly and independently associated with a 30-day readmission when adjusting for survival. Patients who lived in a ZIP code with a high percentage of patients in poverty and without health insurance were also more likely to be readmitted within 30 days (aOR, 1.26 [1.22-1.29] and aOR, 1.28 [1.26-1.29], respectively). Finally, we found that having a primary care provider and health insurance were associated with low odds of an unplanned 30-day readmission. CONCLUSIONS: In this multicenter retrospective cohort, several SDoH variables were strongly associated with unplanned 30-day readmission. Models predicting readmission following sepsis hospitalization may benefit from the addition of SDoH factors to traditional clinical variables.

Unsupervised Detection and Correction of Model Calibration Shift at Test-Time.

The wide adoption of predictive models into clinical practice require generalizability across hospitals and maintenance of consistent performance across time. Model calibration shift, caused by factors such as changes in prevalence rates or data distribution shift, can affect the generalizability of such models. In this work, we propose a model calibration detection and correction (CaDC) method, specifically designed to utilize only unlabeled data at a target hospital. The proposed method is very flexible and can be used alongside any deep learning-based clinical predictive model. As a case study, we focus on the problem of detecting and correcting model calibration shift in the context of early prediction of sepsis. Three patient cohorts consisting of 545,089 adult patients admitted to the emergency departments at three geographically diverse healthcare systems in the United States were used to train and externally validate the proposed method. We successfully show that utilizing the CaDC model can help assist the sepsis prediction model in achieving a predefined positive predictive value (PPV). For instance, when trained to achieve a PPV of 20%, the performance of the sepsis prediction model with and without the calibration shift estimation model was 18.0% vs 12.9% and 23.1% vs 13.4% at the two external validation cohorts, respectively. As such, the proposed CaDC method has potential applications in maintaining performance claims of predictive models deployed across hospital systems.Clinical relevance- Model generalizability is a requirement of wider adoption of clinical predictive models.

Development & Deployment of a Real-time Healthcare Predictive Analytics Platform.

The deployment of predictive analytic algorithms that can safely and seamlessly integrate into existing healthcare workflows remains a significant challenge. Here, we present a scalable, cloud-based, fault-tolerant platform that is capable of extracting and processing electronic health record (EHR) data for any patient at any time following admission and transferring results back into the EHR. This platform has been successfully deployed within the UC San Diego Health system and utilizes interoperable data standards to enable portability.Clinical relevance- This platform is currently hosting a deep learning model for the early prediction of sepsis that is operational in two emergency departments.

Predicting Hospital Readmission among Patients with Sepsis Using Clinical and Wearable Data.

Sepsis is a life-threatening condition that occurs due to a dysregulated host response to infection. Recent data demonstrate that patients with sepsis have a significantly higher readmission risk than other common conditions, such as heart failure, pneumonia and myocardial infarction and associated economic burden. Prior studies have demonstrated an association between a patient's physical activity levels and readmission risk. In this study, we show that distribution of activity level prior and post-discharge among patients with sepsis are predictive of unplanned rehospitalization in 90 days (P-value<1e-3). Our preliminary results indicate that integrating Fitbit data with clinical measurements may improve model performance on predicting 90 days readmission.Clinical relevance Sepsis, Activity level, Hospital readmission, Wearable data.

Development & Deployment of a Real-time Healthcare Predictive Analytics Platform.

The deployment of predictive analytic algorithms that can safely and seamlessly integrate into existing healthcare workflows remains a significant challenge. Here, we present a scalable, cloud-based, fault-tolerant platform that is capable of extracting and processing electronic health record (EHR) data for any patient at any time following admission and transferring results back into the EHR. This platform has been successfully deployed within the UC San Diego Health system and utilizes interoperable data standards to enable portability.

Predicting Hospital Readmission among Patients with Sepsis using Clinical and Wearable Data.

Sepsis is a life-threatening condition that occurs due to a dysregulated host response to infection. Recent data demonstrate that patients with sepsis have a significantly higher readmission risk than other common conditions, such as heart failure, pneumonia and myocardial infarction and associated economic burden. Prior studies have demonstrated an association between a patient's physical activity levels and readmission risk. In this study, we show that distribution of activity level prior and post-discharge among patients with sepsis are predictive of unplanned rehospitalization in 90 days (P-value<1e-3). Our preliminary results indicate that integrating Fitbit data with clinical measurements may improve model performance on predicting 90 days readmission.

Prediction of Unplanned Hospital Readmission using Clinical and Longitudinal Wearable Sensor Features.

Predictive models have been suggested as potential tools for identifying highest risk patients for hospital readmissions, in order to improve care coordination and ultimately long-term patient outcomes. However, the accuracy of current predictive models for readmission prediction is still moderate and further data enrichment is needed to identify at risk patients. This paper describes models to predict 90-day readmission, focusing on testing the predictive performance of wearable sensor features generated using multiscale entropy techniques and clinical features. Our study explores ways to incorporate pre-discharge and post-discharge wearable sensor features to make robust patient predictions. Data were used from participants enrolled in the AllofUs Research program. We extracted the inpatient cohort of patients and integrated clinical data from the electronic health records (EHR) and Fitbit sensor measurements. Entropy features were calculated from the longitudinal wearable sensor data, such as heart rate and mobility-related measurements, in order to characterize time series variability and complexity. Our best performing model acheived an AUC of 83%, and at 80% sensitivity acheived 75% specificity and 57% positive predictive value. Our results indicate that it would be possible to improve the ability to predict unplanned hospital readmissions by considering pre-discharge and post-discharge wearable features.

Inclusion of social determinants of health improves sepsis readmission prediction models.

OBJECTIVE: Sepsis has a high rate of 30-day unplanned readmissions. Predictive modeling has been suggested as a tool to identify high-risk patients. However, existing sepsis readmission models have low predictive value and most predictive factors in such models are not actionable. MATERIALS AND METHODS: Data from patients enrolled in the AllofUs Research Program cohort from 35 hospitals were used to develop a multicenter validated sepsis-related unplanned readmission model that incorporates clinical and social determinants of health (SDH) to predict 30-day unplanned readmissions. Sepsis cases were identified using concepts represented in the Observational Medical Outcomes Partnership. The dataset included over 60 clinical/laboratory features and over 100 SDH features. RESULTS: Incorporation of SDH factors into our model of clinical and demographic features improves model area under the receiver operating characteristic curve (AUC) significantly (from 0.75 to 0.80; P < .001). Model-agnostic interpretability techniques revealed demographics, economic stability, and delay in getting medical care as important SDH predictive features of unplanned hospital readmissions. DISCUSSION: This work represents one of the largest studies of sepsis readmissions using objective clinical data to date (8935 septic index encounters). SDH are important to determine which sepsis patients are more likely to have an unplanned 30-day readmission. The AllofUS dataset provides granular data from a diverse set of individuals, making this model potentially more generalizable than prior models. CONCLUSION: Use of SDH improves predictive performance of a model to identify which sepsis patients are at high risk of an unplanned 30-day readmission.

Leveraging clinical data across healthcare institutions for continual learning of predictive risk models.

The inherent flexibility of machine learning-based clinical predictive models to learn from episodes of patient care at a new institution (site-specific training) comes at the cost of performance degradation when applied to external patient cohorts. To exploit the full potential of cross-institutional clinical big data, machine learning systems must gain the ability to transfer their knowledge across institutional boundaries and learn from new episodes of patient care without forgetting previously learned patterns. In this work, we developed a privacy-preserving learning algorithm named WUPERR (Weight Uncertainty Propagation and Episodic Representation Replay) and validated the algorithm in the context of early prediction of sepsis using data from over 104,000 patients across four distinct healthcare systems. We tested the hypothesis, that the proposed continual learning algorithm can maintain higher predictive performance than competing methods on previous cohorts once it has been trained on a new patient cohort. In the sepsis prediction task, after incremental training of a deep learning model across four hospital systems (namely hospitals H-A, H-B, H-C, and H-D), WUPERR maintained the highest positive predictive value across the first three hospitals compared to a baseline transfer learning approach (H-A: 39.27% vs. 31.27%, H-B: 25.34% vs. 22.34%, H-C: 30.33% vs. 28.33%). The proposed approach has the potential to construct more generalizable models that can learn from cross-institutional clinical big data in a privacy-preserving manner.

AIDEx - An Open-source Platform for Real-Time Forecasting Sepsis and A Case Study on Taking ML Algorithms to Production.

Sepsis, a dysregulated immune response to infection, has been the leading cause of morbidity and mortality in critically ill patients. Multiple studies have demonstrated improved survival outcomes when early treatment is initiated for septic patients. In our previous work, we developed a real-time machine learning algorithm capable of predicting onset of sepsis four to six hours prior to clinical recognition. In this work, we develop AIDEx, an open-source platform that consumes data as FHIR resources. It is capable of consuming live patient data, securely transporting it into a cloud environment, and monitoring patients in real-time. We build AIDEx as an EHR vendor-agnostic open-source platform that can be easily deployed in clinical environments. Finally, the computation of the sepsis risk scores uses a common design pattern that is seen in streaming clinical informatics and predictive analytics applications. AIDEx provides a comprehensive case study in the design and development of a production-ready ML platform that integrates with Healthcare IT systems.

Contextual Embeddings from Clinical Notes Improves Prediction of Sepsis.

Sepsis, a life-threatening organ dysfunction, is a clinical syndrome triggered by acute infection and affects over 1 million Americans every year. Untreated sepsis can progress to septic shock and organ failure, making sepsis one of the leading causes of morbidity and mortality in hospitals. Early detection of sepsis and timely antibiotics administration is known to save lives. In this work, we design a sepsis prediction algorithm based on data from electronic health records (EHR) using a deep learning approach. While most existing EHR-based sepsis prediction models utilize structured data including vitals, labs, and clinical information, we show that incorporation of features based on clinical texts, using a pre-trained neural language representation model, allows for incorporation of unstructured data without an explicit need for ontology-based named-entity recognition and classification. The proposed model is trained on a large critical care database of over 40,000 patients, including 2805 septic patients, and is compared against competing baseline models. In comparison to a baseline model based on structured data alone, incorporation of clinical texts improved AUC from 0.81 to 0.84. Our findings indicate that incorporation of clinical text features via a pre-trained language representation model can improve early prediction of sepsis and reduce false alarms.

Establishment of cardiac rehabilitation program in Yazd-Iran: An experience of a developing country.

BACKGROUND: Cardiovascular diseases are the most common causes of mortality in the world including Iran and are one of the main causes of disability. Cardiac Rehabilitation (CR) is a multidisciplinary program that helps CVD patients recover faster after a heart attack and avoid any subsequent incident. This report determined the current state of CR in Yazd, Iran. CHARACTERISTICS OF THE PROGRAM: Hospital-based Afshar CR program in Yazd, Iran, is the only CR facility in Yazd province, which is located in the centre of Iran. Currently, the Afshar CR program has four phases including inpatient, sub-acute, outpatient and maintenance. The CR team includes cardiologists and heart surgeons as physicians, and physical medicine rehabilitation specialist, outpatient and inpatient resident medical officers, psychiatrists, nutritionists, psychologists, physiotherapists and social workers. DISCUSSION: Given the facilities and training programs mentioned above, the rate of patient referral to the center by the inpatient CR team during the short life of CR in this center was 60%, the patient participation rate was 6.9% and the enrollment rate was 55%. In addition, over the past three years, 57% of registered patients completed the program. CONCLUSION: The Afshar CR is trying to get closer to the world standard setting. But it seems that it is necessary to develop the standard of CR in Iran based on the culture and socio-economic status of Iranian community.

Multi-faceted computational assessment of risk and progression in oligodendroglioma implicates NOTCH and PI3K pathways.

Oligodendrogliomas are diffusely infiltrative gliomas defined by IDH-mutation and co-deletion of 1p/19q. They have highly variable clinical courses, with survivals ranging from 6 months to over 20 years, but little is known regarding the pathways involved with their progression or optimal markers for stratifying risk. We utilized machine-learning approaches with genomic data from The Cancer Genome Atlas to objectively identify molecular factors associated with clinical outcomes of oligodendroglioma and extended these findings to study signaling pathways implicated in oncogenesis and clinical endpoints associated with glioma progression. Our multi-faceted computational approach uncovered key genetic alterations associated with disease progression and shorter survival in oligodendroglioma and specifically identified Notch pathway inactivation and PI3K pathway activation as the most strongly associated with MRI and pathology findings of advanced disease and poor clinical outcome. Our findings that Notch pathway inactivation and PI3K pathway activation are associated with advanced disease and survival risk will pave the way for clinically relevant markers of disease progression and therapeutic targets to improve clinical outcomes. Furthermore, our approach demonstrates the strength of machine learning and computational methods for identifying genetic events critical to disease progression in the era of big data and precision medicine.

Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models.

Translating the vast data generated by genomic platforms into accurate predictions of clinical outcomes is a fundamental challenge in genomic medicine. Many prediction methods face limitations in learning from the high-dimensional profiles generated by these platforms, and rely on experts to hand-select a small number of features for training prediction models. In this paper, we demonstrate how deep learning and Bayesian optimization methods that have been remarkably successful in general high-dimensional prediction tasks can be adapted to the problem of predicting cancer outcomes. We perform an extensive comparison of Bayesian optimized deep survival models and other state of the art machine learning methods for survival analysis, and describe a framework for interpreting deep survival models using a risk backpropagation technique. Finally, we illustrate that deep survival models can successfully transfer information across diseases to improve prognostic accuracy. We provide an open-source software implementation of this framework called SurvivalNet that enables automatic training, evaluation and interpretation of deep survival models.

Prevalence of chronic viral hepatitis infections in Karaj, Iran.

INTRODUCTION: Viral hepatitis is challenging for health and blood safety. Studies carried out on blood donors can help find the frequency and trending of hepatitis B and C infections in a community and also safety of donation. The study aim is to determine the prevalence of HBV and HCV in Karaj blood donors over a four year period between 2010 to 2013. METHODS: This study reports the results of a cross sectional seroepidemiological study of hepatitis B and C in blood donors. Data on hepatitis infection and demographic characteristics of donors were gathered from blood donor registries. Frequency of hepatitis infections were described with 95% confidence interval. Chi square and logistic regression were used for analysis. RESULTS: The frequency of HBV and HCV infection in Karaj blood donors was 0.40% and 0.18% respectively. In first time donors, HBV and HCV positivity risk was respectively 3.59 and 4.8 fold in people with primary education (OR=3.59; 95% CI between 2.68-4.80) comparing to academic level. Frequency of hepatitis B has decreased significantly (P<0.001) during study period but frequency of Hepatitis C has not changed significantly. CONCLUSION: The frequencies of HBV and HCV infection in Karaj blood donor population is low. There are equal infection rates within both genders. This must be considered in controlling transmission of infection in this area.