A Prospective Comparison of Large Language Models for Early Prediction of Sepsis.

We present a comparative study on the performance of two popular open-source large language models for early prediction of sepsis: Llama-3 8B and Mixtral 8x7B. The primary goal was to determine whether a smaller model could achieve comparable predictive accuracy to a significantly larger model in the context of sepsis prediction using clinical data.Our proposed LLM-based sepsis prediction system, COMPOSER-LLM, enhances the previously published COMPOSER model, which utilizes structured EHR data to generate hourly sepsis risk scores.

Development, deployment, and continuous monitoring of a machine learning model to predict respiratory failure in critically ill patients.

Objectives: This study describes the development and deployment of a machine learning (ML) model called Vent.io to predict mechanical ventilation (MV).

Materials And Methods: We trained Vent.

Right ventricular strain as a predictor of trastuzumab-induced chemotherapy-related cardiac dysfunction: A meta-analysis.

Introduction: The survival rates of breast cancer patients have improved drastically in the past few decades due to advancements in anti-neoplastic drugs. Trastuzumab (TZ) chemotherapy is associated with left ventricular dysfunction leading to cardiotoxicity. Two-dimensional speckle-tracking echocardiography has demonstrated efficacy in predicting TZ-induced cardiotoxicity; however, its role in using right ventricular (RV) strain parameters remains unclear.

Development and validation of a deep learning algorithm for the prediction of serum creatinine in critically ill patients.

Objectives: Serum creatinine (SCr) is the primary biomarker for assessing kidney function; however, it may lag behind true kidney function, especially in instances of acute kidney injury (AKI). The objective of the work is to develop Nephrocast, a deep-learning model to predict next-day SCr in adult patients treated in the intensive care unit (ICU).

Materials And Methods: Nephrocast was trained and validated, temporally and prospectively, using electronic health record data of adult patients admitted to the ICU in the University of California San Diego Health (UCSDH) between January 1, 2016 and June 22, 2024.

The impact of laboratory data missingness on sepsis diagnosis timeliness.

Objective: To investigate the impact of missing laboratory measurements on sepsis diagnostic delays.

Materials And Methods: In adult patients admitted to 2 University of California San Diego (UCSD) hospitals from January 1, 2021 to June 30, 2024, we evaluated the relative time of organ failure ( ) and time of clinical suspicion of sepsis ( ) in patients with sepsis according to the Centers for Medicare & Medicaid Services (CMS) definition.

Results: Of the patients studied, 48.

Impact of wearable device data and multi-scale entropy analysis on improving hospital readmission prediction.

Objective: Unplanned readmissions following a hospitalization remain common despite significant efforts to curtail these. Wearable devices may offer help identify patients at high risk for an unplanned readmission.

Materials And Methods: We conducted a multi-center retrospective cohort study using data from the All of Us data repository.

Development and Validation of a Deep Learning Model for Prediction of Adult Physiological Deterioration.

Background: Prediction-based strategies for physiologic deterioration offer the potential for earlier clinical interventions that improve patient outcomes. Current strategies are limited because they operate on inconsistent definitions of deterioration, attempt to dichotomize a dynamic and progressive phenomenon, and offer poor performance.

Objective: Can a deep learning deterioration prediction model (Deep Learning Enhanced Triage and Emergency Response for Inpatient Optimization [DETERIO]) based on a consensus definition of deterioration (the Adult Inpatient Decompensation Event [AIDE] criteria) and that approaches deterioration as a state "value-estimation" problem outperform a commercially available deterioration score?

Derivation Cohort: The derivation cohort contained retrospective patient data collected from both inpatient services (inpatient) and emergency departments (EDs) of two hospitals within the University of California San Diego Health System.

Voice-Enabled Response Analysis Agent (VERAA): Leveraging Large Language Models to Map Voice Responses in SDoH Survey.

Social Determinants of Health (SDoH) have been shown to have profound impacts on health-related outcomes, yet this data suffers from high rates of missingness in electronic health records (EHR). Moreover, limited English proficiency in the United States can be a barrier to communication with health care providers. In this study, we have designed a multilingual conversational agent capable of conducting SDoH surveys for use in healthcare environments.

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.

Physician Group Practices Accrued Large Bonuses Under Medicare's Bundled Payment Model, 2018-20.

The Bundled Payments for Care Improvement Advanced Model (BPCI-A), a voluntary Alternative Payment Model for Medicare, incentivizes hospitals and physician group practices to reduce spending for patient care episodes below preset target prices. The experience of physician groups in BPCI-A is not well understood. We found that physician groups earned $421 million in incentive payments during BPCI-A's first four performance periods (2018-20).

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.

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.

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.

Voice-Enabled Response Analysis Agent (VERAA): Leveraging Large Language Models to Map Voice Responses in SDoH Survey.

Social Determinants of Health (SDoH) have been shown to have profound impacts on health-related outcomes, yet this data suffers from high rates of missingness in electronic health records (EHR). Moreover, limited English proficiency in the United States can be a barrier to communication with health care providers. In this study, we have designed a multilingual conversational agent capable of conducting SDoH surveys for use in healthcare environments.

Making the Improbable Possible: Generalizing Models Designed for a Syndrome-Based, Heterogeneous Patient Landscape.

Syndromic conditions, such as sepsis, are commonly encountered in the intensive care unit. Although these conditions are easy for clinicians to grasp, these conditions may limit the performance of machine-learning algorithms. Individual hospital practice patterns may limit external generalizability.

Changes in Cardiovascular Spending, Care Utilization, and Clinical Outcomes Associated With Participation in Bundled Payments for Care Improvement - Advanced.

Background: Bundled Payments for Care Improvement - Advanced (BPCI-A) is a Medicare initiative that aims to incentivize reductions in spending for episodes of care that start with a hospitalization and end 90 days after discharge. Cardiovascular disease, an important driver of Medicare spending, is one of the areas of focus BPCI-A. It is unknown whether BPCI-A is associated with spending reductions or quality improvements for the 3 cardiovascular medical events or 5 cardiovascular procedures in the model.

Representation Learning and Spectral Clustering for the Development and External Validation of Dynamic Sepsis Phenotypes: Observational Cohort Study.

Background: Recent attempts at clinical phenotyping for sepsis have shown promise in identifying groups of patients with distinct treatment responses. Nonetheless, the replicability and actionability of these phenotypes remain an issue because the patient trajectory is a function of both the patient's physiological state and the interventions they receive.

Objective: We aimed to develop a novel approach for deriving clinical phenotypes using unsupervised learning and transition modeling.

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.

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.