Mortality of Patients With Sepsis Administered Piperacillin-Tazobactam vs Cefepime.

Importance: Experimental and observational studies have suggested that empirical treatment for bacterial sepsis with antianaerobic antibiotics (eg, piperacillin-tazobactam) is associated with adverse outcomes compared with anaerobe-sparing antibiotics (eg, cefepime). However, a recent pragmatic clinical trial of piperacillin-tazobactam and cefepime showed no difference in short-term outcomes at 14 days. Further studies are needed to help clarify the empirical use of these agents.

Two Weeks Versus One Week of Maximal Patient-Intensivist Continuity for Adult Medical Intensive Care Patients: A Two-Center Target Trial Emulation.

Objectives: To compare outcomes for 2 weeks vs. 1 week of maximal patient-intensivist continuity in the ICU.

Design: Retrospective cohort study.

The Lung Microbiome Predicts Mortality and Response to Azithromycin in Lung Transplant Recipients with Chronic Rejection.

Chronic lung allograft dysfunction (CLAD) is the leading cause of death after lung transplant, and azithromycin has variable efficacy in CLAD. The lung microbiome is a risk factor for developing CLAD, but the relationship between lung dysbiosis, pulmonary inflammation, and allograft dysfunction remains poorly understood. Whether lung microbiota predict outcomes or modify treatment response CLAD is unknown.

Using Multi-Modal Electronic Health Record Data for the Development and Validation of Risk Prediction Models for Long COVID Using the Super Learner Algorithm.

Background: Post-Acute Sequelae of COVID-19 (PASC) have emerged as a global public health and healthcare challenge. This study aimed to uncover predictive factors for PASC from multi-modal data to develop a predictive model for PASC diagnoses.

Methods: We analyzed electronic health records from 92,301 COVID-19 patients, covering medical phenotypes, medications, and lab results.

Characteristics of Facilities With Early and Rapid Ustekinumab Adoption for Patients With Inflammatory Bowel Disease.

Introduction: To examine which facility characteristics, including teamwork, are associated with early or rapid inflammatory bowel disease-related ustekinumab adoption.

Methods: We examined the association between ustekinumab adoption and the characteristics of 130 Veterans Affairs facilities.

Results: Mean ustekinumab adoption increased by 3.

External Validation and Comparison of a General Ward Deterioration Index Between Diversely Different Health Systems.

Objectives: Implementing a predictive analytic model in a new clinical environment is fraught with challenges. Dataset shifts such as differences in clinical practice, new data acquisition devices, or changes in the electronic health record (EHR) implementation mean that the input data seen by a model can differ significantly from the data it was trained on. Validating models at multiple institutions is therefore critical.

Characterizing and Predicting Post-Acute Sequelae of SARS CoV-2 Infection (PASC) in a Large Academic Medical Center in the US.

Background: A growing number of Coronavirus Disease-2019 (COVID-19) survivors are affected by post-acute sequelae of SARS CoV-2 infection (PACS). Using electronic health record data, we aimed to characterize PASC-associated diagnoses and develop risk prediction models.

Methods: In our cohort of 63,675 patients with a history of COVID-19, 1724 (2.

Assessment of Symptom, Disability, and Financial Trajectories in Patients Hospitalized for COVID-19 at 6 Months.

Importance: Individuals who survived COVID-19 often report persistent symptoms, disabilities, and financial consequences. However, national longitudinal estimates of symptom burden remain limited.

Objective: To measure the incidence and changes over time in symptoms, disability, and financial status after COVID-19-related hospitalization.

Development and Retention of Early-Career Clinician-Scientists through a Novel Peer Mentorship Program: Multidisciplinary Intensive Care Research Workgroup.

Background: Early-career clinician-scientists often leave academic medicine, but strong mentorship can help facilitate retention. Beyond the traditional dyadic mentor-mentee relationship, formal peer mentoring provides a rich means to augment career development and foster independence.

Objective: To describe a model for early-career peer mentorship and the retention of participating early-career clinician-scientists in academic medicine.

Understanding clinician connections to inform efforts to promote high-quality inflammatory bowel disease care.

Background: Highly connected individuals disseminate information effectively within their social network. To apply this concept to inflammatory bowel disease (IBD) care and lay the foundation for network interventions to disseminate high-quality treatment, we assessed the need for improving the IBD practices of highly connected clinicians. We aimed to examine whether highly connected clinicians who treat IBD patients were more likely to provide high-quality treatment than less connected clinicians.

Characterizing and Predicting Post-Acute Sequelae of SARS CoV-2 infection (PASC) in a Large Academic Medical Center in the US.

Objective: A growing number of Coronavirus Disease-2019 (COVID-19) survivors are affected by Post-Acute Sequelae of SARS CoV-2 infection (PACS). Using electronic health records data, we aimed to characterize PASC-associated diagnoses and to develop risk prediction models.

Methods: In our cohort of 63,675 COVID-19 positive patients, 1,724 (2.

Consensus elements for observational research on COVID-19-related long-term outcomes.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and its long-term outcomes may be jointly caused by a wide range of clinical, social, and economic characteristics. Studies aiming to identify mechanisms for SARS-CoV-2 morbidity and mortality must measure and account for these characteristics to arrive at unbiased, accurate conclusions. We sought to inform the design, measurement, and analysis of longitudinal studies of long-term outcomes among people infected with SARS-CoV-2.

A Quantitative Study of Decision Thresholds for Initiation of Antibiotics in Suspected Sepsis.

Background: Clinicians' decision thresholds for initiating antibiotics in patients with suspected sepsis have not been quantified. We aimed to define an average threshold of infection likelihood at which clinicians initiate antibiotics when treating a patient with suspected infection and to evaluate the influence of severity of illness and clinician-related factors on the threshold.

Design: This was a prospective survey of 153 clinicians responding to 8 clinical vignettes constructed from real-world data from 3 health care systems in the United States.

Association of Surge Conditions with Mortality Among Critically Ill Patients with COVID-19.

Objective: To determine whether surge conditions were associated with increased mortality.

Design: Multicenter cohort study.

Setting: U.

Characteristics and Outcomes of US Patients Hospitalized With COVID-19.

Background: Understanding COVID-19 epidemiology is crucial to clinical care and to clinical trial design and interpretation.

Objective: To describe characteristics, treatment, and outcomes among patients hospitalized with COVID-19 early in the pandemic.

Methods: A retrospective cohort study of consecutive adult patients with laboratory-confirmed, symptomatic SARS-CoV-2 infection admitted to 57 US hospitals from March 1 to April 1, 2020.

Continuing Cardiopulmonary Symptoms, Disability, and Financial Toxicity 1 Month After Hospitalization for Third-Wave COVID-19: Early Results From a US Nationwide Cohort.

Background: Patients discharged after COVID-19 report ongoing needs.

Objectives: To measure incident symptoms after COVID-19 hospitalization.

Design, Setting, And Participants: Preplanned early look at 1-month follow-up surveys from patients hospitalized August 2020 to January 2021 in NHLBI PETAL Network's Biology and Longitudinal Epidemiology: COVID-19 Observational (BLUE CORAL) study.

Predicting Intensive Care Transfers and Other Unforeseen Events: Analytic Model Validation Study and Comparison to Existing Methods.

Background: COVID-19 has led to an unprecedented strain on health care facilities across the United States. Accurately identifying patients at an increased risk of deterioration may help hospitals manage their resources while improving the quality of patient care. Here, we present the results of an analytical model, Predicting Intensive Care Transfers and Other Unforeseen Events (PICTURE), to identify patients at high risk for imminent intensive care unit transfer, respiratory failure, or death, with the intention to improve the prediction of deterioration due to COVID-19.