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

Rationale: 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 after CLAD is unknown. Objectives: To determine whether lung microbiota predict post-CLAD outcomes and clinical response to azithromycin. Methods: Retrospective cohort study using acellular BAL fluid prospectively collected from recipients of lung transplant within 90 days of CLAD onset. Lung microbiota were characterized using 16S rRNA gene sequencing and droplet digital PCR. In two additional cohorts, causal relationships of dysbiosis and inflammation were evaluated by comparing lung microbiota with CLAD-associated cytokines and measuring ex vivo P. aeruginosa growth in sterilized BAL fluid. Measurements and Main Results: Patients with higher bacterial burden had shorter post-CLAD survival, independent of CLAD phenotype, azithromycin treatment, and relevant covariates. Azithromycin treatment improved survival in patients with high bacterial burden but had negligible impact on patients with low or moderate burden. Lung bacterial burden was positively associated with CLAD-associated cytokines, and ex vivo growth of P. aeruginosa was augmented in BAL fluid from transplant recipients with CLAD. Conclusions: In recipients of lung transplants with chronic rejection, increased lung bacterial burden is an independent risk factor for mortality and predicts clinical response to azithromycin. Lung bacterial dysbiosis is associated with alveolar inflammation and may be promoted by underlying lung allograft dysfunction.

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. SETTING: Two U.S. urban, teaching, medical ICUs where intensivists were scheduled for 2-week service blocks: site A was in the Midwest and site B was in the Northeast. PATIENTS: Patients 18 years old or older admitted to a study ICU between March 1, 2017, and February 28, 2020. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We applied target trial emulation to compare admission during an intensivist's first week (as a proxy for 2 wk of maximal continuity) vs. admission during their second week (as a proxy for 1 wk of maximal continuity). Outcomes included hospital mortality, ICU length of stay, and, for mechanically ventilated patients, duration of ventilation. Exploratory outcomes included imaging, echocardiogram, and consultation orders. We used inverse probability weighting to adjust for baseline differences and random-effects meta-analysis to calculate overall effect estimates. Among 2571 patients, 1254 were admitted during an intensivist's first week and 1317 were admitted during a second week. At sites A and B, hospital mortality rates were 25.8% and 24.2%, median ICU length of stay were 4 and 2 days, and median mechanical ventilation durations were 3 and 3 days, respectively. There were no differences in adjusted mortality (odds ratio [OR], 1.01 [95% CI, 0.96-1.06]) or ICU length of stay (-0.25 d [-0.82 d to +0.32 d]) for 2 weeks vs. 1 week of maximal continuity. Among mechanically ventilated patients, there were no differences in adjusted mortality (OR, 1.00 [0.87-1.16]), ICU length of stay (+0.06 d [-0.78 d to +0.91 d]), or duration of mechanical ventilation (+0.37 d [-0.46 d to +1.21 d]) for 2 weeks vs. 1 week of maximal continuity. CONCLUSIONS: Two weeks of maximal patient-intensivist continuity was not associated with differences in clinical outcomes compared with 1 week in two medical ICUs.

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 3.9% from 2016 to 2018 and was higher in urban compared with rural facilities (ß = 0.03, P = 0.033) and among facilities with more teamwork (ß = 0.11, P = 0.041). Compared with nonearly adopters, early adopters were more likely be high-volume facilities (46% vs 19%, P = 0.001). DISCUSSION: Facility variation in medication adoption provides an opportunity for improving inflammatory bowel disease care through targeted dissemination strategies to improve medication uptake.

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. Here, using retrospective data, we demonstrate how Predicting Intensive Care Transfers and other UnfoReseen Events (PICTURE), a deterioration index developed at a single academic medical center, generalizes to a second institution with significantly different patient population. DESIGN: PICTURE is a deterioration index designed for the general ward, which uses structured EHR data such as laboratory values and vital signs. SETTING: The general wards of two large hospitals, one an academic medical center and the other a community hospital. SUBJECTS: The model has previously been trained and validated on a cohort of 165,018 general ward encounters from a large academic medical center. Here, we apply this model to 11,083 encounters from a separate community hospital. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The hospitals were found to have significant differences in missingness rates (> 5% difference in 9/52 features), deterioration rate (4.5% vs 2.5%), and racial makeup (20% non-White vs 49% non-White). Despite these differences, PICTURE's performance was consistent (area under the receiver operating characteristic curve [AUROC], 0.870; 95% CI, 0.861-0.878), area under the precision-recall curve (AUPRC, 0.298; 95% CI, 0.275-0.320) at the first hospital; AUROC 0.875 (0.851-0.902), AUPRC 0.339 (0.281-0.398) at the second. AUPRC was standardized to a 2.5% event rate. PICTURE also outperformed both the Epic Deterioration Index and the National Early Warning Score at both institutions. CONCLUSIONS: Important differences were observed between the two institutions, including data availability and demographic makeup. PICTURE was able to identify general ward patients at risk of deterioration at both hospitals with consistent performance (AUROC and AUPRC) and compared favorably to existing metrics.

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.S. ICUs participating in STOP-COVID. PATIENTS: Consecutive adults with COVID-19 admitted to participating ICUs between March 4 and July 1, 2020. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The main outcome was 28-day in-hospital mortality. To assess the association between admission to an ICU during a surge period and mortality, we used two different strategies: (1) an inverse probability weighted difference-in-differences model limited to appropriately matched surge and non-surge patients and (2) a meta-regression of 50 multivariable difference-in-differences models (each based on sets of randomly matched surge- and non-surge hospitals). In the first analysis, we considered a single surge period for the cohort (March 23 - May 6). In the second, each surge hospital had its own surge period (which was compared to the same time periods in matched non-surge hospitals).Our cohort consisted of 4342 ICU patients (average age 60.8 [sd 14.8], 63.5% men) in 53 U.S. hospitals. Of these, 13 hospitals encountered surge conditions. In analysis 1, the increase in mortality seen during surge was not statistically significant (odds ratio [95% CI]: 1.30 [0.47-3.58], p = .6). In analysis 2, surge was associated with an increased odds of death (odds ratio 1.39 [95% CI, 1.34-1.43], p < .001). CONCLUSIONS: Admission to an ICU with COVID-19 in a hospital that is experiencing surge conditions may be associated with an increased odds of death. Given the high incidence of COVID-19, such increases would translate into substantial excess mortality.

Thrombosis, Bleeding, and the Observational Effect of Early Therapeutic Anticoagulation on Survival in Critically Ill Patients With COVID-19.

BACKGROUND: Hypercoagulability may be a key mechanism of death in patients with coronavirus disease 2019 (COVID-19). OBJECTIVE: To evaluate the incidence of venous thromboembolism (VTE) and major bleeding in critically ill patients with COVID-19 and examine the observational effect of early therapeutic anticoagulation on survival. DESIGN: In a multicenter cohort study of 3239 critically ill adults with COVID-19, the incidence of VTE and major bleeding within 14 days after intensive care unit (ICU) admission was evaluated. A target trial emulation in which patients were categorized according to receipt or no receipt of therapeutic anticoagulation in the first 2 days of ICU admission was done to examine the observational effect of early therapeutic anticoagulation on survival. A Cox model with inverse probability weighting to adjust for confounding was used. SETTING: 67 hospitals in the United States. PARTICIPANTS: Adults with COVID-19 admitted to a participating ICU. MEASUREMENTS: Time to death, censored at hospital discharge, or date of last follow-up. RESULTS: Among the 3239 patients included, the median age was 61 years (interquartile range, 53 to 71 years), and 2088 (64.5%) were men. A total of 204 patients (6.3%) developed VTE, and 90 patients (2.8%) developed a major bleeding event. Independent predictors of VTE were male sex and higher D-dimer level on ICU admission. Among the 2809 patients included in the target trial emulation, 384 (11.9%) received early therapeutic anticoagulation. In the primary analysis, during a median follow-up of 27 days, patients who received early therapeutic anticoagulation had a similar risk for death as those who did not (hazard ratio, 1.12 [95% CI, 0.92 to 1.35]). LIMITATION: Observational design. CONCLUSION: Among critically ill adults with COVID-19, early therapeutic anticoagulation did not affect survival in the target trial emulation. PRIMARY FUNDING SOURCE: None.

AKI Treated with Renal Replacement Therapy in Critically Ill Patients with COVID-19.

BACKGROUND: AKI is a common sequela of coronavirus disease 2019 (COVID-19). However, few studies have focused on AKI treated with RRT (AKI-RRT). METHODS: We conducted a multicenter cohort study of 3099 critically ill adults with COVID-19 admitted to intensive care units (ICUs) at 67 hospitals across the United States. We used multivariable logistic regression to identify patient-and hospital-level risk factors for AKI-RRT and to examine risk factors for 28-day mortality among such patients. RESULTS: A total of 637 of 3099 patients (20.6%) developed AKI-RRT within 14 days of ICU admission, 350 of whom (54.9%) died within 28 days of ICU admission. Patient-level risk factors for AKI-RRT included CKD, men, non-White race, hypertension, diabetes mellitus, higher body mass index, higher d-dimer, and greater severity of hypoxemia on ICU admission. Predictors of 28-day mortality in patients with AKI-RRT were older age, severe oliguria, and admission to a hospital with fewer ICU beds or one with greater regional density of COVID-19. At the end of a median follow-up of 17 days (range, 1-123 days), 403 of the 637 patients (63.3%) with AKI-RRT had died, 216 (33.9%) were discharged, and 18 (2.8%) remained hospitalized. Of the 216 patients discharged, 73 (33.8%) remained RRT dependent at discharge, and 39 (18.1%) remained RRT dependent 60 days after ICU admission. CONCLUSIONS: AKI-RRT is common among critically ill patients with COVID-19 and is associated with a hospital mortality rate of >60%. Among those who survive to discharge, one in three still depends on RRT at discharge, and one in six remains RRT dependent 60 days after ICU admission.

Twelve tips for developing and implementing a medical education Twitter chat.

Live discussions on the social media site Twitter or Twitter chats are gaining popularity as powerful tools for engaging a broad audience in an interactive discussion. Medical education, in particular, is experiencing an increase in the use of this modality to support informal learning, as a means to encourage collaboration and share best practices, and as a platform for large-scale mentorship. Despite this growth in popularity, there are limited data to guide medical educators on the fundamentals of organizing a Twitter chat. In this Twelve Tips article, we discuss strategies relevant to potential Twitter chat organizers. We have arranged the tips chronologically, beginning with a discussion of initial considerations when planning and formulating a chat topic and publicizing the chat to potentially interested people and groups, followed by practical considerations while hosting the chat, and finally strategies for evaluating and extending a Twitter chat's impact.

Estimating ICU Benefit: A Randomized Study of Physicians.

OBJECTIVES: The distinction between overuse and appropriate use of the ICU hinges on whether a patient would benefit from ICU care. We sought to test 1) whether physicians agree about which types of patients benefit from ICU care and 2) whether estimates of ICU benefit are influenced by factors unrelated to severity of illness. DESIGN: Randomized study. SETTING: Online vignettes. SUBJECTS: U.S. critical care physicians. INTERVENTIONS: Physicians were provided with eight vignettes of hypothetical patients. Each vignette had a single patient or hospital factor randomized across participants (four factors related and four unrelated to severity of illness). MEASUREMENTS AND MAIN RESULTS: The primary outcome was the estimate of ICU benefit, assessed with a 4-point Likert-type scale. In total, 1,223 of 8,792 physicians volunteered to participate (14% recruitment rate). Physician agreement of ICU benefit was poor (mean intraclass correlation coefficient for each vignette: 0.06; range: 0-0.18). There were no vignettes in which more than two thirds of physicians agreed about the extent to which a patient would benefit from ICU care. Increasing severity of illness resulted in greater estimated benefit of ICU care. Among factors unrelated to severity of illness, physicians felt ICU care was more beneficial when told one ICU bed was available than if ICU bed availability was unmentioned. Physicians felt ICU care was less beneficial when family was present than when family presence was unmentioned. The patient's age, but not race/ethnicity, also impacted estimates of ICU benefit. CONCLUSIONS: Estimates of ICU benefit are widely dissimilar and influenced by factors unrelated to severity of illness, potentially resulting in inconsistent allocation of ICU care.

Trends in Hospital Utilization After Medicaid Expansion.

BACKGROUND: Medicaid expansion was associated with an increase in hospitalizations funded by Medicaid. Whether this increase reflects an isolated payer shift or broader changes in case-mix among hospitalized adults remains uncertain. RESEEARCH DESIGN: Difference-in-differences analysis of discharge data from 4 states that expanded Medicaid in 2014 (Arizona, Iowa, New Jersey, and Washington) and 3 comparison states that did not (North Carolina, Nebraska, and Wisconsin). SUBJECTS: All nonobstetric hospitalizations among patients aged 19-64 years of age admitted between January 2012 and December 2015. MEASURES: Outcomes included state-level per-capita rates of insurance coverage, several markers of admission severity, and admission diagnosis. RESULTS: We identified 6,516,576 patients admitted during the study period. Per-capita admissions remained consistent in expansion and nonexpansion states, though Medicaid-covered admissions increased in expansion states (274.6-403.8 per 100,000 people vs. 268.9-262.8 per 100,000; P<0.001). There were no significant differences after Medicaid expansion in hospital utilization, based on per-capita rates of patients-designated emergent, admitted via the emergency department, admitted via clinic, discharged within 1 day, or with lengths of stay ≥7 days. Similarly, there were no differences in diagnosis category at admission, admission severity, comorbidity burden, or mortality associated with Medicaid expansion (P>0.05 for all comparisons). CONCLUSIONS: Medicaid expansion was associated with a shift in payers among nonelderly hospitalized adults without significant changes in case-mix or in several markers of acuity. These findings suggest that Medicaid expansion may reduce uncompensated care without shifting admissions practices or acuity among hospitalized adults.

Hospital Contributions to Variability in the Use of ICUs Among Elderly Medicare Recipients.

OBJECTIVE: Hospitals vary widely in ICU admission rates across numerous medical diagnoses. The extent to which variability in ICU use is specific to individual diagnoses or is a function of the hospital, regardless of disease, is unknown. DESIGN: Retrospective cohort study. SETTING: A total of 1,120 acute care hospitals with ICU capabilities. PATIENTS: Medicare beneficiaries 65 years old or older admitted for five medical diagnoses (acute myocardial infarction, congestive heart failure, stroke, pneumonia, and chronic obstructive pulmonary disease) and a surgical diagnosis (hip fracture treated with arthroplasty) in 2010. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We used multilevel models to calculate risk- and reliability-adjusted ICU admission rates, examined the correlation in ICU admission rates across diagnosis and calculated intraclass correlation coefficients and median odds ratios to quantify the variability in ICU admission rate that was attributable to hospitals. We also examined the ability of a high ICU-use hospital for one condition to predict high ICU use for other conditions. We identified 348,462 patients with one of the eligible conditions. ICU admission rates were positively correlated within hospitals for included medical diagnoses (r range, 0.38-0.59; p < 0.01). The top hospital quartile of ICU use for congestive heart failure had a sensitivity of 50-60% and specificity of 79-81% for detecting top quartile hospitals for each other conditions. After adjustment for patient and hospital characteristics, hospitals accounted for 17.6% (95% CI, 16.2-19.1%) of variability in ICU admission, corresponding to a median odds ratio of 2.3, compared to 25.8% (95% CI, 24.5-27.1%) and median odds ratio 2.8 for diagnosis. This suggests a patient with median baseline risk of ICU admission would more than double his/her odds of ICU admission if moving to a higher utilizing hospital. CONCLUSIONS: Hospitals account for a significant proportion of variation independent of measured patient and hospital characteristics, suggesting the need for further work to evaluate the causes of variation at the hospital level and potential consequences of variation across hospitals.

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. Objective: To examine the use of piperacillin-tazobactam compared with cefepime in 90-day mortality in patients treated empirically for sepsis, using instrumental variable analysis of a 15-month piperacillin-tazobactam shortage. Design, Setting, and Participants: In a retrospective cohort study, hospital admissions at the University of Michigan from July 1, 2014, to December 31, 2018, including a piperacillin-tazobactam shortage period from June 12, 2015, to September 18, 2016, were examined. Adult patients with suspected sepsis treated with vancomycin and either piperacillin-tazobactam or cefepime for conditions with presumed equipoise between piperacillin-tazobactam and cefepime were included in the study. Data analysis was conducted from December 17, 2022, to April 11, 2023. Main Outcomes and Measures: The primary outcome was 90-day mortality. Secondary outcomes included organ failure-free, ventilator-free, and vasopressor-free days. The 15-month piperacillin-tazobactam shortage period was used as an instrumental variable for unmeasured confounding in antibiotic selection. Results: Among 7569 patients (4174 men [55%]; median age, 63 [IQR 52-73] years) with sepsis meeting study eligibility, 4523 were treated with vancomycin and piperacillin-tazobactam and 3046 were treated with vancomycin and cefepime. Of patients who received piperacillin-tazobactam, only 152 (3%) received it during the shortage. Treatment groups did not differ significantly in age, Charlson Comorbidity Index score, Sequential Organ Failure Assessment score, or time to antibiotic administration. In an instrumental variable analysis, piperacillin-tazobactam was associated with an absolute mortality increase of 5.0% at 90 days (95% CI, 1.9%-8.1%) and 2.1 (95% CI, 1.4-2.7) fewer organ failure-free days, 1.1 (95% CI, 0.57-1.62) fewer ventilator-free days, and 1.5 (95% CI, 1.01-2.01) fewer vasopressor-free days. Conclusions and Relevance: Among patients with suspected sepsis and no clear indication for antianaerobic coverage, administration of piperacillin-tazobactam was associated with higher mortality and increased duration of organ dysfunction compared with cefepime. These findings suggest that the widespread use of empirical antianaerobic antibiotics in sepsis may be harmful.

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 (2.7%) had a recorded PASC diagnosis. We used a case-control study design and phenome-wide scans to characterize PASC-associated phenotypes of the pre-, acute-, and post-COVID-19 periods. We also integrated PASC-associated phenotypes into phenotype risk scores (PheRSs) and evaluated their predictive performance. RESULTS: In the post-COVID-19 period, known PASC symptoms (e.g., shortness of breath, malaise/fatigue) and musculoskeletal, infectious, and digestive disorders were enriched among PASC cases. We found seven phenotypes in the pre-COVID-19 period (e.g., irritable bowel syndrome, concussion, nausea/vomiting) and sixty-nine phenotypes in the acute-COVID-19 period (predominantly respiratory, circulatory, neurological) associated with PASC. The derived pre- and acute-COVID-19 PheRSs stratified risk well, e.g., the combined PheRSs identified a quarter of the cohort with a history of COVID-19 with a 3.5-fold increased risk (95% CI: 2.19, 5.55) for PASC compared to the bottom 50%. CONCLUSIONS: The uncovered PASC-associated diagnoses across categories highlighted a complex arrangement of presenting and likely predisposing features, some with potential for risk stratification approaches.

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. We used a Super Learner-based prediction approach to identify predictive factors. We integrated the model outputs into individual and composite risk scores and evaluated their predictive performance. RESULTS: Our analysis identified several factors predictive of diagnoses of PASC, including being overweight/obese and the use of HMG CoA reductase inhibitors prior to COVID-19 infection, and respiratory system symptoms during COVID-19 infection. We developed a composite risk score with a moderate discriminatory ability for PASC (covariate-adjusted AUC (95% confidence interval): 0.66 (0.63, 0.69)) by combining the risk scores based on phenotype and medication records. The combined risk score could identify 10% of individuals with a 2.2-fold increased risk for PASC. CONCLUSIONS: We identified several factors predictive of diagnoses of PASC and integrated the information into a composite risk score for PASC prediction, which could contribute to the identification of individuals at higher risk for PASC and inform preventive efforts.

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. We treated each hour in the vignette as a decision to treat or not treat with antibiotics and assigned an infection probability to each hour using a previously developed infection prediction model. We then estimated decision thresholds using regression models based on the timing of antibiotic initiation. We compared thresholds across categories of severity of illness and clinician-related factors. RESULTS: Overall, the treatment threshold occurred at a 69% probability of infection, but the threshold varied significantly across severity of illness categories-when patients had high severity of illness, the treatment threshold occurred at a 55% probability of infection; when patients had intermediate severity, the threshold for antibiotic initiation occurred at an infection probability of 69%, and the threshold was 84% when patients had low severity of illness (P < 0.001 for group differences). Thresholds differed significantly across specialty, highest among infectious disease and lowest among emergency medicine clinicians and across years of experience, decreasing with increasing years of experience. CONCLUSIONS: The threshold infection probability above which physicians choose to initiate antibiotics in suspected sepsis depends on illness severity as well as clinician factors. IMPLICATIONS: Incorporating these context-dependent thresholds into discriminating and well-calibrated models will inform the development of future sepsis clinical decision support systems. Clinician-related differences in treatment thresholds suggests potential unwarranted variation and opportunities for performance improvement. HIGHLIGHTS: Decision making about antibiotic initiation in suspected sepsis occurs under uncertainty, and little is known about clinicians' thresholds for treatment.In this prospective study, 153 clinicians from 3 health care systems reviewed 8 real-world clinical vignettes representing patients with sepsis and indicated the time that they would initiate antibiotics.Using a model-based approach, we estimated decision thresholds and found that thresholds differed significantly across illness severity categories and by clinician specialty and years of experience.

Social Network Diversity and the Daily Burden of Inflammatory Bowel Disease.

INTRODUCTION: To examine the association between social network, daily inflammatory bowel disease (IBD) burden, and related cognitive factors such as loneliness and psychological well-being. METHODS: Using survey data, we compared the relationship between social network diversity and daily IBD burden with multivariable linear regression. RESULTS: Patients with IBD with higher social network diversity reported a lower daily IBD burden. This association was more common among those who reported a higher degree of loneliness than those with a low degree of loneliness. DISCUSSION: We should consider diverse social connections as an indicator of risk for higher IBD burden, especially among lonely patients.

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. Design, Setting, and Participants: A national US multicenter prospective cohort study with 1-, 3-, and 6-month postdischarge visits was conducted at 44 sites participating in the National Heart, Lung, and Blood Institute Prevention and Early Treatment of Acute Lung Injury Network's Biology and Longitudinal Epidemiology: COVID-19 Observational (BLUE CORAL) study. Participants included hospitalized English- or Spanish-speaking adults without severe prehospitalization disabilities or cognitive impairment. Participants were enrolled between August 24, 2020, and July 20, 2021, with follow-up occurring through March 30, 2022. Exposure: Hospitalization for COVID-19 as identified with a positive SARS-CoV-2 molecular test. Main Outcomes and Measures: New or worsened cardiopulmonary symptoms, financial problems, functional impairments, perceived return to baseline health, and quality of life. Logistic regression was used to identify factors associated with new cardiopulmonary symptoms or financial problems at 6 months. Results: A total of 825 adults (444 [54.0%] were male, and 379 [46.0%] were female) met eligibility criteria and completed at least 1 follow-up survey. Median age was 56 (IQR, 43-66) years; 253 (30.7%) participants were Hispanic, 145 (17.6%) were non-Hispanic Black, and 360 (43.6%) were non-Hispanic White. Symptoms, disabilities, and financial problems remained highly prevalent among hospitalization survivors at month 6. Rates increased between months 1 and 6 for cardiopulmonary symptoms (from 67.3% to 75.4%; P = .001) and fatigue (from 40.7% to 50.8%; P < .001). Decreases were noted over the same interval for prevalent financial problems (from 66.1% to 56.4%; P < .001) and functional limitations (from 55.3% to 47.3%; P = .004). Participants not reporting problems at month 1 often reported new symptoms (60.0%), financial problems (23.7%), disabilities (23.8%), or fatigue (41.4%) at month 6. Conclusions and Relevance: The findings of this cohort study of people discharged after COVID-19 hospitalization suggest that recovery in symptoms, functional status, and fatigue was limited at 6 months, and some participants reported new problems 6 months after hospital discharge.

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 (2.7 %) had a recorded PASC diagnosis. We used a case control study design and phenome-wide scans to characterize PASC-associated phenotypes of the pre-, acute-, and post-COVID-19 periods. We also integrated PASC-associated phenotypes into Phenotype Risk Scores (PheRSs) and evaluated their predictive performance. Results: In the post-COVID-19 period, known PASC symptoms (e.g., shortness of breath, malaise/fatigue) and musculoskeletal, infectious, and digestive disorders were enriched among PASC cases. We found seven phenotypes in the pre-COVID-19 period (e.g., irritable bowel syndrome, concussion, nausea/vomiting) and 69 phenotypes in the acute-COVID-19 period (predominantly respiratory, circulatory, neurological) associated with PASC. The derived pre- and acute-COVID-19 PheRSs stratified risk well, e.g., the combined PheRSs identified a quarter of the COVID-19 positive cohort with an at least 2.9-fold increased risk for PASC. Conclusions: The uncovered PASC-associated diagnoses across categories highlighted a complex arrangement of presenting and likely predisposing features, some with a potential for risk stratification approaches.