Sachin Yende

The Early versus Delayed Enteral Nutrition (EDEN) trial found no significant difference in mortality between trophic and full enteral nutrition strategies in acute respiratory distress syndrome (ARDS) patients. Heterogeneity of treatment effect (HTE) has been identified in prior ARDS trials. We previously identified intestine-derived incretin hormones (glucose-dependent insulinotropic peptide [GIP] and glucagon-like peptide [GLP]-1 as potential predictive biomarkers in the response to nutrition. To investigate incretins as biomarkers predictive of HTE in EDEN. GIP, GLP-1, and host immune response biomarkers were measured from pre-intervention EDEN plasma samples. We investigated HTE with 60-day mortality as a primary outcome by testing interaction of treatment with circulating incretin levels in analyses adjusted for demographics, severity of illness, diabetes mellitus, and circulating interleukin-6, and assessed mortality by treatment arm across incretin tertiles. We additionally tested for HTE by de novo ARDS subphenotypes and by risk of mortality. 889 participants were included (452 randomized to trophic and 437 to full enteral nutrition). GIP predicted HTE to enteral nutrition strategies (adjusted interaction p-value 0.01) with lower mortality from trophic feeds (14.1% vs 27.2% in full) in patients in the highest GIP tertile but similar mortality in other tertiles. GLP-1, ARDS subphenotypes, and baseline risk of mortality did not predict HTE. GIP was unique among incretins in predicting HTE to enteral nutrition strategies in EDEN. Further studies are needed to validate our findings as GIP might serve as a biomarker to guide level of enteral nutrition for ARDS patients.

Importance: The Centers for Medicare & Medicaid Services reimburses remote monitoring to reduce hospital readmissions, yet its effectiveness remains uncertain. Objective: To evaluate effectiveness of remote monitoring in reducing readmissions following hospitalizations for serious infections overall and across prespecified subgroups. Design, Setting, and Participants: This randomized clinical trial was conducted from March 25, 2021, to December 9, 2024, using response adaptive randomization among postdischarge patients with sepsis or lower respiratory tract infection across 19 hospitals. Eligible patients were 21 years or older, lived in western Pennsylvania, were insured through the UPMC Health Plan or traditional fee-for-service Medicare, had a smartphone or other internet-connected device, had no cognitive impairment, and were at moderate or high risk for readmission at index hospitalization admission based on an internal predictive model. Analyses were based on intention to treat; data were analyzed from April to June 2025. Intervention: Four remote monitoring strategies combining questionnaire intensity (low vs high) and clinical response teams (standard vs enhanced) compared with usual care. Main Outcomes and Measures: The primary end point consisted of days at home at 90 days after discharge, assessed by posterior probability distribution for the cumulative odds ratios (CORs) in each study arm compared with usual care. Secondary end points included mortality, readmission, functional status, quality of life, and use of emergency department and hospice services. Results: Among the 1286 patients included in the analysis, the median age was 63 (IQR, 54-71) years; 665 patients (51.7%) were female. The median Charlson Comorbidity Index was 6 (IQR, 3-9), and 386 patients (32.6%) received intensive care. Patients were randomized to usual care (n = 399) or remote patient monitoring (RPM) with low- or high-intensity questionnaires and standard or enhanced clinical response team combinations: RPM-low standard response (n = 204), RPM-high standard response (n = 129), RPM-low enhanced response (n = 383), and RPM-high enhanced response (n = 171). Of 887 patients assigned to remote monitoring arms, 529 (59.6%) enrolled in the remote monitoring program. The median home days were similar across all study arms: 90 (IQR, 83-90) for usual care, 90 (IQR, 84-90) for RPM-low standard response, 90 (IQR, 85-90) for RPM-high standard response, 90 (IQR, 83-90) for RPM-low enhanced response, and 90 (IQR, 84-90) for RPM-high enhanced response. Compared with usual care, the CORs were 0.96 (credible interval [CrI], 0.70-1.32) for RPM-low standard response, 0.86 (95% CrI, 0.60-1.23) for RPM-high standard response, 1.01 (95% CrI, 0.76-1.33) for RPM-low enhanced response, and 0.96 (95% CrI, 0.69-1.36) for RPM-high enhanced response, and superiority probability was less than 55% for all comparisons. At least 1 readmission occurred in 151 of 399 patients (37.8%) in the usual care arm, 81 of 204 (39.7%) in the RPM-low standard response arm, 57 of 129 (44.2%) in the RPM-high standard response arm, 143 of 383 (37.3%) in the RPM-low enhanced response arm, and 62 of 171 (36.3%) in the RPM-high enhanced response arm. Among patients 65 years and older, standard and enhanced response arms had fewer home days compared with usual care (COR, 0.56 [95% CrI, 0.36-0.85] and 0.67 [95% CrI, 0.45-0.98], respectively; inferiority probability, 99.6% and 97.9%, respectively). Conclusions and Relevance: Among trial patients discharged after hospitalization for serious infections, remote monitoring did not increase time spent alive at home but reduced it in those 65 years and older. These findings support reevaluating and tailoring remote monitoring after acute care for sepsis and lower respiratory tract infection to support further alignment with patients’ needs and desire for personalized monitoring. Trial Registration: ClinicalTrials.gov Identifier: NCT04829188

Introduction: Smoked tobacco is a leading risk factor for cardiopulmonary disease. Pipe and cigar use remains common among US adults, yet its risks remain insufficiently understood. Methods: We analysed data from five pooled cohorts with adults enrolled from 1971 to 2011 with follow-up through 2018. Pipe/cigar use was defined by baseline self-report. Forced expiratory volume in 1 s (FEV ₁ ), forced vital capacity (FVC) and FEV ₁ /FVC ratio were measured by spirometry. All-cause mortality, coronary heart disease mortality and hospitalisation, respiratory-related mortality, and chronic obstructive pulmonary disease (COPD) mortality and hospitalisation were classified via adjudication or validated algorithms. Associations were estimated with linear mixed models and Fine-Gray subdistribution hazards models adjusted for sociodemographic and clinical factors. Results: Among 22 823 participants (mean (SD) age 48.0 (15.7) years; 44.9% male sex; 70.5% white, 25.4% black, 2.4% Hispanic/Latino), 2621 (11.5%) reported ever pipe/cigar use, including 518 (2.3%) exclusive users without cigarette history. Compared with never tobacco users (n=9931), exclusive pipe/cigar use was associated with faster decline in FEV ₁ (3.36 mL/year; 95% CI 1.99 to 4.72), FVC (3.73 mL/year; 95% CI 2.05 to 5.42) and FEV ₁ /FVC (0.031 per year; 95% CI 0.008 to 0.054). Exclusive users had higher all-cause mortality (adjusted HR (aHR) 1.24; 95% CI 1.08 to 1.41), COPD hospitalisation/mortality (aHR 2.02; 95% CI 1.41 to 2.90) and preserved ratio impaired spirometry (aHR 1.83; 95% CI 1.24 to 2.71). Conclusion: Pipe and cigar use was associated with accelerated lung function decline and increased mortality and cardiopulmonary events, including among never cigarette users. These findings underscore the need for prevention and cessation efforts targeting non-cigarette tobacco.

Background: Implementation fidelity—the degree to which an intervention is executed as intended—is critical for evaluating healthcare interventions' success. Contextual determinants such as organizational culture, communication, and leadership influence how interventions unfold at the site level. The Veterans Health Administration (VA) developed the Patient Safety Events in Community Care: Reporting, Investigation, and Improvement Guidebook to improve standardization of patient safety reporting across VA-delivered and VA-purchased care. While the Guidebook aims to enhance reporting fidelity, little is known about which local contextual factors influence its implementation success across diverse VA sites. This study examined the contextual determinants associated with site-level variation in Guidebook implementation fidelity. Methods: We conducted a cross-sectional, mixed-methods evaluation of 18 geographically diverse VA Medical Centers. Data were collected from 32 interviews with 45 key personnel involved in Guidebook implementation. Using the 2009 Consolidated Framework for Implementation Research (CFIR), 12 constructs were rated at each site. Fidelity was assessed across three safety processes (reporting, investigation, and improvement) on a three-point scale. We used Coincidence Analysis, a configurational comparative method, to identify combinations of CFIR constructs (difference-makers) that consistently distinguished higher- from lower-fidelity sites. Results: Guidebook fidelity varied across sites (range = 0.23 to 1.59). We identified three key CFIR constructs associated with higher fidelity: Networks & Communications, Relative Priority, and Leadership Engagement. Of these, Networks & Communications was both a necessary and sufficient condition for higher fidelity, serving as a precondition for high levels of Leadership Engagement. Sites that rated highly in Relative Priority were more likely to fully implement Guidebook processes. These constructs fostered strong collaboration, timely information exchange, and internal alignment on the importance of patient safety reporting. Conclusions: Effective communication networks and perceived priority of the intervention were central to high-fidelity implementation of the VA’s safety reporting Guidebook . These findings highlight critical levers for improving implementation fidelity in complex healthcare systems. Targeted strategies that strengthen communication and emphasize the value of safety interventions may enhance implementation success, offering valuable insights for patient safety efforts both within and beyond the VA.

OBJECTIVES: Sepsis trials likely include patients who vary in response to therapeutic interventions. The optimal approach to identify such differences in treatment response remains unclear. Estimating individualized absolute risk differences (iARDs) to model treatment response at an individual patient level using supervised effect models applied to randomized trial data may be informative. We explored the relationship between two subgrouping approaches and a recently published iARD model for the effect of early goal-directed therapy (EGDT) resuscitation in sepsis. DESIGN: Secondary analysis of the Protocolized Care for Early Septic Shock (ProCESS) and Australasian Resuscitation in Sepsis Evaluation (ARISE) trials. We applied clinical subtypes (α, β, γ, δ) to 829 ProCESS and 1588 ARISE patients and biologic “hyperinflammatory” and “nonhyperinflammatory” subphenotypes to 363 ProCESS patients with biomarker data using established methods. We predicted iARDs with supervised learning using clinical variables as predictors and 90-day mortality as the primary outcome. We evaluated iARD variability within subgroups. SETTING: Eighty-one sites worldwide. PATIENTS/SUBJECTS: Adults with septic shock. INTERVENTIONS: EGDT or usual care. MEASUREMENTS AND MAIN RESULTS: The average treatment effect of EGDT appeared to vary within both clinical and biologic subphenotypes. EGDT appeared potentially beneficial in the β and nonhyperinflammatory subphenotypes but harmful in the γ and hyperinflammatory subphenotypes. However, the predicted iARDs within each subgroup ranged from considerable harm to considerable benefit. For example, for the β-subtype, the average mortality reduction from EGDT was 8.5% (95% CI, –0.4 to 17.5), but the iARDs ranged from a 29% increase to a 16% reduction in mortality, with 39% of patients predicted to be harmed. CONCLUSIONS: Although both clinical and biologic phenotyping may identify subgroups whose average treatment effect is beneficial or harmful, individual risks and benefits within subgroups still vary dramatically, raising concern that phenotyping may not reliably or safely personalize sepsis care.

Rationale: Chronic lung diseases are associated with increased risk of mortality due to coronary heart disease (CHD). Nonetheless, the population attributable fraction (PAF) of lung function impairment relative to other established cardiovascular risk factors is unclear. Objectives: To evaluate the PAF of low lung function for CHD mortality Methods: We harmonized and pooled lung function and clinical data across eight U.S. general population cohorts. Impaired lung function was defined as forced expiratory volume in 1 second (FEV1) and/or forced vital capacity ≤ 95% predicted on baseline spirometry. The association between CHD mortality and risk factors was assessed using cause-specific proportional hazards and Fine-Gray proportional subdistribution hazard models, treating non-CHD mortality as a competing risk. Models were adjusted for lung function as well as age, sex, race/ethnicity, educational attainment, body mass index, smoking status, pack-years of smoking, diabetes mellitus, high-density lipoprotein, and high low-density lipoprotein (≥130 mg/dl). PAF was calculated as the relative change in the average absolute risk of 10-year CHD mortality by elimination of lung function lower than 95% predicted. Results: Among 35,143 participants, 1,844 of 13,174 (14.0%) deaths were due to CHD. Compared with percentage predicted FEV1 (FEV1pp) > 95%, the subdistribution adjusted hazard ratio for low FEV1pp was 1.30 (95% confidence interval, 1.18–1.44). The PAF for FEV1pp ≤ 95% was 12%, ranking low FEV1 third on the list of PAF for CHD mortality, after hypertension and diabetes. Low FEV1pp ranked second in the subgroup of active smokers (PAF 14%), after hypertension. Conclusions: Low lung function, even in the range considered clinically normal, ranks high on the list of attributable risk factors for CHD mortality and should be considered in cardiovascular risk stratification.

OBJECTIVES: The optimal approach for resuscitation in septic shock remains unclear despite multiple randomized controlled trials (RCTs). Our objective was to investigate whether previously uncharacterized variation across individuals in their response to resuscitation strategies may contribute to conflicting average treatment effects in prior RCTs. DESIGN: We randomly split study sites from the Australian Resuscitation of Sepsis Evaluation (ARISE) and Protocolized Care for Early Septic Shock (ProCESS) trials into derivation and validation cohorts. We trained machine learning models to predict individual absolute risk differences (iARDs) in 90-day mortality in derivation cohorts and tested for heterogeneity of treatment effect (HTE) in validation cohorts and swapped these cohorts in sensitivity analyses. We fit the best-performing model in a combined dataset to explore roles of patient characteristics and individual components of early goal-directed therapy (EGDT) to determine treatment responses. SETTING: Eighty-one sites in Australia, New Zealand, Hong Kong, Finland, Republic of Ireland, and the United States. PATIENTS: Adult patients presenting to the emergency department with severe sepsis or septic shock. INTERVENTIONS: EGDT vs. usual care. MEASUREMENTS AND MAIN RESULTS: A local-linear random forest model performed best in predicting iARDs. In the validation cohort, HTE was confirmed, evidenced by an interaction between iARD prediction and treatment (p < 0.001). When patients were grouped based on predicted iARDs, treatment response increased from the lowest to the highest quintiles (absolute risk difference [95% CI], –8% [–19% to 4%] and relative risk reduction, 1.34 [0.89–2.01] in quintile 1 suggesting harm from EGDT, and 12% [1–23%] and 0.64 [0.42–0.96] in quintile 5 suggesting benefit). Sensitivity analyses showed similar findings. Pre-intervention albumin contributed the most to HTE. Analyses of individual EGDT components were inconclusive. CONCLUSIONS: Treatment response to EGDT varied across patients in two multicenter RCTs with large benefits for some patients while others were harmed. Patient characteristics, including albumin, were most important in identifying HTE.

Introduction Cigarette smoking leads to altered DNA methylation at the aryl-hydrocarbon receptor repressor (AHRR) gene. However, it remains unknown whether pipe or cigar smoking is associated with AHRR methylation. We evaluated associations of non-cigarette tobacco use with AHRR methylation and determined if AHRR methylation was associated with smoking-related health outcomes. Methods Data were pooled across four population-based cohorts that enrolled participants from 1985 to 2002. Tobacco exposures were evaluated using smoking questionnaires. AHRR cg05575921 methylation was measured in peripheral blood leucocyte DNA. Spirometry and respiratory symptoms were evaluated at the time of methylation measurements and in subsequent visits. Vital status was monitored using the National Death Index. Results Among 8252 adults (mean age 56.7±10.3 years, 58.1% women, 40.6% black), 4857 (58.9%) participants used cigarettes and 634 (7.7%) used non-cigarette tobacco products. Exclusive use of non-cigarette tobacco products was independently associated with lower AHRR methylation (−2.44 units, 95% CI −4.42 to −0.45), though to a lesser extent than exclusive use of cigarettes (−6.01 units, 95% CI −6.01 to −4.10). Among participants who exclusively used non-cigarette tobacco products, reduced AHRR methylation was associated with increased respiratory symptom burden (OR 1.60, 95% CI 1.03 to 2.68) and higher all-cause mortality (log-rank p=0.02). Conclusion Pipe and cigar smoking were independently associated with lower AHRR methylation in a multiethnic cohort of US adults. Among users of non-cigarette tobacco products, lower AHRR methylation was associated with poor respiratory health outcomes and increased mortality. AHRR methylation may identify non-cigarette tobacco users with an increased risk of adverse smoking-related health outcomes.

Objective: To evaluate nationwide implementation of a Guidebook designed to standardize safety practices across VA‐delivered and VA‐purchased care (i.e., Community Care) and identify lessons learned and strategies to improve them. Data Sources and Study Setting: Qualitative data collected from key informants at 18 geographically diverse VA facilities across 17 Veterans Integrated Services Networks (VISNs). Study Design: We conducted semi‐structured interviews from 2019 to 2022 with VISN Patient Safety Officers (PSOs) and VA facility patient safety and quality managers (PSMs and QMs) and VA Facility Community Care (CC) staff to assess lessons learned by examining organizational contextual factors affecting Guidebook implementation based on the Consolidated Framework for Implementation Research (CFIR). Data Collection/Extraction Methods: Interviews were conducted virtually with 45 facility staff and 10 VISN PSOs. Using directed content analysis, we identified CFIR factors affecting implementation. These factors were mapped to the Expert Recommendations for Implementing Change (ERIC) strategy compilation to identify lessons learned that could be useful to our operational partners in improving implementation processes. We met frequently with our partners to discuss findings and plan next steps. Principal Findings: Six CFIR constructs were identified as both facilitators and barriers to Guidebook implementation: (1) planning for implementation; (2) engaging key knowledge holders; (3) available resources; (4) networks and communications; (5) culture; and (6) external policies. The two CFIR constructs that were only barriers included: (1) cosmopolitanism and (2) executing implementation. Conclusions: Our findings suggest several important lessons: (1) engage all collaborators involved in implementation; (2) ensure end‐users have opportunities to provide feedback; (3) describe collaborators' purpose and roles/responsibilities clearly at the start; (4) communicate information widely and repeatedly; and (5) identify how multiple high priorities can be synergistic. This evaluation will help our partners and key VA leadership to determine next steps and future strategies for improving Guidebook implementation through collaboration with VA staff.