Results for “Capillary Isoelectric Focusing” — Page 12

PubMed2026

Integrated Telehealth Rehabilitation and Quality of Life in Mechanically Ventilated Adults: A Randomized Clinical Trial.

IMPORTANCE: Whether integrated rehabilitation strategies spanning intensive care unit (ICU), hospital, and postdischarge phases improve quality of life after acute respiratory failure is uncertain. OBJECTIVE: To evaluate the effect of an integrated multicomponent telehealth-based rehabilitation intervention on health-related quality of life at 90 days after hospital discharge among adults with acute hypoxemic respiratory failure requiring invasive mechanical ventilation. DESIGN, SETTING, AND PARTICIPANTS: This stepped-wedge cluster randomized clinical trial in ICUs of 20 public hospitals in Brazil enrolled adults with acute hypoxemic respiratory failure requiring invasive mechanical ventilation between June 2024 and May 2025, with follow-up through September 2025. INTERVENTIONS: A multicomponent telehealth-based rehabilitation program integrating an ICU telehealth-based rehabilitation intervention focused on ventilator liberation; a ward telehealth-based rehabilitation intervention targeting risk stratification and initiation of individualized rehabilitation plans; and a postdischarge telehealth-based rehabilitation intervention consisting of a 2-month personalized centralized telerehabilitation program. MAIN OUTCOMES AND MEASURES: Health-related quality of life at 90 days after hospital discharge, measured using the EuroQol 5-Dimension 3-Level (EQ-5D-3L) utility score (range, -0.17 [worse than death] to 1 [best health state], with 0 representing death). RESULTS: Among 1916 enrolled patients (mean [SD] age, 60.6 [17.3] years; 43.6% female), 1063 were assigned to the intervention and 853 to usual care per local protocols. At 90 days after hospital discharge, mean (SD) EQ-5D-3L utility scores were higher in the intervention group than in the usual care group (0.16 [0.31] vs 0.12 [0.28]; adjusted difference, 0.049; 95% CI, 0.0002 to 0.098; P = .04) but did not differ among survivors (0.60 [0.32] vs 0.59 [0.32]; adjusted difference, -0.045; 95% CI, -0.138 to 0.045; P = .34). Compared with usual care, the intervention resulted in lower 90-day all-cause mortality (71.8% [676 of 941] vs 78.3% [584 of 746]; adjusted difference, -7.6%; 95% CI, -14.7% to -0.6%; P = .03) and shorter mean (SD) mechanical ventilation duration (9.9 [10.3] vs 15.5 [15.9] days; adjusted difference, -6.2 days; 95% CI, -8.5 to -3.9; P < .001). CONCLUSIONS AND RELEVANCE: In this study, an integrated telehealth-based rehabilitation strategy delivered across ICU, hospital, and postdischarge phases improved 90-day health-related quality of life, potentially influenced by reduced mortality. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT06343545.

Humans↗

PubMed2026

Methods for defining equity-stratifying variables: a systematic review of validation studies.

BACKGROUND AND OBJECTIVE: Disease burden is often disproportionally higher among those who are socially disadvantaged by factors defined in the PROGRESS-Plus framework (ie, Place of residence, Race/ethnicity/culture/language, Occupation, Gender/sex, Religion, Education, Socioeconomic status, and Social capital, with "Plus" covering features like age and disability). The accuracy and applicability of case definitions to identify these variables from administrative and clinical health data are unknown. We conducted a systematic review to explore how equity-stratifying variables, as categorized by the PROGRESS-Plus framework, have been defined and validated in epidemiologic studies using administrative health, population-level, or electronic health record (EHR) data. METHODS: Medline, EMBASE, CINAHL, Web of Science, and Google Scholar were searched from the inception of the databases to 2024 for validation studies of equity-stratifying variables in adults using administrative health datasets, health registries, or EHR data. Titles and abstracts, followed by relevant full-text articles, were screened in duplicate by two reviewers for eligibility. The data sources utilized, algorithms employed, and their associated performance measures were extracted and synthesized from included studies. Given substantial heterogeneity in study design, equity-stratifying variable definition, and performance metrics, meta-analysis was not possible. RESULTS: Of the 9099 unique citations screened, 188 full texts were reviewed and 116 were included in this review. Most studies were published between 2019 and 2024 (n = 64, 55%) and were validation studies of race/ethnicity definitions that used race/ethnicity codes or surname list algorithms (n = 66, 57%). No studies examined religion. Regarding the reported performance measure estimates, the race/ethnicity/culture/language equity-stratifying variables category had the largest variability across sensitivity, positive predictive value (PPV), and Cohen's Kappa. Occupation validation studies had the lowest variation in sensitivity and PPV. CONCLUSION: Despite an increasing number of publications reporting on the validation of equity-stratifying variables relevant to the PROGRESS-Plus framework, performance measures varied widely across studies. The significant heterogeneity in equity-stratifying variable definitions and methods used to validate them support the need for further rigorous validation of equity-stratifying variables in administrative and clinical health data. PLAIN LANGUAGE SUMMARY: Disease burden is often higher in people who experience financial hardships, lower level of education, discrimination due to race/ethnicity, and unstable housing. These social factors can be considered health equity factors and are important for understanding health inequalities. Health researchers often use large datasets, such as hospital or electronic health records (EHRs), to study these health equity factors. However, it is not clear how accurately these data sources capture information about people's social circumstances and how these factors are defined. In this study, we reviewed existing research to understand how health equity factors have been defined across health data sources and how accurate they are at measuring aspects of health equity and social disadvantage. Of the more than 9000 studies we identified, we included 116 that met our criteria for this systematic review. Most included studies focused on identifying race and ethnicity, often using codes or surname-based methods. We found that the accuracy of these methods varied widely across studies, meaning results may not always be reliable or comparable. Overall, our findings show that there are inconsistencies in how social factors are defined and measured in health data. This makes it difficult to fully understand and address health inequalities using routinely collected health data. More work is needed to develop and validate better quality and more consistent methods for capturing these important social factors.

Humans↗