Impact of social complexity on outcomes in cystic fibrosis after transfer to adult care

Objective This study evaluates the roles of medical and social complexity in health care use outcomes in cystic fibrosis (CF) after transfer from pediatric to adult care. Methods Retrospective cohort design included patients with CF who were transitioned into adult care at Indiana University from 2005 to 2015. Predictor variables included demographic and comorbidity data, age at transition, treatment complexity score (TCS), and an objective scoring measure of their social complexity (Bob's Level of Social Support, BLSS). Outcome variables included outpatient visit rates and hospitalization rates. Pearson's correlations and linear regression were used to analyze the data. Results The median age of the patients (N = 133) at the time of transition was 20 (IQR 19‐23) years. The mean FEV1 % predicted at transition was 69 ± 24%. TCS correlated with outpatient visit rates (r = 0.3, P = 0.003), as well as hospitalization rates (r = 0.4, P < 0.001); while the BLSS only correlated with hospitalization rates (r = 0.7, P < 0.001). After adjusting for covariates, the strongest predictors of post‐transfer hospitalizations are BLSS (P < 0.0001) and pre‐transfer hospitalization rate (P < 0.0001). Conclusion Greater treatment complexity is associated with greater healthcare utilization overall, while greater social complexity is associated with increased hospitalizations (but not outpatient visits). Screening young adults for social complexity may identify high‐risk subpopulations and allow for patient centered interventions to support them and prevent avoidable health care use

Mortality Rates in a Diverse Cohort of Mechanically Ventilated Patients With Novel Coronavirus in the Urban Midwest

Objectives: Differences in mortality rates previously reported in critically ill patients with coronavirus disease 2019 have increased the need for additional data on mortality and risk factors for death. We conducted this study to describe length of stay, mortality, and risk factors associated with in-hospital mortality in mechanically ventilated patients with coronavirus disease 2019. Design: Observational study. Setting: Two urban, academic referral hospitals in Indianapolis, Indiana. Patients or Subjects: Participants were critically ill patients 18 years old and older, admitted with coronavirus disease 2019 between March 1, 2020, and April 27, 2020. Interventions: None. Measurements and Main Results: Outcomes included in-hospital mortality, duration of mechanical ventilation, and length of stay. A total of 242 patients were included with mean age of 59.6 years (sd, 15.5 yr), 41.7% female and 45% African American. Mortality in the overall cohort was 19.8% and 20.5% in the mechanically ventilated subset. Patients who died were older compared with those that survived (deceased: mean age, 72.8 yr [sd, 10.6 yr] vs patients discharged alive: 54.3 yr [sd, 14.8 yr]; p < 0.001 vs still hospitalized: 59.5 yr [sd, 14.4 yr]; p < 0.001) and had more comorbidities compared with those that survived (deceased: 2 [0.5–3] vs survived: 1 [interquartile range, 0–1]; p = 0.001 vs still hospitalized: 1 [interquartile range, 0–2]; p = 0.015). Older age and end-stage renal disease were associated with increased hazard of in-hospital mortality: age 65–74 years (hazard ratio, 3.1 yr; 95% CI, 1.2–7.9 yr), age 75+ (hazard ratio, 4.1 yr; 95% CI, 1.6–10.5 yr), and end-stage renal disease (hazard ratio, 5.9 yr; 95% CI, 1.3–26.9 yr). The overall median duration of mechanical ventilation was 9.3 days (interquartile range, 5.7–13.7 d), and median ICU length of stay in those that died was 8.7 days (interquartile range, 4.0–14.9 d), compared with 9.2 days (interquartile range, 4.0–14.0 d) in those discharged alive, and 12.7 days (interquartile range, 7.2–20.3 d) in those still remaining hospitalized. Conclusions: We found mortality rates in mechanically ventilated patients with coronavirus disease 2019 to be lower than some previously reported with longer lengths of stay.Drs. Perkins, Gao, and Khan are supported through National Institutes of Health (NIH)-National Institute on Aging (NIA) R01 AG 055391, R01 AG 052493, and National Heart, Lung, and Blood Institute R01 HL131730. Dr. Twigg is supported through NIH-NIA U01 AG060900. The remaining authors have disclosed that they do not have any potential conflicts of interest