Objective To determine if a counseling intervention using the principles of motivational interviewing (MI) would impact uptake of long-acting reversible contraception (LARC) after abortion. Methods We conducted a pilot randomized controlled trial comparing an MI-based contraception counseling intervention to only non-standardized counseling. Sixty women 15-29 years-old were randomized. Primary outcome: uptake of LARC within four weeks of abortion. Secondary outcomes: uptake of any effective contraceptive, contraceptive use three months after abortion and satisfaction with counseling. Bivariate analysis was used to compare outcomes. Results In the intervention arm, 65.5% of participants received a long-acting method within four weeks compared to 32.3% in the control arm (p=0.01). Three months after the abortion, differences in LARC use endured (60.0% vs. 30.8%, p=0.05). Uptake and use of any effective method were not statistically different. More women in the intervention arm reported satisfaction with their counseling than women in the control arm (92.0% vs. 65.4%, p=0.04). Conclusion Twice as many women in the MI-based contraception counseling intervention initiated and continued to use LARC compared to women who received only non-standardized counseling. Practice Implications A contraception counseling session using the principles and skills of motivational interviewing has the potential to impact LARC use after abortion.
Word count: 4,584 (main text); citations 90 (main text) + 4 (figures only). Remaining citations are included in the supplement. Tables/Figures: 4 tables, 2 figures Pages of Text: 20
Background In patients with acute respiratory distress syndrome undergoing mechanical ventilation, positive end-expiratory pressure (PEEP) can lead to recruitment or overdistension. Current strategies utilized for PEEP titration do not permit the distinction. Electric impedance tomography (EIT) detects and quantifies the presence of both collapse and overdistension. We investigated whether using EIT-guided PEEP titration leads to decreased mechanical power compared to high-PEEP/FiO2 tables. Methods A single-center, randomized crossover pilot trial comparing EIT-guided PEEP selection versus PEEP selection using the High-PEEP/FiO2 table in patients with moderate–severe acute respiratory distress syndrome. The primary outcome was the change in mechanical power after each PEEP selection strategy. Secondary outcomes included changes in the 4 × driving pressure + respiratory rate (4 ΔP, + RR index) index, driving pressure, plateau pressure, PaO2/FiO2 ratio, and static compliance. Results EIT was consistently associated with a decrease in mechanical power compared to PEEP/FiO2 tables (mean difference − 4.36 J/min, 95% CI − 6.7, − 1.95, p = 0.002) and led to lower values in the 4ΔP + RR index (− 11.42 J/min, 95% CI − 19.01, − 3.82, p = 0.007) mainly driven by a decrease in the elastic–dynamic power (− 1.61 J/min, − 2.99, − 0.22, p = 0.027). The elastic–static and resistive powers were unchanged. Similarly, EIT led to a statistically significant change in set PEEP (− 2 cmH2O, p = 0.046), driving pressure, (− 2.92 cmH2O, p = 0.003), peak pressure (− 6.25 cmH2O, p = 0.003), plateau pressure (− 4.53 cmH2O, p = 0.006), and static respiratory system compliance (+ 7.93 ml/cmH2O, p = 0.008). Conclusions In patients with moderate–severe acute respiratory distress syndrome, EIT-guided PEEP titration reduces mechanical power mainly through a reduction in elastic–dynamic power. Trial registration This trial was prospectively registered on Clinicaltrials.gov (NCT 03793842) on January 4th, 2019.
ImportanceIndividuals who survived COVID-19 often report persistent symptoms, disabilities, and financial consequences. However, national longitudinal estimates of symptom burden remain limited.ObjectiveTo measure the incidence and changes over time in symptoms, disability, and financial status after COVID-19–related hospitalization.Design, Setting, and ParticipantsA 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.ExposureHospitalization for COVID-19 as identified with a positive SARS-CoV-2 molecular test.Main Outcomes and MeasuresNew 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.ResultsA 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 RelevanceThe 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.
Objectives: Acute respiratory distress syndrome is frequently under recognized and associated with increased mortality. Previously, we developed a model that used machine learning and natural language processing of text from radiology reports to identify acute respiratory distress syndrome. The model showed improved performance in diagnosing acute respiratory distress syndrome when compared to a rule-based method. In this study, our objective was to externally validate the natural language processing model in patients from an independent hospital setting. Design: Secondary analysis of data across five prospective clinical studies. Setting: An urban, tertiary care, academic hospital. Patients: Adult patients admitted to the medical ICU and at-risk for acute respiratory distress syndrome. Interventions: None. Measurements and Main Results: The natural language processing model was previously derived and internally validated in burn, trauma, and medical patients at Loyola University Medical Center. Two machine learning models were examined with the following text features from qualifying radiology reports: 1) word representations (n-grams) and 2) standardized clinical named entity mentions mapped from the National Library of Medicine Unified Medical Language System. The models were externally validated in a cohort of 235 patients at the University of Chicago Medicine, among which 110 (47%) were diagnosed with acute respiratory distress syndrome by expert annotation. During external validation, the n-gram model demonstrated good discrimination between acute respiratory distress syndrome and nonacute respiratory distress syndrome patients (C-statistic, 0.78; 95% CI, 0.72–0.84). The n-gram model had a higher discrimination for acute respiratory distress syndrome when compared with the standardized named entity model, although not statistically significant (C-statistic 0.78 vs 0.72; p = 0.09). The most important features in the model had good face validity for acute respiratory distress syndrome characteristics but differences in frequencies did occur between hospital settings. Conclusions: Our computable phenotype for acute respiratory distress syndrome had good discrimination in external validation and may be used by other health systems for case-identification. Discrepancies in feature representation are likely due to differences in characteristics of the patient cohorts.
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