Citations

Background: Deciding to use oral anticoagulation (OAC) for stroke prevention in patients with atrial fibrillation (AF) is complex. For many, the decision between treatment and non-treatment requires a primary tradeoff between the risks of potential stroke and increased bleeding. Hypothesis: We hypothesized that patient values and empiric stroke risk measured by CHA 2 DS 2 -VASc score would influence OAC decisions. Methods: We conducted a secondary analysis of a randomized clinical trial where eligible clinicians and patients were independently randomized to one of two decision aids (DAs) – a patient decision aid and/or an encounter decision aid. Eligible patients were adults with AF with CHA 2 DS 2 -VASc≥1 for men or ≥2 for women. Patients newly prescribed (OAC-naive subgroup) or already taking OAC (OAC-experienced subgroup) were included. We assessed patient values using a 5-point Likert scale between stroke prevention and bleeding risk after SDM during a clinical encounter. We report preliminary results using descriptive statistics and adjusted analysis to investigate the effects of CHA 2 DS 2 -VASc and expressed values on OAC use. Results: 1005 patients were included in the analysis. 828 (82%) patients valued stroke prevention over bleeding risk. Among the OAC-naive group, 175 (63%) valued stroke prevention more than bleed avoidance, 58 (20%) valued bleed avoidance more than stroke prevention, and 47 (17%) were neutral. In the unadjusted analysis, patients were more likely to value stroke prevention if they were OAC-experienced (p<0.001), older (p<0.001), female (p< 0.001), had patient-reported bleeding (p=0.003), or if CHA 2 DS 2 -VASc ≥ 2 if male, ≥3 if female (p<0.001) Table 1 . After adjustments, there were significant associations for OAC use among patients who valued stroke prevention (OR 5.35, 95%CI 3.13-9.13, p<0.001) or had higher CHA 2 DS 2 -VASc (OR 2.03, 95%CI 1.26-3.27, p=0.004). Patients who valued bleed avoidance had an opposing association not to use OAC (OR 0.23, 95%CI 0.11-0.48, p<0.001). Conclusions: Patients with elevated risk (CHA 2 DS 2 -VASc score, age, and female), and those who reported a prior bleeding event valued stroke prevention more than bleeding avoidance. Both empiric stroke risk and patient values were associated with the decision to use OAC. While the tradeoff between stroke prevention and bleeding risk is only one aspect of the decision-making process, these results indicate variability in patient values may influence OAC use.

Abstract Objective To evaluate the effectiveness of multiple decision aid strategies in promoting high quality shared decision making for prevention of stroke in patients with non-valvular atrial fibrillation. Design Cluster randomized controlled trial. Setting Six academic medical centers in the United States. Participants Patient participants were aged ≥18 with a diagnosis of non-valvular atrial fibrillation, at risk for stroke (CHA 2 DS 2 -VASc ≥1 for men, ≥2 for women), and scheduled for a clinical appointment to discuss stroke prevention strategies. Participating clinicians were those who manage stroke prevention strategies for participating patients. Intervention Patients were randomized to use a patient decision aid or usual care; clinicians were randomized to use an encounter decision aid or usual care with all participating patients. Main outcome measures Primary outcome measures were quality of shared decision making measured by OPTION12, knowledge of atrial fibrillation and its management, and decisional conflict. Results 1117 participants across six sites were included in the analysis. Compared with usual care, the combined use of both the patient decision aid and the encounter decision aid improved the quality of shared decision making (adjusted mean difference 12.1 (95% confidence interval (CI) 8.0 to 16.2; P<0.001), improved patients’ knowledge (odds ratio 1.68 (95% CI 1.35 to 2.09; P<0.001), and reduced patients’ decisional conflict (adjusted mean difference −6.3 (95% CI −9.6 to −3.1; P<0.001). Statistically significant improvements were also observed with the encounter decision aid alone versus usual care for all three outcomes and with the patient decision aid alone versus usual care for quality of shared decision making and knowledge. No important differences were observed in treatment choices for stroke prevention or in participants’ satisfaction. No statistically significant difference in the length of visit across study groups was detected. Conclusion Patients who received any decision aid (encounter decision aid, patient decision aid, or both) had lower decisional conflict, better shared decision making, and greater knowledge than those receiving no decision aid, except for the effect of the patient decision aid on decisional conflict, which did not reach statistical significance. The study establishes that use of either pre-visit or in-visit decision aids individually or in combination is advantageous compared with usual care. Trial registration ClinicalTrials.gov NCT04357288 .

Background: Guidelines recommend shared decision-making (SDM) for patients with atrial fibrillation (AF) making decisions regarding anticoagulation for stroke prevention. Decision support tools (e.g., Decision Aids [DAs]) facilitate SDM. However, little research has compared the effectiveness of the two most common types of DAs: (1) encounter decision aid (EDA) and (2) pre-encounter patient decision aid (PDA). Our study assessed the comparative effectiveness of PDAs and EDAs on SDM during AF clinical encounters where stroke prevention strategies were discussed. Research Question: Do decision aids (PDA or EDA), alone or in combination, improve patients’ AF decision-making experience compared to usual care? Methods: A cluster randomized multi-center trial in patients with AF comparing usual care (no DA) vs. one DA (EDA or PDA) or both DAs. Clinicians and patients were randomized independently into 4 study arms. Clinicians were randomized to use or not use the EDA for all study visits, and patients were randomized to use or not use the PDA. Co-primary outcome domains were: (1) quality of SDM (2) patient knowledge, and (3) decisional conflict. Secondary outcomes included: (1) treatment choice, (2) treatment initiation, and (3) treatment persistence. Patient characteristics were assessed. We will present comparisons of the randomized DAs on the co-primary and secondary outcomes and subgroup analyses. Results: Between December 2020-July 2023, investigators in 6 U.S. healthcare systems enrolled 1117 patients (mean (M) age 69, 63% male, 89% White) and 107 clinicians. Compared to usual care (no DA, M=31.6), SDM was better in patients receiving PDA (M=35.4) and EDA (44.5) alone or together (M=43.7; p’s<0.001). Knowledge about AF was greatest for those receiving both DAs or the PDA alone (84% accurate responses for PDA and both vs 76% with no DA, p<0.001), followed closely by the EDA alone (82%, p=0.003). Finally, both DAs (M=19.5) and EDA alone (M=20.0) had better decisional conflict scores compared to usual care (M=25.8; p’s<0.001). We will present analyses that assess the impact of DAs on secondary outcomes and interactions with DA, patient, and clinical characteristics across each outcome. Conclusions: In one of the first randomized trials examining the comparative effectiveness of EDAs and PDAs on SDM in clinical medicine, both PDAs and EDAs improved shared decision-making, decisional conflict, and knowledge compared to patients receiving usual care.

OBJECTIVES Early warning scores detecting clinical deterioration in pediatric inpatients have wide-ranging performance and use a limited number of clinical features. This study developed a machine learning model leveraging multiple static and dynamic clinical features from the electronic health record to predict the composite outcome of unplanned transfer to the ICU within 24 hours and inpatient mortality within 48 hours in hospitalized children. METHODS Using a retrospective development cohort of 17 630 encounters across 10 388 patients, 2 machine learning models (light gradient boosting machine [LGBM] and random forest) were trained on 542 features and compared with our institutional Pediatric Early Warning Score (I-PEWS). RESULTS The LGBM model significantly outperformed I-PEWS based on receiver operating characteristic curve (AUROC) for the composite outcome of ICU transfer or mortality for both internal validation and temporal validation cohorts (AUROC 0.785 95% confidence interval [0.780–0.791] vs 0.708 [0.701–0.715] for temporal validation) as well as lead-time before deterioration events (median 11 hours vs 3 hours; P = .004). However, LGBM performance as evaluated by precision recall curve was lesser in the temporal validation cohort with associated decreased positive predictive value (6% vs 29%) and increased number needed to evaluate (17 vs 3) compared with I-PEWS. CONCLUSIONS Our electronic health record based machine learning model demonstrated improved AUROC and lead-time in predicting clinical deterioration in pediatric inpatients 24 to 48 hours in advance compared with I-PEWS. Further work is needed to optimize model positive predictive value to allow for integration into clinical practice.

Background: Collaborative care management (CCM) is an evidence-based model that contributes to better outcomes for depression treatment in the primary care setting. Tobacco use increases overall economic costs, morbidity, and mortality and has been shown to impact behavioral health outcomes. Our study aims to observe clinical outcomes for depression treatment for patients with comorbid tobacco use and depression within the CCM model. Methods: A retrospective chart review study of 2826 adult patients with depression enrolled in CCM was performed to determine the association between regular tobacco use and depression outcomes. Baseline intake data consisting of clinical and demographic variables along with 6-month follow-up of Patient Health Questionnaire-9 (PHQ-9) scores for smokers (n = 727, 25.7%) and nonsmokers (n = 2099, 74.3%) were obtained. Depression remission was defined as a PHQ-9 score <5 and persistent depressive symptoms (PDS) as a PHQ-9 score ≥10 at 6 months. Results: Using an intention-to-treat analysis, the multivariate modeling demonstrated that smokers, at 6 months, had an increased adjusted odds ratio (AOR) for PDS: 1.624 (95% CI: 1.353-1.949). Furthermore, smokers had a lower AOR of depression remission: 0.603 (95% CI: 0.492-0.739). Patient adherence to treatment was also lower in smokers with an AOR of 0.666 (95% CI: 0.553-0.802). Conclusions: Smokers enrolled in CCM were associated with reduced treatment adherence and worse outcomes for depression treatment at 6 months compared to nonsmokers, even when baseline clinical and demographic variables were controlled. Thus, new tailored practices may be warranted within the CCM model to treat comorbid depression and tobacco use disorders.