Trajectories of school refusal: sequence analysis using retrospective parent reports.

School refusal (SR) is a form of school attendance problem (SAP) that requires specific mental health care. Despite improvements in the definition of SAPs, the course of SR is not well characterized. To explore three-year patterns of SR course in children, as reported by their parents, we deployed an anonymous web-based survey. We defined SR onset as the absence of ≥ 2 school weeks during one academic year, combined with emotional distress. We defined standard SR trajectories using sequence analysis of parents' recollection of three consecutive years of school attendance. We obtained 1970 responses, 1328 (67%) completed by a parent and meeting the definition of SR. Of these, 729 (55%) responses included three years of school attendance recollection. We identified five prototypical trajectories of SR: two profiles for children: beaded absences (n = 272), and rapid recovery (n = 132); and three for adolescents: prolonged recovery (n = 93), gradual decline (n = 89), and rapid decline (n = 143). We found five distinct trajectories of retrospective recall of SR course. Through pattern recognition, this typology could help with timely identification of SR and implementation of evidence-based interventions to optimize outcomes. Prospective replication of these findings and their field application is warranted.

Survey of the Impact of Decision Support in Preoperative Management of Anemia (i-Anemia): Survey Study.

BACKGROUND: Major surgery on patients with anemia has demonstrated an increased risk of perioperative blood transfusions and postoperative morbidity and mortality. Recent studies have shown that integrating preoperative anemia treatment as a component of perioperative blood management may reduce blood product utilization and improve outcomes in both cardiac and noncardiac surgery. However, outpatient management of anemia falls outside of daily practice for most anesthesiologists and is probably weakly understood. OBJECTIVE: We conducted a simulated case survey with anesthesiologists to accomplish the following aims: (1) evaluate the baseline knowledge of the preoperative optimization of anemia and (2) determine the impact of real-time clinical decision support on anemia management. METHODS: We sent a digital survey (i-Anemia) to members of the French Society of Anaesthesia and Critical Care. The i-Anemia survey contained 7 simulated case vignettes, each describing a patient's brief clinical history and containing up to 3 multiple-choice questions related to preoperative anemia management (12 questions in total). The cases concerned potential situations of preoperative anemia and were created and validated with a committee of patient blood management experts. Correct answers were determined by the current guidelines or by expert consensus. Eligible participants were randomly assigned to control or decision support groups. In the decision support group, the primary outcome measured was the correct response rate. RESULTS: Overall, 1123 participants were enrolled and randomly divided into control (n=568) and decision support (n=555) groups. Among them, 763 participants fully responded to the survey. We obtained a complete response rate of 65.6% (n=364) in the group receiving cognitive aid and 70.2% (n=399) in the group without assistance. The mean duration of response was 10.2 (SD 6.8) minutes versus 7.8 (SD 5) minutes for the decision support and control groups, respectively (P<.001). The score significantly improved with cognitive aid (mean 10.3 out of 12, SD 2.1) in comparison to standard care (mean 6.2 out of 12, SD 2.1; P<.001). CONCLUSIONS: Management strategies to optimize preoperative anemia are not fully known and applied by anesthesiologists in daily practice despite their clinical importance. However, adding a decision support tool can significantly improve patient care by reminding practitioners of current recommendations.

Home care aides' observations and machine learning algorithms for the prediction of visits to emergency departments by older community-dwelling individuals receiving home care assistance: A proof of concept study.

BACKGROUND: Older individuals receiving home assistance are at high risk for emergency visits and unplanned hospitalization. Anticipating their health difficulties could prevent these events. This study investigated the effectiveness of an at-home monitoring method using social workers' observations to predict risk for 7- and 14-day emergency department (ED) visits. METHODS: This was a prospective cohort study of persons ≥75 years, living at home and receiving assistance from home care aides (HCA) at 6 French facilities. After each home visit, HCAs reported on participants' functional status using a smartphone application that recorded 27 functional items about each participant (e.g., ability to stand, move, eat, mood, loneliness). We recorded ED visits. Finally, we used machine learning techniques (i.e., leveraging random forest predictors) to develop a 7- and 14-day predictive algorithm for the risk of ED visit. RESULTS: The study included 301 participants, and the HCA made 9,987 observations. Over the mean 10-month follow-up, 97 participants (32%) had at least one ED visit. Modeling techniques identified 9 contributory factors from the longitudinal records of the HCA and developed a predictive algorithm for the risk of ED visit. The predictive performance (i.e., the area under the ROC curve) was 0.70 at 7 days and 0.67 at 14 days. INTERPRETATION: For frail elders receiving in-home care, information on functional status collected by HCA helps predict the risk of ED visits 7 to 14 days in advance. A survey system for real-time identification of risks could be developed using this exploratory work.