Booklet and Motivational Interviewing to Promote Self-efficacy in Parents/Caregivers of Children with Asthma: A Clinical Trial.

Background: Asthma is the most common chronic disease in childhood which accounts for numerous annual hospitalizations due to a lack of management and proper management of the disease. Thus, this study aimed to evaluate the effect of using an educational booklet with or without combination with motivational interviewing (MI) on the self-efficacy of parents/caregivers in the control and management of childhood asthma. Methods: A clinical trial was carried out with 86 parents/caregivers of children with asthma aged between 2 and 12 years who were followed up in primary health care units from March 2019 to December 2020. Participants were randomly assigned to two groups: one of the groups read the booklet and the other read the booklet combined with the MI. The Brazilian version of the Self-Efficacy and Their Child's Level of Asthma Control scale was applied before and 30 days after the intervention for assessment of self-efficacy. Data were analyzed using SPSS version 20.0 and R 3.6.3 software. P values<0.05 were considered significant. Results: There were 46 participants in the booklet group and 40 in the booklet and MI group. Both groups were effective in increasing total self-efficacy scores after the intervention (P<0.001). No statistically significant difference was found between the scores of the two groups (P=0.257). Conclusion: The educational booklet with or without combination with MI can increase the self-efficacy of parents/caregivers of children with asthma. The findings could be considered by healthcare providers for the empowerment of caregivers of children with asthma in the control and management of their children's asthma.Trial Registration Number: U1111-1254-7256.

Early prediction of clinical deterioration using data-driven machine-learning modeling of electronic health records.

OBJECTIVES: To develop and evaluate a high-dimensional, data-driven model to identify patients at high risk of clinical deterioration from routinely collected electronic health record (EHR) data. MATERIALS AND METHODS: In this single-center, retrospective cohort study, 488 patients with single-ventricle and shunt-dependent congenital heart disease <6 months old were admitted to the cardiac intensive care unit before stage 2 palliation between 2014 and 2019. Using machine-learning techniques, we developed the Intensive care Warning Index (I-WIN), which systematically assessed 1028 regularly collected EHR variables (vital signs, medications, laboratory tests, and diagnoses) to identify patients in the cardiac intensive care unit at elevated risk of clinical deterioration. An ensemble of 5 extreme gradient boosting models was developed and validated on 203 cases (130 emergent endotracheal intubations, 34 cardiac arrests requiring cardiopulmonary resuscitation, 10 extracorporeal membrane oxygenation cannulations, and 29 cardiac arrests requiring cardiopulmonary resuscitation onto extracorporeal membrane oxygenation) and 378 control periods from 446 patients. RESULTS: At 4 hours before deterioration, the model achieved an area under the receiver operating characteristic curve of 0.92 (95% confidence interval, 0.84-0.98), 0.881 sensitivity, 0.776 positive predictive value, 0.862 specificity, and 0.571 Brier skill score. Performance remained high at 8 hours before deterioration with 0.815 (0.688-0.921) area under the receiver operating characteristic curve. CONCLUSIONS: I-WIN accurately predicted deterioration events in critically-ill infants with high-risk congenital heart disease up to 8 hours before deterioration, potentially allowing clinicians to target interventions. We propose a paradigm shift from conventional expert consensus-based selection of risk factors to a data-driven, machine-learning methodology for risk prediction. With the increased availability of data capture in EHRs, I-WIN can be extended to broader applications in data-rich environments in critical care.

Early prediction of critical events for infants with single-ventricle physiology in critical care using routinely collected data.

OBJECTIVE: Critical events are common and difficult to predict among infants with congenital heart disease and are associated with mortality and long-term sequelae. We aimed to achieve early prediction of critical events, that is, cardiopulmonary resuscitation, emergency endotracheal intubation, and extracorporeal membrane oxygenation in infants with single-ventricle physiology before second-stage surgery. We hypothesized that naïve Bayesian models learned from expert knowledge and clinical data can predict critical events early and accurately. METHODS: We collected 93 patients with single-ventricle physiology admitted to intensive care units in a single tertiary pediatric hospital between 2014 and 2017. Using knowledge elicited from experienced cardiac-intensive-care-unit providers and machine-learning techniques, we developed and evaluated the Cardiac-intensive-care Warning INdex (C-WIN) system, consisting of a set of naïve Bayesian models that leverage routinely collected data. We evaluated predictive performance using the area under the receiver operating characteristic curve, sensitivity, and specificity. We performed the evaluation at 5 different prediction horizons: 1, 2, 4, 6, and 8 hours before the onset of critical events. RESULTS: The area under the receiver operating characteristic curves of the C-WIN models ranged between 0.73 and 0.88 at different prediction horizons. At 1 hour before critical events, C-WIN was able to detect events with an area under the receiver operating characteristic curve of 0.88 (95% confidence interval, 0.84-0.92) and a sensitivity of 84% at the 81% specificity level. CONCLUSIONS: Predictive models may enhance clinicians' ability to identify infants with single-ventricle physiology at high risk of critical events. Early prediction of critical events may indicate the need to perform timely interventions, potentially reducing morbidity, mortality, and health care costs.

A delta-sarcoglycan gene polymorphism as a risk factor for hypertrophic cardiomyopathy.

BACKGROUND: The C allele of c.-94C>G polymorphism of the delta-sarcoglycan gene was associated as a risk factor for coronary spasm in Japanese patients with hypertrophic cardiomyopathy (HCM). AIM: We evaluated whether the c.-94C>G polymorphism can be a risk factor for HCM in Mexican patients. METHODS: The polymorphism was genotyped and the risk was estimated in 35 HCM patients and 145 healthy unrelated individuals. Data of this polymorphism reported in Mexican Amerindian populations were included. RESULTS: The C allele frequency in HCM patients was higher with an odds ratio (OR) of 2.37, and the risk for the CC genotype increased to 5.0. The analysis with Mexican Amerindian populations showed that the C allele frequency was significantly higher in HCM patients with an OR of 2.96 and for CC genotype the risk increased to 7.60. CONCLUSIONS: The C allele of the c.-94C>G polymorphism is a risk factor for HCM, which is increased by the Amerindian component and can play an important role in the etiology and progression of disease in Mexican patients.