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.

Predicting pediatric emergence delirium using data-driven machine learning applied to electronic health record dataset at a quaternary care pediatric hospital.

Objectives: Pediatric emergence delirium is an undesirable outcome that is understudied. Development of a predictive model is an initial step toward reducing its occurrence. This study aimed to apply machine learning (ML) methods to a large clinical dataset to develop a predictive model for pediatric emergence delirium. Materials and Methods: We performed a single-center retrospective cohort study using electronic health record data from February 2015 to December 2019. We built and evaluated 4 commonly used ML models for predicting emergence delirium: least absolute shrinkage and selection operator, ridge regression, random forest, and extreme gradient boosting. The primary outcome was the occurrence of emergence delirium, defined as a Watcha score of 3 or 4 recorded at any time during recovery. Results: The dataset included 54 776 encounters across 43 830 patients. The 4 ML models performed similarly with performance assessed by the area under the receiver operating characteristic curves ranging from 0.74 to 0.75. Notable variables associated with increased risk included adenoidectomy with or without tonsillectomy, decreasing age, midazolam premedication, and ondansetron administration, while intravenous induction and ketorolac were associated with reduced risk of emergence delirium. Conclusions: Four different ML models demonstrated similar performance in predicting postoperative emergence delirium using a large pediatric dataset. The prediction performance of the models draws attention to our incomplete understanding of this phenomenon based on the studied variables. The results from our modeling could serve as a first step in designing a predictive clinical decision support system, but further optimization and validation are needed. Clinical trial number and registry URL: Not applicable.

Extracting social determinants of health events with transformer-based multitask, multilabel named entity recognition.

OBJECTIVE: Social determinants of health (SDOH) are nonclinical, socioeconomic conditions that influence patient health and quality of life. Identifying SDOH may help clinicians target interventions. However, SDOH are more frequently available in narrative notes compared to structured electronic health records. The 2022 n2c2 Track 2 competition released clinical notes annotated for SDOH to promote development of NLP systems for extracting SDOH. We developed a system addressing 3 limitations in state-of-the-art SDOH extraction: the inability to identify multiple SDOH events of the same type per sentence, overlapping SDOH attributes within text spans, and SDOH spanning multiple sentences. MATERIALS AND METHODS: We developed and evaluated a 2-stage architecture. In stage 1, we trained a BioClinical-BERT-based named entity recognition system to extract SDOH event triggers, that is, text spans indicating substance use, employment, or living status. In stage 2, we trained a multitask, multilabel NER to extract arguments (eg, alcohol "type") for events extracted in stage 1. Evaluation was performed across 3 subtasks differing by provenance of training and validation data using precision, recall, and F1 scores. RESULTS: When trained and validated on data from the same site, we achieved 0.87 precision, 0.89 recall, and 0.88 F1. Across all subtasks, we ranked between second and fourth place in the competition and always within 0.02 F1 from first. CONCLUSIONS: Our 2-stage, deep-learning-based NLP system effectively extracted SDOH events from clinical notes. This was achieved with a novel classification framework that leveraged simpler architectures compared to state-of-the-art systems. Improved SDOH extraction may help clinicians improve health outcomes.

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.

An argument for reporting data standardization procedures in multi-site predictive modeling: case study on the impact of LOINC standardization on model performance.

OBJECTIVES: We aimed to gain a better understanding of how standardization of laboratory data can impact predictive model performance in multi-site datasets. We hypothesized that standardizing local laboratory codes to logical observation identifiers names and codes (LOINC) would produce predictive models that significantly outperform those learned utilizing local laboratory codes. MATERIALS AND METHODS: We predicted 30-day hospital readmission for a set of heart failure-specific visits to 13 hospitals from 2008 to 2012. Laboratory test results were extracted and then manually cleaned and mapped to LOINC. We extracted features to summarize laboratory data for each patient and used a training dataset (2008-2011) to learn models using a variety of feature selection techniques and classifiers. We evaluated our hypothesis by comparing model performance on an independent test dataset (2012). RESULTS: Models that utilized LOINC performed significantly better than models that utilized local laboratory test codes, regardless of the feature selection technique and classifier approach used. DISCUSSION AND CONCLUSION: We quantitatively demonstrated the positive impact of standardizing multi-site laboratory data to LOINC prior to use in predictive models. We used our findings to argue for the need for detailed reporting of data standardization procedures in predictive modeling, especially in studies leveraging multi-site datasets extracted from electronic health records.

A study of the transferability of influenza case detection systems between two large healthcare systems.

OBJECTIVES: This study evaluates the accuracy and transferability of Bayesian case detection systems (BCD) that use clinical notes from emergency department (ED) to detect influenza cases. METHODS: A BCD uses natural language processing (NLP) to infer the presence or absence of clinical findings from ED notes, which are fed into a Bayesain network classifier (BN) to infer patients' diagnoses. We developed BCDs at the University of Pittsburgh Medical Center (BCDUPMC) and Intermountain Healthcare in Utah (BCDIH). At each site, we manually built a rule-based NLP and trained a Bayesain network classifier from over 40,000 ED encounters between Jan. 2008 and May. 2010 using feature selection, machine learning, and expert debiasing approach. Transferability of a BCD in this study may be impacted by seven factors: development (source) institution, development parser, application (target) institution, application parser, NLP transfer, BN transfer, and classification task. We employed an ANOVA analysis to study their impacts on BCD performance. RESULTS: Both BCDs discriminated well between influenza and non-influenza on local test cases (AUCs > 0.92). When tested for transferability using the other institution's cases, BCDUPMC discriminations declined minimally (AUC decreased from 0.95 to 0.94, p<0.01), and BCDIH discriminations declined more (from 0.93 to 0.87, p<0.0001). We attributed the BCDIH decline to the lower recall of the IH parser on UPMC notes. The ANOVA analysis showed five significant factors: development parser, application institution, application parser, BN transfer, and classification task. CONCLUSION: We demonstrated high influenza case detection performance in two large healthcare systems in two geographically separated regions, providing evidentiary support for the use of automated case detection from routinely collected electronic clinical notes in national influenza surveillance. The transferability could be improved by training Bayesian network classifier locally and increasing the accuracy of the NLP parser.

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.

Inventario de Obsesiones y Compulsiones-Revisado (OCI-R): Aplicabilidad a la población general / Obsessive-Compulsive Inventory – Revised (OCI-R): Applicability to general population

El Obsessive-Compulsive Inventory - Revised (OCI-R; Foa et al., 2002) se ha convertido en instrumento de elección para la evaluación de los comportamientos obsesivo-compulsivos, dada su validez y el corto tiempo que requiere su administración. Sin embargo, el OCI-R aún no ha sido estudiado sobre muestras de la población general. En el presente estudio se analizan las propiedades psicométricas de la versión española del OCI-R en dos grupos de sujetos: estudiantes universitarios (n = 247), lo que permitirá comparar los resultados obtenidos con los de estudios previos, y población general (n = 395), lo que permitirá generalizar dichos resultados a personas sin patología clínica conocida o con sintomatología subclínica. Se ha analizado la estructura factorial, la fiabilidad y la validez convergente, divergente y de criterio del OCI-R. Los resultados muestran una estructura factorial y unas propiedades psicométricas similares a las halladas con la versión original y, tal y como se esperaba, un grado medio de relación entre el OCI-R y otras variables con las que se hipotetizó que estaría relacionado: intrusiones, creencias disfuncionales, preocupaciones y perfeccionismo. Se concluye que la versión española del OCI-R es aplicable a la población general(AU)

The Obsessive-Compulsive Inventory - Revised (OCI-R; Foa et al., 2002) has became the measure of election for the assessment of obsessive-compulsive behaviors, given its validity and the short time that its administration requires. Nevertheless, the OCI-R not yet has been studied on samples from the general population. In the present study the psychometric properties of the Spanish version of the OCI-R in two groups of subjects have been analyzed: university students (n = 247), which will allow comparing the results with those of previous studies, and general population (n = 395), which will allow generalizing these results to people without known clinical pathology or with subclinical symptoms. The factorial structure, the reliability and the convergent, divergent and criterion validity of the OCI-R were analyzed. The results show a factorial structure and psychometric properties of the Spanish version of the OCI-R similar to the ones found on the original version and, as expected, moderate relationship between the OCI-R scores and related variables: intrusions, dysfunctional beliefs, worry, and perfectionism. It is concluded that the Spanish version of the OCI-R is applicable to the general population(AU)

Personality, cognitive coping strategies, and adaptational outcomes in a sample from the general Spanish population.

The relationship among scores on two personality dimensions, Emotional Stability and Extraversion, and on two cognitive coping strategies, Positive Thinking and Wishful Thinking, and on the Consequences of Coping scale were examined in 169 Spanish persons (78 men and 91 women; Mage = 36.3 yr., SD = 12.1). Positive Thinking was associated with high scores on the two personality dimensions and positive consequences, whereas Wishful Thinking was associated with low scores on both Emotional Stability and Extraversion and with negative consequences.

Emotional stability, positive and wishful thinking, and consequences of coping in a Spanish sample.

The relationship between scores on Emotional Stability and on two cognitive coping strategies-Positive Thinking and Wishful Thinking-and the Consequences of Coping scale were examined in a group of 99 Spanish undergraduates. Positive Thinking was associated with high Emotional Stability and positive consequences, whereas Wishful Thinking was associated with low Emotional Stability and negative consequences.

The five-factor model of personality, subjective well-being, and social adaptation: generalizability to the Spanish context.

The relationships between the five-factor model of personality, subjective well-being, and social adaptation were examined in two Spanish groups, one of 112 undergraduate students and one of 177 participants from the general population. Analyses showed a clear pattern of low but positive associations among scores on well-being, social adaptation, and four of the five factors of personality (Extraversion, Agreeableness, Conscientiousness, and Emotional Stability), very similar to those obtained by previous research in the American context.