Two-step predictive model for early detection of emergency department patients with prolonged stay and its management implications.

OBJECTIVE: To develop a novel model for predicting Emergency Department (ED) prolonged length of stay (LOS) patients upon triage completion, and further investigate the benefit of a targeted intervention for patients with prolonged ED LOS. MATERIALS AND METHODS: A two-step model to predict patients with prolonged ED LOS (>16 h) was constructed. This model was initially used to predict ED resource usage and was subsequently adapted to predict patient ED LOS based on the number of ED resources using binary logistic regressions and was validated internally with accuracy. Finally, a discrete event simulation was used to move patients with predicted prolonged ED LOS directly to a virtual Clinical Decision Unit (CDU). The changes of ED crowding status (Overcrowding, Crowding, and Not-Crowding) and savings of ED bed-hour equivalents were estimated as the measures of the efficacy of this intervention. RESULTS: We screened a total of 123,975 patient visits with final enrollment of 110,471 patient visits. The overall accuracy of the final model predicting prolonged patient LOS was 67.8%. The C-index of this model ranges from 0.72 to 0.82. By implementing the proposed intervention, the simulation showed a 12% (1044/8760) reduction of ED overcrowded status - an equivalent savings of 129.3 ED bed-hours per day. CONCLUSIONS: Early prediction of prolonged ED LOS patients and subsequent (simulated) early CDU transfer could lead to more efficiently utilization of ED resources and improved efficacy of ED operations. This study provides evidence to support the implementation of this novel intervention into real healthcare practice.

Late-Onset T1DM and Older Age Predict Risk of Additional Autoimmune Disease.

OBJECTIVE: Type 1 diabetes (T1DM) is associated with other autoimmune diseases (AIDs), which may have serious health consequences. The epidemiology of AIDs in T1DM is not well defined in adults with T1DM. In this cross-sectional cohort study, we sought to characterize the incident ages and prevalence of AIDs in adults with T1DM across a wide age spectrum. RESEARCH DESIGN AND METHODS: A total of 1,212 adults seen at the Washington University Diabetes Center from 2011 to 2018 provided informed consent for the collection of their age, sex, race, and disease onset data. We performed paired association analyses based on age at onset of T1DM. Multivariate logistic regression was used to evaluate the independent effects of sex, race, T1DM age of onset, and T1DM duration on the prevalence of an additional AID. RESULTS: Mean ± SD age of T1DM onset was 21.2 ± 14.4 years. AID incidence and prevalence increased with age. Female sex strongly predicted AID risk. The most prevalent T1DM-associated AIDs were thyroid disease, collagen vascular diseases, and pernicious anemia. T1DM age of onset and T1DM duration predicted AID risk. Patients with late-onset T1DM after 30 years of age had higher risks of developing additional AIDs compared with patients with younger T1DM onset. CONCLUSIONS: The prevalence of AIDs in patients with T1DM increases with age and female sex. Later onset of T1DM is an independent and significant risk factor for developing additional AIDs. Individuals who are diagnosed with T1DM at older ages, particularly women, should be monitored for other autoimmune conditions.

The Association of Low Parental Monitoring With Early Substance Use in European American and African American Adolescent Girls.

OBJECTIVE: Research indicates that low parental monitoring increases the risk for early substance use. Because low parental monitoring tends to co-occur with other familial and neighborhood factors, the specificity of the association is challenging to establish. Using logistic regression and propensity score analyses, we examined associations between low parental monitoring and early substance use in European American (EA) and African American (AA) girls, controlling for risk factors associated with low parental monitoring. METHOD: Participants were 3,133 EA and 523 AA girls from the Missouri Adolescent Female Twin Study with data on parental monitoring assessed via self-report questionnaire, and with ages at first use of alcohol, tobacco, and cannabis queried in at least one of three diagnostic interviews (median ages = 15, 22, and 24 years). RESULTS: The rate of early alcohol use was greater in EA than AA girls, whereas the proportion of AA girls reporting low parental monitoring was higher than in EA girls. EA girls who experienced low parental monitoring were at elevated risk for early alcohol, tobacco, and cannabis use, findings supported in both logistic regression and propensity score analyses. Evidence regarding associations between low parental monitoring and risk for early substance use was less definitive for AA girls. CONCLUSIONS: Findings highlight the role of parental monitoring in modifying risk for early substance use in EA girls. However, we know little regarding the unique effects, if any, of low parental monitoring on the timing of first substance use in AA girls.