Weight status and associated comorbidities in children and adults with Down syndrome, autism spectrum disorder and intellectual and developmental disabilities

This is the peer reviewed version of the following article: Ptomey, L. T., Walpitage, D. L., Mohseni, M., Dreyer Gillette, M. L., Davis, A. M., Forseth, B., Dean, E. E., and Waitman, L. R. (2020) Weight status and associated comorbidities in children and adults with Down syndrome, autism spectrum disorder and intellectual and developmental disabilities. Journal of Intellectual Disability Research, 64: 725– 737. https://doi.org/10.1111/jir.12767, which has been published in final form at https://doi.org/10.1111/jir.12767. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.Background Little is known about body weight status and the association between body weight and common comorbidities in children and adults with Down syndrome (DS), autism spectrum disorder (ASD) and other intellectual and developmental disabilities (IDDs). Methods Data were extracted from the University of Kansas Medical Center's Healthcare Enterprise Repository for Ontological Narration clinical integrated data repository. Measures included demographics (sex, age and race), disability diagnosis, comorbid health conditions, height, weight and body mass index percentiles (BMI%ile; <18 years of age) or BMI (≥18 years of age). Results Four hundred and sixty-eight individuals with DS (122 children and 346 adults), 1659 individuals with ASD (1073 children and 585 adults) and 604 individuals with other IDDs (152 children and 452 adults) were identified. A total of 47.0% (DS), 41.9% (ASD) and 33.5% (IDD) of children had overweight/obese (OW/OB), respectively. Children with DS were more likely to have OW/OB compared with children with IDD or ASD [odds ratio (OR) = 1.91, 95% confidence interval (CI): (1.49, 2.46); OR = 1.43, 95% CI: (1.19, 1.72)], respectively. A total of 81.1% (DS), 62.1% (ASD), and 62.4% (IDD) of adults were OW/OB, respectively. Adults with DS were more likely to have OW/OB compared with those with IDD [OR = 2.56, 95% CI: (2.16, 3.02)]. No significant differences were observed by race. In children with ASD, higher OW/OB was associated with significantly higher (compared with non-OW/OB) occurrence of sleep apnoea [OR = 2.94, 95% CI: (2.22, 3.89)], hypothyroidism [OR = 3.14, 95% CI: (2.17, 4.25)] and hypertension [OR = 4.11, 95% CI: (3.05, 5.54)]. In adults with DS, OW/OB was significantly associated with higher risk of sleep apnoea and type 2 diabetes [OR = 2.93, 95% CI: (2.10, 4.09); OR = 1.76, 95% CI: (1.11, 2.79) respectively]. Similarly, in adults with ASD and IDD, OW/OB was significantly associated with higher risk of sleep apnoea [OR = 3.39, 95% CI: (2.37, 4.85) and OR = 6.69, 95% CI: (4.43, 10.10)], type 2 diabetes [OR = 2.25, 95 % CI: (1.68, 3.01) and OR = 5.49, 95% CI: (3.96, 7.61)] and hypertension [OR = 3.55, 95% CI: (2.76, 4.57) and 3.97, 95% CI: (3.17, 4.97)]. Conclusion Findings suggest higher rates of OW/OB in individuals with DS compared with ASD and IDD. Given the increased risk of comorbidities associated with the increased risk of OW/OB, identification of effective interventions for this special population of individuals is critical

A tree-based decision model to support prediction of the severity of asthma exacerbations in children

This paper describes the development of a tree-based decision model to predict the severity of pediatric asthma exacerbations in the emergency department (ED) at 2 h following triage. The model was constructed from retrospective patient data abstracted from the ED charts. The original data was preprocessed to eliminate questionable patient records and to normalize values of age-dependent clinical attributes. The model uses attributes routinely collected in the ED and provides predictions even for incomplete observations. Its performance was verified on independent validating data (split-sample validation) where it demonstrated AUC (area under ROC curve) of 0.83, sensitivity of 84%, specificity of 71% and the Brier score of 0.18. The model is intended to supplement an asthma clinical practice guideline, however, it can be also used as a stand-alone decision tool

Effect of sitagliptin on cardiovascular outcomes in type 2 diabetes

BACKGROUND: Data are lacking on the long-term effect on cardiovascular events of adding sitagliptin, a dipeptidyl peptidase 4 inhibitor, to usual care in patients with type 2 diabetes and cardiovascular disease. METHODS: In this randomized, double-blind study, we assigned 14,671 patients to add either sitagliptin or placebo to their existing therapy. Open-label use of antihyperglycemic therapy was encouraged as required, aimed at reaching individually appropriate glycemic targets in all patients. To determine whether sitagliptin was noninferior to placebo, we used a relative risk of 1.3 as the marginal upper boundary. The primary cardiovascular outcome was a composite of cardiovascular death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for unstable angina. RESULTS: During a median follow-up of 3.0 years, there was a small difference in glycated hemoglobin levels (least-squares mean difference for sitagliptin vs. placebo, -0.29 percentage points; 95% confidence interval [CI], -0.32 to -0.27). Overall, the primary outcome occurred in 839 patients in the sitagliptin group (11.4%; 4.06 per 100 person-years) and 851 patients in the placebo group (11.6%; 4.17 per 100 person-years). Sitagliptin was noninferior to placebo for the primary composite cardiovascular outcome (hazard ratio, 0.98; 95% CI, 0.88 to 1.09; P<0.001). Rates of hospitalization for heart failure did not differ between the two groups (hazard ratio, 1.00; 95% CI, 0.83 to 1.20; P = 0.98). There were no significant between-group differences in rates of acute pancreatitis (P = 0.07) or pancreatic cancer (P = 0.32). CONCLUSIONS: Among patients with type 2 diabetes and established cardiovascular disease, adding sitagliptin to usual care did not appear to increase the risk of major adverse cardiovascular events, hospitalization for heart failure, or other adverse events

The effectiveness of computerized clinical guidelines in the process of care: a systematic review

<p>Abstract</p> <p>Background</p> <p>Clinical practice guidelines have been developed aiming to improve the quality of care. The implementation of the computerized clinical guidelines (CCG) has been supported by the development of computerized clinical decision support systems.</p> <p>This systematic review assesses the impact of CCG on the process of care compared with non-computerized clinical guidelines.</p> <p>Methods</p> <p>Specific features of CCG were studied through an extensive search of scientific literature, querying electronic databases: Pubmed/Medline, Embase and Cochrane Controlled Trials Register. A multivariable logistic regression was carried out to evaluate the association of CCG's features with positive effect on the process of care.</p> <p>Results</p> <p>Forty-five articles were selected. The logistic model showed that Automatic provision of recommendation in electronic version as part of clinician workflow (Odds Ratio [OR]= 17.5; 95% confidence interval [CI]: 1.6-193.7) and Publication Year (OR = 6.7; 95%CI: 1.3-34.3) were statistically significant predictors.</p> <p>Conclusions</p> <p>From the research that has been carried out, we can conclude that after implementation of CCG significant improvements in process of care are shown. Our findings also suggest clinicians, managers and other health care decision makers which features of CCG might improve the structure of computerized system.</p

Identification of adverse drug-drug interactions through causal association rule discovery from spontaneous adverse event reports

This work is licensed under a Creative Commons Attribution Non-Commercial-No Derivatives 4.0 International License.Objective Drug-drug interaction (DDI) is of serious concern, causing over 30% of all adverse drug reactions and resulting in significant morbidity and mortality. Early discovery of adverse DDI is critical to prevent patient harm. Spontaneous reporting systems have been a major resource for drug safety surveillance that routinely collects adverse event reports from patients and healthcare professionals. In this study, we present a novel approach to discover DDIs from the Food and Drug Administration’s adverse event reporting system. Methods Data-driven discovery of DDI is an extremely challenging task because higher-order associations require analysis of all combinations of drugs and adverse events and accurate estimate of the relationships between drug combinations and adverse event require cause-and-effect inference. To efficiently identify causal relationships, we introduce the causal concept into association rule mining by developing a method called Causal Association Rule Discovery (CARD). The properties of V-structures in Bayesian Networks are utilized in the search for causal associations. To demonstrate feasibility, CARD is compared to the traditional association rule mining (AR) method in DDI identification. Results Based on physician evaluation of 100 randomly selected higher-order associations generated by CARD and AR, CARD is demonstrated to be more accurate in identifying known drug interactions compared to AR, 20% vs. 10% respectively. Moreover, CARD yielded a lower number of drug combinations that are unknown to interact, i.e., 50% for CARD and 79% for AR. Conclusion Evaluation analysis demonstrated that CARD is more likely to identify true causal drug variables and associations to adverse event