Treatment outcome clustering patterns correspond to discrete asthma phenotypes in children.

Despite widely and regularly used therapy asthma in children is not fully controlled. Recognizing the complexity of asthma phenotypes and endotypes imposed the concept of precision medicine in asthma treatment. By applying machine learning algorithms assessed with respect to their accuracy in predicting treatment outcome, we have successfully identified 4 distinct clusters in a pediatric asthma cohort with specific treatment outcome patterns according to changes in lung function (FEV1 and MEF50), airway inflammation (FENO) and disease control likely affected by discrete phenotypes at initial disease presentation, differing in the type and level of inflammation, age of onset, comorbidities, certain genetic and other physiologic traits. The smallest and the largest of the 4 clusters- 1 (N = 58) and 3 (N = 138) had better treatment outcomes compared to clusters 2 and 4 and were characterized by more prominent atopic markers and a predominant allelic (A allele) effect for rs37973 in the GLCCI1 gene previously associated with positive treatment outcomes in asthmatics. These patients also had a relatively later onset of disease (6 + yrs). Clusters 2 (N = 87) and 4 (N = 64) had poorer treatment success, but varied in the type of inflammation (predominantly neutrophilic for cluster 4 and likely mixed-type for cluster 2), comorbidities (obesity for cluster 2), level of systemic inflammation (highest hsCRP for cluster 2) and platelet count (lowest for cluster 4). The results of this study emphasize the issues in asthma management due to the overgeneralized approach to the disease, not taking into account specific disease phenotypes.

Limited Documentation and Treatment Quality of Glycemic Inpatient Care in Relation to Structural Deficits of Heterogeneous Insulin Charts at a Large University Hospital.

OBJECTIVES: Insulin charts represent a key component in the inpatient glycemic management process. The aim was to evaluate the quality of structure, documentation, and treatment of diabetic inpatient care to design a new standardized insulin chart for a large university hospital setting. METHODS: Historically grown blank insulin charts in use at 39 general wards were collected and evaluated for quality structure features. Documentation and treatment quality were evaluated in a consecutive snapshot audit of filled-in charts. The primary end point was the percentage of charts with any medication error. RESULTS: Overall, 20 different blank insulin charts with variable designs and significant structural deficits were identified. A medication error occurred in 55% of the 102 audited filled-in insulin charts, consisting of prescription and management errors in 48% and 16%, respectively. Charts of insulin-treated patients had more medication errors relative to patients treated with oral medication (P < 0.01). Chart design did support neither clinical authorization of individual insulin prescription (10%), nor insulin administration confirmed by nurses' signature (25%), nor treatment of hypoglycemia (0%), which resulted in a reduced documentation and treatment quality in clinical practice 7%, 30%, 25%, respectively. CONCLUSIONS: A multitude of charts with variable design characteristics and structural deficits were in use across the inpatient wards. More than half of the inpatients had a chart displaying a medication error. Lack of structure quality features of the charts had an impact on documentation and treatment quality. Based on identified deficits and international standards, a new insulin chart was developed to overcome these quality hurdles.

Diabetes Management According to Health Status in Older Adults with Type 2 Diabetes Staying in Geriatric Care Facilities.

BACKGROUND: About 25% of adults >70 years suffer from type 2 diabetes. Due to the heterogeneity of the geriatric population, guidelines emphasize the need to individualize glycemic goals and simplify treatment strategies with the main focus of avoiding hypoglycemia. The aim of this study was to assess glycemic control in patients with type 2 diabetes in geriatric care facilities based on their individual health status. METHODS: 170 medical records of older adults with type 2 diabetes in geriatric care facilities were retrospectively assessed (64.7% female, age 80 ± 9 years; glycated hemoglobin 6.8% ± 3.6% [51 ± 16 mmol/mol]; body mass index 27.9 ± 5.8 kg/m2). Based on the individual health status, patients were allocated to three groups (healthy n = 27, complex n = 86, and poor n = 57). RESULTS: The overall blood glucose (BG) value was highest in the poor health group with 188 ± 47 mg/dL (poor) vs 167 ± 42 mg/dL (complex) vs 150 ± 34 mg/dL (healthy). BG values of 1.6% (poor) vs 2.8% (complex) vs 1.5% (healthy) of patients were below 90 mg/dL. 36.8% (poor) vs 23.4% (complex) vs 18.5% (healthy) of patients received insulin as the main diabetes therapy, but of these only 14.3% (poor) vs 20% (complex) vs 40% (healthy) were treated with basal insulin. CONCLUSIONS: Overall, BG values were higher in the poor and complex health group. There were a few low BG values in all groups. Although recommended by international guidelines, basal insulin therapy with its low complexity and low hypoglycemic risk is still underused, especially in the poor health group. Therefore, simplification of diabetes therapy should be considered further.