RESUMO
AIM: To compare open-source AndroidAPS (AAPS) and commercially available Control-IQ (CIQ) automated insulin delivery (AID) systems in a prospective, open-label, single-arm clinical trial. METHODS: Adults with type 1 diabetes who had been using AAPS by their own decision entered the first 3-month AAPS phase then were switched to CIQ for 3 months. The results of this treatment were compared with those after the 3-month AAPS phase. The primary endpoint was the change in time in range (% TIR; 70-80 mg/dL). RESULTS: Twenty-five people with diabetes (mean age 34.32 ± 11.07 years; HbA1c 6.4% ± 3%) participated in this study. CIQ was comparable with AAPS in achieving TIR (85.72% ± 7.64% vs. 84.24% ± 8.46%; P = .12). Similarly, there were no differences in percentage time above range (> 180 and > 250 mg/dL), mean sensor glucose (130.3 ± 13.9 vs. 128.3 ± 16.9 mg/dL; P = .21) or HbA1c (6.3% ± 2.1% vs. 6.4% ± 3.1%; P = .59). Percentage time below range (< 70 and < 54 mg/dL) was significantly lower using CIQ than AAPS. Even although participants were mostly satisfied with CIQ (63.6% mostly agreed, 9.1% strongly agreed), they did not plan to switch to CIQ. CONCLUSIONS: The CODIAC study is the first prospective study investigating the switch between open-source and commercially available AID systems. CIQ and AAPS were comparable in achieving TIR. However, hypoglycaemia was significantly lower with CIQ.
Assuntos
Diabetes Mellitus Tipo 1 , Insulinas , Adulto , Humanos , Pessoa de Meia-Idade , Adulto Jovem , Glicemia , Automonitorização da Glicemia/métodos , Diabetes Mellitus Tipo 1/tratamento farmacológico , Hemoglobinas Glicadas , Hipoglicemiantes/uso terapêutico , Insulina/uso terapêutico , Sistemas de Infusão de Insulina , Estudos ProspectivosRESUMO
Background: Personality pathology does not have to be a contraindication to a bariatric surgery if a proper pre-surgical assessment is done. Indicating subgroups of patients with their specific needs could help tailor interventions and improve surgical treatment outcomes. Objectives: Using the Alternative DSM-5 model for personality disorders (AMPD) and the ICD-11 model for PDs to detect subgroups of patients with obesity based on a specific constellation of maladaptive personality traits and the level of overall personality impairment. Methods: 272 consecutively consented patients who underwent a standard pre-surgical psychological assessment. The majority were women (58.0%), age range was 22-79 years (M = 48.06, SD = 10.70). Patients' average body mass index (BMI) was 43.95 kg/m2. All participants were administered the Personality Inventory for DSM-5 (PID-5) from which Level of Personality Functioning Scale-Self Report (LPFS-SR) and Standardized Assessment of Severity of Personality Disorder (SASPD) scores were gained using the "crosswalk" for common metric for self-reported severity of personality disorder. The k-means clustering method was used to define specific subgroups of patients with obesity and replicated for equality testing to the samples of non-clinical respondents and psychiatric patients. Results: The cluster analysis detected specific groups in the sample of patients with obesity, which differed quantitatively from the samples of non-clinical respondents and psychiatric patients. A vast majority of patients with obesity showed above-average values in most of the PID-5 facets compared to the United States representative general community sample. In two out of the three clusters defined, patients demonstrated moderate (> M + 1.5 × SD) to severe (> M + 2.0 × SD) personality psychopathology within the Detachment and Negative Affectivity domains according to PID-5, which in one of the clusters corresponded to the mild overall impairment in both, LPFS-SR (M = 2.18, SD = 0.27) and SASPD (M = 8.44, SD = 2.38). Moreover, higher levels of psychopathology prove to be associated with higher age and use of psychiatric medication. Conclusions: The dimensional DSM-5 and ICD-11 trait models are suitable procedures for defining specific "characters" of patients in a pre-bariatric setting. As such, they help to identify subgroups of patients with obesity who are different from general population and psychiatric patients. Implications for clinical practice and further research are discussed.