Adaptive bolus calculators for people with type 1 diabetes: A systematic review.

AIM: To conduct a systematic review of studies assessing adaptive insulin bolus calculators for people with type 1 diabetes (T1D). METHODS: Electronic databases (Medline, Embase and Web of Science) were systematically searched from date of inception to 13 October 2022 for single-arm or randomized controlled studies assessing adaptive bolus calculators only, in children or adults with T1D on multiple daily injections or insulin pumps with glycaemic outcomes reported. The Clinicaltrials.gov registry was searched for recently completed studies evaluating decision support in T1D. The quality of extracted studies was assessed using the Standard Quality Assessment criteria and the Cochrane Risk of Bias assessment tool. RESULTS: Six studies were identified. Extracted data were synthesized in a descriptive review because of heterogeneity. All the studies were small feasibility studies or were not suitably powered, and all were deemed to be at a high risk of performance and detection bias because they were unblinded. Overall, these studies did not show a significant glycaemic improvement. Two studies showed a reduction in postprandial time below range or an incremental change in blood glucose concentration; however, these were in controlled environments over a short duration. CONCLUSIONS: There are limited clinical trials evaluating adaptive bolus calculators. Although results from small trials or in-silico data are promising, further studies are required to support personalized and adaptive management of T1D.

Photodermatoses: environmentally induced conditions with high psychological impact.

Photodermatoses are a group of skin disorders caused or exacerbated by ultraviolet and/or visible radiation, which collectively affect a high proportion of the population and substantially affect quality of life (QoL). Our objective was to assess the psychological impact of these conditions. Patients with a range of photodermatoses diagnosed at a specialist investigation centre in the UK completed questionnaires evaluating (i) anxiety and (ii) depression, using the Hospital Anxiety and Depression Scale (HADS), (iii) social anxiety, using the Fear of Negative Evaluation measure (FNE), (iv) coping strategies (brief COPE) and (v) QoL, using the Dermatology Life Quality Index (DLQI). Questionnaires were returned by 185 of 260 patients (71.1% response rate). Mean age was 50.2 years (SD 14.5, range 20-85), 80.3% female. Polymorphic light eruption was the most common diagnosis, followed by photoaggravated eczema, other photoaggravated dermatological conditions and solar urticaria. Across the sample, high percentages, i.e. 23% and 7.9% of individuals, showed scores indicating clinical levels of anxiety and depression, respectively. Facial involvement was a strong indicator for depression (t = 2.7, p < 0.01). In regression analyses psychological factors (particularly depression and adaptive coping) were the principle predictors of QoL, accounting for 17.7% of the variance (F = 7.61, p < 0.01), while clinical variables accounted for an additional 10.1% (F = 8.96, p < 0.01), with number of months/year affected exerting a significant effect (p < 0.01). This study demonstrates the high psychological comorbidity of these conditions; more awareness of this is required, with adoption of a biopsychosocial approach to their management.

Development and Validation of Binary Classifiers to Predict Nocturnal Hypoglycemia in Adults With Type 1 Diabetes.

BACKGROUND: One of the biggest challenges for people with type 1 diabetes (T1D) using multiple daily injections (MDIs) is nocturnal hypoglycemia (NH). Recurrent NH can lead to serious complications; hence, prevention is of high importance. In this work, we develop and externally validate, device-agnostic Machine Learning (ML) models to provide bedtime decision support to people with T1D and minimize the risk of NH. METHODS: We present the design and development of binary classifiers to predict NH (blood glucose levels occurring below 70 mg/dL). Using data collected from a 6-month study of 37 adult participants with T1D under free-living conditions, we extract daytime features from continuous glucose monitor (CGM) sensors, administered insulin, meal, and physical activity information. We use these features to train and test the performance of two ML algorithms: Random Forests (RF) and Support Vector Machines (SVMs). We further evaluate our model in an external population of 20 adults with T1D using MDI insulin therapy and wearing CGM and flash glucose monitoring sensors for two periods of eight weeks each. RESULTS: At population-level, SVM outperforms RF algorithm with a receiver operating characteristic-area under curve (ROC-AUC) of 79.36% (95% CI: 76.86%, 81.86%). The proposed SVM model generalizes well in an unseen population (ROC-AUC = 77.06%), as well as between the two different glucose sensors (ROC-AUC = 77.74%). CONCLUSIONS: Our model shows state-of-the-art performance, generalizability, and robustness in sensor devices from different manufacturers. We believe it is a potential viable approach to inform people with T1D about their risk of NH before it occurs.

Safety and Efficacy of an Adaptive Bolus Calculator for Type 1 Diabetes: A Randomized Controlled Crossover Study.

Background: The Advanced Bolus Calculator for Type 1 Diabetes (ABC4D) is a decision support system using the artificial intelligence technique of case-based reasoning to adapt and personalize insulin bolus doses. The integrated system comprises a smartphone application and clinical web portal. We aimed to assess the safety and efficacy of the ABC4D (intervention) compared with a nonadaptive bolus calculator (control). Methods: This was a prospective randomized controlled crossover study. Following a 2-week run-in period, participants were randomized to ABC4D or control for 12 weeks. After a 6-week washout period, participants crossed over for 12 weeks. The primary outcome was difference in % time in range (%TIR) (3.9-10.0 mmol/L [70-180 mg/dL]) change during the daytime (07:00-22:00) between groups. Results: Thirty-seven adults with type 1 diabetes on multiple daily injections of insulin were randomized, median (interquartile range [IQR]) age 44.7 (28.2-55.2) years, diabetes duration 15.0 (9.5-29.0) years, and glycated hemoglobin 61.0 (58.0-67.0) mmol/mol (7.7 [7.5-8.3]%). Data from 33 participants were analyzed. There was no significant difference in daytime %TIR change with ABC4D compared with control (median [IQR] +0.1 [-2.6 to +4.0]% vs. +1.9 [-3.8 to +10.1]%; P = 0.53). Participants accepted fewer meal dose recommendations in the intervention compared with control (78.7 [55.8-97.6]% vs. 93.5 [73.8-100]%; P = 0.009), with a greater reduction in insulin dosage from that recommended. Conclusion: The ABC4D is safe for adapting insulin bolus doses and provided the same level of glycemic control as the nonadaptive bolus calculator. Results suggest that participants did not follow the ABC4D recommendations as frequently as control, impacting its effectiveness. Clinical Trials Registration: clinicaltrials.gov NCT03963219 (Phase 5).

Improved glycaemia during the Covid-19 pandemic lockdown is sustained post-lockdown and during the "Eat Out to Help Out" Government Scheme, in adults with Type 1 diabetes in the United Kingdom.

AIMS: The majority of studies report that the Covid-19 pandemic lockdown did not have a detrimental effect on glycaemia. We sought to explore the impact of lockdown on glycaemia and whether this is sustained following easing of restrictions. METHODS: Retrospective, observational analysis in adults and children with type 1 diabetes attending a UK specialist centre, using real-time or intermittently scanned continuous glucose monitoring. Data from the following 28-day time periods were collected: (i) pre-lockdown; (ii) during lockdown; (iii) immediately after lockdown; and (iv) a month following relaxation of restrictions (coinciding with Government-subsidised restaurant food). Data were analysed for times in glycaemic ranges and are expressed as median (IQR). RESULTS: 145 adults aged 35.5 (25.8-51.3) years with diabetes duration of 19.0 (7.0-29.0) years on multiple daily injections of insulin (60%) and continuous insulin infusion (40%) were included. In adults, % time in range (70-180mg/dL) increased during lockdown (60.2 (45.2-69.3)%) compared to pre-lockdown (56.7 (43.5-65.3)%; p<0.001). This was maintained in the post-lockdown time periods. Similarly, % time above range (>180mg/dL) reduced in lockdown compared to pre-lockdown (p = 0.01), which was sustained thereafter. In children, no significant changes to glycaemia were observed during lockdown. In multivariable analysis, a greater increase in %TIR 3.9-10mmol/L (70-180mg/dL) during lockdown was associated with higher levels of deprivation (coefficient: 4.208, 95% CI 0.588 to 7.828; p = 0.02). CONCLUSIONS: Glycaemia in adults improved during lockdown, with people from more deprived areas most likely to benefit. This effect was sustained after easing of restrictions, with government-subsidised restaurant eating having no adverse impact on glycaemia.