Search

Objective: Recent years have seen an increase in machine learning (ML)-based blood glucose (BG) forecasting models, with a growing emphasis on potential application to hybrid or closed-loop predictive glucose controllers. However, current approaches focus on evaluating the accuracy of these models using benchmark data generated under the behavior policy, which may differ significantly from the data the model may encounter in a control setting. This study challenges the efficacy of such evaluation approaches, demonstrating that they can fail to accurately capture an ML-based model's true performance in closed-loop control settings. Methods: Forecast error measured using current evaluation approaches was compared to the control performance of two forecasters – a ML-based model (LSTM) and a rule-based model (Loop) – in silico when the forecasters were utilized with a model-based controller in a hybrid closed-loop setting. Results: Under current evaluation standards, LSTM achieves a significantly lower (better) forecast error than Loop with a root mean squared error (RMSE) of $\text{11.57} \pm \text{0.05}\,\text{mg}/\text{dL}$ vs. $\text{18.46} \pm \text{0.07}\,\text{mg}/\text{dL}$ at the 30-minute prediction horizon. Yet in a control setting, LSTM led to significantly worse control performance with only 77.14% (IQR 66.57–84.03) time-in-range compared to 86.20% (IQR 78.28–91.21) for Loop. Conclusion: Prevailing evaluation methods can fail to accurately capture the forecaster's performance when utilized in closed-loop settings. Significance: Our findings underscore the limitations of current evaluation standards and the need for alternative evaluation metrics and training strategies when developing BG forecasters for closed-loop control systems.

Objective:The development of in-hospital acquired pressure ulcers (PUs) is of great concern for both patients and professionals in health care. Based on the hypothesis that identification of patients who are prone to develop PU will enhance preventive measures in this group of patients, we investigated a new tool, Qscale, for in-hospital prediction of PU.Approach:A total of 383 patients were recruited from three departments. The investigations were performed in two steps: 252 patients were used to train the algorithm, and 131 patients were used in the validation. The new scale combines observational and on-site available information regarding patient mobility.Results:The validation data yielded an area under the curve (AUC) of 0.82. The Qscale had a significantly higher AUC compared with that of the Braden Scale with an AUC of 0.76 (p< 0.05). When comparing the performance at specific thresholds, a sensitivity of 47% and a specificity of 94% were observed. This was significantly (p< 0.05) better than the Braden score with a sensitivity of 20% and a specificity of 94%.Innovation:Our study showed promising results on both the training and validation data of the Qscale in comparison with the Braden Scale.Conclusion:The new scale has a potential benefit in the prevention of PU in a hospital setting.

Obstructive sleep apnoea (OSA) is frequent in type 2 diabetes (T2D). The aim was to investigate the effect of a 12‐week treatment with continuous positive airway pressure (CPAP) on glycaemic control assessed by continuous glucose monitoring (CGM), HbA1c and fasting blood glucose in patients with T2D and newly detected OSA. In a randomized controlled multicentre study, 72 participants with T2D and moderate to severe OSA (78% male, age 62 ± 7, AHI 35 ± 15) were recruited from outpatient clinics in three Danish hospitals and were randomized to CPAP intervention or control. The main outcome was glycaemic control assessed by 6 days CGM at baseline and after 12‐week therapy, as well as by HbA1c and fasting blood glucose. No significant changes were found in average glucose levels, time in glucose range, time with hypoglycaemia, time with hyperglycaemia or coefficient of variability. HbA1c decreased 0.7 mmol/mol (0.07%; P= .8) in the CPAP group and increased 0.8 mmol/mol (0.08%; P= .6) in the control group (intergroup difference, P= .6). Fasting blood glucose increased by 0.2 mmol/L (P= .02) in the CPAP group and by 0.4 mmol/L (P= .01) in the control group (intergroup difference, P= .7). In a prespecified subgroup analysis comparing participants with high adherence (minimum usage of four hours/night for 70% of all nights) to CPAP to the control group, no significant changes were observed either, although these participants had a tendency towards better glycaemic indices. CPAP treatment for 12 weeks does not significantly change glycaemic control in patients with type 2 diabetes and OSA. Former studies show a high prevalence of obstructive sleep apnoea (OSA) in people with type 2 diabetes (T2D). Previous data are divergent on whether continuous positive airway pressure (CPAP) therapy improves glycaemic control in people with T2D and OSA. In this randomized controlled study, no effect was found of 12‐week CPAP treatment on glycaemic control measured by continuous glucose monitoring, HbA1c and fasting blood glucose. This study does not support implementation of routine CPAP to people with T2D and OSA with the purpose of improving glycaemic control.