Prendin Francesco, Streicher Olivia, Cappon Giacomo, Rolfes Eva, Herzig David, Bally Lia, Facchinetti Andrea
Department of Information Engineering (DEI), University of Padova, Via G. Gradenigo 6/B, Padua, 35131, Italy.
Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
BMC Med Inform Decis Mak. 2025 Jan 20;25(1):33. doi: 10.1186/s12911-025-02856-5.
Post bariatric hypoglycaemic (PBH) is a late complication of weight loss surgery, characterised by critically low blood glucose levels following meal-induced glycaemic excursions. The disabling consequences of PBH underline the need for the development of a decision support system (DSS) that can warn individuals about upcoming PBH events, thus enabling preventive actions to avoid impending episodes. In view of this, we developed various algorithms based on linear and deep learning models to forecast PBH episodes in the short-term.
We leveraged a dataset obtained from 50 patients with PBH after Roux-en-Y gastric bypass, monitored for up to 50 days under unrestricted real-life conditions. Algorithms' performance was assessed by measuring Precision, Recall, F1-score, False-alarms-per-day and Time Gain (TG).
The run-to-run forecasting algorithm based on recursive autoregressive model (rAR) outperformed the other techniques, achieving Precision of 64.38%, Recall of 84.43%, F1-score of 73.06%, a median TG of 10 min and 1 false alarm every 6 days. More complex deep learning models demonstrated similar median TG but inferior forecasting capabilities with F1-score ranging from 54.88% to 64.10%.
Real-time forecasting of PBH events using CGM data as a single input imposes high demands on various types of prediction algorithms, with CGM data noise and rapid postprandial glucose dynamics representing the key challenges. In this study, the run-to-run rAR yielded most satisfactory results with accurate PBH event predictive capacity and few false alarms, thereby indicating potential for the development of DSS for people with PBH.
减重术后低血糖（PBH）是减重手术的一种晚期并发症，其特征是餐后血糖波动后血糖水平极低。PBH的致残后果凸显了开发一种决策支持系统（DSS）的必要性，该系统可以警告个体即将发生的PBH事件，从而使他们能够采取预防措施以避免即将发生的发作。鉴于此，我们基于线性和深度学习模型开发了各种算法，以短期预测PBH发作。
我们利用了从50例接受Roux-en-Y胃旁路手术的PBH患者获得的数据集，在不受限制的现实生活条件下对其进行了长达50天的监测。通过测量精确率、召回率、F1分数、每日误报率和时间增益（TG）来评估算法的性能。
基于递归自回归模型（rAR）的逐次运行预测算法优于其他技术，精确率达到64.38%，召回率为84.43%，F1分数为73.06%，中位数TG为10分钟，每6天出现1次误报。更复杂的深度学习模型显示出相似的中位数TG，但预测能力较差，F1分数在54.88%至64.10%之间。
使用连续血糖监测（CGM）数据作为单一输入对PBH事件进行实时预测，对各种类型的预测算法提出了很高的要求，CGM数据噪声和餐后血糖快速动态变化是关键挑战。在本研究中，逐次运行的rAR产生了最令人满意的结果，具有准确的PBH事件预测能力且误报较少，从而表明为PBH患者开发DSS具有潜力。
