Artificial Intelligence (AI) based machine learning models predict glucose variability and hypoglycaemia risk in patients with type 2 diabetes on a multiple drug regimen who fast during ramadan (The PROFAST – IT Ramadan study)

Abstract

gender, BMI and HbA1c followed by glucose levels and physical activity. Interestingly, the blood glucose level prediction by our model was influenced by use of SGLT2i. Conclusion:XGBoost, a machine learning AI algorithm achieves high predictive performance for normal and hyperglycaemic excursions, but has limited predictive value for hypoglycaemia in patients on multiple therapies who fast during Ramadan.

Ability of Current Machine Learning Algorithms to Predict and Detect Hypoglycemia in Patients With Diabetes Mellitus: Meta-analysis

“…Table 1 shows the summary of study characteristics. Of the 33 studies, 19 studies (58%) [26][27][28][29][30][31]33,35,36,[38][39][40][41][42][44][45][46][47]54] predicted hypoglycemia, and the remaining 14 studies (42%) detected hypoglycemia [15,20,25,32,34,37,43,[48][49][50][51][52][53]55]. As much as 25 of the 33 included studies (76%) [15,20,[25][26][27]29,30,32,35,36,38,39,[41][42][43][44]…”

“…As much as 25 of the 33 included studies (76%) [15,20,[25][26][27]29,30,32,35,36,38,39,[41][42][43][44][46][47][48][49][50][51][52][53]55] specified type 1 as the type of DM. Type 2 DM was specified in only 3 of these studies (9%) [28,31,45] and the remaining 5 studies [33,34,37,40,54] did not specify the type of DM.…”

“…Regarding the time of day when hypoglycemic events occurred, nocturnal hypoglycemia was the most frequently reported (14 studies of the 33 included studies; 42%) [15,20,26,30,32,35,36,41,44,[49][50][51][52][53]). As to the place of the supposed hypoglycemic episode, 16 of the 19 studies that predicted hypoglycemia (84%) [26][27][28][29][30]35,36,[38][39][40][41][42][44][45][46][47] supposed the event took place in an out-of-hospital setting. The remaining 3 studies (16%) [31,33,54] supposed hypoglycemia occurring in an in-hospital setting.…”

“…Multimedia Appendix 5 shows the profiling data input into the ML algorithm for testing its performance in detecting or predicting hypoglycemia. In the majority of the 19 studies for predicting hypoglycemia (13 studies; 68%) [26][27][28][29][30]35,36,38,[40][41][42]46,47], historical CGM data were input into the ML algorithm while the remaining 6 studies (32%) [31,33,39,44,45,54] did not use CGM. Of the 14 studies that detected hypoglycemia using ML, 7 studies (50%) [20,25,32,49,50,52,55] used information from electroencephalograms (EEGs) and 4 studies (29%) [15,43,51,53] used results of electrocardiography (ECG).…”

“…Mean score (SD) was 5.6 (1.1), which corresponded to 80% of full marks (=7). The applicability of the reference test was evaluated to be low in 61% of the 33 included studies (20 studies) because hypoglycemia was not diagnosed by measuring blood glucose levels or ICD codes but by CGM (ie, glucose levels in blood are indirectly estimated from those in interstitial tissue) (19 studies) [15,[26][27][28][29][30]35,38,[40][41][42][43]46,47,[49][50][51][52]54] or experts' subjective judgement (1 study) [34]. The 2 factors were mainly responsible for lowering the study quality.…”