An artificial intelligence decision support system for the management of type 1 diabetes

Abstract

Type 1 diabetes (T1D) is characterized by pancreatic beta cell dysfunction and insulin depletion. Over 40% of people with T1D manage their glucose through multiple injections of long-acting basal and short-acting bolus insulin, so-called multiple daily injections (MDI)1,2. Errors in dosing can lead to life-threatening hypoglycaemia events (<70 mg dl-1) and hyperglycaemia (>180 mg dl-1), increasing the risk of retinopathy, neuropathy, and nephropathy. Machine learning (artificial intelligence) approaches are being harnessed to incorporate decision support into many medical specialties. Here, we report an algorithm that provides weekly insulin dosage recommendations to adults with T1D using MDI therapy. We employ a unique virtual platform3 to generate over 50,000 glucose observations to train a k-nearest neighbours4 decision support system (KNN-DSS) to identify causes of hyperglycaemia or hypoglycaemia and determine necessary insulin adjustments from a set of 12 potential recommendations. The KNN-DSS algorithm achieves an overall agreement with board-certified endocrinologists of 67.9% when validated on real-world human data, and delivers safe recommendations, per endocrinologist review. A comparison of inter-physician-recommended adjustments to insulin pump therapy indicates full agreement of 41.2% among endocrinologists, which is consistent with previous measures of inter-physician agreement (41-45%)5. In silico3,6 benchmarking using a platform accepted by the United States Food and Drug Administration for evaluation of artificial pancreas technologies indicates substantial improvement in glycaemic outcomes after 12 weeks of KNN-DSS use. Our data indicate that the KNN-DSS allows for early identification of dangerous insulin regimens and may be used to improve glycaemic outcomes and prevent life-threatening complications in people with T1D.

Trial registration: ClinicalTrials.gov NCT03443713.

Figures

Extended Data Fig. 1. Quality control algorithm to assess need for insulin titration.

Quality control algorithm to assess need for insulin titration. User data and glycaemic outcomes are loaded and compared against metrics for percent time in hypoglycaemia, percent time in target range, and percent time in serious hypoglycaemia. If users meet all metrics, recommendations for insulin titration are not required.

Extended Data Fig. 2. Quality control algorithm to assess increasing basal insulin dosage.

Quality control algorithm to assess increasing basal insulin dosage. User features and glycaemic outcomes are loaded by the algorithm and assessed for physician-informed metrics of nocturnal hypoglycaemia, near hypoglycaemia episodes, subject time in target range, subject adherence, and insulin formulation-dependent requirements.

Extended Data Fig. 3. Quality control algorithm to assess decreasing basal insulin dosage.

Quality control algorithm to assess decreasing basal insulin dosage. User features and glycaemic outcomes are loaded by the algorithm and assessed for subject adherence, and insulin formulation-dependent requirements.

Extended Data Fig. 4. Quality control algorithm to assess increasing meal bolus insulin dosage.

Quality control algorithm to assess increasing meal bolus insulin dosage. User features and glycaemic outcomes are loaded by the algorithm and assessed for physician-informed metrics of postprandial hypoglycaemia, subject adherence, and factors returned by the ALPHA algorithm.

Extended Data Fig. 5. Quality control algorithm to assess decreasing meal bolus insulin dosage.

Quality control algorithm to assess decreasing meal bolus insulin dosage. User features and glycaemic outcomes are loaded by the algorithm and assessed for physician-informed metrics of postprandial severe hyperglycaemia, subject adherence, and factors returned by the ALPHA algorithm

Extended Data Fig. 6. Quality control algorithm to assess increasing correction bolus insulin dosage.

Quality control algorithm to assess increasing correction bolus insulin dosage. User features and glycaemic outcomes are loaded by the algorithm and assessed for physician-informed metrics of postprandial and correction-related hypoglycaemia, subject adherence, and factors returned by the ALPHA algorithm.

Extended Data Fig. 7. Quality control algorithm to assess decreasing correction bolus insulin dosage.

Quality control algorithm to assess decreasing correction bolus insulin dosage. User features and glycaemic outcomes are loaded by the algorithm and assessed for physician-informed metrics of subject adherence, postprandial and correction-related hypoglycaemia, and factors returned by the ALPHA algorithm.

Extended Data Fig. 8. KNN-DSS engine performance in improving subject outcomes in an independent virtual patient population.

KNN-DSS engine performance in improving subject outcomes in an independent virtual patient population. Glycaemic outcomes during a 52-week study of the FDA-approved UVA-Padova virtual patient simulator. Percent time in hypoglycaemia is indicated by the blue circular radius.

Extended Data Fig. 9. Outcomes of a human pilot study evaluating KNN-DSS augmented decision support.

Outcomes of a human pilot study evaluating KNN-DSS augmented decision support over 4 weeks where the first recommendation is given at the start of week 2. For panels a-f, boxplot limits indicate the first and third quartiles, centerline indicates the median, and whiskers mark the last non-outlier data-point within 1.5xIQR. For panels a-f, participant data collected during week 1 and the final week of the study were compared using a two-tailed Wilcoxon signed-rank test, with significance level of 5%. a, Frequency of hypoglycaemia was nominally reduced on the final week compared to week 1 of the study (0.86 vs 0.64, P = 0.051, n = 16 independent subjects). b Serious hypoglycaemia was nominally reduced on the final week compared with week 1 of the study (0.34% vs. 0.19%, P = 0.56, n = 16 independent subjects). c Postprandial hypoglycaemia events were nominally reduced on the final week compared with week 1 (0.29 vs 0.14, P = 0.08, n = 16 independent subjects). d Frequency of overnight hypoglycaemia was significantly reduced on the final week compared to week 1 (0.50 to 0.29, P= 0.04, n = 16 independent subjects). e Serious hypoglycemia overnight was significantly reduced on the final week compared to week 1 (0.48% to 0.11%, P = 0.03, n = 16 independent subjects). f Postprandial hypoglycemia overnight was nominally reduced on the final week compared to week 1 (0.14 to 0.07, P = 0.06, n = 16 independent subjects).

Figure 1:. Decision support engine framework to identify user-specific insulin titrations.

a, The user data are aggregated and processed for extracting glucose, insulin, meal, and exercise features that may be used to optimally titrate insulin doses. b, The user features F_user are matched to the closest examples in the look-up table for the K-nearest neighbours algorithm, F_lookup. The distance between user features and the examples in the look-up table are calculated as {D}, and the K examples within minimum distance to user features, {n}, are weighted by distance, w_d, and class-size, w_c; the final insulin dosage recommendations, R_c, are returned by the KNN algorithm. c, For those recommendations indicated by the KNN-DSS, the ALPHA algorithm assigns an aggressiveness factor that titrates carbohydrate ratios and correction factors to improve time in target range and reduce time in hypoglycaemia. d, A quality control algorithm is employed to ensure that KNN-DSS recommendations adhere to physician standards

Figure 2:. Engine performance in improving subject outcomes in silico.

a, Outcomes of the in silico evaluation of the KNN-DSS over 52 weeks. Virtual patients from the OHSU T1D simulator undergo weekly use of the KNN-DSS engine. b, At 52 weeks, new insulin settings and dosing behaviors are imposed on patients, the effects of which are measured at 53 weeks. c, Evolution of patient settings 20 weeks from baseline following a disturbance at week 52. The red dashed line indicates 10% from baseline.

References (Methods Only)