Monitoring Approaches for a Pediatric Chronic Kidney Disease Machine Learning Model.

OBJECTIVE: The purpose of this study is to evaluate the ability of three metrics to monitor for a reduction in performance of a chronic kidney disease (CKD) model deployed at a pediatric hospital. METHODS: The CKD risk model estimates a patient's risk of developing CKD 3 to 12 months following an inpatient admission. The model was developed on a retrospective dataset of 4,879 admissions from 2014 to 2018, then run silently on 1,270 admissions from April to October, 2019. Three metrics were used to monitor its performance during the silent phase: (1) standardized mean differences (SMDs); (2) performance of a "membership model"; and (3) response distribution analysis. Observed patient outcomes for the 1,270 admissions were used to calculate prospective model performance and the ability of the three metrics to detect performance changes. RESULTS: The deployed model had an area under the receiver-operator curve (AUROC) of 0.63 in the prospective evaluation, which was a significant decrease from an AUROC of 0.76 on retrospective data (p = 0.033). Among the three metrics, SMDs were significantly different for 66/75 (88%) of the model's input variables (p <0.05) between retrospective and deployment data. The membership model was able to discriminate between the two settings (AUROC = 0.71, p <0.0001) and the response distributions were significantly different (p <0.0001) for the two settings. CONCLUSION: This study suggests that the three metrics examined could provide early indication of performance deterioration in deployed models' performance.

Adding glycemic and physical activity metrics to a multimodal algorithm-enabled decision-support tool for type 1 diabetes care: Keys to implementation and opportunities.

Algorithm-enabled patient prioritization and remote patient monitoring (RPM) have been used to improve clinical workflows at Stanford and have been associated with improved glucose time-in-range in newly diagnosed youth with type 1 diabetes (T1D). This novel algorithm-enabled care model currently integrates continuous glucose monitoring (CGM) data to prioritize patients for weekly reviews by the clinical diabetes team. The use of additional data may help clinical teams make more informed decisions around T1D management. Regular exercise and physical activity are essential to increasing cardiovascular fitness, increasing insulin sensitivity, and improving overall well-being of youth and adults with T1D. However, exercise can lead to fluctuations in glycemia during and after the activity. Future iterations of the care model will integrate physical activity metrics (e.g., heart rate and step count) and physical activity flags to help identify patients whose needs are not fully captured by CGM data. Our aim is to help healthcare professionals improve patient care with a better integration of CGM and physical activity data. We hypothesize that incorporating exercise data into the current CGM-based care model will produce specific, clinically relevant information such as identifying whether patients are meeting exercise guidelines. This work provides an overview of the essential steps of integrating exercise data into an RPM program and the most promising opportunities for the use of these data.

Efficacy and safety of the pregnancy-IVI, an intravenous insulin protocol for pregnancy, following antenatal betamethasone in type 1 and type 2 diabetes.

AIMS: Hyperglycaemia following antenatal corticosteroids is common in women with diabetes in pregnancy, and validated algorithms to maintain pregnancy-specific glucose targets are lacking. The Pregnancy-IVI, an intravenous-insulin (IVI) algorithm, has been validated in gestational diabetes; however, its performance in pre-existing diabetes (Type 1 and Type 2 diabetes) is not known. We hypothesised that Pregnancy-IVI would be superior to a generic Adult-IVI protocol (prior standard of care) following betamethasone in women with pre-existing diabetes. METHODS: A retrospective cohort study enrolled all women with pre-existing diabetes at a tertiary centre receiving betamethasone and treated with IVI according to one of two protocols: Adult-IVI (n = 73, 2014-2017) or Pregnancy-IVI (n = 62, 2017-2020). The primary outcome was on-IVI glycaemic time-in-range (capillary blood glucose (BGL) 3.8-7.0 mmol/L). Secondary outcomes included time with critical hyperglycaemia (BGL > 10 mmol/L); occurrence of maternal hypoglycaemia (BGL < 3.8 mmol/l) and incidence of neonatal hypoglycaemia (BGL ≤ 2.5 mmol/L). Analysis was stratified by diabetes type. RESULTS: Overall, Pregnancy-IVI achieved a higher proportion of on-IVI time-in-range (70%, IQR 56-78%) compared to Adult-IVI (52%, IQR 41-69%, p < 0.0001). The duration of critical hyperglycaemia with Pregnancy-IVI was also reduced (2% [IQR 0-7] vs 8% [IQR 4-17], p < 0.0001), without an increase in hypoglycaemia. Glycaemic variability was significantly reduced with Pregnancy-IVI. No difference in the rate of neonatal hypoglycaemia was observed. The Pregnancy-IVI was most effective in women with Type 1 diabetes. CONCLUSION: The Pregnancy-IVI algorithm is safe and effective when used following betamethasone in type 1 diabetes in pregnancy. Further study of women with type 2 diabetes is required.

Antenatal corticosteroid administration for foetal lung maturation.

Antenatal corticosteroids are an essential component in the management of women at risk for preterm labour. They promote lung maturation and reduce the risk of other preterm neonatal complications. This narrative review discusses the contentious issues and controversies around the optimal use of antenatal corticosteroids and their consequences for both the mother and the neonate. The most recent evidence base is presented.