Imputing HbA1c from capillary blood glucose levels in patients with type 2 diabetes in Sri Lanka: a cross-sectional study

Abstract

ObjectiveTo develop a population-specific methodology for estimating glycaemic control that optimises resource allocation for patients with diabetes in rural Sri Lanka.DesignCross-sectional study.SettingTrincomalee, Sri Lanka.ParticipantsPatients with non-insulin-treated type 2 diabetes (n=220) from three hospitals in Trincomalee, Sri Lanka.Outcome measureCross-validation was used to build and validate linear regression models to identify predictors of haemoglobin A1c (HbA1c). Validation of models that regress HbA1c on known determinants of glycaemic control was thus the major outcome. These models were then used to devise an algorithm for categorising the patients based on estimated levels of glycaemic control.ResultsTime since last oral intake other than water and capillary blood glucose were the statistically significant predictors of HbA1c and thus included in the final models. In order to minimise type II error (misclassifying a high-risk individual as low-risk or moderate-risk), an algorithm for interpreting estimated glycaemic control was created. With this algorithm, 97.2% of the diabetic patients with HbA1c ≥9.0% were correctly identified.ConclusionsOur calibrated algorithm represents a highly sensitive approach for detecting patients with high-risk diabetes while optimising the allocation of HbA1c testing. Implementation of these methods will optimise the usage of resources devoted to the management of diabetes in Trincomalee, Sri Lanka. Further external validation with diverse patient populations is required before applying our algorithm more widely.

Artificial intelligence and machine learning for early detection and diagnosis of colorectal cancer in sub-Saharan Africa

“…Although, despite available diagnostic testing, studies have shown that they are not optimally used in managing patient care, and tools to bridge the diagnostic–treatment divide are needed 41 42. MAAAs offer one approach to help bridge this gap and can be coupled with simple paper-based tools (eg, nomograms) to more complex mobile app-based tools or lightweight, field-deployed (cloud-based) Laboratory Information Systems designed for use in LMICs 43 44. In addition to the use of MAAAs as a tool for CRC diagnosis, the approach could be adapted for the prediction of CRC prognosis and treatment outcomes as both the CBC and CMP profiles of patients have been associated with disease stage, metastasis and treatment outcomes 45–47…”