Prediction of the risk for diabetic neuropathy among diabetes mellitus patients at selected referral hospitals, in Amhara regional state Northwest Ethiopia, January, 2005- December 2021; Development and validation of risk prediction model

Abstract

Background:-Diabetic neuropathy is the most common complication in both Type-1 and Type-2 DM patients with more than one half of all patients developing nerve dysfunction in their lifetime. Although, risk prediction model was developed for diabetic neuropathy in developed countries, It is not applicable in clinical practice, due to poor data, methodological problems, inappropriately analyzed and reported. To date, no risk prediction model developed for diabetic neuropathy among DM in Ethiopia, Therefore, this study aimed prediction the risk of diabetic neuropathy among DM patients, used for guiding in clinical decision making for clinicians Objective:- Prediction of the risk for diabetic neuropathy among diabetes mellitus patients, in selected referral hospitals, Amhara regional state, northwest Ethiopia,2022 Methods:- A retrospective follow up study was conducted with a total of 808 DM patients were enrolled from January 1,2005 to December 30,2021 at two selected referral hospitals in Amhara regional state. Multi-stage sampling techniques were used and the data was collected by checklist from medical records by Kobo collect and exported to STATA version-17 for analysis. Lasso method were used to select predictors and entered to multivariable logistic regression with P-value<0.05 was used for nomogram development. Model performance was assessed by AUC and calibration plot. Internal validation was done through bootstrapping method and decision curve analysis was performed to evaluate net benefit of model Results:-The incidence proportion of diabetic neuropathy among DM patients was 21.29 %( 95% CI; 18.59, 24.25).In multivariable logistic regression glycemic control, other comorbidities, physical activity, hypertension, alcohol drinking, type of treatment , white blood cells and red blood cells count were statistically significant. Nomogram was developed, has discriminating power AUC; 73.2 %( 95% CI; 69.0%, 77.3%) and calibration test (P-value = 0.45). It was internally validated by bootstrapping method with discrimination performance 71.7(95% CI; 67.2 %, 75.9%).It had less optimism coefficient (0.015).To make nomogram accessible, mobile based tool were developed. In machine learning, classification and regression tree has discriminating performance of 70.2 %( 95 % CI; 65.8%, 74.6%).The model had high net benefit at different threshold probabilities in both nomogram and classification and regression tree. Conclusion:- The developed nomogram and decision tree, has good level of accuracy and well calibration, easily individualized prediction of diabetic neuropathy. Both models had added net benefit in clinical practice and to be clinically applicable mobile based tool were developed.

Survival, incidence, and predictors of diabetic neuropathy among type 2 diabetic patients in hospitals of Addis Ababa

“…For the data collection, an English version checklist was developed from up-to-date similar literatures that contains socio-demographic characteristics, clinical and comorbidity characteristics, biochemical characteristics of type 2 DM patients, and outcomes of type 2 diabetes mellitus patients ( 5 , 13 , 14 , 22 , 25 ). During data collection, the diagnosis of diabetic neuropathy was considered after ruling out other potential explanations, the physicians contemplate the existence of symptoms and sign of peripheral nerve damage and abnormality of nerve conduction velocity confirmed using electromyography.…”