Machine learning prediction of future peripheral neuropathy in type 2 diabetics with percussion entropy and body mass indices

Construction of an Early Risk Prediction Model for Type 2 Diabetic Peripheral Neuropathy Based on Random Forest

Diabetic peripheral neuropathy is a major cause of disability and death in the later stages of diabetes. A retrospective chart review was performed using a hospital-based electronic medical record database to identify 1020 patients who met the criteria. The objective of this study was to explore and analyze the early risk factors for peripheral neuropathy in patients with type 2 diabetes, even in the absence of specific clinical symptoms or signs. Finally, the random forest algorithm was used to rank the influencing factors and construct a predictive model, and then the model performance was evaluated. Logistic regression analysis revealed that vitamin D plays a crucial protective role in preventing diabetic peripheral neuropathy. The top three risk factors with significant contributions to the model in the random forest algorithm eigenvalue ranking were glycosylated hemoglobin, disease duration, and vitamin D. The areas under the receiver operating characteristic curve of the model ware 0.90. The accuracy, precision, specificity, and sensitivity were 0.85, 0.83, 0.92, and 0.71, respectively. The predictive model, which is based on the random forest algorithm, is intended to support clinical decision-making by healthcare professionals and help them target timely interventions to key factors in early diabetic peripheral neuropathy.

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

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

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

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.

Correlation between health literacy and health‐related quality of life in patients with diabetic peripheral neuropathy: The mediating role of self‐management

AIM

The aims of the study were to analyse the current situation of health literacy (HL), self-management and health-related quality of life (HRQOL) in patients with diabetic peripheral neuropathy (DPN), to explore the correlation between the three and to verify the mediating role of self-management in HL and HRQOL.

DESIGN

A cross-sectional survey.

METHODS

The convenience sampling method was used to select 401 DPN patients attending the First Hospital of Jinzhou Medical University in Liaoning Province, China, from December 2020 to December 2021 as the study population. The research instrument included socio-demographic characteristics questionnaire, Health Literacy Management Scale (HeLMS), Summary of Diabetes Self-Care Activities (SDSCA) and Short-Form 12-item Health Survey (SF-12). SPSS 25.0 was applied to the data for descriptive analysis, Pearson correlation analysis and stratified multiple regression analysis. Mediating effects were tested using SPSS PROCESS macro 4.0 software.

RESULTS

HL and self-management of DPN patients correlated positively with HRQOL. The mediation role of self-management was significant in the relationship between HL and physical and mental HRQOL (physical component summary: β = 0.26, P < 0.01; mental component summary: β = 0.18, P < 0.01), with mediating effects accounting for 35.62% and 34.62% of the total effect.

CONCLUSIONS

There was a positive correlation between HL, self-management and HRQOL in patients with DPN. Self-management plays a partially mediating role in the relationship between HL and HRQOL in DPN patients. It means that HRQOL in this population can be improved by increasing HL and thus self-management in DPN patients.

PATIENT OR PUBLIC CONTRIBUTION

None.

Application of photoplethysmography signals for healthcare systems: An in-depth review

BACKGROUND AND OBJECTIVES

Photoplethysmography (PPG) is a device that measures the amount of light absorbed by the blood vessel, blood, and tissues, which can, in turn, translate into various measurements such as the variation in blood flow volume, heart rate variability, blood pressure, etc. Hence, PPG signals can produce a wide variety of biological information that can be useful for the detection and diagnosis of various health problems. In this review, we are interested in the possible health disorders that can be detected using PPG signals.

METHODS

We applied PRISMA guidelines to systematically search various journal databases and identified 43 PPG studies that fit the criteria of this review.

RESULTS

Twenty-five health issues were identified from these studies that were classified into six categories: cardiac, blood pressure, sleep health, mental health, diabetes, and miscellaneous. Various routes were employed in these PPG studies to perform the diagnosis: machine learning, deep learning, and statistical routes. The studies were reviewed and summarized.

CONCLUSIONS

We identified limitations such as poor standardization of sampling frequencies and lack of publicly available PPG databases. We urge that future work should consider creating more publicly available databases so that a wide spectrum of health problems can be covered. We also want to promote the use of PPG signals as a potential precision medicine tool in both ambulatory and hospital settings.

Reactive Hyperemia-Triggered Wrist Pulse Analysis for Early Monitoring of Young Men with High Atherosclerotic Risk

The high prevalence of cardiovascular disease in young adults has raised significant concern regarding the early identification of risk factors to allow for timely intervention. This study aimed to identify young males at risk of atherosclerosis using a noninvasive instrument and an initial application percussion entropy analysis of the wrist pressure pulse (WPP). In total, 49 young males aged 18 to 28, without any known history of vascular disease, were recruited. Blood samples were obtained whereby a TC/HDL cutoff value of 4 was used to divide the young men into low-risk (Group 1, TC/HDL < 4, N = 32) and high-risk (Group 2, TC/HDL ≥ 4, N = 17) groups regarding atherosclerosis. The reactive hyperemia-triggered WPPs were measured using a modified air-pressure-sensing system (MAPSS). The dilation index (DI) of the endothelial function and percussion entropy index (PEI) of the heart rate variability (HRV) assessments, calculated using pragmatic signal-processing techniques, were compared between the two groups. The nonparametric Mann-Whitney U test showed that the DI and PEI of the two groups showed statistical differences (both p < 0.05). Not only could the MAPSS assess endothelial function and HRV in young males, but the results also showed that waist circumference and PEI may serve as indicators for the early identification of young males at risk of atherosclerosis.