Diabetes Retinopathy (DR) Relates to Complication Risk in Young Persons with T1D

Objectives: Diabetic patients are often diagnosed with several comorbidities. The aim of the present study was to investigate the relationship between different combinations of risk factors and complications in diabetic patients. Research design and methods: We used a longitudinal, population-wide dataset of patients with hospital diagnoses and identified all patients (n = 195,575) receiving a diagnosis of diabetes in the observation period from 2003–2014. We defined nine ICD-10-codes as risk factors and 16 ICD-10 codes as complications. Using a computational algorithm, cohort patients were assigned to clusters based on the risk factors they were diagnosed with. The clusters were defined so that the patients assigned to them developed similar complications. Complication risk was quantified in terms of relative risk (RR) compared with healthy control patients. Results: We identified five clusters associated with an increased risk of complications. A combined diagnosis of arterial hypertension (aHTN) and dyslipidemia was shared by all clusters and expressed a baseline of increased risk. Additional diagnosis of (1) smoking, (2) depression, (3) liver disease, or (4) obesity made up the other four clusters and further increased the risk of complications. Cluster 9 (aHTN, dyslipidemia and depression) represented diabetic patients at high risk of angina pectoris “AP” (RR: 7.35, CI: 6.74–8.01), kidney disease (RR: 3.18, CI: 3.04–3.32), polyneuropathy (RR: 4.80, CI: 4.23–5.45), and stroke (RR: 4.32, CI: 3.95–4.71), whereas cluster 10 (aHTN, dyslipidemia and smoking) identified patients with the highest risk of AP (RR: 10.10, CI: 9.28–10.98), atherosclerosis (RR: 4.07, CI: 3.84–4.31), and loss of extremities (RR: 4.21, CI: 1.5–11.84) compared to the controls. Conclusions: A comorbidity of aHTN and dyslipidemia was shown to be associated with diabetic complications across all risk-clusters. This effect was amplified by a combination with either depression, smoking, obesity, or non-specific liver disease.

Download Full-text

Diabetic Retinopathy Prediction by Ensemble Learning Based on Biochemical and Physical Data

Sensors ◽

10.3390/s21113663 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3663

Author(s):

Zun Shen ◽

Qingfeng Wu ◽

Zhi Wang ◽

Guoyi Chen ◽

Bin Lin

Keyword(s):

Diabetic Retinopathy ◽

Prediction Model ◽

Ensemble Learning ◽

Positive Impact ◽

Reduction Rate ◽

Developed Countries ◽

Small Sample ◽

Feature Reduction ◽

Physical Data ◽

Diabetes Retinopathy

(1) Background: Diabetic retinopathy, one of the most serious complications of diabetes, is the primary cause of blindness in developed countries. Therefore, the prediction of diabetic retinopathy has a positive impact on its early detection and treatment. The prediction of diabetic retinopathy based on high-dimensional and small-sample-structured datasets (such as biochemical data and physical data) was the problem to be solved in this study. (2) Methods: This study proposed the XGB-Stacking model with the foundation of XGBoost and stacking. First, a wrapped feature selection algorithm, XGBIBS (Improved Backward Search Based on XGBoost), was used to reduce data feature redundancy and improve the effect of a single ensemble learning classifier. Second, in view of the slight limitation of a single classifier, a stacking model fusion method, Sel-Stacking (Select-Stacking), which keeps Label-Proba as the input matrix of meta-classifier and determines the optimal combination of learners by a global search, was used in the XGB-Stacking model. (3) Results: XGBIBS greatly improved the prediction accuracy and the feature reduction rate of a single classifier. Compared to a single classifier, the accuracy of the Sel-Stacking model was improved to varying degrees. Experiments proved that the prediction model of XGB-Stacking based on the XGBIBS algorithm and the Sel-Stacking method made effective predictions on diabetes retinopathy. (4) Conclusion: The XGB-Stacking prediction model of diabetic retinopathy based on biochemical and physical data had outstanding performance. This is highly significant to improve the screening efficiency of diabetes retinopathy and reduce the cost of diagnosis.

Download Full-text

DyCRS: Dynamic Interpretable Postoperative Complication Risk Scoring

Proceedings of The Web Conference 2020 ◽

10.1145/3366423.3380253 ◽

2020 ◽

Author(s):

Wen Wang ◽

Han Zhao ◽

Honglei Zhuang ◽

Nirav Shah ◽

Rema Padman

Keyword(s):

Postoperative Complication ◽

Complication Risk ◽

Risk Scoring

Download Full-text

Risk Factors for Postpartum Depression in Active Duty Women

Military Medicine ◽

10.1093/milmed/usab161 ◽

2021 ◽

Author(s):

Valencia Garcia ◽

Eric Meyer ◽

Catherine Witkop

Keyword(s):

Risk Factors ◽

Postpartum Depression ◽

Smoking Status ◽

Technical Information ◽

Active Duty ◽

Military Spouses ◽

Inclusion Criteria ◽

Medical Subject Headings ◽

Complication Risk ◽

Screening And Prevention

ABSTRACT Introduction Postpartum depression (PPD) is a common perinatal complication. Risk factors previously found to correlate with PPD in civilians include prenatal depression, childcare stress, limited social support, difficult infant temperament, and maternity blues. Previously identified risk factors in military spouses include spouse deployment/redeployment cycles. It is unclear if these previously identified risk factors are also a risk factor for AD women or if the additional stressors associated with being on active duty (AD) are risk factors for PPD. The purpose of this review is to determine if civilian risk factors have been found to put AD women at risk for PPD and to identify unique risk factors for PPD in AD women. Materials and Methods A scoping literature review was performed using PubMed, Defense Technical Information Center, and PsychINFO. The searches were conducted using relevant medical subject headings and keywords. The inclusion criteria included articles published since 1948 (the year women were legally allowed to join the military) that reference risk factors for postpartum/peripartum depression in AD women serving in the U.S. military. The following exclusion criteria were also applied: in a language other than English, opinion papers, and/or not published in a peer-reviewed journal. Articles meeting criteria were evaluated and mapped to stressors previously identified in the literature for civilian and military spouses with PPD with novel stressors identified as mapping outside this framework. Results Only two articles met the inclusion criteria. The first study included 87 AD women. The second study, a cohort study between 2001 and 2008, included 1660 AD women. Unique risk factors identified in AD women include previous deployments, serving in the Army, smoking status, alcohol use, and low self-esteem. Conclusions Few studies have investigated the risk factors for PPD in AD women. It appears that AD women share many risk factors, or variants of those risk factors, for PPD as their civilian and AD spouse counterparts, but there are also unique risks to consider. More work is needed to improve screening and prevention efforts.

Download Full-text