Ten-year risk prediction models of complications and mortality of Chinese patients with diabetes mellitus in primary care in Hong Kong: a study protocol

New risk score model for identifying individuals at risk for diabetes in southwest China

The prevalence of diabetes is increasing rapidly and becoming a major public health issue worldwide. We aimed to develop a novel nomogram model for long-term diabetic risk prediction in a Chinese population. A prospective cohort study was performed on 687 nondiabetic individuals who underwent routine physical examination in 1992 and 2007. Using the least absolute shrinkage and selection operator model to optimize feature selection. Multiple Cox regression analysis was performed, and a simple nomogram was constructed. The area under receiver operating characteristic curve (AUC) and calibration plot were conducted to assess the predictive accuracy of the model. The model was subjected to bootstrap internal validation. Of the 687 participants without diabetes at baseline, 74 developed diabetes during the follow-up time. This simple nomogram model was constructed by family history of diabetes, height, waist circumference, triglycerides, fasting plasma glucose and white blood cell count. The AUCs were 0.812 (95% CI: 0.729-0.895) and 0.794 (95% CI: 0.734-0.854) for 10-year and 15-year diabetic risk. The bootstrap corrected c-index was 0.771 (95% CI: 0.721-0.821). The calibration plot also achieved good agreement between observational and actual diabetic incidence. The stratification into different risk groups by optimal cut-off value of 12.8 allowed significant distinction between cumulative diabetic incidence curves in the whole cohort and several subgroups. We established and internally validated a novel nomogram which can provide individual diabetic risk prediction for Chinese population and this practical screening model may help clinicians to identify individuals at high risk of diabetes.

Generative adversarial networks for imputing missing data for big data clinical research

Background

Missing data is a pervasive problem in clinical research. Generative adversarial imputation nets (GAIN), a novel machine learning data imputation approach, has the potential to substitute missing data accurately and efficiently but has not yet been evaluated in empirical big clinical datasets.

Objectives

This study aimed to evaluate the accuracy of GAIN in imputing missing values in large real-world clinical datasets with mixed-type variables. The computation efficiency of GAIN was also evaluated. The performance of GAIN was compared with other commonly used methods, MICE and missForest.

Methods

Two real world clinical datasets were used. The first was that of a cohort study on the long-term outcomes of patients with diabetes (50,000 complete cases), and the second was of a cohort study on the effectiveness of a risk assessment and management programme for patients with hypertension (10,000 complete cases). Missing data (missing at random) to independent variables were simulated at different missingness rates (20, 50%). The normalized root mean square error (NRMSE) between imputed values and real values for continuous variables and the proportion of falsely classified (PFC) for categorical variables were used to measure imputation accuracy. Computation time per imputation for each method was recorded. The differences in accuracy of different imputation methods were compared using ANOVA or non-parametric test.

Results

Both missForest and GAIN were more accurate than MICE. GAIN showed similar accuracy as missForest when the simulated missingness rate was 20%, but was more accurate when the simulated missingness rate was 50%. GAIN was the most accurate for the imputation of skewed continuous and imbalanced categorical variables at both missingness rates. GAIN had a much higher computation speed (32 min on PC) comparing to that of missForest (1300 min) when the sample size is 50,000.

Conclusion

GAIN showed better accuracy as an imputation method for missing data in large real-world clinical datasets compared to MICE and missForest, and was more resistant to high missingness rate (50%). The high computation speed is an added advantage of GAIN in big clinical data research. It holds potential as an accurate and efficient method for missing data imputation in future big data clinical research.

Trial registration

ClinicalTrials.gov ID: NCT03299010; Unique Protocol ID: HKUCTR-2232

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-021-01272-3.

Prediction models and nomograms for 10-year risk of end-stage renal disease in Chinese type 2 diabetes mellitus patients in primary care

AIMS

To develop and validate 10-year risk prediction models, nomograms and charts for end-stage renal disease (ESRD) in Chinese patients with type 2 diabetes mellitus (T2DM) in primary care, in order to guide individualized treatment.

MATERIALS AND METHODS

This was a 10-year population-based observational cohort study. A total of 141 516 Chinese T2DM patients without history of cardiovascular disease or ESRD who were managed in public primary care clinics in 2008 were included and followed up until December 2017. Two-thirds of these patients were randomly selected to develop sex-specific ESRD risk prediction models using Cox regressions. The validity and accuracy of the models were tested on the remaining third of patients using Harrell's C-index. We selected variables based on their clinical and statistical importance to construct the nomograms and charts.

RESULTS

The median follow-up period was 9.75 years. The cumulative incidence of ESRD was 6.0% (men: 6.1%, women: 5.9%). Age, diabetes duration, systolic blood pressure (SBP), SBP variability, diastolic blood pressure, triglycerides, glycated haemoglobin (HbA1c), HbA1c variability, urine albumin to creatinine ratio (UACR), and estimated glomerular filtration rate (eGFR) were significant predictors for both sexes. Smoking and total cholesterol to HDL cholesterol ratio were additional significant predictors for men and women, respectively. The models showed Harrell's C-statistics of 0.889/0.889 (women/men). Age, eGFR, UACR, SBP and HbA1c were selected for both sexes to develop nomograms and charts.

CONCLUSIONS

Using routinely available variables, the 10-year ESRD risk of Chinese T2DM patients in primary care can be predicted with approximately 90% accuracy. We have developed different tools to facilitate routine ESRD risk prediction in primary care, so that individualized care can be provided to prevent or delay ESRD in T2DM patients.

Cardiovascular Risk Prediction, Glycemic Control, and Determinants in Diabetic and Hypertensive Patients in Massawa Hospital, Eritrea: Cross-Sectional Study on 600 Subjects

BACKGROUND

Hypertension and diabetes are key determinants of cardiovascular risks. The objective of this study was to calculate 10-year incidence of cardiovascular risk, determine cardiovascular risk factors, and evaluate how diabetes and hypertension are controlled in patients in Massawa Hospital, Eritrea.

METHODS

This was a hospital-based cross-sectional study using census sampling. A checklist and interview were used as data-collection tool from October 10 to November 20, 2020. Written consent was obtained from each study participant before starting the study. Descriptive statistics were wasused, and results are presented in percentages in tables, p<0.05 was considered significant.

RESULTS

A total of 600 patients were enrolled in the study, dominated by the Tigrigna (58.7%) and Tigre (26.7%) ethnic groups. About half the patients (58.8%) had a body-mass index of 18-25 kg/m², with abdominal circumference of <95 cm (74%). Most (93.5%) patients had <10% risk of cardiovascular complications in the coming 10 years. Age showed significant association with hypertension, diabetes mellitus, cardiovascular risk, and poor glycemic and blood-pressure control (p<0.001). Body-mass index, abdominal obesity, and history of stroke were associated with hypertension and diabetes mellitus (p<0.001). Moreover, smoking, hypertension, and monthly income were associated with higher cardiovascular risk (p<0.001). In addition, hypertension and abdominal obesity were associated with glycemic control (p<0.001), and blood-pressure control was significantly associated with diabetes and hypertension (p<0.001).

CONCLUSION

Age and hypertension were associated with diabetes, cardiovascular risk and poor glycemic control, and smoking, abdominal obesity, and monthly income also significant associations with higher cardiovascular risk and glycemic control. Cessation and adjustment of modifiable factors, such as smoking, hypertension, and regular exercise are highly recommended.

A nomogram model for screening the risk of diabetes in a large-scale Chinese population: an observational study from 345,718 participants

Our study is major to establish and validate a simple type||diabetes mellitus (T2DM) screening model for identifying high-risk individuals among Chinese adults. A total of 643,439 subjects who participated in the national health examination had been enrolled in this cross-sectional study. After excluding subjects with missing data or previous medical history, 345,718 adults was included in the final analysis. We used the least absolute shrinkage and selection operator models to optimize feature selection, and used multivariable logistic regression analysis to build a predicting model. The results showed that the major risk factors of T2DM were age, gender, no drinking or drinking/time > 25 g, no exercise, smoking, waist-to-height ratio, heart rate, systolic blood pressure, fatty liver and gallbladder disease. The area under ROC was 0.811 for development group and 0.814 for validation group, and the p values of the two calibration curves were 0.053 and 0.438, the improvement of net reclassification and integrated discrimination are significant in our model. Our results give a clue that the screening models we conducted may be useful for identifying Chinses adults at high risk for diabetes. Further studies are needed to evaluate the utility and feasibility of this model in various settings.

Individualised risk prediction model for new-onset, progression and regression of chronic kidney disease in a retrospective cohort of patients with type 2 diabetes under primary care in Hong Kong

OBJECTIVES

This study is aimed to develop and validate a prediction model for multistate transitions across different stages of chronic kidney disease (CKD) in patients with type 2 diabetes mellitus under primary care.

SETTING

We retrieved the anonymised electronic health records of a population-based retrospective cohort in Hong Kong.

PARTICIPANTS

A total of 26 197 patients were included in the analysis.

PRIMARY AND SECONDARY OUTCOME MEASURES

The new-onset, progression and regression of CKD were defined by the transitions of four stages that were classified by combining glomerular filtration rate and urine albumin-to-creatinine ratio. We applied a multiscale multistate Poisson regression model to estimate the rates of the stage transitions by integrating the baseline demographic characteristics, routine laboratory test results and clinical data from electronic health records.

RESULTS

During the mean follow-up time of 1.8 years, there were 2632 patients newly diagnosed with CKD, 1746 progressed to the next stage and 1971 regressed into an earlier stage. The models achieved the best performance in predicting the new-onset and progression with the predictors of sex, age, body mass index, systolic blood pressure, diastolic blood pressure, serum creatinine, haemoglobin A1c, total cholesterol, low-density lipoprotein, high-density lipoprotein, triglycerides and drug prescriptions.

CONCLUSIONS

This study demonstrated that individual risks of new-onset and progression of CKD can be predicted from the routine physical and laboratory test results. The individualised prediction curves developed from this study could potentially be applied to routine clinical practices, to facilitate clinical decision making, risk communications with patients and early interventions.