A simple nomogram score for screening patients with type 2 diabetes to detect those with hypertension: A cross-sectional study based on a large community survey in China

Development and validation of a prediction model for all-cause mortality in maintenance dialysis patients: a multicenter retrospective cohort study

BACKGROUND

The mortality risk varies considerably among individual dialysis patients. This study aimed to develop a user-friendly predictive model for predicting all-cause mortality among dialysis patients.

METHODS

Retrospective data regarding dialysis patients were obtained from two hospitals. Patients in training cohort (N = 1421) were recruited from the Fifth Affiliated Hospital of Sun Yat-sen University, and patients in external validation cohort (N = 429) were recruited from the First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine. The follow-up endpoint event was all-cause death. Variables were selected by LASSO-Cox regression, and the model was constructed by Cox regression, which was presented in the form of nomogram and web-based tool. The discrimination and accuracy of the prediction model were assessed using C-indexes and calibration curves, while the clinical value was assessed by decision curve analysis (DCA).

RESULTS

The best predictors of 1-, 3-, and 5-year all-cause mortality contained nine independent factors, including age, body mass index (BMI), diabetes mellitus (DM), cardiovascular disease (CVD), cancer, urine volume, hemoglobin (HGB), albumin (ALB), and pleural effusion (PE). The 1-, 3-, and 5-year C-indexes in the training set (0.840, 0.866, and 0.846, respectively) and validation set (0.746, 0.783, and 0.741, respectively) were consistent with comparable performance. According to the calibration curve, the nomogram predicted survival accurately matched the actual survival rate. The DCA showed the nomogram got more clinical net benefit in both the training and validation sets.

CONCLUSIONS

The effective and convenient nomogram may help clinicians quantify the risk of mortality in maintenance dialysis patients.

A diagnostic prediction model for hypertension in Han and Yugur population from the China National Health Survey (CNHS)

BACKGROUND

The prevalence of hypertension is high among Chinese adults, thus, identifying non-hypertensive individuals at high risk for intervention will help to improve the efficiency of primary prevention strategies.

METHODS

The cross-sectional data on 9699 participants aged 20 to 80 years were collected from the China National Health Survey in Gansu and Hebei provinces in 2016 to 2017, and they were nonrandomly split into the training set and validation set based on location. Multivariable logistic regression analysis was performed to develop the diagnostic prediction model, which was presented as a nomogram and a website with risk classification. Predictive performances of the model were evaluated using discrimination and calibration, and were further compared with a previously published model. Decision curve analysis was used to calculate the standardized net benefit for assessing the clinical usefulness of the model.

RESULTS

The Lasso regression analysis identified the significant predictors of hypertension in the training set, and a diagnostic model was developed using logistic regression. A nomogram with risk classification was constructed to visualize the model, and a website ( https://chris-yu.shinyapps.io/hypertension_risk_prediction/ ) was developed to calculate the exact probabilities of hypertension. The model showed good discrimination and calibration, with the C-index of 0.789 (95% confidence interval [CI]: 0.768, 0.810) through internal validation and 0.829 (95% CI: 0.816, 0.842) through external validation. Decision curve analysis demonstrated that the model was clinically useful. The model had a higher area under receiver operating characteristic curves in training and validation sets compared with a previously published diagnostic model based on Northern China population.

CONCLUSION

This study developed and validated a diagnostic model for hypertension prediction in Gansu Province. A nomogram and a website were developed to make the model conveniently used to facilitate the individualized prediction of hypertension in the general population of Han and Yugur.

Development and validation of a nomogram model for individualized prediction of hypertension risk in patients with type 2 diabetes mellitus

Type 2 diabetes mellitus (T2DM) with hypertension (DH) is the most common diabetic comorbidity. Patients with DH have significantly higher rates of cardiovascular disease morbidity and mortality. The objective of this study was to develop and validate a nomogram model for the prediction of an individual's risk of developing DH. A total of 706 T2DM patients who met the criteria were selected and divided into a training set (n = 521) and a validation set (n = 185) according to the discharge time of patients. By using multivariate logistic regression analysis and stepwise regression, the DH nomogram prediction model was created. Calibration curves were used to evaluate the model's accuracy, while decision curve analysis (DCA) and receiver operating characteristic (ROC) curves were used to evaluate the model's clinical applicability and discriminatory power. Age, body mass index (BMI), diabetic nephropathy (DN), and diabetic retinopathy (DR) were all independent risk factors for DH (P < 0.05). Based on independent risk factors identified by multivariate logistic regression, the nomogram model was created. The model produces accurate predictions. If the total nomogram score is greater than 120, there is a 90% or higher chance of developing DH. In the training and validation sets, the model's ROC curves are 0.762 (95% CI 0.720-0.803) and 0.700 (95% CI 0.623-0.777), respectively. The calibration curve demonstrates that there is good agreement between the model's predictions and the actual outcomes. The decision curve analysis findings demonstrated that the nomogram model was clinically helpful throughout a broad threshold probability range. The DH risk prediction nomogram model constructed in this study can help clinicians identify individuals at high risk for DH at an early stage, which is a guideline for personalized prevention and treatments.

Establishment and evaluation of a risk-prediction model for hypertension in elderly patients with NAFLD from a health management perspective

Elderly patients with nonalcoholic fatty liver disease are at a higher risk of developing. This study established an effective, individualised, early Hypertension risk-prediction model and proposed health management advice for patients over 60 years of age with NAFLD. Questionnaire surveys, physical examinations, and biochemical tests were conducted in 11,136 participants. The prevalence of NAFLD among 11,136 participants was 52.1%. Risk factors were screened using the least absolute shrinkage and selection operator model and random forest model. A risk-prediction model was established using logistic regression analysis and a dynamic nomogram was drawn. The model was evaluated for discrimination, calibration, and clinical applicability using receiver operating characteristic curves, calibration curves, decision curve analysis, net reclassification index (NRI), and external validation. The results suggested that the model showed moderate predictive ability. The area under curve (AUC) of internal validation was 0.707 (95% CI: 0.688-0.727) and the AUC of external validation was 0.688 (95% CI: 0.672-0.705). The calibration plots showed good calibration, the risk threshold of the decision curve was 30-56%, and the NRI value was 0.109. This Hypertension risk factor model may be used in clinical practice to predict the Hypertension risk in NAFLD patients.

Development and validation of a clinical model to predict hypertension in consecutive patients with obstructive sleep apnea hypopnea syndrome: a hospital-based study and nomogram analysis

BACKGROUND

To screen for risk predictors of hypertension in patients with Obstructive Sleep Apnea Hypopnea Syndrome (OSAHS) and develop and validate a clinical model for individualized prediction of hypertension in consecutive patients with OSAHS.

METHODS

114 consecutive patients with OSAHS confirmed by PSG monitoring participated in this study. Those individuals were divided into two sets at a ratio of 7:3, using computer-generated random numbers: 82 individuals were assigned to the training set and 32 to the validation set. Important risk predictors of hypertension in individuals with OSAHS were confirmed using the LASSO method and a clinical nomogram constructed. The predictive accuracy was assessed by unadjusted concordance index (C-index) and calibration plot.

RESULTS

Univariate and multivariate regression analysis identified BMI, REM-AHI, REM-MSpO₂ and T90% as predictive risk factors of OSAHS. Those risk factors were used to construct a clinical predictive nomogram. The calibration curves for hypertension in patients with OSAHS risk revealed excellent accuracy of the predictive nomogram model, internally and externally. The unadjusted concordance index (C-index) for the training and validation set was 0.897 [95% CI 0.795-0.912] and 0.894 [95% CI 0.788-0.820] respectively. The AUC of the training and validation set was 0.8175882 and 0.8031522, respectively. Decision curve analysis showed that the predictive model could be applied clinically when the threshold probability was 20 to 80%.

CONCLUSION

We constructed and validated a clinical nomogram to individually predict the occurrence of hypertension in patients with OSAHS. We determined that BMI, REM-AHI, REM-MSpO₂ and T90% were independent risk predictors for hypertension in patients with OSAHS. This practical prognostic nomogram may help improve clinical decision making.

A Non-Invasive Prediction Model for Non-Alcoholic Fatty Liver Disease in Adults with Type 2 Diabetes Based on the Population of Northern Urumqi, China

BACKGROUND

High prevalence of non-alcoholic fatty liver disease (NAFLD) occurs in type 2 diabetes mellitus (T2DM), and about 13% of diabetic patients eventually die of liver cirrhosis or liver cancer. The purpose of our research was to develop a non-invasive predictive model of NAFLD in adults with T2DM.

PATIENTS AND METHODS

Adult patients diagnosed with T2DM during physical examination in 2018 in Urumqi were recruited, in total 40,921 cases. We chose questionnaire and physical measurement variables to build a simple, low-cost model. Variables were selected by the least absolute shrinkage and selection operator regression (LASSO). The features chosen by LASSO were used to build the nomogram prediction model of NAFLD. The receiver operating curve (ROC) and calibration were used for model validation.

RESULTS

Determinants in the nomogram included age, ethnicity, sex, exercise, smoking, dietary ratio, heart rate, systolic blood pressure (SBP), BMI, waist circumference, and atherosclerotic vascular disease (ASCVD). The area under ROC of developing group and validation group was 0.756 (95% confidence interval 0.750-0.761) and 0.755 (95% confidence interval 0.746-0.763), respectively, and the P values of the two calibration curves were 0.694 and 0.950, suggesting that the nomogram had good disease recognition ability and calibration.

CONCLUSION

A nomogram constructed with accuracy can calculate the possibility of NAFLD in adults with T2DM. If validated externally, this tool could be utilized as a non-invasive method to diagnose non-alcoholic fatty liver in adults with T2DM.

Identification of Potential Type II Diabetes in a Large-Scale Chinese Population Using a Systematic Machine Learning Framework

BACKGROUND

An estimated 425 million people globally have diabetes, accounting for 12% of the world's health expenditures, and the number continues to grow, placing a huge burden on the healthcare system, especially in those remote, underserved areas.

METHODS

A total of 584,168 adult subjects who have participated in the national physical examination were enrolled in this study. The risk factors for type II diabetes mellitus (T2DM) were identified by p values and odds ratio, using logistic regression (LR) based on variables of physical measurement and a questionnaire. Combined with the risk factors selected by LR, we used a decision tree, a random forest, AdaBoost with a decision tree (AdaBoost), and an extreme gradient boosting decision tree (XGBoost) to identify individuals with T2DM, compared the performance of the four machine learning classifiers, and used the best-performing classifier to output the degree of variables' importance scores of T2DM.

RESULTS

The results indicated that XGBoost had the best performance (accuracy = 0.906, precision = 0.910, recall = 0.902, F-1 = 0.906, and AUC = 0.968). The degree of variables' importance scores in XGBoost showed that BMI was the most significant feature, followed by age, waist circumference, systolic pressure, ethnicity, smoking amount, fatty liver, hypertension, physical activity, drinking status, dietary ratio (meat to vegetables), drink amount, smoking status, and diet habit (oil loving).

CONCLUSIONS

We proposed a classifier based on LR-XGBoost which used fourteen variables of patients which are easily obtained and noninvasive as predictor variables to identify potential incidents of T2DM. The classifier can accurately screen the risk of diabetes in the early phrase, and the degree of variables' importance scores gives a clue to prevent diabetes occurrence.