Development and validation of a nomogram to better predict hypertension based on a 10-year retrospective cohort study in China

A nomogram for prediction of early mortality in patients undergoing cardiac surgery for infective endocarditis: a retrospective single-center study

OBJECTIVE

Despite advancements in surgical techniques, operations for infective endocarditis (IE) remain associated with relatively high mortality. The aim of this study was to develop a nomogram model to predict the early postoperative mortality in patients undergoing cardiac surgery for infective endocarditis based on the preoperative clinical features.

METHODS

We retrospectively analyzed the clinical data of 357 patients with IE who underwent surgeries at our center between January 2007 and June 2023. Independent risk factors for early postoperative mortality were identified using univariate and multivariate logistic regression models. Based on these factors, a predictive model was developed and presented in a nomogram. The performance of the nomogram was evaluated through the receiver operating characteristic (ROC) curve, calibration plot, and decision curve analysis (DCA). Internal validation was performed utilizing the bootstrapping method.

RESULTS

The nomogram included nine predictors: age, stroke, pulmonary embolism, albumin level, cardiac function class IV, antibotic use <4weeks, vegetation size ≥1.5 cm, perivalvular abscess and preoperative dialysis. The area under the ROC curve (AUC) of the model was 0.88 (95%CI:0.80-0.96). The calibration plot indicated strong prediction consistency of the nomogram with satisfactory Hosmer-Lemeshow test results (χ2 = 13.490, p = 0.142). Decision curve analysis indicated that the nomogram model provided greater clinical net benefits compared to "operate-all" or "operate-none" strategies.

CONCLUSIONS

The innovative nomogram model offers cardiovascular surgeons a tool to predict the risk of early postoperative mortality in patients undergoing IE operations. This model can serve as a valuable reference for preoperative decision-making and can enhance the clinical outcomes of IE patients.

Multimodal data-driven, vertical visualization prediction model for early prediction of atherosclerotic cardiovascular disease in patients with new-onset hypertension

BACKGROUND

: Hypertension is an important contributing factor to atherosclerotic cardiovascular disease (ASCVD), and multiple risk factors, many of which are implicated in metabolic disorders, contribute to the cause of hypertension. Despite the promise of multimodal data-driven prediction model, no such prediction model was available to predict the risk of ASCVD in Chinese individuals with new-onset hypertension and no history of ASCVD.

METHODS

: A total of 514 patients were randomly allocated to training and verification cohorts (ratio, 7 : 3). We employed Boruta feature selection and conducted multivariate Cox regression analyses to identify variables associated with ASCVD in these patients, which were subsequently utilized for constructing the predictive model. The performance of prediction model was assessed in terms of discriminatory power (C-index), calibration (calibration curves), and clinical utility [decision curve analysis (DCA)].

RESULTS

: This model was derived from four clinical variables: 24-h SBP coefficient of variation, 24-h DBP coefficient of variation, urea nitrogen and the triglyceride-glucose (TyG) index. Bootstrapping with 500 iterations was conducted to adjust the C-indexes were C-index = 0.731, 95% confidence interval (CI) 0.620-0.794 and C-index: 0.799, 95% CI 0.677-0.892 in the training and verification cohorts, respectively. Calibration plots with 500 bootstrapping iterations exhibited a strong correlation between the predicted and observed occurrences of ASCVD in both the training and verification cohorts. DCA analysis confirmed the clinical utility of this prediction model. The constructed nomogram demonstrated significant additional prognostic utility for ASCVD, as evidenced by improvements in the C-index, net reclassification improvement, integrated discrimination improvement, and DCA compared with the overall ASCVD risk assessment.

CONCLUSION

The developed longitudinal prediction model based on multimodal data can effectively predict ASCVD risk in individuals with an initial diagnosis of hypertension.

TRIAL REGISTRATION

: The trial was registered in the Chinese Clinical Trial Registry (ChiCTR2300074392).

Development and validation of a prediction nomogram for sleep disorders in hospitalized patients with acute myocardial infarction

PURPOSE

Sleep disorders are becoming more prevalent in hospitalized patients with acute myocardial infarction (AMI). We aimed to investigate the risk factors for sleep disorders in hospitalized patients with AMI, then develop and validate a prediction nomogram for the risk of sleep disorders.

METHODS

Clinical data were collected from patients with AMI hospitalized in our hospital from January 2020 to June 2023. All patients were divided into the training group and the validation group with a ratio of 7:3 in sequential order. The LASSO regression analysis and multivariate logistic regression analysis were used to screen potential risk factors for sleep disorders. The concordance index (C-index), calibration curves, and decision curve analysis (DCA) were plotted.

RESULTS

A total of 256 hospitalized patients with AMI were enrolled. Patients were divided into the training group (180) and the validation group (76) according to a scale of 7:3. Of the 256 patients, 90 patients (35.16%) suffered from sleep disorders, and 33 patients (12.89%) needed hypnotics. The variables screened by LASSO regression included age, smoking, NYHA class, anxiety status at admission, depression status at admission, and strangeness of environment. A nomogram model was established by incorporating the risk factors selected. The C-index, calibration curve, and DCA showed good predictive performance.

CONCLUSIONS

We identified six clinical characteristics as predictors of sleep disorders in hospitalized patients with AMI. It helps nurses make appropriate decisions in clinical practice.

Multimodal Data Integration Enhance Longitudinal Prediction of New-Onset Systemic Arterial Hypertension Patients with Suspected Obstructive Sleep Apnea

Background

It is crucial to accurately predict the disease progression of systemic arterial hypertension in order to determine the most effective therapeutic strategy. To achieve this, we have employed a multimodal data-integration approach to predict the longitudinal progression of new-onset systemic arterial hypertension patients with suspected obstructive sleep apnea (OSA) at the individual level.

Methods

We developed and validated a predictive nomogram model that utilizes multimodal data, consisting of clinical features, laboratory tests, and sleep monitoring data. We assessed the probabilities of major adverse cardiac and cerebrovascular events (MACCEs) as scores for participants in longitudinal cohorts who have systemic arterial hypertension and suspected OSA. In this cohort study, MACCEs were considered as a composite of cardiac mortality, acute coronary syndrome and nonfatal stroke. The least absolute shrinkage and selection operator (LASSO) regression and multiple Cox regression analyses were performed to identify independent risk factors for MACCEs among these patients.

Results

448 patients were randomly assigned to the training cohort while 189 were assigned to the verification cohort. Four clinical variables were enrolled in the constructed nomogram: age, diabetes mellitus, triglyceride, and apnea-hypopnea index (AHI). This model accurately predicted 2-year and 3-year MACCEs, achieving an impressive area under the receiver operating characteristic (ROC) curve of 0.885 and 0.784 in the training cohort, respectively. In the verification cohort, the performance of the nomogram model had good discriminatory power, with an area under the ROC curve of 0.847 and 0.729 for 2-year and 3-year MACCEs, respectively. The correlation between predicted and actual observed MACCEs was high, provided by a calibration plot, for training and verification cohorts.

Conclusions

Our study yielded risk stratification for systemic arterial hypertension patients with suspected OSA, which can be quantified through the integration of multimodal data, thus highlighting OSA as a spectrum of disease. This prediction nomogram could be instrumental in defining the disease state and long-term clinical outcomes.

Association between triglyceride glucose index and H-type hypertension in postmenopausal women

Background

Recent studies have reported better predictive performance of triglyceride glucose (TyG) index for cardiovascular events in women, however, whether this association persists in postmenopausal women is inconclusive. We investigated the association between TyG index and H-type hypertension (HHT) in postmenopausal women.

Methods

1,301 eligible women with hypertension were included in this cross-sectional study. Concomitant homocysteine levels >10 μmol/L were defined as H-type hypertension. The TyG index was calculated as ln [triglycerides (mg/dl) × fasting glucose (mg/dl)/2]. Multivariable logistic regression models and restricted cubic spline models were used to assess the association between TyG index and H-type hypertension in postmenopausal women, and subgroup analyses were performed for potential confounders.

Results

Of the 1,301 hypertensive patients, 634 (48.7%) participants had H-type hypertension. In each adjusted model, TyG index was significantly associated with the risk of H-type hypertension. each 1-unit increase in TyG index was associated with an increased risk of H-type hypertension in all participants (OR = 1.6; 95% CI, 1.3-2.0; P < 0.001), and there was a linear relationship between TyG index and H-type hypertension (P for linear trend < 0.001).

Conclusion

TyG index is positively associated with H-type hypertension in postmenopausal women, suggesting that TyG index may be a promising marker for H-type hypertension. By controlling lipid levels and blood glucose levels, it may help prevent H-type hypertension in postmenopausal women.

Relationship between occupational noise exposure and hypertension: Cross-sectional evidence from real-world

Background

Occupational noise is one of the most common and prevalent occupational hazards worldwide and may induce adverse auditory and/or non-auditory health effects. However, the relationship between occupational noise exposure and hypertension is controversial and has long been debated.

Methods

Based on large sample cross-sectional data from all registered occupational health examination units from 2021 to 2022 (N = 101,605), this study aimed to analyze the prevalence of hearing loss and hypertension and to explore the influencing factors of hypertension of workers in Wuhan. Descriptive statistics, univariate analyses and multivariate analyses were used. Forest plot and nomograms were constructed for the visualization of predictive results. The ROC curve, AUC, C-index and calibration curves were used to assess the predictive accuracy and validity. DCA was performed to evaluate the net benefit that workers could receive.

Results

Higher rate of high-frequency hearing loss (25.3%), speech frequency hearing loss (8.8%), ECG abnormalities (31.9%) and hypertension (21.0%) were found in workers exposed to occupational noise in Wuhan. Occupational noise exposure (OR = 1.09, 95% CI: 1.01-1.18, p = 0.04), growth of age (OR: 1.07, 95% CI: 1.07-1.07, p < 0.001), overweight (OR: 1.82, 95% CI: 1.73-1.92, p < 0.001), obesity (OR: 3.62, 95% CI: 3.42-3.83, p < 0.001), hyperglycemia (OR: 1.84, 95% CI: 1.73-1.96, p < 0.001), hypercholesterolemia (OR = 1.34; 95% CI 1.22-1.48; p < 0.001), ECG abnormalities (OR = 1.11; 95% CI 1.07-1.15; p < 0.001) and family history of hypertension (OR = 1.69; 95% CI 1.58-1.81; p < 0.001) were risk factors of hypertension for workers. Male workers had a relatively higher hypertension risk than female workers (OR = 1.61; 95% CI 1.54-1.69; p < 0.001). Ear protective measures could not reduce the risk of hypertension in workers. Our nomogram has good predictive accuracy and validity. A dynamic nomogram to predict the workers' risk of hypertension was established publicly available online.

Conclusion

Occupational noise exposure may elevate workers' hypertension risk. More effective and relevant prevention measures should be taken. Our nomogram may help identify high-risk workers and facilitate timely interventions.

Conditional survival nomogram predicting real-time prognosis of locally advanced breast cancer: Analysis of population-based cohort with external validation

Background

Locally advanced breast cancer (LABC) is generally considered to have a relatively poor prognosis. However, with years of follow-up, what is its real-time survival and how to dynamically estimate an individualized prognosis? This study aimed to determine the conditional survival (CS) of LABC and develop a CS-nomogram to estimate overall survival (OS) in real-time.

Methods

LABC patients were recruited from the Surveillance, Epidemiology, and End Results (SEER) database (training and validation groups, n = 32,493) and our institution (testing group, n = 119). The Kaplan-Meier method estimated OS and calculated the CS at year (x+y) after giving x years of survival according to the formula CS(y|x) = OS(y+x)/OS(x). y represented the number of years of continued survival under the condition that the patient was determined to have survived for x years. Cox regression, best subset regression, and the least absolute shrinkage and selection operator (LASSO) regression were used to screen predictors, respectively, to determine the best model to develop the CS-nomogram and its network version. Risk stratification was constructed based on this model.

Results

CS analysis revealed a dynamic improvement in survival occurred with increasing follow-up time (7 year survival was adjusted from 63.0% at the time of initial diagnosis to 66.4, 72.0, 77.7, 83.5, 89.0, and 94.7% year by year [after surviving for 1-6 years, respectively]). In addition, this improvement was non-linear, with a relatively slow increase in the second year after diagnosis. The predictors identified were age, T and N status, grade, estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER 2), surgery, radiotherapy and chemotherapy. A CS-nomogram developed by these predictors and the CS formula was used to predict OS in real-time. The model's concordance indexes (C-indexes) in the training, validation and testing groups were 0.761, 0.768 and 0.810, which were well-calibrated according to the reality. In addition, the web version was easy to use and risk stratification facilitated the identification of high-risk patients.

Conclusions

The real-time prognosis of LABC improves dynamically and non-linearly over time, and the novel CS-nomogram can provide real-time and personalized prognostic information with satisfactory clinical utility.

Construction and internal validation of a novel nomogram for predicting prognosis of infective endocarditis

To develop a nomogram prediction model capable of early identification of high-risk infective endocarditis (IE) patients. We retrospectively analyzed the clinical data of 383 patients with IE and divided them into survival and non-survival groups according to different hospitalization outcomes. Univariate and multivariate logistic regression methods were used to screen independent risk factors affecting the survival outcome of IE, and a Nomogram prediction model was constructed by these factors. The Hosmer-Lemeshow goodness-of-fit test was applied to assess the model fit, the discrimination and calibration of the model were evaluated by plotting ROC curves and calibration curves. Advanced age, embolic symptoms, abnormal leukocyte count, low hemoglobin level and double-sided IE were associated with higher in-hospital mortality in patients with IE (P < 0.05). The Hosmer-Lemeshow goodness-of-fit test for the model was χ² = 7.107, P = 0.311. The AUC of the ROC curve of the model was 0.738 (95% CI 0.677-0.800). The bootstrap method was used to validate the prediction model. The results showed that the prediction accuracy of the model in the validation cohort was 0.842. The nomogram prediction model can accurately predict the in-hospital mortality risk of IE and can help clinicians identify high-risk IE patients early.

Development and validation of prediction models for hypertension risks: A cross-sectional study based on 4,287,407 participants

Objective

To develop an optimal screening model to identify the individuals with a high risk of hypertension in China by comparing tree-based machine learning models, such as classification and regression tree, random forest, adaboost with a decision tree, extreme gradient boosting decision tree, and other machine learning models like an artificial neural network, naive Bayes, and traditional logistic regression models.

Methods

A total of 4,287,407 adults participating in the national physical examination were included in the study. Features were selected using the least absolute shrinkage and selection operator regression. The Borderline synthetic minority over-sampling technique was used for data balance. Non-laboratory and semi-laboratory analyses were carried out in combination with the selected features. The tree-based machine learning models, other machine learning models, and traditional logistic regression models were constructed to identify individuals with hypertension, respectively. Top features selected using the best algorithm and the corresponding variable importance score were visualized.

Results

A total of 24 variables were finally included for analyses after the least absolute shrinkage and selection operator regression model. The sample size of hypertensive patients in the training set was expanded from 689,025 to 2,312,160 using the borderline synthetic minority over-sampling technique algorithm. The extreme gradient boosting decision tree algorithm showed the best results (area under the receiver operating characteristic curve of non-laboratory: 0.893 and area under the receiver operating characteristic curve of semi-laboratory: 0.894). This study found that age, systolic blood pressure, waist circumference, diastolic blood pressure, albumin, drinking frequency, electrocardiogram, ethnicity (uyghur, hui, and other), body mass index, sex (female), exercise frequency, diabetes mellitus, and total bilirubin are important factors reflecting hypertension. Besides, some algorithms included in the semi-laboratory analyses showed less improvement in the predictive performance compared to the non-laboratory analyses.

Conclusion

Using multiple methods, a more significant prediction model can be built, which discovers risk factors and provides new insights into the prediction and prevention of hypertension.

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 Nomogram-Based Prognostic Model to Predict High Blood Pressure in Children and Adolescents—Findings From 342,736 Individuals in China

Objectives

Predicting the potential risk factors of high blood pressure (HBP) among children and adolescents is still a knowledge gap. Our study aimed to establish and validate a nomogram-based model for identifying youths at risk of developing HBP.

Methods

HBP was defined as systolic blood pressure or diastolic blood pressure above the 95th percentile, using age, gender, and height-specific cut-off points. Penalized regression with Lasso was used to identify the strongest predictors of HBP. Internal validation was conducted by a 5-fold cross-validation and bootstrapping approach. The predictive variables and the advanced nomogram plot were identified by conducting univariate and multivariate logistic regression analyses. A nomogram was constructed by a training group comprised of 239,546 (69.9%) participants and subsequently validated by an external group with 103,190 (30.1%) participants.

Results

Of 342,736 children and adolescents, 55,480 (16.2%) youths were identified with HBP with mean age 11.51 ± 1.45 years and 183,487 were boys (53.5%). Nine significant relevant predictors were identified including: age, gender, weight status, birth weight, breastfeeding, gestational hypertension, family history of obesity and hypertension, and physical activity. Acceptable discrimination [area under the receiver operating characteristic curve (AUC): 0.742 (development group), 0.740 (validation group)] and good calibration (Hosmer and Lemeshow statistics, P > 0.05) were observed in our models. An available web-based nomogram was built online on https://hbpnomogram.shinyapps.io/Dyn_Nomo_HBP/.

Conclusions

This model composed of age, gender, early life factors, family history of the disease, and lifestyle factors may predict the risk of HBP among youths, which has developed a promising nomogram that may aid in more accurately identifying HBP among youths in primary care.