Development of the prediction model for hypertension in patients with idiopathic inflammatory myopathies

Abstract

Cardiac involvement is an important cause of morbidity and mortality in patients with idiopathic inflammatory myopathies (IIMs). Hypertension, an important cardiovascular risk factor for the general population, has a crucial role in heart involvement. However, few studies have focused on the hypertension associated with IIMs. This study aimed to develop and assess the prediction model for incident hypertension in patients with IIMs. A retrospective cohort study was performed on 362 patients with IIMs, of whom 54 (14.9%) were given a diagnosis of new‐onset hypertension from January 2008 to December 2018. The predictors of hypertension in IIMs were selected by least absolute shrinkage and selection operator (LASSO) regression, multivariable logistic regression, and clinically relevance, and then these predictors were used to draw the nomogram. Discrimination, calibration and clinical usefulness of the model were evaluated using the C‐index, calibration plot, and decision curve analysis, respectively. The predicting model was validated by the bootstrapping validation. The nomogram mainly included predictors such as age, diabetes mellitus, triglyceride, low‐density lipoprotein‐cholesterol (LDL‐C), antinuclear antibodies (ANA), and smoking. This prediction model demonstrated good discrimination with a C‐index of 0.754 (95%CI, 0.684 to 0.824) and good calibration. The C‐index of internal validation was 0.728, and decision curve analysis demonstrated that this nomogram was clinically useful. Clinicians can use this prediction model to assess the risk of hypertension in IIMs patients, and early preventive measures should be taken to reduce the incidence of hypertension in high‐risk patients.

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

“…At present, many models for hypertension have been established, which show good predicting results. However, these models are limited to a specific population (40)(41)(42)(43)(44) or disease (45)(46)(47) Semi-laboratory model. The variable importance was calculated using the absolute z-statistic of each predictor.…”

“…At present, many models for hypertension have been established, which show good predicting results. However, these models are limited to a specific population ( 40 – 44 ) or disease ( 45 – 47 ). For example, Xu Y et al ( 41 ) established a prediction model for hypertension in the Xinjiang kazak population by using 14 predictors, including age, smoking, alcohol consumption, baseline BMI, baseline DBP, baseline SBP, daily salt intake, and yak butter intake.…”

Editorial: Women in hypertension

“…Risk prediction and an early diagnosis of hypertension are essential for the primary prevention and management of this condition and its cardiovascular complications. Therefore, effective, and easy-tomanage hypertension risk prediction models (machine learning models) have been generated to identify individuals at high risk of developing hypertension (12,13). Practice guidelines are available for the management of hypertension, indicating the most effective drugs, therapeutic associations, and lifestyle modifications (14-15) to prevent cardiovascular events and reduce mortality, but many patients with hypertension remain uncontrolled.…”

Idiopathic inflammatory myopathies and hypertension: Possible involvement of hormonal factors

“…Qin et al 1 developed a predictive model for the assessment of hypertension associated with idiopathic inflammatory myopathies (IIMs). Fifty‐four patients with IIMs with a diagnosis of new‐onset hypertension and 308 normotensive controls with the same diseases were studied.…”