Department of Rheumatology, the First Affiliated Hospital, Zhejiang University School of Medicine, #79 Qingchun Road, Zhejiang Province, Hangzhou, 310003, China.
Department of Respiratory Diseases, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Clin Rheumatol. 2024 Jun;43(6):1959-1969. doi: 10.1007/s10067-024-06948-x. Epub 2024 Apr 8.
This study aimed at identifying clinical and laboratory risk factors for myocardial involvement (MI) in idiopathic inflammatory myopathies (IIMs) patients as well as constructing a risk-predicted nomogram for prediction and early identification of MI.
An IIMs cohort in southeastern China was constructed, including 504 adult IIMs patients who met the inclusion and exclusion criteria, and were hospitalized at four divisions of the First Affiliated Hospital, Zhejiang University School of Medicine from January 1st 2018 to April 30st 2022. After dividing patients into the training cohort and the validation cohort, risk factors for MI were identified through least absolute shrinkage and selection operator regression and multivariate logistic regression. A risk-predicted nomogram was established and validated internally and externally for discrimination, calibration and practicability.
In this cohort, 17.7% of patients developed MI and the survival was significantly inferior to that of IIMs patients without MI (P < 0.001). In the training cohort, age > 55 years old (P < 0.001), disease activity > 10 points (P < 0.001), interleukin-17A (IL-17A) > 7.5 pg/ml (P < 0.001), lactic dehydrogenase (LDH) > 425 U/L (P < 0.001), anti-mitochondrial antibodies (AMAs, P = 0.017), and anti-MDA5 antibody (P = 0.037) were significantly correlated with development of MI. A nomogram was established by including the above values to predict MI and was found efficient in discrimination, calibration, and practicability through internal and external validation.
This study developed and validated a nomogram model to predict the risk of MI in adult IIMs patients, which can benefit the prediction and early identification of MI as well as timely intervention in these patients.
本研究旨在确定特发性炎性肌病(IIM)患者心肌受累(MI)的临床和实验室危险因素,并构建预测 MI 的风险预测列线图。
构建了中国东南部的一个 IIM 队列,纳入了 504 名符合纳入和排除标准的成年 IIM 患者,他们于 2018 年 1 月 1 日至 2022 年 4 月 30 日在浙江大学医学院附属第一医院的四个科室住院。将患者分为训练队列和验证队列后,通过最小绝对收缩和选择算子回归以及多变量逻辑回归确定 MI 的危险因素。建立并内部和外部验证了风险预测列线图,以用于区分、校准和实用性。
在该队列中,17.7%的患者发生了 MI,其生存率明显低于未发生 MI 的 IIM 患者(P<0.001)。在训练队列中,年龄>55 岁(P<0.001)、疾病活动度>10 分(P<0.001)、白细胞介素-17A(IL-17A)>7.5pg/ml(P<0.001)、乳酸脱氢酶(LDH)>425U/L(P<0.001)、抗线粒体抗体(AMAs,P=0.017)和抗 MDA5 抗体(P=0.037)与 MI 的发生显著相关。通过纳入上述值,建立了一个预测 MI 的列线图,并通过内部和外部验证发现其在区分、校准和实用性方面均有效。
本研究建立并验证了一个预测成年 IIM 患者 MI 风险的列线图模型,该模型有助于预测和早期识别 MI,并及时对这些患者进行干预。