Center for Clinical Epidemiology and Clinical Statistics, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand.
General Thoracic Unit, Department of Surgery, Faculty of Medicine, Chiang Mai University Hospital, Chiang Mai, Thailand.
PLoS One. 2024 Sep 30;19(9):e0308890. doi: 10.1371/journal.pone.0308890. eCollection 2024.
Despite the well-established significance of the CAC score as a cardiovascular risk marker, the timing of using CAC score in routine clinical practice remains unclear. We aim to develop a prediction model for patients visiting outpatient cardiology units, which can recommend whether CAC score screening is necessary. A prediction model using retrospective cross-sectional design was conducted. Patients who underwent CAC score screening were included. Eight candidate predictors were preselected, including age, gender, DM or primary hypertension, angina chest pain, LDL-C (≥130 mg/dl), presence of low HDL-C, triglyceride (≥150 mg/dl), and eGFR. The outcome of interest was the level of CAC score (CAC score 0, CAC score 1-99, CAC score ≥100). The model was developed using ordinal logistic regression, and model performance was evaluated in terms of discriminative ability and calibration. A total of 360 patients were recruited for analysis, comprising 136 with CAC score 0, 133 with CAC score 1-99, and 111 with CAC score ≥100. The final predictors identified were age, male gender, presence of hypertension or DM, and low HDL-C. The model demonstrated excellent discriminative ability (Ordinal C-statistics of 0.81) with visually good agreement on calibration plots. The implementation of this model (CAC-prob) has the potential to enhance precision in recommending CAC screening. However, external validation is necessary to assess its robustness in new patient cohorts.
尽管 CAC 评分作为心血管风险标志物的意义已经得到充分证实，但在常规临床实践中何时使用 CAC 评分仍然不清楚。我们旨在为就诊于门诊心内科的患者开发一种预测模型，以推荐是否需要 CAC 评分筛查。采用回顾性横断面设计进行预测模型构建。纳入接受 CAC 评分筛查的患者。预筛选了 8 个候选预测因子，包括年龄、性别、糖尿病或原发性高血压、心绞痛胸痛、LDL-C（≥130mg/dl）、低 HDL-C、甘油三酯（≥150mg/dl）和 eGFR。感兴趣的结局是 CAC 评分水平（CAC 评分 0、CAC 评分 1-99、CAC 评分≥100）。使用有序逻辑回归构建模型，并根据判别能力和校准来评估模型性能。共纳入 360 例患者进行分析，包括 CAC 评分 0 者 136 例、CAC 评分 1-99 者 133 例和 CAC 评分≥100 者 111 例。最终确定的预测因子为年龄、男性、高血压或糖尿病以及低 HDL-C。该模型具有出色的判别能力（有序 C 统计量为 0.81），校准图上的视觉一致性良好。该模型的实施（CAC-prob）有可能提高 CAC 筛查推荐的精准度。然而，需要进行外部验证以评估其在新患者队列中的稳健性。
