Background: There is a significant lack of effective pharmaceutical interventions for treating coronary artery calcification (CAC). Severe CAC (sCAC) poses a formidable challenge to interventional surgery and exhibits robust associations with adverse cardiovascular outcomes. Therefore, it is imperative to develop tools capable of early-stage detection and risk assessment for both CAC and sCAC. This study aims to develop and validate nomograms for the accurate prediction of CAC and sCAC. Methods: This retrospective cross-sectional study was conducted in Taizhou, Jiangsu Province, China. CAC assessment was performed using non-gated thoracic CT scans. Demographic data and clinical information were collected from patients who were then randomly divided into a training set (70%) or a validation set (30%). Least absolute shrinkage and selection operator (LASSO) regression as well as multiple logistic regression analyses were utilized to identify predictive factors for both CAC and sCAC development. Nomograms were developed to predict the occurrence of CAC or sCAC events. The models' performance was evaluated through discrimination analysis, calibration analysis, as well as assessment of their clinical utility. Results: This study included 666 patients with an average age of 75 years, of whom 56% were male. 391 patients had CAC, with sCAC in 134 cases. Through LASSO and multiple logistic regression analysis, age increase, hypertension, carotid artery calcification, CHD, and CHADS2 score were identified for the CAC risk predictive nomogram with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.845(95%CI 0.809-0.881) in the training set and 0.810(95%CI 0.751-0.870) in the validation set. Serum calcium level, carotid artery calcification, and CHD were identified for the sCAC risk predictive nomogram with an AUC of 0.863(95%CI 0.825-0.901) in the training set and 0.817(95%CI 0.744-0.890) in the validation set. Calibration plots indicated that two models exhibited good calibration ability. According to the decision curve analysis (DCA) results, both models have demonstrated a positive net benefit within a wide range of risks. Conclusions: The present study has successfully developed and validated two nomograms to accurately predict CAC and sCAC, both of which have demonstrated robust predictive capabilities.

The authors have declared no competing interest.

The study was supported by grants from the Research Project on Elderly Health of Jiangsu Commission of Health (Jiangsu Province, China; LK2021058), and the Chinese Medicine Science and Technology Development Project of Taizhou Commission of Health (TZ202205).

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Not Applicable

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The study was approved by the Ethics Review Committee of Taizhou Hospital of Traditional Chinese Medicine Ethics statement (Ethics Approval Number: 2024-033-01), and informed consent was waived. Before data analysis, all patient information was anonymized. The research conducted in this study adhered to the principles outlined in the Declaration of Helsinki.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Not Applicable

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Not Applicable

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Not Applicable

The datasets generated and/or analyzed during the current study are not publicly available due to hospital restrictions, but they can be obtained from the corresponding author on reasonable request.