A nomogram is a visualized clinical prediction models, which offer a scientific basis for clinical decision-making. There is a lack of reports on its use in predicting the risk of arrhythmias in trauma patients. This study aims to develop and validate a straightforward nomogram for predicting the risk of arrhythmias in trauma patients.

We retrospectively collected clinical data from 1119 acute trauma patients who were admitted to the Advanced Trauma Center of the Affiliated Hospital of Zunyi Medical University between January 2016 and May 2022. Data recorded included intra-hospital arrhythmia, ICU stay, and total hospitalization duration. Patients were classified into arrhythmia and non-arrhythmia groups. Data was summarized according to the occurrence and prognosis of post-traumatic arrhythmias, and randomly allocated into a training and validation sets at a ratio of 7:3. The nomogram was developed according to independent risk factors identified in the training set. Finally, the predictive performance of the nomogram model was validated.

Arrhythmias were observed in 326 (29.1%) of the 1119 patients. Compared to the non-arrhythmia group, patients with arrhythmias had longer ICU and hospital stays and higher in-hospital mortality rates. Significant factors associated with post-traumatic arrhythmias included cardiovascular disease, catecholamine use, glasgow coma scale (GCS) score, abdominal abbreviated injury scale (AIS) score, injury severity score (ISS), blood glucose (GLU) levels, and international normalized ratio (INR). The area under the receiver operating characteristic curve (AUC) values for both the training and validation sets exceeded 0.7, indicating strong discriminatory power. The calibration curve showed good alignment between the predicted and actual probabilities of arrhythmias. Decision curve analysis (DCA) indicated a high net benefit for the model in predicting arrhythmias. The Hosmer-Lemeshow goodness-of-fit test confirmed the model's good fit.

The nomogram developed in this study is a valuable tool for accurately predicting the risk of post-traumatic arrhythmias, offering a novel approach for physicians to tailor risk assessments to individual patients.