Impact of atrial fibrillation on outcomes with motor vehicle accidents

A dynamic online nomogram predicting post-traumatic arrhythmias: A retrospective cohort study

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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.

Development and Internal Validation of a Nomogram to Predict Mortality During the ICU Stay of Thoracic Fracture Patients Without Neurological Compromise: An Analysis of the MIMIC-III Clinical Database

Background: This study aimed to develop and validate a nomogram for predicting mortality in patients with thoracic fractures without neurological compromise and hospitalized in the intensive care unit. Methods: A total of 298 patients from the Medical Information Mart for Intensive Care III (MIMIC-III) database were included in the study, and 35 clinical indicators were collected within 24 h of patient admission. Risk factors were identified using the least absolute shrinkage and selection operator (LASSO) regression. A multivariate logistic regression model was established, and a nomogram was constructed. Internal validation was performed by the 1,000 bootstrap samples; a receiver operating curve (ROC) was plotted, and the area under the curve (AUC), sensitivity, and specificity were calculated. In addition, the calibration of our model was evaluated by the calibration curve and Hosmer-Lemeshow goodness-of-fit test (HL test). A decision curve analysis (DCA) was performed, and the nomogram was compared with scoring systems commonly used during clinical practice to assess the net clinical benefit. Results: Indicators included in the nomogram were age, OASIS score, SAPS II score, respiratory rate, partial thromboplastin time (PTT), cardiac arrhythmias, and fluid-electrolyte disorders. The results showed that our model yielded satisfied diagnostic performance with an AUC value of 0.902 and 0.883 using the training set and on internal validation. The calibration curve and the Hosmer-Lemeshow goodness-of-fit (HL). The HL tests exhibited satisfactory concordance between predicted and actual outcomes (P = 0.648). The DCA showed a superior net clinical benefit of our model over previously reported scoring systems. Conclusion: In summary, we explored the incidence of mortality during the ICU stay of thoracic fracture patients without neurological compromise and developed a prediction model that facilitates clinical decision making. However, external validation will be needed in the future.

Development of atrial fibrillation following trauma increases short term risk of cardiovascular events

Context:

New onset atrial fibrillation (AF) is associated with poor outcomes in several different patient populations.

Objectives:

To assess the effect of developing AF on cardiovascular events such as myocardial infarction (MI) and cerebrovascular accident (CVA) during the acute index hospitalization for trauma patients.

Methods:

The Healthcare Cost and Utilization Project State Inpatient Databases for California and Florida were used to identify adult trauma patients (18 years of age or older) who were admitted between 2007 and 2010. After excluding patients with a history of AF and prior history of cardiovascular events, patients were evaluated for MI, CVA, and death during the index hospitalization. A secondary analysis was performed using matched propensity scoring based on age, race, and preexisting comorbidities.

Results:

During the study period, 1,224,828 trauma patients were admitted. A total of 195,715 patients were excluded for a prior history of AF, MI, or CVA. Of the remaining patients, 15,424 (1.5%) met inclusion criteria and had new onset AF after trauma. There was an associated increase in incidence of MI (2.9 vs. 0.7%; p<0.001), CVA (2.6 vs. 0.4%; p<0.001), and inpatient mortality (8.5 vs. 2.1%; p<0.001) during the index hospitalization in patients who developed new onset AF compared with those who did not. Cox proportional hazards regression demonstrated an increased risk of MI (odds ratio [OR], 2.35 [2.13–2.60]), CVA (OR, 3.90 [3.49–4.35]), and inpatient mortality (OR, 2.83 [2.66–3.00]) for patients with new onset AF after controlling for all other potential risk factors.

Conclusions:

New onset AF in trauma patients was associated with increased incidence of myocardial infarction (MI), cerebral vascular accident (CVA), and mortality during index hospitalization in this study.