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An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
[Development and validation on death risk model of Stanford type A aortic dissection based on Cox regression]
Zhiran Guo1, Sufang Huang1, Qiansheng Wu2
1Department of Emergency, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan 430030, Hubei, China.
Insights
This study developed a Cox regression model to predict death risk in Stanford type A aortic dissection (AAD) patients. The model effectively identifies high-risk individuals, aiding in timely treatment adjustments and improving patient outcomes.
Area of Science:
- Cardiovascular Surgery
- Medical Prediction Modeling
- Thoracic Surgery
Background:
- Stanford type A aortic dissection (AAD) is a life-threatening condition with significant mortality.
- Accurate risk stratification is crucial for optimizing treatment strategies and improving patient survival in AAD.
Purpose of the Study:
- To construct and validate a Cox proportional risk regression model for predicting the death risk in patients with Stanford type A aortic dissection (AAD).
Main Methods:
- A cohort of 454 AAD patients treated surgically was retrospectively analyzed.
- Lasso regression identified key prognostic variables, and a multivariate Cox model was built.
- Model performance was assessed using ROC curves, calibration curves, and decision curve analysis (DCA).
Main Results:
- The final model included 10 variables: abdominal pain, syncope, lower limb pain/numbness, admission mode, preoperative SBP, hs-cTnI levels, De Bakey type, pulmonary infection, and postoperative delirium.
- The prediction model demonstrated good discrimination with an AUC of 0.873 in the model group and 0.828 in the verification group.
- Calibration and DCA confirmed the model's accuracy and clinical utility.
Conclusions:
- The developed AAD death risk prediction model effectively identifies high-risk patients.
- This tool aids clinicians in evaluating postoperative survival and tailoring treatment strategies for AAD.
- The model's variables provide insights into factors influencing mortality in AAD.
Objective:
To construct the prediction model of death risk of Stanford type A aortic dissection (AAD) based on Cox proportional risk regression model.
Methods:
AAD patients who were diagnosed and received surgical treatment admitted to the department of cardiothoracic surgery of Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology from January 1st, 2019 to April 30th, 2020 were enrolled. The general situation, clinical manifestations, pre-hospital data, laboratory examination and imaging examination results of the patients were collected. The observation period was up to the death of the patients or ended on April 30th, 2021. They were divided into the model group and the verification group according to the ratio of 7:3. Lasso method was used to screen prognostic variables from the data of the modeling group, and multivariate Cox regression analysis was included to construct the AAD death risk prediction model, which was displayed by nomogram. The receiver operator characteristic curve (ROC curve) was used to evaluate the discrimination of the model, the calibration curve to evaluate the accuracy of the model, and the clinical decision curve (DCA) to evaluate the effectiveness of the model.
Results:
A total of 454 patients with AAD were finally included, and the mortality was 19.4% (88/454). Lasso regression analysis was used to screen out 10 variables from the data of 317 patients in the model group, and the prediction model of death risk was constructed: 0.511×abdominal pain+1.061×syncope+0.428×lower limb pain/numbness-0.365×emergency admission-1.933×direct admission-1.493×diagnosis before referral+0.662×preoperative systolic blood pressure (SBP) < 100 mmHg (1 mmHg = 0.133 kPa)+0.632×hypersensitivity cardiac troponin I (hs-cTnI) > 34.2 ng/L+1.402×De Bakey type+0.641× pulmonary infection+1.472×postoperative delirium. The area under the ROC curve (AUC) and 95% confidence interval (95%CI) of the AAD death risk prediction model were 0.873 (0.817-0.928), and that of the verification group was 0.828 (0.740-0.916). DCA showed that the net benefit value of the model was higher. The calibration curve showed that there was a good correlation between the actual observation results and the model prediction results.
Conclusions:
The AAD death risk prediction model based on abdominal pain, syncope, lower limb pain/numbness, mode of admission, diagnosis before referral, preoperative SBP < 100 mmHg, hs-cTnI > 34.2 ng/L, De Bakey type , pulmonary infection, and postoperative delirium can effectively help clinicians identify patients at high risk for AAD, evaluate their postoperative survival and timely adjust treatment strategies.
