ArticleJournal of clinical hypertension (Greenwich, Conn.)2024
A predictive model for postoperative adverse outcomes following surgical treatment of acute type A aortic dissection based on machine learning.
Article in Journal of clinical hypertension (Greenwich, Conn.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 12 citations in OpenAlex.
- Performance and incremental value of preoperative multimodal imaging and perioperative clinical characteristics in predicting postoperative major adverse events in type A aortic dissection.Quantitative imaging in medicine and surgery · 2026Article
- Aortic Arch and Frozen Elephant Trunk Surgery: Anesthetic Challenges and Strategies for Organ Protection.Journal of clinical medicine · 2026Review
- Machine learning approaches for risk prediction in aortic dissection: a systematic review and meta-analysis.Frontiers in cardiovascular medicine · 2026Review
- Construction and validation of explainable machine learning models to predict in-hospital mortality for patients with acute type A aortic dissection surgery.Frontiers in medicine · 2026Article
- Development and validation of machine learning-based in-hospital mortality predictive models for acute aortic syndrome in emergency departments.World journal of emergency medicine · 2026Article
- Applications of Artificial Intelligence as a Prognostic Tool in the Management of Acute Aortic Syndrome and Aneurysm: A Comprehensive Review.Journal of clinical medicine · 2025Review
- Preoperative prediction of major adverse outcomes after total arch replacement in acute type A aortic dissection based on machine learning ensemble.Scientific reports · 2025Article
- Interpretable prognostic modeling for long-term survival of Type A aortic dissection patients using support vector machine algorithm.European journal of medical research · 2025Article
- Predicting the risk of heart failure after acute myocardial infarction using an interpretable machine learning model.Frontiers in cardiovascular medicine · 2025Article
- Revolutionizing Cardiology through Artificial Intelligence-Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment-A Comprehensive Review of the Past 5 Years.Diagnostics (Basel, Switzerland) · 2024Review
- A predictive model for postoperative adverse outcomes following surgical treatment of acute type A aortic dissection based on machine learning.Journal of clinical hypertension (Greenwich, Conn.) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
Acute type A aortic dissection (AAAD) has a high probability of postoperative adverse outcomes (PAO) after emergency surgery, so exploring the risk factors for PAO during hospitalization is key to reducing postoperative mortality and improving prognosis. An artificial intelligence approach was used to build a predictive model of PAO by clinical data-driven machine learning to predict the incidence of PAO after total arch repair for AAAD. This study included 380 patients with AAAD. The clinical features that are associated with PAO were selected using the LASSO regression analysis. Six different machine learning algorithms were tried for modeling, and the performance of each model was analyzed comprehensively using receiver operating characteristic curves, calibration curve, precision recall curve, and decision analysis curves. Explain the optimal model through Shapley Additive Explanation (SHAP) and perform an individualized risk assessment. After comprehensive analysis, the authors believe that the extreme gradient boosting (XGBoost) model is the optimal model, with better performance than other models. The authors successfully built a prediction model for PAO in AAAD patients based on the XGBoost algorithm and interpreted the model with the SHAP method, which helps to identify high-risk AAAD patients at an early stage and to adjust individual patient-related clinical treatment plans in a timely manner.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.