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find Keyword "risk prediction models" 2 results
  • Current status of research on models for predicting acute kidney injury following cardiac surgery

    Acute kidney injury (AKI) is a complication with high morbidity and mortality after cardiac surgery. In order to predict the incidence of AKI after cardiac surgery, many risk prediction models have been established worldwide. We made a detailed introduction to the composing features, clinical application and predictive capability of 14 commonly used models. Among the 14 risk prediction models, age, congestive heart failure, hypertension, left ventricular ejection fraction, diabetes, cardiac valve surgery, coronary artery bypass grafting (CABG) combined with cardiac valve surgery, emergency surgery, preoperative creatinine, preoperative estimated glomerular filtration rate (eGFR), preoperative New York Heart Association (NYHA) score>Ⅱ, previous cardiac surgery, cadiopulmonary bypass (CPB) time and low cardiac output syndrome (LCOS) are included in many risks prediction models (>3 times). In comparison to Mehta and SRI models, Cleveland risk prediction model shows the best discrimination for the prediction of renal replacement therapy (RRT)-AKI and AKI in the European. However, in Chinese population, the predictive ability of the above three risk prediction models for RRT-AKI and AKI is poor.

    Release date:2018-03-05 03:32 Export PDF Favorites Scan
  • Progress in predictive models for the risk of postoperative pulmonary complications after lung cancer surgery

    Postoperative pulmonary complications (PPCs) risk prediction models can help healthcare professionals identify the probability of PPCs occurring in patients after surgery and provide a foundation for rapid decision-making by clinical healthcare professionals. This study evaluated PPCs of lung cancer models' merits, limitations, and challenges, covering construction methods, model performance, and clinical applications. The current risk prediction models for PPCs after lung cancer surgery have a certain predictive effect on the occurrence of PPCs. However, deficiencies persist in study design, clinical implementation, and reporting transparency. Future research should prioritize large-sample, prospective, multi-center studies for multiomics models, ensuring robust data for precise predictions, thereby facilitating clinical translation, adoption, and promotion.

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