{"title":"Advanced statistical methods for hazard modeling in cardiothoracic surgery: a comprehensive review of techniques and approaches.","authors":"H Shafeeq Ahmed","doi":"10.1007/s12055-024-01799-2","DOIUrl":null,"url":null,"abstract":"<p><p>Hazard modeling in cardiothoracic surgery, crucial for understanding patient outcomes, utilizes survival analysis like the Cox proportional hazards model. Kaplan-Meier curves are employed in survival analysis to represent the probability of survival over time. While Cox assumes proportional hazards, the Fine-Gray model deals with competing risks. Parametric models (e.g., Weibull) specify survival distributions, unlike Cox. Bayesian analysis integrates prior knowledge with data. Machine learning, including decision trees and support vector machines, enhances risk prediction by analyzing extensive datasets. However, it is important to note that whatever new approaches one may adopt will enhance the quality of risk assessment and not the risk assessment as such. Preprocessing is vital for data quality in complex cardiovascular datasets, alongside robust validation methods like cross-validation for model reliability across patient cohorts.</p>","PeriodicalId":13285,"journal":{"name":"Indian Journal of Thoracic and Cardiovascular Surgery","volume":null,"pages":null},"PeriodicalIF":0.7000,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11329482/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Indian Journal of Thoracic and Cardiovascular Surgery","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s12055-024-01799-2","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/7/31 0:00:00","PubModel":"Epub","JCR":"Q4","JCRName":"CARDIAC & CARDIOVASCULAR SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Hazard modeling in cardiothoracic surgery, crucial for understanding patient outcomes, utilizes survival analysis like the Cox proportional hazards model. Kaplan-Meier curves are employed in survival analysis to represent the probability of survival over time. While Cox assumes proportional hazards, the Fine-Gray model deals with competing risks. Parametric models (e.g., Weibull) specify survival distributions, unlike Cox. Bayesian analysis integrates prior knowledge with data. Machine learning, including decision trees and support vector machines, enhances risk prediction by analyzing extensive datasets. However, it is important to note that whatever new approaches one may adopt will enhance the quality of risk assessment and not the risk assessment as such. Preprocessing is vital for data quality in complex cardiovascular datasets, alongside robust validation methods like cross-validation for model reliability across patient cohorts.
期刊介绍:
The primary aim of the Indian Journal of Thoracic and Cardiovascular Surgery is education. The journal aims to dissipate current clinical practices and developments in the area of cardiovascular and thoracic surgery. This includes information on cardiovascular epidemiology, aetiopathogenesis, clinical manifestation etc. The journal accepts manuscripts from cardiovascular anaesthesia, cardiothoracic and vascular nursing and technology development and new/innovative products.The journal is the official publication of the Indian Association of Cardiovascular and Thoracic Surgeons which has a membership of over 1000 at present.DescriptionThe journal is the official organ of the Indian Association of Cardiovascular-Thoracic Surgeons. It was started in 1982 by Dr. Solomon Victor and ws being published twice a year up to 1996. From 2000 the editorial office moved to Delhi. From 2001 the journal was extended to quarterly and subsequently four issues annually have been printed out at time and regularly without fail. The journal receives manuscripts from members and non-members and cardiovascular surgeons. The manuscripts are peer reviewed by at least two or sometimes three or four reviewers who are on the panel. The manuscript process is now completely online. Funding the journal comes partially from the organization and from revenue generated by subscription and advertisement.