{"title":"Perioperative risk assessment for emergency general surgery in those with multimorbidity or frailty.","authors":"Yasmin Arda, Haytham M A Kaafarani","doi":"10.1097/MCC.0000000000001269","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose of review: </strong>This review explores advances in risk stratification tools and their applicability in identifying and managing high-risk emergency general surgery (EGS) patients.</p><p><strong>Recent findings: </strong>Traditional risk assessment tools have several limitations when applied to complex EGS patients as comorbidities are generally treated in a binary, linear and sequential fashion. Additionally, some tools are only usable in the postoperative period, and some require multidisciplinary involvement and are not suitable in an emergency setting. Frailty in particular - for which there are multiple calculators-maladaptively influences outcomes. Artificial intelligence tools, such as the machine-learning-based POTTER calculator, demonstrate superior performance by addressing nonlinear interactions among patient factors, offering a dynamic and more accurate approach to risk prediction.</p><p><strong>Summary: </strong>Integrating advanced, data-driven risk assessment tools into clinical practice can help identify and manage high-risk patients as well as forecast outcomes for EGS patients. Such tools are intended to trigger preoperative interventions as well as discussions that ensure goal-concordant care, align expectations with anticipated outcomes and support both facility and patient-relevant outcomes.</p>","PeriodicalId":10851,"journal":{"name":"Current Opinion in Critical Care","volume":" ","pages":""},"PeriodicalIF":3.5000,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Opinion in Critical Care","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1097/MCC.0000000000001269","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CRITICAL CARE MEDICINE","Score":null,"Total":0}
引用次数: 0
Abstract
Purpose of review: This review explores advances in risk stratification tools and their applicability in identifying and managing high-risk emergency general surgery (EGS) patients.
Recent findings: Traditional risk assessment tools have several limitations when applied to complex EGS patients as comorbidities are generally treated in a binary, linear and sequential fashion. Additionally, some tools are only usable in the postoperative period, and some require multidisciplinary involvement and are not suitable in an emergency setting. Frailty in particular - for which there are multiple calculators-maladaptively influences outcomes. Artificial intelligence tools, such as the machine-learning-based POTTER calculator, demonstrate superior performance by addressing nonlinear interactions among patient factors, offering a dynamic and more accurate approach to risk prediction.
Summary: Integrating advanced, data-driven risk assessment tools into clinical practice can help identify and manage high-risk patients as well as forecast outcomes for EGS patients. Such tools are intended to trigger preoperative interventions as well as discussions that ensure goal-concordant care, align expectations with anticipated outcomes and support both facility and patient-relevant outcomes.
期刊介绍:
Current Opinion in Critical Care delivers a broad-based perspective on the most recent and most exciting developments in critical care from across the world. Published bimonthly and featuring thirteen key topics – including the respiratory system, neuroscience, trauma and infectious diseases – the journal’s renowned team of guest editors ensure a balanced, expert assessment of the recently published literature in each respective field with insightful editorials and on-the-mark invited reviews.