{"id":2046,"date":"2022-05-18T00:00:00","date_gmt":"2022-05-18T04:00:00","guid":{"rendered":"https:\/\/icesontario.wpengine.com\/journal-articles\/derivation-and-external-validation-of-a-30-day-mortality-risk-prediction-model-for-older-patients-having-emergency-general-surgery\/"},"modified":"2023-06-14T19:23:35","modified_gmt":"2023-06-14T23:23:35","slug":"derivation-and-external-validation-of-a-30-day-mortality-risk-prediction-model-for-older-patients-having-emergency-general-surgery","status":"publish","type":"journal_article","link":"https:\/\/www.ices.on.ca\/fr\/publications\/journal-articles\/derivation-and-external-validation-of-a-30-day-mortality-risk-prediction-model-for-older-patients-having-emergency-general-surgery\/","title":{"rendered":"Derivation and external validation of a 30-day mortality risk prediction model for older patients having emergency general surgery"},"content":{"rendered":"<p><strong>Background<\/strong> &#x2014; Older people (&#x2265;65 yr) are at increased risk of morbidity and mortality after emergency general surgery. Risk prediction models are needed to guide decision making in this high-risk population. Existing models have substantial limitations and lack external validation, potentially limiting their applicability in clinical use. We aimed to derive and validate, both internally and externally, a multivariable model to predict 30-day mortality risk in older patients undergoing emergency general surgery.<\/p>\n<p><strong>Methods<\/strong> &#x2014; After protocol publication, we used the National Surgical Quality Improvement Program (NSQIP) database (2012&#x2013;6; estimated to contain 90% data from the USA and 10% from Canada) to derive and internally validate a model to predict 30-day mortality for older people having emergency general surgery using logistic regression with elastic net regularisation. Internal validation was done with 10-fold cross-validation. External validation was done using a temporally separate health administrative database exclusively from Ontario, Canada.<\/p>\n<p><strong>Results<\/strong> &#x2014; Overall, 6012 (12.0%) of the 50 221 patients died within 30 days. The model demonstrated strong discrimination (area under the curve [AUC]=0.871) and calibration across the spectrum of observed and predicted risks. Ten-fold internal cross-validation demonstrated minimal optimism (AUC=0.851, optimism 0.019 [standard deviation=0.06]) with excellent calibration. External validation demonstrated lower discrimination (AUC=0.700) and degraded calibration.<\/p>\n<p><strong>Conclusion<\/strong> &#x2014; A multivariable mortality risk prediction model was strongly discriminative and well calibrated internally. However, poor external validation suggests the model may not be generalisable to non-NSQIP data and hospitals. The findings highlight the importance of external validation before clinical application of risk models.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Background &#x2014; Older people (&#x2265;65 yr) are at increased risk of morbidity and mortality after emergency general surgery. Risk prediction models are needed to guide decision making in this high-risk population. Existing models have substantial limitations and lack external validation, potentially limiting their applicability in clinical use. We aimed to derive and validate, both internally [&hellip;]<\/p>\n","protected":false},"template":"","migration-helper-automated":[],"migration-manual":[],"topic":[],"migration-helper-qa-sample-set":[],"class_list":["post-2046","journal_article","type-journal_article","status-publish","hentry"],"acf":{"citation":"Feng S, van Walraven C, Lalu MM, Moloo H, Musselman R, McIsaac DI. <em>Br J Anaesth<\/em>. 2022; 129(1):33-40. Epub 2022 May 18.","source_url":"https:\/\/doi.org\/10.1016\/j.bja.2022.04.007","ices_scientist":[1319,1119],"site":[6734],"research_program":[6740],"news_release":[],"journal_article":[],"atlas":[],"research_report":[],"infographic":[],"video":[],"downloads":null,"links":null,"sitecore_item_id":"FF5E9448-B352-441A-9BF8-186604EACC1D","sitecore_item_name":"Derivation-and-external-validation-of-a-30-day-mortality-risk-prediction-model-for-older-patients","sitecore_field_values":"{\n  \"Title\": \"Derivation and external validation of a 30-day mortality risk prediction model for older patients having emergency general surgery\",\n  \"Short title\": \"Derivation and external validation of a\",\n  \"Summary\": \"The study findings highlight the importance of external validation before clinical application of risk models.\",\n  \"Citation\": \"<p>Feng S, van Walraven C, Lalu MM, Moloo H, Musselman R, McIsaac DI. <em>Br J Anaesth<\/em>. 2022; 129(1):33-40. Epub 2022 May 18. DOI: <a href=\"https:\/\/doi.org\/10.1016\/j.bja.2022.04.007\" title=\"opens external link\">https:\/\/doi.org\/10.1016\/j.bja.2022.04.007<\/a><\/p>\",\n  \"Abstract\": \"<p><strong>Background<\/strong> &mdash; Older people (&ge;65 yr) are at increased risk of morbidity and mortality after emergency general surgery. Risk prediction models are needed to guide decision making in this high-risk population. Existing models have substantial limitations and lack external validation, potentially limiting their applicability in clinical use. We aimed to derive and validate, both internally and externally, a multivariable model to predict 30-day mortality risk in older patients undergoing emergency general surgery.<\/p>n<p><strong>Methods<\/strong> &mdash; After protocol publication, we used the National Surgical Quality Improvement Program (NSQIP) database (2012&ndash;6; estimated to contain 90% data from the USA and 10% from Canada) to derive and internally validate a model to predict 30-day mortality for older people having emergency general surgery using logistic regression with elastic net regularisation. Internal validation was done with 10-fold cross-validation. External validation was done using a temporally separate health administrative database exclusively from Ontario, Canada.<\/p>n<p><strong>Results<\/strong> &mdash; Overall, 6012 (12.0%) of the 50 221 patients died within 30 days. The model demonstrated strong discrimination (area under the curve [AUC]=0.871) and calibration across the spectrum of observed and predicted risks. Ten-fold internal cross-validation demonstrated minimal optimism (AUC=0.851, optimism 0.019 [standard deviation=0.06]) with excellent calibration. External validation demonstrated lower discrimination (AUC=0.700) and degraded calibration.<\/p>n<p><strong>Conclusion<\/strong> &mdash; A multivariable mortality risk prediction model was strongly discriminative and well calibrated internally. However, poor external validation suggests the model may not be generalisable to non-NSQIP data and hospitals. The findings highlight the importance of external validation before clinical application of risk models.<\/p>\",\n  \"Research Programs\": \"{46DF28D2-EDE8-4DF2-8CC0-87CEF464E435}\",\n  \"ICES Locations\": \"{0AD77E2C-C883-4E93-8B9A-40B82D50E3A5}\",\n  \"ICES Scientists\": \"{EFFC898B-0B98-49BF-BC64-4A0482273C1C}|{372EE381-9B1A-49F5-AC1B-0D8675558A8D}\",\n  \"Posted Date\": \"20220518T000000\",\n  \"Show on Publications Landing Page\": \"1\"\n}","previous_url":"https:\/\/www.ices.on.ca\/Publications\/Journal-Articles\/2022\/May\/Derivation-and-external-validation-of-a-30-day-mortality-risk-prediction-model-for-older-patients"},"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>ICES | Derivation and external validation of a 30-day mortality risk prediction model for older patients having emergency general surgery<\/title>\n<meta name=\"description\" content=\"Background &#x2014; Older people (&#x2265;65 yr) are at increased risk of morbidity and mortality after emergency general surgery. 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