Development and Validation of HAS (Hajibandeh Index, ASA Status, Sarcopenia) - A Novel Model for Predicting Mortality after Emergency Laparotomy
To develop and validate a predictive model to predict the risk of postoperative mortality after emergency laparotomy taking into account the following variables: age, age ≥ 80, ASA status, clinical frailty score, sarcopenia, Hajibandeh Index (HI), bowel resection, and intraperitoneal contamination....
Gespeichert in:
| Veröffentlicht in: | Annals of surgery 2024-03, Vol.279 (3), p.501-509 |
|---|---|
| Hauptverfasser: | , , , , , , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 509 |
|---|---|
| container_issue | 3 |
| container_start_page | 501 |
| container_title | Annals of surgery |
| container_volume | 279 |
| creator | Hajibandeh, Shahab Hajibandeh, Shahin Hughes, Ioan Mitra, Kalyan Puthiyakunnel Saji, Alwin Clayton, Amy Alessandri, Giorgio Duncan, Trish Cornish, Julie Morris, Chris O’Reilly, David Kumar, Nagappan |
| description | To develop and validate a predictive model to predict the risk of postoperative mortality after emergency laparotomy taking into account the following variables: age, age ≥ 80, ASA status, clinical frailty score, sarcopenia, Hajibandeh Index (HI), bowel resection, and intraperitoneal contamination.
The discriminative powers of the currently available predictive tools range between adequate and strong; none has demonstrated excellent discrimination yet.
The TRIPOD and STROCSS statement standards were followed to protocol and conduct a retrospective cohort study of adult patients who underwent emergency laparotomy due to non-traumatic acute abdominal pathology between 2017 and 2022. Multivariable binary logistic regression analysis was used to develop and validate the model via two protocols (Protocol A and B). The model performance was evaluated in terms of discrimination (ROC curve analysis), calibration (calibration diagram and Hosmer-Lemeshow test), and classification (classification table).
One thousand forty-three patients were included (statistical power = 94%). Multivariable analysis kept HI (Protocol-A: P =0.0004; Protocol-B: P =0.0017), ASA status (Protocol-A: P =0.0068; Protocol-B: P =0.0007), and sarcopenia (Protocol-A: P |
| doi_str_mv | 10.1097/SLA.0000000000005897 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2809542292</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2809542292</sourcerecordid><originalsourceid>FETCH-LOGICAL-c3521-99ae7051d29a9a863cfaca3a53317b60cb630642676b55ce98733ae1963eea7d3</originalsourceid><addsrcrecordid>eNpdkdtu1DAQhi0EokvhDRDyZZGa1o7jOL6MSmErLQUpwG00cSbdlCRObYeyb8Ej19UuBzEXM9IcvtHMT8hrzs440-q82pRn7B-ThVZPyIrLtEg4z9hTsopZkWRapEfkhfe3jPGsYOo5ORKKC610viK_3uEPHOw84hQoTC39BkPfQujtRG1H12VFT9Zw2zexhlt6Ff3PU1pWJa0ChMWf0gqcsTNOPbylCS3ptY1A-tG20XfW0c8O296EfrqJSRciPuwodAEdvRzR3eBkdnQDMzgb7Lh7SZ51MHh8dYjH5Ov7yy8X62Tz6cPVRblJjJApT7QGVEzyNtWgociF6cCAACkEV03OTJMLlmdprvJGSoO6UEIAcp0LRFCtOCYne-7s7N2CPtRj7w0OA0xoF1-nBdMyS1OdxtZs32qc9d5hV8-uH8Htas7qRy3qqEX9vxZx7M1hw9KM2P4Z-v38v9x7O8R3-O_Dco-u3iIMYbvn5bKIpx6oCWOZ5OIB596Teg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2809542292</pqid></control><display><type>article</type><title>Development and Validation of HAS (Hajibandeh Index, ASA Status, Sarcopenia) - A Novel Model for Predicting Mortality after Emergency Laparotomy</title><source>PubMed Central</source><source>Journals@Ovid Complete</source><creator>Hajibandeh, Shahab ; Hajibandeh, Shahin ; Hughes, Ioan ; Mitra, Kalyan ; Puthiyakunnel Saji, Alwin ; Clayton, Amy ; Alessandri, Giorgio ; Duncan, Trish ; Cornish, Julie ; Morris, Chris ; O’Reilly, David ; Kumar, Nagappan</creator><creatorcontrib>Hajibandeh, Shahab ; Hajibandeh, Shahin ; Hughes, Ioan ; Mitra, Kalyan ; Puthiyakunnel Saji, Alwin ; Clayton, Amy ; Alessandri, Giorgio ; Duncan, Trish ; Cornish, Julie ; Morris, Chris ; O’Reilly, David ; Kumar, Nagappan</creatorcontrib><description>To develop and validate a predictive model to predict the risk of postoperative mortality after emergency laparotomy taking into account the following variables: age, age ≥ 80, ASA status, clinical frailty score, sarcopenia, Hajibandeh Index (HI), bowel resection, and intraperitoneal contamination.
The discriminative powers of the currently available predictive tools range between adequate and strong; none has demonstrated excellent discrimination yet.
The TRIPOD and STROCSS statement standards were followed to protocol and conduct a retrospective cohort study of adult patients who underwent emergency laparotomy due to non-traumatic acute abdominal pathology between 2017 and 2022. Multivariable binary logistic regression analysis was used to develop and validate the model via two protocols (Protocol A and B). The model performance was evaluated in terms of discrimination (ROC curve analysis), calibration (calibration diagram and Hosmer-Lemeshow test), and classification (classification table).
One thousand forty-three patients were included (statistical power = 94%). Multivariable analysis kept HI (Protocol-A: P =0.0004; Protocol-B: P =0.0017), ASA status (Protocol-A: P =0.0068; Protocol-B: P =0.0007), and sarcopenia (Protocol-A: P <0.0001; Protocol-B: P <0.0001) as final predictors of 30-day postoperative mortality in both protocols; hence the model was called HAS (HI, ASA status, sarcopenia). The HAS demonstrated excellent discrimination (AUC: 0.96, P <0.0001), excellent calibration ( P <0.0001), and excellent classification (95%) via both protocols.
The HAS is the first model demonstrating excellent discrimination, calibration, and classification in predicting the risk of 30-day mortality following emergency laparotomy. The HAS model seems promising and is worth attention for external validation using the calculator provided. HAS mortality risk calculator https://app.airrange.io/#/element/xr3b_E6yLor9R2c8KXViSAeOSK .</description><identifier>ISSN: 0003-4932</identifier><identifier>EISSN: 1528-1140</identifier><identifier>DOI: 10.1097/SLA.0000000000005897</identifier><identifier>PMID: 37139796</identifier><language>eng</language><publisher>United States: Lippincott Williams & Wilkins</publisher><ispartof>Annals of surgery, 2024-03, Vol.279 (3), p.501-509</ispartof><rights>Lippincott Williams & Wilkins</rights><rights>Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3521-99ae7051d29a9a863cfaca3a53317b60cb630642676b55ce98733ae1963eea7d3</citedby><cites>FETCH-LOGICAL-c3521-99ae7051d29a9a863cfaca3a53317b60cb630642676b55ce98733ae1963eea7d3</cites><orcidid>0000-0001-6159-1068 ; 0000-0002-3294-4335</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37139796$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Hajibandeh, Shahab</creatorcontrib><creatorcontrib>Hajibandeh, Shahin</creatorcontrib><creatorcontrib>Hughes, Ioan</creatorcontrib><creatorcontrib>Mitra, Kalyan</creatorcontrib><creatorcontrib>Puthiyakunnel Saji, Alwin</creatorcontrib><creatorcontrib>Clayton, Amy</creatorcontrib><creatorcontrib>Alessandri, Giorgio</creatorcontrib><creatorcontrib>Duncan, Trish</creatorcontrib><creatorcontrib>Cornish, Julie</creatorcontrib><creatorcontrib>Morris, Chris</creatorcontrib><creatorcontrib>O’Reilly, David</creatorcontrib><creatorcontrib>Kumar, Nagappan</creatorcontrib><title>Development and Validation of HAS (Hajibandeh Index, ASA Status, Sarcopenia) - A Novel Model for Predicting Mortality after Emergency Laparotomy</title><title>Annals of surgery</title><addtitle>Ann Surg</addtitle><description>To develop and validate a predictive model to predict the risk of postoperative mortality after emergency laparotomy taking into account the following variables: age, age ≥ 80, ASA status, clinical frailty score, sarcopenia, Hajibandeh Index (HI), bowel resection, and intraperitoneal contamination.
The discriminative powers of the currently available predictive tools range between adequate and strong; none has demonstrated excellent discrimination yet.
The TRIPOD and STROCSS statement standards were followed to protocol and conduct a retrospective cohort study of adult patients who underwent emergency laparotomy due to non-traumatic acute abdominal pathology between 2017 and 2022. Multivariable binary logistic regression analysis was used to develop and validate the model via two protocols (Protocol A and B). The model performance was evaluated in terms of discrimination (ROC curve analysis), calibration (calibration diagram and Hosmer-Lemeshow test), and classification (classification table).
One thousand forty-three patients were included (statistical power = 94%). Multivariable analysis kept HI (Protocol-A: P =0.0004; Protocol-B: P =0.0017), ASA status (Protocol-A: P =0.0068; Protocol-B: P =0.0007), and sarcopenia (Protocol-A: P <0.0001; Protocol-B: P <0.0001) as final predictors of 30-day postoperative mortality in both protocols; hence the model was called HAS (HI, ASA status, sarcopenia). The HAS demonstrated excellent discrimination (AUC: 0.96, P <0.0001), excellent calibration ( P <0.0001), and excellent classification (95%) via both protocols.
The HAS is the first model demonstrating excellent discrimination, calibration, and classification in predicting the risk of 30-day mortality following emergency laparotomy. The HAS model seems promising and is worth attention for external validation using the calculator provided. HAS mortality risk calculator https://app.airrange.io/#/element/xr3b_E6yLor9R2c8KXViSAeOSK .</description><issn>0003-4932</issn><issn>1528-1140</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNpdkdtu1DAQhi0EokvhDRDyZZGa1o7jOL6MSmErLQUpwG00cSbdlCRObYeyb8Ej19UuBzEXM9IcvtHMT8hrzs440-q82pRn7B-ThVZPyIrLtEg4z9hTsopZkWRapEfkhfe3jPGsYOo5ORKKC610viK_3uEPHOw84hQoTC39BkPfQujtRG1H12VFT9Zw2zexhlt6Ff3PU1pWJa0ChMWf0gqcsTNOPbylCS3ptY1A-tG20XfW0c8O296EfrqJSRciPuwodAEdvRzR3eBkdnQDMzgb7Lh7SZ51MHh8dYjH5Ov7yy8X62Tz6cPVRblJjJApT7QGVEzyNtWgociF6cCAACkEV03OTJMLlmdprvJGSoO6UEIAcp0LRFCtOCYne-7s7N2CPtRj7w0OA0xoF1-nBdMyS1OdxtZs32qc9d5hV8-uH8Htas7qRy3qqEX9vxZx7M1hw9KM2P4Z-v38v9x7O8R3-O_Dco-u3iIMYbvn5bKIpx6oCWOZ5OIB596Teg</recordid><startdate>20240301</startdate><enddate>20240301</enddate><creator>Hajibandeh, Shahab</creator><creator>Hajibandeh, Shahin</creator><creator>Hughes, Ioan</creator><creator>Mitra, Kalyan</creator><creator>Puthiyakunnel Saji, Alwin</creator><creator>Clayton, Amy</creator><creator>Alessandri, Giorgio</creator><creator>Duncan, Trish</creator><creator>Cornish, Julie</creator><creator>Morris, Chris</creator><creator>O’Reilly, David</creator><creator>Kumar, Nagappan</creator><general>Lippincott Williams & Wilkins</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0001-6159-1068</orcidid><orcidid>https://orcid.org/0000-0002-3294-4335</orcidid></search><sort><creationdate>20240301</creationdate><title>Development and Validation of HAS (Hajibandeh Index, ASA Status, Sarcopenia) - A Novel Model for Predicting Mortality after Emergency Laparotomy</title><author>Hajibandeh, Shahab ; Hajibandeh, Shahin ; Hughes, Ioan ; Mitra, Kalyan ; Puthiyakunnel Saji, Alwin ; Clayton, Amy ; Alessandri, Giorgio ; Duncan, Trish ; Cornish, Julie ; Morris, Chris ; O’Reilly, David ; Kumar, Nagappan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3521-99ae7051d29a9a863cfaca3a53317b60cb630642676b55ce98733ae1963eea7d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hajibandeh, Shahab</creatorcontrib><creatorcontrib>Hajibandeh, Shahin</creatorcontrib><creatorcontrib>Hughes, Ioan</creatorcontrib><creatorcontrib>Mitra, Kalyan</creatorcontrib><creatorcontrib>Puthiyakunnel Saji, Alwin</creatorcontrib><creatorcontrib>Clayton, Amy</creatorcontrib><creatorcontrib>Alessandri, Giorgio</creatorcontrib><creatorcontrib>Duncan, Trish</creatorcontrib><creatorcontrib>Cornish, Julie</creatorcontrib><creatorcontrib>Morris, Chris</creatorcontrib><creatorcontrib>O’Reilly, David</creatorcontrib><creatorcontrib>Kumar, Nagappan</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Annals of surgery</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hajibandeh, Shahab</au><au>Hajibandeh, Shahin</au><au>Hughes, Ioan</au><au>Mitra, Kalyan</au><au>Puthiyakunnel Saji, Alwin</au><au>Clayton, Amy</au><au>Alessandri, Giorgio</au><au>Duncan, Trish</au><au>Cornish, Julie</au><au>Morris, Chris</au><au>O’Reilly, David</au><au>Kumar, Nagappan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Development and Validation of HAS (Hajibandeh Index, ASA Status, Sarcopenia) - A Novel Model for Predicting Mortality after Emergency Laparotomy</atitle><jtitle>Annals of surgery</jtitle><addtitle>Ann Surg</addtitle><date>2024-03-01</date><risdate>2024</risdate><volume>279</volume><issue>3</issue><spage>501</spage><epage>509</epage><pages>501-509</pages><issn>0003-4932</issn><eissn>1528-1140</eissn><abstract>To develop and validate a predictive model to predict the risk of postoperative mortality after emergency laparotomy taking into account the following variables: age, age ≥ 80, ASA status, clinical frailty score, sarcopenia, Hajibandeh Index (HI), bowel resection, and intraperitoneal contamination.
The discriminative powers of the currently available predictive tools range between adequate and strong; none has demonstrated excellent discrimination yet.
The TRIPOD and STROCSS statement standards were followed to protocol and conduct a retrospective cohort study of adult patients who underwent emergency laparotomy due to non-traumatic acute abdominal pathology between 2017 and 2022. Multivariable binary logistic regression analysis was used to develop and validate the model via two protocols (Protocol A and B). The model performance was evaluated in terms of discrimination (ROC curve analysis), calibration (calibration diagram and Hosmer-Lemeshow test), and classification (classification table).
One thousand forty-three patients were included (statistical power = 94%). Multivariable analysis kept HI (Protocol-A: P =0.0004; Protocol-B: P =0.0017), ASA status (Protocol-A: P =0.0068; Protocol-B: P =0.0007), and sarcopenia (Protocol-A: P <0.0001; Protocol-B: P <0.0001) as final predictors of 30-day postoperative mortality in both protocols; hence the model was called HAS (HI, ASA status, sarcopenia). The HAS demonstrated excellent discrimination (AUC: 0.96, P <0.0001), excellent calibration ( P <0.0001), and excellent classification (95%) via both protocols.
The HAS is the first model demonstrating excellent discrimination, calibration, and classification in predicting the risk of 30-day mortality following emergency laparotomy. The HAS model seems promising and is worth attention for external validation using the calculator provided. HAS mortality risk calculator https://app.airrange.io/#/element/xr3b_E6yLor9R2c8KXViSAeOSK .</abstract><cop>United States</cop><pub>Lippincott Williams & Wilkins</pub><pmid>37139796</pmid><doi>10.1097/SLA.0000000000005897</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0001-6159-1068</orcidid><orcidid>https://orcid.org/0000-0002-3294-4335</orcidid></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0003-4932 |
| ispartof | Annals of surgery, 2024-03, Vol.279 (3), p.501-509 |
| issn | 0003-4932 1528-1140 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_2809542292 |
| source | PubMed Central; Journals@Ovid Complete |
| title | Development and Validation of HAS (Hajibandeh Index, ASA Status, Sarcopenia) - A Novel Model for Predicting Mortality after Emergency Laparotomy |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-02T16%3A35%3A14IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Development%20and%20Validation%20of%20HAS%20(Hajibandeh%20Index,%20ASA%20Status,%20Sarcopenia)%20-%20A%20Novel%20Model%20for%20Predicting%20Mortality%20after%20Emergency%20Laparotomy&rft.jtitle=Annals%20of%20surgery&rft.au=Hajibandeh,%20Shahab&rft.date=2024-03-01&rft.volume=279&rft.issue=3&rft.spage=501&rft.epage=509&rft.pages=501-509&rft.issn=0003-4932&rft.eissn=1528-1140&rft_id=info:doi/10.1097/SLA.0000000000005897&rft_dat=%3Cproquest_cross%3E2809542292%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2809542292&rft_id=info:pmid/37139796&rfr_iscdi=true |