{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T18:00:44Z","timestamp":1772560844404,"version":"3.50.1"},"reference-count":21,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2017,8,9]],"date-time":"2017-08-09T00:00:00Z","timestamp":1502236800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2017,8,9]],"date-time":"2017-08-09T00:00:00Z","timestamp":1502236800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["World j. surg."],"published-print":{"date-parts":[[2018,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>\nDespite the existence of multiple validated risk assessment and quality benchmarking tools in surgery, their utility outside of high\u2010income countries is limited. We sought to derive, validate and apply a scoring system that is both (1) feasible, and (2) reliably predicts mortality in a middle\u2010income country (MIC) context.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>\nA 5\u2010step methodology was used: (1) development of a de novo surgical outcomes database modeled around the American College of Surgeons\u2019 National Surgical Quality Improvement Program (ACS\u2010NSQIP) in South Africa (SA dataset), (2) use of the resultant data to identify all predictors of in\u2010hospital death with more than 90% capture indicating feasibility of collection, (3) use these predictors to derive and validate an integer\u2010based score that reliably predicts in\u2010hospital death in the 2012 ACS\u2010NSQIP, (4) apply the score in the original SA dataset and demonstrate its performance, (5) identify threshold cutoffs of the score to prompt action and drive quality improvement.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>\nFollowing step one\u2010three above, the 13 point Codman\u2019s score was derived and validated on 211,737 and 109,079 patients, respectively, and includes: age 65 (1), partially or completely dependent functional status (1), preoperative transfusions \u22654 units (1), emergency operation (2), sepsis or septic shock (2) American Society of Anesthesia score \u22653 (3) and operative procedure (1\u20133). Application of the score to 373 patients in the SA dataset showed good discrimination and calibration to predict an in\u2010hospital death. A Codman Score of 8 is an optimal cutoff point for defining expected and unexpected deaths.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>We have designed a novel risk prediction score specific for a MIC context. The Codman Score can prove useful for both (1) preoperative decision\u2010making and (2) benchmarking the quality of surgical care in MIC\u2019s.<\/jats:p><\/jats:sec>","DOI":"10.1007\/s00268-017-4177-2","type":"journal-article","created":{"date-parts":[[2017,8,9]],"date-time":"2017-08-09T17:53:51Z","timestamp":1502301231000},"page":"533-540","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Derivation, Validation and Application of a Pragmatic Risk Prediction Index for Benchmarking of Surgical Outcomes"],"prefix":"10.1002","volume":"42","author":[{"given":"Richard T.","family":"Spence","sequence":"first","affiliation":[{"name":"Department of General Surgery, Codman Center for Clinical Effectiveness in Surgery Massachusetts General Hospital Boston MA USA"},{"name":"Department of Surgery University of Cape Town Cape Town South Africa"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David C.","family":"Chang","sequence":"additional","affiliation":[{"name":"Department of General Surgery, Codman Center for Clinical Effectiveness in Surgery Massachusetts General Hospital Boston MA USA"},{"name":"Harvard Medical School Boston MA USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haytham M. 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