{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T22:55:23Z","timestamp":1788908123973,"version":"build-2803163510"},"reference-count":34,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"2","content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":["Kidney360"],"published-print":{"date-parts":[[2021,2]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>AKI after surgery is associated with high mortality and morbidity. The purpose of this study is to develop and validate a risk prediction tool for the occurrence of postoperative AKI requiring RRT (AKI-dialysis).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      This retrospective cohort study had 2,299,502 surgical patients over 2015\u20132017 from the American College of Surgeons National Surgical Quality Improvement Program Database (ACS NSQIP). Eleven predictors were selected for the predictive model: age, history of congestive heart failure, diabetes, ascites, emergency surgery, hypertension requiring medication, preoperative serum creatinine, hematocrit, sodium, preoperative sepsis, and surgery type. The predictive model was trained using 2015\u20132016 data (\n                      <jats:italic toggle=\"yes\">n<\/jats:italic>\n                      =1,487,724) and further tested using 2017 data (\n                      <jats:italic toggle=\"yes\">n<\/jats:italic>\n                      =811,778). A risk model was developed using multivariable logistic regression.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      AKI-dialysis occurred in 0.3% (\n                      <jats:italic toggle=\"yes\">n<\/jats:italic>\n                      =6853) of patients. The unadjusted 30-day postoperative mortality rate associated with AKI-dialysis was 37.5%. The AKI risk prediction model had high area under the receiver operating characteristic curve (AUC; training cohort: 0.89, test cohort: 0.90) for postoperative AKI-dialysis.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This model provides a clinically useful bedside predictive tool for postoperative AKI requiring dialysis.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.34067\/kid.0004732020","type":"journal-article","created":{"date-parts":[[2020,12,29]],"date-time":"2020-12-29T18:10:14Z","timestamp":1609265414000},"page":"215-223","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":17,"title":["Development and Validation of a Web-Based Prediction Model for AKI after Surgery"],"prefix":"10.34067","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8805-3443","authenticated-orcid":false,"given":"Sang H.","family":"Woo","sequence":"first","affiliation":[{"name":"Division of Hospital Medicine, Department of Medicine, Thomas Jefferson University, Philadelphia, Pennsylvania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jillian","family":"Zavodnick","sequence":"additional","affiliation":[{"name":"Division of Hospital Medicine, Department of Medicine, Thomas Jefferson University, Philadelphia, Pennsylvania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lily","family":"Ackermann","sequence":"additional","affiliation":[{"name":"Division of Hospital Medicine, Department of Medicine, Thomas Jefferson University, Philadelphia, Pennsylvania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Omar H.","family":"Maarouf","sequence":"additional","affiliation":[{"name":"Division of Nephrology, Department of Medicine, Thomas Jefferson University, Philadelphia, Pennsylvania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingjing","family":"Zhang","sequence":"additional","affiliation":[{"name":"Division of Nephrology, Department of Medicine, Thomas Jefferson University, Philadelphia, Pennsylvania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Scott W.","family":"Cowan","sequence":"additional","affiliation":[{"name":"Department of Surgery, Thomas Jefferson University, Philadelphia, Pennsylvania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","published-online":{"date-parts":[[2021,2,25]]},"reference":[{"key":"B1-20250825","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1097\/SLA.0b013e3181a40a0b","article-title":"Long-term risk of mortality and acute kidney injury during hospitalization after major surgery","volume":"249","author":"Bihorac","year":"2009","journal-title":"Ann Surg"},{"key":"B2-20250825","doi-asserted-by":"crossref","first-page":"878","DOI":"10.1161\/01.CIR.95.4.878","article-title":"Preoperative renal risk stratification","volume":"95","author":"Chertow","year":"1997","journal-title":"Circulation"},{"key":"B3-20250825","doi-asserted-by":"crossref","first-page":"1237","DOI":"10.1503\/cmaj.110895","article-title":"Secular trends in acute dialysis after elective major surgery--1995 to 2009","volume":"184","author":"Siddiqui","year":"2012","journal-title":"CMAJ"},{"key":"B4-20250825","doi-asserted-by":"crossref","first-page":"1207","DOI":"10.1097\/SLA.0000000000000732","article-title":"Cost and mortality associated with postoperative acute kidney injury","volume":"261","author":"Hobson","year":"2015","journal-title":"Ann Surg"},{"key":"B5-20250825","doi-asserted-by":"crossref","first-page":"1551","DOI":"10.1007\/s00134-016-4670-3","article-title":"Prevention of cardiac surgery-associated AKI by implementing the KDIGO guidelines in high risk patients identified by biomarkers: The PrevAKI randomized controlled trial","volume":"43","author":"Meersch","year":"2017","journal-title":"Intensive Care Med"},{"key":"B6-20250825","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s40140-018-0246-9","article-title":"Risk assessment","volume":"8","author":"Ajitsaria","year":"2018","journal-title":"Curr Anesthesiol Rep"},{"key":"B7-20250825","doi-asserted-by":"crossref","first-page":"e77","DOI":"10.1016\/j.jacc.2014.07.944","article-title":"2014 ACC\/AHA guideline on perioperative cardiovascular evaluation and management of patients undergoing noncardiac surgery: A report of the American College of Cardiology\/American Heart Association Task Force on practice guidelines","volume":"64","author":"Fleisher","year":"2014","journal-title":"J Am Coll Cardiol"},{"key":"B8-20250825","doi-asserted-by":"crossref","first-page":"2383","DOI":"10.1093\/eurheartj\/ehu282","article-title":"2014 ESC\/ESA Guidelines on non-cardiac surgery: Cardiovascular assessment and management: The Joint Task Force on non-cardiac surgery: Cardiovascular assessment and management of the European Society of Cardiology (ESC) and the European Society of Anaesthesiology (ESA)","volume":"35","author":"Kristensen","year":"2014","journal-title":"Eur Heart J"},{"key":"B9-20250825","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1161\/CIRCULATIONAHA.110.015701","article-title":"Development and validation of a risk calculator for prediction of cardiac risk after surgery","volume":"124","author":"Gupta","year":"2011","journal-title":"Circulation"},{"key":"B10-20250825","doi-asserted-by":"crossref","first-page":"581","DOI":"10.7326\/0003-4819-144-8-200604180-00009","article-title":"Preoperative pulmonary risk stratification for noncardiothoracic surgery: Systematic review for the American College of Physicians","volume":"144","author":"Smetana","year":"2006","journal-title":"Ann Intern Med"},{"key":"B11-20250825","doi-asserted-by":"crossref","first-page":"1076","DOI":"10.1503\/cmaj.151447","article-title":"A new model to predict acute kidney injury requiring renal replacement therapy after cardiac surgery","volume":"188","author":"Pannu","year":"2016","journal-title":"CMAJ"},{"key":"B12-20250825","doi-asserted-by":"crossref","first-page":"e003754","DOI":"10.1161\/JAHA.116.003754","article-title":"Dynamic predictive scores for cardiac surgery-associated acute kidney injury","volume":"5","author":"Jiang","year":"2016","journal-title":"J Am Heart Assoc"},{"key":"B13-20250825","doi-asserted-by":"crossref","first-page":"704","DOI":"10.1097\/EJA.0b013e328365ae64","article-title":"How can we best predict acute kidney injury following cardiac surgery?: A prospective observational study","volume":"30","author":"Berg","year":"2013","journal-title":"Eur J Anaesthesiol"},{"key":"B14-20250825","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1681\/ASN.2004040331","article-title":"A clinical score to predict acute renal failure after cardiac surgery","volume":"16","author":"Thakar","year":"2005","journal-title":"J Am Soc Nephrol"},{"key":"B15-20250825","doi-asserted-by":"crossref","first-page":"2208","DOI":"10.1161\/CIRCULATIONAHA.106.635573","article-title":"Bedside tool for predicting the risk of postoperative dialysis in patients undergoing cardiac surgery","volume":"114","author":"Mehta","year":"2006","journal-title":"Circulation"},{"key":"B16-20250825","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1016\/j.jamcollsurg.2013.07.385","article-title":"Development and evaluation of the universal ACS NSQIP surgical risk calculator: A decision aid and informed consent tool for patients and surgeons","volume":"217","author":"Bilimoria","year":"2013","journal-title":"J Am Coll Surg"},{"key":"B17-20250825","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1016\/j.jamcollsurg.2013.02.027","article-title":"Optimizing ACS NSQIP modeling for evaluation of surgical quality and risk: Patient risk adjustment, procedure mix adjustment, shrinkage adjustment, and surgical focus","volume":"217","author":"Cohen","year":"2013","journal-title":"J Am Coll Surg"},{"key":"B18-20250825","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1097\/SLA.0b013e3181b4148f","article-title":"Does surgical quality improve in the American College of Surgeons National Surgical Quality Improvement Program: An evaluation of all participating hospitals","volume":"250","author":"Hall","year":"2009","journal-title":"Ann Surg"},{"key":"B21-20250825","doi-asserted-by":"crossref","first-page":"857","DOI":"10.1016\/j.mayocp.2015.03.026","article-title":"Long-term outcomes and prognostic factors for patients requiring renal replacement therapy after cardiac surgery","volume":"90","author":"Thongprayoon","year":"2015","journal-title":"Mayo Clin Proc"},{"key":"B22-20250825","doi-asserted-by":"crossref","first-page":"789","DOI":"10.1177\/000331979804900902","article-title":"Risk factors for development of acute renal failure (ARF) requiring dialysis in patients undergoing cardiac surgery","volume":"49","author":"Suen","year":"1998","journal-title":"Angiology"},{"key":"B23-20250825","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J Mach Learn Res"},{"key":"B24-20250825","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1097\/ACO.0000000000000329","article-title":"Patient with chronic renal failure undergoing surgery","volume":"29","author":"Meersch","year":"2016","journal-title":"Curr Opin Anaesthesiol"},{"key":"B25-20250825","doi-asserted-by":"crossref","first-page":"1375","DOI":"10.1213\/ANE.0b013e3181ee8456","article-title":"Chronic kidney disease and postoperative morbidity after elective orthopedic surgery","volume":"112","author":"Ackland","year":"2011","journal-title":"Anesth Analg"},{"key":"B26-20250825","doi-asserted-by":"crossref","first-page":"1069","DOI":"10.1038\/ki.2008.29","article-title":"Chronic kidney disease and postoperative mortality: A systematic review and meta-analysis","volume":"73","author":"Mathew","year":"2008","journal-title":"Kidney Int"},{"key":"B27-20250825","doi-asserted-by":"crossref","first-page":"258","DOI":"10.4097\/kjae.2017.70.3.258","article-title":"Postoperative acute kidney injury","volume":"70","author":"Park","year":"2017","journal-title":"Korean J Anesthesiol"},{"key":"B28-20250825","first-page":"451","article-title":"Prevention of renal dysfunction in postoperative elderly patients","volume":"20","author":"M\u00e5rtensson","year":"2014","journal-title":"Curr Opin Crit Care"},{"key":"B29-20250825","doi-asserted-by":"crossref","first-page":"1013","DOI":"10.1097\/SLA.0000000000002485","article-title":"Biomarker-guided intervention to prevent acute kidney injury after major surgery: The prospective randomized BigpAK study","volume":"267","author":"G\u00f6cze","year":"2018","journal-title":"Ann Surg"},{"key":"B30-20250825","doi-asserted-by":"crossref","first-page":"1235","DOI":"10.1016\/j.jtcvs.2019.10.034","article-title":"Using urinary biomarkers to reduce acute kidney injury following cardiac surgery","volume":"160","author":"Engelman","year":"2020","journal-title":"J Thorac Cardiovasc Surg"},{"key":"B31-20250825","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1111\/j.1365-2753.2010.01549.x","article-title":"The importance of communication in collaborative decision making: Facilitating shared mind and the management of uncertainty","volume":"17","author":"Politi","year":"2011","journal-title":"J Eval Clin Pract"},{"key":"B32-20250825","doi-asserted-by":"crossref","first-page":"e0155705","DOI":"10.1371\/journal.pone.0155705","article-title":"Application of machine learning techniques to high-dimensional clinical data to forecast postoperative complications","volume":"11","author":"Thottakkara","year":"2016","journal-title":"PLoS One"},{"key":"B33-20250825","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1186\/s12911-016-0277-4","article-title":"Prediction and detection models for acute kidney injury in hospitalized older adults","volume":"16","author":"Kate","year":"2016","journal-title":"BMC Med Inform Decis Mak"},{"key":"B34-20250825","doi-asserted-by":"crossref","first-page":"1052","DOI":"10.1093\/jamia\/ocx030","article-title":"Calibration drift in regression and machine learning models for acute kidney injury","volume":"24","author":"Davis","year":"2017","journal-title":"J Am Med Inform Assoc"},{"key":"B35-20250825","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1159\/000351509","article-title":"Identifying the patient at risk of acute kidney injury: A predictive scoring system for the development of acute kidney injury in acute medical patients","volume":"123","author":"Forni","year":"2013","journal-title":"Nephron Clin Pract"},{"key":"B36-20250825","doi-asserted-by":"crossref","first-page":"1070","DOI":"10.1097\/CCM.0000000000003123","article-title":"The development of a machine learning inpatient acute kidney injury prediction model","volume":"46","author":"Koyner","year":"2018","journal-title":"Crit Care Med"}],"container-title":["Kidney360"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.ovid.com\/10.34067\/KID.0004732020","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T20:02:11Z","timestamp":1786478531000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ovid.com\/10.34067\/KID.0004732020"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2]]},"references-count":34,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2021]]}},"URL":"https:\/\/doi.org\/10.34067\/kid.0004732020","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2020.07.03.20145094","asserted-by":"object"}]},"ISSN":["2641-7650"],"issn-type":[{"value":"2641-7650","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2]]},"assertion":[{"value":"2020-08-05","name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-12-28","name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}}]}}