{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T01:35:22Z","timestamp":1792200922755,"version":"4.4.0"},"reference-count":19,"publisher":"SAGE Publications","issue":"12","license":[{"start":{"date-parts":[[2021,3,22]],"date-time":"2021-03-22T00:00:00Z","timestamp":1616371200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Feline Medicine and Surgery"],"published-print":{"date-parts":[[2021,12]]},"abstract":"\n Objectives<\/jats:title>\n The aim of this study was to develop an algorithm capable of predicting short- and medium-term survival in cases of intrinsic acute-on-chronic kidney disease (ACKD) in cats.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n \n The medical record database was searched to identify cats hospitalised for acute clinical signs and azotaemia of at least 48\u2009h duration and diagnosed to have underlying chronic kidney disease based on ultrasonographic renal abnormalities or previously documented azotaemia. Cases with postrenal azotaemia, exposure to nephrotoxicants, feline infectious peritonitis or neoplasia were excluded. Clinical variables were combined in a clinical severity score (CSS). Clinicopathological and ultrasonographic variables were also collected. The following variables were tested as inputs in a machine learning system: age, body weight (BW), CSS, identification of small kidneys or nephroliths by ultrasonography, serum creatinine at 48\u2009h (Crea\n 48<\/jats:sub>\n ), spontaneous feeding at 48\u2009h (SpF\n 48<\/jats:sub>\n ) and aetiology. Outputs were outcomes at 7, 30, 90 and 180 days. The machine-learning system was trained to develop decision tree algorithms capable of predicting outputs from inputs. Finally, the diagnostic performance of the algorithms was calculated.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n Crea\n 48<\/jats:sub>\n was the best predictor of survival at 7 days (threshold 1043\u2009\u00b5mol\/l, sensitivity 0.96, specificity 0.53), 30 days (threshold 566\u2009\u00b5mol\/l, sensitivity 0.70, specificity 0.89) and 90 days (threshold 566\u2009\u00b5mol\/l, sensitivity 0.76, specificity 0.80), with fewer cats still alive when their Crea\n 48<\/jats:sub>\n was above these thresholds. A short decision tree, including age and Crea\n 48<\/jats:sub>\n , predicted the 180-day outcome best. When Crea\n 48<\/jats:sub>\n was excluded from the analysis, the generated decision trees included CSS, age, BW, SpF\n 48<\/jats:sub>\n and identification of small kidneys with an overall diagnostic performance similar to that using Crea\n 48<\/jats:sub>\n .\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions and relevance<\/jats:title>\n \n Crea\n 48<\/jats:sub>\n helps predict short- and medium-term survival in cats with ACKD. Secondary variables that helped predict outcomes were age, CSS, BW, SpF\n 48<\/jats:sub>\n and identification of small kidneys.\n <\/jats:p>\n <\/jats:sec>","DOI":"10.1177\/1098612x211001273","type":"journal-article","created":{"date-parts":[[2021,3,22]],"date-time":"2021-03-22T09:56:41Z","timestamp":1616407001000},"page":"1140-1148","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":14,"title":["Machine-learning algorithm as a prognostic tool in non-obstructive acute-on-chronic kidney disease in the cat"],"prefix":"10.1177","volume":"23","author":[{"given":"Jade","family":"Renard","sequence":"first","affiliation":[{"name":"Alliance Small Animal Clinic, Bordeaux, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3136-737X","authenticated-orcid":false,"given":"Mathieu R","family":"Faucher","sequence":"additional","affiliation":[{"name":"Alliance Small Animal Clinic, Bordeaux, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ana\u00efs","family":"Combes","sequence":"additional","affiliation":[{"name":"Alliance Small Animal Clinic, Bordeaux, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Didier","family":"Concordet","sequence":"additional","affiliation":[{"name":"InTheRes, University of Toulouse, INRAE, ENVT, Toulouse, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brice S","family":"Reynolds","sequence":"additional","affiliation":[{"name":"InTheRes, University of Toulouse, INRAE, ENVT, Toulouse, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2021,3,22]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"127","article-title":"Feline renal failure: questions, answers, questions.","volume":"14","author":"Lulich JP","year":"1992","unstructured":"Lulich JP, Osborne CA, O\u2019Brien TD, et al. Feline renal failure: questions, answers, questions. Compend Cont Ed Pract Vet 1992; 14: 127\u2013152.","journal-title":"Compend Cont Ed Pract Vet"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1177\/1098612X13511446"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1939-1676.2008.0163.x"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1111\/jvim.13625"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1939-1676.2007.tb03042.x"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1939-1676.2006.tb02892.x"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1939-1676.2012.00920.x"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.2460\/javma.232.5.728"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1111\/jvim.12108"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1111\/jvim.15808"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1177\/0300985819829524"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11259-017-9694-9"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1177\/1098612X13477415"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1111\/jep.12779"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cvsm.2016.06.001"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1476-4431.2012.00733.x"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1111\/jvim.14525"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1939-1676.2010.0483.x"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cvsm.2016.06.002"}],"container-title":["Journal of Feline Medicine and Surgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/1098612X211001273","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/1098612X211001273","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/1098612X211001273","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T05:41:59Z","timestamp":1784094119000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/1098612X211001273"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,22]]},"references-count":19,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["10.1177\/1098612X211001273"],"URL":"https:\/\/doi.org\/10.1177\/1098612x211001273","relation":{},"ISSN":["1098-612X","1532-2750"],"issn-type":[{"value":"1098-612X","type":"print"},{"value":"1532-2750","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,22]]}}}