{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T21:44:49Z","timestamp":1789508689376,"version":"build-2803163510"},"reference-count":27,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T00:00:00Z","timestamp":1768867200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Kaiser Permanente Northern California Community Benefit Programs"},{"name":"Bayer Pharma AG"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,2,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>To develop and internally validate electronic health record (EHR)-based machine-learning models to predict worsening heart failure (WHF) events across care settings and all-cause mortality among adults with mild-to-moderate chronic kidney disease (CKD).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We studied adults with mild-to-moderate CKD [estimated glomerular filtration rate (eGFR) 30\u201359\u2005ml\/min\/1.73\u2005m\u00b2 or eGFR \u226560 with albuminuria] receiving care in a large health system from 2012 to 2021; outcomes were ascertained through 31 December 2022. Primary outcomes were (i) WHF events\u2014outpatient encounters, emergency department (ED)\/observation stays, and hospitalizations\u2014identified using a validated natural language processing algorithm, and (ii) all-cause mortality. Models [extreme gradient boosting (XGBoost)] used an 80:20 train-test split and &amp;gt;500 EHR-derived covariates. Discrimination [area under the curve (AUC)] and calibration (slope) were evaluated overall and across subgroups by age, sex, race and ethnicity, and CKD stage.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Among 375 495 adults (mean age 64 \u00b1 16 years; 54% women; 53% non-Hispanic White; mean eGFR 76 \u00b1 26\u2005ml\/min\/1.73\u2005m\u00b2), the WHF model achieved AUC 0.887 (95% CI 0.879\u20130.893) with calibration slope 0.955; the mortality model achieved AUC 0.875 (95% CI 0.868\u20130.883) with calibration slope 0.914 in the test set. Performance was consistent across age, sex, and race and ethnicity, with a slight decrement as CKD stage worsened.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Electronic health record-based machine-learning models accurately predicted WHF and mortality in mild-to-moderate CKD with strong calibration across key subgroups. These models are positioned for EHR deployment to support risk-stratified cardiovascular-kidney-metabolic care\u2014prioritizing guideline-directed therapies and care pathways for those at highest risk.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/eschf\/xvaf037","type":"journal-article","created":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T12:31:14Z","timestamp":1767702674000},"source":"Crossref","is-referenced-by-count":1,"title":["Clinical risk prediction models for worsening heart failure events and all-cause mortality in adults with mild-to-moderate chronic kidney disease"],"prefix":"10.1093","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4900-1402","authenticated-orcid":false,"given":"Soham","family":"Patel","sequence":"first","affiliation":[{"name":"Department of Medicine, Kaiser Permanente San Francisco Medical Center , San Francisco, CA 94115 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,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rami","family":"Halaseh","sequence":"additional","affiliation":[{"name":"Departments of Medicine, Epidemiology, and Biostatistics, University of California , San Francisco, San Francisco, CA 94158 ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ankeet S","family":"Bhatt","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Kaiser Permanente San Francisco Medical Center , San Francisco, CA 94115 ,","place":["USA"]},{"name":"Division of Research, Kaiser Permanente Northern California , Pleasanton, CA 94588 ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leonid","family":"Pravoverov","sequence":"additional","affiliation":[{"name":"Department of Nephrology, Kaiser Permanente Oakland Medical Center , Oakland, CA 94611 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Walnut Creek, CA 94596 ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keane K","family":"Lee","sequence":"additional","affiliation":[{"name":"Division of Research, Kaiser Permanente Northern California , Pleasanton, CA 94588 ,","place":["USA"]},{"name":"Department of Cardiology, Kaiser Permanente Santa Clara Medical Center , Santa Clara, CA 95051 ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sirtaz","family":"Adatya","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Kaiser Permanente Santa Clara Medical Center , Santa Clara, CA 95051 ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Ouyang","sequence":"additional","affiliation":[{"name":"Division of Research, Kaiser Permanente Northern California , Pleasanton, CA 94588 ,","place":["USA"]},{"name":"Department of Cardiology, Kaiser Permanente Santa Clara Medical Center , Santa Clara, CA 95051 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