Identifying Novel Phenotypes of Renal Recovery and Progression in Sepsis-Associated Acute Kidney Injury: A Dynamic Time Warping Clustering Study in MIMIC-III

Identifying Novel Phenotypes of Renal Recovery and Progression in Sepsis-Associated Acute Kidney Injury: A Dynamic Time Warping Clustering Study in MIMIC-III | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Identifying Novel Phenotypes of Renal Recovery and Progression in Sepsis-Associated Acute Kidney Injury: A Dynamic Time Warping Clustering Study in MIMIC-III Avichandra Singh Ningthoujam, Gomathi Thiyagarajan, Pragya Vaishnav, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7560995/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose: Sepsis-associated acute kidney injury (SA-AKI) is a grave condition with high morbidity. The current understanding of renal recovery is often limited to binary outcomes, which fail to capture the complex and heterogeneous temporal dynamics of kidney function. This study was undertaken to identify distinct, clinically meaningful phenotypes of renal function trajectories in patients with SA-AKI. Methods: We conducted a retrospective cohort study using the MIMIC-III v1.4 database. Adult patients with a diagnosis of both sepsis (using Sepsis-3 criteria) and AKI (based on KDIGO guidelines) were included. Time-series serum creatinine (SCr) and urine output (UO) data for the first 14 days following AKI diagnosis were extracted. We employed a multivariate Dynamic Time Warping (DTW) distance metric coupled with K-medoids clustering to group patients based on the similarity of their renal function trajectories. The optimal number of clusters was determined using the average silhouette score. The identified phenotypes were then characterized based on baseline patient characteristics, clinical interventions, and outcomes. Results: From an initial pool of 12,542 patients with sepsis, our final cohort comprised 4,067 patients with SA-AKI. We identified four distinct phenotypes: Phenotype 1 'Rapid Reversal' (n=1430, 35.2%), Phenotype 2 'Gradual Recovery' (n=828, 20.4%), Phenotype 3 'Fluctuating Non-Recovery' (n=1293, 31.8%), and Phenotype 4 'Progressive Worsening' (n=516, 12.7%). While 90-day mortality was similar across phenotypes (ranging from 20.7% to 22.5%, p=0.893), baseline severity and clinical outcomes varied significantly. The 'Progressive Worsening' phenotype had the highest baseline SOFA score (13.6 ± 3.8) and the highest rate of RRT initiation (72.1%), in stark contrast to the 'Fluctuating Non-Recovery' phenotype (SOFA 9.0 ± 3.7, RRT 3.9%). The 'Rapid Reversal' and 'Gradual Recovery' groups showed intermediate severity and RRT rates. Conclusion: Our study demonstrates that a data-driven approach using DTW clustering can successfully unravel the significant heterogeneity in renal recovery patterns among SA-AKI patients. The four identified phenotypes, while not differing in 90-day mortality, represent distinct clinical pathways characterized by different levels of illness severity and need for organ support. This nuanced phenotyping could pave the way for more personalized prognostication and the design of targeted therapeutic strategies in the future. Sepsis Acute Kidney Injury MIMIC-III Dynamic Time Warping Time-Series Clustering Clinical Phenotypes Full Text Additional Declarations No competing interests reported. Supplementary Files Appendix.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7560995","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":531904602,"identity":"c3358265-25ed-4123-88ce-3b9e0f292ca3","order_by":0,"name":"Avichandra Singh Ningthoujam","email":"","orcid":"","institution":"Manipal University Jaipur, Jaipur","correspondingAuthor":false,"prefix":"","firstName":"Avichandra","middleName":"Singh","lastName":"Ningthoujam","suffix":""},{"id":531904603,"identity":"c0f966b7-c18d-4a71-a50f-ca6b5216e9d8","order_by":1,"name":"Gomathi Thiyagarajan","email":"","orcid":"","institution":"CMR Institute of 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MIMIC-III","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Sepsis, Acute Kidney Injury, MIMIC-III, Dynamic Time Warping, Time-Series Clustering, Clinical Phenotypes","lastPublishedDoi":"10.21203/rs.3.rs-7560995/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7560995/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose:\u003c/h2\u003e\u003cp\u003eSepsis-associated acute kidney injury (SA-AKI) is a grave condition with high morbidity. The current understanding of renal recovery is often limited to binary outcomes, which fail to capture the complex and heterogeneous temporal dynamics of kidney function. This study was undertaken to identify distinct, clinically meaningful phenotypes of renal function trajectories in patients with SA-AKI.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e\u003cp\u003eWe conducted a retrospective cohort study using the MIMIC-III v1.4 database. Adult patients with a diagnosis of both sepsis (using Sepsis-3 criteria) and AKI (based on KDIGO guidelines) were included. Time-series serum creatinine (SCr) and urine output (UO) data for the first 14 days following AKI diagnosis were extracted. We employed a multivariate Dynamic Time Warping (DTW) distance metric coupled with K-medoids clustering to group patients based on the similarity of their renal function trajectories. The optimal number of clusters was determined using the average silhouette score. The identified phenotypes were then characterized based on baseline patient characteristics, clinical interventions, and outcomes.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e\u003cp\u003eFrom an initial pool of 12,542 patients with sepsis, our final cohort comprised 4,067 patients with SA-AKI. We identified four distinct phenotypes: Phenotype 1 'Rapid Reversal' (n=1430, 35.2%), Phenotype 2 'Gradual Recovery' (n=828, 20.4%), Phenotype 3 'Fluctuating Non-Recovery' (n=1293, 31.8%), and Phenotype 4 'Progressive Worsening' (n=516, 12.7%). While 90-day mortality was similar across phenotypes (ranging from 20.7% to 22.5%, p=0.893), baseline severity and clinical outcomes varied significantly. The 'Progressive Worsening' phenotype had the highest baseline SOFA score (13.6 \u0026plusmn; 3.8) and the highest rate of RRT initiation (72.1%), in stark contrast to the 'Fluctuating Non-Recovery' phenotype (SOFA 9.0 \u0026plusmn; 3.7, RRT 3.9%). The 'Rapid Reversal' and 'Gradual Recovery' groups showed intermediate severity and RRT rates.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e\u003cp\u003eOur study demonstrates that a data-driven approach using DTW clustering can successfully unravel the significant heterogeneity in renal recovery patterns among SA-AKI patients. The four identified phenotypes, while not differing in 90-day mortality, represent distinct clinical pathways characterized by different levels of illness severity and need for organ support. This nuanced phenotyping could pave the way for more personalized prognostication and the design of targeted therapeutic strategies in the future.\u003c/p\u003e","manuscriptTitle":"Identifying Novel Phenotypes of Renal Recovery and Progression in Sepsis-Associated Acute Kidney Injury: A Dynamic Time Warping Clustering Study in MIMIC-III","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-23 19:59:36","doi":"10.21203/rs.3.rs-7560995/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"047cc4ed-5c8d-442e-bf7d-0494ebcc232d","owner":[],"postedDate":"October 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-19T11:41:05+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-23 19:59:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7560995","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7560995","identity":"rs-7560995","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}