{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T20:01:17Z","timestamp":1792785677233,"version":"4.5.0"},"reference-count":75,"publisher":"IOP Publishing","issue":"10","license":[{"start":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T00:00:00Z","timestamp":1792108800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T00:00:00Z","timestamp":1792108800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"},{"start":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T00:00:00Z","timestamp":1792108800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/questions\/alternative-author-rights-policies\/"},{"start":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T00:00:00Z","timestamp":1792108800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/questions\/alternative-author-rights-policies\/"}],"funder":[{"name":"ZonMw","award":["10430102110005"],"award-info":[{"award-number":["10430102110005"]}],"id":[{"id":"https:\/\/ror.org\/01yaj9a77","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Physiol. Meas."],"published-print":{"date-parts":[[2026,10,30]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    Physiological resilience\u2014the ability to maintain or regain homeostasis after a stressor\u2014is rarely quantified in clinical practice. Complex-systems theory proposes dynamic indicators of resilience based on variation, autocorrelation, and complexity in physiological signals. We examined whether such indicators, derived from routine ward-monitor heart-rate (HR) data, were associated with in-hospital COVID-19 severity and long-term survival.\n                    <jats:italic>Approach.<\/jats:italic>\n                    Routine ward-monitor HR data from 181 hospitalized COVID-19 patients were analyzed at 1 min resolution. Dynamic indicators were calculated for the first valid 270 min interval after admission and for an extended 1620 min summary based on the first six sequential valid 270 min intervals within the first three days post-admission. For the extended summary, indicators were calculated per interval and summarized as change from the first interval. Logistic regression was used to assess associations with an in-hospital composite outcome of COVID-19-related complications, ICU admission, or death. Cox proportional hazards models were used to examine survival; systematic post-discharge follow-up was conducted only in patients aged \u2a7e60 years. Models were adjusted for age and sex.\n                    <jats:italic>Main Results.<\/jats:italic>\n                    Greater minute-level HR variation during the early 270 min interval, reflected by standard deviation (SD) and range, was associated with lower odds of severe in-hospital disease (OR = 0.62,\n                    <jats:italic>p<\/jats:italic>\n                    = 0.02 for SD; OR = 0.64,\n                    <jats:italic>p<\/jats:italic>\n                    = 0.01 for range) and better survival (hazard ratio = 0.51,\n                    <jats:italic>p<\/jats:italic>\n                    = 0.045 for SD). Short-lag autocorrelations during the 270 min interval were positively associated with quick sequential organ failure assessment, but were not clearly associated with the in-hospital severity composite. Sample entropy showed time-scale-dependent associations with in-hospital severity: higher sample entropy during the 270 min interval was associated with higher odds of severe disease (OR = 1.46,\n                    <jats:italic>p<\/jats:italic>\n                    = 0.04), whereas higher sample entropy in the extended 1620 min summary was associated with lower odds of severe disease (OR = 0.68,\n                    <jats:italic>p<\/jats:italic>\n                    = 0.02). Higher fractal dimension (FracDim) in the extended summary was associated with increased long-term mortality in age-interaction models (hazard ratio = 1.93,\n                    <jats:italic>p<\/jats:italic>\n                    = 0.04). Age-dependent patterns were observed, most clearly for the association between FracDim and long-term mortality.\n                    <jats:italic>Significance.<\/jats:italic>\n                    Minute-level HR dynamics may contain clinically relevant information beyond conventional static HR summaries. In this exploratory study, early HR variation and autocorrelation appeared most relevant for acute disease severity, whereas extended-window complexity indicators showed more mixed and age-dependent associations. These findings suggest that dynamic resilience indicators can be extracted from routinely collected monitoring data, but require validation in larger cohorts with standardized preprocessing, transparent handling of monitoring gaps, and careful consideration of time scale.\n                  <\/jats:p>","DOI":"10.1088\/1361-6579\/aeafa9","type":"journal-article","created":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T22:53:34Z","timestamp":1790981614000},"page":"105009","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Dynamic indicators of resilience in COVID-19 patients: a comprehensive analysis of heart rate dynamics"],"prefix":"10.1088","volume":"47","author":[{"given":"Anna","family":"Kuranova","sequence":"first","affiliation":[{"name":"Radboud University Medical Center"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4460-7026","authenticated-orcid":true,"given":"Geeske","family":"Peeters","sequence":"additional","affiliation":[{"name":"Radboud University Medical Center"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"given":"Jan","family":"Wijgerse","sequence":"additional","affiliation":[{"name":"Radboud University Medical Center"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"given":"Jeannette B","family":"Peters","sequence":"additional","affiliation":[{"name":"Radboud University Medical Center"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"data-curation","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"given":"Evertine J","family":"Abbink","sequence":"additional","affiliation":[{"name":"Radboud University Medical Center"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"data-curation","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"given":"Harry","family":"van Goor","sequence":"additional","affiliation":[{"name":"Radboud University Medical Center"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"given":"Sebastian","family":"Bredie","sequence":"additional","affiliation":[{"name":"Radboud University Medical Center"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"given":"Ren\u00e9","family":"Melis","sequence":"additional","affiliation":[{"name":"Radboud University Medical Center"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"name":"on behalf of the COOP consortium","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2026,10,16]]},"reference":[{"key":"pmeaaeafa9bib1","doi-asserted-by":"publisher","first-page":"175","DOI":"10.3390\/computers13070175","type":"journal-article","article-title":"A framework for cleaning streaming data in healthcare: a context and user-supported approach","volume":"13","author":"Alotaibi","year":"2024","journal-title":"Computers"},{"key":"pmeaaeafa9bib2","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2022.1008970","type":"journal-article","article-title":"Heart rate variability: a biomarker of frailty in older adults?","volume":"9","author":"Arantes","year":"2022","journal-title":"Front. 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