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This prospective multicentre cohort study compares the performance of a LR and five ML\u00a0models on the contribution of influencing predictors and predictor-to-event relationships on prediction model\u00b4s performance.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We used 25 baseline variables of 490 COVID-19 patients admitted to 8 hospitals in Germany (March\u2013November 2020) to develop and validate (75\/25 random-split) 3 linear (L1 and L2 penalty, elastic net [EN]) and 2 non-linear (support vector machine [SVM] with radial kernel, random forest [RF]) ML approaches for predicting critical events defined by intensive care unit transfer, invasive ventilation and\/or death (composite end-point: 181 patients). Models were compared for performance (area-under-the-receiver-operating characteristic-curve [AUC], Brier score) and predictor importance (performance-loss metrics, partial-dependence profiles).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Models performed close with a small benefit for LR (utilizing restricted cubic splines for non-linearity) and RF (AUC means: 0.763\u20130.731 [RF\u2013L1]); Brier scores: 0.184\u20130.197 [LR\u2013L1]). Top ranked predictor variables (consistently highest importance: C-reactive protein) were largely identical across models, except creatinine, which exhibited marginal (L1, L2, EN, SVM) or high\/non-linear effects (LR, RF) on events.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Although the LR and ML models analysed showed no strong differences in performance and the most influencing predictors for COVID-19-related event prediction, our results indicate a predictive benefit from taking account for non-linear predictor-to-event relationships and effects. Future efforts should focus on leveraging data-driven ML technologies from static towards dynamic modelling solutions that continuously learn and adapt to changes in data environments during the evolving pandemic.<\/jats:p><jats:p><jats:italic>Trial registration number<\/jats:italic>: NCT04659187.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12911-022-02057-4","type":"journal-article","created":{"date-parts":[[2022,11,28]],"date-time":"2022-11-28T04:30:40Z","timestamp":1669609840000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Comparison of machine learning methods with logistic regression analysis in creating predictive models for risk of critical in-hospital events in COVID-19 patients on hospital admission"],"prefix":"10.1186","volume":"22","clinical-trial-number":[{"clinical-trial-number":"nct04659187","registry":"10.18810\/clinical-trials-gov"}],"author":[{"given":"Aaron W.","family":"Sievering","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter","family":"Wohlmuth","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nele","family":"Ge\u00dfler","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Melanie A.","family":"Gunawardene","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Klaus","family":"Herrlinger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Berthold","family":"Bein","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dirk","family":"Arnold","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martin","family":"Bergmann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lorenz","family":"Nowak","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christian","family":"Gloeckner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ina","family":"Koch","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martin","family":"Bachmann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christoph U.","family":"Herborn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Axel","family":"Stang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,11,28]]},"reference":[{"key":"2057_CR1","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.m1090","volume":"368","author":"JH Tanne","year":"2020","unstructured":"Tanne JH, Hayasaki E, Zastrow M, Pulla P, Smith P, Rada AG. 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Nat Commun. 2020;11(1):5493.","journal-title":"Nat Commun"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-022-02057-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-022-02057-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-022-02057-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T16:06:38Z","timestamp":1728489998000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-022-02057-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,28]]},"references-count":52,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["2057"],"URL":"https:\/\/doi.org\/10.1186\/s12911-022-02057-4","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,28]]},"assertion":[{"value":"21 February 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 November 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 November 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The exploratory cohort study operates under the \u201cCORONA Germany\u201d - <i>Clinical Outcome and Risk in hospitalized COVID-19 patients<\/i> - study (ClinicalTrials.gov, NCT04659187). Data were collected as part of routine care by the responsible clinical teams. Data were anonymised at the point of extraction and no patient identifiable data is reported in the analysis. The study protocol was approved by the ethics committees of the General Medical Councils (Aerztekammer) for the cities Hamburg and Munich prior to data extraction (Ethics committee Aerztekammer Hamburg, Protocol WF-046\/20, Date: 26.03.2020) and waived the need for participant consent because any data were collected and analysed on a fully anonymized basis. All data processing and analyses presented in this study have been conducted in accordance with the Helsinki declaration.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"309"}}