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Treatment of this acute condition often includes establishing an external ventricular drainage (EVD). However, chronic hydrocephalus develops in some patients, who then require placement of a permanent ventriculoperitoneal (VP) shunt. The aim of this study was to employ recurrent neural network (RNN)-based machine learning techniques to identify patients who require VP shunt placement at an early stage. This retrospective single-centre study included all patients who were diagnosed with aSAH and treated in the intensive care unit (ICU) between November 2010 and May 2020 (<jats:italic>n<\/jats:italic>\u2009=\u2009602). More than 120 parameters were analysed, including routine neurocritical care data, vital signs and blood gas analyses. Various machine learning techniques, including RNNs and gradient boosting machines, were evaluated for their ability to predict VP shunt dependency. VP-shunt dependency could be predicted using an RNN after just one day of ICU stay, with an AUC-ROC of 0.77 (CI: 0.75\u20130.79). The accuracy of the prediction improved after four days of observation (Day 4: AUC-ROC 0.81, CI: 0.79\u20130.84). At that point, the accuracy of the prediction was 76% (CI: 75.98\u201383.09%), with a sensitivity of 85% (CI: 83\u201388%) and a specificity of 74% (CI: 71\u201378%). RNN-based machine learning has the potential to predict VP shunt dependency on Day 4 after ictus in aSAH patients using routine data collected in the ICU. The use of machine learning may allow early identification of patients with specific therapeutic needs and accelerate the execution of required procedures.<\/jats:p>","DOI":"10.1007\/s10877-024-01151-4","type":"journal-article","created":{"date-parts":[[2024,3,21]],"date-time":"2024-03-21T05:01:56Z","timestamp":1710997316000},"page":"1175-1186","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Early prediction of ventricular peritoneal shunt dependency in aneurysmal subarachnoid haemorrhage patients by recurrent neural network-based machine learning using routine intensive care unit data"],"prefix":"10.1007","volume":"38","author":[{"given":"Nils","family":"Schweingruber","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jan","family":"Bremer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anton","family":"Wiehe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marius Marc-Daniel","family":"Mader","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christina","family":"Mayer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marcel Seungsu","family":"Woo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefan","family":"Kluge","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J\u00f6rn","family":"Grensemann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fanny","family":"Quandt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jens","family":"Gempt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marlene","family":"Fischer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"G\u00f6tz","family":"Thomalla","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christian","family":"Gerloff","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jennifer","family":"Sauvigny","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0865-241X","authenticated-orcid":false,"given":"Patrick","family":"Czorlich","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,3,21]]},"reference":[{"issue":"10355","key":"1151_CR1","doi-asserted-by":"publisher","first-page":"846","DOI":"10.1016\/S0140-6736(22)00938-2","volume":"400","author":"J Claassen","year":"2022","unstructured":"Claassen J, Park S. 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The manuscript has been prepared in accordance with the journal\u2019s formatting and stylistic requirements.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"We hereby confirm and attest that this manuscript has not been previously published in any form or medium and is not concurrently under consideration by another scientific journal.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"We hereby confirm that all individuals listed as authors have significantly contributed to the development of this manuscript and satisfy the authorship criteria. Further, we affirm that the final version of the manuscript has been reviewed and approved by all authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The study protocol was reported to the local ethics committee (Ethics committee of the Hamburg Chamber of Physicians, reference number WF-059\/20) and was conducted according to the Declaration of Helsinki. Written informed consent was waived because all datasets have been de-identified prior to processing and evaluation for the purposes of the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"For the present study, the RECORD (REporting of studies Conducted using Observational Routinely collected Data) checklist was rigorously followed to ensure robust reporting standards. The completed RECORD checklist will be uploaded concomitantly with the manuscript submission.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Use of reporting Checklist"}},{"value":"The authors declare no competing interests and no conflict of interest. M.F. receives financial support from the External Research Program, Medtronic GmbH, unrelated to this work. C.G. reports personal fees from Amgen, personal fees from Boehringer Ingelheim, personal fees from Daiichi Sankyo, personal fees from Abbott, personal fees from Prediction Biosciences, personal fees from Novartis, and personal fees from Bayer outside the submitted work. G.T. reports personal fees from Acandis, Alexion, Astra Zeneca, Bayer, Boehringer Ingelheim, BristolMyersSquibb, Pfizer, Amarin, Daiichi Sankyo, Stryker, Portola outside the submitted work. J.Gr. received financial study support from Infectopharm and Pfizer, and consultant and lecture fees from Dr\u00e4gerwerk, General Electric Healthcare, and Infectopharm outside of the submitted work.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}