[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121827-en":3,"doc-seo-121827-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121827,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",7,"Healthcare","IntraCranial pressure prediction AlgoRithm using machinE learning (I-CARE) - Training and Validation Study","Elevated intracranial pressure (ICP) is a serious complication in neurologic injury, and current therapies require time to take effect. This retrospective training and validation study develops an ensemble machine learning algorithm (I-CARE) to predict a patient’s ICP 30 minutes ahead using historical ICP, vitals, laboratory data, medications/infusions, input/output, and Glasgow Coma Scale components. Performance is assessed with a left-out test set and external validation on MIMIC-III waveform data, showing promising predictive accuracy and identifying key drivers of ICP evolution.","UCSF  \nUC San Francisco Previously Published Works  \nTitle  \nIntraCranial pressure prediction AlgoRithm using machinE learning (I-CARE): Training and Validation Study.  \nPermalink  \n[https://escholarship.org/uc/item/19c3158v](https://escholarship.org/uc/item/19c3158v)  \nJournal  \nCritical Care Explorations, 6(1)  \nAuthors  \nFong, Nicholas  \nFeng, Jean Hubbard, Alan  \net al.  \nPublication Date  \n2024  \nDOI  \n10.1097/CCE.0000000000001024 Peer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nORIGINAL CLINICAL REPORT  \nIntraCranial pressure prediction AlgoRithm using machinE learning (I-CARE): Training and Validation Study  \nOBJECTIVES: Elevated intracranial pressure (ICP) is a potentially devastating complication of neurologic injury. Developing an ICP prediction algorithm to help the clinician adjust treatments and potentially prevent elevated ICP episodes. DESIGN: Retrospective study.  \nSETTING: Three hundred thirty-five ICUs at 208 hospitals in the United States. SUBJECTS: Adults patients from the electronic ICU (eICU) Collaborative Research Database was used to train an ensemble machine learning model to predict the ICP 30 minutes in the future. Predictive performance was evaluated using a left-out test dataset and externally evaluated on the Medical Information Mart for Intensive Care-III (MIMIC-III) Matched Waveform Database.  \nINTERVENTIONS: None.  \nMEASUREMENTS AND MAIN RESULTS: Predictors included age, assigned sex, laboratories, medications and infusions, input/output, Glasgow Coma Scale (GCS) components, and time-series vitals (heart rate, ICP, mean arterial pressure, respiratory rate, and temperature) . Each patient ICU stay was divided into successive 95-minute timeblocks. For each timeblock, the model was trained on nontimevarying covariates as well as on 12 observations of time-varying covariates at 5-minute intervals and asked to predict the 5-minute median ICP 30 minutes after the last observed ICP value. Data from 931 patients with ICP monitoring in theeICU dataset were extracted (46,207 timeblocks) . The root mean squared error was 4.51 mm Hg in the eICU test set and 3.56 mm Hg in the MIMIC-III dataset. The most important variables driving ICP prediction were previous ICP history, patients’ temperature, weight, serum creatinine, age, GCS, and hemodynamic parameters.  \nCONCLUSIONS: IntraCranial pressure prediction AlgoRithm using machinE learning, an ensemble machine learning model, trained to predict the ICP of a patient 30 minutes in the future based on baseline characteristics and vitals data from the past hour showed promising predictive performance including in an external validation dataset.  \nKEYWORDS: artificial intelligence; brain injury; intracranial pressure; machine learning; prediction  \nElevated intracranial pressure (ICP) is a potentially devastating complica  \ntion of neurologic injury. The 2016 guidelines (1) for the management  \nof patients with severe traumatic brain injury recommend using ICP monitoring to reduce in-hospital and 2-week post-injury mortality (level IIb) . The same guidelines recommend treating ICP greater than 22 mm Hg because values above this level are associated with increased mortality (level IIb) .  \nUnfortunately, the treatments available to decrease the ICP and optimize the cerebral perfusion pressure take time to be effective. The intensity and  \nNicholas Fong1,2 Jean Feng, PhD3 Alan Hubbard, PhD4  \nLauren Eyler Dang, MD4  \nRomain Pirracchio, MD, MPH, PhD1,3,4  \nCopyright © 2023 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine. This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No  \nDerivatives License 4.0 (CCBYNC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from th","cbCainQyMu8dL2mf","https://ap.wps.com/l/cbCainQyMu8dL2mf","pdf",1492309,1,11,"English","en",105,"# Objectives\n## Study design and setting\n## Participants and data sources\n# Methods\n## Prediction horizon and timeblock strategy\n## Model inputs and features\n# Results\n## Predictive performance and error metrics\n## Most important variables\n# Conclusions","[{\"question\":\"What is the main goal of the I-CARE algorithm?\",\"answer\":\"To predict intracranial pressure (ICP) 30 minutes in the future so clinicians can adjust treatments proactively and potentially prevent intracranial hypertension episodes.\"},{\"question\":\"How was the model trained and evaluated?\",\"answer\":\"A retrospective ensemble machine learning model was trained using the eICU Collaborative Research Database, evaluated on a left-out test dataset, and externally validated using the MIMIC-III Matched Waveform Database.\"},{\"question\":\"Which variables were most influential for ICP prediction?\",\"answer\":\"Previous ICP history, temperature, weight, serum creatinine, age, Glasgow Coma Scale (GCS), and hemodynamic parameters were among the most important predictors driving ICP prediction.\"}]","IntraCranial pressure prediction AlgoRithm using machinE learning (I-CARE) - 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