FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4285404191> ?p ?o ?g. }

• W4285404191 endingPage "612" @default.
• W4285404191 startingPage "604" @default.
• W4285404191 abstract "Background Currently, very few preventive or therapeutic strategies are used for mechanical ventilation (MV)-associated severe acute kidney injury (AKI). Objectives We developed clinical prediction models to detect the onset of severe AKI in the first week of intensive care unit (ICU) stay during the initiation of MV. Methods A large ICU database Medical Information Mart for Intensive Care IV (MIMIC-IV) was analysed retrospectively. Data were collected from the clinical information recorded at the time of ICU admission and during the initial 12 h of MV. Using univariate and multivariate analyses, the predictors were selected successively. For model development, two machine learning algorithms were compared. The primary goal was to predict the development of AKI stage 2 or 3 (AKI-23) and AKI stage 3 (AKI-3) in the first week of patients’ ICU stay after initial 12 h of MV. The developed models were externally validated using another multicentre ICU database (eICU Collaborative Research Database, eICU) and evaluated in various patient subpopulations. Results Models were developed using data from the development cohort (MIMIC-IV: 2008–2016; n = 3986); the random forest algorithm outperformed the logistic regression algorithm. In the internal (MIMIC-IV: 2017–2019; n = 1210) and external (eICU; n = 1494) validation cohorts, the incidences of AKI-23 were 154 (12.7%) and 119 (8.0%), respectively, with areas under the receiver operator characteristic curve of 0.78 (95% confidence interval [CI]: 0.74–0.82) and 0.80 (95% CI: 0.76–0.84); the incidences of AKI-3 were 81 (6.7%) and 67 (4.5%), with areas under the receiver operator characteristic curve of 0.81 (95% CI: 0.76–0.87) and 0.80 (95% CI: 0.73–0.86), respectively. Conclusions Models driven by machine learning and based on routine clinical data may facilitate the early prediction of MV-associated severe AKI. The validated models can be found at: https://apoet.shinyapps.io/mv_aki_2021_v2/." @default.
• W4285404191 created "2022-07-14" @default.
• W4285404191 creator A5001000619 @default.
• W4285404191 creator A5036508191 @default.
• W4285404191 creator A5058002676 @default.
• W4285404191 creator A5076455280 @default.
• W4285404191 creator A5085198253 @default.
• W4285404191 creator A5086582726 @default.
• W4285404191 date "2023-07-01" @default.
• W4285404191 modified "2023-10-16" @default.
• W4285404191 title "Internal and external validation of machine learning–assisted prediction models for mechanical ventilation–associated severe acute kidney injury" @default.
• W4285404191 cites W1194393158 @default.
• W4285404191 cites W1552974878 @default.
• W4285404191 cites W1934177287 @default.
• W4285404191 cites W1972649334 @default.
• W4285404191 cites W1982595904 @default.
• W4285404191 cites W2000785819 @default.
• W4285404191 cites W2042289252 @default.
• W4285404191 cites W2058471727 @default.
• W4285404191 cites W2065737867 @default.
• W4285404191 cites W2083175765 @default.
• W4285404191 cites W2141559993 @default.
• W4285404191 cites W2143851667 @default.
• W4285404191 cites W2146081758 @default.
• W4285404191 cites W2286158066 @default.
• W4285404191 cites W2396881363 @default.
• W4285404191 cites W2471686427 @default.
• W4285404191 cites W2480383475 @default.
• W4285404191 cites W2519345742 @default.
• W4285404191 cites W2520709829 @default.
• W4285404191 cites W2580745727 @default.
• W4285404191 cites W2582793731 @default.
• W4285404191 cites W2586582651 @default.
• W4285404191 cites W2589631808 @default.
• W4285404191 cites W2593239058 @default.
• W4285404191 cites W2604449545 @default.
• W4285404191 cites W2732471580 @default.
• W4285404191 cites W2735809879 @default.
• W4285404191 cites W2757504960 @default.
• W4285404191 cites W2759850233 @default.
• W4285404191 cites W2781246281 @default.
• W4285404191 cites W2848799999 @default.
• W4285404191 cites W2893893301 @default.
• W4285404191 cites W2895242940 @default.
• W4285404191 cites W2896893468 @default.
• W4285404191 cites W2914416972 @default.
• W4285404191 cites W2923757114 @default.
• W4285404191 cites W2946302516 @default.
• W4285404191 cites W2964696298 @default.
• W4285404191 cites W2965822798 @default.
• W4285404191 cites W2972538248 @default.
• W4285404191 cites W2982640547 @default.
• W4285404191 cites W2996218315 @default.
• W4285404191 cites W4205910988 @default.
• W4285404191 doi "https://doi.org/10.1016/j.aucc.2022.06.001" @default.
• W4285404191 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35842332" @default.
• W4285404191 hasPublicationYear "2023" @default.
• W4285404191 type Work @default.
• W4285404191 citedByCount "1" @default.
• W4285404191 countsByYear W42854041912023 @default.
• W4285404191 crossrefType "journal-article" @default.
• W4285404191 hasAuthorship W4285404191A5001000619 @default.
• W4285404191 hasAuthorship W4285404191A5036508191 @default.
• W4285404191 hasAuthorship W4285404191A5058002676 @default.
• W4285404191 hasAuthorship W4285404191A5076455280 @default.
• W4285404191 hasAuthorship W4285404191A5085198253 @default.
• W4285404191 hasAuthorship W4285404191A5086582726 @default.
• W4285404191 hasConcept C119857082 @default.
• W4285404191 hasConcept C126322002 @default.
• W4285404191 hasConcept C151956035 @default.
• W4285404191 hasConcept C161584116 @default.
• W4285404191 hasConcept C177713679 @default.
• W4285404191 hasConcept C194828623 @default.
• W4285404191 hasConcept C2776376669 @default.
• W4285404191 hasConcept C2777080012 @default.
• W4285404191 hasConcept C2780472472 @default.
• W4285404191 hasConcept C2987404301 @default.
• W4285404191 hasConcept C38180746 @default.
• W4285404191 hasConcept C41008148 @default.
• W4285404191 hasConcept C44249647 @default.
• W4285404191 hasConcept C58471807 @default.
• W4285404191 hasConcept C71924100 @default.
• W4285404191 hasConcept C72563966 @default.
• W4285404191 hasConceptScore W4285404191C119857082 @default.
• W4285404191 hasConceptScore W4285404191C126322002 @default.
• W4285404191 hasConceptScore W4285404191C151956035 @default.
• W4285404191 hasConceptScore W4285404191C161584116 @default.
• W4285404191 hasConceptScore W4285404191C177713679 @default.
• W4285404191 hasConceptScore W4285404191C194828623 @default.
• W4285404191 hasConceptScore W4285404191C2776376669 @default.
• W4285404191 hasConceptScore W4285404191C2777080012 @default.
• W4285404191 hasConceptScore W4285404191C2780472472 @default.
• W4285404191 hasConceptScore W4285404191C2987404301 @default.
• W4285404191 hasConceptScore W4285404191C38180746 @default.
• W4285404191 hasConceptScore W4285404191C41008148 @default.
• W4285404191 hasConceptScore W4285404191C44249647 @default.
• W4285404191 hasConceptScore W4285404191C58471807 @default.
• W4285404191 hasConceptScore W4285404191C71924100 @default.

• next