Predicting Duration of Invasive Mechanical Ventilation in the Pediatric ICU.
BACKGROUND: Timely ventilator liberation can prevent morbidities associated with invasive mechanical ventilation in the pediatric ICU (PICU). There currently exists no standard benchmark for duration of invasive mechanical ventilation in the PICU. This study sought to develop and validate a multi-ce...
| Publicado en: | Respiratory Care Vol. 68; no. 12; pp. 1623 - 1631 |
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| Autores principales: | , , , , , |
| Formato: | CEU research tables/charts Journal Article |
| Publicado: |
Mary Ann Liebert, Inc.
Dec2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=173910574&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 173910574 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00201324 4GG jtl: Respiratory Care issn: 00201324 maglogo: N pubinfo: dt: Dec2023 vid: 68 iid: 12 pid: 1365 pub: Mary Ann Liebert, Inc. place: New Rochelle, New York artinfo: ui: 173910574 173910574 173910574 10.4187/respcare.11015 173910574 ppf: 1623 ppct: 8 formats: fmt: @attributes: type: P tig: atl: Predicting Duration of Invasive Mechanical Ventilation in the Pediatric ICU. aug: au: Rogerson, Colin M. Abu-Sultaneh, Samer Loberger, Jeremy M. Ross, Patrick Khemani, Robinder G. Sanchez-Pinto, L. Nelson affil: Indiana University School of Medicine, Riley Hospital for Children, Indianapolis, Indiana sug: subj: Respiratory Failure Therapy Respiration, Artificial Standards Invasive Procedures Standards Treatment Duration Standards Machine Learning Prediction Models Benchmarking Intensive Care Units, Pediatric Human Education, Continuing (Credit) Validation Studies Ventilator Patients Ventilator Weaning Prospective Studies Retrospective Design Record Review Confidence Intervals Descriptive Statistics Nonexperimental Studies Infant, Newborn Infant Child, Preschool Child Adolescence Inpatients Scales Data Analysis Software Chi Square Test Kruskal-Wallis Test Random Forest Male Female Infant, Newborn: birth-1 month Infant: 1-23 months Child, Preschool: 2-5 years Child: 6-12 years Adolescent: 13-18 years Male Female ab: BACKGROUND: Timely ventilator liberation can prevent morbidities associated with invasive mechanical ventilation in the pediatric ICU (PICU). There currently exists no standard benchmark for duration of invasive mechanical ventilation in the PICU. This study sought to develop and validate a multi-center prediction model of invasive mechanical ventilation duration to determine a standardized duration of invasive mechanical ventilation ratio. METHODS: This was a retrospective cohort study using registry data from 157 institutions in the Virtual Pediatric Systems database. The study population included encounters in the PICU between 2012-2021 involving endotracheal intubation and invasive mechanical ventilation in the first day of PICU admission who received invasive mechanical ventilation for > 24 h. Subjects were stratified into a training cohort (2012-2017) and 2 validation cohorts (2018-2019/2020-2021). Four models to predict the duration of invasive mechanical ventilation were trained using data from the first 24 h, validated, and compared. RESULTS: The study included 112,353 unique encounters. All models had observed-to-expected (O/E) ratios close to one but low mean squared error and R² values. The random forest model was the best performing model and achieved an O/E ratio of 1.043 (95% CI 1.030-1.056) and 1.004 (95% CI 0.990-1.019) in the validation cohorts and 1.009 (95% CI 1.004-1.016) in the full cohort. There was a high degree of institutional variation, with single- unit O/E ratios ranging between 0.49-1.91. When stratified by time period, there were observ- able changes in O/E ratios at the individual PICU level over time. CONCLUSIONS: We derived and validated a model to predict the duration of invasive mechanical ventilation that performed well in aggregated predictions at the PICU and the cohort level. This model could be beneficial in quality improvement and institutional benchmarking initiatives for use at the PICU level and for tracking of performance over time. pubtype: Academic Journal doctype: CEU research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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