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A risk prediction model for ICU-acquired weakness


- candidate number15927
- NTR NumberNTR4331
- ISRCTNISRCTN wordt niet meer aangevraagd.
- Date ISRCTN created
- date ISRCTN requested
- Date Registered NTR23-dec-2013
- Secondary IDsW13_193#13.17.0239 METC reference number
- Public TitleA risk prediction model for ICU-acquired weakness
- Scientific TitleICU-acquired Weakness Risk Prediction Study
- ACRONYMWARP
- hypothesisA risk prediction model, using patient characteristics, early available clinical parameters, laboratory results and use of medication as parameters could reliably predict the risk of ICU-AW two days after ICU admission.
- Healt Condition(s) or Problem(s) studiedIntensive Care (IC) patients
- Inclusion criteria-Adult patients newly admitted to the ICU
-Mechanically ventilated at 48h after admission
- Exclusion criteria- Cardiac arrest, any central nervous system (CNS) disorder (stroke, traumatic brain or spinal injury, CNS infection, CNS tumor) or neuromuscular disease as reason for admission
- Pre-existing spinal injury
- Poor pre-hospital functional status (modified Rankin score ≥4)
- Poor prognosis; death likely within 48 hours after inclusion
- mec approval receivedno
- multicenter trialyes
- randomisedno
- group[default]
- Type[default]
- Studytypeobservational
- planned startdate 1-jan-2014
- planned closingdate1-feb-2015
- Target number of participants500
- InterventionsAt 48 hours after ICU admission, patient characteristics, early available clinical parameters, laboratory results and use of medication parameters will be collected from the patient file. The candidate predictors are: age, gender, pre-existent polyneuropathy, polyneuropathy risk factor, systemic corticosteroid use prior to ICU admission, unplanned admission, suspected sepsis, presence of shock, RASS score, average urine production, highest glucose, lowest glucose, lowest pO2, FiO2 at lowest pO2, lowest pH, highest lactate, lowest platelets, lowest ionized calcium, highest ionized calcium, lowest phosphate, red blood cell transfusion, treatment with any corticosteroid, repeated treatment with any neuromuscular blocker and treatment with any aminoglycoside. Muscle strength will be investigated by the attending physiotherapist, using the Medical Research Council (MRC) sumscore to assess the presence of ICU-AW.
- Primary outcomePredictive performance (discrimination, calibration and classification) of a risk prediction model for ICU-AW.
- Secondary outcomePredictive performance after updating of the prediction model.
- TimepointsAt 48 hours after ICU admission, after screening of the in-and exclusion criteria, the patient will be included in the study. Thereafter the candidate predictors will be collected from the patient file. As soon as the patient is awake and attentive the attending physiotherapist will assess the muscle strength of the patient.
- Trial web siteN/A
- statusinclusion stopped: follow-up
- CONTACT FOR PUBLIC QUERIESMD. PhD. Janneke Horn
- CONTACT for SCIENTIFIC QUERIESMD. PhD. Janneke Horn
- Sponsor/Initiator Academic Medical Center (AMC), Amsterdam
- Funding
(Source(s) of Monetary or Material Support)
Academic Medical Center (AMC)
- PublicationsN/A
- Brief summaryIntensive Care Unit–acquired weakness (ICU–AW) is a frequent and debilitating complication of critical illness. It is important to identify ICU-AW early after onset of critical illness to provide accurate prognostic information to patients and their families and to enable timely initiation of supportive interventions, like intensive physiotherapy and tracheostomy. Using the current diagnostic reference standard, assessment of muscle strength using the Medical Research Council (MRC) score, an early diagnosis of ICU–AW is frequently precluded due to impaired consciousness or attentiveness. A prediction model using easily available data might be able to predict the risk of ICU-AW and might enable early interventions. In a previous study, we found that it was possible to predict the risk of ICU-AW using simple and widely available data. However, that risk prediction model was based on single center data which limits the generalizability. Therefore we aim to validate and update the risk prediction model for ICU-AW using prospective data from multiple centers.
- Main changes (audit trail)
- RECORD23-dec-2013 - 3-nov-2016


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