Predictive Algorithms for Critical Rehabilitation Outcomes

Official title

Development and Validation of a Prediction Algorithms to Estimate the Clinical Effect of Early Rehabilitation on ICU Survivors Received Mechanical Ventilation in the ICU

Overview

An increasing amount of evidence from evidence-based medicine indicates that early rehabilitation intervention for patients receiving mechanical ventilation is safe and feasible, and can promote functional recovery and reduce hospital stay. However, the conscious state, respiratory function, and daily living activities of these patients after being discharged from the ICU vary greatly, and some patients do not show obvious benefits. How to identify which patients may have benefit from early rehabilitation is a key issue that needs to be addressed in critical care rehabilitation. This study aims to investigate the clinical data related to the disease of the ICU survivors who received mechanical ventilation as the research object, by collecting their clinical data when receiving early rehabilitation intervention, and constructing a clinical prediction model for the efficacy of early rehabilitation intervention in the ICU through the selection of optimal regression equation or machine learning algorithm. The application of this model can effectively determine whether ICU inpatients need early rehabilitation intervention, thereby reducing complication rates and improving their quality of life.

Detailed description

An increasing amount of evidence from evidence-based medicine suggests that early rehabilitation intervention (including early active and passive exercises, position management, pulmonary rehabilitation, etc.) for mechanical ventilation patients is safe and feasible, and can promote certain degree of functional recovery and reduce the length of stay in the intensive care unit (ICU). However, the differences in consciousness state, muscle strength, respiratory function, and activity of daily living (ADL) among patients who are discharged from the ICU after condition stabilization are very large, even some patients did not obtain obvious benefits. Therefore, how to identify which patients may have better benefit from early rehabilitation intervention is a key issue that needs to be focused on in ICU.

This study used "Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD)" as the guideline. Survivors undergoing mechanical ventilation in the ICU were recruited as the participants, whether patients gained progress in ADL function at different time points after receiving early rehabilitation intervention in the ICU was used as the outcome which is a time-to-event indicator. Demographic data, clinical diagnostic data and disease intervention data of the subjects were collected as alternative predictors. Variable transformation and variable screening were used to find predictors that could predict the outcome. The process of constructing clinical predictive models is completed by fitting models through regression equations and machine learning algorithms, internal validation, external validation, and clinical value assessment. The model with the best prediction efficiency is selected based on the differentiation and calibration of different models after validation. This model will be presented with a nomogram or a web app. The application of this clinical predictive model will identify whether and when this patient can received better recovery on ADL after receiving early rehabilitation intervention, so as to further optimize the timing of early intervention in rehabilitation and improve his survival quality.

Interventions

• Other Early rehabilitation intervention
  Based on the indications for early rehabilitation intervention outlined in the "Chinese Expert Consensus on Neurological Critical Care Rehabilitation," early rehabilitation interventions are categorized into three stages according to the patient's consciousness level (GCS score), degree of cooperation (S5Q score), and sedation status (RASS score)

Primary outcome measures

• Functional Independence Measure (FIM) scale [Time frame: From date of enrollment until the date of ADL improvement (FIM increase ≥ 5) or date of participants are transferred out of the ICU. FIM score was assessed every other day after treatment starts and up to 6 weeks.]

Eligibility criteria

Inclusion criteria

• Age older than 18 years;
• Received mechanical ventilation, including endotracheal intubation and tracheostomy, during ICU admission;
• Met the rehabilitation intervention indications outlined in the "Chinese Expert Consensus on Neurocritical Rehabilitation" during ICU admission and underwent corresponding early rehabilitation interventions, including but not limited to arousal therapy for consciousness disorders, early active/passive mobilization, comprehensive pulmonary rehabilitation, etc.;
• No mortality events occurred during ICU admission;
• Informed consent form signed by family members or the patient.

Exclusion criteria

• Pediatric patients under 18 years of age;
• Hospitalized patients in the ICU who did not receive mechanical ventilation;
• Patients in the ICU who did not undergo early rehabilitation interventions;
• mortality events occurred during ICU admission;
• Patients transferred out of the ICU due to treatment abandonment by family members;
• Family refusal to sign the informed consent form or patient refusal to sign the informed consent form when conscious and competent.

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Observational model

Cohort

Study locations

China · 1 center

• Zhongnan hospital of Wuhan University — Wuhan

Publications

• Adhikari NK, Fowler RA, Bhagwanjee S, Rubenfeld GD. Critical care and the global burden of critical illness in adults. Lancet. 2010 Oct 16;376(9749):1339-46. doi: 10.1016/S0140-6736(10)60446-1. Epub 2010 Oct 11. PMID 20934212
• Liu L, Gao Z, Yang Y, Li M, Mu X, Ma X, Li G, Sun W, Wang X, Gu Q, Zheng R, Zhao H, Xie J, Qiu H. Economic variations in patterns of care and outcomes of patients receiving invasive mechanical ventilation in China: a national cross-sectional survey. J Thorac Dis. 2019 Jul;11(7):2878-2889. doi: 10.21037/jtd.2019.07.51. PMID 31463117
• Stiller K. Physiotherapy in intensive care: towards an evidence-based practice. Chest. 2000 Dec;118(6):1801-13. doi: 10.1378/chest.118.6.1801. No abstract available. PMID 11115476
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• Schaller SJ, Anstey M, Blobner M, Edrich T, Grabitz SD, Gradwohl-Matis I, Heim M, Houle T, Kurth T, Latronico N, Lee J, Meyer MJ, Peponis T, Talmor D, Velmahos GC, Waak K, Walz JM, Zafonte R, Eikermann M; International Early SOMS-guided Mobilization Research Initiative. Early, goal-directed mobilisation in the surgical intensive care unit: a randomised controlled trial. Lancet. 2016 Oct 1;388(1005 PMID 27707496
• Balas MC, Burke WJ, Gannon D, Cohen MZ, Colburn L, Bevil C, Franz D, Olsen KM, Ely EW, Vasilevskis EE. Implementing the awakening and breathing coordination, delirium monitoring/management, and early exercise/mobility bundle into everyday care: opportunities, challenges, and lessons learned for implementing the ICU Pain, Agitation, and Delirium Guidelines. Crit Care Med. 2013 Sep;41(9 Suppl 1):S11 PMID 23989089
• Hodgson CL, Stiller K, Needham DM, Tipping CJ, Harrold M, Baldwin CE, Bradley S, Berney S, Caruana LR, Elliott D, Green M, Haines K, Higgins AM, Kaukonen KM, Leditschke IA, Nickels MR, Paratz J, Patman S, Skinner EH, Young PJ, Zanni JM, Denehy L, Webb SA. Expert consensus and recommendations on safety criteria for active mobilization of mechanically ventilated critically ill adults. Crit Care. 201 PMID 25475522
• Bakhru RN, Wiebe DJ, McWilliams DJ, Spuhler VJ, Schweickert WD. An Environmental Scan for Early Mobilization Practices in U.S. ICUs. Crit Care Med. 2015 Nov;43(11):2360-9. doi: 10.1097/CCM.0000000000001262. PMID 26308435

Identifiers

NCT: NCT06532994 · ZNYYIIT2024004

Primary sources (government registries)

• ClinicalTrials.gov (US NIH)

View this study on ClinicalTrials.gov ↗