Development of an Early Warning Model for Intensive Care Unit-Acquired Weakness in Mechanically Ventilated Children: A Disease-Specific Cohort and Database Study

Fudan University

Status

Not yet enrolling

Conditions

ICU Acquired Weakness

Treatments

Diagnostic Test: ICU-AW

Study type

Observational

Funder types

Other

Identifiers

NCT07150637

ICU-AW 20250824

Details and patient eligibility

About

Acquired weakness (AW) is a common complication among patients in the Intensive Care Unit (ICU). It is a systemic muscle weakness and dysfunction associated with critical illness, often related to prolonged bed rest, mechanical ventilation, systemic inflammatory response syndrome (SIRS), and multiple organ dysfunction syndrome (MODS). The primary clinical manifestations include weakness in limb and respiratory muscles, particularly diminished strength in distal muscle groups. As a result, the weaning process from mechanical ventilation becomes more challenging, leading to prolonged ICU stays, increased mortality, and a higher risk of long-term functional disability. The significance of AW lies not only in its substantial impediment to short-term recovery but also in its role as a core component of Post-Intensive Care Syndrome (PICS), profoundly affecting patients' long-term outcomes.

Mechanical ventilation is a vital life-support technology for critically ill children in the Pediatric Intensive Care Unit (PICU). However, complications associated with mechanical ventilation have garnered increasing attention, particularly Acquired Weakness in mechanically ventilated children. With improving survival rates in the PICU, a growing number of pediatric critical illness survivors are at risk of developing AW. Despite rapid advancements in pediatric critical care medicine in China, there is currently a lack of an early warning system for AW in children receiving mechanical ventilation, resulting in significantly delayed clinical interventions. This project aims to identify novel biomarkers for pediatric ICU-AW and develop an early warning model. It holds promise for transitioning from the traditional post-symptomatic diagnostic approach to subclinical prediction of AW in children, which is of great clinical value for reducing disability rates and optimizing critical care rehabilitation strategies.

Full description

A prospective cohort study of mechanically ventilated children was established to systematically analyze epidemiological characteristics. The modified Pediatric Medical Research Council (MRC) muscle strength scale (pMRC) combined with simplified bedside neuroelectrophysiological testing (measurement of common peroneal nerve compound muscle action potential amplitude) was used to determine the occurrence rate, subtype distribution (CIP/CIM/Mixed), and natural disease course of intensive care unit-acquired weakness (ICU-AW) among mechanically ventilated children in China. An age-stratified model was applied to analyze differences in the occurrence rate of ICU-AW among children of different age groups. A Cox regression model was employed to quantify the dose-response relationship between dynamic parameters-such as duration of mechanical ventilation, cumulative doses of sedative and analgesic drugs, and glycemic variability-and the development of ICU-AW, and to construct a risk prediction nomogram.

Clinical parameters-including demographic characteristics, disease types, critical illness scores, treatment indicators such as mechanical ventilation parameters, laboratory indicators (e.g., inflammatory and biomarkers, metabolic genes), imaging data (muscle and diaphragmatic ultrasound, electrophysiology), molecular biomarkers, and muscle biopsy data-were integrated. Data mining and machine learning techniques were applied to develop an early warning model for ICU-AW based on Cox regression. A logistic regression preliminary screening model was constructed by integrating demographic characteristics and biomarkers. Quantitative parameters from muscle ultrasound (e.g., diaphragmatic excursion, muscle thickness) were incorporated, and dynamic risk assessment was optimized using the Random Forest algorithm. The sensitivity and specificity of the model were evaluated.

1. Study Design: This study employed a multicenter prospective cohort design.

Grouping: Pediatric patients undergoing mechanical ventilation were grouped based on the occurrence of ICU-AW at the study endpoint. The diagnostic criteria required meeting any one of the following: i) New-onset muscle dysfunction or weaning difficulty (unrelated to the primary disease, or in non-neuromuscular diseases with normal cardiopulmonary function); ii) Daily MRC score assessment for enrolled PICU patients [for patients under analgesic sedation, drug dosages were adjusted to maintain a modified Comfort-B sedation score between 11 and 23]. Patients with a total MRC score < 48 on two consecutive assessments 24 hours apart, and excluding those with other neuromuscular disorders, were assigned to the ICU-AW group; those with a total MRC score ≥ 48 were assigned to the non-ICU-AW group; patients who could not be evaluated were excluded. Subtype criteria: CIP: In addition to the above criteria, also meeting iii) Slowed sensory and/or motor nerve conduction velocity; CIM: In addition to criteria i and ii, also meeting iv) Reduced CMAP amplitude with prolonged duration, and normal SNAP amplitude; Mixed: Meeting both criteria iii and iv.
Sample Size Estimation (Data Management + Statistical Methods): The occurrence rate of pediatric ICU-AW was estimated at 2%. With a permissible error of 0.03 and a confidence level of 0.95, the sample size was calculated as 1500 cases using PASS software. Assuming a non-response rate of 10% among study subjects, the required sample size was N = 1500 / 0.9 = 1666 cases. Statistical analysis was performed using Stata 15.0 software. Continuous variables following a normal distribution in measurement data are presented as mean (SD); comparisons of means between groups were conducted using one-way and multi-way ANOVA. Those with a skewed distribution are presented as median (range or interquartile range, IQR). Count data (binary or multi-category) are expressed as incidence rates or composition ratios, with comparisons of incidence rates or composition ratios performed using the chi-square test. Logistic regression analysis was used to examine the relationship between biomarkers and the occurrence and development of ICU-AW, as well as the weight and contribution of each variable in the early warning model. ROC curve analysis was employed to assess model performance. A P-value < 0.01 was considered statistically significant for all analyses.

2. Case Collection and Data Analysis: An electronic database was established to collect clinical data from pediatric patients undergoing mechanical ventilation, including clinical baseline characteristics, laboratory test results, and imaging data. Each research center designated dedicated research personnel to begin enrolling study cases on the same start date (cases already hospitalized on the start date who met the inclusion criteria were enrolled). These personnel were responsible for data cleaning, organization, and standardization to ensure data quality. For all enrolled cases, demographic characteristics, clinical features, and laboratory test information were recorded in CRF forms on the day of enrollment (D0), day 3 (D3), day 10 (D10), the day of PICU discharge (Ddis), or the day of death (DD). Patient examinations for data collection were performed only when deemed appropriate by the physician. If an examination was not performed, the variable value was assumed to be normal or consistent with the previous measurement. Specific recorded information included:

Demographic characteristics: Age, sex, length/height, weight, body mass index.
Baseline clinical characteristics: Past medical history and underlying diseases, primary diagnosis upon PICU admission, critical illness score (PELOD-2), nutritional status (skinfold thickness/mid-upper arm circumference), CK, myoglobin, neurofilament light chain, a panel of twelve inflammatory factors, urinary titin, blood glucose, MRC muscle strength assessment, gastrocnemius muscle electrophysiology, rectus femoris muscle ultrasound, diaphragm thickness/excursion, treatments received (corticosteroids, e.g., dexamethasone, prednisone, methylprednisolone, hydrocortisone succinate; neuromuscular blocking agents, e.g., vecuronium), mechanical ventilation (duration in days, mode, fraction of inspired oxygen, mean airway pressure, tidal volume ml/kg, respiratory rate, P0.1), days on cardiopulmonary bypass (CPB), days on extracorporeal membrane oxygenation (ECMO), days of rehabilitation, whole-exome sequencing (WES) of family lineage, etc. Patient examinations for data collection were performed only when deemed appropriate by the physician. If an examination was not performed, the variable value was assumed to be normal or consistent with the previous measurement.
Determination of inflammatory markers: All inflammatory marker determinations were performed using EDTA-anticoagulated plasma samples. These included: Pro-inflammatory cytokines: IL-1β, IL-6, IL-8, TNF-α, IFN-γ, GM-CSF; Anti-inflammatory cytokines: IL-10, IL-13; Chemoattractant (CX3CL1), soluble intercellular adhesion molecule-1 (sICAM-1), soluble E-selectin (sE-selectin), and soluble P-selectin (sP-selectin). Each inflammatory marker was quantitatively determined using standardized immunoassay methods, such as ELISA (Enzyme-Linked Immunosorbent Assay).
Follow-up indicators: Nutritional status (skinfold thickness/mid-upper arm circumference), CK, myoglobin, blood glucose (D0, D3, D7, D10), MRC score, muscle ultrasound, diaphragm ultrasound, electromyography, muscle biopsy (if necessary) (D0, D3, D7, D10).
Outcome measures: The occurrence rate of ICU-AW in pediatric patients undergoing mechanical ventilation on day 3 or day 10. Defined as MRC score < 48, or slowed nerve conduction velocity on electromyography; CIP: Normal or mildly reduced nerve conduction velocity, reduced CMAP amplitude, reduced mixed SNAP amplitude; CIM: Normal or mildly reduced nerve conduction velocity, reduced CMAP amplitude, reduced muscle excitability to direct stimulation, increased CMAP duration, normal SNAP; or definitive diagnosis by muscle biopsy.

Enrollment

1,500 estimated patients

Sex

All

Ages

1 month to 18 years old

Volunteers

No Healthy Volunteers

Inclusion criteria

Age between 28 days and 18 years;
Patients continuously admitted to the PICU during the study period and subjected to invasive mechanical ventilation.

Exclusion criteria

Mechanical ventilation duration < 72 hours;
Presence of primary central or neuromuscular diseases that significantly affect central or peripheral respiratory failure, including: traumatic brain injury, cerebrovascular disease history (e.g., cerebral hemorrhage, cerebral infarction), intracranial tumors, central nervous system infections, spinal cord injury, Guillain-Barré syndrome, porphyria, paraneoplastic neuropathy, etc.;
Conditions requiring immobilization such as limb joint surgery or fractures.