Development and Validation of a Prediction Algorithms to Estimate the Clinical Effect of Early Rehabilitation on ICU Survivors Received Mechanical Ventilation in the ICU
Conditions: Intensive Care, Mechanical Ventilation, Rehabilitation, Algorithms
Sponsor: Wuhan University

Study Protocol and Statistical Analysis Plan 
 
 
Development and validation of a prediction algorithms to 
estimate the clinical effect of early rehabilitation on ICU 
survivors received mechanical ventilation in the ICU 
 
 
Lead Unit: Zhongnan Hospital of Wuhan University 
Project Leader: Qing Shu 
Responsible Department: Department of Rehabilitation 
Contact Number: +86-13971081682 
Participating Units: Wuhan No.1 Hospital; General Hospital of the 
Yangtze River Shipping/Wuhan Brain Hospital; Wuhan No.6 Hospital 
Research Duration: July 2024 - December 2025 
Version Number: V2.0.0621 
Version Date: June 21, 2024 
 
 
 
 

1. Background 
Approximately 13 to 20 million people worldwide receive treatment 
in intensive care units (ICU) each year. Mechanical ventilation is one of 
the critical treatment measures to maintain vital signs in ICU patients, with 
50-70% of ICU patients in China undergoing mechanical ventilation. 
Critically ill patients receiving mechanical ventilation are often subjected 
to deep sedation and bed rest, especially during the early stages of their 
ICU stay. Extensive research has demonstrated that prolonged bed rest can 
lead to a series of complications such as ICU-acquired weakness, increased 
infection risk, and decreased cardiopulmonary function, which in turn 
increases the difficulty of patient recovery, extends hospital stays, and 
elevates the risk of in-hospital mortality. To prevent various complications 
during ICU hospitalization and improve patient outcomes, early 
rehabilitation intervention is crucial. In recent years, an increasing amount 
of evidence-based medical research has proven that early rehabilitation 
intervention for mechanically ventilated patients (including early passive 
and active activities, postural management, and pulmonary rehabilitation) 
is safe and feasible, and can to some extent promote functional recovery 
and reduce ICU length of stay. 
In recent years, clinical prediction models for the prognosis of ICU 
survivors have become increasingly prevalent, with related outcome 
indicators including the incidence of sepsis, survival rates of elderly 

patients, mortality risk from acute gastrointestinal bleeding, survival rates 
of patients with acute respiratory distress syndrome (ARDS), and ICU 
length of stay. A comprehensive review of existing literature reveals that 
due to the unique nature of ICU hospitalized patients, clinicians tend to 
focus more on indicators such as vital signs, mortality rates, and the 
incidence of severe complications in ICU patients, rather than the 
functional status of ICU survivors. As medical technology has advanced in 
recent years, ICU mortality rates have steadily declined, bringing increased 
attention to the quality of life of ICU survivors. The key issue that early 
rehabilitation intervention must address is how to facilitate the 
reintegration of ICU survivors into society and preserve their daily living 
abilities as much as possible. Currently, no prediction models specifically 
address the post-discharge physical function of ICU survivors. Upon ICU 
admission, patients undergo comprehensive clinical examinations or 
assessments, including evaluations of consciousness, liver and kidney 
function, cardiopulmonary function, gastrointestinal function, nutritional 
status, and imaging studies of critical organs. Before receiving 
rehabilitation interventions, they must also undergo various types of 
rehabilitation assessments. These clinical data significantly impact patient 
prognosis. 
Although early rehabilitation intervention in the ICU has begun to be 
implemented in some hospitals in China in recent years, it is still in its early 

stages of development. There are significant differences in the severity of 
ICU patients among different hospitals, and the level of professional 
knowledge in critical care medicine among rehabilitation physicians and 
therapists varies widely. Additionally, ICU inpatients within the same 
hospital come from various departments, with primary diseases exhibiting 
significant heterogeneity, and their physical conditions when starting early 
rehabilitation intervention are not entirely consistent. While there are 
currently guidelines or expert consensus recommending certain indicators 
for initiating early rehabilitation intervention and suggesting early 
intervention methods, the early intervention programs that patients with 
different physical conditions can undergo also vary. These factors result in 
considerable differences in indicators such as consciousness, muscle 
strength, respiratory function, and activity of daily living (ADL) when 
patients who have received early rehabilitation intervention are transferred 
out of the ICU once their condition stabilizes. Some patients may not 
experience significant benefits. Therefore, identifying which patients 
might have a higher potential for benefiting from early rehabilitation 
intervention is a crucial issue that needs to be addressed in early ICU 
rehabilitation. 
Clinical prediction models estimate the probability of having a certain 
disease or the likelihood of a specific outcome occurring in the future by 
using multifactorial models. Prognostic models focus on estimating the 

probability of disease recurrence, mortality, disability, and complications 
over a future period based on the current disease state. This study aims to 
collect relevant clinical data (including basic patient information, 
physiological indicators, medical history, etc.) and rehabilitation 
assessment results (including muscle strength, consciousness status, ADL, 
etc.) from ICU inpatients undergoing early rehabilitation intervention. By 
screening and transforming these data, we will use regression equations to 
construct a prognostic model that can predict the outcomes of ICU patients 
receiving early rehabilitation intervention. This will help determine 
whether a patient can benefit from early rehabilitation intervention. The 
clinical application of this model will enable a more precise determination 
of whether ICU inpatients need early rehabilitation intervention, thereby 
reducing the incidence of complications among mechanically ventilated 
ICU patients and improving their quality of life. 
 
2. Objectives 
2.1 Primary Objective: To establish a clinical prediction model that can 
predict the timeframe in which mechanically ventilated ICU survivors will 
begin to benefit from early rehabilitation intervention, thus providing 
guidance for the early initiation of rehabilitation in critical care settings. 
2.2 Secondary Objectives: Understanding the Role of Individual 
Differences in Rehabilitation Benefits. Analyze the impact of individual 

differences on the timing of rehabilitation benefits, including factors such 
as age, gender, and baseline health status. Identify potential disparities in 
rehabilitation benefits among different patient populations to provide 
reference for developing personalized rehabilitation strategies. This will 
help make early rehabilitation interventions more individualized and 
precise, maximizing patient recovery benefits. 
 
3. Study Design Type, Principles, and Experimental Steps 
3.1 Study Design 
This study is a multicenter, prospective cohort study, planning to 
recruit 250 ICU patients undergoing mechanical ventilation. Three 
additional hospitals in Wuhan (Wuhan No.1 Hospital, Wuhan Brain 
Hospital, and Wuhan No.6 Hospital) will serve as sub-centers, enrolling 
ICU patients who receive early rehabilitation intervention for the 
validation cohort of the model. 
Sample Size Calculation: This study plans to include approximately 
10 predictive factors to develop the model. According to the one-tenth rule 
of EPV (Events per Variable), and assuming that 50% of ICU patients will 
benefit from early rehabilitation intervention during hospitalization, the 
sample size for the model development cohort is calculated to be at least 
(10×10)/0.5 = 200 patients. The study aims to collect data on all patients 
undergoing early rehabilitation intervention within one year from the 

enrollment of the first patient. Based on the current number of patients 
receiving early rehabilitation intervention in our ICU, it is estimated that 
subject recruitment can be completed within one year. The external 
validation dataset will consist of a quarter of the model development cohort, 
amounting to 50 cases. A multicenter prospective cohort study will also be 
conducted simultaneously, incorporating relevant data from ICU survivors 
in other hospitals for external validation. 
Indications: Based on the detailed assessment and classification of 
different patient states outlined in the "Chinese Expert Consensus on 
Neurological Critical Care Rehabilitation," assessment indicators include 
consciousness level, degree of cooperation, sedation status, and other 
aspects to ensure the personalization and specificity of rehabilitation 
interventions. The determination of indications is guided by the " Expert 
consensus and recommendations on safety criteria for active mobilization 
of mechanically ventilated critically ill adults", as well as specific 
assessment tools and standards used in this study design. 
3.2 Research procedure 
(1) Identify Clinical Issues and Outcome Indicators. 
(2) Collection of Predictive Factors: Gather clinical data from ICU 
patients receiving early rehabilitation intervention, including demographic 
data, disease diagnosis, and intervention data. 
(3) Data Processing: Check for missing values, outliers, or anomalies 

in the data, and conduct necessary data processing. 
(4) Variable Selection: Perform predictive factor selection using 
methods such as collinearity analysis, LASSO regression, and expert 
opinion. 
(5) 
Variable 
Transformation: 
Choose 
appropriate 
variable 
transformation methods based on the distribution characteristics of the 
predictive factors. 
(6) Model developing: This study plans to use two main types of 
modeling approaches. The first approach is the Cox proportional hazards 
regression model. The second approach involves machine learning 
algorithms. 
(7) Model Diagnosis and Evaluation: Diagnose the efficiency of the 
model by calculating linear relationships between models, influential 
points, multicollinearity, and the proportional hazards assumption. 
(8) Internal Validation of the Model: Perform internal validation using 
the bootstrap method. 
(9) External Validation of the Model: Utilize data from external sub-
centers for external validation. 
(10) Assessing Clinical Value of the Model: Apply Decision Curve 
Analysis (DCA) to evaluate the clinical value of the model. 
(11) Model Presentation: Present the predictive model using 
nomograms and a web-based application. 

4. Participants recruitment 
4.1 Inclusion Criteria: 
(1) Age greater than 18 years; 
(2) Received mechanical ventilation during ICU hospitalization, including 
both endotracheal intubation and tracheostomy; 
(3) Met the indications for rehabilitation intervention according to the 
“Chinese Expert Consensus on Neurological Critical Care Rehabilitation” 
during ICU hospitalization and underwent corresponding early 
rehabilitation interventions, including but not limited to awakening from 
altered 
consciousness, 
early 
active/passive 
mobilization, 
and 
comprehensive pulmonary rehabilitation; 
(4) No mortality events occurred during ICU hospitalization; 
(5) Informed consent signed by family members or the patient. 
4.2 Exclusion Criteria: 
(1) Minors under the age of 18; 
(2) Inpatients who did not receive mechanical ventilation during ICU 
hospitalization; 
(3) Patients who did not undergo early rehabilitation intervention in the 
ICU; 
(4) Occurrence of mortality events during ICU hospitalization; 
(5) Patients transferred out of the ICU due to family decision to withdraw 
treatment; 

(6) Family members who refuse to sign the informed consent or patients 
who refuse to sign the informed consent while having autonomous 
consciousness. 
 
5. Methods and flow chart 
5.1 Name and Specifications of Study Medication 
None 
5.2 Early rehabilitation intervention procedure 
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): 
(1) Stage 1:  
Patients with altered consciousness or sedation status who are unable 
to cooperate or only minimally cooperate with rehabilitation treatment, 
with assessment results indicating the following conditions: altered 
consciousness (GCS score < 8); S5Q < 3; manual muscle strength testing 
scoring 0-1; mechanical ventilation (P-SIMV mode); CRRT (CVVHDF 
mode). The rehabilitation protocol includes: early passive mobilization of 
limbs, neuromuscular electrical stimulation, chest wall mobilization, 
postural drainage for secretions, external diaphragm pacing therapy, and 

comprehensive awakening treatments (sensory input, ice stimulation, 
median nerve electrical stimulation, acupuncture, etc.). 
(2) Stage 2:  
Patients with partial consciousness who can somewhat cooperate with 
rehabilitation treatment but are unable to get out of bed, with assessment 
results indicating the following conditions: GCS (8-12); S5Q = 3; MMT 
muscle strength 2-3; mechanical ventilation (P-CPAP mode with no 
assisted ventilation) or extubated; CRRT on CVVHDF/CVVH mode or 
removed; able to complete swallowing assessment. The rehabilitation 
protocol includes: active/passive mobilization of limbs, sensory 
stimulation, neuromuscular electrical stimulation, respiratory muscle 
training, airway clearance techniques (ACBT), external diaphragm pacing 
therapy, and swallowing function training. 
(3) Stage 3:  
Patients with full consciousness who can largely cooperate with 
rehabilitation training and begin sitting, standing, and other out-of-bed 
training, approaching transfer out of the ICU, with assessment results 
indicating the following conditions: GCS (13-15); S5Q = 5; RASS (0); 
muscle strength MMT (most muscle strength ≥ 3); intermittent extubation 
or extubated; CRRT has been removed, no pressure on catheter sites, and 
no positional restrictions; able to complete activities of daily living (ADL) 
assessment; able to complete swallowing function assessment (Wakatani 

water test, dye test). The rehabilitation protocol includes: active muscle 
strength training (dumbbells, resistance bands, sandbags), endurance 
training, out-of-bed training, standing training, assisted walking training, 
neuromuscular electrical stimulation, respiratory muscle training, airway 
clearance techniques (ACBT), external diaphragm pacing therapy, 
swallowing function training, and occupational therapy (training for 
activities of daily living). 
5.3 Concomitant Medications: 
None. 
 
6. Parameters and Time Points 
The primary observation items in this study involve collecting 
predictive factors that serve as variables for the predictive model, which 
are categorized into the following three major groups. All predictive 
factors will be collected prior to the initiation of early rehabilitation 
intervention. 
5.1 Demographic Data: This includes gender, age, weight, height, 
and FIM score. All data are collected prior to the initiation of early 
rehabilitation intervention. 
5.2 Clinical Diagnosis Data: This includes current diagnoses 
determined based on the patient's recent imaging or blood tests, as well as 
medical history, detailed as follows: 

(1) Acute Myocardial Infarction: Acute myocardial infarction 
occurring after admission, clearly diagnosed by ICU physicians. 
(2) Acute Arrhythmia: Acute arrhythmias occurring after admission, 
including rapid atrial fibrillation, atrial flutter, paroxysmal tachycardia, 
premature contractions, and pathological conduction block, with 
appropriate clinical interventions. 
(3) Acute Heart Failure: Acute heart failure due to various causes 
after admission, with serum BNP or NT-BNP levels above normal, and 
receiving appropriate clinical interventions. 
(4) Acute Cerebral Infarction: Cerebral infarction occurring within 
one week prior to admission until ICU discharge, clearly diagnosed by CT, 
MRI, or angiography. 
(5) Acute Intracerebral Hemorrhage: Non-traumatic intracerebral 
hemorrhage due to various causes occurring within three weeks prior to 
admission until ICU discharge, clearly diagnosed by CT or MRI. 
(6) Traumatic Brain Injury: Primary reason for admission being 
trauma-related skull fractures, intracerebral hemorrhage, brain contusions, 
or subarachnoid hemorrhage, confirmed by imaging studies. 
(7) History of Stroke: History of stroke within the past five years, 
resulting in unilateral limb muscle strength decline or hemiplegia. 
(8) Postoperative Brain Tumor: Diagnosed with a brain tumor prior 
to ICU admission and having undergone surgical treatment, with tumor 

types including but not limited to malignant brain tumors, hemangiomas, 
etc. 
(9) Level of Consciousness: Assessed using the Glasgow Coma 
Scale (GCS) to evaluate the level of consciousness, treated as a continuous 
variable. 
(10) Respiratory Failure: Respiratory failure due to various causes 
requiring mechanical ventilation. 
(11) Pulmonary Infection: Clinically diagnosed pulmonary infection 
with unilateral pulmonary consolidation greater than 1/2. 
(12) Pleural Effusion: Moderate or greater (500 ml-1000 ml) pleural 
effusion confirmed by imaging before starting rehabilitation treatment. 
(13) Acute Gastrointestinal Bleeding: Diagnosed gastrointestinal 
bleeding occurring during this hospitalization, accompanied by 
hemoglobin levels dropping to moderate anemia (below 90 g/L). 
(14) Acute Kidney Injury: Sudden decline in renal function within 
48 hours, with serum creatinine increasing by ≥0.3 mg/dl, or within 7 days, 
serum creatinine increasing to 1.5 times the baseline value, confirmed by 
clinicians as acute kidney injury. 
(15) Chronic Kidney Disease: History of chronic kidney disease with 
serum creatinine exceeding 1.5 times the upper limit during this admission. 
(16) Malignant Tumor: Various diagnosed malignant tumors, 
including those of the digestive tract, urinary system, endocrine glands, 

hematologic system, spine, and skeletal system, excluding malignant 
tumors of the cranial region. 
(17) Sepsis: Diagnosed with sepsis by ICU physicians through 
serological indicators, bacterial cultures, imaging studies, etc. 
(18) Multidrug-Resistant Infection: Development of pulmonary, 
gastrointestinal, urinary infections, or sepsis during hospitalization, 
confirmed as multidrug-resistant bacteria through sputum, urine, or blood 
cultures. 
(19) Spinal Cord Injury: Severe functional impairment of the lower 
limbs due to trauma, spinal tumors, or spinal ischemia. 
(20) Multiple Trauma: Various causes leading to multiple traumas, 
excluding traumatic brain injury, and requiring surgical treatment, 
including but not limited to pulmonary contusion, hemothorax, abdominal 
injuries, renal contusion, and multiple fractures. 
(21) Fractures Without Surgery: Patients with fractures due to trauma 
who have not yet undergone surgical treatment due to the presence of 
other severe conditions. 
5.3 Disease Intervention Data: This includes important life support 
and rehabilitation interventions received by ICU patients undergoing 
mechanical ventilation, specifically as follows: 
(1) History of ECMO Application: Whether the patient received 
ECMO (Extracorporeal Membrane Oxygenation) for life support before 

starting rehabilitation interventions. 
(2) Continuous Renal Replacement Therapy (CRRT): Whether the 
patient received CRRT before starting rehabilitation interventions, or if 
they were undergoing CRRT near the time of intervention. 
(3) Duration of CRRT: The total number of days the patient received 
CRRT during their entire ICU stay, as a continuous variable. 
(4) Mode of Mechanical Ventilation: Whether the patient received 
mechanical ventilation through endotracheal intubation or tracheostomy, 
as a categorical variable. 
(5) Duration of Mechanical Ventilation: The number of days the 
patient received mechanical ventilation in the ICU before starting 
rehabilitation interventions, as a continuous variable. 
(6) Mode of Ventilatory Support: Whether the patient received 
ventilatory support in the form of SIMV (Synchronized Intermittent 
Mandatory Ventilation), including P-SIMV (Pressure-SIMV) and V-
SIMV (Volume-SIMV), or CPAP (Continuous Positive Airway Pressure), 
as a categorical variable. 
(7) Analgesic Treatment: Whether the patient received analgesic 
treatment before and during rehabilitation interventions, including but not 
limited to NSAIDs (Non-Steroidal Anti-Inflammatory Drugs), opioids, 
and opioid-like analgesics. 
(8) Nutritional Support: The type of nutritional support the patient 

received before starting rehabilitation interventions, either enteral 
nutrition or parenteral nutrition, as a categorical variable. 
(9) Comprehensive Pulmonary Rehabilitation: Whether the patient's 
rehabilitation plan in the ICU included comprehensive pulmonary 
rehabilitation, including airway clearance, active breathing cycle 
techniques, and diaphragm pacing. 
(10) Early Active/Passive Limb Activity: Whether the patient's 
rehabilitation plan included early active/passive limb activity and 
neuromuscular electrical stimulation. 
(11) Arousal Therapy for Patients with Consciousness Disorders: 
Whether patients with consciousness disorders received arousal therapy, 
including sensory stimulation and median nerve electrical stimulation. 
(12) Swallowing Assessment and Therapy: Whether ICU patients 
underwent swallowing assessments (e.g., water swallow test, VVST) and 
swallowing therapy (including the Mendelsohn maneuver and swallowing 
electrical stimulation). 
 
7. Outcome Measurements: 
The purpose of this study is to investigate how soon ICU survivors 
can benefit from early rehabilitation interventions. By observing the 
changes in ADL (Activities of Daily Living) scores of ICU survivors 
during their ICU stay, we can determine whether they have benefited. 

Thus, the outcome measure is a time-to-event outcome, specifically 
whether the patient has achieved improvement in ADL function at 
different time points after receiving early rehabilitation interventions in 
the ICU. All enrolled participants will undergo FIM (Functional 
Independence Measure) scoring before the early rehabilitation 
intervention and every other day after the intervention. An increase of ≥5 
points in the FIM score compared to the pre-intervention score will be 
considered as the patient benefiting from the early rehabilitation 
intervention. 
 
8. Statistical Analysis 
8.1 Data Preprocessing 
(1) Processing of Missing Data: This project is a prospective cohort 
study, and theoretically, the predictor data for each patient can be 
collected. However, small amounts of data loss due to various reasons 
cannot be excluded. First, the “ggplot” package in R will be used to 
visualize and determine if the missing data is random or related. Then, the 
MICE package will be utilized for multiple imputations, with the number 
of imputations equal to the percentage of missing data (if n% of the data 
is missing, n imputations will be performed). 
(2) Transformation of Predictors (Variables): Based on the 
distribution characteristics of the predictor variables, appropriate 

transformation methods will be selected. For continuous variables, 
transformations 
such 
as 
logarithmic, 
square, 
or 
square 
root 
transformations will be applied to approximate a normal distribution as 
closely as possible. For binary or categorical variables, the factor() 
function in R will be used for conversion. If machine learning algorithms 
such as SVM or GBM are used for model development, binary or 
categorical variables do not need to be transformed. 
8.2 Model Development Methods 
This study plans to use two main model development methods. The 
first method is the COX proportional hazards regression model. The 
second method involves machine learning algorithms, such as Support 
Vector Machines (SVM) and Gradient Boosting Machine (GBM) 
algorithms. 
8.3 Model Diagnostics and Evaluation 
The efficiency of the models will be diagnosed by calculating the 
linear relationships between models, identifying influential points, 
detecting multicollinearity, and testing the proportional hazards 
assumption. For the SVM model, evaluation metrics will include accuracy, 
precision, recall, and F1 score. 
8.4 Internal Validation of the Model-Bootstrap Validation 
Given that this study is a prospective cohort with a relatively small 
development sample size, the bootstrap method will be used for internal 

validation. The `validate()` function in the RMS package will be applied 
to calculate model performance metrics such as discrimination and 
calibration. The C-statistic and calibration will be evaluated by calculating 
the difference (optimism) between the model's performance in the training 
set and its bootstrap samples. 
8.5 External Validation of the Model 
Once the external validation data is collected, the same variable 
transformation methods used in the development cohort will be applied to 
the corresponding variables. Linear predictor values and predicted 
probabilities will be calculated. The model's performance in the external 
validation cohort will be comprehensively evaluated using metrics such 
as the C-statistic, Brier score, calibration intercept, and calibration slope. 
8.6 Evaluation of the Model's Clinical Value 
The clinical value of the model will be assessed using Decision 
Curve Analysis (DCA). This will be performed using the `mda` and 
`ggDCA` packages in R. 
8.7 Model Presentation 
(1) Nomogram: The RMS package will be used to create a nomogram 
for the fitted COX model, providing a visual representation of the model. 
(2) Web Application: The DynNom package will be used to develop 
a web-based application for the fitted COX model, facilitating easier 
access and interaction with the model. 

 
9. Research Schedule 
July 2024-December 2024: After completing the ethics approval, 
the clinical trial registration will be finalized. Enroll 100 subjects into the 
model development cohort while continuously optimizing the machine 
learning algorithms using the collected data. The writing of the Chinese 
literature review will also be completed. 
January 2025-February 2025: Based on the data from the model 
development cohort, attempts will be made to construct predictive models 
using different modeling approaches and to complete the internal 
validation of the models. The writing of related papers on clinical 
prediction models will also commence. 
January 2025-June 2025: Complete the inclusion of 50 subjects in 
the external validation cohort. Once inclusion is complete, proceed with 
the external validation of the models. 
July 2025-December 2025: Organize clinical data, write, and 
publish relevant papers. Plan to establish a database for critical care 
rehabilitation based on the clinical data framework developed in this study, 
continue collecting relevant clinical data, and conduct retrospective 
studies to further train the model.