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The study aims to develop and validate a prognostic prediction model for adverse outcomes in neurocritical patients receiving enteral nutrition based on key inflammatory and metabolic markers. This model will serve as a clinical tool to help physicians identify high-risk patients and guide individualized nutritional support strategies.
A multi-center, prospective case data collection study will be conducted across 19 tertiary hospitals in China. Based on this, a predictive assessment model for poor prognosis in neurocritically ill patients receiving enteral nutrition support will be developed and validated, using key inflammatory and metabolic markers. During the data collection process, comprehensive clinical information will be extracted, including patient demographic data, clinical indicators, and hematological markers. By conducting in-depth analysis and processing of this vast and detailed clinical and laboratory data, a nomogram for predicting poor prognosis in neurocritical care patients receiving enteral nutrition support will be constructed using statistical methods and data analysis techniques in R. Once the model is built, it will undergo rigorous validation on an independent external dataset to ensure its accuracy and reliability. The goal is to create a precise assessment tool for clinicians, helping them to quickly and accurately identify high-nutritional-risk patients, thereby providing a solid scientific foundation for the formulation of individualized nutrition support strategies, ultimately improving the prognosis of neurocritical patients.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| mild Inflammation & Metabolic dysfunction | First, a predictive model for poor prognosis is constructed through screening of independent variables after data collection. Then, stratified analysis is conducted with inflammatory markers such as C-reactive protein and interleukin-6, and metabolic markers such as blood glucose and insulin dosage. | ||
| Moderate Inflammation & Metabolic Dysregulation | First, a predictive model for poor prognosis is constructed through screening of independent variables after data collection. Then, stratified analysis is conducted with inflammatory markers such as C-reactive protein and interleukin-6, and metabolic markers such as blood glucose and insulin dosage. | ||
| High Inflammation & Severe Metabolic Dysregulation | First, a predictive model for poor prognosis is constructed through screening of independent variables after data collection. Then, stratified analysis is conducted with inflammatory markers such as C-reactive protein and interleukin-6, and metabolic markers such as blood glucose and insulin dosage. | ||
| Peptide-Based Nutrition | Select patients who received peptide-based formulas from the entire database for poor prognosis analysis. | ||
| Whole Protein Nutrition | Select patients who received whole protein formulas from the database for poor prognosis analysis. |
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| Measure | Description | Time Frame |
|---|---|---|
| All-cause mortality at Day 28 of enteral nutrition therapy | From enrollment to 28 days after the initiation of enteral nutrition |
| Measure | Description | Time Frame |
|---|---|---|
| Nutritional goal achievement rate at Day 3 (caloric and protein intake reaching 70%-100% of calculated target) | At Day 3 after the initiation of enteral nutrition | |
| Adverse outcome rate at Day 90 (defined as a Modified Rankin Scale score ≥ 3) | At Day 90 after the initiation of enteral nutrition |
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Inclusion Criteria:
Exclusion Criteria:
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The study population consists of critically ill neurocritical patients in the acute phase who are receiving enteral nutrition (EN) treatment in the Neurocritical Care Unit (NICU).
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Yan Zhang, MD., Ph D. | Contact | 8613671376710 | zhangylq@sina.com | |
| Fei Tian, MD., Ph D. | Contact | 8618201303685 | smile21tian@163.com |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Beijing Hui People's Hospital | Beijing | Beijing Municipality | 100053 | China |
De-identified individual participant data (IPD) will be shared. Available data includes demographic information, clinical characteristics, laboratory results, and outcome measures.
Data will be available upon reasonable request from 6 months after study completion to 3 years post-publication.
Access will be granted to qualified researchers upon reasonable request through a formal data-sharing agreement.
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Blood and gastric juice
| Incidence of infectious complications within 14 days (including pneumonia, urinary tract infections, bloodstream infections, skin infections, and Clostridium difficile infections) | Within 14 days after the initiation of enteral nutrition |
| Gastrointestinal intolerance within 14 days (gastric residual volume > 200 mL, nausea, vomiting, bloating, diarrhea) | The gastric residual volume is assessed every 4 hours by the nurse through aspiration via the nasogastric tube. Other clinical manifestations such as nausea, vomiting, bloating, and diarrhea are assessed through daily observation by the attending physician. | Within 14 days after the initiation of enteral nutrition |
| Incidence of gastrointestinal bleeding within 14 days (gastric occult blood, fecal occult blood, hematemesis, melena, hematochezia) | Within 14 days after the initiation of enteral nutrition |
| Number of days with random blood glucose > 10 mmol/L within 14 days | Within 14 days after the initiation of enteral nutrition |
| Average daily insulin requirement within 14 days | Within 14 days after the initiation of enteral nutrition |
| Incidence of hypophosphatemia within 3 days | Within 3 days after the initiation of enteral nutrition |
| Duration of mechanical ventilation within 14 days | Within 14 days after the initiation of enteral nutrition |
| Length of NICU stay | From enrollment to discharge from the NICU, up to 1 year. |
| Total hospital stay duration | From enrollment to discharge, up to 1 year. |
| 90-day readmission rate post-discharge | Within 90 days after discharge |
| Xuanwu Hospital, Capital Medical University | Beijing | Beijing Municipality | 100053 | China |
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| You'anmen Hospital | Beijing | Beijing Municipality | 100069 | China |
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| The Ninth Medical Center of Chinese PLA General Hospital | Beijing | Beijing Municipality | 100700 | China |
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| The 940th Hospital of Joint Logistics Support Force of Chinese PLA | Lanzhou | Gansu | 730050 | China |
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| Guizhou Provincial People's Hospital | Guiyang | Guizhou | 550002 | China |
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| Affiliated Hospital of Zunyi Medical University | Zunyi | Guizhou | 563003 | China |
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| Tangshan People's Hospital | Tangshan | Hebei | 063000 | China |
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| The First Hospital of Hebei Medical University | Shijiazhuang | Heibei | 050031 | China |
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| Inner Mongolia Autonomous Region People's Hospital | Hohhot | Inner Mongolia | 010017 | China |
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| The Second Affiliated Hospital of Suzhou University | Suzhou | Jiangsu | 215004 | China |
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| The First Hospital of Jilin University | Jilin City | Jilin | 130021 | China |
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| Chifeng Municipal Hospital | Chifeng | Neimenggu | 024000 | China |
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| General Hospital of Ningxia Medical University | Yinchuan | Ningxia | 750004 | China |
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| Qilu Hospital of Shandong University | Jinan | Shandong | 250012 | China |
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| The 960th Hospital of Joint Logistics Support Force of Chinese PLA | Jinan | Shandong | 250031 | China |
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| Liaocheng People's Hospital | Liaocheng | Shandong | 252000 | China |
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| The Second Hospital of Shanxi Medical University | Taiyuan | Shanxi | 030001 | China |
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| The First People's Hospital of Yunnan Province | Kunming | Yunnan | 650032 | China |
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| ID | Term |
|---|---|
| D020521 | Stroke |
| D004660 | Encephalitis |
| ID | Term |
|---|---|
| D002561 | Cerebrovascular Disorders |
| D001927 | Brain Diseases |
| D002493 | Central Nervous System Diseases |
| D009422 | Nervous System Diseases |
| D014652 | Vascular Diseases |
| D002318 | Cardiovascular Diseases |
| D000090862 | Neuroinflammatory Diseases |
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