Glucose Disposal Rate, Inflammation, Mortality, Dyslipidemia
- Okay, here's a breakdown of the details provided, focusing on the key details about the study's methodology and variables.I'll organize it for clarity:
- * Nomogram Use: The study utilized a nomogram (referenced as Chen et al., 2023 - Reference 18) to predict in-hospital mortality risk in elderly patients with acute...
- * Demographic: * Age * Sex (Male/Female) * Race (Non-Hispanic Black, mexican American, Non-Hispanic White, Other) * Educational Attainment (Less than high school,...
Okay, here’s a breakdown of the details provided, focusing on the key details about the study’s methodology and variables.I’ll organize it for clarity:
1. Risk Prediction & Cut-off Values:
* Nomogram Use: The study utilized a nomogram (referenced as Chen et al., 2023 – Reference 18) to predict in-hospital mortality risk in elderly patients with acute myocardial infarction.
* MSRSM: The Modified Recursive Summation Method (MSRSM) was employed to determine optimal cut-off values for two inflammatory/nutritional markers:
* eGDR (enhanced Glasgow-durham Risk Score):
* Low: < 7.84
* High: ≥ 7.84
* SIRI (systemic Immune-Inflammation Index):
* Low: ≤ 1.54
* High: > 1.54
* Reference for MSRSM: Zhang et al., 2023 (Reference 28) provides the basis for the MSRSM application in this context, specifically for predicting prognosis in multiple myeloma patients.
* Stratification: Participants were categorized into four groups based on the combinations of eGDR and SIRI levels, allowing for risk stratification.
2. Study Variables (Data Collected):
the study collected a wide range of data, categorized as follows:
* Demographic:
* Age
* Sex (Male/Female)
* Race (Non-Hispanic Black, mexican American, Non-Hispanic White, Other)
* Educational Attainment (Less than high school, High school/equivalent, More than high school)
* Family Income-to-Poverty Ratio (PIR) (<1, 1-3, >3)
* Lifestyle:
* Smoking Status (Never, Former, Current)
* Alcohol Consumption (Never, Former, Mild, Moderate, Heavy)
* Clinical/Laboratory Data:
* Height
* Weight
* Waist Circumference
* HbA1c (Glycated Hemoglobin)
* FBG (Fasting Blood Glucose)
* TG (Triglycerides)
* TC (total Cholesterol)
* LDL-C (Low-density Lipoprotein Cholesterol)
* HDL-C (High-Density Lipoprotein Cholesterol)
* White Blood Cell Count
* Neutrophil Count
* Monocyte Count
* Lymphocyte Count
* Medical Conditions (Defined by specific criteria):
* Hypertension: Self-reported diagnosis, medication use, or SBP ≥ 140 mmHg and/or DBP ≥ 90 mmHg.
* Diabetes Mellitus (DM): Fasting plasma glucose ≥ 7.0 mmol/L, 2-hour OGTT ≥ 11.1 mmol/L, random blood glucose ≥ 11.1 mmol/L,HbA1c ≥ 6.5%, medication use, or self-reported diagnosis.
3. Data Collection Standards:
* All routine biochemical tests were conducted following the guidelines in the NHANES Laboratory/Medical Technologist Manual of Procedures.
4. references:
* Reference 18: Chen Y, Xie K, Han Y, Xu Q, Zhao X. An easy-to-use nomogram based on SII and SIRI to predict in-hospital mortality risk in elderly patients with acute myocardial infarction. J Inflamm Res. 2023;16:4061-71.
* Reference 28: Zhang L, Chen S, Wang W, Wang Y, Liang Y. Inflammatory and nutritional scoring system for predicting prognosis in patients with newly diagnosed multiple myeloma. J Inflamm Res.2023;16:7-17.
* Reference 29: Wan Z, Guo J, Pan A, chen C, Liu L, liu