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ด้านล่างเป็นบทคัดย่อต้นฉบับภาษาอังกฤษจากข้อมูลบรรณานุกรม โปรดตรวจบทความต้นฉบับก่อนอ้างอิง
Acute kidney injury (AKI) alters metabolic balance, making accurate assessment of energy requirements important for nutritional support in critically ill patients. Although indirect calorimetry (IC) is the gold standard for measuring energy expenditure, its clinical use is limited by availability. This study aimed to identify factors associated with energy expenditure, develop and internally validate an exploratory prediction equation and evaluate its agreement and prediction error, and categorize patients into hypometabolic, normometabolic, and hypermetabolic groups. A cross-sectional study was conducted prospectively alongside the ENGINE randomized controlled trial at two medical intensive care units (MICU) between March and November 2023. Eighty critically ill patients with AKI who were not receiving renal replacement therapy (RRT) were enrolled. Indirect calorimeter was used to measure energy expenditure. The model was internally validated using repeated 10-fold cross-validation. Cross-validated R², root mean square error (RMSE), mean absolute error, and mean absolute percentage error, calibration intercept and slope, and the proportions of predictions within ± 10% and ± 15% of measured IC-derived energy expenditure (ICEE) were evaluated. Eight variables were associated with ICEE in univariable analyses: age, body weight (BW), body mass index (BMI), current creatinine (Cr), AKI stage, serum lactate, PaO₂, and PaCO₂. After reducing overlapping predictors, six were considered for multivariable model development. The developed predictive equation, “TEE-cAKI,” was: predicted ICEE (kcal) = 1275.385 + 9.517(BW) + 71.257(Cr) − 6.694(age). Metabolic categorization showed 45% of patients were hypermetabolic, 36.25% normometabolic, and 18.75% hypometabolic. Bland–Altman analyses comparing measured ICEE with TEE-cAKI, weight-based methods (20, 25, and 30 kcal/kg/day), the Harris–Benedict equation, and Harris–Benedict multiplied by a stress factor of 1.3 demonstrated that the TEE-cAKI equation showed the smallest mean bias. However, all predictive methods exhibited wide limits of agreement with measured ICEE, indicating substantial variability at the individual-patient level. Considerable heterogeneity in energy expenditure was observed among critically ill patients with AKI not receiving RRT. The TEE-cAKI equation showed the smallest mean bias and lowest RMSE among the evaluated methods, but its explanatory and individual-level predictive performance were modest. IC remains the preferred method whenever available. The clinical trial number is not applicable.
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ประเด็นที่เกี่ยวข้อง: Clinical Nutrition and Gastroenterology · Acute Kidney Injury Research · Hemodynamic Monitoring and Therapy
บทบาทของนักวิจัยและสถาบันไทย
Wankawee Jeerangsapasuk · Wittawat Hongmeng · Pichaya Tantiyavarong · Autchara Khantason · Aphichat Chatkrailert · Thammasat University · Thammasat University Hospital
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หน้านี้เป็นระเบียนบรรณานุกรมและข้อมูลจากบทคัดย่อ ยังไม่ใช่บทวิเคราะห์ฉบับเต็มหรือการประเมินคุณภาพงานวิจัย ควรตรวจสอบ DOI และเอกสารต้นฉบับก่อนนำไปอ้างอิง