A Proposed Predictive Equation for Energy Expenditure Estimation Among Noncritically Ill Patients With Acute Kidney Injury.

OBJECTIVE: The incidence of acute kidney injury (AKI) is identified more frequently in noncritical compared with intensive care settings. The prognosis of malnourished AKI patients is far worse than those with normal nutritional status. However, a method for estimating the optimal amount of energy required to guide nutritional support among noncritically ill AKI patients is yet to be determined. METHODS: We evaluated the performance of weight-based formulas (20-30 kcal/kg/day) with the reference values of energy expenditure (EE) measured by indirect calorimetry (IC) among noncritically ill AKI patients during hospitalization. The statistics for assessing agreement, including total deviation index and accuracy within 10% represent the percentage of estimations falling within the IC value range of ±10%, were tested. Parameters for predicting the EE equation were also developed using a regression analysis model. RESULTS: A total of 40 noncritically ill AKI patients were recruited. The mean age of participants was 62.5 ± 16.5 years with 50% being male. The average IC-derived EE was 1,124.6 ± 278.9 kcal/day with respiratory quotients 0.8-1.3, indicating good validity of the IC test. Receiving dialysis, protein catabolic rate, and age was not significantly associated with measured EE. Nearly all weight-based formulas overestimated measured EE. The magnitude of total deviation index values was broad with the proportion of patients achieving an accuracy of 10% being as low as 20%. The proposed equation to predict EE derived from this study was EE (kcal/day) = 618.27 + (8.98 x weight in kg) + 137.0 if diabetes - 199.7 if female (r2 = 0.68, P < .001). In the validation study with an independent group of noncritically ill AKI patients, predicted EE using the newly derived equation was also significantly correlated with measured EE by IC (r = 0.69, P = .004). CONCLUSION: Estimation of EE by weight-based formulas usually overestimated measured EE among noncritically ill AKI patients. In the absence of IC, the proposed predictive equation, specifically for noncritically ill AKI patients might be useful, in addition to weight-based formulas, for guiding caloric dosing in clinical practice.

Diagnostic values of inferior Q-waves for myocardial scar identification detected by 3.0 T cardiac MRI.

While Q-waves in inferior leads, particularly lead III, can be regarded as a minor abnormality, it can also indicate the presence of myocardial scar. This study assessed the diagnostic value of pathologic inferior Q-waves (lead II, III, aVF) for detecting ischemic scars using a high-resolution 3.0 T cardiac magnetic resonance (CMR). We retrospectively analyzed 1692 patients with suspected or known coronary artery disease who underwent stress CMR perfusion or viability assessment. Pathologic Q-waves were defined as duration of ≥ 30 ms and depth of ≥ 1 mm or QS-complex. Eleven models were created to evaluate the presence of Q-waves in different combinations of inferior leads. Of the 1692 patients, 436 (25.8%) had pathologic Q-waves. Models with Q-waves in leads II + aVF (model 7) and II + III + aVF (model 9) showed high specificity (100% and 99.6%), positive predictive value (PPV) (80.0% and 86.7%), and negative predictive value (NPV) (82.6% and 84.3%) but low sensitivity (1.3% and 13.1%). Other models also maintained high specificity and NPV but poor sensitivity and PPV. Notably, 21% of patients with an isolated pathologic Q-wave in lead III (model 4) exhibited scars. These findings highlight the need for careful clinical assessment when pathologic Q-waves are present.