The sarcopenia index is a fair measure for muscle mass estimation among ICU patients and can modestly predict hospital and 90-day mortality among patients who do not have acute kidney injury at the time of measurement.

Sarcopenia index is a potentially objective measure for estimating muscle mass that is noninvasive and less expensive. Sarcopenia index could be considered in lung transplant candidate selection following prospective validation in larger cohorts.

These findings suggest that even a short-term yoga-based lifestyle intervention may be an important modality to reduce the risk for CVD as indicated by weight loss, reduction in systolic blood pressure, an increase in adiponectin, and decrease in IL-6 in overweight and obese men.

IntroductionDyschloremia is common in critically ill patients, although its impact has not been well studied. We investigated the epidemiology of dyschloremia and its associations with the incidence of acute kidney injury and other intensive care unit outcomes.Material and MethodsThis is a single-center, retrospective cohort study at Mayo Clinic Hospital—Rochester. All adult patients admitted to intensive care units from January 1st, 2006, through December 30th, 2012 were included. Patients with known acute kidney injury and chronic kidney disease stage 5 before intensive care unit admission were excluded. We evaluated the association of dyschloremia with ICU outcomes, after adjustments for the effect of age, gender, Charlson comorbidity index and severity of illness score.ResultsA total of 6,025 patients were enrolled in the final analysis following the implementation of eligibility criteria. From the cohort, 1,970 patients (33%) developed acute kidney injury. Of the total patients enrolled, 4,174 had a baseline serum chloride. In this group, 1,530 (37%) had hypochloremia, and 257 (6%) were hyperchloremic. The incidence of acute kidney injury was higher in hypochloremic and hyperchloremic patients compared to those with a normal serum chloride level (43% vs.30% and 34% vs. 30%, respectively; P < .001). Baseline serum chloride was lower in the acute kidney injury group vs. the non-acute kidney injury group [100 mmol/L (96–104) vs. 102 mmol/L (98–105), P < .0001]. In a multivariable logistic regression model, baseline serum chloride of ≤94 mmol/L found to be independently associated with the risk of acute kidney injury (OR 1.7, 95% CI 1.1–2.6; P = .01).DiscussionDyschloremia is common in critically ill patients, and severe hypochloremia is independently associated with an increased risk of development of acute kidney injury.

Background: With higher adoption of electronic health records at health-care centers, electronic search algorithms (computable phenotype) for identifying acute decompensated heart failure (ADHF) among hospitalized patients can be an invaluable tool to enhance data abstraction accuracy and efficacy in order to improve clinical research accrual and patient centered outcomes. We aimed to derive and validate a computable phenotype for ADHF in hospitalized patients. Methods: We screened 256, 443 eligible (age > 18 years and with prior research authorization) individuals who were admitted to Mayo Clinic Hospital in Rochester, MN, from January 1, 2006, through December 31, 2014. Using a randomly selected derivation cohort of 938 patients, several iterations of a free-text electronic search were developed and refined. The computable phenotype was subsequently validated in an independent cohort 100 patients. The sensitivity and specificity of the computable phenotype were compared to the gold standard (expert review of charts) and International Classification of Diseases-9 (ICD-9) codes for Acute Heart Failure. Results: In the derivation cohort, the computable phenotype achieved a sensitivity of 97.5%, and specificity of 100%, whereas ICD-9 codes for Acute Heart Failure achieved a sensitivity of 47.5% and specificity of 96.7%. When all Heart Failure codes (ICD-9) were used, sensitivity and specificity were 97.5 and 86.6%, respectively. In the validation cohort, the sensitivity and specificity of the computable phenotype were 100 and 98.5%. The sensitivity and specificity for the ICD-9 codes (Acute Heart Failure) were 42 and 98.5%. Upon use of all Heart Failure codes (ICD-9), sensitivity and specificity were 96.8 and 91.3%. Conclusions: Our results suggest that using computable phenotype to ascertain ADHF from the clinical notes contained within the electronic medical record are feasible and reliable. Our computable phenotype outperformed ICD-9 codes for the detection of ADHF.

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This yoga-based lifestyle intervention program significantly reduced the CVD risk, as shown by lowered FRS and estimated 10-year CVD risk. Further testing of this promising intervention is warranted in the long term.

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