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      Frailty and Sarcopenia Assessment upon Hospital Admission to Internal Medicine Predicts Length of Hospital Stay and Re-Admission: A Prospective Study of 980 Patients

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          Abstract

          Background: Frailty and sarcopenia are associated with frequent hospitalizations and poor clinical outcomes in geriatric patients. Ascertaining this association for younger patients hospitalized in internal medicine departments could help better prognosticate patients in the realm of internal medicine. Methods: During a 1-year prospective study in an internal medicine department, we evaluated patients upon admission for sarcopenia and frailty. We used the FRAIL questionnaire, blood alanine-amino transferase (ALT) activity, and mid-arm muscle circumference (MAMC) measurements. Results: We recruited 980 consecutive patients upon hospital admission (median age 72 years (IQR 65–79); 56.8% males). According to the FRAIL questionnaire, 106 (10.8%) patients were robust, 368 (37.5%) pre-frail, and 506 (51.7%) were frail. The median ALT value was 19IU/L (IQR 14–28). The median MAMC value was 27.8 (IQR 25.7–30.2). Patients with low ALT activity level (<17IU/L) were frailer according to their FRAIL score (3 (IQR 2–4) vs. 2 (IQR 1–3); p < 0.001). Higher MAMC values were associated with higher ALT activity, both representing robustness. The rate of 30 days readmission in the whole cohort was 17.4%. Frail patients, according to the FRAIL score (FS), had a higher risk for 30 days readmission (for FS > 2, HR = 1.99; 95CI = 1.29–3.08; p = 0.002). Frail patients, according to low ALT activity, also had a significantly higher risk for 30 days readmission (HR = 2.22; 95CI = 1.26–3.91; p = 0.006). After excluding patients whose length of stay (LOS) was ≥10 days, 252 (27.5%) stayed in-hospital for 4 days or longer. Frail patients according to FS had a higher risk for LOS ≥4 days (for FS > 2, HR = 1.87; 95CI = 1.39–2.52; p < 0.001). Frail patients, according to low ALT activity, were also at higher risk for LOS ≥4 days (HR = 1.87; 95CI = 1.39–2.52; p < 0.001). MAMC values were not correlated with patients’ LOS or risk for re-admission. Conclusion: Frailty and sarcopenia upon admission to internal medicine departments are associated with longer hospitalization and increased risk for re-admission.

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          US Health Care Spending by Payer and Health Condition, 1996-2016

          How does spending on different health conditions vary by payer (public insurance, private insurance, or out-of-pocket payments) and how has this spending changed over time? From 1996 to 2016, total health care spending increased from an estimated $1.4 trillion to an estimated $3.1 trillion. In 2016, private insurance accounted for 48.0% (95% CI, 48.0%-48.0%) of health care spending, public insurance for 42.6% (95% CI, 42.5%-42.6%) of health care spending, and out-of-pocket payments for 9.4% (95% CI, 9.4%-9.4%) of health care spending. After adjusting for population size and aging, the annualized spending growth rate was 2.6% (95% CI, 2.6%-2.6%) for private insurance, 2.9% (95% CI, 2.9%-2.9%) for public insurance, and 1.1% (95% CI, 1.0%-1.1%) for out-of-pocket payments. Understanding how much each payer spent on each health condition and how these amounts have changed over time can inform health care policy. US health care spending has continued to increase and now accounts for 18% of the US economy, although little is known about how spending on each health condition varies by payer, and how these amounts have changed over time. To estimate US spending on health care according to 3 types of payers (public insurance [including Medicare, Medicaid, and other government programs], private insurance, or out-of-pocket payments) and by health condition, age group, sex, and type of care for 1996 through 2016. Government budgets, insurance claims, facility records, household surveys, and official US records from 1996 through 2016 were collected to estimate spending for 154 health conditions. Spending growth rates (standardized by population size and age group) were calculated for each type of payer and health condition. Ambulatory care, inpatient care, nursing care facility stay, emergency department care, dental care, and purchase of prescribed pharmaceuticals in a retail setting. National spending estimates stratified by health condition, age group, sex, type of care, and type of payer and modeled for each year from 1996 through 2016. Total health care spending increased from an estimated $1.4 trillion in 1996 (13.3% of gross domestic product [GDP]; $5259 per person) to an estimated $3.1 trillion in 2016 (17.9% of GDP; $9655 per person); 85.2% of that spending was included in this study. In 2016, an estimated 48.0% (95% CI, 48.0%-48.0%) of health care spending was paid by private insurance, 42.6% (95% CI, 42.5%-42.6%) by public insurance, and 9.4% (95% CI, 9.4%-9.4%) by out-of-pocket payments. In 2016, among the 154 conditions, low back and neck pain had the highest amount of health care spending with an estimated $134.5 billion (95% CI, $122.4-$146.9 billion) in spending, of which 57.2% (95% CI, 52.2%-61.2%) was paid by private insurance, 33.7% (95% CI, 30.0%-38.4%) by public insurance, and 9.2% (95% CI, 8.3%-10.4%) by out-of-pocket payments. Other musculoskeletal disorders accounted for the second highest amount of health care spending (estimated at $129.8 billion [95% CI, $116.3-$149.7 billion]) and most had private insurance (56.4% [95% CI, 52.6%-59.3%]). Diabetes accounted for the third highest amount of the health care spending (estimated at $111.2 billion [95% CI, $105.7-$115.9 billion]) and most had public insurance (49.8% [95% CI, 44.4%-56.0%]). Other conditions estimated to have substantial health care spending in 2016 were ischemic heart disease ($89.3 billion [95% CI, $81.1-$95.5 billion]), falls ($87.4 billion [95% CI, $75.0-$100.1 billion]), urinary diseases ($86.0 billion [95% CI, $76.3-$95.9 billion]), skin and subcutaneous diseases ($85.0 billion [95% CI, $80.5-$90.2 billion]), osteoarthritis ($80.0 billion [95% CI, $72.2-$86.1 billion]), dementias ($79.2 billion [95% CI, $67.6-$90.8 billion]), and hypertension ($79.0 billion [95% CI, $72.6-$86.8 billion]). The conditions with the highest spending varied by type of payer, age, sex, type of care, and year. After adjusting for changes in inflation, population size, and age groups, public insurance spending was estimated to have increased at an annualized rate of 2.9% (95% CI, 2.9%-2.9%); private insurance, 2.6% (95% CI, 2.6%-2.6%); and out-of-pocket payments, 1.1% (95% CI, 1.0%-1.1%). Estimates of US spending on health care showed substantial increases from 1996 through 2016, with the highest increases in population-adjusted spending by public insurance. Although spending on low back and neck pain, other musculoskeletal disorders, and diabetes accounted for the highest amounts of spending, the payers and the rates of change in annual spending growth rates varied considerably. This study estimates health care spending for the most common health conditions in the United States, including low back pain and musculoskeletal disorders, diabetes, and ischemic heart disease, between 1996 and 2016.
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            Measurement of muscle mass in sarcopenia: from imaging to biochemical markers.

            Sarcopenia encompasses the loss of muscle mass and strength/function during aging. Several methods are available for the estimation of muscle or lean body mass. Popular assessment tools include body imaging techniques (e.g., magnetic resonance imaging, computed tomography, dual X-ray absorptiometry, ultrasonography), bioelectric impedance analysis, anthropometric parameters (e.g., calf circumference, mid-arm muscle circumference), and biochemical markers (total or partial body potassium, serum and urinary creatinine, deuterated creatine dilution method). The heterogeneity of the populations to be evaluated as well as the setting in which sarcopenia is investigated impacts the definition of "gold standard" assessment techniques. The aim of this article is to critically review available methods for muscle mass estimation, highlighting strengths and weaknesses of each of them as well as their proposed field of application.
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              Importance of frailty in patients with cardiovascular disease.

              Cardiovascular diseases (CVDs) are the leading cause of morbidity and mortality. With the ageing population, the prognostic determinants among others include frailty, health status, disability, and cognition. These constructs are seldom measured and factored into clinical decision-making or evaluation of the prognosis of these at-risk older adults, especially as it relates to high-risk interventions. Addressing this need effectively requires increased awareness and their recognition by the treating cardiologists, their incorporation into risk prediction models when treating an elderly patient with underlying complex CVD, and timely referral for comprehensive geriatric management. Simple measures such as gait speed, the Fried score, or the Rockwood Clinical Frailty Scale can be used to assess frailty as part of routine care of elderly patients with CVD. This review examines the prevalence and outcomes associated with frailty with special emphasis in patients with CVD.
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                Author and article information

                Journal
                J Clin Med
                J Clin Med
                jcm
                Journal of Clinical Medicine
                MDPI
                2077-0383
                17 August 2020
                August 2020
                : 9
                : 8
                : 2659
                Affiliations
                [1 ]Internal Medicine Department “T”, Chaim Sheba Medical Center. Sackler faculty of medicine, Tel-Aviv University, Tel Aviv P.O. Box 39040, Tel Aviv 6997801, Israel; sapir.anani@ 123456sheba.health.gov.il (S.A.); gal.goldhaber@ 123456sheba.health.gov.il (G.G.); adi.brom@ 123456sheba.health.gov.il (A.B.); nir.lasman@ 123456sheba.health.gov.il (N.L.); natia.turpashvili@ 123456sheba.health.gov.il (N.T.); gilat.shenhavzaltsman@ 123456sheba.health.gov.il (G.S.-s.); chen.avaky@ 123456sheba.health.gov.il (C.A.); Liat.negru@ 123456sheba.health.gov.il (L.N.); muhamad.agbaria@ 123456sheba.health.gov.il (M.A.); sigalit.ariam@ 123456sheba.health.gov.il (S.A.); yishay.wasserstrum@ 123456sheba.health.gov.il (Y.W.)
                [2 ]Internal Medicine Department, Sackler faculty of medicine, Tel-Aviv University, Tel Aviv 6997801, Israel; doron.portal@ 123456sheba.health.gov.il
                Author notes
                [* ]Correspondence: Gad.segal@ 123456sheba.health.gov.il ; Tel.: +97-25-2666-9580
                [†]

                Authors with equal contribution.

                Author information
                https://orcid.org/0000-0001-8063-2806
                https://orcid.org/0000-0002-3851-3245
                Article
                jcm-09-02659
                10.3390/jcm9082659
                7464238
                32824484
                5f31e3ac-8786-486a-9c50-53b7e9e33bc6
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 27 July 2020
                : 13 August 2020
                Categories
                Article

                sarcopenia,frailty,alt,mamc,frail questionnaire,internal medicine

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