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Purpose
Malnutrition is a strong predictor of mortality in hemodialysis patients. Several scoring systems for evaluating nutritional status have been proposed. However, they rely on different sets of anthropometric and laboratory markers to make a diagnosis of malnutrition and assess its impact on prognosis. To validate them, nutritional scores should be compared with clinical outcomes. Thus, the purpose of this study was to assess malnutrition by three different nutrition scoring systems and determine which best predicts mortality in hemodialysis patients.Methods
This prospective study included 106 adult chronic hemodialysis patients. Their mean age was 56.3 ± 14.9 years and mean body mass index 24.8 (21.8–28.9); 52 % were men and they had been on dialysis for 24 (5–55) months. Nutritional status was classified according to the diagnostic systems proposed by Wolfson et al. (Am J Clin Nutr 39(4):547–555, 1984), International Society of Renal Nutrition and Metabolism (ISRNM) (Fouque et al. in Kidney Int 73(4):391–398, 2008), and Beberashvili et al. (Nephrol Dial Transplant 25(8):2662–2671, 2010). During about 2 years of follow-up, mortality was assessed by Kaplan–Meier curves, log-rank, and Cox’s models adjusted for diabetes, sex, C-reactive protein, time on dialysis, age, and fractional urea clearance.Results
Twenty-three deaths (21.5 %) occurred during the study period. According to the systems of Wolfson, Beberashvili, and the ISRNM, 54, 32, and 20 % of patients, respectively, had malnutrition. Both univariate and multivariate analyses showed that the ISRNM system was the only one that predicted poorer survival (fourfold higher death risk) in malnourished patients.Conclusions
The scoring system proposed by the ISRNM most accurately identifies patients at higher risk of death. 相似文献Background
It is a challenge in low-resource settings to ensure the availability of complete, timely disease surveillance information. Smartphone applications (apps) have the potential to enhance surveillance data transmission.Methods
The Central African Republic (CAR) Ministry of Health and Médecins Sans Frontières (MSF) conducted a 15-week pilot project to test a disease surveillance app, Argus, for 20 conditions in 21 health centers in Mambéré Kadéi district (MK 2016). Results were compared to the usual paper-based surveillance in MK the year prior (MK 2015) and simultaneously in an adjacent health district, Nana-Mambére (NM 2016). Wilcoxon rank sum and Kaplan-Meier analyses compared report completeness and timeliness; the cost of the app, and users’ perceptions of its usability were assessed.Results
Two hundred seventy-one weekly reports sent by app identified 3403 cases and 63 deaths; 15 alerts identified 28 cases and 4 deaths. Median completeness (IQR) for MK 2016, 81% (81–86%), was significantly higher than in MK 2015 (31% (24–36%)), and NM 2016 (52% (48–57)) (p?<?0.01). Median timeliness (IQR) for MK 2016, 50% (39–57%) was also higher than in MK 2015, 19% (19–24%), and NM 2016 29% (24–36%) (p?<?0.01). Kaplan-Meier Survival Analysis showed a significant progressive reduction in the time taken to transmit reports over the 15-week period (p?<?0.01). Users ranked the app’s usability as greater than 4/5 on all dimensions. The total cost of the 15-week pilot project was US$40,575. It is estimated that to maintain the app in the 21 health facilities of MK will cost approximately US$18,800 in communication fees per year.Conclusions
The app-based data transmission system more than doubled the completeness and timeliness of disease surveillance reports. This simple, low-cost intervention may permit the early detection of disease outbreaks in similar low-resource settings elsewhere.Evidence-based policy measures need non-interest-guided information about the health status of a population and the diseases that affect the population the most. In such cases, a national burden of disease study can provide reliable insights at the regional level.
AimThis article presents the potential of the BURDEN 2020 project and its expected outcome for Germany at the national and regional level.
MethodsThe BURDEN 2020 project uses several indicators including years of life lost (YLL) to cover the impact of mortality and years lived with disability (YLD) to cover morbidity. The sum of both is the measure of population health called disability adjusted life years (DALY).
ResultsThe study ranks individual diseases and risk factors based on their impact on population health. The burden of disease approach is assumed to be sensitive to subnational differences and may generate immediate benefits for regional planning. The BURDEN 2020 study will pilot a national burden of disease study for Germany that will later be transformed into a continuous data processing and visualization tool. This is done by using, modifying and supplementing the methodology employed by the Global Burden of Disease (GBD) study to better fit the needs of health policy in Germany. This study is aimed at calculating the disease burden for up to 17 preselected diseases. Furthermore, the estimates of burden of disease are attributed to a selected set of risk factors.
ConclusionThe Burden 2020 study will provide the results of a new, health-related data processing system to the public. This includes a noninterest-guided presentation of the burden of disease (DALY) in Germany at the national and regional level.
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