Additional file 4 of Frailty transition and depression among community-dwelling older adults: the Korean Longitudinal Study of Aging (2006–2020)

Additional file 4: Supplementary Table 4. Generalized linear model using the GEE with CES-D-10 score in 2008-2020

Additional file 1 of Frailty transition and depression among community-dwelling older adults: the Korean Longitudinal Study of Aging (2006–2020)

Additional file 1: Supplementary Table 1. General characteristics of the study population (baseline 2008)

Additional file 2 of Frailty transition and depression among community-dwelling older adults: the Korean Longitudinal Study of Aging (2006–2020)

Additional file 2: Supplementary Table 2. Generalized linear model using the GEE with CES-D-10 score in 2008-2020 with employing imputation-based approach for missingdata

Additional file 1 of Marital transition and cognitive function among older adults: the korean Longitudinal Study of Aging (2006–2020)

Additional file 1

Additional file 3 of Frailty transition and depression among community-dwelling older adults: the Korean Longitudinal Study of Aging (2006–2020)

Additional file 3: Supplementary Table 3. Generalized linear model using the GEE with CES-D-10 score in 2008-2020 without employing imputation-based approach formissing data

Additional file 1: of Association between eating behaviour and diet quality: eating alone vs. eating with others

Appendix 1. Characteristics of study population by eating behaviour Appendix 2. Results of unadjusted and adjusted multiple regression associated with MAR Appendix 3. NAR of nutrients and total energy intake. (DOCX 140 kb

Data_Sheet_1_LACE Index to Predict the High Risk of 30-Day Readmission in Patients With Acute Myocardial Infarction at a University Affiliated Hospital.PDF

BackgroundThe LACE index (length of stay, acuity of admission, comorbidity index, and emergency room visit in the past 6 months) has been used to predict the risk of 30-day readmission after hospital discharge in both medical and surgical patients. This study aimed to utilize the LACE index to predict the risk of 30-day readmission in hospitalized patients with acute myocardial infraction (AMI).MethodsThis was a retrospective study. Data were extracted from the hospital's electronic medical records of patients admitted with AMI between 2015 and 2019. LACE index was built on admission patient demographic data, and clinical and laboratory findings during the index of admission. The multivariate logistic regression was performed to determine the association and the risk prediction ability of the LACE index, and 30-day readmission were analyzed by receiver operator characteristic curves with C-statistic.ResultsOf the 3,607 patients included in the study, 5.7% (205) were readmitted within 30 days of discharge from the hospital. The adjusted odds ratio based on logistic regression of all baseline variables showed a statistically significant association with the LACE score and revealed an increased risk of readmission within 30 days of hospital discharge. However, patients with high LACE scores (≥10) had a significantly higher rate of emergency revisits within 30 days from the index discharge than those with low LACE scores. Despite this, analysis of the receiver operating characteristic curve indicated that the LACE index had favorable discrimination ability C-statistic 0.78 (95%CI; 0.75–0.81). The Hosmer–Lemeshow goodness- of-fit test P value was p = 0.920, indicating that the model was well-calibrated to predict risk of the 30-day readmission.ConclusionThe LACE index demonstrated the good discrimination power to predict the risk of 30-day readmissions for hospitalized patients with AMI. These results can help clinicians to predict the risk of 30-day readmission at the early stage of hospitalization and pay attention during the care of high-risk patients. Future work is to be focused on additional factors to predict the risk of 30-day readmissions; they should be considered to improve the model performance of the LACE index with other acute conditions by using administrative data.</p

Dinuclear gold(I) pseudohalogen complexes bridged by a ferrocenyl bisphosphine ligand: synthesis, structure, and reactivity toward isothiocyanate

Ferrocenyl bisphosphine-bridged dinuclear gold(I) complexes containing CF3COO as a pseudohalogen ligand were synthesized from the corresponding halides using CF3COOAg. The sequential treatment of gold(I) trifluoroacetates with aqueous NaN3 afforded the corresponding ferrocenyl bisphosphine-bridged dinuclear gold(I) azides. Isolated gold(I) pseudohalogen complexes were characterized through IR, NMR and X-ray crystallography. The characteristic IR absorption bands of ν(CO), ν(CF), and ν(N3) were at 1690, 1190, and 2040 cm−1, respectively, corresponding to the gold(I) trifluoroacetates and azides, verifying gold(I) pseudohalogen formation. The molecular structure of [Au2(η1-CF3CO2)2(µ-dippf)] (dippf = 1,1’-bis(diisopropylphosphino)ferrocene) through X-ray diffraction showed intermolecular Au⋅⋅⋅Au interactions with close contacts between molecules. The crystallographic images illustrate the polymeric chain of bis(phosphino)ferrocenyl gold(I) formed via intermolecular Au⋅⋅⋅Au bonds and layered packing arrays. In contrast, the molecular structures of [Au2(η1-CF3CO2)2(µ-dtbpf)] (dtbpf = 1,1’-bis(di-tert-butylphosphino)ferrocene) and Au2(N3)2(µ-dippf) showed intramolecular Au⋅⋅⋅Au aurophilic interactions, but the structures of [Au2(η1-CF3CO2)2(µ-dcpf)] (dcpf = 1,1′-bis(dicyclohexylphosphino)ferrocene) and Au2(N3)2(µ-dppf) revealed no direct interaction within the dinuclear gold(I) system. Gold(I) azides gradually react with isothiocyanates (allyl, (S)-(+)-1-phenylethyl, and benzyl) to afford the corresponding gold(I) tetrazole-thiolates [Au2X2(µ-dippf)] or [Au2X2(µ-dtbpf)] (X = S[CN4(Y)]) (Y = allyl, (S)-(+)-1-phenylethyl, and benzyl) via dipolar cycloaddition of the isothiocyanates into the Au-N3 bond.</p

Data_Sheet_1_A scoping review on population-centered indicators for cancer care continuum.PDF

PurposeThe purpose of this study was to develop prioritized cancer indicators and measure the population-based monitoring of the entire life cycle of cancer care, guiding the improvement of care delivery systems.MethodsScoping review was performed based on the Joanna Briggs Institute's methodology. Electronic databases were searched in PubMed, Cochrane Library, EMBASE, Ovid Medline, RISS, KISS, and KoreaMed. The searches were limited to articles published in English between 2010 and 2020. No restrictions were applied regarding the publication status or country of origin, and all study designs were included. Gray literature was used to broaden the search's scope, identify new recommendations, need to be in connect with subject experts, and explore pertinent websites. The process and selected indicators were analyzed based on their frequency distribution and percentage.ResultsThe literature search yielded 6,202 works. In addition, national and international cancer guidelines were obtained from official database reports. A total of 35 articles and 20 reports regarding cancer indicators were finally selected for data synthesis. Based on them, 254 core sets of cancer indicators were identified. The selected indicators were classified into six domains based on the continuum of cancer care and survivor's life cycle, namely, primary prevention (61, 24.0%), secondary prevention (46, 18.1%), treatment (85, 33.5%), quality of care (33, 13.0%), survivor management (33, 13.0%), and end-of-life care (14, 5.5%).ConclusionThere is a growing interest in developing specific areas of cancer care. Cancer indicators can help organizations, care providers, and patients strive for optimal care outcomes. The identified indicators could guide future innovations by identifying weaknesses in cancer prevention and management.</p