Inotropic agents and vasodilator strategies for the treatment of cardiogenic shock or low cardiac output syndrome.

BackgroundTo address the need for personalized prevention, we conducted a subject‐level meta‐analysis within the framework of the Heart “OMics” in AGEing (HOMAGE) study to develop a risk prediction model for heart failure (HF) based on routinely available clinical measurements.Methods and ResultsThree studies with elderly persons (Health Aging and Body Composition [Health ABC], Valutazione della PREvalenza di DIsfunzione Cardiaca asinTOmatica e di scompenso cardiaco [PREDICTOR], and Prospective Study of Pravastatin in the Elderly at Risk [PROSPER]) were included to develop the HF risk function, while a fourth study (Anglo‐Scandinavian Cardiac Outcomes Trial [ASCOT]) was used as a validation cohort. Time‐to‐event analysis was conducted using the Cox proportional hazard model. Incident HF was defined as HF hospitalization. The Cox regression model was evaluated for its discriminatory performance (area under the receiver operating characteristic curve) and calibration (Grønnesby‐Borgan χ2 statistic). During a follow‐up of 3.5 years, 470 of 10 236 elderly persons (mean age, 74.5 years; 51.3% women) developed HF. Higher age, BMI, systolic blood pressure, heart rate, serum creatinine, smoking, diabetes mellitus, history of coronary artery disease, and use of antihypertensive medication were associated with increased HF risk. The area under the receiver operating characteristic curve of the model was 0.71, with a good calibration (χ2 7.9, P=0.54). A web‐based calculator was developed to allow easy calculations of the HF risk.ConclusionsSimple measurements allow reliable estimation of the short‐term HF risk in populations and patients. The risk model may aid in risk stratification and future HF prevention strategies.

Accurate identification of pathogenic species is important for early appropriate patient management, but growing diversity of infectious species/strains makes the identification of clinical yeasts increasingly difficult. Among conventional methods that are commercially available, the API ID32C, AuxaColor, and Vitek 2 systems are currently the most used systems in routine clinical microbiology. We performed a systematic review and meta-analysis to estimate and to compare the accuracy of the three systems, in order to assess whether they are still of value for the species-level identification of medically relevant yeasts. After adopting rigorous selection criteria, we included 26 published studies involving Candida and non-Candida yeasts that were tested with the API ID32C (674 isolates), AuxaColor (1,740 T he epidemiology of yeast infections, particularly those involving the bloodstream (i.e., fungemia) or other normally sterile sites, continues to evolve throughout the world (1), likely due to the diversity of susceptible hosts, including mainly patients undergoing transplantations or receiving treatment for underlying malignant diseases (2). Prophylactic use of antifungals has also contributed to alterations in the patterns of such infections, leading to increases in the incidence of non-albicans Candida species, compared to Candida albicans (3-6). Although the latter species is the most frequently isolated yeast in hospital settings (7), the emergence of less-common or "cryptic" non-albicans Candida species, as well as uncommon yeasts such as Rhodotorula, Geotrichum, Pichia, Malassezia, Saccharomyces, and cryptococci other than Cryptococcus neoformans (8), poses a threat due to their potential to develop antifungal resistance (9-12) or their intrinsically decreased susceptibility to one or more antifungal agents (13-15). As differences in antifungal drug susceptibilities can exist not only among genera but also among members of the same genus or species complex (8), accurate species identification is important for initiating early effective antifungal therapy, especially when susceptibility testing results are not promptly available.As simple, rapid, reliable alternatives to traditional methods based on the Wickerham assimilation/fermentation patterns (16), manual (e.g., AuxaColor [Bio-Rad, Marnes-la-Coquette, France]) and automated (i.e., Vitek 2 [bioMérieux, Marcy l'Etoile, France]) commercial identification systems are currently being used in clinical microbiology laboratories, but their ability to identify yeast isolates to the species level is dependent on the types and numbers of biochemical (carbohydrate/enzyme) substrates tested (17). Although with a limited database of only 26 taxa/species (updated to include 33 yeast species in a newer version of the system [AuxaColor 2], which was launched in 2002), the AuxaColor system uses colorimetric tests for assimilation substrates,

Purpose This systematic review aims to summarize factors that influence the quality of life (QOL) of advanced cancer patients in palliative care (PC) in developing countries. Understanding this context in developing countries milieu is necessary; however, this outcome is rarely reported. Methods Following the PRISMA guidelines, the electronic databases MEDLINE, Embase, CINAHL, and Web of Science were systematically searched using the search terms: QOL, cancer, PC, and names of all developing countries. Studies with less than ten subjects, qualitative or pilot studies, reviews, conference abstracts, and that reported validation of QOL questionnaires were excluded. Results Fifty-five studies from 15 developing countries in the African (n = 5), Latin America and the Caribbean (n = 10), and Asian (n = 40) region were included in the narrative synthesis. 65.4% were cross-sectional, 27.3% were cohort studies, 7.3% were RCTs or quasi-experimental studies. Around 30 QOL factors were studied with 20 different types of QOL instruments. Advanced cancer patients who were older, married/ever married, participated in additional care within PC, used complementary and alternative medicine (CAM), and practiced spirituality/religiosity showed higher QOL score. Low educational level and high depression were associated with a lower QOL. Conclusion Various factors affect QOL among cancer patients in PC. Patients valued the use of CAMs; however, the quality and safety aspects should be properly addressed. Important factors that influenced the QOL score were social and spiritual support. While there is a general need to develop PC strategies further, recognizing patients’ needs should be prioritized in national cancer programs.

IntroductionAlong with the proliferation of Open Access (OA) publishing, the interest for comparing the scientific quality of studies published in OA journals versus subscription journals has also increased. With our study we aimed to compare the methodological quality and the quality of reporting of primary epidemiological studies and systematic reviews and meta-analyses published in OA and non-OA journals.MethodsIn order to identify the studies to appraise, we listed all OA and non-OA journals which published in 2013 at least one primary epidemiologic study (case-control or cohort study design), and at least one systematic review or meta-analysis in the field of oncology. For the appraisal, we picked up the first studies published in 2013 with case-control or cohort study design from OA journals (Group A; n = 12), and in the same time period from non-OA journals (Group B; n = 26); the first systematic reviews and meta-analyses published in 2013 from OA journals (Group C; n = 15), and in the same time period from non-OA journals (Group D; n = 32). We evaluated the methodological quality of studies by assessing the compliance of case-control and cohort studies to Newcastle and Ottawa Scale (NOS) scale, and the compliance of systematic reviews and meta-analyses to Assessment of Multiple Systematic Reviews (AMSTAR) scale. The quality of reporting was assessed considering the adherence of case-control and cohort studies to STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) checklist, and the adherence of systematic reviews and meta-analyses to Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) checklist.ResultsAmong case-control and cohort studies published in OA and non-OA journals, we did not observe significant differences in the median value of NOS score (Group A: 7 (IQR 7–8) versus Group B: 8 (7–9); p = 0.5) and in the adherence to STROBE checklist (Group A, 75% versus Group B, 80%; p = 0.1). The results did not change after adjustment for impact factor. The compliance with AMSTAR and adherence to PRISMA checklist were comparable between systematic reviews and meta-analyses published in OA and non-OA journals (Group C, 46.0% versus Group D, 55.0%; p = 0.06), (Group C, 72.0% versus Group D, 76.0%; p = 0.1), respectively).ConclusionThe epidemiological studies published in OA journals in the field of oncology approach the same methodological quality and quality of reporting as studies published in non-OA journals.