Background The clinical presentation of COVID-19 in patients admitted to hospital is heterogeneous. We aimed to determine whether clinical phenotypes of patients with COVID-19 can be derived from clinical data, to assess the reproducibility of these phenotypes and correlation with prognosis, and to derive and validate a simplified probabilistic model for phenotype assignment. Phenotype identification was not primarily intended as a predictive tool for mortality. MethodsIn this study, we used data from two cohorts: the COVID-19@Spain cohort, a retrospective cohort including 4035 consecutive adult patients admitted to 127 hospitals in Spain with COVID-19 between Feb 2 and March 17, 2020, and the COVID-19@HULP cohort, including 2226 consecutive adult patients admitted to a teaching hospital in Madrid between Feb 25 and April 19, 2020. The COVID-19@Spain cohort was divided into a derivation cohort, comprising 2667 randomly selected patients, and an internal validation cohort, comprising the remaining 1368 patients. The COVID-19@HULP cohort was used as an external validation cohort. A probabilistic model for phenotype assignment was derived in the derivation cohort using multinomial logistic regression and validated in the internal validation cohort. The model was also applied to the external validation cohort. 30-day mortality and other prognostic variables were assessed in the derived phenotypes and in the phenotypes assigned by the probabilistic model. Findings Three distinct phenotypes were derived in the derivation cohort (n=2667)-phenotype A (516 [19%] patients), phenotype B (1955 [73%]) and phenotype C (196 [7%])-and reproduced in the internal validation cohort (n=1368)phenotype A (233 [17%] patients), phenotype B (1019 [74%]), and phenotype C (116 [8%]). Patients with phenotype A were younger, were less frequently male, had mild viral symptoms, and had normal inflammatory parameters. Patients with phenotype B included more patients with obesity, lymphocytopenia, and moderately elevated inflammatory parameters. Patients with phenotype C included older patients with more comorbidities and even higher inflammatory parameters than phenotype B. We developed a simplified probabilistic model (validated in the internal validation cohort) for phenotype assignment, including 16 variables. In the derivation cohort, 30-day mortality rates were 2•5% (95% CI 1•4-4•3) for patients with phenotype A, 30•5% (28•5-32•6) for patients with phenotype B, and 60•7% (53•7-67•2) for patients with phenotype C (log-rank test p<0•0001). The predicted phenotypes in the internal validation cohort and external validation cohort showed similar mortality rates to the assigned phenotypes (internal validation cohort: 5•3% [95% CI 3•4-8•1] for phenotype A, 31•3% [28•5-34•2] for phenotype B, and 59•5% [48•8-69•3] for phenotype C; external validation cohort: 3•7% [2•0-6•4] for phenotype A, 23•7% [21•8-25•7] for phenotype B, and 51•4% [41•9-60•7] for phenotype C).Interpretation Patients admitted to hospital with COVID-19 can be classified into three...

IntroductionThe present study describes the distribution of selected micronutrients and anaemia among school-aged children living in Libo Kemkem and Fogera (Amhara State, Ethiopia), assessing differences by socio-demographic characteristics, health status and dietary habits.MethodsA cross-sectional survey was carried out during May–December 2009. Socio-demographic characteristics, health status and dietary habits were collected. Biomarkers were determined for 764 children. Bivariate and multivariable statistical methods were employed to assess micronutrient deficiencies (MD), anaemia, and their association with different factors.ResultsMore than two thirds of the school-aged children (79.5%) had at least one MD and 40.5% had two or more coexisting micronutrient deficiencies. The most prevalent deficiencies were of zinc (12.5%), folate (13.9%), vit A (29.3%) and vit D (49%). Anaemia occurred in 30.9% of the children. Children living in rural areas were more likely to have vit D insufficiency [OR: 5.9 (3.7–9.5)] but less likely to have folate deficiency [OR: 0.2 (0.1–0.4)] and anaemia [OR: 0.58 (0.35–0.97)]. Splenomegaly was positively associated with folate deficiency and anaemia [OR: 2.77 (1.19–6.48) and 4.91 (2.47–9.75)]. Meat and fish consumption were inversely correlated with zinc and ferritin deficiencies [OR: 0.2 (0.1–0.8) and 0.2 (0.1–0.9)], while oil consumption showed a negative association with anaemia and deficiencies of folate and vitamin A [0.58 (0.3–0.9), OR: 0.5 (0.3–0.9) and 0.6 (0.4–0.9)]. Serum ferritin levels were inversely correlated to the presence of anaemia (p<0.005).ConclusionThere is a high prevalence of vitamin A deficiency and vitamin D insufficiency and a moderate prevalence of zinc and folate deficiencies in school-aged children in this area. The inverse association of anaemia and serum ferritin levels may be due to the presence of infectious diseases in the area. To effectively tackle malnutrition, strategies should target not only isolated micronutrient supplementation but also diet diversification.

Stability of a measurand in a specimen is a function of the property variation over time in specific storage conditions, which can be expressed as a stability equation, and is usually simplified to stability limits (SLs). Stability studies show differences or even inconsistent results due to the lack of standardized experimental designs and heterogeneity of the chosen specifications. Although guidelines for the validation of sample collection tubes have been published recently, the measurand stability evaluation is not addressed. This document provides an easy guideline for the development of a stability test protocol based on a two-step process. A preliminary test is proposed to evaluate the stability under laboratory habitual conditions. The loss of stability is assessed by comparing measurement values of two samples obtained from the same patient and analyzed at different time points. One of them is analyzed under optimal conditions (basal sample). The other is stored under specific stability conditions for a time set by the laboratory (test sample). Differences are expressed using percentage deviation (PD%) to facilitate comparison with specifications. When the preliminary test demonstrates instability, a comprehensive test is proposed in order to define the stability equation and to specify SLs. Several samples are collected from a set of patients. The basal sample is analyzed under optimal conditions, whereas analysis of test samples is delayed at time intervals. For each patient PD% is calculated as the difference between measurements for every test sample and its basal one and represented in a coordinate graph versus time.

In early normal gestation, low plasma antioxidant status, assessed through a global score, associates with later development of pregnancy complications. Larger population studies could help to determine the value of Antiox-S as predictive tool and the relevance of nutrition on maternal antioxidant status.

Time is the main variable affecting stability in medical laboratory samples. Bibliographic studies differ in recommedations of stability limits mainly because of different specifications for maximum allowable error. Definition of a consensus stability function in specific conditions can help laboratories define stability limits using their own quality specifications.

In June, 1997, we initiated a prospective study to analyze the effect of granulocyte colony-stimulating factor (G-CSF) on coagulation system in peripheral blood stem cells (PBSC) donors following G-CSF administration. Since, 25 consecutively healthy donors received G-CSF (filgrastim) to mobilize and collect PBSC and 20 donors were finally included in the study. Blood samples were collected immediately before starting G-CSF and prior to PBSC collection to analyze the following parameters: prothrombin time (PT), activated partial thromboplastin time (APTT), fibrinogen, hypercoagulability markers (D-dimer, TAT complex, F1 + 2), natural anticoagulants (antithrombin, protein C, protein S), endothelial activation markers [von Willebrand factor antigen (vWF:Ag) and angiotensin converting enzyme (ACE)], and resistance to activated protein C. We found a significant increase in F1 + 2 and D-dimer while a significant decrease of antithrombin and protein C activity was evidenced. Regarding endothelial cell activation markers, a significant increase of vWF:Ag with a slightly significant decrease of ACE were also observed. Therefore, in PBSC donors receiving G-CSF our results reveal activation of both coagulation and endothelial cells that could favor the developing of thrombotic events. In consequence, a careful monitoring should be considered in those cases with risk factors for thrombosis.

To ensure that clinical laboratories produce results that are both accurate and of clinical utility it is essential that only samples of adequate quality are analysed. Although various studies and databases assessing the stability of analytes in different settings do exist, guidance on how to perform and report stability studies is lacking. This results in studies that often do not report essential information, thus compromising transferability of the data. The aim of this manuscript is to describe the Checklist for Reporting Stability Studies (CRESS) against which future studies should be reported to ensure standardisation of reporting and easy assessment of transferability of studies to other healthcare settings. The EFLM WG-PRE (European Federation of Clinical Chemistry and Laboratory Medicine Working Group for the Preanalytical Phase) produced the CRESS checklist following a detailed literature review and extensive discussions resulting in consensus agreement. The checklist consists of 20 items covering all the aspects that should be considered when producing a report on a stability study including details of what should be included for each item and a rationale as to why. Adherence to the CRESS checklist will ensure that studies are reported in a transparent and replicable way. This will allow other laboratories to assess whether published data meet the stability criteria required in their own particular healthcare scenario. The EFLM WG-PRE encourage researchers and authors to use the CRESS checklist as a guide to planning stability studies and to produce standardised reporting of future stability studies.

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