Challenge to Predict Mobilized Peripheral Blood Stem Cells on the Fourth Day of Granulocyte Colony-Stimulating Factor Treatment in Healthy Donors: Predictive Value of Basal CD34+ Cell and Platelet Counts.

A longitudinal, prospective, observational, single-center cohort study on healthy donors was designed to identify predictors of CD34+ cell mobilization on day 4 after granulocyte colony-stimulating factor (G-CSF) administration. As potential predictors of mobilization, age, sex, body weight, height, blood volume, WBC count, peripheral blood (PB) mononuclear cell count, platelet (Plt) count, and hematocrit and hemoglobin levels were considered. Two different evaluations of CD34+ cell counts were determined for each donor: baseline (before G-CSF administration) and in PB on day 4 after G-CSF administration. One hundred twenty-two consecutive healthy donors with a median age of 47.5 years were enrolled. The median value of CD34+ on day 4 was 43 cells/µL (interquartile range, 23 to 68), and 81.1% of donors had ≥20 cells/µL. Basal WBC count, Plt count, and CD34+ were significantly higher for the subjects with CD34+ levels over median values on day 4. A multivariate quartile regression analysis, adjusted by sex, age, basal CD34+, and basal Plt count, showed a progressively stronger relationship between baseline CD34+ and Plt levels and the CD34+ levels on day 4. The basal CD34+ cut-off level to predict the levels of CD34+ on day 4 was either ≤2 cells/µL or ≥3 cells/µL and that of basal Plt count was ≤229 × 109/L or ≥230 × 109/L, respectively, to determine whether mobilization therapy should or should not be attempted. PB stem cell mobilization with G-CSF was highly effective on day 4, and herein we describe a model for predicting the probability of performing PB stem cell collection after a short course of G-CSF.

Metabolic syndrome and subclinical carotid damage: a meta-analysis from population-based studies.

AIM: Metabolic syndrome (MetS) is a phenotype of growing prevalence in the general population. Information on the association between MetS and vascular damage in this setting is only based on data provided by single reports. We performed a meta-analysis of population-based studies aimed to assess the association of MetS with carotid atherosclerosis. DESIGN: Studies were identified by the following search terms: 'metabolic syndrome', 'general population,' 'carotid intima-media thickness' (IMT), 'carotid atherosclerosis,' 'carotid damage,' 'ultrasonography.' The OVID-MEDLINE, PubMed, and the Cochrane Central Register of Controlled Trials databases were searched for English-language articles without time restriction up to 30 September 2016. RESULTS: Overall, 34 635 study participants (22.9% with MetS) of both sexes were included in 21 studies (sample size range 182-11 502). Mean common carotid IMT was higher in MetS study participants as compared with their non-MetS counterparts (759 ±â€Š41 vs. 695 ±â€Š27 µm), the standard means difference being 0.39 ±â€Š0.05 (confidence interval: 0.29-0.48, P < 0.0001). This was also the case when pooled data were separately analysed according to sex. Differences in carotid IMT were unaffected by the presence of publication bias or single-study effect. CONCLUSION: Our findings support the view that MetS is a risk factor for early carotid atherosclerosis in members of the general population, regardless of sex. From a practical perspective, the ultrasound search of subclinical carotid disease may refine cardiovascular risk stratification and decision-making strategies in MetS individuals.

Nocturnal Hypertension and Altered Night-Day BP Profile and Atherosclerosis in Renal Transplant Patients.

BACKGROUND: The clinical relevance of ambulatory blood pressure monitoring (ABPM) for risk stratification in renal transplant patients still remains poorly defined. METHODS: We investigated the association between clinic and ABPM with an established biomarker of atherosclerosis (intima-media thickness [IMT] by echo-color Doppler) in a large, inclusive survey (n = 172) in renal transplant patients at a single institution. RESULTS: Forty-two patients (24%) were classified as hypertensive by ABPM criteria and 29 (17%) by clinic blood pressure (BP) criteria. Average daytime and nighttime BP was 126 ± 12/78 ± 9 mm Hg and 123 ± 13/74 ± 10 mm Hg, respectively. Forty-five patients (26%) were classified as hypertensive by the daytime criterion (>135/85 mm Hg) and a much higher proportion (n = 119, 69%) by the nighttime criterion (>120/70 mm Hg). Sixty-two patients (36%) had a night-day ratio of 1 or greater, indicating clear-cut nondipping. The average nighttime systolic BP (r = 0.24, P = 0.001) and the night-day systolic BP ratio (r = 0.23, P = 0.002) were directly related to IMT, and these associations were much more robust than the 24-hour systolic BP-IMT relationship (r = 0.16, P = 0.04). Average daytime BP and clinic B were unrelated to IMT. In a multiple regression analysis adjusting for confounders, the night-day systolic BP ratio maintained an independent association with IMT (ß = 0.14, P = 0.04). CONCLUSIONS: In renal transplant patients, the prevalence of nocturnal hypertension by far exceeds the prevalence of hypertension as assessed by clinic, daytime, and 24-hour ABPM. Nighttime systolic BP and the night-day ratio but no other BP metrics are independently associated with IMT. Blood pressure during nighttime may provide unique information for the assessment of cardiovascular risk attributable to BP burden in renal transplant patients.

The quality of reporting in clinical research: the CONSORT and STROBE initiatives.

Inaccurate reporting of data hampers the generalizability and the correct interpretation of results of scientific medical papers. The Consolidated Standards of Reporting Trials (CONSORT) and Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) initiatives, both included in the Enhancing the Quality and Transparency of Health Research (EQUATOR) international network, have elaborated appropriate guidelines in order to improve the transparence, clearness and completeness of scientific literature. The CONSORT statement consists of a 25 items checklist and a flow-chart diagram which provide guidance to Authors on how to report randomized clinical trials. The STROBE is a checklist of 22 items which should be addressed when observational studies (case-control, cohort and cross-sectional) are made up. Many editorial committees and prestigious international journals have now embraced these guidelines to improve the quality and methodology of their scientific reports.

Prognostic models in the clinical arena.

Making a prognosis is to predict the course of a disease and estimate the probability (or risk) of the appearance of a given outcome in relationship to clinical or non-clinical characteristics. Prognostic assessment is usually modelled by multivariable mathematic equations (prognostic models). In this article we describe what a prognostic model is, how to build a good one, why and how it is important to evaluate its generalizability and accuracy by means of discrimination, calibration and reclassification.

An overview on standard statistical methods for assessing exposure-outcome link in survival analysis (Part II): the Kaplan-Meier analysis and the Cox regression method.

The Kaplan-Meier and the Cox regression methods are the most used statistical techniques for performing "time to event analysis" in epidemiological and clinical research. The Kaplan-Meier analysis allows to build up one or more survival curves describing the occurrence of the outcome of interest over time according to the presence/absence of one or more exposures. The Cox regression method models the relationship between a specific exposure (either a continuous one like age, and systolic blood pressure or a categorical one like diabetes, degree of obesity, etc.) and the occurrence of a given outcome taking into account multiple confounders and/or predictors.

An overview of standard statistical methods for assessing exposure-outcome link in survival analysis (Part I): basic concepts.

The occurrence of a given disease in a population is measured by prevalence and incidence. Prevalence reflects the burden of a given disease in a specific population and is calculated as point prevalence or period prevalence. Incidence denotes the number of new cases of a given disease which occur in a cohort of individuals followed up for a given period of time, and may be expressed in terms of cumulative risk or incidence rate. The relationship between disease frequency (e.g., myocardial infarction) and specific exposure (e.g., smoking) is assessed by calculating relative (relative risk and odds ratio) and absolute (risk difference) measures of effect.