IntroductionThe Sysmex XN-series haematology analyser has newly adopted a fluorescent channel to measure immature platelet fraction (IPF). To promote the clinical utility of this promising parameter, establishing a reliable reference interval is mandatory. According to previous studies, IPF values may be affected by the employed analyser and the ethnic background of the individual, but no differences seem to be found between individuals’ genders. Therefore, this study aimed to define the reference interval for IPF in a Spanish population following Clinical and Laboratory Standard Institute (CLSI) guidelines.Materials and methodsA total of 153 healthy Caucasian adults from Spain met the inclusion criteria. IPF measurement was performed by means of a Sysmex XN-2000 haematology analyser. A non-parametric percentile method was used to calculate the reference intervals in accordance with CLSI guidelines.ResultsThe obtained reference interval for IPF on the Sysmex XN-2000 was 1.6–9.6% (90% confidence intervals (CIs) were 1.5–1.8 and 9.3–11.5, respectively). No significant gender difference in IPF reference intervals was observed (P = 0.101).ConclusionsThis study provides, for the first time, a reference interval for IPF using a Sysmex XN-2000 in a Spanish population, ranging from 1.6 to 9.6%. These data are needed to evaluate platelet production in several conditions such as thrombocytopenia, inflammatory states and cardiovascular diseases, as well as for future research.

AimsCardiac surgery (CS) can induce an inflammatory response (IR) that is associated with poorer outcomes. Immature platelets are among the factors that may be associated with IR development. We aimed to evaluate whether immature platelet fraction (IPF) could be a predictive biomarker for IR and whether IPF could improve the prognosis assessment of IR for Acute Physiologic and Chronic Health Evaluation (APACHE II) and Sequential Organ Failure Assessment (SOFA) following CS.MethodsThree-hundred and twenty-seven (327) patients who underwent CS were enrolled during the study period. IR was defined according to the need for vasopressor support (>48 hours). Perioperative variables and outcomes were registered in our database. IPF was measured immediately following CS and at 24 hours by Sysmex XN analyzer and the difference between both measurements (ΔIPF) was calculated. To assess the relationship between ΔIPF and IR, univariate and multivariate logistic regression were performed. To analyse the additive value of ΔIPF in APACHE II and SOFA scores in predicting IR, an area under the receiver operating characteristic (AUROC) curve was calculated.ResultsAmong 327 patients included, 60 patients (18.3%) developed IR. Multivariate analysis showed ΔIPF was significantly associated with IR (OR: 1.26; 95% CI: 1.01 to 1.56; p=0.038). The combination of ΔIPF with scores improved the AUROC for IR prediction: 0.629 vs 0.728 (p=0.010) for APACHE II and 0.676 vs 0.715 (p=0.106) for SOFA.ConclusionThese findings suggested that ΔIPF may be a useful and low-cost biomarker for the early identification of patients at risk of IR development.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection in some hospitalized patients has shown some important alterations in laboratory tests. The aim of this study was to establish the most relevant quantities associated with the worst prognosis related to COVID-19. This was a descriptive, longitudinal, observational and retrospective study, in a cohort of 845 adult inpatients from Bellvitge University Hospital (L’Hospitalet de Llobregat, Barcelona, Spain). A multivariate regression analysis was carried out in demographic, clinical and laboratory data, comparing survivors (SURV) and non-survivors (no-SURV). A receiver operating characteristic analysis was also carried out to establish the cut-off point for poor prognostic with better specificity and sensibility. Dynamic changes in clinical laboratory measurements were tracked from day 1 to day 28 after the onset of symptoms. During their hospital stay, 18% of the patients died. Age, kidney disease, creatinine (CREA), lactate-dehydrogenase (LD), C-reactive-protein (CRP) and lymphocyte (LYM) concentration showed the strongest independent associations with the risk of death in the multivariate regression analysis. Established cut-off values for poor prognosis for CREA, LD, CRP and LYM concentrations were 75.0 μmol /L, 320 U/L, 80.9 mg/L and 0.69 x109/L. Dynamic profile of laboratory findings, were in agreement with the consequences of organ damage and tissue destruction. Age, kidney disease, CREA, LD, CRP and LYM concentrations in COVID-19 patients from the southern region of Catalonia provide important information for their prognosis. Measurement of LD has demonstrated to be very good indicator of poor prognosis at initial evaluation because of its stability over time.

Objectives Change limits, more commonly called delta check, are those in which a change in a patient’s measured result in relation to their corresponding preceding measurement is suspected of being erroneous and should be considered as a doubtful result. The aim of this study was to provide change limits for some biochemical and haematological quantities to detect doubtful measured results and to assess its effectiveness to detect erroneous results for their application in and the standardization of the plausibility control. Methods Change limits have been estimated for 13 biochemical and 6 haematological quantities. For each quantity, relative differences (D), expressed as a percentage between the two consecutive measured results from the same patient (from scheduled laboratory requests), were calculated. From these differences (D), the p5 and p95 percentiles of the data distribution were calculated. To assess the effectiveness of the change limits to detect laboratory errors, 43 erroneous laboratory reports, containing different biochemical and haematological quantities, were obtained from the standard laboratory plausibility control procedure. Results From the 43 erroneous laboratory reports, 31 (72%) were due to endovenous administration errors and 12 (28%) were due to mislabeling errors. All erroneous laboratory reports were detected when the change limits of the quantities were combined and applied together. Conclusions The best combination of quantities, which detect all the erroneous reports in the same specimen were: potassium, albumin, creatinine, glucose and haemoglobin.

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