Sepsis is a medical emergency that describes the body’s systemic immune response to an infection and can lead to end-stage organic dysfunction and death. Despite the advances in understanding the pathophysiology of this syndrome and therapies, sepsis remains one of the leading causes of morbidity and mortality in critically ill patients. Early diagnosis and rapid intervention are essential to improve outcomes, which inspired the concept “golden hour,” during which the correction of shock and organic dysfunction can improve the patients’ outcomes. But the initial presentation of sepsis is often nonspecific and its severity is difficult to assess. Anomalies in temperature, heart and respiratory rates and leukocyte counts are manifestations of systemic inflammatory response syndrome (SIRS). Diagnosis, management and follow-up of patients with sepsis remains a challenge, and diverse biomarkers have been proposed for the timely diagnosis and prognosis of septic patients: lactic acid, procalcitonin (PCT), C-reactive protein, immature granulocytes. The host’s initial response to infection is a humoral, cellular and neuroendocrine reaction to infection, and leukocytes interact with endothelial cells. The new generation of hematological analyzers incorporates technological innovations allowing to expand the information derived from the complete blood count: new leukocyte derived parameters are emerging as potentially useful markers in different clinical situations. Additional research parameters cell population data (CPD), characterizing different leukocyte populations have become available, and preliminary observations suggest their utility in the diagnosis of sepsis. This review emphasizes the value of CPD, reported by modern cellular counters for early recognition of sepsis, and therefore the potential improvement in patient outcomes.

A targeted and timely treatment can be a beneficial tool for patients with hematological emergencies (particularly acute leukemias). The key challenges in the early diagnosis of leukemias and related hematological disorders are their symptom-sharing nature and prolonged turnaround time as well as the expertise needed in reporting confirmatory tests. The present study made use of the potential morphological and immature fraction-related parameters (research items or cell population data) generated during complete blood cell count (CBC), through artificial intelligence (AI)/machine learning (ML) predictive modeling for early (at the pre-microscopic level) differentiation of various types of leukemias: acute from chronic as well as myeloid from lymphoid. The routine CBC parameters along with research CBC items from a hematology analyzer in the diagnosis of 1577 study subjects with hematological neoplasms were collected. The statistical and data visualization tools, including heat-map and principal component analysis (PCA,) helped in the evaluation of the predictive capacity of research CBC items. Next, research CBC parameter-driven artificial neural network (ANN) predictive modeling was developed to use the hidden trend (disease’s signature) by increasing the auguring accuracy of these potential morphometric parameters in differentiation of leukemias. The classical statistics for routine and research CBC parameters showed that as a whole, all study items are significantly deviated among various types of leukemias (study groups). The CPD parameter-driven heat-map gave clustering (separation) of myeloid from lymphoid leukemias, followed by the segregation (nodding) of the acute from the chronic class of that particular lineage. Furthermore, acute promyelocytic leukemia (APML) was also well individuated from other types of acute myeloid leukemia (AML). The PCA plot guided by research CBC items at notable variance vindicated the aforementioned findings of the CPD-driven heat-map. Through training of ANN predictive modeling, the CPD parameters successfully differentiate the chronic myeloid leukemia (CML), AML, APML, acute lymphoid leukemia (ALL), chronic lymphoid leukemia (CLL), and other related hematological neoplasms with AUC values of 0.937, 0.905, 0.805, 0.829, 0.870, and 0.789, respectively, at an agreeably significant (10.6%) false prediction rate. Overall practical results of using our ANN model were found quite satisfactory with values of 83.1% and 89.4.7% for training and testing datasets, respectively. We proposed that research CBC parameters could potentially be used for early differentiation of leukemias in the hematology–oncology unit. The CPD-driven ANN modeling is a novel practice that substantially strengthens the predictive potential of CPD items, allowing the clinicians to be confident about the typical trend of the “disease fingerprint” shown by these automated potential morphometric items.

% Microcytic and % hypochromic red cells show an inverse trend in beta thalassemia trait and in iron deficiency anemia (IDA). The aim of this study was to assess the discriminant value of % microcytic/% hypochromic ratio, as can be obtained from the Advia 2120 (Siemens) analyzer, in the differential diagnosis of microcytic anemia, compared to the conventional England and Fraser, Green and King, Mentzer, Ricerca, Shine and Lal and Srivastava indices. Receiver operating characteristic (ROC) curves, sensitivity, specificity and Youden index of indices were calculated for a set of 148 IDA patients and 170 beta thalassemia carriers. The Green and King index showed the best area under the curve (AUC, 0.962), with a Youden index of 80.9%; the % microcytic/% hypochromic ratio ranked second (AUC 0.943), with a Youden index of 76.3% and a sensitivity of 99.2%. This index, with high sensitivity for beta thalassemia screening, can be a useful tool in the differential diagnosis of microcytic anemia, so samples can be chosen for HbA(2) analysis, to confirm the presumptive diagnosis of the disease. As specificity is 77.1%, we assume it will be 22.9% for false positives, but these samples will have HbA(2) values within the reference range, for correct diagnosis.

Iron status is the result of the balance between the rate of erythropoiesis and the amount of the iron stores. Direct consequence of an imbalance between the erythroid marrow iron requirements and the actual supply is a reduction of red cell hemoglobin content, which causes hypochromic mature red cells and reticulocytes. The diagnosis of iron deficiency is particularly challenging in patients with acute or chronic inflammatory conditions because most of the biochemical markers for iron metabolism (serum ferritin and transferrin ) are affected by acute phase reaction. For these reasons, interest has been generated in the use of erythrocyte and reticulocyte parameters, available on the modern hematology analyzers. Reported during blood analysis routinely performed on the instrument, these parameters can assist in early detection of clinical conditions (iron deficiency, absolute, or functional; ineffective erythropoiesis, including iron restricted or thalassemia), without additional cost. Technological progress has meant that in recent years modern analyzers report new parameters that provide further information from the traditional count. Nevertheless these new parameters are exclusive of each manufacturer, and they are patented. This is an update of these new laboratory test biomarkers of hypochromia reported by different manufactures, their meaning, and clinical utility on daily practice.

New erythrocyte parameters are reported by the Sysmex XE 5000 analyzer. This instrument measures the hemoglobin (Hb) content of individual red cells, calculates the percentage of hypochromic red cells (%Hypo He) and the percentage of hyperchromic red cells (%Hyper He) and quantifies the proportion of marginally sized erythrocytes (%Micro R and %Macro R). The goals of the study were to establish the reference range for erythrocyte extended parameters, their value in different types of anemia and to investigate their reliability in the study of disorders of iron metabolism. Three hundred and ninety samples were analyzed. The Kolmogorov-Smirnoff test, independent samples t-test and Pearson correlation were calculated; receiver operating characteristic (ROC) curve analysis was used to determine their diagnostic performance. The values of the four parameters studied were normally distributed and statistically different (p<0.0001) in the different groups of patients; the only exception was %Hypo He in cases of iron deficiency and thalassemia (p=0.3758). Results of ROC curve analysis for %Hypo He in the diagnosis of restricted erythropoiesis reticulocyte Hb content (reticulocyte hemoglobin content, Ret He <29 pg) were: area under the curve 0.963; cut-off 1.8%; sensitivity 98.3%; specificity 91.1%. The new parameters appear to be sensitive for detecting small changes in the number of red cells with inadequate hemoglobinization and volume.