RESUMEN
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.
RESUMEN
OBJECTIVES: To investigate the clinical significance of numeric and morphologic peripheral blood (PB) changes in coronavirus disease 2019 (COVID-19)-positive patients in predicting the outcome, as well as to compare these changes between critically ill COVID-19-positive and COVID-19-negative patients. METHODS: The study included 90 COVID-19-positive (51 intensive care unit [ICU] and 39 non-ICU) patients and 30 COVID-19-negative ICU patients. We collected CBC parameters (both standard and research) and PB morphologic findings, which were independently scored by two hematopathologists. RESULTS: All patients with COVID-19 demonstrated striking numeric and morphologic WBC changes, which were different between mild and severe disease states. More severe disease was associated with significant neutrophilia and lymphopenia, which was intensified in critically ill patients. Abnormal WBC morphology, most pronounced in monocytes and lymphocytes, was associated with more mild disease; the changes were lost with disease progression. Between COVID-19-positive and COVID-19-negative ICU patients, significant differences in morphology-associated research parameters were indicative of changes due to the severe acute respiratory syndrome coronavirus 2 virus, including higher RNA content in monocytes, lower RNA content in lymphocytes, and smaller hypogranular neutrophils. CONCLUSIONS: Hospitalized patients with COVID-19 should undergo a comprehensive daily CBC with manual WBC differential to monitor for numerical and morphologic changes predictive of poor outcome and signs of disease progression.