Shifa Maqsood¹, A Sharadrutha²

¹Second year Postgraduate student,²Professor,Department of Pathology, Prathima Institute of Medical Sciences, Nagunur, Karimnagar, Telangana-India.

Address for correspondence: Dr. A.Sharadrutha, Professor, Department of Pathology,Prathima Institute of Medical Sciences, Nagunur, Karimnagar, Telangana, India.

Email: drutha77@yahoo.com.

Introduction: Complete blood count by automated hematology analyzers and peripheral smear examination complement each other to provide a comprehensive report on patient’s blood sample. Data displayed as visual image, can convey information with more impact than numbers. It helps laboratory personnel to diagnose different anemias directly from automated hematology analyzer and correlate with peripheral smear.

Objectives: To know utility and advantage of red cell histograms and to study automated histogram patterns in various anemias.

Materials and Methods :This is prospective study conducted from January 2017 to June 2017 in Prathima Institute of Medical Sciences on 100 patients of >1year age who were anemic (Hb<12gm%). Complete blood count including hemoglobin, RBC indices, haematocrit ,total WBC count, differential count, platelet countwas obtained from ALFASWELAB,3 part automated hematology analyzer. Peripheral smear was obtained for all cases.

Results :Microcytic hypochromic anemia was the most common (68%). Representing the histogram variation in various anemias: Out of the 7% cases of normocytic normochromic anemia, 5% showed RBC histogram with short peak and 2% showed mild broad base curve histogram. Out of the 68% of microcytic hypochromic anemia, 5% showed normal histogram, 40% showed left shift histogram, 21% showed broad base curve and 2% showed bimodal histogram. Out of 7% cases of Macrocytic anemia, 5% showed right shift with broad base curve, 1% showed bimodal curve and 1% showed short peak histogram. Out of 6% cases of dimorphic anemia 1% showed normal histogram, 1% showed broad base histogram, 1% right and 1% left shift histogram and 2% showed bimodal histogram. Out of the 6% cases of the pancytopenia, 3% showed right shift and 3% showed short peak. Among 4% cases of thalassemia, 2% showed abnormal histogram which was not starting at the baseline with left shift bimodal, broad base curve, 1% showed bimodal curve. 2% cases of sickle cell anemia showed broad based curves with short peak.

Conclusion :: Different patterns ofhistograms are obtained in different anemias. Histogram gives information about abnormality of sample and need for follow up on peripheral smear. Histograms can be useful to prioritize cases and help in speedy disposal of samples in laboratorybut will not identify conditions like malarial parasites, membrane abnormalities that cause anemia.

Keywords :: Histograms, anemias, automated hematology analyzer.