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Introduction: For the release of precise and accurate reports of routine tests, its necessary to follow a proper quality management system in the clinical laboratory. One of the most popular quality management system tools, for process improvement, six sigma has been accepted widely in the laboratory testing process. It gives an objective assessment of analytical methods and instrumentation. Six sigma measures the outcome of a process, on a scale of 0 to 6. The poor outcomes are measured in terms of defects per million opportunities (DPMO). Aim: To do the performance assessment of each analyte by six sigma analysis and to plan and chart out a better, customised, quality control plan for each analyte, according to its own sigma value. Materials and Methods: This was a retrospective observational study, conducted from January 2022 to June 2022, in the Department of Central Laboratory, KMCT Medical College, Kozhikode, Kerala, India. The precision and accuracy of 26 parameters in both haematology and biochemistry were assessed via Internal Quality Control (IQC) and External Quality Assurance (EQAS) Programme, analysis, and their performance was assessed by sigma analysis. Results: Clinical chemistry parameters showed an average percentage of Coefficient of Variation (CV%) of 2.65% and 2.3% for all the parameters in L2 (normal level) and L3 (abnormal levels) respectively. In haematology, the average CV% came out as, 1.3% (high level),1.82% (low level), and 1.35% (normal level). These values indicate excellent precision for all parameters in both clinical chemistry and haematology; with CV% below 3%. It was observed in the month of May, due to reconstitution errors, bias% showed a setback in a few chemistry parameters, due to which the sigma was lowered. Parameters with <3 sigma metrics (poor performance) occupy 37%, 3-6 sigma metrics (good performance) occupy 29% and >6 sigma metrics (world-class performance) occupy 34% of all the 26 parameters of clinical chemistry and haematology. Mean, Standard Deviation (SD) for biochemistry parameters were calculated using the daily IQC data. Conclusion: With the present study, sigma metric analysis provides a benchmark for the laboratory to design a protocol for IQC, address poor assay performance, and assess the efficiency of the existing laboratory processes. It is on the basis of strict quality control measures and sigma analysis, the present Institute, was able to achieve world-class performance in many analytes of clinical chemistry and haematology disciplines. However, a few analytes like alkaline phosphatase, alanine transaminase, aspartate transaminase, and total protein needed more stringent external quality assurance monitoring and modified quality control measures.

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Introduction: Diffuse Large B-cell Lymphoma (DLBCL) is the most common Non-Hodgkin’s Lymphoma (NHL). Using Gene Expression Profiling (GEP) DLBCL has been subtyped into two groups of prognostic importance, Germinal Center B-cell (GCB) like and activated B-cell like. GCB DLBCL has a better survival and can be identified using Hans algorithm with three immunohistochemical markers Cluster Differentiation (CD10), B-cell lymphoma 6 (BCL6) and Multiple Myeloma Oncogene-1 (MUM1). Aim: To analyse DLBCL using Hans algorithm as both GCB lymphoma and non GCB lymphoma have different treatment and prognosis. Materials and Methods: This was a cross-sectional study conducted in the Department of Pathology, Government Medical College, Kozhikode, Kerala, India, from January 2019 to December 2020. A total of 97 DLBCL cases received in the Department of Pathology, from January 2016 to December 2019 were included in the study were subtyped using Hans algorithm. CD10, BCL6 and MUM1 were considered positive, if more than 30% of the tumour cells showed staining by the respective antibodies. The relation between DLBCL subtypes and the age, gender, symptoms, site of initial involvement, organomegaly, Ann Arbor stage, treatment response and overall survival. Findings in the patients were analysed using Chi-square test. Statistical Package for Social Sciences (SPSS) software version 18.0. Overall survival was estimated using Kaplan-Meier method. Results: The median age of study population was 60 years (age range: 31-85 years) and there were 55 (56.7%) males and 42 (43.3%) females. Out of the 97 DLBCL cases 47 (48.5%) were GCB and 50 (51.5%) were non GCB subtype. Statistical analysis was done only in 88 patients (excluded nine recurrent lymphoma patients, which may have a different outcome). There was significant association (p-value=0.003) between stage and subtypes as majority of the non GCB cases presented in an advanced stage. The rate of complete remission with Rituximab Cyclophosphamid Hydroxydaunorubicin Oncovin Prednisone (RCHOP) chemotherapy was higher in GCB (58.75%) compared to non GCB (15.25%) subtypes (p-value=0.001). Overall survival rate of GCB was 74.4% and non GCB was 31% with a p-value of 0.001. There was no statistically significant relation between DLBCL subtypes and other clinicopathological factors. Conclusion: In the present study, the patients within the GCB subtype had better treatment response and overall survival rate compared to non GCB subtype. Non germinal center subtype presented in advanced stage and had a worse prognosis. Therefore, it is essential to subtype DLBCL in all cases to identify non GCB subtype, which may need additional treatment after RCHOP chemotherapy.