Evaluation of Characteristics and Outcomes of COVID-19 Between First Wave and Second Wave: Study From a Tertiary Cancer Care Centre, Delhi, India.

Background and objectives The second wave of coronavirus disease-19 (COVID-19) had several severe consequences in the form of rising cases, deaths, and overwhelming health infrastructure in India. However, the similarities and differences between the characteristics of the first and second waves have yet to be explained. The objectives of the study were to compare the incidence, clinical management, and mortality rates between two waves. Methods The COVID-19 data collated from Rajiv Gandhi Cancer Institute and Research Centre, Delhi between the first wave (1 April 2020 to 27 February 2021) and second wave (1 March 2021 to 30 June 2021) were evaluated in terms of incidence, the clinical course of the disease, and mortality rates. Results The number of subjects hospitalized in the first and second waves was 289 and 564, respectively. Compared to the first wave, the proportion of patients with severe disease was higher (9.7% vs. 37.8%). Several parameters such as age group, grade of disease, the reason for hospitalization, values of peripheral oxygen saturation, type of respiratory support, response to therapy, vital status, and others show statistically significant differences between the two waves (P<0.001). The mortality rate in the second wave was significantly higher (20.2% vs. 2.4%, P<0.001) than in the first wave. Interpretation and conclusions The clinical course and outcomes of COVID-19 significantly differ between the first and second waves. There is a higher incidence of hospitalized patients (66.1% vs. 33.9%) with drastically increased case fatality rate in the second wave. Disease severity in the first wave is four times lower than in the second wave. The second wave was quite devastating, which led to the shortage of critical care facilities and the loss of a significant number of lives.

Cross-Sectional Study to Establish the Utility of Serum Tumor Markers in the Diagnosis of Lung Cancer.

BACKGROUND: Reliable blood markers for aiding lung cancer (LC) diagnosis and differentiating LC from tuberculosis are lacking in India. METHODOLOGY: In this single-centre, cross-sectional, real-world study, serum levels of 5 TMs (CEA, CYFRA 21-1, SCC, ProGRP, NSE) were measured from consented patients with suspicious lung nodules who were candidates for biopsy, and also from healthy controls. TM level measurement was done through electrochemiluminescent immunoassay, followed by histological diagnosis on the biopsied specimen. Using package insert cut-offs, sensitivity and specificity of the 5 TMs were evaluated individually and in combination. Using receiver operating characteristic (ROC) curves of individual TMs, the ability of CEA, CYFRA 21-1, and ProGRP taken together was evaluated for its ability to differentiate LC from no-LC. RESULTS: Out of 178 subjects, 160 had LC (147 NSCLC; 13 SCLC). NSCLC patients had higher median values of CYFRA 21-1 and SCC; SCLC patients had higher median values of CEA, NSE, and ProGRP. Adenocarcinoma-NSCLC patients had higher median values of CEA, CYFRA 21-1, NSE, and ProGRP; squamous-NSCLC patients had higher median value of SCC. For differentiating LC from no-LC, the combination of all 5 TMs (sensitivity:97.5%, specificity:33.3%) and combination of CEA, CYFRA 21-1 and ProGRP (sensitivity:91.3%, specificity:88.9%) were found suitable. CONCLUSION: Combination of all 5 TMs, and combination of CEA, CYFRA 21-1, and ProGRP represents an easy and non-invasive method for aid in LC diagnosis that does not require technical expertise. TM evaluation can also supplement histological diagnosis of LC. 
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COVID-19 mortality in cancer patients: a report from a tertiary cancer centre in India.

BACKGROUND: Cancer patients, especially those receiving cytotoxic therapy, are assumed to have a higher probability of death from COVID-19. We have conducted this study to identify the Case Fatality Rate (CFR) in cancer patients with COVID-19 and have explored the relationship of various clinical factors to mortality in our patient cohort. METHODS: All confirmed cancer cases presented to the hospital from June 8 to August 20, 2020, and developed symptoms/radiological features suspicious of COVID-19 were tested by Real-time polymerase chain reaction assay and/or cartridge-based nucleic acid amplification test from a combination of naso-oropharyngeal swab for SARS-CoV-2. Clinical data, treatment details, and outcomes were assessed from the medical records. RESULTS: Of the total 3,101 cancer patients admitted to the hospital, 1,088 patients were tested and 186 patients were positive for SARS-CoV-2. The CFR in the cohort was 27/186 (14.52%). Univariate analysis showed that the risk of death was significantly associated with the presence of any comorbidity (OR: 2.68; (95% CI [1.13-6.32]); P = 0.025), multiple comorbidities (OR: 3.01; (95% CI [1.02-9.07]); P = 0.047 for multiple vs. single), and the severity of COVID-19 presentation (OR: 27.48; (95% CI [5.34-141.49]); P < 0.001 for severe vs. not severe symptoms). Among all comorbidities, diabetes (OR: 3.31; (95% CI [1.35-8.09]); P = 0.009) and cardiovascular diseases (OR: 3.77; (95% CI [1.02-13.91]); P = 0.046) were significant risk factors for death. Anticancer treatments including chemotherapy, surgery, radiotherapy, targeted therapy, and immunotherapy administered within a month before the onset of COVID-19 symptoms had no significant effect on mortality. CONCLUSION: To the best of our knowledge, this is the first study from India reporting the CFR, clinical associations, and risk factors for mortality in SARS-CoV-2 infected cancer patients. Our study shows that the frequency of COVID-19 in cancer patients is high. Recent anticancer therapies are not associated with mortality. Pre-existing comorbidities, especially diabetes, multiple comorbidities, and severe symptoms at presentation are significantly linked with COVID-19 related death in the cohort.

Bisalbuminemia: A Rare Finding on Serum Electrophoresis.

Bisalbuminemia is an uncommon finding that is seen as bifid albumin peak on serum protein electrophoresis. We report here this unusual finding in an adult male diagnosed with multiple myeloma on routine workup.

Preanalytical error occurrence rate in clinical chemistry laboratory of a public hospital in India.

BACKGROUND: The pre and post analytical phase in a testing cycle contributes up to 93% of total laboratory errors. Hence, it is of utter importance for the laboratory to study error occurrence rates during the testing cycle and implement a quality improvement plan to release an accurate result. METHODS: The present study was conducted during the period 2008 - 2009 in the clinical biochemistry lab in Lady Hardinge Medical College and associated hospitals. During a 6 month period 32,589 samples were monitored for major preanalytical problems at the receiving counter of the laboratory. RESULTS: Out of all laboratory problems, up to 61% are associated with the preanalytical phase in the lab. Out of this, 33% of the errors are associated with the test request forms, 18% errors with sample collection in glass vials, and 3% errors associated with sample processing in the laboratory. As per Quality Improvement policy of the lab, a closed blood evacuation system (vacutainers) has been implemented for sample collection. Post implementation, preanalytical error rate has been reduced to 48%, however, tube filling errors still remain the major problem noted. CONCLUSIONS: Based on these observations, transcription errors related to test request forms is of great concern and needs corrective measures via proper educational programs. If this area is compromised it can lead to adverse patient outcome. However, with the use of vacutainers a better specimen quality and health care worker safety is achieved. It also decreases inconvenience to the patient.

Metabolic effects of obesity: A review.

With the many recent advances in the biomedical world, vast changes are taking place in our growing knowledge of the physiological aspects of almost all the tissues and organs of the human body. One of the most prevalent topics of discussion is the question of obesity and its effect on the metabolic changes in the human body. The original classical role of adipose tissue as an energy storage organ has been greatly modified. We now know that it is an endocrine organ, producing adipokines like leptin, adiponectin, visfatin, resistin, apelin, etc, which modulate metabolic processes in the body. Since obesity is associated with an increase in the adipose tissue mass, these hormones may be expected to be produced in increased concentrations and may thus have a significant impact on the macronutrient metabolism. Further, these adipokines may interact with long term energy modulators like insulin. Even though the scientific community has started unravelling the mysteries of the close linkage between obesity, its hormones and their physiological effects, a lot still remains to be discovered. The present discussion makes an attempt to trace the basic modern day concepts of the role of obesity in various metabolic processes.