Cancer care continuum at a tertiary care centre in India during the Covid-19 pandemic and nationwide lockdown: Healthcare delivery through telemedicine.

Background The Covid-19 pandemic and subsequent lockdown in India caused disruptions in cancer treatment due to the restriction on movement of patients. We aimed to maintain continuity in cancer treatment during the lockdown through teleconsultations. We tried to reach out to our patients using telephonic consultations by establishing a Teleconsult Centre facility run by a team of doctors and patient navigators. Methods We telephonically contacted all patients who had outpatient appointments from 23 March to 30 April 2020 at our centre through the Teleconsult Centre to understand their current circumstances, feasibility of follow-up, local resources and offered best possible alternatives to continue cancer treatment, if required. Results Of the 2686 patients scheduled for follow-up during this period, we could contact 1783 patients in 9 working days. Through teleconsultations, we could defer follow-ups of 1034 patients (57.99%, 95% confidence interval [CI] 55.6%-60.3%), thus reducing the need for patients to travel to the hospital. Change in systemic therapy was made in 75 patients (4.2%, 95% CI 3.3%-5.2%) as per the requirements and available resources. Symptoms suggestive of disease progression were picked up in 12 patients (0.67%, 95% CI 0.35%-1.17%), who were advised to meet local physicians. Conclusion Our study suggests that the majority of patients on follow-up can be managed with teleconsultation in times of crisis. Teleconsultation has the potential of being one of the standard methods of patient follow-up even during periods of normalcy.

Predicting the impact of the third wave of COVID-19 in India using hybrid statistical machine learning models: A time series forecasting and sentiment analysis approach.

BACKGROUND: Since January 2020, India has faced two waves of COVID-19; preparation for the upcoming waves is the primary challenge for public health sectors and governments. Therefore, it is important to forecast future cumulative confirmed cases to plan and implement control measures effectively. METHODS: This study proposed a hybrid autoregressive integrated moving average (ARIMA) and Prophet model to predict daily confirmed and cumulative confirmed cases. The built-in auto.arima function was first used to select the optimal hyperparameter values of the ARIMA model. Then, the modified ARIMA model was used to find the best fit between the test and forecast data to find the best model parameter combinations. Articles, blog posts, and news stories from virologists, scientists, and health experts related to the third wave of COVID-19 were gathered using the Python web scraping package Beautiful Soup. Their opinions (sentiments) toward the potential third wave were analyzed using natural language processing (NLP) libraries. RESULTS: A spike in daily confirmed and cumulative confirmed cases was predicted in India in the next 180 days based on past time series data. The results were validated using various analytical tools and evaluation metrics, producing a root mean square error (RMSE) of 0.14 and a mean absolute percentage error (MAPE) of 0.06. The NLP processing results revealed negative sentiments in most articles and blogs, with few exceptions. CONCLUSION: The findings of this study suggest that there will be more active cases in the upcoming days. The proposed models can forecast future daily confirmed and cumulative confirmed cases. This study will help the country and states plan appropriate public health measures for the upcoming waves of COVID-19.

Clinical characteristics, laboratory parameters and outcomes of COVID-19 in cancer and non-cancer patients from a tertiary Cancer Centre in India.

BACKGROUND: There is paucity of data regarding clinical characteristics, laboratory parameters and outcomes of coronavirus disease (COVID-19) in cancer versus non-cancer patients, particularly from India. MATERIALS AND METHODS: This was an observational, single-centre, retrospective analysis of patients with laboratory-confirmed COVID-19 hospitalised in our institution between 22 May 2020 and 1 December 2020. We compared baseline clinical characteristics, laboratory parameters and outcomes of COVID-19 (overall mortality, time to discharge) between cancer and non-cancer patients. RESULTS: A total of 200 COVID-19 infection episodes were analysed of which 109 (54.5%) were patients with cancer and 91 (45.5%) were patients without cancer. The median age was 43 (interquartile range [IQR]:32-57), 51 (IQR: 33-62) and 38 (IQR: 31.5-49.3) years; of whole cohort, cancer and non-cancer patients, respectively. Comparison of outcomes showed that oxygen requirement (31.2% [95% CI: 22.6-40.7] vs. 17.6% [95% CI: 10.4-26.9]; p = 0.03), median time to discharge (11 days [IQR: 6.75-16] vs. 6 days [IQR: 3-9.75]; p < 0.001) and mortality (10.0% [95% CI: 5.2-17.3] vs. 1.1% [95% CI: 0.03-5.9]; p = 0.017) were significantly higher in patients with cancer. In univariable analysis, factors associated with higher mortality in the whole cohort included diagnosis of cancer (10.1% vs. 1.1%; p = 0.027; odds ratio [OR]: 7.04), age ≥60 (17.4% vs. 2.6%; p = 0.001; OR: 7.38), oxygen requirement (22% vs. 0.6%; p < 0.001; OR: 29.01), chest infiltrates (19.2% vs. 1.4%; p < 0.001; OR: 22.65), baseline absolute lymphocyte count <1 × 109 /L (10.8% vs. 1.9%; p = 0.023; OR:5.1), C-reactive protein >1 mg% (12.8% vs. 0%; p = 0.027; OR: 24.69), serum procalcitonin >0.05 ng/ml (22.65% vs. 0%; p = 0.004; OR: 4.49) and interleukin-6 >6 pg/ml (10.8% vs. 1.3%; p = 0.036; OR: 3.08). In multivariable logistic regression, factors significantly associated with mortality were oxygen requirement (p = 0.005; OR: 13.11) and high baseline procalcitonin level (p = 0.014; OR: 37.6). CONCLUSION: Cancer patients with COVID-19 have higher mortality and require longer hospital stay. High procalcitonin levels and oxygen requirement during admission are other factors that affect outcomes adversely.

Clinical course of severe COVID19 treated with tocilizumab and antivirals post-allogeneic stem cell transplant with extensive chronic GVHD.

Recipients of allogeneic hematopoietic stem cell transplantation (allo-HSCT) are an immunocompromised group who are likely to develop severe complications and mortality because of coronavirus disease 2019 (COVID-19). We report here a 61-year-old male patient of primary myelofibrosis who underwent an allo-HSCT 6 years earlier, had chronic graft-versus-host disease (cGVHD) involving the liver, lung, eyes, and skin, (with recurrent episodes of pulmonary infections) who developed severe COVID-19. The patient was treated with tocilizumab, and a combination of lopinavir/ritonavir, ribavirin, interferon-ß1b. He was discharged after 31 days with full recovery. Tocilizumab, a humanized monoclonal antibody against IL6, has been shown to benefit respiratory manifestations in severe COVID19. However, this is first report, to our knowledge, of its use and benefit in a post HSCT recipient.