11
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      COVID-19 Mortality in the Delta Wave in India: A Hospital-Based Study From Ramanagara District, Karnataka

      research-article
      1 , 2 , 3 , , 4 , 2 , 5
      ,
      Cureus
      Cureus
      covid-19, sars cov-2, delta variant, second wave, covid-19 epidemiology, inhospital mortality

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Introduction: Coronavirus 19 (COVID-19) disease spread rapidly over the world since its inception in December 2019 in Wuhan, China. India too was crippled by the burden of high caseloads and deaths. The first death caused by COVID-19 in Karnataka was reported on March 13, 2020. There is a plethora of information on the descriptive statistics, epidemiology, and management of COVID-19 cases. However, there has not been an in-depth and extensive exploration of COVID-19 mortality data in terms of published research from India. The study area was a 300 bedded tertiary care center in Ramnagara district, Karnataka. During the second wave, 150 beds were dedicated to COVID-19 cases referred from government centers. This study was carried out to assess the in-hospital mortality at this institute during the second wave. The expected outcome of this study was to shed light on co-morbidities associated with mortality, the age and sex distribution in mortality, and any other significant factors influencing mortality due to COVID-19.

          Methodology: A hospital-based, retrospective, and observational-analytical study was carried out during April-August 2021, the second wave of COVID-19. The data included all deaths recorded in-hospital during the dedicated COVID-19 referral center status. Data were collected from case sheets and mortality audit forms that included basic demographics, symptoms, co-morbidities, admission pathway, transfer to ICU, oxygen therapy, ventilator requirement, duration of hospitalization, laboratory profile, and management modalities prior to death. Data were compiled into Microsoft Excel and were analyzed with JASP software (open source). Data were interpreted in terms of frequencies, averages with standard deviation, and bivariate and multivariate analysis.

          Results: We analyzed mortality audits of 91 adult patients and one neonate. The male-to-female ratio was 1.67:1 (> 60% male), with an average age of 53.4 years (standard deviation 15.4 years). Most of the patients fell in the age range of 36 to 65 years (65%). The average duration was 5.6 days (range 0-35 days). The most common symptom was fever (84, 92.31%), followed by breathlessness (77, 84.62%) and fatigue (65, 71.43%). Only 10 had a positive contact history and only one patient reported travel to a containment zone. The source of infection was indeterminate in the majority of cases. Diabetes mellitus and hypertension were the commonest associated comorbidities. Almost three-quarters of the patients were tachypneic at admission and nearly 90% had low levels which included 43 patients with critically low SpO 2. The inflammatory indicators, such as WBC count, CRP, and d-dimer, were raised in many patients (WBC count raised in 40% and d-dimer, CRP raised in > 50% of cases). A striking 83% of the patients had hyperglycemia. The most common immediate cause of death pertained to the respiratory system (ARDS, refractory hypoxia, respiratory) in more than half of the patients.

          Conclusion: This study reported the clinical and laboratory characteristics of 91 adult COVID-19 mortality cases at a teaching hospital at the peak of the Delta wave in Karnataka. While inflammatory indicators such as WBC count, CRP, and d-dimer were raised in many patients, our most remarkable finding was the high frequency of hyperglycemia. The findings of our study would contribute to enhancing the understanding of the clinical correlates and progression of COVID-19.

          Related collections

          Most cited references20

          • Record: found
          • Abstract: found
          • Article: not found

          A Novel Coronavirus from Patients with Pneumonia in China, 2019

          Summary In December 2019, a cluster of patients with pneumonia of unknown cause was linked to a seafood wholesale market in Wuhan, China. A previously unknown betacoronavirus was discovered through the use of unbiased sequencing in samples from patients with pneumonia. Human airway epithelial cells were used to isolate a novel coronavirus, named 2019-nCoV, which formed a clade within the subgenus sarbecovirus, Orthocoronavirinae subfamily. Different from both MERS-CoV and SARS-CoV, 2019-nCoV is the seventh member of the family of coronaviruses that infect humans. Enhanced surveillance and further investigation are ongoing. (Funded by the National Key Research and Development Program of China and the National Major Project for Control and Prevention of Infectious Disease in China.)
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            An interactive web-based dashboard to track COVID-19 in real time

            In December, 2019, a local outbreak of pneumonia of initially unknown cause was detected in Wuhan (Hubei, China), and was quickly determined to be caused by a novel coronavirus, 1 namely severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The outbreak has since spread to every province of mainland China as well as 27 other countries and regions, with more than 70 000 confirmed cases as of Feb 17, 2020. 2 In response to this ongoing public health emergency, we developed an online interactive dashboard, hosted by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, Baltimore, MD, USA, to visualise and track reported cases of coronavirus disease 2019 (COVID-19) in real time. The dashboard, first shared publicly on Jan 22, illustrates the location and number of confirmed COVID-19 cases, deaths, and recoveries for all affected countries. It was developed to provide researchers, public health authorities, and the general public with a user-friendly tool to track the outbreak as it unfolds. All data collected and displayed are made freely available, initially through Google Sheets and now through a GitHub repository, along with the feature layers of the dashboard, which are now included in the Esri Living Atlas. The dashboard reports cases at the province level in China; at the city level in the USA, Australia, and Canada; and at the country level otherwise. During Jan 22–31, all data collection and processing were done manually, and updates were typically done twice a day, morning and night (US Eastern Time). As the outbreak evolved, the manual reporting process became unsustainable; therefore, on Feb 1, we adopted a semi-automated living data stream strategy. Our primary data source is DXY, an online platform run by members of the Chinese medical community, which aggregates local media and government reports to provide cumulative totals of COVID-19 cases in near real time at the province level in China and at the country level otherwise. Every 15 min, the cumulative case counts are updated from DXY for all provinces in China and for other affected countries and regions. For countries and regions outside mainland China (including Hong Kong, Macau, and Taiwan), we found DXY cumulative case counts to frequently lag behind other sources; we therefore manually update these case numbers throughout the day when new cases are identified. To identify new cases, we monitor various Twitter feeds, online news services, and direct communication sent through the dashboard. Before manually updating the dashboard, we confirm the case numbers with regional and local health departments, including the respective centres for disease control and prevention (CDC) of China, Taiwan, and Europe, the Hong Kong Department of Health, the Macau Government, and WHO, as well as city-level and state-level health authorities. For city-level case reports in the USA, Australia, and Canada, which we began reporting on Feb 1, we rely on the US CDC, the government of Canada, the Australian Government Department of Health, and various state or territory health authorities. All manual updates (for countries and regions outside mainland China) are coordinated by a team at Johns Hopkins University. The case data reported on the dashboard aligns with the daily Chinese CDC 3 and WHO situation reports 2 for within and outside of mainland China, respectively (figure ). Furthermore, the dashboard is particularly effective at capturing the timing of the first reported case of COVID-19 in new countries or regions (appendix). With the exception of Australia, Hong Kong, and Italy, the CSSE at Johns Hopkins University has reported newly infected countries ahead of WHO, with Hong Kong and Italy reported within hours of the corresponding WHO situation report. Figure Comparison of COVID-19 case reporting from different sources Daily cumulative case numbers (starting Jan 22, 2020) reported by the Johns Hopkins University Center for Systems Science and Engineering (CSSE), WHO situation reports, and the Chinese Center for Disease Control and Prevention (Chinese CDC) for within (A) and outside (B) mainland China. Given the popularity and impact of the dashboard to date, we plan to continue hosting and managing the tool throughout the entirety of the COVID-19 outbreak and to build out its capabilities to establish a standing tool to monitor and report on future outbreaks. We believe our efforts are crucial to help inform modelling efforts and control measures during the earliest stages of the outbreak.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Elevated glucose level leads to rapid COVID-19 progression and high fatality

              Objectives We aimed to identify high-risk factors for disease progression and fatality for coronavirus disease 2019 (COVID-19) patients. Methods We enrolled 2433 COVID-19 patients and used LASSO regression and multivariable cause-specific Cox proportional hazard models to identify the risk factors for disease progression and fatality. Results The median time for progression from mild-to-moderate, moderate-to-severe, severe-to-critical, and critical-to-death were 3.0 (interquartile range: 1.8–5.5), 3.0 (1.0–7.0), 3.0 (1.0–8.0), and 6.5 (4.0–16.3) days, respectively. Among 1,758 mild or moderate patients at admission, 474 (27.0%) progressed to a severe or critical stage. Age above 60 years, elevated levels of blood glucose, respiratory rate, fever, chest tightness, c-reaction protein, lactate dehydrogenase, direct bilirubin, and low albumin and lymphocyte count were significant risk factors for progression. Of 675 severe or critical patients at admission, 41 (6.1%) died. Age above 74 years, elevated levels of blood glucose, fibrinogen and creatine kinase-MB, and low plateleta count were significant risk factors for fatality. Patients with elevated blood glucose level were 58% more likely to progress and 3.22 times more likely to die of COVID-19. Conclusions Older age, elevated glucose level, and clinical indicators related to systemic inflammatory responses and multiple organ failures, predict both the disease progression and the fatality of COVID-19 patients.
                Bookmark

                Author and article information

                Journal
                Cureus
                Cureus
                2168-8184
                Cureus
                Cureus (Palo Alto (CA) )
                2168-8184
                18 August 2023
                August 2023
                : 15
                : 8
                : e43678
                Affiliations
                [1 ] Community Medicine, Institute of Health Management Research Bangalore (IHMR-B), Bangalore, IND
                [2 ] Forensic Medicine, Dr. Chandramma Dayananda Sagar Institute of Medical Education and Research, Bangalore, IND
                [3 ] Community Medicine, Kalinga Institute of Medical Sciences, Bhubaneswar, IND
                [4 ] Anesthesiology, DM Wayanand Institute of Medical Sciences, Wayanad, IND
                [5 ] Forensic Medicine, Karpaga Vinayaga Institute of Medical Sciences and Research Center, Maduranthakam, IND
                Author notes
                Article
                10.7759/cureus.43678
                10505258
                37724226
                785646df-2ed6-4214-9118-6a531e7b547d
                Copyright © 2023, Mukhopadhyay et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 17 August 2023
                Categories
                Infectious Disease
                Public Health
                Forensic Medicine

                covid-19,sars cov-2,delta variant,second wave,covid-19 epidemiology,inhospital mortality

                Comments

                Comment on this article

                scite_
                0
                0
                0
                0
                Smart Citations
                0
                0
                0
                0
                Citing PublicationsSupportingMentioningContrasting
                View Citations

                See how this article has been cited at scite.ai

                scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.

                Similar content296

                Most referenced authors333