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Background India witnessed a massive surge of rhino orbital cerebral mucormycosis (ROCM) cases during the second wave of COVID-19, recording the highest number of cases in the world, indeed, an epidemic within the pandemic. Objectives To describe the clinical profile of patients with COVID-19-associated mucormycosis (CAM) and the clinical suspects for mucormycosis. Methods This single-center descriptive, observational study/audit was done at Indira Gandhi Medical College, Pondicherry, South India. This study is about the clinical profile of 7 CAM patients and 14 COVID-19 patients who were suspects of CAM, based on their risk factors and clinical symptoms, and were referred to the ENT department. Statistical analysis All the descriptive variables were summarized as mean, frequency, and percentages for qualitative data. Results All 7 CAM patients were COVID-19 positive and were not vaccinated against COVID-19, All 7 were known diabetic, all 7 had steroid therapy for their COVID status, and 5 out of 7 (71%) had uncontrolled diabetes mellitus at the time of diagnosis. Facial pain, nasal discharge, and eye swelling were the presenting symptoms of CAM. Maxillary and ethmoid sinuses were the most commonly involved para nasal sinuses. Four out of seven (57.1%) CAM patients survived after 16 months of follow-up, after surgical and medical treatment for CAM. Of the 14 clinical suspects who were negative for CAM, 2 were negative for COVID-19, their risk factors were brought under control, 3 expired due to COVID complications, and 9 patients are alive till date. Conclusion Uncontrolled diabetes is a risk factor for ROCM/CAM, another possible risk factor is steroid therapy, and we hypothesize that COVID infection could also be a possible risk factor that needs to be studied more extensively in a larger sample. Early clinical suspicion, withdrawal of steroids, rapid control of diabetes mellitus, appropriate investigations, and early surgical intervention combined with medical treatment offers better outcome. Supplementary Information The online version contains supplementary material available at 10.1186/s43163-023-00430-2.
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Objectives During the COVID-19 pandemic, several laboratories used different RNA extraction methods based on the resources available. Hence this study was done to compare the Ct values in qRT-PCR, time taken (sample processing-loading to PCR), manpower requirement, and cost of consumables between manual and automated methods. Materials and methods A cross-sectional study was done on 120 nasopharyngeal/oropharyngeal swabs received in VRDL for RT-PCR testing. Based on the results of automated RNA extraction (Genetix, HT 96 Purifier) and RT-PCR (Trivitron PCR Kit) detecting E gene (screening) and ORF gene (confirmatory), the division into Group- I (Ct 15-22), Group- II (Ct 23-29), Group-III (Ct 30-36) and Group-IV (Ct >36) was done. Manual RNA extraction was done using magnetic beads (Lab system, Trivitron). Statistical analysis Data were analyzed by SPSS 19.0 version software. Ct values obtained in the two methods were compared by paired t-test, GroupWise. Z test was used to compare the other parameters. Results The difference in Ct values for target genes was statistically significant (p<0.05) in Group-I to III; however, no variation in result interpretation. The difference in time, manpower, and cost were statistically significant (p<0.05). The manual method required twice more manpower; 40 minutes more time & automated method cost 3.5 times more for consumables. Conclusion The study showed that RNA yield was better with automated extraction in comparison to manual extraction. The samples extracted by the automated method detected the virus at a lower Ct range by PCR than the manual method. Automated method processed samples in less time and with less manpower. Considering the cost factor, manual extraction can be preferred in resource-limited settings as there was no difference in the results of the test. The manual method requires more hands-on time with potential chances of cross-contamination and technical errors.
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BACKGROUND: The COVID-19 pandemic prompted safety-net health care systems to rapidly implement telemedicine services with little prior experience, causing disparities in access to virtual visits. While much attention has been given to patient barriers, less is known regarding system-level factors influencing telephone versus video-visit adoption. As telemedicine remains a preferred service for patients and providers, and reimbursement parity will not continue for audio visits, health systems must evaluate how to support higher-quality video visit access. OBJECTIVE: This study aimed to assess health system-level factors and their impact on telephone and video visit adoption to inform sustainability of telemedicine for ambulatory safety-net sites. METHODS: We conducted a cross-sectional survey among ambulatory care clinicians at a hospital-linked ambulatory clinic network serving a diverse, publicly insured patient population between May 28 and July 14, 2020. We conducted bivariate analyses assessing health care system-level factors associated with (1) high telephone adoption (4 or more visits on average per session); and (2) video visit adoption (at least 1 video visit on average per session). RESULTS: We collected 311 responses from 643 eligible clinicians, yielding a response rate of 48.4%. Clinician respondents (N=311) included 34.7% (n=108) primary or urgent care, 35.1% (n=109) medical, and 7.4% (n=23) surgical specialties. Our sample included 178 (57.2%) high telephone adopters and 81 (26.05%) video adopters. Among high telephone adopters, 72.2% utilized personal devices for telemedicine (vs 59.0% of low telephone adopters, P=.04). Video nonadopters requested more training in technical aspects than adopters (49.6% vs 27.2%, P<.001). Primary or urgent care had the highest proportion of high telephone adoption (84.3%, compared to 50.4% of medical and 37.5% of surgical specialties, P<.001). Medical specialties had the highest proportion of video adoption (39.1%, compared to 14.8% of primary care and 12.5% of surgical specialties, P<.001). CONCLUSIONS: Personal device access and department specialty were major factors associated with high telephone and video visit adoption among safety-net clinicians. Desire for training was associated with lower video visit use. Secure device access, clinician technical trainings, and department-wide assessments are priorities for safety-net systems implementing telemedicine.
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Objective. The COVID-19 pandemic prompted unprecedented expansion of telemedicine services. We sought to describe clinician experiences providing telemedicine to publiclyinsured, lowincome patients during COVID-19. Methods. Online survey of ambulatory clinicians in an urban safetynet hospital system, conducted May 28, 2020–July 14, 2020. Results. Among 311 participants (response rate 48.3%), 34.7% (n=108/311) practiced in primary/urgent care, 37.0% (n=115/311) medical specialty, and 7.7% (n=24/311) surgical clinics. A large majority (87.8%, 273/311) had conducted telephone visits, 26.0% (81/311) video. Participants reported observing both technical and nontechnical patient barriers. Clinicians reported concerns about the diagnostic safety of telephone (58.9%, 129/219) vs. video (35.3%, 24/68). However, clinician comfort with telemedicine was high for telephone (89.3%, 216/242) and for video (91.0%, 61/67), with many clinicians (92.1%, 220/239 telephone;90.9%, 60/66 video) planning to continue telemedicine after COVID-19. Conclusions. Clinicians in a safetynet health care system report great comfort with and intention to continue telemedicine after the pandemic, despite safety concerns and patient challenges.
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The use of telemedicine in U.S. perinatal care has drastically increased during the coronavirus disease 2019 (COVID-19) pandemic, and will likely continue given the national focus on high-value, patient-centered care. If implemented in an equitable manner, telemedicine has the potential to reduce disparities in care access and related outcomes that stem from systemic racism, implicit biases and other forms of discrimination within our health care system. In this commentary, we address implementation factors that should be considered to ensure that disparities are not widened as telemedicine becomes more integrated into care delivery.