A Case Report of Mucormycosis in a Patient with a Recent History of COVID-19 Infection in India

Introduction: Mucormycosis is an angio-invasive fungal infection that increased significantly during the 2nd wave of the Covid-19 pandemic in India. The rise of cases was attributed to inflammatory changes, poor quality oxygen, immune suppression, and corticosteroid therapy.Case Presentation: This case study reports the history, treatment, and rehabilitation of a case of post-Covid-19 mucormycosis infection. The patient was admitted to the hospital following respiratory distress, at the beginning of the 2nd Covid-19 wave in India. Intravenous antibiotics, steroids, and moist O2 were administered, intensive support was provided and the patient was discharged after 13 days. Following extraction of 17, the patient reported signs of oro-antral communication which was managed by performing antral lavage and buccal advanced flap closure. Histopathological investigation of tissue salvaged during the procedure revealed the presence of fungal hyphae.Management and Prognosis: Following diagnosis, anti-fungal medication was prescribed, and a maxillectomy was performed to remove the affected tissue. On follow-up, the tissue healed with no further complications or symptoms, and rehabilitation was performed using an obturators and are movable complete denture. Histopathological investigations were carried out on the tissue salvaged during maxillectomy which confirmed Mucormycosis infection. Conclusion: The importance of histopathological investigation in the diagnosis of any infectious disease is enumerated in this paper. Oral and Maxillofacial Pathology Journal (2023): https://www.ompj.org/archives

Cotrimoxazole in hospitalised patients with severe COVID-19 - interim results from the CoTroxCov study

Background: Severe COVID-19 has been attributed to a hyperimmune response mediated by cytokines. The mainstay of therapy remains largely supportive along with steroids. Co-trimoxazole in addition to having antimicrobial properties has immunomodulatory and anti-inflammatory properties and could potentially improve outcomes in patients with severe COVID-19 . Hypothesis: We hypothesised that Co-trimoxazole given to patients with severe COVID-19 could prevent progression to critical illness, mortality and reduce time to recovery. Method(s): We conducted an interim analysis in our single center open-label randomised control trial, in which hospitalised patients with severe COVID-19 requiring supplemental oxygen via non-rebreathe mask between 10 -15 Litres per minute and maintaining saturations between 92-96% were assigned in a 1:1 ratio to receive either oral Cotrimoxazole in addition to standard therapy or standard therapy alone. Result(s): 111 patients were recruited into the study, of which 56 patients received Co-trimoxazole and 55 received standard therapy alone. The mean age was 50 years in the Co-trimoxazole group versus 53 years in the standard therapy group (p=0.083). In-hospital mortality was 11% in the Co-trimoxazole group vs 29% in the standard therapy group (p=0.020). Mechanical ventilation was offered to 9% of the patients in the Co-trimoxazole group versus 13% of the patients in the standard therapy group. Time to recovery was 6 days in the Co-trimoxazole group versus 7 days in the standard therapy group (p=0.466). Conclusion(s): In this interim analysis oral Co-trimoxazole reduces mortality in patients with severe Covid-19. Further recruitment is underway.

High Seroconversion Rates after 2 Doses of Vaccine against Sars-Cov-2 in Kidney Transplant Recipients

Introduction: Immunoglobulin (Ig)G antibodies against SARS-CoV-2 are implicated in herd immunity. Humoral response to vaccines in kidney transplant recipients (KTRs) is documented to be sub-optimal. However, the response to non-messenger RNA(mRNA) based vaccines in KTRs is not known Methods: SARS-CoV-2 spike protein IgG antibody response was assessed in KTRs using chemiluminescence immunoassay. Patients were characterized by the number of vaccine doses received and Coronavirus disease 2019 (COVID-19) infection in past. Result(s): Out of 224 KTRs evaluated, 197 (87.94%) had positive S1/S2 IgG anti-SARS-CoV-2 antibodies with a median [IQR] titre of 307.5 AU/ml [91 AU/ml - 400 AU/ml]. High titres (in neutralizing range) were found in 170/224 (75.9%) KTRs. Seropositivity rates after 2 doses of vaccination were significantly higher than unvaccinated KTRs (88.67% vs 66.7%;p = 0.006). After adjusting for cofounders, KTRs with diabetes at the time of vaccination were less likely to develop antibody response (aOR 0.31, 95% confidence interval [CI] - 0.10, 0.90;p = 0.032). Higher eGFR was also an independent predictor of antibody response (aOR 1.04 95% CI - 1.01, 1.08;p = 0.005). KTRs vaccinated with CovishieldTM developed higher antibody response as compared to CovaxinTM (aOR 5.04, 95% CI - 1.56, 16.22;p = 0.007). Conclusion(s): A high rate of seroconversion was seen in KTRs after SARS-CoV-2 vaccination with non mRNA vaccines. The presence of diabetes and decreased eGFR independently predicted lower seroconversion rates. No conflict of interestCopyright © 2023

Biserial targeted feature projection based radial kernel regressive deep belief neural learning for covid-19 prediction.

Coronavirus disease 2019 (COVID-19) is a highly infectious viral disease caused by the novel SARS-CoV-2 virus. Different prediction techniques have been developed to predict the coronavirus disease's existence in patients. However, the accurate prediction was not improved and time consumption was not minimized. In order to address these existing problems, a novel technique called Biserial Targeted Feature Projection-based Radial Kernel Regressive Deep Belief Neural Learning (BTFP-RKRDBNL) is introduced to perform accurate disease prediction with lesser time consumption. The BTFP-RKRDBNL techniques perform disease prediction with the help of different layers such as two visible layers namely input and layer and two hidden layers. Initially, the features and data are collected from the dataset and transmitted to the input layer. The Point Biserial Correlative Target feature projection is used to select relevant features and other irrelevant features are removed with minimizing the disease prediction time. Then the relevant features are sent to the hidden layer 2. Next, Radial Kernel Regression is applied to analyze the training features and testing disease features to identify the disease with higher accuracy and a lesser false positive rate. Experimental analysis is planned to measure the prediction accuracy, sensitivity, and specificity, and prediction time for different numbers of patients. The result illustrates that the method increases the prediction accuracy, sensitivity, and specificity by 10, 6, and 21% and reduces the prediction time by 10% as compared to state-of-the-art works.

THE FUTURE OF OUTPATIENT APPOINTMENTS: DO PATIENTS WITH EPILEPSY PREFER REMOTE CONSULTATIONS?

In light of the COVID 19 pandemic, outpatient services have been restructured to facilitate remote con-sultations. As the pandemic has entered second and third 'phases', patients' perspectives regarding remote consultation are essential. Methods Questionnaires were sent to PWE and carers attending tertiary epilepsy services at University Hospitals Birmingham. 25% of the population is from an ethnic minority. Results 378 questionnaires were analysed-278 in phase A, 100 in phase B. 57.0% respondents were female, 42.9% over 50 years. 8.2% had comorbid non epileptic attacks. 55.8% respondents preferred telephone consultation in phase A, 66% in phase B. 34.9% preferred face to face in phase A, dropping to 32% by phase B, the majority being non-white. 6.83% selected video consultation in phase A, none in phase B. The proportion citing 'safety' as a reason for remote consultation in phase A (23%) was greater than in B (5%) P<0.001 The proportion citing 'ease of access' as key increased by phase B, though non significantly p=0.03. Conclusion Ease of access continues to drive preferences for telephone consultations with infection risk in outpatients becoming less of a concern. Translating services are essential if remote consultation is to be accessible to all.

Control Of Epidemic Spreads Via Testing And Lock-Down

Testing and lock-down are interventions that can combat the spread of an infectious disease. Testing is a targeted instrument that permits the isolation of infectious individuals. Lock-down, on the other hand, is blunt and restricts the mobility of all people. In this paper, we present a compartmental epidemic model that accounts for the impact of lock-down and different kinds of testing, motivated by the nature of the ongoing COVID-19 outbreak. We consider the testing of symptomatic, contact traced, and randomly chosen asymptomatic individuals. Using the model, we first characterize static mobility levels and testing requirements that can dampen the spread asymptotically. We then characterize a threshold-type optimal lock-down policy that minimizes the social impact of an epidemic, modeled via a sum of infection and lockdown costs. Our results are contextualized with realistic parameter values for COVID-19.

IoT and wearables for detection of COVID-19 diagnosis using fusion-based feature extraction with multikernel extreme learning machine

Presently, wearables act as a vital part of healthcare sector and they are able to offer exclusive perceptions about the person's health conditions. In contrast to traditional diagnosis in a hospital environment, wearables can give unrestricted access to real-time physiological data. COVID-19 epidemic is increasing at a faster rate with limited test kits. Hence, it becomes essential to develop a novel COVID-19 diagnostic model. Numerous studies were based on the utilization of artificial intelligence techniques on radiological images to precisely identify the disease. This chapter presents an efficient fusion-based feature extraction with multikernel extreme learning machine (FFE-MKELM) for COVID-19 diagnosis using internet of things (IoT) and wearables. Primarily, the wearables and IoT are used to capture the radiological images of the patient. The presented FFE-MKELM model incorporates Gaussian filtering based preprocessing for removing the noise that exists in the radiological image. Besides, directional local extreme patterns with deep features based on Inception v4 model are applied for the FFE process. In addition, MKELM model is utilized as a classification model to determine the appropriate class label of the input radiological images. Moreover, monarch butterfly optimization algorithm is applied to fine tune the parameters involved in the MKELM model. Experimental validation of the FFE-MKELM model is performed against benchmark dataset and the outcomes are inspected under different measures. The resultant simulation outcome ensured the betterment of the FFE-MKELM method by demonstrating an increased sensitivity of 97.34%, specificity of 97.26%, accuracy of 97.14%, and F-measure of 97.01%. © 2022 Elsevier Inc. All rights reserved.

Predicting NCOVID-19 Probability Factor with Severity Index

Hospitals worldwide are struggling to cope up with patient’s admission issues related with the increasing number of COVID-19 patients’ cases mainly driven by Delta variant, as severely ill nCOVID patients are found waiting for hospital beds, which are occupied by non-critical COVID patients. To make the situation worse, people who are partially or fully vaccinated against COVID-19 are also getting re-infected. Due to the absence of prior knowledge of an index of severity for COVID-19 patients, hospitals, with limited number of ventilators and medical equipment, fail to admit patients on any priority basis. With multiple tests kit available in market till now, there is none with an instantaneous index for severity prediction for COVID. This research develops a free and user-friendly algorithm titled “SAHAYATA 1427” (renamed herein Sahayata) which predicts a factor for a patient having the probability of disease nCOVID-19 termed as “probability factor” of COVID-19 for each patient. Concurrently, the algorithm also provides an index for severity by which the patient is affected by nCOVID, termed as “severity index.” The input data is both demographic and patient provided. The severity index is determined using artificial intelligence. Using a logistic regression model with data set of existing COVID patients, Sahayata predicts the probability factor for an nCOVID-19 patient with an accuracy, precision and recall of 88.17%, 100% and 87.3%, respectively. Results indicate that it can be used effectively both at hospitals by trained medical personnel and at home by the general population. Sahayata helps the COVID-19 patients living in rural communities with smaller patients care facilities with limited equipment by providing a way for efficient treatment care. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Polypharmacy mediates the effect of mechanical ventilation on incidence of delirium

INTRODUCTION: Delirium in ICU is associated with poor outcomes. Delirium among critically ill COVID-19 patients is due at least in part to iatrogenic causes such as staffing constraints, restricted mobility, and polypharmacy stemming from drug shortages. The aim of this study was to describe the sedation practices and prevalence of delirium at a tertiary level academic medical center. We tested the hypothesis that polypharmacy (PP, use of ≥ 4 classes of sedatives), is a mediator in the causal pathway of mechanical ventilation and delirium. METHODS: 212 patients admitted to ICUs at a tertiary level academic medical center in Boston, MA between Jan 2020 and April 2021 with a primary diagnosis of SARS-CoV2 were included. Mediation analysis was conducted with bootstrap estimation to assess whether association between mechanical ventilation and incidence of delirium was mediated by PP. Analyses were adjusted for potential confounders found to be related to the treatment, mediator, and outcome, including age, gender, vasopressor use, median RASS scores, and maximum CRP levels. RESULTS: Of the 212 patients in the cohort, 72.6% had delirium during their ICU stay, 76.9% were mechanically ventilated, and 54.7% received ≥ 4 classes of sedatives. The percentage of patients given Opioids, Benzodiazepines, Ketamine, Propofol, and Dexmedetomidine, were 81.1%, 60.4%, 40.6%, 75.9%, and 54.3%, respectively. Adjusting for potential confounders, patients given ≥ 4 classes of sedatives had 7.4 (95% CI: 2.5 - 22.4) times the odds of developing delirium compared to those given < 4. Mechanically ventilated patients had 4.9 (95% CI: 1.6 - 15.2) times the odds of developing delirium compared to patients not mechanically ventilated. Approximately 42.1% (95% CI: 39.8 - 50.6) of the mechanical ventilation effect is attributed to the mediation of PP. CONCLUSIONS: Mechanical ventilation is associated with higher risk of delirium and PP mediates > 40% of this effect which is clinically and statistically significant. Prospective studies should explore whether limiting PP among mechanically ventilated patients could reduce delirium.

Hierarchy of values in recovery of respiratory failure: Prospective study of patients and caregivers

BACKGROUND: Survivors of acute respiratory failure (ARF) face challenges that impact their quality of life across multiple domains. This prospective study aims to identify a hierarchy of preferred outcomes by ranking nine domains of recovery, over the period of six months post discharge among patients and their caregivers. METHODS: This is a single-center companion study to the multi-center APICS (Addressing Post Intensive Care Syndrome) study. This protocol has been expanded to enroll a maximum of 80 patient-caregiver dyads at BIDMC including a subset of COVID positive patients. Eligible patients are those who meet ARF criteria in the ICUs at BIDMC for at least 24 hours and are expected to be discharged home are recruited. Patients and caregivers participate in a survey in which they rank 9 aspects of recovery from critical illness from being most important (1) to least important (9). Patients also participate in the MOCA/MOCA-blind questionnaire at baseline and 6-month follow-up. RESULTS: This study is actively enrolling. To date, we have enrolled 21 patients and 5 caregivers. At discharge, 43% of patients ranked survival as most important, while 24% ranked cognitive function as most important. 80% of caregivers ranked survival as most important at discharge. Survival remained the highest priority for patients at 6 months followed by physical, cognitive and pulmonary recovery respectively. CONCLUSIONS: Both survivors and caregivers valued survival as the most important construct of recovery. Cognitive function followed survival as the second most important construct. Preliminary results indicate that these preferences may change over a period of time however small sample size limits broad generalizations. Final results are expected to help delineate a hierarchy of patient centered outcomes in this population.

Covid-19—Analysis and Prediction—A Case Study Using Machine Learning

In this research paper, machine learning-based models are used for predicting the number of countries that have been affected by the COVID-19 virus and what was the situation of them. Then, the analysis is performed whether the “Lockdown” is the savior or not, and how India faced the whole problem. After that the prediction model for India was also prepared. These are the selected ones that are comprised in this paper. The COVID virus has spread globally, causing thousands of deaths and having an enormous impact on our health systems and economies. As soon as the Covid-19 pandemic was hit hard worldwide, lots of researchers were trying to figure out lot more things, among them when and from where did the virus make its an outbreak. The whole analysis part is based on the data science and machine learning algorithms and utterly dependent on the data collected from various trusted sources. In this paper, decision tree-based linear (DTBL) model and Random Forest Regression (RFR) models are applied to ensure the accuracy of the models. The Auto-Regressive Integrated Moving Average (ARIMA) and Prophet Algorithm are used for the prediction model. Preventive measures to reduce the spread of the COVID virus in respective zones are also suggested. We hope that this research work will help in the understanding and eradication of the threatening disease. This study can be applied by other countries for predicting COVID-19 cases at the state or national level. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

1389P Real world outcomes of locally advanced gastric/GOJ cancers receiving perioperative chemotherapy with FLOT regimen during the COVID-19 pandemic: Results from a regional cancer care centre in the UK

Background: In locally advanced gastroesophageal and gastric cancers, which constitute the majority of the presentation, the postoperative 5 year survival rate remains only 30-40%. It is in these patients that perioperative chemotherapy has helped improve radical resection rates control preoperative micrometastases and improve survival. Since 2017,FLOT based chemotherapy has largely replaced ECF/ECX in this setting. We did a retrospective analysis on our patients treated with FLOT regimen during the COVID-19 pandemic to assess its efficacy, tolerance and pathological response (TRG). Methods: Patients with resectable gastric and GOJ cancers who presented to us from August 2019 – March 2020 and treated with FLOT based perioperative chemotherapy were analyzed with SPSS (version 26, IBM, Armonk, NY). Pathologic assessment of tumour regression was done by Mandard's TRG scoring. A total of 36 patients were analysed, out of which 91% were males, median age 68 years,cT3/T4 86%, cN1/2 72.2%,GOJ 77.8%,Gastric 22%,Grade 2/3 94%.Total of 80.6% patients completed all 4 cycles of neoadjuvant FLOT and 88.9% patients underwent surgery(all R0).Median interval between last dose of chemo and surgery was 7 weeks.A total of 52.8% patients completed all 4 cycles of adjuvant FLOT. Treatment was delayed due to COVID-19 in 11%. Results: Median followup was 16.3 mths.1 year DFS was 66.3% and OS was 91.4%.Pathological CR(TRG 1) was seen in 2.8% patients.3 patients died due to postop complications. Most common grd-3 toxicities were oral mucositis(6%), diarrhea (6%),neutropenia(8.3%).5FU cardiotoxicity noted in 5.6%. [Formula presented] Conclusions: In our subset of patients,treatment delivery,surgery rates and toxicity profile were comparable to the seminal FLOT4 trial.Our histological responses are lower with pathCR in only 2.8% (vs 16%)patients,with most having TRG 3.The survival rates are better but a longer followup is required. Legal entity responsible for the study: The authors. Funding: Has not received any funding. Disclosure: All authors have declared no conflicts of interest.

Feasibility of self-sampling for HPV Dna detection in a community-based cervical cancer screening project

Objective: Paucity of resources and trained professionals makes it difficult to implement a cervical cancer screening program in India. This study aims to evaluate the feasibility of self-sampling for detection of human papillomavirus (HPV) DNA in a communitybased cervical cancer screening project. Methods: Women (30-60 years) were assigned to do self-sampling or get their samples collected by healthcare workers in outreach clinics. The samples were brought to the institute and were tested by hybrid capture2 (HC2) test for 13 high-risk HPV types. HC2 positive women were brought to the institute where they underwent colposcopy, biopsy and treatment by Thermal ablation, Cryotherapy or Loop Electrosurgical Excision Procedure (LEEP). A focussed group discussion was done with health workers involved with this project in the form of a questionnaire. Results: From May 2017 and December 2020, 15,311 women were recruited. Amongst them, 4916 (32.1%) had self-sampling and 10,395 (67.9%) had health-worker collected sample. The HC2 positivity rates in both groups were 269 (5.5%) and 652 (6.3%), which was not significantly different statistically (P = 0.06). The colposcopy rates and Cervical Intraepithelial Neoplasia (CIN) 2 and 3 detection rates were also similar. All women were comfortable with self-sampling with no sample inadequacy or wastage of collection kits. The health workers rated both procedures as acceptable. The advantage of selfsampling was that no examining table or light source was required and the screened women were less ''shy'' while sample collection. Conclusion: Self-sampling for HPV may increase participation in cervical cancer screening programs, especially in the COVID19 era.

Development of a point-of-care mortality risk score for hospitalized COVID-19 patients

Rationale: While several COVID-19-specific mortality risk scores exist, they lack the ease of use given their dependence on online calculators and algorithms. Objectives: The objectives of this study were (1) to design, validate, and calibrate a simple, easy-to-use mortality risk score in a hospitalized COVID-19 population. Methods: Multi-hospital health system in New York City. Patients (n=4840) with laboratory-confirmed SARS-CoV2 infection who were admitted between March 1 and April 28, 2020. Gray's K-sample test for the cumulative incidence of a competing risk was used to assess and rank 48 different variables' associations with mortality. Candidate variables were added to the composite score using DeLong's test to evaluate their effect on predictive performance (AUC) of in-hospital mortality. Final AUCs for the new score, SOFA, qSOFA, and CURB-65 were assessed on an independent test set. Results: Of 48 variables investigated, 36 (75%) displayed significant (p<0.05 by Gray's test) associations with mortality. The variables selected for the final score were (1) oxygen support level, (2) troponin, (3) blood urea nitrogen, (4) lymphocyte percentage, (5) Glasgow Coma Score, and (6) age. The new score, COBALT, outperforms SOFA, qSOFA, and CURB-65 at predicting mortality in this COVID-19 population: AUCs for initial, maximum, and mean COBALT scores were 0.81, 0.91, and 0.92, compared to 0.77, 0.87, and 0.87 for SOFA. Conclusions: The COBALT score provides a point-of-care tool to estimate mortality in hospitalized COVID-19 patients with superior performance to SOFA and other scores currently in widespread use.

Cardiovascular disease and severe hypoxemia are associated with higher rates of non-invasive respiratory support failure in COVID-19 pneumonia

RATIONALE Acute hypoxemic respiratory failure (AHRF) is the major complication of coronavirus disease 2019 (COVID-19), yet optimal respiratory support strategies are uncertain. We aimed to describe outcomes with highflow oxygen delivered through nasal cannula (HFNC) and non-invasive positive pressure ventilation (NIPPV) in COVID-19 AHRF and identify individual factors associated with non-invasive respiratory support failure. METHODS We conducted a retrospective cohort study of hospitalized adults with COVID-19 within a large academic health system in New York City early in the pandemic to describe outcomes with HFNC and NIPPV. Patients were categorized into the HFNC cohort if they received HFNC but not NIPPV, whereas the NIPPV cohort included patients who received NIPPV with or without HFNC. We described rates of HFNC and NIPPV success, defined as live discharge without endotracheal intubation (ETI). Further, using Fine-Gray sub-distribution hazard models, we identified demographic and patient characteristics associated with HFNC and NIPPV failure, defined as the need for ETI and/or in-hospital mortality. RESULTS Of the 331 patients in the HFNC cohort, 154 (46.5%) patients were successfully discharged without requiring ETI. Of the 177 (53.5%) who experienced HFNC failure, 100 (56.5%) required ETI and 135 (76.3%) patients ultimately died. Among the 747 patients in the NIPPV cohort, 167 (22.4%) patients were successfully discharged without requiring ETI, and 8 (1.1%) were censored. Of the 572 (76.6%) patients who failed NIPPV, 338 (59.1%) required ETI and 497 (86.9%) ultimately died. In adjusted models, significantly increased risk of HFNC and NIPPV failure was observed among patients with co-morbid cardiovascular disease (sub-distribution hazard ratio (sHR) 1.82;95% confidence interval (CI), 1.17-2.83 and sHR 1.40;95% CI 1.06-1.84, respectively). Conversely, a higher oxygen saturation to fraction of inspired oxygen ratio (SpO2/FiO2) at HFNC and NIPPV initiation was associated with reduced risk of failure (sHR, 0.32;95% CI 0.19-0.54, and sHR 0.34;95% CI 0.21-0.55, respectively). CONCLUSIONS A subset of patients with COVID-19 AHRF was effectively managed with non-invasive respiratory modalities and achieved successful hospital discharge without requiring ETI. Notably, patients with co-morbid cardiovascular disease and more severe hypoxemia experienced lower success rates with both HFNC and NIPPV. Identification of specific patient factors may help inform more selective use of non-invasive respiratory strategies, and allow for a more personalized approach to the management of COVID-19 AHRF in pandemic settings.

Predictors of pre-icu duration and its association with mortality in COVID-19 infection: A secondary analysis of the stop-covid registry

Rationale: High patient volume and limited ICU resources associated with the COVID-19 pandemic have exacerbated ICU capacity strain, leading to longer pre-ICU lengths-of-stay (LOS). We examined the patient- and hospital-level predictors of pre-ICU LOS, and the association of pre-ICU LOS on in-hospital mortality for patients with COVID-19. Methods: Data were derived from the Study of the Treatment and Outcomes in Critically Ill Patients with COVID-19 (STOP-COVID), a multicenter cohort study of critically ill adults with COVID-19 admitted to 68 US hospitals. All patients had a minimum of 28-day follow-up;those discharged from hospital were presumed alive. The primary outcome was pre-ICU LOS, dichotomized into brief (≤1 day) vs. prolonged (>1 day). We constructed a multivariate mixed effects model, adjusting for patient factors (e.g., demographics, comorbidities, and pre-hospital symptom duration) and hospital factors (pre-COVID ICU beds number, countylevel case rates of COVID-19 (number of cases per 100,000 residents), and the hospital site itself) to determine predictors of pre-ICU LOS. Using 1:3 propensity score matching for pre-ICU period, we used multivariate mixed effect modelling to examine the association between pre-ICU LOS and in-hospital mortality. Results: A total of 4738 patients with complete data were admitted to the ICU, 36.6% were female, with median age 62 years (IQR 52-71). The majority (85.5%) were admitted from the ED or wards, with 62.5% classified as having a brief pre- ICU LOS. While demographics and co-morbidities (cancer, diabetes, and end-stage renal disease) were not associated with pre-ICU LOS, pre-existing lung disease (OR 1.33, 95% CI 1.02-1.74) was a patient-level predictor of a brief pre-ICU LOS as compared to a prolonged LOS. Having more available ICU beds (>100 vs. 0-48 ICU beds, OR 1.41, 95% CI 1.03-1.92) was a hospital-level predictor of a brief pre-ICU LOS. More patients were intubated at the time of ICU arrival in the prolonged pre-ICU LOS group, compared to the brief LOS group (64.6% vs. 59.2%, p≤0.001). In the mixed model, propensity matched for pre-ICU LOS, and adjusted for patient/hospital characteristics, differential pre-ICU LOS was not predictive of in-hospital mortality (OR 1.22, 95%CI 0.81-1.87), though oxygen support modality was associated with mortality. Conclusion: Patient- and hospital-level factors, such as ICU capacity, had an impact on pre-ICU duration, with more patients requiring a higher level of oxygen support at ICU arrival if admitted later in their course. However, once adjusting for clinical and hospital factors, pre-ICU LOS was not associated with in-hospital mortality.

Evolution of personal protective equipment from its inception to COVID-19

A bizarre illness, identified in a group of patients with respiratory problems in Wuhan, China;was then ascertained as the coronavirus disease 2019 (COVID- 19) – that proliferated into a global pandemic, with lakhs of people getting infected per day. Given this pandemic situation, there is a need globally for the use of personal protective equipment (PPE) to protect oneself from getting infected by the disease. This is very much critical in the healthcare sector as healthcare units like hospitals and clinics might act as potential epicentres for the spread of a disease and cause the healthcare workers to act as vehicles for disease transmission. Given this context, this review article primely focuses on the different ways to prevent transmission of virus and the different PPE that exist in the market. The various anti-viral technologies which are currently available in the market to tackle SARS-CoV-2 have been described along with few interesting literature which can be looked upon to develop anti-viral PPE that can be effective against enveloped viruses like the current SARS-CoV-2. © 2021

Effects of Covid 19 on dentistry - A review

People affected by coronavirus disease 2019 (COVID-19) out of proportion are also at high risk for oral diseases and experience oral health and oral health care inconsistencies at high rates. COVID-19 has led to reduced hours of dental practices or even closure except for emergency services. Dental care includes aerosol releasing procedures that may increase the transmission of virus. This pandemic offers a chance for the dental professionals to change towards procedures without much aerosol, preventive approaches to care and away from surgical interventions. Regular barrier changes to oral health care access during the pandemic could have a valid impact if maintained in the future.