ABSTRACT
Healthcare is a sector that is expeditiously developing in technology and services. In recent years, the Covid-19 pandemic has drastically affected the working of the healthcare sector;people are apprehended to visit hospitals for any treatment. But evolution in modern technologies has opened multiple paths to improve and modernize the working of the healthcare sector. The proposed system is a multi-layered disease prediction model that analyzes numerous factors for predicting diseases. The system analyzes the symptoms using a modified decision tree algorithm that predicts the possible illness and suggests the test accordingly. The model is trained individually for each type of test format. For image type, reports were classified with convolutional neural networks. For PDF type, the data was extracted using optical character recognition (OCR). The model uses the Levenshtein distance to find unigrams and bigrams. The match is further analyzed, and a detailed summary of the report gets generated. Report summary and the predicted disease are provided to the patient with the list of home remedies. Further, a specialized doctor receives all the medical diagnosis details when a patient books an appointment. Hospitals usually face the problem of patient versus nurse ratio. It creates management issues to the critical ward. Patients are left unattended and can cause death threats. The proposed system analyzes multiple and dynamic factors. It increases the accuracy of the prediction. The proposed hospital monitoring system observes the vital signs on the patient monitors beside the ICU beds and notifies the hospital staff after encountering the abnormality. The model dynamically calculates the threshold value for each vital sign considering multiple factors like age, gender, and medical history of the patient. By understanding the patient's current medical condition, the model responds to change in vital signs and gives an idea about the organ's condition. Machine learning algorithm-random forest regression helps in calculating the threshold values of heart rate (HR) and respiratory rate (RR). Equations for blood pressure (BP) get the threshold values depending on age and gender. These custom thresholds for specific patients reduce false alarms, which was a significant concern in the previous monitoring system. © 2023 River Publishers.
ABSTRACT
Background: There is limited information on effectiveness of COVID-19 therapies in immunocompromised patients, who are at higher risk of hospitalizations, complications, and mortality due to COVID-19. We examined hospital all-cause mortality for early RDV use vs. no RDV use among immunocompromised COVID-19 patients across several distinct dominant variants of concern (VOC) periods: pre-Delta (Dec'20-Apr'21), Delta (May-Nov'21) and Omicron (Dec'21-Apr'22). Method(s): Using the Premier Healthcare Database, we identified adults with an immunocompromised condition (cancer, solid organ and hematopoietic stem cell transplant, hematologic malignancies, primary immunodeficiencies, asplenia, bone marrow failure/aplastic anemia, severe combined immunodeficiencies or HIV), hospitalized with a primary diagnosis of COVID-19. Patients treated with RDV in first 2 days of admission vs. those not treated with RDV during the hospitalization were matched using 1:1 preferential withinhospital propensity matching with replacement. Patients were excluded if discharged within 3 days of RDV initiation. Cox Proportional Hazards Model was used to examine time to 14-and 28-day mortality. Result(s): Overall (Dec'20-Apr'22), 14,169 RDV-treated patients were matched to 5,341 unique non-RDV patients. Post-matching balance was achieved with 59% being 65+ years, 40.5% with no supplementary oxygen charges, 39% received low-flow oxygen, 19% on high-flow oxygen/non-invasive ventilation and 1.5% on invasive mechanical ventilation/ECMO at baseline. During the study period, unadjusted mortality rate was significantly lower for RDV patients at 14 days (11% [95% CI: 11%-12%] vs 15% [15%-16%];p< .0001) and 28 days (18% [17%-18%];p< .0001 vs 22% [22%-23%];p< .0001) as compared to patients that did not receive RDV. After adjusting for baseline and clinical covariates, 14-day results showed that RDV had significantly lower mortality risk compared to non-RDV across all VOC periods [overall (30% lower risk), pre-delta (41%), Delta (23%), Omicron (25%)]. Similarly, 28-day results showed that RDV had significantly lower mortality risk compared to non-RDV across all VOC periods [overall (25%), pre-delta (35%), Delta (21%), Omicron (16%)] (Fig). Conclusion(s): Timely initiation of RDV in first two days of hospital admission demonstrated significant mortality reduction in immunocompromised patients hospitalized with primary diagnosis of COVID-19. RDV demonstrated consistent benefit in an immunocompromised cohort across all variant periods of the pandemic.
ABSTRACT
Background: Clinical management of COVID-19 based on oxygenation requirements continues to change over time as variants of concern (VOC) evolve. We examine hospital all-cause mortality for early hospital RDV use vs. no RDV use across dominant VOC periods: pre-Delta (Dec'20-Apr'21), Delta (May-Nov'21) and Omicron (Dec'21-Apr'22). Method(s): We examined adults with a primary discharge diagnosis of COVID-19 (ICD-10: U07.1) using the Premier Healthcare Database. Patients treated with RDV in the first 2 days of admission vs. those not treated with RDV during the hospitalization were matched using a 1:1 preferential within-hospital propensity matching with replacement. Patients were excluded if discharged within 3 days of RDV initiation. Time to mortality at 14-and 28-days was examined for patients with no supplemental oxygen charges (NSOc), low-flow oxygen (LFO), high-flow oxygen/non-invasive ventilation (HFO/NIV) and invasive mechanical ventilation/ECMO (IMV/ECMO) at baseline. Baseline was defined as first 2 days of hospitalization. Result(s): 164,791 RDV-treated patients were matched to 48,473 unique non-RDV patients. Post-matching balance was achieved across groups with different baseline oxygenation levels and VOC periods. In the matched weighted cohort, 35% required NSOc, 41% LFO, 21% HFO/NIV and 3% IMV/ECMO. During the overall study period (Dec'20-Apr'22), unadjusted mortality rate was significantly lower for RDV patients across all oxygenation levels at 14 days (NSOc: 5.4% vs. 7.3%, LFO: 6.4% vs. 8.8%, HFO/NIV: 16.8% vs. 19.4%, IMV/ECMO: 27.8% vs. 35.3%) and 28 days (NSOc: 8.0% vs. 9.8%, LFO: 9.8% vs. 12.3%, HFO/ NIV: 25.8% vs. 28.3%, IMV/ECMO: 41.4% vs. 50.6%). After adjusting for baseline and clinical covariates, 14-day mortality results showed that RDV significantly lower risk compared to non-RDV across all oxygenation levels at baseline [NSO (26%), LFO (28%), HFO/NIV (17%), IMV/ ECMO (27%)]. Similarly, 28-day mortality results showed that RDV significantly lower risk compared to non-RDV across all oxygenation levels at baseline [NSO (19%), LFO (21%), HFO/NIV (12%), IMV/ECMO (26%)]. This lower mortality risk associated with RDV was consistently observed across all variant periods (Figure). Conclusion(s): Timely initiation of RDV within first two days of hospital admission demonstrated significant mortality reduction in patients hospitalized for a primary diagnosis of COVID-19 across all oxygenation levels. Remdesivir demonstrated consistent benefit across all variant periods of the pandemic to-date.
ABSTRACT
Background: There is limited data on the association between COVID-19 therapy and hospital readmissions, including during evolution of the pandemic over time. We examine all cause 30-day readmissions after a COVID-19 hospitalization among remdesivir (RDV)-treated vs non-RDV treated patients across different dominant variants of concern (VOC) periods: pre-Delta (May'20-Apr'21), Delta (May-Nov'21) and Omicron (Dec'21-Apr'22). Method(s): Using the Premier Healthcare Database, we examined adults hospitalized with a primary diagnosis of COVID-19 (ICD-10:U07.1) who were discharged alive from the COVID-19 hospitalization. All-cause readmission to the same hospital was examined using multivariate logistic regression. The model adjusted for: age, corticosteroids use, VOC period, Charlson comorbidity index, maximum oxygenation requirements and ICU admission during COVID-19 hospitalization. Result(s): In the study period (May'20-Apr'22), 440,601 patients with a primary diagnosis of COVID-19 were discharged alive, of which 53% received RDV. As compared to non-RDV, RDV patients were younger (median[IQR]: 62[51-73] vs 64[52-76]), with a lower proportion with no supplementary oxygen charges (30% vs 52%), a higher proportion with low-flow oxygen (46% vs 36%), highflow oxygen/non-invasive ventilation (20% vs 10%), and invasive mechanical ventilation/ECMO (4% vs 2%). Among RDV-treated, the all-cause 30-day readmission was 6.3% compared to 9.1% (p< .0001) in non-RDV treated. Lower readmission for RDV vs non-RDV was seen in Pre-delta (6.3% vs 9.3%;p< .0001), Delta (5.1% vs 7.8%;p< .0001), and Omicron (8.7% vs 9.9%;p< .0001) (Fig). After adjusting for age and characteristics at index hospitalization including corticosteroid, RDV patients had significantly lower likelihood of all-cause 30-day readmission (OR[95% CI]:0.73[0.72-0.75]) as compared to non-RDV. Significantly Lower odds of 30-day readmission for RDV vs non-RDV patients were observed in Pre-delta (0.69[0.67-0.71]), Delta (0.72[0.68-0.76]) and Omicron-(0.87[0.83-0.92]) (Fig). Similarly, RDV-related reduction in readmissions was also seen for COVID-19 related readmissions. Conclusion(s): RDV use during the COVID-19 hospitalization was associated with significantly lower likelihood of all-cause 30-day readmission across the VOC periods of the pandemic May 2020 till April 2022. The lower rate of hospital re-admission for RDV-treated patients was observed despite the RDV group having higher supplemental oxygen requirement during their index COVID-19 hospitalization.
ABSTRACT
Methods: This retrospective study included adults with COVID-19 (ICD-10: U07.1) and pneumonia (ICD-10 subcodes within J11.x - J16.x, J18.x) May 2020-December 2021 in the Premier Healthcare Database, analyzing severity, treatment patterns and clinical outcomes. Result(s): Between May 2020 and December 2021: N=338,930 patients in 856 hospitals 79% of patients received any dexamethasone(DEX);>50% received any remdesivir(RDV) Combination therapy use increased: DEX+RDV only from <1% of patients to 29%;DEX+RDV with baricitinib or tocilizumab from <1% to 19% RDV initiation in the first 2 days of hospitalization increased 41% to 88% Overall all-cause mortality increased 19% to 24% with large differences between severity subgroups: in December 2021, 20%, 32%, 46% and 60%, respectively, in no supplementary oxygen(NSOc), low-flow(LFO), highflow/non-invasive(HFO/NIV) and invasive mechanical ventilation/ECMO(IMV/ECMO) Overall median hospital LOS and ICU LOS remained between 6-10 days, with notable variation by severity subgroup and over time Overall ICU use was 35%-38%, with large differences by severity subgroups: in December 2021, 28%, 47%, 67% and 94%, respectively, in NSOc, LFO, HFO/NIV and IMV/EC Conclusion(s): COVID-19 can result in severe outcomes;understanding treatment and severity trends can improve prognosis.
ABSTRACT
With an exponential increase in transition of various business to technical platforms there has been a huge data transfer between various platforms occurring every second. In 2020, there was a significant increase in the number of businesses shifting to online platforms during COVID, which not only increased the data traffic but also led both academic and IT industry to think of various techniques to improve data quality and data transformations in order to achieve optimal solutions from their captured data. The current review paper provides insight about how data analytics has been used by various phases within Supply chain management and also addresses the usage of data analytics in creation of various SCM models. The paper also addresses a number of research gaps while creating these SCM models, which leads to future research directions. © 2023 IEEE.
ABSTRACT
COVID-19 has provided an unprecedented opportunity to expand access and coverage to the country's healthcare system via telehealth. Because of the growing need for telemedicine by healthcare providers, the Medical Council of India issued Practice Guidelines in March 2020. Medical specialties like ophthalmology, dermatology, and neurology offered telehealth services during pandemics. Low-middle-income countries like India are highly dependent on out-of-pocket expenses for health services. Thus, there is a need to understand telehealth's accessibility, feasibility and affordability. This review aims to understand trends regarding the access and patient response to telehealth in India during the COVID-19 pandemic. We reviewed published papers to understand better accessibility and patient response to the healthcare delivery systems via telehealth in India. The results of this review showed that patients were satisfied with the use of telehealth. Healthcare providers and patients believe telehealth can be suitable for various healthcare services, including follow-up visits in clinical disciplines and minor health problems. In conclusion, for Telehealth to understand further, quality evidence must be available, and its role in developing integrated parts of the healthcare system to be defined. © 2023, Intelektual Pustaka Media Utama. All rights reserved.
ABSTRACT
Machine learning (ML] helps with the future prediction of action and take decision. A variety of prediction techniques are used for the future prediction of risks and effectively dealing it. This work shows how ML models can predict death rates of COVID-19 patients so that we can do effective treatment and try to minimize the effect of the causes. Coronavirus 2019, COVID-19 is a member of the Coronaviridae genus. A virus without a cure causes unpredictable devastation to people's lives as well as the financial and economic systems of every nation on earth. We have taken certain features from the COVID-19 dataset to study and comprehend the future circumstance using machine learning algorithms, various prediction models are created, and their performances are calculated and assessed. We have compared machine learning algorithms viz. Random Forest and Linear Regression, Decision Tree to predict a number of cases.
ABSTRACT
COVID-19 has provided an unprecedented opportunity to expand access and coverage to the country's healthcare system via telehealth. Because of the growing need for telemedicine by healthcare providers, the Medical Council of India issued Practice Guidelines in March 2020. Medical specialties like ophthalmology, dermatology, and neurology offered telehealth services during pandemics. Low-middle-income countries like India are highly dependent on out-of-pocket expenses for health services. Thus, there is a need to understand telehealth's accessibility, feasibility and affordability. This review aims to understand trends regarding the access and patient response to telehealth in India during the COVID-19 pandemic. We reviewed published papers to understand better accessibility and patient response to the healthcare delivery systems via telehealth in India. The results of this review showed that patients were satisfied with the use of telehealth. Healthcare providers and patients believe telehealth can be suitable for various healthcare services, including follow-up visits in clinical disciplines and minor health problems. In conclusion, for Telehealth to understand further, quality evidence must be available, and its role in developing integrated parts of the healthcare system to be defined. © 2023, Intelektual Pustaka Media Utama. All rights reserved.
ABSTRACT
Purpose: To characterize demographics, treatment patterns, and outcomes among 3,998 transplant patients hospitalized for COVID-19 over 16 months of the pandemic (May '20-Aug '21). Method(s): Adult patients in a transplant cohort (TC) and non-transplant cohort (NTC) hospitalized with COVID-19 (ICD-10: U07.1) were compared in the Premier Healthcare Database from May '20-Aug '21. Baseline measures in first two days, demographics, comorbidity, COVID-19 treatments and immunosuppressants were analyzed. Outcomes included mortality (discharge status expired or hospice) and hospital and ICU LOS. Result(s): 3,998 TC patients were hospitalized for COVID-19 in 587 US hospitals. Compared to NTC, TC were younger (61 vs 64 yrs;p<.0001), less likely to be white (59% vs 67%;p<.0001), obese (24% vs 33%;p<.0001) or have COPD (17% vs 24%;p<.0001). TC had higher rates hypertension (84% vs 69%;p<.0001), renal disease (80% vs 22%, p<.0001), diabetes (48% vs 29%;p<.0001) and chronic heart failure (23% vs 18%;p<.0001). During hospitalization, a lower proportion of TC needed any oxygen therapy compared to NTC (p<.05). Compared to NTC, fewer TC received remdesivir (RDV) (44% vs 48%;p<.0001), but more received corticosteroids (87% vs 78%;p<.0001), anticoagulants (44% vs 29%;p<.0001) and convalescent plasma (18% vs 16%;p=0.007). In TC, 44% received MMF, 73% calcineurin inhibitors and 5% mTOR. Use of MMF did not change over time (43% May-Jul 2020;43% Aug- Dec 2020;45% 2021). TC had higher ICU admission rates (31% vs 28%;p.001), but similar hospital LOS and ICU LOS compared to NTC. All-cause mortality in NTC (15% overall;16% May-Jul 2020;16% Aug-Dec 2020;14% 2021) was not significantly different than TC over time (16% overall;13% May-Jul 2020;16% Aug-Dec 2020;16% 2021). Conclusion(s): Very few large studies have assessed COVID-19 management in transplant patients over time. All-cause mortality was comparable in both cohorts despite TC immunosuppression. RDV use was lower in TC. Uncertainty around MMF use in COVID-19 patients did not impact reported use of MMF. Further analyses are needed to evaluate confounding factors (medication sequence, time since transplant, disease severity) and impact of external factors such as earlier testing and treatment for COVID-19, vaccination, and new variants. (Table Presented).
ABSTRACT
Virtual Reality (VR) has been incorporated in almost every possible application domain, but majority of the online shopping platforms are providing their customers only simple two-dimensional image-based and text-based interfaces to shop upon. This kind of monotonous shopping environment not only makes consumers lose their interest in shopping but also restricts personalized services to consumers such as providing healthier alternatives to the products or providing with suggestions based on their past shopping pattern [1]. Keeping these issues in mind the prototype of a VR based game 'Virtual Bazar' is proposed which allows its users to shop in a Virtual Reality supermarket environment. The proposed model keeps a check on the Calorie requirements set by the user, shows Nutritional Information of products and create awareness for Healthier Products. It consists of two runs, in the first run users add items to their cart based on their desires with no warnings and nutritional values being shown. In the second run users are shown Nutritional information regarding products along with warnings on products having high Sugar or Carbohydrate values and with these suggestions the user again adds items to the cart. Based on the items of the two shopping lists, separate scores are calculated and analyzed. 50 different people within the age bracket of 20-35 years have played this game and ANNOVA Test has been performed on the scores obtained by these players. The results suggest that the players were benefited by the game as they learnt to choose healthier products. © 2022 IEEE.
ABSTRACT
Background An outbreak of 2019 coronavirus pandemic (COVID-19) in urban centre, China, has unfolded quickly nationwide. There is a scarcity of human resource for observance of the symptoms of COVID-19 in patients or suspected person. Additionally, humans in the market for the monitorization of the symptoms are vulnerable because the COVID-19 will have an effect on those too. Methods Presently, there are many smartwatches/ smart bands available in the market which monitor the health parameters, but do not alert the user for any symptomatic changes in those parameters. And, the currently available watches/bands do not locate the person. Result This paper consists of an idea of inventing a smart healthcare device named as "Corona Warrior Smart Band" which measures the temperature, pulse rate and blood oxygen levels of a person without any human intervention and alerts the user and nearby COVID care centre (CCC) if any of the health parameters shows unusual observations in accordance with the COVID-19 conditions. This band also helps the COVID care centre's team to detect the live location of the suspected person. Conclusion This band is the best example of preventing the spreading and affection of COVID-19 virus in the society.
ABSTRACT
Background. Remdesivir (RDV) reduced time to recovery and mortality in some subgroups of hospitalized patients in the NIAID ACTT-1 RCT compared to placebo. Comparative effectiveness data in clinical practice are limited. Methods. Using the Premier Healthcare Database, we compared survival for adult non-mechanically ventilated hospitalized COVID-19 patients between Aug-Nov 2020 and treated with RDV within 2 days of hospitalization vs. those who did not receive RDV. Preferential within-hospital propensity score matching with replacement was used. Patients were matched on baseline O2 and 2-month admission period and were excluded if discharged within 3 days of RDV initiation (to exclude anticipated discharges/transfers within 72 hrs consistent with ACTT-1 study). Time to 14- and 28-day mortality was examined separately for patients on high-flow/non-invasive ventilation (NIV), low-flow, and no supplemental O2 using Cox Proportional Hazards models. Results. RDV patients (n=27,559) were matched to unique non-RDV patients (n=15,617) (Fig 1). The two groups were balanced;median age 66 yrs and 73% white (RDV);68 yrs and 74% white (non-RDV), and 55% male. At baseline, 21% required high-flow O2, 50% low-flow O2, and 29% no O2, overall. Mortality in RDV patients was 9.6% and 13.8% on days 14 and 28, respectively. For non-RDV patients, mortality was 14.0% and 17.3% on days 14 and 28, respectively. Kaplan-Meier curves for time to mortality are shown in Fig 2. After adjusting for baseline and clinical covariates, RDV patients on no O2 and low-flow O2 had a significantly lower risk of death within 14 days (no O2, HR: 0.69, 95% CI: 0.57-0.83;low-flow, HR: 0.67, 95% CI: 0.59-0.77) and 28 days (no O2, HR: 0.80, 95% CI: 0.68-0.94;low-flow, HR: 0.76, 95% CI: 0.68-0.86). Additionally, RDV patients on high-flow O2/NIV had a significantly lower risk of death within 14 days (HR: 0.81, 95% CI: 0.70-0.93);but not at 28 days (Fig 3). Conclusion. In this large study of patients in clinical care hospitalized with COVID-19, we observed a significant reduction of mortality in RDV vs. non-RDV treated patients in those on no O2 or low-flow O2. Mortality reduction was also seen in patients on high-flow O2 at day 14, but not day 28. These data support the use of RDV early in the course of COVID-19 in hospitalized patients.
ABSTRACT
Objectives: In this comparative effectiveness study, we compare the survival outcomes for hospitalized COVID-19 patients treated with remdesivir (RDV) upon admission vs. those not treated with RDV. Methods: We used the Premier Healthcare Database to examine patients hospitalized between Aug-Nov 2020 and treated with RDV within 2 days of hospitalization vs. those who did not receive RDV during their hospitalization. Preferential within-hospital propensity score matching with replacement was used. Patients were matched on baseline oxygen requirement and 2-month admission period and were excluded if discharged within 3 days of RDV initiation (to exclude anticipated discharges/transfers within 72 hrs consistent with ACTT-1 study). Cox Proportional Hazards models were used to examine 14- and 28-day mortality overall and for patients on no supplemental oxygen (NSO), low-flow oxygen (LFO), high-flow oxygen/non-invasive ventilation (HFO/NIV) and invasive mechanical ventilation/ECMO (IMV/ECMO) separately. Results: RDV patients (n=28,855) were matched to unique non-RDV patients (n=16,687). The two groups were balanced. At baseline, 28% required NSO, 48% LFO, 20% HFO/NIV and 4% IMV/ECMO. Mortality in RDV patients was 10.6% and 15.4% on days 14 and 28, respectively. For non-RDV patients, mortality was 15.4% and 19.1% on days 14 and 28, respectively. After adjusting for baseline and clinical covariates, RDV patients had significantly lower risk of mortality at 14-days (HR[95% CI]: 0.76[0.70−0.83]) and 28-days (0.89[0.82−0.96]). This mortality benefit was also seen for NSO, LFO and IMV/ECMO patients at 14-days (NSO: 0.69[0.57−0.83], LFO: 0.68[0.80−0.77], IMV/ECMO: 0.70[0.58−0.84]) and 28-days (NSO: 0.80[0.68−0.94], LFO: 0.77[0.68−0.86], IMV/ECMO: 0.81[0.69−0.94]). Additionally, HFO/NIV RDV patients had a significantly lower risk of mortality at 14-days (0.81[0.70−0.93]);but not at 28-days. Conclusions: In this observational study, treatment with RDV was associated with statistically significant reduction in mortality among hospitalized COVID-19 patients. These results complement the findings from the ACTT-1 and contribute to the growing body of evidence on the survival benefits of RDV.
ABSTRACT
Objectives: Remdesivir is an FDA approved treatment for hospitalized patients with COVID-19 infection and, in randomized controlled trials, RDV shortened time to recovery and improved clinical outcomes. Data are scarce on RDV utilization in real-world settings or how use has changed over the course of the pandemic. Using chargemaster inpatient data from the Premier Healthcare Database, we describe the patient population and use of RDV following Emergency Use Authorization. Methods: In this retrospective cohort study, adult patients admitted May 1st - Nov 30th 2020 with a primary or secondary discharge diagnosis of COVID-19 (ICD-10-CM: U07.1) were identified and their first COVID-related hospital admission was considered. Descriptive statistics were reported for demographic characteristics of RDV and non-RDV treated patients. RDV utilization over time and by region was examined. Results: Of the 190,529 patients hospitalized for COVID-19 in 823 hospitals, 55,030 (29%) were treated with RDV in 589 hospitals. RDV utilization over time increased from 5% of patients in May to 47% in Nov 2020. In Nov, RDV utilization was 57% in the West, followed by 49% in the South, 48% in the Midwest and 27% in the Northeast. Over time, RDV was initiated earlier in the course of hospitalization. Initiation within the first 2 days of hospitalization increased from 40% to 85% from May to Nov 2020. The average age was 63.6 years (SD=15.3) and 63.5 years (SD=17.3) for RDV-treated and non-RDV treated patients, respectively. More than half of the patients were male (RDV: 56%;Non-RDV: 52%) and about a quarter had commercial insurance (RDV: 28%;Non-RDV: 22%). Racial distribution (white, black, and other) was similar between RDV and non-RDV patients. Conclusions: Overall use of RDV and initiation within the first two days of hospitalization have substantially increased over the course of the pandemic in the United States.
ABSTRACT
Background: Clinical practice patterns for hospitalized COVID-19 patients have rapidly evolved, including specific treatment utilization. In turn, outcomes including time to improvement and mortality have also changed, but some reports have shown disproportionate mortality in Blacks. Data on the use of COVID-19 treatments over time and temporal association with hospital mortality and length of stay (LOS), along with assessments by race, are lacking. Methods: This was a retrospective cohort study of adult patients with a discharge diagnosis of COVID-19 (ICD-10-CM: U07.1) admitted between May-Nov 2020 using the chargemaster inpatient data from the Premier Healthcare Database. Demographic characteristics of the cohort were summarized. Utilization of remdesivir (RDV), dexamethasone, anticoagulants, tocilizumab, sarilumab and baricitinib were examined. Median hospital and intensive care unit (ICU) LOS were assessed over time. In-hospital mortality was identified through discharge status. Unadjusted mortality rates over time are reported. Results: Between May-Nov 2020, 190,529 patients were hospitalized for COVID-19 in 823 US hospitals. Patients had a mean age of 64 years, 64% were White, 19% Black, 53% male and 65% had Medicare/ Medicaid as primary payor. Black patients were younger than White (mean 60 vs. 66 years). Significant comorbidities (>20%) were similar between overall cohort and Black patients and included chronic pulmonary disease, hypertension and obesity. From May to Nov, overall RDV utilization increased from 5% to 47%, dexamethasone utilization increased from 7% to 77% and anticoagulant treatment utilization decreased from 32% to 24% (Figure). Few patients received tocilizumab (5%), sarilumab (0.02%) and baricitinib (0.003%). Among Black patients, RDV use increased from 5% to 39% and dexamethasone use increased from 6% to 74%. The median LOS of the overall cohort and Black cohort decreased from 6 days in May to 5 days in Nov, and overall ICU LOS for patients decreased from 5 to 4 days during this time;5 to 3 in Black patients. Overall in-hospital mortality rate decreased by 35%, and by 38% in Black patients. Conclusion:In US hospitalized patients, use of both dexamethasone and RDV has increased approximately 10-fold from May to Nov. Over this same time, a 35% reduction in mortality, a 17% reduction in LOS and 20% reduction in ICU stay were observed. Besides age, no notable differences were apparent by race. Understanding the drivers of improvement in outcomes requires further analyses.
ABSTRACT
Ecosystem, which consists of the physical environment and all the living organisms, on which we all depend, is declining rapidly because of its destruction caused by humans. It's a two-way relationship between the humans and mother nature. If we destroy the natural environment around us, human life will be seriously affected, and the life of next generation will be endangered unless serious steps are taken. One such effect of human overexploitations has come in the form of coronavirus outbreak. Coronavirus, a contagious disease of 2019 known as Covid-19, is the latest swiftly spreading global infection. The aetiology of Covid-19 is different from SARS-CoV which has the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), but it has the same host receptor, human angiotensin converting enzyme 2 (ACE2). The novel coronavirus which is zoonotic (spreading from an animal to a human) and mainly found in the bats and pangolins is a single stranded ribonucleic acid virus of Coronaviridae family.<sup>1</sup> The typical structure of 2019-nCoV possessed 'spike protein' in the membrane envelope, also expressed various polyproteins, nucleoproteins and membrane protein. The S protein binds to the receptor cell of host to facilitate the entry of virus in the host. Currently four genera for coronavirus are found a-CoV, ss-CoV, P-CoV, -CoV. SARS-CoV first originated in Wuhan, China and has spread across the globe. World Health Organization (WHO) and public health emergency of international concern declared it as 2019 - 2020 pandemic disease.<sup>2</sup> According to WHO report, (7<sup>th</sup> April 2020) update on this pandemic coronavirus disease, there have been more than 13,65,004 confirmed cases and 76,507 deaths across the world and these figures are rapidly increasing. Therefore, actions for proper recognition, management and its prevention must be prompted for relevant alleviation of its outspread.<sup>3</sup> Health care professionals are mainly indulged in the national crises and are working diligently around-the-clock, small ratio of the health care workers have become affected and few died tragically. Dentists are most often the first ones to be affected because they work with patients in close proximity. On 15<sup>th</sup> March 2020, the New York Times published an article titled "The workers who face the greatest Coronavirus risk" described the dentists are highly exposed, than the paramedical staffs and general physicians, to the risk of novel coronavirus disease 19.<sup>4</sup>.
ABSTRACT
In this paper, we discussed the available architectures used for smart ultrasound transducer probes and implemented the most popular architecture, which is very practical for most researchers or engineers. Field II simulation, hardware and software integration, and imaging acquisition were carried out. The open platform consists of 64 low-noise receiver channels, 128 transmitter channels, FPGA, USB controller and power management circuits to handle signals from uVpp to 160200 Vpp. This open platform is powered and accessed via high-speed USB Type-C™ port available on most PADs or PCs. RF data data can be available for transducer characterization, power consumption optimization and algorithm development. The open platform is capable of achieving real-time triplex B-mode/color/PW Doppler mode imaging. The hardware resource files can be requested by contacting the authors. © 2020 IEEE.
ABSTRACT
An outburst of coronavirus has tremendously affected the life of every individ-ual. However, indeed, it has drastically been a setback for everyone around the world, but Healthcare professionals are the ones who are suffering as well as serving the most. Specifically, it has affected dental professionals who are at a maximum higher exposure to this coronavirus disease. So this study article aims to assess fear psychosis and practice modifications in dental fraternity to fight against COVID-19. This pandemic has changed the lifestyles of people as well their perspective towards life. Moreover keeping in mind, the current scenario it’s vital to assess the knowledge and modifications which Dentist are adopting in their daily practice considering the pandemic situation. A cross-sectional study was conducted using an online survey form from 24th to 31st August 2020. A questionnaire was formulated and uploaded online and circu-lated amongst dental professionals in central India population. One thousand participants were included from various parts of Central India More than two-thirds of the general dental practitioners (78%) from various regions Were having anxiety about the anxious and scared by the distressing effects of Coro-navirus disease. There was awareness seen in almost 85% of dentists about changes in the treatment protocols. Nevertheless, carrying out of the recent protocol for treatment was estimated to be around 60%. The majority of the dentists (66%) were working in the hospital setting, 20% were from govern-ment settings. Despite having a high standard of knowledge and practice, dental practitioners around the globe are in a state of anxiety and fear while working in their respective fields due to the COVID-19 pandemic impact on human-ity. It has been evaluated that a vast number of population has just shut down their practices for an uncertain period or have restarted the practices with new protocols.