Coronavirus diagnosis using cough sounds: Artificial intelligence approaches

Introduction The Coronavirus disease 2019 (COVID-19) pandemic has caused irreparable damage to the world. In order to prevent the spread of pathogenicity, it is necessary to identify infected people for quarantine and treatment. The use of artificial intelligence and data mining approaches can lead to prevention and reduction of treatment costs. The purpose of this study is to create data mining models in order to diagnose people with the disease of COVID-19 through the sound of coughing. Method In this research, Supervised Learning classification algorithms have been used, which include Support Vector Machine (SVM), random forest, and Artificial Neural Networks, that based on the standard "Fully Connected” neural network, Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) recurrent neural networks have been established. The data used in this research was from the online site sorfeh.com/sendcough/en, which has data collected during the spread of COVID-19. Result With the data we have collected (about 40,000 people) in different networks, we have reached acceptable accuracies. Conclusion These findings show the reliability of this method for using and developing a tool as a screening and early diagnosis of people with COVID-19. This method can also be used with simple artificial intelligence networks so that acceptable results can be expected. Based on the findings, the average accuracy was 83% and the best model was 95%.

Coronavirus diagnosis using cough sounds: Artificial intelligence approaches.

Introduction: The Coronavirus disease 2019 (COVID-19) pandemic has caused irreparable damage to the world. In order to prevent the spread of pathogenicity, it is necessary to identify infected people for quarantine and treatment. The use of artificial intelligence and data mining approaches can lead to prevention and reduction of treatment costs. The purpose of this study is to create data mining models in order to diagnose people with the disease of COVID-19 through the sound of coughing. Method: In this research, Supervised Learning classification algorithms have been used, which include Support Vector Machine (SVM), random forest, and Artificial Neural Networks, that based on the standard "Fully Connected" neural network, Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) recurrent neural networks have been established. The data used in this research was from the online site sorfeh.com/sendcough/en, which has data collected during the spread of COVID-19. Result: With the data we have collected (about 40,000 people) in different networks, we have reached acceptable accuracies. Conclusion: These findings show the reliability of this method for using and developing a tool as a screening and early diagnosis of people with COVID-19. This method can also be used with simple artificial intelligence networks so that acceptable results can be expected. Based on the findings, the average accuracy was 83% and the best model was 95%.

Personalized predictions of adverse side effects of the COVID-19 vaccines.

Background: Misconceptions about adverse side effects are thought to influence public acceptance of the Coronavirus disease 2019 (COVID-19) vaccines negatively. To address such perceived disadvantages of vaccines, a novel machine learning (ML) approach was designed to generate personalized predictions of the most common adverse side effects following injection of six different COVID-19 vaccines based on personal and health-related characteristics. Methods: Prospective data of adverse side effects following COVID-19 vaccination in 19943 participants from Iran and Switzerland was utilized. Six vaccines were studied: The AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2, and the mRNA-1273 vaccine. The eight side effects were considered as the model output: fever, fatigue, headache, nausea, chills, joint pain, muscle pain, and injection site reactions. The total input parameters for the first and second dose predictions were 46 and 54 features, respectively, including age, gender, lifestyle variables, and medical history. The performances of multiple ML models were compared using Area Under the Receiver Operating Characteristic Curve (ROC-AUC). Results: The total number of people receiving the first dose of the AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2, and mRNA-1273 were 6022, 7290, 5279, 802, 277, and 273, respectively. For the second dose, the numbers were 2851, 5587, 3841, 599, 242 and 228. The Logistic Regression model for predicting different side effects of the first dose achieved ROC-AUCs of 0.620-0.686, 0.685-0.716, 0.632-0.727, 0.527-0.598, 0.548-0.655, 0.545-0.712 for the AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2 and mRNA-1273 vaccines, respectively. The second dose models yielded ROC-AUCs of 0.777-0.867, 0.795-0.848, 0.857-0.906, 0.788-0.875, 0.683-0.850, and 0.486-0.680, respectively. Conclusions: Using a large cohort of recipients vaccinated with COVID-19 vaccines, a novel and personalized strategy was established to predict the occurrence of the most common adverse side effects with high accuracy. This technique can serve as a tool to inform COVID-19 vaccine selection and generate personalized factsheets to curb concerns about adverse side effects.

Five consecutive epidemiological waves of COVID-19: a population-based cross-sectional study on characteristics, policies, and health outcome.

BACKGROUND: This study was conducted with the intension of providing a more detailed view about the dynamics of COVID-19 pandemic. To this aim, characteristics, implemented public health measures, and health outcome of COVID-19 patients during five consecutive waves of the disease were assessed. METHODS: This study was a population-based cross-sectional analysis of data on adult patients who were diagnosed with COVID-19 during five waves of the disease in Iran. Chi-squared test, One-way ANOVA, and Logistic Regression analysis were applied. A detailed literature review on implemented public health policies was performed by studying published documents and official websites responsible for conveying information about COVID-19. RESULTS: Data on 328,410 adult patients was analyzed. Main findings indicated that the probability of dying with COVID-19 has increased as the pandemic wore on, showing its highest odd during the third wave (odds ratio: 1.34, CI: 1.283-1.395) and has gradually decreased during the next two waves. The same pattern was observed in the proportion of patients requiring ICU admission (P < 0.001). First wave presented mainly with respiratory symptoms, gastrointestinal complaints were added during the second wave, neurological manifestations with peripheral involvement replaced the gastrointestinal complaints during the third wave, and central nervous system manifestations were added during the fourth and fifth waves. A significant difference in mean age of patients was revealed between the five waves (P < 0.001). Moreover, results showed a significant difference between men and women infected with COVID-19, with men having higher rates of the disease at the beginning. However, as the pandemic progressed the proportion of women gradually increased, and ultimately more women were diagnosed with COVID-19 during the fifth wave. Our observations pointed to the probability that complete lockdowns were the key measures that helped to mitigate the virus spread during the first twenty months of the pandemic in the country. CONCLUSION: A changing pattern in demographic characteristics, clinical manifestations, and severity of the disease has been revealed as the pandemic unfolded. Reviewing COVID-19-related public health interventions highlighted the importance of immunization and early implementation of restrictive measures as effective strategies for reducing the acute burden of the disease.

Mortality among hospitalized COVID-19 patients during surges of SARS-CoV-2 alpha (B.1.1.7) and delta (B.1.617.2) variants.

The aim of this study was to evaluate the death proportion and death risk of COVID-19 hospitalized patients over time and in different surges of COVID-19. This multi-center observational study was conducted from March 21, 2021 to October 3, 2021 which included the alpha and delta SARS-CoV-2 surges occurred in April and August in Tehran, respectively. The risk of COVID-19 death was compared in different months of admission. A total of 270,624 patients with COVID-19, of whom 6.9% died, were admitted to hospitals in Tehran province. Compared to patients admitted in March, a higher risk of COVID-19 death was observed among patients admitted to the hospital in July (HR 1.28; 95% CI 1.17, 1.40), August (HR 1.40; 95% CI 1.28, 1.52), September (HR 1.37; 95% CI 1.25, 1.50) and October (HR 4.63; 95% CI 2.77, 7.74). The ICU death proportion was 36.8% (95% CI: 35.5, 38.1) in alpha surge and increased significantly to 39.8 (95% CI 38.6, 41.1) in delta surge. The risk of COVID-19 death was significantly higher in delta surge compared to alpha surge (HR 1.22; 95% CI 1.17, 1.27). Delta surge was associated with a higher risk of death compared to alpha surge. High number of hospitalizations, a shortage of hospital beds, ICU spaces and medical supplies, poor nutritional status of hospitalized patients, and lack of the intensivist physicians or specialized nurses in the ICU were factors that contributed to the high mortality rate in the delta surge in Iran.

Migrant Health and COVID-19 Pandemic: A Cross-sectional Study of Characteristics, Clinical Features, and Health Outcome from Iran.

BACKGROUND: As the pandemic unfolds, major concerns remain with those in disadvantaged positions who may be disproportionately affected. This paper aimed to present the characteristics of COVID-19 immigrant patients and investigate whether they were disproportionately affected by COVID-19 pandemic. METHODS: A cross-sectional study was performed using data on 589,146 patients diagnosed with COVID-19 in Iran. Descriptive analyses were used to summarize the study population's characteristics. Chi-squared test and logistic regression model were applied. RESULTS: After accounting for possible confounding covariates, being an immigrant was significantly associated with increased risk of death due to COVID-19 (OR 1.64, CI 1.568-1.727). When compared to Iranian-born patients, the prevalence of low blood oxygen levels on admission was higher among immigrant patients (53.9% versus 47.7%, P value < 0.001). Moreover, greater proportions of immigrants who were diagnosed with COVID-19 were admitted to an ICU (17% versus 15.8%, P value < 0.001). Patients aged 65 and above were the largest age category in both populations. However, there was a significant difference between the age profiles of patients, with children under the age of eighteen presenting 16% of immigrant patients vs 6.6% of Iranian-born patients (P value < 0.001). In both groups, more men were affected by COVID-19 than women, yet the sex bias was more prominent for migrant patients (P value < 0.001). CONCLUSION: The evidence from this study revealed that immigrant patients infected with COVID-19 were more likely to suffer from severe health outcome of the disease compared to Iranian-born patients.

National Research Institute of Tuberculosis and Lung Disease (NRITLD) Protocol for the Treatment of Patients with COVID-19.

More than a year after the onset of the coronavirus disease pandemic in 2019, the disease remains a major global health issue. During this time, health organizations worldwide have tried to provide integrated treatment guidelines to control coronavirus disease 2019 (COVID-19) at different levels. However, due to the novel nature of the disease and the emergence of new variants, medical teams' updating medical information and drug prescribing guidelines should be given special attention. This version is an updated instruction of the National Research Institute of Tuberculosis and Lung Disease (NRITLD) in collaboration with a group of specialists from Masih Daneshvari Hospital in Tehran, Iran, which is provided to update the information of caring clinicians for the treatment and care of COVID-19 hospitalized patients.

Urban and sub-urban disparities in health outcomes among patients with COVID-19; a cross-sectional study of 234 418 patients in Iran.

BACKGROUND: It remains crucial to understand socio-demographic determinants of COVID-19 infection to improve access to care and recovery rates from the disease. This study aimed to investigate the urban and sub-urban disparities associated with COVID-19 in patients visiting healthcare facilities in the province of Tehran, Iran. METHODS: Data from 234 418 patients who were diagnosed with COVID-19 infection from March 2020 to March 2021 in the province of Tehran were used in this analysis. Descriptive statistics were used to describe the characteristics of the study population. Chi-Squared test was applied to examine the association of study variables with residing area. Independent samples t-test was performed to compare mean age of patients in urban and sub-urban areas. Multiple Logistic Regression model was applied to examine the association of study variables with disease outcome. RESULTS: Overall, most patients resided in the urban settings (73%). Mean age of patients was significantly lower in sub-urban areas compared to their counterparts in urban settings (49 ± 23.1 years versus 53 ± 21.1 years, P < 0.001). Positive PCR test results were more common in urban areas (48.5% versus 41.3%, P < 0.001). Yet, sub-urban settings had higher rates of positive chest CT scan reports (62.8% versus 53.4%, P < 0.001). After accounting for age and sex covariates, residing in urban areas was associated with higher likelihood of being admitted to an ICU (OR = 1.27, CI: 1.240-1.305). Yet, a greater vulnerability to fatal outcome of COVID-19 infection was shown in patients living in sub-urban areas (OR = 1.13, CI: 1.105-1.175). CONCLUSIONS: This study revealed a clear disparity in the health outcome of patients infected with COVID-19 between urban and sub-urban areas.

Effect of short-term exposure to air pollution on COVID-19 mortality and morbidity in Iranian cities.

Purpose: The association between air pollutant (PM2.5, PM10, NO2, and O3) concentrations and daily number of COVID-19 confirmed cases and related deaths were evaluated in three major Iranian cities (Tehran, Mashhad, and Tabriz). Methods: Hourly concentrations of air pollutants and daily number of PCR-confirmed cases and deaths of COVID-19 were acquired (February 20th, 2020 to January 4th, 2021). A generalized additive model (GAM) assuming a quasi-Poisson distribution was used to model the associations in each city up to lag-day 7 (for mortality) and 14 (for morbidity). Then, the city-specific estimates were meta-analyzed using a fixed effect model to obtain the overall relative risks (RRs). Results: A total of 114,964 confirmed cases and 21,549 deaths were recorded in these cities. For confirmed cases, exposure to PM2.5, NO2, and O3 for several lag-days showed significant associations. In case of mortality, meta-analysis estimated that the RRs for PM2.5, PM10, NO2, and O3 concentrations were 1.06 (95% CI: 0.99, 1.13), 1.06 (95% CI: 0.93, 1.19), 1.15 (95% CI: 0.93, 1.38), and 1.07 (95% CI: 0.84, 1.31), respectively. Despite several positive associations with all air pollutants over multiple lag-days, COVID-19 mortality was only significantly associated with NO2 on lag-days 0-1 and 1 with the RRs of 1.35 (95% CI: 1.04, 1.67) and 1.16 (95% CI: 1.02, 1.31), respectively. Conclusion: This study showed that air pollution can be a factor exacerbating COVID-19 infection and clinical outcomes. Actions should be taken to reduce the exposure of the public and particularly patients to ambient air pollutants. Supplementary Information: The online version contains supplementary material available at 10.1007/s40201-021-00736-4.

Corticosteroids on the Management of Coronavirus Disease 2019 (COVID-19): A Systemic Review and Meta-Analysis.

BACKGROUND: We aimed to examine the available evidence regarding the efficacy and safety of corticosteroids on the management of coronavirus disease 2019 (COVID-19), severe acute respiratory syndrome (SARS-CoV) and Middle East respiratory syndrome (MERS-CoV). METHOD: An extensive search was conducted in Medline, Embase, and Central databases until the end of March 2020, using keywords related to corticosteroids, COVID-19, SARS-CoV and MERS-CoV. The main outcome was considered to be the mortality rate, length of stay, virus clearance time, symptom improvement, and lung function improvement. The findings are presented as odds ratio (OR) with 95% confidence interval (95% CI). RESULTS: Fifteen paper compromising 5 studies on COVID-19, 8 studies on SARS-CoV and 2 studies on MERS-CoV were included. One study was clinical trial and the rest were cohort. The analyses showed that corticosteroids were not reduce the mortality rate of COVID-19 (OR=1.08; 95% CI: 0.34 to 3.50) and SARS-CoV (OR=0.77; 95% CI: 0.34 to 1.3) patients, while they were associated with higher mortality rate of patients with MERS-CoV (OR = 2.52; 95% CI: 1.41 to 4.50). Moreover, it appears that corticosteroids administration would not be effective in shortening viral clearance time, length of hospitalization, and duration of relief symptoms following viral severe acute respiratory infections. CONCLUSION: There is no evidences that corticosteroids are safe and effective on the treatment of severe acute respiratory infection when COVID-19 disease is suspected. Therefore, corticosteroids prescription in COVID-19 patients should be avoided.

Using Machine Learning to Predict Mortality for COVID-19 Patients on Day 0 in the ICU.

Rationale: Given the expanding number of COVID-19 cases and the potential for new waves of infection, there is an urgent need for early prediction of the severity of the disease in intensive care unit (ICU) patients to optimize treatment strategies. Objectives: Early prediction of mortality using machine learning based on typical laboratory results and clinical data registered on the day of ICU admission. Methods: We retrospectively studied 797 patients diagnosed with COVID-19 in Iran and the United Kingdom (U.K.). To find parameters with the highest predictive values, Kolmogorov-Smirnov and Pearson chi-squared tests were used. Several machine learning algorithms, including Random Forest (RF), logistic regression, gradient boosting classifier, support vector machine classifier, and artificial neural network algorithms were utilized to build classification models. The impact of each marker on the RF model predictions was studied by implementing the local interpretable model-agnostic explanation technique (LIME-SP). Results: Among 66 documented parameters, 15 factors with the highest predictive values were identified as follows: gender, age, blood urea nitrogen (BUN), creatinine, international normalized ratio (INR), albumin, mean corpuscular volume (MCV), white blood cell count, segmented neutrophil count, lymphocyte count, red cell distribution width (RDW), and mean cell hemoglobin (MCH) along with a history of neurological, cardiovascular, and respiratory disorders. Our RF model can predict patient outcomes with a sensitivity of 70% and a specificity of 75%. The performance of the models was confirmed by blindly testing the models in an external dataset. Conclusions: Using two independent patient datasets, we designed a machine-learning-based model that could predict the risk of mortality from severe COVID-19 with high accuracy. The most decisive variables in our model were increased levels of BUN, lowered albumin levels, increased creatinine, INR, and RDW, along with gender and age. Considering the importance of early triage decisions, this model can be a useful tool in COVID-19 ICU decision-making.

Favipiravir Effects on the Control of Clinical Symptoms of Hospitalized COVID-19 Cases: An Experience with Iranian Formulated Dosage Form.

Coronavirus disease -19 (COVID-19) pandemic, caused by SARS-CoV-2, has gradually spread worldwide, becoming a major public health event. This situation requires designing a novel antiviral agent against the SARS-CoV-2; however, this is time-consuming and the use of repurposed medicines may be promising. One such medicine is favipiravir, primarily introduced as an anti-influenza agent in east world. The aim of this study was to evaluate the efficacy and safety of favipiravir in comparison with lopinavir-ritonavir in SARS-CoV-2 infection. In this randomized clinical trial, 62 patients were recruited. These patients had bilateral pulmonary infiltration with peripheral oxygen saturation lower than 93%. The median time from symptoms onset to intervention initiation was seven days. Favipiravir was not available in the Iranian pharmaceutical market, and it was decided to formulate it at the research laboratory of School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, Iran. The patients received favipiravir tablet at a dose of 1600 mg orally twice a day for day one and then 600 mg orally twice a day for days 2 to 6. In the second group, the patients received lopinavir-ritonavir combination tablet at a dose of 200/50 mg twice a day for seven days. Fever, cough, and dyspnea were improved significantly in favipiravir group in comparison with lopinavir-ritonavir group on days four and five. Mortality rate and ICU stay in both groups were similar, and there was no significant difference in this regard (P = 0.463 and P = 0.286, respectively). Chest X-ray improvement also was not significantly different between the two groups. Adverse drug reactions occurred in both groups, and impaired liver enzymes were the most frequent adverse effect. In conclusion, early administration of oral favipiravir may reduce the duration of clinical signs and symptoms in patients with COVID-19 and hospitalization period. The mortality rate also should be investigated in future clinical trials.

Transverse myelitis after SARS-CoV-2 infection: Report of two cases with COVID-19.

Transverse myelitis has been reported as a complication of COVID-19 in recent studies. Here, we report two cases of transverse myelitis related to COVID-19. Both patients underwent plasma exchange after being treated with antibiotics and corticosteroids which lead to the recovery of one of them.

Description of Epidemiological Features, Symptoms and Mortality of the Patients with COVID-19 in Some Provinces of Iran.

BACKGROUND: Clinical manifestations of COVID-19 are different. There are some risk factors for COVID-19. This study aimed to describe the epidemiological features, symptoms and mortality of the patients with COVID-19 in Iran. METHODS: This were a cohort study performed on 103,179 patients with COVID-19. The demographic and clinical data were collected in selected provinces. The required data of all patients was extracted from the COVID registry system and analyzed using STATA version 14 and Excel 2016. RESULTS: The mean age was 52.40 years for men and 52.41 years for women. About 55.2% of the study population were male and 44.8% were female. Totally, 60.9% (5085) of deaths happened in men and 39.1% (3263) in women. The mean time from onset of symptoms to hospitalization in men and women were 3.47 and 3.48 days, respectively. The mean time from onset of symptoms to isolation was 2.81 days in men and was 2.87 days in women, from onset of symptoms to death was 9.29 and 9.54 days, respectively, from onset of symptoms to discharge was 7.47 and 7.39 days, and from hospitalization to death was 6.76 and 7.05 days. Cough and shortness of breath were the most common symptoms in the patients. CONCLUSION: According to the results, the overall mortality rate was higher in men than women. Women with cardiovascular disease and diabetes were more likely to die. The mean time from onset of symptoms to hospitalization, isolation, and discharge was similar in men and women.

Parkinson's disease and COVID-19: a systematic review and meta-analysis.

BACKGROUND: Patients with Parkinson's disease (PD) are at higher risk of COVID-19 infection as most of them are at older age. The goal of this study is to update the pooled prevalence of COVID-19 infection in patients with PD. METHODS: Two researchers systematically searched PubMed, Scopus, EMBASE, Web of Science, Google Scholar, and also gray literature including references of the included studies which were published before September 2021. We extracted data regarding the total number of participants, first author, publication year, the country of origin, mean age, number with COVID-19, symptoms, hospitalization, and death. RESULTS: We found 1693 articles by literature search; after deleting duplicates, 798 remained. Thirty articles remained for meta-analysis. The pooled prevalence of COVID-19 infection in PD cases was 5% (95%CI: 4-6%) (I2 = 98.1%, P < 0.001). The pooled prevalence of fever in cases with PD was 4% (95%CI: 2-6%) (I2 = 96%, P < 0.001). The pooled prevalence of cough in cases with PD was 3% (95%CI: 2-4%) (I2 = 95.9%, P < 0.001). The pooled prevalence of hospitalization in cases with COVID-19 infection was 49% (95%CI: 29-52%) (I2: 93.5%, P < 0.001). The pooled prevalence of mortality in COVID-19 cases was 12% (95%CI: 10-14%) (I2 = 97.6%, P < 0.001). CONCLUSION: The results of this systematic review and meta-analysis show that the pooled prevalence of COVID-19 infection in PD cases is 5% besides hospitalization and mortality rates which are 49% and 12%.

A description of spatial-temporal patterns of the novel COVID-19 outbreak in the neighbourhoods' scale in Tehran, Iran.

Background: Analyzing and monitoring the spatial-temporal patterns of the new coronavirus disease (COVID-19) pandemic can assist local authorities and researchers in detecting disease outbreaks in the early stages. Because of different socioeconomic profiles in Tehran's areas, we will provide a clear picture of the pandemic distribution in Tehran's neighbourhoods during the first months of its spread from February to July 2020, employing a spatial-temporal analysis applying the geographical information system (GIS). Disease rates were estimated by location during the 5 months, and hot spots and cold spots were highlighted. Methods: This study was performed using the COVID-19 incident cases and deaths recorded in the Medical Care Monitoring Centre from February 20, to July 20, 2020. The local Getis-Ord Gi* method was applied to identify the hotspots where the infectious disease distribution had significantly clustered spatially. A statistical analysis for incidence and mortality rates and hot spots was conducted using ArcGIS 10.7 software. Results: The addresses of 43,000 Tehrani patients (15,514 confirmed COVID-19 cases and 27,486 diagnosed as probable cases) were changed in its Geo-codes in the GIS. The highest incidence rate from February to July 2020 was 48 per 10,000 and the highest 5-month incidence rate belonged to central and eastern neighbourhoods. According to the Cumulative Population density of patients, the higher number is estimated by more than 2500 people in the area; however, the lower number is highlighted by about 500 people in the neighborhood. Also, the results from the local Getis-Ord Gi* method indicate that COVID-19 has formed a hotspot in the eastern, southeast, and central districts in Tehran since February. We also observed a death rate hot spot in eastern areas. Conclusion: Because of the spread of COVID-19 disease throughout Tehran's neighborhoods with different socioeconomic status, it seems essential to pay attention to health behaviors to prevent the next waves of the disease. The findings suggest that disease distribution has formed a hot spot in Tehran's eastern and central regions.

The Impact of the Coronavirus Disease 2019 Pandemic on Neurosurgical Practice and Feasibility of Safe Resumption of Elective Procedures During this Era in a Large Referral Center in Tehran, Iran: An Unmatched Case-Control Study.

OBJECTIVE: The coronavirus disease 2019 (COVID-19) pandemic has considerably affected surgical practice. The present study aimed to investigate the effects of the pandemic on neurosurgical practice and the safety of the resumption of elective procedures through implementing screening protocols in a high-volume academic public center in Iran, as one of the countries severely affected by the pandemic. METHODS: This unmatched case-control study compared 2 populations of patients who underwent neurosurgical procedures between June 1, 2019 and September 1, 2019 and the same period in 2020. In the prospective part of the study, patients who underwent elective procedures were tested for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection postoperatively to evaluate the viability of our screening protocol. RESULTS: Elective and emergency procedures showed significant reduction during the pandemic (59.4%, n = 168 vs. 71.3%, n = 380) and increase (28.7%, n = 153 vs. 40.6%, n = 115, respectively; P = 0.003). The proportional distribution of neurosurgical categories remained unchanged during the pandemic. Poisson regression showed that the reduction in total daily admissions and some categories, including spine, trauma, oncology, and infection were significantly correlated with the pandemic. Among patients who underwent elective procedures, 0 (0.0%) and 26 (16.25%) had positive test results on days 30 and 60 postoperatively, respectively. Overall mortality was comparable between the pre-COVID-19 and COVID-19 periods, yet patients with concurrent SARS-CoV-2 infection showed substantially higher mortality (65%). CONCLUSIONS: By implementing safety and screening protocols with proper resource allocation, the emergency care capacity can be maintained and the risk minimized of hospital-acquired SARS-CoV-2 infection, complications, and mortality among neurosurgical patients during the pandemic. Similarly, for elective procedures, according to available resources, hospital beds can be allocated for patients with a higher risk of delayed hospitalization and those who are concerned about the risk of hospital-acquired infection can be reassured.

Covid-19 in Parkinson's Disease treated by drugs or brain stimulation.

PURPOSE: Covid-19 has affected all people, especially those with chronic diseases, including Parkinson's Disease (PD). Covid-19 may affect both motor and neuropsychiatric symptoms of PD patients. We intend to evaluate different aspects of Covid-19 impact on PD patients. METHODS: 647 PD patients were evaluated in terms of PD-related and Covid-19-related clinical presentations in addition to past medical history during the pandemic through an online questioner. They were compared with an age-matched control group consist of 673 individuals and a sample of the normal population consist of 1215 individuals. RESULTS: The prevalence of Covid-19 in PD patients was 11.28%. The mortality was 1.23% among PD patients. The prevalence of Covid-19 in PD patients who undergone Deep Brain Stimulation (DBS) was 18.18%. No significant association was found between the duration of disease and the prevalence of Covid-19. A statistically significant higher prevalence of Covid-19 in PD patients who had direct contact with SARS-CoV-19 infected individuals was found. No statistically significant association has been found between the worsening of motor symptoms and Covid-19. PD patients and the normal population may differ in the prevalence of some psychological disorders, including anxiety and sleeping disorders, and Covid-19 may affect the psychological status. CONCLUSION: PD patients possibly follow tighter preventive protocols, which lead to lower prevalence and severity of Covid-19 and its consequences in these patients. Although it seems Covid-19 does not affect motor and psychological aspects of PD as much as it was expected, more accurate evaluations are suggested in order to clarify such effects.

Symptom Prediction and Mortality Risk Calculation for COVID-19 Using Machine Learning.

Background: Early prediction of symptoms and mortality risks for COVID-19 patients would improve healthcare outcomes, allow for the appropriate distribution of healthcare resources, reduce healthcare costs, aid in vaccine prioritization and self-isolation strategies, and thus reduce the prevalence of the disease. Such publicly accessible prediction models are lacking, however. Methods: Based on a comprehensive evaluation of existing machine learning (ML) methods, we created two models based solely on the age, gender, and medical histories of 23,749 hospital-confirmed COVID-19 patients from February to September 2020: a symptom prediction model (SPM) and a mortality prediction model (MPM). The SPM predicts 12 symptom groups for each patient: respiratory distress, consciousness disorders, chest pain, paresis or paralysis, cough, fever or chill, gastrointestinal symptoms, sore throat, headache, vertigo, loss of smell or taste, and muscular pain or fatigue. The MPM predicts the death of COVID-19-positive individuals. Results: The SPM yielded ROC-AUCs of 0.53-0.78 for symptoms. The most accurate prediction was for consciousness disorders at a sensitivity of 74% and a specificity of 70%. 2,440 deaths were observed in the study population. MPM had a ROC-AUC of 0.79 and could predict mortality with a sensitivity of 75% and a specificity of 70%. About 90% of deaths occurred in the top 21 percentile of risk groups. To allow patients and clinicians to use these models easily, we created a freely accessible online interface at www.aicovid.net. Conclusion: The ML models predict COVID-19-related symptoms and mortality using information that is readily available to patients as well as clinicians. Thus, both can rapidly estimate the severity of the disease, allowing shared and better healthcare decisions with regard to hospitalization, self-isolation strategy, and COVID-19 vaccine prioritization in the coming months.