Prevalence of the Risk Factors of Delirium Among COVID-19 Patients in the Intensive Care Unit at King Abdullah Medical City, Makkah: A Single-Center Study.

Background Coronavirus disease 2019 (COVID-19) patients admitted to the intensive care unit (ICU) are at a higher risk of developing delirium. In this study, we estimated the incidence of delirium and its risk factors in ICU patients with COVID-19 at King Abdullah Medical City (KAMC), Makkah, Saudi Arabia. Methodology We conducted a retrospective, analytical, cohort study of adult COVID-19 patients admitted to the ICU of KAMC between May 2020 and July 2021. Data were collected from electronic medical records. Results Of the 406 examined patients with COVID-19 aged >18 years, 55 developed delirium in the ICU setting. The incidence rate was 0.59% per 100 ICU days in these 55 patients; the mean age was 62.36 ± 17.9 years, and 65.5% were men. Binary logistic regression showed that age (p = 0.027), nationality (p = 0.045), presence of infectious diseases other than COVID-19 (p = 0.047), and ICU outcome (p = 0.013) were significant risk factors for developing delirium. The clinical presentation and prognosis of patients who developed delirium were assessed using the Acute Physiology and Chronic Health Evaluation II and Sequential Organ Failure Assessment scores, and the mean scores were 16.13 ± 7.96 and 5.25 ± 3.48, respectively. The mean length of ICU stay was 22.2 ± 33.3 days; 39 (70.9%) patients were discharged and 16 (29.1%) died. Conclusions Older age, nationality, infections, and ICU outcomes were risk factors for developing delirium in hospitalized COVID-19 patients at KAMC. Early detection of cognitive comorbidities and delirium in these patients is important.

Hospital and laboratory outcomes of patients with COVID-19 who received vitamin D supplementation: a systematic review and meta-analysis of randomized controlled trials.

Coronavirus disease 2019 (COVID-19) has a wide-ranging spectrum of clinical symptoms, from asymptomatic/mild to severe. Recent research indicates that, among several factors, a low vitamin D level is a modifiable risk factor for COVID-19 patients. This study aims to evaluate the effect of vitamin D on hospital and laboratory outcomes of patients with COVID-19.Five databases (PubMed, Embase, Scopus, Web of Science, and Cochrane Library) and clinicaltrials.gov were searched until July 2022, using relevant keywords/Mesh terms. Only randomized clinical trials (RCTs) that addressed the topic were included. The Cochrane tool was used to assess the studies' risk of bias, and the data were analyzed using the review manager (RevMan 5.4).We included nine RCTs with 1586 confirmed COVID-19 patients. Vitamin D group showed a significant reduction of intensive care unit (ICU) admission (risk ratio = 0.59, 95% confidence interval (CI) [0.41, 0.84], P = 0.003), and higher change in vitamin D level (standardized mean difference = 2.27, 95% CI [2.08, 2.47], P < 0.00001) compared to the control group. Other studied hospital and laboratory outcomes showed non-significant difference between vitamin D and the control group (P ≥ 0.05).In conclusion, vitamin D reduced the risk of ICU admission and showed superiority in changing vitamin D level compared to the control group. However, other outcomes showed no difference between the two groups. More RCTs are needed to confirm these results.

Prevalence of the Risk Factors of Delirium Among COVID-19 Patients in the Intensive Care Unit at King Abdullah Medical City, Makkah: A Single-Center Study

Background Coronavirus disease 2019 (COVID-19) patients admitted to the intensive care unit (ICU) are at a higher risk of developing delirium. In this study, we estimated the incidence of delirium and its risk factors in ICU patients with COVID-19 at King Abdullah Medical City (KAMC), Makkah, Saudi Arabia. Methodology We conducted a retrospective, analytical, cohort study of adult COVID-19 patients admitted to the ICU of KAMC between May 2020 and July 2021. Data were collected from electronic medical records. Results Of the 406 examined patients with COVID-19 aged >18 years, 55 developed delirium in the ICU setting. The incidence rate was 0.59% per 100 ICU days in these 55 patients;the mean age was 62.36 ± 17.9 years, and 65.5% were men. Binary logistic regression showed that age (p = 0.027), nationality (p = 0.045), presence of infectious diseases other than COVID-19 (p = 0.047), and ICU outcome (p = 0.013) were significant risk factors for developing delirium. The clinical presentation and prognosis of patients who developed delirium were assessed using the Acute Physiology and Chronic Health Evaluation II and Sequential Organ Failure Assessment scores, and the mean scores were 16.13 ± 7.96 and 5.25 ± 3.48, respectively. The mean length of ICU stay was 22.2 ± 33.3 days;39 (70.9%) patients were discharged and 16 (29.1%) died. Conclusions Older age, nationality, infections, and ICU outcomes were risk factors for developing delirium in hospitalized COVID-19 patients at KAMC. Early detection of cognitive comorbidities and delirium in these patients is important.

Statistical analysis for the pitch of mask-wearing Arabic speech

According to Fourier analysis, any periodic function can be analyzed as an infinite series of trigonometric functions (sets of sines and cosines). The kernel of decay cosine yields an extension for the previous frequency-based, sieve-type detection algorithm by giving smooth peaks for decaying amplitudes with the harmonics of the signal correlation. The sequential outline of the RAPT algorithm is: 1) Providing speech samples with their sampling rate and with a reduced sampling rate. 2) Periodically, computing normalized cross-correlation function (NCCF) of the reduced sampling rate speech signal with lags in the F0 range. 3) Indicating the locations of maximum at the 1st pass of NCCF. 4) For the vicinity of the peaks in that 1st pass, calculate the NCCF for the original sampling rate. 5) Again, finding the maximum in that NCCF. Obtaining the location and amplitude of the modified peak. 6) For each peak obtained from the NCCF (high resolution), estimate the F0 of the processed frame. 7) The hypothesis of the frame for unvoiced/voiced is advanced for each frame. 8) Finding the group of the NCCF peaks via optimization process for the unvoiced/voiced hypotheses for all the frames which have the best match with the above characteristics. 9) Using the well-known speech pitch tracking algorithm (PTA), RAPT has the following differences: - PTA computes the NCCF in the linear prediction coding (LPC).

Colorimetry-Based Detection of Nitric Oxide from Exhaled Breath for Quantification of Oxidative Stress in Human Body.

Monitoring exhaled breath is a safe, noninvasive method for determining the health status of the human body. Most of the components in our exhaled breath can act as health biomarkers, and they help in providing information about various diseases. Nitric oxide (NO) is one such important biomarker in exhaled breath that indicates oxidative stress in our body. This work presents a simple and noninvasive quantitative analysis approach for detecting NO from exhaled breath. The sensing is based on the colorimetric assisted detection of NO by m-Cresol Purple, Bromophenol Blue, and Alizaringelb dye. The sensing performance of the dye was analyzed by ultraviolet-visible (UV-Vis) spectroscopy. The study covers various sampling conditions like the pH effect, temperature effect, concentration effect, and selective nature of the dye. The m-Cresol Purple dye exhibited a high sensitivity towards NO with a detection limit of ~0.082 ppm in the linear range of 0.002-0.5 ppm. Moreover, the dye apprehended a high degree of selectivity towards other biocompounds present in the breath, and no possible interfering cross-reaction from these species was observed. The dye offered a high sensitivity, selectivity, fast response, and stability, which benchmark its potential for NO sensing. Further, m-Cresol Purple dye is suitable for NO sensing from the exhaled breath and can assist in quantifying oxidative stress levels in the body for the possible detection of COVID-19.

Comparing COVID-19 Risk Factors in Brazil Using Machine Learning: The Importance of Socioeconomic, Demographic and Structural Factors (preprint)

Background: The COVID-19 pandemic continues to have a devastating impact on Brazil. Brazil's social, health and economic crises are aggravated by strong societal inequities and persisting political disarray. This complex scenario motivates careful study of the clinical, socioeconomic, demographic and structural factors contributing to increased risk of mortality from SARS-CoV-2 in Brazil specifically.Methods: We consider the Brazilian SIVEP-Gripe catalog, a very rich respiratory infection dataset which allows us to estimate the importance of several non-laboratorial and socio-geographic factors on COVID-19 mortality. We analyze the catalog using machine learning algorithms to account for likely complex interdependence between metrics.Findings: The XGBoost algorithm achieved excellent performance, producing an AUC-ROC of 0.813 (95%CI 0.810-0.817), and outperforming logistic regression. Using our model we found that, in Brazil, socioeconomic, geographical and structural factors are more important than individual comorbidities. Particularly important factors were: The state of residence and its development index; the distance to the hospital (especially for rural and less developed areas); the level of education; hospital funding model and strain. Ethnicity is also confirmed to be more important than comorbidities but less than the aforementioned factors.Interpretation: Socioeconomic and structural factors are as important as biological factors in determining the outcome of COVID-19. This has important consequences for policy making, especially on vaccination/non-pharmacological preventative measures, hospital management and healthcare network organization.Funding: None.Declaration of Interests: We declare no competing interests.

Comparing COVID-19 risk factors in Brazil using machine learning: the importance of socioeconomic, demographic and structural factors (preprint)

Background The COVID-19 pandemic continues to have a devastating impact on Brazil. Brazil’s social, health and economic crises are aggravated by strong societal inequities and persisting political disarray. This complex scenario motivates careful study of the clinical, socioeconomic, demographic and structural factors contributing to increased risk of mortality from SARS-CoV-2 in Brazil specifically. Methods We consider the Brazilian SIVEP-Gripe catalog, a very rich respiratory infection dataset which allows us to estimate the importance of several non-laboratorial and socio-geographic factors on COVID-19 mortality. We analyze the catalog using machine learning algorithms to account for likely complex interdependence between metrics. Findings The XGBoost algorithm achieved excellent performance, producing an AUC-ROC of 0.813 (95%CI 0.810–0.817), and outperforming logistic regression. Using our model we found that, in Brazil, socioeconomic, geographical and structural factors are more important than individual comorbidities. Particularly important factors were: The state of residence and its development index; the distance to the hospital (especially for rural and less developed areas); the level of education; hospital funding model and strain. Ethnicity is also confirmed to be more important than comorbidities but less than the aforementioned factors. Interpretation Socioeconomic and structural factors are as important as biological factors in determining the outcome of COVID-19. This has important consequences for policy making, especially on vaccination/non-pharmacological preventative measures, hospital management and healthcare network organization. Funding None.