Diabetes Mellitus and Hypertension Increase Risk of Death in Novel Corona Virus Patients Irrespective of Age: a Prospective Observational Study of Co-morbidities and COVID-19 from India

Age, sex, and pre-arrest comorbidities shape the risk trajectory of sudden cardiac death- Patterns highlighted by population data in Taiwan

OBJECTIVES

Few reports have indicated the secular trend in the sudden cardiac death (SCD) incidence and pre-arrest comorbidities. This study aimed to comprehensively analyze the trend of SCD incidence and its association with pre-arrest comorbidities.

METHODS

This population-based cohort study analyzed Taiwan's National Health Insurance (NHI) research database and identified SCD incidents by inspecting data from all emergency department visits from 2011 to 2018. The inclusion criteria were ICD-9:427.5 or 427.41, or ICD-10:I46.9, I46.8, or I46.2. Pre-existing comorbidities were confirmed based on medication use. Multivariable logistic regression was adopted with covariates age, sex, and pre-existing comorbidities.

RESULTS

This study reviewed a total of 184,164,969 person-year records, and identified 92,138 SCD incidents. From 2011 to 2018, the SCD incidence rate increased from 36.3 to 55.4 per 100,000 enrollees in Taiwan. The top five pre-arrest comorbidities were stable, while the prevalence of chronic kidney disease rose significantly. Compared to those aged 20-29, enrollees aged >65 years had significantly higher odds of SCD (aOR:27.30, 95% CI:26.05-28.61). Higher odds of SCD were noted in the enrollees who had a seizure (aOR:2.24, 95% CI:2.12-2.38), parkinsonism (aOR:1.81, 95% CI:1.73-1.89), psychological disorders (aOR:1.59, 95% CI:1.56-1.62), diabetes mellitus (aOR:1.44, 95% CI:1.41-1.46), heart diseases (aOR:1.41, 95% CI:1.38-1.44).

CONCLUSIONS

The incidence of SCD steadily increased in Taiwan from 2011 to 2018. Hypertension, diabetes mellitus, heart disease, psychological disorders, and arthritis were major pre-arrest comorbidities. Age is the most important risk factor for SCD. Further large-scaled population-based study that investigated in diverse ethnicities from countries in addition to Asians would be warranted.

Persistent high mortality rates for Diabetes Mellitus and Hypertension after excluding deaths associated with COVID-19 in Brazil, 2020-2022

INTRODUCTION

The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) posed a significant public health challenge globally, with Brazil being no exception. Excess mortality during this period reached alarming levels. Cardiovascular diseases (CVD), Systemic Hypertension (HTN), and Diabetes Mellitus (DM) were associated with increased mortality. However, the specific impact of DM and HTN on mortality during the pandemic remains poorly understood.

METHODS

This study analyzed mortality data from Brazil's mortality system, covering the period from 2015 to 2022. Data included all causes of death as listed on death certificates, categorized by International Classification of Diseases 10th edition (ICD-10) codes. Population data were obtained from the Brazilian Census. Mortality ratios (MRs) were calculated by comparing death rates in 2020, 2021, and 2022 to the average rates from 2015 to 2019. Adjusted MRs were calculated using Poisson models.

RESULTS

Between 2015 and 2022, Brazil recorded a total of 11,423,288 deaths. Death rates remained relatively stable until 2019 but experienced a sharp increase in 2020 and 2021. In 2022, although a decrease was observed, it did not return to pre-pandemic levels. This trend persisted even when analyzing records mentioning DM, HTN, or CVD. Excluding death certificates mentioning COVID-19 codes, the trends still showed increases from 2020 through 2022, though less pronounced.

CONCLUSION

This study highlights the persistent high mortality rates for DM and HTN in Brazil during the years 2020-2022, even after excluding deaths associated with COVID-19. These findings emphasize the need for continued attention to managing and preventing DM and HTN as part of public health strategies, both during and beyond the COVID-19 pandemic. There are complex interactions between these conditions and the pandemic's impact on mortality rates.

Machine learning algorithms for predicting determinants of COVID-19 mortality in South Africa

Background

COVID-19 has strained healthcare resources, necessitating efficient prognostication to triage patients effectively. This study quantified COVID-19 risk factors and predicted COVID-19 intensive care unit (ICU) mortality in South Africa based on machine learning algorithms.

Methods

Data for this study were obtained from 392 COVID-19 ICU patients enrolled between 26 March 2020 and 10 February 2021. We used an artificial neural network (ANN) and random forest (RF) to predict mortality among ICU patients and a semi-parametric logistic regression with nine covariates, including a grouping variable based on K-means clustering. Further evaluation of the algorithms was performed using sensitivity, accuracy, specificity, and Cohen's K statistics.

Results

From the semi-parametric logistic regression and ANN variable importance, age, gender, cluster, presence of severe symptoms, being on the ventilator, and comorbidities of asthma significantly contributed to ICU death. In particular, the odds of mortality were six times higher among asthmatic patients than non-asthmatic patients. In univariable and multivariate regression, advanced age, PF1 and 2, FiO2, severe symptoms, asthma, oxygen saturation, and cluster 4 were strongly predictive of mortality. The RF model revealed that intubation status, age, cluster, diabetes, and hypertension were the top five significant predictors of mortality. The ANN performed well with an accuracy of 71%, a precision of 83%, an F1 score of 100%, Matthew's correlation coefficient (MCC) score of 100%, and a recall of 88%. In addition, Cohen's k-value of 0.75 verified the most extreme discriminative power of the ANN. In comparison, the RF model provided a 76% recall, an 87% precision, and a 65% MCC.

Conclusion

Based on the findings, we can conclude that both ANN and RF can predict COVID-19 mortality in the ICU with accuracy. The proposed models accurately predict the prognosis of COVID-19 patients after diagnosis. The models can be used to prioritize COVID-19 patients with a high mortality risk in resource-constrained ICUs.

Prevalence and Impact of Preexisting Comorbidities on Overall Clinical Outcomes of Hospitalized COVID-19 Patients

COVID-19 risk increases with comorbidities, and the effect is magnified due to the contribution of individual and combined comorbidities to the overall clinical outcomes. We aimed to explore the influence of demographic factors, clinical manifestations, and underlying comorbidities on mortality, severity, and hospital stay in COVID-19 patients. Therefore, retrospective chart reviews were performed to identify all laboratory-confirmed cases of SARS-CoV-2 infection in Apollo Hospitals, Hyderabad, between March 2020 and August 2020.A total of 369 confirmed SARS-CoV-2 cases were identified: 272 (73.7%) patients were male, and 97 (26.2%) were female. Of the confirmed cases, 218 (59.1%) had comorbidities, and 151 (40.9%) were devoid of comorbidities. This study showed that old age and underlying comorbidities significantly increase mortality, hospital stay, and severity due to COVID-19 infection. The presence of all four comorbidities, diabetes mellitus (DM) + Hypertension (HTN) + coronary artery disease (CAD) + chronic kidney disease (CKD), conferred the most severity (81%). The highest mortality (OR: 44.03, 95% CI: 8.64-224.27) was observed during the hospital stay (12.73 ± 11.38; 95% CI: 5.08-20.38) in the above group. Multivariate analysis revealed that nonsurvivors are highest (81%) in (DM + HTN + CAD + CKD) category with an odds ratio (95% CI) of 44.03 (8.64-224.27). Age, gender, and comorbidities adjusted odds ratio decreased to 20.25 (3.77-108.77). Median survival of 7 days was observed in the (DM + HTN + CAD + CKD) category. In summary, the presence of underlying comorbidities has contributed to a higher mortality rate, greater risk of severe disease, and extended hospitalization periods, hence, resulting in overall poorer clinical outcomes in hospitalized COVID-19 patients.

Mortality and Clinical Outcomes among Patients with COVID-19 and Diabetes

Background Diabetes mellitus (DM) is a decisive risk factor for severe illness in coronavirus disease 2019 (COVID-19). India is home to a large number of people with DM, and many of them were infected with COVID-19. It is critical to understand the impact of DM on mortality and other clinical outcomes of COVID-19 infection from this region. Aims The primary objective of our study was to analyze the mortality rate in people with DM infected with COVID-19. The secondary objectives were to assess the effect of various comorbidities on mortality and study the impact of DM on other clinical outcomes. Methods This is a retrospective study of COVID-19 infected patients admitted to a tertiary care hospital in north India in the early phase of the pandemic. Results Of the 1211 cases admitted, 19 were excluded because of incomplete data, and 1192 cases were finally considered for analysis. DM constituted 26.8% of total patients. The overall mortality rate was 6.1%, and the rate was 10.7% in the presence of diabetes (p < 0.01, OR 2.55). In univariate analysis, increased age, chronic kidney disease (CKD), coronary artery disease (CAD), stroke, and cancer were associated with mortality. On multiple logistic regression, the independent predictors of mortality were CAD, CKD, and cancer. Breathlessness and low SpO₂ at presentation, extensive involvement in CXR, and elevated ANC/ALC ratio were also significantly associated with mortality. Conclusions The presence of comorbidities such as DM, hypertension, CAD, CKD, and cancer strongly predict the risk of mortality in COVID-19 infection. Early triaging and aggressive therapy of patients with these comorbidities can optimize clinical outcomes.

Standardizing PaO2 for PaCO2 in P/F ratio predicts in-hospital mortality in acute respiratory failure due to Covid-19: A pilot prospective study

INTRODUCTION

Up to fifteen percent of patients with novel pandemic coronavirus disease (Covid-19) have acute respiratory failure (ARF). Ratio between arterial partial pressure of oxygen (PaO2) and fraction of inspired oxygen (FiO2), P/F, is currently used as a marker of ARF severity in Covid-19. P/F does not reflect the respiratory efforts made by patients to maintain arterial blood oxygenation, such as tachypnea and hyperpnea, leading to hypocapnia. Standard PaO2, the value of PaO2 adjusted for arterial partial pressure of carbon dioxide (PaCO2) of the subject, better reflects the pathophysiology of hypoxemic ARF. We hypothesized that the ratio between standard PaO2 over FiO2 (STP/F) better predicts Covid-19 ARF severity compared to P/F.

METHODS

Aim of this pilot prospectic observational study was to observe differences between STP/F and P/F in predicting outcome failure, defined as need of invasive mechanical ventilation and/or deaths in Covid-19 ARF. Accuracy was calculated using Receiver Operating Characteristics (ROC) analysis and areas under the ROC curve (AUROC) were compared.

RESULTS

349 consecutive subjects admitted to our respiratory wards due to Covid-19 ARF were enrolled. STP/F was accurate to predict mortality and superior to P/F with, respectively, AUROC 0.710 versus 0.688, p = 0.012.Both STP/F and PF were accurate to predict outcome failure (AUROC respectively of 0.747 and 0.742, p = 0.590).

DISCUSSION

This is the first study assessing the role of STP/F in describing severity of ARF in Covid-19. According to results, STP/F is accurate and superior to P/F in predicting in-hospital mortality.

Mortality and Years of Potential Life Lost Due to COVID-19 in Brazil

In November 2020, Brazil ranked third in the number of cases of coronavirus disease 2019 (COVID-19) and second in the number of deaths due to the disease. We carried out a descriptive study of deaths, mortality rate, years of potential life lost (YPLL) and excess mortality due to COVID-19, based on SARS-CoV-2 records in SIVEP-Gripe (Ministry of Health of Brazil) from 16 February 2020, to 1 January 2021. In this period, there were 98,025 deaths from COVID-19 in Brazil. Men accounted for 60.5% of the estimated 1.2 million YPLLs. High YPLL averages showed prematurity of deaths. The population aged 45–64 years (both sexes) represented more than 50% of all YPLLs. Risk factors were present in 69.5% of deaths, with heart disease, diabetes and obesity representing the most prevalent comorbidities in both sexes. Indigenous people had the lowest number of deaths and the highest average YPLL. However, in indigenous people, pregnant women and mothers had an average YPLL of over 35 years. The excess mortality for Brazil was estimated at 122,914 deaths (9.2%). The results show that the social impacts of YPLL due to COVID-19 are different depending on gender, race and risk factors. YPLL and excess mortality can be used to guide the prioritization of health interventions, such as prioritization of vaccination, lockdowns, or distribution of facial masks for the most vulnerable populations.

Comparison of Generalized Anxiety and Sleep Disturbance among Frontline and Second-Line Healthcare Workers during the COVID-19 Pandemic

Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) infection, also known as COVID-19, has developed into an alarming situation around the world. Healthcare workers are playing the role of frontline defense to safeguard the lives of everyone during the COVID-19 pandemic. The present study aimed to investigate the anxiety levels and sleep quality among frontline and second-line healthcare workers during the COVID-19 pandemic. In this cross-sectional study, a validated, self-administered, electronic questionnaire was distributed through email to healthcare workers. The selection of 1678 healthcare workers was based on a convenience sampling technique. The General Anxiety Disorder-7 (GAD-7) and Pittsburgh Sleep Quality Index (PSQI) instrument scales were used to assess healthcare workers' anxiety levels and sleep quality during the COVID-19 pandemic. Out of 1678 respondents, 1200 (71.5%) were frontline healthcare workers, while 478 (28.5%) were second-line healthcare workers. Among all the healthcare workers, 435 (25.92%) were experiencing moderate to severe anxiety. Among them, 713 (59.4%) frontline healthcare workers were experiencing anxiety in comparison with 277 (57.9%) second-line healthcare workers. Severe anxiety symptoms were seen in 137 (11.41%) frontline healthcare workers compared to 44 (9.20%) second-line healthcare workers. In total, 1376 (82.0%) healthcare workers were found to have poor sleep quality; 975 (58.10%) were frontline, and 407 (23.89%) were second-line healthcare workers. The highest poor sleep quality levels were found among 642 (84.6%) of the healthcare workers who work in frontline areas (emergency departments, intensive care units, and wards) compared to 734 (79.9%) of the healthcare workers who work in second-line areas. These findings provide a substantial contribution to the consolidation of evidence concerning the negative impact of the pandemic on the mental health of healthcare workers (HCWs). These results have established an association that the COVID-19 pandemic causes larger negative psychological symptoms in frontline healthcare workers, such as severe anxiety and poor sleep quality. Preventive measures to minimize anxiety levels and maintain sleep quality, addressing this issue nationally and globally, are essential to support the healthcare workers who are sacrificing their mental health for the future of our nations.

Impact of Kidney Failure on the Severity of COVID-19

Background: Patients with kidney failure are at an increased risk of progression to a severe form of coronavirus disease 2019 (COVID-19) with high mortality. The current analysis was aimed to assess the impact of renal failure on the severity of COVID-19 and identify the risk factors of the fatal outcome in this population. Methods: The analysis included patients from the SARSTer database, a national real-world study evaluating treatment for COVID-19 in 30 Polish centers. Data were completed retrospectively and submitted online. Results: A total of 2322 patients were included in the analysis. Kidney failure was diagnosed in 455 individuals (19.65%), of whom 373 presented moderate stage and 82 patients, including 14 dialysis individuals, presented severe renal failure. Patients with kidney failure were significantly older and demonstrated a more severe course of COVID-19. The age, baseline SpO₂, the ordinal scale of 4 and 5, neutrophil and platelet count, estimated glomerular filtration rate, and C-reactive protein concentration as well as malignancy and arterial hypertension were the independent predictors of 28-day mortality in logistic regression analysis. Conclusions: Underlying kidney disease in patients with COVID-19 is among the leading factors associated with a higher risk of severe clinical presentation and increased mortality rate.