Incidence, clinical features, and outcomes of COVID-19 in Canada: Impact of sex and age

O’Brien, Jacob A.; Du, Kevin Y.; Peng, Chun

doi:10.21203/rs.3.rs-85876/v1

Cited by 6 publications

(7 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Both models con rm that age is one of the strongest predictors of ICU survival, with a probability of death reaching almost 100% in older patients. The strong association between age and mortality is a constant nding in COVID-19 literature (4,14). Male gender is a negative predicting factor, a nding con rmed by previous studies (15).…”

Section: Discussionsupporting

confidence: 70%

Artificial Intelligence to Predict Mortality in Critically ill COVID-19 Patients Using Data from the First 24h: A Case Study from Lombardy Outbreak

Greco

Angelotti

Caruso

et al. 2021

Preprint

View full text Add to dashboard Cite

Introduction: SARS-CoV-2 infection was first identified at the end of 2019 in China, and subsequently spread globally. COVID-19 disease frequently affects the lungs leading to bilateral viral pneumonia, progressing in some cases to severe respiratory failure requiring ICU admission and mechanical ventilation. Risk stratification at ICU admission is fundamental for resource allocation and decision making, considering that baseline comorbidities, age, and patient conditions at admission have been associated to poorer outcomes. Supervised machine learning techniques are increasingly diffuse in clinical medicine and can predict mortality and test associations reaching high predictive performance. We assessed performances of a machine learning approach to predict mortality in COVID-19 patients admitted to ICU using data from the Lombardy ICU Network.Methods: this is a secondary analysis of prospectively collected data from Lombardy ICU network. To predict survival at 7-,14- and 28 days we built two different models; model A included patient demographics, medications before admission and comorbidities, while model B also included the data of the first day since ICU admission. 10-fold cross validation was repeated 2500 times, to ensure optimal hyperparameter choice. The only constrain imposed to model optimization was the choice of logistic regression as final layer to increase clinical interpretability. Different imputation and over-sampling techniques were employed in model training.Results 1503 patients were included, with 766 deaths (51%). Exploratory analysis and Kaplan-Meier curves demonstrated mortality association with age and gender. Model A and B reached the greatest predictive performance at 28 days (AUC 0.77 and 0.79), with lower performance at 14 days (AUC 0.72 and 0.74) and 7 days (AUC 0.68 and 0.71). Male gender, age and number of comorbidities were strongly associated with mortality in both models. Among comorbidities, chronic kidney disease and chronic obstructive pulmonary disease demonstrated association. Mode of ventilatory assistance at ICU admission and Fraction of Inspired oxygen were associated with mortality in model B.Conclusions Supervised machine learning models demonstrated good performance in prediction of 28-day mortality. 7-days and 14-days predictions demonstrated lower performance. Machine learning techniques may be useful in emergency phases to reach higher predictive performance with reduced human supervision using complex data.

show abstract

Section: Discussionsupporting

confidence: 70%

Artificial Intelligence to Predict Mortality in Critically ill COVID-19 Patients Using Data from the First 24h: A Case Study from Lombardy Outbreak

Greco

Angelotti

Caruso

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…In another study with Canadian patients, they compared differences between women and men in different age groups in terms of hospital admission, ICU admission, time to recovery, and mortality, and found that there were large differences in women under 30 years of age and men of the same age in terms of hospitalization and admission to the ICU, and observed that, from the age of 60, there was a significant decrease in mortality in women compared to men of the same age group. They concluded that estrogens in women after puberty and before menopause could play a fundamental role, and that there may be a possible mechanism between estrogen levels and mortality/time to recovery that is not yet well defined, although in women aged over 60 years there would have to be other additional factors [ 18 ].…”

Section: Discussionmentioning

confidence: 99%

Gender-Based Differences by Age Range in Patients Hospitalized with COVID-19: A Spanish Observational Cohort Study

Josa-Laorden

Vieitez

Andreu

et al. 2021

JCM

View full text Add to dashboard Cite

There is some evidence that male gender could have a negative impact on the prognosis and severity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. The aim of the present study was to compare the characteristics of coronavirus disease 2019 (COVID-19) between hospitalized men and women with confirmed SARS-CoV-2 infection. This multicenter, retrospective, observational study is based on the SEMI-COVID-19 Registry. We analyzed the differences between men and women for a wide variety of demographic, clinical, and treatment variables, and the sex distribution of the reported COVID-19 deaths, as well as intensive care unit (ICU) admission by age subgroups. This work analyzed 12,063 patients (56.8% men). The women in our study were older than the men, on average (67.9 vs. 65.7 years; p < 001). Bilateral condensation was more frequent among men than women (31.8% vs. 29.9%; p = 0.007). The men needed non-invasive and invasive mechanical ventilation more frequently (5.6% vs. 3.6%, p < 0.001, and 7.9% vs. 4.8%, p < 0.001, respectively). The most prevalent complication was acute respiratory distress syndrome, with severe cases in 19.9% of men (p < 0.001). In men, intensive care unit admission was more frequent (10% vs. 6.1%; p < 0.001) and the mortality rate was higher (23.1% vs. 18.9%; p < 0.001). Regarding mortality, the differences by gender were statistically significant in the age groups from 55 years to 89 years of age. A multivariate analysis showed that female sex was significantly and independently associated with a lower risk of mortality in our study. Male sex appears to be related to worse progress in COVID-19 patients and is an independent prognostic factor for mortality. In order to fully understand its prognostic impact, other factors associated with sex must be considered.

show abstract

“…Moreover, among those hospitalized in ICU-CCU, males over 65 years old are 50% more likely to die than females. It has been shown that a considerable association between ICU admission and age exists so that cases younger than 20 years old, are less prone to experience hospitalization, and ICU admission, and death (38). It has been argued that some chronic diseases such as diabetes and cardiovascular diseases are more common among males than in females, especially in older ages (39).…”

Section: Discussionmentioning

confidence: 99%

Assessing Sex Differential in COVID-19 Mortality Rate by Age and Polymerase Chain Reaction Test Results; An Iranian Multi-Center Study

Looha

Rezaei‐Tavirani

Rostami-Nejad

et al. 2021

Preprint

View full text Add to dashboard Cite

Background: Males are more likely to die from COVID-19 than females. In addition, the mortality rate among positive and suspected COVID-19 patients were reported in many literatures. The aim of this study is to evaluate the sex differential effect in the COVID-19 mortality by different age groups and polymerase chain reaction (PCR) test results. Methods: in a multicenter cross-sectional study from 55 hospitals in Tehran, Iran, patients were categorized as the positive, negative and suspected cases. Age group, sex and hospital wards were also assessed in analysis. Results: A total of 25481 cases (14791 males) were included in the study with the mortality rate of 12.0%. The mortality rate in positive, negative and suspected cases were 20.55%, 9.97% and 7.31%, respectively. Although the mortality in negative test group was considerable, sex was not associated with the death rate in this group. Using Cox regression model, sex had a significant effect on the hazard of death due to COVID-19 in adults and senior patients having positive and suspected PCR test results. However, sex was not found as significant factor for mortality in patients with negative PCR test occurring to different age groups. Conclusion: Regardless of other risk factors, we found that sex differential effect in COVID-19 mortality varies significantly in different age groups; therefore, appropriate strategies should be designed to protect adult and senior men from this deadly infectious disease. Furthermore, owing to the considerable death rate of COVID-19 patients with negative test results, new policies should be launched increase the accuracy of diagnosis tests.

show abstract

Incidence, clinical features, and outcomes of COVID-19 in Canada: Impact of sex and age

Cited by 6 publications

References 24 publications

Artificial Intelligence to Predict Mortality in Critically ill COVID-19 Patients Using Data from the First 24h: A Case Study from Lombardy Outbreak

Artificial Intelligence to Predict Mortality in Critically ill COVID-19 Patients Using Data from the First 24h: A Case Study from Lombardy Outbreak

Gender-Based Differences by Age Range in Patients Hospitalized with COVID-19: A Spanish Observational Cohort Study

Assessing Sex Differential in COVID-19 Mortality Rate by Age and Polymerase Chain Reaction Test Results; An Iranian Multi-Center Study

Contact Info

Product

Resources

About