Trends in all-cause mortality of hospitalized patients due to SARS-CoV-2 infection from a monocentric cohort in Milan (Lombardy, Italy)

Predictors of Intensive Care Unit Admissions in Patients Presenting with Coronavirus Disease 2019

Background  Increased mortality rates among coronavirus disease 2019 (COVID-19) positive patients admitted to intensive care units (ICUs) highlight a compelling need to establish predictive criteria for ICU admissions. The aim of our study was to identify criteria for recognizing patients with COVID-19 at elevated risk for ICU admission. Methods  We identified patients who tested positive for COVID-19 and were hospitalized between March and May 2020. Patients' data were manually abstracted through review of electronic medical records. An ICU admission prediction model was derived from a random sample of half the patients using multivariable logistic regression. The model was validated with the remaining half of the patients using c-statistic. Results  We identified 1,094 patients; 204 (18.6%) were admitted to the ICU. Correlates of ICU admission were age, body mass index (BMI), quick Sequential Organ Failure Assessment (qSOFA) score, arterial oxygen saturation to fraction of inspired oxygen ratio, platelet count, and white blood cell count. The c-statistic in the derivation subset (0.798, 95% confidence interval [CI]: 0.748, 0.848) and the validation subset (0.764, 95% CI: 0.706, 0.822) showed excellent comparability. At 22% predicted probability for ICU admission, the derivation subset estimated sensitivity was 0.721, (95% CI: 0.637, 0.804) and specificity was 0.763, (95% CI: 0.722, 0.804). Our pilot predictive model identified the combination of age, BMI, qSOFA score, and oxygenation status as significant predictors for ICU admission. Conclusion  ICU admission among patients with COVID-19 can be predicted by age, BMI, level of hypoxia, and severity of illness.

Is the 4C Score Still a Valid Item to Predict In-Hospital Mortality in People with SARS-CoV-2 Infections in the Omicron Variant Era?

Since the start of the SARS-CoV-2 pandemic, several scores have been proposed to identify infected individuals at a higher risk of progression and death. The most famous is the 4C score. However, it was developed in early 2020. Our study aimed to evaluate the accuracy of the 4C score during the wave in which the Omicron variant was prevalent. An observational study was conducted at an Italian University Hospital between 1 January and 31 July 2022. A receiver operating characteristic (ROC) curve analysis was performed to evaluate the ability of the 4C score to predict mortality. Overall, 1186 people were recruited, of which 160 (13.5%) died. According to the 4C score, 177 (11.6%) were classified as having a low risk of mortality, 302 (25.5%) were intermediate, 596 (50.3%) were high, and 151 (12.7%) were very high. The ROC curve of the 4C score showed an AUC (95% CI) value of 0.78 (0.74−0.82). At the criterion value of > 10, the sensitivity was 76.2% and the specificity was 62.67%. Similar to previous studies, the 4C mortality score performed well in our sample, and it is still a useful tool for clinicians to identify patients with a high risk of progression. However, clinicians must be aware that the mortality rate reported in the original studies was higher than that observed in our study.

Decline of Admission for Acute Coronary Syndromes and Acute Cardiovascular Conditions during COVID-19 Pandemic in Veneto Region

Background: The present study aimed to examine longitudinal trends in hospitalizations for acute coronary syndrome (ACS) before and during the COVID-19 pandemic, by reviewing the data from 13 hospitals of the Veneto Region, in the north-east of Italy. Methods: We performed a multicenter, retrospective analysis including all the consecutive patients presenting with ACS and other acute cardiovascular (CV) conditions (defined as heart failure, arrhythmias, cardiac arrest and venous thromboembolism) hospitalized in 13 different hospitals of the Veneto Region covering a population of 2,554,818 inhabitants, during the first (between 15 March 2020 and 30 April 2020) and second (between 15 November 2020 and 30 December 2020) COVID-19 pandemic waves (the 2020 cohort). Data were compared with those obtained at the same time-windows of years 2018 and 2019 (the historical cohorts). Results: Compared to the historical cohorts, a significant decrease in the number of ACS cases was observed in 2020 (−27.3%, p = 0.01 and −32%, p < 0.001, comparing 2018 versus 2020 and 2019 and 2020, respectively). The proportion of patients hospitalized for acute CV conditions decreased during the first and second wave COVID-19 pandemic when compared to the historical cohorts (−36.5%, p < 0.001 and −40.6%, p < 0.001, comparing 2018 versus 2020 and 2019 and 2020, respectively). Pearson’s correlation evidenced a significant inverse relationship between the number of COVID-19 cases and both ACS hospital admissions (r = −0.881, p = 0.005) and hospitalizations for acute CV conditions (r = −0.738, p = 0.01), respectively. Conclusions: The decrease in hospitalizations for ACS and other acute CV conditions will strongly affect future patients’ management since undiagnosed nonfatal CV events represent a source of increased (and unknown) CV morbidity and mortality.

Decline of Admission for Acute Coronary Syndromes and Acute Cardiovascular Conditions during COVID-19 Pandemic in Veneto Region

Background: The present study aimed to examine longitudinal trends in hospitalizations for acute coronary syndrome (ACS) before and during the COVID-19 pandemic, by reviewing the data from 13 hospitals of the Veneto Region, in the north-east of Italy. Methods: We performed a multicenter, retrospective analysis including all the consecutive patients presenting with ACS and other acute cardiovascular (CV) conditions (defined as heart failure, arrhythmias, cardiac arrest and venous thromboembolism) hospitalized in 13 different hospitals of the Veneto Region covering a population of 2,554,818 inhabitants, during the first (between 15 March 2020 and 30 April 2020) and second (between 15 November 2020 and 30 December 2020) COVID-19 pandemic waves (the 2020 cohort). Data were compared with those obtained at the same time-windows of years 2018 and 2019 (the historical cohorts). Results: Compared to the historical cohorts, a significant decrease in the number of ACS cases was observed in 2020 (−27.3%, p = 0.01 and −32%, p < 0.001, comparing 2018 versus 2020 and 2019 and 2020, respectively). The proportion of patients hospitalized for acute CV conditions decreased during the first and second wave COVID-19 pandemic when compared to the historical cohorts (−36.5%, p < 0.001 and −40.6%, p < 0.001, comparing 2018 versus 2020 and 2019 and 2020, respectively). Pearson’s correlation evidenced a significant inverse relationship between the number of COVID-19 cases and both ACS hospital admissions (r = −0.881, p = 0.005) and hospitalizations for acute CV conditions (r = −0.738, p = 0.01), respectively. Conclusions: The decrease in hospitalizations for ACS and other acute CV conditions will strongly affect future patients’ management since undiagnosed nonfatal CV events represent a source of increased (and unknown) CV morbidity and mortality.

Analysis of Risk Factors for In-Hospital Death Due to COVID-19 in Patients Hospitalised at the Temporary Hospital Located at the National Stadium in Warsaw: A Retrospective Analysis

The outbreak of the SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) pandemic has affected all aspects of social life and brought massive changes to the healthcare sector. The aim of this study was to identify the factors affecting the mortality of COVID-19 patients at a temporary hospital in Warsaw (Poland). The present study was conducted based on a retrospective analysis of the medical records of patients hospitalised at the temporary hospital located at the National Stadium in Warsaw between 1 March 2020 and 30 April 2021. The study included all cases of patients who were brought directly or transferred to the National Hospital from other hospitals for further treatment. With regard to comorbidities, the analysis found that five comorbidities—namely, diabetes (OR = 1.750, 95% CI: 1.009−2.444, p < 0.05), stroke history (OR = 2.408, 95% CI: 1.208−4.801, p < 0.05), renal failure (OR = 2.141, 95% CI: 1.052−4.356, p < 0.05), chronic obstructive pulmonary disease (OR = 2.044, 95% CI: 1.133−3.690, p < 0.05) and heart failure (OR = 1.930, 95% CI: 1.154−3.227, p < 0.05)—had a significant impact on the survival of COVID-19 patients. The analysis identified 14 factors that had a significant impact on the prognosis and mortality of the COVID-19 patients studied.