Changes in severity, mortality, and virus genome among a Spanish cohort of patients hospitalized with SARS-CoV-2.

Comparing pandemic waves could aid in understanding the evolution of COVID-19. The objective of the present study was to compare the characteristics and outcomes of patients hospitalized for COVID-19 in different pandemic waves in terms of severity and mortality. We performed an observational retrospective cohort study of 5,220 patients hospitalized with SARS-CoV-2 infection from February to September 2020 in Aragon, Spain. We compared ICU admissions and 30-day mortality, clinical characteristics, and risk factors of the first and second waves of COVID-19. The SARS-CoV-2 genome was also analyzed in 236 samples. Patients in the first wave (n = 2,547) were older (median age 74 years [IQR 60-86] vs. 70 years [53-85]; p < 0.001) and had worse clinical and analytical parameters related to severe COVID-19 than patients in the second wave (n = 2,673). The probability of ICU admission at 30 days was 16% and 10% (p < 0.001) and the cumulative 30-day mortality rates 38% and 32% in the first and second wave, respectively (p = 0.007). Survival differences were observed among patients aged 60 to 80 years. We also found some variability among death risk factors and the viral genome between waves. Therefore, the two analyzed COVID-19 pandemic waves were different in terms of disease severity and mortality.

A Clinical Decision Web to Predict ICU Admission or Death for Patients Hospitalised with COVID-19 Using Machine Learning Algorithms.

The purpose of the study was to build a predictive model for estimating the risk of ICU admission or mortality among patients hospitalized with COVID-19 and provide a user-friendly tool to assist clinicians in the decision-making process. The study cohort comprised 3623 patients with confirmed COVID-19 who were hospitalized in the SALUD hospital network of Aragon (Spain), which includes 23 hospitals, between February 2020 and January 2021, a period that includes several pandemic waves. Up to 165 variables were analysed, including demographics, comorbidity, chronic drugs, vital signs, and laboratory data. To build the predictive models, different techniques and machine learning (ML) algorithms were explored: multilayer perceptron, random forest, and extreme gradient boosting (XGBoost). A reduction dimensionality procedure was used to minimize the features to 20, ensuring feasible use of the tool in practice. Our model was validated both internally and externally. We also assessed its calibration and provide an analysis of the optimal cut-off points depending on the metric to be optimized. The best performing algorithm was XGBoost. The final model achieved good discrimination for the external validation set (AUC = 0.821, 95% CI 0.787-0.854) and accurate calibration (slope = 1, intercept = -0.12). A cut-off of 0.4 provides a sensitivity and specificity of 0.71 and 0.78, respectively. In conclusion, we built a risk prediction model from a large amount of data from several pandemic waves, which had good calibration and discrimination ability. We also created a user-friendly web application that can aid rapid decision-making in clinical practice.

Screening of severe acute respiratory syndrome coronavirus-2 infection during labor and delivery using polymerase chain reaction and immunoglobulin testing.

AIMS: To assess severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection during labor and delivery with polymerase chain reaction (PCR) and using immunoglobulin G and M testing to correlate with maternal and perinatal outcomes. MAIN METHODS: Pregnant women admitted for labor and delivery at two Spanish hospitals were screened for SARS-CoV-2 infection by PCR test and by detection of serum immunoglobulins G and M. Maternal and perinatal outcomes were compared in women with laboratory evidence of SARS-CoV-2 infection with those with negative tests. KEY FINDINGS: Between March 31st and September 30th, 2020, 1211 pregnant women were screened for SARS-CoV-2 infection. The prevalence of laboratory evidence of SARS-CoV-2 infections was 5.4% (n = 65), corresponding to (i) 22 ongoing infections at admission, including two with mild clinical symptoms and 20 asymptomatic women; (ii) 43 cases of previous SARS-CoV-2 exposure; (iii) and 1146 women who were negative for both SARS-CoV-2 PCR and serological test. None of the screened mothers required hospital admission for coronavirus disease before or after delivery, nor were any of the newborns admitted to the intensive care unit. All newborns from mothers with positive PCR on admission were PCR negative. There were no significant differences in maternal or perinatal outcomes among the three studied groups. SIGNIFICANCE: Ongoing or previous SARS-CoV-2 infection with asymptomatic or mild clinical symptoms detected during screening in pregnant women at labor and delivery do not have a higher rate of adverse maternal or perinatal outcomes.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) universal screening in gravids during labor and delivery.

OBJECTIVE: To screen pregnant women at risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection during delivery using reverse-transcription polymerase chain reaction (RT-PCR) test and serum immunoglobulin (Ig) testing. METHOD: Between March 31 st and August 31 st of 2020, consecutive pregnant women admitted for labor and delivery in a single hospital were screened for SARS-CoV-2 with nasopharyngeal RT-PCR swab tests and detection of serum IgG and IgM. RESULTS: We studied 266 pregnant women admitted for labor and delivery. The prevalence of acute or past SARS-CoV-2 infection was 9.0 %, including (i) two cases with respiratory symptoms of SARS-Co-V-2 infection and positive RT-PCR; (ii) four asymptomatic women with positive RT-PCR without clinical symptoms and negative serological tests between two and 15 weeks later; and (iii) two women with false positive RT-PCR due to technical problems. All newborns of the 6 pregnant women with RT-PCR positive had negative RT-PCR and did not require Neonatal Intensive Care Unit admission. There were eighteen asymptomatic women with positive serological IgG tests and negative RT-PCR. CONCLUSION: In our cohort of gravids, we found 2.2 % of women with positive RT-PRC tests and 6.7 % with positive serological tests during the first wave of the SARS-CoV-2 pandemic.