Near real-time observation reveals increased prevalence of young patients in the ICU during the emerging third SARS-CoV-2 wave in Switzerland.

AIMS OF THE STUDY: During the ongoing COVID-19 pandemic, the launch of a large-scale vaccination campaign and virus mutations have hinted at possible changes in transmissibility and the virulence affecting disease progression up to critical illness, and carry potential for future vaccination failure. To monitor disease development over time with respect to critically ill COVID-19 patients, we report near real-time prospective observational data from the RISC-19-ICU registry that indicate changed characteristics of critically ill patients admitted to Swiss intensive care units (ICUs) at the onset of a third pandemic wave. METHODS: 1829 of 3344 critically ill COVID-19 patients enrolled in the international RISC-19-ICU registry as of 31 May 2021 were treated in Switzerland and were included in the present study. Of these, 1690 patients were admitted to the ICU before 1 February 2021 and were compared with 139 patients admitted during the emerging third pandemic wave RESULTS: Third wave patients were a mean of 5.2 years (95% confidence interval [CI] 3.2–7.1) younger (median 66.0 years, interquartile range [IQR] 57.0–73.0 vs 62.0 years, IQR 54.5–68.0; p <0.0001) and had a higher body mass index than patients admitted in the previous pandemic period. They presented with lower SAPS II and APACHE II scores, less need for circulatory support and lower white blood cell counts at ICU admission. P/F ratio was similar, but a 14% increase in ventilatory ratio was observed over time (p = 0.03) CONCLUSION: Near real-time registry data show that the latest COVID-19 patients admitted to ICUs in Switzerland at the onset of the third wave were on average 5 years younger, had a higher body mass index, and presented with lower physiological risk scores but a trend towards more severe lung failure. These differences may primarily be related to the ongoing nationwide vaccination campaign, but the possibility that changes in virus-host interactions may be a co-factor in the age shift and change in disease characteristics is cause for concern, and should be taken into account in the public health and vaccination strategy during the ongoing pandemic. (ClinicalTrials.gov Identifier: NCT04357275).

Machine learning using the extreme gradient boosting (XGBoost) algorithm predicts 5-day delta of SOFA score at ICU admission in COVID-19 patients.

Background: Accurate risk stratification of critically ill patients with coronavirus disease 2019 (COVID-19) is essential for optimizing resource allocation, delivering targeted interventions, and maximizing patient survival probability. Machine learning (ML) techniques are attracting increased interest for the development of prediction models as they excel in the analysis of complex signals in data-rich environments such as critical care. Methods: We retrieved data on patients with COVID-19 admitted to an intensive care unit (ICU) between March and October 2020 from the RIsk Stratification in COVID-19 patients in the Intensive Care Unit (RISC-19-ICU) registry. We applied the Extreme Gradient Boosting (XGBoost) algorithm to the data to predict as a binary outcome the increase or decrease in patients' Sequential Organ Failure Assessment (SOFA) score on day 5 after ICU admission. The model was iteratively cross-validated in different subsets of the study cohort. Results: The final study population consisted of 675 patients. The XGBoost model correctly predicted a decrease in SOFA score in 320/385 (83%) critically ill COVID-19 patients, and an increase in the score in 210/290 (72%) patients. The area under the mean receiver operating characteristic curve for XGBoost was significantly higher than that for the logistic regression model (0.86 vs. 0.69, P < 0.01 [paired t-test with 95% confidence interval]). Conclusions: The XGBoost model predicted the change in SOFA score in critically ill COVID-19 patients admitted to the ICU and can guide clinical decision support systems (CDSSs) aimed at optimizing available resources.

Characterisation of clinical manifestations and treatment strategies for invasive beta-haemolytic streptococcal infections in a Swiss tertiary hospital.

AIMS OF THE STUDY: Invasive streptococcal infections affect more than half a million patients worldwide every year and have a high lethality. Little is known about the epidemiology and microbiological characteristics of streptococcal infections in Switzerland. This case series study aims to describe the demographics, known risk factors for streptococcal skin and soft tissue infections, clinical presentations, treatment and outcomes of patients admitted to the University Hospital Zurich between 2000 and 2014 with invasive streptococcal infections caused by Streptococcus pyogenes (group A Streptococcus), Streptococcus dysgalactiae ssp. equisimilis or the Streptococcus anginosus group, as well as the microbiological characteristics of the clinical isolates. METHODS: Data collected retrospectively from patients hospitalised between 2000 and 2014 with invasive streptococcal infections were analysed. M protein gene (emm) typing of the bacterial clinical isolates was carried out according to the Centers for Disease Control and Prevention guidelines. RESULTS: A total of 86 patients with invasive beta-haemolytic streptococcal infections were included in this study, of which 49% presented with necrotising fasciitis (NF). The median age was 44 years and half were female. The most common risk factor was acute skin lesions. C-reactive protein levels were significantly higher in patients with NF, as were acute renal failure and distributive shock. Beta-lactam antibiotics were given to most patients, and intravenous immunoglobulins were given to 18% of patients within the first 24 hours. All patients suffering from NF underwent surgery. The overall case fatality rate was 8.1% at 30 days post admission. All Group A Streptococcus strains were susceptible to penicillin and clindamycin, and we found resistance to tetracycline in 11.9% of strains. The most common emm-type isolated was emm1 (44.4%). CONCLUSIONS: Invasive beta-haemolytic streptococcal infections, the most severe presentation of which is NF, remain a serious clinical issue and require rapid diagnosis and treatment. This is the first representative analysis monitoring clinical and microbiological characteristics of patients with a severe invasive beta-haemolytic streptococcal infection and treated in Zurich, Switzerland. In addition to the detailed reporting of various clinical and microbiological characteristics, we show that C-reactive protein levels, acute renal failure and distributive shock were higher in the patients with NF. We also found a low case fatality rate compared to other reports. The detailed clinical data and microbiological characteristics depicted in this study will lead to a better understanding of regional differences in severe invasive streptococcal infections.