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The Covid-19 pandemic has pushed many hospitals to their capacity limits. Therefore, a triage of patients has been discussed controversially primarily through an ethical perspective. The term triage contains many aspects such as urgency of treatment, severity of the disease and pre-existing conditions, access to critical care, or the classification of patients regarding subsequent clinical pathways starting from the emergency department. The determination of the pathways is important not only for patient care, but also for capacity planning in hospitals. We examine the performance of a human-made triage algorithm for clinical pathways which is considered a guideline for emergency departments in Germany based on a large multicenter dataset with over 4,000 European Covid-19 patients from the LEOSS registry. We find an accuracy of 28 percent and approximately 15 percent sensitivity for the ward class. The results serve as a benchmark for our extensions including an additional category of palliative care as a new label, analytics, AI, XAI, and interactive techniques. We find significant potential of analytics and AI in Covid-19 triage regarding accuracy, sensitivity, and other performance metrics whilst our interactive human-AI algorithm shows superior performance with approximately 73 percent accuracy and up to 76 percent sensitivity. The results are independent of the data preparation process regarding the imputation of missing values or grouping of comorbidities. In addition, we find that the consideration of an additional label palliative care does not improve the results.
Background
Coronavirus disease 2019 (COVID-19) is an infection which can affect the central nervous system. In this study, we sought to investigate associations between neuroimaging findings with clinical, demographic, blood and cerebrospinal fluid (CSF) parameters, pre-existing conditions and the severity of acute COVID-19.
Materials and methods
Retrospective multicenter data retrieval from 10 university medical centers in Germany, Switzerland and Austria between February 2020 and September 2021. We included patients with COVID-19, acute neurological symptoms and cranial imaging. We collected demographics, neurological symptoms, COVID-19 severity, results of cranial imaging, blood and CSF parameters during the hospital stay.
Results
442 patients could be included. COVID-19 severity was mild in 124 (28.1%) patients (moderate n = 134/30.3%, severe n = 43/9.7%, critical n = 141/31.9%). 220 patients (49.8%) presented with respiratory symptoms, 167 (37.8%) presented with neurological symptoms first. Acute ischemic stroke (AIS) was detected in 70 (15.8%), intracranial hemorrhage (IH) in 48 (10.9%) patients. Typical risk factors were associated with AIS; extracorporeal membrane oxygenation therapy and invasive ventilation with IH. No association was found between the severity of COVID-19 or blood/CSF parameters and the occurrence of AIS or IH.
Discussion
AIS was the most common finding on cranial imaging. IH was more prevalent than expected but a less common finding than AIS. Patients with IH had a distinct clinical profile compared to patients with AIS. There was no association between AIS or IH and the severity of COVID-19. A considerable proportion of patients presented with neurological symptoms first. Laboratory parameters have limited value as a screening tool.
Purpose
The influence of new SARS-CoV-2 variants on the post-COVID-19 condition (PCC) remains unanswered. Therefore, we examined the prevalence and predictors of PCC-related symptoms in patients infected with the SARS-CoV-2 variants delta or omicron.
Methods
We compared prevalences and risk factors of acute and PCC-related symptoms three months after primary infection (3MFU) between delta- and omicron-infected patients from the Cross-Sectoral Platform of the German National Pandemic Cohort Network. Health-related quality of life (HrQoL) was determined by the EQ-5D-5L index score and trend groups were calculated to describe changes of HrQoL between different time points.
Results
We considered 758 patients for our analysis (delta: n = 341; omicron: n = 417). Compared with omicron patients, delta patients had a similar prevalence of PCC at the 3MFU (p = 0.354), whereby fatigue occurred most frequently (n = 256, 34%). HrQoL was comparable between the groups with the lowest EQ-5D-5L index score (0.75, 95% CI 0.73–0.78) at disease onset. While most patients (69%, n = 348) never showed a declined HrQoL, it deteriorated substantially in 37 patients (7%) from the acute phase to the 3MFU of which 27 were infected with omicron.
Conclusion
With quality-controlled data from a multicenter cohort, we showed that PCC is an equally common challenge for patients infected with the SARS-CoV-2 variants delta and omicron at least for the German population. Developing the EQ-5D-5L index score trend groups showed that over two thirds of patients did not experience any restrictions in their HrQoL due to or after the SARS-CoV-2 infection at the 3MFU.
Clinical Trail registration
The cohort is registered at ClinicalTrials.gov since February 24, 2021 (Identifier: NCT04768998).
