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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
Infection-associated secondary hemophagocytic lymphohistiocytosis (sHLH) is a potentially life-threatening hyperinflammatory condition caused by various infectious diseases. Malaria has rarely been described as trigger. The aim of this study is to collect data on frequency, clinical spectrum, and outcome of sHLH induced by malaria.
Methods
We collected case numbers on malaria and malaria-associated sHLH from specialized centers in Germany from 2015 to 2022. In addition, we conducted a literature search on published cases of malaria-associated sHLH and systematically analyzed the literature regarding clinical and diagnostic criteria.
Results
We obtained data from 13 centers treating 1461 malaria cases with different Plasmodium species, of which 5 patients (0.34%) also were diagnosed with sHLH. The literature search revealed detailed case reports from further 51 patients and case series comprising the description of further 24 patients with malaria-associated sHLH. Most cases (48/80; 60%) were reported from Asia. The median time interval between onset of malaria symptoms and hospital admission was 7 days. Severe complications of sHLH were documented in 36% (20/56) of patients, including two patients with multiple organ failure in our case series. Only 41% (23/56) of patients received specific treatment for sHLH, nevertheless the mortality rate (CFR) of 5% is lower compared to the CFR reported for sHLH triggered by other infectious diseases (e.g., 25% in sHLH due to EBV infection).
Conclusion
Malaria-associated sHLH appears to have a comparatively good prognosis but may still represent an underdiagnosed and potentially fatal complication of malaria, especially in resource-poor settings.
