Covid-19 triage in the emergency department 2.0: how analytics and AI transform a human-made algorithm for the prediction of clinical pathways

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

Case report of rare congenital cardiovascular anomalies associated with truncus arteriosus type 2

Truncus arteriosus (TA) is a very rare congenital anomaly with complex cardiovascular anatomy and high lethality also due to severe associated anatomical variants and pathologies. As TA has a massive impact on the survival of a newborn and usually has to be surgically treated. Thus, it is of high importance to understand this congenital cardiovascular disease and associated complications, to improve life expectancy and outcome of these patients. We recently came across a newborn female patient with a rare complex case of persistent TA type 2 associated with further complex cardiovascular anomalies, who received a contrast enhanced CT scan on the 3 rd day post-partum, showing complex cardiovascular abnormalities that were ultimately incompatible with life

A case of myocarditis after COVID-19 vaccination: incidental or consequential?

Vaccination represents one of the fundamentals in the fight against SARS-CoV-2. Myocarditis has been reported as a rare but possible adverse consequence of different vaccines, and its clinical presentation can range from mild symptoms to acute heart failure. We report a case of a 29-year-old man who presented with fever and retrosternal pain after receiving SARS-CoV-2 vaccine. Cardiac magnetic resonance imaging and laboratory data revealed typical findings of acute myocarditis

DataSheet_1_15-month post-COVID syndrome in outpatients: Attributes, risk factors, outcomes, and vaccination status - longitudinal, observational, case-control study.pdf

BackgroundWhile the short-term symptoms of post-COVID syndromes (PCS) are well-known, the long-term clinical characteristics, risk factors and outcomes of PCS remain unclear. Moreover, there is ongoing discussion about the effectiveness of post-infection vaccination against severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) to aid in PCS recovery.MethodsIn this longitudinal and observational case-control study we aimed at identifying long-term PCS courses and evaluating the effects of post-infection vaccinations on PCS recovery. Individuals with initial mild COVID-19 were followed for a period of 15 months after primary infection. We assessed PCS outcomes, distinct symptom clusters (SC), and SARS-CoV-2 immunoglobulin G (IgG) levels in patients who received SARS-CoV-2 vaccination, as well as those who did not. To identify potential associating factors with PCS, we used binomial regression models and reported the results as odds ratios (OR) with 95% confidence intervals (95%CI).ResultsOut of 958 patients, follow-up data at 15 month after infection was obtained for 222 (23.2%) outpatients. Of those individuals, 36.5% (81/222) and 31.1% (69/222) were identified to have PCS at month 10 and 15, respectively. Fatigue and dyspnea (SC2) rather than anosmia and ageusia (SC1) constituted PCS at month 15. SARS-CoV-2 IgG levels were equally distributed over time among age groups, sex, and absence/presence of PCS. Of the 222 patients, 77.0% (171/222) were vaccinated between 10- and 15-months post-infection, but vaccination did not affect PCS recovery at month 15. 26.3% of unvaccinated and 25.8% of vaccinated outpatients improved from PCS (p= .9646). Baseline headache (SC4) and diarrhoea (SC5) were risk factors for PCS at months 10 and 15 (SC4: OR 1.85 (95%CI 1.04-3.26), p=.0390; SC5: OR 3.27(95%CI 1.54-6.64), p=.0009).ConclusionBased on the specific symptoms of PCS our findings show a shift in the pattern of recovery. We found no effect of SARS-CoV-2 vaccination on PCS recovery and recommend further studies to identify predicting biomarkers and targeted PCS therapeutics.</p

COVID-19 among heart transplant recipients in Germany: a multicenter survey

Aims Heart transplantation may represent a particular risk factor for severe coronavirus infectious disease 2019 (COVID-19) due to chronic immunosuppression and frequent comorbidities. We conducted a nation-wide survey of all heart transplant centers in Germany presenting the clinical characteristics of heart transplant recipients with COVID-19 during the first months of the pandemic in Germany. Methods and results A multicenter survey of all heart transplant centers in Germany evaluating the current status of COVID-19 among adult heart transplant recipients was performed. A total of 21 heart transplant patients with COVID-19 was reported to the transplant centers during the first months of the pandemic in Germany. Mean patient age was 58.6 +/- 12.3 years and 81.0% were male. Comorbidities included arterial hypertension (71.4%), dyslipidemia (71.4%), diabetes mellitus (33.3%), chronic kidney failure requiring dialysis (28.6%) and chronic-obstructive lung disease/asthma (19.0%). Most patients received an immunosuppressive drug regimen consisting of a calcineurin inhibitor (71.4%), mycophenolate mofetil (85.7%) and steroids (71.4%). Eight of 21 patients (38.1%) displayed a severe course needing invasive mechanical ventilation. Those patients showed a high mortality (87.5%) which was associated with right ventricular dysfunction (62.5% vs. 7.7%;p = 0.014), arrhythmias (50.0% vs. none;p = 0.012), and thromboembolic events (50.0% vs. none;p = 0.012). Elevated high-sensitivity cardiac troponin T- and N-terminal prohormone of brain natriuretic peptide were significantly associated with the severe form of COVID-19 (p = 0.017 andp < 0.001, respectively). Conclusion Severe course of COVID-19 was frequent in heart transplanted patients. High mortality was associated with right ventricular dysfunction, arrhythmias, thromboembolic events, and markedly elevated cardiac biomarkers

Image_1_Serum interleukin-6, procalcitonin, and C-reactive protein at hospital admission can identify patients at low risk for severe COVID-19 progression.PDF

BackgroundCOVID-19 can show a variable course, from asymptomatic infections to acute respiratory failure and death. For efficient allocation of resources, patients should be stratified according to their risk for a severe course as early as possible.Methods135 hospitalized patients with COVID-19 pneumonia at four German hospitals were prospectively included in this observational study. A standardized clinical laboratory profile was taken at hospital admission and a panel of serum markers with possible roles in the COVID-associated cytokine storm were also determined. 112 patients could be evaluated. The primary endpoint of ventilator requirement or death within 30 days of symptom onset was met by 13 patients.ResultsSerum elevations of interleukin-6 (IL-6), procalcitonin (PCT), and C-reactive protein (CRP) at hospital admission were each highly significantly (p ConclusionNegative likelihood ratios indicate that IL-6, PCT, and CRP at hospital admission can be used for identifying patients at low risk for severe COVID-19 progression.</p

First results of the “Lean European Open Survey on SARS-CoV-2-Infected Patients (LEOSS)"

Purpose Knowledge regarding patients' clinical condition at severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) detection is sparse. Data in the international, multicenter Lean European Open Survey on SARS-CoV-2-Infected Patients (LEOSS) cohort study may enhance the understanding of COVID-19. Methods Sociodemographic and clinical characteristics of SARS-CoV-2-infected patients, enrolled in the LEOSS cohort study between March 16, 2020, and May 14, 2020, were analyzed. Associations between baseline characteristics and clinical stages at diagnosis (uncomplicated vs. complicated) were assessed using logistic regression models. Results We included 2155 patients, 59.7% (1,287/2,155) were male; the most common age category was 66-85 years (39.6%; 500/2,155). The primary COVID-19 diagnosis was made in 35.0% (755/2,155) during complicated clinical stages. A significant univariate association between age; sex; body mass index; smoking; diabetes; cardiovascular, pulmonary, neurological, and kidney diseases; ACE inhibitor therapy; statin intake and an increased risk for complicated clinical stages of COVID-19 at diagnosis was found. Multivariable analysis revealed that advanced age [46-65 years: adjusted odds ratio (aOR): 1.73, 95% CI 1.25-2.42,p = 0.001; 66-85 years: aOR 1.93, 95% CI 1.36-2.74,p 85 years: aOR 2.38, 95% CI 1.49-3.81,p < 0.001 vs. individuals aged 26-45 years], male sex (aOR 1.23, 95% CI 1.01-1.50,p = 0.040), cardiovascular disease (aOR 1.37, 95% CI 1.09-1.72,p = 0.007), and diabetes (aOR 1.33, 95% CI 1.04-1.69,p = 0.023) were associated with complicated stages of COVID-19 at diagnosis. Conclusion The LEOSS cohort identified age, cardiovascular disease, diabetes and male sex as risk factors for complicated disease stages at SARS-CoV-2 diagnosis, thus confirming previous data. Further data regarding outcomes of the natural course of COVID-19 and the influence of treatment are required

First results of the Lean European Open Survey on SARS-CoV-2-Infected Patients (LEOSS)

Purpose Knowledge regarding patients' clinical condition at severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) detection is sparse. Data in the international, multicenter Lean European Open Survey on SARS-CoV-2-Infected Patients (LEOSS) cohort study may enhance the understanding of COVID-19. Methods Sociodemographic and clinical characteristics of SARS-CoV-2-infected patients, enrolled in the LEOSS cohort study between March 16, 2020, and May 14, 2020, were analyzed. Associations between baseline characteristics and clinical stages at diagnosis (uncomplicated vs. complicated) were assessed using logistic regression models. Results We included 2155 patients, 59.7% (1,287/2,155) were male; the most common age category was 66-85 years (39.6%; 500/2,155). The primary COVID-19 diagnosis was made in 35.0% (755/2,155) during complicated clinical stages. A significant univariate association between age; sex; body mass index; smoking; diabetes; cardiovascular, pulmonary, neurological, and kidney diseases; ACE inhibitor therapy; statin intake and an increased risk for complicated clinical stages of COVID-19 at diagnosis was found. Multivariable analysis revealed that advanced age [46-65 years: adjusted odds ratio (aOR): 1.73, 95% CI 1.25-2.42,p = 0.001; 66-85 years: aOR 1.93, 95% CI 1.36-2.74,p 85 years: aOR 2.38, 95% CI 1.49-3.81,p < 0.001 vs. individuals aged 26-45 years], male sex (aOR 1.23, 95% CI 1.01-1.50,p = 0.040), cardiovascular disease (aOR 1.37, 95% CI 1.09-1.72,p = 0.007), and diabetes (aOR 1.33, 95% CI 1.04-1.69,p = 0.023) were associated with complicated stages of COVID-19 at diagnosis. Conclusion The LEOSS cohort identified age, cardiovascular disease, diabetes and male sex as risk factors for complicated disease stages at SARS-CoV-2 diagnosis, thus confirming previous data. Further data regarding outcomes of the natural course of COVID-19 and the influence of treatment are required

Prediction of COVID-19 deterioration in high-risk patients at diagnosis: an early warning score for advanced COVID-19 developed by machine learning

Purpose!#!While more advanced COVID-19 necessitates medical interventions and hospitalization, patients with mild COVID-19 do not require this. Identifying patients at risk of progressing to advanced COVID-19 might guide treatment decisions, particularly for better prioritizing patients in need for hospitalization.!##!Methods!#!We developed a machine learning-based predictor for deriving a clinical score identifying patients with asymptomatic/mild COVID-19 at risk of progressing to advanced COVID-19. Clinical data from SARS-CoV-2 positive patients from the multicenter Lean European Open Survey on SARS-CoV-2 Infected Patients (LEOSS) were used for discovery (2020-03-16 to 2020-07-14) and validation (data from 2020-07-15 to 2021-02-16).!##!Results!#!The LEOSS dataset contains 473 baseline patient parameters measured at the first patient contact. After training the predictor model on a training dataset comprising 1233 patients, 20 of the 473 parameters were selected for the predictor model. From the predictor model, we delineated a composite predictive score (SACOV-19, Score for the prediction of an Advanced stage of COVID-19) with eleven variables. In the validation cohort (n = 2264 patients), we observed good prediction performance with an area under the curve (AUC) of 0.73 ± 0.01. Besides temperature, age, body mass index and smoking habit, variables indicating pulmonary involvement (respiration rate, oxygen saturation, dyspnea), inflammation (CRP, LDH, lymphocyte counts), and acute kidney injury at diagnosis were identified. For better interpretability, the predictor was translated into a web interface.!##!Conclusion!#!We present a machine learning-based predictor model and a clinical score for identifying patients at risk of developing advanced COVID-19