Evaluation of patients with respiratory infections during the first pandemic wave in Germany: characteristics of COVID-19 versus non-COVID-19 patients

BACKGROUND Characteristics of COVID-19 patients have mainly been reported within confirmed COVID-19 cohorts. By analyzing patients with respiratory infections in the emergency department during the first pandemic wave, we aim to assess differences in the characteristics of COVID-19 vs. Non-COVID-19 patients. This is particularly important regarding the second COVID-19 wave and the approaching influenza season. METHODS We prospectively included 219 patients with suspected COVID-19 who received radiological imaging and RT-PCR for SARS-CoV-2. Demographic, clinical and laboratory parameters as well as RT-PCR results were used for subgroup analysis. Imaging data were reassessed using the following scoring system: 0 - not typical, 1 - possible, 2 - highly suspicious for COVID-19. RESULTS COVID-19 was diagnosed in 72 (32,9%) patients. In three of them (4,2%) the initial RT-PCR was negative while initial CT scan revealed pneumonic findings. 111 (50,7%) patients, 61 of them (55,0%) COVID-19 positive, had evidence of pneumonia. Patients with COVID-19 pneumonia showed higher body temperature (37,7~± 0,1 vs. 37,1~± 0,1 °C; p = 0.0001) and LDH values (386,3~± 27,1 vs. 310,4~± 17,5 U/l; p = 0.012) as well as lower leukocytes (7,6~± 0,5 vs. 10,1~± 0,6G/l; p = 0.0003) than patients with other pneumonia. Among abnormal CT findings in COVID-19 patients, 57 (93,4%) were evaluated as highly suspicious or possible for COVID-19. In patients with negative RT-PCR and pneumonia, another third was evaluated as highly suspicious or possible for COVID-19 (14 out of 50; 28,0%). The sensitivity in the detection of patients requiring isolation was higher with initial chest CT than with initial RT-PCR (90,4% vs. 79,5%). CONCLUSIONS COVID-19 patients show typical clinical, laboratory and imaging parameters which enable a sensitive detection of patients who demand isolation measures due to COVID-19

Clinically focused multi-cohort benchmarking as a tool for external validation of artificial intelligence algorithm performance in basic chest radiography analysis

Artificial intelligence (AI) algorithms evaluating [supine] chest radiographs ([S]CXRs) have remarkably increased in number recently. Since training and validation are often performed on subsets of the same overall dataset, external validation is mandatory to reproduce results and reveal potential training errors. We applied a multicohort benchmarking to the publicly accessible (S)CXR analyzing AI algorithm CheXNet, comprising three clinically relevant study cohorts which differ in patient positioning ([S]CXRs), the applied reference standards (CT-/[S]CXR-based) and the possibility to also compare algorithm classification with different medical experts’ reading performance. The study cohorts include [1] a cohort, characterized by 563 CXRs acquired in the emergency unit that were evaluated by 9 readers (radiologists and non-radiologists) in terms of 4 common pathologies, [2] a collection of 6,248 SCXRs annotated by radiologists in terms of pneumothorax presence, its size and presence of inserted thoracic tube material which allowed for subgroup and confounding bias analysis and [3] a cohort consisting of 166 patients with SCXRs that were evaluated by radiologists for underlying causes of basal lung opacities, all of those cases having been correlated to a timely acquired computed tomography scan (SCXR and CT within < 90 min). CheXNet non-significantly exceeded the radiology resident (RR) consensus in the detection of suspicious lung nodules (cohort [1], AUC AI/RR: 0.851/0.839, p = 0.793) and the radiological readers in the detection of basal pneumonia (cohort [3], AUC AI/reader consensus: 0.825/0.782, p = 0.390) and basal pleural effusion (cohort [3], AUC AI/reader consensus: 0.762/0.710, p = 0.336) in SCXR, partly with AUC values higher than originally published (“Nodule”: 0.780, “Infiltration”: 0.735, “Effusion”: 0.864). The classifier “Infiltration” turned out to be very dependent on patient positioning (best in CXR, worst in SCXR). The pneumothorax SCXR cohort [2] revealed poor algorithm performance in CXRs without inserted thoracic material and in the detection of small pneumothoraces, which can be explained by a known systematic confounding error in the algorithm training process. The benefit of clinically relevant external validation is demonstrated by the differences in algorithm performance as compared to the original publication. Our multi-cohort benchmarking finally enables the consideration of confounders, different reference standards and patient positioning as well as the AI performance comparison with differentially qualified medical readers

Interpretation of Thoracic Radiography Shows Large Discrepancies Depending on the Qualification of the Physician—Quantitative Evaluation of Interobserver Agreement in a Representative Emergency Department Scenario

(1) Background: Chest radiography (CXR) is still a key diagnostic component in the emergency department (ED). Correct interpretation is essential since some pathologies require urgent treatment. This study quantifies potential discrepancies in CXR analysis between radiologists and non-radiology physicians in training with ED experience. (2) Methods: Nine differently qualified physicians (three board-certified radiologists [BCR], three radiology residents [RR], and three non-radiology residents involved in ED [NRR]) evaluated a series of 563 posterior-anterior CXR images by quantifying suspicion for four relevant pathologies: pleural effusion, pneumothorax, pneumonia, and pulmonary nodules. Reading results were noted separately for each hemithorax on a Likert scale (0–4; 0: no suspicion of pathology, 4: safe existence of pathology) adding up to a total of 40,536 reported pathology suspicions. Interrater reliability/correlation and Kruskal–Wallis tests were performed for statistical analysis. (3) Results: While interrater reliability was good among radiologists, major discrepancies between radiologists’ and non-radiologists’ reading results could be observed in all pathologies. Highest overall interrater agreement was found for pneumothorax detection and lowest agreement in raising suspicion for malignancy suspicious nodules. Pleural effusion and pneumonia were often suspected with indifferent choices (1–3). In terms of pneumothorax detection, all readers mainly decided for a clear option (0 or 4). Interrater reliability was usually higher when evaluating the right hemithorax (all pathologies except pneumothorax). (4) Conclusions: Quantified CXR interrater reliability analysis displays a general uncertainty and strongly depends on medical training. NRR can benefit from radiology reporting in terms of time efficiency and diagnostic accuracy. CXR evaluation of long-time trained ED specialists has not been tested

Agreement Between 24-Hour Salt Ingestion and Sodium Excretion in a Controlled Environment

Accurately collected 24-hour urine collections are presumed to be valid for estimating salt intake in individuals. We performed 2 independent ultralong-term salt balance studies lasting 105 (4 men) and 205 (6 men) days in 10 men simulating a flight to Mars. We controlled dietary intake of all constituents for months at salt intakes of 12, 9, and 6 g/d and collected all urine. The subjects' daily menus consisted of 27 279 individual servings, of which 83.0% were completely consumed, 16.5% completely rejected, and 0.5% incompletely consumed. Urinary recovery of dietary salt was 92% of recorded intake, indicating long-term steady-state sodium balance in both studies. Even at fixed salt intake, 24-hour urine collection for sodium excretion (UNaV) showed infradian rhythmicity. We defined a ±25 mmol deviation from the average difference between recorded sodium intake and UNaV as the prediction interval to accurately classify a 3-g difference in salt intake. Because of the biological variability in UNaV, only every other daily urine sample correctly classified a 3-g difference in salt intake (49%). By increasing the observations to 3 consecutive 24-hour collections and sodium intakes, classification accuracy improved to 75%. Collecting seven 24-hour urines and sodium intake samples improved classification accuracy to 92%. We conclude that single 24-hour urine collections at intakes ranging from 6 to 12 g salt per day were not suitable to detect a 3-g difference in individual salt intake. Repeated measurements of 24-hour UNaV improve precision. This knowledge could be relevant to patient care and the conduct of intervention trials

Long-term space flight simulation reveals infradian rhythmicity in human Na(+) balance

The steady-state concept of Na(+) homeostasis, based on short-term investigations of responses to high salt intake, maintains that dietary Na(+) is rapidly eliminated into urine, thereby achieving constant total-body Na(+) and water content. We introduced the reverse experimental approach by fixing salt intake of men participating in space flight simulations at 12 g, 9 g, and 6 g/day for months and tested for the predicted constancy in urinary excretion and total-body Na(+) content. At constant salt intake, daily Na(+) excretion exhibited aldosterone-dependent, weekly (circaseptan) rhythms, resulting in periodic Na(+) storage. Changes in total-body Na(+) (±200-400 mmol) exhibited longer infradian rhythm periods (about monthly and longer period lengths) without parallel changes in body weight and extracellular water and were directly related to urinary aldosterone excretion and inversely to urinary cortisol, suggesting rhythmic hormonal control. Our findings define rhythmic Na(+) excretory and retention patterns independent of blood pressure or body water, which occur independent of salt intake