Risk factors for in-hospital mortality in laboratory-confirmed COVID-19 patients in the Netherlands: A competing risk survival analysis.

BACKGROUND: To date, survival data on risk factors for COVID-19 mortality in western Europe is limited, and none of the published survival studies have used a competing risk approach. This study aims to identify risk factors for in-hospital mortality in COVID-19 patients in the Netherlands, considering recovery as a competing risk. METHODS: In this observational multicenter cohort study we included adults with PCR-confirmed SARS-CoV-2 infection that were admitted to one of five hospitals in the Netherlands (March to May 2020). We performed a competing risk survival analysis, presenting cause-specific hazard ratios (HRCS) for the effect of preselected factors on the absolute risk of death and recovery. RESULTS: 1,006 patients were included (63.9% male; median age 69 years, IQR: 58-77). Patients were hospitalized for a median duration of 6 days (IQR: 3-13); 243 (24.6%) of them died, 689 (69.9%) recovered, and 74 (7.4%) were censored. Patients with higher age (HRCS 1.10, 95% CI 1.08-1.12), immunocompromised state (HRCS 1.46, 95% CI 1.08-1.98), who used anticoagulants or antiplatelet medication (HRCS 1.38, 95% CI 1.01-1.88), with higher modified early warning score (MEWS) (HRCS 1.09, 95% CI 1.01-1.18), and higher blood LDH at time of admission (HRCS 6.68, 95% CI 1.95-22.8) had increased risk of death, whereas fever (HRCS 0.70, 95% CI 0.52-0.95) decreased risk of death. We found no increased mortality risk in male patients, high BMI or diabetes. CONCLUSION: Our competing risk survival analysis confirms specific risk factors for COVID-19 mortality in a the Netherlands, which can be used for prediction research, more intense in-hospital monitoring or prioritizing particular patients for new treatments or vaccination.

Automated Assessment of COVID-19 Reporting and Data System and Chest CT Severity Scores in Patients Suspected of Having COVID-19 Using Artificial Intelligence.

Background The coronavirus disease 2019 (COVID-19) pandemic has spread across the globe with alarming speed, morbidity, and mortality. Immediate triage of patients with chest infections suspected to be caused by COVID-19 using chest CT may be of assistance when results from definitive viral testing are delayed. Purpose To develop and validate an artificial intelligence (AI) system to score the likelihood and extent of pulmonary COVID-19 on chest CT scans using the COVID-19 Reporting and Data System (CO-RADS) and CT severity scoring systems. Materials and Methods The CO-RADS AI system consists of three deep-learning algorithms that automatically segment the five pulmonary lobes, assign a CO-RADS score for the suspicion of COVID-19, and assign a CT severity score for the degree of parenchymal involvement per lobe. This study retrospectively included patients who underwent a nonenhanced chest CT examination because of clinical suspicion of COVID-19 at two medical centers. The system was trained, validated, and tested with data from one of the centers. Data from the second center served as an external test set. Diagnostic performance and agreement with scores assigned by eight independent observers were measured using receiver operating characteristic analysis, linearly weighted κ values, and classification accuracy. Results A total of 105 patients (mean age, 62 years ± 16 [standard deviation]; 61 men) and 262 patients (mean age, 64 years ± 16; 154 men) were evaluated in the internal and external test sets, respectively. The system discriminated between patients with COVID-19 and those without COVID-19, with areas under the receiver operating characteristic curve of 0.95 (95% CI: 0.91, 0.98) and 0.88 (95% CI: 0.84, 0.93), for the internal and external test sets, respectively. Agreement with the eight human observers was moderate to substantial, with mean linearly weighted κ values of 0.60 ± 0.01 for CO-RADS scores and 0.54 ± 0.01 for CT severity scores. Conclusion With high diagnostic performance, the CO-RADS AI system correctly identified patients with COVID-19 using chest CT scans and assigned standardized CO-RADS and CT severity scores that demonstrated good agreement with findings from eight independent observers and generalized well to external data. © RSNA, 2020 Supplemental material is available for this article.

Ribavirin plasma concentration measurements in patients with hepatitis C: early ribavirin concentrations predict steady-state concentrations.

BACKGROUND: Ribavirin is an essential component in the treatment of chronic hepatitis C (HCV) infection. Although ribavirin dose is weight-based, data in the literature suggest large between-patient variability in plasma ribavirin concentrations. Recent studies indicate that higher ribavirin exposure results in higher sustained viral response rates. Monitoring ribavirin concentration is suggested in the literature, but it is unclear at what time point during treatment plasma ribavirin concentrations should be monitored. AIM: To investigate the association between early plasma ribavirin concentrations and ribavirin dosing with steady-state (Css) concentration and the between- and within-patient variability in plasma ribavirin concentration in clinical practice. METHODS: We performed a prospective observational cohort study in patients with HCV who received pegylated interferon in combination with oral weight-based ribavirin (12-15 mg/kg) twice daily. Trough plasma ribavirin concentrations at Weeks 1, 2, 4, 8, 12, 16, 20, and 24 were studied using a validated high-performance liquid chromatography assay. RESULTS: In total, 53 patients (37 male, 16 female) with a mean age of 51 years (range, 26-68 years) were included and 209 samples were collected. There was a significant correlation between Week 2 as well as Week 4 and plasma ribavirin Css (r = 0.589 and r = 0.714, P < 0.05, respectively). Ribavirin Css was reached at Week 8 of HCV treatment. There was no correlation between dose in mg/kg and Css (r = 0.181, P = 0.263). The between- and within-patient coefficients of variation of plasma ribavirin concentrations at Week 8 and beyond were 43% and 13%, respectively. CONCLUSION: In HCV-infected patients, ribavirin steady-state concentrations can be predicted by measurement of concentrations made early after the start of therapy.

A prospective multicenter study on fever of unknown origin: the yield of a structured diagnostic protocol.

We conducted a prospective study to update our knowledge of fever of unknown origin (FUO) and to explore the utility of a structured diagnostic protocol. From December 2003 to July 2005, 73 patients with FUO were recruited from 1 university hospital (n = 40) and 5 community hospitals (n = 33) in the same region in The Netherlands. FUO was defined as a febrile illness of >3 weeks' duration, a temperature of >38.3 degrees C on several occasions, without a diagnosis after standardized history-taking, physical examination, and certain obligatory investigations. Immunocompromised patients were excluded. A structured diagnostic protocol was used. Patients from the university hospital were characterized by more secondary referrals and a higher percentage of periodic fever than those referred to community hospitals. Infection was the cause in 16%, a neoplasm in 7%, noninfectious inflammatory diseases in 22%, miscellaneous causes in 4%, and in 51%, the cause of fever was not found (no differences between university and community hospitals). There were no differences regarding the number and type of investigations between university and community hospitals. Significant predictors for reaching a diagnosis included continuous fever; fever present for <180 days; elevated erythrocyte sedimentation rate, C-reactive protein, or lactate dehydrogenase; leukopenia; thrombocytosis; abnormal chest computed tomography (CT); and abnormal F-fluorodeoxyglucose positron emission tomography (FDG-PET). For future FUO studies, inclusion of outpatients and the use of a set of obligated investigations instead of a time-related criterion are recommended. Except for tests from the obligatory part of our protocol and cryoglobulins in an early stage, followed by FDG-PET, and in a later stage by abdominal and chest CT, temporal artery biopsy in patients aged 55 years or older, and possibly bone marrow biopsy, other tests should not be used as screening investigations.