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Transl Vis Sci Technol ; 10(12): 32, 2021 10 04.
Article in English | MEDLINE | ID: covidwho-1484163


Purpose: The putative presence of SARS-CoV-2 in ocular specimen puts healthcare workers at risk. We thoroughly examined conjunctival swabs and tear fluid in a large cohort of COVID-19 patients. Methods: A total of 243 symptomatic laboratory-confirmed COVID-19 patients were included in this observational multicenter study. Conjunctival swabs were analyzed by reverse transcription polymerase chain reaction for detection of SARS-CoV-2 RNA. Next-generation sequencing and phylogenetic analysis were performed to identify viral strains and to determine tissue tropism. Schirmer tear samples from 43 hospitalized COVID-19 patients and 25 healthy controls were analyzed by multiplex cytokine immunoassays. Results: Viral SARS-CoV-2 RNA was detected in conjunctival swabs from 17 (7.0%) of 243 COVID-19 patients. Conjunctival samples were positive for viral SARS-CoV-2 RNA as long as 12 days after disease onset. Cycle threshold (Ct) values for conjunctival swabs (mean 34.5 ± 5.1) were significantly higher than nasopharyngeal swabs (mean 16.7 ± 3.6). No correlation between Ct values of conjunctival and nasopharyngeal swabs was observed. The majority of positive conjunctival samples were detected only once and primarily during the first visit. Next-generation sequencing analysis revealed that the virus strain found in the conjunctiva was most often identical to the one found in the nasopharynx. Tear cytokine levels IL-1ß and IL-6 were elevated in COVID-19 patients compared to healthy controls. Conclusions: Conjunctival samples that were positive for SARS-CoV-2 RNA contained the same viral strain as the nasopharynx. Translational Relevance: The presence of SARS-CoV-2 viral RNA and elevated cytokines in tear fluid confirm the involvement of the ocular surface in COVID-19 disease.

COVID-19 , RNA, Viral , Cohort Studies , Humans , Phylogeny , SARS-CoV-2
PLoS One ; 16(4): e0249920, 2021.
Article in English | MEDLINE | ID: covidwho-1186609


OBJECTIVE: To establish whether one can build a mortality prediction model for COVID-19 patients based solely on demographics and comorbidity data that outperforms age alone. Such a model could be a precursor to implementing smart lockdowns and vaccine distribution strategies. METHODS: The training cohort comprised 2337 COVID-19 inpatients from nine hospitals in The Netherlands. The clinical outcome was death within 21 days of being discharged. The features were derived from electronic health records collected during admission. Three feature selection methods were used: LASSO, univariate using a novel metric, and pairwise (age being half of each pair). 478 patients from Belgium were used to test the model. All modeling attempts were compared against an age-only model. RESULTS: In the training cohort, the mortality group's median age was 77 years (interquartile range = 70-83), higher than the non-mortality group (median = 65, IQR = 55-75). The incidence of former/active smokers, male gender, hypertension, diabetes, dementia, cancer, chronic obstructive pulmonary disease, chronic cardiac disease, chronic neurological disease, and chronic kidney disease was higher in the mortality group. All stated differences were statistically significant after Bonferroni correction. LASSO selected eight features, novel univariate chose five, and pairwise chose none. No model was able to surpass an age-only model in the external validation set, where age had an AUC of 0.85 and a balanced accuracy of 0.77. CONCLUSION: When applied to an external validation set, we found that an age-only mortality model outperformed all modeling attempts (curated on using three feature selection methods on 22 demographic and comorbid features.

COVID-19/mortality , Age Factors , Aged , Aged, 80 and over , Belgium/epidemiology , COVID-19/diagnosis , COVID-19/epidemiology , Cohort Studies , Communicable Disease Control , Comorbidity , Electronic Health Records , Female , Hospitalization , Humans , Male , Middle Aged , Netherlands/epidemiology , Prognosis , Risk Assessment , Risk Factors , SARS-CoV-2/isolation & purification
Thromb Res ; 196: 486-490, 2020 12.
Article in English | MEDLINE | ID: covidwho-887135


BACKGROUND: The risk of pulmonary embolism (PE) in patients with Coronavirus Disease 2019 (COVID-19) is recognized. The prevalence of PE in patients with respiratory deterioration at the Emergency Department (ED), the regular ward, and the Intensive Care Unit (ICU) are not well-established. OBJECTIVES: We aimed to investigate how often PE was present in individuals with COVID-19 and respiratory deterioration in different settings, and whether or not disease severity as measured by CT-severity score (CTSS) was related to the occurrence of PE. PATIENTS/METHODS: Between April 6th and May 3rd, we enrolled 60 consecutive adult patients with confirmed COVID-19 from the ED, regular ward and ICU who met the pre-specified criteria for respiratory deterioration. RESULTS: A total of 24 (24/60: 40% (95% CI: 28-54%)) patients were diagnosed with PE, of whom 6 were in the ED (6/23: 26% (95% CI: 10-46%)), 8 in the regular ward (8/24: 33% (95% CI: 16-55%)), and 10 in the ICU (10/13: 77% (95% CI: 46-95%)). CTSS (per unit) was not associated with the occurrence of PE (age and sex-adjusted OR 1.06 (95%CI 0.98-1.15)). CONCLUSION: The number of PE diagnosis among patients with COVID-19 and respiratory deterioration was high; 26% in the ED, 33% in the regular ward and 77% in the ICU respectively. In our cohort CTSS was not associated with the occurrence of PE. Based on the high number of patients diagnosed with PE among those scanned we recommend a low threshold for performing computed tomography angiography in patients with COVID-19 and respiratory deterioration.

COVID-19/epidemiology , Emergency Service, Hospital , Intensive Care Units , Pulmonary Embolism/epidemiology , Respiratory Insufficiency/epidemiology , Aged , Aged, 80 and over , COVID-19/diagnostic imaging , Computed Tomography Angiography , Female , Humans , Male , Middle Aged , Netherlands/epidemiology , Prevalence , Prognosis , Pulmonary Embolism/diagnostic imaging , Respiratory Insufficiency/diagnostic imaging , Risk Assessment , Risk Factors , Severity of Illness Index