Increased COVID-19 mortality rate in rare disease patients: a retrospective cohort study in participants of the Genomics England 100,000 Genomes project.

BACKGROUND: Several common conditions have been widely recognised as risk factors for COVID-19 related death, but risks borne by people with rare diseases are largely unknown. Therefore, we aim to estimate the difference of risk for people with rare diseases comparing to the unaffected. METHOD: To estimate the correlation between rare diseases and COVID-19 related death, we performed a retrospective cohort study in Genomics England 100k Genomes participants, who tested positive for Sars-Cov-2 during the first wave (16-03-2020 until 31-July-2020) of COVID-19 pandemic in the UK (n = 283). COVID-19 related mortality rates were calculated in two groups: rare disease patients (n = 158) and unaffected relatives (n = 125). Fisher's exact test and logistic regression was used for univariable and multivariable analysis, respectively. RESULTS: People with rare diseases had increased risk of COVID19-related deaths compared to the unaffected relatives (OR [95% CI] = 3.47 [1.21- 12.2]). Although, the effect was insignificant after adjusting for age and number of comorbidities (OR [95% CI] = 1.94 [0.65-5.80]). Neurology and neurodevelopmental diseases was significantly associated with COVID19-related death in both univariable (OR [95% CI] = 4.07 [1.61-10.38]) and multivariable analysis (OR [95% CI] = 4.22 [1.60-11.08]). CONCLUSIONS: Our results showed that rare disease patients, especially ones affected by neurology and neurodevelopmental disorders, in the Genomics England cohort had increased risk of COVID-19 related death during the first wave of the pandemic in UK. The high risk is likely associated with rare diseases themselves, while we cannot rule out possible mediators due to the small sample size. We would like to raise the awareness that rare disease patients may face increased risk for COVID-19 related death. Proper considerations for rare disease patients should be taken when relevant policies (e.g., returning to workplace) are made.

Increased COVID-19 related mortality rate for patients with rare diseases: a retrospective cohort study with data from Genomics England

Background The ongoing COVID-19 pandemic has had a high incidence and mortality so far. Several common conditions have been widely recognised as risk factors for COVID-19-related death, but the risks for patients with rare diseases are largely unknown. Therefore, we aimed to estimate the difference in the risk of mortality for patients with rare diseases compared to the risk for the general population. Methods To estimate the correlation between rare diseases and COVID-19-related death, we performed a retrospective cohort study of Genomics England participants who tested positive for SARS-CoV-2 (n=283) during the first wave (March 16 to July 31, 2020) of the COVID-19 pandemic in the UK. Participants with one of 190 rare diseases and their biological relatives (mostly to the first or second degree) were recruited by Genomics England, where patients had a provisional diagnosis but not a molecular diagnosis. COVID-19-related mortality rates were calculated in two groups: patients with rare diseases and unaffected relatives. Univariable analysis on the associations between rare diseases and COVID-19-related death was done with Fisher's exact test. Adjusted odds ratio (OR;for age and number of common comorbidities) was calculated with multivariable logistic regression. The study was approved by Genomic England (reference GEL-79143). Findings There were 20 (13%) COVID-19-related deaths in patients with rare diseases (n=158) and five (4%) COVID-19-related deaths in unaffected relatives (n=125), translating to an increased risk of mortality in patients with rare diseases (OR 3·47 [95% CI 1·21–12·2], Fisher's exact p=0·011). A greater OR was observed in participants younger than 60 years (univariable 5·11 [0·56–245·16];p=0·212), although the trend was not significant. Having a rare disease (multivariable 1·94 [0·65–5·80];p=0·233) and the number of comorbidities (multivariable 2·10 [0·79–5·58];p=0·135) contributed similarly to COVID-19-related death in multivariable logistic regression analysis in this cohort. Sex was not found to affect the mortality rate. Interpretation Our results show that patients with rare diseases in the Genomics England cohort had an increased risk of COVID-19-related mortality during the first wave of the pandemic in UK. The high risk is probably associated with the rare diseases themselves, but we cannot rule out possible mediators due to the small sample size. We would like to raise the awareness that patients with rare diseases might face increased risk for COVID-19-related death. Proper considerations for these patients should be taken when relevant decisions (eg, returning to a workplace) are made. Funding HZ is supported by Wellcome Trust ITPA funding (grant number PIII026/013). HW is supported by Wellcome Trust ITPA (grant number PIII0054/005) and Medical Research Council (grant number MR/S004149/2).

Ensemble learning for poor prognosis predictions: A case study on SARS-CoV-2.

OBJECTIVE: Risk prediction models are widely used to inform evidence-based clinical decision making. However, few models developed from single cohorts can perform consistently well at population level where diverse prognoses exist (such as the SARS-CoV-2 [severe acute respiratory syndrome coronavirus 2] pandemic). This study aims at tackling this challenge by synergizing prediction models from the literature using ensemble learning. MATERIALS AND METHODS: In this study, we selected and reimplemented 7 prediction models for COVID-19 (coronavirus disease 2019) that were derived from diverse cohorts and used different implementation techniques. A novel ensemble learning framework was proposed to synergize them for realizing personalized predictions for individual patients. Four diverse international cohorts (2 from the United Kingdom and 2 from China; N = 5394) were used to validate all 8 models on discrimination, calibration, and clinical usefulness. RESULTS: Results showed that individual prediction models could perform well on some cohorts while poorly on others. Conversely, the ensemble model achieved the best performances consistently on all metrics quantifying discrimination, calibration, and clinical usefulness. Performance disparities were observed in cohorts from the 2 countries: all models achieved better performances on the China cohorts. DISCUSSION: When individual models were learned from complementary cohorts, the synergized model had the potential to achieve better performances than any individual model. Results indicate that blood parameters and physiological measurements might have better predictive powers when collected early, which remains to be confirmed by further studies. CONCLUSIONS: Combining a diverse set of individual prediction models, the ensemble method can synergize a robust and well-performing model by choosing the most competent ones for individual patients.

Evaluation and improvement of the National Early Warning Score (NEWS2) for COVID-19: a multi-hospital study.

BACKGROUND: The National Early Warning Score (NEWS2) is currently recommended in the UK for the risk stratification of COVID-19 patients, but little is known about its ability to detect severe cases. We aimed to evaluate NEWS2 for the prediction of severe COVID-19 outcome and identify and validate a set of blood and physiological parameters routinely collected at hospital admission to improve upon the use of NEWS2 alone for medium-term risk stratification. METHODS: Training cohorts comprised 1276 patients admitted to King's College Hospital National Health Service (NHS) Foundation Trust with COVID-19 disease from 1 March to 30 April 2020. External validation cohorts included 6237 patients from five UK NHS Trusts (Guy's and St Thomas' Hospitals, University Hospitals Southampton, University Hospitals Bristol and Weston NHS Foundation Trust, University College London Hospitals, University Hospitals Birmingham), one hospital in Norway (Oslo University Hospital), and two hospitals in Wuhan, China (Wuhan Sixth Hospital and Taikang Tongji Hospital). The outcome was severe COVID-19 disease (transfer to intensive care unit (ICU) or death) at 14 days after hospital admission. Age, physiological measures, blood biomarkers, sex, ethnicity, and comorbidities (hypertension, diabetes, cardiovascular, respiratory and kidney diseases) measured at hospital admission were considered in the models. RESULTS: A baseline model of 'NEWS2 + age' had poor-to-moderate discrimination for severe COVID-19 infection at 14 days (area under receiver operating characteristic curve (AUC) in training cohort = 0.700, 95% confidence interval (CI) 0.680, 0.722; Brier score = 0.192, 95% CI 0.186, 0.197). A supplemented model adding eight routinely collected blood and physiological parameters (supplemental oxygen flow rate, urea, age, oxygen saturation, C-reactive protein, estimated glomerular filtration rate, neutrophil count, neutrophil/lymphocyte ratio) improved discrimination (AUC = 0.735; 95% CI 0.715, 0.757), and these improvements were replicated across seven UK and non-UK sites. However, there was evidence of miscalibration with the model tending to underestimate risks in most sites. CONCLUSIONS: NEWS2 score had poor-to-moderate discrimination for medium-term COVID-19 outcome which raises questions about its use as a screening tool at hospital admission. Risk stratification was improved by including readily available blood and physiological parameters measured at hospital admission, but there was evidence of miscalibration in external sites. This highlights the need for a better understanding of the use of early warning scores for COVID.

Differences of Severe Acute Respiratory Syndrome Coronavirus 2 Shedding Duration in Sputum and Nasopharyngeal Swab Specimens Among Adult Inpatients With Coronavirus Disease 2019.

BACKGROUND: The viral shedding duration of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has not been fully defined. Consecutive detection of SARS-CoV-2 RNA from respiratory tract specimens is essential for determining duration of virus shedding and providing evidence to optimize the clinical management of coronavirus disease 2019 (COVID-19). RESEARCH QUESTION: What are the shedding durations of SARS-CoV-2 RNA in the upper and lower respiratory tract specimens? What are their associated risk factors? STUDY DESIGN AND METHODS: A total of 68 patients with COVID-19 admitted to Wuhan Taikang Tongji Hospital and Huoshenshan Hospital from February 10, 2020, to March 20, 2020, were recruited. Consecutive SARS-CoV-2 RNA detection from paired specimens of nasopharyngeal swab (NPS) and sputum were carried out. The clinical characteristics of patients were recorded for further analysis. RESULTS: SARS-CoV-2 RNA was detected from NPSs in 48 patients (70.6%), and from sputum specimens in 30 patients (44.1%). The median duration of viral shedding from sputum specimens (34 days; interquartile range [IQR], 24-40) was significantly longer than from NPSs (19 days; IQR, 14-25; P < .001). Elderly age was an independent factor associated with prolonged virus shedding time of SARS-CoV-2 (hazard ratio, 1.71; 95% CI, 1.01-2.93). It was noteworthy that in 9 patients, the viral RNA was detected in sputum after NPS turned negative. Chronic lung disease and steroids were associated with virus detection in sputum, and diabetes mellitus was associated with virus detection in both NPS and sputum. INTERPRETATION: These findings may impact a test based clearance discharge criteria given patients with COVID-19 may shed virus longer in their lower respiratory tracts, with potential implication for prolonged transmission risk. In addition, more attention should be given to elderly patients who might have prolonged viral shedding duration.