Detection and Staging of Chronic Obstructive Pulmonary Disease Using a Computed Tomography-based Weakly Supervised Deep Learning Approach (preprint)

BackgroundChronic obstructive pulmonary disease (COPD) remains underdiagnosed globally. The coronavirus disease 2019 pandemic has also severely restricted spirometry, the primary tool used for COPD diagnosis and severity evaluation, due to concerns of virus transmission. Computed tomography (CT)-based deep learning (DL) approaches have been suggested as a cost-effective alternative for COPD identification within smokers. The present study aims to develop weakly supervised DL models that utilize CT image data for the automated detection and staging of spirometry-defined COPD among natural population.MethodsA large, highly heterogenous dataset was established comprising 1393 participants recruited from outpatient, inpatient and physical examination center settings of 4 large public hospitals in China. CT scans, spirometry data, demographic data, and clinical information of each participant were collected for the purpose of model development and evaluation. An attention-based multi-instance learning (MIL) model for COPD detection was trained using CT scans from 837 participants and evaluated using a test set comprised of data from 278 non-overlapping participants. External validation of the COPD detection was performed with 620 low-dose CT (LDCT) scans acquired from the National Lung Screening Trial (NLST) cohort. A multi-channel 3D residual network was further developed to categorize GOLD stages among confirmed COPD patients and evaluated using 5-fold cross validation. Spirometry tests were used to diagnose COPD, with stages defined according to the GOLD criteria.ResultsThe attention-based MIL model used for COPD detection achieved an area under the receiver operating characteristic curve (AUC) of 0.934 on the test set and 0.866 on the LDCT subset acquired from NLST. The model exhibited high generalizability across distinct scanning devices and slice thicknesses, with an AUC above 0.90. The multi-channel 3D residual network was able to correctly grade 76.4% of COPD patients in the test set (423/553) using the GOLD scale, with a Cohen’s weighted Kappa of 0.619 for the assessment of GOLD categorization .ConclusionThe proposed chest CT-DL approach can automatically identify spirometry-defined COPD and categorize patients according to the GOLD scale, with clinically acceptable performance. As such, this approach may be a powerful novel tool for COPD diagnosis and staging at the population level.

Risk Prediction for Poor Outcome and Death in Hospital In-Patients with COVID-19: Derivation in Wuhan, China and External Validation in London, UK (preprint)

Background: Accurate risk prediction of clinical outcome would usefully inform clinical decisions and intervention targeting in COVID-19. The aim of this study was to derive and validate risk prediction models for poor outcome and death in adult inpatients with COVID-19. Methods: Model derivation using data from Wuhan, China used logistic regression with death and poor outcome (death or severe disease) as outcomes. Predictors were demographic, comorbidity, symptom and laboratory test variables. The best performing models were externally validated in data from London, UK. Findings: 4.3% of the derivation cohort (n=775) died and 9.7% had a poor outcome, compared to 34.1% and 42.9% of the validation cohort (n=226). In derivation, prediction models based on age, sex, neutrophil count, lymphocyte count, platelet count, C-reactive protein and creatinine had excellent discrimination (death c-index=0.91, poor outcome c-index=0.88), with good-to-excellent calibration. Using two cut-offs to define low, high and very-high risk groups, derivation patients were stratified in groups with observed death rates of 0.34%, 15.0% and 28.3% and poor outcome rates 0.63%, 8.9% and 58.5%. External validation discrimination was good (c-index death=0.74, poor outcome=0.72) as was calibration. However, observed rates of death were 16.5%, 42.9% and 58.4% and poor outcome 26.3%, 28.4% and 64.8% in predicted low, high and very-high risk groups. Interpretation: Our prediction model using demography and routinely-available laboratory tests performed very well in internal validation in the lower-risk derivation population, but less well in the much higher-risk external validation population. Further external validation is needed. Collaboration to create larger derivation datasets, and to rapidly externally validate all proposed prediction models in a range of populations is needed, before routine implementation of any risk prediction tool in clinical care. Funding Statement: HW and HZ are supported by Medical Research Council and Health Data Research UK Grant (MR/S004149/1), Industrial Strategy Challenge Grant (MC_PC_18029) and Wellcome Institutional Translation Partnership Award (PIII054). RD is supported by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. DMB is funded by a UKRI Innovation Fellowship as part of Health Data Research UK MR/S00310X/1 (https://www.hdruk.ac.uk). KD is supported by LifeArc STOPCOVID award. This work uses data provided by patients and collected by the NHS as part of their care and support. XW is supported by National Natural Science Foundation of China (grant number:81700006). QL is supported by National Key R&D Program (2018YFC1313700), National Natural Science Foundation of China (grant number: 81870064) and the “Gaoyuan” project of Pudong Health and Family Planning Commission (PWYgy2018-06).Declaration of Interests: The authors declare no competing interests.Ethics Approval Statement: The derivation study was approved by the Research Ethics Committee of Shanghai Dongfang Hospital and Taikang Tongji Hospital. The external validation study operated under London South East Research Ethics Committee (reference 18/LO/2048) approval granted to the King’s Electronic Records Research Interface (KERRI).

Association between Cardiovascular Burden and Requirement of Intensive Care among Patients with Mild COVID-19 (preprint)

BackgroundInformation regarding the impact of cardiovascular disease (CVD) on disease progression among patients with mild coronavirus disease 2019 (COVID-19) is limited. MethodsThis study evaluated the association of underlying CVD with disease progression in patients with mild COVID-19. The primary outcome was the need to be transferred to intensive care due to disease progression. The patients were divided with and without CVD as well as stable and intensive care groups. ResultsOf 332 patients with mild COVID-19, median age was 51 years (IQR, 40-59 years), and 200 (61.2%) were female. Of 48 (14.5%) patients with CVD, 23 (47.9%) progressed to severe disease status and required intensive care. Compared with patients without CVD, patients with CVD were older, and more likely to have fatigue, chest tightness, and myalgia. The rate of requiring intensive care was significantly higher among patients with CVD than in patients without CVD (47.92% vs. 12.4%; P<0.001). In subgroup analysis, rate of requiring intensive care was also higher among patients with either hypertension or coronary heart disease than in patients without hypertension or coronary heart disease. The multivariable regression model showed CVD served as an independent risk factor for intensive care (Odd ratio [OR], 2.652 [95% CI, 1.019-6.899]) after adjustment for various cofounders. ConclusionsPatients with mild COVID-19 complicating CVD in are susceptible to develop severe disease status and requirement for intensive care. Key PointsO_ST_ABSQuestionC_ST_ABSWhat is the impact of coexisting cardiovascular diseases (CVD) on disease progression in patients with mild COVID-19? FindingsAlthough most patients with mild COVID-19 were discharged alive from hospital, approximately 47.9% patients with coexisting CVD developed severe disease status and required intensive care. CVD is an independent risk factor of intensive care among patients with mild COVID-19. MeaningCoexisting CVD is associated with unfavorable outcomes among patients with mild COVID-19. Special monitoring is required for these patients to improve their outcome.

Risk prediction for poor outcome and death in hospital in-patients with COVID-19: derivation in Wuhan, China and external validation in London, UK (preprint)

Background Accurate risk prediction of clinical outcome would usefully inform clinical decisions and intervention targeting in COVID-19. The aim of this study was to derive and validate risk prediction models for poor outcome and death in adult inpatients with COVID-19. Methods Model derivation using data from Wuhan, China used logistic regression with death and poor outcome (death or severe disease) as outcomes. Predictors were demographic, comorbidity, symptom and laboratory test variables. The best performing models were externally validated in data from London, UK. Findings 4.3% of the derivation cohort (n=775) died and 9.7% had a poor outcome, compared to 34.1% and 42.9% of the validation cohort (n=226). In derivation, prediction models based on age, sex, neutrophil count, lymphocyte count, platelet count, C-reactive protein and creatinine had excellent discrimination (death c-index=0.91, poor outcome c-index=0.88), with good-to-excellent calibration. Using two cut-offs to define low, high and very-high risk groups, derivation patients were stratified in groups with observed death rates of 0.34%, 15.0% and 28.3% and poor outcome rates 0.63%, 8.9% and 58.5%. External validation discrimination was good (c-index death=0.74, poor outcome=0.72) as was calibration. However, observed rates of death were 16.5%, 42.9% and 58.4% and poor outcome 26.3%, 28.4% and 64.8% in predicted low, high and very-high risk groups. Interpretation Our prediction model using demography and routinely-available laboratory tests performed very well in internal validation in the lower-risk derivation population, but less well in the much higher-risk external validation population. Further external validation is needed. Collaboration to create larger derivation datasets, and to rapidly externally validate all proposed prediction models in a range of populations is needed, before routine implementation of any risk prediction tool in clinical care.

Supplementing the National Early Warning Score (NEWS2) for anticipating early deterioration among patients with COVID-19 infection (preprint)

BackgroundThe National Early Warning Score (NEWS2) is currently recommended in the United Kingdom for risk stratification of COVID outcomes, but little is known about its ability to detect severe cases. We aimed to evaluate NEWS2 for severe COVID outcome and identify and validate a set of routinely-collected blood and physiological parameters taken at hospital admission to improve the score. MethodsTraining cohorts comprised 1276 patients admitted to Kings College Hospital NHS Foundation Trust with COVID-19 disease from 1st March to 30th April 2020. External validation cohorts included 5037 patients from four UK NHS Trusts (Guys and St Thomas Hospitals, University Hospitals Southampton, University Hospitals Bristol and Weston NHS Foundation Trust, University College London Hospitals), and two hospitals in Wuhan, China (Wuhan Sixth Hospital and Taikang Tongji Hospital). The outcome was severe COVID disease (transfer to intensive care unit 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. ResultsA baseline model of NEWS2 + age had poor-to-moderate discrimination for severe COVID infection at 14 days (AUC in training sample = 0.700; 95% 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, CRP, estimated GFR, neutrophil count, neutrophil/lymphocyte ratio) improved discrimination (AUC = 0.735; 95% CI: 0.715, 0.757) and these improvements were replicated across five UK and non-UK sites. However, there was evidence of miscalibration with the model tending to underestimate risks in most sites. ConclusionsNEWS2 score had poor-to-moderate discrimination for medium-term COVID 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. KO_SCPLOWEYC_SCPLOWO_SCPCAP C_SCPCAPO_SCPLOWMESSAGESC_SCPLOWO_LIThe National Early Warning Score (NEWS2), currently recommended for stratification of severe COVID-19 disease in the UK, showed poor-to-moderate discrimination for medium-term outcomes (14-day transfer to ICU or death) among COVID-19 patients. C_LIO_LIRisk stratification was improved by the addition of routinely-measured blood and physiological parameters routinely at hospital admission (supplemental oxygen, urea, oxygen saturation, CRP, estimated GFR, neutrophil count, neutrophil/lymphocyte ratio) which provided moderate improvements in a risk stratification model for 14-day ICU/death. C_LIO_LIThis improvement over NEWS2 alone was maintained across multiple hospital trusts but the model tended to be miscalibrated with risks of severe outcomes underestimated in most sites. C_LIO_LIWe benefited from existing pipelines for informatics at KCH such as CogStack that allowed rapid extraction and processing of electronic health records. This methodological approach provided rapid insights and allowed us to overcome the complications associated with slow data centralisation approaches. C_LI