A Quantitative Lung Computed Tomography Image Feature for Multi-Center Severity Assessment of COVID-19

Ghosh, Biswajoy; Kumar, Nikhil; Sadhu, Anup; Ghosh, Nirmalya; Mitra, Pabitra; Chatterjee, Jyotirmoy

doi:10.1101/2020.07.13.20152231

Cited by 7 publications

(11 citation statements)

References 40 publications

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“…Nineteen papers developed models for the prognosis of patients with COVID-19 51,[63][64][65][66][67][68][69][70][71][72][73][74][75][76][77][78][79][80] , fifteen using CT and four using CXR. These models were developed for predicting severity of outcomes including: death or need for ventilation 72,78,79 , a need for intensive care unit (ICU) admission 63,73,[77][78][79] , progression to acute respiratory distress syndrome 80 , the length of hospital stay 51,74 , likelihood of conversion to severe disease 64,65,75 and the extent of lung infection 76 . Most papers used models based on a multivariable Cox proportional hazards model 51,72,78,79 , logistic regression 65,[73][74][75]80 , linear regression 75,76 , random forest 74,7...…”

Section: Diagnostic Models For Covid-19 Diagnosis Models Using Cxrsmentioning

confidence: 99%

“…These models were developed for predicting severity of outcomes including: death or need for ventilation 72,78,79 , a need for intensive care unit (ICU) admission 63,73,[77][78][79] , progression to acute respiratory distress syndrome 80 , the length of hospital stay 51,74 , likelihood of conversion to severe disease 64,65,75 and the extent of lung infection 76 . Most papers used models based on a multivariable Cox proportional hazards model 51,72,78,79 , logistic regression 65,[73][74][75]80 , linear regression 75,76 , random forest 74,77 or compare a huge variety of machine learning models such as tree-based methods, support vector machines, neural networks and nearestneighbour clustering 63,64 .…”

Section: Diagnostic Models For Covid-19 Diagnosis Models Using Cxrsmentioning

confidence: 99%

“…Predictors from radiological data were extracted using either handcrafted radiomic features 63,64,[68][69][70]72,74,75,[77][78][79][80] or deep learning 51,66,70,71,73,76 . Clinical data included basic observations, serology and comorbidities.…”

Section: Diagnostic Models For Covid-19 Diagnosis Models Using Cxrsmentioning

confidence: 99%

“…Only ten papers have a low risk of bias for their analysis. The high risk of bias in most papers was principally due to a small sample size of patients with COVID-19 (leading to highly imbalanced datasets), use of only a single internal holdout set for validating their algorithm (rather than cross-validation or bootstrapping) and a lack of appropriate evaluation of the performance metrics (for example, no discussion of calibration/discrimination) [18][19][20]22,23,44,48,52,64,72,80 . One paper with a high risk of bias 32 claimed external validation on the dataset from ref.…”

Section: Risks Of Biasmentioning

confidence: 99%

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Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

et al. 2021

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Machine learning methods offer great promise for fast and accurate detection and prognostication of coronavirus disease 2019 (COVID-19) from standard-of-care chest radiographs (CXR) and chest computed tomography (CT) images. Many articles have been published in 2020 describing new machine learning-based models for both of these tasks, but it is unclear which are of potential clinical utility. In this systematic review, we consider all published papers and preprints, for the period from 1 January 2020 to 3 October 2020, which describe new machine learning models for the diagnosis or prognosis of COVID-19 from CXR or CT images. All manuscripts uploaded to bioRxiv, medRxiv and arXiv along with all entries in EMBASE and MEDLINE in this timeframe are considered. Our search identified 2,212 studies, of which 415 were included after initial screening and, after quality screening, 62 studies were included in this systematic review. Our review finds that none of the models identified are of potential clinical use due to methodological flaws and/or underlying biases. This is a major weakness, given the urgency with which validated COVID-19 models are needed. To address this, we give many recommendations which, if followed, will solve these issues and lead to higher-quality model development and well-documented manuscripts.

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Section: Diagnostic Models For Covid-19 Diagnosis Models Using Cxrsmentioning

confidence: 99%

Section: Diagnostic Models For Covid-19 Diagnosis Models Using Cxrsmentioning

confidence: 99%

Section: Diagnostic Models For Covid-19 Diagnosis Models Using Cxrsmentioning

confidence: 99%

Section: Risks Of Biasmentioning

confidence: 99%

See 2 more Smart Citations

Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

et al. 2021

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“…Multiple previous studies have used ML-based algorithms to predict COVID-19 prognosis by combining basic patient demographics, vital signs, laboratory measurements, and comorbidities (15-17), sociodemographic information, comorbidities and current medications (19), or chest computed tomography (CT) scan with patient demographics (16). Some studies have also sought to predict prognosis by CT alone (19, 20). The performance of our tests compared favorably with that of two existing risk assessment models for which we were able to generate classifications for our development set, with our test achieving similar negative predictive values in the lowest risk groups but superior specificity.…”

Section: Discussionmentioning

confidence: 99%

Predicting Prognosis in COVID-19 Patients using Machine Learning and Readily Available Clinical Data

Campbell¹,

Wilson

Röder³

et al. 2021

Preprint

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RationalePrognostic tools for aiding in the treatment of hospitalized COVID-19 patients could help improve outcome by identifying patients at higher or lower risk of severe disease.ObjectivesThe study objective was to develop models to stratify patients by risk of severe outcomes during COVID-19 hospitalization using readily available information at hospital admission.MethodsHierarchical ensemble classification models were trained on a set of 229 patients hospitalized with COVID-19 to predict severe outcomes, including ICU admission, development of ARDS, or intubation, using easily attainable attributes including basic patient characteristics, vital signs at admission, and basic lab results collected at time of presentation. Each test stratifies patients into groups of increasing risk. An additional cohort of 330 patients was used for blinded, independent validation. Shapley value analysis evaluated which attributes contributed most to the models’ predictions of risk.Measurements and Main ResultsTest performance was assessed using precision (positive predictive value) and recall (sensitivity) of the final risk groups. All test cut-offs were fixed prior to blinded validation. In both development and validation, the tests achieved precision in the lowest risk groups near or above 0.9. The proportion of patients with severe outcomes significantly increased across increasing risk groups. While the importance of attributes varied by test and patient, CRP, LDH, and D-dimer were often found to be important in the assignment of risk label.ConclusionsRisk of severe outcomes for patients hospitalized with COVID-19 infection can be assessed using machine learning-based models based on attributes routinely collected at hospital admission.

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Predicting prognosis in COVID-19 patients using machine learning and readily available clinical data

Campbell

Wilson

Röder

et al. 2021

International Journal of Medical Informatics

View full text Add to dashboard Cite

Rationale: Prognostic tools for aiding in the treatment of hospitalized COVID-19 patients could help improve outcome by identifying patients at higher or lower risk of severe disease. The study objective was to develop models to stratify patients by risk of severe outcomes during COVID-19 hospitalization using readily available information at hospital admission. Methods Hierarchical ensemble classification models were trained on a set of 229 patients hospitalized with COVID-19 to predict severe outcomes, including ICU admission, development of acute respiratory distress syndrome, or intubation, using easily attainable attributes including basic patient characteristics, vital signs at admission, and basic lab results collected at time of presentation. Each test stratifies patients into groups of increasing risk. An additional cohort of 330 patients was used for blinded, independent validation. Shapley value analysis evaluated which attributes contributed most to the models’ predictions of risk. Main Results: Test performance was assessed using precision (positive predictive value) and recall (sensitivity) of the final risk groups. All test cut-offs were fixed prior to blinded validation. In development and validation, the tests achieved precision in the lowest risk groups near or above 0.9. The proportion of patients with severe outcomes significantly increased across increasing risk groups. While the importance of attributes varied by test and patient, C-reactive protein, lactate dehydrogenase, and D-dimer were often found to be important in the assignment of risk. Conclusions Risk of severe outcomes for patients hospitalized with COVID-19 infection can be assessed using machine learning-based models based on attributes routinely collected at hospital admission.

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A Quantitative Lung Computed Tomography Image Feature for Multi-Center Severity Assessment of COVID-19

Cited by 7 publications

References 40 publications

Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

Predicting Prognosis in COVID-19 Patients using Machine Learning and Readily Available Clinical Data

Predicting prognosis in COVID-19 patients using machine learning and readily available clinical data

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