A predictive tool for identification of SARS-CoV-2 PCR-negative emergency department patients using routine test results

Joshi, Rohan P.; Pejaver, Vikas; Hammarlund, Noah; Sung, Heungsup; Lee, Seong-Kyu; Furmanchuk, Al’ona; Lee, Hye Young; Scott, Gregory D.; Gombar, Saurabh; Shah, Nigam H.; Shen, Sam; Nassiri, Anna; Schneider, Daniel; Ahmad, Faraz; Liebovitz, David; Kho, Abel N.; Mooney, Sean D.; Pinsky, Benjamin A.; Banaei, Niaz

doi:10.1016/j.jcv.2020.104502

Cited by 51 publications

(50 citation statements)

References 7 publications

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“…Notably, the authors report a number of complete instances (510) which is different from that reported in (32). Joshi et al (34) developed a logistic regression model trained using CBC data on a dataset of 380 cases, reporting good sensitivity (93%) but low specificity (43%).…”

Section: Discussionmentioning

confidence: 99%

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Development, evaluation, and validation of machine learning models for COVID-19 detection based on routine blood tests

Cabitza

Campagner

Chiara

et al. 2020

Preprint

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Background: The rRT PCR test, the current gold standard for the detection of coronavirus disease (COVID19), presents with known shortcomings, such as long turnaround time, potential shortage of reagents, false-negative rates around 15/20%, and expensive equipment. The hematochemical values of routine blood exams could represent a faster and less expensive alternative. Methods: Three different training data set of hematochemical values from 1,624 patients (52% COVID19 positive), admitted at San Raphael Hospital (OSR) from February to May 2020, were used for developing machine learning (ML) models: the complete OSR dataset (72 features: complete blood count (CBC), biochemical, coagulation, hemogasanalysis and CO-Oxymetry values, age, sex and specific symptoms at triage) and two subdatasets (COVID specific and CBC dataset, 32 and 21 features respectively). 58 cases (50% COVID19 positive) from another hospital, and 54 negative patients collected in 2018 at OSR, were used for internal external and external validation. Results: We developed five ML models: for the complete OSR dataset, the area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.83 to 0.90; for the COVID specific dataset from 0.83 to 0.87; and for the CBC dataset from 0.74 to 0.86. The validations also achieved good results: respectively, AUC from 0.75 to 0.78; and specificity from 0.92 to 0.96. Conclusions: ML can be applied to blood tests as both an adjunct and alternative method to rRT PCR for the fast and cost-effective identification of COVID19 positive patients. This is especially useful in developing countries, or in countries facing an increase in contagions.

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Section: Discussionmentioning

confidence: 99%

“…which is different from that reported in (32). Joshi et al (34) developed a logistic regression model trained using…”

mentioning

confidence: 99%

Development, evaluation, and validation of machine learning models for COVID-19 detection based on routine blood tests

Cabitza

Campagner

Chiara

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Notably, the authors report a number of complete instances (510) which is different from that reported in [32]. Joshi et al [34] developed a logistic regression model trained using CBC data on a dataset of 380 cases, reporting good sensitivity (93%) but low specificity (43%). More in general, a recent critical survey [5] raised some concerns about these and other evaluated studies (most of which have not yet undergone peer-review), noting the possibility of high rates of bias and over-fitting, and little compliance with reporting and replication guidelines [18].…”

Section: Datasetmentioning

confidence: 96%

Development, evaluation, and validation of machine learning models for COVID-19 detection based on routine blood tests

Cabitza

Campagner

Resta

et al. 2020

Clinical Chemistry and Laboratory Medicine (CCLM)

127

156

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ObjectivesThe rRT-PCR test, the current gold standard for the detection of coronavirus disease (COVID-19), presents with known shortcomings, such as long turnaround time, potential shortage of reagents, false-negative rates around 15–20%, and expensive equipment. The hematochemical values of routine blood exams could represent a faster and less expensive alternative.MethodsThree different training data set of hematochemical values from 1,624 patients (52% COVID-19 positive), admitted at San Raphael Hospital (OSR) from February to May 2020, were used for developing machine learning (ML) models: the complete OSR dataset (72 features: complete blood count (CBC), biochemical, coagulation, hemogasanalysis and CO-Oxymetry values, age, sex and specific symptoms at triage) and two sub-datasets (COVID-specific and CBC dataset, 32 and 21 features respectively). 58 cases (50% COVID-19 positive) from another hospital, and 54 negative patients collected in 2018 at OSR, were used for internal-external and external validation.ResultsWe developed five ML models: for the complete OSR dataset, the area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.83 to 0.90; for the COVID-specific dataset from 0.83 to 0.87; and for the CBC dataset from 0.74 to 0.86. The validations also achieved good results: respectively, AUC from 0.75 to 0.78; and specificity from 0.92 to 0.96.ConclusionsML can be applied to blood tests as both an adjunct and alternative method to rRT-PCR for the fast and cost-effective identification of COVID-19-positive patients. This is especially useful in developing countries, or in countries facing an increase in contagions.

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“…Joshi et al [ 36 ] developed a LR [ 31 ] model to predict COVID-19 from 3 blood count components (absolute neutrophil count, absolute lymphocyte count, and hematocrit) and patient sex. The model was trained with dataset of 390 patient samples (with 33 confirmed positive) collected from Stanford Health Care.…”

Section: Related Workmentioning

confidence: 99%

Ensemble learning model for diagnosing COVID-19 from routine blood tests

AlJame

Ahmad

Imtiaz

et al. 2020

Informatics in Medicine Unlocked

108

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Background and objectives The pandemic of novel coronavirus disease 2019 (COVID-19) has severely impacted human society with a massive death toll worldwide. There is an urgent need for early and reliable screening of COVID-19 patients to provide better and timely patient care and to combat the spread of the disease. In this context, recent studies have reported some key advantages of using routine blood tests for initial screening of COVID-19 patients. In this article, first we present a review of the emerging techniques for COVID-19 diagnosis using routine laboratory and/or clinical data. Then, we propose ERLX which is an ensemble learning model for COVID-19 diagnosis from routine blood tests. Method The proposed model uses three well-known diverse classifiers, extra trees, random forest and logistic regression, which have different architectures and learning characteristics at the first level, and then combines their predictions by using a second level extreme gradient boosting (XGBoost) classifier to achieve a better performance. For data preparation, the proposed methodology employs a KNNImputer algorithm to handle null values in the dataset, isolation forest (iForest) to remove outlier data, and a synthetic minority oversampling technique (SMOTE) to balance data distribution. For model interpretability, features importance are reported by using the SHapley Additive exPlanations (SHAP) technique. Results The proposed model was trained and evaluated by using a publicly available data set from Albert Einstein Hospital in Brazil, which consisted of 5,644 data samples with 559 confirmed COVID-19 cases. The ensemble model achieved outstanding performance with an overall accuracy of 99.88% [95% CI: 99.6 - 100], AUC of 99.38% [95% CI: 97.5 - 100], a sensitivity of 98.72% [95% CI: 94.6 - 100] and a specificity of 99.99% [95% CI: 99.99- 100]. Discussion The proposed model revealed better performance when compared against existing state-of-the-art studies [ 3 , 22 , 56 , 71 ] for the same set of features employed by them. As compared to the best performing Bayes Net model [ 22 ] average accuracy of 95.159%, ERLX achieved an average accuracy of 99.94%. In comparison with AUC of 85% reported by the SVM model [ 56 ], ERLX obtained AUC of 99.77% in addition to improvements in sensitivity, and specificity. As compared with ER-COV model [ 71 ] average sensitivity of 70.25% and specificity of 85.98%, ERLX model achieved sensitivity of 99.47% and specificity of 99.99%. The ERLX model obtained considerable higher score as compared with ANN model [ 3 ] in all performance metrics. Therefore, the model presented is robust and can be deployed for reliable early and rapid screening of COVID-19 patients.

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A predictive tool for identification of SARS-CoV-2 PCR-negative emergency department patients using routine test results

Cited by 51 publications

References 7 publications

Development, evaluation, and validation of machine learning models for COVID-19 detection based on routine blood tests

Development, evaluation, and validation of machine learning models for COVID-19 detection based on routine blood tests

Development, evaluation, and validation of machine learning models for COVID-19 detection based on routine blood tests

Ensemble learning model for diagnosing COVID-19 from routine blood tests

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