A COVID-19 Prediction Model from Standard Laboratory Tests and Vital Signs

BACKGROUND Early detection and intervention are the key factors for improving outcomes in COVID-19. OBJECTIVE To detect severity subgroups among COVID-19 patients, based only on clinical data and standard laboratory tests obtained during the assessment at the emergency department. METHODS We applied unsupervised machine learning to a dataset of 853 COVID-19 patients from HM hospitals in Spain. RESULTS From a total of 850 variables, four tests, the serum levels of aspartate transaminase (AST), lactate dehydrogenase (LDH) and C-reactive protein (CRP), and the number of neutrophils, were enough to segregate the entire patient pool into three separate clusters. Further, the percentage of monocytes and lymphocytes and the levels of alanine transaminase (ALT) distinguished the cluster 3 from the other two clusters. The cluster 1 was characterized by the higher mortality rate and higher levels of AST, ALT, LDH, CRP and number of neutrophils, and low percentage of monocytes and lymphocytes. The cluster 2 included patients with a moderate mortality rate and medium levels of the previous laboratory determinations. The cluster 3 was characterized by the lower mortality rate and lower levels of AST, ALT, LDH, CRP and number of neutrophils, and higher percentage of monocytes and lymphocytes. Age, sex, comorbidities, and vital signs did not allow us to separate the three clusters. An online cluster assignment tool can be found at https://g-nec.car.upm-csic.es/COVID19-severity-group-assessment/. CONCLUSIONS A few standard laboratory tests, deemed to be available in all emergency departments, have shown far discriminative power for characterization of severity subgroups among COVID-19 patients.

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Changes in Coagulation in Standard Laboratory Tests and ROTEM in Trauma Patients Between On-Scene and Arrival in the Emergency Department

Anesthesia & Analgesia ◽

10.1213/ane.0000000000000561 ◽

2015 ◽

Vol 120 (3) ◽

pp. 627-635 ◽

Cited By ~ 26

Author(s):

Oliver M. Theusinger ◽

Werner Baulig ◽

Burkhardt Seifert ◽

Stefan M. Müller ◽

Sergio Mariotti ◽

...

Keyword(s):

Emergency Department ◽

Laboratory Tests ◽

Trauma Patients ◽

Standard Laboratory

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Semen Analysis and Other Standard Laboratory Tests

Male Infertility ◽

10.1007/978-1-4471-1029-3_3 ◽

1994 ◽

pp. 37-73 ◽

Cited By ~ 2

Author(s):

D. Mortimer

Keyword(s):

Laboratory Tests ◽

Semen Analysis ◽

Standard Laboratory

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REGULATORY COMPLIANCE AND STANDARD LABORATORY TESTS CAN POTENTIALLY IMPROVE THE PUBLIC HEALTH AND FOOD SAFETY

Advanced Food and Nutritional Sciences ◽

10.21065/advfoonutrsci.4.8 ◽

2020 ◽

Vol 5 ◽

Keyword(s):

Public Health ◽

Food Safety ◽

Laboratory Tests ◽

Regulatory Compliance ◽

Standard Laboratory ◽

The Public

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Validation of a simple hypoplastic constitutive model for overconsolidated clays

Acta Geotechnica ◽

10.1007/s11440-020-01105-5 ◽

2020 ◽

Author(s):

Shun Wang ◽

Wei Wu

Keyword(s):

Laboratory Tests ◽

Constitutive Models ◽

Standard Laboratory ◽

Hypoplastic Model ◽

Proposed Model ◽

Modified Cam Clay ◽

Overconsolidated Clays ◽

Hypoplastic Constitutive Model ◽

Comparison With Experiments ◽

Cam Clay

AbstractHypoplastic constitutive models are able to describe history dependence using a single nonlinear tensorial function with a set of parameters. A hypoplastic model including a structure tensor for consolidation history was introduced in our previous paper (Wang and Wu in Acta Geotechnica, 2020, 10.1007/s11440-020-01000-z). The present paper focuses mainly on the model validation with experiments. This model is as simple as the modified Cam Clay model but with better performance. The model requires five parameters, which are easy to calibrate from standard laboratory tests. In particular, the model is capable of capturing the unloading behavior without introducing loading criteria. Numerical simulations of element tests and comparison with experiments show that the proposed model is able to reproduce the salient features of normally consolidated and overconsolidated clays.

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A classification model for prediction of clinical severity level using qSOFA medical score

Information Discovery and Delivery ◽

10.1108/idd-02-2019-0013 ◽

2020 ◽

Vol 48 (1) ◽

pp. 41-77

Author(s):

Diana Olivia ◽

Ashalatha Nayak ◽

Mamatha Balachandra ◽

Jaison John

Keyword(s):

Prediction Model ◽

Vital Signs ◽

Clinical Severity ◽

Classification Model ◽

Experimental Result ◽

Training Dataset ◽

Severity Level ◽

Content Type ◽

Score Method ◽

Qsofa Score

Purpose The purpose of this study is to develop an efficient prediction model using vital signs and standard medical score systems, which predicts the clinical severity level of the patient in advance based on the quick sequential organ failure assessment (qSOFA) medical score method. Design/methodology/approach To predict the clinical severity level of the patient in advance, the authors have formulated a training dataset that is constructed based on the qSOFA medical score method. Further, along with the multiple vital signs, different standard medical scores and their correlation features are used to build and improve the accuracy of the prediction model. It is made sure that the constructed training set is suitable for the severity level prediction because the formulated dataset has different clusters each corresponding to different severity levels according to qSOFA score. Findings From the experimental result, it is found that the inclusion of the standard medical scores and their correlation along with multiple vital signs improves the accuracy of the clinical severity level prediction model. In addition, the authors showed that the training dataset formulated from the temporal data (which includes vital signs and medical scores) based on the qSOFA medical scoring system has the clusters which correspond to each severity level in qSOFA score. Finally, it is found that RAndom k-labELsets multi-label classification performs better prediction of severity level compared to neural network-based multi-label classification. Originality/value This paper helps in identifying patient' clinical status.

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