Dataset Information

CBCovid19EC: A dataset complete blood count and PCR test for COVID-19 detection in Ecuadorian population.

ABSTRACT: In this work, we present the complete blood count data and PCR test results of a population of Ecuadorians from different provinces, primarily residing in the Andean region, especially in Quito. PCR was the standard test to detect Covid-19 during the pandemic since 2020. The data were obtained between March 1st and August 12th, 2021. Segurilab and Previne Salud laboratories performed the tests. The dataset contains about 400 clinical cases. Each patient agreed to participate in the study by sharing the results of their PCR (reverse transcription polymerase chain reaction) tests and CBC (complete blood count). CBC test measured several components and features of the blood, including red blood cells, white blood cells, hemoglobin, hematocrit, and platelets. The shared data are intended to provide researchers with input to analyze various events associated with the diagnosis of Covid-19 linked to potential diseases identified in the components measured in the CBC test. These data are helpful for pattern analysis of blood components in modeling prediction and clustering problems. The components measured in the complete blood count and CRP together can be helpful for the analysis of different medical conditions using machine learning algorithms.

SUBMITTER: Ordonez-Avila R

PROVIDER: S-EPMC10023941 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature

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CBCovid19EC: A dataset complete blood count and PCR test for COVID-19 detection in Ecuadorian population.

Ordoñez-Avila R R Parraga-Alava J J Hormaza J Meza JM Vaca-Cárdenas L L Portmann E E Terán L L Dorn M M

Data in brief 20230301

In this work, we present the complete blood count data and PCR test results of a population of Ecuadorians from different provinces, primarily residing in the Andean region, especially in Quito. PCR was the standard test to detect Covid-19 during the pandemic since 2020. The data were obtained between March 1st and August 12th, 2021. Segurilab and Previne Salud laboratories performed the tests. The dataset contains about 400 clinical cases. Each patient agreed to participate in the ...[more]

PMID: 36942101

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Project description:BackgroundBiomarkers, such as leukocyte count, C-reactive protein (CRP), and procalcitonin (PCT), have been commonly used to predict the occurrence of life-threatening bacteremia and provide prognostic information, given the need for prompt intervention. However, such diagnosis methods require much time and money. Therefore, we propose a method with a high prediction capability using machine learning (ML) models based on complete blood count (CBC) and differential leukocyte count (DC) and compare its performance with traditional CRP or PCT biomarker methods and those of models incorporating CRP or PCT biomarkers.MethodsWe collected 366,586 daily blood culture (BC) results, of which 350,775 (93.2%), 308,803 (82.1%), and 23,912 (6.4%) cases were issued CBC/DC (CBC/DC group), CRP with CBC/DC (CRP&CBC/DC group), and PCT with CBC/DC (PCT&CBC/DC group), respectively. For the ML methods, conventional logistic regression and random forest models were selected, trained, applied, and validated for each group. Fivefold validation and prediction capability were also evaluated and reported.ResultsOverall, the ML methods, such as the random forest model, demonstrated promising performances. When trained with CBC/DC data, it achieved an area under the ROC curve (AUC) of 0.802, which is superior to the prediction conventionally made with CRP/PCT levels (0.699/0.731). Upon evaluating the performance enhanced by incorporating CRP or PCT biomarkers, it reported no substantial AUC increase with the addition of either CRP or PCT to CBC/DC data, which suggests the predicting power and applicability of using only CBC/DC data. Moreover, it showed competitive prognostic capability compared to the PCT test with similar all-cause in-hospital mortality (45.10% vs. 47.40%) and overall median survival time (27 vs. 25 days).ConclusionsThe ML models using only CBC/DC data yielded more accurate bacteremia predictions compared to those by methods using CRP and PCT data and reached similar prognostic performance as by PCT data. Thus, such models are potentially complementary and competitive with traditional CRP and PCT biomarkers for conducting and guiding antibiotic usage.

Dataset Information

CBCovid19EC: A dataset complete blood count and PCR test for COVID-19 detection in Ecuadorian population.

Publications

<i>CBC</i>ovid19EC: A dataset complete blood count and PCR test for COVID-19 detection in Ecuadorian population.

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