Dynamic profile and clinical implications of hematological parameters in hospitalized patients with coronavirus disease 2019

Ding, Xiurong; Yu, Yanhua; Lu, Bichao; Huo, J M; Chen, Ming; Kang, Yu; Lou, Jinli; Liu, Zhizhong

doi:10.1515/cclm-2020-0411

Cited by 72 publications

(94 citation statements)

References 27 publications

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“…It is possible that differences are also present across the complete data spectrum, even though they are not clearly visualized with PDF data. These results are in accordance to previous reports of changes in laboratory findings in COVID-19 infected patients, where conditions as leukopenia, lymphocytopenia and thrombopenia were reported (Fan et al, 2020;Ding et al, 2020). It is important to highlight that data analysis is not sufficient to characterize clinical hematological alterations in evaluated patients (when compared to demographic hematologic parameters data), once data was normalized for the evaluated sample set only.…”

Section: Discussionsupporting

confidence: 90%

Hemogram data as a tool for decision-making in COVID-19 management: applications to resource scarcity scenarios

Avila

Kahmann²,

Alho

et al. 2020

PeerJ

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Background COVID-19 pandemics has challenged emergency response systems worldwide, with widespread reports of essential services breakdown and collapse of health care structure. A critical element involves essential workforce management since current protocols recommend release from duty for symptomatic individuals, including essential personnel. Testing capacity is also problematic in several countries, where diagnosis demand outnumbers available local testing capacity. Purpose This work describes a machine learning model derived from hemogram exam data performed in symptomatic patients and how they can be used to predict qRT-PCR test results. Methods Hemogram exams data from 510 symptomatic patients (73 positives and 437 negatives) were used to model and predict qRT-PCR results through Naïve-Bayes algorithms. Different scarcity scenarios were simulated, including symptomatic essential workforce management and absence of diagnostic tests. Adjusts in assumed prior probabilities allow fine-tuning of the model, according to actual prediction context. Results Proposed models can predict COVID-19 qRT-PCR results in symptomatic individuals with high accuracy, sensitivity and specificity, yielding a 100% sensitivity and 22.6% specificity with a prior of 0.9999; 76.7% for both sensitivity and specificity with a prior of 0.2933; and 0% sensitivity and 100% specificity with a prior of 0.001. Regarding background scarcity context, resources allocation can be significantly improved when model-based patient selection is observed, compared to random choice. Conclusions Machine learning models can be derived from widely available, quick, and inexpensive exam data in order to predict qRT-PCR results used in COVID-19 diagnosis. These models can be used to assist strategic decision-making in resource scarcity scenarios, including personnel shortage, lack of medical resources, and testing insufficiency.

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Section: Discussionsupporting

confidence: 90%

Hemogram data as a tool for decision-making in COVID-19 management: applications to resource scarcity scenarios

Avila

Kahmann²,

Alho

et al. 2020

PeerJ

View full text Add to dashboard Cite

show abstract

“…In consistent with our study, Yang et al suggested NLR ≥3.3 as an indicator with clinical symptoms to change disease status from mild to severe disease [24]. NLR is reported to be used as an early indicator for the severe disease, similar to previous findings from a different patient population [24,25].…”

supporting

confidence: 92%

Do initial hematologic indices predict the severity of COVID-19 patients?

Aşan¹,

Üstündağ²,

Koca³

et al. 2021

Turk J Med Sci

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Background/aim: In this study, we aimed to evaluate the initial hematological findings analyzed on admission in confirmed COVID-19 patients who were transferred to the intensive care unit (ICU), to predict possible hematological indices. Materials and methods: Initial neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), monocyte to lymphocyte ratio (MLR), red cell distribution width to platelet ratio (RPR), mean platelet volume to platelet ratio, and lymphocyte multiplied by platelet count (LYM x PLT), of 695 patients with laboratory-confirmed COVID-19 were investigated and compared between the mild/moderate and severe groups. Results: The proportion of COVID-19 cases admitted to ICU was 3.9%. The median age of patients admitted to ICU was significantly higher than those who were not; (68.5 (interquartile range (IQR); 21.5) years vs. 41.0 (IQR; 15.7) years; p <0.001). Severe cases had higher NLR (6.6 vs 2.4; P <0.001), and MLR (0.40 vs 0.28; P=0.004) and lower PLR (180.0 vs 129.0; P <0.001) compared to that of mild or moderate patients. Among all of the parameters, the ROC curve of NLR gave us the best ability to distinguish serious patients at an early stage (AUC = 0. 819, 95% confidence interval 0.729-0.910; p<0.001). Conclusion: These data showed that age, initial NLR, PLR, and LYM x PLT were associated with the severity of COVID-19 disease and patients' need for the ICU. Therefore, initial hemogram parameters may be essential to predict the prognosis of COVID-19 patients.

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Section: /14supporting

confidence: 91%

Peer Review #1 of "Hemogram data as a tool for decision-making in COVID-19 management: applications to resource scarcity scenarios (v0.1)"

2020

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Background: COVID-19 pandemics has challenged emergency response systems worldwide, with widespread reports of essential services breakdown and collapse of health care structure. A critical element involves essential workforce management since current protocols recommend release from duty for symptomatic individuals, including essential personnel. Testing capacity is also problematic in several countries, where diagnosis demand outnumbers available local testing capacity. Purpose: This work describes a machine learning model derived from hemogram exam data performed in symptomatic patients and how they can be used to predict qRT-PCR test results. Methods: Hemogram exams data from 510 symptomatic patients (73 positives and 437 negatives) were used to model and predict qRT-PCR results through Naïve-Bayes algorithms. Different scarcity scenarios were simulated, including symptomatic essential workforce management and absence of diagnostic tests. Adjusts in assumed prior probabilities allow fine-tuning of the model, according to actual prediction context. Results: Proposed models can predict COVID-19 qRT-PCR results in symptomatic individuals with high accuracy, sensitivity and specificity, yielding a 100% sensitivity and 22.6% specificity with a prior of 0.9999; 76.7% for both sensitivity and specificity with a prior of 0.2933; and 0% sensitivity and 100% specificity with a prior of 0.001. Regarding background scarcity context, resources allocation can be significantly improved when model-based patient selection is observed, compared to random choice. Conclusions: Machine learning models can be derived from widely available, quick, and inexpensive exam data in order to predict qRT-PCR results used in COVID-19 diagnosis. These models can be used to assist strategic decision-making in resource scarcity scenarios, including personnel shortage, lack of medical resources, and testing insufficiency.

show abstract

Dynamic profile and clinical implications of hematological parameters in hospitalized patients with coronavirus disease 2019

Cited by 72 publications

References 27 publications

Hemogram data as a tool for decision-making in COVID-19 management: applications to resource scarcity scenarios

Hemogram data as a tool for decision-making in COVID-19 management: applications to resource scarcity scenarios

Do initial hematologic indices predict the severity of COVID-19 patients?

Peer Review #1 of "Hemogram data as a tool for decision-making in COVID-19 management: applications to resource scarcity scenarios (v0.1)"

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