Development and validation of a clinical score to estimate progression to severe or critical state in COVID-19 pneumonia hospitalized patients

Gudé, Francisco; Riveiro, Vanessa; Rodríguez-Núñez, Nuria; Ricoy, Jorge; Lado-Baleato, Óscar; Lourido, Tamara; Rábade, Carlos; Lama, Adriana; Casal, Ana; Abelleira-Paris, Romina; Ferreiro, Lucía; Suárez-Antelo, Juan; Toubes, María E.; Pou, Cristina; Taboada-Muñiz, Manuel; Calle-Velles, Felipe; Mayán-Conesa, Placido; Molino, María Luisa Pérez del; Galbán-Rodríguez, Cristóbal; Escudero, J. Álvarez; Beceiro-Abad, Carmen; Molinos-Castro, Sonia; Agra-Vázquez, Néstor; Pazo-Núñez, María; Páez-Guillán, Emilio; Varela-García, Pablo Manuel; Martínez-Rey, Carmen; Pernas-Pardavila, Hadrián; Domínguez-Santalla, María Jesús; Vidal-Vázquez, Martín; Marques-Afonso, Ana T.; González‐Quintela, Arturo; González-Juanàtey, José Ramón; Pose, Antonio; Valdés, Luís

doi:10.1038/s41598-020-75651-z

Cited by 24 publications

(24 citation statements)

References 26 publications

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“…In this retrospective analysis of 1,108 hospitalized COVID-19 patients at an epicenter during the first wave of the pandemic, we were able to identify risk factors and associations with mortality. Consistent with previous reports, we observed that increasing age and certain pre-existing medical conditions relating to major organ systems were associated with increased chance for death [10,11].…”

Section: Discussionsupporting

confidence: 92%

Predictors for inpatient mortality during the first wave of the SARS-CoV-2 pandemic: A retrospective analysis

et al. 2021

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Background The coronavirus disease 2019 (COVID-19) pandemic overwhelmed healthcare systems, highlighting the need to better understand predictors of mortality and the impact of medical interventions. Methods This retrospective cohort study examined data from every patient who tested positive for COVID-19 and was admitted to White Plains Hospital between March 9, 2020, and June 3, 2020. We used binomial logistic regression to analyze data for all patients, and propensity score matching for those treated with hydroxychloroquine and convalescent plasma (CP). The primary outcome of interest was inpatient mortality. Results 1,108 admitted patients with COVID-19 were available for analysis, of which 124 (11.2%) were excluded due to incomplete data. Of the 984 patients included, 225 (22.9%) died. Risk for death decreased for each day later a patient was admitted [OR 0.970, CI 0.955 to 0.985; p < 0.001]. Elevated initial C-reactive protein (CRP) value was associated with a higher risk for death at 96 hours [OR 1.007, 1.002 to 1.012; p = 0.006]. Hydroxychloroquine and CP administration were each associated with increased mortality [OR 3.4, CI 1.614 to 7.396; p = 0.002, OR 2.8560, CI 1.361 to 6.160; p = 0.006 respectively]. Conclusions Elevated CRP carried significant odds of early death. Hydroxychloroquine and CP were each associated with higher risk for death, although CP was without titers and was administered at a median of five days from admission. Randomized or controlled studies will better describe the impact of CP. Mortality decreased as the pandemic progressed, suggesting that institutional capacity for dynamic evaluation of process and outcome measures may benefit COVID-19 survival.

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Section: Discussionsupporting

confidence: 92%

Predictors for inpatient mortality during the first wave of the SARS-CoV-2 pandemic: A retrospective analysis

et al. 2021

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“…For comparison with CT we considered several established blood biomarkers of severity of COVID-19 pneumonia including serum LDH, D-dimer, CRP and lymphocyte counts that can predict ICU admission (10,12,13,15,16,23). As well, we considered the P/F ratio which is a marker of non-responsive hypoxiemia in these patients (10,14,16).…”

Section: Discussionmentioning

confidence: 99%

“…Hence prediction of ICU admission has been frequently investigated in studies addressing diagnostic and prognostic models for COVID-19 (5). Among the variables potentially useful for such a purpose, clinical features yielded mixed results (6)(7)(8)(9)(10)(11)(12), while blood laboratory and arterial gas analysis features generally improved the overall prediction capacity (8,10,11,(13)(14)(15)(16).…”

Section: Introductionmentioning

confidence: 99%

The incremental value of computed tomography of COVID-19 pneumonia in predicting ICU admission

Bartolucci

Benelli

Betti

et al. 2021

Preprint

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RationaleTriage is crucial for patient’s management and estimation of the required Intensive Care Unit (ICU) beds is fundamental for Health Systems during the COVID-19 pandemic.ObjectiveTo assess whether chest Computed Tomography (CT) of COVID-19 pneumonia has an incremental role in predicting patient’s admission to ICU.MethodsWe performed volumetric and texture analysis of the areas of the affected lung in CT of 115 outpatients with COVID-19 infection presenting to the Emergency Room with dyspnea and unresponsive hypoxyemia. Admission blood laboratory including lymphocyte count, serum lactate dehydrogenase, D-dimer and C-Reactive Protein and the ratio between the arterial partial pressure of oxygen and inspired oxygen were collected. By calculating the areas under the receiver-operating characteristic curves (AUC), we compared the performance of blood laboratory-arterial gas analyses features alone and combined with the CT features in two hybrid models (Hybrid radiological and Hybrid radiomics)for predicting ICU admission. Following a machine learning approach, 63 patients were allocated to the training and 52 to the validation set.Measurements and Main ResultsTwenty-nine (25%) of patients were admitted to ICU. The Hybrid radiological model comprising the lung %consolidation performed significantly (p=0.04) better in predicting ICU admission in the validation (AUC=0.82; 95%Confidence Interval 0.68-0.95) set than the blood laboratory-arterial gas analyses features alone (AUC=0.71; 95%Confidence Interval 0.56-0.86). A risk calculator for ICU admission was derived and is available at:https://github.com/cgplab/covidappConclusionsThe volume of the consolidated lung in CT of patients with COVID-19 pneumonia has a mild but significant incremental value in predicting ICU admission.

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“…For comparison with CT we considered several established blood biomarkers of severity of COVID-19 pneumonia including serum LDH, D-dimer, CRP and lymphocyte counts that can predict ICU admission 10 , 12 , 13 , 15 , 16 , 24 . As well, we considered the P/F ratio which is a marker of non-responsive hypoxiemia in these patients 10 , 14 , 16 .…”

Section: Discussionmentioning

confidence: 99%

“…Hence prediction of ICU admission has been frequently investigated in studies addressing diagnostic and prognostic models for COVID-19 5 . Among the variables potentially useful for such a purpose, clinical features yielded mixed results 6 – 12 , while blood laboratory and arterial gas analysis features generally improved the overall prediction capacity 8 , 10 , 11 , 13 – 16 .…”

Section: Introductionmentioning

confidence: 99%

The incremental value of computed tomography of COVID-19 pneumonia in predicting ICU admission

Bartolucci

Benelli

Betti

et al. 2021

Sci Rep

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Triage is crucial for patient’s management and estimation of the required intensive care unit (ICU) beds is fundamental for health systems during the COVID-19 pandemic. We assessed whether chest computed tomography (CT) of COVID-19 pneumonia has an incremental role in predicting patient’s admission to ICU. We performed volumetric and texture analysis of the areas of the affected lung in CT of 115 outpatients with COVID-19 infection presenting to the emergency room with dyspnea and unresponsive hypoxyemia. Admission blood laboratory including lymphocyte count, serum lactate dehydrogenase, D-dimer and C-reactive protein and the ratio between the arterial partial pressure of oxygen and inspired oxygen were collected. By calculating the areas under the receiver-operating characteristic curves (AUC), we compared the performance of blood laboratory-arterial gas analyses features alone and combined with the CT features in two hybrid models (Hybrid radiological and Hybrid radiomics)for predicting ICU admission. Following a machine learning approach, 63 patients were allocated to the training and 52 to the validation set. Twenty-nine (25%) of patients were admitted to ICU. The Hybrid radiological model comprising the lung %consolidation performed significantly (p = 0.04) better in predicting ICU admission in the validation (AUC = 0.82; 95% confidence interval 0.73–0.97) set than the blood laboratory-arterial gas analyses features alone (AUC = 0.71; 95% confidence interval 0.56–0.86). A risk calculator for ICU admission was derived and is available at: https://github.com/cgplab/covidapp. The volume of the consolidated lung in CT of patients with COVID-19 pneumonia has a mild but significant incremental value in predicting ICU admission.

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Development and validation of a clinical score to estimate progression to severe or critical state in COVID-19 pneumonia hospitalized patients

Cited by 24 publications

References 26 publications

Predictors for inpatient mortality during the first wave of the SARS-CoV-2 pandemic: A retrospective analysis

Predictors for inpatient mortality during the first wave of the SARS-CoV-2 pandemic: A retrospective analysis

The incremental value of computed tomography of COVID-19 pneumonia in predicting ICU admission

The incremental value of computed tomography of COVID-19 pneumonia in predicting ICU admission

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