Combined Model of Quantitative Evaluation of Chest Computed Tomography and Laboratory Values for Assessing the Prognosis of Coronavirus Disease 2019

Abstract

Purpose To assess the prognostic power of quantitative analysis of chest CT, laboratory values, and their combination in COVID-19 pneumonia.

Materials and Methods Retrospective analysis of patients with PCR-confirmed COVID-19 pneumonia and chest CT performed between March 07 and November 13, 2020. Volume and percentage (PO) of lung opacifications and mean HU of the whole lung were quantified using prototype software. 13 laboratory values were collected. Negative outcome was defined as death, ICU admittance, mechanical ventilation, or extracorporeal membrane oxygenation. Positive outcome was defined as care in the regular ward or discharge. Logistic regression was performed to evaluate the prognostic value of CT parameters and laboratory values. Independent predictors were combined to establish a scoring system for prediction of prognosis. This score was validated on a separate validation cohort.

Results 89 patients were included for model development between March 07 and April 27, 2020 (mean age: 60.3 years). 38 patients experienced a negative outcome. In univariate regression analysis, all quantitative CT parameters as well as C-reactive protein (CRP), relative lymphocyte count (RLC), troponin, and LDH were associated with a negative outcome. In a multivariate regression analysis, PO, CRP, and RLC were independent predictors of a negative outcome. Combination of these three values showed a strong predictive value with a C-index of 0.87. A scoring system was established which categorized patients into 4 groups with a risk of 7 %, 30 %, 67 %, or 100 % for a negative outcome. The validation cohort consisted of 28 patients between May 5 and November 13, 2020. A negative outcome occurred in 6 % of patients with a score of 0, 50 % with a score of 1, and 100 % with a score of 2 or 3.

Conclusion The combination of PO, CRP, and RLC showed a high predictive value for a negative outcome. A 4-point scoring system based on these findings allows easy risk stratification in the clinical routine and performed exceptionally in the validation cohort.

Key Points: 

• A high PO is associated with an unfavorable outcome in COVID-19

• PO, CRP, and RLC are independent predictors of an unfavorable outcome, and their combination has strong predictive power

• A 4-point scoring system based on these values allows quick risk stratification in a clinical setting

Citation Format

• Scharf G, Meiler S, Zeman F et al. Combined Model of Quantitative Evaluation of Chest Computed Tomography and Laboratory Values for Assessing the Prognosis of Coronavirus Disease 2019. Fortschr Röntgenstr 2022; 194: 737 – 746

Zusammenfassung

Ziel Evaluation der prognostischen Aussagekraft der quantitativen Auswertung von Thorax-CTs, Laborwerten und deren Kombination bei COVID-19.

Material und Methoden Retrospektive Auswertung von Patienten mit einer mittels PCR bestätigten COVID-19-Pneumonie und Thorax-CT zwischen 07.03.20 und 13.11.20. Mithilfe eines Software-Prototypen wurden das Volumen und der prozentuale Anteil (PO) von Dichteanhebungen der Lunge und die durchschnittlichen HU der gesamten Lunge quantifiziert. 13 Laborwerte wurden erhoben. Als negatives Outcome wurden definiert: Tod, Einweisung auf Intensivstation, mechanische Beatmung oder extrakorporale Membranoxygenierung. Behandlung auf Normalstation oder Entlassung wurden als positives Outcome definiert. Mittels logistischer Regression wurde die prognostische Wertigkeit der CT-Parameter und der Laborwerte bestimmt. Auf Grundlage der unabhängigen Prädiktoren wurde ein Scoring-System zur Vorhersage der Prognose entwickelt. Der Score wurde in einer separaten Validierungskohorte überprüft.

Ergebnisse 89 Patienten wurden zwischen 07.03.20 und 27.04.20 in die Hauptkohorte eingeschlossen (Durchschnittsalter 60,3 Jahre), 38 Patienten hatten ein negatives Outcome. In der univariaten Regressionsanalyse waren alle CT-Parameter, CRP, relative Lymphozytenzahl (RLC), Troponin und LDH mit einem negativen Outcome assoziiert. In der multivariaten Regressionsanalyse waren PO, CRP und RLC unabhängige Prädiktoren eines negativen Outcomes. Die Kombination dieser Werte zeigte eine starke Vorhersagekraft mit einem C-Index von 0,87. Eine Punkteskala basierend auf diesen Werten ermöglichte die Einteilung der Patienten in 4 Gruppen mit einem Risiko von 7 %, 30 %, 67 % und 100 % für ein negatives Outcome. Die Validierungskohorte bestand aus 28 Patienten zwischen 05.05.20 und 13.11.20. Ein negatives Outcome hatten 6 % der Patienten mit 0 Punkten, 50 % mit 1 Punkt und 100 % bei 2 oder 3 Punkten.

Schlussfolgerung Die Kombination aus PO, CRP und RLC zeigte eine sehr hohe Vorhersagekraft für ein negatives Outcome. Eine auf diesen Werten basierende 4-Punkte-Skala erleichtert die Risikostratifizierung im klinischen Alltag und zeigte eine gute Übereinstimmung in der Validierungskohorte.

Kernaussagen: 

• Eine hohe PO im Thorax-CT ist mit einem negativen Outcome bei COVID-19 assoziiert.

• PO, CRP und RLC sind unabhängige Prädiktoren eines negativen Outcomes und ihre Kombination hat eine hohe Vorhersagekraft.

• Eine 4-Punkte-Skala, die auf diesen Werten basiert, erlaubt eine schnelle Risikostratifizierung im klinischen Alltag.

Key words

COVID-19 - computed X-Ray tomography - pneumonia - artificial intelligence - lung volume measurements - laboratory tests

Publication History

Received: 08 May 2021

Accepted: 20 December 2021

Article published online:
10 March 2022

• References

• 1 American College of Radiology. ACR Recommendations for the use of Chest Radiography and Computed Tomography (CT) for Suspected COVID-19 Infection (28.12.2020). Im Internet (Stand: 28.12.2020): https://www.acr.org/Advocacy-and-Economics/ACR-Position-Statements/Recommendations-for-Chest-Radiography-and-CT-for-Suspected-COVID19-Infection
• 2 Li X, Fang X, Bian Y. et al. Comparison of chest CT findings between COVID-19 pneumonia and other types of viral pneumonia: a two-center retrospective study. Eur Radiol 2020; 30: 5470-5478 DOI: 10.1007/s00330-020-06925-3.
• 3 Roberto G, Roberta F, Paola BM. et al. Coronavirus disease 2019 (COVID-19) in Italy: features on chest computed tomography using a structured report system. Scientific reports 2020; 10: 17236 DOI: 10.1038/s41598-020-73788-5.
• 4 Zhou Z, Guo D, Li C. et al. Coronavirus disease 2019: initial chest CT findings. Eur Radiol 2020; 30: 4398-4406 DOI: 10.1007/s00330-020-06816-7.
• 5 Feng Z, Yu Q, Yao S. et al. Early prediction of disease progression in COVID-19 pneumonia patients with chest CT and clinical characteristics. Nat Commun 2020; 11: 4968 DOI: 10.1038/s41467-020-18786-x.
• 6 Francone M, Iafrate F, Masci GM. et al. Chest CT score in COVID-19 patients: correlation with disease severity and short-term prognosis. Eur Radiol 2020; DOI: 10.1007/s00330-020-07033-y.
• 7 Jalaber C, Lapotre T, Morcet-Delattre T. et al. Chest CT in COVID-19 pneumonia: A review of current knowledge. Diagnostic and interventional imaging 2020; 101: 431-437 DOI: 10.1016/j.diii.2020.06.001.
• 8 Gieraerts C, Dangis A, Janssen L. et al. Prognostic Value and Reproducibility of AI-assisted Analysis of Lung Involvement in COVID-19 on Low-Dose Submillisievert Chest CT: Sample Size Implications for Clinical Trials. Radiology: Cardiothoracic Imaging 2020; 2: e200441 DOI: 10.1148/ryct.2020200441.
• 9 Grodecki K, Lin A, Cadet S. et al. Quantitative Burden of COVID-19 Pneumonia on Chest CT Predicts Adverse Outcomes: A Post-Hoc Analysis of a Prospective International Registry. Radiology: Cardiothoracic Imaging 2020; 2: e200389 DOI: 10.1148/ryct.2020200389.
• 10 Colombi D, Bodini FC, Petrini M. et al. Well-aerated Lung on Admitting Chest CT to Predict Adverse Outcome in COVID-19 Pneumonia. Radiology 2020; 296: E86-E96 DOI: 10.1148/radiol.2020201433.
• 11 Liu F, Zhang Q, Huang C. et al. CT quantification of pneumonia lesions in early days predicts progression to severe illness in a cohort of COVID-19 patients. Theranostics 2020; 10: 5613-5622 DOI: 10.7150/thno.45985.
• 12 Shen C, Yu N, Cai S. et al. Quantitative computed tomography analysis for stratifying the severity of Coronavirus Disease 2019. Journal of pharmaceutical analysis 2020; 10: 123-129 DOI: 10.1016/j.jpha.2020.03.004.
• 13 Lanza E, Muglia R, Bolengo I. et al. Quantitative chest CT analysis in COVID-19 to predict the need for oxygenation support and intubation. Eur Radiol 2020; DOI: 10.1007/s00330-020-07013-2.
• 14 Wang Y, Chen Y, Wei Y. et al. Quantitative analysis of chest CT imaging findings with the risk of ARDS in COVID-19 patients: a preliminary study. Annals of translational medicine 2020; 8: 594 DOI: 10.21037/atm-20-3554.
• 15 Yu Q, Wang Y, Huang S. et al. Multicenter cohort study demonstrates more consolidation in upper lungs on initial CT increases the risk of adverse clinical outcome in COVID-19 patients. Theranostics 2020; 10: 5641-5648 DOI: 10.7150/thno.46465.
• 16 Meiler S, Schaible J, Poschenrieder F. et al. Can CT performed in the early disease phase predict outcome of patients with COVID 19 pneumonia? Analysis of a cohort of 64 patients from Germany. European journal of radiology 2020; 131: 109256 DOI: 10.1016/j.ejrad.2020.109256.
• 17 Tan L, Wang Q, Zhang D. et al. Lymphopenia predicts disease severity of COVID-19: a descriptive and predictive study. Signal Transduct Target Ther 2020; 5: 33 DOI: 10.1038/s41392-020-0148-4.
• 18 Yu H-H, Qin C, Chen M. et al. D-dimer level is associated with the severity of COVID-19. Thromb Res 2020; 195: 219-225 DOI: 10.1016/j.thromres.2020.07.047.
• 19 Ghahramani S, Tabrizi R, Lankarani KB. et al. Laboratory features of severe vs. non-severe COVID-19 patients in Asian populations: a systematic review and meta-analysis. European journal of medical research 2020; 25: 30 DOI: 10.1186/s40001-020-00432-3.
• 20 Bonetti G, Manelli F, Patroni A. et al. Laboratory predictors of death from coronavirus disease 2019 (COVID-19) in the area of Valcamonica, Italy. Clinical chemistry and laboratory medicine 2020; 58: 1100-1105 DOI: 10.1515/cclm-2020-0459.
• 21 Zheng Y, Xiao A, Yu X. et al. Development and Validation of a Prognostic Nomogram Based on Clinical and CT Features for Adverse Outcome Prediction in Patients with COVID-19. Korean journal of radiology 2020; 21: 1007-1017 DOI: 10.3348/kjr.2020.0485.
• 22 Schalekamp S, Huisman M, van Dijk RA. et al. Model-based Prediction of Critical Illness in Hospitalized Patients with COVID-19. Radiology 2021; 298: E46-E54 DOI: 10.1148/radiol.2020202723.
• 23 Chaganti S, Grenier P, Balachandran A. et al. Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT. Radiology: Artificial Intelligence 2020; 2: e200048 DOI: 10.1148/ryai.2020200048.
• 24 Harrel jr FE. Regresion modeling strategies. Springer Series in Statistics. 2. Aufl.. New York: Springer; 2015
• 25 Zhou F, Yu T, Du R. et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. The Lancet 2020; 395: 1054-1062 DOI: 10.1016/S0140-6736(20)30566-3.
• 26 Bonanad C, García-Blas S, Tarazona-Santabalbina F. et al. The Effect of Age on Mortality in Patients With COVID-19: A Meta-Analysis With 611583 Subjects. J Am Med Dir Assoc 2020; 21: 915-918 DOI: 10.1016/j.jamda.2020.05.045.
• 27 Heussel CP, Kappes J, Hantusch R. et al. Contrast enhanced CT-scans are not comparable to non-enhanced scans in emphysema quantification. European journal of radiology 2010; 74: 473-478 DOI: 10.1016/j.ejrad.2009.03.023.
• 28 Mascalchi M, Camiciottoli G, Diciotti S. Lung densitometry: why, how and when. J Thorac Dis 2017; 9: 3319-3345 DOI: 10.21037/jtd.2017.08.17.
• 29 Coxson HO. Sources of variation in quantitative computed tomography of the lung. Journal of Thoracic Imaging 2013; 28: 272-279 DOI: 10.1097/RTI.0b013e31829efbe9.