Cargando…
Clinical and chest CT features as a predictive tool for COVID-19 clinical progress: introducing a novel semi-quantitative scoring system
OBJECTIVE: Proposing a scoring tool to predict COVID-19 patients’ outcomes based on initially assessed clinical and CT features. METHODS: All patients, who were referred to a tertiary-university hospital respiratory triage (March 27–April 26, 2020), were highly clinically suggestive for COVID-19 and...
Autores principales: | , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7809225/ https://www.ncbi.nlm.nih.gov/pubmed/33449185 http://dx.doi.org/10.1007/s00330-020-07623-w |
_version_ | 1783637075586711552 |
---|---|
author | Salahshour, Faeze Mehrabinejad, Mohammad-Mehdi Nassiri Toosi, Mohssen Gity, Masoumeh Ghanaati, Hossein Shakiba, Madjid Nosrat Sheybani, Sina Komaki, Hamidreza Kolahi, Shahriar |
author_facet | Salahshour, Faeze Mehrabinejad, Mohammad-Mehdi Nassiri Toosi, Mohssen Gity, Masoumeh Ghanaati, Hossein Shakiba, Madjid Nosrat Sheybani, Sina Komaki, Hamidreza Kolahi, Shahriar |
author_sort | Salahshour, Faeze |
collection | PubMed |
description | OBJECTIVE: Proposing a scoring tool to predict COVID-19 patients’ outcomes based on initially assessed clinical and CT features. METHODS: All patients, who were referred to a tertiary-university hospital respiratory triage (March 27–April 26, 2020), were highly clinically suggestive for COVID-19 and had undergone a chest CT scan were included. Those with positive rRT-PCR or highly clinically suspicious patients with typical chest CT scan pulmonary manifestations were considered confirmed COVID-19 for additional analyses. Patients, based on outcome, were categorized into outpatient, ordinary-ward admitted, intensive care unit (ICU) admitted, and deceased; their demographic, clinical, and chest CT scan parameters were compared. The pulmonary chest CT scan features were scaled with a novel semi-quantitative scoring system to assess pulmonary involvement (PI). RESULTS: Chest CT scans of 739 patients (mean age = 49.2 ± 17.2 years old, 56.7% male) were reviewed; 491 (66.4%), 176 (23.8%), and 72 (9.7%) cases were managed outpatient, in an ordinary ward, and ICU, respectively. A total of 439 (59.6%) patients were confirmed COVID-19 cases; their most prevalent chest CT scan features were ground-glass opacity (GGO) (93.3%), pleural-based peripheral distribution (60.3%), and multi-lobar (79.7%), bilateral (76.6%), and lower lobes (RLL and/or LLL) (89.1%) involvement. Patients with lower SpO(2), advanced age, RR, total PI score or PI density score, and diffuse distribution or involvement of multi-lobar, bilateral, or lower lobes were more likely to be ICU admitted/expired. After adjusting for confounders, predictive models found cutoffs of age ≥ 53, SpO(2) ≤ 91, and PI score ≥ 8 (15) for ICU admission (death). A combination of all three factors showed 89.1% and 95% specificity and 81.9% and 91.4% accuracy for ICU admission and death outcomes, respectively. Solely evaluated high PI score had high sensitivity, specificity, and NPV in predicting the outcome as well. CONCLUSION: We strongly recommend patients with age ≥ 53, SpO(2) ≤ 91, and PI score ≥ 8 or even only high PI score to be considered as high-risk patients for further managements and care plans. KEY POINTS: • Chest CT scan is a valuable tool in prioritizing the patients in hospital triage. • A more accurate and novel 35-scale semi-quantitative scoring system was designed to predict the COVID-19 patients’ outcome. • Patients with age ≥ 53, SpO(2) ≤ 91, and PI score ≥ 8 or even only high PI score should be considered high-risk patients. |
format | Online Article Text |
id | pubmed-7809225 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-78092252021-01-15 Clinical and chest CT features as a predictive tool for COVID-19 clinical progress: introducing a novel semi-quantitative scoring system Salahshour, Faeze Mehrabinejad, Mohammad-Mehdi Nassiri Toosi, Mohssen Gity, Masoumeh Ghanaati, Hossein Shakiba, Madjid Nosrat Sheybani, Sina Komaki, Hamidreza Kolahi, Shahriar Eur Radiol Chest OBJECTIVE: Proposing a scoring tool to predict COVID-19 patients’ outcomes based on initially assessed clinical and CT features. METHODS: All patients, who were referred to a tertiary-university hospital respiratory triage (March 27–April 26, 2020), were highly clinically suggestive for COVID-19 and had undergone a chest CT scan were included. Those with positive rRT-PCR or highly clinically suspicious patients with typical chest CT scan pulmonary manifestations were considered confirmed COVID-19 for additional analyses. Patients, based on outcome, were categorized into outpatient, ordinary-ward admitted, intensive care unit (ICU) admitted, and deceased; their demographic, clinical, and chest CT scan parameters were compared. The pulmonary chest CT scan features were scaled with a novel semi-quantitative scoring system to assess pulmonary involvement (PI). RESULTS: Chest CT scans of 739 patients (mean age = 49.2 ± 17.2 years old, 56.7% male) were reviewed; 491 (66.4%), 176 (23.8%), and 72 (9.7%) cases were managed outpatient, in an ordinary ward, and ICU, respectively. A total of 439 (59.6%) patients were confirmed COVID-19 cases; their most prevalent chest CT scan features were ground-glass opacity (GGO) (93.3%), pleural-based peripheral distribution (60.3%), and multi-lobar (79.7%), bilateral (76.6%), and lower lobes (RLL and/or LLL) (89.1%) involvement. Patients with lower SpO(2), advanced age, RR, total PI score or PI density score, and diffuse distribution or involvement of multi-lobar, bilateral, or lower lobes were more likely to be ICU admitted/expired. After adjusting for confounders, predictive models found cutoffs of age ≥ 53, SpO(2) ≤ 91, and PI score ≥ 8 (15) for ICU admission (death). A combination of all three factors showed 89.1% and 95% specificity and 81.9% and 91.4% accuracy for ICU admission and death outcomes, respectively. Solely evaluated high PI score had high sensitivity, specificity, and NPV in predicting the outcome as well. CONCLUSION: We strongly recommend patients with age ≥ 53, SpO(2) ≤ 91, and PI score ≥ 8 or even only high PI score to be considered as high-risk patients for further managements and care plans. KEY POINTS: • Chest CT scan is a valuable tool in prioritizing the patients in hospital triage. • A more accurate and novel 35-scale semi-quantitative scoring system was designed to predict the COVID-19 patients’ outcome. • Patients with age ≥ 53, SpO(2) ≤ 91, and PI score ≥ 8 or even only high PI score should be considered high-risk patients. Springer Berlin Heidelberg 2021-01-15 2021 /pmc/articles/PMC7809225/ /pubmed/33449185 http://dx.doi.org/10.1007/s00330-020-07623-w Text en © European Society of Radiology 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Chest Salahshour, Faeze Mehrabinejad, Mohammad-Mehdi Nassiri Toosi, Mohssen Gity, Masoumeh Ghanaati, Hossein Shakiba, Madjid Nosrat Sheybani, Sina Komaki, Hamidreza Kolahi, Shahriar Clinical and chest CT features as a predictive tool for COVID-19 clinical progress: introducing a novel semi-quantitative scoring system |
title | Clinical and chest CT features as a predictive tool for COVID-19 clinical progress: introducing a novel semi-quantitative scoring system |
title_full | Clinical and chest CT features as a predictive tool for COVID-19 clinical progress: introducing a novel semi-quantitative scoring system |
title_fullStr | Clinical and chest CT features as a predictive tool for COVID-19 clinical progress: introducing a novel semi-quantitative scoring system |
title_full_unstemmed | Clinical and chest CT features as a predictive tool for COVID-19 clinical progress: introducing a novel semi-quantitative scoring system |
title_short | Clinical and chest CT features as a predictive tool for COVID-19 clinical progress: introducing a novel semi-quantitative scoring system |
title_sort | clinical and chest ct features as a predictive tool for covid-19 clinical progress: introducing a novel semi-quantitative scoring system |
topic | Chest |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7809225/ https://www.ncbi.nlm.nih.gov/pubmed/33449185 http://dx.doi.org/10.1007/s00330-020-07623-w |
work_keys_str_mv | AT salahshourfaeze clinicalandchestctfeaturesasapredictivetoolforcovid19clinicalprogressintroducinganovelsemiquantitativescoringsystem AT mehrabinejadmohammadmehdi clinicalandchestctfeaturesasapredictivetoolforcovid19clinicalprogressintroducinganovelsemiquantitativescoringsystem AT nassiritoosimohssen clinicalandchestctfeaturesasapredictivetoolforcovid19clinicalprogressintroducinganovelsemiquantitativescoringsystem AT gitymasoumeh clinicalandchestctfeaturesasapredictivetoolforcovid19clinicalprogressintroducinganovelsemiquantitativescoringsystem AT ghanaatihossein clinicalandchestctfeaturesasapredictivetoolforcovid19clinicalprogressintroducinganovelsemiquantitativescoringsystem AT shakibamadjid clinicalandchestctfeaturesasapredictivetoolforcovid19clinicalprogressintroducinganovelsemiquantitativescoringsystem AT nosratsheybanisina clinicalandchestctfeaturesasapredictivetoolforcovid19clinicalprogressintroducinganovelsemiquantitativescoringsystem AT komakihamidreza clinicalandchestctfeaturesasapredictivetoolforcovid19clinicalprogressintroducinganovelsemiquantitativescoringsystem AT kolahishahriar clinicalandchestctfeaturesasapredictivetoolforcovid19clinicalprogressintroducinganovelsemiquantitativescoringsystem |