Literature DB >> 32500700

Novel coronavirus disease 2019: predicting prognosis with a computed tomography-based disease severity score and clinical laboratory data.

Ali Sabri1, Amir H Davarpanah2, Arash Mahdavi3, Alireza Abrishami4, Mehdi Khazaei5, Saman Heydari5, Reyhane Asgari5, Seyyed Mojtaba Nekooghadam6, Julian Dobranowski7, Morteza Sanei Taheri8.   

Abstract

INTRODUCTION: Currently, there are known contributing factors but no comprehensive methods for predicting the mortality risk or intensive care unit (ICU) admission in patients with novel coronavirus disease 2019 (COVID‑19).
OBJECTIVES: The aim of this study was to explore risk factors for mortality and ICU admission in patients with COVID‑19, using computed tomography (CT) combined with clinical laboratory data. PATIENTS AND METHODS: Patients with polymerase chain reaction-confirmed COVID‑19 (n = 63) from university hospitals in Tehran, Iran, were included. All patients underwent CT examination. Subsequently, a total CT score and the number of involved lung lobes were calculated and compared against collected laboratory and clinical characteristics. Univariable and multivariable proportional hazard analyses were used to determine the association among CT, laboratory and clinical data, ICU admission, and in‑hospital death.
RESULTS: By univariable analysis, in‑hospital mortality was higher in patients with lower oxygen saturation on admission (below 88%), higher CT scores, and a higher number of lung lobes (more than 4) involved with a diffuse parenchymal pattern. By multivariable analysis, in‑hospital mortality was higher in those with oxygen saturation below 88% on admission and a higher number of lung lobes involved with a diffuse parenchymal pattern. The risk of ICU admission was higher in patients with comorbidities (hypertension and ischemic heart disease), arterial oxygen saturation below 88%, and pericardial effusion.
CONCLUSIONS: We can identify factors affecting in‑hospital death and ICU admission in COVID-19. This can help clinicians to determine which patients are likely to require ICU admission and to inform strategic healthcare planning in critical conditions such as the COVID‑19 pandemic.

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Mesh:

Year:  2020        PMID: 32500700     DOI: 10.20452/pamw.15422

Source DB:  PubMed          Journal:  Pol Arch Intern Med        ISSN: 0032-3772


  5 in total

1.  A systematic review and meta-analysis of regional risk factors for critical outcomes of COVID-19 during early phase of the pandemic.

Authors:  Hyung-Jun Kim; Hyeontaek Hwang; Hyunsook Hong; Jae-Joon Yim; Jinwoo Lee
Journal:  Sci Rep       Date:  2021-05-07       Impact factor: 4.379

2.  The challenge of deciding between home-discharge versus hospitalization in COVID-19 patients: The role of initial imaging and clinicolaboratory data.

Authors:  Abolfazl Mozafari; Mojtaba Miladinia; Ali Sabri; Fatemeh Movaseghi; Mehdi Gholamzadeh Baeis
Journal:  Clin Epidemiol Glob Health       Date:  2020-12-03

3.  Risk Factors for the Mortality in Hospitalized Patients with COVID-19: A Brief Report.

Authors:  Ramin Sami; Mohammad-Reza Hajian; Babak Amra; Forogh Soltaninejad; Marjan Mansourian; Sam Mirfendereski; Raheleh Sadegh; Nilufar Khademi; Soheila Jalali; Nafiseh Shokri-Mashhadi
Journal:  Iran J Med Sci       Date:  2021-11

Review 4.  [Korean Clinical Imaging Guidelines for Justification of Diagnostic Imaging Study for COVID-19].

Authors:  Kwang Nam Jin; Kyung-Hyun Do; Bo Da Nam; Sung Ho Hwang; Miyoung Choi; Hwan Seok Yong
Journal:  Taehan Yongsang Uihakhoe Chi       Date:  2022-01-06

5.  An Easy-to-Use Machine Learning Model to Predict the Prognosis of Patients With COVID-19: Retrospective Cohort Study.

Authors:  Hyung-Jun Kim; Deokjae Han; Jeong-Han Kim; Daehyun Kim; Beomman Ha; Woong Seog; Yeon-Kyeng Lee; Dosang Lim; Sung Ok Hong; Mi-Jin Park; JoonNyung Heo
Journal:  J Med Internet Res       Date:  2020-11-09       Impact factor: 5.428

  5 in total

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