tetano
Editor, Senior Moderator
Pol Arch Intern Med
. 2020 Jun 5.
doi: 10.20452/pamw.15422. Online ahead of print.
Novel Coronavirus Disease 2019: Predicting Prognosis by Using a Computed Tomography Severity Score and Clinicolaboratory Data
Ali Sabri, Amir H Davarpanah, Arash Mahdavi, Alireza Abrishami, Mehdi Khazaei, Saman Heydari, Reyhane Asgari, Seyyed Mojtaba Nekooghadam, Julian Dobranowski, Morteza Sanei Taheri
Abstract
Introduction: Currently there are known contributing factors but no comprehensive methods for predicting the risk of mortality or ICU admission in patients with Novel Corona Virus infection (COVID-19).
Objectives: The aim of this study is to explore risk factors of mortality and ICU admission in COVID-19 patients using combined CT and clinicolaboratory data.
Patients and methods: Patients with polymerase chain reaction (PCR) confirmed COVID-19 (N= 63) from University Hospitals in Tehran, Iran were studied. All patients underwent CT examination and a total CT score and the number of involved lung lobes were calculated and compared against collected laboratory and clinical information. Univariable and multivariate proportional hazards analysis were used to determine the relationship between the CT, laboratory and clinical data and ICU admission and in-hospital death.
Results: By univariable analysis, in-hospital mortality was higher in patients with lower O2 saturation on admission (<88%), higher CT scores and higher number of lung lobes (> 4) involved with a diffuse parenchymal pattern. By multivariable analysis, in-hospital mortality was higher in patients with O2 saturation below 88% on admission and a higher number of lung lobes involved with diffuse parenchymal pattern. The risk of ICU admission was higher with comorbidities (hypertension and ischemic heart disease), SaO2 below 88% and pericardial effusion.
Conclusions: We can identify factors affecting in-hospital death and ICU admission in COVID-19. This can help to determine which patients are likely to require ICU admission and to help inform strategic health care planning in critical conditions such as the COVID-19 pandemic.
. 2020 Jun 5.
doi: 10.20452/pamw.15422. Online ahead of print.
Novel Coronavirus Disease 2019: Predicting Prognosis by Using a Computed Tomography Severity Score and Clinicolaboratory Data
Ali Sabri, Amir H Davarpanah, Arash Mahdavi, Alireza Abrishami, Mehdi Khazaei, Saman Heydari, Reyhane Asgari, Seyyed Mojtaba Nekooghadam, Julian Dobranowski, Morteza Sanei Taheri
- PMID: 32500700
- DOI: 10.20452/pamw.15422
Abstract
Introduction: Currently there are known contributing factors but no comprehensive methods for predicting the risk of mortality or ICU admission in patients with Novel Corona Virus infection (COVID-19).
Objectives: The aim of this study is to explore risk factors of mortality and ICU admission in COVID-19 patients using combined CT and clinicolaboratory data.
Patients and methods: Patients with polymerase chain reaction (PCR) confirmed COVID-19 (N= 63) from University Hospitals in Tehran, Iran were studied. All patients underwent CT examination and a total CT score and the number of involved lung lobes were calculated and compared against collected laboratory and clinical information. Univariable and multivariate proportional hazards analysis were used to determine the relationship between the CT, laboratory and clinical data and ICU admission and in-hospital death.
Results: By univariable analysis, in-hospital mortality was higher in patients with lower O2 saturation on admission (<88%), higher CT scores and higher number of lung lobes (> 4) involved with a diffuse parenchymal pattern. By multivariable analysis, in-hospital mortality was higher in patients with O2 saturation below 88% on admission and a higher number of lung lobes involved with diffuse parenchymal pattern. The risk of ICU admission was higher with comorbidities (hypertension and ischemic heart disease), SaO2 below 88% and pericardial effusion.
Conclusions: We can identify factors affecting in-hospital death and ICU admission in COVID-19. This can help to determine which patients are likely to require ICU admission and to help inform strategic health care planning in critical conditions such as the COVID-19 pandemic.