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Clinical and imaging features predict mortality in COVID-19 infection in Iran.

ABSTRACT: The new coronavirus disease 2019 (COVID-19) pandemic has challenged many healthcare systems around the world. While most of the current understanding of the clinical features of COVID-19 is derived from Chinese studies, there is a relative paucity of reports from the remaining global health community. In this study, we analyze the clinical and radiologic factors that correlate with mortality odds in COVID-19 positive patients from a tertiary care center in Tehran, Iran. A retrospective cohort study of 90 patients with reverse transcriptase-polymerase chain reaction (RT-PCR) positive COVID-19 infection was conducted, analyzing demographics, co-morbidities, presenting symptoms, vital signs, laboratory values, chest radiograph findings, and chest CT features based on mortality. Chest radiograph was assessed using the Radiographic Assessment of Lung Edema (RALE) scoring system. Chest CTs were assessed according to the opacification pattern, distribution, and standardized severity score. Initial and follow-up Chest CTs were compared if available. Multiple logistic regression was used to generate a prediction model for mortality. The 90 patients included 59 men and 31 women (59.4 ± 16.6 years), including 21 deceased and 69 surviving patients. Among clinical features, advanced age (p = 0.02), low oxygenation saturation (p<0.001), leukocytosis (p = 0.02), low lymphocyte fraction (p = 0.03), and low platelet count (p = 0.048) were associated with increased mortality. High RALE score on initial chest radiograph (p = 0.002), presence of pleural effusions on initial CT chest (p = 0.005), development of pleural effusions on follow-up CT chest (p = 0.04), and worsening lung severity score on follow-up CT Chest (p = 0.03) were associated with mortality. A two-factor logistic model using patient age and oxygen saturation was created, which demonstrates 89% accuracy and area under the ROC curve of 0.86 (p<0.0001). Specific demographic, clinical, and imaging features are associated with increased mortality in COVID-19 infections. Attention to these features can help optimize patient management.

SUBMITTER: Homayounieh F

PROVIDER: S-EPMC7514030 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Clinical and imaging features predict mortality in COVID-19 infection in Iran.

Homayounieh Fatemeh F Zhang Eric W EW Babaei Rosa R Karimi Mobin Hadi H Sharifian Maedeh M Mohseni Iman I Kuo Anderson A Arru Chiara C Kalra Mannudeep K MK Digumarthy Subba R SR

PloS one 20200924 9

The new coronavirus disease 2019 (COVID-19) pandemic has challenged many healthcare systems around the world. While most of the current understanding of the clinical features of COVID-19 is derived from Chinese studies, there is a relative paucity of reports from the remaining global health community. In this study, we analyze the clinical and radiologic factors that correlate with mortality odds in COVID-19 positive patients from a tertiary care center in Tehran, Iran. A retrospective cohort st ...[more]

PMID: 32970733

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Project description:BackgroundThe COVID-19 pandemic has affected millions of individuals and caused hundreds of thousands of deaths worldwide. Predicting mortality among patients with COVID-19 who present with a spectrum of complications is very difficult, hindering the prognostication and management of the disease. We aimed to develop an accurate prediction model of COVID-19 mortality using unbiased computational methods, and identify the clinical features most predictive of this outcome.MethodsIn this prediction model development and validation study, we applied machine learning techniques to clinical data from a large cohort of patients with COVID-19 treated at the Mount Sinai Health System in New York City, NY, USA, to predict mortality. We analysed patient-level data captured in the Mount Sinai Data Warehouse database for individuals with a confirmed diagnosis of COVID-19 who had a health system encounter between March 9 and April 6, 2020. For initial analyses, we used patient data from March 9 to April 5, and randomly assigned (80:20) the patients to the development dataset or test dataset 1 (retrospective). Patient data for those with encounters on April 6, 2020, were used in test dataset 2 (prospective). We designed prediction models based on clinical features and patient characteristics during health system encounters to predict mortality using the development dataset. We assessed the resultant models in terms of the area under the receiver operating characteristic curve (AUC) score in the test datasets.FindingsUsing the development dataset (n=3841) and a systematic machine learning framework, we developed a COVID-19 mortality prediction model that showed high accuracy (AUC=0·91) when applied to test datasets of retrospective (n=961) and prospective (n=249) patients. This model was based on three clinical features: patient's age, minimum oxygen saturation over the course of their medical encounter, and type of patient encounter (inpatient vs outpatient and telehealth visits).InterpretationAn accurate and parsimonious COVID-19 mortality prediction model based on three features might have utility in clinical settings to guide the management and prognostication of patients affected by this disease. External validation of this prediction model in other populations is needed.FundingNational Institutes of Health.

Project description:IntroductionWithin a few months coronavirus disease 2019 (COVID-19) evolved into a pandemic causing millions of cases worldwide, but it remains challenging to diagnose the disease in a timely fashion in the emergency department (ED). In this study we aimed to construct machine-learning (ML) models to predict severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection based on the clinical features of patients visiting an ED during the early COVID-19 pandemic.MethodsWe retrospectively collected the data of all patients who received reverse transcriptase polymerase chain reaction (RT-PCR) testing for SARS-CoV-2 at the ED of Baylor Scott & White All Saints Medical Center, Fort Worth, from February 23-May 12, 2020. The variables collected included patient demographics, ED triage data, clinical symptoms, and past medical history. The primary outcome was the confirmed diagnosis of COVID-19 (or SARS-CoV-2 infection) by a positive RT-PCR test result for SARS-CoV-2, and was used as the label for ML tasks. We used univariate analyses for feature selection, and variables with P<0.1 were selected for model construction. Samples were split into training and testing cohorts on a 60:40 ratio chronologically. We tried various ML algorithms to construct the best predictive model, and we evaluated performances with the area under the receiver operating characteristic curve (AUC) in the testing cohort.ResultsA total of 580 ED patients were tested for SARS-CoV-2 during the study periods, and 98 (16.9%) were identified as having the SARS-CoV-2 infection based on the RT-PCR results. Univariate analyses selected 21 features for model construction. We assessed three ML methods for performance: of the three methods, random forest outperformed the others with the best AUC result (0.86), followed by gradient boosting (0.83) and extra trees classifier (0.82).ConclusionThis study shows that it is feasible to use ML models as an initial screening tool for identifying patients with SARS-CoV-2 infection. Further validation will be necessary to determine how effectively this prediction model can be used prospectively in clinical practice.

Project description:BackgroundDuring the coronavirus disease 2019 (COVID-19) pandemic, Iran reported its first confirmed cases of syndrome coronavirus 2 (SARS-CoV-2) infections on 19 February 2020 in Qom. Although the numbers of cases are increasing, no report about clinical manifestations, laboratory results, and imaging findings of the children infected with COVID-19 in Iran has been published. The aim of this study was to evaluate the epidemiological, clinical, and radiological and laboratory findings of 24 children who had proven SARS-CoV-2 infection and performed chest computed tomographic (CT) in Qom, Iran.MethodsDemographic information and clinical characteristics of the patients including signs and symptoms, chest CT scan manifestation, laboratory findings and clinical outcomes were collected. Diagnosing of the confirmed case was based on positive real-time reverse-transcriptase-polymerase-chain-reaction test for SARS-CoV-2.FindingsDuring the first 3 months of the epidemic in Qom, Iran, 24 children with confirmed diagnosis of COVID-19 were included. The median age of the cases was 6 years [inter-quartile range 3.5-9.5 years]. The most common presenting symptoms were fever (100%), dry cough (62.5%), tachypnea (29%), abdominal pain (21%), and vomiting (21%). Three cases (12.5%) presented with a history of diarrhea in addition to fever and cough. According to the chest CT findings, 2 cases (8%) showed no abnormality. Typical CT findings were found in 6 patients (25%), 2 patients showed indeterminate appearance, and 14 patients (58%) showed atypical findings. Two children with SARS-CoV-2 infection manifested as a hyperinflammatory syndrome with multi-organ involvement similar to Kawasaki disease shock syndrome. Seventy-one percent of the patients showed severe SARS-CoV-2 infection and the mortality of 12.5% (3 cases) were reported.InterpretationHigh frequency of atypical chest CT finding in children should raise concern for pediatricians. Early recognition of patients with SARS-CoV-2 infection is of crucial importance in controlling of the outbreak and atypical imaging features should be interpreted with caution.

Project description:BackgroundCoronavirus disease 2019 (COVID-19) is associated with excess mortality after hospital discharge. Identification of patients at increased risk of death following hospital discharge is needed to guide clinical monitoring and early intervention. Herein, we aimed to identify predictors of early vs. late mortality in COVID-19 patients.MethodsA total of 471 patients with polymerase chain reaction-confirmed COVID-19 were followed up for 9 months [median (inter-quartile range) of follow-up time: 271 (14) days] after hospital admission. COVID-19-related signs and symptoms, laboratory features, co-morbidities, Coronavirus Clinical Characterisation Consortium (4C) mortality and Clinical Frailty Scale (CFS) scores were analysed by logistic regression for association with early (28 day) vs. late mortality. Receiver operating characteristic (ROC) analysis was used to determine the discriminative value of 4C and CFS scores for early vs. late mortality.ResultsA total of 120 patients died within 28 days from hospital admission. Of the remaining 351 patients, 41 died within the next 8 months. Respiratory failure, systemic inflammation, and renal impairment were associated with early mortality, while active cancer and dementia were associated with late mortality, after adjustment for age and sex. 4C mortality score and CFS were associated with both early [odds ratio (OR) (95% confidence interval-CI): 4C: 1.34 (1.25-1.45); CFS: 1.49 (1.33-1.66)] and late [OR (95% CI): 4C: 1.23 (1.12-1.36); CFS: 2.04 (1.62-2.56)] mortality. After adjustment for CFS, the association between 4C and late mortality was lost. By ROC analysis, 4C mortality score was superior to CFS for 28 day mortality [area under the curve (AUC) (95% CI): 0.779 (0.732-0.825) vs. 0.723 (0.673-0.773), respectively; P = 0.039]. In contrast, CFS had higher predictive value for late mortality compared with 4C mortality score [AUC (95% CI): 0.830 (0.776-0.883) vs. 0.724 (0.650-0.798), respectively; P = 0.007].ConclusionsIn our cohort, late mortality in COVID-19 patients is more strongly associated with premorbid clinical frailty than with severity of the acute infection phase.

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Clinical and imaging features predict mortality in COVID-19 infection in Iran.

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Clinical and imaging features predict mortality in COVID-19 infection in Iran.

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