Longitudinal Chest X-ray Scores and their Relations with Clinical Variables and Outcomes in COVID-19 Patients

Shen, Beiyi; Hou, Wei; Zhao, Jin; Li, Haifang; Singer, Adam J.; Hoshmand-Kochi, Mahsa; Abbasi, Almas; Glass, S.; Thode, Henry C.; Levsky, Jeffrey M.; Lipton, Michael L.; Duong, Timothy Q.

doi:10.3390/diagnostics13061107

Cited by 4 publications

(4 citation statements)

References 40 publications

(58 reference statements)

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“…Ji et al (20) found that COVID-19 patients with comorbidities were more likely to progress to severe disease than those without comorbidities. Shen et al (60) found that mortality was significantly associated with comorbidities (e.g., hypertension, COPD, coronary artery disease, heart failure, and chronic kidney disease) in COVID-19 patients (p < 0.05). Some studies found that comorbidity at presentation was an independent high-risk factor for COVID-19 progression and mortality (20,61).…”

Section: Discussionmentioning

confidence: 99%

Predicting omicron pneumonia severity and outcome: a single-center study in Hangzhou, China

Cao

Miao

et al. 2023

Front. Med.

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BackgroundIn December 2022, there was a large Omicron epidemic in Hangzhou, China. Many people were diagnosed with Omicron pneumonia with variable symptom severity and outcome. Computed tomography (CT) imaging has been proven to be an important tool for COVID-19 pneumonia screening and quantification. We hypothesized that CT-based machine learning algorithms can predict disease severity and outcome in Omicron pneumonia, and we compared its performance with the pneumonia severity index (PSI)-related clinical and biological features.MethodsOur study included 238 patients with the Omicron variant who have been admitted to our hospital in China from 15 December 2022 to 16 January 2023 (the first wave after the dynamic zero-COVID strategy stopped). All patients had a positive real-time polymerase chain reaction (PCR) or lateral flow antigen test for SARS-CoV-2 after vaccination and no previous SARS-CoV-2 infections. We recorded patient baseline information pertaining to demographics, comorbid conditions, vital signs, and available laboratory data. All CT images were processed with a commercial artificial intelligence (AI) algorithm to obtain the volume and percentage of consolidation and infiltration related to Omicron pneumonia. The support vector machine (SVM) model was used to predict the disease severity and outcome.ResultsThe receiver operating characteristic (ROC) area under the curve (AUC) of the machine learning classifier using PSI-related features was 0.85 (accuracy = 87.40%, p < 0.001) for predicting severity while that using CT-based features was only 0.70 (accuracy = 76.47%, p = 0.014). If combined, the AUC was not increased, showing 0.84 (accuracy = 84.03%, p < 0.001). Trained on outcome prediction, the classifier reached the AUC of 0.85 using PSI-related features (accuracy = 85.29%, p < 0.001), which was higher than using CT-based features (AUC = 0.67, accuracy = 75.21%, p < 0.001). If combined, the integrated model showed a slightly higher AUC of 0.86 (accuracy = 86.13%, p < 0.001). Oxygen saturation, IL-6, and CT infiltration showed great importance in both predicting severity and outcome.ConclusionOur study provided a comprehensive analysis and comparison between baseline chest CT and clinical assessment in disease severity and outcome prediction in Omicron pneumonia. The predictive model accurately predicts the severity and outcome of Omicron infection. Oxygen saturation, IL-6, and infiltration in chest CT were found to be important biomarkers. This approach has the potential to provide frontline physicians with an objective tool to manage Omicron patients more effectively in time-sensitive, stressful, and potentially resource-constrained environments.

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Section: Discussionmentioning

confidence: 99%

Predicting omicron pneumonia severity and outcome: a single-center study in Hangzhou, China

Cao

Miao

et al. 2023

Front. Med.

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“…It has been reported that initial chest X-ray scores of patients with COVID-19 are linked to clinical outcomes, such as mortality, the length of hospitalization and the duration of invasive ventilation ( 37 ). Since the beginning of the pandemic, comorbidities have been known to affect the outcomes of patients with COVID-19.…”

Section: Discussionmentioning

confidence: 99%

Risk factors for the in‑hospital and 1‑year mortality of elderly patients hospitalized due to COVID‑19‑related pneumonia

Georgakopoulou,

Gkoufa,

Makrodimitri

et al. 2023

Exp Ther Med

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Coronavirus disease 2019 (COVID-19) is characterized by poor outcomes and a high mortality rate, particularly among elderly patients. Since the beginning of the pandemic, an older age has been recognized as a critical risk factor for disease severity, with increasing mortality rates in each decade of life. This phenomenon may be a consequence of a poor previous health status, with a higher prevalence of pre-existing comorbidities and a higher degree of frailty. The majority of studies on the outcomes and risk factors of elderly patients refer to the first waves of the pandemic and the predictors of in-hospital mortality in these patients. The aim of the present study was to provide a detailed description of the clinical characteristics and management of a cohort of elderly patients (≥65 years of age) who were hospitalized with COVID-19-related pneumonia in all phases of the pandemic, presenting their outcomes, and investigating predictors of in-hospital and out-of-hospital mortality over a period of 1 year in this particularly vulnerable population. A total of 1,124 elderly patients (603 males, 53.7%) with a mean age of 78.51±7.42 years and a median Charlson comorbidity index (CCI) of 5 were included in the study. Of these patients, 104 (9.3%) were hospitalized during the period of prevalence of the original strain Wuhan, 385 (34.3%) were hospitalized during the period of prevalence of the Alpha variant, 221 (19.7%) were hospitalized during the period of prevalence of the Delta variant, and 414 (36.8%) were hospitalized during the period of prevalence of the Omicron variant. Overall, the in-hospital mortality rate was 33.4% (375 patients), and the 1-year mortality rate was 44.7% (502 patients). The majority of patients had not been vaccinated or had not completed full vaccination against severe acute respiratory syndrome coronavirus-2 (843 patients, 75%), given the period of infection. Age, immature granulocytes, lactate dehydrogenase (LDH) levels, ferritin levels, chest X-ray score, as well as the absence of full vaccination, cough and fatigue, were statistically significantly and independently associated with in-hospital mortality, while age, LDH levels, ferritin levels, alanine aminotransferase levels, CCI, chest X-ray score, the absence of cough and fatigue, and a history of dementia were statistically significantly and independently associated with 1-year mortality. On the whole, the present study demonstrates that both the in-hospital mortality and 1-year mortality rates of elderly patients hospitalized due to COVID-19-related pneumonia are high.

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“…Furthermore, these features can be utilized as inputs in ML algorithms to develop predictive models for brain tumor diagnosis and treatment. Mathematically, it can be represented as: (10) The intensity value of each tumor region pixel is P(xi, yi). Calculate the tumor region's perimeter by measuring its boundary.…”

Section: B Feature Extraction and Selectionmentioning

confidence: 99%

“…Several ML methods, such as supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning, are used in the analysis of medical images. [10]. Each of these techniques is tailored to specific tasks, and their use in medical image analysis is expected to expand, leading to improved patient outcomes and reduced pressure on healthcare providers.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Model for Brain Cancer-Grading and Classification

Ullah,

Nadeem,

Abrar

et al. 2023

IEEE Access

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Brain cancer is a bad disease and affects millions of people in worldwide. Approximately 70% of patients diagnosed with this disease do not survive. The Machine learning is a promising and recent development in this area. However, very limited research is performed in this direction. Therefore, in this research, we propose an evolutionary lightweight model aimed at detecting brain cancer and classification, starting from the analysis of magnetic resonance images. The proposed model named lightweight ensemble combines (weighted average and lightweight combines multiple XGBoost decision trees) is the modified version of the recent Multimodal Lightweight XGBoost. Herein, we provide prediction explain ability by considering the preprocessing of Magnetic Resonance Imaging (MRI) data and the feature extraction (Intensity, texture, and shape). The process in the evolutionary model involves a various step -first, prepare the data, extract important features, and finally, merge together using a special kind of classification called ensemble classification. We evaluate our proposed model using BraTS 2020 dataset. The dataset consists of 285 MRI scans of patients diagnosed with gliomas. The simulation results showed that our proposed model achieved 93.0% accuracy, 0.94 precision, 0.93 recall, 0.94 F1 score, and an area under Receiver Operating Characteristic Curve (AUC-ROC) value of 0.984. The efficient results demonstrate the effectiveness of our proposed model for brain tumor grading and classification using four grades. The efficient results show the potential of our proposed approach as a valuable tool for early diagnosis and effective treatment planning of brain tumors. Finally, the proposed model holds promise for aiding in early cancer diagnosis and treatment.INDEX TERMS disease classification; Brain cancer; Machine learning.

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Longitudinal Chest X-ray Scores and their Relations with Clinical Variables and Outcomes in COVID-19 Patients

Cited by 4 publications

References 40 publications

Predicting omicron pneumonia severity and outcome: a single-center study in Hangzhou, China

Predicting omicron pneumonia severity and outcome: a single-center study in Hangzhou, China

Risk factors for the in‑hospital and 1‑year mortality of elderly patients hospitalized due to COVID‑19‑related pneumonia

Evolutionary Model for Brain Cancer-Grading and Classification

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