Idioma:

Risk factors analysis of COVID-19 patients with ARDS and prediction based on machine learning

Xu, Wan ; Sun, Nan-Nan ; Gao, Hai-Nv ; Chen, Zhi-Yuan ; Yang, Ya ; Ju, Bin ; Tang, Ling-Ling

Scientific reports, 2021-02, Vol.11 (1), p.2933-12, Article 2933 [Periódico revisado por pares]

England: Nature Publishing Group

Texto completo disponível

Citações Citado por

Enviar para

Título:
Risk factors analysis of COVID-19 patients with ARDS and prediction based on machine learning
Autor: Xu, Wan ; Sun, Nan-Nan ; Gao, Hai-Nv ; Chen, Zhi-Yuan ; Yang, Ya ; Ju, Bin ; Tang, Ling-Ling
Assuntos: Adult ; Area Under Curve ; Artificial intelligence ; Body Mass Index ; Cell number ; Comorbidity ; Coronaviruses ; Cough ; COVID-19 ; COVID-19 - complications ; COVID-19 - pathology ; COVID-19 - virology ; Deep learning ; Female ; Humans ; Infectious diseases ; Learning algorithms ; Lymphocyte Count ; Lymphocytes ; Machine Learning ; Male ; Middle Aged ; Polypnea ; Prediction models ; Respiratory distress syndrome ; Respiratory Distress Syndrome - diagnosis ; Respiratory Distress Syndrome - etiology ; Risk Factors ; ROC Curve ; SARS-CoV-2 - isolation & purification ; Severity of Illness Index ; Sex Factors
É parte de: Scientific reports, 2021-02, Vol.11 (1), p.2933-12, Article 2933
Descrição: COVID-19 is a newly emerging infectious disease, which is generally susceptible to human beings and has caused huge losses to people's health. Acute respiratory distress syndrome (ARDS) is one of the common clinical manifestations of severe COVID-19 and it is also responsible for the current shortage of ventilators worldwide. This study aims to analyze the clinical characteristics of COVID-19 ARDS patients and establish a diagnostic system based on artificial intelligence (AI) method to predict the probability of ARDS in COVID-19 patients. We collected clinical data of 659 COVID-19 patients from 11 regions in China. The clinical characteristics of the ARDS group and no-ARDS group of COVID-19 patients were elaborately compared and both traditional machine learning algorithms and deep learning-based method were used to build the prediction models. Results indicated that the median age of ARDS patients was 56.5 years old, which was significantly older than those with non-ARDS by 7.5 years. Male and patients with BMI > 25 were more likely to develop ARDS. The clinical features of ARDS patients included cough (80.3%), polypnea (59.2%), lung consolidation (53.9%), secondary bacterial infection (30.3%), and comorbidities such as hypertension (48.7%). Abnormal biochemical indicators such as lymphocyte count, CK, NLR, AST, LDH, and CRP were all strongly related to the aggravation of ARDS. Furthermore, through various AI methods for modeling and prediction effect evaluation based on the above risk factors, decision tree achieved the best AUC, accuracy, sensitivity and specificity in identifying the mild patients who were easy to develop ARDS, which undoubtedly helped to deliver proper care and optimize use of limited resources.
Editor: England: Nature Publishing Group
Idioma: Inglês

Links

View this record in MEDLINE/PubMed

Voltar para lista de resultados

Realização: Logos de Redes Sociais:

Risk factors analysis of COVID-19 patients with ARDS and prediction based on machine learning

Xu, Wan ; Sun, Nan-Nan ; Gao, Hai-Nv ; Chen, Zhi-Yuan ; Yang, Ya ; Ju, Bin ; Tang, Ling-Ling

England: Nature Publishing Group

Buscando em bases de dados remotas. Favor aguardar.