Incidence and affecting factors of pulmonary diffusing capacity impairment with COVID-19 survivors 18 months after discharge in Wuhan, China

Xu, Bo; Ma, Fu-Qiang; He, Cong; WU, Zi-qi; Fan, Cun-yu; MAO, He-rong; Zhang, Jixian; Yang, Min; Hu, Zuowei

doi:10.1016/j.jinf.2021.12.020

Cited by 3 publications

(3 citation statements)

References 10 publications

(12 reference statements)

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“…However, one and a half years later, our study found that PLT was still closely related to PDCI of COVID-19 survivors. Consistent with our previous study [45], these observations suggest that clinically obtained MVV, PLT, Hb and severity of illness are the key factors for using the XGBoost model to predict pulmonary function status in COVID-19 survivors. Besides, compared with the indicators directly affecting pulmonary function, the SHAP pooled analysis exhibited that the increased UA and BUN may be correlated with a growing risk of the retrogressive pulmonary diffusing capacity of the COVID-19 patients.…”

Section: Discussionsupporting

confidence: 90%

“…The convalescent COVID-19 patients have demonstrated particularly pronounced PDCI. Our previous study has found that the incidence of DLCO impairment of the COVID-19 patients reached 57.92% in 18 months after discharge [3]. Studies show that pulmonary diffusing capacity of the COVID-19 patients is also significantly impaired in the 1-24 month recovery phase.…”

Section: Introductionmentioning

confidence: 90%

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Interpretable machine-learning model for Predicting the Convalescent COVID-19 patients with pulmonary diffusing capacity impairment

Ma,

He,

Yang

et al. 2023

BMC Med Inform Decis Mak

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Introduction The COVID-19 patients in the convalescent stage noticeably have pulmonary diffusing capacity impairment (PDCI). The pulmonary diffusing capacity is a frequently-used indicator of the COVID-19 survivors’ prognosis of pulmonary function, but the current studies focusing on prediction of the pulmonary diffusing capacity of these people are limited. The aim of this study was to develop and validate a machine learning (ML) model for predicting PDCI in the COVID-19 patients using routinely available clinical data, thus assisting the clinical diagnosis. Methods Collected from a follow-up study from August to September 2021 of 221 hospitalized survivors of COVID-19 18 months after discharge from Wuhan, including the demographic characteristics and clinical examination, the data in this study were randomly separated into a training (80%) data set and a validation (20%) data set. Six popular machine learning models were developed to predict the pulmonary diffusing capacity of patients infected with COVID-19 in the recovery stage. The performance indicators of the model included area under the curve (AUC), Accuracy, Recall, Precision, Positive Predictive Value(PPV), Negative Predictive Value (NPV) and F1. The model with the optimum performance was defined as the optimal model, which was further employed in the interpretability analysis. The MAHAKIL method was utilized to balance the data and optimize the balance of sample distribution, while the RFECV method for feature selection was utilized to select combined features more favorable to machine learning. Results A total of 221 COVID-19 survivors were recruited in this study after discharge from hospitals in Wuhan. Of these participants, 117 (52.94%) were female, with a median age of 58.2 years (standard deviation (SD) = 12). After feature selection, 31 of the 37 clinical factors were finally selected for use in constructing the model. Among the six tested ML models, the best performance was accomplished in the XGBoost model, with an AUC of 0.755 and an accuracy of 78.01% after experimental verification. The SHAPELY Additive explanations (SHAP) summary analysis exhibited that hemoglobin (Hb), maximal voluntary ventilation (MVV), severity of illness, platelet (PLT), Uric Acid (UA) and blood urea nitrogen (BUN) were the top six most important factors affecting the XGBoost model decision-making. Conclusion The XGBoost model reported here showed a good prognostic prediction ability for PDCI of COVID-19 survivors during the recovery period. Among the interpretation methods based on the importance of SHAP values, Hb and MVV contributed the most to the prediction of PDCI outcomes of COVID-19 survivors in the recovery period.

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Section: Discussionsupporting

confidence: 90%

Section: Introductionmentioning

confidence: 90%

Interpretable machine-learning model for Predicting the Convalescent COVID-19 patients with pulmonary diffusing capacity impairment

Ma,

He,

Yang

et al. 2023

BMC Med Inform Decis Mak

Self Cite

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“…4. Neurological symptoms: COVID-19 has been associated with neurological effects such as headaches, dizziness, loss of taste and smell, and brain fog 30 .…”

Section: Possible Long Term Side Effects Resulting From Covid-19mentioning

confidence: 99%

COVID-19 Tedavisinde Kullanılan İlaçlar ve Farmakokinetiği

Demirtürk

2024

Arşiv Kaynak Tarama Dergisi

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Farmakokinetik, bir ilacın vücuda girme, dağılma, etki alanına ulaşma, etkisini gösterme ve atılma derecesi ve hızı dahil olmak üzere bu süreci kontrol eden tüm faktörlerle ilgilenir. Ayrıca bu süreç boyunca ilacın farklı kompartımanlardaki seviyesinin zamana bağlı olarak nasıl değiştiğini de inceler. Farmakokinetiğin birincil amacı hasta ve ilaçla ilgili kinetik parametreleri etkileyen değişkenleri belirlemek iken, ikincil amacı doz-konsantrasyon-zaman ilişkisindeki kinetik parametreleri tanımlamaktır. Bu nedenle, acil durumlarda veya özel popülasyonlarda kullanılan herhangi bir ilacın farmakokinetik parametreleri öğreticidir. COVID-19 bağlamında, kullanılan ilaçların farmakokinetiğini anlamak, optimum terapötik sonuçlara ulaşmak için çok önemlidir. Bu bilgi, COVID-19 hastaları için uygun dozajın, uygulama sıklığının ve tedavi süresinin belirlenmesine yardımcı olur. Farmakokinetik, potansiyel ilaç-ilaç etkileşimlerinin ve advers ilaç reaksiyonlarının olasılığının anlaşılmasında da rol oynar. Ayrıca, farmakokinetik çalışmalar COVID-19 için yeni tedavi stratejilerinin ve terapötik müdahalelerin geliştirilmesine yardımcı olabilir. Bu makalede, COVID 19 tedavisinde kullanılması gereken favipiravir, remdesivir, lopinavir, ritonavir, klorokin ve hidroksiklorokin için temel farmakokinetik parametreler açıklanmaktadır. Bu çalışma, çeşitli COVID-19 ilaçlarının farmakokinetiği hakkında bir bilgi tabanı sağlayacaktır.

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The Short- and Long-Term Clinical, Radiological and Functional Consequences of COVID-19

Gao

Liang

et al. 2022

Archivos de Bronconeumología

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Incidence and affecting factors of pulmonary diffusing capacity impairment with COVID-19 survivors 18 months after discharge in Wuhan, China

Cited by 3 publications

References 10 publications

Interpretable machine-learning model for Predicting the Convalescent COVID-19 patients with pulmonary diffusing capacity impairment

Interpretable machine-learning model for Predicting the Convalescent COVID-19 patients with pulmonary diffusing capacity impairment

COVID-19 Tedavisinde Kullanılan İlaçlar ve Farmakokinetiği

The Short- and Long-Term Clinical, Radiological and Functional Consequences of COVID-19

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