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HIV infection is associated with reduced pulmonary diffusing capacity.

ABSTRACT: INTRODUCTION:Prior studies comparing abnormalities in pulmonary function between HIV-infected and HIV-uninfected persons in the current era are limited. OBJECTIVES:To determine the pattern and severity of impairment in pulmonary function in HIV-infected compared with HIV-uninfected individuals. METHODS:Cross-sectional analysis of 300 HIV-infected men and 289 HIV-uninfected men enrolled from 2009 to 2011 in 2 clinical centers of the Lung HIV Study. Participants completed pre- and postbronchodilator spirometry, diffusing capacity of the lung for carbon monoxide (DLCO) measurement, and standardized questionnaires. RESULTS:Most participants had normal airflow; 18% of HIV-infected and 16% of HIV-uninfected men had airflow obstruction. The mean percent predicted DLCO was 69% in HIV-infected vs. 76% in HIV-uninfected men (P < 0.001). A moderately to severely reduced DLCO of ?60% was observed in 30% of HIV-infected compared with 18% of HIV-uninfected men (P < 0.001), despite the fact that 89% of those with HIV were on antiretroviral therapy. A reduced DLCO was significantly associated with HIV and CD4 cell count in linear regression adjusting for smoking and other confounders. The DLCO was lowest in HIV-infected men with CD4 cell counts <200 cells per microliter compared with those with CD4 cell counts ?200 cells per microliter and to HIV-uninfected men. Respiratory symptoms of cough, phlegm and dyspnea were more prevalent in HIV-infected patients particularly those with abnormal pulmonary function compared with HIV-uninfected patients. CONCLUSIONS:HIV infection is an independent risk factor for reduced DLCO, particularly in individuals with a CD4 cell count below 200 cells per microliter. Abnormalities in pulmonary function among HIV-infected patients manifest clinically with increased respiratory symptoms. Mechanisms accounting for the reduced DLCO require further evaluation.

SUBMITTER: Crothers K

PROVIDER: S-EPMC3845879 | biostudies-literature | 2013 Nov

REPOSITORIES: biostudies-literature

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HIV infection is associated with reduced pulmonary diffusing capacity.

Crothers Kristina K McGinnis Kathleen K Kleerup Eric E Wongtrakool Cherry C Hoo Guy S GS Kim Joon J Sharafkhaneh Amir A Huang Laurence L Luo Zhaoyu Z Thompson Bruce B Diaz Philip P Kirk Gregory D GD Rom William W Detels Roger R Kingsley Lawrence L Morris Alison A

Journal of acquired immune deficiency syndromes (1999) 20131101 3

<h4>Introduction</h4>Prior studies comparing abnormalities in pulmonary function between HIV-infected and HIV-uninfected persons in the current era are limited.<h4>Objectives</h4>To determine the pattern and severity of impairment in pulmonary function in HIV-infected compared with HIV-uninfected individuals.<h4>Methods</h4>Cross-sectional analysis of 300 HIV-infected men and 289 HIV-uninfected men enrolled from 2009 to 2011 in 2 clinical centers of the Lung HIV Study. Participants completed pre ...[more]

PMID: 23979001

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Project description:IntroductionThe 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.MethodsCollected 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.ResultsA 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.ConclusionThe 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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HIV infection is associated with reduced pulmonary diffusing capacity.

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HIV infection is associated with reduced pulmonary diffusing capacity.

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