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Jankovic J, Milenkovic B, Simic A, Skrobic O, Valipour A, Ivanovic N, Buha I, Milin-Lazovic J, Djurdjevic N, Jandric A, Colic N, Stojkovic S, Stjepanovic M. Influence of Achalasia on the Spirometry Flow-Volume Curve and Peak Expiratory Flow. Diagnostics (Basel) 2024; 14:933. [PMID: 38732346 PMCID: PMC11083519 DOI: 10.3390/diagnostics14090933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Revised: 04/25/2024] [Accepted: 04/26/2024] [Indexed: 05/13/2024] Open
Abstract
BACKGROUND Achalasia is an esophageal motor disorder characterized by aperistalsis and the failure of the relaxation of the lower esophageal sphincter. We want to find out whether external compression or recurrent micro-aspiration of undigested food has a functional effect on the airway. METHODS The aim of this research was to analyze the influence of achalasia on the peak expiratory flow and flow-volume curve. All of the 110 patients performed spirometry. RESULTS The mean diameter of the esophagus was 5.4 ± 2.1 cm, and nine of the patients had mega-esophagus. Seven patients had a plateau in the inspiratory part of the flow-volume curve, which coincides with the patients who had mega-esophagus. The rest of the patients had a plateau in the expiration part of the curve. The existence of a plateau in the diameter of the esophagus of more than 5 cm was significant (p 0.003). Statistical significance between the existence of a plateau and a lowered PEF (PEF < 80) has been proven (p 0.001). Also, a statistical significance between the subtype and diameter of more than 4 cm has been proved. There was no significant improvement in the PEF values after operation. In total, 20.9% of patients had a spirometry abnormality finding. The frequency of the improvement in the spirometry values after surgery did not differ significantly by achalasia subtype. The improvement in FEV1 was statistically significant compared to the FVC values. CONCLUSIONS Awareness of the influence of achalasia on the pulmonary parameters is important because low values of PEF with a plateau on the spirometry loop can lead to misdiagnosis. The recognition of various patterns of the spirometry loop may help in identifying airway obstruction caused by another non-pulmonary disease such as achalasia.
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Affiliation(s)
- Jelena Jankovic
- Clinic for Pulmonology, University Clinical Center of Serbia, 11000 Belgrade, Serbia; (J.J.); (B.M.); (I.B.); (N.D.); (A.J.)
- Medical Faculty, University of Belgrade, 11000 Belgrade, Serbia; (A.S.); (O.S.); (J.M.-L.); (N.C.)
| | - Branislava Milenkovic
- Clinic for Pulmonology, University Clinical Center of Serbia, 11000 Belgrade, Serbia; (J.J.); (B.M.); (I.B.); (N.D.); (A.J.)
- Medical Faculty, University of Belgrade, 11000 Belgrade, Serbia; (A.S.); (O.S.); (J.M.-L.); (N.C.)
| | - Aleksandar Simic
- Medical Faculty, University of Belgrade, 11000 Belgrade, Serbia; (A.S.); (O.S.); (J.M.-L.); (N.C.)
- Clinic for Digestive Surgery, University Clinical Centre of Serbia, 11000 Belgrade, Serbia;
| | - Ognjan Skrobic
- Medical Faculty, University of Belgrade, 11000 Belgrade, Serbia; (A.S.); (O.S.); (J.M.-L.); (N.C.)
- Clinic for Digestive Surgery, University Clinical Centre of Serbia, 11000 Belgrade, Serbia;
| | - Arschang Valipour
- Karl Landsteiner Institute for Lung Research and Pulmonary Oncology, Klinik Floridsdorf, Vienna Health Care Group, 1210 Vienna, Austria;
| | - Nenad Ivanovic
- Clinic for Digestive Surgery, University Clinical Centre of Serbia, 11000 Belgrade, Serbia;
| | - Ivana Buha
- Clinic for Pulmonology, University Clinical Center of Serbia, 11000 Belgrade, Serbia; (J.J.); (B.M.); (I.B.); (N.D.); (A.J.)
- Medical Faculty, University of Belgrade, 11000 Belgrade, Serbia; (A.S.); (O.S.); (J.M.-L.); (N.C.)
| | - Jelena Milin-Lazovic
- Medical Faculty, University of Belgrade, 11000 Belgrade, Serbia; (A.S.); (O.S.); (J.M.-L.); (N.C.)
- Institute for Medical Statistics and Informatics, University of Belgrade, 11000 Belgrade, Serbia
| | - Natasa Djurdjevic
- Clinic for Pulmonology, University Clinical Center of Serbia, 11000 Belgrade, Serbia; (J.J.); (B.M.); (I.B.); (N.D.); (A.J.)
- Medical Faculty, University of Belgrade, 11000 Belgrade, Serbia; (A.S.); (O.S.); (J.M.-L.); (N.C.)
| | - Aleksandar Jandric
- Clinic for Pulmonology, University Clinical Center of Serbia, 11000 Belgrade, Serbia; (J.J.); (B.M.); (I.B.); (N.D.); (A.J.)
| | - Nikola Colic
- Medical Faculty, University of Belgrade, 11000 Belgrade, Serbia; (A.S.); (O.S.); (J.M.-L.); (N.C.)
- Center for Radiology and MR, University Clinical Center of Serbia, 11000 Belgrade, Serbia
| | - Stefan Stojkovic
- Clinic for Gastroenterohepatology, University Clinical Center of Serbia, 11000 Belgrade, Serbia;
| | - Mihailo Stjepanovic
- Clinic for Pulmonology, University Clinical Center of Serbia, 11000 Belgrade, Serbia; (J.J.); (B.M.); (I.B.); (N.D.); (A.J.)
- Medical Faculty, University of Belgrade, 11000 Belgrade, Serbia; (A.S.); (O.S.); (J.M.-L.); (N.C.)
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Wang X, Gan H, Wang Y, Yu X, An J, Sun B, Gao Y, Zhu Z. Body mass index affects spirometry indices in patients with chronic obstructive pulmonary disease and asthma. Front Physiol 2023; 14:1132078. [PMID: 38107480 PMCID: PMC10722288 DOI: 10.3389/fphys.2023.1132078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Accepted: 11/20/2023] [Indexed: 12/19/2023] Open
Abstract
Background: Body mass index (BMI) is known to affect the outcomes of spirometry indices. However, its association with spirometry indices in COPD and asthma is less studied. We aimed to explore the impact of BMI on these patients. Methods: Patients with COPD or asthma who completed bronchodilator tests (BDTs) between 2017 and 2021 were reviewed. Spirometry indices were compared among patients with COPD or asthma that were subclassified as underweight (BMI< 18.5 kg/m2), normal weight (≥18.5 to < 25), overweight (≥ 25 to < 30), and obesity (≥ 30). Results. Results: Analysis was conducted on 3891 COPD patients (age:66.5 ± 7.8 years) and 1208 asthma patients (age:59.7 ± 7.5 years). COPD patients classified as underweight demonstrated significantly lower values of pre-and post FEV1 (L, %), pre-and post FVC (L, %), and pre- and post-FEV1/FVC (all p < 0.05). In contrast, COPD patients who were overweight or obese exhibited higher values for pre-and post FEV1 (L, %), and pre and post FEV1/FVC (all p < 0.05). Within the cohort of asthma patients, those underweight had lower pre-and post FEV1 (L, %), pre and post FVC (L, %), pre and post FEV1/FVC %. Obese asthma patients displayed higher pre and post FEV1/FVC (all p < 0.05). Conclusion: Significant BMI category differences in spirometry indices can be seen in patients with COPD or asthma. Both underweight and obesity could affect the diagnosis and severity of these diseases. Recognizing these effects is essential to better management and diagnosis of these patients.
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Affiliation(s)
- Xiaohu Wang
- Department of Respiratory and Critical Care Medicine, People’s Hospital of Yangjiang, Yangjiang, China
| | - Hui Gan
- National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yimin Wang
- National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Xinxin Yu
- National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Jiaying An
- National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Baoqing Sun
- National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- Department of Allergy and Clinical Immunology, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yi Gao
- National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Zheng Zhu
- National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- Department of Allergy and Clinical Immunology, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
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Wang Y, Li Q, Chen W, Jian W, Liang J, Gao Y, Zhong N, Zheng J. Deep Learning-Based Analytic Models Based on Flow-Volume Curves for Identifying Ventilatory Patterns. Front Physiol 2022; 13:824000. [PMID: 35153838 PMCID: PMC8831887 DOI: 10.3389/fphys.2022.824000] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Accepted: 01/06/2022] [Indexed: 11/13/2022] Open
Abstract
IntroductionSpirometry, a pulmonary function test, is being increasingly applied across healthcare tiers, particularly in primary care settings. According to the guidelines set by the American Thoracic Society (ATS) and the European Respiratory Society (ERS), identifying normal, obstructive, restrictive, and mixed ventilatory patterns requires spirometry and lung volume assessments. The aim of the present study was to explore the accuracy of deep learning-based analytic models based on flow–volume curves in identifying the ventilatory patterns. Further, the performance of the best model was compared with that of physicians working in lung function laboratories.MethodsThe gold standard for identifying ventilatory patterns was the rules of ATS/ERS guidelines. One physician chosen from each hospital evaluated the ventilatory patterns according to the international guidelines. Ten deep learning models (ResNet18, ResNet34, ResNet18_vd, ResNet34_vd, ResNet50_vd, ResNet50_vc, SE_ResNet18_vd, VGG11, VGG13, and VGG16) were developed to identify patterns from the flow–volume curves. The patterns obtained by the best-performing model were cross-checked with those obtained by the physicians.ResultsA total of 18,909 subjects were used to develop the models. The ratio of the training, validation, and test sets of the models was 7:2:1. On the test set, the best-performing model VGG13 exhibited an accuracy of 95.6%. Ninety physicians independently interpreted 100 other cases. The average accuracy achieved by the physicians was 76.9 ± 18.4% (interquartile range: 70.5–88.5%) with a moderate agreement (κ = 0.46), physicians from primary care settings achieved a lower accuracy (56.2%), while the VGG13 model accurately identified the ventilatory pattern in 92.0% of the 100 cases (P < 0.0001).ConclusionsThe VGG13 model identified ventilatory patterns with a high accuracy using the flow–volume curves without requiring any other parameter. The model can assist physicians, particularly those in primary care settings, in minimizing errors and variations in ventilatory patterns.
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