Physiological measurement最新文献

{"title":"Automated system for diagnosing pulmonary fibrosis using crackle analysis in recorded lung sounds based on iterative envelope mean fractal dimension filter.","authors":"Ravi Pal, Anna Barney, Giacomo Sgalla, Simon L F Walsh, Nicola Sverzellati, Sophie Fletcher, Stefania Cerri, Maxime Cannesson, Luca Richeldi","doi":"10.1088/1361-6579/ada9c0","DOIUrl":"10.1088/1361-6579/ada9c0","url":null,"abstract":"<p><p><i>Objective.</i>Patients with pulmonary fibrosis (PF) often experience long waits before getting a correct diagnosis, and this delay in reaching specialized care is associated with increased mortality, regardless of the severity of the disease. Early diagnosis and timely treatment of PF can potentially extend life expectancy and maintain a better quality of life. Crackles present in the recorded lung sounds may be crucial for the early diagnosis of PF.<i>Approach.</i>This paper describes an automated system for differentiating lung sounds related to PF from other pathological lung conditions using the average number of crackles per breath cycle (NOC/BC). The system is divided into four main parts: (1) pre-processing, (2) separation of crackles from normal breath sounds using the iterative envelope mean fractal dimension filter, (3) crackle verification and counting, and (4) estimating NOC/BC. The system was tested on a dataset consisting of 48 (24 fibrotic and 24 non-fibrotic) subjects and the results were compared with an assessment by two expert respiratory physicians. The set of high-resolution computed tomography images, reviewed by two expert radiologists for the presence or absence of PF, was used as the ground truth for evaluating the PF and non-PF classification performance of the system.<i>Main results.</i>The overall performance of the automatic classifier based on receiver operating curve-derived cut-off value for average NOC/BC of 18.65 (AUC = 0.845, 95% CI 0.739-0.952,<i>p</i>< 0.001; sensitivity = 91.7%; specificity = 59.3%) compares favourably with the averaged performance of the physicians (sensitivity = 83.3%; specificity = 56.25%).<i>Significance.</i>Although radiological assessment should remain the gold standard for diagnosis of fibrotic interstitial lung disease (ILD), the automatic classification system has strong potential for diagnostic support, especially in assisting general practitioners in the auscultatory assessment of lung sounds to prompt further diagnostic work up of patients with suspect of ILD.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143009917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"One-week test-retest stability of heart rate variability during rest and deep breathing.","authors":"Andy Schumann, Franziska Lukas, Katrin Rieger, Yubraj Gupta, Karl-Jürgen Bär","doi":"10.1088/1361-6579/adae51","DOIUrl":"10.1088/1361-6579/adae51","url":null,"abstract":"<p><p><i>Objective</i>. Heart rate variability (HRV) is an important indicator of cardiac autonomic function. Given its clinical significance, reliable HRV assessment is crucial. Here, we assessed test-retest stability, as a key aspect of reliability, quantifying the consistency of a measure when repeated under the same conditions.<i>Approach</i>. This observational study includes healthy individuals. A 20 min electrocardiogram was recorded at rest in a supine position and during deep breathing in two lab sessions within one week, at the same time of day. HRV indices from time domain, frequency domain, nonlinear dynamics, and information-theoretic complexity were assessed using a validated toolbox. Additionally, heart rate variations per respiratory cycle were evaluated during deep breathing. Lifestyle factors such as perceived stress, mood, physical activity, sleep quality were assessed prior to both sessions. Intra-class correlation (ICC) and coefficients of variation (CVs) were used to assess the concordance between the two measurements and the relative deviation, respectively.<i>Main results</i>. From 62 screened individuals, 51 participants were recruited from the local community. One participant opted out for personal reasons, and another with frequent premature beats was excluded, leaving a final sample of 49 individuals. Most self-rated psychological and lifestyle indicators showed substantial agreement, though participants reported less stress and better mood in the second session. At rest, ICC of HRV ranged from 0.50 to 0.83, with CV from 5% to 41%. Spectral HRV measures were less reliable than time domain parameters. Nonlinear and time domain features had substantial to nearly perfect agreement. Complexity measures had low CVs but limited test-retest correlation. The stability indices of HRV during deep breathing were not significantly different from those during rest. Test-retest differences in root mean square of the successive beat-to-beat interval difference were not sufficiently explained by lifestyle factors.<i>Significance.</i>Test-retest stability of HRV depends considerably on chosen measures. Our data suggest that HRV can be assessed reliably using time-domain indices at rest.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143040959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rotating radial injection pattern for highly sensitive electrical impedance tomography of human lung anomalies.","authors":"Oumaima Bader, Najoua Essoukri Ben Amara, Oliver G Ernst, Olfa Kanoun","doi":"10.1088/1361-6579/ada9c2","DOIUrl":"10.1088/1361-6579/ada9c2","url":null,"abstract":"<p><p><i>Objective.</i>Electrical impedance tomography (EIT) is a non-invasive technique used for lung imaging. A significant challenge in EIT is reconstructing images of deeper thoracic regions due to the low sensitivity of boundary voltages to internal conductivity variations. The current injection pattern is decisive as it influences the current path, boundary voltages, and their sensitivity to tissue changes.<i>Approach.</i>This study introduces a novel current injection pattern with radially placed electrodes excited in a rotating radial pattern. The effectiveness of the proposed pattern was investigated using a 3D computational model that mimics the human thorax, replicating its geometry and tissue electrical properties. To examine the detection of lung anomalies, models representing both healthy and unhealthy states, including cancer-like anomalies in three different positions, were developed. The new pattern was compared to common patterns-adjacent, skip 1, and opposite-using finite element analysis. The comparison focused on the current density within lung nodules and the sensitivity to changes in anomaly positions.<i>Main results.</i>Results showed that the new pattern achieved the maximum current density within anomalies compared to surrounding tissues, with peak values near the closest electrode pairs to the anomalies. Specifically, current density magnitudes reached72.73⋅10-9A⋅m,145.24⋅10-9A⋅m, and26.43⋅10-9A⋅mfor the three different positions, respectively. Furthermore, the novel pattern's sensitivity to anomaly position changes surpassed the common patterns.<i>Significance.</i>These results demonstrate the efficiency of the proposed injection pattern in detecting lung anomalies compared to the common injection patterns.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143009921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The regional ventilation distribution monitored by electrical impedance tomography during anesthesia induction with head-rotated mask ventilation.","authors":"Qing Xu, Yijiao Fang, Congxia Pan, Lingling Gao, Yun Zhu, Jun Zhang, Zhanqi Zhao, Li Yang","doi":"10.1088/1361-6579/adad2f","DOIUrl":"10.1088/1361-6579/adad2f","url":null,"abstract":"<p><p><i>Objective.</i>Abnormal regional lung ventilation can lead to undesirable outcomes during the induction of anesthesia. Head rotated ventilation has proven to change the airflow of upper airway tract and be effective in increasing the tidal volume. This study aimed to investigate the influence of head rotated mask ventilation on regional ventilation distribution during the induction phase of anesthesia.<i>Approach.</i>Ninety patients undergoing anesthesia induction were randomly assigned to receive either neutral head (neutral-head group) or rotated right side head (rotated-head group) mask ventilation. Pressure-controlled mode was used in all mechanical ventilation. The regional lung ventilation was monitored by electrical impedance tomography. The primary outcome was the ratio of left/right lung ventilation distribution. The secondary outcomes were global inhomogeneity index (GI), center of ventilation (CoV, 100% = entirely dorsal), and the regional lung distribution differences between spontaneous and mask ventilation.<i>Main results.</i>Forty-two patients with neutral-head and 38 with rotated-head mask ventilation were analyzed finally. Compared with spontaneous ventilation, mask positive-pressure ventilation caused significant changes in the ratio of left/right lung ventilation distribution [0.85 (0.27) versus 0.94 (0.30);<i>P</i>= 0.022]. However, there were no differences in the ratio of left/right lung ventilation distribution between neutral and rotated head groups (<i>P</i>= 0.128). When compared with spontaneous ventilation, mask ventilation caused regional distributions of ventilation shifts towards ventral lung areas (CoV: 45.7 ± 5.0% versus 39.2 ± 4.8%;<i>P</i>< 0.001), and significant lung ventilation inhomogeneity (GI: 0.40 ± 0.07 versus 0.49 ± 0.14;<i>P</i>< 0.001). Compared with neutral-head mask ventilation, rotated-head mask ventilation was associated with higher expiratory tidal volume (TVe) (575.1 ± 148.6 ml versus 654.2 ± 204.0 ml;<i>P</i>= 0.049).<i>Significance.</i>Mask positive ventilation caused regional lung ventilation changes. When compared with neutral-head mask ventilation, rotated-head mask ventilation did not improve the regional ventilation towards to left lung. However, rotated-head mask ventilation was associated with higher TVe, and has the potential for better oxygenation.<b>Trial Registration.</b>This study was registered on Chinese Clinical Trial Registry on 6 August, 2024 (ChiCTR2400087858).</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143024507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The UNICA sleep HRV analysis tool: an integrated open-source tool for heart rate variability analysis during sleep.","authors":"Parisa Sattar, Giulia Baldazzi, Monica Puligheddu, Danilo Pani","doi":"10.1088/1361-6579/adaad5","DOIUrl":"10.1088/1361-6579/adaad5","url":null,"abstract":"<p><p>Heart rate variability (HRV) analysis during sleep plays a key role for understanding autonomic nervous system function and assessing cardiovascular health. The UNICA Sleep HRV analysis (UNICA-HRV) tool is a novel, open-source MATLAB tool designed to fill the gap in current HRV analysis tools. In particular, the integration of ECG and HRV data with hypnogram information, which illustrates the progression through the different sleep stages, eases the computation of HRV metrics in polysomnographic recordings. This integration is crucial for accurate phase-specific analysis, as autonomic regulation changes markedly across different sleep stages. The tool supports single- and multiple-subject analyses and is tailored to enhance usability and accessibility for researchers and clinicians without requiring extensive technical expertise. It implements and supports a variety of data inputs and configurations, allowing for flexible, detailed HRV analyses across sleep stages, employing classical and advanced metrics, such as time-domain, frequency-domain, non-linear, complexity, and Poincaré plot indexes. Validation of the tool against established tools like Kubios and PhysioZoo indicates its robustness and precision in generating reliable HRV metrics, that are essential not only for sleep research, but also for clinical diagnostics. The introduction of UNICA-HRV represents a significant simplification for sleep studies, and its open-source nature (licensed under a Creative Commons Attribution 4.0 International License) allows to easily extend the functionality to other needs.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143009309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ECG signal generation using feature disentanglement auto-encoder.","authors":"Hanbin Xiao, Yong Xia","doi":"10.1088/1361-6579/adab4f","DOIUrl":"10.1088/1361-6579/adab4f","url":null,"abstract":"<p><p><i>Objective.</i>The demand for electrocardiogram (ECG) datasets, particularly those containing rare classes, poses a significant challenge as deep learning becomes increasingly prevalent in ECG signal research. While generative adversarial networks (GANs) and variational autoencoders (VAEs) are widely adopted, they encounter difficulties in effectively generating samples for classes with limited instances.<i>Approach.</i>To address this issue, we propose a novel<u>F</u>eature<u>D</u>isentanglement Auto-Encoder (FDAE) designed to dissect various generative factors under a contrastive learning framework within ECG data to facilitate the generation of new ECG samples. The FDAE enhances and extends the AE structure with novel methodologies, which involve: (1) partitioning the latent space into three distinct representations to capture various generative factors; (2) utilizing a contrastive loss function to improve feature disentanglement capabilities; and (3) incorporating additional classifiers to enhance representation learning, alongside a discriminator aimed at boosting the realism of synthesized signals. Furthermore, our FDAE generates new signals by swapping latent codes of existing signals and combining freely or substituting patient-independent representations with those randomly generated by a VAE.<i>Main results.</i>To validate our approach, we conduct heartbeat classification experiments on the publicly available MIT-BIH arrhythmia database, using FAKE-train/FAKE-test partitions and data augmentation. The results highlight the FDAE's ability to improve ECG classifier performance and excel in synthesizing ECG signals. Furthermore, we apply the model to the Icentia11K dataset and conducted classification enhancement experiments. The results further highlight the model's strong generalization ability in ECG synthesis.<i>Significance.</i>This work has the potential to improve the robustness and generalization of deep learning models for ECG analysis, particularly in medical applications where rare cardiac events are often underrepresented in available datasets.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143009919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnosing anaemia via smartphone colorimetry of the eye in a population of pregnant women.","authors":"Thomas Alan Wemyss, Anubhuti Rana, Sara L Hillman, Miranda Nixon-Hill, Kapil Yadav, Vatsla Dadhwal, Terence S Leung","doi":"10.1088/1361-6579/adab4d","DOIUrl":"10.1088/1361-6579/adab4d","url":null,"abstract":"<p><p><i>Objective.</i>Screening for disease using a smartphone camera is an emerging tool for conditions such as jaundice and anaemia, which are associated with a colour change (yellowing in jaundice; pallor in anaemia) of the external tissues. Based on this, we aimed to test a technique to non-invasively screen for anaemia in a population highly affected by anaemia: pregnant women in India. In this group, anaemia can have severe health consequences for both the mother and child.<i>Approach.</i>Over 3 years of data collection, in 486 pregnant women in India, we attempted to replicate a previously successful smartphone imaging technique to screen for anaemia. Using smartphone images of the eye and eyelid, we compared two techniques (white balancing and ambient subtraction) to control for variation in ambient lighting, and then extracted 'redness' features from images, which we used as features to predict anaemia via statistical modelling.<i>Main results.</i>We found that we were not able to predict anaemia with enough accuracy to be clinically useful, at 89.6% sensitivity and 26.1% specificity. We consider the hypothesis that this may be due to pigmentation on the sclera and palpebral conjunctiva. Visual judgement showed that pigmentation on the sclera, which may affect the measured colour, is more prevalent in pregnant women in India than in preschool aged children in Ghana (a population previously studied in this context). When participants with subjectively judged visible scleral pigmentation are removed, ability to screen for anaemia using the smartphone images slightly improves (93.1% sensitivity, 28.6% specificity).<i>Significance.</i>These findings provide evidence to reinforce that applying smartphone imaging techniques to understudied populations in the real world requires caution-a promising result in one group may not necessarily transfer to another demographic.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143009918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The acute effect of bitemporal electroconvulsive therapy on synchronous changes in heart rate variability and heart rate in patients with depression.","authors":"Xiang Chen, Changjiang He, Hui Zhang, Han Yang, Jin Li","doi":"10.1088/1361-6579/adaad6","DOIUrl":"10.1088/1361-6579/adaad6","url":null,"abstract":"<p><p><i>Objective.</i>The transient autonomic nervous system responses induced by electroconvulsive therapy (ECT) may serve as critical indicators of treatment efficacy and potential side effects; however, their precise characteristics remains unclear. Considering that the intense stimulation of ECT may disrupt the typical antagonistic relationship between the sympathetic and parasympathetic branches, this study aims to conduct a meticulous analysis of the rapid changes in heart rate variability (HRV) and HR during ECT, with a particular focus on their synchronized interplay.<i>Methods.</i>Pulse interval sequences were collected from 50 sessions of bitemporal ECT administered to 27 patients diagnosed with major depressive disorder. The average HR and ultra-short term HRV indices RMSSD and SDNN, as well as the Poincaré indices SD1, SD2 and SD2/SD1, were calculated using a 10 s sliding window with a step size of 1 s. In particular, the synchronous changes between SD1, SD2, SD2/SD1 and HR were analyzed.<i>Results.</i>The synchronous changes of the indices showed different characteristics over time. In particular, SD1, SD2 and HR increased significantly by 41.50 ± 11.45 ms, 33.97 ± 10.98 ms and 9.68 ± 2.00 bpm respectively between 8 and 20 s, whereas they decreased significantly by 19.89 ± 9.07 ms, 17.54 ± 8.54 ms and 3.80 ± 1.33 bpm respectively between 45 and 53 s after ECT stimulus onset. SD1 and SD2 both had highly significant positive correlations with HR in the above phases.<i>Conclusion.</i>The results suggest that bitemporal ECT induces the sympathetic and parasympathetic co-activation during the early ictal period and brief co-inhibition approximately 45 s after stimulus. Our findings may provide new insights comprehending the mechanisms of ECT and its associated cardiovascular risks.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143009922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Self-critical strategy adjustment based artificial intelligence method in generating diagnostic reports of respiratory diseases.","authors":"Binyue Chen, Guohua Liu, Quan Zhang","doi":"10.1088/1361-6579/ada869","DOIUrl":"10.1088/1361-6579/ada869","url":null,"abstract":"<p><p><i>Objective</i>. Humanity faces many health challenges, among which respiratory diseases are one of the leading causes of human death. Existing AI-driven pre-diagnosis approaches can enhance the efficiency of diagnosis but still face challenges. For example, single-modal data suffer from information redundancy or loss, difficulty in learning relationships between features, and revealing the obscure characteristics of complex diseases. Therefore, it is critical to explore a method that can assist clinicians in detecting lesions early and in pre-diagnosing corresponding diseases.<i>Approach.</i>This paper introduces a novel network structure, strong constraint self-critical strategy network (SCSCS-Net), which can effectively extract image features from chest x-ray images and generate medical image descriptions, assist clinicians in analyzing patients' medical imaging information, deeply explore potential disease characteristics, and assist in making pre-diagnostic decisions. The SCSCS-Net consists of a reinforced cross-modal feature representation model and a self-critical cross-modal alignment model, which are responsible for learning the features interdependence between images and reports by using a multi-subspace self-attention structure and guiding the model in learning report generation strategies to improve the professionalism and consistency of medical terms in generated reports, respectively.<i>Main results.</i>We further compare our model with some advanced models on the same dataset, and the results demonstrate that our method achieves better performance. Finally, the CE and NLG metrics further confirm that the proposed method acquires the ability to generate high-quality medical reports with higher clinical consistency in generating medical reports.<i>Significance.</i>Our novel method has the potential to improve the early detection and pre-diagnosis of respiratory diseases. The model proposed in this paper allows to narrow the gap between artificial intelligence technology and clinical medical diagnosis and provides the possibility for in-depth integration.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142953092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ventilation-perfusion matching in early-stage of prone position ventilation: a prospective cohort study between COVID-19 ARDS and ARDS from other etiologies.","authors":"Yingying Yang, Hantian Li, Yi Chi, Inéz Frerichs, Zhanqi Zhao, Yuan Li, Chunyang Zhang, Huiwen Chu, Huaiwu He, Yun Long","doi":"10.1088/1361-6579/ada8f1","DOIUrl":"10.1088/1361-6579/ada8f1","url":null,"abstract":"&lt;p&gt;&lt;p&gt;&lt;i&gt;Objective.&lt;/i&gt;Prone positioning has been established as a therapeutic strategy for severe acute respiratory distress syndrome (ARDS). In COVID-19-associated ARDS (CARDS), the application of prone position has shown varying responses, influenced by factors such as lung recruitability and SARS-CoV-2-induced pulmonary endothelial dysfunction. This study aimed to compare the early impact of pronation on lung ventilation-perfusion matching (VQmatch) in CARDS and non-COVID-19 ARDS patients (non-CARDS).&lt;i&gt;Approach.&lt;/i&gt;This was a two-center, prospective study comparing between CARDS and non-CARDS. Electrical impedance tomography (EIT) was used to compare the VQmatch between supine and early-stage prone positions (∼2 h). The study identified the areas of Deadspace, shunt, and VQmatch. Within the defined VQmatch region, the global inhomogeneity index (VQmatch-GI) was computed to evaluate the degree of heterogeneity. Paired Wilcoxon signed-rank test and Chi-square test were used in statistical analysis.&lt;i&gt;Main results.&lt;/i&gt;15 CARDS patients and 14 non-CARDS patients undergoing mechanical ventilation were included. In comparison to the non-CARDS group, the CARDS group exhibited a higher prevalence of diffuse lung disease (15 [100%] vs. 4 [28.6%], CARDS vs. Non-CARDS,&lt;i&gt;p&lt;/i&gt;&lt; 0.001), along with elevated SOFA score, PCO&lt;sub&gt;2&lt;/sub&gt;, PEEP, and Ppeak. Among non-CARDS patients, 11/14 demonstrated improved oxygenation, whereas only 5/15 CARDS patients exhibited oxygenation improvement in prone ventilation. In 13/29 patients with oxygenation improvement (defined as above 20% increase in SpO&lt;sub&gt;2&lt;/sub&gt;/FiO&lt;sub&gt;2&lt;/sub&gt;), there was a significant decreased deadspace (21.3 [11.5, 33.1] vs. 9.7 [7.3, 16.9],&lt;i&gt;p&lt;/i&gt;= 0.039), and VQmatch showed an upward trend. When comparing prone ventilation to supine ventilation, non-CARDS patients showed a significant improvement in overall VQmatch (Supine 65.7 [49.7, 68.5] vs. Prone 67.4 [60.8, 72.6],&lt;i&gt;p&lt;/i&gt;= 0.019). CARDS patients had a notable decrease in ventral VQmatch (VQmatch_Ventral: Supine 35.0 [26.9, 42.0] vs. Prone 22.7 [12.4, 32.9],&lt;i&gt;p&lt;/i&gt;= 0.003), and an improvement in dorsal VQmatch (VQmatch_Dorsal: Supine 33.4 [20.4, 39.4] vs. Prone 46.4 [37.4, 48.4],&lt;i&gt;p&lt;/i&gt;= 0.031), leading to no significant improvement in overall VQmatch. Ten CARDS patients with no improvement in VQmatch had increased shunting and VQmatch-GI.&lt;i&gt;Significance.&lt;/i&gt;In non-CARDS patients, the improvement in oxygenation and VQmatch following prone positioning exhibits a consistent pattern. Conversely, in CARDS patients, the impact of prone positioning reveals considerable individual variability. This study indicates that the response to short-time prone ventilation can vary in ARDS patients with different etiologies.&lt;b&gt;Trial registration:&lt;/b&gt;NCT05816928, 04/17/2023, retrospectively registered. Ventilation-Perfusion Matching in Early-stage Prone Position Ventilation, NCT05816928. Registered 17 April 2023 - Retrospectively registered,https://clini","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142966402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}