Frequency-domain band-pass filtering enhanced pulsation method for pulmonary embolism diagnosis and risk stratification: A two-center retrospective study

Frequency-domain band-pass filtering enhanced pulsation method for pulmonary embolism diagnosis and risk stratification: A two-center retrospective study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Frequency-domain band-pass filtering enhanced pulsation method for pulmonary embolism diagnosis and risk stratification: A two-center retrospective study shaofei xu, Jiazheng Li, Ziqi Li, Yuxuan Cai, Junlai Zhao, Rongrong Zhu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9270053/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 7 You are reading this latest preprint version Abstract Background: Pulmonary embolism (PE) remains a major cause of morbidity and mortality in critical care, yet traditional diagnostic methods face limitations, especially in critically ill patients. This study introduces a novel frequency-domain band-pass filtering enhanced electrical impedance tomography (EIT) pulsation method for rapid, non-invasive PE diagnosis and risk stratification. Methods: In a two-center retrospective study, 106 participants (53 PE patients, 53 healthy controls) were enrolled. A 16-electrode EIT system recorded pulmonary blood flow pulsation signals, with a heart rate-adaptive 0.8–2 Hz band-pass filter to mitigate respiratory and motion artifacts. Key parameters (Matching Index （MI）, Dispersion Index (DI), Shunt Index (SI), Electrical Impedance VQ ratio(EVIQ)) were analyzed for PE diagnosis and risk stratification. Results: PE patients showed significantly lower MI and higher DI and SI compared to controls (P<0.001). For risk stratification, intermediate-high-risk PE patients had notably lower MI and higher DI than lower-risk groups (P<0.001). The combined MI+DI+SI model demonstrated the highest diagnostic efficacy for PE (AUC=0.820, 95% CI: 0.739–0.892), outperforming individual parameters. EIVQ showed no significant discriminative value. Conclusions: The enhanced EIT pulsation method effectively mitigates respiratory and motion artifacts. MI, DI, and SI are reliable non-invasive parameters for PE diagnosis and risk stratification, offering a practical bedside tool in critical care. Electrical impedance tomography Pulmonary embolism Frequency-domain band-pass filtering Pulsatility method Risk stratification Embolus location Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Pulmonary embolism (PE) is a life-threatening cardiovascular emergency and ranks as the third leading cause of cardiovascular mortality worldwide. The in-hospital mortality rate for untreated PE is 20-30%, with mortality rates exceeding 60% in critically ill intensive care unit (ICU) patients [1-3] . For ICU patients, prompt bedside assessment is crucial, as delayed diagnosis and intervention can significantly worsen outcomes. Computed tomography pulmonary angiography (CTPA), the gold standard for PE diagnosis, has inherent limitations for critically ill patients. These include ionizing radiation exposure, the risk of contrast-induced nephropathy—particularly in patients with renal impairment—and the need for patient transfer, which may be unfeasible for hemodynamically unstable individuals [4-5] . D-dimer, a commonly used non-invasive screening tool, has poor specificity (40-60%) in elderly, post-surgical, and oncological patients, who are core ICU subgroups. This leads to overutilization of CTPA and unnecessary resource consumption [6] . Although clinical probability scores such as 4PEPS and YEARS have optimized CTPA use by reducing unnecessary scans [7-8] , they lack direct diagnostic value and do not address the need for real-time bedside perfusion assessment. Pulsation-based pulmonary perfusion assessment, particularly using electrical impedance tomography (EIT) in pulsation mode, has emerged as a promising non-invasive method for evaluating ventilation-perfusion (V/Q) matching, a key pathophysiological feature of PE [9] . Advances such as three-dimensional EIT and saline contrast-enhanced techniques have improved perfusion imaging. However, conventional EIT approaches still face limitations due to signal artifacts, including respiratory-induced impedance changes, patient movement, and conductivity drift [10-11] . Standard low-pass filtering can attenuate respiratory harmonics but may also distort the amplitude and timing of cardiac signals. Additionally, traditional saline-contrast EIT studies rely on rapid injection of a hypertonic saline bolus through a central venous catheter, typically during a breath-hold of 8-15 seconds, which restricts use in ICU patients who cannot tolerate apnea [12-13] . To overcome these limitations, we developed a heart rate–adaptive frequency-domain band-pass filtering method. This method uses a 0.8–2 Hz band-pass filter tuned to the cardiac cycle, preserving pulmonary blood flow signals while suppressing respiratory (0.2–0.33 Hz) and low-frequency motion (<0.1 Hz) artifacts [14] . Unlike fixed low-pass filters that distort cardiac signal amplitude and timing, our heart rate–adaptive approach aligns with each patient's cardiac rhythm, enhancing signal specificity without the need for contrast agents [15] . This approach enables reliable, non-invasive signal acquisition during spontaneous breathing and is compatible with mechanically ventilated patients, addressing key barriers to bedside EIT use in critically ill populations. In this two-center retrospective study, we assessed the diagnostic performance of this heart rate–adaptive, frequency-domain filtered EIT method in critically ill patients with PE. To our knowledge, this is the first focused evaluation of such an approach for PE diagnosis in the ICU, providing evidence for a rapid, non-invasive bedside tool that bridges the gap between emerging pulmonary perfusion imaging technologies and their clinical implementation. Materials and Methods 2.1. Study Design and Participants This two-center, retrospective case-control study was conducted at Tsinghua Chang Gung Hospital in Beijing and Chenzhou First People’s Hospital in Hunan. The study was approved by the institutional review boards of both centers and conducted in accordance with the Declaration of Helsinki (2013 revision). Written informed consent was obtained from all participants or their legally authorized representatives. PE patients confirmed by CTPA were enrolled. All patients were treatment-naïve with complete clinical data, including biomarkers for disease severity. Healthy controls had normal health screening results and no history of significant cardiopulmonary disease, surgery, trauma, or infection. Exclusion criteria included severe cardiopulmonary dysfunction, pregnancy or lactation, and incomplete clinical or follow-up data. PE patients were independently evaluated by two senior radiologists and intensivists in a double-blind manner, with disagreements resolved by consensus. Embolus location was classified as central (main pulmonary artery or left/right main pulmonary arteries) or non-central (lobar, segmental, or subsegmental arteries). Key hemodynamic and laboratory parameters were used to assess disease severity and prognostic risk. 2.2. Frequency-Domain Band-Pass Filtering Enhanced Pulsation Method Pulsation imaging was performed using a dedicated system (Model ET1000, Infivision Medical Imaging, Beijing, China) with a 16-electrode chest belt and an integrated signal processing workstation. Measurements were conducted at the bedside, lasting about 5 minutes. Participants were positioned supine, with mechanically ventilated patients maintained on their original ventilator settings. The electrode belt was placed at the 4th–6th intercostal spaces using conductive paste, and pulmonary blood flow signals were recorded at 20 Hz for 60 seconds. Raw signals were processed using Fast Fourier Transform and a heart rate–adaptive 0.8–2 Hz band-pass filter to isolate cardiac-related pulsations. Respiratory interference and low-frequency motion were attenuated using notch and sliding-average filters. The signals were reconstructed in MATLAB R2023b through differential processing and periodic superposition averaging. Four key parameters were derived: Matching Index (MI), Dispersion Index (DI), Shunt Index (SI), and Electrical Impedance VQ ratio (EIVQ). 2.3. Clinical Data Collection Demographic data, including age, sex, and body mass index (BMI), along with laboratory parameters (D-dimer, troponin, NT-proBNP), were extracted from electronic medical records. Imaging data included CTPA and echocardiographic assessment of right ventricular (RV) function. RV dysfunction was defined by either an RV-to-left ventricular diameter ratio > 1.0 on CTPA or a tricuspid annular plane systolic excursion (TAPSE) < 16 mm on echocardiography [ 16 ] . 2.4. Statistical Analysis Statistical analyses were performed using SPSS 26.0 and Python 3.9. A two-sided P < 0.05 was considered significant. Continuous variables were tested for normality and presented as mean ± SD for normally distributed data and median (IQR) for non-normally distributed data. Categorical variables were reported as n (%). Between-group comparisons used the Mann–Whitney U test (two groups) and Kruskal–Wallis H test (multiple groups), with Dunn’s post-hoc test and Bonferroni correction for pairwise comparisons. Diagnostic performance was evaluated using ROC curve analysis (AUC, 95% CI, optimal cutoff, sensitivity, specificity). DeLong’s test was used to compare AUC differences between models. A combined model of MI, DI, and SI was built using Z-score standardized logistic regression, with parameter contributions expressed as percentages of total absolute standardized coefficients. Results 3.1. Baseline Characteristics A total of 106 participants were enrolled, including 53 PE patients and 53 healthy controls. Of the PE patients, 17 were recruited from Tsinghua Chang Gung Hospital and 36 from Chenzhou First People's Hospital. As shown in Table 1 , there were no significant differences in age (66.77 ± 14.36 vs. 62.55 ± 9.12 years; P = 0.0739), gender distribution (67.9% male in the PE group vs. 60.4% in controls; P = 0.5434), or body mass index (BMI, median 24.22 [IQR 22.48–26.01] vs. 24.88 [IQR 22.79–26.99] kg/m²; P = 0.3105) between the two groups. However, PE patients had significantly higher median D-dimer levels compared to the control group (6.19 [IQR 3.61–13.53] vs. 0.30 [IQR 0.19–0.51] mg/L; P < 0.001). Table 1 Baseline characteristics of the study population Characteristic PE Group (n = 53) Healthy Control Group (n = 53) Statistic P -value Age, years (mean ± SD,95%CI) 66.77 ± 14.36 (62.82–70.73) 62.55 ± 9.12 (60.03–65.06) t = 1.81 0.0739 Gender, n (%) χ²=0.37 0.5434 - Female 17(32.1%) 21(39.6%) - Male 36(67.9%) 32(60.4%) BMI (kg/m², median, IQR) 24.22(22.48–26.01) 24.88(22.79–26.99) U = 1243.50 0.3105 D-dimer (mg/L, median, IQR) 6.19(3.61–13.53) 0.30(0.19–0.51) U = 2777.50 < 0.001 Normally distributed data are presented as mean ± SD (95%CI), non-normally distributed data as median (IQR), and categorical data as n (%). Between-group comparisons were performed using the independent samples t-test, Mann-Whitney U test, or χ² test. PE, pulmonary embolism; BMI, body mass index. Two-tailed P < 0.05 was considered statistically significant. 3.2. Pulsation Parameters: PE Patients vs. Healthy Controls MI, DI, and SI, the three core pulsation parameters, differed significantly between the PE and control groups, with MI showing the strongest discriminative power. No significant difference was found for EVIQ (Table 2 , Fig. 1 ). Specifically, MI was lower in the PE group (0.81 [0.72–0.85] vs. 0.91 [0.88–0.93]; P < 0.001), while DI (0.18 [0.10–0.21] vs. 0.07 [0.05–0.10]; P < 0.001) and SI (0.03 [0.00–0.10] vs. 0.01 [0.00–0.02]; P < 0.001) were higher in the PE group. Table 2 Comparisons of MI, DI, SI, and EVIQ between PE group and healthy control group Parameter PE (n = 53), median (IQR) Healthy(n = 53), median (IQR) Z value P value MI 0.81 (0.72–0.85) 0.91 (0.88–0.93) -5.63 < 0.001 DI 0.18 (0.10–0.21) 0.07 (0.05–0.10) 3.61 < 0.001 SI 0.03 (0.00–0.10) 0.01 (0.00–0.02) 2.77 < 0.001 EIVQ 0.48 (0.24–0.89) 1.36 (1.24–1.67) -1.52 0.13 Abbreviations: MI:Matching Index; DI:Dispersion Index; SI:Shunt Index; EVIQ:Electrical Impedance VQ ratio Table 3 EIT-related parameters across different risk stratification groups of pulmonary embolism Parameter Low-risk (n = 19) Int.-low (n = 15) Int.-high (n = 19) Kruskal-Wallis Dunn’s post-hoc test with Bonferroni correction MI 0.87 (0.81–0.91) 0.84 (0.80–0.87) 0.68 (0.56–0.74) χ²=24.70, P < 0.001 Low-risk vs Int.-low: ns; Low-risk vs Int.-high: ***; Int.-low vs Int.-high: ** DI 0.09 (0.04–0.12) 0.13 (0.03–0.14) 0.22 (0.17–0.32) χ²=15.20, P < 0.001 Low-risk vs Int.-low: ns; Low-risk vs Int.-high: ***; Int.-low vs Int.-high: ** SI 0.05 (0.01–0.10) 0.07 (0.01–0.11) 0.15 (0.04–0.24) χ²=4.68, P = 0.096 Low-risk vs Int.-low: ns; Low-risk vs Int.-high: ns; Int.-low vs Int.-high: ns EIVQ 1.43 (1.16–1.79) 1.41 (0.53–1.99) 0.42 (0.17–1.72) χ²=4.41, P = 0.110 Low-risk vs Int.-low: ns; Low-risk vs Int.-high: ns; Int.-low vs Int.-high: ns Data are presented as median (interquartile range, IQR).Comparisons among groups were performed using the Kruskal–Wallis test, followed by Dunn’s post-hoc test with Bonferroni correction.Significance levels: ***P < 0.001, **P < 0.01, ns = not significant.Bonferroni-corrected significance level α = 0.017. 3.3. Pulsation Parameters and PE Risk Stratification MI and DI showed significant overall differences across three PE risk stratification subgroups (MI: χ²=24.70, P < 0.001; DI: χ²=15.20, P < 0.001; Kruskal-Wallis test). Dunn’s post-hoc test with Bonferroni correction (α = 0.017) revealed that intermediate-high-risk patients had significantly lower MI and higher DI than low-risk and intermediate-low-risk patients (all ***P 0.017). SI and EIVQ exhibited no significant overall differences among the three subgroups (SI: χ²=4.68, P = 0.096; EIVQ: χ²=4.41, P = 0.110). 3.4. Pulsation Parameters and Embolus Location Central PE patients had significantly lower MI and higher SI than Non-central PE patients, with no significant differences in DI or EVIQ between the two subgroups (Table 4 , Fig. 3 ). MI in Central PE patients was 0.76 [0.67–0.85], lower than that in Non-central PE patients (0.88 [0.81–0.89]; P < 0.05). In contrast, SI was higher in Central PE patients (0.11 [0.02–0.18] vs. 0.05 [0.00–0.07] in Non-central PE patients; P < 0.05). Table 4 Comparison of EIT-related parameters between Central and Non-central PE groups Parameter Central PE(n = 35) (median, IQR) Non-centralPE(n = 18) (median, IQR) Z value P value MI 0.76 (0.67–0.85) 0.88 (0.81–0.89) -2.85 P < 0.05 DI 0.16 (0.08–0.23) 0.10 (0.05–0.14) 1.37 0.1727 SI 0.11 (0.02–0.18) 0.05 (0.00–0.07) 1.97 P < 0.05 EIVQ 1.41 (0.32–1.99) 1.33 (0.48–1.78) 0.27 0.7926 3.5. Diagnostic Efficacy of Individual Parameters and Combined Model MI was the most effective single parameter for PE diagnosis. The combined MI + DI+SI model optimized diagnostic performance with balanced sensitivity and specificity (Table 5 , Fig. 4 ). DeLong’s test confirmed that the combined model had a significantly higher AUC than DI and SI (both P < 0.001). MI had an AUC of 0.817 (95% CI: 0.734–0.891); at the optimal cutoff value of ≤ 0.81, the sensitivity was 73.6% and specificity was 84.9%. The combined MI + DI+SI model achieved an AUC of 0.820 (95% CI: 0.739–0.892); with an optimal cutoff value of ≥ 0.46, it had a sensitivity of 66.0% and specificity of 84.9%. Table 5 Diagnostic efficacy for PE: individual parameters vs. combined model Parameter/Model AUC (95% CI) Cut-off value Sensitivity, (%) Specificity, (%) Youden index MI 0.817 (0.734–0.891) ≤ 0.81 73.6 84.9 0.585 DI 0.703 (0.597–0.804) ≥ 0.13 54.7 88.7 0.434 SI 0.656 (0.544–0.758) ≥ 0.11 39.6 92.5 0.321 Combined (MI + DI+SI) 0.820 (0.739–0.892) ≥ 0.46 66.0 84.9 0.509 Abbreviations: AUC, area under the curve; CI, confidence interval. Discussion Pulmonary embolism (PE) remains a leading cause of morbidity and mortality in critically ill patients, and accurate diagnosis is crucial. Conventional diagnostic methods, including SPECT ventilation/perfusion (V/Q) imaging, clinical scoring systems with D-dimer thresholds, bedside ultrasound, and CTPA, have limitations in the ICU. These limitations include operator dependence, difficulties in assessing regional perfusion defects, and transport risks in unstable patients, especially for CTPA, which also involves radiation exposure and contrast-related nephropathy [ 17 – 23 ] . In this context, electrical impedance tomography (EIT) offers a promising non-invasive, contrast-free alternative for pulmonary perfusion assessment. Previous clinical studies have shown that EIT is effective in detecting regional perfusion defects and monitoring dynamic changes in PE, emphasizing its potential clinical utility [ 24 – 25 ] . Furthermore, international consensus supports EIT’s role in evaluating ventilation and perfusion in critically ill adults [ 26 ] . Preclinical studies have demonstrated that pulsatility-based signals in EIT reflect pulmonary blood flow, and three-dimensional EIT enhances spatial resolution and coverage [ 27 – 28 ] . Building on these advances, we applied a heart rate–adaptive frequency-domain band-pass filtering method, isolating cardiac-related impedance changes while minimizing respiratory and motion artifacts. This approach enables real-time, bedside assessment of pulmonary perfusion without the need for breath-holding or contrast agents, offering a clinically actionable tool for monitoring PE in critically ill patients. Our findings showed that PE patients exhibited significantly lower MI, and higher DI and SI compared to healthy controls, which aligns with the ventilation-perfusion mismatch characteristic of PE [ 29 – 30 ] . Specifically, the MI + DI+SI combined model demonstrated an AUC of 0.820 (95% CI: 0.739–0.892), confirming the diagnostic efficacy of this method and supporting its clinical relevance for PE detection. The MI parameter exhibited the strongest discriminative power, consistent with its ability to reflect global perfusion disturbances in PE. The stepwise changes in MI and DI with increasing PE severity further validate their utility in risk stratification. Intermediate-high-risk PE patients had markedly lower MI and higher DI compared to low-risk patients, highlighting the potential of EIT-derived indices to quantify the extent of perfusion disturbance and aid in tailoring treatment strategies. Our study also provides new insights into the relationship between embolus location and regional perfusion abnormalities. Central PE patients had significantly lower MI and higher SI compared to non-central PE patients. These findings suggest that EIT-derived indices can detect regional differences in pulmonary perfusion, which are not readily identified by traditional clinical evaluation methods. This supports the growing body of evidence that EIT can provide valuable information about embolus burden and location, potentially improving patient management. The heart rate–adaptive frequency-domain band-pass filtering method represents an innovation in EIT, addressing the limitations of fixed filtering techniques and enabling better signal fidelity in critically ill patients. The dynamic nature of this method, which adapts to each patient’s cardiac rhythm, significantly improves diagnostic accuracy while reducing the need for contrast agents and breath-holding maneuvers. This is particularly important in the ICU, where patients may be unable to cooperate with traditional imaging procedures. Our findings suggest that EIT could be integrated into routine ICU practice, providing a non-invasive, real-time tool for monitoring PE and other pulmonary diseases. Several limitations must be considered. The retrospective, two-center design limits the generalizability of our findings. A larger, multi-center, prospective study is needed to validate the diagnostic performance of this method in diverse ICU populations. Another limitation is the potential heterogeneity of patients, which may affect the consistency of EIT measurements. Variations in comorbidities and hemodynamics could impact the method’s accuracy. Future studies should address how these factors influence EIT performance. Finally, while our study focused on PE diagnosis, its role in monitoring therapeutic response remains unclear. Further research should explore its potential for dynamic monitoring during treatment, especially in patients receiving thrombolytics or anticoagulation. In conclusion, the heart rate–adaptive frequency-domain band-pass filtering method for EIT provides a rapid, non-invasive, bedside tool for assessing pulmonary perfusion in critically ill PE patients. It offers valuable physiological insights that can complement conventional diagnostic methods and improve risk stratification. Future research should focus on prospective validation, direct comparisons with other imaging modalities, and exploring its role in monitoring treatment response to further establish its clinical utility in ICU settings. Declarations Author Contributions Shaofei Xu: Study design, clinical data acquisition, initial manuscript drafting. Jiazheng Li: Data analysis, manuscript revision, study design refinement. Ziqi Li: Study design optimization, clinical data validation, data analysis. Yuxuan Cai: Theoretical framework, data validation. Junlai Zhao: Clinical data curation, data analysis. Rongrong Zhu: Data organization, figure preparation, manuscript review. Maokun Li: Study supervision, data interpretation guidance, critical manuscript revision. Weiwei Wu: Overall study design, funding acquisition, team coordination, journal correspondence. Funding Information This study was supported by the Beijing Natural Science Foundation (L246021), the Beijing Science and Technology Plan (Z231100004623007), the Health Commission of Hunan Province (20257230) and the Hunan Provincial Natural Science Foundation (2026JJ80637). Acknowledgments The authors thank the medical staff of the Department of Vascular Surgery, Beijing Tsinghua Changgung Hospital, for clinical data collection support. Data Availability Data supporting the findings are available from corresponding authors upon reasonable request. Ethical Approval This study was approved by the Ethics Committee of Beijing Tsinghua Changgung Hospital (Ethical Approval No: 25893-0-01). All procedures were performed in accordance with the Declaration of Helsinki.Written informed consent was obtained from the patient for publication of this case report and any accompanying images. Conflict of Interest Disclosure The authors have no conflicts of interest to declare Consent for publication All authors have agreed to the publication of this manuscript. Competing interests The authors declare that they have no competing interests. References Konstantinides SV, Meyer G, Becattini C, et al. 2024 ESC guidelines for the diagnosis and management of acute pulmonary embolism. Eur Heart J. 2024;45(47):4713–808. 10.1093/eurheartj/ehu456 . Chopard R, Behr J, Vidoni C, Ecarnot F, Meneveau N. An update on the management of acute high-risk pulmonary embolism. J Clin Med. 2022;11(16):4807. 10.3390/jcm11164807 . de Wit K, D'Arsigny CL. Risk stratification of acute pulmonary embolism. J Thromb Haemost. 2023;21(11):3016–23. 10.1016/j.jtha.2023.05.003 . 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Bedside evaluation of pulmonary perfusion heterogeneity using electrical impedance tomography. Crit Care. 2024;28:299. 10.1186/s13613-024-08889-9 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 13 May, 2026 Reviews received at journal 15 Apr, 2026 Reviewers agreed at journal 14 Apr, 2026 Reviewers invited by journal 14 Apr, 2026 Editor assigned by journal 01 Apr, 2026 Submission checks completed at journal 01 Apr, 2026 First submitted to journal 30 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9270053","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":624434509,"identity":"c24535df-ba45-4830-b7a1-6ef839907b10","order_by":0,"name":"shaofei xu","email":"","orcid":"","institution":"Chenzhou First People's Hospital and the first Affiliated Hospital of Xiangnan University","correspondingAuthor":false,"prefix":"","firstName":"shaofei","middleName":"","lastName":"xu","suffix":""},{"id":624434510,"identity":"2207f90f-1683-4504-9b57-9f377e3f5164","order_by":1,"name":"Jiazheng Li","email":"","orcid":"","institution":"Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University","correspondingAuthor":false,"prefix":"","firstName":"Jiazheng","middleName":"","lastName":"Li","suffix":""},{"id":624434511,"identity":"8b10472c-2b5e-43f0-b15a-c4188433d720","order_by":2,"name":"Ziqi Li","email":"","orcid":"","institution":"Tsinghua University","correspondingAuthor":false,"prefix":"","firstName":"Ziqi","middleName":"","lastName":"Li","suffix":""},{"id":624434513,"identity":"1e47a039-56ae-4b85-b64a-e3fc667f08a9","order_by":3,"name":"Yuxuan Cai","email":"","orcid":"","institution":"Tsinghua University","correspondingAuthor":false,"prefix":"","firstName":"Yuxuan","middleName":"","lastName":"Cai","suffix":""},{"id":624434514,"identity":"8f057c6b-3f42-4f5a-852f-788fc9ef5462","order_by":4,"name":"Junlai Zhao","email":"","orcid":"","institution":"Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University","correspondingAuthor":false,"prefix":"","firstName":"Junlai","middleName":"","lastName":"Zhao","suffix":""},{"id":624434518,"identity":"783d21d4-2aaa-403b-a00d-796c4d46d97e","order_by":5,"name":"Rongrong Zhu","email":"","orcid":"","institution":"Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University","correspondingAuthor":false,"prefix":"","firstName":"Rongrong","middleName":"","lastName":"Zhu","suffix":""},{"id":624434519,"identity":"208f3191-d68e-4017-8365-71eb2bb710ad","order_by":6,"name":"Maokun Li","email":"","orcid":"","institution":"Tsinghua University","correspondingAuthor":false,"prefix":"","firstName":"Maokun","middleName":"","lastName":"Li","suffix":""},{"id":624434520,"identity":"5ea9faeb-82f9-4619-941b-269002852808","order_by":7,"name":"weiwei Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYPACCQYGZuYDBz5UkKaFLfHgjDOk2cRjfJi3hQh15hLZiZ95KiwS+9l5PhzgbWCQ5xc7gF+L5YzczdI8ZyQSZzbzbjgguYPBcObsBPxaDG7kbmPObZPI3XAYqMXwDEOCwW2itPyTyN1/mOfBgcQ2orU0AG1h5mE4cJAYLZY9bzdL/zkmUT/jMJvBwYYzEoT9Ys6eu/HjjJo6Y/7+w48//6mwkeeXJuQwNL4EfuXYtIyCUTAKRsEowAQAUEJG1jiOK7sAAAAASUVORK5CYII=","orcid":"","institution":"Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University","correspondingAuthor":true,"prefix":"","firstName":"weiwei","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2026-03-30 16:54:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9270053/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9270053/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107616086,"identity":"0b02c401-0950-461d-9a35-f32f0a7202e6","added_by":"auto","created_at":"2026-04-23 09:12:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":113034,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of EIT pulsation parameters between PE patients and healthy controls. Data presented as median (IQR). *P\u0026lt;0.05, **P\u0026lt;0.01, ***P\u0026lt;0.001, ns=not significant (Mann-Whitney U test); EIT=electrical impedance tomography, PE=pulmonary embolism, MI=Matching Index, DI=Dispersion Index, SI=Shunt Index,\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9270053/v1/d89382bfbf9103e06f25bab5.png"},{"id":107616087,"identity":"ae8beb7d-2991-4b10-91ab-ce15d55739f0","added_by":"auto","created_at":"2026-04-23 09:12:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":291771,"visible":true,"origin":"","legend":"\u003cp\u003eEIT pulsation parameters across PE risk stratification subgroups .Data are median (IQR). Data presented as median (IQR). Kruskal-Wallis H test:MI was significantly lower and DI significantly higher in the intermediate-high-risk group versus the low-risk group (both P\u0026lt;0.001). No significant differences were found in SI or EIVQ (all P\u0026gt;0.05). Pairwise comparisons: Dunn’s post-hoc test with Bonferroni correction.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9270053/v1/e1c026e642f0fece9119f8b5.png"},{"id":107616062,"identity":"61e123aa-f1b5-4fc4-a9ec-7fabca928b0f","added_by":"auto","created_at":"2026-04-23 09:12:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":109396,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of EIT pulsation parameters between central PE (n=35) and non-central PE (n=18) patients. Data presented as median (IQR). *P\u0026lt;0.05, **P\u0026lt;0.01, ns=not significant (Mann-Whitney U test).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9270053/v1/5e06ed24bee2ae79827c2cc1.png"},{"id":107616056,"identity":"26d7e24c-5440-461c-926d-dabf7eaa4454","added_by":"auto","created_at":"2026-04-23 09:12:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":138153,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves for diagnostic efficacy of individual EIT parameters and the MI+DI+SI combined model for PE. AUC (95% CI): MI=0.817, DI=0.703, SI=0.656, combined model=0.820. DeLong’s test confirmed the combined model had a significantly higher AUC than DI and SI (both P\u0026lt;0.001); ROC=receiver operating characteristic, AUC=area under the curve.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9270053/v1/1b37bc205d5c23839c72bf25.png"},{"id":107616408,"identity":"7e38cb83-1b7c-4ea3-b3de-85553a325562","added_by":"auto","created_at":"2026-04-23 09:13:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1030150,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9270053/v1/0cd255f3-fdb8-4683-aac0-4247d89e0893.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Frequency-domain band-pass filtering enhanced pulsation method for pulmonary embolism diagnosis and risk stratification: A two-center retrospective study","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePulmonary embolism (PE) is a life-threatening cardiovascular emergency and ranks as the third leading cause of cardiovascular mortality worldwide. The in-hospital mortality rate for untreated PE is 20-30%, with mortality rates exceeding 60% in critically ill intensive care unit (ICU) patients\u003csup\u003e\u0026nbsp;[1-3]\u003c/sup\u003e. For ICU patients, prompt bedside assessment is crucial, as delayed diagnosis and intervention can significantly worsen outcomes.\u003c/p\u003e\n\u003cp\u003eComputed tomography pulmonary angiography (CTPA), the gold standard for PE diagnosis, has inherent limitations for critically ill patients. These include ionizing radiation exposure, the risk of contrast-induced nephropathy\u0026mdash;particularly in patients with renal impairment\u0026mdash;and the need for patient transfer, which may be unfeasible for hemodynamically unstable individuals\u003csup\u003e[4-5]\u003c/sup\u003e. D-dimer, a commonly used non-invasive screening tool, has poor specificity (40-60%) in elderly, post-surgical, and oncological patients, who are core ICU subgroups. This leads to overutilization of CTPA and unnecessary resource consumption\u003csup\u003e[6]\u003c/sup\u003e. Although clinical probability scores such as 4PEPS and YEARS have optimized CTPA use by reducing unnecessary scans\u003csup\u003e[7-8]\u003c/sup\u003e, they lack direct diagnostic value and do not address the need for real-time bedside perfusion assessment.\u003c/p\u003e\n\u003cp\u003ePulsation-based pulmonary perfusion assessment, particularly using electrical impedance tomography (EIT) in pulsation mode, has emerged as a promising non-invasive method for evaluating ventilation-perfusion (V/Q) matching, a key pathophysiological feature of PE\u003csup\u003e\u0026nbsp;[9]\u003c/sup\u003e. Advances such as three-dimensional EIT and saline contrast-enhanced techniques have improved perfusion imaging. However, conventional EIT approaches still face limitations due to signal artifacts, including respiratory-induced impedance changes, patient movement, and conductivity drift\u003csup\u003e[10-11]\u003c/sup\u003e. Standard low-pass filtering can attenuate respiratory harmonics but may also distort the amplitude and timing of cardiac signals. Additionally, traditional saline-contrast EIT studies rely on rapid injection of a hypertonic saline bolus through a central venous catheter, typically during a breath-hold of 8-15 seconds, which restricts use in ICU patients who cannot tolerate apnea\u003csup\u003e[12-13]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eTo overcome these limitations, we developed a heart rate\u0026ndash;adaptive frequency-domain band-pass filtering method. This method uses a 0.8\u0026ndash;2 Hz band-pass filter tuned to the cardiac cycle, preserving pulmonary blood flow signals while suppressing respiratory (0.2\u0026ndash;0.33 Hz) and low-frequency motion (\u0026lt;0.1 Hz) artifacts\u003csup\u003e[14]\u003c/sup\u003e. Unlike fixed low-pass filters that distort cardiac signal amplitude and timing, our heart rate\u0026ndash;adaptive approach aligns with each patient\u0026apos;s cardiac rhythm, enhancing signal specificity without the need for contrast agents \u003csup\u003e[15]\u003c/sup\u003e. This approach enables reliable, non-invasive signal acquisition during spontaneous breathing and is compatible with mechanically ventilated patients, addressing key barriers to bedside EIT use in critically ill populations.\u003c/p\u003e\n\u003cp\u003eIn this two-center retrospective study, we assessed the diagnostic performance of this heart rate\u0026ndash;adaptive, frequency-domain filtered EIT method in critically ill patients with PE. To our knowledge, this is the first focused evaluation of such an approach for PE diagnosis in the ICU, providing evidence for a rapid, non-invasive bedside tool that bridges the gap between emerging pulmonary perfusion imaging technologies and their clinical implementation.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study Design and Participants\u003c/h2\u003e \u003cp\u003eThis two-center, retrospective case-control study was conducted at Tsinghua Chang Gung Hospital in Beijing and Chenzhou First People\u0026rsquo;s Hospital in Hunan. The study was approved by the institutional review boards of both centers and conducted in accordance with the Declaration of Helsinki (2013 revision). Written informed consent was obtained from all participants or their legally authorized representatives. PE patients confirmed by CTPA were enrolled. All patients were treatment-na\u0026iuml;ve with complete clinical data, including biomarkers for disease severity. Healthy controls had normal health screening results and no history of significant cardiopulmonary disease, surgery, trauma, or infection. Exclusion criteria included severe cardiopulmonary dysfunction, pregnancy or lactation, and incomplete clinical or follow-up data. PE patients were independently evaluated by two senior radiologists and intensivists in a double-blind manner, with disagreements resolved by consensus. Embolus location was classified as central (main pulmonary artery or left/right main pulmonary arteries) or non-central (lobar, segmental, or subsegmental arteries). Key hemodynamic and laboratory parameters were used to assess disease severity and prognostic risk.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Frequency-Domain Band-Pass Filtering Enhanced Pulsation Method\u003c/h2\u003e \u003cp\u003ePulsation imaging was performed using a dedicated system (Model ET1000, Infivision Medical Imaging, Beijing, China) with a 16-electrode chest belt and an integrated signal processing workstation. Measurements were conducted at the bedside, lasting about 5 minutes. Participants were positioned supine, with mechanically ventilated patients maintained on their original ventilator settings. The electrode belt was placed at the 4th\u0026ndash;6th intercostal spaces using conductive paste, and pulmonary blood flow signals were recorded at 20 Hz for 60 seconds. Raw signals were processed using Fast Fourier Transform and a heart rate\u0026ndash;adaptive 0.8\u0026ndash;2 Hz band-pass filter to isolate cardiac-related pulsations. Respiratory interference and low-frequency motion were attenuated using notch and sliding-average filters. The signals were reconstructed in MATLAB R2023b through differential processing and periodic superposition averaging. Four key parameters were derived: Matching Index (MI), Dispersion Index (DI), Shunt Index (SI), and Electrical Impedance VQ ratio (EIVQ).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Clinical Data Collection\u003c/h2\u003e \u003cp\u003eDemographic data, including age, sex, and body mass index (BMI), along with laboratory parameters (D-dimer, troponin, NT-proBNP), were extracted from electronic medical records. Imaging data included CTPA and echocardiographic assessment of right ventricular (RV) function. RV dysfunction was defined by either an RV-to-left ventricular diameter ratio\u0026thinsp;\u0026gt;\u0026thinsp;1.0 on CTPA or a tricuspid annular plane systolic excursion (TAPSE)\u0026thinsp;\u0026lt;\u0026thinsp;16 mm on echocardiography \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Statistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using SPSS 26.0 and Python 3.9. A two-sided P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant. Continuous variables were tested for normality and presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD for normally distributed data and median (IQR) for non-normally distributed data. Categorical variables were reported as n (%). Between-group comparisons used the Mann\u0026ndash;Whitney U test (two groups) and Kruskal\u0026ndash;Wallis H test (multiple groups), with Dunn\u0026rsquo;s post-hoc test and Bonferroni correction for pairwise comparisons. Diagnostic performance was evaluated using ROC curve analysis (AUC, 95% CI, optimal cutoff, sensitivity, specificity). DeLong\u0026rsquo;s test was used to compare AUC differences between models. A combined model of MI, DI, and SI was built using Z-score standardized logistic regression, with parameter contributions expressed as percentages of total absolute standardized coefficients.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Baseline Characteristics\u003c/h2\u003e \u003cp\u003eA total of 106 participants were enrolled, including 53 PE patients and 53 healthy controls. Of the PE patients, 17 were recruited from Tsinghua Chang Gung Hospital and 36 from Chenzhou First People's Hospital. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, there were no significant differences in age (66.77\u0026thinsp;\u0026plusmn;\u0026thinsp;14.36 vs. 62.55\u0026thinsp;\u0026plusmn;\u0026thinsp;9.12 years; P\u0026thinsp;=\u0026thinsp;0.0739), gender distribution (67.9% male in the PE group vs. 60.4% in controls; P\u0026thinsp;=\u0026thinsp;0.5434), or body mass index (BMI, median 24.22 [IQR 22.48\u0026ndash;26.01] vs. 24.88 [IQR 22.79\u0026ndash;26.99] kg/m\u0026sup2;; P\u0026thinsp;=\u0026thinsp;0.3105) between the two groups. However, PE patients had significantly higher median D-dimer levels compared to the control group (6.19 [IQR 3.61\u0026ndash;13.53] vs. 0.30 [IQR 0.19\u0026ndash;0.51] mg/L; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of the study population\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePE Group (n\u0026thinsp;=\u0026thinsp;53)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHealthy Control Group (n\u0026thinsp;=\u0026thinsp;53)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD,95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66.77\u0026thinsp;\u0026plusmn;\u0026thinsp;14.36 (62.82\u0026ndash;70.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.55\u0026thinsp;\u0026plusmn;\u0026thinsp;9.12 (60.03\u0026ndash;65.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0739\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ\u0026sup2;=0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5434\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e- Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17(32.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21(39.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e- Male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36(67.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32(60.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u0026sup2;, median, IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.22(22.48\u0026ndash;26.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.88(22.79\u0026ndash;26.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eU\u0026thinsp;=\u0026thinsp;1243.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD-dimer (mg/L, median, IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.19(3.61\u0026ndash;13.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.30(0.19\u0026ndash;0.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eU\u0026thinsp;=\u0026thinsp;2777.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNormally distributed data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (95%CI), non-normally distributed data as median (IQR), and categorical data as n (%). Between-group comparisons were performed using the independent samples t-test, Mann-Whitney U test, or χ\u0026sup2; test. PE, pulmonary embolism; BMI, body mass index. Two-tailed P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Pulsation Parameters: PE Patients vs. Healthy Controls\u003c/h2\u003e \u003cp\u003eMI, DI, and SI, the three core pulsation parameters, differed significantly between the PE and control groups, with MI showing the strongest discriminative power. No significant difference was found for EVIQ (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Specifically, MI was lower in the PE group (0.81 [0.72\u0026ndash;0.85] vs. 0.91 [0.88\u0026ndash;0.93]; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while DI (0.18 [0.10\u0026ndash;0.21] vs. 0.07 [0.05\u0026ndash;0.10]; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and SI (0.03 [0.00\u0026ndash;0.10] vs. 0.01 [0.00\u0026ndash;0.02]; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were higher in the PE group.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparisons of MI, DI, SI, and EVIQ between PE group and healthy control group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePE (n\u0026thinsp;=\u0026thinsp;53), median (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHealthy(n\u0026thinsp;=\u0026thinsp;53),\u003c/p\u003e \u003cp\u003emedian (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.81 (0.72\u0026ndash;0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.91 (0.88\u0026ndash;0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-5.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.18 (0.10\u0026ndash;0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.07 (0.05\u0026ndash;0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.03 (0.00\u0026ndash;0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01 (0.00\u0026ndash;0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEIVQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.48 (0.24\u0026ndash;0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.36 (1.24\u0026ndash;1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eAbbreviations: MI:Matching Index; DI:Dispersion Index; SI:Shunt Index; EVIQ:Electrical Impedance VQ ratio\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEIT-related parameters across different risk stratification groups of pulmonary embolism\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow-risk (n\u0026thinsp;=\u0026thinsp;19)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInt.-low (n\u0026thinsp;=\u0026thinsp;15)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInt.-high (n\u0026thinsp;=\u0026thinsp;19)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKruskal-Wallis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDunn\u0026rsquo;s post-hoc test with Bonferroni correction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.87 (0.81\u0026ndash;0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.84 (0.80\u0026ndash;0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.68 (0.56\u0026ndash;0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=24.70, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLow-risk vs Int.-low: ns; Low-risk vs Int.-high: ***; Int.-low vs Int.-high: **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.09 (0.04\u0026ndash;0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.13 (0.03\u0026ndash;0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.22 (0.17\u0026ndash;0.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=15.20, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLow-risk vs Int.-low: ns; Low-risk vs Int.-high: ***; Int.-low vs Int.-high: **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.05 (0.01\u0026ndash;0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.07 (0.01\u0026ndash;0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.15 (0.04\u0026ndash;0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=4.68, P\u0026thinsp;=\u0026thinsp;0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLow-risk vs Int.-low: ns; Low-risk vs Int.-high: ns; Int.-low vs Int.-high: ns\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEIVQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.43 (1.16\u0026ndash;1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.41 (0.53\u0026ndash;1.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.42 (0.17\u0026ndash;1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=4.41, P\u0026thinsp;=\u0026thinsp;0.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLow-risk vs Int.-low: ns; Low-risk vs Int.-high: ns; Int.-low vs Int.-high: ns\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eData are presented as median (interquartile range, IQR).Comparisons among groups were performed using the Kruskal\u0026ndash;Wallis test, followed by Dunn\u0026rsquo;s post-hoc test with Bonferroni correction.Significance levels: ***P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ns\u0026thinsp;=\u0026thinsp;not significant.Bonferroni-corrected significance level α\u0026thinsp;=\u0026thinsp;0.017.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Pulsation Parameters and PE Risk Stratification\u003c/h2\u003e \u003cp\u003eMI and DI showed significant overall differences across three PE risk stratification subgroups (MI: χ\u0026sup2;=24.70, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; DI: χ\u0026sup2;=15.20, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Kruskal-Wallis test). Dunn\u0026rsquo;s post-hoc test with Bonferroni correction (α\u0026thinsp;=\u0026thinsp;0.017) revealed that intermediate-high-risk patients had significantly lower MI and higher DI than low-risk and intermediate-low-risk patients (all ***P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while no significant differences were observed between low-risk and intermediate-low-risk subgroups (ns, P\u0026thinsp;\u0026gt;\u0026thinsp;0.017). SI and EIVQ exhibited no significant overall differences among the three subgroups (SI: χ\u0026sup2;=4.68, P\u0026thinsp;=\u0026thinsp;0.096; EIVQ: χ\u0026sup2;=4.41, P\u0026thinsp;=\u0026thinsp;0.110).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Pulsation Parameters and Embolus Location\u003c/h2\u003e \u003cp\u003eCentral PE patients had significantly lower MI and higher SI than Non-central PE patients, with no significant differences in DI or EVIQ between the two subgroups (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). MI in Central PE patients was 0.76 [0.67\u0026ndash;0.85], lower than that in Non-central PE patients (0.88 [0.81\u0026ndash;0.89]; P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In contrast, SI was higher in Central PE patients (0.11 [0.02\u0026ndash;0.18] vs. 0.05 [0.00\u0026ndash;0.07] in Non-central PE patients; P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of EIT-related parameters between Central and Non-central PE groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCentral PE(n\u0026thinsp;=\u0026thinsp;35)\u003c/p\u003e \u003cp\u003e(median, IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-centralPE(n\u0026thinsp;=\u0026thinsp;18)\u003c/p\u003e \u003cp\u003e(median, IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.76 (0.67\u0026ndash;0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.88 (0.81\u0026ndash;0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.16 (0.08\u0026ndash;0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.10 (0.05\u0026ndash;0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1727\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.11 (0.02\u0026ndash;0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.05 (0.00\u0026ndash;0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEIVQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.41 (0.32\u0026ndash;1.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.33 (0.48\u0026ndash;1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7926\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Diagnostic Efficacy of Individual Parameters and Combined Model\u003c/h2\u003e \u003cp\u003eMI was the most effective single parameter for PE diagnosis. The combined MI\u0026thinsp;+\u0026thinsp;DI+SI model optimized diagnostic performance with balanced sensitivity and specificity (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). DeLong\u0026rsquo;s test confirmed that the combined model had a significantly higher AUC than DI and SI (both P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). MI had an AUC of 0.817 (95% CI: 0.734\u0026ndash;0.891); at the optimal cutoff value of \u0026le;\u0026thinsp;0.81, the sensitivity was 73.6% and specificity was 84.9%. The combined MI\u0026thinsp;+\u0026thinsp;DI+SI model achieved an AUC of 0.820 (95% CI: 0.739\u0026ndash;0.892); with an optimal cutoff value of \u0026ge;\u0026thinsp;0.46, it had a sensitivity of 66.0% and specificity of 84.9%.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiagnostic efficacy for PE: individual parameters vs. combined model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter/Model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCut-off value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSensitivity, (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpecificity, (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYouden index\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.817 (0.734\u0026ndash;0.891)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e73.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e84.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.585\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.703 (0.597\u0026ndash;0.804)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e88.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.434\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.656 (0.544\u0026ndash;0.758)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e92.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.321\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCombined (MI\u0026thinsp;+\u0026thinsp;DI+SI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.820 (0.739\u0026ndash;0.892)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e84.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.509\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eAbbreviations: AUC, area under the curve; CI, confidence interval.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003ePulmonary embolism (PE) remains a leading cause of morbidity and mortality in critically ill patients, and accurate diagnosis is crucial. Conventional diagnostic methods, including SPECT ventilation/perfusion (V/Q) imaging, clinical scoring systems with D-dimer thresholds, bedside ultrasound, and CTPA, have limitations in the ICU. These limitations include operator dependence, difficulties in assessing regional perfusion defects, and transport risks in unstable patients, especially for CTPA, which also involves radiation exposure and contrast-related nephropathy \u003csup\u003e[\u003cspan additionalcitationids=\"CR18 CR19 CR20 CR21 CR22\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this context, electrical impedance tomography (EIT) offers a promising non-invasive, contrast-free alternative for pulmonary perfusion assessment. Previous clinical studies have shown that EIT is effective in detecting regional perfusion defects and monitoring dynamic changes in PE, emphasizing its potential clinical utility \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Furthermore, international consensus supports EIT\u0026rsquo;s role in evaluating ventilation and perfusion in critically ill adults \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Preclinical studies have demonstrated that pulsatility-based signals in EIT reflect pulmonary blood flow, and three-dimensional EIT enhances spatial resolution and coverage \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Building on these advances, we applied a heart rate\u0026ndash;adaptive frequency-domain band-pass filtering method, isolating cardiac-related impedance changes while minimizing respiratory and motion artifacts. This approach enables real-time, bedside assessment of pulmonary perfusion without the need for breath-holding or contrast agents, offering a clinically actionable tool for monitoring PE in critically ill patients.\u003c/p\u003e \u003cp\u003eOur findings showed that PE patients exhibited significantly lower MI, and higher DI and SI compared to healthy controls, which aligns with the ventilation-perfusion mismatch characteristic of PE \u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Specifically, the MI\u0026thinsp;+\u0026thinsp;DI+SI combined model demonstrated an AUC of 0.820 (95% CI: 0.739\u0026ndash;0.892), confirming the diagnostic efficacy of this method and supporting its clinical relevance for PE detection. The MI parameter exhibited the strongest discriminative power, consistent with its ability to reflect global perfusion disturbances in PE. The stepwise changes in MI and DI with increasing PE severity further validate their utility in risk stratification. Intermediate-high-risk PE patients had markedly lower MI and higher DI compared to low-risk patients, highlighting the potential of EIT-derived indices to quantify the extent of perfusion disturbance and aid in tailoring treatment strategies.\u003c/p\u003e \u003cp\u003eOur study also provides new insights into the relationship between embolus location and regional perfusion abnormalities. Central PE patients had significantly lower MI and higher SI compared to non-central PE patients. These findings suggest that EIT-derived indices can detect regional differences in pulmonary perfusion, which are not readily identified by traditional clinical evaluation methods. This supports the growing body of evidence that EIT can provide valuable information about embolus burden and location, potentially improving patient management.\u003c/p\u003e \u003cp\u003eThe heart rate\u0026ndash;adaptive frequency-domain band-pass filtering method represents an innovation in EIT, addressing the limitations of fixed filtering techniques and enabling better signal fidelity in critically ill patients. The dynamic nature of this method, which adapts to each patient\u0026rsquo;s cardiac rhythm, significantly improves diagnostic accuracy while reducing the need for contrast agents and breath-holding maneuvers. This is particularly important in the ICU, where patients may be unable to cooperate with traditional imaging procedures. Our findings suggest that EIT could be integrated into routine ICU practice, providing a non-invasive, real-time tool for monitoring PE and other pulmonary diseases.\u003c/p\u003e \u003cp\u003eSeveral limitations must be considered. The retrospective, two-center design limits the generalizability of our findings. A larger, multi-center, prospective study is needed to validate the diagnostic performance of this method in diverse ICU populations. Another limitation is the potential heterogeneity of patients, which may affect the consistency of EIT measurements. Variations in comorbidities and hemodynamics could impact the method\u0026rsquo;s accuracy. Future studies should address how these factors influence EIT performance. Finally, while our study focused on PE diagnosis, its role in monitoring therapeutic response remains unclear. Further research should explore its potential for dynamic monitoring during treatment, especially in patients receiving thrombolytics or anticoagulation.\u003c/p\u003e \u003cp\u003eIn conclusion, the heart rate\u0026ndash;adaptive frequency-domain band-pass filtering method for EIT provides a rapid, non-invasive, bedside tool for assessing pulmonary perfusion in critically ill PE patients. It offers valuable physiological insights that can complement conventional diagnostic methods and improve risk stratification. Future research should focus on prospective validation, direct comparisons with other imaging modalities, and exploring its role in monitoring treatment response to further establish its clinical utility in ICU settings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShaofei Xu: Study design, clinical data acquisition, initial manuscript drafting.\u003c/p\u003e\n\u003cp\u003eJiazheng Li: Data analysis, manuscript revision, study design refinement.\u003c/p\u003e\n\u003cp\u003eZiqi Li: Study design optimization, clinical data validation, data analysis.\u003c/p\u003e\n\u003cp\u003eYuxuan Cai: Theoretical framework, data validation.\u003c/p\u003e\n\u003cp\u003eJunlai Zhao: Clinical data curation, data analysis.\u003c/p\u003e\n\u003cp\u003eRongrong Zhu: Data organization, figure preparation, manuscript review.\u003c/p\u003e\n\u003cp\u003eMaokun Li: Study supervision, data interpretation guidance, critical manuscript revision.\u003c/p\u003e\n\u003cp\u003eWeiwei Wu: Overall study design, funding acquisition, team coordination, journal correspondence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Beijing Natural Science Foundation (L246021), the Beijing Science and Technology Plan (Z231100004623007), the Health Commission of Hunan Province (20257230) and the Hunan Provincial Natural Science Foundation (2026JJ80637).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the medical staff of the Department of Vascular Surgery, Beijing Tsinghua Changgung Hospital, for clinical data collection support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData supporting the findings are available from corresponding authors upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of Beijing Tsinghua Changgung Hospital (Ethical Approval No: 25893-0-01). All procedures were performed in accordance with the Declaration of Helsinki.Written informed consent was obtained from the patient for publication of this case report and any accompanying images.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Disclosure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have agreed to the publication of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKonstantinides SV, Meyer G, Becattini C, et al. 2024 ESC guidelines for the diagnosis and management of acute pulmonary embolism. 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Crit Care. 2024;28:299. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13613-024-08889-9\u003c/span\u003e\u003cspan address=\"10.1186/s13613-024-08889-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"critical-care","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cric","sideBox":"Learn more about [Critical Care](http://ccforum.biomedcentral.com/)","snPcode":"13054","submissionUrl":"https://submission.nature.com/new-submission/13054/3","title":"Critical Care","twitterHandle":"@Crit_Care","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Electrical impedance tomography, Pulmonary embolism, Frequency-domain band-pass filtering, Pulsatility method, Risk stratification, Embolus location","lastPublishedDoi":"10.21203/rs.3.rs-9270053/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9270053/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Pulmonary embolism (PE) remains a major cause of morbidity and mortality in critical care, yet traditional diagnostic methods face limitations, especially in critically ill patients. This study introduces a novel frequency-domain band-pass filtering enhanced electrical impedance tomography (EIT) pulsation method for rapid, non-invasive PE diagnosis and risk stratification.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eIn a two-center retrospective study, 106 participants (53 PE patients, 53 healthy controls) were enrolled. A 16-electrode EIT system recorded pulmonary blood flow pulsation signals, with a heart rate-adaptive 0.8–2 Hz band-pass filter to mitigate respiratory and motion artifacts. Key parameters (Matching Index （MI）, Dispersion Index (DI), Shunt Index (SI), Electrical Impedance VQ ratio(EVIQ)) were analyzed for PE diagnosis and risk stratification.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e PE patients showed significantly lower MI and higher DI and SI compared to controls (P\u0026lt;0.001). For risk stratification, intermediate-high-risk PE patients had notably lower MI and higher DI than lower-risk groups (P\u0026lt;0.001). The combined MI+DI+SI model demonstrated the highest diagnostic efficacy for PE (AUC=0.820, 95% CI: 0.739–0.892), outperforming individual parameters. EIVQ showed no significant discriminative value.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThe enhanced EIT pulsation method effectively mitigates respiratory and motion artifacts. MI, DI, and SI are reliable non-invasive parameters for PE diagnosis and risk stratification, offering a practical bedside tool in critical care.\u003c/p\u003e","manuscriptTitle":"Frequency-domain band-pass filtering enhanced pulsation method for pulmonary embolism diagnosis and risk stratification: A two-center retrospective study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-23 09:11:07","doi":"10.21203/rs.3.rs-9270053/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-13T14:10:31+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-16T00:12:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"309719625861284689983999857519678808720","date":"2026-04-15T00:19:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-14T14:36:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-02T01:35:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-02T01:34:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"Critical Care","date":"2026-03-30T16:45:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"critical-care","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cric","sideBox":"Learn more about [Critical Care](http://ccforum.biomedcentral.com/)","snPcode":"13054","submissionUrl":"https://submission.nature.com/new-submission/13054/3","title":"Critical Care","twitterHandle":"@Crit_Care","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"41245b20-155e-4c6d-ada8-6570c60cff5c","owner":[],"postedDate":"April 23rd, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-13T14:10:31+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-13T20:58:08+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-23 09:11:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9270053","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9270053","identity":"rs-9270053","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}