Pulmonary transit time of cardiovascular magnetic resonance perfusion scans for quantification of cardiopulmonary haemodynamics

Segeroth, Martin; Winkel, David; Strebel, Ivo; Yang, Shan; Stouwe, Jan Gerrit van der; Formambuh, Jude; Badertscher, Patrick; Cyriac, Joshy; Wasserthal, Jakob; Caobelli, Federico; Madaffari, Antonio; Lopez-Ayala, Pedro; Zellweger, Michael J.; Sauter, Alexander; Mueller, Christian; Bremerich, Jens; Haaf, Philip

doi:10.1093/ehjci/jead001

Cited by 7 publications

(7 citation statements)

References 39 publications

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“…In this study, the LungCTAnalyzer module uses a nnU-Net TotalSegmentator AI tool (11), which allowed autonomous computer analysis including lung lobes without human intervention. The use of AI-powered tools like the nnU-Net TotalSegmentator can provide several bene ts, including reduced time spent on image analysis, increased consistency in assessments, and potentially improved patient outcomes due to faster and more accurate diagnosis.…”

Section: Discussionmentioning

confidence: 99%

“…Utilizing the TotalSegmentator (nnU-Net) (11) for segmentation, we were able to automatically generate segments corresponding to the trachea and the left erector spinae muscle in the CT datasets. We then evaluated the median Houns eld units (HU) of both segmented structures in both scanners (Toshiba: Trachea: -931.6 Muscle: 22.68 [mean HU], Siemens: Trachea: -958.2 Muscle: 24.25 [mean HU] ) A Python function then standardized CT scans by calibrating Houns eld unit (HU) values for air and muscle.…”

Section: Data Calibrationmentioning

confidence: 99%

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From Voxels to Prognosis: AI-Driven Quantitative Chest CT Analysis Forecasts ICU Requirements in 81 COVID-19 Cases

Bumm

Zaffino

Lassó

et al. 2023

Preprint

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Background: The current study aimed to investigate the distribution and extent of lung involvement in patients with COVID-19, assess the relationship between lung involvement and the need for intensive care unit (ICU) admission and compare the performance of computer analysis with the judgment of radiological experts. Methods: A total of 81 patients from an open-source COVID database with confirmed COVID-19 infection were included in the study. Lung involvement was assessed using computed tomography (CT) scans, and the extent of infiltration and collapse was quantified across various lung lobes and regions. The associations between lung involvement and ICU admission were analyzed. Additionally, the computer analysis of COVID-19 involvement was compared against the expert rating provided by a radiological expert. Results: The results showed a higher degree of infiltration and collapse in the left lower lobe compared to the upper lobe (p < 0.05) and a similar pattern in the right lower lobes (p < 0.05). No significant difference was detected in the COVID-19-related involvement of the left and right lower lobes. The right middle lobes demonstrated lower involvement compared to the right lower lobes (p < 0.05). When examining the regions, no significant difference in COVID-19 involvement was found when comparing the anterior and posterior lungs. The middle third displayed greater COVID-19 involvement than the upper and lower thirds. Patients requiring ICU admission during their inpatient treatment exhibited significantly higher COVID-19 involvement in their lung parenchyma according to computer analysis, compared to patients who remained in general wards. Patients with more than 40% COVID-19 involvement were almost exclusively treated in intensive care. A high correlation was observed between computer detection of COVID-19 affections and expert rating by a radiological expert. Conclusion: The findings suggest that the extent of lung involvement, particularly in the lower lobes and middle third, may be associated with the need for ICU admission in patients with COVID-19. Computer analysis showed a high correlation with expert rating, highlighting its potential utility in clinical settings for assessing lung involvement. This information may help guide clinical decision-making and resource allocation during ongoing or future pandemics. Further studies with larger sample sizes are warranted to validate these findings.

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Section: Discussionmentioning

confidence: 99%

Section: Data Calibrationmentioning

confidence: 99%

From Voxels to Prognosis: AI-Driven Quantitative Chest CT Analysis Forecasts ICU Requirements in 81 COVID-19 Cases

Bumm

Zaffino

Lassó

et al. 2023

Preprint

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“…There is extensive literature examining the relationship of PTT with cardiomegaly, systolic dysfunction, pulmonary hypertension and heart failure due to reduced LVEF [ 8 – 17 ]. PTT is also correlated with B-type natriuretic peptide [ 29 ]. Recently, longer PTT duration has been observed in heart failure patients with preserved LVEF [ 24 ] as it is closely associated with altered hemodynamics such as elevated pulmonary artery wedge pressure [ 17 , 24 ].…”

Section: Discussionmentioning

confidence: 99%

Association of pulmonary transit time by cardiac magnetic resonance with heart failure hospitalization in a large prospective cohort with diverse cardiac conditions

Cao,

Nashta,

Weber

et al. 2023

Journal of Cardiovascular Magnetic Resonance

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Background Longer pulmonary transit time (PTT) is closely associated with hemodynamic abnormalities. However, the implications on heart failure (HF) risk have not been investigated broadly in patients with diverse cardiac conditions. In this study we examined the long-term risk of HF hospitalization associated with longer PTT in a large prospective cohort with a broad spectrum of cardiac conditions. Methods All subjects were prospectively recruited to undergo cardiac magnetic resonance (CMR). The dynamic images of first-pass perfusion were acquired to assess peak-to-peak pulmonary transit time (PTT) which was subsequently normalized to RR interval duration. The risk of HF was examined using Cox proportional hazards models adjusted for baseline confounding risk factors. Results Among 506 consecutively consented patients undergoing clinical cardiac MR with diverse cardiac conditions, the mean age was 63 ± 14 years and 373 (73%) were male. After a mean follow up duration of 4.5 ± 3.0 years, 70 (14%) patients developed hospitalized HF and of these 6 died. A normalized PTT ≥ 8.2 was associated with a significantly increased adjusted HF hazard ratio of 3.69 (95% CI 2.02, 6.73). The HF hazard ratio was 1.26 (95% CI 1.18, 1.33) for each 1 unit increase in PTT which was higher among those preserved (1.70, 95% CI 1.20, 2.41) compared to those with reduced left ventricular ejection fraction (< 50%) (1.18, 95% CI 1.09, 1.27). PTT remained a significant risk factor of hospitalized HF after additional adjustment for N-terminal pro-hormone brain natriuretic peptide (NT-proBNP) or left ventricular global longitudinal strain with additionally demonstrated incremental model improvement through likelihood ratio testing. Conclusions Our findings support the role of PTT in assessing HF risk among patients with broad spectrum of cardiac conditions with reduced as well as preserved ejection fraction. Longer PTT duration is an incremental risk factor for HF when baseline global longitudinal strain and NT-proBNP are taken into consideration.

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“…During a preliminary scan of all datasets, it became evident that the CT scans exhibited some heterogeneity in HU range, quality, windowing, and the presence of artifacts. Utilizing the TotalSegmentator (nnU-Net) (11) for segmentation, we were able to automatically generate segments corresponding to the trachea and the left erector spinae muscle in the CT datasets. We then evaluated the median Houns eld units (HU) of both segmented structures in both scanners (Toshiba: Trachea: -931.…”

Section: Data Calibrationmentioning

confidence: 99%

From Voxels to Prognosis: AI-Driven Quantitative Chest CT Analysis Forecasts ICU Requirements in 78 COVID-19 Cases

Bumm

Zaffino

Lassó

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

Background: The aim of the current study was to investigate the distribution and extent of lung involvement in patients with COVID-19 with AI-supported, automated computer analysis and to assess the relationship between lung involvement and the need for intensive care unit (ICU) admission. A secondary aim was to compare the performance of computer analysis with the judgment of radiological experts. Methods: A total of 81 patients from an open-source COVID database with confirmed COVID-19 infection were included in the study. Three patients were excluded. Lung involvement was assessed in 78 patients using computed tomography (CT) scans, and the extent of infiltration and collapse was quantified across various lung lobes and regions. The associations between lung involvement and ICU admission were analyzed. Additionally, the computer analysis of COVID-19 involvement was compared against a human rating provided by radiological experts. Results: The results showed a higher degree of infiltration and collapse in the lower lobes compared to the upper lobes (p < 0.05) No significant difference was detected in the COVID-19-related involvement of the left and right lower lobes. The right middle lobe demonstrated lower involvement compared to the right lower lobes (p < 0.05). When examining the regions, significantly more COVID-19 involvement was found when comparing the posterior vs. the anterior halves of the lungs and the lower vs. the upper half of the lungs. Patients, who required ICU admission during their treatment exhibited significantly higher COVID-19 involvement in their lung parenchyma according to computer analysis, compared to patients who remained in general wards. Patients with more than 40% COVID-19 involvement were almost exclusively treated in intensive care. A high correlation was observed between computer detection of COVID-19 affections and expert rating by radiological experts. Conclusion: The findings suggest that the extent of lung involvement, particularly in the lower lobes, dorsal lungs, and lower half of the lungs, may be associated with the need for ICU admission in patients with COVID-19. Computer analysis showed a high correlation with expert rating, highlighting its potential utility in clinical settings for assessing lung involvement. This information may help guide clinical decision-making and resource allocation during ongoing or future pandemics. Further studies with larger sample sizes are warranted to validate these findings.

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Pulmonary transit time of cardiovascular magnetic resonance perfusion scans for quantification of cardiopulmonary haemodynamics

Cited by 7 publications

References 39 publications

From Voxels to Prognosis: AI-Driven Quantitative Chest CT Analysis Forecasts ICU Requirements in 81 COVID-19 Cases

From Voxels to Prognosis: AI-Driven Quantitative Chest CT Analysis Forecasts ICU Requirements in 81 COVID-19 Cases

Association of pulmonary transit time by cardiac magnetic resonance with heart failure hospitalization in a large prospective cohort with diverse cardiac conditions

From Voxels to Prognosis: AI-Driven Quantitative Chest CT Analysis Forecasts ICU Requirements in 78 COVID-19 Cases

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