Pub Date : 2023-04-21eCollection Date: 2023-08-01DOI: 10.1093/ehjdh/ztad028
Thomas Newman, Raunak Borker, Louise Aubiniere-Robb, Justin Hendrickson, Dipankar Choudhury, Ian Halliday, John Fenner, Andrew Narracott, D Rodney Hose, Rebecca Gosling, Julian P Gunn, Paul D Morris
Aims: Over the last ten years, virtual Fractional Flow Reserve (vFFR) has improved the utility of Fractional Flow Reserve (FFR), a globally recommended assessment to guide coronary interventions. Although the speed of vFFR computation has accelerated, techniques utilising full 3D computational fluid dynamics (CFD) solutions rather than simplified analytical solutions still require significant time to compute.
Methods and results: This study investigated the speed, accuracy and cost of a novel 3D-CFD software method based upon a graphic processing unit (GPU) computation, compared with the existing fastest central processing unit (CPU)-based 3D-CFD technique, on 40 angiographic cases. The novel GPU simulation was significantly faster than the CPU method (median 31.7 s (Interquartile Range (IQR) 24.0-44.4s) vs. 607.5 s (490-964 s), P < 0.0001). The novel GPU technique was 99.6% (IQR 99.3-99.9) accurate relative to the CPU method. The initial cost of the GPU hardware was greater than the CPU (£4080 vs. £2876), but the median energy consumption per case was significantly less using the GPU method (8.44 (6.80-13.39) Wh vs. 2.60 (2.16-3.12) Wh, P < 0.0001).
Conclusion: This study demonstrates that vFFR can be computed using 3D-CFD with up to 28-fold acceleration than previous techniques with no clinically significant sacrifice in accuracy.
{"title":"Rapid virtual fractional flow reserve using 3D computational fluid dynamics.","authors":"Thomas Newman, Raunak Borker, Louise Aubiniere-Robb, Justin Hendrickson, Dipankar Choudhury, Ian Halliday, John Fenner, Andrew Narracott, D Rodney Hose, Rebecca Gosling, Julian P Gunn, Paul D Morris","doi":"10.1093/ehjdh/ztad028","DOIUrl":"10.1093/ehjdh/ztad028","url":null,"abstract":"<p><strong>Aims: </strong>Over the last ten years, virtual Fractional Flow Reserve (vFFR) has improved the utility of Fractional Flow Reserve (FFR), a globally recommended assessment to guide coronary interventions. Although the speed of vFFR computation has accelerated, techniques utilising full 3D computational fluid dynamics (CFD) solutions rather than simplified analytical solutions still require significant time to compute.</p><p><strong>Methods and results: </strong>This study investigated the speed, accuracy and cost of a novel 3D-CFD software method based upon a graphic processing unit (GPU) computation, compared with the existing fastest central processing unit (CPU)-based 3D-CFD technique, on 40 angiographic cases. The novel GPU simulation was significantly faster than the CPU method (median 31.7 s (Interquartile Range (IQR) 24.0-44.4s) vs. 607.5 s (490-964 s), <i>P</i> < 0.0001). The novel GPU technique was 99.6% (IQR 99.3-99.9) accurate relative to the CPU method. The initial cost of the GPU hardware was greater than the CPU (£4080 vs. £2876), but the median energy consumption per case was significantly less using the GPU method (8.44 (6.80-13.39) Wh vs. 2.60 (2.16-3.12) Wh, <i>P</i> < 0.0001).</p><p><strong>Conclusion: </strong>This study demonstrates that vFFR can be computed using 3D-CFD with up to 28-fold acceleration than previous techniques with no clinically significant sacrifice in accuracy.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"4 4","pages":"283-290"},"PeriodicalIF":3.9,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10393878/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9938895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-11DOI: 10.1101/2023.04.09.23287650
A. Javed, D. Kim, S. Hershman, A. Shcherbina, Anderson Johnson, Alex Tolas, J. O’Sullivan, Michael V. McConnell, L. Lazzeroni, A. King, J. Christle, M. Oppezzo, C. Mattsson, Robert A. Harrington, M. Wheeler, Euan A Ashley
Background: Physical activity is strongly protective against the development of chronic diseases associated with aging. We previously demonstrated that digital interventions delivered through a smartphone app can increase short-term physical activity. Our randomized crossover trial has continued to digitally enroll participants, allowing increasing statistical power for greater precision in subsequent analyses. Methods: We offered enrollment to adults aged >=18 years with access to an iPhone and the MyHeart Counts app. After completion of a 1-week baseline period, e-consented participants were randomly allocated to four 7-day interventions. Interventions consisted of: 1) daily personalized e-coaching based on the individuals baseline activity patterns, 2) daily prompts to complete 10,000 steps, 3) hourly prompts to stand following inactivity, and 4) daily instructions to read guidelines from the American Heart Association website. The trial was completed in a free-living setting, where neither the participants or investigators were blinded to the intervention. The primary outcome was change in mean daily step count from baseline for each of the four interventions, assessed in a modified intention-to-treat analysis. This trial is registered with ClinicalTrials.gov, NCT03090321. Findings: Between January 1, 2017 and April 1, 2022, 4500 participants consented to enroll in the trial, of whom 2458 completed 7-days of baseline monitoring (mean daily steps 4232+/-73) and at least one day of one of the four interventions. The greater statistical power afforded by continued passive enrollment revealed that e-coaching prompts, tailored to an individual, increased step count significantly more than other interventions (402+/-71 steps, P=7.1x10-8). Interpretation: Digital studies can continuously recruit participants in a cost-effective manner, allowing for new insights provided by increased statistical power and refinement of prior signals. Here, we show that digital interventions tailored to an individual are effective in increasing short-term physical activity in a free-living cohort. Funding: Stanford Data Science Initiative and Catalyst Program, Apple, Google
{"title":"Personalized digital behaviour interventions increase short-term physical activity: a randomized control crossover trial substudy of the MyHeart Counts Cardiovascular Health Study","authors":"A. Javed, D. Kim, S. Hershman, A. Shcherbina, Anderson Johnson, Alex Tolas, J. O’Sullivan, Michael V. McConnell, L. Lazzeroni, A. King, J. Christle, M. Oppezzo, C. Mattsson, Robert A. Harrington, M. Wheeler, Euan A Ashley","doi":"10.1101/2023.04.09.23287650","DOIUrl":"https://doi.org/10.1101/2023.04.09.23287650","url":null,"abstract":"Background: Physical activity is strongly protective against the development of chronic diseases associated with aging. We previously demonstrated that digital interventions delivered through a smartphone app can increase short-term physical activity. Our randomized crossover trial has continued to digitally enroll participants, allowing increasing statistical power for greater precision in subsequent analyses. Methods: We offered enrollment to adults aged >=18 years with access to an iPhone and the MyHeart Counts app. After completion of a 1-week baseline period, e-consented participants were randomly allocated to four 7-day interventions. Interventions consisted of: 1) daily personalized e-coaching based on the individuals baseline activity patterns, 2) daily prompts to complete 10,000 steps, 3) hourly prompts to stand following inactivity, and 4) daily instructions to read guidelines from the American Heart Association website. The trial was completed in a free-living setting, where neither the participants or investigators were blinded to the intervention. The primary outcome was change in mean daily step count from baseline for each of the four interventions, assessed in a modified intention-to-treat analysis. This trial is registered with ClinicalTrials.gov, NCT03090321. Findings: Between January 1, 2017 and April 1, 2022, 4500 participants consented to enroll in the trial, of whom 2458 completed 7-days of baseline monitoring (mean daily steps 4232+/-73) and at least one day of one of the four interventions. The greater statistical power afforded by continued passive enrollment revealed that e-coaching prompts, tailored to an individual, increased step count significantly more than other interventions (402+/-71 steps, P=7.1x10-8). Interpretation: Digital studies can continuously recruit participants in a cost-effective manner, allowing for new insights provided by increased statistical power and refinement of prior signals. Here, we show that digital interventions tailored to an individual are effective in increasing short-term physical activity in a free-living cohort. Funding: Stanford Data Science Initiative and Catalyst Program, Apple, Google","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"4 1","pages":"411 - 419"},"PeriodicalIF":0.0,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62358665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-06eCollection Date: 2023-05-01DOI: 10.1093/ehjdh/ztad025
Le Li, Zhuxin Zhang, Likun Zhou, Zhenhao Zhang, Yulong Xiong, Zhao Hu, Yan Yao
Aims: Life-threatening ventricular arrhythmias (LTVAs) are common manifestations of sepsis. The majority of sepsis patients with LTVA are unresponsive to initial standard treatment and thus have a poor prognosis. There are very limited studies focusing on the early identification of patients at high risk of LTVA in sepsis to perform optimal preventive treatment interventions. We aimed to develop a prediction model to predict LTVA in sepsis using machine learning (ML) approaches.
Methods and results: Six ML algorithms including CatBoost, LightGBM, and XGBoost were employed to perform the model fitting. The least absolute shrinkage and selection operator (LASSO) regression was used to identify key features. Methods of model evaluation involved in this study included area under the receiver operating characteristic curve (AUROC), for model discrimination, calibration curve, and Brier score, for model calibration. Finally, we validated the prediction model both internally and externally. A total of 27 139 patients with sepsis were identified in this study, 1136 (4.2%) suffered from LTVA during hospitalization. We screened out 10 key features from the initial 54 variables via LASSO regression to improve the practicability of the model. CatBoost showed the best prediction performance among the six ML algorithms, with excellent discrimination (AUROC = 0.874) and calibration (Brier score = 0.157). The remarkable performance of the model was presented in the external validation cohort (n = 9492), with an AUROC of 0.836, suggesting certain generalizability of the model. Finally, a nomogram with risk classification of LTVA was shown in this study.
Conclusion: We established and validated a machine leaning-based prediction model, which was conducive to early identification of high-risk LTVA patients in sepsis, thus appropriate methods could be conducted to improve outcomes.
{"title":"Use of machine learning algorithms to predict life-threatening ventricular arrhythmia in sepsis.","authors":"Le Li, Zhuxin Zhang, Likun Zhou, Zhenhao Zhang, Yulong Xiong, Zhao Hu, Yan Yao","doi":"10.1093/ehjdh/ztad025","DOIUrl":"10.1093/ehjdh/ztad025","url":null,"abstract":"<p><strong>Aims: </strong>Life-threatening ventricular arrhythmias (LTVAs) are common manifestations of sepsis. The majority of sepsis patients with LTVA are unresponsive to initial standard treatment and thus have a poor prognosis. There are very limited studies focusing on the early identification of patients at high risk of LTVA in sepsis to perform optimal preventive treatment interventions. We aimed to develop a prediction model to predict LTVA in sepsis using machine learning (ML) approaches.</p><p><strong>Methods and results: </strong>Six ML algorithms including CatBoost, LightGBM, and XGBoost were employed to perform the model fitting. The least absolute shrinkage and selection operator (LASSO) regression was used to identify key features. Methods of model evaluation involved in this study included area under the receiver operating characteristic curve (AUROC), for model discrimination, calibration curve, and Brier score, for model calibration. Finally, we validated the prediction model both internally and externally. A total of 27 139 patients with sepsis were identified in this study, 1136 (4.2%) suffered from LTVA during hospitalization. We screened out 10 key features from the initial 54 variables via LASSO regression to improve the practicability of the model. CatBoost showed the best prediction performance among the six ML algorithms, with excellent discrimination (AUROC = 0.874) and calibration (Brier score = 0.157). The remarkable performance of the model was presented in the external validation cohort (<i>n</i> = 9492), with an AUROC of 0.836, suggesting certain generalizability of the model. Finally, a nomogram with risk classification of LTVA was shown in this study.</p><p><strong>Conclusion: </strong>We established and validated a machine leaning-based prediction model, which was conducive to early identification of high-risk LTVA patients in sepsis, thus appropriate methods could be conducted to improve outcomes.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"4 3","pages":"245-253"},"PeriodicalIF":0.0,"publicationDate":"2023-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/b8/c8/ztad025.PMC10232270.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9568846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-28eCollection Date: 2023-05-01DOI: 10.1093/ehjdh/ztad023
Joseph Keunhong Yi, Tyler Hyungtaek Rim, Sungha Park, Sung Soo Kim, Hyeon Chang Kim, Chan Joo Lee, Hyeonmin Kim, Geunyoung Lee, James Soo Ghim Lim, Yong Yu Tan, Marco Yu, Yih-Chung Tham, Ameet Bakhai, Eduard Shantsila, Paul Leeson, Gregory Y H Lip, Calvin W L Chin, Ching-Yu Cheng
Aims: This study aims to evaluate the ability of a deep-learning-based cardiovascular disease (CVD) retinal biomarker, Reti-CVD, to identify individuals with intermediate- and high-risk for CVD.
Methods and results: We defined the intermediate- and high-risk groups according to Pooled Cohort Equation (PCE), QRISK3, and modified Framingham Risk Score (FRS). Reti-CVD's prediction was compared to the number of individuals identified as intermediate- and high-risk according to standard CVD risk assessment tools, and sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated to assess the results. In the UK Biobank, among 48 260 participants, 20 643 (42.8%) and 7192 (14.9%) were classified into the intermediate- and high-risk groups according to PCE, and QRISK3, respectively. In the Singapore Epidemiology of Eye Diseases study, among 6810 participants, 3799 (55.8%) were classified as intermediate- and high-risk group according to modified FRS. Reti-CVD identified PCE-based intermediate- and high-risk groups with a sensitivity, specificity, PPV, and NPV of 82.7%, 87.6%, 86.5%, and 84.0%, respectively. Reti-CVD identified QRISK3-based intermediate- and high-risk groups with a sensitivity, specificity, PPV, and NPV of 82.6%, 85.5%, 49.9%, and 96.6%, respectively. Reti-CVD identified intermediate- and high-risk groups according to the modified FRS with a sensitivity, specificity, PPV, and NPV of 82.1%, 80.6%, 76.4%, and 85.5%, respectively.
Conclusion: The retinal photograph biomarker (Reti-CVD) was able to identify individuals with intermediate and high-risk for CVD, in accordance with existing risk assessment tools.
{"title":"Cardiovascular disease risk assessment using a deep-learning-based retinal biomarker: a comparison with existing risk scores.","authors":"Joseph Keunhong Yi, Tyler Hyungtaek Rim, Sungha Park, Sung Soo Kim, Hyeon Chang Kim, Chan Joo Lee, Hyeonmin Kim, Geunyoung Lee, James Soo Ghim Lim, Yong Yu Tan, Marco Yu, Yih-Chung Tham, Ameet Bakhai, Eduard Shantsila, Paul Leeson, Gregory Y H Lip, Calvin W L Chin, Ching-Yu Cheng","doi":"10.1093/ehjdh/ztad023","DOIUrl":"10.1093/ehjdh/ztad023","url":null,"abstract":"<p><strong>Aims: </strong>This study aims to evaluate the ability of a deep-learning-based cardiovascular disease (CVD) retinal biomarker, Reti-CVD, to identify individuals with intermediate- and high-risk for CVD.</p><p><strong>Methods and results: </strong>We defined the intermediate- and high-risk groups according to Pooled Cohort Equation (PCE), QRISK3, and modified Framingham Risk Score (FRS). Reti-CVD's prediction was compared to the number of individuals identified as intermediate- and high-risk according to standard CVD risk assessment tools, and sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated to assess the results. In the UK Biobank, among 48 260 participants, 20 643 (42.8%) and 7192 (14.9%) were classified into the intermediate- and high-risk groups according to PCE, and QRISK3, respectively. In the Singapore Epidemiology of Eye Diseases study, among 6810 participants, 3799 (55.8%) were classified as intermediate- and high-risk group according to modified FRS. Reti-CVD identified PCE-based intermediate- and high-risk groups with a sensitivity, specificity, PPV, and NPV of 82.7%, 87.6%, 86.5%, and 84.0%, respectively. Reti-CVD identified QRISK3-based intermediate- and high-risk groups with a sensitivity, specificity, PPV, and NPV of 82.6%, 85.5%, 49.9%, and 96.6%, respectively. Reti-CVD identified intermediate- and high-risk groups according to the modified FRS with a sensitivity, specificity, PPV, and NPV of 82.1%, 80.6%, 76.4%, and 85.5%, respectively.</p><p><strong>Conclusion: </strong>The retinal photograph biomarker (Reti-CVD) was able to identify individuals with intermediate and high-risk for CVD, in accordance with existing risk assessment tools.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"4 3","pages":"236-244"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/98/11/ztad023.PMC10232236.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9571920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-23eCollection Date: 2023-05-01DOI: 10.1093/ehjdh/ztad022
Fabian Theurl, Michael Schreinlechner, Nikolay Sappler, Michael Toifl, Theresa Dolejsi, Florian Hofer, Celine Massmann, Christian Steinbring, Silvia Komarek, Kurt Mölgg, Benjamin Dejakum, Christian Böhme, Rudolf Kirchmair, Sebastian Reinstadler, Axel Bauer
Aims: We aimed to investigate the concordance between heart rate variability (HRV) derived from the photoplethysmographic (PPG) signal of a commercially available smartwatch compared with the gold-standard high-resolution electrocardiogram (ECG)-derived HRV in patients with cardiovascular disease.
Methods and results: We prospectively enrolled 104 survivors of acute ST-elevation myocardial infarction, 129 patients after an ischaemic stroke, and 30 controls. All subjects underwent simultaneous recording of a smartwatch (Garmin vivoactive 4; Garmin Ltd, Olathe, KS, USA)-derived PPG signal and a high-resolution (1000 Hz) ECG for 30 min under standardized conditions. HRV measures in time and frequency domain, non-linear measures, as well as deceleration capacity (DC) were calculated according to previously published technologies from both signals. Lin's concordance correlation coefficient (ρc) between smartwatch-derived and ECG-based HRV markers was used as a measure of diagnostic accuracy. A very high concordance within the whole study cohort was observed for the mean heart rate (ρc = 0.9998), standard deviation of the averages of normal-to-normal (NN) intervals in all 5min segments (SDANN; ρc = 0.9617), and very low frequency power (VLF power; ρc = 0.9613). In contrast, detrended fluctuation analysis (DF-α1; ρc = 0.5919) and the square mean root of the sum of squares of adjacent NN-interval differences (rMSSD; ρc = 0.6617) showed only moderate concordance.
Conclusion: Smartwatch-derived HRV provides a practical alternative with excellent accuracy compared with ECG-based HRV for global markers and those characterizing lower frequency components. However, caution is warranted with HRV markers that predominantly assess short-term variability.
{"title":"Smartwatch-derived heart rate variability: a head-to-head comparison with the gold standard in cardiovascular disease.","authors":"Fabian Theurl, Michael Schreinlechner, Nikolay Sappler, Michael Toifl, Theresa Dolejsi, Florian Hofer, Celine Massmann, Christian Steinbring, Silvia Komarek, Kurt Mölgg, Benjamin Dejakum, Christian Böhme, Rudolf Kirchmair, Sebastian Reinstadler, Axel Bauer","doi":"10.1093/ehjdh/ztad022","DOIUrl":"10.1093/ehjdh/ztad022","url":null,"abstract":"<p><strong>Aims: </strong>We aimed to investigate the concordance between heart rate variability (HRV) derived from the photoplethysmographic (PPG) signal of a commercially available smartwatch compared with the gold-standard high-resolution electrocardiogram (ECG)-derived HRV in patients with cardiovascular disease.</p><p><strong>Methods and results: </strong>We prospectively enrolled 104 survivors of acute ST-elevation myocardial infarction, 129 patients after an ischaemic stroke, and 30 controls. All subjects underwent simultaneous recording of a smartwatch (Garmin vivoactive 4; Garmin Ltd, Olathe, KS, USA)-derived PPG signal and a high-resolution (1000 Hz) ECG for 30 min under standardized conditions. HRV measures in time and frequency domain, non-linear measures, as well as deceleration capacity (DC) were calculated according to previously published technologies from both signals. Lin's concordance correlation coefficient (<i>ρ</i><sub>c</sub>) between smartwatch-derived and ECG-based HRV markers was used as a measure of diagnostic accuracy. A very high concordance within the whole study cohort was observed for the mean heart rate (<i>ρ</i><sub>c</sub> = 0.9998), standard deviation of the averages of normal-to-normal (NN) intervals in all 5min segments (SDANN; <i>ρ</i><sub>c</sub> = 0.9617), and very low frequency power (VLF power; <i>ρ</i><sub>c</sub> = 0.9613). In contrast, detrended fluctuation analysis (DF-α1; <i>ρ</i><sub>c</sub> = 0.5919) and the square mean root of the sum of squares of adjacent NN-interval differences (rMSSD; <i>ρ</i><sub>c</sub> = 0.6617) showed only moderate concordance.</p><p><strong>Conclusion: </strong>Smartwatch-derived HRV provides a practical alternative with excellent accuracy compared with ECG-based HRV for global markers and those characterizing lower frequency components. However, caution is warranted with HRV markers that predominantly assess short-term variability.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"4 3","pages":"155-164"},"PeriodicalIF":3.9,"publicationDate":"2023-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/c0/d9/ztad022.PMC10232241.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9568842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-06eCollection Date: 2023-05-01DOI: 10.1093/ehjdh/ztad017
Alessandra Scoccia, Peter de Jaegere
{"title":"Infrared spectral analysis for the classification of patients with acute coronary syndrome. The questions run so deep.","authors":"Alessandra Scoccia, Peter de Jaegere","doi":"10.1093/ehjdh/ztad017","DOIUrl":"10.1093/ehjdh/ztad017","url":null,"abstract":"","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"4 3","pages":"143-144"},"PeriodicalIF":3.9,"publicationDate":"2023-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10232277/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9571919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-02eCollection Date: 2023-05-01DOI: 10.1093/ehjdh/ztad016
Tobias Paul Seraphin, Mark Luedde, Christoph Roderburg, Marko van Treeck, Pascal Scheider, Roman D Buelow, Peter Boor, Sven H Loosen, Zdenek Provaznik, Daniel Mendelsohn, Filip Berisha, Christina Magnussen, Dirk Westermann, Tom Luedde, Christoph Brochhausen, Samuel Sossalla, Jakob Nikolas Kather
Aims: One of the most important complications of heart transplantation is organ rejection, which is diagnosed on endomyocardial biopsies by pathologists. Computer-based systems could assist in the diagnostic process and potentially improve reproducibility. Here, we evaluated the feasibility of using deep learning in predicting the degree of cellular rejection from pathology slides as defined by the International Society for Heart and Lung Transplantation (ISHLT) grading system.
Methods and results: We collected 1079 histopathology slides from 325 patients from three transplant centres in Germany. We trained an attention-based deep neural network to predict rejection in the primary cohort and evaluated its performance using cross-validation and by deploying it to three cohorts. For binary prediction (rejection yes/no), the mean area under the receiver operating curve (AUROC) was 0.849 in the cross-validated experiment and 0.734, 0.729, and 0.716 in external validation cohorts. For a prediction of the ISHLT grade (0R, 1R, 2/3R), AUROCs were 0.835, 0.633, and 0.905 in the cross-validated experiment and 0.764, 0.597, and 0.913; 0.631, 0.633, and 0.682; and 0.722, 0.601, and 0.805 in the validation cohorts, respectively. The predictions of the artificial intelligence model were interpretable by human experts and highlighted plausible morphological patterns.
Conclusion: We conclude that artificial intelligence can detect patterns of cellular transplant rejection in routine pathology, even when trained on small cohorts.
{"title":"Prediction of heart transplant rejection from routine pathology slides with self-supervised deep learning.","authors":"Tobias Paul Seraphin, Mark Luedde, Christoph Roderburg, Marko van Treeck, Pascal Scheider, Roman D Buelow, Peter Boor, Sven H Loosen, Zdenek Provaznik, Daniel Mendelsohn, Filip Berisha, Christina Magnussen, Dirk Westermann, Tom Luedde, Christoph Brochhausen, Samuel Sossalla, Jakob Nikolas Kather","doi":"10.1093/ehjdh/ztad016","DOIUrl":"10.1093/ehjdh/ztad016","url":null,"abstract":"<p><strong>Aims: </strong>One of the most important complications of heart transplantation is organ rejection, which is diagnosed on endomyocardial biopsies by pathologists. Computer-based systems could assist in the diagnostic process and potentially improve reproducibility. Here, we evaluated the feasibility of using deep learning in predicting the degree of cellular rejection from pathology slides as defined by the International Society for Heart and Lung Transplantation (ISHLT) grading system.</p><p><strong>Methods and results: </strong>We collected 1079 histopathology slides from 325 patients from three transplant centres in Germany. We trained an attention-based deep neural network to predict rejection in the primary cohort and evaluated its performance using cross-validation and by deploying it to three cohorts. For binary prediction (rejection yes/no), the mean area under the receiver operating curve (AUROC) was 0.849 in the cross-validated experiment and 0.734, 0.729, and 0.716 in external validation cohorts. For a prediction of the ISHLT grade (0R, 1R, 2/3R), AUROCs were 0.835, 0.633, and 0.905 in the cross-validated experiment and 0.764, 0.597, and 0.913; 0.631, 0.633, and 0.682; and 0.722, 0.601, and 0.805 in the validation cohorts, respectively. The predictions of the artificial intelligence model were interpretable by human experts and highlighted plausible morphological patterns.</p><p><strong>Conclusion: </strong>We conclude that artificial intelligence can detect patterns of cellular transplant rejection in routine pathology, even when trained on small cohorts.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"4 3","pages":"265-274"},"PeriodicalIF":3.9,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/33/45/ztad016.PMC10232288.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9568841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
[This corrects the article DOI: 10.1093/ehjdh/ztac026.].
[这更正了文章DOI: 10.1093/ehjdh/ztac026.]。
{"title":"Corrigendum to: ESC Working Group on e-Cardiology Position Paper: accuracy and reliability of electrocardiogram monitoring in the detection of atrial fibrillation in cryptogenic stroke patients : In collaboration with the Council on Stroke, the European Heart Rhythm Association, and the Digital Health Committee.","authors":"","doi":"10.1093/ehjdh/ztad019","DOIUrl":"https://doi.org/10.1093/ehjdh/ztad019","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.1093/ehjdh/ztac026.].</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"4 2","pages":"138"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/d1/71/ztad019.PMC10039420.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9547118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Peter de Jaegere, Robert van der Boon, Joost Lumens, Nico Bruining
{"title":"PubMed indexation for the European Heart Journal - Digital Health: a small step for the European Heart Journal family, a giant leap in the digital future of cardiovascular disease management.","authors":"Peter de Jaegere, Robert van der Boon, Joost Lumens, Nico Bruining","doi":"10.1093/ehjdh/ztad013","DOIUrl":"https://doi.org/10.1093/ehjdh/ztad013","url":null,"abstract":"","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"4 2","pages":"63-64"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10039422/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9567929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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