Robyn Gallagher, Clara K Chow, Helen Parker, Lis Neubeck, David S Celermajer, Julie Redfern, Geoffrey Tofler, Thomas Buckley, Tracy Schumacher, Karice Hyun, Farzaneh Boroumand, Gemma Figtree
Aims: Secondary prevention reduces coronary heart disease (CHD) progression. Traditional prevention programs including cardiac rehabilitation are under-accessed, which smartphone apps may overcome. To evaluate the effect of a game-based mobile app intervention (MyHeartMate) to improve cardiovascular risk factors and lifestyle behaviours.
Methods and results: Single-blind randomized trial of CHD patients in Sydney, 2017-2021. Intervention group were provided the MyHeartMate app for 6 months. Co-designed features included an avatar of the patient's heart and tokens earned by risk factor work (tracking, challenges, and quizzes). The control group received usual care. Primary outcome was self-reported physical activity [metabolic equivalents (METs), Global Physical Activity Questionnaire] and secondary outcomes included lipid levels, blood pressure (BP), body mass index, and smoking. Pre-specified sample size was achieved (n = 390), age 61.2 ± 11.5 years; 82.5% men and 9.2% current smokers. At 6 months, adjusted for baseline levels, the intervention group achieved more physical activity than control (median difference 329 MET mins/wk), which was not statistically significant (95% CI -37.4, 696; P = 0.064). No differences occurred between groups on secondary outcomes except for lower triglyceride levels in the intervention [mean difference -0.3 (95% CI -0.5, -0.1 mmoL/L, P = 0.004)]. Acceptability was high: 94.8% of intervention participants engaged by tracking exercise or BP and completing missions; 26.8% continued to engage for ≥30 days. Participants (n = 14) reported the app supported tracking behaviours and risk factors, reinforcing and improving self-care confidence, and decreasing anxiety.
Conclusion: A game-based app proved highly acceptable for patients with CHD but did not improve risk factors or lifestyle behaviours other than triglyceride levels.
目的:二级预防减少冠心病(CHD)的进展。包括心脏康复在内的传统预防项目很少有人参与,而智能手机应用可能会克服这一点。评估基于游戏的移动应用程序干预(MyHeartMate)对改善心血管危险因素和生活方式行为的影响。方法和结果:2017-2021年悉尼冠心病患者的单盲随机试验。干预组使用MyHeartMate应用程序6个月。共同设计的功能包括患者心脏的化身和通过风险因素工作(跟踪、挑战和测验)获得的代币。对照组接受常规护理。主要结局是自我报告的身体活动[代谢当量(METs),全球身体活动问卷],次要结局包括脂质水平、血压(BP)、体重指数和吸烟。达到预先规定的样本量(n = 390),年龄61.2±11.5岁;男性占82.5%,目前吸烟者占9.2%。在6个月时,调整基线水平,干预组比对照组获得更多的身体活动(中位数差329 MET分钟/周),这在统计学上没有显著意义(95% CI -37.4, 696;P = 0.064)。除了干预中甘油三酯水平较低外,两组间的次要结局无差异[平均差异-0.3 (95% CI -0.5, -0.1 mmoL/L, P = 0.004)]。可接受性高:94.8%的干预参与者通过跟踪锻炼或BP和完成任务参与;26.8%的患者持续治疗≥30天。参与者(n = 14)报告说,该应用程序支持跟踪行为和风险因素,增强和提高自我护理信心,减少焦虑。结论:基于游戏的应用程序被证明对冠心病患者是高度可接受的,但除了甘油三酯水平外,并没有改善危险因素或生活方式行为。
{"title":"The effect of a game-based mobile app 'MyHeartMate' to promote lifestyle change in coronary disease patients: a randomized controlled trial.","authors":"Robyn Gallagher, Clara K Chow, Helen Parker, Lis Neubeck, David S Celermajer, Julie Redfern, Geoffrey Tofler, Thomas Buckley, Tracy Schumacher, Karice Hyun, Farzaneh Boroumand, Gemma Figtree","doi":"10.1093/ehjdh/ztac069","DOIUrl":"https://doi.org/10.1093/ehjdh/ztac069","url":null,"abstract":"<p><strong>Aims: </strong>Secondary prevention reduces coronary heart disease (CHD) progression. Traditional prevention programs including cardiac rehabilitation are under-accessed, which smartphone apps may overcome. To evaluate the effect of a game-based mobile app intervention (MyHeartMate) to improve cardiovascular risk factors and lifestyle behaviours.</p><p><strong>Methods and results: </strong>Single-blind randomized trial of CHD patients in Sydney, 2017-2021. Intervention group were provided the MyHeartMate app for 6 months. Co-designed features included an avatar of the patient's heart and tokens earned by risk factor work (tracking, challenges, and quizzes). The control group received usual care. Primary outcome was self-reported physical activity [metabolic equivalents (METs), Global Physical Activity Questionnaire] and secondary outcomes included lipid levels, blood pressure (BP), body mass index, and smoking. Pre-specified sample size was achieved (<i>n</i> = 390), age 61.2 ± 11.5 years; 82.5% men and 9.2% current smokers. At 6 months, adjusted for baseline levels, the intervention group achieved more physical activity than control (median difference 329 MET mins/wk), which was not statistically significant (95% CI -37.4, 696; <i>P</i> = 0.064). No differences occurred between groups on secondary outcomes except for lower triglyceride levels in the intervention [mean difference -0.3 (95% CI -0.5, -0.1 mmoL/L, <i>P</i> = 0.004)]. Acceptability was high: 94.8% of intervention participants engaged by tracking exercise or BP and completing missions; 26.8% continued to engage for ≥30 days. Participants (<i>n</i> = 14) reported the app supported tracking behaviours and risk factors, reinforcing and improving self-care confidence, and decreasing anxiety.</p><p><strong>Conclusion: </strong>A game-based app proved highly acceptable for patients with CHD but did not improve risk factors or lifestyle behaviours other than triglyceride levels.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9890079/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10663668","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}
{"title":"The explainability of the latent variables is limited to the synthesis of electrocardiogram.","authors":"Akinori Higaki, Osamu Yamaguchi","doi":"10.1093/ehjdh/ztac052","DOIUrl":"https://doi.org/10.1093/ehjdh/ztac052","url":null,"abstract":"We","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/7e/fc/ztac052.PMC9779878.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10734973","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}
Aims: The detection of white-coat hypertension/white-coat uncontrolled hypertension (WCH/WUCH) with out-of-office blood pressure (BP) monitoring is time- and resource-consuming. We aim to develop a machine learning (ML)-derived prediction model based on the characteristics of patients from a single outpatient visit.
Methods and results: Data from two cohorts in Taiwan were used. Cohort one (970 patients) was used for development and internal validation, and cohort two (464 patients) was used for external validation. WCH/WUCH was defined as an office BP of ≥140/90 mmHg and daytime ambulatory BP of <135/85 mmHg in treatment-naïve or treated individuals. Logistic regression, random forest (RF), eXtreme Gradient Boosting, and artificial neural network models were trained using 26 patient parameters. We used SHapley Additive exPlanations values to provide explanations for the risk factors. All models achieved great area under the receiver operating characteristic curve (AUROC), specificity, and negative predictive value in both validations (AUROC = 0.754-0.891; specificity = 0.682-0.910; negative predictive value = 0.831-0.968). The RF model was the best performing (AUROC = 0.884; sensitivity = 0.619; specificity = 0.887; negative predictive value = 0.872; accuracy = 0.819). The five most influential features of the RF model were office diastolic BP, office systolic BP, current smoker, estimated glomerular filtration rate, and fasting glucose level.
Conclusion: Our prediction models achieved good performance, underlining the feasibility of applying ML models to outpatient populations for the diagnosis of WCH and WUCH. Further validation with other prospective data sets should be considered in the future.
{"title":"Prediction of white-coat hypertension and white-coat uncontrolled hypertension using machine learning algorithm.","authors":"Ling-Chieh Shih, Yu-Ching Wang, Ming-Hui Hung, Han Cheng, Yu-Chieh Shiao, Yu-Hsuan Tseng, Chin-Chou Huang, Shing-Jong Lin, Jaw-Wen Chen","doi":"10.1093/ehjdh/ztac066","DOIUrl":"https://doi.org/10.1093/ehjdh/ztac066","url":null,"abstract":"<p><strong>Aims: </strong>The detection of white-coat hypertension/white-coat uncontrolled hypertension (WCH/WUCH) with out-of-office blood pressure (BP) monitoring is time- and resource-consuming. We aim to develop a machine learning (ML)-derived prediction model based on the characteristics of patients from a single outpatient visit.</p><p><strong>Methods and results: </strong>Data from two cohorts in Taiwan were used. Cohort one (970 patients) was used for development and internal validation, and cohort two (464 patients) was used for external validation. WCH/WUCH was defined as an office BP of ≥140/90 mmHg and daytime ambulatory BP of <135/85 mmHg in treatment-naïve or treated individuals. Logistic regression, random forest (RF), eXtreme Gradient Boosting, and artificial neural network models were trained using 26 patient parameters. We used SHapley Additive exPlanations values to provide explanations for the risk factors. All models achieved great area under the receiver operating characteristic curve (AUROC), specificity, and negative predictive value in both validations (AUROC = 0.754-0.891; specificity = 0.682-0.910; negative predictive value = 0.831-0.968). The RF model was the best performing (AUROC = 0.884; sensitivity = 0.619; specificity = 0.887; negative predictive value = 0.872; accuracy = 0.819). The five most influential features of the RF model were office diastolic BP, office systolic BP, current smoker, estimated glomerular filtration rate, and fasting glucose level.</p><p><strong>Conclusion: </strong>Our prediction models achieved good performance, underlining the feasibility of applying ML models to outpatient populations for the diagnosis of WCH and WUCH. Further validation with other prospective data sets should be considered in the future.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/4f/0b/ztac066.PMC9779877.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10734974","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}
Aims: The incremental usefulness of circulating biomarkers from different pathological pathways for predicting mortality has not been evaluated in acute Type A aortic dissection (ATAAD) patients. We aim to develop a risk prediction model and investigate the impact of arch repair strategy on mortality based on distinct risk stratifications.
Methods and results: A total of 3771 ATAAD patients who underwent aortic surgery retrospectively included were randomly divided into training and testing cohorts at a ratio of 7:3 for the development and validation of the risk model based on multiple circulating biomarkers and conventional clinical factors. Extreme gradient boosting was used to generate the risk models. Subgroup analyses were performed by risk stratifications (low vs. middle-high risk) and arch repair strategies (proximal vs. extensive arch repair). Addition of multiple biomarkers to a model with conventional factors fitted an ABC risk model consisting of platelet-leucocyte ratio, mean arterial pressure, albumin, age, creatinine, creatine kinase-MB, haemoglobin, lactate, left ventricular end-diastolic dimension, urea nitrogen, and aspartate aminotransferase, with adequate discrimination ability {area under the receiver operating characteristic curve (AUROC): 0.930 [95% confidence interval (CI) 0.906-0.954] and 0.954, 95% CI (0.930-0.977) in the derivation and validation cohort, respectively}. Compared with proximal arch repair, the extensive repair was associated with similar mortality risk among patients at low risk [odds ratio (OR) 1.838, 95% CI (0.559-6.038); P = 0.316], but associated with higher mortality risk among patients at middle-high risk [OR 2.007, 95% CI (1.460-2.757); P < 0.0001].
Conclusion: In ATAAD patients, the simultaneous addition of circulating biomarkers of inflammatory, cardiac, hepatic, renal, and metabolic abnormalities substantially improved risk stratification and individualized arch repair strategy.
目的:在急性A型主动脉夹层(ATAAD)患者中,尚未评估不同病理途径的循环生物标志物对预测死亡率的增量有用性。我们的目标是建立一个风险预测模型,并研究基于不同风险分层的弓修复策略对死亡率的影响。方法和结果:回顾性纳入3771例接受主动脉手术的ATAAD患者,按7:3的比例随机分为训练组和测试组,以建立和验证基于多种循环生物标志物和常规临床因素的风险模型。采用极值梯度增强方法生成风险模型。根据风险分层(低风险vs中高风险)和弓修复策略(近端弓修复vs广泛弓修复)进行亚组分析。在常规因素模型中加入多种生物标志物,拟合由血小板-白细胞比、平均动脉压、白蛋白、年龄、肌酐、肌酸激酶- mb、血红蛋白、乳酸、左室舒张末期尺寸、尿素氮和天冬氨酸转氨酶组成的ABC风险模型,具有足够的识别能力{受试者工作特征曲线下面积(AUROC):0.930[95%置信区间(CI) 0.906-0.954]和0.954,95% CI(0.930-0.977)在推导和验证队列中分别}。与近端弓修复相比,低风险患者的广泛修复与相似的死亡风险相关[优势比(OR) 1.838, 95% CI (0.559-6.038);P = 0.316],但中高危患者的死亡风险较高[OR 2.007, 95% CI (1.460-2.757);P < 0.0001]。结论:在ATAAD患者中,同时添加炎症、心脏、肝脏、肾脏和代谢异常的循环生物标志物可显著改善风险分层和个体化弓修复策略。
{"title":"Circulating biomarker-based risk stratifications individualize arch repair strategy of acute Type A aortic dissection via the XGBoosting algorithm.","authors":"Hong Liu, Si-Chong Qian, Lu Han, Ying-Yuan Zhang, Ying Wu, Liang Hong, Ji-Nong Yang, Ji-Sheng Zhong, Yu-Qi Wang, Dong-Kai Wu, Guo-Liang Fan, Jun-Quan Chen, Sheng-Qiang Zhang, Xing-Xing Peng, Zhi-Wei Tang, Al-Wajih Hamzah, Yong-Feng Shao, Hai-Yang Li, Hong-Jia Zhang","doi":"10.1093/ehjdh/ztac068","DOIUrl":"https://doi.org/10.1093/ehjdh/ztac068","url":null,"abstract":"<p><strong>Aims: </strong>The incremental usefulness of circulating biomarkers from different pathological pathways for predicting mortality has not been evaluated in acute Type A aortic dissection (ATAAD) patients. We aim to develop a risk prediction model and investigate the impact of arch repair strategy on mortality based on distinct risk stratifications.</p><p><strong>Methods and results: </strong>A total of 3771 ATAAD patients who underwent aortic surgery retrospectively included were randomly divided into training and testing cohorts at a ratio of 7:3 for the development and validation of the risk model based on multiple circulating biomarkers and conventional clinical factors. Extreme gradient boosting was used to generate the risk models. Subgroup analyses were performed by risk stratifications (low vs. middle-high risk) and arch repair strategies (proximal vs. extensive arch repair). Addition of multiple biomarkers to a model with conventional factors fitted an ABC risk model consisting of platelet-leucocyte ratio, mean arterial pressure, albumin, age, creatinine, creatine kinase-MB, haemoglobin, lactate, left ventricular end-diastolic dimension, urea nitrogen, and aspartate aminotransferase, with adequate discrimination ability {area under the receiver operating characteristic curve (AUROC): 0.930 [95% confidence interval (CI) 0.906-0.954] and 0.954, 95% CI (0.930-0.977) in the derivation and validation cohort, respectively}. Compared with proximal arch repair, the extensive repair was associated with similar mortality risk among patients at low risk [odds ratio (OR) 1.838, 95% CI (0.559-6.038); <i>P</i> = 0.316], but associated with higher mortality risk among patients at middle-high risk [OR 2.007, 95% CI (1.460-2.757); <i>P</i> < 0.0001].</p><p><strong>Conclusion: </strong>In ATAAD patients, the simultaneous addition of circulating biomarkers of inflammatory, cardiac, hepatic, renal, and metabolic abnormalities substantially improved risk stratification and individualized arch repair strategy.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/70/b5/ztac068.PMC9779759.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10747228","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}
James N Cameron, Andrea Comella, Nigel Sutherland, Adam J Brown, Thanh G Phan
Aims: Hyperaemic and non-hyperaemic pressure ratios (NHPR) are routinely used to identify significant coronary lesions. Machine learning (ML) techniques may help better understand these indices and guide future practice. This study assessed the ability of a purpose-built ML algorithm to classify coronary ischaemia during non-hyperaemia compared with the existing gold-standard technique (fractional flow reserve, FFR). Further, it investigated whether ML could identify components of coronary and aortic pressure cycles indicative of ischaemia.
Methods and results: Seventy-seven coronary vessel lesions (39 FFR defined ischaemia, 53 patients) with proximal and distal non-hyperaemic pressure waveforms and FFR values were assessed using supervised and unsupervised learning techniques in combination with principal component analysis (PCA). Fractional flow reserve measurements were obtained from the right coronary artery (13), left anterior descending (46), left circumflex (11), left main (1), obtuse marginal (2), and diagonal (4). The most accurate supervised learning classification utilized whole-cycle aortic with diastolic distal blood pressure waveforms, yielding a classification accuracy of 86.9% (sensitivity 86.8%, specificity 87.2%, positive predictive value 86.8%, negative predictive value 87.2%). Principal component analysis showed subtle variations in coronary pressures at the start of diastole have significant relation to ischaemia, and whole-cycle aortic pressure data are important for determining ischaemia.
Conclusions: Our ML algorithm classifies significant coronary lesions with accuracy similar to previous studies comparing time-domain NHPRs with FFR. Further, it has identified characteristics of pressure waveforms that relate to function. These results provide an application of ML to ischaemia requiring only standard data from non-hyperaemic pressure measurements.
{"title":"Non-hyperaemic assessment of coronary ischaemia: application of machine learning techniques.","authors":"James N Cameron, Andrea Comella, Nigel Sutherland, Adam J Brown, Thanh G Phan","doi":"10.1093/ehjdh/ztac050","DOIUrl":"https://doi.org/10.1093/ehjdh/ztac050","url":null,"abstract":"<p><strong>Aims: </strong>Hyperaemic and non-hyperaemic pressure ratios (NHPR) are routinely used to identify significant coronary lesions. Machine learning (ML) techniques may help better understand these indices and guide future practice. This study assessed the ability of a purpose-built ML algorithm to classify coronary ischaemia during non-hyperaemia compared with the existing gold-standard technique (fractional flow reserve, FFR). Further, it investigated whether ML could identify components of coronary and aortic pressure cycles indicative of ischaemia.</p><p><strong>Methods and results: </strong>Seventy-seven coronary vessel lesions (39 FFR defined ischaemia, 53 patients) with proximal and distal non-hyperaemic pressure waveforms and FFR values were assessed using supervised and unsupervised learning techniques in combination with principal component analysis (PCA). Fractional flow reserve measurements were obtained from the right coronary artery (13), left anterior descending (46), left circumflex (11), left main (1), obtuse marginal (2), and diagonal (4). The most accurate supervised learning classification utilized whole-cycle aortic with diastolic distal blood pressure waveforms, yielding a classification accuracy of 86.9% (sensitivity 86.8%, specificity 87.2%, positive predictive value 86.8%, negative predictive value 87.2%). Principal component analysis showed subtle variations in coronary pressures at the start of diastole have significant relation to ischaemia, and whole-cycle aortic pressure data are important for determining ischaemia.</p><p><strong>Conclusions: </strong>Our ML algorithm classifies significant coronary lesions with accuracy similar to previous studies comparing time-domain NHPRs with FFR. Further, it has identified characteristics of pressure waveforms that relate to function. These results provide an application of ML to ischaemia requiring only standard data from non-hyperaemic pressure measurements.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/2b/41/ztac050.PMC9779890.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10747232","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}
Rutger R van de Leur, Rutger J Hassink, René van Es
We appreciate the opportunity to address Higaki and Yamaguchi and their detailed commentary on our study. 1 In the referenced study, we show that variational auto-encoders (VAEs), which use deep neural networks (DNNs) to learn the underlying factors of variation in the median beat electrocardiogram (ECG), can be used to provide improved explainability over previous attempts to open the ‘black box’ of ECG-based DNNs using saliency-based heatmaps. There are currently conflicting definitions of explainability and interpretability in the literature and both are used interchangeably
{"title":"Variational auto-encoders improve explainability over currently employed heatmap methods for deep learning-based interpretation of the electrocardiogram.","authors":"Rutger R van de Leur, Rutger J Hassink, René van Es","doi":"10.1093/ehjdh/ztac063","DOIUrl":"https://doi.org/10.1093/ehjdh/ztac063","url":null,"abstract":"We appreciate the opportunity to address Higaki and Yamaguchi and their detailed commentary on our study. 1 In the referenced study, we show that variational auto-encoders (VAEs), which use deep neural networks (DNNs) to learn the underlying factors of variation in the median beat electrocardiogram (ECG), can be used to provide improved explainability over previous attempts to open the ‘black box’ of ECG-based DNNs using saliency-based heatmaps. There are currently conflicting definitions of explainability and interpretability in the literature and both are used interchangeably","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/7b/bc/ztac063.PMC9779792.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10747230","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}
Chengyi Zheng, Benjamin C Sun, Yi-Lin Wu, Maros Ferencik, Ming-Sum Lee, Rita F Redberg, Aniket A Kawatkar, Visanee V Musigdilok, Adam L Sharp
Aims: Stress echocardiography (SE) findings and interpretations are commonly documented in free-text reports. Reusing SE results requires laborious manual reviews. This study aimed to develop and validate an automated method for abstracting SE reports in a large cohort.
Methods and results: This study included adult patients who had SE within 30 days of their emergency department visit for suspected acute coronary syndrome in a large integrated healthcare system. An automated natural language processing (NLP) algorithm was developed to abstract SE reports and classify overall SE results into normal, non-diagnostic, infarction, and ischaemia categories. Randomly selected reports (n = 140) were double-blindly reviewed by cardiologists to perform criterion validity of the NLP algorithm. Construct validity was tested on the entire cohort using abstracted SE data and additional clinical variables. The NLP algorithm abstracted 6346 consecutive SE reports. Cardiologists had good agreements on the overall SE results on the 140 reports: Kappa (0.83) and intraclass correlation coefficient (0.89). The NLP algorithm achieved 98.6% specificity and negative predictive value, 95.7% sensitivity, positive predictive value, and F-score on the overall SE results and near-perfect scores on ischaemia findings. The 30-day acute myocardial infarction or death outcomes were highest among patients with ischaemia (5.0%), followed by infarction (1.4%), non-diagnostic (0.8%), and normal (0.3%) results. We found substantial variations in the format and quality of SE reports, even within the same institution.
Conclusions: Natural language processing is an accurate and efficient method for abstracting unstructured SE reports. This approach creates new opportunities for research, public health measures, and care improvement.
{"title":"Automated interpretation of stress echocardiography reports using natural language processing.","authors":"Chengyi Zheng, Benjamin C Sun, Yi-Lin Wu, Maros Ferencik, Ming-Sum Lee, Rita F Redberg, Aniket A Kawatkar, Visanee V Musigdilok, Adam L Sharp","doi":"10.1093/ehjdh/ztac047","DOIUrl":"https://doi.org/10.1093/ehjdh/ztac047","url":null,"abstract":"<p><strong>Aims: </strong>Stress echocardiography (SE) findings and interpretations are commonly documented in free-text reports. Reusing SE results requires laborious manual reviews. This study aimed to develop and validate an automated method for abstracting SE reports in a large cohort.</p><p><strong>Methods and results: </strong>This study included adult patients who had SE within 30 days of their emergency department visit for suspected acute coronary syndrome in a large integrated healthcare system. An automated natural language processing (NLP) algorithm was developed to abstract SE reports and classify overall SE results into normal, non-diagnostic, infarction, and ischaemia categories. Randomly selected reports (<i>n</i> = 140) were double-blindly reviewed by cardiologists to perform criterion validity of the NLP algorithm. Construct validity was tested on the entire cohort using abstracted SE data and additional clinical variables. The NLP algorithm abstracted 6346 consecutive SE reports. Cardiologists had good agreements on the overall SE results on the 140 reports: Kappa (0.83) and intraclass correlation coefficient (0.89). The NLP algorithm achieved 98.6% specificity and negative predictive value, 95.7% sensitivity, positive predictive value, and <i>F</i>-score on the overall SE results and near-perfect scores on ischaemia findings. The 30-day acute myocardial infarction or death outcomes were highest among patients with ischaemia (5.0%), followed by infarction (1.4%), non-diagnostic (0.8%), and normal (0.3%) results. We found substantial variations in the format and quality of SE reports, even within the same institution.</p><p><strong>Conclusions: </strong>Natural language processing is an accurate and efficient method for abstracting unstructured SE reports. This approach creates new opportunities for research, public health measures, and care improvement.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/97/ff/ztac047.PMC9779789.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10734966","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}
Ilke Ozcan, Takumi Toya, Michal Cohen-Shelly, Hyun Woong Park, Ali Ahmad, Alp Ozcan, Peter A Noseworthy, Suraj Kapa, Lilach O Lerman, Zachi I Attia, Sudhir S Kushwaha, Paul A Friedman, Amir Lerman
Aims: An artificial intelligence algorithm detecting age from 12-lead electrocardiogram (ECG) has been suggested to reflect 'physiological age'. An increased physiological age has been associated with a higher risk of cardiac mortality in the non-transplant population. We aimed to investigate the utility of this algorithm in patients who underwent heart transplantation (HTx).
Methods and results: A total of 540 patients were studied. The average ECG ages within 1 year before and after HTx were used to represent pre- and post-HTx ECG ages. Major adverse cardiovascular event (MACE) was defined as any coronary revascularization, heart failure hospitalization, re-transplantation, and mortality. Recipient pre-transplant ECG age (mean 63 ± 11 years) correlated significantly with recipient chronological age (mean 49 ± 14 years, R = 0.63, P < 0.0001), while post-transplant ECG age (mean 54 ± 10 years) correlated with both the donor (mean 32 ± 13 years, R = 0.45, P < 0.0001) and the recipient ages (R = 0.38, P < 0.0001). During a median follow-up of 8.8 years, 307 patients experienced MACE. Patients with an increase in ECG age post-transplant showed an increased risk of MACE [hazard ratio (HR): 1.58, 95% confidence interval (CI): (1.24, 2.01), P = 0.0002], even after adjusting for potential confounders [HR: 1.58, 95% CI: (1.19, 2.10), P = 0.002].
Conclusion: Electrocardiogram age-derived cardiac ageing after transplantation is associated with a higher risk of MACE. This study suggests that physiological age change of the heart might be an important determinant of MACE risk post-HTx.
目的:提出了一种从12导联心电图(ECG)检测年龄的人工智能算法,以反映“生理年龄”。在非移植人群中,生理年龄的增加与心脏死亡风险的增加有关。我们的目的是研究该算法在接受心脏移植(HTx)的患者中的效用。方法与结果:共对540例患者进行研究。用HTx前后1年内的平均心电年龄表示HTx前后的心电年龄。主要不良心血管事件(MACE)定义为任何冠状动脉血运重建术、心力衰竭住院、再移植和死亡。受者移植前心电图年龄(平均63±11岁)与受者实时年龄(平均49±14岁,R = 0.63, P < 0.0001)相关,移植后心电图年龄(平均54±10岁)与供者年龄(平均32±13岁,R = 0.45, P < 0.0001)和受者年龄(R = 0.38, P < 0.0001)相关。在中位随访8.8年期间,307例患者经历了MACE。移植后心电图年龄增加的患者发生MACE的风险增加[危险比(HR): 1.58, 95%可信区间(CI): (1.24, 2.01), P = 0.0002],即使校正了潜在的混杂因素[HR: 1.58, 95% CI: (1.19, 2.10), P = 0.002]。结论:移植后心电图年龄源性心脏老化与MACE发生风险增高有关。本研究提示心脏的生理年龄变化可能是htx后MACE风险的重要决定因素。
{"title":"Artificial intelligence-derived cardiac ageing is associated with cardiac events post-heart transplantation.","authors":"Ilke Ozcan, Takumi Toya, Michal Cohen-Shelly, Hyun Woong Park, Ali Ahmad, Alp Ozcan, Peter A Noseworthy, Suraj Kapa, Lilach O Lerman, Zachi I Attia, Sudhir S Kushwaha, Paul A Friedman, Amir Lerman","doi":"10.1093/ehjdh/ztac051","DOIUrl":"https://doi.org/10.1093/ehjdh/ztac051","url":null,"abstract":"<p><strong>Aims: </strong>An artificial intelligence algorithm detecting age from 12-lead electrocardiogram (ECG) has been suggested to reflect 'physiological age'. An increased physiological age has been associated with a higher risk of cardiac mortality in the non-transplant population. We aimed to investigate the utility of this algorithm in patients who underwent heart transplantation (HTx).</p><p><strong>Methods and results: </strong>A total of 540 patients were studied. The average ECG ages within 1 year before and after HTx were used to represent pre- and post-HTx ECG ages. Major adverse cardiovascular event (MACE) was defined as any coronary revascularization, heart failure hospitalization, re-transplantation, and mortality. Recipient pre-transplant ECG age (mean 63 ± 11 years) correlated significantly with recipient chronological age (mean 49 ± 14 years, <i>R</i> = 0.63, <i>P</i> < 0.0001), while post-transplant ECG age (mean 54 ± 10 years) correlated with both the donor (mean 32 ± 13 years, <i>R</i> = 0.45, <i>P</i> < 0.0001) and the recipient ages (<i>R</i> = 0.38, <i>P</i> < 0.0001). During a median follow-up of 8.8 years, 307 patients experienced MACE. Patients with an increase in ECG age post-transplant showed an increased risk of MACE [hazard ratio (HR): 1.58, 95% confidence interval (CI): (1.24, 2.01), <i>P</i> = 0.0002], even after adjusting for potential confounders [HR: 1.58, 95% CI: (1.19, 2.10), <i>P</i> = 0.002].</p><p><strong>Conclusion: </strong>Electrocardiogram age-derived cardiac ageing after transplantation is associated with a higher risk of MACE. This study suggests that physiological age change of the heart might be an important determinant of MACE risk post-HTx.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/db/48/ztac051.PMC9779895.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9310426","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}
Bjarke Skogstad Larsen, Simon Winther, Louise Nissen, Axel Diederichsen, Morten Bøttcher, Matthias Renker, Johannes Jan Struijk, Mads Græsbøll Christensen, Samuel Emil Schmidt
Aims: Current early risk stratification of coronary artery disease (CAD) consists of pre-test probability scoring such as the 2019 ESC guidelines on chronic coronary syndromes (ESC2019), which has low specificity and thus rule-out capacity. A newer clinical risk factor model (risk factor-weighted clinical likelihood, RF-CL) showed significantly improved rule-out capacity over the ESC2019 model. The aim of the current study was to investigate if the addition of acoustic features to the RF-CL model could improve the rule-out potential of the best performing clinical risk factor models.
Methods and results: Four studies with heart sound recordings from 2222 patients were pooled and distributed into two data sets: training and test. From a feature bank of 40 acoustic features, a forward-selection technique was used to select three features that were added to the RF-CL model. Using a cutoff of 5% predicted risk of CAD, the developed acoustic-weighted clinical likelihood (A-CL) model showed significantly (P < 0.05) higher specificity of 48.6% than the RF-CL model (specificity of 41.5%) and ESC 2019 model (specificity of 6.9%) while having the same sensitivity of 84.9% as the RF-CL model. Area under the curve of the receiver operating characteristic for the three models was 72.5% for ESC2019, 76.7% for RF-CL, and 79.5% for A-CL.
Conclusion: The proposed A-CL model offers significantly improved rule-out capacity over the ESC2019 model and showed better overall performance than the RF-CL model. The addition of acoustic features to the RF-CL model was shown to significantly improve early risk stratification of symptomatic patients suspected of having stable CAD.
{"title":"Improved pre-test likelihood estimation of coronary artery disease using phonocardiography.","authors":"Bjarke Skogstad Larsen, Simon Winther, Louise Nissen, Axel Diederichsen, Morten Bøttcher, Matthias Renker, Johannes Jan Struijk, Mads Græsbøll Christensen, Samuel Emil Schmidt","doi":"10.1093/ehjdh/ztac057","DOIUrl":"https://doi.org/10.1093/ehjdh/ztac057","url":null,"abstract":"<p><strong>Aims: </strong>Current early risk stratification of coronary artery disease (CAD) consists of pre-test probability scoring such as the 2019 ESC guidelines on chronic coronary syndromes (ESC2019), which has low specificity and thus rule-out capacity. A newer clinical risk factor model (risk factor-weighted clinical likelihood, RF-CL) showed significantly improved rule-out capacity over the ESC2019 model. The aim of the current study was to investigate if the addition of acoustic features to the RF-CL model could improve the rule-out potential of the best performing clinical risk factor models.</p><p><strong>Methods and results: </strong>Four studies with heart sound recordings from 2222 patients were pooled and distributed into two data sets: training and test. From a feature bank of 40 acoustic features, a forward-selection technique was used to select three features that were added to the RF-CL model. Using a cutoff of 5% predicted risk of CAD, the developed acoustic-weighted clinical likelihood (A-CL) model showed significantly (<i>P</i> < 0.05) higher specificity of 48.6% than the RF-CL model (specificity of 41.5%) and ESC 2019 model (specificity of 6.9%) while having the same sensitivity of 84.9% as the RF-CL model. Area under the curve of the receiver operating characteristic for the three models was 72.5% for ESC2019, 76.7% for RF-CL, and 79.5% for A-CL.</p><p><strong>Conclusion: </strong>The proposed A-CL model offers significantly improved rule-out capacity over the ESC2019 model and showed better overall performance than the RF-CL model. The addition of acoustic features to the RF-CL model was shown to significantly improve early risk stratification of symptomatic patients suspected of having stable CAD.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/1f/57/ztac057.PMC9779903.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10747226","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}