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INTRODUCTION: Hypercholesterolemia is associated with increased inflammation and impaired serotonin neurotransmission, potentially contributing to depressive symptoms. However, the role of statins, particularly pitavastatin, in modulating serotonin transporter (SERT) function within this context remains underexplored. This study aimed to investigate whether pitavastatin counteracts the neurobiological effects of hypercholesterolemia. METHODS: Low-density lipoprotein receptor knockout (LDLR-/-) mice on a C57BL/6 background were assigned to three groups: a control group fed a standard chow diet, a group fed a high-fat diet (HFD), and a third group fed a high-fat diet supplemented with pitavastatin (HFD + Pita). We evaluated the effects of HFD with or without pitavastatin on lipid profiles, inflammatory markers, and SERT availability using small-animal positron emission tomography (PET) scans with the radioligand 4-[18F]-ADAM over a 20-week period. RESULTS: Pitavastatin treatment in HFD-fed mice significantly reduced both total cholesterol and LDL cholesterol levels in HFD-fed mice compared to those on HFD alone. Elevated inflammatory markers such as IL-1α, MCP-1/CCL2, and TNF-α in HFD mice were notably decreased in the HFD + Pita group. PET scans showed reduced SERT availability in the brains of HFD mice; however, pitavastatin improved this in brain regions associated with mood regulation, suggesting enhanced serotonin neurotransmission. Additionally, the sucrose preference test showed a trend towards increased preference in the HFD + Pita group compared to the HFD group, indicating a potential reduction in depressive-like behavior. CONCLUSION: Our findings demonstrate that pitavastatin not only lowers cholesterol and reduces inflammation but also enhances SERT availability, suggesting a potential role in alleviating depressive symptoms associated with hypercholesterolemia. These results highlight the multifaceted benefits of pitavastatin, extending beyond its lipid-lowering effects to potentially improving mood regulation and neurotransmitter function.
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Dieta Alta en Grasa , Hipercolesterolemia , Ratones Endogámicos C57BL , Quinolinas , Proteínas de Transporte de Serotonina en la Membrana Plasmática , Animales , Quinolinas/farmacología , Quinolinas/uso terapéutico , Hipercolesterolemia/tratamiento farmacológico , Hipercolesterolemia/metabolismo , Proteínas de Transporte de Serotonina en la Membrana Plasmática/metabolismo , Proteínas de Transporte de Serotonina en la Membrana Plasmática/genética , Ratones , Dieta Alta en Grasa/efectos adversos , Masculino , Ratones Noqueados , Receptores de LDL/metabolismo , Receptores de LDL/genética , Tomografía de Emisión de Positrones , LDL-Colesterol/sangre , Encéfalo/metabolismo , Encéfalo/efectos de los fármacos , Encéfalo/diagnóstico por imagen , Inhibidores de Hidroximetilglutaril-CoA Reductasas/farmacología , Inhibidores de Hidroximetilglutaril-CoA Reductasas/uso terapéuticoRESUMEN
The diagnosis and treatment of pulmonary hypertension have changed dramatically through the re-defined diagnostic criteria and advanced drug development in the past decade. The application of Artificial Intelligence for the detection of elevated pulmonary arterial pressure (ePAP) was reported recently. Artificial Intelligence (AI) has demonstrated the capability to identify ePAP and its association with hospitalization due to heart failure when analyzing chest X-rays (CXR). An AI model based on electrocardiograms (ECG) has shown promise in not only detecting ePAP but also in predicting future risks related to cardiovascular mortality. We aimed to develop an AI model integrating ECG and CXR to detect ePAP and evaluate their performance. We developed a deep-learning model (DLM) using paired ECG and CXR to detect ePAP (systolic pulmonary artery pressure > 50 mmHg in transthoracic echocardiography). This model was further validated in a community hospital. Additionally, our DLM was evaluated for its ability to predict future occurrences of left ventricular dysfunction (LVD, ejection fraction < 35%) and cardiovascular mortality. The AUCs for detecting ePAP were as follows: 0.8261 with ECG (sensitivity 76.6%, specificity 74.5%), 0.8525 with CXR (sensitivity 82.8%, specificity 72.7%), and 0.8644 with a combination of both (sensitivity 78.6%, specificity 79.2%) in the internal dataset. In the external validation dataset, the AUCs for ePAP detection were 0.8348 with ECG, 0.8605 with CXR, and 0.8734 with the combination. Furthermore, using the combination of ECGs and CXR, the negative predictive value (NPV) was 98% in the internal dataset and 98.1% in the external dataset. Patients with ePAP detected by the DLM using combination had a higher risk of new-onset LVD with a hazard ratio (HR) of 4.51 (95% CI: 3.54-5.76) in the internal dataset and cardiovascular mortality with a HR of 6.08 (95% CI: 4.66-7.95). Similar results were seen in the external validation dataset. The DLM, integrating ECG and CXR, effectively detected ePAP with a strong NPV and forecasted future risks of developing LVD and cardiovascular mortality. This model has the potential to expedite the early identification of pulmonary hypertension in patients, prompting further evaluation through echocardiography and, when necessary, right heart catheterization (RHC), potentially resulting in enhanced cardiovascular outcomes.
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Medical advances prolonging life have led to more permanent pacemaker implants. When pacemaker implantation (PMI) is commonly caused by sick sinus syndrome or conduction disorders, predicting PMI is challenging, as patients often experience related symptoms. This study was designed to create a deep learning model (DLM) for predicting future PMI from ECG data and assess its ability to predict future cardiovascular events. In this study, a DLM was trained on a dataset of 158,471 ECGs from 42,903 academic medical center patients, with additional validation involving 25,640 medical center patients and 26,538 community hospital patients. Primary analysis focused on predicting PMI within 90 days, while all-cause mortality, cardiovascular disease (CVD) mortality, and the development of various cardiovascular conditions were addressed with secondary analysis. The study's raw ECG DLM achieved area under the curve (AUC) values of 0.870, 0.878, and 0.883 for PMI prediction within 30, 60, and 90 days, respectively, along with sensitivities exceeding 82.0% and specificities over 81.9% in the internal validation. Significant ECG features included the PR interval, corrected QT interval, heart rate, QRS duration, P-wave axis, T-wave axis, and QRS complex axis. The AI-predicted PMI group had higher risks of PMI after 90 days (hazard ratio [HR]: 7.49, 95% CI: 5.40-10.39), all-cause mortality (HR: 1.91, 95% CI: 1.74-2.10), CVD mortality (HR: 3.53, 95% CI: 2.73-4.57), and new-onset adverse cardiovascular events. External validation confirmed the model's accuracy. Through ECG analyses, our AI DLM can alert clinicians and patients to the possibility of future PMI and related mortality and cardiovascular risks, aiding in timely patient intervention.
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Enfermedades Cardiovasculares , Aprendizaje Profundo , Electrocardiografía , Marcapaso Artificial , Humanos , Electrocardiografía/métodos , Femenino , Masculino , Anciano , Persona de Mediana Edad , Inteligencia Artificial , Síndrome del Seno EnfermoRESUMEN
Background: Central venous catheterization (CVC) is a critical clinical procedure. To avoid complications, possessing good knowledge regarding the CVC care bundle and skills for the proper insertion and maintenance of CVC are important. Objectives: To evaluate the effectiveness of an educational intervention and the use of an interactive response system in enhancing the CVC bundle care and insertion skills of medical students undergoing critical care medicine training. Materials and Methods: Sixth-year medical students (equivalent to fourth-year students in the United States) engaged in didactic lessons, interactive demonstrations, and simulator training facilitated by a CVC team comprising three thoracic and two vascular surgeons (all with a minimum 5 years of experience in central venous access) during their intensive care unit (ICU) rotation. Self-reported knowledge and confidence levels were assessed using pre-and posttests administered through the Zuvio App, an interactive response system. Results: A total of 60 students underwent the educational intervention, of which 54 completed the pretest and 40 completed the posttest. In the posttest, significant improvement was found in the CVC bundle care competency and understanding (P = 0.002), preprocedural preparation (P = 0.002), insertion procedures (P = 0.004), complications (P = 0.003), and insertion depth decisions (P = 0.001). Staff and students reported that assessment and interaction via the Zuvio App were valuable, practical, and feasible in a clinical setting, providing trainees with an individual competency portfolio of receiving precise medical education. Conclusions: Integrating the training provided by a specialized team with an interactive response system enhanced the knowledge and competency level in CVC insertion among medical students in this study.
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Background Diagnosing osteoporosis is challenging due to its often asymptomatic presentation, which highlights the importance of providing screening for high-risk populations. Purpose To evaluate the effectiveness of dual-energy x-ray absorptiometry (DXA) screening in high-risk patients with osteoporosis identified by an artificial intelligence (AI) model using chest radiographs. Materials and Methods This randomized controlled trial conducted at an academic medical center included participants 40 years of age or older who had undergone chest radiography between January and December 2022 without a history of DXA examination. High-risk participants identified with the AI-enabled chest radiographs were randomly allocated to either a screening group, which was offered fully reimbursed DXA examinations between January and June 2023, or a control group, which received usual care, defined as DXA examination by a physician or patient on their own initiative without AI intervention. A logistic regression was used to test the difference in the primary outcome, new-onset osteoporosis, between the screening and control groups. Results Of the 40 658 enrolled participants, 4912 (12.1%) were identified by the AI model as high risk, with 2456 assigned to the screening group (mean age, 71.8 years ± 11.5 [SD]; 1909 female) and 2456 assigned to the control group (mean age, 72.1 years ± 11.8; 1872 female). A total of 315 of 2456 (12.8%) participants in the screening group underwent fully reimbursed DXA, and 237 of 315 (75.2%) were identified with new-onset osteoporosis. After including DXA results by means of usual care in both screening and control groups, the screening group exhibited higher rates of osteoporosis detection (272 of 2456 [11.1%] vs 27 of 2456 [1.1%]; odds ratio [OR], 11.2 [95% CI: 7.5, 16.7]; P < .001) compared with the control group. The ORs of osteoporosis diagnosis were increased in screening group participants who did not meet formalized criteria for DXA compared with those who did (OR, 23.2 [95% CI: 10.2, 53.1] vs OR, 8.0 [95% CI: 5.0, 12.6]; interactive P = .03). Conclusion Providing DXA screening to a high-risk group identified with AI-enabled chest radiographs can effectively diagnose more patients with osteoporosis. Clinical trial registration no. NCT05721157 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Smith and Rothenberg in this issue.
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Absorciometría de Fotón , Redes Neurales de la Computación , Osteoporosis , Radiografía Torácica , Humanos , Femenino , Osteoporosis/diagnóstico por imagen , Masculino , Radiografía Torácica/métodos , Absorciometría de Fotón/métodos , Anciano , Tamizaje Masivo/métodos , Persona de Mediana EdadRESUMEN
BACKGROUND: Valvular heart disease (VHD) is becoming increasingly important to manage the risk of future complications. Electrocardiographic (ECG) changes may be related to multiple VHDs, and (AI)-enabled ECG has been able to detect some VHDs. We aimed to develop five deep learning models (DLMs) to identify aortic stenosis, aortic regurgitation, pulmonary regurgitation, tricuspid regurgitation, and mitral regurgitation. METHODS: Between 2010 and 2021, 77,047 patients with echocardiography and 12-lead ECG performed within 7 days were identified from an academic medical center to provide DLM development (122,728 ECGs), and internal validation (7,637 ECGs). Additional 11,800 patients from a community hospital were identified to external validation. The ECGs were classified as with or without moderate-to-severe VHDs according to transthoracic echocardiography (TTE) records, and we also collected the other echocardiographic data and follow-up TTE records to identify new-onset valvular heart diseases. RESULTS: AI-ECG adjusted for age and sex achieved areas under the curves (AUCs) of >0.84, >0.80, >0.77, >0.83, and >0.81 for detecting aortic stenosis, aortic regurgitation, pulmonary regurgitation, tricuspid regurgitation, and mitral regurgitation, respectively. Since predictions of each DLM shared similar components of ECG rhythms, the positive findings of each DLM were highly correlated with other valvular heart diseases. Of note, a total of 37.5-51.7% of false-positive predictions had at least one significant echocardiographic finding, which may lead to a significantly higher risk of future moderate-to-severe VHDs in patients with initially minimal-to-mild VHDs. CONCLUSION: AI-ECG may be used as a large-scale screening tool for detecting VHDs and a basis to undergo an echocardiography.
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Inteligencia Artificial , Electrocardiografía , Enfermedades de las Válvulas Cardíacas , Humanos , Electrocardiografía/métodos , Femenino , Masculino , Enfermedades de las Válvulas Cardíacas/diagnóstico , Enfermedades de las Válvulas Cardíacas/diagnóstico por imagen , Enfermedades de las Válvulas Cardíacas/fisiopatología , Anciano , Persona de Mediana Edad , Aprendizaje Profundo , Ecocardiografía , Anciano de 80 o más AñosRESUMEN
The early identification of vulnerable patients has the potential to improve outcomes but poses a substantial challenge in clinical practice. This study evaluated the ability of an artificial intelligence (AI)-enabled electrocardiogram (ECG) to identify hospitalized patients with a high risk of mortality in a multisite randomized controlled trial involving 39 physicians and 15,965 patients. The AI-ECG alert intervention included an AI report and warning messages delivered to the physicians, flagging patients predicted to be at high risk of mortality. The trial met its primary outcome, finding that implementation of the AI-ECG alert was associated with a significant reduction in all-cause mortality within 90 days: 3.6% patients in the intervention group died within 90 days, compared to 4.3% in the control group (4.3%) (hazard ratio (HR) = 0.83, 95% confidence interval (CI) = 0.70-0.99). A prespecified analysis showed that reduction in all-cause mortality associated with the AI-ECG alert was observed primarily in patients with high-risk ECGs (HR = 0.69, 95% CI = 0.53-0.90). In analyses of secondary outcomes, patients in the intervention group with high-risk ECGs received increased levels of intensive care compared to the control group; for the high-risk ECG group of patients, implementation of the AI-ECG alert was associated with a significant reduction in the risk of cardiac death (0.2% in the intervention arm versus 2.4% in the control arm, HR = 0.07, 95% CI = 0.01-0.56). While the precise means by which implementation of the AI-ECG alert led to decreased mortality are to be fully elucidated, these results indicate that such implementation assists in the detection of high-risk patients, prompting timely clinical care and reducing mortality. ClinicalTrials.gov registration: NCT05118035 .
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Inteligencia Artificial , Electrocardiografía , Anciano , Femenino , Humanos , Masculino , Persona de Mediana EdadRESUMEN
BACKGROUND: Hyperthyroidism is frequently under-recognized and leads to heart failure and mortality. Timely identification of high-risk patients is a prerequisite to effective antithyroid therapy. Since the heart is very sensitive to hyperthyroidism and its electrical signature can be demonstrated by electrocardiography, we developed an artificial intelligence model to detect hyperthyroidism by electrocardiography and examined its potential for outcome prediction. METHODS: The deep learning model was trained using a large dataset of 47,245 electrocardiograms from 33,246 patients at an academic medical center. Patients were included if electrocardiograms and measurements of serum thyroid-stimulating hormone were available that had been obtained within a three day period. Serum thyroid-stimulating hormone and free thyroxine were used to define overt and subclinical hyperthyroidism. We tested the model internally using 14,420 patients and externally using two additional test sets comprising 11,498 and 596 patients, respectively. RESULTS: The performance of the deep learning model achieves areas under the receiver operating characteristic curves (AUCs) of 0.725-0.761 for hyperthyroidism detection, AUCs of 0.867-0.876 for overt hyperthyroidism, and AUC of 0.631-0.701 for subclinical hyperthyroidism, superior to a traditional features-based machine learning model. Patients identified as hyperthyroidism-positive by the deep learning model have a significantly higher risk (1.97-2.94 fold) of all-cause mortality and new-onset heart failure compared to hyperthyroidism-negative patients. This cardiovascular disease stratification is particularly pronounced in subclinical hyperthyroidism, surpassing that observed in overt hyperthyroidism. CONCLUSIONS: An innovative algorithm effectively identifies overt and subclinical hyperthyroidism and contributes to cardiovascular risk assessment.
Hyperthyroidism occurs when the thyroid gland produces too much hormone and can cause various symptoms including faster heartbeat, weight loss, and nervousness. Diagnosis is often missed, which can lead to heart problems and even death. Measurements of the heart's electrical activity can be obtained using Electrocardiograms (ECGs). We made a computational model that can detect hyperthyroidism from ECGs. Our model was better able to identify people with hyperthyroidism than currently available methods, especially the more severe forms of the condition. If future work demonstrates our model is safe and accurate, it could potentially be used to detect hyperthyroidism sooner, enabling faster treatment and improved health of people with hyperthyroidism.
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Calcium channel blockers (CCBs) are commonly used as antihypertensive agents. While certain L-type CCBs exhibit antiatherogenic effects, the impact of Cav3.1 T-type CCBs on antiatherogenesis and lipid metabolism remains unexplored. NNC 55-0396 (NNC) is a highly selective blocker of T-type calcium channels (Cav3.1 channels). We investigated the effects of NNC on relevant molecules and molecular mechanisms in human THP-1 macrophages. Cholesterol efflux, an indicator of reverse cholesterol transport (RCT) efficiency, was assessed using [3H]-labeled cholesterol. In vivo, high cholesterol diet (HCD)-fed LDL receptor knockout (Ldlr-/-) mice, an atherosclerosis-prone model, underwent histochemical staining to analyze plaque burden. Treatment of THP-1 macrophages with NNC facilitated cholesterol efflux and reduced intracellular cholesterol accumulation. Pharmacological and genetic interventions demonstrated that NNC treatment or Cav3.1 knockdown significantly enhanced the protein expression of scavenger receptor B1 (SR-B1), ATP-binding cassette transporter A1 (ABCA1), ATP-binding cassette transporter G1 (ABCG1), and liver X receptor alpha (LXRα) transcription factor. Mechanistic analysis revealed that NNC activates p38 and c-Jun N-terminal kinase (JNK) phosphorylation, leading to increased expression of ABCA1, ABCG1, and LXRα-without involving the microRNA pathway. LXRα isrequired for NNC-induced ABCA1 and ABCG1 expression. Administering NNC diminished atherosclerotic lesion area and lipid deposition in HCD-fed Ldlr-/- mice. NNC's anti-atherosclerotic effects, achieved through enhanced cholesterol efflux and inhibition of lipid accumulation, suggest a promising therapeutic approach for hypertensive patients with atherosclerosis. This research highlights the potential of Cav3.1 T-type CCBs in addressing cardiovascular complications associated with hypertension.
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Aterosclerosis , Bencimidazoles , Ciclopropanos , Hipercolesterolemia , Naftalenos , Humanos , Animales , Ratones , Bloqueadores de los Canales de Calcio/farmacología , Bloqueadores de los Canales de Calcio/uso terapéutico , Aterosclerosis/tratamiento farmacológico , Aterosclerosis/prevención & control , Aterosclerosis/metabolismo , Receptores X del Hígado/metabolismo , Colesterol/metabolismo , Hipercolesterolemia/tratamiento farmacológico , Transportadoras de Casetes de Unión a ATP/metabolismo , Transportador 1 de Casete de Unión a ATP/genética , Transportador 1 de Casete de Unión a ATP/metabolismoRESUMEN
A deep learning model was developed to identify osteoporosis from chest X-ray (CXR) features with high accuracy in internal and external validation. It has significant prognostic implications, identifying individuals at higher risk of all-cause mortality. This Artificial Intelligence (AI)-enabled CXR strategy may function as an early detection screening tool for osteoporosis. The aim of this study was to develop a deep learning model (DLM) to identify osteoporosis via CXR features and investigate the performance and clinical implications. This study collected 48,353 CXRs with the corresponding T score according to Dual energy X-ray Absorptiometry (DXA) from the academic medical center. Among these, 35,633 CXRs were used to identify CXR- Osteoporosis (CXR-OP). Another 12,720 CXRs were used to validate the performance, which was evaluated by the area under the receiver operating characteristic curve (AUC). Furthermore, CXR-OP was tested to assess the long-term risks of mortality, which were evaluated by KaplanâMeier survival analysis and the Cox proportional hazards model. The DLM utilizing CXR achieved AUCs of 0.930 and 0.892 during internal and external validation, respectively. The group that underwent DXA with CXR-OP had a higher risk of all-cause mortality (hazard ratio [HR] 2.59, 95% CI: 1.83-3.67), and those classified as CXR-OP in the group without DXA also had higher all-cause mortality (HR: 1.67, 95% CI: 1.61-1.72) in the internal validation set. The external validation set produced similar results. Our DLM uses CXRs for early detection of osteoporosis, aiding physicians to identify those at risk. It has significant prognostic implications, improving life quality and reducing mortality. AI-enabled CXR strategy may serve as a screening tool.
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Aprendizaje Profundo , Osteoporosis , Humanos , Inteligencia Artificial , Rayos X , Osteoporosis/diagnóstico por imagen , Absorciometría de Fotón/métodosRESUMEN
BACKGROUND: The burden of asymptomatic left ventricular dysfunction (LVD) is greater than that of heart failure; however, a cost-effective tool for asymptomatic LVD screening has not been well validated. We aimed to prospectively validate an artificial intelligence (AI)-enabled electrocardiography (ECG) algorithm for asymptomatic LVD detection and evaluate its cost-effectiveness for opportunistic screening. METHODS: In this prospective observational study, patients undergoing ECG at outpatient clinics or health check-ups were enrolled in 2 hospitals in Taiwan. Patients were stratified into LVD (left ventricular ejection fraction ≤ 40%) risk groups according to a previously developed ECG algorithm. The performance of AI-ECG was used to conduct a cost-effectiveness analysis of LVD screening compared with no screening. Incremental cost-effectiveness ratio (ICER) and sensitivity analyses were used to examine the cost-effectiveness and robustness of the results. RESULTS: Among the 29,137 patients, the algorithm demonstrated areas under the receiver operating characteristic curves of 0.984 and 0.945 for detecting LVD within 28 days in the 2 hospital cohorts. For patients not initially scheduled for ECG, the algorithm predicted future echocardiograms (high-risk, 46.2%; medium-risk, 31.4%; low-risk, 14.6%) and LVD (high-risk, 26.2%; medium-risk, 3.4%; low-risk, 0.1%) at 12 months. Opportunistic screening with AI-ECG could result in a negative ICER of -$7,439 for patients aged 65 years, with consistent cost-savings across age groups and particularly in men. Approximately 91.5% of the cases were found to be cost-effective at the willingness-to-pay threshold of $30,000 in the probabilistic analysis. CONCLUSIONS: The use of AI-ECG for asymptomatic LVD risk stratification is promising, and opportunistic screening in outpatient clinics has the potential to reduce costs.
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Background: The early diagnosis of pulmonary embolism (PE) remains a challenge. Electrocardiograms (ECGs) and D-dimer levels are used to screen potential cases. Objective: To develop a deep learning model (DLM) to detect PE using ECGs and investigate the clinical value of false detections in patients without PE. Methods: Among patients who visited the emergency department between 2011 and 2019, PE cases were identified through a review of medical records. Non-PE ECGs were collected from patients without a diagnostic code for PE. There were 113 PE and 51,456 non-PE ECGs in the training and validation sets for developing the DLM, respectively, and 27 PE and 13,105 non-PE cases in an independent testing set for performance validation. A human-machine competition was conducted from the testing set to compare the performance of the DLM with that of physicians. Receiver operating characteristic (ROC) curves, sensitivity, and specificity were used to determine the diagnostic value. Survival analysis was used to assess the prognosis of the patients without PE, stratified by DLM prediction. Results: The DLM was as effective as physicians in diagnosing PE, with 70.8% sensitivity and 69.7% specificity. The area under the ROC curve of DLM was 0.778 in the testing set and up to 0.9 with D-dimer and demographic data. The non-PE patients whose ECG was misclassified as PE by DLM had higher all-cause mortality [hazard ratio (HR) 2.13 (1.51-3.02)] and risk of non-cardiovascular hospitalization [HR 1.55 (1.42-1.68)] than those correctly classified. Conclusions: A DLM-enhanced ECG system may prompt PE recognition and provide prognostic outcomes in patients with false-positive predictions.
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BACKGROUND: The B-type natriuretic peptide (BNP) and N-terminal pro-brain natriuretic peptide (pBNP) are predictors of cardiovascular morbidity and mortality. Since the artificial intelligence (AI)-enabled electrocardiogram (ECG) system is widely used in the management of many cardiovascular diseases (CVDs), patients requiring intensive monitoring may benefit from an AI-ECG with BNP/pBNP predictions. This study aimed to develop an AI-ECG to predict BNP/pBNP and compare their values for future mortality. METHODS: The development, tuning, internal validation, and external validation sets included 47,709, 16,249, 4001, and 6042 ECGs, respectively. Deep learning models (DLMs) were trained using a development set for estimating ECG-based BNP/pBNP (ECG-BNP/ECG-pBNP), and the tuning set was used to guide the training process. The ECGs in internal and external validation sets belonging to nonrepeating patients were used to validate the DLMs. We also followed-up all-cause mortality to explore the prognostic value. RESULTS: The DLMs accurately distinguished mild (≥500 pg/mL) and severe (≥1000 pg/mL) an abnormal BNP/pBNP with AUCs of ≥0.85 in the internal and external validation sets, which provided sensitivities of 68.0-85.0% and specificities of 77.9-86.2%. In continuous predictions, the Pearson correlation coefficient between ECG-BNP and ECG-pBNP was 0.93, and they were both associated with similar ECG features, such as the T wave axis and correct QT interval. ECG-pBNP provided a higher all-cause mortality predictive value than ECG-BNP. CONCLUSIONS: The AI-ECG can accurately estimate BNP/pBNP and may be useful for monitoring the risk of CVDs. Moreover, ECG-pBNP may be a better indicator to manage the risk of future mortality.
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Emergency department (ED) triage scale determines the priority of patient care and foretells the prognosis. However, the information retrieved from the initial assessment is limited, hindering the risk identification accuracy of triage. Therefore, we sought to develop a 'dynamic' triage system as secondary screening, using artificial intelligence (AI) techniques to integrate information from initial assessment data and subsequent examinations. This retrospective cohort study included 134,112 ED visits with at least one electrocardiography (ECG) and chest X-ray (CXR) in a medical center from 2012 to 2022. Additionally, an independent community hospital provided 45,614 ED visits as an external validation set. We trained an eXtreme gradient boosting (XGB) model using initial assessment data to predict all-cause mortality in 7 days. Two deep learning models (DLMs) using ECG and CXR were trained to stratify mortality risks. The dynamic triage levels were based on output from the XGB-triage and DLMs from ECG and CXR. During the internal and external validation, the area under the receiver operating characteristic curve (AUC) of the XGB-triage model was >0.866; furthermore, the AUCs of DLMs using ECG and CXR were >0.862 and >0.886, respectively. The dynamic triage scale provided a higher C-index (0.914-0.920 vs. 0.827-0.843) than the original one and demonstrated better predictive ability for 5-year mortality, 30-day ED revisit, and 30-day discharge. The AI-based risk scale provides a more accurate and dynamic stratification of mortality risk in ED patients, particularly in identifying patients who tend to be overlooked due to atypical symptoms.
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Inteligencia Artificial , Servicio de Urgencia en Hospital , Humanos , Estudios Retrospectivos , Triaje/métodos , Electrocardiografía , Medición de RiesgoRESUMEN
Background: The electrocardiogram (ECG) may be the most popular test in the management of cardiovascular disease (CVD). Although wide applications of artificial intelligence (AI)-enabled ECG have been developed, an integrating indicator for CVD risk stratification was not investigated. Since mortality may be the most important global outcome, this study aimed to develop a survival deep learning model (DLM) to establish a critical ECG value and explore the associations with various CVD events. Methods: We trained a DLM with 451,950 12-lead resting ECGs obtained from 210,552 patients, for whom 23,592 events occurred. The internal validation set included 27,808 patients with one ECG for each patient. The external validations were performed in a community hospital with 33,047 patients and two transnational data sets with 233,647 and 1631 ECGs. We distinguished the cause of mortality and additionally investigated CVD-related outcomes, including new-onset acute myocardial infarction (AMI), stroke (STK), and heart failure (HF). Results: The DLM achieved C-indices of 0.858/0.836 in internal/external validation sets by using ECG over a 10-year period. The high-mortality-risk group identified by the proposed DLM presented a hazard ratio (HR) of 14.16 (95% confidence interval (CI): 11.33-17.70) compared to the low-risk group in the internal validation and presented a higher risk of cardiovascular (CV) mortality (HR: 18.50, 95% CI: 9.82-34.84), non-CV mortality (HR: 13.68, 95% CI: 10.76-17.38), AMI (HR: 4.01, 95% CI: 2.24-7.17), STK (HR: 2.15, 95% CI: 1.70-2.72), and HF (HR: 6.66, 95% CI: 4.54-9.77), which was consistent in an independent community hospital. The transnational validation also revealed HRs of 4.91 (95% CI: 2.63-9.16) and 2.29 (95% CI: 2.15-2.44) for all-cause mortality in the SaMi-Trop and Clinical Outcomes in Digital Electrocardiography 15% (CODE15) cohorts. Conclusions: The mortality risk by AI-enabled ECG may be applied in passive electronic-health-record-based CVD risk screening, which may identify more asymptomatic and unaware high-risk patients.
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Patients bitten by Protobothrops mucrosquamatus typically experience significant pain, substantial swelling, and potentially blister formation. The appropriate dosage and efficacy of FHAV for alleviating local tissue injury remain uncertain. Between 2017 and 2022, 29 snakebite patients were identified as being bitten by P. mucrosquamatus. These patients underwent point-of-care ultrasound (POCUS) assessments at hourly intervals to measure the extent of edema and evaluate the rate of proximal progression (RPP, cm/hour). Based on Blaylock's classification, seven patients (24%) were classified as Group I (minimal), while 22 (76%) were classified as Group II (mild to severe). In comparison to Group I patients, Group II patients received more FHAV (median of 9.5 vials vs. two vials, p-value < 0.0001) and experienced longer median complete remission times (10 days vs. 2 days, p-value < 0.001). We divided the Group II patients into two subgroups based on their clinical management. Clinicians opted not to administer antivenom treatment to patients in Group IIA if their RPP decelerated. In contrast, for patients in Group IIB, clinicians increased the volume of antivenom in the hope of reducing the severity of swelling or blister formation. Patients in Group IIB received a significantly higher median volume of antivenom (12 vials vs. six vials; p-value < 0.001) than those in Group IIA. However, there was no significant difference in outcomes (disposition, wound necrosis, and complete remission times) between subgroups IIA and IIB. Our study found that FHAV does not appear to prevent local tissue injuries, such as swelling progression and blister formation, immediately after administration. When administering FHAV to patients bitten by P. mucrosquamatus, the deceleration of RPP may serve as an objective parameter to help clinicians decide whether to withhold FHAV administration.
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BACKGROUND AND PURPOSE: Vascular smooth muscle cells (SMCs) undergo phenotypic switching during sustained inflammation, contributing to an unfavourable atherosclerotic plaque phenotype. PPARδ plays an important role in regulating SMC functions; however, its role in atherosclerotic plaque vulnerability remains unclear. Here, we explored the pathological roles of PPARδ in atherosclerotic plaque vulnerability in severe atherosclerosis and elucidated the underlying mechanisms. EXPERIMENTAL APPROACH: Plasma levels of PPARδ were measured in patients with acute coronary syndrome (ACS) and stable angina (SA). SMC contractile and synthetic phenotypic markers, endoplasmic reticulum (ER) stress, and features of atherosclerotic plaque vulnerability were analysed for the brachiocephalic artery of apolipoprotein E-knockout (ApoE-/- ) mice, fed a high-cholesterol diet (HCD) and treated with or without the PPARδ agonist GW501516. In vitro, the role of PPARδ was elucidated using human aortic SMCs (HASMCs). KEY RESULTS: Patients with ACS had significantly lower plasma PPARδ levels than those with SA. GW501516 reduced atherosclerotic plaque vulnerability, a synthetic SMC phenotype, ER stress markers, and NLRP3 inflammasome expression in HCD-fed ApoE-/- mice. ER stress suppressed PPARδ expression in HASMCs. PPARδ activation inhibited ER stress-induced synthetic phenotype development, ER stress-NLRP3 inflammasome axis activation and matrix metalloproteinase 2 (MMP2) expression in HASMCs. PPARδ inhibited NFκB signalling and alleviated ER stress-induced SMC phenotypic switching. CONCLUSIONS AND IMPLICATIONS: Low plasma PPARδ levels may be associated with atherosclerotic plaque vulnerability. Our findings provide new insights into the mechanisms underlying the protective effect of PPARδ on SMC phenotypic switching and improvement the features of atherosclerotic plaque vulnerability.
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PPAR delta , Placa Aterosclerótica , Animales , Humanos , Ratones , Apolipoproteínas E/genética , Apolipoproteínas E/metabolismo , Inflamasomas/metabolismo , Metaloproteinasa 2 de la Matriz/metabolismo , Ratones Endogámicos C57BL , Miocitos del Músculo Liso/metabolismo , Proteína con Dominio Pirina 3 de la Familia NLR/metabolismo , Fenotipo , Placa Aterosclerótica/metabolismo , PPAR delta/genéticaRESUMEN
Aims: Deep learning models (DLMs) have shown superiority in electrocardiogram (ECG) analysis and have been applied to diagnose dyskalaemias. However, no study has explored the performance of DLM-enabled ECG in continuous follow-up scenarios. Therefore, we proposed a dynamic revision of DLM-enabled ECG to use personal pre-annotated ECGs to enhance the accuracy in patients with multiple visits. Methods and results: We retrospectively collected 168 450 ECGs with corresponding serum potassium (K+) levels from 103 091 patients as development samples. In the internal/external validation sets, the numbers of ECGs with corresponding K+ were 37 246/47 604 from 13 555/20 058 patients. Our dynamic revision method showed better performance than the traditional direct prediction for diagnosing hypokalaemia [area under the receiver operating characteristic curve (AUC) = 0.730/0.720-0.788/0.778] and hyperkalaemia (AUC = 0.884/0.888-0.915/0.908) in patients with multiple visits. Conclusion: Our method has shown a distinguishable improvement in DLMs for diagnosing dyskalaemias in patients with multiple visits, and we also proved its application in ejection fraction prediction, which could further improve daily clinical practice.
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BACKGROUND AND OBJECTIVE: Deep learning models (DLMs) have been successfully applied in biomedicine primarily using supervised learning with large, annotated databases. However, scarce training resources limit the potential of DLMs for electrocardiogram (ECG) analysis. METHODS: We have developed a novel pre-training strategy for unsupervised identity identification with an area under the receiver operating characteristic curve (AUC) >0.98. Accordingly, a DLM pre-trained with identity identification can be applied to 70 patient characteristic predictions using transfer learning (TL). These ECG-based patient characteristics were then used for cardiovascular disease (CVD) risk prediction. The DLMs were trained using 507,729 ECGs from 222,473 patients and validated using two independent validation sets (n = 27,824/31,925). RESULTS: The DLMs using our method exhibited better performance than directly trained DLMs. Additionally, our DLM performed better than those of previous studies in terms of gender (AUC [internal/external] = 0.982/0.968), age (correlation = 0.886/0.892), low ejection fraction (AUC = 0.942/0.951), and critical markers not addressed previously, including high B-type natriuretic peptide (AUC = 0.921/0.899). Additionally, approximately 50% of the ECG-based characteristics provided significantly more prediction information for cardiovascular risk than real characteristics. CONCLUSIONS: This is the first study to use identity identification as a pre-training task for TL in ECG analysis. An extensive exploration of the relationship between ECG and 70 patient characteristics was conducted. Our DLM-enhanced ECG interpretation system extensively advanced ECG-related patient characteristic prediction and mortality risk management for cardiovascular diseases.
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Enfermedades Cardiovasculares , Sistema Cardiovascular , Aprendizaje Profundo , Humanos , Electrocardiografía , Bases de Datos FactualesRESUMEN
Background: Artificial intelligence-enabled electrocardiogram has become a substitute tool for echocardiography in left ventricular ejection fraction estimation. However, the direct use of artificial intelligence-enabled electrocardiogram may be not trustable due to the uncertainty of the prediction. Objective: The study aimed to establish an artificial intelligence-enabled electrocardiogram with a degree of confidence to identify left ventricular dysfunction. Methods: The study collected 76,081 and 11,771 electrocardiograms from an academic medical center and a community hospital to establish and validate the deep learning model, respectively. The proposed deep learning model provided the point estimation of the actual ejection fraction and its standard deviation derived from the maximum probability density function of a normal distribution. The primary analysis focused on the accuracy of identifying patients with left ventricular dysfunction (ejection fraction ≤ 40%). Since the standard deviation was an uncertainty indicator in a normal distribution, we used it as a degree of confidence in the artificial intelligence-enabled electrocardiogram. We further explored the clinical application of estimated standard deviation and followed up on the new-onset left ventricular dysfunction in patients with initially normal ejection fraction. Results: The area under receiver operating characteristic curves (AUC) of detecting left ventricular dysfunction were 0.9549 and 0.9365 in internal and external validation sets. After excluding the cases with a lower degree of confidence, the artificial intelligence-enabled electrocardiogram performed better in the remaining cases in internal (AUC = 0.9759) and external (AUC = 0.9653) validation sets. For the application of future left ventricular dysfunction risk stratification in patients with initially normal ejection fraction, a 4.57-fold risk of future left ventricular dysfunction when the artificial intelligence-enabled electrocardiogram is positive in the internal validation set. The hazard ratio was increased to 8.67 after excluding the cases with a lower degree of confidence. This trend was also validated in the external validation set. Conclusion: The deep learning model with a degree of confidence can provide advanced improvements in identifying left ventricular dysfunction and serve as a decision support and management-guided screening tool for prognosis.