Identifying patients with acute aortic dissection using an electrocardiogram with convolutional neural network.

Background: The potential of utilizing artificial intelligence with electrocardiography (ECG) for initial screening of aortic dissection (AD) is promising. However, achieving a high positive predictive rate (PPR) remains challenging. Methods and results: This retrospective analysis of a single-center, prospective cohort study (Shinken Database 2010-2017, N = 19,170) used digital 12-lead ECGs from initial patient visits. We assessed a convolutional neural network (CNN) model's performance for AD detection with eight-lead (I, II, and V1-6), single-lead, and double-lead (I, II) ECGs via five-fold cross-validation. The mean age was 63.5 ± 12.5 years for the AD group (n = 147) and 58.1 ± 15.7 years for the non-AD group (n = 19,023). The CNN model achieved an area under the curve (AUC) of 0.936 (standard deviation [SD]: 0.023) for AD detection with eight-lead ECGs. In the entire cohort, the PPR was 7 %, with 126 out of 147 AD cases correctly diagnosed (sensitivity 86 %). When applied to patients with D-dimer levels ≥1 µg/dL and a history of hypertension, the PPR increased to 35 %, with 113 AD cases correctly identified (sensitivity 86 %). The single V1 lead displayed the highest diagnostic performance (AUC: 0.933, SD: 0.03), with PPR improvement from 8 % to 38 % within the same population. Conclusions: Our CNN model using ECG data for AD detection achieved an over 30% PPR when applied to patients with elevated D-dimer levels and hypertension history while maintaining sensitivity. A similar level of performance was observed with a single-lead V1 ECG in the CNN model.

Evaluating convolutional neural network-enhanced electrocardiography for hypertrophic cardiomyopathy detection in a specialized cardiovascular setting.

The efficacy of convolutional neural network (CNN)-enhanced electrocardiography (ECG) in detecting hypertrophic cardiomyopathy (HCM) and dilated HCM (dHCM) remains uncertain in real-world applications. This retrospective study analyzed data from 19,170 patients (including 140 HCM or dHCM) in the Shinken Database (2010-2017). We evaluated the sensitivity, positive predictive rate (PPR), and F1 score of CNN-enhanced ECG in a ''basic diagnosis'' model (total disease label) and a ''comprehensive diagnosis'' model (including disease subtypes). Using all-lead ECG in the "basic diagnosis" model, we observed a sensitivity of 76%, PPR of 2.9%, and F1 score of 0.056. These metrics improved in cases with a diagnostic probability of ≥ 0.9 and left ventricular hypertrophy (LVH) on ECG: 100% sensitivity, 8.6% PPR, and 0.158 F1 score. The ''comprehensive diagnosis'' model further enhanced these figures to 100%, 13.0%, and 0.230, respectively. Performance was broadly consistent across CNN models using different lead configurations, particularly when including leads viewing the lateral walls. While the precision of CNN models in detecting HCM or dHCM in real-world settings is initially low, it improves by targeting specific patient groups and integrating disease subtype models. The use of ECGs with fewer leads, especially those involving the lateral walls, appears comparably effective.

Lead-Specific Performance for Atrial Fibrillation Detection in Convolutional Neural Network Models Using Sinus Rhythm Electrocardiography.

Background: We developed a convolutional neural network (CNN) model to detect atrial fibrillation (AF) using the sinus rhythm ECG (SR-ECG). However, the diagnostic performance of the CNN model based on different ECG leads remains unclear. Methods and Results: In this retrospective analysis of a single-center, prospective cohort study, we identified 616 AF cases and 3,412 SR cases for the modeling dataset among new patients (n=19,170). The modeling dataset included SR-ECGs obtained within 31 days from AF-ECGs in AF cases and SR cases with follow-up ≥1,095 days. We evaluated the CNN model's performance for AF detection using 8-lead (I, II, and V1-6), single-lead, and double-lead ECGs through 5-fold cross-validation. The CNN model achieved an area under the curve (AUC) of 0.872 (95% confidence interval (CI): 0.856-0.888) and an odds ratio of 15.24 (95% CI: 12.42-18.72) for AF detection using the eight-lead ECG. Among the single-lead and double-lead ECGs, the double-lead ECG using leads I and V1 yielded an AUC of 0.871 (95% CI: 0.856-0.886) with an odds ratio of 14.34 (95% CI: 11.64-17.67). Conclusions: We assessed the performance of a CNN model for detecting AF using eight-lead, single-lead, and double-lead SR-ECGs. The model's performance with a double-lead (I, V1) ECG was comparable to that of the 8-lead ECG, suggesting its potential as an alternative for AF screening using SR-ECG.

Identification of patients with dilated phase of hypertrophic cardiomyopathy using a convolutional neural network applied to multiple, dual, and single lead electrocardiograms.

Background: This study sought to develop an artificial intelligence-derived model to detect the dilated phase of hypertrophic cardiomyopathy (dHCM) on digital electrocardiography (ECG) and to evaluate the performance of the model applied to multiple-lead or single-lead ECG. Methods: This is a retrospective analysis using a single-center prospective cohort study (Shinken Database 2010-2017, n = 19,170). After excluding those without a normal P wave on index ECG (n = 1,831) and adding dHCM patients registered before 2009 (n = 39), 17,378 digital ECGs were used. Totally 54 dHCM patients were identified of which 11 diagnosed at baseline, 4 developed during the time course, and 39 registered before 2009. The performance of the convolutional neural network (CNN) model for detecting dHCM was evaluated using eight-lead (I, II, and V1-6), single-lead, and double-lead (I, II) ECGs with the five-fold cross validation method. Results: The area under the curve (AUC) of the CNN model to detect dHCM (n = 54) with eight-lead ECG was 0.929 (standard deviation [SD]: 0.025) and the odds ratio was 38.64 (SD 9.10). Among the single-lead and double-lead ECGs, the AUC was highest with the single lead of V5 (0.953 [SD: 0.038]), with an odds ratio of 58.89 (SD:68.56). Conclusion: Compared with the performance of eight-lead ECG, the most similar performance was achieved with the model with a single V5 lead, suggesting that this single-lead ECG can be an alternative to eight-lead ECG for the screening of dHCM.

Cardiovascular events and artificial intelligence-predicted age using 12-lead electrocardiograms.

Background: There is increasing evidence that 12-lead electrocardiograms (ECG) can be used to predict biological age, which is associated with cardiovascular events. However, the utility of artificial intelligence (AI)-predicted age using ECGs remains unclear. Methods: Using a single-center database, we developed an AI-enabled ECG using 17 042 sinus rhythm ECGs (SR-ECG) to predict chronological age (CA) with a convolutional neural network that yields AI-predicted age. Using the 5-fold cross validation method, AI-predicted age deriving from the test dataset was yielded for all ECGs. The incidence by AgeDiff and the areas under the curve by receiver operating characteristic curve with AI-predicted age for cardiovascular events were analyzed. Results: During the mean follow-up period of 460.1 days, there were 543 cardiovascular events. The annualized incidence of cardiovascular events was 2.24 %, 2.44 %, and 3.01 %/year for patients with AgeDiff < -6, -6 to ≤6, and >6 years, respectively. The areas under the curve for cardiovascular events with CA and AI-predicted age, respectively, were 0.673 and 0.679 (Delong's test, P = 0.388) for all patients; 0.642 and 0.700 (P = 0.003) for younger patients (CA < 60 years); and 0.584 and 0.570 (P = 0.268) for older patients (CA ≥ 60 years). Conclusions: AI-predicted age using 12-lead ECGs showed superiority in predicting cardiovascular events compared with CA in younger patients, but not in older patients.

Identifying patients with atrial fibrillation during sinus rhythm on ECG: Significance of the labeling in the artificial intelligence algorithm.

BACKGROUND: This study aimed to increase the knowledge on how to enhance the performance of artificial intelligence (AI)-enabled electrocardiography (ECG) to detect atrial fibrillation (AF) on sinus rhythm ECG (SR-ECG). METHODS: It is a retrospective analysis of a single-center, prospective cohort study (Shinken Database). We developed AI-enabled ECG using SR-ECG to predict AF with a convolutional neural network (CNN). Among new patients in our hospital (n = 19,170), 276 AF label (having ECG on AF [AF-ECG] in the ECG database) and 1896 SR label with following three conditions were identified in the derivation dataset: (1) without structural heart disease, (2) in AF label, SR-ECG was taken within 31 days from AF-ECG, and (3) in SR label, follow-up ≥ 1,095 days. Three patterns of AF label were analyzed by timing of SR-ECG to AF-ECG (before/after/before-or-after, CNN algorithm 1 to 3). The outcome measurement was area under the curve (AUC), sensitivity, specificity, accuracy, and F1 score. As an extra-testing dataset, the performance of AI-enabled ECG was tested in patients with structural heart disease. RESULTS: The AUC of AI-enabled ECG with CNN algorithm 1, 2, and 3 in the derivation dataset was 0.83, 0.88, and 0.86, respectively; when tested in patients with structural heart disease, 0.75, 0.81, and 0.78, respectively. CONCLUSION: We confirmed high performance of AI-enabled ECG to detect AF on SR-ECG in patients without structural heart disease. The performance enhanced especially when SR-ECG after index AF-ECG was included in the algorithm, which was consistent in patients with structural heart disease.

Effect of substituent of ß-diketones on the synergistic extraction of lanthanoids with linear polyether.

The synergistic extraction of 14 trivalent lanthanoids (Ln(3+)) into 1,2-dichloroethane with a linear polyether (DEO6), HO(C(2)H(4)O)(6)C(12)H(25), and ß-diketones (HA) having different substituents was investigated at 25.0°C. The HAs used were trifluoroacetylacetone (Htfa), thenoyltrifluoroacetone (Htta), benzoyltrifluoroacetone (Hbta), naphthoyltrifluoroacetone (Hnta), and pivaloyltrifluoroacetone (Hpta). By the extraction of Ln(3+) with ß-diketone alone, the extraction constants of the neutral LnA(3) complex, [Formula: see text] , were determined. The intrinsic extraction constants, [Formula: see text] , were evaluated by employing the regular solution theory. Results indicate that the extractability of LnA(3) is dependent on the lipophilicity of the ligand, and the planar aromatic rings do not cause steric hindrance in the formation of the binary complex. Addition of DEO6 significantly enhanced the extraction of Ln(3+) by the formation of LnA(3)(DEO6). The ternary complex formation constants, ß(add), were determined for all the Ln(3+) and HA. The ß(add) of bta(-) and nta(-) complexes is similar with those of tfa(-) complexes, indicating that planar aromatic rings do not sterically hinder even the formation of the ternary complex. The higher values of ß(add) for the complexes of tta(-), which has a slightly dipolar thenoyl moiety, can be accounted for the presence of ligand-ligand interaction. The formation constants of the ternary complexes of pta(-) were lower compared to complexes of other ß-diketones because of steric hindrance due to the bulky t-butyl moiety. The detailed structures of the ternary complexes in solution were elucidated by NMR spectroscopy. Estimated structures sufficiently explain the variation in stability constants of LnA(3)(DEO6) among HAs and across the series of Ln(3+). The structures thus obtained were ascertained by the molecular models created by MM2 calculation.

1H-NMR studies on the ternary complexes of rare-earth ions with thenoyltrifluoroacetone and polyethers in dichloromethane.

The structures of the ternary complexes of lanthanoid and yttrium (Ln3+)-thenoyltrifluoroacetonates (tta-) with polyether (POE) in organic phase were investigated by 1H-NMR spectroscopy, where the POEs are crown ethers (18-crown-6 and benzo-18-crown-6) and monodispersed linear polyethers (DEOn: HO-(CH2CH2O-)nC12H25, where n=4, 6, 8). The changes in chemical shift of methylene protons of POE by addition of the adduct complex [Ln(tta)3(POE)] were measured at various Ln3+-to-POE concentration ratios. Chemical shift changes for each proton of POE by the formation of [Ln(tta)3(POE)] were determined. Results revealed that oxygen atoms at the hydroxyl terminal of linear POE have higher tendency to coordinate to the metal ion in [Ln(tta)3] complex. Three (for La3+) or two (for Lu3+ or Y3+) oxygen atoms of the POE coordinate to the metal ion without substitution of tta- ligands to satisfy the metal ion's coordination number of nine or eight, respectively. In the case of 18-membered crown ether complexes, La3+ is incorporated inside the cavity of the POE, displacing one of the three tta- from the inner coordination sphere while the other two remain coordinated to the metal ion. On the other hand, for the adduct of Y3+ complex with crown ether, all three tta- ligands are directly coordinating to the metal ion.

Prospective evaluation of occlusive hydrocolloid dressing versus conventional gauze dressing regarding the healing effect after abdominal operations: randomized controlled trial.

OBJECTIVE: To compare occlusive hydrocolloid dressing (OHD; Karayahesive) and gauze dressing (GD) with regard to the cost and incidence of wound infection after abdominal surgery. METHODS: A total of 134 patients who underwent incisions were randomized to have their wounds dressed with either OHD or GD. OHD was left on until the sutures were removed, and GD was changed everyday postoperatively. The cost calculations represent the number of dressings required for each treatment group as determined by the frequency of required dressing changes and cost per dressing. RESULTS: There were no differences between the groups regarding the need for dressings to be changed or the incidence of infection. OHD was less expensive and complicated than GD, which needed to be changed everyday (p < 0.0001). CONCLUSION: The results suggest that OHD is less expensive to use than GD, and the risk of wound infection is not increased compared to GD.

Structures of linear poly(ethylene oxide) compounds and potassium complexes in dichloromethane.

The H-1 NMR spectra of a series of linear poly(ethylene oxides) compounds (POE compounds) were measured in dichloromethane-d2 at 25 degrees C, where the POE compounds (HO-(CH2CH2O-)n-R) were unsubstituted POE, HEOn (R=H, n=3, 4, 6), and alkyl-substituted POE, DEOn (R=C12H25, n=4, 6, 8) and MeEO6 (R=CH3, n=6). All the peaks of H-1 NMR signals were assigned to each methylene proton of POE. The chemical shifts and coupling constant between vicinal protons were evaluated by a complete spin analysis. The spectral changes of POE compounds by the addition of potassium ion were measured at various metal-to-POE ratios. The chemical shift change of each methylene proton by the formation of the complex was evaluated. The downfield shift of methylene protons caused by the complex formation indicates that the ethylene oxide that the ethylene oxide moiety is coordinating to surround the potassium ion in the same manner as the cyclic crown ether complexes. The results of spin lattice relaxation time measurements of DEO6 suggest that all the methylenes of the ethylene oxides are immobilized by the coordination to the metal ion. Thus, it was confirmed that all oxygens of POE are participating in the complex formation.

Regulation of the expressions of HCO3- uptake and intracellular carbonic anhydrase in response to CO2 concentration in the marine diatom Phaeodactylum sp.

The marine diatom, Phaeodactylum tricornutum Bohlin, is probably one of the most extensively studied marine alga with respect to carbon acquisition and assimilation mechanisms. However, quantitative analyses of HCO3-utilization and the detailed process of acclimation of cells from high CO2 to limited CO2 are yet to be done extensively. Suitable molecular markers for this acclimation process are not established, either. Recently, it became clear that the rate of CO2 formation in artificial seawater is about eight times slower than that in freshwater, and thatP. tricornutum cells utilize HCO3- quite efficiently. Despite their great capacity to take up HCO3-, the signal controlling photosynthetic affinity for dissolved inorganic carbon has been shown to be CO2 in the medium. Furthermore, light seems to be required for this process. Internal carbonic anhydrase (CA) activity has been shown to be crucial for high-affinity photosynthesis in a number of algae, including marine diatoms. Internal ß-type CA, which has been isolated in one strain of P. tricornutum, was clearly shown to be a low-CO2 inducible enzyme. This review paper additionally includes data showing that this CA occurs generally in P. tricornutum species.

Advanced gastric glandular-endocrine cell carcinoma with 1-year survival after gastrectomy.

Primary gastric endocrine cell carcinoma (ECC) is extremely rare. In general, when it is advanced, gastric ECC causes extensive ulceration (type 2) and invades or metastasizes to other organs, frequently to the liver and sometimes to the lungs or bones, and carries a poor prognosis. We herein report a 67-year-old man with advanced gastric ECC of extensive-polypoid shape (type 1) but without distant metastasis, who underwent total gastrectomy and treatment with oral tegafur-uracil (UFT), and showed no sign of recurrence 1 year later.