Brood success of sex-role-reversed pheasant-tailed jacanas: the effects of social polyandry, seasonality, and male mating order.

Multiple mating by avian females may increase hatching and overall brood success; however, reproductive effort and parental investment are costly, and females may be gradually depleted, with lowered outputs over time. Thus, males in social polyandry systems may differ greatly in their reproductive gains. In the present study, we investigated the reproductive outputs of social polyandrous and sex-role-reversed pheasant-tailed jacanas, Hydrophasianus chirurgus, to assess the effects of polyandry, seasonality, and male mating order on breeding success. Female jacanas produced multiple clutches, either by leaving two or more clutches with an individual male (22%), or by mating with two or more males (78%). The polyandrous females laid both the first and second clutches earlier and showed a breeding period more than twice as long as that of monandrous females. Both polyandry and seasonality affected the fate of a clutch, where clutches from polyandrous females and the early season had higher hatching and brood success rates, but the number of polyandrous females declined over the season. Polyandrous females not only laid more clutches and eggs, and gained more hatchlings and fledglings, but also achieved higher per-clutch outputs and hatching rates than monandrous females. In polyandry groups, males gained higher total hatchlings and fledglings, although not total clutches or eggs, than males in monandry or bi-andry groups. Moreover, males in polyandry groups achieved higher hatchlings and fledglings per clutch and higher hatching and brood success rates. In polyandry groups, the first-mating males obtained more clutches, eggs, and hatchlings; however, they did not have higher success rates, nor total fledglings and per-clutch outputs, than males who mated later. Overall, the results indicate a selective advantage of polyandry for the jacanas studied, particularly in the early breeding season. This advantage, however, differs both between the sexes and intra-sexually, suggesting strong connections with certain ecological/environmental conditions in addition to the jacanas' own quality.

Comparing Artificial Intelligence-Enabled Electrocardiogram Models in Identifying Left Atrium Enlargement and Long-term Cardiovascular Risk.

BACKGROUND: The role of P-wave in identifying left atrial enlargement (LAE) with the use of artificial intelligence (AI)-enabled electrocardiography (ECG) models is unclear. It is also unknown if AI-enabled single-lead ECG could be used as a diagnostic tool for LAE surveillance. We aimed to build AI-enabled P-wave and single-lead ECG models to identify LAE using sinus rhythm (SR) and non-SR ECGs, and compare the prognostic ability of severe LAE, defined as left atrial diameter ≥ 50 mm, assessed by AI-enabled ECG models vs echocardiography. METHODS: This retrospective study used data from 382,594 consecutive adults with paired 12-lead ECG and echocardiography performed within 2 weeks of each other at Chang Gung Memorial Hospital. UNet++ was used for P-wave segmentation. ResNet-18 was used to develop deep convolutional neural network-enabled ECG models for discriminating LAE. External validation was performed with the use of data from 11,753 patients from another hospital. RESULTS: The AI-enabled 12-lead ECG model outperformed other ECG models for classifying LAE, but the single-lead ECG models also showed excellent performance at a left atrial diameter cutoff of 50 mm. AI-enabled ECG models had excellent and fair discrimination on LAE using the SR and the non-SR data set, respectively. Severe LAE identified by AI-enabled ECG models was more predictive of future cardiovascular disease than echocardiography; however, the cumulative incidence of new-onset atrial fibrillation and heart failure was higher in patients with echocardiography-severe LAE than with AI-enabled ECG-severe LAE. CONCLUSIONS: P-Wave plays a crucial role in discriminating LAE in AI-enabled ECG models. AI-enabled ECG models outperform echocardiography in predicting new-onset cardiovascular diseases associated with severe LAE.

Machine learning-based prediction of acute mortality in emergency department patients using twelve-lead electrocardiogram.

Background: The risk of mortality is relatively high among patients who visit the emergency department (ED), and stratifying patients at high risk can help improve medical care. This study aimed to create a machine-learning model that utilizes the standard 12-lead ECG to forecast acute mortality risk in ED patients. Methods: The database included patients who visited the EDs and underwent standard 12-lead ECG between October 2007 and December 2017. A convolutional neural network (CNN) ECG model was developed to classify survival and mortality using 12-lead ECG tracings acquired from 345,593 ED patients. For machine learning model development, the patients were randomly divided into training, validation and testing datasets. The performance of the mortality risk prediction in this model was evaluated for various causes of death. Results: Patients who visited the ED and underwent one or more ECG examinations experienced a high incidence of 30-day mortality [18,734 (5.42%)]. The developed CNN model demonstrated high accuracy in predicting acute mortality (hazard ratio 8.50, 95% confidence interval 8.20-8.80) with areas under the receiver operating characteristic (ROC) curve of 0.84 for the 30-day mortality risk prediction models. This CNN model also demonstrated good performance in predicting one-year mortality (hazard ratio 3.34, 95% confidence interval 3.30-3.39). This model exhibited good predictive performance for 30-day mortality not only for cardiovascular diseases but also across various diseases. Conclusions: The machine learning-based ECG model utilizing CNN screens the risks for 30-day mortality. This model can complement traditional early warning scoring indexes as a useful screening tool for mortality prediction.

Artificial intelligence-enabled electrocardiographic screening for left ventricular systolic dysfunction and mortality risk prediction.

Background: Left ventricular systolic dysfunction (LVSD) characterized by a reduced left ventricular ejection fraction (LVEF) is associated with adverse patient outcomes. We aimed to build a deep neural network (DNN)-based model using standard 12-lead electrocardiogram (ECG) to screen for LVSD and stratify patient prognosis. Methods: This retrospective chart review study was conducted using data from consecutive adults who underwent ECG examinations at Chang Gung Memorial Hospital in Taiwan between October 2007 and December 2019. DNN models were developed to recognize LVSD, defined as LVEF <40%, using original ECG signals or transformed images from 190,359 patients with paired ECG and echocardiogram within 14 days. The 190,359 patients were divided into a training set of 133,225 and a validation set of 57,134. The accuracy of recognizing LVSD and subsequent mortality predictions were tested using ECGs from 190,316 patients with paired data. Of these 190,316 patients, we further selected 49,564 patients with multiple echocardiographic data to predict LVSD incidence. We additionally used data from 1,194,982 patients who underwent ECG only to assess mortality prognostication. External validation was performed using data of 91,425 patients from Tri-Service General Hospital, Taiwan. Results: The mean age of patients in the testing dataset was 63.7 ± 16.3 years (46.3% women), and 8,216 patients (4.3%) had LVSD. The median follow-up period was 3.9 years (interquartile range 1.5-7.9 years). The area under the receiver-operating characteristic curve (AUROC), sensitivity, and specificity of the signal-based DNN (DNN-signal) to identify LVSD were 0.95, 0.91, and 0.86, respectively. DNN signal-predicted LVSD was associated with age- and sex-adjusted hazard ratios (HRs) of 2.57 (95% confidence interval [CI], 2.53-2.62) for all-cause mortality and 6.09 (5.83-6.37) for cardiovascular mortality. In patients with multiple echocardiograms, a positive DNN prediction in patients with preserved LVEF was associated with an adjusted HR (95% CI) of 8.33 (7.71 to 9.00) for incident LVSD. Signal- and image-based DNNs performed equally well in the primary and additional datasets. Conclusion: Using DNNs, ECG becomes a low-cost, clinically feasible tool to screen LVSD and facilitate accurate prognostication.

Efficacy of portable sleep monitoring device in diagnosing central sleep apnea in patients with congestive heart failure.

Introduction: Central sleep apnea (CSA) is a common and serious comorbidity mainly occurring in patients with heart failure (HF), which tends to be underdiagnosed and has not been widely studied. Overnight polysomnography (PSG) is the gold standard for diagnosing CSA; however, the time and expense of the procedure limit its applicability. Portable monitoring (PM) devices are convenient and easy to use; however, they have not been widely studied as to their effectiveness in detecting CSA in patients with HF. In the current study, we examined the diagnostic value of PM as a screening tool to identify instances of CSA among patients with HF. Methods: A total of 22 patients under stable heart failure conditions with an ejection fraction of <50% were enrolled. All patients underwent PM and overnight PSG within a narrow time frame. The measurements of the apnea-hypopnea index (AHI), hypopnea index (HI), central apnea index (CAI), and obstructive apnea index (OAI) obtained from PSG, automatic scoring, and manual scoring of PM were recorded. The results obtained from PSG and those from PM (automatic and manual scoring) were compared to assess the accuracy of PM. Results: Among the patients, CSA in 11 patients was found by PSG. The AHI measurements performed using manual scoring of PM showed a significant correlation with those performed using PSG (r = 0.69; P = 0.01). Nonetheless, mean AHI measurements showed statistically significant differences between PSG and automatic scoring of PM (40.0 vs. 23.7 events/hour, respectively; P < 0.001), as well as between automatic and manual scoring of PM (23.7 vs. 29.5 events/hour; P < 0.001). Central sleep apnea was detected by PM; however, the results were easily misread as obstructive apnea, particularly in automatic scoring. Conclusion: PM devices could be used to identify instances of central sleep apnea among patients with HF. The results from PM were well-correlated with standard PSG results, and manual scoring was preferable to automated scoring.

Identification of Location Specific Feature Points in a Cardiac Cycle Using a Novel Seismocardiogram Spectrum System.

Seismocardiogram (SCG) or mechanocardiography is a noninvasive cardiac diagnostic method; however, previous studies used only a single sensor to detect cardiac mechanical activities that will not be able to identify location-specific feature points in a cardiac cycle corresponding to the four valvular auscultation locations. In this study, a multichannel SCG spectrum measurement system was proposed and examined for cardiac activity monitoring to overcome problems like, position dependency, time delay, and signal attenuation, occurring in traditional single-channel SCG systems. ECG and multichannel SCG signals were simultaneously recorded in 25 healthy subjects. Cardiac echocardiography was conducted at the same time. SCG traces were analyzed and compared with echocardiographic images for feature point identification. Fifteen feature points were identified in the corresponding SCG traces. Among them, six feature points, including left ventricular lateral wall contraction peak velocity, septal wall contraction peak velocity, transaortic peak flow, transpulmonary peak flow, transmitral ventricular relaxation flow, and transmitral atrial contraction flow were identified. These new feature points were not observed in previous studies because the single-channel SCG could not detect the location-specific signals from other locations due to time delay and signal attenuation. As the results, the multichannel SCG spectrum measurement system can record the corresponding cardiac mechanical activities with location-specific SCG signals and six new feature points were identified with the system. This new modality may help clinical diagnoses of valvular heart diseases and heart failure in the future.

Comparison of clinical outcomes in peripartum cardiomyopathy and age-matched dilated cardiomyopathy: A 15-year nationwide population-based study in Asia.

Peripartum cardiomyopathy (PPCM), often classified as a form of dilated cardiomyopathy (DCM), is the myocardial dysfunction that occurs in late pregnancy and through the first few postpartum months.The aim of this study is to investigate the differences in the clinical outcomes of PPCM and DCM.Electronic medical records from 1997 to 2011 were retrieved from the Taiwan National Health Insurance Research Database. Patients with PPCM were compared with age- and clinical characteristics-matched patients with DCM. Primary outcomes were 1- and 3-year heart failure (HF) readmission, cardiac death, all-cause mortality, and major adverse cardiovascular events. Secondary outcomes were myocardial infarction, new onset of dialysis, heart transplant, and cerebrovascular accident. Follow-up period was divided into "within the first year" and "after the first year."A total of 527,979 patients (253,166 females) were hospitalized with a principal diagnosis of HF during 1997 to 2011 period. After excluding patients aged <18 and >50 years, patients with other forms of HF, and those with a history of cerebrovascular accidents or coronary artery disease, 797 patients with PPCM and 1267 patients with DCM were evaluated. Propensity score matching yielded 391 patients in each group. Patients with DCM had a significantly worse prognosis compared to those with PPCM for all primary and secondary outcomes at the 1- and 3-year follow-ups. After 1 year, the HF readmission rate did not significantly differ between the 2 diseases, suggesting that HF medications should be aggressively instituted in patients with PPCM.This is the first study to directly compare the clinical outcomes between age-matched patients with PPCM and DCM. Patients with PPCM had a significantly better prognosis across all cardiovascular endpoints compared to patients with DCM.

The N-terminal propeptide of type III procollagen in patients with acute coronary syndrome: a link between left ventricular end-diastolic pressure and cardiovascular events.

BACKGROUND: Despite the usefulness of N-terminal propeptide of type III procollagen (PIIINP) in detecting enhanced collagen turnover in patients with congestive heart failure, the value added by PIIINP to the use of clinical variables and echocardiography in relation to directly measured left ventricular (LV) end-diastolic pressure (EDP) and the outcome of acute coronary syndrome (ACS) has not been clearly defined. METHODS AND RESULTS: This study involved 168 adult patients with ACS, who underwent echocardiography, measurement of serum PIIINP levels, and cardiac catheterization. Pulsed wave tissue Doppler imaging (PWTDI), which revealed mean peak systolic (s'), early (e'), and late diastolic (a') velocities, was carried out and the eas index of LV function was evaluated: e'/(a'×s'). The patients were divided into three study groups based on the degree of LVEDP--normal (<16 mmHg), intermediate (16-30 mmHg), and high (>30 mmHg) LVEDP. All patients were followed-up to determine cardiac-related death and revascularization. Patients with high LVEDP had significantly more PIIINP than those with intermediate or normal LVEDP (all post hoc p<0.05). The presence of coronary artery disease, the left atrial volume index (LAVI), the ratio of transmitral early and late diastolic flow velocities, a', and the eas index were significantly correlated with LVEDP. According to multiple stepwise analysis, PIIINP, LAVI and the eas index were the three independent predictors of the level of LVEDP (PIIINP, p <0.001; LAVI, p = 0.007; eas index, p = 0.021). During follow-up (median, 24 months), 32 participants suffered from cardiac events, PIIINP and LAVI were significant predictors of cardiac mortality and hospitalization (PIIINP, hazard ratio (HR) 2.589, p = 0.002; LAVI, HR 1.040, p = 0.027). CONCLUSIONS: PIIINP is a highly effective means to evaluate LVEDP in patients with ACS. The PIIINP is also correlated with cardiac mortality and revascularization, providing an additional means of evaluating and managing patients with ACS.

Effects of using a portable navigation system and paper map in real driving.

Navigation systems are very useful tools because they display a user's location and guide them to a destination using graphics, text and voice information. Recent work has revealed that millions of consumers received driving directions using their cell phone or PDA. This present work aimed to explore whether the efficiency to destination and driver behavior were distinguishable when using a portable navigation system compared to a paper map. Thirty-two subjects were paid to participate in this research, with field experiments being carried out in both urban and rural environments. A smart phone was adopted as the portable navigation system in the study. The results revealed that the drivers performed better when using a portable navigation system compared to those using a paper map, in terms of efficiency to destination and driving performance. In addition, drivers could save time and gasoline using a portable navigation system when in an unfamiliar region, and driving performance may be safer, despite the fact that the display screen of the phone is small.