Cardiovascular diseases are the most common causes of death around the world. To detect and treat heart-related diseases, continuous blood pressure (BP) monitoring along with many other parameters are required. Several invasive and non-invasive methods have been developed for this purpose. Most existing methods used in hospitals for continuous monitoring of BP are invasive. On the contrary, cuff-based BP monitoring methods, which can predict systolic blood pressure (SBP) and diastolic blood pressure (DBP), cannot be used for continuous monitoring. Several studies attempted to predict BP from non-invasively collectible signals such as photoplethysmograms (PPG) and electrocardiograms (ECG), which can be used for continuous monitoring. In this study, we explored the applicability of autoencoders in predicting BP from PPG and ECG signals. The investigation was carried out on 12,000 instances of 942 patients of the MIMIC-II dataset, and it was found that a very shallow, one-dimensional autoencoder can extract the relevant features to predict the SBP and DBP with state-of-the-art performance on a very large dataset. An independent test set from a portion of the MIMIC-II dataset provided a mean absolute error (MAE) of 2.333 and 0.713 for SBP and DBP, respectively. On an external dataset of 40 subjects, the model trained on the MIMIC-II dataset provided an MAE of 2.728 and 1.166 for SBP and DBP, respectively. For both the cases, the results met British Hypertension Society (BHS) Grade A and surpassed the studies from the current literature. Despite tremendous advancements in the healthcare sector, cardiovascular diseases (CVDs) still secured the top positions last year in the list of leading causes of death globally. The most fatal CVD was Ischaemic Heart Disease which is termed by the World Health Organization (WHO) as the “world’s biggest killer” as it accounted for 16% of the total deaths from 2000 to 2019 ,4. HyperSeveral studies attempted to tackle this problem following various methodologies and a handful of them  were adIn recent years, convolution neural network (CNN)-based deep learning (DL) techniques have been utilized to solve complex problems on large datasets in 1D , 2D  settings. However, there are not many deep learning-based approaches in the literature for BP estimation. Slapnicặr et al.  predicteU-Net is an encoder-decoder-based deep CNN architecture that was originally used for image (2D) segmentation. Many studies used U-Net to perform tasks, such as biomedical image segmentation , shape rIn this study, two different datasets have been used, which are briefly described below.The Cuff-Less Blood Pressure Estimation Dataset  from theThe external validation dataset used in this work has been collected and shared recently by Carlson et al.  . The whole data pre-processing procedure is shown in Algorithm 1 Pseudo-Code: Baseline Drift CorrectionInputs: X Check X is a Row Vector else X = Transpose (X)1.1.1. Initialize Time_Vector = Transpose , length(X)))1.2.1. try:2.1.1. findpeaks(X)2.2.1.     = Initialize peak_dist2.3.1.    for i = 1: (length(peak_locations) − 1)2.4.1.    2.4.2.      peak_dist(i) = peak_locations(i + 1) − peak_locations(i)end for2.4.3.    median (peak_dist)2.5.1.    median_peak_dist = movmin 2.6.1.    Baseline = polyfit )2.7.1.    P = except:3.1.1. Initialize polynomial_order3.2.1.    polyfit 3.3.1.    P = polyval 4.1.1. Baseline_Fit = 4.2.1. Y = X – Baseline_Fitmin(Y)4.3.1. Y = Y – max(X) − min(X)4.4.1. X_amp = max(Y) − min(Y)4.5.1. Y_amp = 4.6.1. Y = Y*(X_amp/Y_amp)Outputs: Y Baseline drift correction: this was undertaken using the built-in functions of MATLAB ) while ABP waveforms were min-max normalized globally, in terms of the minimum and maximum of the ABP waveforms across the whole dataset (Equation (2)).ABP signals were not range normalized between 0 to 1 to retain their relative amplitude feature  which was found to be helpful during BP prediction. Mentionable that the bold quantities in the equations mean signals, similar to vectors.Algorithm 2 Pseudo-Code: Deriving PPG DerivativesInputs: PPGInitialize Bandpass Filter Parameters 1.1.1. designfilt(Bandpass Filter using Filter Parameters)2.1.1 bandpass_filter = mean(grpdelay(bandpass_filter))2.2.1 delay = Initialize Sample_Num = linspace, length(PPG))3.1.1 3.3.1 PPG = Normalize(PPG)3.4.1 VPG = Normalize(bandpass_filter(PPG)/dt)3.5.1 APG = Normalize(bandpass_filter(VPG)/dt)3.6.1 PPG = PPG(1:end−2*delay)3.7.1 VPG = VPG(delay+1:end)3.8.1 APG = APG(2*delay+1:end)Outputs: PPG, VPG, APG3.2.1 dt = Sample_Num(2) − Sample_Num(1)Derivatives of PPG: according to literature, the first and second derivatives of PPG also provide valuable information or features while predicting BP. They are called various names such as PPG’, PPG’’ or Velocity of PPG (VPG), Acceleration of PPG (APG), or FDPPG (First Derivative of PPG), SDPPG (Second Derivative of PPG) ,53,54 F. To findAlgorithm 3 Pseudo-Code: Deriving PPG DerivativesInputs: PPG, ABP, Signal_Length1.1.1 Normalize both signals1.2.1 PPG_Size = size(PPG)1.3.1 ABP_Size = size(ABP)1.4.1 if (PPG_Size(1) or ABP_Size(1)) > 1 then Transpose2.1.1 Initialize Time_Vector = linspace, length(PPG))2.2.1  = findpeaks(PPG)2.3.1 num_peaks_PPG = length(peaks_PPG)2.4.1 std_peaks_PPG = std(peaks_PPG)2.5.1 std_peaks_dist_PPG = std(peak_locations_PPG)2.6.1  = findpeaks(ABP)2.7.1 num_peaks_ABP = length(peaks_ABP)2.8.1 std_peaks_ABP = std(peaks_ABP)2.9.1 std_peaks_dist_ABP = std(peak_locations_ABP)3.1.1 Initialize thresholds3.2.1 if  satisfies thresholds then Decision = 03.2.2 else Decision = 1Outputs: Decision (0 or 1)Removing bad signals: the signal samples extracted from the UCI dataset contains many highly distorted signals that can potentially affect the performance of the deep learning model significantly as the network tries to learn from them. Hence, the following types of samples were removed from the dataset: ABP signals with extreme SBP and DBP values, blank samples, and signals which exceed a certain distortion threshold. In particular, ABP signals with SBP values smaller than 80 and greater than 190, DBP values greater than 120 and smaller than 50, and ABP signals which had a BP range (SBP–DBP) less than 20 or more than 120 were removed since it was observed that apart from some extreme cases, highly distorted signals normally had such a BP range. Under this scheme, around 2% of signals were removed from the datasets. After performing some signal processing and taking the derivatives, a few samples became blank due to being extremely distorted; these were also removed. There are levels of distortions for various samples and a sample remains acceptable up to a certain level of distortion. As shown in Histograms of ABP and SBP in The rationale behind this study was to extract an effective set of features from a very large dataset containing PPG, ECG, and ABP signals which can be used to reliably predict BP. While studies mostly use PPG and ECG signals for BP prediction or extracting features directly, we propose to use an approach inspired by the power of autoencoders to extract the latent features automatically and check for the performance. In traditional autoencoders, usually, the input is given to the network to reconstruct it through a latent space compact transformation. This enforces the model to learn the distinctive attributes of the input and thus has shown great success in feature extraction. Therefore, an obvious idea would be to train an autoencoder using PPG and ECG signals both as inputs and outputs. This will provide us with a latent space aware of the diverse patterns of the PPG and ECG signals. Then, we could simply use this feature representation to train regressor models and predict BP by keeping BP values as labels. However, despite that this approach should prove a concise set of attributes of the PPG and ECG signals, this feature set may still not be suitable for BP prediction. On the other hand, from the literature, there have been many studies in which deep convolutional (CNN) networks were trained to extract features from PPG and/or ECG against BP labels and perform regression in the topmost layer to predict BP ,25. HoweThe BP prediction pipeline consists of mainly two sections, namely the U-Net based autoencoder for feature extraction from the raw signals, and the machine learning-based regressor to perform regression on the extracted features for BP prediction. The complete pipeline is shown in The U-Net-based autoencoder is used for extracting a feature map from the raw input data. The dimensionality of the feature map may vary depending on the network setup (discussed elaborately in the experiments section). The general training setup for the U-Net based feature extractor (autoencoder) consisted of a batch size = 64, number of epochs = 100, patience (stopping criterion) = 15, mean squared error (MSE) as the loss function, Adam as the optimizer, and MAE as the metric being monitored. Batch size, number of training epochs, and the patience value were varied a few times initially to determine their optimal values.Extracted features were regressed using traditional machine learning (ML) techniques, such as k-nearest neighbor (KNN), SVM, stochastic gradient descent (SGD), various ensemble techniques , and artificial neural network (ANN)-based MLP. For all these ML algorithms, various parameters were tweaked and tuned to get the optimum outcome. As shown in The primary aim of the experiments was to find the best performing U-Net architecture which can be used as an autoencoder for optimum feature extraction. Later, the same pipeline can be used to evaluate external datasets. Therefore, mainly two types of experiments were performed in this study as discussed below.The UCI dataset  was originally divided into four equal ‘parts’. The first three parts of the UCI dataset were combined to make the train set (75% of the dataset) and the fourth part was taken as an independent test set (25% of the dataset). These four parts being independent in terms of subjects . During training, a randomly selected 20% of the training set was used for validation. Four combinations of the four input signals, namely PPG, ECG, VPG, and APG were used in this experiment, while the target signal was ABP. Total predictor signal segments used for the four-channel approach  were 147,116 while the test set size was 53,043, as shown in Various sub-experiments were performed in Experiment 1 to determine the best U-Net architecture as an autoencoder (at least for this study). Their respective MAE was recorded in each case.Variable depth of the encoder: the depth (number of levels) of the U-Net was varied from 1 to 4 to determine whether the depth of the architecture had any effect on the extracted latent features from the autoencoder.Variable width of the encoder and number of features: the width of the encoder, which represents the number of kernels or filters present in the input layer, was varied from 32 to 256.Variable kernel size: the kernel size was varied from 3 to 11 to see the effect of Kernel size on performance.Variable number of channels: four combinations of the four predictor signals were used for BP prediction. For one channel: only PPG, for two channels: PPG and ECG, for three channels: PPG and its two derivatives, and for four channels: all four types of signal were utilized. Experiments on regression techniques: the extracted features were used to train some traditional Machine Learning regression techniques, namely, MLP, SGD, SVR, XGBoost, GradBoost, AdaBoost, k-nearest neighbor, and random forest to predict BP. BP Prediction from PPG-to-PPG Feature Mapping: Apart from the primary approach of this study which aimed at mapping PPG and ECG features to ABP features for BP prediction, an additional experiment was performed aiming at predicting BP by mapping PPG (or PPG and ECG) to PPG i.e., PPG was taken as the target signal instead of ABP while using the same BP labels and ground truths. The significance of this study lies in taking the ABP signal completely out of the equation which would help avoid acquiring simultaneous ABP data during data acquisition and BP can be predicted from PPG alone.The external BCG dataset was investigated using two different methods. Firstly, the model trained on the whole UCI dataset is evaluated on the full BCG dataset (Method 1). Secondly, an exercise similar to Experiment 1 was performed on the UCI dataset, i.e., the model was trained using the BCG dataset through 5-Fold Cross-Validation (Method 2).Train on UCI, test on BCG Dataset (Method 1): in this experiment, the BCG Dataset was tested against a model trained on the whole UCI Dataset. The outcome from this experiment proved the performance and generalizability of a model trained using the proposed shallow U-Net-based autoencoder on a completely unknown dataset. The training and testing sets used for this experiment are described in Five-fold CV on BCG Dataset (Method 2): the BCG dataset was divided into train-test fold (80:20) and validated using a five-fold cross-validation approach. The training set, in this case, contained 1498 samples while the test set contained 374 samples .Primary Evaluation Metric:y1, y2, y3, …, yn] and ground truth values y = , MAE is defined as in Equation (3) [Mean absolute error (MAE)  was usedtion (3) :(3)MAE=∑British Hypertension Society (BHS) Standard:The BHS introduced a structured protocol  to act aAssociation for the Advancement of Medical Instrumentation (AAMI) Standard:AAMI has proposed a similar standard  as BHS fStatistical analyses:Mainly two types of statistical analysis were performed in this study, namely linear regression and the Bland–Altman plots . The liniY and iX are the dependent and independent variables, respectively. β0 is the offset or the y-intercept and β1 is the slope. The most positive correlation results in a slope of 1, which in turn varies between −1 and 1. In this study, we also represent the linear correlation performance with the Pearson correlation coefficient (PCC). PCC is the covariance of the two variables divided by the product of their standard deviations, as shown in Equation (5) [Here, tion (5) .(5)rxy=Here, it is necessary to mention that the PCC formula for an entire population and a sample of the population is different due to considering population and sample means, respectively, during computation. In this case, PCC formulae for a sample have been used since the dataset is a sample of the originally collected dataset in MIMIC-II. On the contrary, we also computed and plotted the Bland–Altman plots to show the difference between the ground truths and the predictions over the whole BP range, which cannot be reflected upon properly from normal correlation plots.Several different studies were carried out in Experiment 1 as mentioned earlier to identify the best network architecture with optimized parameters. In what follows, we will report the results of these studies. Variable depth of the encoder: as shown in Variable width of the encoder and number of features: as shown in The U-Net-based autoencoder was used to extract features from both train and test sets. So, the optimal number of features to be extracted is also crucial to investigate. Variable number of channels: it can be noticed from One significant outcome from this experiment is that PPG and its first two derivatives perform similarly to PPG alone with ECG for BP prediction. Therefore, ECG can be replaced just by deriving two derivatives of the PPG signal and supplying them as two additional channels in U-Net. Removing ECG while maintaining the performance greatly reduces the complexity of the test setup.Variable kernel size: the kernel size, k = 3 performed best as the kernel size was varied from 3 to 11. The performance dropped as the kernel size was increased .Based on these experiments, the best U-Net architecture as an autoencoder is shown in Experiments on regression techniques: the extracted features from the best autoencoder architecture, were trained using some traditional machine learning regression techniques to predict BP. As is evident from BP prediction from PPG-to-PPG feature mapping: from BHS Standard:The criteria of the three grades along with the model performance of this study are presented in Histograms of MAE for SBP and DBP predictions for all of them are provided in AAMI Standard:As presented in Error measurements for all SBP and DBP predictions are plotted in Statistical Analysis:p-values of approximately 0.01 for both cases indicate the statistical significance of the outcomes of this experiment when the test set contains 53,043 samples. Thus, the null hypothesis, which was rejected, stated that there is no relation between the predictions and the ground truths.The response plots for SBP and DBP regression outcomes are shown in In this experiment, after training on the whole pre-processed UCI dataset, the created model has been tested on the whole (similarly pre-processed) BCG dataset. The main aim was to prove the effectiveness of the shallow U-Net model trained on a large dataset on an external dataset.Performance Evaluation:MAE for SBP and DBP was found to be 2.728 and 1.166, respectively, after testing the whole BCG dataset with 1872 samples by the model trained on the whole UCI dataset. MAE was slightly higher than the results obtained from Experiment 1 with the UCI dataset but still better than any past study. The performance is excellent considering that the BCG dataset is completely unknown compared to the MIMIC-II (UCI repository) from all aspects of the data acquisition setup to data pre-processing. However, when there was no transfer learning, the MAE for five-Fold CV on the BCG dataset was found to be 6.336 and 2.658 for SBP and DBP, respectively. This can imply that the autoencoder requires a good amount of nicely varying balanced datasets to extract quality features. Therefore, it is important to train the proposed model using a large, general dataset that contains an ample number of features. Note that BHS and AAMI metrics information for external validation have not been provided since the number of patients in the BCG dataset does not suffice the minimum requirements for these metrics.Various research groups around the world attempted to predict BP from PPG and ECG signals separately or in combination using various machine learning techniques. It is hard to directly compare and evaluate the performances of those studies due to multiple factors such as the number of patients, data pre-processing, signal length, machine learning models, so on and so forth. In Performance metrics such as MAE do not always show the complete picture of the performance of a study. For this reason, many studies in this domain represent their results in terms of BHS metrics. A comparison of BHS metrics of the current work with some past studies is shown in This study aimed at developing a novel pipeline for BP prediction from PPG and ECG signals by experimenting with the U-Net architecture being used as an autoencoder to extract optimal features. Instead of the raw signals, the extracted features were regressed using machine learning techniques to predict SBP and DBP. The strength of this work lies in how the U-Net architecture was utilized for feature extraction, thereby achieving very high performance from the shallowest version of the U-Net architecture on the current largest possible dataset from the UCI repository. The extracted features were efficient enough in predicting the SBP and DBP, causing a significant performance boost compared to any previous study. Our lightweight network can be helpful for deployment in a resource-constrained setting. Independent test sets were used for evaluation purposes for both experiments performed in this study proving the robustness of the proposed pipeline. The dataset used for the second experiment was acquired through a completely different process , but still our model achieved high performance when evaluated thereon thereby showing the generalizability thereof. This strongly suggests that extracting features from this large dataset using the shallow autoencoder provided the trained model with enough generalizable features to perform robustly even on external datasets. Some studies  reporte


Left atrial (LA) enlargement is an independent risk factor for atrial fibrillation (AF). Interestingly, some athletes have increased risk of AF, which may be linked to LA enlargement; however, little is known about the relationship between LA enlargement and AF risk at moderate-level physical activity (PA). We aimed to explore the associations between PA, LA size and risk of incident AF, and if PA can attenuate the risk of AF with LA enlargement.This prospective study followed 2479 participants , free from known cardiac pathology, for median 20.2 years. Participants were followed up for hospital-diagnosed AF, confirmed by electrocardiography, from 1994-95 through 2016. At baseline, LA size was evaluated by anteroposterior LA diameter, and PA was self-reported by questionnaire.adjusted 0.68, 95% CI 0.50 to 0.93). Participants with LA enlargement had 38% higher AF risk compared with participants with normal LA size . However, the increased AF risk with LA enlargement was attenuated by PA; compared with inactive participants with LA enlargement, the AF risk was 45% lower among active with LA enlargement . AF risk in active participants with LA enlargement did not differ from active with normal LA size. These patterns were observed in both men and women, and in participants over/under 65 years.We observed a U-shaped relationship between PA and AF, and moderately active had 32% lower AF risk than inactive (HRModerate PA was associated with reduced AF risk, and PA attenuated the increased risk of AF with LA enlargement in both men and women and all age groups. Left atrial (LA) enlargement is an independent risk factor for atrial fibrillation (AF).Moderate physical activity (PA) is associated with reduced risk of cardiovascular disease including AF.Little is known about the role of moderate PA in the association between LA size and risk of AF in the general population.In this prospective study, PA was associated with lower risk of AF in a U-shaped pattern.Participants with LA enlargement had higher AF risk compared with participants with normal LA size.PA attenuated the increased risk of AF with LA enlargement in both men and women and all age groups.We suggest that the protective effect of moderate PA outweighs the potential risk of AF with LA enlargement.Increased understanding of the relationship between PA and AF may guide clinicians in implementing PA in primary and secondary prevention of AF.Atrial fibrillation (AF) is the most common clinical arrhythmia worldwide, associated with considerably morbidity, such as stroke and heart failure.Physical activity (PA) is recommended for health promotion and disease prevention,9Several studies have reported that left atrial (LA) enlargement is an independent risk factor for AF in the general population,19However, despite increased risk of AF with both LA enlargement and with vigorous exercise, convincing data linking the combination of exercise and LA size to AF are lacking and are largely speculative.Current knowledge of the relationship between exercise, LA remodelling and AF is mainly derived from studies of athletes. Thus, little is known about the role of moderate PA in the association between LA size and risk of AF in the general population. Therefore, our objective was to explore the associations between PA, LA size and incident risk of AF in a general adult and elderly population. Furthermore, we aimed to explore if PA attenuates the increased risk of AF seen with LA enlargement.The Tromsø Study is a single-centre population-based cohort study with seven repeated health surveys of the population of the Tromsø municipality, Norway.In Tromsø4, all inhabitants ≥25 years were invited (n=37 558), with 27 158 men and women attending the first visit (77% attendance). Of these, all men aged 55–74 years, all women aged 50–74 years and 5%–8% random samples of the other age groups aged <85 years were invited to a second visit with extended examinations. Of these, a total of 7965 participants (76% of the 10 542 invited) attended the second visit. These participants were alternately allocated by computer to one of two lines of examination when attending the first visit, and 3287 participants in one of the lines were examined by echocardiography. These participants did not differ from the total sample attending the second visit.23In total, 2714 participants provided valid data on PA in combination with valid echocardiography data and status of myocardial infarction at baseline. We excluded participants with previous or present documented AF (n=45), valvular heart disease (n=27), left ventricular (LV) ejection fraction <40% (n=13), previous myocardial infarction (n=129) and LA below normal reference range (n=16) at baseline. Furthermore, one participant was excluded due to missing data on a covariate (systolic blood pressure). Finally, our analytical sample consisted of 2479 participants free from known cardiac pathology, with valid data on PA, AF, echocardiography and covariates at baseline .PA was assessed by a validated questionnaire measuring duration of light and hard leisure time PA,For sensitivity analysis, we combined questions about light and hard PA into five categories: (1) light and hard PA 0 hours/week (sedentary), (2) light PA >0 hours/week and hard PA 0 hours/week (inactive), (3) hard PA 0–1 hour/week (low), (4) hard PA 1–2 hours/week (moderate), (5) hard PA ≥3 hours/week (vigorous).Echocardiography was performed by three medical doctors (one trained in echocardiography and two expert cardiologists), using a VingMed CFM 750 ultrasound scanner . The echocardiographic assessment was performed with the use of standard imaging planes in a supine left lateral position. The echocardiographic assessment was performed online in one heart cycle, but remeasured if deviating from eye-balled estimates. Procedures and details of echocardiographic assessments in Tromsø4 are described elsewhere.232). LA diameter index was categorised into small (<1.5 cm/m2), normal (1.5–2.3 cm/m2) and enlarged (≥2.3 cm/m2).29LA anteroposterior diameter was measured at the end of the LV systole by M-mode echocardiography in the parasternal short axis view at the aortic valve level, after alignment of LV in long axis view, using the leading edge-to-leading edge convention.2) by two-dimensional colour Doppler imaging and/or (d) aortic regurgitation , by colour M-mode.Valvular heart disease was defined by of the following criteria: (a) mitral stenosis identified by pulsed Doppler, (b) aortic stenosis (peak gradient >36 mm Hg) by continuous Doppler, (c) mitral regurgitation , coffee consumption (cups/day), diabetes (yes/no), use of antihypertensives (currently or previously/never), myocardial infarction (previously/no), stroke (previously/no), thyroid disease (yes/no) and palpitations (yes/no). Alcohol consumption was summarised from three questions reporting number of glasses of beer, wine or spirit normally consumed within a 14-day period.2).Heart rate and blood pressure were recorded three times with 1 min intervals after 2 min seated rest, by specially trained nurses using an automatic device . For resting heart rate, the lowest recorded reading was used. For blood pressure, the average from reading two and three was used. Blood pressure was classified into hypertension groups: (a) normotensive , (b) hypertensive, controlled , (c) hypertensive, uncontrolled (systolic blood pressure ≥140 mm Hg and/or diastolic blood pressure ≥90 mm Hg and self-reported use of antihypertensives) or (d) hypertension, untreated (systolic blood pressure ≥140 mm Hg and/or diastolic blood pressure ≥90 mm Hg and no self-reported use of antihypertensives). Height and weight were measured with participants wearing light clothing and no shoes. Body mass index was calculated as weight (kg) divided by height squared  and C-reactive protein (mg/L) at the Department of Clinical Chemistry, University Hospital of North Norway. Low-density lipoprotein (LDL) cholesterol was estimated by subtracting high-density lipoprotein cholesterol from total cholesterol.The participants were followed from the date of examination in 1994–95 (Tromsø4), until (a) the date of first documented AF, (b) date of censoring due to death or migration or (c) end of follow-up on 31 December 2016, whichever came first.The follow-up data on AF were derived from the diagnosis registry of the University Hospital of North Norway, the only hospital in the region, by linking the hospital records of AF to the participants’ unique Norwegian national 11-digit identification number. Hospital records were searched for incident cases of AF confirmed by electrocardiography confirmation. Additionally, in participants with diagnoses of cerebrovascular or cardiovascular events, but without diagnosis of arrhythmias, hospital records were manually searched for notes on AF. Details of ascertainment of AF are described elsewhere.Characteristics of the study population are presented as means with SD or percentages with number of observations (n). Associations between PA, LA size and AF risk were evaluated using univariable and multivariable Cox proportional hazard regression analyses. The associations are presented as HRs with 95% CI. The proportional hazard assumption was confirmed by visual inspection of log-minus-log plots. Model 1 was unadjusted, model 2 was adjusted for age, sex, body mass index and systolic blood pressure.To test the robustness of the fully adjusted model 2, we performed sensitivity analyses with additional covariates  stepwise, or jointly, added to the model.In the fully adjusted model 2, we tested for possible interactions between PA*sex, PA*age, PA*hypertension groups, PA*body mass index and PA*LA size in a stepwise analysis of the association between PA and AF. We observed a significant interaction between PA*LA size (p=0.023), whereas no significant interactions were indicated for the other interaction terms (p>0.50). However, given previously reported age and sex differences in the association between PA and AF risk, we also stratified our main analyses by sex and age in addition to LA size.All statistical analyses were performed using SPSS V.26 , with a two-sided p≤0.05 considered statistically significant.In total, 1181 men (57.7±10.7 years) and 1298 women (59.3±10.7 years), 25–83 years, were included in our analyses. Of these, 399 participants (214 men and 185 women) were diagnosed with AF during a mean follow-up of 16.6±6.6 years . Descriptive characteristics for the participants stratified by level of PA are presented in adjusted 0.68, 95% CI 0.50 to 0.93), and a U-shaped relationship was observed .Overall, we observed 32% lower risk of AF in moderately active than in inactive participants . Adjusted cumulative hazard for AF stratified by joint associations of PA and LA size is presented in adjusted 0.55, 95% CI 0.39 to 0.79). Furthermore, the risk of AF in active participants with LA enlargement did not differ from participants with normal LA size   . Adjusteto 2.19) .adjusted 2.59, 95% CI 2.09 to 3.20)  . Adjusteto 3.20) .Adjusted cumulative hazard for AF stratified by joint associations of PA, LA size and age, stratified by sex, is presented in When we combined questions about light and hard PA into five categories , the assTo the best of our knowledge, this is the first study that has analysed both PA and LA size jointly as risk factors for AF in a general population. The main findings from our study are that PA was associated with lower risk of AF in a U-shaped pattern, and the increased AF risk with LA enlargement was attenuated by PA. Furthermore, the risk of AF in active participants with LA enlargement did not differ from active with normal LA size. These patterns were observed in both men and women, and in participants over/under 65 years.Therefore, in our study of participants free from known cardiac pathology, LA enlargement does not represent increased risk of AF in physically active individuals. Furthermore, inclusion of several risk factors for AF in the analysis did not influence the associations between PA, LA size and AF.Overall, we observed 32% lower risk of AF in moderate active than in inactive participants, and a U-shaped association was seen between PA and risk of AF. The protective effect of PA on cardiovascular disease is well documented,To our knowledge, no previous studies have examined how the relationship between LA size and AF varies with PA, and if PA attenuates the risk of AF seen with LA enlargement, in the general population. Our results, that LA enlargement increases the risk of AF, are consistent with previous research in both the general populationThe higher risk of AF observed in inactive with LA enlargement may be due to pathological conditions elevating chronic LA pressure and volume overload,Atrial stretch in response to LA volume and pressure overload is a key stimulus for LA enlargement and may potentially induce release of factors leading to atrial myocyte hypertrophy, oxidative stress and fibrosis, which increases the risk of AF.During vigorous exercise, atrial stretch may potentially release similar factors as in pathological conditions and thereby increase risk of AF,12the athlete’s heart37Cardiac chamber enlargement is assumed to be a physiological and reversible adaptation to exercise training.Interestingly, the pathophysiology seems to differ between ‘classical’ AF and exercise-related AF. AF is usually associated with high age, comorbidities and other risk factors.It is well documented that increasing age is a prominent risk factor for AF, and that men have higher risk of AF than women.3 32 39In sex stratified analyses, we observed 79% higher risk of AF in men than in women, and when we stratified by age, participants ≥65 years had 2.6-fold higher risk of AF compared with participants <65 years. Furthermore, despite a higher overall risk of AF with LA enlargement, the risk of AF in active with LA enlargement was not higher in any sex, or age groups, compared with active participants with normal LA size.Our study has several strengths. First, the prospective design with a long follow-up period in combination with echocardiography data. Second, hospital-diagnosed AF confirmed by electrocardiography and validated by an independent expert endpoint committee. Third, the large number of participants, the high attendance and the large age span strengthen our generalisability to other Caucasian populations. Fourth, as the University Hospital of North Norway is the only hospital within a large geographical region, most hospital confirmed AF cases in our study population are likely uncovered. Finally, the broad diversity of covariates allowed us to adjust for multiple potential confounders.Our study has limitations that should be addressed. First, LA size was assessed by M-mode anteroposterior LA diameter, which is less accurate and have more geometrically assumptions than the recommended biplane calculated LA volume.In conclusion, our prospective study of participants free from known cardiac pathology suggests a U-shaped relationship between PA and AF. Moderate PA was associated with reduced risk of AF, whereas vigorous PA attenuated the protective effect of moderate PA. Moreover, PA attenuated the increased AF risk with LA enlargement in both men and women, and in participants over/under 65 years. We suggest that the protective effect of moderate PA outweighs the potential risk of AF with LA enlargement. Further research is warranted to clarify the association between LA enlargement and AF in relation to PA, preferably assessed with biplane-derived LA volume and objective measures of PA.


Inadequate physical activity (PA), excess screen time (ST), and sub-optimal sleep quality tend to co-occur during adolescence. Yet, little is known about the associations of these behaviors as a cluster with adiposity indicators in Indian adolescents. This study aimed to evaluate the independent and combined influences of PA, ST, and sleep quality on body mass index (BMI) and waist to height ratio (WHtR) in 10–15 years old adolescents in Mumbai, India. A secondary aim was to explore if these influences vary between sexes.n = 772, mean age 13.2 (1.4) years) reported frequency and duration of moderate to vigorous PA (MVPA) and time spent using screens on a previously validated instrument. Sleep quality was estimated using the Pittsburg Sleep Quality Index (PSQI). Weight, height, and waist circumference were measured. Mixed effect logistic regression analyses were performed to explore associations between adiposity indicators (BMI z scores > +1SD and WHtR > 0.5) and unhealthy behaviors , stratified by sex.Cross-sectional study. Adolescents (p < 0.001). Clustering of low PA and excess ST was observed in 69.2% and of all three unhealthy behaviors in 18.8%. Among girls, MVPA < 60 min/d  and PSQI scores > 5  predicted increased BMI. The odds of overweight/obesity were 2.10 times higher in boys reporting low PA and 4.13 times higher in those with low PA+ ST > 120 min/d. Clustering of all three unhealthy behaviors increased prevalence of obesity in both sexes.The combined prevalence of overweight and obesity was 38.3%. Overall, 62.0 and 85.0% reported MVPA< 60 min/d and ST > 120 min/d respectively. Girls reported higher ST (218.21 (69.01) min/d) as compared to boys (165.3 (101.22) min/d, The results indicated a co-existence of multiple unhealthy lifestyle factors of obesity and that clustering of these behaviors can further aggravate obesity risk as compared to their independent effects. Integrated interventions that leverage the cumulative benefits of being active, less sedentary and sufficient sleep are warranted to facilitate greater improvements in obesity risk behaviors.The online version contains supplementary material available at 10.1186/s12889-021-12183-9. The immediate and long-term health benefits of adequate physical activity (PA) levels, low recreational screen time (ST), and optimum sleep quality in adolescents are well documented –4. PreviTo date, studies have examined the independent effects of inadequate PA, and excess ST  on the body weight status of adolescents. Previous studies have consistently indicated an inverse association between PA and body weight status , a dose-The majority of the epidemiological studies have used BMI to characterize entire body fatness or general obesity , 26, desAs the health risk behaviors tend to exist concurrently during adolescence , 33, theInvestigating the cluster of modifiable risk factors simultaneously in adolescents may help identify specific targets for culturally relevant behavior change interventions. We hypothesized that examining the clustering of low PA, excess ST, and poor sleep quality in adolescents in India and exploring whether the effect of one unhealthy behavior supersedes the effect of another on the risk of obesity can guide appropriate obesity prevention strategies. Therefore, we conducted this study to evaluate the independent and combined influences of lifestyle behaviors such as PA, ST, and sleep quality on the measures of adiposity – BMI and WHtR among 10–15 years old adolescents in Mumbai, India. The secondary objective was to determine whether these influences differed by sex. This information can help harness the synergistic benefits of improving PA, SB and sleep in adolescents in India and inform more targeted programs for prevention and management of obesity.n = 772) attending three government-funded public schools and three unaided private schools in the city of Mumbai, India. A convenience sampling method informed the selection of the schools. In India, public schools are typically attended by children belonging to low and low middle socioeconomic class families and private schools usually cater to children from high middle to high socioeconomic class families. So, the selection of participants from private and public schools ensured representation across socioeconomic classes. At these six schools, a class each from grades 6 to 9 was selected at random as the participating class. As per the study design, all students (n = 988) attending these classes  were eligible to participate. Details of the protocol were explained, and information sheets were sent home to receive parental consent. Written informed assent was obtained from the adolescents who provided parental consent. The survey was administered at a central facility such as the library or school recreation center/auditorium in the presence of school teachers and a trained field research team. The study protocol was reviewed and approved by an independent research ethics committee, the Intersystem Biomedica Ethics Committee, Mumbai, India (ISBEC Protocol Ver 2 -February 2019).This cross-sectional study was conducted among a sample of 10–15 years old adolescents , MVPA, recreational screen-based SB, and sleep quality. The parents provided information regarding the mother’s highest education and present occupation and the family’s monthly income while completing the parental consent forms. A separate section for the researcher recorded anthropometry measurements were also included.never’ to ‘every day’, scored 0 to 7 and the duration of engaging in each activity was reported in minutes/d.Adolescents were asked to recall the frequency and duration of engaging in each of the listed MVPA and screen-based sedentary activities (SA) in the past week. A detailed description of the development and validation of these items included in this study is provided elsewhere . ActivitThe psychometric properties of the items assessing PA and SA were tested using several measures of validity and reproducibility. Item content validity indices for clarity and relevance were found to be satisfactory (> 0.80), the internal consistency  was acceptable and the intraclass correlation coefficient estimates ranged from 0.954–0.968, indicating good to excellent reliability of the items included in the questionnaire. The total time spent in MVPA/d was calculated by multiplying the frequency of engaging in each activity with the time spent/d and then dividing the value by 7 (the number of days in a week). The time spent on each of the MVPA items was aggregated to estimate the total MVPA minutes/d/. Similar calculations were done to estimate the total time spent on ST/ d. Similar to previous studies , MVPA ≥6A widely used instrument, Pittsburg Sleep Quality Index (PSQI), assessed the adolescents’ sleep duration and quality during the previous month . The que. Each measurement was repeated twice and the average of these consecutive readings was used to calculate BMI using the formula- weight in kilogram divided by height in meter squared and WHtR by dividing WC in centimeters by height. The presence of overweight and obesity were determined by calculating BMI for age z scores using Anthro Plus software and then comparing with age-specific and sex-specific BMI z score cut-offs provided by the World Health Organization [Weight, height, and waist circumference (WC) were measured using standard procedures and calibrated equipment by trained field investigators. All field investigators had received systematic training for collecting and recording anthropometric measurements before data collection. Weight was measured standing erect and barefoot on an electronic weighing scale  to the nearest 0.1 kg, height was taken using a portable stadiometer  to the nearest 0.1 cm and the WC was measured to the nearest 0.1 cm using a non-elastic measuring tape . The participants were asked to stand erect and WC was measured at the midpoint between the lowest part of the last rib and the iliac crest (top part of the hip bone) along the midline of the armpitnization . The BMInization , 47. Thenization .n = 24) who were not present in school on the survey day and those (n = 78), who had provided > 30% incomplete information in the survey, the analyses were performed on the final sample  of adolescents, attending private (n = 382) and public schools (n = 390). Data were analyzed using IBM SPSS for Windows version 24.0 . Descriptive characteristics were calculated as mean and SD or percentages for the entire sample and also compared between adolescent boys and girls using chi-square tests for categorical and t-tests for continuous variables respectively.Out of 988, a total of 874 (88.5% of those who were contacted) students provided written parental consent and comprised the study sample. After excluding students  low PA and excess ST, (2) low PA and poor sleep, (3) excess ST and poor sleep and (4) combination of all three unhealthy behaviors such as low PA, excess ST, and poor sleep. The cutoff points for low PA (engagement in MVPA < 60 min/ day), excess ST (time spent using screens > 2 h/d), and poor sleep  were operationalized based on the recent WHO 2020 report recommendations , 49 and The mean age of the adolescents was 13.2 (1.4) years. Of 772 adolescents, 49.0% were girls, 50.5% attended public schools and 66.7% reported their living arrangement as nuclear families. The prevalence of overweight, and obesity were 27.8, and 10.5% respectively and 42.2% had WHtR > 0.5. The age-specific WC percentile data showed that 32.5% were centrally overweight (75th – 90th percentile) and 9.5% had central obesity (>95th percentile). Girls reported a higher prevalence of central obesity as compared to boys (Table p ≤  0.001).Among participants, the total time spent in MVPA was 68.33 (29.2) minutes/d, and the mean ST was reported as 172.41(88.32) minutes/d. Overall, 62.1% reported PA < 60 min/d and 85% engaged in ST > 2 h/d. Significant differences were observed in the mean frequency of performing activities such as football/basketball, swimming/bicycling, playing cricket/badminton, household chores between adolescent boys and girls but not in the total time spent in MVPA/ d. Also, girls reported spending significantly higher time in ST (218.21 (69.01) minutes/d) as compared to boys (165.34 (101.22) minutes/d, Analyses indicated that only 53.3% were sleeping > 7 h/d, 34.6% had < 75% habitual sleep efficiency , almost three-fourths (74.6%) had experienced some sleep disturbances in the past month and 36.9% had PSQI scores > 5  , indicating ‘fairly good’ to ‘fairly bad’ sleep quality, and the mean sleep latency score was 1.29 (1.09), suggesting that adolescents usually took 15–30 min to fall asleep after going to bed. Girls had significantly higher mean global PSQI scores (7.12 (1.76)) as compared to boys (6.78 (2.02), 2 = 16.260 p < 0.001), ST , sleep duration  and PSQI scores  were observed between younger (10–12 years) and older adolescents (13–15 years). Private school adolescents reported significantly higher ST  and PSQI scores .Additional Fig. More than two-thirds 69.2%) engaged in both PA < 60 min/d and ST > 120 min/d and almost one-third (31.0%) reported having excess ST and poor sleep. The clustering of all three unhealthy behaviors was observed in 18.8% of sampled adolescents. No significant sex differences were observed in the proportion of participants reporting various combinations of unhealthy behaviors (Table % engagedp ≤ 0.001) and PSQI scores were > 5  and for central adiposity when ST > 120 min/d . Attending private schools, having low PA and increased ST were associated with higher BMI z scores and WHtR in boys. , a higher amount of time spent on screens (mean ST was 172.4 min/ d), and a clustering of low PA and excess ST in more than two-thirds (69.2%) of the sampled adolescents. Second, the prevalence of sleep insufficiency (sleep duration < 7 h/d) and poor sleep quality (PSQI scores > 5) were observed to be substantially high with a preponderance in girls and older adolescents. Third, sex-stratified regression analyses showed that the predictors of higher BMI and greater central obesity within PA, ST, and sleep patterns vary between adolescent boys and girls. Finally, the findings indicated a co-existence of multiple unhealthy lifestyle risk factors of obesity in the sample and that the clustering of these behaviors significantly increased the likelihood of general and central adiposity in both adolescent boys and girls.. This is of particular relevance as physical activity patterns and sedentary behaviors during adolescence are known to track into adulthood.We observed that the odds of obesity were 2.10 times higher when MVPA < 60 min/d, and 1.73 times higher when ST > 2 h/d, but increased to 4.13 times in those who reported a combination of low PA and excess ST. A significantly higher prevalence of obesity in adolescents who spent lesser time in PA and had greater ST has been observed in previous studies –53. The To encourage adequate PA among adolescents, efforts should be taken to adopt school-level strategies such as structured physical education as a part of the academic curriculum, encouragement of students to participate in organized sports, better provision of sports coaching and availability of sports equipment, and having designated play areas or fields at schools. Parents and peers play key roles in encouraging, supporting, and facilitating physical activity in adolescents . In addiBesides PA and ST, the sleep correlates of adiposity in adolescents were evaluated. A greater prevalence of poor sleep quality in older adolescents (13–15 years old) as compared to the younger participants, ages 10–12 years in this study can be attributed to alterations in school timings, increased academic demands, and greater electronic device usage , 66 combIn this study, the sex differences in PA, ST, and sleep and the influences of these behaviors on adiposity measures were explored. Girls reported higher ST, decreased sleep duration, and greater PSQI scores as compared to boys. Given that excess ST and insufficient sleep are independently associated with an increased risk of developing non-communicable diseases including obesity, the combination of these unhealthy behaviors might predispose adolescent girls to a higher risk of poorer health outcomes. We used sex-stratified regression analyses to determine factors associated with BMI and WHtR. The majority of previous studies had adjusted the regression models for sex , 71. HowThe novelty of this study lies in evaluating the combined effect of unhealthy behaviors on adiposity measures in Indian adolescents. Previous studies that were conducted in high-income countries have reported favorable associations between meeting movement behavior recommendations and better health outcomes in adolescents , 9, 72. As such, the multifactorial nature of obesity accentuates the importance of identifying and addressing the risk factors that contribute to obesity in adolescents. The results of this study reiterate the critical importance of targeting each component of the ‘movement continuum’  that occA few limitations of our study must also be mentioned. First, the objective assessments of PA, ST, or sleep using accelerometers could not be done due to resource constraints. Second, though we used validated tools to estimate the activity and sleep patterns, the data was self-reported and can be subject to recall and social desirability bias. Third, only maternal factors such as education and occupation were assessed as indicators of familial support for engaging in healthy lifestyle behaviors. Finally, the convenience sampling method and the cross-sectional design of the study may limit the interpretations of associations between variables and formulation of hypotheses for future experimental studies.However, the strength of this study is that it is the first large-scale survey to evaluate the combined effects of low physical activity levels, excess sedentary behaviors, and poor sleep quality on body weight status and central adiposity indicators in adolescents in India, albeit in a non-representative manner. Adolescents in the early and mid-adolescence and attending both private and public schools were selected and the sex, age, and socioeconomic differences in the activity, sedentary, and sleep behaviors were assessed. Also, we used BMI as well as WHtR to determine obesity, providing evidence of demographic and lifestyle-related risk factors associated with general and central adiposity in adolescents in India. Practical applications of this study include regular assessments of simple anthropometric measurements to characterize obesity in adolescents in school settings, early identification of sleep disturbances and excess sedentary behaviors using validated questionnaires, and designing educational interventions and feasible activity guidelines to foster health-promoting lifestyle habits and reduce risk of immediate and long-term consequences of adiposity in the pediatric population.Adolescence is a critical phase of life when hormonal, physiological, and lifestyle factors contribute to alterations in metabolic pathways to support increased nutrient requirements and facilitate growth spurt. In adolescents with obesity, these manifestations may continue to persist and eventually contribute to an early onset of metabolic derangements such as hyperinsulinemia, hypertension, and other cardiometabolic disorders , 72. TheAdditional file 1.Additional file 2.


Postoperative pancreatic fistula (POPF) remains the main cause of morbidity in patientsafter distal pancreatectomy. The objective of this study was to investigate whether anabsorbable fibrin sealant patch could prevent POPF after distal pancreatectomy.A multicentre, patient-blinded, parallel-group randomized superiority trial wasperformed in seven Dutch hospitals. Allocation was done using a computer-generatedrandomization list with a 1 : 1 allocation ratio and concealed varying permuted blocksizes. Pancreatic stump closure with a fibrin patch was compared with standard treatmentin patients undergoing distal pancreatectomy. The primary endpoint was the developmentof grade B/C POPF. A systematic review and meta-analysis was performed which combinedthe present findings with all available evidence.versus 29 of 122 in the control group . No related adverse effectswere observed. In the meta-analysis, no significant difference was seen between thepatch and control groups .Between October 2010 and August 2017, 247 patients were enrolled. Fifty-four patients(22.2 per cent) developed a POPF, 25 of 125 patients in the patch groupApplication of a fibrin patch to the pancreatic stump does not reduce the incidence ofPOPF in distal pancreatectomy. Future studies should focus on alternative fistulamitigation strategies, considering pancreatic neck thickness and duct size as riskfactors. Trial registration number NL5876 . Postoperative pancreatic fistula (POPF) remains a major concern after distalpancreatectomy. Although stump closure with a fibrin sealant patch has shown promisingresults, patch application did not reduce the incidence of POPF in this multicentre,patient-blinded RCT. No clear guidelines exist for closure of the pancreatic stump, or howto prevent POPF after DP. The use of absorbable fibrin patches has been investigated inpancreatic surgery for several yearsIn 2016, the International Study Group of Pancreatic Surgery (ISGPS)Appendices 1 and 2.The CPR  trial was designedas a multicentre, investigator-initiated, patient-blinded, parallel-group, randomizedsuperiority trial. The study was conducted in seven Dutch hospitals belonging to the DutchPancreatic Cancer Group and followed the CONSORT guidelinesAdult patients undergoing open or minimally invasive DP were eligible for inclusion, ifthey had an expected survival time of at least 12 months. Exclusion criteria were: currentimmunosuppressive therapy, recent chemotherapy (less than 2 weeks before surgery),psychiatric/neurological disease, and/or drug or alcohol abuse. All patients gave writteninformed consent before surgery.A central study coordinator allocated patients randomly during surgery using a concealedrandomization list. This study coordinator was involved only in group assignment of trialsubjects. The randomization list was computer-generated with a 1 : 1 allocation ratio, andconcealed varying permuted block sizes of two, four, six, or eight patients. Nostratification was applied. Randomization was done during surgery after the pancreas hadbeen transected. Patients remained blinded to the group allocation for at least 12 monthsafter surgery.®; Takeda Pharmaceutical Company, Tokio, Japan) was placed to coverthe transection surface, including an overlap on the pancreas. All participating surgeonshad received a video demonstrating this procedure. In the control group, patients receivedstandard treatment without a fibrin patch. In both groups, no other additional stumpclosure techniques were allowed. One or more intra-abdominal surgical drains were placednear the pancreatic remnant. On the third postoperative day, amylase levels were measuredin serum and drain fluid.In both groups, the pancreas was transected using a stapler or surgical scalpel withsuturing. In the fibrin patch group, a fibrin and thrombin-coated collagen patch, method of stump closure, and the meta-analysiswere exploratory analyses. All other analyses were confirmatory. A logistic regressionmodel was used to assess the effect of the fibrin patch in the presence of known riskfactors: pancreatic thickness, pancreatic duct size, pathology, and method of stumpclosure. The goal of this analysis was both to explore whether known risk factors werepredictive in the present data set, and to reduce confounding bias in analysis of theprimary endpoint. The selection of these risk factors was based on currently availableliterature® version 22.0 .Analyses were done according to the intention-to-treat principle. The normality ofdistribution was checked by visual inspection of histograms. For continuous variables,normally distributed data were summarized as mean(s.d.), and non-normally distributed dataas median (i.q.r.), with testing for differences between groups using Student’s®) after DP. A meta-analysis of all available RCTs of fibrin patchesin DP was done, according to the PRISMA guidelinesDuring the study interval, other RCTs assessed the impact of fibrin patches(TachoSil®). The search in PubMed was as follows: ‘ AND ‘distal pancreatect*’. Similar search strategieswere used for the Cochrane Library and Embase. The major clinical trial registries were checked for any ongoing trials. Theterm ‘randomized trial’ was not included in the search strategy to ensure that noclinical trials were missed during the screening process. Titles, abstracts, andfull-text articles were all independently assessed by two authors to establisheligibility. References of included studies were screened manually for possibleadditional studies.A systematic literature search was conducted in Embase, Cochrane Central Register ofControlled Trials, and PubMed databases to search for RCTs on this topic up to April2020. Search terms were based on procedure (pancreatectomy) and intervention, Englewood(NJ), United States), version 3.0 (CMA 3.0) software. Categorical data are presented asfrequencies and percentages. A DerSimonian and Laird random-effects model was used topool the dataFig. 1). The fibrin patch group consisted of 125 patients and therewere 122 patients in the control group. Baseline characteristics, intraoperative variables,and pathological outcomes were comparable between groups (Tables 1 and 2). More than half of the patients were women (56 percent) and the median age was 62 years. Minimally invasive DP was performed in 37 per cent ofpatients. Somatostatin analogues (such as octreotide) were used in 16 per cent of patients:in 15 (12 per cent) in the patch group versus 24 (20 per cent) in thecontrol group (P = 0.117). The most common indications for DP wereadenocarcinoma  and neuroendocrine tumours . Median duration of operation was 258 (i.q.r. 203–333) min.Between 11 October 2010 and 7 August 2017, 252 patients were randomized. Randomization wasperformed too early for two patients, and DP was ultimately not carried out becausemetastases were detected. Three patients were lost to follow-up (no data available), and 247patients were included in the final analysis (versus 29 of 122 (24 per cent) in the controlgroup (P = 0.539) (Table 3). In the logistic regression model,application of a fibrin patch was not a statistically significant predictor of grade B/CPOPF (P = 0·473) (Table S1) in thepresence of pancreatic neck thickness, pancreatic duct size, and pathology . Independent risk factors for POPF wereincreasing pancreatic neck thickness (OR 1·19 (95 per cent c.i.1·10 to 1·30) per mm increase) and increasing pancreaticduct size (OR 1·68 (1·22 to 2·32) per mm increase) (Table S1). The originalprimary endpoint was analysed and reported in Overall, the rate of grade B/C POPF was 22 per cent: 25 of 125 patients (20 per cent)in the fibrin patch group Table3). No difference was found regarding DGE and PPH (Table 3), or in othercomplications (TableS3). Hospital stay was shorter in the fibrin patch group.Reoperations were performed in four patients (3.2 per cent) in the fibrin patch groupversus 12 (9.8 per cent) in the control group(P = 0·040), and 23 (18 per cent)versus 30 (25 per cent) respectively were readmitted to hospital(P = 0·279). The 90-day mortality rate did notdiffer significantly between groups: 2 of 125 (1.6 per cent) versus 6of 122 (4·9 per cent) (P = 0·168). Deaths of threepatients were related to POPF: 0 (0 per cent) versus 3 (2·5 per cent)in patch versus control group. There was no difference between groupsregarding day of drain removal. No association was found between surgical technique andfibrin patch regarding the development of POPF. Ad hoclogistic regression also showed that method of stump closure was not a significantpredictor of POPF (P = 0·504).The incidence of major morbidity (Clavien–Dindo grade at least III) did not differbetween groups , along with data from the present trial. Therisk of bias of included studies is summarized in Fig. 2. This risk was regarded as moderate in allof the included trials. All trials used the definition developed by the ISGPS. In twostudies,versus 95 of 441 (22 per cent)respectively  . There was low heterogeneity betweenstudies (I2 = 27 per cent).A total of 241 studies were identified, which were screened based on title andabstract, after which 234 studies were excluded as they did not meet the inclusioncriteria. Of seven studies that remained, four were excluded after full-text assessment.Two studies were excluded owing to the design (not an RCT), and the other two usedfibrin glue instead of a fibrin patch. As the comparability of a fibrin patch and fibringlue is uncertain, only fibrin patch studies were included. In total, three studiesIn this multicentre patient-blinded RCT, grade B/C POPF developed in 22 per cent ofpatients after DP in experienced centres. No significant reduction in POPF was seen withapplication of a fibrin patch. After adjusting for known risk factors, no beneficial effectof the fibrin patch was noted. The reoperation rate was lower in the fibrin patch group,although specific indications for reoperation did not differ much between groups. Thesystematic review and meta-analysis, which combined the present results with those fromother available RCTs on this subject, confirmed that fibrin patches do not decrease theincidence of POPF in DP.,,,The overall 22 per cent rate of POPF after DP signifies the relevance of this complicationand justifies the rationale of this study. Despite numerous studies on this topic, there isno consensus regarding the optimal method of stump management in DP. Three randomizedtrialsAlthough POPF rates were similar in the two groups in the present study, a shorter hospitalstay and lower reintervention rate were observed in the fibrin patch group. The shorterhospital stay could be explained by the lower reoperation rate. As patch use was not aprotective factor for POPF in the multivariable analysis, this seems coincidental.Increasing pancreatic thickness and pancreatic duct size were independent prognosticfactors for POPF. The relationship between duct size and POPF risk is contrary to that forpancreatoduodenectomy, where larger duct size reduces the risk of POPFversus routine drainage, and hand-sewnversus stapler stump closure,versus67 per cent; P = 0.02). However, the old definition of POPF was used; whenonly grade B/C POPF was assessed, there was no significant difference  . Follow-up studies havequestioned the value of this drug in DPNumerous fistula mitigation strategies in DP have been proposed, such as fibrin patchapplication, no drain versus 32 per cent), which may have biased theresults. However, lack of benefit of a fibrin patch was confirmed in multivariable analysisthat adjusted for malignant versus non-malignant lesions. Second, thisstudy was conducted over a relatively long interval (2010–2017), during which minimallyinvasive DP was implemented in the Netherlands, as well as enhanced recovery pathways.Additionally, a new definition and grading system for POPF was published, which led to anadjustment in the primary outcome. Although a shorter period of inclusion would have beenpreferred, DP is performed less commonly than pancreatoduodenectomy, and fewer patients wereavailable for the study than expected. Because of the randomized design, the authors feelthis did not influence the study outcomes negatively. Finally, only the patients wereblinded to the group allocation. The 90-day mortality rate (3 per cent) may seem slightlyhigher than that in more recent reportsThe present study has some limitations. First, more patients had non-malignant lesions inthe fibrin patch group ; M. Luyer ; K. van Laarhoven; M. Stommel ; R. de Kleine ; Q. Molenaar .zrab001_Supplementary_DataClick here for additional data file.


P < 0.05). RMSSD was significantly associated with APTT, i.e., the value of APTT increased by 1.032 ± 0.470 s for each 1 − SD increase in RMSSD (P < 0.05). HRV parameters were associated with RCIs in patients with BC. These observations suggest that the autonomic nervous system and coagulation indices in BC patients are linked, potentially explaining the reason that they are both associated with the prognosis.The purpose of this study was to investigate the relationship between heart rate variability (HRV), a non-invasive tool for evaluating autonomic function, and routine coagulation indices (RCIs) in patients with breast cancer (BC). Forty-six BC patients were enrolled in this study. Blood biochemistry tests were performed to extract RCIs, including prothrombin time (PT), activated partial thromboplastin time (APTT), and thrombin time (TT). Five-minute electrocardiograms were collected for analysis of HRV parameters . Multiple linear regression models examined the relationship of HRV parameters with RCIs. RMSSD, LF n.u., HF n.u., LF/HF were significantly associated with PT. Specifically, the value of PT increased by 0.192 ± 0.091 or 0.231 ± 0.088 s, respectively for each 1 standard deviation (SD) increase in RMSSD or HF n.u.; it increased by 0.230 ± 0.088 or 0.215 ± 0.088 s, respectively for each 1 − SD decrease in LF n.u. or ln (LF/HF) (all  Prior studies found that, compared with patients with other benign breast diseases, BC patients demonstrated changes in routine coagulation indices (RCIs), such as decreased prothrombin time (PT), decreased activated partial thromboplastin time (APTT), and decreased thrombin time (TT)4. Besides, regular coagulation tests and the use of appropriate anticoagulant drugs in BC patients were reported to help reduce the occurrence of thrombotic disease and tumour metastasis6.Breast cancer (BC) patients often have disrupted coagulation function8. As a quantitative assessment of the function of cardiac autonomic nervous system (ANS), heart rate variability (HRV) was found to be associated with survival and tumour stage in BC patients. For example, Arab et al. found that greater HRV reduction was associated with more profound tumour stage9. Giese-Davis et al. found that higher high-frequency (HF) power of HRV significantly predicted longer overall survival10.Autonomic nerve activity also plays a significant role in tumorigenesis of BC patients12. Mais et al. showed that SNS activation through adrenergic agonists that stimulates adrenergic responses could induce the procoagulant state in healthy individuals and lead to the shortening of APTT and the increase of D-dimer and prothrombin fragment F1 + 213. Westerloo et al. showed that vagus nerve stimulation can inhibit the activation of coagulation and fibrinolysis system induced by dose of lipopolysaccharide in rats through cholinergic anti-inflammatory pathway14. Based on HRV analysis, Wang et al. reported associations between frequency-domain HRV parameters and FIB level in gastric cancer (GC) patients15. Shi et al. studied nonlinear HRV characteristics of GC patients and found that increased short-range temporal correlations, decreased asymmetry, and increased irregularity of heartbeat fluctuations were associated with elevated FIB level16. However, it is yet to verify whether these changes in HRV are also linked to coagulation function in BC patients. The aim of this work is to investigate the association between short-term HRV parameters and RCIs in BC patients. To this end, we enrolled 46 patients who were first diagnosed with BC. Prior to surgical treatments, 5-min resting electrocardiogram (ECG) was recorded to enable HRV analysis, and fasting venous blood was collected for performing coagulation assay. Bivariate correlation of RCIs  with time- and frequency-domain HRV parameters was performed followed by linear regression models to account for potential confounding factors including age and body mass index (BMI).Other studies also observed that certain associations between ANS function and coagulation existed in cancer patients. Activation of the sympathetic nervous system (SNS) has been reported to initiate coagulation by increasing blood fibrinogen (FIB), clotting factor VIII, von Willebrand factor, and platelet activityBC patients admitted to The First Affiliated Hospital of Bengbu Medical College between November 2018 and June 2019 were enrolled. All patients were female and were first diagnosed with BC by needle biopsy and/or pathological examination, and none of them had undergone any treatments . Patients with primary diseases of vital organs , severe infections, long-term use of hormones, a history of anticoagulant thrombolytic therapy in the past three months, or was breastfeeding or pregnancy were excluded. This study was approved by the Clinical Medical Research Ethics Committee of The First Affiliated Hospital of Bengbu Medical College  (registration number: 2019KY031). The experiments were conducted in strict accordance with the ethical standards set forth in the 1964 Declaration of Helsinki and its later amendments. Ultimately, 46 BC patients were included in this study. All the patients were informed of the objectives, contents, latent risks, and signed written informed consents.17; therefore, we chose a sampling rate of 400 Hz. During the test, subjects were in a sitting position. Two disposable Ag/AgCl ECG electrodes  were adhered at the V6 positions for use.Single-lead ECG data were collected from each patient for 5 min using a Micro-ECG recorder . The Healink ECG recorder has three sampling modes: 100 Hz, 200 Hz, and 400 Hz. Ziemssen et al. recommended that the sampling rate for HRV analysis be no lower than 250 HzFasting venous blood was collected from all patients in the morning before breast-conserving surgery and modified radical mastectomy. The time interval between ECG measurement and blood test was within 2 days. The blood samples were combined with sodium citrate to prevent coagulation; the ratio of anticoagulant volume to blood volume was 1:9. PT, APTT, and TT were analysed using an automatic coagulation analyser . PT, APTT and TT were all detected by the solidification method. All the test reagents used above were the original reagents imported with the instrument.https://www.healink.ltd, HeaLink Ltd., Bengbu, China) with visual inspections afterwards to remove artefacts (such as ectopic beats). Seven of them had fewer than three ectopic beats, and one had a rate of less than 6%. The number of ectopic beats in each subject was less than 10% of all beats. Then, HRV time- and frequency-domain analyses were performed.The collected ECG data were transmitted to a computer via Bluetooth. The R-to-R interval (RRI) time series were extracted by ECGViewer software  intervals (SDNN) and the root mean square of the normal difference between successive NN intervals (RMSSD)19. The fast Fourier transform (FFT) with the Welch’s periodogram method (with 150 s window width and 50% overlapping window) was applied to estimate the power spectral density of the RRI time series20. The spectrum is obtained by averaging the spectra of these overlapping segments for decreasing the variance of the FFT spectrum.The frequency-domain parameters included the low-frequency  power, the high-frequency  power, the LF power in normalized units (LF n.u.), the HF power in normalized units (HF n.u.), and the ratio of LF power to HF power (LF/HF). Prior to frequency-domain analysis, the RRI time series was resampled at 4 Hz using cubic spline interpolationhttps://www.kubios.com, Kubios Oy, Kuopio, Finland)21.The analyses of the above parameters were performed with Kubios HRV Premium software  across multiple comparisons. Statistical significance was accepted if the FDR-corrected P < 0.05. SPSS statistical software  was used for the above analysis.Bivariate Pearson correlation was used to analyse the relationship of HRV parameters and RCIs. Significant correlation was considered if The histograms of HRV parameters  are shown in Fig. P < 0.05). Besides, APTT was also positively correlated with RMSSD (P < 0.05). After adjusting for age and BMI, RMSSD, LF n.u., HF n.u., LF/HF were still significantly associated with PT. Specifically, the value of PT increased by 0.192 ± 0.091 or 0.231 ± 0.088 s, respectively for each 1 standard deviation (SD) increase in RMSSD or HF n.u.; it increased by 0.230 ± 0.088 or 0.215 ± 0.088 s, respectively for each 1 − SD decrease in LF n.u. or ln (LF/HF) [all false discovery rate (FDR)-corrected P < 0.05; Table P < 0.05; Table The results of the bivariate correlation analysis are shown in Table Correlation between short-term HRV parameters and RCIs was observed in this study involving 46 BC patients. Specifically, PT was positively correlated with RMSSD and HF n.u. and was negatively correlated with LF n.u. and LF/HF. Moreover, and APTT was significantly positively correlated with RMSSD. However, we did not find an association between HRV and TT in patients with BC.22. The interpretation of LF, LF n.u. and LF/HF is controversial. LF is earlier considered to be related to SNS activity and is more recently believed to mainly reflect baroreceptor reflex activity24. As a result, the opinion that LF n.u. or LF/HF represents sympathetic-vagal balance has also been challenged. The interaction between sympathetic and vagal modulation is likely complex and nonlinear in nature that also challenges the interpretation and assessment of their balance25. APTT and PT are indicators reflecting endogenous and exogenous coagulation pathways, respectively, which can be used to determine the content and activity of coagulation factors in the blood26. Therefore, our results directly support the relationship between ANS and coagulation in BC patients.RMSSD and HF n.u. are considered to reflect parasympathetic nerve (vagus nerve) modulation29. There is growing evidence that vagus neuromodulation in tumorigenesis is partially mediated by its anti-inflammatory effect through the cholinergic anti-inflammatory pathway30. Specifically, the acetylcholine produced by the vagus nerve can bind to the α-7 nicotinic acetylcholine receptor expressed on tissue macrophages and inhibit the release of inflammatory cytokines through signal transduction of receptors, such as interleukin-1 (IL-1), interleukin-6 (IL-6) and tumour necrosis factor-α (TNF-α), blocking the interaction between tumour cells and the surrounding inflammatory microenvironment32. The role of vagal activity in regulating tumour progression has also been shown in several human and animal studies. For example, in patients with various cancers, including colon cancer (CC), prostate cancer (PC) and GC, high vagus nerve activity, as indicated by HRV, significantly predicts lower levels of tumour inflammatory markers35. In vagotomised animals, enhanced tumour metastasis and worse prognosis was also observed36.Autonomic nervous function is closely related to the tumorigenesis and development of cancer37. Many studies have shown that coagulation disorders caused by tumours not only can promote tumour progression, invasion, angiogenesis and metastasis, but also are closely related to tumour stage and prognosis38. In fact, coagulation abnormalities in cancer patients, including prolongation or shortening of APTT or PT, thrombocytosis, hyperfibrinogenaemia and elevated D-dimer levels, have been confirmed in different cancers, such as GC, lung cancer, BC, or CC42. APTT and PT are the two most commonly used RCIs in laboratory examinations, and their prognostic value for cancer has been reported. For example, Qi et al. found that the level of PT in patients with non-small-cell lung cancer was significantly higher than that in healthy people, and the survival rate of patients with prolonged PT was significantly lower43. Similarly, Wang et al. reported that elevated PT level was an effective predictor of survival for cholangiocarcinoma patients which was independent of age, tumour differentiation, and TNM stage44. In BC patients, the shortening of PT and APTT directly promoted thrombosis, which facilitated infiltration and metastasis of tumour cells and was significantly related to poorer prognosis of patients4.Patients with malignant tumours usually demonstrate aberrant activation of the coagulation system45. This transmembrane glycoprotein can form a complex (TF/FVIIa) with coagulation factor VIIa and trigger coagulation by activating coagulation factors IX and X. TF is the main initiator of the coagulation pathway46. Studies have shown that in addition to activating blood coagulation, TF also plays an important role in tumour progression and is an important mediator between coagulation, inflammation, thrombosis, tumour growth and metastasis48. Inflammatory cytokines, including TNF-α and IL-1b, can induce tumour-associated macrophages and endothelial cells to express TF. In addition, these inflammatory factors downregulate important physiological anticoagulant pathways through a variety of mechanisms, which together with the upregulation of TF lead to the procoagulant effect50. The vagus nerve has a regulatory effect on inflammation, while the anti-inflammatory effect inhibits the expression of TF on monocytes51. Therefore, the vagus nerve may inhibit the expression of TF by regulating inflammation to achieve the effect of inhibiting coagulation. Future studies are warranted to further elucidate this mechanism.The activation of coagulation cascades is mainly caused by tissue factor (TF) expressed and released by tumour cells52. In addition, several studies have shown that higher vagal modulation measured by HRV is associated with longer survival and lower level of C-reactive protein54. Our study extends the results of previous studies, associating vagal modulation  with RCIs, and reveals the inhibitory effect of vagus nerve on blood coagulation in BC patients. It is thus of great significance to understand the beneficial role of the vagal activity in cancer. Further studies are warranted to examine whether an intervention on vagal activity can prevent coagulopathy, slow down the progression of cancer, and improve the prognosis of patients. Besides, the physiological mechanism connecting the ANS with coagulation function is unclear. Surgery, radiotherapy, chemotherapy or other treatments may also change the coagulation function of BC patients56. How the associations between RCIs and HRV change during and after the treatment in BC patients also required further elucidation.In conclusion, previous studies showed that vagus nerve modulation was associated with cancer prognosis. Erin et al. showed that anti-inflammatory drugs that activate the vagus nerve reduced tumor metastasis in tumor-bearing mice