Synthetic data for annotation and extraction of family history information from clinical text

Background The limited availability of clinical texts for Natural Language Processing purposes is hindering the progress of the field. This article investigates the use of synthetic data for the annotation and automated extraction of family history information from Norwegian clinical text. We make use of incrementally developed synthetic clinical text describing patients’ family history relating to cases of cardiac disease and present a general methodology which integrates the synthetically produced clinical statements and annotation guideline development. The resulting synthetic corpus contains 477 sentences and 6030 tokens. In this work we experimentally assess the validity and applicability of the annotated synthetic corpus using machine learning techniques and furthermore evaluate the system trained on synthetic text on a corpus of real clinical text, consisting of de-identified records for patients with genetic heart disease. Results For entity recognition, an SVM trained on synthetic data had class weighted precision, recall and F1-scores of 0.83, 0.81 and 0.82, respectively. For relation extraction precision, recall and F1-scores were 0.74, 0.75 and 0.74. Conclusions A system for extraction of family history information developed on synthetic data generalizes well to real, clinical notes with a small loss of accuracy. The methodology outlined in this paper may be useful in other situations where limited availability of clinical text hinders NLP tasks. Both the annotation guidelines and the annotated synthetic corpus are made freely available and as such constitutes the first publicly available resource of Norwegian clinical text

Impact of transcatheter aortic valve implantation on mechanical dispersion

Objectives The physiological determinants of left ventricular (LV) mechanical dispersion (MD) are not fully explored. We aimed to investigate the impact of afterload reduction and changes in ventricular conduction on LV MD after transcatheter aortic valve implantation (TAVI). Methods Patients with severe aortic stenosis (AS) were examined in a prospective, repeated measures observational cohort study before and after an uncomplicated transfemoral TAVI in a single tertiary centre. LV MD was assessed by speckle tracking echocardiography. Valvulo-arterial impedance (ZVA) was used as a measure of global afterload. Results We included 140 consecutive patients (83±8 years old, 49% women, logistic EuroSCORE 16±10) with severe AS (valve area 0.7±0.2 cm 2 , mean transvalvular gradient 54±18 mm Hg) and a relatively preserved LV ejection fraction (52%±11%). After TAVI, we observed favourable changes in transvalvular gradients and ZVA in all patients. Compared with baseline, postprocedural MD was significantly lower in 108 patients with unchanged ventricular conduction (55±17 ms vs 51±17 ms, p=0.02) and higher in 28 patients with TAVI-induced left bundle branch block (51±13 ms vs 62±19 ms, p≤0.001). During 22±9 months observation, 22 patients died. Postprocedural MD was associated with mortality in a univariate Cox regression model (HR=1.24 (1.01–1.52), p<0.04, per 10 ms increase). Conclusions Isolated afterload reduction was associated with reduction of MD, while concomitant impairment of ventricular conduction resulted in a more pronounced MD after TAVI, indicating that loading conditions and conduction should be considered when evaluating MD. A pronounced postprocedural LV MD was associated with mortality

Classical mechanical dyssynchrony is rare in transcatheter aortic valve implantation-induced left bundle branch block

Aims Left bundle branch block (LBBB) is a frequent conduction abnormality after transcatheter aortic valve implantation (TAVI). We aimed to investigate how TAVI procedure related conduction abnormalities influence ventricular mechanics and prognosis, with particular focus on new-onset persistent LBBB. Methods and results A total of 140 consecutive patients with severe aortic stenosis (83 ± 8 years old, 49% women) undergoing TAVI in a single tertiary centre were included in a repeated measures study. Changes in myocardial function and contraction patterns were investigated in relation to changes in electrical conduction and afterload by speckle tracking echocardiography. Whether patients with new-onset LBBB acquired classical dyssynchronous contractions was assessed by longitudinal strain in apical four-chamber view. Global longitudinal strain improvement was seen in all patients (−15.1 ± 4.3 vs. −16.1 ± 3.9%, P < 0.01, n = 140), and all subgroups, regardless of pre-existing or procedure-acquired conduction abnormalities immediately after TAVI. New-onset LBBB fulfilling strict electrocardiogram (ECG) criteria was observed in 28 patients (20%). The vast majority of new-onset LBBB patients (n = 26, 93%) had homogenous contractions. Classical dyssynchronous LBBB contractions were only observed in 2 patients (7%) with new-onset LBBB. Patients with new-onset LBBB and patients without acquired conduction disorders had similar mortality rates during 19 ± 9 months of follow-up [11.1, 95% confidence interval (CI) 4.6–26.8 vs. 8.1, 95% CI 4.8–13.7 per 100 patients years, P = 0.53]. Conclusion Classical dyssynchronous LBBB contractions were absent in most patients with new-onset post-TAVI LBBB, even when applying strict ECG criteria. Patients with and without new-onset LBBB experienced similar prognosis with regards to mortality

Two methods for modifed Doo-Sabin modeling of nonsmooth surfaces - Applied to right ventricle modeling

Purpose: In recent years, there has been increased clinical interest in the right ventricle (RV) of the heart. RV dysfunction is an important prognostic marker for several cardiac diseases. Accurate modeling of the RV shape is important for estimating the performance. We have created computationally effective models that allow for accurate estimation of the RV shape. Approach: Previous approaches to cardiac shape modeling, including modeling the RV geometry, has used Doo–Sabin surfaces. Doo–Sabin surfaces allow effective computation and adapt to smooth, organic surfaces. However, they struggle with modeling sharp corners or ridges without many control nodes. We modified the Doo–Sabin surface to allow for sharpness using weighting of vertices and edges instead. This was done in two different ways. For validation, we compared the standard Doo–Sabin versus the sharp Doo–Sabin models in modeling the RV shape of 16 cardiac ultrasound images, against a ground truth manually drawn by a cardiologist. A Kalman filter fitted the models to the ultrasound images, and the difference between the volume of the model and the ground truth was measured. Results: The two modified Doo–Sabin models both outperformed the standard Doo–Sabin model in modeling the RV. On average, the regular Doo–Sabin had an 8-ml error in volume, whereas the sharp models had 7- and 6-ml error, respectively. Conclusions: Compared with the standard Doo–Sabin, the modified Doo–Sabin models can adapt to a larger variety of surfaces while still being compact models. They were more accurate on modeling the RV shape and could have uses elsewhere

Cardiac Structure and Function in Epilepsy Patients with Drug-Resistant Convulsive Seizures

High frequency of convulsive seizures and long-lasting epilepsy are associated with an increased risk of sudden unexpected death in epilepsy (SUDEP). Structural changes in the myocardium have been described in SUDEP victims. It is speculated that these changes are secondary to frequent convulsive seizures and may predispose to SUDEP. The aim of this cross-sectional study was to investigate the impact of chronic drug-resistant epilepsy on cardiac function and structure in patients with a high frequency of convulsive seizures. We consecutively included 21 patients (17 women, 4 men) aged 18–40 years, with at least 10 years with epilepsy and a minimum of six convulsive seizures in the last year and without a history of status epilepticus or nonepileptic events. A complete clinical examination, resting 12-lead electrocardiogram, 72-h Holter monitoring, and echocardiography were recorded in all patients. Ten patients were assessed by 3-Tesla cardiac magnetic resonance imaging. Echocardiography and MRI data were compared with those from age- and sex-matched healthy control individuals. No significant changes in cardiac structure or function were found among patients with chronic drug-resistant epilepsy and high frequency of convulsive seizures. However, we cannot exclude that there are subgroups of patients who are more prone to epilepsy-associated cardiac alterations

Cardiac Structure and Function in Epilepsy Patients with Drug-Resistant Convulsive Seizures

High frequency of convulsive seizures and long-lasting epilepsy are associated with an increased risk of sudden unexpected death in epilepsy (SUDEP). Structural changes in the myocardium have been described in SUDEP victims. It is speculated that these changes are secondary to frequent convulsive seizures and may predispose to SUDEP. The aim of this cross-sectional study was to investigate the impact of chronic drug-resistant epilepsy on cardiac function and structure in patients with a high frequency of convulsive seizures. We consecutively included 21 patients (17 women, 4 men) aged 18–40 years, with at least 10 years with epilepsy and a minimum of six convulsive seizures in the last year and without a history of status epilepticus or nonepileptic events. A complete clinical examination, resting 12-lead electrocardiogram, 72-h Holter monitoring, and echocardiography were recorded in all patients. Ten patients were assessed by 3-Tesla cardiac magnetic resonance imaging. Echocardiography and MRI data were compared with those from age- and sex-matched healthy control individuals. No significant changes in cardiac structure or function were found among patients with chronic drug-resistant epilepsy and high frequency of convulsive seizures. However, we cannot exclude that there are subgroups of patients who are more prone to epilepsy-associated cardiac alterations

Cardiac Structure and Function in Epilepsy Patients with Drug-Resistant Convulsive Seizures

High frequency of convulsive seizures and long-lasting epilepsy are associated with an increased risk of sudden unexpected death in epilepsy (SUDEP). Structural changes in the myocardium have been described in SUDEP victims. It is speculated that these changes are secondary to frequent convulsive seizures and may predispose to SUDEP. The aim of this cross-sectional study was to investigate the impact of chronic drug-resistant epilepsy on cardiac function and structure in patients with a high frequency of convulsive seizures. We consecutively included 21 patients (17 women, 4 men) aged 18–40 years, with at least 10 years with epilepsy and a minimum of six convulsive seizures in the last year and without a history of status epilepticus or nonepileptic events. A complete clinical examination, resting 12-lead electrocardiogram, 72-h Holter monitoring, and echocardiography were recorded in all patients. Ten patients were assessed by 3-Tesla cardiac magnetic resonance imaging. Echocardiography and MRI data were compared with those from age- and sex-matched healthy control individuals. No significant changes in cardiac structure or function were found among patients with chronic drug-resistant epilepsy and high frequency of convulsive seizures. However, we cannot exclude that there are subgroups of patients who are more prone to epilepsy-associated cardiac alterations

Lamin A/C cardiomyopathy: Young onset, high penetrance, and frequent need for heart transplantation

Aims: Lamin A/C (LMNA) mutations cause familial dilated cardiomyopathy (DCM) with frequent conduction blocks and arrhythmias. We explored the prevalence, cardiac penetrance, and expressivity of LMNA mutations among familial DCM in Norway. Furthermore, we explored the risk factors and the outcomes in LMNA patients. Methods and results: During 2003–15, genetic testing was performed in patients referred for familial DCM. LMNA genotype-positive subjects were examined by electrocardiography, Holter monitoring, cardiac magnetic resonance imaging, and echocardiography. A positive cardiac phenotype was defined as the presence of atrioventricular (AV) block, atrial fibrillation/flutter (AF), ventricular tachycardia (VT), and/or echocardiographic DCM. Heart transplantation was recorded and compared with non-ischaemic DCM of other origin. Of 561 unrelated familial DCM probands, 35 (6.2%) had an LMNA mutation. Family screening diagnosed an additional 93 LMNA genotype-positive family members. We clinically followed up 79 LMNA genotype-positive [age 42 ± 16 years, ejection fraction (EF) 45 ± 13%], including 44 (56%) with VT. Asymptomatic LMNA genotype-positive family members (age 31 ± 15 years) had a 9% annual incidence of a newly documented cardiac phenotype and 61% (19/31) of cardiac penetrance during 4.4 ± 2.9 years of follow-up. Ten (32%) had AV block, 7 (23%) AF, and 12 (39%) non-sustained VT. Heart transplantation was performed in 15 of 79 (19%) LMNA patients during 7.8 ± 6.3 years of follow-up. Conclusion: LMNA mutation prevalence was 6.2% of familial DCM in Norway. Cardiac penetrance was high in young asymptomatic LMNA genotype-positive family members with frequent AV block and VT, highlighting the importance of early family screening and cardiological follow-up. Nearly 20% of the LMNA patients required heart transplantation

Prevalence and prognostic significance of hyponatremia in patients with acute exacerbation of chronic obstructive pulmonary disease: Data from the akershus cardiac examination (ACE) 2 study

Background Hyponatremia is prevalent and associated with mortality in patients with heart failure (HF). The prevalence and prognostic implications of hyponatremia in acute exacerbation of chronic obstructive pulmonary (AECOPD) have not been established. Method We included 313 unselected patients with acute dyspnea who were categorized by etiology of dyspnea according to established guidelines (derivation cohort). Serum Na+ was determined on hospital admission and corrected for hyperglycemia, and hyponatremia was defined as [Na+]<137 mmol/L. Survival was ascertained after a median follow-up of 816 days and outcome was analyzed in acute HF (n = 143) and AECOPD (n = 83) separately. Results were confirmed in an independent AECOPD validation cohort (n = 99). Results In the derivation cohort, median serum Na+ was lower in AECOPD vs. acute HF (138.5 [135.9–140.5] vs. 139.2 [136.7–141.3] mmol/L, p = 0.02), while prevalence of hyponatremia (27% [22/83] vs. 20% [29/143], p = 0.28) and mortality rate (42% [35/83] vs. 46% [66/143], p = 0.56) were similar. By univariate Cox regression analysis, hyponatremia was associated with increased mortality in acute HF (HR 1.85 [95% CI 1.08, 3.16], p = 0.02), but not in AECOPD (HR 1.00 [0.47, 2.15], p = 1.00). Analogous to the results of the derivation cohort, hyponatremia was prevalent also in the AECOPD validation cohort (25% [25/99]), but not associated with mortality. The diverging effect of hyponatremia on outcome between AECOPD and acute HF was statistically significant (p = 0.04). Conclusion Hyponatremia is prevalent in patients with acute HF and AECOPD, but is associated with mortality in patients with acute HF only

Additional file 1 of Deep learning for automated left ventricular outflow tract diameter measurements in 2D echocardiography

Additional file 1: Supplemental Table 1. Distribution of patients and echocardiographic views in the training and test set. Supplemental Table 2. Distribution of repeated LVOTd measurements for patients in the training and test set. Supplemental Matrix 1. Data quality labels for the training set. Supplemental Matrix 2. Data quality labels for the test set. Supplemental Table 3. Means and medians from 5-fold validation of common data extension methods and alternate loss functions. Best values are marked in bold. Supplemental Table 4. Means and medians from 5-fold validation of various data configurations for model training. Best values are marked in bold. Supplemental Figure 1. Bland-Altman plot comparing the DL predicted and clinical reference LVOTds in pixel values. Limits of agreement were -6.43 to 6.30 pixels. The grey dotted line denotes the signed mean. The red dotted lines denote the limits of agreement. High, Medium and Low denote the image quality of the echocardiographic still frame. Supplemental Figure 2. Correlation plot comparing the DL predicted and clinical reference LVOTds in pixel values. Pearson R was 0.97 (p < 0.001). The grey line denotes the reference. The red line denotes best fit. High, Medium and Low denote the image quality of the echocardiographic still frame. Supplemental Table 5. Absolute- and relative LVOTd errors for the DL model on the test set derived from pixel values. Supplemental Table 6. Mean point-wise ED in pixels and angle deviation for the DL model on the test set derived from pixel values. Supplemental Figure 3. Box-plots comparing precision for the DL model and clinicians for patients with exactly 3 repeated LVOTd measurements in the test set. Solid boxes represent the interquartile range. The whiskers represent the upper and lower adjacent values. The dots represent the outliers. Supplemental Table 7. Precision with coefficient of variation for the DL model and clinicians for patients with exactly 3 repeated LVOTd measurements in the test set. Supplemental Figure 4. Box-plots showing the individual effect of image quality and ground truth quality on LVOTd error for the DL predictions on the test set. Solid boxes represent the interquartile range. The whiskers represent the upper and lower adjacent values. Supplemental Table 8. Absolute- and relative LVOTd errors for the DL model on images the test set in grouped by individual image and ground truth quality labels. Supplemental Figure 5. Line-plot showing the effect of patient number and use of data extension methods during DL model training on mean absolute LVOTd error for DL predictions on the test set. Supplemental Table 9. Absolute LVOTd error and mean point-wise ED for a DL implementation using a standard U-Net. Only image augmentations were used as data extension methods since a standard U-Net does not have pre-trained ImageNet weights. Supplemental Table 10. Absolute LVOTd error and mean point-wise ED for a DL implementation using a U-Net with a ResNet50 encoder. Both image augmentations and pre-trained ImageNet weights were used as data extension methods