Cine and 4D flow cardiac MRI are two important non-invasive MR imaging techniques to assess cardiac function and diagnose cardiovascular diseases. Cine MRI offers great soft tissue detail which... Show moreCine and 4D flow cardiac MRI are two important non-invasive MR imaging techniques to assess cardiac function and diagnose cardiovascular diseases. Cine MRI offers great soft tissue detail which allows clinical experts to evaluate structure and function of the heart. 4D flow MRI further has the ability of three-dimensional time-resolved acquisition of blood flow velocity, which can be used to derive intra-cardiac hemodynamic parameters. In this thesis, we developed deep learning-based approaches to analyze cine and 4D flow cardiac MRI. This thesis proposes deep learning based methods for quantifying cardiac MRI. The described methods can be applied for cine MR image quality classification and ventricle segmentation without any human interactions. Investigating combining and fusing magnitude and velocity images can be helpful for left ventricle segmentation in 4D flow MRI, which is not fully explored yet. Moreover, we proposed a network to predict the blood flow pattern from the cine MRI. By combining visualization of the blood flow and myocardial motion in the routinely acquired standard CMR exams, the method can be potentially used in clinical studies. Show less
The validation of objective and easy-to-implement biomarkers that can monitor the effects of fast-acting drugs among Parkinson's disease (PD) patients would benefit antiparkinsonian drug... Show moreThe validation of objective and easy-to-implement biomarkers that can monitor the effects of fast-acting drugs among Parkinson's disease (PD) patients would benefit antiparkinsonian drug development. We developed composite biomarkers to detect levodopa/carbidopa effects and to estimate PD symptom severity. For this development, we trained machine learning algorithms to select the optimal combination of finger tapping task features to predict treatment effects and disease severity. Data were collected during a placebo-controlled, crossover study with 20 PD patients. The alternate index and middle finger tapping (IMFT), alternative index finger tapping (IFT), and thumb-index finger tapping (TIFT) tasks and the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) III were performed during treatment. We trained classification algorithms to select features consisting of the MDS-UPDRS III item scores; the individual IMFT, IFT, and TIFT; and all three tapping tasks collectively to classify treatment effects. Furthermore, we trained regression algorithms to estimate the MDS-UPDRS III total score using the tapping task features individually and collectively. The IFT composite biomarker had the best classification performance (83.50% accuracy, 93.95% precision) and outperformed the MDS-UPDRS III composite biomarker (75.75% accuracy, 73.93% precision). It also achieved the best performance when the MDS-UPDRS III total score was estimated (mean absolute error: 7.87, Pearson's correlation: 0.69). We demonstrated that the IFT composite biomarker outperformed the combined tapping tasks and the MDS-UPDRS III composite biomarkers in detecting treatment effects. This provides evidence for adopting the IFT composite biomarker for detecting antiparkinsonian treatment effect in clinical trials. & COPY; 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. Show less
Romano, F.; Cozzi, M.; Monteduro, D.; Oldani, M.; Boon, C.J.F.; Staurenghi, G.; Salvetti, A.P. 2023
Purpose:To describe the imaging characteristics and topographic expansion of retinal pigment epithelium (RPE) and outer retinal atrophy in extensive macular atrophy with pseudodrusen-like... Show morePurpose:To describe the imaging characteristics and topographic expansion of retinal pigment epithelium (RPE) and outer retinal atrophy in extensive macular atrophy with pseudodrusen-like appearance.Methods:Three-year, prospective, observational study. Nine patients with extensive macular atrophy with pseudodrusen-like appearance (17 eyes; 6 women) with no other ocular conditions were annually examined; one eye was excluded because of macular neovascularization. Best-corrected visual acuity measurement, fundus photographs, blue-light autofluorescence, and optical coherence tomography were performed at each visit. Formation of atrophy was analyzed on optical coherence tomography at foveal and extrafoveal areas following the Classification of Atrophy Meeting recommendations. Spatial enlargement throughout four sectors was assessed on blue-light autofluorescence after placing an Early Treatment for Diabetic Retinopathy Study grid centered on the foveola.Results:Mean age was 53.0 +/- 2.1 years at baseline with a follow-up of 36.6 +/- 0.7 months. Thinning of the outer nuclear layer and disruption of the ellipsoid zone initially appeared above areas of RPE-Bruch membrane separation and preceded RPE atrophy. Subfoveal fibrosis was seen in 65% of the eyes. Superior sector involvement was found in all patients at baseline and was significantly larger than the other sectors at any time point (P < 0.001). Best-corrected visual acuity declined from 68.0 +/- 15.7 letters to 44.8 +/- 14.9 letters during the follow-up and was significantly associated with subfoveal atrophy (P < 0.001) and fibrosis (P = 0.02).Conclusion:Our findings suggest that primary alterations in patients with extensive macular atrophy with pseudodrusen-like appearance are present at the outer segment-RPE interface, with the superior Early Treatment for Diabetic Retinopathy Study sector being the most vulnerable, which progresses to extensive atrophy of the RPE and outer retinal layers. Accordingly, we propose a three-stage disease classification. Show less
Central serous chorioretinopathy (CSC) has remained an enigmatic disease since its initial description by Von Graefe. Over the years, multiple risk factors have been recognized: these include... Show moreCentral serous chorioretinopathy (CSC) has remained an enigmatic disease since its initial description by Von Graefe. Over the years, multiple risk factors have been recognized: these include psychological stress, behavioral traits, and corticosteroids. The basic pathophysiology of CSC involves choroidal thickening, vascular congestion, altered choroidal blood flow (ChBF), and choroidal hyperpermeability, leading to retinal pigment epithelium decompensation and subsequent neurosensory detachment. Multiple organ systems, mainly the nervous, cardiovascular, endocrinal, and renal systems participate in the control of the vascular tone and the ChBF via hypothalamus-pituitary-adrenal axis and renin-angiotensin-aldosterone system, while others such as the hepatic system regulate the enzymatic degradation of corticosteroids. Many vasoactive and psychotropic drugs also modulate the ocular perfusion. In addition, there are anatomical and genetic predispositions that determine its progression to the chronic or recurrent form, through cellular response and angiogenesis. We herein review the basic pathophysiology and immunogenetics in CSC along with the role of multiple organ systems. With this background, we propose an etiological classification that should provide a framework for customized therapeutic interventions. Show less
Maleki, G.; Zhuparris, A.; Koopmans, I.; Doll, R.J.; Voet, N.; Cohen, A.; ... ; Maeyer, J. de 2022
Background: Facioscapulohumeral dystrophy (FSHD) is a progressive muscle dystrophy disorder leading to significant disability. Currently, FSHD symptom severity is assessed by clinical assessments... Show moreBackground: Facioscapulohumeral dystrophy (FSHD) is a progressive muscle dystrophy disorder leading to significant disability. Currently, FSHD symptom severity is assessed by clinical assessments such as the FSHD clinical score and the Timed Up-and-Go test. These assessments are limited in their ability to capture changes continuously and the full impact of the disease on patients' quality of life. Real-world data related to physical activity, sleep, and social behavior could potentially provide additional insight into the impact of the disease and might be useful in assessing treatment effects on aspects that are important contributors to the functioning and well-being of patients with FSHD.Objective: This study investigated the feasibility of using smartphones and wearables to capture symptoms related to FSHD based on a continuous collection of multiple features, such as the number of steps, sleep, and app use. We also identified features that can be used to differentiate between patients with FSHD and non-FSHD controls.Methods: In this exploratory noninterventional study, 58 participants (n=38, 66%, patients with FSHD and n=20, 34%, non-FSHD controls) were monitored using a smartphone monitoring app for 6 weeks. On the first and last day of the study period, clinicians assessed the participants' FSHD clinical score and Timed Up-and-Go test time. Participants installed the app on their Android smartphones, were given a smartwatch, and were instructed to measure their weight and blood pressure on a weekly basis using a scale and blood pressure monitor. The user experience and perceived burden of the app on participants' smartphones were assessed at 6 weeks using a questionnaire. With the data collected, we sought to identify the behavioral features that were most salient in distinguishing the 2 groups (patients with FSHD and non-FSHD controls) and the optimal time window to perform the classification.Results: Overall, the participants stated that the app was well tolerated, but 67% (39/58) noticed a difference in battery life using all 6 weeks of data, we classified patients with FSHD and non-FSHD controls with 93% accuracy, 100% sensitivity, and 80% specificity. We found that the optimal time window for the classification is the first day of data collection and the first week of data collection, which yielded an accuracy, sensitivity, and specificity of 95.8%, 100%, and 94.4%, respectively. Features relating to smartphone acceleration, app use, location, physical activity, sleep, and call behavior were the most salient features for the classification.Conclusions: Remotely monitored data collection allowed for the collection of daily activity data in patients with FSHD and non-FSHD controls for 6 weeks. We demonstrated the initial ability to detect differences in features in patients with FSHD and non-FSHD controls using smartphones and wearables, mainly based on data related to physical and social activity. Show less
Background: Deep Endometriosis (DE) classification studies with Enzian never compared solitary (A, B, C, F), and combinations of anatomical locations (A&B, A&C, B&C, A&B&C), in... Show moreBackground: Deep Endometriosis (DE) classification studies with Enzian never compared solitary (A, B, C, F), and combinations of anatomical locations (A&B, A&C, B&C, A&B&C), in correlation to pain. Therefore, the results of these studies are challenging to translate to the clinical situation.Objectives: We studied pain symptoms and their correlation with the solitary and combinations of anatomical locations of deep endometriosis lesion(s) classified by the Enzian score.Materials and Methods: A prospective multi-centre study was conducted with data from university and non -university hospitals. A total of 419 surgical DE cases were collected with the web-based application called EQUSUM (www.equsum.org).Main outcome measures: Preoperative reported numeric rating scale (NRS) were collected along with the Enzian classification. Baseline characteristics, pain scores, surgical procedure and extent of the disease were also collected.Results: In general, more extensive involvement of DE does not lead to an increase in the numerical rating scale for pain measures. However, dysuria and bladder involvement do show a clear correlation AUC 0.62 (SE 0.04, CI 0.54-0.71, p< 0.01). Regarding the predictive value of dyschezia, we found a weak, but significant correlation with ureteric involvement; AUC 0.60 (SE 0.04, CI 0.53-0.67, p< 0.01).Conclusions:TPain symptoms poorly correlate with anatomical locations of deep endometriosis in almost all pain scores, with the exception of bladder involvement and dysuria which did show a correlation. Also, dyschezia seems to have predictive value for DE ureteric involvement and therefore MRI or ultrasound imaging (ureter and kidney) could be recommended in the preoperative workup of these patients.What's new? Dyschezia might have a predictive value in detecting ureteric involvement. Show less
Hoof, J. van; Dikken, J.; Staalduinen, W.H. van; Pas, S. van der; Hoven, R.F.M. van den; Hulsebosch-Janssen, L.M.T. 2022
The sense of safety and security of older people is a widely acknowledged action domain for policy and practice in age-friendly cities. Despite an extensive body of knowledge on the matter, the... Show moreThe sense of safety and security of older people is a widely acknowledged action domain for policy and practice in age-friendly cities. Despite an extensive body of knowledge on the matter, the theory is fragmented, and a classification is lacking. Therefore, this study investigated how older people experience the sense of safety and security in an age-friendly city. A total of four focus group sessions were organised in The Hague comprising 38 older people. Based on the outcomes of the sessions, the sense of safety and security was classified into two main domains: a sense of safety and security impacted by intentional acts and negligence (for instance, burglary and violence), and a sense of safety and security impacted by non-intentional acts (for instance, incidents, making mistakes online). Both domains manifest into three separate contexts, namely the home environment, the outdoor environment and traffic and the digital environment. In the discussions with older people on these derived domains, ideas for potential improvements and priorities were also explored, which included access to information on what older people can do themselves to improve their sense of safety and security, the enforcement of rules, and continuous efforts to develop digital skills to improve safety online. Show less
Bokma, W.A.; Zhutovsky, P.; Giltay, E.J.; Schoevers, R.A.; Penninx, B.W.J.H.; Balkom, A.L.J.M. van; ... ; Wingen, G.A. van 2022
Background Disease trajectories of patients with anxiety disorders are highly diverse and approximately 60% remain chronically ill. The ability to predict disease course in individual patients... Show moreBackground Disease trajectories of patients with anxiety disorders are highly diverse and approximately 60% remain chronically ill. The ability to predict disease course in individual patients would enable personalized management of these patients. This study aimed to predict recovery from anxiety disorders within 2 years applying a machine learning approach. Methods In total, 887 patients with anxiety disorders (panic disorder, generalized anxiety disorder, agoraphobia, or social phobia) were selected from a naturalistic cohort study. A wide array of baseline predictors (N = 569) from five domains (clinical, psychological, sociodemographic, biological, lifestyle) were used to predict recovery from anxiety disorders and recovery from all common mental disorders (CMDs: anxiety disorders, major depressive disorder, dysthymia, or alcohol dependency) at 2-year follow-up using random forest classifiers (RFCs). Results At follow-up, 484 patients (54.6%) had recovered from anxiety disorders. RFCs achieved a cross-validated area-under-the-receiving-operator-characteristic-curve (AUC) of 0.67 when using the combination of all predictor domains (sensitivity: 62.0%, specificity 62.8%) for predicting recovery from anxiety disorders. Classification of recovery from CMDs yielded an AUC of 0.70 (sensitivity: 64.6%, specificity: 62.3%) when using all domains. In both cases, the clinical domain alone provided comparable performances. Feature analysis showed that prediction of recovery from anxiety disorders was primarily driven by anxiety features, whereas recovery from CMDs was primarily driven by depression features. Conclusions The current study showed moderate performance in predicting recovery from anxiety disorders over a 2-year follow-up for individual patients and indicates that anxiety features are most indicative for anxiety improvement and depression features for improvement in general. Show less
This International Consensus Classification and Nomenclature for the congenital bicuspid aortic valve condition recognizes 3 types of bicuspid valves: 1. The fused type (right-left cusp fusion,... Show moreThis International Consensus Classification and Nomenclature for the congenital bicuspid aortic valve condition recognizes 3 types of bicuspid valves: 1. The fused type (right-left cusp fusion, right-non-coronary cusp fusion and left-non-coronary cusp fusion phenotypes); 2. The 2-sinus type (latero-lateral and antero-posterior phenotypes); and 3. The partial-fusion (forme fruste) type. The presence of raphe and the symmetry of the fused type phenotypes are critical aspects to describe. The International Consensus also recognizes 3 types of bicuspid valve-associated aortopathy: 1. The ascending phenotype; 2. The root phenotype; and 3. Extended phe Show less
OBJECTIVES This study aimed to evaluate the prevalence and prognostic value of the extent of extra-aortic valvular cardiac abnormalities in a large multicenter registry of patients with moderate AS... Show moreOBJECTIVES This study aimed to evaluate the prevalence and prognostic value of the extent of extra-aortic valvular cardiac abnormalities in a large multicenter registry of patients with moderate AS.BACKGROUND The prognostic significance of a new classification system that incorporates the extent of cardiac injury (beyond the aortic valve) has been proposed in patients with severe aortic stenosis (AS). Whether this can be applied to patients with moderate AS is unclear.METHODS Based on the echocardiographic findings at the time of diagnosis of moderate AS (aortic valve area between 1.0 and 1.5 cm(2) and dimensionless velocity index ratio of >= 0.25), a total of 1,245 patients were included and analyzed retrospectively. They were recategorized into 5 groups according to the extent of extra-aortic valvular cardiac abnormalities: none (Group 0), involving the left ventricle (Group 1), the left atrial or mitral valve (Group 2), the pulmonary artery vasculature or tricuspid valve (Group 3), or the right ventricle (Group 4). Patients were followed for all-cause mortality and combined endpoint (all-cause mortality, stroke, heart failure, or myocardial infarction).RESULTS The distribution of patients according to the proposed classification was 13.1%, 26.8%, 42.6%, 10.6%, and 6.9% in Groups 0, 1, 2, 3, and 4, respectively. During a median follow-up of 4.3 (2.4 to 6.9) years, 564 (45.3%) patients died. There was a significant higher mortality rates with increasing extent of extra-aortic valvular cardiac abnormalities (log-rank p < 0.001). On multivariable analysis, the presence of extra-aortic valvular cardiac abnormalities remained independently associated with all-cause mortality and combined outcome, adjusted for aortic valve replacement as a time-dependent covariable. In particular, Group 2 and above were independently associated with all-cause mortality.CONCLUSIONS In patients with moderate AS, the presence of extra-aortic valvular cardiac abnormalities is associated with poor outcome. (C) 2021 Published by Elsevier on behalf of the American College of Cardiology Foundation. Show less
During the ISNS meeting "Newborn Screening for SCID 'State of the Art'" on 26 and 27 January 2021, the topic of case definitions and related issues were discussed. There is currently a lack of... Show moreDuring the ISNS meeting "Newborn Screening for SCID 'State of the Art'" on 26 and 27 January 2021, the topic of case definitions and related issues were discussed. There is currently a lack of uniform definitions and therefore a lack of uniform registration of screen-positive cases. This severely hampers the comparison of outcomes of different screening programs and the exchange of experiences gained by the different countries performing SCID screening, which is essential to improve screening programs. In this letter, I outline the current situation and indicate the need for uniform definitions and classification, which in my view needs to be a joined effort of screeners and immunologists. Show less
The classification of the central disorders of hypersomnolence has undergone multiple iterations in an attempt to capture biologically meaningful disease entities in the absence of known... Show moreThe classification of the central disorders of hypersomnolence has undergone multiple iterations in an attempt to capture biologically meaningful disease entities in the absence of known pathophysiology. Accumulating data suggests that further refinements may be necessary. At the 7th International Symposium on Narcolepsy, a group of clinician-scientists evaluated data in support of keeping or changing classifications, and as a result suggest several changes. First, idiopathic hypersomnia with long sleep durations appears to be an identifiable and meaningful disease subtype. Second, idiopathic hypersomnia without long sleep time and narcolepsy without cataplexy share substantial phenotypic overlap and cannot reliably be distinguished with current testing, and so combining them into a single disease entity seems warranted at present. Moving forward, it is critical to phenotype patients across a wide variety of clinical and biological features, to aid in future refinements of disease classification. Show less
Traumatic brain injury (TBI) is currently classified as mild, moderate, or severe TBI by trichotomizing the Glasgow Coma Scale (GCS). We aimed to explore directions for a more refined... Show moreTraumatic brain injury (TBI) is currently classified as mild, moderate, or severe TBI by trichotomizing the Glasgow Coma Scale (GCS). We aimed to explore directions for a more refined multidimensional classification system. For that purpose, we performed a hypothesis-free cluster analysis in the Collaborative European NeuroTrauma Effectiveness Research for TBI (CENTER-TBI) database: a European all-severity TBI cohort (n = 4509). The first building block consisted of key imaging characteristics, summarized using principal component analysis from 12 imaging characteristics. The other building blocks were demographics, clinical severity, secondary insults, and cause of injury. With these building blocks, the patients were clustered into four groups. We applied bootstrap resampling with replacement to study the stability of cluster allocation. The characteristics that predominantly defined the clusters were injury cause, major extracranial injury, and GCS. The clusters consisted of 1451, 1534, 1006, and 518 patients, respectively. The clustering method was quite stable: the proportion of patients staying in one cluster after resampling and reclustering was 97.4% (95% confidence interval [CI]: 85.6-99.9%). These clusters characterized groups of patients with different functional outcomes: from mild to severe, 12%, 19%, 36%, and 58% of patients had unfavorable 6 month outcome. Compared with the mild and the upper intermediate cluster, the lower intermediate and the severe cluster received more key interventions. To conclude, four types of TBI patients may be defined by injury mechanism, presence of major extracranial injury and GCS. Describing patients according to these three characteristics could potentially capture differences in etiology and care pathways better than with GCS only. Show less
Feis, R.A.; Grond, J. van der; Bouts, M.J.R.J.; Panman, J.L.; Poos, J.M.; Schouten, T.M.; ... ; Rombouts, S.A.R.B. 2020
Frontotemporal dementia is a highly heritable and devastating neurodegenerative disease. About 10-20% of all frontotemporal dementia is caused by known pathogenic mutations, but a reliable tool to... Show moreFrontotemporal dementia is a highly heritable and devastating neurodegenerative disease. About 10-20% of all frontotemporal dementia is caused by known pathogenic mutations, but a reliable tool to predict clinical conversion in mutation carriers is lacking. In this retrospective proof-of-concept case-control study, we investigate whether MRI-based and cognition-based classifiers can predict which mutation carriers from genetic frontotemporal dementia families will develop symptoms ('convert') within 4 years. From genetic frontotemporal dementia families, we included 42 presymptomatic frontotemporal dementia mutation carriers. We acquired anatomical, diffusion-weighted imaging, and resting-state functional MRI, as well as neuropsychological data. After 4 years, seven mutation carriers had converted to frontotemporal dementia ('converters'), while 35 had not ('non-converters'). We trained regularized logistic regression models on baseline MRI and cognitive data to predict conversion to frontotemporal dementia within 4 years, and quantified prediction performance using area under the receiver operating characteristic curves. The prediction model based on fractional anisotropy, with highest contribution of the forceps minor, predicted conversion to frontotemporal dementia beyond chance level (0.81 area under the curve, family-wise error corrected P = 0.025 versus chance level). Other MRI-based and cognitive features did not outperform chance level. Even in a small sample, fractional anisotropy predicted conversion in presymptomatic frontotemporal dementia mutation carriers beyond chance level. After validation in larger data sets, conversion prediction in genetic frontotemporal dementia may facilitate early recruitment into clinical trials. Show less
Blanco, E.; Perez-Andres, M.; Arriba-Mendez, S.; Serrano, C.; Criado, I.; Pino-Molina, L. del; ... ; EuroFlow PID Grp 2019
Dementie is een verwoestende ziekte waar wereldwijd miljoenen mensen aan leiden. De meest voorkomende oorzaak van dementie is de ziekte van Alzheimer. Voor het ontwikkelen van effectieve... Show moreDementie is een verwoestende ziekte waar wereldwijd miljoenen mensen aan leiden. De meest voorkomende oorzaak van dementie is de ziekte van Alzheimer. Voor het ontwikkelen van effectieve behandelingen is het belangrijk om dementie in een vroeg stadium te detecteren. Traditioneel alzheimeronderzoek is voornamelijk gericht op groepsverschillen tussen patiënten en controles. Recent onderzoek is deels verschoven naar individuele classificatie met machine learning. In dit proefschrift onderzoeken we het gebruik van magnetic resonance imaging (MRI) voor automatische detectie van de ziekte van Alzheimer, en vroege detectie van cognitieve achteruitgang. In dit proefschrift laten we zien dat het combineren van MRI modaliteiten de classificatie kan verbeteren. Ook laten we zien dat diffusie MRI een goede maat is om alzheimer te diagnosticeren. Bij toepassing van dezelfde methoden op een groep presymptomatische gendragers die amyloïdangiopathie zullen ontwikkelen vonden we geen verschillen tussen de gendragers en controles. Tevens waren we niet in staat om cognitieve achteruitgang na 4 jaar te voorspellen in een groep ouderen met verhoogd risico op achteruitgang. Met MRI kunnen betrouwbare individuele uitspraken gedaan kan worden over patiënten, maar het is met de huidige methoden niet gevoelig voor vroege detectie van cognitieve achteruitgang. Show less
The multifactorial likelihood analysis method has demonstrated utility for quantitative assessment of variant pathogenicity for multiple cancer syndrome genes. Independent data types currently... Show moreThe multifactorial likelihood analysis method has demonstrated utility for quantitative assessment of variant pathogenicity for multiple cancer syndrome genes. Independent data types currently incorporated in the model for assessing BRCA1 and BRCA2 variants include clinically calibrated prior probability of pathogenicity based on variant location and bioinformatic prediction of variant effect, co-segregation, family cancer history profile, co-occurrence with a pathogenic variant in the same gene, breast tumor pathology, and case-control information. Research and clinical data for multifactorial likelihood analysis were collated for 1,395 BRCA1/2 predominantly intronic and missense variants, enabling classification based on posterior probability of pathogenicity for 734 variants: 447 variants were classified as (likely) benign, and 94 as (likely) pathogenic; and 248 classifications were new or considerably altered relative to ClinVar submissions. Classifications were compared with information not yet included in the likelihood model, and evidence strengths aligned to those recommended for ACMG/AMP classification codes. Altered mRNA splicing or function relative to known nonpathogenic variant controls were moderately to strongly predictive of variant pathogenicity. Variant absence in population datasets provided supporting evidence for variant pathogenicity. These findings have direct relevance for BRCA1 and BRCA2 variant evaluation, and justify the need for gene-specific calibration of evidence types used for variant classification. Show less
Vollema, E.M.; Amanullah, M.R.; Ng, A.C.T.; Bijl, P. van der; Prevedello, F.; Sin, Y.K.; ... ; Bax, J.J. 2019
BACKGROUND In severe aortic stenosis (AS), patients often show extra-aortic valvular injury. Recently, a new staging system for severe AS has been proposed on the basis of the extent of cardiac... Show moreBACKGROUND In severe aortic stenosis (AS), patients often show extra-aortic valvular injury. Recently, a new staging system for severe AS has been proposed on the basis of the extent of cardiac damage.OBJECTIVES The present study evaluated the prevalence and prognostic impact of these different stages of cardiac damage in a large, real-world, multicenter cohort of symptomatic severe AS patients.METHODS From the ongoing registries from 2 academic institutions, a total of 1,189 symptomatic severe AS patients were selected and retrospectively analyzed. According to the extent of cardiac damage on echocardiography, patients were classified as Stage 0 (no cardiac damage), Stage 1 (left ventricular damage), Stage 2 (mitral valve or left atrial damage), Stage 3 (tricuspid valve or pulmonary artery vasculature damage), or Stage 4 (right ventricular damage). Patients were followed for all-cause mortality and combined endpoint (all-cause mortality, stroke, and cardiac-related hospitalization).RESULTS On the basis of the proposed classification, 8% of patients were classified as Stage 0, 24% as Stage 1, 49% as Stage 2, 7% as Stage 3, and 12% as Stage 4. On multivariable analysis, cardiac damage was independently associated with all-cause mortality and combined outcome, although this was mainly determined by Stages 3 and 4.CONCLUSIONS In this large multicenter cohort of symptomatic severe AS patients, stage of cardiac injury as classified by a novel staging system was independently associated with all-cause mortality and combined endpoint, although this seemed to be predominantly driven by tricuspid valve or pulmonary artery vasculature damage (Stage 3) and right ventricular dysfunction (Stage 4). (C) 2019 by the American College of Cardiology Foundation. Show less
Bouts, M.J.R.J.; Grond, J. van der; Vernooij, M.W.; Koini, M.; Schouten, T.M.; Vos, F. de; ... ; Rombouts, S.A.R.B. 2019
