17 research outputs found

    Joint Modelling of Longitudinal and Survival Data with Applications in Heart Valve Data

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    __Abstract__ The heart is one of the most important organs in the entire human body. Specifically, it is a pump composed of muscle which pumps blood throughout the blood vessels to various parts of the body by repeated rhythmic contractions. The four heart valves determine the pathway of blood flow through the heart and they normally allow blood flow in only one direction through the heart. Moreover, they open or close incumbent upon differential blood pressure on each side. Specifically, the four valves are: the tricuspid valve, the pulmonary valve, the mitral valve and the aortic valve. Figure 1.1, represents graphically the heart anatomy. The blood flows from the right atrium to the right ventricle through the tricuspid valve. Thereafter, the blood flows through the pulmonary valve to the lungs, where oxygenation takes place. Next, the blood re-enters the heart into the left atrium, through the mitral valve into the left ventricle. Finally, it enters the aorta through the aortic valve. Another important part of the heart is the aortic root which connects the heart to the systemic circulation. Heart valve disease occurs when one or more valves are not functioning properly due to stenosis and/or regurgitation. Valve stenosis is the disease in which the opening of the valve is narrowed, while valve regurgitation or insufficiency is the leaking of the valve that causes blood to flow in the reverse direction during ventricular diastole. Echoca

    Multivariate joint modeling to identify markers of growth and lung function decline that predict cystic fibrosis pulmonary exacerbation onset

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    BACKGROUND: Attenuated decreases in lung function can signal the onset of acute respiratory events known as pulmonary exacerbations (PEs) in children and adolescents with cystic fibrosis (CF). Univariate joint modeling facilitates dynamic risk prediction of PE onset and accounts for measurement error of the lung function marker. However, CF is a multi-system disease and the extent to which simultaneously modeling growth and nutrition markers improves PE predictive accuracy is unknown. Furthermore, it is unclear which routinely collected clinical indicators of growth and nutrition in early life predict PE onset in CF. METHODS: Using a longitudinal cohort of 17,100 patients aged 6-20 years (US Cystic Fibrosis Foundation Patient Registry; 2003-2015), we fit a univariate joint model of lung-function decline and PE onset and contrasted its predictive performance with a class of multivariate joint models that included combinations of growth markers as additional submodels. Outcomes were longitudinal lung function (forced expiratory volume in 1 s of % predicted), percentiles of body mass index, weight-for-age and height-for-age and PE onset. Relevant demographic/clinical covariates were included in submodels. We implemented a univariate joint model of lung function and time-to-PE and four multivariate joint models including growth outcomes. RESULTS: All five joint models showed that declining lung function corresponded to slightly increased risk of PE onset (hazard ratio from univariate joint model: 0.97, P  0.70). None of the growth markers alongside lung function as outcomes in multivariate joint modeling appeared to have an association with hazard of PE. Jointly modeling only lung function and PE onset yielded the most accurate (area under the receiver-operator characteristic curve = 0.75) and precise (narrowest interquartile range) predictions. Dynamic predictions were accurate across forecast horizons (0.5, 1 and 2 years) and precision improved with age. CONCLUSIONS: Including growth markers via multivariate joint models did not yield gains in prediction performance, compared to a univariate joint model with lung function. Individualized dynamic predictions from joint modeling could enhance physician monitoring of CF disease progression by providing PE risk assessment over a patient's clinical course

    Integrating latent classes in the Bayesian shared parameter joint model of longitudinal and survival outcomes

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    Cystic fibrosis is a chronic lung disease requiring frequent lung-function monitoring to track acute respiratory events (pulmonary exacerbations). The association between lung-function trajectory and time-to-first exacerbation can be characterized using joint longitudinal-survival modeling. Joint models specified through the shared parameter framework quantify the strength of association between such outcomes but do not incorporate latent sub-populations reflective of heterogeneous disease progression. Conversely, latent class joint models explicitly postulate the existence of sub-populations but do not directly quantify the strength of association. Furthermore, choosing the optimal number of classes using established metrics like deviance information criterion is computationally intensive in complex models. To overcome these limitations, we integrate latent classes in the shared parameter joint model through a fully Bayesian approach. To choose the optimal number of classes, we construct a mixture model assuming more latent classes than present in the data, thereby asymptotically “emptying” superfluous latent classes, provided the Dirichlet prior on class proportions is sufficiently uninformative. Model properties are evaluated in simulation studies. Application to data from the US Cystic Fibrosis Registry supports the existence of three sub-populations corresponding to lung-function trajectories with high initial forced expiratory volume in 1 s (FEV1), rapid FEV1 decline, and low but steady FEV1 progression. The association between FEV1 and hazard of exacerbation was negative in each class, but magnitude varied

    Airway disease on chest computed tomography of preschool children with cystic fibrosis is associated with school-age bronchiectasis

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    Airway wall thickening and mucus plugging are important characteristics of cystic fibrosis (CF) lung disease in the first 5 years of life.The aim of this study is to investigate the association of lung disease in preschool children (age, 2-6) with bronchiectasis and other clinical outcome measures in the school age (age >7). Deidentified computed tomography-scans were annotated using Perth-Rotterdam annotated grid morphometric analysis for CF. Preschool %disease (a composite score of %airway wall thickening, %mucus plugging, and %bronchiectasis) and %MUPAT (a composite score of %airway wall thickening and %mucus plugging) were used as predictors for %bronchiectasis and several other school-age clinical outcomes. For statistical analysis, we used regression analysis, linear mixed-effects models and two-way mixed models. Sixty-one patients were included. %Disease increased significantly with age (P .05). Cross-sectional, %disease in school-age was associated with a low FEV1% predicted and low quality of life (P =.01 and P =.007, respectively). %Disease can be considered an early marker of diffuse airways disease and is a risk factor for school-age bronchiectasis

    Dynamic prediction of outcome for patients with severe aortic stenosis: Application of joint models for longitudinal and time-to-event data

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    Background: Physicians utilize different types of information to predict patient prognosis. For example: confronted with a new patient suffering from severe aortic stenosis (AS), the cardiologist considers not only the severity of the AS but also patient characteristics, medical history, and markers such as BNP. Intuitively, doctors adjust their prediction of prognosis over time, with the change in clinical status, aortic valve area and BNP at each outpatient clinic visit. With the help of novel statistical approaches to model outcomes, it is now possible t

    Assessment of early lung disease in young children with CF: A comparison between pressure-controlled and free-breathing chest computed tomography

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    Background: Chest computed tomography (CT) in children with cystic fibrosis (CF) is sensitive in detecting early airways disease. The pressure-controlled CT-protocol combines a total lung capacity scan (TLC PC-CT) with a near functional residual capacity scan (FRC PC-CT) under general anesthesia, while another CT-protocol is acquired during free breathing (FB-CT) near functional residual capacity. The aim of this study was to evaluate the sensitivity in detecting airways disease of both protocols in two cohorts. Methods: Routine PC-CTs (Princess Margaret Children's Hospital) and FB-CTs (Erasmus MC—Sophia Children's Hospital) were retrospectively collected from CF children aged 2 to 6 years. Total airways disease (%disease), bronchiectasis (%Bx), and low attenuation regions (%LAR) were scored on CTs using the Perth-Rotterdam annotated grid morphometric analysis-CF method. The Wilcoxon signed-rank test was used for differences between TLC and FRC PC-CTs and the Wilcoxon rank-sum test for differences between FRC PC-CTs and FB-CTs. Results: Fifty patients with PC-CTs (21 male, aged 2.5-5.5 years) and 42 patients with FB-CTs (26 male, aged 2.3-6.8 years) were included. %Disease was higher on TLC PC-CTs compared with FRC PC-CTs (median 4.51 vs 2.49; P <.001). %Disease and %Bx were not significantly different between TLC PC-CTs and FB-CTs (median 4.51% vs 3.75%; P =.143 and 0.52% vs 0.57%; P =.849). %Disease, %Bx, and %LAR were not significantly different between FRC PC-CTs and FB-CTs (median 2.49% vs 3.75%; P =.055, 0.54% vs 0.57%; P =.797, and 2.49% vs 1.53%; P =.448). Conclusions: Our data suggest that FRC PC-CTs are less sensitive than TLC PC-CTs and that FB-CTs have similar sensitivity to PC-CTs in detecting lung disease. FB-CTs seem to be a viable alternative for PC-CTs to track CF lung disease in young patients with CF

    Smaller Foveal Avascular Zone in Deep Capillary Plexus Is Associated with Better Visual Acuity in Patients after Macula-off Retinal Detachment Surgery

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    Purpose: To associate the change in the foveal avascular zone (FAZ) and vessel density (VD) with final best corrected visual acuity (BCVA) in eyes after macula-off rhegmatogenous retinal detachment surgery, and to investigate the evolution of FAZ and VD during 12 months of follow-up. Methods: We prospectively evaluated 47 patients with macula-off rhegmatogenous retinal detachment and healthy fellow eyes. At 1.5, 3.0, 6.0, and 12.0 months postoperatively, optical coherence tomography angiography scans were obtained from both eyes on a 3.0 Ă— 3.0 mm macula-centered grid. En face images of the superficial vascular plexus, intermediate capillary plexus and deep capillary plexus were used to quantify FAZ and VD. BCVA was assessed with ETDRS-charts (logarithm of the minimal angle of resolution). At 12 months postoperatively, the association between the change in optical coherence tomography angiography parameters and visual function in study eyes was evaluated using the Spearman correlation coefficient. We calculated the BCVA difference and the percentage difference of FAZ and VD between the study and control eye. The evolution of FAZ and VD was investigated with linear mixed-effects models with nested random effects (eyes nested within patients). Results: At 12 months postoperatively, FAZ difference of the deep capillary plexus and BCVA difference were correlated (P = 0.0004, rs = 0.5). Furthermore, there was no evidence that FAZ and VD changed during follow-up. Conclusions: Although FAZ and VD remained stable during 12 months after surgery for macula-off rhegmatogenous retinal detachment, a smaller FAZ in the deep capillary plexus is associated with better BCVA. Translational relevance: Reduction in FAZ area may be caused by angiogenesis to counteract ischemia, therefore therapeutic stimulation of angiogenesis could be beneficial to visual recovery

    Predicting Upper Limb Motor Impairment Recovery after Stroke: A Mixture Model

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    Objective: Spontaneous recovery is an important determinant of upper extremity recovery after stroke and has been described by the 70% proportional recovery rule for the Fugl–Meyer motor upper extremity (FM-UE) scale. However, this rule is criticized for overestimating the predictability of FM-UE recovery. Our objectives were to develop a longitudinal mixture model of FM-UE recovery, identify FM-UE recovery subgroups, and internally validate the model predictions. Methods: We developed an exponential recovery function with the following parameters: subgroup assignment probability, proportional recovery coefficient rk, time constant in weeks τk, and distribution of the initial FM-UE scores. We fitted the model to FM-UE measurements of 412 first-ever ischemic stroke patients and cross-validated endpoint predictions and FM-UE recovery cluster assignment. Results: The model distinguished 5 subgroups with different recovery parameters (r1 = 0.09, τ1 = 5.3, r2 = 0.46, τ2 = 10.1, r3 = 0.86, τ3 = 9.8, r4 = 0.89, τ4 = 2.7, r5 = 0.93, τ5 = 1.2). Endpoint FM-UE was predicted with a median absolute error of 4.8 (interquartile range [IQR] = 1.3–12.8) at 1 week poststroke and 4.2 (IQR = 1.3–9.8) at 2 weeks. Overall accuracy of assignment to the poor (subgroup 1), moderate (subgroups 2 and 3), and good (subgroups 4 and 5) FM-UE recovery clusters was 0.79 (95% equal-tailed interval [ETI] = 0.78–0.80) at 1 week poststroke and 0.81 (95% ETI = 0.80–0.82) at 2 weeks. Interpretation: FM-UE recovery reflects different subgroups, each with its own recovery profile. Cross-validation indicates that FM-UE endpoints and FM-UE recovery clusters can be well predicted. Results will contribute to the understanding of upper limb recovery patterns in the first 6 months after stroke. ANN NEUROL 2020

    Chest computed tomography outcomes in a randomized clinical trial in cystic fibrosis: Lessons learned from the first ataluren phase 3 study

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    A phase 3 randomized double blind controlled, trial in 238 people with cystic fibrosis (CF) and at least one nonsense mutation (nmCF) investigated the effect of ataluren on FEV1. The study was of 48 weeks duration and failed to meet its primary endpoint. Unexpectedly

    Positive association between physical outcomes and patient-reported outcomes in late-onset Pompe disease: a cross sectional study

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    BACKGROUND: Pompe disease is a rare, progressive metabolic myopathy. The aim of this study is to investigate the associations of physical outcomes with patient-reported outcome measures (PROMs) in late-onset Pompe disease. METHODS: We included 121 Dutch adult patients with Pompe disease. Physical outcomes comprised muscle strength (manual muscle testing using Medical Research Council [MRC] grading, hand-held dynamometry [HHD]), walking ability (6-min walk test [6MWT]), and pulmonary function (forced vital capacity [FVC] in upright and supine positions). PROMs comprised quality of life (Short Form 36 health survey [SF-36]), participation (Rotterdam Handicap Scale [RHS]) and daily-life activities (Rasch-Built Pompe-Specific Activity [R-PAct] Scale). Analyses were cross-sectional: the time-point before, and closest to, start of Enzyme Replacement Therapy was chosen. Associations between PROMs and physical outcomes were investigated using linear regression models. RESULTS: RHS and R-PAct scores were better in patients with higher FVC supine and upright, HHD, MRC and 6MWT scores, accounting for the effect of sex, disease duration, use of wheelchair and ventilator support. While the SF-36 Physical Component Summary (PCS) was correlated positively with FVC upright, HHD, MRC and 6MWT scores, there was no significant relationship between the SF-36 Mental Component Summary (MCS) and any of the physical outcomes. CONCLUSIONS: Participation, daily-life activities, and the physical component of quality of life of adult Pompe patients are positively correlated to physical outcomes. This work serves as a first step towards assessing how changes over time in physical outcomes are related to changes in PROMs, and to define the minimal change in physical outcomes required to make an important difference for the patient
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