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

__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

Improved Dynamic Predictions from Joint Models of Longitudinal and Survival Data with Time-Varying Effects using P-splines

In the field of cardio-thoracic surgery, valve function is monitored over time after surgery. The motivation for our research comes from a study which includes patients who received a human tissue valve in the aortic position. These patients are followed prospectively over time by standardized echocardiographic assessment of valve function. Loss of follow-up could be caused by valve intervention or the death of the patient. One of the main characteristics of the human valve is that its durability is limited. Therefore, it is of interest to obtain a prognostic model in order for the physicians to scan trends in valve function over time and plan their next intervention, accounting for the characteristics of the data. Several authors have focused on deriving predictions under the standard joint modeling of longitudinal and survival data framework that assumes a constant effect for the coefficient that links the longitudinal and survival outcomes. However, in our case this may be a restrictive assumption. Since the valve degenerates, the association between the biomarker with survival may change over time. To improve dynamic predictions we propose a Bayesian joint model that allows a time-varying coefficient to link the longitudinal and the survival processes, using P-splines. We evaluate the performance of the model in terms of discrimination and calibration, while accounting for censoring

Dynamic Predictions with Time-Dependent Covariates in Survival Analysis using Joint Modeling and Landmarking

A key question in clinical practice is accurate prediction of patient prognosis. To this end, nowadays, physicians have at their disposal a variety of tests and biomarkers to aid them in optimizing medical care. These tests are often performed on a regular basis in order to closely follow the progression of the disease. In this setting it is of medical interest to optimally utilize the recorded information and provide medically-relevant summary measures, such as survival probabilities, that will aid in decision making. In this work we present and compare two statistical techniques that provide dynamically-updated estimates of survival probabilities, namely landmark analysis and joint models for longitudinal and time-to-event data. Special attention is given to the functional form linking the longitudinal and event time processes, and to measures of discrimination and calibration in the context of dynamic prediction.Comment: 34 pages, 4 figures. arXiv admin note: substantial text overlap with arXiv:1303.279

A Bayesian joint model for zero‐inflated integers and left‐truncated event times with a time‐varying association: Applications to senior health care

Population aging in most industrialized societies has led to a dramatic increase in emergency medical demand among the elderly. In the context of private health care, an optimal allocation of the medical resources for seniors is commonly done by forecasting their life spans. Accounting for each subject's particularities is therefore indispensable, so the available data must be processed at an individual level. We use a large and unique dataset of insured parties aged 65 and older to appropriately relate the emergency care usage with mortality risk. Longitudinal and time‐to‐event processes are jointly modeled, and their underlying relationship can therefore be assessed. Such an application, however, requires some special features to also be considered. First, longitudinal demand for emergency services exhibits a nonnegative integer response with an excess of zeros due to the very nature of the data. These subject‐specific responses are handled by a zero‐inflated version of the hierarchical negative binomial model. Second, event times must account for the left truncation derived from the fact that policyholders must reach the age of 65 before they may begin to be observed. Consequently, a delayed entry bias arises for those individuals entering the study after this age threshold. Third, and as the main challenge of our analysis, the association parameter between both processes is expected to be age‐dependent, with an unspecified association structure. This is well‐approximated through a flexible functional specification provided by penalized B‐splines. The parameter estimation of the joint model is derived under a Bayesian scheme

Diffusion weighted imaging in cystic fibrosis disease: beyond morphological imaging

To explore the feasibility of diffusion-weighted imaging (DWI) to assess inflammatory lung changes in patients with Cystic Fibrosis (CF) METHODS: CF patients referred for their annual check-up had spirometry, chest-CT and MRI on the same day. MRI was performed in a 1.5 T scanner with BLADE and EPI-DWI sequences (b = 0-600 s/mm(2)). End-inspiratory and end-expiratory scans were acquired in multi-row scanners. DWI was scored with an established semi-quantitative scoring system. DWI score was correlated to CT sub-scores for bronchiectasis (CF-CTBE), mucus (CF-CTmucus), total score (CF-CTtotal-score), FEV1, and BMI. T-test was used to assess differences between patients with and without DWI-hotspots

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

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

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

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

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

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

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 × 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

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