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
r
k, time constant in weeks
τ
k, 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;87:383–393 Ann Neurol 2020;87:383–393
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.
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.
Abstract
Individualized prediction is a hallmark of clinical medicine and decision making. However, most existing prediction models rely on biomarkers and clinical outcomes available at a single ...time. This is in contrast to how health states progress and how physicians deliver care, which relies on progressively updating a prognosis based on available information. With the use of joint models of longitudinal and survival data, it is possible to dynamically adjust individual predictions regarding patient prognosis. This article aims to introduce the reader to the development of dynamic risk predictions and to provide the necessary resources to support their implementation and assessment, such as adaptable R code, and the theory behind the methodology. Furthermore, measures to assess the predictive performance of the derived predictions and extensions that could improve the predictions are presented. We illustrate personalized predictions using an online dataset consisting of patients with chronic liver disease (primary biliary cirrhosis).
Background
The extent to which environmental exposures and community characteristics of the built environment collectively predict rapid lung function decline, during adolescence and early adulthood ...in cystic fibrosis (CF), has not been examined.
Objective
To identify built environment characteristics predictive of rapid CF lung function decline.
Methods
We performed a retrospective, single‐center, longitudinal cohort study (n = 173 individuals with CF aged 6–20 years, 2012–2017). We used a stochastic model to predict lung function, measured as forced expiratory volume in 1 s (FEV1) of % predicted. Traditional demographic/clinical characteristics were evaluated as predictors. Built environmental predictors included exposure to elemental carbon attributable to traffic sources (ECAT), neighborhood material deprivation (poverty, education, housing, and healthcare access), greenspace near the home, and residential drivetime to the CF center.
Measurements and Main Results
The final model, which included ECAT, material deprivation index, and greenspace, alongside traditional demographic/clinical predictors, significantly improved fit and prediction, compared with only demographic/clinical predictors (Likelihood Ratio Test statistic: 26.78, p < 0.0001; the difference in Akaike Information Criterion: 15). An increase of 0.1 μg/m3 of ECAT was associated with 0.104% predicted/yr (95% confidence interval: 0.024, 0.183) more rapid decline. Although not statistically significant, material deprivation was similarly associated (0.1‐unit increase corresponded to additional decline of 0.103% predicted/year −0.113, 0.319). High‐risk regional areas of rapid decline and age‐related heterogeneity were identified from prediction mapping.
Conclusion
Traffic‐related air pollution exposure is an important predictor of rapid pulmonary decline that, coupled with community‐level material deprivation and routinely collected demographic/clinical characteristics, enhance CF prognostication and enable personalized environmental health interventions.
Objective
To evaluate lung disease progression using airway and artery (AA) dimensions on chest CT over 2‐year interval in young CF patients longitudinally and compare to disease controls ...cross‐sectionally.
Methods
Retrospective analysis of pressure controlled end‐inspiratory CTs, 12 routine baseline (CT1) and follow up (CT2) from AREST CF cohort; 12 disease controls with normal CT. All visible AA‐pairs were measured perpendicular to the airway axis. Inner and outer airway diameters and wall (outer‐inner radius) thickness were divided by adjacent arteries to compute AinA‐, AoutA‐, and AWTA‐ratios, respectively. Differences between CF and control data were assessed using mixed effects models predicting AA‐ratios per segmental generation (SG). Power calculations were performed with 80% power and ɑ = 0.05.
Results
CF, median age CT1 2 years; CT2 3.9 years, 5 males. Controls, median age 2.9 years, 10 males. Total of 4798 AA‐pairs measured. Cross‐sectionally: AinA‐ratio showed no difference between controls and CF CT1 or CT2. AoutA‐ratio was significantly higher in CF CT1 (SG 2‐4) and CT2 (SG 2‐5) compared to controls. AWTA‐ratio was increased for CF CT1 (SG 1‐5) and CT2 (SG 2‐6) compared to controls. CF longitudinally: AinA‐ratio was significantly higher at CT2 compared to CT1. Increase in AoutA‐ratio at CT2 compared to CT1 was visible in SG ≥4. Sample sizes of 21 and 58 would be necessary for 50% and 30% AoutA‐ratio reductions, respectively, between CF CT2 and controls.
Conclusion
AA‐ratio differences were present in young CF patients relative to disease controls. AoutA‐ratio as an objective parameter for bronchiectasis could reduce sample sizes for clinical trials.
Pompe disease is a rare, progressive, and metabolic myopathy. Reduced pulmonary function is one of the main problems seen in adult patients with late‐onset Pompe disease (LOPD). We aimed to explore ...the association between changes over time in pulmonary function and in patient‐reported outcome measures (PROMs), in these patients treated with enzyme replacement therapy (ERT). This is a post hoc analysis of two cohort studies. Pulmonary function was assessed as forced vital capacity in the upright position (FVCup). As PROMs, we assessed the physical component summary score (PCS) of the Medical Outcome Study 36‐item Short‐Form Health Survey (SF‐36) and daily life activities (Rasch‐Built Pompe‐Specific Activity R‐PACT scale). We fitted Bayesian multivariate mixed‐effects models. In the models of PROMs, we assumed a linear association with FVCup, and adjusted for time (nonlinear), sex, and age and disease duration at the start of ERT. One hundred and one patients were eligible for analysis. PCS and R‐PAct were positively associated with FVCup, while their relation with time was nonlinear (initial increase then decrease). A 1%‐point increase in FVCup is expected to increase PCS by 0.14 points (95% Credible Interval: 0.09;0.19) and R‐PACT by 0.41 points 0.33;0.49 at the same time point. In the first year of ERT, we expect a change of PCS and R‐PAct scores by +0.42 and +0.80 points, and in the 5th year of +0.16 and +0.45, respectively. We conclude that the physical domain of quality of life and daily life activities improve when FVCup increases during ERT.
Neurophysiologic correlates of motor learning that can be monitored during neurorehabilitation interventions can facilitate the development of more effective learning methods. Previous studies have ...focused on the role of the beta band (14–30 Hz) because of its clear response during motor activity. However, it is difficult to discriminate between beta activity related to learning a movement and performing the movement. In this study, we analysed differences in the electroencephalography (EEG) power spectra of complex and simple explicit sequential motor tasks in healthy young subjects. The complex motor task (CMT) allowed EEG measurement related to motor learning. In contrast, the simple motor task (SMT) made it possible to control for EEG activity associated with performing the movement without significant motor learning. Source reconstruction of the EEG revealed task-related activity from 5 clusters covering both primary motor cortices (M1) and 3 clusters localised to different parts of the cingulate cortex (CC). We found no association between M1 beta power and learning, but the CMT produced stronger bilateral beta suppression compared to the SMT. However, there was a positive association between contralateral M1 theta (5–8 Hz) and alpha (8–12 Hz) power and motor learning, and theta and alpha power in the posterior mid-CC and posterior CC were positively associated with greater motor learning. These findings suggest that the theta and alpha bands are more related to motor learning than the beta band, which might merely relate to the level of perceived difficulty during learning.
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 < .01). Preschool %disease and %MUPAT were significantly associated with school‐age %bronchiectasis (P < .01 and P < .01, respectively). No significant association was found between preschool %disease and %MUPAT and school‐age forced expiratory volume 1 (FEV1%) predicted and quality of life (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.
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