Structural Lung Disease and Clinical Phenotype in Bronchiectasis Patients: The EMBARC CT Study.

Rationale: Chest computed tomography (CT) scans are essential to diagnose and monitor bronchiectasis (BE). To date, few quantitative data are available about the nature and extent of structural lung abnormalities (SLAs) on CT scans of patients with BE. Objectives: To investigate SLAs on CT scans of patients with BE and the relationship of SLAs to clinical features using the EMBARC (European Multicenter Bronchiectasis Audit and Research Collaboration) registry. Methods: CT scans from patients with BE included in the EMBARC registry were analyzed using the validated Bronchiectasis Scoring Technique for CT (BEST-CT). The subscores of this instrument are expressed as percentages of total lung volume. The items scored are atelectasis/consolidation, BE with and without mucus plugging (MP), airway wall thickening, MP, ground-glass opacities, bullae, airways, and parenchyma. Four composite scores were calculated: total BE (i.e., BE with and without MP), total MP (i.e., BE with MP plus MP alone), total inflammatory changes (i.e., atelectasis/consolidation plus total MP plus ground-glass opacities), and total disease (i.e., all items but airways and parenchyma). Measurements and Main Results: CT scans of 524 patients with BE were analyzed. Mean subscores were 4.6 (range, 2.3-7.7) for total BE, 4.2 (1.2-8.1) for total MP, 8.3 (3.5-16.7) for total inflammatory changes, and 14.9 (9.1-25.9) for total disease. BE associated with primary ciliary dyskinesia was associated with more SLAs, whereas chronic obstructive pulmonary disease was associated with fewer SLAs. Lower FEV1, longer disease duration, Pseudomonas aeruginosa and nontuberculous mycobacterial infections, and severe exacerbations were all independently associated with worse SLAs. Conclusions: The type and extent of SLAs in patients with BE are highly heterogeneous. Strong relationships between radiological disease and clinical features suggest that CT analysis may be a useful tool for clinical phenotyping.

Predicting lung function decline in cystic fibrosis: the impact of initiating ivacaftor therapy.

BACKGROUND: Modulator therapies that seek to correct the underlying defect in cystic fibrosis (CF) have revolutionized the clinical landscape. Given the heterogeneous nature of lung disease progression in the post-modulator era, there is a need to develop prediction models that are robust to modulator uptake. METHODS: We conducted a retrospective longitudinal cohort study of the CF Foundation Patient Registry (N = 867 patients carrying the G551D mutation who were treated with ivacaftor from 2003 to 2018). The primary outcome was lung function (percent predicted forced expiratory volume in 1 s or FEV1pp). To characterize the association between ivacaftor initiation and lung function, we developed a dynamic prediction model through covariate selection of demographic and clinical characteristics. The ability of the selected model to predict a decline in lung function, clinically known as an FEV1-indicated exacerbation signal (FIES), was evaluated both at the population level and individual level. RESULTS: Based on the final model, the estimated improvement in FEV1pp after ivacaftor initiation was 4.89% predicted (95% confidence interval [CI]: 3.90 to 5.89). The rate of decline was reduced with ivacaftor initiation by 0.14% predicted/year (95% CI: 0.01 to 0.27). More frequent outpatient visits prior to study entry and being male corresponded to a higher overall FEV1pp. Pancreatic insufficiency, older age at study entry, a history of more frequent pulmonary exacerbations, lung infections, CF-related diabetes, and use of Medicaid insurance corresponded to lower FEV1pp. The model had excellent predictive accuracy for FIES events with an area under the receiver operating characteristic curve of 0.83 (95% CI: 0.83 to 0.84) for the independent testing cohort and 0.90 (95% CI: 0.89 to 0.90) for 6-month forecasting with the masked cohort. The root-mean-square errors of the FEV1pp predictions for these cohorts were 7.31% and 6.78% predicted, respectively, with standard deviations of 0.29 and 0.20. The predictive accuracy was robust across different covariate specifications. CONCLUSIONS: The methods and applications of dynamic prediction models developed using data prior to modulator uptake have the potential to inform post-modulator projections of lung function and enhance clinical surveillance in the new era of CF care.

Establishing how much improvement in lung function and distance walked is clinically important for adult patients with Pompe disease.

BACKGROUND AND PURPOSE: Pompe disease is a rare, inheritable, progressive metabolic myopathy. This study aimed to estimate the minimal clinically important difference (MCID) for an improvement in forced vital capacity in the upright seated position (FVCup) and the 6-min walk test (6MWT) after a year of treatment with enzyme replacement therapy. METHODS: Data were obtained from two prospective follow-up studies. Between-group and within-group MCIDs were estimated using anchor-based methods. Additionally, a distribution-based method was used to generate supportive evidence. As anchors, self-reported change in health and in physical functioning, shortness of breath and a categorization of the Short-Form 36 Physical Component Summary score were used. Anchor appropriateness was assessed using Spearman correlations (absolute values ≥0.29) and a sufficient number of observations in each category. RESULTS: In all, 102 patients had at least one FVCup or 6MWT measurement during enzyme replacement therapy. Based on the anchors assessed as appropriate, the between-group MCID for an improvement in FVCup ranged from 2.47% to 4.83% points. For the 6MWT, it ranged from 0.35% to 7.47% points which is equivalent to a distance of 2.18-46.61 m and 1.97-42.13 m for, respectively, a man and a woman of age 50, height 1.75 m and weight 80 kg. The results of the distribution-based method were within these ranges when applied to change in the outcome values. CONCLUSION: The MCIDs for FVCup and 6MWT derived in this study can be used to interpret differences between and within groups of patients with Pompe disease in clinical trials and cohort studies.

Automatic analysis of bronchus-artery dimensions to diagnose and monitor airways disease in cystic fibrosis.

BACKGROUND: Cystic fibrosis (CF) lung disease is characterised by progressive airway wall thickening and widening. We aimed to validate an artificial intelligence-based algorithm to assess dimensions of all visible bronchus-artery (BA) pairs on chest CT scans from patients with CF. METHODS: The algorithm fully automatically segments the bronchial tree; identifies bronchial generations; matches bronchi with the adjacent arteries; measures for each BA-pair bronchial outer diameter (Bout), bronchial lumen diameter (Bin), bronchial wall thickness (Bwt) and adjacent artery diameter (A); and computes Bout/A, Bin/A and Bwt/A for each BA pair from the segmental bronchi to the last visible generation. Three datasets were used to validate the automatic BA analysis. First BA analysis was executed on 23 manually annotated CT scans (11 CF, 12 control subjects) to compare automatic with manual BA-analysis outcomes. Furthermore, the BA analysis was executed on two longitudinal datasets (Copenhagen 111 CTs, ataluren 347 CTs) to assess longitudinal BA changes and compare them with manual scoring results. RESULTS: The automatic and manual BA analysis showed no significant differences in quantifying bronchi. For the longitudinal datasets the automatic BA analysis detected 247 and 347 BA pairs/CT in the Copenhagen and ataluren dataset, respectively. A significant increase of 0.02 of Bout/A and Bin/A was detected for Copenhagen dataset over an interval of 2 years, and 0.03 of Bout/A and 0.02 of Bin/A for ataluren dataset over an interval of 48 weeks (all p<0.001). The progression of 0.01 of Bwt/A was detected only in the ataluren dataset (p<0.001). BA-analysis outcomes showed weak to strong correlations (correlation coefficient from 0.29 to 0.84) with manual scoring results for airway disease. CONCLUSION: The BA analysis can fully automatically analyse a large number of BA pairs on chest CTs to detect and monitor progression of bronchial wall thickening and bronchial widening in patients with CF.

HER2-Low Breast Cancer: Incidence, Clinicopathologic Features, and Survival Outcomes From Real-World Data of a Large Nationwide Cohort.

Patients with breast cancer (BC) with low levels of human epidermal growth factor receptor 2 (HER2) expression (HER2-low) could benefit from novel antibody-drug conjugates. However, there is conflicting information regarding the characteristics of HER2-low BC and its outcome. We assessed the clinicopathologic characteristics and outcomes of HER2-low BC using real-world data from the Dutch National Pathology Registry. This retrospective study incorporated all patients with primary invasive BC, without neoadjuvant therapy, reported in the Dutch National Pathology Registry synoptic reporting module between 2014 and 2022. HER2 status was categorized as HER2-0 (defined as an immunohistochemistry score of 0 according to the current American Society of Clinical Oncology/College of American Pathologists guidelines) or HER2-low (immunohistochemistry score 1+ or 2+ without amplification). Clinicopathologic characteristics and overall survival of HER2-low BC were compared with HER2-0, adjusted for estrogen receptor (ER) status. We included 65,035 patients with BC, resulting in 69,424 tumors. The proportion of HER2-low BC was 62% in the ER+ cohort and 38% in the ER- cohort. A substantial number of patients had a different HER2 category between the needle biopsy and the corresponding surgical resection (28%) or among multiple tumors (28%). After multivariable logistic analysis, HER2-low tumors were significantly associated with histologic subtype, a higher ER, and lower progesterone receptor expression in the ER+ cohort, whereas within the ER-cohort, HER2-low tumors were associated with a lower tumor grade. However, the absolute differences were limited, and there was no significant difference in overall survival between HER2-low and HER2-0 tumors within the ER+ or ER- cohort. The classification of HER2 expression (HER2-0 vs HER2-low) varies between biopsies and corresponding resection specimens and within multiple tumors in the same patient, which could affect clinical decision making in case only HER2-low cases are eligible for novel HER2-targeting agents. The limited follow-up time and the lack of substantial clinicopathologic differences between HER2-low and HER2-0-cases could explain the lack of differences in overall survival.

Association between changes in pulmonary function and in patient reported outcomes during enzyme therapy of adult patients with late-onset Pompe disease.

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.

Projecting the impact of triple CFTR modulator therapy on intravenous antibiotic requirements in cystic fibrosis using patient registry data combined with treatment effects from randomised trials.

BACKGROUND: Cystic fibrosis (CF) is a life-threatening genetic disease, affecting around 10 500 people in the UK. Precision medicines have been developed to treat specific CF-gene mutations. The newest, elexacaftor/tezacaftor/ivacaftor (ELEX/TEZ/IVA), has been found to be highly effective in randomised controlled trials (RCTs) and became available to a large proportion of UK CF patients in 2020. Understanding the potential health economic impacts of ELEX/TEZ/IVA is vital to planning service provision. METHODS: We combined observational UK CF Registry data with RCT results to project the impact of ELEX/TEZ/IVA on total days of intravenous (IV) antibiotic treatment at a population level. Registry data from 2015 to 2017 were used to develop prediction models for IV days over a 1-year period using several predictors, and to estimate 1-year population total IV days based on standards of care pre-ELEX/TEZ/IVA. We considered two approaches to imposing the impact of ELEX/TEZ/IVA on projected outcomes using effect estimates from RCTs: approach 1 based on effect estimates on FEV1% and approach 2 based on effect estimates on exacerbation rate. RESULTS: ELEX/TEZ/IVA is expected to result in significant reductions in population-level requirements for IV antibiotics of 16.1% (~17 800 days) using approach 1 and 43.6% (~39 500 days) using approach 2. The two approaches require different assumptions. Increased understanding of the mechanisms through which ELEX/TEZ/IVA acts on these outcomes would enable further refinements to our projections. CONCLUSIONS: This work contributes to increased understanding of the changing healthcare needs of people with CF and illustrates how Registry data can be used in combination with RCT evidence to estimate population-level treatment impacts.

Small airways targeted treatment with smart nebulizer technology could improve severe asthma in children: a retrospective analysis.

OBJECTIVE: Conventional inhaler devices have a low efficacy in targeting small airways. Smart nebulizers can be used to increase deposition to small airways by adjusting the flow and depth of each inhalation based on patients 'individual inspiratory capacity. We investigated whether targeting of high dose inhaled corticosteroids (ICS) to small airways with a smart nebulizer could reduce exacerbation rate in children with severe asthma (SA). METHODS: We conducted a retrospective study in children with SA using a smart nebulizer (Akita® Jet nebulizer) for the administration of high dose ICS in our outpatient clinic at the Erasmus MC - Sophia Children's Hospital. Clinical data before and after start of treatment were collected. The primary outcome was exacerbation rate, defined as: number of asthma exacerbations for which oral corticosteroid courses (OCS) were prescribed. The exacerbation rate 1 year before treatment was compared with the exacerbation rate 1 year after start of treatment. Secondary outcomes were changes in spirometry parameters, hospital admissions and medication use. RESULTS: Data on OCS use was available for 28/31 patients. Median number of asthma exacerbations requiring OCS courses 1 year before decreased from 2 (interquartile range(IQR) 2) to 0.5 (IQR 3) 1 year after treatment (p = 0.021). Hospital admission decreased from 1 (IQR 3) to 0 (IQR 1)(p = 0.028). FEV1, FEF25-75 and FEF75 were not significantly improved after one year of treatment with the smart nebulizer (p = 0.191; p = 0.248; p = 0.572). CONCLUSION: Targeting small airways with high dose ICS using a smart nebulizer resulted in a significant reduction in exacerbations requiring OCS after one year of treatment.

Machine Learning Can be Used to Predict Function but Not Pain After Surgery for Thumb Carpometacarpal Osteoarthritis.

BACKGROUND: Surgery for thumb carpometacarpal osteoarthritis is offered to patients who do not benefit from nonoperative treatment. Although surgery is generally successful in reducing symptoms, not all patients benefit. Predicting clinical improvement after surgery could provide decision support and enhance preoperative patient selection. QUESTIONS/PURPOSES: This study aimed to develop and validate prediction models for clinically important improvement in (1) pain and (2) hand function 12 months after surgery for thumb carpometacarpal osteoarthritis. METHODS: Between November 2011 and June 2020, 2653 patients were surgically treated for thumb carpometacarpal osteoarthritis. Patient-reported outcome measures were used to preoperatively assess pain, hand function, and satisfaction with hand function, as well as the general mental health of patients and mindset toward their condition. Patient characteristics, medical history, patient-reported symptom severity, and patient-reported mindset were considered as possible predictors. Patients who had incomplete Michigan Hand outcomes Questionnaires at baseline or 12 months postsurgery were excluded, as these scores were used to determine clinical improvement. The Michigan Hand outcomes Questionnaire provides subscores for pain and hand function. Scores range from 0 to 100, with higher scores indicating less pain and better hand function. An improvement of at least the minimum clinically important difference (MCID) of 14.4 for the pain score and 11.7 for the function score were considered "clinically relevant." These values were derived from previous reports that provided triangulated estimates of two anchor-based and one distribution-based MCID. Data collection resulted in a dataset of 1489 patients for the pain model and 1469 patients for the hand function model. The data were split into training (60%), validation (20%), and test (20%) dataset. The training dataset was used to select the predictive variables and to train our models. The performance of all models was evaluated in the validation dataset, after which one model was selected for further evaluation. Performance of this final model was evaluated on the test dataset. We trained the models using logistic regression, random forest, and gradient boosting machines and compared their performance. We chose these algorithms because of their relative simplicity, which makes them easier to implement and interpret. Model performance was assessed using discriminative ability and qualitative visual inspection of calibration curves. Discrimination was measured using area under the curve (AUC) and is a measure of how well the model can differentiate between the outcomes (improvement or no improvement), with an AUC of 0.5 being equal to chance. Calibration is a measure of the agreement between the predicted probabilities and the observed frequencies and was assessed by visual inspection of calibration curves. We selected the model with the most promising performance for clinical implementation (that is, good model performance and a low number of predictors) for further evaluation in the test dataset. RESULTS: For pain, the random forest model showed the most promising results based on discrimination, calibration, and number of predictors in the validation dataset. In the test dataset, this pain model had a poor AUC (0.59) and poor calibration. For function, the gradient boosting machine showed the most promising results in the validation dataset. This model had a good AUC (0.74) and good calibration in the test dataset. The baseline Michigan Hand outcomes Questionnaire hand function score was the only predictor in the model. For the hand function model, we made a web application that can be accessed via https://analyse.equipezorgbedrijven.nl/shiny/cmc1-prediction-model-Eng/. CONCLUSION: We developed a promising model that may allow clinicians to predict the chance of functional improvement in an individual patient undergoing surgery for thumb carpometacarpal osteoarthritis, which would thereby help in the decision-making process. However, caution is warranted because our model has not been externally validated. Unfortunately, the performance of the prediction model for pain is insufficient for application in clinical practice. LEVEL OF EVIDENCE: Level III, therapeutic study.

Theta but not beta power is positively associated with better explicit motor task learning.

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.

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 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.

Computerised patient-specific prediction of the recovery profile of upper limb capacity within stroke services: the next step.

INTRODUCTION: Predicting upper limb capacity recovery is important to set treatment goals, select therapies and plan discharge. We introduce a prediction model of the patient-specific profile of upper limb capacity recovery up to 6 months poststroke by incorporating all serially assessed clinical information from patients. METHODS: Model input was recovery profile of 450 patients with a first-ever ischaemic hemispheric stroke measured using the Action Research Arm Test (ARAT). Subjects received at least three assessment sessions, starting within the first week until 6 months poststroke. We developed mixed-effects models that are able to deal with one or multiple measurements per subject, measured at non-fixed time points. The prediction accuracy of the different models was established by a fivefold cross-validation procedure. RESULTS: A model with only ARAT time course, finger extension and shoulder abduction performed as good as models with more covariates. For the final model, cross-validation prediction errors at 6 months poststroke decreased as the number of measurements per subject increased, from a median error of 8.4 points on the ARAT (Q1-Q3:1.7-28.1) when one measurement early poststroke was used, to 2.3 (Q1-Q3:1-7.2) for seven measurements. An online version of the recovery model was developed that can be linked to data acquisition environments. CONCLUSION: Our innovative dynamic model can predict real-time, patient-specific upper limb capacity recovery profiles up to 6 months poststroke. The model can use all available serially assessed data in a flexible way, creating a prediction at any desired moment poststroke, stand-alone or linked with an electronic health record system.

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.

Patients With Higher Treatment Outcome Expectations Are More Satisfied With the Results of Nonoperative Treatment for Thumb Base Osteoarthritis: A Cohort Study.

OBJECTIVE: To investigate how satisfaction with treatment outcome is associated with patient mindset and Michigan Hand Outcome Questionnaire (MHQ) scores at baseline and 3 months in patients receiving nonoperative treatment for first carpometacarpal joint (CMC-1) osteoarthritis (OA). DESIGN: Cohort study SETTING: A total of 20 outpatient locations of a clinic for hand surgery and hand therapy in the Netherlands. PARTICIPANTS: Patients (N=308) receiving nonoperative treatment for CMC-1 OA, including exercise therapy, an orthosis, or both, between September 2017 and February 2019. INTERVENTIONS: Nonoperative treatment (ie, exercise therapy, an orthosis, or both) MAIN OUTCOME MEASURES: Satisfaction with treatment outcomes was measured after 3 months of treatment. We measured total MHQ score at baseline and at 3 months. As baseline mindset factors, patients completed questionnaires on treatment outcome expectations, illness perceptions, pain catastrophizing, and psychological distress. We used multivariable logistic regression analysis and mediation analysis to identify factors associated with satisfaction with treatment outcomes. RESULTS: More positive pretreatment outcome expectations were associated with a higher probability of being satisfied with treatment outcomes at 3 months (odds ratio, 1.15; 95% confidence interval, 1.07-1.25). Only a relatively small part (33%) of this association was because of a higher total MHQ score at 3 months. None of the other mindset and hand function variables at baseline were associated with satisfaction with treatment outcomes. CONCLUSIONS: This study demonstrates that patients with higher pretreatment outcome expectations are more likely to be satisfied with treatment outcomes after 3 months of nonoperative treatment for CMC-1 OA. This association could only partially be explained by a better functional outcome at 3 months for patients who were satisfied. Health care providers treating patients nonoperatively for CMC-1 OA should be aware of the importance of expectations and may take this into account in pretreatment counseling.

Metabolic events in HIV-infected patients using abacavir are associated with erythrocyte inosine triphosphatase activity.

Objectives: Abacavir use has been associated with an increased risk of cardiovascular disease (CVD) and metabolic events in HIV-infected patients, although this finding was not consistently found. It is unclear whether abacavir only increases this risk in subpopulations of HIV-infected patients. It may be hypothesized that inosine 5'-triphosphate pyrophosphohydrolase (ITPase), an enzyme involved in the metabolism of purine analogues used in HIV treatment, plays a role in the risk of CVD and metabolic events in HIV-infected patients. Methods: ITPase activity and ITPA genotype were determined in 393 HIV-infected patients. ITPase activity <4 mmol IMP/mmol Hb/h was considered decreased. ITPA polymorphisms tested were: c.94C>A (rs1127354) and c.124 + 21A>C (rs7270101). ORs were determined using generalized estimating equation models for developing CVD in patients who had ever been exposed to abacavir, tenofovir or didanosine and for developing metabolic events in patients currently using these drugs. Results: In patients using abacavir, metabolic events were associated with ITPase activity. No association was demonstrated for tenofovir or didanosine. The OR for metabolic events was 3.11 in patients using abacavir with normal ITPase activity (95% CI 1.34-7.21; P = 0.008) compared with patients with decreased ITPase activity [adjusted for age, BMI, cumulative duration of combination ART (cART) use and the use of PI and NNRTI]. CVD was not associated with ITPase activity or ITPA genotype. Conclusions: This study shows, for the first time, that ITPase activity is associated with the occurrence of metabolic events in patients using abacavir. Further studies are needed to confirm this association and to elucidate the possible mechanism.

The effect of a multidisciplinary intervention program for overweight and obese children on cardiorespiratory fitness and blood pressure.

BACKGROUND: Multidisciplinary intervention programs for overweight and obese children mainly focus on reducing bodyweight and body mass index (BMI), but they may also positively impact blood pressure (BP), and cardiorespiratory fitness (CRF), which is a stronger predictor for all-cause mortality than BMI. OBJECTIVE: To evaluate whether Kids4Fit, a multidisciplinary weight reduction program, has a positive effect on CRF and BP in overweight and obese children in socially deprived areas. METHODS: A quasi-experimental study design with a waiting list control period including children who participated in a multidisciplinary intervention program of 12 weeks was set-up. Blood pressure measurements and shuttle-run test (SRT) were performed at baseline, at the start of the intervention, at the end of intervention and after 52 weeks. The effect of Kids4Fit on BP and on SRT scores were analyzed using mixed models. RESULTS: A total of 154 children were included [mean age 8.5 years (SD 1.8)]. No significant change was seen in systolic BP percentiles at 52 weeks after start of the Kids4Fit intervention (ß 0.08, (95%CI -0.06, 0.22)). Diastolic BP percentiles increased significantly over time (ß 0.20 (0.08, 0.31)). Effect plots showed an initial significant increase of the SRT scores but this effect diluted after the intervention. CONCLUSION: A local multidisciplinary intervention program in deprived areas had a significant positive effect on CRF, but this effect diluted after the intervention. Diastolic BP percentiles significantly increased over time. However, systolic BP did not change over time.

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.

Reference Values for Central Airway Dimensions on CT Images of Children and Adolescents.

OBJECTIVE: The purpose of this study was to acquire normative data on central airway dimensions on chest CT scans in the pediatric population. MATERIALS AND METHODS: Chest CT findings reported as normal by a radiologist were collected retrospectively at 10 international centers. An experienced and independent thoracic radiologist reevaluated all CT scans for image quality and for normal findings. Semiautomated image analysis was performed to measure dimensions of the trachea and right and left main bronchi at inspiration. Intrathoracic tracheal length was measured from carina to thorax inlet. Cross-sectional area and short and long axes were measured perpendicular to the longitudinal airway axis starting from the carina every centimeter upward for the trachea and every 0.5 cm downward for the main bronchi. The effects on airway diameters of age, sex, intrathoracic tracheal length, and distance from the carina were investigated by use of mixed-effects models analysis. RESULTS: Among 1160 CT scans collected, 388 were evaluated as normal by the independent radiologist with sufficient image quality and adequate inspiratory volume level. Central airways were successfully semiautomatically analyzed in 294 of 388 CT studies. Age, sex, intrathoracic tracheal length, and distance from carina were all significant predictors in the models for tracheal and right and left main bronchial diameters (p < 0.001). The central airway dimensions increased with age up to 20 years, and dimensions were larger in male than in female adolescents. CONCLUSION: Normative data were determined for the central airways of children and adolescents. Central airway dimensions depended on distance from the carina and on intrathoracic tracheal length.

Detection and monitoring of lung inflammation in cystic fibrosis during respiratory tract exacerbation using diffusion-weighted magnetic resonance imaging.

The aim was to investigate whether diffusion-weighted magnetic resonance imaging (DWI) detects and monitors inflammatory and lung function changes during respiratory tract exacerbations (RTE) treatment in patients with cystic fibrosis (CF).29 patients with RTE underwent DWI pre- and post-antibiotic treatment. A control group of 27 stable patients, matched for age and sex, underwent DWI with the same time gap as those undergoing RTE treatment. Clinical status and lung function were assessed at each DWI time point. The CF-MRI scoring system was used to assess structural lung changes in both CF groups.Significant reduction in the DWI score over the course of antibiotic treatment (p<0.0001) was observed in patients with RTE, but not in the control group. DWI score had a strong inverse correlation with clinical status (r=-0.504, p<0.0001) and lung function (r=-0.635, p<0.0001) in patients with RTE. Interestingly, there were persistent significant differences in the CF-MRI score between the RTE and control group at both baseline and follow-up (p<0.001), while the differences in DWI score were only observed at baseline (p<0.001).DWI is a promising imaging method for noninvasive detection of pulmonary inflammation during RTE, and may be used to monitor treatment efficacy of anti-inflammatory treatment.