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.

Azithromycin reduces bronchial wall thickening in infants with cystic fibrosis.

BACKGROUND: COMBAT-CF showed that children aged 0-3 years treated with azithromycin did clinically better than placebo but there was no effect on CT-scores. We reanalysed CTs using an automatic bronchus-artery (BA) analysis. METHOD: Inspiratory and expiratory CTs at 12 and 36 months were analysed. BA-analysis measures BA-diameters: bronchial outer wall (Bout), bronchial inner wall (Bin), artery (A), and bronchial wall thickness (Bwt) and computes BA-ratios: Bout/A and Bin/A for bronchial widening, Bwt/A and Bwa/Boa (bronchial wall area/bronchial outer area) for bronchial wall thickening. Low attenuation regions (LAR) were analysed using an automatic method. Mixed-effect model was used to compare BA-outcomes at 36 months between treatment groups. RESULTS: 228 CTs (59 placebo; 66 azithromycin) were analysed. The azithromycin group had lower Bwa/Boa (p = 0.0034) and higher Bin/A (p = 0.001) relative to placebo. Bout/A (p = 0.0088) was higher because of a reduction in artery diameters which correlated to a reduction in LAR. CONCLUSION: Azithromycin-treated infants with CF show a reduction in bronchial wall thickness and possibly a positive effect on lung perfusion.

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.

Key inflammatory markers in bronchoalveolar lavage predict bronchiectasis progression in young children with CF.

INTRODUCTION: Inflammation appears early in cystic fibrosis (CF) pathogenesis, with specific elevated inflammatory markers in bronchoalveolar lavage fluid (BALF) correlating with structural lung disease. Our aim was to identify markers of airway inflammation able to predict bronchiectasis progression over two years with high sensitivity and specificity. METHODS: Children with CF with two chest computed tomography (CT) scans and bronchoscopies at a two-year interval were included (n= 10 at 1 and 3 years and n= 27 at 3 and 5 years). Chest CTs were scored for increase in bronchiectasis (Δ%Bx), using the PRAGMA-CF score. BALF collected with the first CT scan were analyzed for neutrophil% (n= 36), myeloperoxidase (MPO) (n= 25), neutrophil elastase (NE) (n= 26), and with a protein array for inflammatory and fibrotic markers (n= 26). RESULTS: MPO, neutrophil%, and inducible T-cell costimulator ligand (ICOSLG), but not clinical characteristics, correlated significantly with Δ%Bx. Evaluation of neutrophil%, NE, MPO, interleukin-8 (IL-8), ICOSLG, and hepatocyte growth factor (HGF), for predicting an increase of > 0.5% of Δ%Bx in two years, showed that IL-8 had the best sensitivity (82%) and specificity (73%). Neutrophil%, ICOSLG and HGF had sensitivities of 85, 82, and 82% and specificities of 59, 67 and 60%, respectively. The odds ratio for risk of >0.5% Δ%Bx was higher for IL-8 (12.4) than for neutrophil%, ICOSLG, and HGF (5.9, 5.3, and 6.7, respectively). Sensitivity and specificity were lower for NE and MPO). CONCLUSIONS: High levels of IL-8, neutrophil%, ICOSGL and HGF in BALF may be good predictors for progression of bronchiectasis in young children with CF.

Individualized Dynamic Prediction Model for Patient-Reported Voice Quality in Early-Stage Glottic Cancer.

OBJECTIVE: Early-stage glottic cancer (ESGC) is a malignancy of the head and neck. Besides disease control, preservation and improvement of voice quality are essential. To enable expectation management and well-informed decision-making, patients should be sufficiently counseled with individualized information on expected voice quality. This study aims to develop an individualized dynamic prediction model for patient-reported voice quality. This model should be able to provide individualized predictions at every time point from intake to the end of follow-up. STUDY DESIGN: Longitudinal cohort study. SETTING: Tertiary cancer center. METHODS: Patients treated for ESGC were included in this study (N = 294). The Voice Handicap Index was obtained prospectively. The framework of mixed and joint models was used. The prognostic factors used are treatment, age, gender, comorbidity, performance score, smoking, T-stage, and involvement of the anterior commissure. The overall performance of these models was assessed during an internal cross-validation procedure and presentation of absolute errors using box plots. RESULTS: The mean age in this cohort was 67 years and 81.3% are male. Patients were treated with transoral CO2 laser microsurgery (57.8%), single vocal cord irradiation up to (24.5), or local radiotherapy (17.5%). The mean follow-up was 43.4 months (SD 21.5). Including more measurements during prediction improves predictive performance. Including more clinical and demographic variables did not provide better predictions. Little differences in predictive performance between models were found. CONCLUSION: We developed a dynamic individualized prediction model for patient-reported voice quality. This model has the potential to empower patients and professionals in making well-informed decisions and enables tailor-made counseling.

Social-environmental phenotypes of rapid cystic fibrosis lung disease progression in adolescents and young adults living in the United States.

Background: Cystic fibrosis (CF) is a genetic disease but is greatly impacted by non-genetic (social/environmental and stochastic) influences. Some people with CF experience rapid decline, a precipitous drop in lung function relative to patient- and/or center-level norms. Those who experience rapid decline in early adulthood, compared to adolescence, typically exhibit less severe clinical disease but greater loss of lung function. The extent to which timing and degree of rapid decline are informed by social and environmental determinants of health (geomarkers) is unknown. Methods: A longitudinal cohort study was performed (24,228 patients, aged 6-21 years) using the U.S. CF Foundation Patient Registry. Geomarkers at the ZIP Code Tabulation Area level measured air pollution/respiratory hazards, greenspace, crime, and socioeconomic deprivation. A composite score quantifying social-environmental adversity was created and used in covariate-adjusted functional principal component analysis, which was applied to cluster longitudinal lung function trajectories. Results: Social-environmental phenotyping yielded three primary phenotypes that corresponded to early, middle, and late timing of peak decline in lung function over age. Geographic differences were related to distinct cultural and socioeconomic regions. Extent of peak decline, estimated as forced expiratory volume in 1 s of % predicted/year, ranged from 2.8 to 4.1 % predicted/year depending on social-environmental adversity. Middle decliners with increased social-environmental adversity experienced rapid decline 14.2 months earlier than their counterparts with lower social-environmental adversity, while timing was similar within other phenotypes. Early and middle decliners experienced mortality peaks during early adolescence and adulthood, respectively. Conclusion: While early decliners had the most severe CF lung disease, middle and late decliners lost more lung function. Higher social-environmental adversity associated with increased risk of rapid decline and mortality during young adulthood among middle decliners. This sub-phenotype may benefit from enhanced lung-function monitoring and personalized secondary environmental health interventions to mitigate chemical and non-chemical stressors.

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.

Automatic bronchus and artery analysis on chest computed tomography to evaluate the effect of inhaled hypertonic saline in children aged 3-6 years with cystic fibrosis in a randomized clinical trial.

BACKGROUND: SHIP-CT showed that 48-week treatment with inhaled 7% hypertonic saline (HS) reduced airway abnormalities on chest CT using the manual PRAGMA-CF method relative to isotonic saline (IS) in children aged 3-6 years with cystic fibrosis (CF). An algorithm was developed and validated to automatically measure bronchus and artery (BA) dimensions of BA-pairs on chest CT. Aim of the study was to assess the effect of HS on bronchial wall thickening and bronchial widening using the BA-analysis. METHODS: The BA-analysis (LungQ, version 2.1.0.1, Thirona, Netherlands) automatically segments the bronchial tree and identifies the segmental bronchi (G0) and distal generations (G1-G10). Dimensions of each BA-pair are measured: diameters of bronchial outer wall (Bout), bronchial inner wall (Bin), bronchial wall thickness (Bwt), and artery (A). BA-ratios are computed: Bout/A and Bin/A to detect bronchial widening and Bwt/A and Bwa/Boa (=bronchial wall area/bronchial outer area) to detect bronchial wall thickening. RESULTS: 113 baseline and 102 48-week scans of 115 SHIP-CT participants were analysed. LungQ measured at baseline and 48-weeks respectively 6,073 and 7,407 BA-pairs in the IS-group and 6,363 and 6,840 BA-pairs in the HS-group. At 48 weeks, Bwt/A (mean difference 0.011; 95%CI, 0.0017 to 0.020) and Bwa/Boa (mean difference 0.030; 95% 0.009 to 0.052) was significantly higher (worse) in the IS-group compared to the HS-group representing more severe bronchial wall thickening in the IS-group (p=0.025 and p=0.019 respectively). Bwt/A and Bwa/Boa decreased and Bin/A remained stable from baseline to 48 weeks in the HS while it declined in the IS-group (all p<0.001). There was no difference in progression of Bout/A between two treatment groups. CONCLUSION: The automatic BA-analysis showed a positive impact of inhaled HS on bronchial lumen and wall thickness, but no treatment effect on progression of bronchial widening over 48 weeks.

Lung function and secondhand smoke exposure among children with cystic fibrosis: A Bayesian meta-analysis.

BACKGROUND: Secondhand smoke exposure, an important environmental health factor in cystic fibrosis (CF), remains uniquely challenging to children with CF as they strive to maintain pulmonary function during early stages of growth and throughout adolescence. Despite various epidemiologic studies among CF populations, little has been done to coalesce estimates of the association between secondhand smoke exposure and lung function decline. METHODS: A systematic review was performed using PRISMA guidelines. A Bayesian random-effects model was employed to estimate the association between secondhand smoke exposure and change in lung function (measured as FEV1% predicted). RESULTS: Quantitative synthesis of study estimates indicated that second-hand smoke exposure corresponded to a significant drop in FEV1 (estimated decrease: -5.11% predicted; 95% CI: -7.20, -3.47). The estimate of between-study heterogeneity was 1.32% predicted (95% CI: 0.05, 4.26). There was moderate heterogeneity between the 6 analyzed studies that met review criteria (degree of heterogeneity: I2=61.9% [95% CI: 7.3-84.4%] and p = 0.022 from the frequentist method.) CONCLUSIONS: Our results quantify the impact at the pediatric population level and corroborate the assertion that secondhand smoke exposure negatively affects pulmonary function in children with CF. Findings highlight challenges and opportunities for future environmental health interventions in pediatric CF care.

Lung Function Decline in Cystic Fibrosis: Impact of Data Availability and Modeling Strategies on Clinical Interpretations.

Rationale: Studies estimating the rate of lung function decline in cystic fibrosis have been inconsistent regarding the methods used. How the methodology used impacts the validity of the results and comparability between studies is unknown. Objectives: The Cystic Fibrosis Foundation established a work group whose tasks were to examine the impact of differing approaches to estimating the rate of decline in lung function and to provide analysis guidelines. Methods: We used a natural history cohort of 35,252 individuals with cystic fibrosis aged ⩾6 years in the Cystic Fibrosis Foundation Patient Registry (CFFPR), 2003-2016. Modeling strategies using linear and nonlinear forms of marginal and mixed-effects models, which have previously quantified the rate of forced expiratory volume in 1 second (FEV1) decline (percent predicted per year), were evaluated under clinically relevant scenarios of available lung function data. Scenarios varied by sample size (overall CFFPR, medium-sized cohort of 3,000 subjects, and small-sized cohort of 150), data collection/reporting frequency (encounter, quarterly, and annual), inclusion of FEV1 during pulmonary exacerbation, and follow-up length (<2 yr, 2-5 yr, entire duration). Results: Rate of FEV1 decline estimates (percent predicted per year) differed between linear marginal and mixed-effects models; overall cohort estimates (95% confidence interval) were 1.26 (1.24-1.29) and 1.40 (1.38-1.42), respectively. Marginal models consistently estimated less rapid lung function decline than mixed-effects models across scenarios, except for short-term follow-up (both were ∼1.4). Rate of decline estimates from nonlinear models diverged by age 30. Among mixed-effects models, nonlinear and stochastic terms fit best, except for short-term follow-up (<2 yr). Overall CFFPR analysis from a joint longitudinal-survival model implied that an increase in rate of decline of 1% predicted per year in FEV1 was associated with a 1.52-fold (52%) increase in the hazard of death/lung transplant, but the results exhibited immortal cohort bias. Conclusions: Differences were as high as 0.5% predicted per year between rate of decline estimates, but we found estimates were robust to lung function data availability scenarios, except short-term follow-up and older age ranges. Inconsistencies among previous study results may be attributable to inherent differences in study design, inclusion criteria, or covariate adjustment. Results-based decision points reported herein will support researchers in selecting a strategy to model lung function decline most reflective of nuanced, study-specific goals.

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.

Built environment factors predictive of early rapid lung function decline in cystic fibrosis.

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.

Statistical primer: an introduction to the application of linear mixed-effects models in cardiothoracic surgery outcomes research-a case study using homograft pulmonary valve replacement data.

OBJECTIVES: The emergence of big cardio-thoracic surgery datasets that include not only short-term and long-term discrete outcomes but also repeated measurements over time offers the opportunity to apply more advanced modelling of outcomes. This article presents a detailed introduction to developing and interpreting linear mixed-effects models for repeated measurements in the setting of cardiothoracic surgery outcomes research. METHODS: A retrospective dataset containing serial echocardiographic measurements in patients undergoing surgical pulmonary valve replacement from 1986 to 2017 in Erasmus MC was used to illustrate the steps of developing a linear mixed-effects model for clinician researchers. RESULTS: Essential aspects of constructing the model are illustrated with the dataset including theories of linear mixed-effects models, missing values, collinearity, interaction, nonlinearity, model specification, results interpretation and assumptions evaluation. A comparison between linear regression models and linear mixed-effects models is done to elaborate on the strengths of linear mixed-effects models. An R script is provided for the implementation of the linear mixed-effects model. CONCLUSIONS: Linear mixed-effects models can provide evolutional details of repeated measurements and give more valid estimates compared to linear regression models in the setting of cardio-thoracic surgery outcomes research.

Supplemental oxygen strategies in infants with bronchopulmonary dysplasia after the neonatal intensive care unit period: study protocol for a randomised controlled trial (SOS BPD study).

INTRODUCTION: Supplemental oxygen is the most important treatment for preterm born infants with established bronchopulmonary dysplasia (BPD). However, it is unknown what oxygen saturation levels are optimal to improve outcomes in infants with established BPD from 36 weeks postmenstrual age (PMA) onwards. The aim of this study is to compare the use of a higher oxygen saturation limit (≥95%) to a lower oxygen saturation limit (≥90%) after 36 weeks PMA in infants diagnosed with moderate or severe BPD. METHODS AND ANALYSIS: This non-blinded, multicentre, randomised controlled trial will recruit 198 preterm born infants with moderate or severe BPD between 36 and 38 weeks PMA. Infants will be randomised to either a lower oxygen saturation limit of 95% or to a lower limit of 90%; supplemental oxygen and/or respiratory support will be weaned based on the assigned lower oxygen saturation limit. Adherence to the oxygen saturation limit will be assessed by extracting oxygen saturation profiles from pulse oximeters regularly, until respiratory support is stopped. The primary outcome is the weight SD score at 6 months of corrected age. Secondary outcomes include anthropometrics collected at 6 and 12 months of corrected age, rehospitalisations, respiratory complaints, infant stress, parental quality of life and cost-effectiveness. ETHICS AND DISSEMINATION: Ethical approval for the trial was obtained from the Medical Ethics Review Committee of the Erasmus University Medical Centre, Rotterdam, the Netherlands (MEC-2018-1515). Local approval for conducting the trial in the participating hospitals has been or will be obtained from the local institutional review boards. Informed consent will be obtained from the parents or legal guardians of all study participants. TRIAL REGISTRATION NUMBER: NL7149/NTR7347.

The effect of inhaled hypertonic saline on lung structure in children aged 3-6 years with cystic fibrosis (SHIP-CT): a multicentre, randomised, double-blind, controlled trial.

BACKGROUND: In the Saline Hypertonic in Preschoolers (SHIP) study, inhaled 7% hypertonic saline improved the lung clearance index in children aged 3-6 years with cystic fibrosis, but it remained unclear whether improvement is also seen in structural lung disease. We aimed to assess the effect of inhaled hypertonic saline on chest CT imaging in children aged 3-6 years with cystic fibrosis. METHODS: Children with cystic fibrosis were enrolled in this multicentre, randomised, double-blind, controlled study at 23 cystic fibrosis centres in Spain, Denmark, the Netherlands, Italy, France, Belgium, the USA, Canada, and Australia. Eligible participants were children aged 3-6 years who were able to cooperate with chest CT imaging and comply with daily nebuliser treatment. Participants were randomly assigned 1:1 to receive inhaled 2 puffs of 100 µg salbutamol followed by 4mL of either 7% hypertonic saline or 0·9% isotonic saline twice per day for 48 weeks. Randomisation was stratified by age in North America and Australia, and by age and country in Europe. Chest CTs were obtained at baseline and 48 weeks and scored using the Perth-Rotterdam Annotated Grid Morphometric Analysis for Cystic Fibrosis (PRAGMA-CF) method. The primary outcome was the difference between groups in the percentage of total lung volume occupied by abnormal airways (PRAGMA-CF %Disease) measured by chest CT at 48 weeks. Analysis was by intention-to-treat. This study is registered with Clinicaltrials.gov, NCT02950883. FINDINGS: Between May 24, 2016, and Dec 18, 2019, 134 children were assessed for inclusion. 18 patients were excluded (nine had incomplete or unsuccessful chest CT at enrolment visit, two could not comply with CT training, two had acute respiratory infection, two withdrew consent, two for reasons unknown, and one was already on hypertonic saline). 116 participants were enrolled and randomly assigned to hypertonic saline (n=56) or isotonic saline (n=60). 12 patients dropped out of the study (seven in the hypertonic saline group and five in the isotonic saline group). Mean PRAGMA-CF %Disease at 48 weeks was 0·88% (95% CI 0·60-1·16) in the hypertonic saline group and 1·55% (1·25-1·84) in the isotonic saline group (mean difference 0·67%, 95% CI 0·26-1·08; p=0·0092) based on a linear regression model adjusted for baseline %Disease values and baseline age. Most adverse events in both groups were rated as mild, and the most common adverse event in both groups was cough. INTERPRETATION: Inhaled hypertonic saline for 48 weeks had a positive effect on structural lung changes in children aged 3-6 years with cystic fibrosis relative to isotonic saline. This is the first demonstration of an intervention that alters structural lung disease in children aged 3-6 years with cystic fibrosis. FUNDING: Cystic Fibrosis Foundation.

Alternative prostate cancer grading systems incorporating percent pattern 4/5 (IQ-Gleason) and cribriform architecture (cGrade) improve prediction of outcome after radical prostatectomy.

Percentage Gleason pattern 4, invasive cribriform and/or intraductal carcinoma (IC/IDC) and minor pattern 5 are recognized as independent parameters for prostate cancer outcome, but are not incorporated in current grade groups (GGs). Two proof-of-principle studies have proposed alternative grading schemes based on percentage Gleason pattern 4/5 (integrated quantitative Gleason score; IQ-Gleason) and IC/IDC presence (cribriform grade; cGrade). Our objective was to compare the performance of GG, IQ-Gleason and cGrade for predicting biochemical recurrence and metastasis after radical prostatectomy (RP). RP specimens of 1064 patients were pathologically reviewed and graded according to the three schemes. Discriminative power for prediction of biochemical recurrence-free (BCRFS) and metastasis-free (MFS) survival was compared using Harrell's c-index. The GG distribution at RP was 207 (19.4%) GG1, 472 (44.4%) GG2, 126 (11.8%) GG3, 140 (13.2%) GG4 and 119 (11.2%) GG5. Grading according to 5-tier IQ-Gleason and cGrade systems led to categorical shifts in 49.8% and 29.7% of cases, respectively. Continuous IQ-Gleason had the best performance for predicting BCRFS (c-index 0.743, 95% confidence interval (CI) 0.715-0.771), followed by cGrade (c-index 0.738, 95%CI 0.712-0.759), 5-tier categorical IQ-Gleason (c-index 0.723, 95%CI 0.695-0.750) and GG (c-index 0.718, 95%CI 0.691-0.744). Continuous IQ-Gleason (c-index 0.834, 95%CI 0.802-0.863) and cGrade (c-index 0.834, 95%CI 0.808-0.866) both had better predictive value for MFS than categorical IQ-Gleason (c-index 0.823, 95%CI 0.788-0.857) and GG (c-index 0.806, 95%CI 0.777-0.839). In conclusion, the performance of prostate cancer grading can be improved by alternative grading schemes incorporating percent Gleason pattern 4/5 and IC/IDC.

Predicting Clinically Relevant Patient-Reported Symptom Improvement After Carpal Tunnel Release: A Machine Learning Approach.

BACKGROUND: Symptom improvement is an important goal when considering surgery for carpal tunnel syndrome. There is currently no prediction model available to predict symptom improvement for patients considering a carpal tunnel release (CTR). OBJECTIVE: To predict using a model the probability of clinically relevant symptom improvement at 6 mo after CTR. METHODS: We split a cohort of 2119 patients who underwent a mini-open CTR and completed the Boston Carpal Tunnel Questionnaire preoperatively and 6 mo postoperatively into training (75%) and validation (25%) data sets. Patients who improved more than the minimal clinically important difference of 0.8 at the Boston Carpal Tunnel Questionnaire-symptom severity scale were classified as "improved." Logistic regression, random forests, and gradient boosting machines were considered to train prediction models. The best model was selected based on discriminative ability (area under the curve) and calibration in the validation data set. This model was further assessed in a holdout data set (N = 397). RESULTS: A gradient boosting machine with 5 predictors was chosen as optimal trade-off between discriminative ability and the number of predictors. In the holdout data set, this model had an area under the curve of 0.723, good calibration, sensitivity of 0.77, and specificity of 0.55. The positive predictive value was 0.50, and the negative predictive value was 0.81. CONCLUSION: We developed a prediction model for clinically relevant symptom improvement 6 mo after a CTR, which required 5 patient-reported predictors (18 questions) and has reasonable discriminative ability and good calibration. The model is available online and might help shared decision making when patients are considering a CTR.

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.