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- de Vries, Claire E. E., et al.
(författare)
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Outcomes of the first global multidisciplinary consensus meeting including persons living with obesity to standardize patient-reported outcome measurement in obesity treatment research
- 2022
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Ingår i: Obesity Reviews. - : John Wiley & Sons. - 1467-7881 .- 1467-789X. ; 23:8
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Tidskriftsartikel (refereegranskat)abstract
- Quality of life is a key outcome that is not rigorously measured in obesity treatment research due to the lack of standardization of patient-reported outcomes (PROs) and PRO measures (PROMs). The S.Q.O.T. initiative was founded to Standardize Quality of life measurement in Obesity Treatment. A first face-to-face, international, multidisciplinary consensus meeting was conducted to identify the key PROs and preferred PROMs for obesity treatment research. It comprised of 35 people living with obesity (PLWO) and healthcare providers (HCPs). Formal presentations, nominal group techniques, and modified Delphi exercises were used to develop consensus-based recommendations. The following eight PROs were considered important: self-esteem, physical health/functioning, mental/psychological health, social health, eating, stigma, body image, and excess skin. Self-esteem was considered the most important PRO, particularly for PLWO, while physical health was perceived to be the most important among HCPs. For each PRO, one or more PROMs were selected, except for stigma. This consensus meeting was a first step toward standardizing PROs (what to measure) and PROMs (how to measure) in obesity treatment research. It provides an overview of the key PROs and a first selection of the PROMs that can be used to evaluate these PROs.
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- Osterborg, Anders, et al.
(författare)
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Ofatumumab retreatment and maintenance in fludarabine-refractory chronic lymphocytic leukaemia patients
- 2015
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Ingår i: British Journal of Haematology. - : Wiley. - 0007-1048 .- 1365-2141. ; 170:1, s. 40-49
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Tidskriftsartikel (refereegranskat)abstract
- There are limited data on retreatment with monoclonal antibodies (mAb) in patients with chronic lymphocytic leukaemia (CLL). In a pivotal study, ofatumumab (human anti-CD20 mAb) monotherapy demonstrated a 47% objective response rate (ORR) in fludarabine refractory CLL patients. From this study, a subset of 29 patients who had at least stable disease and then progressed were retreated with eight weekly ofatumumab infusions (induction treatment period), followed by monthly infusions for up to 2years (maintenance treatment period). The ORR after 8weeks of induction retreatment was 45% and 24% had continued disease control after maintenance at 52weeks. Efficacy and safety of the retreated patients were compared with their initial results in the pivotal study. Response duration was 241months vs. 68months; time to next therapy was 148months vs. 123months; and progression-free survival was 74months vs. 79months (medians). Upon retreatment, 72% had infusion reactions, mostly Grade 1-2. Three patients had fatal infections. In summary, ofatumumab retreatment and maintenance therapy was feasible in patients with heavily pretreated CLL and appeared to result in more durable disease control than initial ofatumumab treatment in this subset of patients who may have a more favourable disease profile.
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- Romagnoni, A, et al.
(författare)
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Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data
- 2019
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Ingår i: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 9:1, s. 10351-
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Tidskriftsartikel (refereegranskat)abstract
- Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers.
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