Thresholds for the Oxford Hip Score after total hip replacement surgery:a novel approach to postoperative evaluation

Abstract Background This is a prospective cohort study to define the thresholds to distinguish patients with a satisfactory or unsatisfactory outcome after total hip replacement (THR) based on patient-reported outcome measures (PROMs) including the Oxford Hip Score (OHS), and using patient satisfaction and patient-perceived function as global transition items. The thresholds are intended to be used as a tool in the process of determining which patients are in need of postoperative outpatient evaluation. Methods One hundred and three THR patients who had completed a preoperative questionnaire containing the OHS questionnaire were invited to complete the same questionnaire and supplementary questions at a mean of 6 (4–9) months after surgery. Correlations between outcome measures and anchors were calculated using Pearson’s correlation coefficient. Thresholds were established by receiver operating characteristic (ROC) analysis, using multiple anchors. Results Significant correlations were found between outcome measures and anchors. Thresholds were determined for outcome measures coupled with satisfaction, patient-perceived function and a combination thereof using a cut-off of 50 and 70. Conclusions We have established a set of thresholds for Oxford scores that may help determine which THR patients are in need of postoperative evaluation. These thresholds can be implemented in clinical practice. Level of evidence Level 3

Machine-learning vs. logistic regression for preoperative prediction of medical morbidity after fast-track hip and knee arthroplasty-a comparative study

BACKGROUND: Machine-learning models may improve prediction of length of stay (LOS) and morbidity after surgery. However, few studies include fast-track programs, and most rely on administrative coding with limited follow-up and information on perioperative care. This study investigates potential benefits of a machine-learning model for prediction of postoperative morbidity in fast-track total hip (THA) and knee arthroplasty (TKA).METHODS: Cohort study in consecutive unselected primary THA/TKA between 2014-2017 from seven Danish centers with established fast-track protocols. Preoperative comorbidity and prescribed medication were recorded prospectively and information on length of stay and readmissions was obtained through the Danish National Patient Registry and medical records. We used a machine-learning model (Boosted Decision Trees) based on boosted decision trees with 33 preoperative variables for predicting "medical" morbidity leading to LOS &gt; 4 days or 90-days readmissions and compared to a logistical regression model based on the same variables. We also evaluated two parsimonious models, using the ten most important variables in the full machine-learning and logistic regression models. Data collected between 2014-2016 (n:18,013) was used for model training and data from 2017 (n:3913) was used for testing. Model performances were analyzed using precision, area under receiver operating (AUROC) and precision recall curves (AUPRC), as well as the Mathews Correlation Coefficient. Variable importance was analyzed using Shapley Additive Explanations values.RESULTS: Using a threshold of 20% "risk-patients" (n:782), precision, AUROC and AUPRC were 13.6%, 76.3% and 15.5% vs. 12.4%, 74.7% and 15.6% for the machine-learning and logistic regression model, respectively. The parsimonious machine-learning model performed better than the full logistic regression model. Of the top ten variables, eight were shared between the machine-learning and logistic regression models, but with a considerable age-related variation in importance of specific types of medication.CONCLUSION: A machine-learning model using preoperative characteristics and prescriptions slightly improved identification of patients in high-risk of "medical" complications after fast-track THA and TKA compared to a logistic regression model. Such algorithms could help find a manageable population of patients who may benefit most from intensified perioperative care.</p

Psychometric evaluation of an item bank for computerized adaptive testing of the EORTC QLQ-C30 cognitive functioning dimension in cancer patients

Background The European Organisation of Research and Treatment of Cancer (EORTC) Quality of Life Group is developing computerized adaptive testing (CAT) versions of all EORTC Quality of Life Questionnaire (QLQ-C30) scales with the aim to enhance measurement precision. Here we present the results on the field-testing and psychometric evaluation of the item bank for cognitive functioning (CF). Methods In previous phases (I-III), 44 candidate items were developed measuring CF in cancer patients. In phase IV, these items were psychometrically evaluated in a large sample of international cancer patients. This evaluation included an assessment of dimensionality, fit to the item response theory (IRT) model, differential item functioning (DIF), and measurement properties. Results A total of 1030 cancer patients completed the 44 candidate items on CF. Of these, 34 items could be included in a unidimensional IRT model, showing an acceptable fit. Although several items showed DIF, these had a negligible impact on CF estimation. Measurement precision of the item bank was much higher than the two original QLQ-C30 CF items alone, across the whole continuum. Moreover, CAT measurement may on average reduce study sample sizes with about 35-40% compared to the original QLQ-C30 CF scale, without loss of power. Conclusion A CF item bank for CAT measurement consisting of 34 items was established, applicable to various cancer patients across countries. This CAT measurement system will facilitate precise and efficient assessment of HRQOL of cancer patients, without loss of comparability of results

The first draft reference genome of the American mink ( Neovison vison )

Abstract The American mink (Neovison vison) is a semiaquatic species of mustelid native to North America. It’s an important animal for the fur industry. Many efforts have been made to locate genes influencing fur quality and color, but this search has been impeded by the lack of a reference genome. Here we present the first draft genome of mink. In our study, two mink individuals were sequenced by Illumina sequencing with 797 Gb sequence generated. Assembly yielded 7,175 scaffolds with an N50 of 6.3 Mb and length of 2.4 Gb including gaps. Repeat sequences constitute around 31% of the genome, which is lower than for dog and cat genomes. The alignments of mink, ferret and dog genomes help to illustrate the chromosomes rearrangement. Gene annotation identified 21,053 protein-coding sequences present in mink genome. The reference genome’s structure is consistent with the microsatellite-based genetic map. Mapping of well-studied genes known to be involved in coat quality and coat color, and previously located fur quality QTL provide new knowledge about putative candidate genes for fur traits. The draft genome shows great potential to facilitate genomic research towards improved breeding for high fur quality animals and strengthen our understanding on evolution of Carnivora

Detailed statistical analysis plan for the Danish Palliative care trial (DanPaCT)

Acknowledgements We wish to thank the students who sent out the questionnaires, who entered and compared all data, help with data management, made material blind to the investigators, and were/will be outcome assessors of interventions given. They were: Nicla Rohde Christensen, Ellen Lundorff, Marc Klee Olsen, Charlotte Lund Rasmussen, and Nete Skjødt. This work was funded by the Tryg Foundation [journal number 7-10-0838A] and the Danish Cancer Society [journal number R16-A695]. Other than funding the trial, the funding body had no role in the design, conduct, analysis, or reporting of the present trial.Peer reviewedPublisher PD

Development and Psychometric Evaluation of an Item Bank for Computerized Adaptive Testing of the EORTC Insomnia Dimension in Cancer Patients (EORTC CAT-SL)

To further advance assessment of patient-reported outcomes, the European Organisation of Research and Treatment of Cancer (EORTC) Quality of Life Group has developed computerized adaptive test (CAT) versions of all EORTC Quality of Life Core Questionnaire (QLQ-C30) scales/items. The aim of this study was to develop and evaluate an item bank for CAT measurement of insomnia (CAT-SL). In line with the EORTC guidelines, the developmental process comprised four phases: (I) defining the concept insomnia and literature search, (II) selection and formulation of new items, (III) pre-testing and (IV) field-testing, including psychometric analyses of the final item bank. In phase I, the literature search identified 155 items that were compatible with our conceptualisation of insomnia, including both quantity and quality of sleep. In phase II, following a multistep-approach, this number was reduced to 15 candidate items. Pre-testing of these items in cancer patients (phase III) resulted in an item list of 14 items, which were field-tested among 1094 patients in phase IV. Psychometric evaluations showed that eight items could be retained in a unidimensional model. The final item bank yielded greater measurement precision than the original QLQ-C30 insomnia item. It was estimated that administering two or more items from the insomnia item bank with CAT results in a saving in sample size between approximately 15–25%. The 8-item EORTC CAT-SL item bank facilitates precise and efficient measurement of insomnia as part of the EORTC CAT system of health-related quality life assessment in both clinical research and practice