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  • 1.
    Blom, Mathias Carl
    et al.
    Department of Clinical Sciences, Lund University, Lund, Sweden.
    Ashfaq, Awais
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. Halland Hospital, Region Halland, Halmstad, Sweden.
    Pinheiro Sant'Anna, Anita
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Anderson, Philip D.
    Department of Emergency Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, USA & Harvard Medical School, Boston, Massachusetts, USA.
    Lingman, Markus
    Halland Hospital, Region Halland, Sweden & Department of Molecular and Clinical Medicine/Cardiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
    Training machine learning models to predict 30-day mortality in patients discharged from the emergency department: a retrospective, population based registry study2019In: BMJ Open, ISSN 2044-6055, E-ISSN 2044-6055, Vol. 9, no 8, article id e028015Article in journal (Refereed)
    Abstract [en]

    Background: Aggressive treatment at end-of-life (EOL) can be traumatic to patients and may not add clinical benefit. Absent an accurate prognosis of death, individual level biases may prevent timely discussions about the scope of EOL care and patients are at risk of being subject to care against their desire. The aim of this work is to develop predictive algorithms for identifying patients at EOL, with clinically meaningful discriminatory power.

    Methods: Retrospective, population-based study of patients utilizing emergency departments (EDs) in Sweden, Europe. Electronic health records (EHRs) were used to train supervised learning algorithms to predict all-cause mortality within 30 days following ED discharge. Algorithm performance was validated out of sample on EHRs from a separate hospital, to which the algorithms were previously unexposed.

    Results: Of 65,776 visits in the development set, 136 (0.21%) experienced the outcome. The algorithm with highest discrimination attained ROC-AUC 0.945 (95% CI 0.933 - 0.956), with sensitivity 0.869 (95% CI 0.802, 0.931) and specificity 0.858 (0.855, 0.860) on the validation set.

    Conclusions: Multiple algorithms displayed excellent discrimination and outperformed available indexes for short-term mortality prediction. The practical utility of the algorithms increases as the required data were captured electronically and did not require de novo data collection.

    Trial registration number: Not applicable.

  • 2.
    Larsson, Ingrid
    et al.
    Halmstad University, School of Health and Welfare, Centre of Research on Welfare, Health and Sport (CVHI).
    Andersson, M. L. E.
    Spenshult Research and Development Center, Spenshult, Halmstad, Sweden.
    Ajeganova, S.
    Leiden University Medical Center - LUMC, Leiden, Netherlands.
    Bala, V.
    Helsingborgs Lasarett, Department of Medicine, Helsingborg, Sweden.
    Svensson, Björn
    Lunds Universitet, Department of Clinical Sciences, Lund, Sweden.
    Bergman, Stefan
    Halmstad University, School of Health and Welfare, Centre of Research on Welfare, Health and Sport (CVHI).
    Bremander, Ann
    Halmstad University, School of Business, Engineering and Science, The Rydberg Laboratory for Applied Sciences (RLAS).
    Häggström, Åsa
    Keller, Catharina
    Helsingborg Lasarett AB, Helsingborg, Sweden.
    Leden, Ido
    Centralsjukhuset, Kristianstad, Sweden.
    Teleman, Annika
    Spenshult Hospital, Olofstrom, Sweden.
    Theander, Jan
    Kristianstad Central Hospital, Department of Internal Medicine, Kristianstad, Sweden.
    ֖stenson, Anneli
    Reasons to stop drinking alcohol among patients with rheumatoid arthritis in Sweden: A mixed-methods study2018In: BMJ Open, ISSN 2044-6055, E-ISSN 2044-6055, Vol. 8, no 12, article id e024367Article in journal (Refereed)
    Abstract [en]

    Objectives: The aims were to identify patients with rheumatoid arthritis (RA) who had stopped drinking alcohol and compare them with patients drinking alcohol, and to explore reasons for stopping drinking alcohol. Design: A sequential explanatory mixed methods design was used. Setting: Six rheumatology clinics in Southern Sweden Better Anti-Rheumatic FarmacOTherapy cohort. Participants: A total of 1509 patients completed the questions about alcohol and were included in the study. 86 of these had stopped drinking alcohol and 72 responded to the open question and their answers were analysed with qualitative content analysis. Outcome measures: The quantitative data were from a cross-sectional survey assessing disease severity, physical function (Health Assessment Questionnaire, HAQ) and health-related quality of life (EuroQol five dimensions, EQ5D), pain, fatigue, patient global assessment (PatGA) and lifestyle factors, for example, alcohol. The questions assessing alcohol included an open question ’Why have you stopped drinking alcohol?’ Results: The patients who stopped drinking alcohol were older (median (min-max) 69 (36-90) vs 66 (23-95), p=0.011), had worse HAQ (1.00 (0-2.75) vs 0.50 (0-3.00), p<0.001), worse EQ5D (0.69 (-0.02-1.00) vs 0.76 (-0.58-1.00), p<0.001) worse PatGA (5 (0-10) vs 3 (0-10), p<0.001), more pain (5 (0-10) vs 3 (0-10), p<0.001) and more fatigue (6 (0-10) vs 4 (0-10), p<0.001 compared with patients drinking alcohol. The qualitative content analysis revealed five categories describing reasons for patients with RA to stop drinking alcohol: illness and treatment; health and well-being; work and family; faith and belief; and dependences and abuse. Conclusions: The patients who had stopped drinking had worse physical functioning and higher levels in pain-related variables. Most stopped drinking due to their illness or a desire to improve health. © 2018 Author(s).

  • 3.
    Van der Elst, Kristien
    et al.
    Department of Rheumatology, University Hospitals Leuven, Leuven, Belgium & Skeletal Biology and Engineering Research Centre, Department of Development and Regeneration, KU Leuven–University of Leuven, Leuven, Belgium.
    Bremander, Ann
    Halmstad University, School of Business, Engineering and Science, The Rydberg Laboratory for Applied Sciences (RLAS). Department of Clinical Sciences, Section of Rheumatology, Lund University, Lund, Sweden & Research and Development Centre, Spenshult, Halmstad, Sweden.
    De Groef, An
    Department of Rehabilitation Sciences, KU Leuven – University of Leuven, Leuven, Belgium.
    Larsson, Ingrid
    Halmstad University, School of Health and Welfare, Centre of Research on Welfare, Health and Sport (CVHI).
    Mathijssen, Elke G.E.
    Department of Rheumatology, Sint Maartenskliniek, Nijmegen, The Netherlands.
    Vriezekolk, Johanna E.
    Department of Rheumatology, Sint Maartenskliniek, Nijmegen, The Netherlands.
    Westhovens, Rene
    Department of Rheumatology, University Hospitals Leuven, Leuven, Belgium & Skeletal Biology and Engineering Research Centre, Department of Development and Regeneration, KU Leuven–University of Leuven, Leuven, Belgium.
    van Eijk-Hustings, Yvonne J.L.
    Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Centre, Maastricht, The Netherlands & Department of Rheumatology, Maastricht University Medical Centre, Maastricht, The Netherlands.
    European Qualitative research project on Patient-preferred outcomes in Early Rheumatoid Arthritis (EQPERA): rationale, design and methods of a multinational, multicentre, multilingual, longitudinal qualitative study2019In: BMJ Open, ISSN 2044-6055, E-ISSN 2044-6055, Vol. 9, no 3, article id 023606Article in journal (Refereed)
    Abstract [en]

    INTRODUCTION: Including the patient perspective is important to achieve optimal outcomes in the treatment of rheumatoid arthritis (RA). Ample qualitative studies exist on patient outcomes in RA. A Belgian study recently unravelled what matters most to patients throughout the overwhelming and rapidly evolving early stage of RA. The present study, European Qualitative research project on Patient-preferred outcomes in Early Rheumatoid Arthritis (EQPERA) was created to contribute to a more universal understanding of patient-preferred health and treatment outcomes by integrating the perspectives of patients with early RA from three European countries.

    METHODS AND ANALYSIS: In EQPERA, a qualitative, explorative, longitudinal study will be implemented in The Netherlands and Sweden, parallel to the methods applied in the previously conducted Belgian study. In each country, a purposive sample of patients with early RA will be individually interviewed 3-6 months after start of the initial RA treatment and subsequently, the same participants will be invited to take part in a focus group 12-18 months after RA treatment initiation. Data collection and analysis will be independently conducted by the local research teams in their native language. A meta-analysis of the local findings will be performed to explore and describe similarities, differences and patterns across countries.

    ETHICS AND DISSEMINATION: Ethics approval was granted by the responsible local ethics committees. EQPERA follows the recommendations of the Declaration of Helsinki. Two main papers are foreseen (apart from the data reporting on the local findings) for peer-reviewed publication.

    © Author(s) (or their employer(s)) 2019.

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