18 research outputs found
A risk score including body mass index, glycated haemoglobin and triglycerides predicts future glycaemic control in people with type 2 diabetes
AimTo identify, predict and validate distinct glycaemic trajectories among patients with newly diagnosed type 2 diabetes treated in primary care, as a first step towards more effective patient-centred care. MethodsWe conducted a retrospective study in two cohorts, using routinely collected individual patient data from primary care practices obtained from two large Dutch diabetes patient registries. Participants included adult patients newly diagnosed with type 2 diabetes between January 2006 and December 2014 (development cohort, n=10528; validation cohort, n=3777). Latent growth mixture modelling identified distinct glycaemic 5-year trajectories. Machine learning models were built to predict the trajectories using easily obtainable patient characteristics in daily clinical practice. ResultsThree different glycaemic trajectories were identified: (1) stable, adequate glycaemic control (76.5% of patients); (2) improved glycaemic control (21.3% of patients); and (3) deteriorated glycaemic control (2.2% of patients). Similar trajectories could be discerned in the validation cohort. Body mass index and glycated haemoglobin and triglyceride levels were the most important predictors of trajectory membership. The predictive model, trained on the development cohort, had a receiver-operating characteristic area under the curve of 0.96 in the validation cohort, indicating excellent accuracy. ConclusionsThe developed model can effectively explain heterogeneity in future glycaemic response of patients with type 2 diabetes. It can therefore be used in clinical practice as a quick and easy tool to provide tailored diabetes care
Tailored Healthcare: Two Perspectives on the Development and Use of Patient Profiles
Calls for a more tailored approach to the management of cardiometabolic and musculoskeletal diseases have been increasing. Although tailored care is a centuries-old concept, it is still unclear how it should be best practised. The current paper introduces two phenotype-based Dutch approaches to support tailored care. One approach focuses on patients with type 2 diabetes, the other on patients undergoing total joint replacement. Using the patient profiling approach, both projects propose that care can be tailored by the assessment of biopsychosocial patient characteristics, stratification of patients into subgroups of patients with similar care needs, abilities, and preferences (so-called patient profiles) and tailoring of care in concordance with the common care preferences of these profiles. In this article, the advantages and disadvantages of the method are discussed to enable researchers or clinicians who want to extend the patient profiling approach to other patient populations to carefully evaluate these in relation to their project’s focus and available resources. Funding: Novo Nordisk B.V., the Netherlands Organisation for Scientific Research (NWO) (Grant 314-99-118) and Zimmer Biomet Inc.Applied Ergonomics and Desig
Tailored Health Care: Two Perspectives on the Development and Use of Patient Profiles
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Supporting professionals to implement integrated, person-centered care for people with chronic conditions: the TARGET pilot study
AbstractObjective The TARGET program for integrated, person-centered care for people with chronic conditions offers primary care (PC) professionals a set of tools and trainings to actively engage in population segmentation and person-centered needs assessments (PCNAs). A pilot study was conducted to gain insight into the program’s feasibility and acceptability, and identify preconditions for successful implementation.Design and setting Seven Dutch PC practices participated in a half-year pilot study starting in August 2020. We performed a review of the population segmentation tool, observed four training sessions and 15 PCNAs, and interviewed 15 professionals and 12 patients.Results Regarding feasibility and acceptability, we found that the tools and trainings provided professionals with skills to use the segmentation tool and take a more coaching role in the well-appreciated PCNAs. Concerning implementation preconditions, we found that team commitment and network connections need improvement, although work pleasure increased and professionals generally wanted the program to continue.Conclusions While the content of the TARGET program is supported by its users, the implementation process, for instance team commitment to the program, needs more attention in future upscaling efforts
Relevant patient characteristics for guiding tailored integrated diabetes primary care:a systematic review
Aim To identify which patient-related effect modifiers influence the outcomes of integrated care programs for type 2 diabetes in primary care. Background Integrated care is a widespread management strategy for the treatment of type 2 diabetes. However, most integrated care programs are not tailored to patients' needs, preferences and abilities. There is increasing consensus that such a patient-centered approach could improve the management of type 2 diabetes. Thus far, it remains unclear which patient-related effect modifiers should guide such an approach. Methods PubMed, CINAHL and EMBASE were searched for empirical studies published after 1998. A systematic literature review was conducted according to the PRISMA guidelines. Findings In total, 23 out of 1015 studies were included. A total of 21 studies measured the effects of integrated diabetes care programs on hemoglobin A1c (HbA1c) and three on low-density lipoprotein cholesterol, systolic blood pressure and health-care utilization. In total, 49 patient characteristics were assessed as potential effect modifiers with HbA1c as an outcome, of which 46 were person or health-related and only three were context-related. Younger age, insulin therapy and longer disease duration were associated with higher HbA1c levels in cross-sectional and longitudinal studies. Higher baseline HbA1c was associated with higher HbA1c at follow-up in longitudinal studies. Information on context- and person-related characteristics was limited, but is necessary to help identify the care needs of individual patients and implement an effective integrated type 2 diabetes tailored care program.</p
Relevant patient characteristics for estimating healthcare needs according to healthcare providers and people with type 2 diabetes: a Delphi survey
Profiling Patients' Healthcare Needs to Support Integrated, Person-Centered Models for Long-Term Disease Management (Profile): Research Design
<p class="p1"><strong>Background: </strong>This article presents the design of PROFILe, a study investigating which (bio)medical and non-(bio)medical patient characteristics should guide more tailored chronic care. Based on this insight, the project aims to develop and validate ‘patient profiles’ that can be used in practice to determine optimal treatment strategies for subgroups of chronically ill with similar healthcare needs and preferences. <p class="p2"><strong>Methods/Design: </strong>PROFILe is a practice-based research comprising four phases. The project focuses on patients with type 2 diabetes. During the first study phase, patient profiles are drafted based on a systematic literature research, latent class growth modeling, and expert collaboration. In phase 2, the profiles are validated from a clinical, patient-related and statistical perspective. Phase 3 involves a discrete choice experiment to gain insight into the patient preferences that exist per profile. In phase 4, the results from all analyses are integrated and recommendations formulated on which patient characteristics should guide tailored chronic care. <p class="p2"><strong>Discussion: </strong>PROFILe is an innovative study which uses a uniquely holistic approach to assess the healthcare needs and preferences of chronically ill. The patient profiles resulting from this project must be tested in practice to investigate the effects of tailored management on patient experience, population health and costs
Person-related patient characteristics by glycaemic control.
<p>Person-related patient characteristics by glycaemic control.</p