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Multimorbidity is a health issue mostly dealt with in primary care practice. As a result of their generalist and patient-centered approach, long-lasting relationships with patients, and responsibility for continuity and coordination of care, family physicians are particularly well placed to manage patients with multimorbidity. However, conflicts arising from the application of multiple disease oriented guidelines and the burden of diseases and treatments often make consultations challenging. To provide orientation in decision making in multimorbidity during primary care consultations, we developed guiding principles and named them after the Greek mythological figure Ariadne. For this purpose, we convened a two-day expert workshop accompanied by an international symposium in October 2012 in Frankfurt, Germany. Against the background of the current state of knowledge presented and discussed at the symposium, 19 experts from North America, Europe, and Australia identified the key issues of concern in the management of multimorbidity in primary care in panel and small group sessions and agreed upon making use of formal and informal consensus methods. The proposed preliminary principles were refined during a multistage feedback process and discussed using a case example. The sharing of realistic treatment goals by physicians and patients is at the core of the Ariadne principles. These result from i) a thorough interaction assessment of the patient’s conditions, treatments, constitution, and context; ii) the prioritization of health problems that take into account the patient's preferences – his or her most and least desired outcomes; and iii) individualized management realizes the best options of care in diagnostics, treatment, and prevention to achieve the goals. Goal attainment is followed-up in accordance with a re-assessment in planned visits. The occurrence of new or changed conditions, such as an increase in severity, or a changed context may trigger the (re-)start of the process. Further work is needed on the implementation of the formulated principles, but they were recognized and appreciated as important by family physicians and primary care researchers.
Background: We conducted a comprehensive medication review at the patients’ home, using data from electronic patient records, and with input from relevant specialists, general practitioners and pharmacists formulated and implemented recommendations to optimize medication use in patients aged 60+ years with polypharmacy. We evaluated the effect of this medication review on quality of life (QoL) and medication use. Methods: Cluster randomized controlled trial (stepped wedge), randomly assigning general practices to one of three consecutive steps. Patients received usual care until the intervention was implemented. Primary outcome was QoL (SF-36 and EQ-5D); secondary outcomes were medication changes, medication adherence and (instrumental) activities of daily living (ADL, iADL) which were measured at baseline, and around 6- and 12-months post intervention. Results: Twenty-four general practices included 360 women and 410 men with an average age of 75 years (SD 7.5). A positive effect on SF-36 mental health (estimated mean was stable in the intervention, but decreased in the control condition with −6.1, p = 0.009,) was found with a reduced number of medications at follow-up compared to the control condition. No significant effects were found on other QoL subscales, ADL, iADL or medication adherence. Conclusion: The medication review prevented decrease of mental health (SF36), with no significant effects on other outcome measures, apart from a reduction in the number of prescribed medications.
Health-related preferences of older patients with multimorbidity: the protocol for an evidence map
(2019)
Introduction: Interaction of conditions and treatments, complicated care needs and substantial treatment burden make patient–physician encounters involving multimorbid older patients highly complex. To optimally integrate patients’ preferences, define and prioritise realistic treatment goals and individualise care, a patient-centred approach is recommended. However, the preferences of older patients, who are especially vulnerable and frequently multimorbid, have not been systematically investigated with regard to their health status. The purpose of this evidence map is to explore current research addressing health-related preferences of older patients with multimorbidity, and to identify the knowledge clusters and research gaps.
Methods and analysis: To identify relevant research, we will conduct searches in the electronic databases MEDLINE, EMBASE, PsycINFO, PSYNDEX, CINAHL, Social Science Citation Index, Social Science Citation Index Expanded and the Cochrane library from their inception. We will check reference lists of relevant articles and carry out cited reference research (forward citation tracking). Two independent reviewers will screen titles and abstracts, check full texts for eligibility and extract the data. Any disagreement will be resolved and consensus reached with the help of a third reviewer. We will include both qualitative and quantitative studies, and address preferences from the patients’ perspectives in a multimorbid population of 60 years or older. There will be no restrictions on the publication language. Data extraction tables will present study and patient characteristics, aim of study, methods used to identify preferences and outcomes (ie, type of preferences). We will summarise the data using tables and figures (ie, bubble plot) to present the research landscape and to describe clusters and gaps.
Ethics and dissemination: Due to the nature of the proposed evidence map, ethics approval will not be required. Results from our research will be disseminated by means of specifically prepared materials for patients, at relevant (inter)national conferences and via publication in peer-reviewed journals.
Introduction End-of-life care is an essential task performed by most healthcare providers and often involves decision-making about how and where patients want to receive care. To provide decision support to healthcare professionals and patients in this difficult situation, we will systematically review a knowledge cluster of the end-of-life care preferences of older patients with multimorbidity that we previously identified using an evidence map.
Methods and analysis We will systematically search for studies reporting end-of-life care preferences of older patients (mean age ≥60) with multimorbidity (≥2 chronic conditions) in MEDLINE, CINAHL, PsycINFO, Social Sciences Citation Index, Social Sciences Citation Index Expanded, PSYNDEX and The Cochrane Library from inception to September 2019. We will include all primary studies that use quantitative, qualitative and mixed methodologies, irrespective of publication date and language.
Two independent reviewers will assess eligibility, extract data and describe evidence in terms of study/population characteristics, preference assessment method and end-of-life care elements that matter to patients (eg, life-sustaining treatments). Risk of bias/applicability of results will be independently assessed by two reviewers using the Mixed-Methods Appraisal Tool. Using a convergent integrated approach on qualitative/quantitative studies, we will synthesise information narratively and, wherever possible, quantitatively.
Ethics and dissemination Due to the nature of the proposed systematic review, ethics approval is not required. Results from our research will be disseminated at relevant (inter-)national conferences and via publication in peer-reviewed journals. Synthesising evidence on end-of-life care preferences of older patients with multimorbidity will improve shared decision-making and satisfaction in this final period of life.
Objectives: Investigate the effectiveness of a complex intervention aimed at improving the appropriateness of medication in older patients with multimorbidity in general practice.
Design: Pragmatic, cluster randomised controlled trial with general practice as unit of randomisation.
Setting: 72 general practices in Hesse, Germany.
Participants: 505 randomly sampled, cognitively intact patients (≥60 years, ≥3 chronic conditions under pharmacological treatment, ≥5 long-term drug prescriptions with systemic effects); 465 patients and 71 practices completed the study.
Interventions: Intervention group (IG): The healthcare assistant conducted a checklist-based interview with patients on medication-related problems and reconciled their medications. Assisted by a computerised decision support system, the general practitioner optimised medication, discussed it with patients and adjusted it accordingly. The control group (CG) continued with usual care.
Outcome measures: The primary outcome was a modified Medication Appropriateness Index (MAI, excluding item 10 on cost-effectiveness), assessed in blinded medication reviews and calculated as the difference between baseline and after 6 months; secondary outcomes after 6 and 9 months’ follow-up: quality of life, functioning, medication adherence, and so on.
Results: At baseline, a high proportion of patients had appropriate to mildly inappropriate prescriptions (MAI 0–5 points: n=350 patients). Randomisation revealed balanced groups (IG: 36 practices/252 patients; CG: 36/253). Intervention had no significant effect on primary outcome: mean MAI sum scores decreased by 0.3 points in IG and 0.8 points in CG, resulting in a non-significant adjusted mean difference of 0.7 (95% CI −0.2 to 1.6) points in favour of CG. Secondary outcomes showed non-significant changes (quality of life slightly improved in IG but continued to decline in CG) or remained stable (functioning, medication adherence).
Conclusions: The intervention had no significant effects. Many patients already received appropriate prescriptions and enjoyed good quality of life and functional status. We can therefore conclude that in our study, there was not enough scope for improvement.
Trial registration number: ISRCTN99526053. NCT01171339; Results.
Objective To explore factors that potentially impact external validation performance while developing and validating a prognostic model for hospital admissions (HAs) in complex older general practice patients.
Study design and setting Using individual participant data from four cluster-randomised trials conducted in the Netherlands and Germany, we used logistic regression to develop a prognostic model to predict all-cause HAs within a 6-month follow-up period. A stratified intercept was used to account for heterogeneity in baseline risk between the studies. The model was validated both internally and by using internal-external cross-validation (IECV).
Results Prior HAs, physical components of the health-related quality of life comorbidity index, and medication-related variables were used in the final model. While achieving moderate discriminatory performance, internal bootstrap validation revealed a pronounced risk of overfitting. The results of the IECV, in which calibration was highly variable even after accounting for between-study heterogeneity, agreed with this finding. Heterogeneity was equally reflected in differing baseline risk, predictor effects and absolute risk predictions.
Conclusions Predictor effect heterogeneity and differing baseline risk can explain the limited external performance of HA prediction models. With such drivers known, model adjustments in external validation settings (eg, intercept recalibration, complete updating) can be applied more purposefully.
Trial registration number PROSPERO id: CRD42018088129.
Background: Cumulative anticholinergic exposure, also known as anticholinergic burden, is associated with a variety of adverse outcomes. However, studies show that anticholinergic effects tend to be underestimated by prescribers, and anticholinergics are the most frequently prescribed potentially inappropriate medication in older patients. The grading systems and drugs included in existing scales to quantify anticholinergic burden differ considerably and do not adequately account for patients’ susceptibility to medications. Furthermore, their ability to link anticholinergic burden with adverse outcomes such as falls is unclear. This study aims to develop a prognostic model that predicts falls in older general practice patients, to assess the performance of several anticholinergic burden scales, and to quantify the added predictive value of anticholinergic symptoms in this context.
Methods: Data from two cluster-randomized controlled trials investigating medication optimization in older general practice patients in Germany will be used. One trial (RIME, n = 1,197) will be used for the model development and the other trial (PRIMUM, n = 502) will be used to externally validate the model. A priori, candidate predictors will be selected based on a literature search, predictor availability, and clinical reasoning. Candidate predictors will include socio-demographics (e.g. age, sex), morbidity (e.g. single conditions), medication (e.g. polypharmacy, anticholinergic burden as defined by scales), and well-being (e.g. quality of life, physical function). A prognostic model including sociodemographic and lifestyle-related factors, as well as variables on morbidity, medication, health status, and well-being, will be developed, whereby the prognostic value of extending the model to include additional patient-reported symptoms will be also assessed. Logistic regression will be used for the binary outcome, which will be defined as “no falls” vs. “≥1 fall” within six months of baseline, as reported in patient interviews. Discussion: As the ability of different anticholinergic burden scales to predict falls in older patients is unclear, this study may provide insights into their relative importance as well as into the overall contribution of anticholinergic symptoms and other patient characteristics. The results may support general practitioners in their clinical decision-making and in prescribing fewer medications with anticholinergic properties.
Meeting Abstract : 10. Deutscher Kongress für Versorgungsforschung, 18. GAA-Jahrestagung. Deutsches Netzwerk Versorgungsforschung e. V. ; Gesellschaft für Arzneimittelanwendungsforschung und Arzneimittelepidemiologie e. V. 20.-22.10.2011, Köln
Hintergrund: Multimedikation als Folge von Multimorbidität ist ein zentrales Problem der Hausarztpraxis und erhöht das Risiko für unangemessene Arzneimittel-Verordnungen (VO). Um die Medikation bei älteren, multimorbiden Patienten zu optimieren und zu priorisieren, wurde eine computergestützte, durch Medizinische Fachangestellte (MFA) assistierte, komplexe Intervention (checklistengestütztes Vorbereitungsgespräch sowie Überprüfung eingenommener Medikamente durch MFA, Einsatz des web-basierten ArzneimittelinformationsDienstes AiD, spezifisches Arzt-Patienten-Gespräch) entwickelt und in einer 12-monatigen Pilotstudie auf Machbarkeit getestet. Ein auf 9 Items reduzierter MAI [1] wurde eingesetzt, um dessen Eignung als potentielles primäres Outcome der Hauptstudie zu prüfen.
Material und Methoden: In die Pilotstudie in 20 Hausarztpraxen mit Cluster-Randomisation auf Praxisebene in Kontrollgruppe (Regelversorgung b. empfohlenem Standard) vs. Interventionsgruppe (komplexe Intervention b. empfohlenem Standard) wurden 5 Pat./Praxis eingeschlossen (≥65 Jahre, ≥3 chron. Erkrankungen, ≥5 Dauermedikamente, MMSE ≥26, Lebenserwartung ≥6 Monate). Zur Bewertung des MAI wurden an Baseline (T0), 6 Wo. (T1) & 3 Mon. (T2) nach Intervention erhoben: VO, Diagnosen, Natrium, Kalium & Kreatinin i.S., Größe, Gewicht, Geschlecht, Cumulative Illness Rating Scale (CIRS) [2] durch die Hausarztpraxis; Symptome für unerwünschte Arzneimittelwirkungen im Patienten-Telefoninterview.
Für den MAI wurde die Angemessenheit jeder VO in den 9 Kategorien Indikation, Effektivität, Dosierung, korrekter & praktikabler Applikationsweg, Arzneimittelwechselwirkung, Drug-disease-Interaktion, Doppelverordnung, Anwendungsdauer 3-stufig bewertet (1 = korrekt - 3 = unkorrekt) und für die Auswertung auf Patientenebene summiert. Die Bewertung erfolgte ohne Kenntnis der Gruppenzugehörigkeit. Deskriptive Statistiken und Reliabilitätsanalysen, ungewichtete Auswertung und Gewichtung n. Bregnhoj [3].
Ergebnisse: Es wurden N=100 Patienten in die Studie eingeschlossen, im Mittel 76 Jahre (Standardabweichung, SD 6; Range, R: 64-93) , 52% Frauen, durchschnittlich 9 VO/Pat. (SD 2; R 4-16), mittlerer CIRS-Score 10 (SD 4; R 0-23). Basierend auf N=851 VO (100 Pat.) zu T0 betrug der Reliabilitätskoeffizient (RK, Cronbachs Alpha) der ungewichteten 9 Items 0,70. Items 1-5 wiesen akzeptable Trennschärfen auf (0,52-0,64), die der Items 6, 7 & 9 fielen mit 0,21-0,29 niedriger aus, die des Item 8 betrug 0,06. Auf der Basis der 9 gewichteten Items fiel die interne Konsistenz des MAI erwartet höher aus (0,75). Die Reliabilitätsanalysen auf VO-Ebene zeigten einen RK von 0,67 (ungewichtet) vs. 0,75 (gewichtet), die Trennschärfen waren vergleichbar. Zur Zwischenauswertung betrug der MAI (T1-T0) in der Interventionsgruppe (5 Praxen, 24 Pat.) -0,9 (SD 5,6), in der Kontrollgruppe (7 Praxen, 35 Pat.) -0,5 (SD 4,9); die Differenz zwischen beiden Gruppen Mi–Mk -0,4 [95% Konfidenzintervall: -3,4;2,6].
Schlussfolgerung: Der MAI ist als potentielles primäres Outcome in der Hauptstudie geeignet: wenige fehlende Werte, Darstellung von Unterschieden prä-post und zwischen den Gruppen, akzeptable interne Konsistenz. Der niedrige Trennschärfekoeffizient des Items 8 weist darauf hin, dass dieses Item nicht mit dem Gesamt-Skalenwert korreliert, auch die Items 6, 7 und 9 korrelieren wesentlich schwächer mit dem Gesamt-Skalenwert als die Items 1 bis 5. Eine Wichtung z.B. der Items 2, 5, 6 und 9 könnte erwogen werden, um den Fokus der Intervention in der Hauptzielgröße angemessen abzubilden.
Mit den Krankheiten häuft sich im Alter auch die Zahl der einzunehmenden Medikamente. Das bringt viele Probleme mit sich. Das Institut für Allgemeinmedizin der Goethe-Universität untersucht in enger Kooperation mit der Universität Maastricht die Folgen der Multimedikation und entwickelt gemeinsam mit Hausärzten Strategien, um unerwünschte Wirkungen zu vermeiden.
Background: Receiving a cancer diagnosis can be a major life event which causes distress even years after primary treatment.
Aim: To examine the prevalence of distress in older patients with cancer (OPCs) up until 5 years post-diagnosis, and identify predictors present at time of diagnosis. Results are compared with reference groups of middle-aged patients with cancer (MPCs) and older patients without a cancer diagnosis (OPs).
Design & setting: OPCs, MPCs, and OPs participated in a longitudinal cohort study in Belgium and the Netherlands by filling in questionnaires at designated time points from 2010–2019.
Method: Data from 541 patients were analysed using multivariable logistic regression analyses.
Results: At baseline, 40% of OPCs, 37% of MPCs, and 17% of OPs reported distress. After 5 years, 35% of OPCs, 23% of MPCs, and 25% of OPs reported distress. No significant predictors for long-term distress in OPCs and OPs were found. For MPCs, it was found that baseline distress (odds ratio [OR] 2.94; 95% confidence intervals [CI] = 1.40 to 6.19) and baseline fatigue (OR 4.71; 95% CI = 1.81 to 12.31) predicted long-term distress.
Conclusion: Distress is an important problem for people with cancer, with peaks at different moments after diagnosis. Feelings of distress are present shortly after diagnosis but they decrease quickly for the majority of patients. In the long term, however, OPCs in particular appear to be most at risk for distress. This warrants extra attention from primary healthcare professionals, such as GPs who are often patients’ first medical contact point. More research into risk factors occurring later in an illness trajectory might shed more light on predictors for development of long-term distress.