Improving the external validity of clinical trials: the case of multiple chronic conditions

The U.S. Department of Health and Human Services vision and strategic framework on multiple chronic conditions (MCCs) incorporates recommendations designed to facilitate research that will improve our knowledge about interventions and systems that will benefit individuals with MCCs (or multimorbidity). The evidence base supporting the management of patients with MCCs will be built both through intervention trials specifically designed to address multimorbidity and identification of MCCs in participants across the clinical trial range. This article specifically focuses on issues relating to external validity with specific reference to trials involving patients with MCCs. The exclusion of such patients from clinical trials has been well documented. Randomized control trials (RCTs) are considered the “gold standard” of evidence, but may have drawbacks in relation to external validity, particularly in relation to multimorbidity. It may, therefore, be necessary to consider a broader range of research methods that can provide converging evidence on intervention effects to address MCCs. Approaches can also be taken to increase the usefulness of RCTs in general for providing evidence to inform multimorbidity management. Additional improvements to RCTs would include better reporting of inclusion and exclusion criteria and participant characteristics in relation to MCCs. New trials should be considered in terms of how they will add to the existing evidence base and should inform how interventions may work in different settings and patient groups. Research on treatments and interventions for patients with MCCs is badly needed. It is important that this research includes patient-centered measures and that generalizability issues be explicitly addressed.Journal of Comorbidity 2013;3(2)30–3

Self-management interventions in patients with long-term conditions: a structured review of approaches to reporting inclusion, assessment, and outcomes in multimorbidity

Background: Multimorbidity has many potential implications for healthcare delivery, but a particularly important impact concerns the validity of trial evidence underpinning clinical guidelines for individual conditions. Objective: To review how authors of published trials of self-management interventions reported inclusion criteria, sample descriptions, and consideration of the impact of multimorbidity on trial outcomes. Methods: We restricted our analysis to a small number of exemplar long-term conditions: type 2 diabetes mellitus, coronary heart disease, and chronic obstructive pulmonary disease. We focussed our search on published Cochrane reviews. Data were extracted from the trials on inclusion/exclusion, sample description, and impact on outcomes. Results: Eleven reviews consisting of 164 unique trials were identified. Sixty percent of trials reported excluding patients with forms of multimorbidity. Reasons for exclusion were poorly described or defined. Reporting of multimorbidity within the trials was poor, with only 35% of trials reporting on multimorbidity in their patient samples. Secondary analyses, exploring the impact of multimorbidity, were very rare. Conclusions: The importance of multimorbidity in trials is only going to become more important over time, but trials often exclude patients with multimorbidity, and reporting of multimorbidity in trials including such patients is generally poor. This limits judgements about the external validity of the results for clinical populations. A consistent approach to the conduct and reporting of secondary analyses of the effects of multimorbidity on outcomes, using current best-practice guidance, could lead to a rapid development of the evidence base. Journal of Comorbidity 2014;4(1):37–4

Comorbidity patterns in patients with chronic diseases in general practice

INTRODUCTION: Healthcare management is oriented toward single diseases, yet multimorbidity is nevertheless the rule and there is a tendency for certain diseases to occur in clusters. This study sought to identify comorbidity patterns in patients with chronic diseases, by reference to number of comorbidities, age and sex, in a population receiving medical care from 129 general practitioners in Spain, in 2007. METHODS: A cross-sectional study was conducted in a health-area setting of the Madrid Autonomous Region (Comunidad Autónoma), covering a population of 198,670 individuals aged over 14 years. Multiple correspondences were analyzed to identify the clustering patterns of the conditions targeted. RESULTS: Forty-two percent (95% confidence interval [CI]: 41.8-42.2) of the registered population had at least one chronic condition. In all, 24.5% (95% CI: 24.3-24.6) of the population presented with multimorbidity. In the correspondence analysis, 98.3% of the total information was accounted for by three dimensions. The following four, age- and sex-related comorbidity patterns were identified: pattern B, showing a high comorbidity rate; pattern C, showing a low comorbidity rate; and two patterns, A and D, showing intermediate comorbidity rates. CONCLUSIONS: Four comorbidity patterns could be identified which grouped diseases as follows: one showing diseases with a high comorbidity burden; one showing diseases with a low comorbidity burden; and two showing diseases with an intermediate comorbidity burden.This study was partially supported by the CENIT Program (MICINN-CDTI) [CEN-2007-1010 ‘‘Digital personal environment for health and wellbeing – AmiVital’’ project], a grant from the Ministry of Health & Consumer Affairs [FIS PI08-0435], and the MOBIS Program of the Spanish Vodafone Foundation . The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.S

COMORBIDITY AND MULTIMORBIDITY IN MEDICINE TODAY: CHALLENGES AND OPPORTUNITIES FOR BRINGING SEPARATED BRANCHES OF MEDICINE CLOSER TO EACH OTHER

Comorbidity and multimorbidity represent one of the greatest chalenge to academic medicine. Many disorders are often comorbidly expressed in diverse combinations. In clinical practice comorbidity and multimorbidity are underrecognized, underdiagnosed, underestimated and undertreated. So that one can speak about comorbidity and multimorbidity anosognosia. Comorbidities and multimorbidities are indifferent to medical specializations, so the integrative and complementary medicine is an imperative in the both education and practice. Shifting the paradigm from vertical/mono-morbid interventions to comorbidity and multimorbidity approaches enhances effectiveness and efficiency of human resources utilization. Comorbidity and multimorbidity studies have been expected to be an impetus to research on the validity of current diagnostic systems as well as on establishing more effective and efficient treatment including individualized and personalized pharmacotherapy

A prognostic model predicted deterioration in health-related quality of life in older patients with multimorbidity and polypharmacy

OBJECTIVE: To develop and validate a prognostic model to predict deterioration in health-related quality of life (dHRQoL) in older general practice patients with at least one chronic condition and one chronic prescription. STUDY DESIGN AND SETTING: We used individual participant data from five cluster-randomized trials conducted in the Netherlands and Germany to predict dHRQoL, defined as a decrease in EQ-5D-3L index score of =5 % after six-month follow-up in logistic regression models with stratified intercepts to account for between-study heterogeneity. The model was validated internally, and by using internal-external cross-validation (IECV). RESULTS: - In 3,582 patients with complete data, of whom 1,046 (29.2 %) showed deterioration in HRQoL, 12/87 variables were selected that were related to single (chronic) conditions, inappropriate medication, medication underuse, functional status, well-being and HRQoL. Bootstrap internal validation showed a C-statistic of 0.71 (0.69 to 0.72), and a calibration slope of 0.88 (0.78 to 0.98). In the IECV loop, the model provided a pooled C-statistic of 0.68 (0.65 to 0.70) and calibration-in-the-large of 0 (-0.13 to 0.13). HRQoL/functionality had the strongest prognostic value. CONCLUSION: - The model performed well in terms of discrimination, calibration, and generalizability and might help clinicians identify older patients at high-risk of dHRQoL. REGISTRATION: PROSPERO ID: CRD42018088129

Participant characteristics and exclusion from trials: a meta-analysis of individual participant-level data from phase 3/4 industry-funded trials in chronic medical conditions

Objectives Trials often do not represent their target populations, threatening external validity. The aim was to assess whether age, sex, comorbidity count and/or race/ethnicity are associated with likelihood of screen failure (i.e., failure to be enrolled in the trial for any reason) among potential trial participants.Design Bayesian meta-analysis of individual participant-level data (IPD).SettingIndustry-funded phase 3/4 trials in chronic medical conditions. Participants were identified as “enrolled” or “screen failure” using trial IPD.Participants Data were available for 52 trials involving 72,178 screened individuals of whom 24,733 (34%) failed screening.Main outcome measures For each trial, logistic regression models were constructed to assess likelihood of screen failure in people who had been invited to screening, regressed on age (per 10-year increment), sex (male versus female), comorbidity count (per one additional comorbidity) and race/ethnicity. Trial-level analyses were combined in Bayesian hierarchical models with pooling across condition.ResultsIn age- and sex-adjusted models across all trials, neither age nor sex was associated with increased odds of screen failure, though weak associations were detected after additionally adjusting for comorbidity (age, per 10-year increment: odds ratio [OR] 1.02; 95% credibility interval [CI] 1.01 to 1.04 and male sex: OR 0.95; 95% CI 0.91 to 1.00). Comorbidity count was weakly associated with screen failure, but in an unexpected direction (OR 0.97 per additional comorbidity, 95% CI 0.94 to 1.00, adjusted for age and sex). Those who self-reported as Black were slightly more likely to fail screening (OR 1.04; 95% CI 0.99 to 1.09); an effect which persisted after adjustment for age, sex and comorbidity count (OR 1.05; 95% CI 0.98 to 1.12). The between-trial heterogeneity was generally low, but there was evidence of heterogeneity by sex across conditions (variation in odds ratios on log-scale of 0.01-0.13).Conclusions Though the conclusions are limited by uncertainty about the completeness or accuracy of data collection among non-randomised participants, we identified mostly weak associations between age, sex, comorbidity count and Black race/ethnicity and increased likelihood of screen failure. Proportionate increases in screening these underserved populations may improve representation in trials. Trial registration Relevant trials in chronic medical conditions were identified according to pre-specified criteria (PROSPERO CRD42018048202) then analysed according to availability of IPD. <br/

Trajectories of Health Status in Older People

One of the main successes of healthcare is the continuing increase in life expectancy. This challenges both older people themselves and the society due to the increase in people aged 80 years and over in the coming decades. Older people want to live as independently and healthy for as long as possible. It is therefore important to recognize early signs of health status deterioration in older people, so that effective actions can be undertaken at an early stage, by the elderly themselves and by informal caregivers. This dissertation showed that: 1. Measuring change in health status is not straightforward. Some common and frequently used instruments to measure functional status and frailty status in geriatrics appeared suitable for research purposes, but only at group level. These instruments are not suitable for measuring change in the health status of individual older patients. 2. A small decline in activities of daily living during hospitalization does not reflect the functional decline as experienced by the patient. 3. Chronic illnesses and frailty are predictive of unfavorable health status trajectories in both community-dwelling older people and older hospitalized patients. 4. Improving lifestyle behavior and reducing fatigability potentially reverse or positively affect the negative trend of an unfavorable health status. 5. Further research is needed to identify older people who will benefit the most from interventions to positively affect negative health outcomes. Only then can concrete recommendations be formulated for developing targeted interventions