Support of personalized medicine through risk-stratified treatment recommendations - an environmental scan of clinical practice guidelines

The discussion of risk in German surgical clinical practice guidelines: a qualitative review

Objectives

Clinical practice guidelines (CPGs) have a potentially important role regarding the assessment and communication of the risks of perioperative complications. This study aimed to (1) examine the content of German surgical CPGs in relation to surgical risks and (2) provide baseline results for future research in order to assess the development of surgical CPGs in Germany in relation to this issue.

Methods

In November 2015, all German surgical CPGs that provide guidance regarding illnesses that can be treated with a surgical procedure were collected from the websites of the German umbrella organisation of medical professional associations and the German Association for Cardiology.

Results

Data collection retrieved 230 CPGs of which 214 were included in the final analysis. The analysis identified four different groups: 1) 5% (10/214) of guidelines did not discuss “risks” or “complications” at all; 2) 21% (44/214) of guidelines discussed general risks that are not related to surgical complications; 3) 35% (76/214) of guidelines discussed surgical complications and often discussed their likelihood in terms of “high risk” or “low risk”, but did not provide numeric estimates and 4) 39% (84/214) of guidelines discussed specific surgical risks and also provided numerical risk estimates. Guidelines with higher methodological quality more frequently included numerical risk estimates.

Conclusions

It is positive that the vast majority of German surgical CPGs address the issue of risks. However, it would be helpful if more German surgical CPGs provide explicit and evidence-based estimates and recommendations relating to the surgical risk to support surgeons in providing high-quality care and to meet their ethical obligations to patients.

Learning decision thresholds for risk stratification models from aggregate clinician behavior

Using a risk stratification model to guide clinical practice often requires the choice of a cutoff—called the decision threshold—on the model’s output to trigger a subsequent action such as an electronic alert. Choosing this cutoff is not always straightforward. We propose a flexible approach that leverages the collective information in treatment decisions made in real life to learn reference decision thresholds from physician practice. Using the example of prescribing a statin for primary prevention of cardiovascular disease based on 10-year risk calculated by the 2013 pooled cohort equations, we demonstrate the feasibility of using real-world data to learn the implicit decision threshold that reflects existing physician behavior. Learning a decision threshold in this manner allows for evaluation of a proposed operating point against the threshold reflective of the community standard of care. Furthermore, this approach can be used to monitor and audit model-guided clinical decision making following model deployment.

Large-scale validation of the prediction model risk of bias assessment Tool (PROBAST) using a short form: high risk of bias models show poorer discrimination

OBJECTIVE

To assess whether the Prediction model Risk Of Bias ASsessment Tool (PROBAST) and a shorter version of this tool can identify clinical prediction models (CPMs) that perform poorly at external validation.

STUDY DESIGN AND SETTING

We evaluated risk of bias (ROB) on 102 CPMs from the Tufts CPM Registry, comparing PROBAST to a short form consisting of six PROBAST items anticipated to best identify high ROB. We then applied the short form to all CPMs in the Registry with at least 1 validation (n=556) and assessed the change in discrimination (dAUC) in external validation cohorts (n=1,147).

RESULTS

PROBAST classified 98/102 CPMS as high ROB. The short form identified 96 of these 98 as high ROB (98% sensitivity), with perfect specificity. In the full CPM registry, 527 of 556 CPMs (95%) were classified as high ROB, 20 (3.6%) low ROB, and 9 (1.6%) unclear ROB. Only one model with unclear ROB was reclassified to high ROB after full PROBAST assessment of all low and unclear ROB models. Median change in discrimination was significantly smaller in low ROB models (dAUC -0.9%, IQR -6.2-4.2%) compared to high ROB models (dAUC -11.7%, IQR -33.3-2.6%; P<0.001).

CONCLUSION

High ROB is pervasive among published CPMs. It is associated with poor discriminative performance at validation, supporting the application of PROBAST or a shorter version in CPM reviews.

A systematic review of the status and methodological considerations for estimating risk of first ever stroke in the general population

AIMS

The methodological quality of development, validation, and modification of those models have not been evaluated via a thoroughly literature review. This study aims to describe the overall status and evaluate the methodological quality of risk prediction models for stroke incidence in the general population.

METHODS

We searched the database of EMBASE and MEDLINE by the combination of subject words and key words to collect the research on stroke risk prediction model in the general population. The retrieval time was from the establishment of the database to September 2019. It should be mentioned that risk of bias for each model was assessed, and data on population characteristics and model performance was also extracted.

RESULTS

The search screened 11,386 peer-reviewed publications and 57 citation searching, of which 48 were included in the review, describing the development of 51 prediction models, 47 external validation models, and 12 modification models. Among 51 development models, the predicted outcome concentrated on fatal or non-fatal stroke (n = 37, 73%). Thirty-nine development models (76%) were without internal validation. C-statistic or AUC was adopted for discrimination in 80% models, and Hosmer-Lemeshow test (n = 25, 49%) was also performed for calibration. Twenty-six development models (53%) were externally validated, among which only 2 (8%) were validated by independent researchers. Risk prediction performance was improved when models were modified by adding novel risk factors, such as the internal carotid artery plaque and intima-media thickness.

CONCLUSION

Models for predicting stroke occurrence need further external validation, recalibration, or modification in different populations, to help interpret those models in the practice of stroke prevention.

Stratified treatment recommendation or one-size-fits-all? A health economic insight based on graphical exploration

OBJECTIVES

We sought to explore to what extent the use of Subpopulation Treatment Effect Pattern Plot (STEPP) may help to identify efficient treatment allocation strategy.

METHODS

The analysis was based on data from the COACH study, in which 1023 patients with heart failure were randomly assigned to three treatments: care-as-usual, basic support, and intensive support. First, using predicted 18-month mortality risk as the stratification basis, a suitable strategy for assigning different treatments to different risk groups of patients was developed. To that end, a graphical exploration of the difference in net monetary benefit (NMB) across treatment regimens and baseline risk was used. Next, the efficiency gains resulting from this proposed subgroup strategy were quantified by computing the difference in NMB between our stratified approach and the best performing population-wide strategy.

RESULTS

The analysis using STEPPs suggested that a differentiated approach, based on offering intensive support to low-risk patients (18-month mortality risk ≤ 0.16) and basic support to intermediate- to high-risk patients (18-month mortality risk > 0.16) would be an economically efficient treatment allocation strategy. This was confirmed in the subsequent cost-effectiveness analysis, where the average gain in NMB resulting from the proposed stratified approach compared to basic support for all was found to be €1312 (95% CI €390-€2346) per patient.

CONCLUSIONS

STEPP provides a systematic approach to assess the interaction between baseline risk and the difference in NMB between competing interventions and to identify cutoffs to stratify patients in a health economically optimal manner.

Finding the Balance Between Benefits and Harms When Using Statins for Primary Prevention of Cardiovascular Disease: A Modeling Study

BACKGROUND

Many guidelines use expected risk for cardiovascular disease (CVD) during the next 10 years as a basis for recommendations on use of statins for primary prevention of CVD. However, how harms were considered and weighed against benefits is often unclear.

OBJECTIVE

To identify the expected risk above which statins provide net benefit.

DESIGN

Quantitative benefit-harm balance modeling study.

DATA SOURCES

Network meta-analysis of primary prevention trials, a preference survey, and selected observational studies.

TARGET POPULATION

Persons aged 40 to 75 years with no history of CVD.

TIME HORIZON

10 years.

PERSPECTIVE

Clinicians and guideline developers.

INTERVENTION

Low- or moderate-dose statin versus no statin.

OUTCOME MEASURES

The 10-year risk for CVD at which statins provide at least a 60% probability of net benefit, with baseline risk, frequencies of and preferences for statin benefits and harms, and competing risk for non-CVD death taken into account.

RESULTS OF BASE-CASE ANALYSIS

Younger men had net benefit at a lower 10-year risk for CVD than older men (14% for ages 40 to 44 years vs. 21% for ages 70 to 75 years). In women, the risk required for net benefit was higher (17% for ages 40 to 44 years vs. 22% for ages 70 to 75 years). Atorvastatin and rosuvastatin provided net benefit at lower 10-year risks than simvastatin and pravastatin.

RESULTS OF SENSITIVITY ANALYSIS

Most alternative assumptions led to similar findings.

LIMITATION

Age-specific data for some harms were not available.

CONCLUSION

Statins provide net benefits at higher 10-year risks for CVD than are reflected in most current guidelines. In addition, the level of risk at which net benefit occurs varies considerably by age, sex, and statin type.

PRIMARY FUNDING SOURCE

Swiss Government Excellence Scholarship Office, Béatrice Ederer-Weber Foundation, and North-South Cooperation at the University of Zurich.

Development and validation of modified risk prediction models for cardiovascular disease and its subtypes: The Hisayama Study

BACKGROUND AND AIMS

Predicting cardiovascular events is of practical benefit for disease prevention. The aim of this study was to develop and evaluate an updated risk prediction model for cardiovascular diseases and its subtypes.

METHODS

A total of 2462 community residents aged 40-84 years were followed up for 24 years. A Cox proportional hazards regression model was used to develop risk prediction models for cardiovascular diseases, and separately for stroke and coronary heart diseases. The risk assessment ability of the developed model was evaluated, and a bootstrapping method was used for internal validation. The predicted risk was translated into a simplified scoring system. A decision curve analysis was used to evaluate clinical usefulness.

RESULTS

The multivariable model for cardiovascular diseases included age, sex, systolic blood pressure, hemoglobin A1c, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, smoking habits, and regular exercise as predictors. The models for stroke and coronary heart diseases incorporated both shared and unique variables. The developed models showed good discrimination with little evidence of overfitting (optimism-corrected Harrell's C statistics 0.726-0.777) and calibrations (Hosmer-Lemeshow test, p = 0.44-0.90). The decision curve analysis revealed that the predicted risk-based decision-making would have higher net benefit than either a CVD intervention strategy for all individuals or no individuals.

CONCLUSIONS

The developed risk prediction models showed a good performance and satisfactory internal validity, which may help understand individual risk and setting personalized goals, and promote risk stratification in public health strategies for CVD prevention.

Should statin guidelines consider patient preferences? Eliciting preferences of benefit and harm outcomes of statins for primary prevention of cardiovascular disease in the sub-Saharan African and European contexts

BACKGROUND

Patient preferences are key parameters to evaluate benefit-harm balance of statins for primary prevention but they are not readily available to guideline developers and decision makers. Our study aimed to elicit patient preferences for benefit and harm outcomes related to use of statins for primary cardiovascular disease prevention and to examine how the preferences differ across economically and socio-culturally different environments.

METHODS

We conducted preference-eliciting surveys using best-worst scaling designed with a balanced incomplete-block design (BIBD) on 13 statins-related outcomes on 220 people in Ethiopia and Switzerland. The participants made tradeoff decisions and selected the most and least worrisome outcomes concurrently from each scenario generated using the BIBD. The design yielded 34,320 implied paired-comparisons and 2860 paired-responses as unit of analysis for eliciting the preferences that were analyzed using a conditional-logit model on a relative scale and surface under the cumulative ranking curve from multivariate random-effects meta-analysis model on a scale of 0 to 1.

RESULTS

There was high internal consistency of responses and minimal amount of measurement error in both surveys. Severe stroke was the most worrisome outcome with a ceiling preference of 1 (on 0 to 1 scale) followed by severe myocardial infarction, 0.913 (95% CI, 0.889-0.943), and cancer, 0.846 (0.829-0.855); while treatment discontinuation, 0.090 (0.023-0.123), and nausea/headache, 0.060 (0.034-0.094) were the least worrisome outcomes. Preferences were similar between Ethiopia and Switzerland with overlapping uncertainty intervals and concordance correlation of 0.97 (0.90-0.99).

CONCLUSIONS

Our study provides much needed empirical evidence on preferences that help clinical guidelines consider for weighing the benefit and harm outcomes when recommending for or against statins for primary prevention of cardiovascular disease. The preferences are consistent across the disparate settings; however, we recommend inclusion of more countries in future studies to ensure the generalizability of the preferences to all environments.

Large-scale external validation and comparison of prognostic models: an application to chronic obstructive pulmonary disease

BACKGROUND

External validations and comparisons of prognostic models or scores are a prerequisite for their use in routine clinical care but are lacking in most medical fields including chronic obstructive pulmonary disease (COPD). Our aim was to externally validate and concurrently compare prognostic scores for 3-year all-cause mortality in mostly multimorbid patients with COPD.

METHODS

We relied on 24 cohort studies of the COPD Cohorts Collaborative International Assessment consortium, corresponding to primary, secondary, and tertiary care in Europe, the Americas, and Japan. These studies include globally 15,762 patients with COPD (1871 deaths and 42,203 person years of follow-up). We used network meta-analysis adapted to multiple score comparison (MSC), following a frequentist two-stage approach; thus, we were able to compare all scores in a single analytical framework accounting for correlations among scores within cohorts. We assessed transitivity, heterogeneity, and inconsistency and provided a performance ranking of the prognostic scores.

RESULTS

Depending on data availability, between two and nine prognostic scores could be calculated for each cohort. The BODE score (body mass index, airflow obstruction, dyspnea, and exercise capacity) had a median area under the curve (AUC) of 0.679 [1st quartile-3rd quartile = 0.655-0.733] across cohorts. The ADO score (age, dyspnea, and airflow obstruction) showed the best performance for predicting mortality (difference AUCADO - AUCBODE = 0.015 [95% confidence interval (CI) = -0.002 to 0.032]; p = 0.08) followed by the updated BODE (AUCBODE updated - AUCBODE = 0.008 [95% CI = -0.005 to +0.022]; p = 0.23). The assumption of transitivity was not violated. Heterogeneity across direct comparisons was small, and we did not identify any local or global inconsistency.

CONCLUSIONS

Our analyses showed best discriminatory performance for the ADO and updated BODE scores in patients with COPD. A limitation to be addressed in future studies is the extension of MSC network meta-analysis to measures of calibration. MSC network meta-analysis can be applied to prognostic scores in any medical field to identify the best scores, possibly paving the way for stratified medicine, public health, and research.

Prediction models for exacerbations in patients with COPD

Personalised medicine aims to tailor medical decisions to the individual patient. A possible approach is to stratify patients according to the risk of adverse outcomes such as exacerbations in chronic obstructive pulmonary disease (COPD). Risk-stratified approaches are particularly attractive for drugs like inhaled corticosteroids or phosphodiesterase-4 inhibitors that reduce exacerbations but are associated with harms. However, it is currently not clear which models are best to predict exacerbations in patients with COPD. Therefore, our aim was to identify and critically appraise studies on models that predict exacerbations in COPD patients. Out of 1382 studies, 25 studies with 27 prediction models were included. The prediction models showed great heterogeneity in terms of number and type of predictors, time horizon, statistical methods and measures of prediction model performance. Only two out of 25 studies validated the developed model, and only one out of 27 models provided estimates of individual exacerbation risk, only three out of 27 prediction models used high-quality statistical approaches for model development and evaluation. Overall, none of the existing models fulfilled the requirements for risk-stratified treatment to personalise COPD care. A more harmonised approach to develop and validate high- quality prediction models is needed to move personalised COPD medicine forward.

Improving Prognostic Web Calculators: Violation of Preferential Risk Independence

BACKGROUND

Web-based applications are available for prognostication of individual patients. These prognostic models were developed for groups of patients. No one is the average patient, and using these calculators to inform individual patients could provide misleading results.

OBJECTIVE

This article gives an example of paradoxical results that may emerge when indices used for prognosis of the average person are used for care of an individual patient.

METHODS

We calculated the expected mortality risks of stomach cancer and its associated comorbidities. Mortality risks were calculated using data from 140,699 Veterans Administration nursing home residents.

RESULTS

On average, a patient with hypertension has a higher risk of mortality than one without hypertension. Surprisingly, among patients with lung cancer, hypertension is protective and reduces risk of mortality. This paradoxical result is explained by how group-level, average prognosis could mislead individual patients. In particular, average prognosis of lung cancer patients reflects the impact of various comorbidities that co-occur in lung cancer patients. The presence of hypertension, a relatively mild comorbidity of lung cancer, indicates that more serious comorbidities have not occurred. It is not that hypertension is protective; it is the absence of more serious comorbidities that is protective. The article shows how the presence of these anomalies can be checked through the mathematical concept of preferential risk independence.

CONCLUSION

Instead of reporting average risk scores, web-based calculators may improve accuracy of predictions by reporting the unconfounded risks.

Rethinking risk assessment for emerging technology first-in-human trials

Recent progress in synthetic biology (SynBio) has enabled the development of novel therapeutic opportunities for the treatment of human disease. In the near future, first-in-human trials (FIH) will be indicated. FIH trials mark a key milestone in the translation of medical SynBio applications into clinical practice. Fostered by uncertainty of possible adverse events for trial participants, a variety of ethical concerns emerge with regards to SynBio FIH trials, including 'risk' minimization. These concerns are associated with any FIH trial, however, due to the novelty of the approach, they become more pronounced for medical applications of emerging technologies (emTech) like SynBio. To minimize potential harm for trial participants, scholars, guidelines, regulations and policy makers alike suggest using 'risk assessment' as evaluation tool for such trials. Conversely, in the context of emTech FIH trials, we believe it to be at least questionable to contextualize uncertainty of potential adverse events as 'risk' and apply traditional risk assessment methods. Hence, this issue needs to be discussed to enable alterations of the evaluation process before the translational phase of SynBio applications begins. In this paper, we will take the opportunity to start the debate and highlight how a misunderstanding of the concept of risk, and the possibilities and limitations of risk assessment, respectively, might impair decision-making by the relevant regulatory authorities and research ethics committees, and discuss possible solutions to tackle the issue.

Prediction of Cardiovascular Disease Risk by Cardiac Biomarkers in 2 United Kingdom Cohort Studies: Does Utility Depend on Risk Thresholds For Treatment?

We tested the predictive ability of cardiac biomarkers N-terminal pro B-type natriuretic peptide (NT-proBNP), high-sensitivity troponin T, and midregional pro adrenomedullin for cardiovascular disease (CVD) events using the British Regional Heart Study (BRHS) of men aged 60 to 79 years, and the MIDSPAN Family Study (MFS) of men and women aged 30 to 59 years. They included 3757 and 2226 participants, respectively, and during median 13.0 and 17.3 years follow-up the primary CVD event rates were 16.6 and 5.3 per 1000 patient-years, respectively. In Cox models adjusted for basic classical risk factors, 1 SD increases in log-transformed NT-proBNP, high-sensitivity troponin T, and midregional pro adrenomedullin were generally associated with increased primary CVD risk in both the studies (P<0.006) except midregional pro adrenomedullin in MFS (P=0.10). In BRHS, QRISK2 risk factors yielded a C-index of 0.657, which was improved by 0.017 (P=0.005) by NT-proBNP, but not by other biomarkers. Using 28% 14-year risk as a proxy for 20% 10-year risk, NT-proBNP improved risk classification for primary CVD cases (case net reclassification index, 5.9%; 95% confidence interval, 2.8%-9.2%), but only improved classification of noncases at a 14% 14-year risk threshold (4.6%; 2.9%-6.3%). In MFS, ASSIGN risk factors yielded a C-index of 0.752 for primary CVD; none of the cardiac biomarkers improved the C-index. Improvements in risk classification were only seen using NT-proBNP and high-sensitivity troponin T among cases using the 28% 14-year risk threshold (4.7%; 1.0%-9.2% and 2.6%; 0.0%-5.8%, respectively). In conclusion, the improvement in treatment allocation gained by adding cardiac biomarkers to risk scores seems to depend on the risk threshold chosen for commencing preventative treatments.

Benefit-harm analysis and charts for individualized and preference-sensitive prevention: example of low dose aspirin for primary prevention of cardiovascular disease and cancer

BACKGROUND

Clinical practice guidelines provide separate recommendations for different diseases that may be prevented or treated by the same intervention. Also, they commonly provide recommendations for entire populations but not for individuals. To address these two limitations, our aim was to conduct benefit-harm analyses for a wide range of individuals using the example of low dose aspirin for primary prevention of cardiovascular disease and cancer and to develop Benefit-Harm Charts that show the overall benefit-harm balance for individuals.

METHODS

We used quantitative benefit-harm modeling that included 16 outcomes to estimate the probability that low dose aspirin provides more benefits than harms for a wide range of men and women between 45 and 84 years of age and without a previous myocardial infarction, severe ischemic stroke, or cancer. We repeated the quantitative benefit-harm modeling for different combinations of age, sex, and outcome risks for severe ischemic and hemorrhagic stroke, myocardial infarction, cancers, and severe gastrointestinal bleeds. The analyses considered weights for the outcomes, statistical uncertainty of the effects of aspirin, and death as a competing risk. We constructed Benefit-Harm Charts that show the benefit-harm balance for different combinations of outcome risks.

RESULTS

The Benefit-Harm Charts ( http://www.benefit-harm-balance.com ) we have created show that the benefit-harm balance differs largely across a primary prevention population. Low dose aspirin is likely to provide more benefits than harms in men, elderly people, and in those at low risk for severe gastrointestinal bleeds. Individual preferences have a major impact on the benefit-harm balance. If, for example, it is a high priority for individuals to prevent stroke and severe cancers while severe gastrointestinal bleeds are deemed to be of little importance, the benefit-harm balance is likely to favor low dose aspirin for most individuals. Instead, if severe gastrointestinal bleeds are judged to be similarly important compared to the benefit outcomes, low dose aspirin is unlikely to provide more benefits than harms.

CONCLUSIONS

Benefit-Harm Charts support individualized benefit-harm assessments and decision making. Similarly, individualized benefit-harm assessments may allow guideline developers to issue more finely granulated recommendations that reduce the risk of over- and underuse of interventions. The example of low dose aspirin for primary prevention of cardiovascular disease and cancer shows that it may be time for guideline developers to provide combined recommendations for different diseases that may be prevented or treated by the same intervention.

Outcome Preferences in Patients With Noninfectious Uveitis: Results of a Best-Worst Scaling Study

PURPOSE

To estimate patient preferences regarding potential adverse outcomes of local versus systemic corticosteroid therapies for noninfectious uveitis by using a best-worst scaling (BWS) approach.

METHODS

Local and systemic therapies are alternatives for noninfectious uveitis that have different potential adverse outcomes. Patients participating in the Multicenter Uveitis Steroid Treatment Trial Follow-up Study (MUST FS) and additional patients with a history of noninfectious uveitis treated at two academic medical centers (Johns Hopkins University and University of Pennsylvania) were surveyed about their preferences regarding six adverse outcomes deemed important to patients. Using "case 1" BWS, patients were asked to repeatedly select the most and least worrying from a list of outcomes (in the survey three outcomes per task).

RESULTS

Eighty-two patients in the MUST FS and 100 patients treated at the academic medical centers completed the survey. According to BWS, patients were more likely to select vision not meeting the requirement for driving (individual BWS score: median = 3, interquartile range, 0-5), development of glaucoma (2, 1-4), and needing eye surgery (1, 0-3) as the most worrying outcomes as compared to needing medicine for high blood pressure/cholesterol (-2, -4 to 0), development of cataracts (-2, -3 to -1), or infection (sinusitis) (-3, -5 to 0). Larger BWS scores indicated the outcomes were more worrying to patients.

CONCLUSIONS

Patients with noninfectious uveitis considered impaired vision, development of glaucoma, and need for eye surgery worrying adverse outcomes, which suggests that it is especially desirable to avoid these outcomes if possible. (ClinicalTrials.gov number, NCT00132691.)

Does personalized medicine exist and can you test it in a clinical trial?

The idea that different patients will respond differently to the same treatment is not new. The recent advances in genomics and laboratory medicine have led to the hope that it will be possible to maximize the benefit and minimize the harms of each medical therapy by using an individuals' biomarker status to 'personalize' their treatment. The selection of treatment for each individual would then be determined, not just by their disease status (or an estimate of the risk of developing a disease or disease progression), but also by their genetic makeup or by other measurable characteristics, such as the level of a particular biomarker in the blood. This review discusses the extent to which personalized medicine might be applied in stroke, and the implications for global stroke health care.

Quantitative benefit-harm assessment for setting research priorities: the example of roflumilast for patients with COPD

BACKGROUND

When faced with uncertainties about the effects of medical interventions regulatory agencies, guideline developers, clinicians, and researchers commonly ask for more research, and in particular for more randomized trials. The conduct of additional randomized trials is, however, sometimes not the most efficient way to reduce uncertainty. Instead, approaches such as value of information analysis or other approaches should be used to prioritize research that will most likely reduce uncertainty and inform decisions.

DISCUSSION

In situations where additional research for specific interventions needs to be prioritized, we propose the use of quantitative benefit-harm assessments that illustrate how the benefit-harm balance may change as a consequence of additional research. The example of roflumilast for patients with chronic obstructive pulmonary disease shows that additional research on patient preferences (e.g., how important are exacerbations relative to psychiatric harms?) or outcome risks (e.g., what is the incidence of psychiatric outcomes in patients with chronic obstructive pulmonary disease without treatment?) is sometimes more valuable than additional randomized trials. We propose that quantitative benefit-harm assessments have the potential to explore the impact of additional research and to identify research priorities Our approach may be seen as another type of value of information analysis and as a useful approach to stimulate specific new research that has the potential to change current estimates of the benefit-harm balance and decision making.

The Ariadne principles: how to handle multimorbidity in primary care consultations

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.Please see related article: http://www.biomedcentral.com/1741-7015/12/222.

Benefits and harms of roflumilast in moderate to severe COPD

BACKGROUND

Roflumilast, a phosphodiesterase 4 inhibitor, has been approved for the prevention of chronic obstructive pulmonary disease (COPD) exacerbations. It is unclear which patients will have a favourable benefit-harm balance with roflumilast. Our aim was to quantitatively assess the benefits and harms of roflumilast (500 µg/day) compared with placebo.

METHODS

We used summary data released by the US Food and Drug Administration to estimate the treatment effects of roflumilast. Data from trials and observational studies were used to estimate the baseline risks for COPD exacerbations and gastrointestinal, neurological and psychiatric harms associated with roflumilast. Using simulation, we calculated the probability that roflumilast provides net benefit. We examined the impacts of different baseline risks for exacerbations and the severity of exacerbations, and varied weights (ie, relative importance) for outcomes and treated death as a competing risk in the analyses.

RESULTS

The probability that roflumilast provides net benefit approximates 0% across different age categories of men and women with varying baseline risks for exacerbations. Using different weights for outcomes did not change the probability that roflumilast provides a net benefit. Only in the sensitivity analysis restricted to the prevention of severe exacerbations was there a probability of >50% that roflumilast provides a net benefit if the baseline risk of having at least one severe exacerbation per year exceeds 22%.

CONCLUSIONS

Our results suggest that roflumilast only provides a net benefit to patients at a high risk of severe exacerbations. Guideline developers should consider different recommendations for patients with COPD at different baseline risks for exacerbations.

Using internally developed risk models to assess heterogeneity in treatment effects in clinical trials

BACKGROUND

Recent proposals suggest that risk-stratified analyses of clinical trials be routinely performed to better enable tailoring of treatment decisions to individuals. Trial data can be stratified using externally developed risk models (eg, Framingham risk score), but such models are not always available. We sought to determine whether internally developed risk models, developed directly on trial data, introduce bias compared with external models.

METHODS AND RESULTS

We simulated a large patient population with known risk factors and outcomes. Clinical trials were then simulated by repeatedly drawing from the patient population assuming a specified relative treatment effect in the experimental arm, which either did or did not vary according to a subject's baseline risk. For each simulated trial, 2 internal risk models were developed on either the control population only (internal controls only) or the whole trial population blinded to treatment (internal whole trial). Bias was estimated for the internal models by comparing treatment effect predictions to predictions from the external model. Under all treatment assumptions, internal models introduced only modest bias compared with external models. The magnitude of these biases was slightly smaller for internal whole trial models than for internal controls only models. Internal whole trial models were also slightly less sensitive to bias introduced by overfitting and less sensitive to falsely identifying the existence of variability in treatment effect across the risk spectrum compared with internal controls only models.

CONCLUSIONS

Appropriately developed internal models produce relatively unbiased estimates of treatment effect across the spectrum of risk. When estimating treatment effect, internally developed risk models using both treatment arms should, in general, be preferred to models developed on the control population.

Suggested indications of clinical practice guideline for stem cell-therapy in cardiovascular diseases: A stepwise appropriate use criteria for regeneration therapy

Despite astonishing progress concerning cardiovascular diseases, patients are still suffering from complications of acute insults. Due to reverse remodeling and improper myocyte rebuilding, heart failure has become a common problem these days which needs more powerful myocardial reconstructing strategies. Indeed, no option cases afflicted with non-healing peripheral vascular diseases; refractory stable and unstable angina is the other field with paucity of proper treatments. For these cases, stem cell-based therapies became optimistic treatment, but lack of guideline-based indications regarding stem-cell is still a major problem which limits application of these cells for such end-stage cases. Here, an outline of appropriateness criteria for stem cell-based therapy is suggested.