Measures Used to Assess the Impact of Interventions to Reduce Low-Value Care: a Systematic Review.

IMPORTANCE: Studies of interventions to reduce low-value care are increasingly common. However, little is known about how the effects of such interventions are measured. OBJECTIVE: To characterize measures used to assess interventions to reduce low-value care. EVIDENCE REVIEW: We searched PubMed and Web of Science to identify studies published between 2010 and 2016 that examined the effects of interventions to reduce low-value care. We also searched ClinicalTrials.gov to identify ongoing studies. We extracted data on characteristics of studies, interventions, and measures. We then developed a framework to classify measures into the following categories: utilization (e.g., number of tests ordered), outcome (e.g., mortality), appropriateness (e.g., overuse of antibiotics), patient-reported (e.g., satisfaction), provider-reported (e.g., satisfaction), patient-provider interaction (e.g., informed decision-making elements), value, and cost. We also determined whether each measure was designed to assess unintended consequences. FINDINGS: A total of 1805 studies were identified, of which 101 published and 16 ongoing studies were included. Of published studies (N = 101), 68% included at least one measure of utilization, 41% of an outcome, 52% of appropriateness, 36% of cost, 8% patient-reported, and 3% provider-reported. Funded studies were more likely to use patient-reported measures (17% vs 0%). Of ongoing studies (registered trials) (N = 16), 69% included at least one measure of utilization, 75% of an outcome, 50% of appropriateness, 19% of cost, 50% patient-reported, 13% provider-reported, and 6% patient-provider interaction. Of published studies, 34% included at least one measure of an unintended consequence as compared to 63% of ongoing studies. CONCLUSIONS AND RELEVANCE: Most published studies focused on reductions in utilization rather than on clinically meaningful measures (e.g., improvements in appropriateness, patient-reported outcomes) or unintended consequences. Investigators should systematically incorporate more clinically meaningful measures into their study designs, and sponsors should develop standardized guidance for the evaluation of interventions to reduce low-value care.

Predicting in-hospital mortality in acute type B aortic dissection: evidence from International Registry of Acute Aortic Dissection.

BACKGROUND: The outcome of patients with acute type B aortic dissection (ABAD) is strongly related to their clinical presentation. The purpose of this study was to investigate predictors for mortality among patients presenting with ABAD and to create a predictive model to estimate individual risk of in-hospital mortality using the International Registry of Acute Aortic Dissection (IRAD). METHODS AND RESULTS: All patients with ABAD enrolled in IRAD between 1996 and 2013 were included for analysis. Multivariable logistic regression analysis was used to investigate predictors of in-hospital mortality. Significant risk factors for in-hospital death were used to develop a prediction model. A total of 1034 patients with ABAD were included for analysis (673 men; mean age, 63.5±14.0 years), with an overall in-hospital mortality of 10.6%. In multivariable analysis, the following variables at admission were independently associated with increased in-hospital mortality: increasing age (odds ratio [OR], 1.03; 95% confidence interval [CI], 1.00-1.06; P=0.044), hypotension/shock (OR, 6.43; 95% CI, 2.88-18.98; P=0.001), periaortic hematoma (OR, 3.06; 95% CI, 1.38-6.78; P=0.006), descending diameter ≥5.5 cm (OR, 6.04; 95% CI, 2.87-12.73; P<0.001), mesenteric ischemia (OR, 9.03; 95% CI, 3.49-23.38; P<0.001), acute renal failure (OR, 3.61; 95% CI, 1.68-7.75; P=0.001), and limb ischemia (OR, 3.02; 95% CI, 1.05-8.68; P=0.040). Based on these multivariable results, a reliable and simple bedside risk prediction tool was developed. CONCLUSIONS: We present a simple prediction model using variables that are independently associated with in-hospital mortality in patients with ABAD. Although it needs to be validated in an independent population, this model could be used to assist physicians in their choice of management and for informing patients and their families.

Identifying Recommendations for Stopping or Scaling Back Unnecessary Routine Services in Primary Care.

Importance: Much of health care involves established, routine use of medical services for chronic conditions or prevention. Stopping these services when the evidence changes or if the benefits no longer outweigh the risks is essential. Yet, most guidelines focus on escalating care and provide few explicit recommendations to stop or scale back (ie, deintensify) treatment and testing. Objective: To develop a systematic, transparent, and reproducible approach for identifying, specifying, and validating deintensification recommendations associated with routine adult primary care. Design, Setting, and Participants: A focused review of existing guidelines and recommendations was completed to identify and prioritize potential deintensification indications. Then, 2 modified virtual Delphi expert panels examined the synthesized evidence, suggested ways that the candidate recommendations could be improved, and assessed the validity of the recommendations using the RAND/UCLA Appropriateness Method. Twenty-five physicians from Veterans Affairs and US academic institutions with knowledge in relevant clinical areas (eg, geriatrics, primary care, women's health, cardiology, and endocrinology) served as panel members. Main Outcomes and Measures: Validity of the recommendations, defined as high-quality evidence that deintensification is likely to improve patient outcomes, evidence that intense testing and/or treatment could cause harm in some patients, absence of evidence on the benefit of continued or repeated intense treatment or testing, and evidence that deintensification is consistent with high-quality care. Results: A total of 409 individual recommendations were identified representing 178 unique opportunities to stop or scale back routine services (eg, stopping population-based screening for vitamin D deficiency and decreasing concurrent use of opioids and benzodiazepines). Thirty-seven recommendations were prioritized and forwarded to the expert panels. Panelists reviewed the evidence and suggested modifications, resulting in 44 recommendations being rated. Overall, 37 recommendations (84%) were considered to be valid, as assessed by the RAND/UCLA Appropriateness Method. Conclusions and Relevance: In this study, a total of 178 unique opportunities to deintensify routine primary care services were identified, and 37 of these were validated as high-priority deintensification recommendations. To date, this is the first study to develop a model for identifying, specifying, and validating deintensification recommendations that can be implemented and tracked in clinical practice.