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Background Clinical prediction models (CPMs) estimate the probability of clinical outcomes and hold the potential to improve decision making and individualize care. For patients with cardiovascular disease (CVD) there are numerous CPMs available though the extent of this literature is not well described. Methods and Results We conducted a systematic review for articles containing CPMs for CVD published between January 1990 through May 2012. CVD includes coronary heart disease (CHD), heart failure (HF), arrhythmias, stroke, venous thromboembolism (VTE) and peripheral vascular disease (PVD). We created a novel database and characterized CPMs based on the stage of development, population under study, performance, covariates, and predicted outcomes. There are 796 models included in this database. The number of CPMs published each year is increasing steadily over time. 717 (90%) are de novo CPMs, 21 (3%) are CPM recalibrations, and 58 (7%) are CPM adaptations. This database contains CPMs for 31 index conditions including 215 CPMs for patients with CAD, 168 CPMs for population samples, and 79 models for patients with HF. There are 77 distinct index/ outcome (I/O) pairings. Of the de novo models in this database 450 (63%) report a c-statistic and 259 (36%) report some information on calibration. Conclusions There is an abundance of CPMs available for a wide assortment of CVD conditions, with substantial redundancy in the literature. The comparative performance of these models, the consistency of effects and risk estimates across models and the actual and potential clinical impact of this body of literature is poorly understood.

The contingent valuation literature in diagnostics has grown rapidly, and suggests that many respondents place considerable value on diagnostic information. There exists, however, great variation in studies with respect to the type of technologies and diseases assessed, respondent characteristics, and study methodology. The perceived value of diagnostic technologies is also influenced by the study design and elicitation methods.

The evidence of both clinical and economic gains from EN is consistent with ASPEN guidelines recommending use of EN in critically ill hospital patients when possible.

BT is a cost-effective treatment option for patients with poorly controlled, severe, persistent asthma.

The Tufts Cost-Effectiveness Analysis (CEA) Registry (www.cearegistry.org) is a publicly available comprehensive database of cost-utility analyses of health interventions published in the peer-reviewed medical and public health literature. This article discusses the database structure, methodology of data extraction, current trends in cost-utility analyses and impact of the Registry.

We examined Medicare national coverage determinations for medical interventions to determine whether or not they have become more restrictive over time. National coverage determinations address whether particular big-ticket medical items, services, treatment procedures, and technologies can be paid for under Medicare. We found that after we adjusted for the strength of evidence and other factors known to influence the determinations of the Centers for Medicare and Medicaid Services (CMS), the evidentiary bar for coverage has risen. More recent coverage determinations (from mid-March 2008 through August 2012) were twenty times less likely to be positive than earlier coverage determinations (from February 1999 through January 2002). Furthermore, coverage during the study period was increasingly and positively associated both with the degree of consistency of favorable findings in the CMS reviewed clinical evidence and with recommendations made in clinical guidelines. Coverage policy is an important payer tool for promoting the appropriate use of medical interventions, but CMS's rising evidence standards also raise questions about patients' access to new technologies and about hurdles for the pharmaceutical and device industries as they attempt to bring innovations to the market.

Background: Clinical prediction models (CPMs) estimate the probability of clinical outcomes and hold the potential to improve decision making and individualize care. For patients with cardiovascular disease (CVD) there are numerous CPMs available though the extent of this literature is not well described. Methods and Results: We conducted a systematic review for articles containing CPMs for CVD published between January 1990 through May 2012. CVD includes coronary artery disease (CAD), congestive heart failure (CHF), arrhythmias, stroke, venous thromboembolism (VTE) and peripheral vascular disease (PVD). We created a novel database and characterized CPMs based on the stage of development, population under study, performance, covariates, and predicted outcomes. We included articles that describe newly developed CPMs that predict the risk of developing an outcome (prognostic models) or the probability of a specific diagnosis (diagnostic models). There are 796 models included in this database representing 31 distinct index conditions. 717 (90%) are de novo CPMs, 21 (3%) are CPM recalibrations, and 58 (7%) are CPM adaptations. There are 215 CPMs for patients with CAD, 168 CPMs for population samples at risk for incident CVD, and 79 models for patients with CHF (Figure). De novo CPMs predicting mortality were most commonly published for patients with known CAD (98 models) followed by HF (63 models) and stroke (24 models). There are 77 distinct index/ outcome (I/O) pairings and models are roughly evenly split between those predicting short term outcomes (< 3 months) and those predicting long term outcomes (< 6 months). There are 41 diagnostic CPMs included in this database, most commonly predicting diagnoses of CAD (11 models), VTE (10 models), and acute coronary syndrome (5 models). Of the de novo models in this database 450 (63%) report a c-statistic and 259 (36%) report either the Hosmer-Lemeshow statistic or show a calibration plot. Conclusions: There is an abundance of CPMs available for many CVD conditions, with substantial redundancy in the literature. The comparative performance of these models, the consistency of effects and risk estimates across models and the actual and potential clinical impact of this body of literature is poorly understood.