Tissue plasminogen activator was cost-effective compared to streptokinase in only selected patients with acute myocardial infarction

The Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement: Explanation and Elaboration

The PATH (Predictive Approaches to Treatment effect Heterogeneity) Statement was developed to promote the conduct of, and provide guidance for, predictive analyses of heterogeneity of treatment effects (HTE) in clinical trials. The goal of predictive HTE analysis is to provide patient-centered estimates of outcome risk with versus without the intervention, taking into account all relevant patient attributes simultaneously, to support more personalized clinical decision making than can be made on the basis of only an overall average treatment effect. The authors distinguished 2 categories of predictive HTE approaches (a "risk-modeling" and an "effect-modeling" approach) and developed 4 sets of guidance statements: criteria to determine when risk-modeling approaches are likely to identify clinically meaningful HTE, methodological aspects of risk-modeling methods, considerations for translation to clinical practice, and considerations and caveats in the use of effect-modeling approaches. They discuss limitations of these methods and enumerate research priorities for advancing methods designed to generate more personalized evidence. This explanation and elaboration document describes the intent and rationale of each recommendation and discusses related analytic considerations, caveats, and reservations.

Patient Variability Seldom Assessed in Cost-effectiveness Studies

BACKGROUND

Cost-effectiveness analysis (CEA) estimates can vary substantially across patient subgroups when patient characteristics influence preferences, outcome risks, treatment effectiveness, life expectancy, or associated costs. However, no systematic review has reported the frequency of subgroup analysis in CEA, what type of heterogeneity they address, and how often heterogeneity influences whether cost-effectiveness ratios exceed or fall below conventional thresholds.

METHODS

We reviewed the CEA literature cataloged in the Tufts Medical Center CEA Registry, a repository describing cost-utility analyses published through 2016. After randomly selecting 200 of 642 articles published in 2014, we ascertained whether each study reported subgroup results and collected data on the defining characteristics of these subgroups. We identified whether any of the CEA subgroup results crossed conventional cost-effectiveness benchmarks (e.g., $100,000 per QALY) and compared characteristics of studies with and without subgroup-specific findings.

RESULTS

Thirty-eight studies (19%) reported patient subgroup results. Articles reporting subgroup analyses were more likely to be US-based, government funded (v. drug industry- or nonprofit foundation-funded) studies, with a focus on primary or secondary (v. tertiary) prevention (P < 0.05 for comparisons). One or more patient characteristics were used to stratify CEA results 68 times within the 38 studies, with most stratifications using one characteristic (n = 47), most commonly age (n = 35). Among the 23 stratifications reported alongside average ratios in US studies, 13 produced subgroup ratios that crossed a conventional CEA ratio benchmark.

CONCLUSIONS

Most CEAs do not report any subgroup results, and those that do most often stratify only by patient age. Over half of the subgroup analyses reported could lead to different value-based decision making for at least some patients.

Understanding the Value of Individualized Information: The Impact of Poor Calibration or Discrimination in Outcome Prediction Models

BACKGROUND

Risk prediction models allow for the incorporation of individualized risk and clinical effectiveness information to identify patients for whom therapy is most appropriate and cost-effective. This approach has the potential to identify inefficient (or harmful) care in subgroups at different risks, even when the overall results appear favorable. Here, we explore the value of personalized risk information and the factors that influence it.

METHODS

Using an expected value of individualized care (EVIC) framework, which monetizes the value of customizing care, we developed a general approach to calculate individualized incremental cost effectiveness ratios (ICERs) as a function of individual outcome risk. For a case study (tPA v. streptokinase to treat possible myocardial infarction), we used a simulation to explore how an EVIC is influenced by population outcome prevalence, model discrimination (c-statistic) and calibration, and willingness-to-pay (WTP) thresholds.

RESULTS

In our simulations, for well-calibrated models, which do not over- or underestimate predicted v. observed event risk, the EVIC ranged from $0 to $700 per person, with better discrimination (higher c-statistic values) yielding progressively higher EVIC values. For miscalibrated models, the EVIC ranged from -$600 to $600 in different simulated scenarios. The EVIC values decreased as discrimination improved from a c-statistic of 0.5 to 0.6, before becoming positive as the c-statistic reached values of ~0.8.

CONCLUSIONS

Individualizing treatment decisions using risk may produce substantial value but also has the potential for net harm. Good model calibration ensures a non-negative EVIC. Improvements in discrimination generally increase the EVIC; however, when models are miscalibrated, greater discriminating power can paradoxically reduce the EVIC under some circumstances.

Biases in Individualized Cost-effectiveness Analysis: Influence of Choices in Modeling Short-Term, Trial-Based, Mortality Risk Reduction and Post-Trial Life Expectancy

BACKGROUND

The benefits and costs of a treatment are typically heterogeneous across individual patients. Randomized clinical trials permit the examination of individualized treatment benefits over the trial horizon but extrapolation to lifetime horizon usually involves combining trial-based individualized estimates of short-term risk reduction with less detailed (less granular) population life tables. However, the underlying assumption of equal post-trial life expectancy for low- and high-risk patients of the same sex and age is unrealistic. We aimed to study the influence of unequal granularity between models of short-term risk reduction and life expectancy on individualized estimates of cost-effectiveness of aggressive thrombolysis for patients with an acute myocardial infarction.

METHODS

To estimate life years gained, we multiplied individualized estimates of short-term risk reduction either with less granular and with equally granular post-trial life expectancy estimates. Estimates of short-term risk reduction were obtained from GUSTO trial data (30,510 patients) using logistic regression analysis with treatment, sex, and age as predictor variables. Life expectancy estimates were derived from sex- and age-specific US life tables.

RESULTS

Based on sex- and age-specific, short-term risk reductions but average population life expectancy (less granularity), we found that aggressive thrombolysis was cost-effective (incremental cost-effectiveness ratio below $50,000) for women above age 49 y and men above age 53 y (92% and 69% of the population, respectively). Considering sex- and age-specific short-term mortality risk reduction and correspondingly sex- and age-specific life expectancy (equal granularity), aggressive thrombolysis was cost-effective for men above age 45 y and women above age 50 y (95% and 76% of the population, respectively).

CONCLUSIONS

Failure to model short-term risk reduction and life expectancy at an equal level of granularity may bias our estimates of individualized cost-effectiveness and misallocate resources.

A proposal for integrated efficacy-to-effectiveness (E2E) clinical trials

We propose an "efficacy-to-effectiveness" (E2E) clinical trial design, in which an effectiveness trial would commence seamlessly upon completion of the efficacy trial. Efficacy trials use inclusion/exclusion criteria to produce relatively homogeneous samples of participants with the target condition, conducted in settings that foster adherence to rigorous clinical protocols. Effectiveness trials use inclusion/exclusion criteria that generate heterogeneous samples that are more similar to the general patient spectrum, conducted in more varied settings, with protocols that approximate typical clinical care. In E2E trials, results from the efficacy trial component would be used to design the effectiveness trial component, to confirm and/or discern associations between clinical characteristics and treatment effects in typical care, and potentially to test new hypotheses. An E2E approach may improve the evidentiary basis for selecting treatments, expand understanding of the effectiveness of treatments in subgroups with particular clinical features, and foster incorporation of effectiveness information into regulatory processes.

Purification and characterization of a fibrinolytic enzyme from Streptomyces sp. XZNUM 00004

A fibrinolytic enzyme (SFE1) from Streptomyces sp. XZNUM 00004 was purified to electrophoretic homogeneity with the methods including ammonium sulfate precipitation, polyacrylamide gel, DEAE-Sepharose Fast Flow anion exchange and gel-filtration chromatography. The molecular weight of SFE1 was estimated to be 20 kDa by SDS-PAGE, fibrin zymography, and gel filtration chromatography. The isoelectric point was 4.9. K (m) and V (max) values were 0.96 mg/ml and 181.8 unit/ml, respectively. It was very stable at pH 5.0-8.0 and below 65 °C. The optimum pH for enzyme activity was 7.8. The optimum temperature was 35 °C. The fibrinolytic activity of SFE1 was enhanced by Na(+), K(+), Mn(2+), Mg(2+), Zn(2+) and Co(2+). Conversely, Cu(2+) showed strong inhibition. Furthermore, the fibrinolytic activity was strongly inhibited by PMSF, and partly inhibited by EDTA and EGTA. SFE1 rapidly hydrolyzed the Aα-chain of fibrinogen, followed by the Bβ-chain and finally the γ-chain. The first 15 amino acids of the N-terminal sequence were APITLSQGHVDVVDI. Additionally, SFE1 directly digested fibrin and not by plasminogen activators in vitro. SFE1 can be further developed as a potential candidate for thrombolytic therapy.

Comparative effectiveness of ST-segment-elevation myocardial infarction regionalization strategies

BACKGROUND

Primary percutaneous coronary intervention (PCI) is more effective on average than fibrinolytic therapy in the treatment of ST-segment-elevation myocardial infarction. Yet, most US hospitals are not equipped for PCI, and fibrinolytic therapy is still widely used. This study evaluated the comparative effectiveness of ST-segment-elevation myocardial infarction regionalization strategies to increase the use of PCI against standard emergency transport and care.

METHODS AND RESULTS

We estimated incremental treatment costs and quality-adjusted life expectancies of 2000 patients with ST-segment-elevation myocardial infarction who received PCI or fibrinolytic therapy in simulations of emergency care in a regional hospital system. To increase access to PCI across the system, we compared a base case strategy with 12 hospital-based strategies of building new PCI laboratories or extending the hours of existing laboratories and 1 emergency medical services-based strategy of transporting all patients with ST-segment-elevation myocardial infarction to existing PCI-capable hospitals. The base case resulted in 609 (95% CI, 569-647) patients getting PCI. Hospital-based strategies increased the number of patients receiving PCI, the costs of care, and quality-adjusted life years saved and were cost-effective under a variety of conditions. An emergency medical services-based strategy of transporting every patient to an existing PCI facility was less costly and more effective than all hospital expansion options.

CONCLUSION

Our results suggest that new construction and staffing of PCI laboratories may not be warranted if an emergency medical services strategy is both available and feasible.