A standardized framework for risk-based assessment of treatment effect heterogeneity in observational healthcare databases.

Treatment effects are often anticipated to vary across groups of patients with different baseline risk. The Predictive Approaches to Treatment Effect Heterogeneity (PATH) statement focused on baseline risk as a robust predictor of treatment effect and provided guidance on risk-based assessment of treatment effect heterogeneity in a randomized controlled trial. The aim of this study is to extend this approach to the observational setting using a standardized scalable framework. The proposed framework consists of five steps: (1) definition of the research aim, i.e., the population, the treatment, the comparator and the outcome(s) of interest; (2) identification of relevant databases; (3) development of a prediction model for the outcome(s) of interest; (4) estimation of relative and absolute treatment effect within strata of predicted risk, after adjusting for observed confounding; (5) presentation of the results. We demonstrate our framework by evaluating heterogeneity of the effect of thiazide or thiazide-like diuretics versus angiotensin-converting enzyme inhibitors on three efficacy and nine safety outcomes across three observational databases. We provide a publicly available R software package for applying this framework to any database mapped to the Observational Medical Outcomes Partnership Common Data Model. In our demonstration, patients at low risk of acute myocardial infarction receive negligible absolute benefits for all three efficacy outcomes, though they are more pronounced in the highest risk group, especially for acute myocardial infarction. Our framework allows for the evaluation of differential treatment effects across risk strata, which offers the opportunity to consider the benefit-harm trade-off between alternative treatments.

Association of Preexisting Heart Failure With Outcomes in Older Patients With Diffuse Large B-Cell Lymphoma.

Importance: Anthracycline-containing regimens are highly effective for diffuse large B-cell lymphoma (DLBCL); however, patients with preexisting heart failure (HF) may be less likely to receive anthracyclines and may be at higher risk of lymphoma mortality. Objective: To assess the prevalence of preexisting HF in older patients with DLBCL and its association with treatment patterns and outcomes. Design, Setting, and Participants: This longitudinal cohort study used data from the Surveillance, Epidemiology, and End Results (SEER)-Medicare registry from 1999 to 2016. The SEER registry is a system of population-based cancer registries, capturing more than 25% of the US population. Linkage to Medicare offers additional information from billing claims. This study included individuals 65 years and older with newly diagnosed DLBCL from 2000 to 2015 with Medicare Part A or B continuously in the year prior to lymphoma diagnosis. Data were analyzed from September 2020 to December 2022. Exposures: Preexisting HF in the year prior to DLBCL diagnosis ascertained from billing codes required one of the following: (1) 1 primary inpatient discharge diagnosis, (2) 2 outpatient diagnoses, (3) 3 secondary inpatient discharge diagnoses, (4) 3 emergency department diagnoses, or (5) 2 secondary inpatient discharge diagnoses plus 1 outpatient diagnosis. Main Outcomes and Measures: The primary outcome was anthracycline-based treatment. The secondary outcomes were (1) cardioprotective medications and (2) cause-specific mortality. The associations between preexisting HF and cancer treatment were estimated using multivariable logistic regression. The associations between preexisting HF and cause-specific mortality were evaluated using cause-specific Cox proportional hazards models with adjustment for comorbidities and cancer treatment. Results: Of 30 728 included patients with DLBCL, 15 474 (50.4%) were female, and the mean (SD) age was 77.8 (7.2) years. Preexisting HF at lymphoma diagnosis was present in 4266 patients (13.9%). Patients with preexisting HF were less likely to be treated with an anthracycline (odds ratio, 0.55; 95% CI, 0.49-0.61). Among patients with preexisting HF who received an anthracycline, dexrazoxane or liposomal doxorubicin were used in 78 of 1119 patients (7.0%). One-year lymphoma mortality was 41.8% (95% CI, 40.5-43.2) with preexisting HF and 29.6% (95% CI, 29.0%-30.1%) without preexisting HF. Preexisting HF was associated with higher lymphoma mortality in models adjusting for baseline and time-varying treatment factors (hazard ratio, 1.24; 95% CI, 1.18-1.31). Conclusions and Relevance: In this study, preexisting HF in patients with newly diagnosed DLBCL was common and was associated with lower use of anthracyclines and lower use of any chemotherapy. Trials are needed for this high-risk population.

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.

Targeting of the diabetes prevention program leads to substantial benefits when capacity is constrained.

OBJECTIVE: Approximately 84 million people in the USA have pre-diabetes, but only a fraction of them receive proven effective therapies to prevent type 2 diabetes. We estimated the value of prioritizing individuals at highest risk of progression to diabetes for treatment, compared to non-targeted treatment of individuals meeting inclusion criteria for the Diabetes Prevention Program (DPP). METHODS: Using microsimulation to project outcomes in the DPP trial population, we compared two interventions to usual care: (1) lifestyle modification and (2) metformin administration. For each intervention, we compared targeted and non-targeted strategies, assuming either limited or unlimited program capacity. We modeled the individualized risk of developing diabetes and projected diabetic outcomes to yield lifetime costs and quality-adjusted life expectancy, from which we estimated net monetary benefits (NMB) for both lifestyle and metformin versus usual care. RESULTS: Compared to usual care, lifestyle modification conferred positive benefits and reduced lifetime costs for all eligible individuals. Metformin's NMB was negative for the lowest population risk quintile. By avoiding use when costs outweighed benefits, targeted administration of metformin conferred a benefit of $500 per person. If only 20% of the population could receive treatment, when prioritizing individuals based on diabetes risk, rather than treating a 20% random sample, the difference in NMB ranged from $14,000 to $20,000 per person. CONCLUSIONS: Targeting active diabetes prevention to patients at highest risk could improve health outcomes and reduce costs compared to providing the same intervention to a similar number of patients with pre-diabetes without targeted selection.

Prehospital Triage Strategies for the Transportation of Suspected Stroke Patients in the United States.

BACKGROUND AND PURPOSE: Ischemic stroke patients with large vessel occlusion (LVO) could benefit from direct transportation to an intervention center for endovascular treatment, but non-LVO patients need rapid IV thrombolysis in the nearest center. Our aim was to evaluate prehospital triage strategies for suspected stroke patients in the United States. METHODS: We used a decision tree model and geographic information system to estimate outcome of suspected stroke patients transported by ambulance within 4.5 hours after symptom onset. We compared the following strategies: (1) Always to nearest center, (2) American Heart Association algorithm (ie, directly to intervention center if a prehospital stroke scale suggests LVO and total driving time from scene to intervention center is <30 minutes, provided that the delay would not exclude from thrombolysis), (3) modified algorithms with a maximum additional driving time to the intervention center of <30 minutes, <60 minutes, or without time limit, and (4) always to intervention center. Primary outcome was the annual number of good outcomes, defined as modified Rankin Scale score of 0-2. The preferred strategy was the one that resulted in the best outcomes with an incremental number needed to transport to intervention center (NNTI) <100 to prevent one death or severe disability (modified Rankin Scale score of >2). RESULTS: Nationwide implementation of the American Heart Association algorithm increased the number of good outcomes by 594 (+1.0%) compared with transportation to the nearest center. The associated number of non-LVO patients transported to the intervention center was 16 714 (NNTI 28). The modified algorithms yielded an increase of 1013 (+1.8%) to 1369 (+2.4%) good outcomes, with a NNTI varying between 28 and 32. The algorithm without time limit was preferred in the majority of states (n=32 [65%]), followed by the algorithm with <60 minutes delay (n=10 [20%]). Tailoring policies at county-level slightly reduced the total number of transportations to the intervention center (NNTI 31). CONCLUSIONS: Prehospital triage strategies can greatly improve outcomes of the ischemic stroke population in the United States, but increase the number of non-LVO stroke patients transported to an intervention center. The current American Heart Association algorithm is suboptimal as a nationwide policy and should be modified to allow more delay when directly transporting LVO-suspected patients to an intervention center.

Predictably unequal: understanding and addressing concerns that algorithmic clinical prediction may increase health disparities.

The machine learning community has become alert to the ways that predictive algorithms can inadvertently introduce unfairness in decision-making. Herein, we discuss how concepts of algorithmic fairness might apply in healthcare, where predictive algorithms are being increasingly used to support decision-making. Central to our discussion is the distinction between algorithmic fairness and algorithmic bias. Fairness concerns apply specifically when algorithms are used to support polar decisions (i.e., where one pole of prediction leads to decisions that are generally more desired than the other), such as when predictions are used to allocate scarce health care resources to a group of patients that could all benefit. We review different fairness criteria and demonstrate their mutual incompatibility. Even when models are used to balance benefits-harms to make optimal decisions for individuals (i.e., for non-polar decisions)-and fairness concerns are not germane-model, data or sampling issues can lead to biased predictions that support decisions that are differentially harmful/beneficial across groups. We review these potential sources of bias, and also discuss ways to diagnose and remedy algorithmic bias. We note that remedies for algorithmic fairness may be more problematic, since we lack agreed upon definitions of fairness. Finally, we propose a provisional framework for the evaluation of clinical prediction models offered for further elaboration and refinement. Given the proliferation of prediction models used to guide clinical decisions, developing consensus for how these concerns can be addressed should be prioritized.

Assessment of the impact of EHR heterogeneity for clinical research through a case study of silent brain infarction.

BACKGROUND: The rapid adoption of electronic health records (EHRs) holds great promise for advancing medicine through practice-based knowledge discovery. However, the validity of EHR-based clinical research is questionable due to poor research reproducibility caused by the heterogeneity and complexity of healthcare institutions and EHR systems, the cross-disciplinary nature of the research team, and the lack of standard processes and best practices for conducting EHR-based clinical research. METHOD: We developed a data abstraction framework to standardize the process for multi-site EHR-based clinical studies aiming to enhance research reproducibility. The framework was implemented for a multi-site EHR-based research project, the ESPRESSO project, with the goal to identify individuals with silent brain infarctions (SBI) at Tufts Medical Center (TMC) and Mayo Clinic. The heterogeneity of healthcare institutions, EHR systems, documentation, and process variation in case identification was assessed quantitatively and qualitatively. RESULT: We discovered a significant variation in the patient populations, neuroimaging reporting, EHR systems, and abstraction processes across the two sites. The prevalence of SBI for patients over age 50 for TMC and Mayo is 7.4 and 12.5% respectively. There is a variation regarding neuroimaging reporting where TMC are lengthy, standardized and descriptive while Mayo's reports are short and definitive with more textual variations. Furthermore, differences in the EHR system, technology infrastructure, and data collection process were identified. CONCLUSION: The implementation of the framework identified the institutional and process variations and the heterogeneity of EHRs across the sites participating in the case study. The experiment demonstrates the necessity to have a standardized process for data abstraction when conducting EHR-based clinical studies.

Models with interactions overestimated heterogeneity of treatment effects and were prone to treatment mistargeting.

OBJECTIVES: We aimed to compare the performance of different regression modeling approaches for the prediction of heterogeneous treatment effects. STUDY DESIGN AND SETTING: We simulated trial samples (n = 3,600; 80% power for a treatment odds ratio of 0.8) from a superpopulation (N = 1,000,000) with 12 binary risk predictors, both without and with six true treatment interactions. We assessed predictions of treatment benefit for four regression models: a "risk model" (with a constant effect of treatment assignment) and three "effect models" (including interactions of risk predictors with treatment assignment). Three novel performance measures were evaluated: calibration for benefit (i.e., observed vs. predicted risk difference in treated vs. untreated), discrimination for benefit, and prediction error for benefit. RESULTS: The risk modeling approach was well-calibrated for benefit, whereas effect models were consistently overfit, even with doubled sample sizes. Penalized regression reduced miscalibration of the effect models considerably. In terms of discrimination and prediction error, the risk modeling approach was superior in the absence of true treatment effect interactions, whereas penalized regression was optimal in the presence of true treatment interactions. CONCLUSION: A risk modeling approach yields models consistently well calibrated for benefit. Effect modeling may improve discrimination for benefit in the presence of true interactions but is prone to overfitting. Hence, effect models-including only plausible interactions-should be fitted using penalized regression.

Targeted Incentive Programs For Lung Cancer Screening Can Improve Population Health And Economic Efficiency.

Because an intervention's clinical benefit depends on who receives it, a key to improving the efficiency of lung cancer screening with low-dose computed tomography (LDCT) is to incentivize its use among the current or former smokers who are most likely to benefit from it. Despite its clinical advantages and cost-effectiveness, only 3.9 percent of the eligible population underwent LDCT screening in 2015. Using individual lung cancer mortality risk, we developed a policy simulation model to explore the potential impact of implementing risk-targeted incentive programs, compared to either implementing untargeted incentive programs or doing nothing. We found that compared to the status quo, an untargeted incentive program that increased overall LDCT screening from 3,900 (baseline) to 10,000 per 100,000 eligible people would save 12,300 life-years and accrue a net monetary benefit (NMB) of $771 million over a lifetime horizon. Increasing screening by the same amount but targeting higher-risk people would yield an additional 2,470-6,600 life-years and an additional $210-$560 million NMB, depending on the extent of the risk-targeting. Risk-targeted incentive programs could include provider-level bonuses, health plan premium subsidies, and smoking cessation programs to maximize their impact. As clinical medicine becomes more personalized, targeting and incentivizing higher-risk people will help enhance population health and economic efficiency.

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.

Risk-Targeted Lung Cancer Screening: A Cost-Effectiveness Analysis.

Background: Targeting low-dose computed tomography (LDCT) for lung cancer screening to persons at highest risk for lung cancer mortality has been suggested to improve screening efficiency. Objective: To quantify the value of risk-targeted selection for lung cancer screening compared with National Lung Screening Trial (NLST) eligibility criteria. Design: Cost-effectiveness analysis using a multistate prediction model. Data Sources: NLST. Target Population: Current and former smokers eligible for lung cancer screening. Time Horizon: Lifetime. Perspective: Health care sector. Intervention: Risk-targeted versus NLST-based screening. Outcome Measures: Incremental 7-year mortality, life expectancy, quality-adjusted life-years (QALYs), costs, and cost-effectiveness of screening with LDCT versus chest radiography at each decile of lung cancer mortality risk. Results of Base-Case Analysis: Participants at greater risk for lung cancer mortality were older and had more comorbid conditions and higher screening-related costs. The incremental lung cancer mortality benefits during the first 7 years ranged from 1.2 to 9.5 lung cancer deaths prevented per 10 000 person-years for the lowest to highest risk deciles, respectively (extreme decile ratio, 7.9). The gradient of benefits across risk groups, however, was attenuated in terms of life-years (extreme decile ratio, 3.6) and QALYs (extreme decile ratio, 2.4). The incremental cost-effectiveness ratios (ICERs) were similar across risk deciles ($75 000 per QALY in the lowest risk decile to $53 000 per QALY in the highest risk decile). Payers willing to pay $100 000 per QALY would pay for LDCT screening for all decile groups. Results of Sensitivity Analysis: Alternative assumptions did not substantially alter our findings. Limitation: Our model did not account for all correlated differences between lung cancer mortality risk and quality of life. Conclusions: Although risk targeting may improve screening efficiency in terms of early lung cancer mortality per person screened, the gains in efficiency are attenuated and modest in terms of life-years, QALYs, and cost-effectiveness. Primary Funding Source: National Institutes of Health (U01NS086294).

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.

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.

Assessment of the relative effectiveness and tolerability of treatments of type 2 diabetes mellitus: a network meta-analysis.

PURPOSE: The relative effectiveness and tolerability of treatments for type 2 diabetes mellitus (T2DM) is not well understood because few randomized, controlled trials (RCTs) have compared these treatments directly. The purpose of the present study was to evaluate the relative effectiveness and tolerability of treatments of T2DM. METHODS: We performed a network meta-analysis of available RCTs with pharmacologic interventions in T2DM and compared antidiabetic drugs and combination regimens with metformin (the reference drug). Glycemic control (proportion achieving HbA1c goal) and tolerability (risk of hypoglycemia) were the primary outcomes of interest. Direct and indirect relative effects (unadjusted) were expressed as odds ratios and 95% CIs. FINDINGS: Eight treatments (glucagon-like peptide-1 [GLP-1] agonists plus metformin, sulfonylureas plus metformin, dipeptidyl peptidase-4 [DPP-4] inhibitors] plus metformin, colesevelan plus metformin, thiazolidinediones plus metformin, meglitinides plus metformin, α-glucosidase inhibitor plus metformin, and rosiglitazone monotherapy) outperformed metformin (direct effects). Triple combinations of GLP-1, thiazolinedione, insulin, metiglinide, or sulfonylureas added to a metformin backbone improved glycemic control (indirect effects). Higher risk of hypoglycemia was noted for sulfonylureas, α-glycosidases, and metiglinides when added to metformin (direct effects). Across indirect effects, only 17% of comparisons yielded less risk of hypoglycemia (70% were worse and 13% were comparable). IMPLICATIONS: Our results point out the relative superiority of 2- and 3-drug combination regimens over metformin and summarize treatment effects and tolerability in a comprehensive manner, which adds to our knowledge regarding T2DM treatment options.

Detecting mood disorder in resource-limited primary care settings: comparison of a self-administered screening tool to general practitioner assessment.

OBJECTIVES: Although efficacious treatments for mood disorders are available in primary care, under-diagnosis is associated with under-treatment and poorer outcomes. This study compares the accuracy of self-administered screening tests with routine general practitioner (GP) assessment for detection of current mood disorder. METHODS: 197 consecutive patients attending primary care centres in Santiago, Chile enrolled in this cross-sectional study, filling out the Patients Health Questionnaire-9 (PHQ-9) for depression and the Mood Disorder Questionnaire (MDQ) for bipolar disorder, after routine GP assessment. Diagnostic accuracy of these self-administered tools was compared with GP assessment, with gold standard diagnosis established by a structured diagnostic interview with trained clinicians (SCID-I). RESULTS: The sample was 75% female, with a mean age of 48.5 (SD 16.8); 37% had a current mood disorder (positive SCID-I result for depression or bipolar disorder). Sensitivity of the screening instruments (SI) was substantially higher than GP assessment (SI: 0.8, [95% CI 0.71, 0.81], versus GP: 0.2, [95% CI 0.12, 0.25]: p-value < 0.0001), without sacrifice in specificity (SI: 0.9, [95% CI 0.86, 0.96], versus GP: 0.9, [95% CI 0.88, 0.97]: p-value = 0.7). This led to improvement in both positive predictive value (SI: 0.8, [95% CI 0.82, 0.90], versus GP: 0.6, [95% CI 0.50, 0.64]: p-value < 0.001) and negative predictive value (SI: 0.9, [95% CI 0.78, 0.91] versus GP: 0.7, [95% CI 0.56, 0.72]: p-value < 0.01). CONCLUSION: Self-administered screening tools are more accurate than GP assessment in detecting current mood disorder in low-income primary care. Such screening tests may improve detection of current mood disorder if implemented in primary care settings.

Evidence of systematic duplication by new percutaneous coronary intervention programs.

BACKGROUND: Evidence suggests that recent and projected future investments in percutaneous coronary intervention (PCI) programs at US hospitals fail to increase access to timely reperfusion for patients with ST-segment elevation myocardial infarction. METHODS AND RESULTS: We set out to estimate the annual number and costs of new PCI programs in US hospitals from 2004 to 2008 and identify the characteristics of hospitals, neighborhoods, and states where new PCI programs have been introduced. We estimated a discrete-time hazard model to measure the influence of these characteristics on the decision of a hospital to introduce a new PCI program. In 2008, 1739 US hospitals were capable of performing PCI, a relative increase of 16.5% (251 hospitals) over 2004. The percentage of the US population with projected access to timely PCI grew by 1.8%. New PCI programs were more likely to be introduced in areas that already had a PCI program with more competition for market share, near populations with higher rates of private insurance, in states that had weak or no regulation of new cardiac catheterization laboratories, and in wealthier and larger hospitals. CONCLUSIONS: Our data show that new PCI programs were systematically duplicative of existing programs and did not help patients gain access to timely PCI. The total cost of recent US investments in new PCI programs is large and of questionable value for patients.

Effects of chemotherapy on survival of elderly patients with small-cell lung cancer: analysis of the SEER-medicare database.

INTRODUCTION: This retrospective cohort study was designed to analyze factors associated with administration of chemotherapy and to examine the impact of chemotherapy on survival among elderly patients with small-cell lung cancer (SCLC) in the community. METHODS: Elderly patients aged 65 years and older with SCLC diagnosed between 1992 and 2001 were selected from the Surveillance, Epidemiology, and End Results-Medicare database. Logistic regression was used to evaluate which covariates influenced receipt of chemotherapy. Cox proportional hazards regression was used to examine the influence of clinical and demographic variables on survival. The independent effect of chemotherapy on survival was determined using propensity scores and quantile regression. RESULTS: In the final cohort of 10,428 patients, 67.1% received chemotherapy, 39.1% received radiation, 3.4% received surgery, and 21.8% received no treatment. The most common chemotherapy regimens included etoposide combined with either cisplatin or carboplatin. Patients aged 85 years and older were significantly less likely to receive chemotherapy compared with patients aged 65 to 69 years (odds ratio 0.17; 95% confidence interval 0.14-0.21). Median survival for all patients was 7 months. Factors associated with improved survival were being female, black race, having limited-stage disease, receiving any treatment, and having a lower comorbidity score. Quantile regression demonstrated that chemotherapy provided a 6.5-month improvement in median survival (95% confidence interval 6.3-6.6; p<0.001). CONCLUSIONS: Statistically significant differences in the receipt of chemotherapy exist among elderly patients with SCLC. Chemotherapy is associated with a greater than 6-month improvement in median survival among elderly patients with SCLC, even in patients over the age of 80 years.

How to integrate multiple comorbidities in guideline development: article 10 in Integrating and coordinating efforts in COPD guideline development. An official ATS/ERS workshop report.

BACKGROUND: Professional societies, like many other organizations around the world, have recognized the need to use more rigorous processes to ensure that health care recommendations are informed by the best available research evidence. This is the 10th of a series of 14 articles that were prepared to advise guideline developers in respiratory and other diseases. This article deals with how multiple comorbidities (co-existing chronic conditions) may be more effectively integrated into guidelines. METHODS: In this review we addressed the following topics and questions using chronic obstructive pulmonary disease (COPD) as an example. (1) How important are multiple comorbidities for guidelines? (2) How have other organizations involved in the development of guidelines for single chronic disease approached the problem of multiple comorbidities? (3) What are the implications of multiple comorbidities for pharmacological treatment? (4) What are the potential changes induced by multiple comorbidities in guidelines? (5) What are the implications of considering a population of older patients with multiple comorbidities in designing clinical trials? Our conclusions are based on available evidence from the published literature, experience from guideline developers, and workshop discussions. We did not attempt to examine all Clinical Practice Guidelines (CPGs) and relevant literature. Instead, we selected CPGs generated by prominent professional organizations and relevant literature published in widely read journals, which are likely to have a high impact on clinical practice. RESULTS AND CONCLUSIONS: A widening gap exists between the reality of the care of patients with multiple chronic conditions and the practical clinical recommendations driven by CPGs focused on a single disease, such as COPD. Guideline development panels should aim for multidisciplinary representation, especially when contemplating recommendations for individuals aged 65 years or older (who often have multiple comorbidities), and should evaluate the quality of evidence and the strength of recommendations targeted at this population. A priority area for research should be to assess the effect of multiple concomitant medications and assess how their combined effects are altered by genetic, physiological, disease-related, and other factors. One step that should be implemented immediately would be for existing COPD guidelines to add new sections to address the impact of multiple comorbidities on screening, diagnosis, prevention, and management recommendations. Research should focus on the possible interaction of multiple medications. Furthermore, genetic, physiological, disease-related, and other factors that may influence the directness (applicability) of the evidence for the target population in clinical practice guidelines should be examined.