Statin use after acute myocardial infarction by patient complexity: are the rates right?

Generating Practice-Based Evidence in the Use of Guideline-Recommended Combination Therapy for Secondary Prevention of Acute Myocardial Infarction

Background: Clinical guidelines recommend beta-blockers, angiotensin-converting enzyme inhibitors/angiotensin-receptor blockers, and statins for the secondary prevention of acute myocardial infarction (AMI). It is not clear whether variation in real-world practice reflects poor quality-of-care or a balance of outcome tradeoffs across patients. Methods: The study cohort included Medicare fee-for-service beneficiaries hospitalized 2007-2008 for AMI. Treatment within 30-days post-discharge was grouped into one of eight possible combinations for the three drug classes. Outcomes included one-year overall survival, one-year cardiovascular-event-free survival, and 90-day adverse events. Treatment effects were estimated using an Instrumental Variables (IV) approach with instruments based on measures of local-area practice style. Pre-specified data elements were abstracted from hospital medical records for a stratified, random sample to create "unmeasured confounders" (per claims data) and assess model assumptions. Results: Each drug combination was observed in the final sample (N = 124,695), with 35.7% having all three, and 13.5% having none. Higher rates of guideline-recommended treatment were associated with both better survival and more adverse events. Unmeasured confounders were not associated with instrumental variable values. Conclusions: The results from this study suggest that providers consider both treatment benefits and harms in patients with AMIs. The investigation of estimator assumptions support the validity of the estimates.

Assessing the ability of an instrumental variable causal forest algorithm to personalize treatment evidence using observational data: the case of early surgery for shoulder fracture

Background

Comparative effectiveness research (CER) using observational databases has been suggested to obtain personalized evidence of treatment effectiveness. Inferential difficulties remain using traditional CER approaches especially related to designating patients to reference classes a priori. A novel Instrumental Variable Causal Forest Algorithm (IV-CFA) has the potential to provide personalized evidence using observational data without designating reference classes a priori, but the consistency of the evidence when varying key algorithm parameters remains unclear. We investigated the consistency of IV-CFA estimates through application to a database of Medicare beneficiaries with proximal humerus fractures (PHFs) that previously revealed heterogeneity in the effects of early surgery using instrumental variable estimators.

Methods

IV-CFA was used to estimate patient-specific early surgery effects on both beneficial and detrimental outcomes using different combinations of algorithm parameters and estimate variation was assessed for a population of 72,751 fee-for-service Medicare beneficiaries with PHFs in 2011. Classification and regression trees (CART) were applied to these estimates to create ex-post reference classes and the consistency of these classes were assessed. Two-stage least squares (2SLS) estimators were applied to representative ex-post reference classes to scrutinize the estimates relative to known 2SLS properties.

Results

IV-CFA uncovered substantial early surgery effect heterogeneity across PHF patients, but estimates for individual patients varied with algorithm parameters. CART applied to these estimates revealed ex-post reference classes consistent across algorithm parameters. 2SLS estimates showed that ex-post reference classes containing older, frailer patients with more comorbidities, and lower utilizers of healthcare were less likely to benefit and more likely to have detriments from higher rates of early surgery.

Conclusions

IV-CFA provides an illuminating method to uncover ex-post reference classes of patients based on treatment effects using observational data with a strong instrumental variable. Interpretation of treatment effect estimates within each ex-post reference class using traditional CER methods remains conditional on the extent of measured information in the data.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-022-01663-0.

Lipid Testing Trends Before and After Hospitalization for Myocardial Infarction Among Adults in the United States, 2008-2019

Background

Routine monitoring of low-density lipoprotein cholesterol (LDL-C) identifies patients who may benefit from modifying lipid-lowering therapies (LLT). However, the extent to which LDL-C testing is occurring in clinical practice is unclear, specifically among patients hospitalized for a myocardial infarction (MI).

Methods

Using US commercial claims data, we identified patients with an incident MI hospitalization between 01/01/2008-03/31/2019. LDL-C testing was assessed in the year before admission (pre-MI) and the year after discharge (post-MI). Changes in LDL-C testing were evaluated using a Poisson model fit to pre-MI rates and extrapolated to the post-MI period. We predicted LDL-C testing rates if no MI had occurred (ie, based on pre-MI trends) and estimated rate differences and ratios (contrasting observed vs predicted rates).

Results

Overall, 389,367 patients were hospitalized for their first MI during the study period. In the month following discharge, 9% received LDL-C testing, increasing to 27% at 3 months and 52% at 12 months. Mean rates (tests per 1000 patients per month) in the pre- and post-MI periods were 51.9 (95% CI: 51.7, 52.1) and 84.4 (95% CI: 84.1, 84.6), respectively. Over 12 months post-MI, observed rates were higher than predicted rates; the maximum rate difference was 66 tests per 1000 patients in month 2 (rate ratio 2.2), stabilizing at a difference of 15-20 (ratio 1.2-1.3) for months 6-12.

Conclusion

Although LDL-C testing increased following MI hospitalization, rates remained lower than recommended by clinical guidelines. This highlights a potential gap in care, where increased LDL-C testing after MI may provide opportunities for LLT modification and decrease risk of subsequent cardiovascular events.

Association of Surgical Treatment With Adverse Events and Mortality Among Medicare Beneficiaries With Proximal Humerus Fracture

IMPORTANCE

Meta-analyses of randomized clinical trials suggest that the advantages and risks of surgery compared with conservative management as the initial treatment for proximal humerus fracture (PHF) vary, or are heterogeneous across patients. Substantial geographic variation in surgery rates for PHF suggests that the optimal rate of surgery across the population of patients with PHF is unknown.

OBJECTIVE

To use geographic variation in treatment rates to assess the outcomes associated with higher rates of surgery for patients with PHF.

DESIGN, SETTING, AND PARTICIPANTS

This comparative effectiveness research study analyzed all fee-for-service Medicare beneficiaries with proximal humerus fracture in 2011 who were continuously enrolled in Medicare Parts A and B for the 365-day period before and immediately after their index fracture. Data analysis was performed January through June 2019.

EXPOSURE

Undergoing 1 of the commonly used surgical procedures in the 60 days after an index fracture diagnosis.

MAIN OUTCOMES AND MEASURES

Risk-adjusted area surgery ratios were created for each hospital referral region as a measure of local area practice styles. Instrumental variable approaches were used to assess the association between higher surgery rates and adverse events, mortality risk, and cost at 1 year from Medicare's perspective for patients with PHF in 2011. Instrumental variable models were stratified by age, comorbidities, and frailty. Instrumental variable estimates were compared with estimates from risk-adjusted regression models.

RESULTS

The final cohort included 72 823 patients (mean [SD] age, 80.0 [7.9] years; 13 958 [19.2%] men). The proportion of patients treated surgically ranged from 1.8% to 33.3% across hospital referral regions in the United States. Compared with conservatively managed patients, surgical patients were younger (mean [SD] age, 80.4 [8.1] years vs 78.0 [7.2] years; P < .001) and healthier (Charlson Comorbidity Index score of 0, 14 863 [24.4%] patients vs 3468 [29.1%] patients; Function-Related Indicator score of 0, 20 720 [34.0%] patients vs 4980 [41.8%] patients; P < .001 for both), and a larger proportion were women (49 030 [80.5%] patients vs 9835 [82.5%] patients; P < .001). Instrumental variable analysis showed that higher rates of surgery were associated with increased total costs ($8913) during the treatment period, increased adverse event rates (a 1-percentage point increase in the surgery rate was associated with a 0.19-percentage point increase in the 1-year adverse event rate; β = 0.19; 95% CI, 0.09-0.27; P < .001), and increased mortality risk (a 1-percentage point increase in the surgery rate was associated with a 0.09-percentage point increase in the 1-year mortality rate; β = 0.09; 95% CI, 0.04-0.15; P < .01). Instrumental variable mortality results were even more striking for older patients and those with higher comorbidity burdens and greater frailty. Risk-adjusted estimates suggested that surgical patients had higher costs (increase of $17 278) and more adverse events (a 1-percentage point increase in the surgery rate was associated with a 0.12-percentage point increase in the 1-year adverse event rate; β = 0.12; 95% CI, 0.11 to 0.13; P < .001) but lower risk of mortality after PHF (a 1-percentage point increase in the surgery rate was associated with a 0.01-percentage point decrease in the 1-year mortality rate; β = -0.01; 95% CI, -0.015 to -0.005; P < .001).

CONCLUSIONS AND RELEVANCE

This study found that higher rates of surgery for treatment of patients with PHF were associated with increased costs, adverse event rates, and risk of mortality. Orthopedic surgeons should be aware of the harms of extending the use of surgery to more clinically vulnerable patient subgroups.

Treatment for Rotator Cuff Tear Is Influenced by Demographics and Characteristics of the Area Where Patients Live

Background:

Atraumatic rotator cuff tear is a common orthopaedic complaint for people >60 years of age. Lack of evidence or consensus on appropriate treatment for this type of injury creates the potential for substantial discretion in treatment decisions. To our knowledge, no study has assessed the implications of this discretion on treatment patterns across the United States.

Methods:

All Medicare beneficiaries in the United States with a new magnetic resonance imaging (MRI)-confirmed atraumatic rotator cuff tear were identified with use of 2010 to 2012 Medicare administrative data and were categorized according to initial treatment (surgery, physical therapy, or watchful waiting). Treatment was modeled as a function of the clinical and demographic characteristics of each patient. Variation in treatment rates across hospital referral regions and the presence of area treatment signatures, representing the extent that treatment rates varied across hospital referral regions after controlling for patient characteristics, were assessed. Correlations between measures of area treatment signatures and measures of physician access in hospital referral regions were examined.

Results:

Among patients who were identified as having a new, symptomatic, MRI-confirmed atraumatic rotator cuff tear (n = 32,203), 19.8% were managed with initial surgery; 41.3%, with initial physical therapy; and 38.8%, with watchful waiting. Patients who were older, had more comorbidity, or were female, of non-white race, or dual-eligible for Medicaid were less likely to receive surgery (p < 0.0001). Black, dual-eligible females had 0.42-times (95% confidence interval [CI], 0.34 to 0.50) lower odds of surgery and 2.36-times (95% CI, 2.02 to 2.70) greater odds of watchful waiting. Covariate-adjusted odds of surgery varied dramatically across hospital referral regions; unadjusted surgery and physical therapy rates varied from 0% to 73% and from 6% to 74%, respectively. On average, patients in high-surgery areas were 62% more likely to receive surgery than the average patient with identical measured characteristics, and patients in low-surgery areas were half as likely to receive surgery than the average comparable patient. The supply of orthopaedic surgeons and the supply of physical therapists were associated with greater use of initial surgery and physical therapy, respectively.

Conclusions:

Patient characteristics had a significant influence on treatment for atraumatic rotator cuff tear but did not explain the wide-ranging variation in treatment rates across areas. Local-area physician supply and specialty mix were correlated with treatment, independent of the patient’s measured characteristics.

Understanding Treatment Effect Estimates When Treatment Effects Are Heterogeneous for More Than One Outcome

BACKGROUND

Patient-centred care requires evidence of treatment effects across many outcomes. Outcomes can be beneficial (e.g. increased survival or cure rates) or detrimental (e.g. adverse events, pain associated with treatment, treatment costs, time required for treatment). Treatment effects may also be heterogeneous across outcomes and across patients. Randomized controlled trials are usually insufficient to supply evidence across outcomes. Observational data analysis is an alternative, with the caveat that the treatments observed are choices. Real-world treatment choice often involves complex assessment of expected effects across the array of outcomes. Failure to account for this complexity when interpreting treatment effect estimates could lead to clinical and policy mistakes.

OBJECTIVE

Our objective was to assess the properties of treatment effect estimates based on choice when treatments have heterogeneous effects on both beneficial and detrimental outcomes across patients.

METHODS

Simulation methods were used to highlight the sensitivity of treatment effect estimates to the distributions of treatment effects across patients across outcomes. Scenarios with alternative correlations between benefit and detriment treatment effects across patients were used. Regression and instrumental variable estimators were applied to the simulated data for both outcomes.

RESULTS

True treatment effect parameters are sensitive to the relationships of treatment effectiveness across outcomes in each study population. In each simulation scenario, treatment effect estimate interpretations for each outcome are aligned with results shown previously in single outcome models, but these estimates vary across simulated populations with the correlations of treatment effects across patients across outcomes.

CONCLUSIONS

If estimator assumptions are valid, estimates across outcomes can be used to assess the optimality of treatment rates in a study population. However, because true treatment effect parameters are sensitive to correlations of treatment effects across outcomes, decision makers should be cautious about generalizing estimates to other populations.

Use of Angiotensin-Converting Enzyme Inhibitors and Angiotensin Receptor Blockers for Geriatric Ischemic Stroke Patients: Are the Rates Right?

BACKGROUND

Our objective is to estimate the effects associated with higher rates of renin-angiotensin system antagonists, angiotensin-converting enzyme inhibitors and angiotensin receptor blockers (ACEI/ARBs), in secondary prevention for geriatric (aged >65 years) patients with new ischemic strokes by chronic kidney disease (CKD) status.

METHODS AND RESULTS

The effects of ACEI/ARBs on survival and renal risk were estimated by CKD status using an instrumental variable (IV) estimator. Instruments were based on local area variation in ACEI/ARB use. Data abstracted from charts were used to assess the assumptions underlying the instrumental estimator. ACEI/ARBs were used after stroke by 45.9% and 45.2% of CKD and non-CKD patients, respectively. ACEI/ARB rate differences across local areas grouped by practice styles were nearly identical for CKD and non-CKD patients. Higher ACEI/ARB use rates for non-CKD patients were associated with higher 2-year survival rates, whereas higher ACEI/ARB use rates for patients with CKD were associated with lower 2-year survival rates. While the negative survival estimates for patients with CKD were not statistically different from zero, they were statistically lower than the estimates for non-CKD patients. Confounders abstracted from charts were not associated with the instrumental variable used.

CONCLUSIONS

Higher ACEI/ARB use rates had different survival implications for older ischemic stroke patients with and without CKD. ACEI/ARBs appear underused in ischemic stroke patients without CKD as higher use rates were associated with higher 2-year survival rates. This conclusion is not generalizable to the ischemic stroke patients with CKD, as higher ACEI/ARBS use rates were associated with lower 2-year survival rates that were statistically lower than the estimates for non-CKD patients.

Big Data Cohort Extraction to Facilitate Machine Learning to Improve Statin Treatment

Health care Big Data studies hold substantial promise for improving clinical practice. Among analytic tools, machine learning (ML) is an important approach that has been widely used by many industries for data-driven decision support. In Big Data, thousands of variables and millions of patient records are commonly encountered, but most data elements cannot be directly used to support decision making. Although many feature-selection tools can help identify relevant data, these tools are typically insufficient to determine a patient data cohort to support learning. Therefore, domain experts with nursing or clinic knowledge play critical roles in determining value criteria or the type of variables that should be included in the patient cohort to maximize project success. We demonstrate this process by extracting a patient cohort (37,506 individuals) to support our ML work (i.e., the production of a proactive strategy to prevent statin adverse events) from 130 million de-identified lives in the OptumLabs™ Data Warehouse.

Geographic variation in the use of adjuvant therapy among elderly patients with resected non-small cell lung cancer

OBJECTIVES

The purpose of this study was to assess to what extent geographic variation in adjuvant treatment for non-small cell lung cancer (NSCLC) patients would remain, after controlling for patient and area-level characteristics.

MATERIALS AND METHODS

A retrospective cohort of 18,410 Medicare beneficiaries with resected, stage I-IIIA NSCLC was identified from the Surveillance, Epidemiology, and End Results (SEER)-Medicare linked database. Adjuvant therapies were classified as adjuvant chemotherapy (ACT), postoperative radiation therapy (PORT), or no adjuvant therapy. Predicted treatment probabilities were estimated for each patient given their clinical, demographic, and area-level characteristics with multivariate logistic regression. Area Treatment Ratios were used to estimate the propensity of patients in a local area to receive an adjuvant treatment, controlling for characteristics of patients in the area. Areas were categorized as low-, mid- and high-use and mapped for two representative SEER registries.

RESULTS

Overall, 10%, 12%, and 78% of patients received ACT, PORT and no adjuvant therapy, respectively. Age, sex, stage, type and year of surgery, and comorbidity were associated with adjuvant treatment use. Even after adjusting for patient characteristics, substantial geographic treatment variation remained. High- and low-use areas were tightly juxtaposed within and across SEER registries, often within the same county. In some local areas, patients were up to eight times more likely to receive adjuvant therapy than expected, given their characteristics. On the other hand, almost a quarter of patients lived in local areas in which patients were more than three times less likely to receive ACT than would be predicted.

CONCLUSION

Controlling for patient and area-level covariates did not remove geographic variation in adjuvant therapies for resected NSCLC patients. A greater proportion of patients were treated less than expected, rather than more than expected. Further research is needed to better understand its causes and potential impact on outcomes.