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Background: The optimal hemoglobin threshold to guide red blood cell (RBC) transfusion for patients with acute myocardial infarction (MI) and anemia is uncertain., Objective: To estimate the efficacy of 4 individual hemoglobin thresholds (<10 g/dL [<100 g/L], <9 g/dL [<90 g/L], <8 g/dL [<80 g/L], and <7 g/dL [<70 g/L]) to guide transfusion in patients with acute MI and anemia., Design: Prespecified secondary analysis of the MINT (Myocardial Ischemia and Transfusion) trial using target trial emulation methods. (ClinicalTrials.gov: NCT02981407)., Setting: 144 clinical sites in 6 countries., Participants: 3492 MINT trial participants with acute MI and a hemoglobin level below 10 g/dL., Intervention: Four transfusion strategies to maintain patients' hemoglobin concentrations at or above thresholds of 10, 9, 8, or 7 g/dL. Protocol exceptions were permitted for specified adverse clinical events., Measurements: Data from the MINT trial were leveraged to emulate 4 transfusion strategies and estimate per protocol effects on the composite outcome of 30-day death or recurrent MI (death/MI) and 30-day death using inverse probability weighting., Results: The 30-day risk for death/MI was 14.8% (95% CI, 11.8% to 18.4%) for a <10-g/dL strategy, 15.1% (CI, 11.7% to 18.2%) for a <9-g/dL strategy, 15.9% (CI, 12.4% to 19.0%) for a <8-g/dL strategy, and 18.3% (CI, 14.6% to 22.0%) for a <7-g/dL strategy. Absolute risk differences and risk ratios relative to the <10-g/dL strategy for 30-day death/MI increased as thresholds decreased, although 95% CIs were wide. Findings were similar and imprecise for 30-day death., Limitation: Unmeasured confounding may have persisted despite adjustment., Conclusion: The 30-day risks for death/MI and death among patients with acute MI and anemia seem to increase progressively with lower hemoglobin concentration thresholds for transfusion. However, the imprecision around estimates from this target trial analysis precludes definitive conclusions about individual hemoglobin thresholds., Primary Funding Source: National Heart, Lung, and Blood Institute., Competing Interests: Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M24-0571.

Competing Interests: Dr Strom additionally reports research grants from Anumana, HeartSciences, Ultromics, and Philips Healthcare; consulting for Edwards Lifesciences, Bracco Diagnostics, Philips Healthcare, General Electric Healthcare, and EVERSANA Lifesciences and is a member of the scientific advisory boards for Ultromics, EchoIQ, and HeartSciences, and serves on a data safety monitoring board for Pfizer. Dr Osborn reports research grants from the National Heart, Lung, and Blood Institute (R43HL167290), Philips Healthcare, and Zoll Circulation; consulting for Gentuity,NuPulse CV, and OpSens, and is a member of the scientific advisory board for Dyad Medical and holds equity in this company. Ms. Stout reports consulting for Bracco Diagnostics. All other authors report no conflicts.

Background: A strategy of administering a transfusion only when the hemoglobin level falls below 7 or 8 g per deciliter has been widely adopted. However, patients with acute myocardial infarction may benefit from a higher hemoglobin level., Methods: In this phase 3, interventional trial, we randomly assigned patients with myocardial infarction and a hemoglobin level of less than 10 g per deciliter to a restrictive transfusion strategy (hemoglobin cutoff for transfusion, 7 or 8 g per deciliter) or a liberal transfusion strategy (hemoglobin cutoff, <10 g per deciliter). The primary outcome was a composite of myocardial infarction or death at 30 days., Results: A total of 3504 patients were included in the primary analysis. The mean (±SD) number of red-cell units that were transfused was 0.7±1.6 in the restrictive-strategy group and 2.5±2.3 in the liberal-strategy group. The mean hemoglobin level was 1.3 to 1.6 g per deciliter lower in the restrictive-strategy group than in the liberal-strategy group on days 1 to 3 after randomization. A primary-outcome event occurred in 295 of 1749 patients (16.9%) in the restrictive-strategy group and in 255 of 1755 patients (14.5%) in the liberal-strategy group (risk ratio modeled with multiple imputation for incomplete follow-up, 1.15; 95% confidence interval [CI], 0.99 to 1.34; P = 0.07). Death occurred in 9.9% of the patients with the restrictive strategy and in 8.3% of the patients with the liberal strategy (risk ratio, 1.19; 95% CI, 0.96 to 1.47); myocardial infarction occurred in 8.5% and 7.2% of the patients, respectively (risk ratio, 1.19; 95% CI, 0.94 to 1.49)., Conclusions: In patients with acute myocardial infarction and anemia, a liberal transfusion strategy did not significantly reduce the risk of recurrent myocardial infarction or death at 30 days. However, potential harms of a restrictive transfusion strategy cannot be excluded. (Funded by the National Heart, Lung, and Blood Institute and others; MINT ClinicalTrials.gov number, NCT02981407.)., (Copyright © 2023 Massachusetts Medical Society.)

Importance: The addition of a claims-based frailty metric to traditional comorbidity-based risk-adjustment models for acute myocardial infarction (AMI), heart failure (HF), and pneumonia improves the prediction of 30-day mortality and readmission. This may have important implications for hospitals that tend to care for frail populations and participate in Centers for Medicare & Medicaid Services value-based payment programs, which use these risk-adjusted metrics to determine reimbursement., Objective: To determine whether the addition of frailty measures to traditional comorbidity-based risk-adjustment models improved prediction of outcomes for patients with AMI, HF, and pneumonia., Design, Setting, and Participants: A nationwide cohort study included Medicare fee-for-service beneficiaries 65 years and older in the United States between January 1 and December 1, 2016. Analysis began August 2018., Main Outcomes and Measures: Rates of mortality within 30 days of admission and 30 days of discharge, as well as 30-day readmission rates by frailty group. We evaluated the incremental effect of adding the Hospital Frailty Risk Score (HFRS) to current comorbidity-based risk-adjustment models for 30-day outcomes across all conditions., Results: For 785 127 participants, there were 166 200 hospitalizations [21.2%] for AMI, 348 619 [44.4%] for HF, and 270 308 [34.4%] for pneumonia. The mean (SD) age at the time of hospitalization was 79.2 (8.9) years; 656 315 (83.6%) were white and 402 639 (51.3%) were women. The mean (SD) HFRS was 7.3 (7.4) for patients with AMI, 10.8 (8.3) for patients with HF, and 8.2 (5.7) for patients with pneumonia. Among patients hospitalized for AMI, an HFRS more than 15 (compared with an HFRS <5) was associated with a higher risk of 30-day postadmission mortality (adjusted odds ratio [aOR], 3.6; 95% CI, 3.4-3.8), 30-day postdischarge mortality (aOR, 4.0; 95% CI, 3.7-4.3), and 30-day readmission (aOR, 3.0; 95% CI, 2.9-3.1) after multivariable adjustment for age, sex, race, and comorbidities. Similar patterns were observed for patients hospitalized with HF (30-day postadmission mortality: aOR, 3.5; 95% CI, 3.4-3.7; 30-day postdischarge mortality: aOR, 3.5; 95% CI, 3.3-3.6; and 30-day readmission: aOR, 2.9; 95% CI, 2.8-3.0) and among patients with pneumonia (30-day postadmission mortality: aOR, 2.5; 95% CI, 2.3-2.6; 30-day postdischarge mortality: aOR, 3.0; 95% CI, 2.9-3.2; and 30-day readmission: aOR, 2.8; 95% CI, 2.7-2.9). The addition of HFRS to traditional comorbidity-based risk-prediction models improved discrimination to predict outcomes for all 3 conditions., Conclusions and Relevance: Among Medicare fee-for-service beneficiaries, frailty as measured by the HFRS was associated with mortality and readmissions among patients hospitalized for AMI, HF, or pneumonia. The addition of HFRS to traditional comorbidity-based risk-prediction models improved the prediction of outcomes for all 3 conditions.

Readmission after hospitalization for acute myocardial infarction (AMI) significantly contributes to preventable morbidity and health-care costs. Outcomes after AMI vary by sex but the relationship of sex to readmissions warrants further exploration. Using the 2013 Nationwide Readmissions Database, we identified patients with a principal discharge diagnosis of AMI and stratified all-cause 30-day readmissions by sex and age. Of 214,824 patients, 44% were 18 to 64 years of age, 56% were ≥65 years, and 28% and 45%, respectively, were female. For patients 18 to 64 years, the readmission rate was 14% for women and 10% for men (p <0.001). For patients ≥65 years, the readmission rate was 18% for women and 16% for men (p <0.001). After adjusting for co-morbidities, women had a significantly higher risk of 30-day readmission compared with men, an effect that was strongest in younger women (odds ratio [OR] 1.21, 95% confidence interval [CI] 1.06 to 1.39, for ages 18 to 44; OR 1.13, 95% CI 1.07 to 1.18, for ages 45 to 64; OR 1.13, 95% CI 1.07 to 1.19, for ages 65 to 74, interaction p <0.001). The procedure rates during the index hospitalization were significantly lower for women. The most common readmission diagnoses were recurrent AMI, ischemic heart disease, and heart failure. Costs associated with readmissions after AMI totaled $447,506,740, of which $176,743,622 were attributed to readmissions of women. In conclusion, women are at higher risk of short-term readmission after an AMI hospitalization than men, particularly younger women. Sex-specific strategies to reduce these readmissions may be warranted., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Background: In 2010, New York State began excluding selected patients with cardiac arrest and coma from publicly reported mortality statistics after percutaneous coronary intervention. We evaluated the effects of this exclusion on rates of coronary angiography, revascularization, and mortality among patients with acute myocardial infarction and cardiac arrest., Methods and Results: Using statewide hospitalization files, we identified discharges for acute myocardial infarction and cardiac arrest January 2003 to December 2013 in New York and several comparator states. A difference-in-differences approach was used to evaluate the likelihood of coronary angiography, revascularization, and in-hospital mortality before and after 2010. A total of 26 379 patients with acute myocardial infarction and cardiac arrest (5619 in New York) were included. Of these, 17 141 (65%) underwent coronary angiography, 12 183 (46.2%) underwent percutaneous coronary intervention, and 2832 (10.7%) underwent coronary artery bypass grafting. Before 2010, patients with cardiac arrest in New York were less likely to undergo percutaneous coronary intervention compared with referent states (adjusted relative risk, 0.79; 95% confidence interval, 0.73-0.85; P <0.001). This relationship was unchanged after the policy change (adjusted relative risk, 0.82; 95% confidence interval, 0.76-0.89; interaction P =0.359). Adjusted risks of in-hospital mortality between New York and comparator states after 2010 were also similar (adjusted relative risk, 0.94; 95% confidence interval, 0.87-1.02; P =0.152 for post- versus pre-2010 in New York; adjusted relative risk, 0.88; 95% confidence interval, 0.84-0.92; P <0.001 for comparator states; interaction P =0.103)., Conclusions: Exclusion of selected cardiac arrest cases from public reporting was not associated with changes in rates of percutaneous coronary intervention or in-hospital mortality in New York. Rates of revascularization in New York for cardiac arrest patients were lower throughout., (© 2017 American Heart Association, Inc.)

Background: Early readmission after percutaneous coronary intervention is an important quality metric, but prediction models from registry data have only moderate discrimination. We aimed to improve ability to predict 30-day readmission after percutaneous coronary intervention from a previously validated registry-based model., Methods and Results: We matched readmitted to non-readmitted patients in a 1:2 ratio by risk of readmission, and extracted unstructured and unconventional structured data from the electronic medical record, including need for medical interpretation, albumin level, medical nonadherence, previous number of emergency department visits, atrial fibrillation/flutter, syncope/presyncope, end-stage liver disease, malignancy, and anxiety. We assessed differences in rates of these conditions between cases/controls, and estimated their independent association with 30-day readmission using logistic regression conditional on matched groups. Among 9288 percutaneous coronary interventions, we matched 888 readmitted with 1776 non-readmitted patients. In univariate analysis, cases and controls were significantly different with respect to interpreter (7.9% for cases and 5.3% for controls; P=0.009), emergency department visits (1.12 for cases and 0.77 for controls; P<0.001), homelessness (3.2% for cases and 1.6% for controls; P=0.007), anticoagulation (33.9% for cases and 22.1% for controls; P<0.001), atrial fibrillation/flutter (32.7% for cases and 28.9% for controls; P=0.045), presyncope/syncope (27.8% for cases and 21.3% for controls; P<0.001), and anxiety (69.4% for cases and 62.4% for controls; P<0.001). Anticoagulation, emergency department visits, and anxiety were independently associated with readmission., Conclusions: Patient characteristics derived from review of the electronic health record can be used to refine risk prediction for hospital readmission after percutaneous coronary intervention., (© 2015 American Heart Association, Inc.)