Opioid Heart Risks Mapped in 18 Million FDA Reports

A study mining nearly twenty years of U.S. adverse event reports has mapped opioid heart risks across the prescription painkiller class, finding almost eighty thousand statistical warning signals that connect fifteen approved opioids with distinct cardiovascular problems.

The research, published in Frontiers in Drug Safety and Regulation, is the first large-scale pharmacovigilance project to focus exclusively on the heart safety of prescription opioids, according to its authors. The team was led by scientists at the U.S. Food and Drug Administration's National Center for Toxicological Research, working with colleagues at Arkansas State University and the University of Arkansas at Little Rock.

To build the dataset, the researchers pulled roughly eighteen million reports from the FDA Adverse Event Reporting System, known as FAERS, covering the first quarter of 2004 through the third quarter of 2024. Those reports contained more than three hundred forty-two million drug-event pairs. Because FAERS stores drug names as free text, the team standardized thousands of spelling variants, brand names, and salt forms through RxNorm, a clinical drug vocabulary, before running any analysis.

How the team filtered millions of reports

The group applied the Empirical Bayes Geometric Mean method, a conservative Bayesian approach for spotting disproportionate reporting of an event for a single drug versus the database as a whole. A drug-event pair counted as a signal only when the lower bound of the ninety-five percent confidence interval exceeded a value of one and the pair appeared at least three times. Cardiovascular terms from the Medical Dictionary for Regulatory Activities then narrowed the results to heart and vascular events.

That filter left almost eighty thousand signals of disproportionate reporting, about four percent of all signals detected, spread across two hundred ninety-one unique cardiovascular events. The events ranged from high blood pressure and rapid heartbeat to cardiac arrest and cardio-respiratory arrest.

Fentanyl sits at the top of the ranking

Fentanyl was the most frequently reported prescription opioid in the dataset, with more than thirty-two thousand cardiovascular event pairs. After the statistical filtering, it still carried the largest number of signals, nearly twenty-three thousand, or seventy-one percent of its reported pairs, spanning 117 unique cardiovascular events. Oxycodone, tramadol, and morphine followed it in total signal counts, according to the study.

Volume told only part of the story. Remifentanil showed the highest concentration of signals relative to its reporting volume, at ninety percent of its pairs, with sufentanil close behind at about nine in ten and pentazocine at roughly two-thirds. The researchers note that low-reporting drugs can still carry dense risk profiles, which matters for medicines used in anesthesia and intensive care where cardiac events are watched closely.

Among individual events, low blood pressure was the most common signal, with 7,107 occurrences, followed by cardiac arrest with 5,436, rapid heartbeat with 3,600, cardio-respiratory arrest with 3,480, and high blood pressure with 3,358. Fentanyl and morphine together accounted for more than seventy percent of the low blood pressure, cardiac arrest, and rapid heartbeat signals, while codeine and hydromorphone drove the largest share of the high blood pressure cases.

Each drug has a distinct danger signature

The analysis found that individual opioids carry strikingly different cardiovascular profiles rather than a uniform class-wide pattern. Methadone's signals were dominated by torsade de pointes, a life-threatening arrhythmia, while oxycodone's concentrated in inflammation of the heart lining, which accounted for 43.9 percent of its signals. Codeine showed a similar pattern at 19 percent. Hydromorphone stood apart with signals dominated by cardiogenic shock, ventricular fibrillation, and blood clots. Buprenorphine's top signals were rapid heartbeat and cardio-respiratory arrest, each making up more than twenty-eight percent of its total, and tapentadol showed hypertensive crisis and heart failure.

Machine learning reinforced the point. Network analysis showed fentanyl and morphine sharing 51 cardiovascular events, the strongest overlap in the dataset, with fentanyl and remifentanil sharing 48. Hierarchical clustering divided the drugs into a hydromorphone-oxycodone cluster and a larger group holding the remaining thirteen, with the first pair showing a markedly different profile from everything else. Pairwise correlations were exceptionally high for pentazocine and buprenorphine at 0.96, oxymorphone and methadone at 0.92, and remifentanil and sufentanil at 0.91.

The clustering largely tracked chemistry. Fentanyl, remifentanil, and sufentanil all belong to the phenylpiperidine class of synthetic opioids, which the authors say may explain their coherent grouping, though they caution that shared hospital settings and reporting contexts could also drive the similarity. The paper notes that the R code for its correlation analysis was drafted with assistance from Claude, an AI assistant built by Anthropic, and then reviewed and verified by the authors, who take full responsibility for the results.

What the findings change for patients and prescribers

The authors are careful about what the numbers do and do not prove. FAERS is a passive reporting system, subject to reporting bias, underreporting, and uneven report quality, so disproportionality scores cannot estimate how often an event occurs, the absolute risk, or whether a drug caused it. The analysis also could not adjust for patient illness, other medicines taken alongside opioids, dosage, delivery method, or length of treatment.

Still, the researchers argue the patterns point to practical changes: heart screening with an electrocardiogram for patients starting methadone, cardiovascular monitoring while fentanyl is administered, and drug labels that reflect each opioid's distinct cardiac profile instead of treating the class as a single uniform risk. They describe the findings as a foundation for more refined prescribing and regulatory decisions aimed at reducing cardiovascular harm from prescription opioids.

For readers, the study lands as a reminder that the dangers of prescription opioids extend beyond addiction and breathing suppression. The full analysis is available in Frontiers in Drug Safety and Regulation, with a summary of the findings covered by Scienmag. Related coverage of health research and new science findings appears across the site.