Cardiovascular adverse events by non-steroidal anti-inflammatory drugs: when the benefits outweigh the risks

Feasibility of Identifying Factors Related to Alzheimer's Disease and Related Dementia in Real-World Data

A comprehensive view of factors associated with AD/ADRD will significantly aid in studies to develop new treatments for AD/ADRD and identify high-risk populations and patients for prevention efforts. In our study, we summarized the risk factors for AD/ADRD by reviewing existing meta-analyses and review articles on risk and preventive factors for AD/ADRD. In total, we extracted 477 risk factors in 10 categories from 537 studies. We constructed an interactive knowledge map to disseminate our study results. Most of the risk factors are accessible from structured Electronic Health Records (EHRs), and clinical narratives show promise as information sources. However, evaluating genomic risk factors using RWD remains a challenge, as genetic testing for AD/ADRD is still not a common practice and is poorly documented in both structured and unstructured EHRs. Considering the constantly evolving research on AD/ADRD risk factors, literature mining via NLP methods offers a solution to automatically update our knowledge map.

Machine learning risk prediction model for acute coronary syndrome and death from use of non-steroidal anti-inflammatory drugs in administrative data

Our aim was to investigate the usefulness of machine learning approaches on linked administrative health data at the population level in predicting older patients’ one-year risk of acute coronary syndrome and death following the use of non-steroidal anti-inflammatory drugs (NSAIDs). Patients from a Western Australian cardiovascular population who were supplied with NSAIDs between 1 Jan 2003 and 31 Dec 2004 were identified from Pharmaceutical Benefits Scheme data. Comorbidities from linked hospital admissions data and medication history were inputs. Admissions for acute coronary syndrome or death within one year from the first supply date were outputs. Machine learning classification methods were used to build models to predict ACS and death. Model performance was measured by the area under the receiver operating characteristic curve (AUC-ROC), sensitivity and specificity. There were 68,889 patients in the NSAIDs cohort with mean age 76 years and 54% were female. 1882 patients were admitted for acute coronary syndrome and 5405 patients died within one year after their first supply of NSAIDs. The multi-layer neural network, gradient boosting machine and support vector machine were applied to build various classification models. The gradient boosting machine achieved the best performance with an average AUC-ROC of 0.72 predicting ACS and 0.84 predicting death. Machine learning models applied to linked administrative data can potentially improve adverse outcome risk prediction. Further investigation of additional data and approaches are required to improve the performance for adverse outcome risk prediction.

Coronary Risks Associated with Diclofenac and Other NSAIDs: An Update

The risk of coronary events with non-steroidal anti-inflammatory drugs has been the subject of much debate since the original trial of rofecoxib raised the issue. Since then, over almost 20 years, such risks have been shown in clinical trials of long-term high-dose users, and in observational studies comparing users with non-users. The roles of cyclooxygenase (COX)-2/COX-1 selectivity and COX-2 inhibitory potency have been proposed to explain this increased risk of myocardial infarction (MI). Among NSAIDs, diclofenac appeared to be associated with a relatively higher risk of MI, similar to that of rofecoxib, compatible with the drug's high COX-2 inhibitory potency. Recent studies have resulted in further information being available. A study in the Danish healthcare system using active comparators found a slightly increased risk of MI in healthy persons. However, risk decreased with increasing baseline cardiovascular risk, to the point that in patients at high cardiovascular risk, there was no additional risk associated with diclofenac compared with paracetamol or other NSAIDs. The other major study, from the SOS project, studied several million persons in four countries in Europe, comparing the use of many NSAIDs with non-use. That study found a slightly increased risk with diclofenac compared with non-use, but this was not different from other NSAIDs. Comparing risks with selectivity or potency found no effect of either. These studies refute the main hypotheses to explain the coronary risk of NSAIDs. Finding risk in healthy low-risk patients only questions the reality of a link between the use of the drugs and the occurrence of MI in these conditions. Biases or confounding may be the major reason for small increases in cardiovascular risks in healthy users of NSAIDs in real life.

Repurposing drugs to fight aging: The difficult path from bench to bedside

The steady rise in life expectancy occurred across all developed countries during the last century. This demographic trend is, however, not accompanied by the same healthspan extension. This is since aging is the main risk factor for all age-associated pathological conditions. Therefore, slowing the rate of aging is suggested to be more efficient in preventing or delaying age-related diseases than treat them one by one, which is the common approach in a current pharmacological disease-oriented paradigm. To date, a variety of medications designed to treat particular pathological conditions have been shown to exhibit pro-longevity effects in different experimental models. Among them, there are many commonly used prescription and over-the-counter pharmaceuticals such as metformin, rapamycin, aspirin, statins, melatonin, vitamin antioxidants, etc. All of them are being increasingly investigated in preclinical and clinical trials with the aim of determine whether they have potential for extension of human healthspan. The results from these trials are frequently inconclusive and fall short of initial expectations, suggesting that innovative research ideas and additional translational steps are required to overcome obstacles for implementation of such approaches in clinical practice. In this review, recent advances and challenges in the field of repurposing widely used conventional pharmaceuticals to target the aging process are summarized and discussed.

Physician Prescribing Patterns and Risk of Future Long-Term Opioid Use Among Patients With Rheumatoid Arthritis: A Prospective Observational Cohort Study

OBJECTIVE

To identify the extent to which opioid prescribing rates for patients with rheumatoid arthritis (RA) vary in the US and to determine the implications of baseline opioid prescribing rates on the probability of future long-term opioid use.

METHODS

We identified patients with RA from physicians who contributed ≥10 patients within the first 12 months of participation in the Corrona RA Registry. The baseline opioid prescribing rate was calculated by dividing the number of patients with RA reporting opioid use during the first 12 months by the number of patients with RA providing data that year. To estimate odds ratios (ORs) for long-term opioid use, we used generalized linear mixed models.

RESULTS

During the follow-up period, long-term opioid use was reported by 7.0% (163 of 2,322) of patients of physicians with a very low rate of opioid prescribing (referent) compared to 6.8% (153 of 2,254) of patients of physicians with a low prescribing rate, 12.5% (294 of 2,352) of patients of physicians with a moderate prescribing rate, and 12.7% (307 of 2,409) of patients of physicians with a high prescribing rate. The OR for long-term opioid use after the baseline period was 1.16 (95% confidence interval [95% CI] 0.79-1.70) for patients of low-intensity prescribing physicians, 1.89 (95% CI 1.27-2.82) for patients of moderate-intensity prescribing physicians, and 2.01 (95% CI 1.43-2.83) for patients of high-intensity prescribing physicians, compared to very low-intensity prescribing physicians.

CONCLUSION

Rates of opioid prescriptions vary widely. Our findings indicate that baseline opioid prescribing rates are a strong predictor of whether a patient will become a long-term opioid user in the future, after controlling for patient characteristics.

Ibuprofen Safety at the Golden Anniversary: Are all NSAIDs the Same? A Narrative Review

Ibuprofen first came to market about 50 years ago and rapidly moved to over-the-counter (OTC) sales. In April 2019, the National Agency for the Safety of Medicines and Health Products (ANSM) of France issued a warning for NSAID uses by patients with infectious diseases based on an analysis of 20 years of real-world safety data on ibuprofen and ketoprofen. Nevertheless, ibuprofen remains a mainstay in the analgesic armamentarium and with numerous randomized clinical trials, head-to-head studies, and decades of clinical experience. The authors offer a review of the safety of ibuprofen and how it may differ from other NSAIDs. Ibuprofen is associated with certain well-known gastrointestinal adverse effects that are related to dose and patient population. Among nonsteroidal anti-inflammatory drugs (NSAIDs), ibuprofen has a comparatively low risk of cardiovascular adverse effects. It has been associated with renal and hepatic adverse effects, which appear to depend on dose, concomitant medications, and patient population. The association of ibuprofen with infections is more complex in that it confers risk in some situations but benefits in others, the latter in cystic fibrosis. Emerging interest in the literature is providing evidence of the role of ibuprofen as a possible endocrine disrupter as well as its potential antiproliferative effects for cancer cells. Taken altogether, ibuprofen has a favorable safety profile and is an effective analgesic for many acute and chronic pain conditions, although it-like other NSAIDs-is not without risk. After 50 years, evidence is still emerging about ibuprofen and its unique safety profile among NSAIDs. FUNDING: The Rapid Service Fee was funded by Abbott Established Pharmaceuticals Division (EPD).