More than two-dozen prescription drug-based risk scores are available for risk adjustment: A systematic review

Association of polypharmacy scores with the long-term survival of patients with intact aortoiliac aneurysms and indication for repair

OBJECTIVE/BACKGROUND

Our study analyzed the relationship between two polypharmacy scores (addition of chronic prescribed drugs [ACPDs] and Rx-Risk Comorbidity Index) and survival in patients with an intact abdominal aortic and/or common iliac aneurysm (AAA).

METHODS

Consecutive retrospective, single-center cohort of patients attended for an intact AAA with indication for repair from 2008 to 2021. Demographic data, Charlson Comorbidity Index, AAA treatment, ACPD, and Rx-Risk polypharmacy scores were recorded at baseline. Main outcomes were the 5-year and long-term survival rates. The statistical analysis included Cox regression, area under the curve, and continuous net reclassification index.

RESULTS

A total of 424 patients with AAA were evaluated (median age: 76 years; 92.2% male, median Charlson index 2), of whom 314 (74.1%) underwent intervention (80% endovascular and 20% open) and 110 (25.9%) did not. During follow-up (mean 4.6 years), 245 patients (57.8%) died, with 1-month, 1-year, and 5-year survival rates of 98.1%, 86.3%, and 52.7%, respectively. ACPD and Rx-Risk indices (median [interquartile range]: 6 [4-9] and 3 [0-5], respectively) were significantly and linearly associated (P < .001) with survival, with the best cutoff points at 5 and 0, respectively. An ACPD >5 (patients with >5 chronically prescribed drugs at baseline) and an Rx-Risk >0 were associated with a 45.2% (P = .038) and 102% (P = .002) increase in 5-year mortality, respectively, after adjustment for age, sex, Charlson index, and type of AAA treatment. Both polypharmacy indices improved significantly the discriminative power of the Charlson Comorbidity Index in predicting survival.

CONCLUSIONS

Both ACPD and Rx-Risk polypharmacy scores are independently related to survival among patients with an intact AAA and indication for repair. Their behavior is similar, so the simple ACPD >5 appears to be sufficient to identify patients with lower survival rates.

Clinical Utility of Medication-Based Risk Scores to Reduce Polypharmacy and Potentially Avoidable Healthcare Utilization

The management of multiple chronic health conditions often requires patients to be exposed to polypharmacy to improve their health and enhance their quality of life. However, exposure to polypharmacy has been associated with an increased risk for adverse effects, drug-drug interactions, inappropriate prescribing, medication nonadherence, increased healthcare utilization such as emergency department visits and hospitalizations, and costs. Medication-based risk scores have been utilized to identify patients who may benefit from deprescribing interventions and reduce rates of inappropriate prescribing. These risk scores may also be utilized to prompt targeted discussions between patients and providers regarding medications or medication classes contributing to an individual’s risk for harm, eventually leading to the deprescribing of the offending medication(s). This opinion will describe existing medication-based risk scores in the literature, their utility in identifying patients at risk for specific adverse events, and how they may be incorporated in healthcare settings to reduce rates of potentially inappropriate polypharmacy and avoidable healthcare utilization and costs.

Evaluating Drug Risk Using GAN and SMOTE Based on CFDA's Spontaneous Reporting Data

Adverse drug reactions (ADRs) pose health threats to humans. Therefore, the risk re-evaluation of post-marketing drugs has become an important part of the pharmacovigilance work of various countries. In China, drugs are mainly divided into three categories, from high-risk to low-risk drugs, namely, prescription drugs (Rx), over-the-counter drugs A (OTC-A), and over-the-counter drugs B (OTC-B). Until now, there has been a lack of automated evaluation methods for the three status switch of drugs. Based on China Food and Drug Administration's (CFDA) spontaneous reporting database (CSRD), we proposed a classification model to predict risk level of drugs by using feature enhancement based on Generative Adversarial Networks (GAN) and Synthetic Minority Over-Sampling Technique (SMOTE). A total of 985,960 spontaneous reports from 2011 to 2018 were selected from CSRD in Jiangsu Province as experimental data. After data preprocessing, a class-imbalance data set was obtained, which contained 887 Rx (accounting for 84.72%), 113 OTC-A (10.79%), and 47 OTC-B (4.49%). Taking drugs as the samples, ADRs as the features, and signal detection results obtained by proportional reporting ratio (PRR) method as the feature values, we constructed the original data matrix, where the last column represents the category label of each drug. Our proposed model expands the ADR data from both the sample space and the feature space. In terms of feature space, we use feature selection (FS) to screen ADR symptoms with higher importance scores. Then, we use GAN to generate artificial data, which are added to the feature space to achieve feature enhancement. In terms of sample space, we use SMOTE technology to expand the minority samples to balance three categories of drugs and minimize the classification deviation caused by the gap in the sample size. Finally, we use random forest (RF) algorithm to classify the feature-enhanced and balanced data set. The experimental results show that the accuracy of the proposed classification model reaches 98%. Our proposed model can well evaluate drug risk levels and provide automated methods for status switch of post-marketing drugs.