Active surveillance systems for pediatric adverse drug reactions: an idea whose time has come

Evaluation of medication errors in patients with kidney diseases in Quetta, Pakistan

BACKGROUND

Medication errors represent a significant challenge in healthcare, as they can lead to enduring harm for patients and impose substantial financial burdens on the healthcare system. To effectively mitigate medication errors, it is imperative to gain a comprehensive understanding of their frequency and the contributing variables. Thus, the primary objective of this study was to evaluate the occurrence of medication errors among patients with kidney diseases in Quetta, Pakistan.

METHODS

The objective of this study was to assess medication errors in patients diagnosed with kidney diseases in Quetta, Pakistan. The research was conducted at the Balochistan Institute of Nephro-Urology Quetta (BINUQ) Hospital, which serves as a tertiary care center specializing in the treatment of kidney diseases. A cross-sectional descriptive study design was employed over a period of six months. The study population consisted of patients admitted to the Nephro-urology wards at BINUQ Hospital during the specified duration. Data collection encompassed various methodologies, including checklist-guided observation, review of prescription order forms, documentation of drug administration, and comprehensive analysis of patient medical records. Descriptive and analytical analyses were conducted using SPSS version 23. Univariate analysis was employed to identify independent variables associated with medication errors, employing a significance level of p<0.01. The multivariate logistic regression analysis incorporated variables that exhibited a significant association with medication errors during the univariate analysis. Only those variables demonstrating a p-value of less than 0.05 at a 95% confidence level were considered significant predictors of medication administration errors within the final multivariate model.

RESULTS

Among the 274 medication errors identified in the study, documentation errors accounted for 118 cases (12.06%), administration errors for 97 cases (9.91%), prescribing errors for 34 cases (3.47%), and dispensing errors for 25 cases (2.55%). Statistical analysis revealed significant associations (p<0.05) between forgetfulness and duty shift, and medication errors in the documentation process. Similarly, inattention was significantly associated (p<0.05) with both prescribing and dispensing errors. Furthermore, the number of medications received emerged as the most influential factor associated with medication errors. Patients receiving 4-6 medications exhibited an odds ratio of 9.08 (p<0.001) compared to patients receiving 1-3 medications, while patients receiving more than 6 medications had an odds ratio of 4.23 (p<0.001) in relation to patients receiving 1-3 medications.

CONCLUSION

In conclusion, this study determined that documentation errors were the most prevalent medication errors observed in patients with kidney disease in Quetta, Pakistan. Forgetfulness and duty shift were associated with documentation errors, whereas inattention was linked to prescribing and dispensing errors. The significant risk factor for medication errors was found to be a high number of prescribed medications. Therefore, strategies aimed at reducing medication errors should prioritize enhancements in documentation practices, alleviating medication burden, and increasing awareness among healthcare providers.

A secure distributed logistic regression protocol for the detection of rare adverse drug events

Background

There is limited capacity to assess the comparative risks of medications after they enter the market. For rare adverse events, the pooling of data from multiple sources is necessary to have the power and sufficient population heterogeneity to detect differences in safety and effectiveness in genetic, ethnic and clinically defined subpopulations. However, combining datasets from different data custodians or jurisdictions to perform an analysis on the pooled data creates significant privacy concerns that would need to be addressed. Existing protocols for addressing these concerns can result in reduced analysis accuracy and can allow sensitive information to leak.

Objective

To develop a secure distributed multi-party computation protocol for logistic regression that provides strong privacy guarantees.

Methods

We developed a secure distributed logistic regression protocol using a single analysis center with multiple sites providing data. A theoretical security analysis demonstrates that the protocol is robust to plausible collusion attacks and does not allow the parties to gain new information from the data that are exchanged among them. The computational performance and accuracy of the protocol were evaluated on simulated datasets.

Results

The computational performance scales linearly as the dataset sizes increase. The addition of sites results in an exponential growth in computation time. However, for up to five sites, the time is still short and would not affect practical applications. The model parameters are the same as the results on pooled raw data analyzed in SAS, demonstrating high model accuracy.

Conclusion

The proposed protocol and prototype system would allow the development of logistic regression models in a secure manner without requiring the sharing of personal health information. This can alleviate one of the key barriers to the establishment of large-scale post-marketing surveillance programs. We extended the secure protocol to account for correlations among patients within sites through generalized estimating equations, and to accommodate other link functions by extending it to generalized linear models.

Quantifying adverse drug events : are systematic reviews the answer?

Quantifying adverse drug events (ADEs) is critical to clinicians, consumers and policy makers. Most ADE information comes from large clinical trials. Systematic reviews have become a popular tool in quantifying the efficacy of different therapeutic interventions and ADE data collected in randomised trials may be helpful in quantifying the risk associated with a specific pharmacological agent. However, clinicians who are interested in conducting systematic reviews of ADEs may face many challenges. These challenges are geared towards two main areas: poor quality of ADE reporting in randomised trials and poor indexing of ADEs in medical databases. In this review, we will discuss these challenges in detail using some examples from the literature. Where possible, we also discuss strategies that may overcome these problems. More rigourous standards of reporting ADEs in randomised trials, as well as better indexing of ADE terminology in medical databases, could one day make systematic reviews of ADEs a powerful tool for practising clinicians.