Dataset Information

Data Mining for Adverse Drug Events With a Propensity Score-matched Tree-based Scan Statistic.

ABSTRACT: The tree-based scan statistic is a statistical data mining tool that has been used for signal detection with a self-controlled design in vaccine safety studies. This disproportionality statistic adjusts for multiple testing in evaluation of thousands of potential adverse events. However, many drug safety questions are not well suited for self-controlled analysis. We propose a method that combines tree-based scan statistics with propensity score-matched analysis of new initiator cohorts, a robust design for investigations of drug safety. We conducted plasmode simulations to evaluate performance. In multiple realistic scenarios, tree-based scan statistics in cohorts that were propensity score matched to adjust for confounding outperformed tree-based scan statistics in unmatched cohorts. In scenarios where confounding moved point estimates away from the null, adjusted analyses recovered the prespecified type 1 error while unadjusted analyses inflated type 1 error. In scenarios where confounding moved point estimates toward the null, adjusted analyses preserved power, whereas unadjusted analyses greatly reduced power. Although complete adjustment of true confounders had the best performance, matching on a moderately mis-specified propensity score substantially improved type 1 error and power compared with no adjustment. When there was true elevation in risk of an adverse event, there were often co-occurring signals for clinically related concepts. TreeScan with propensity score matching shows promise as a method for screening and prioritization of potential adverse events. It should be followed by clinical review and safety studies specifically designed to quantify the magnitude of effect, with confounding control targeted to the outcome of interest.

SUBMITTER: Wang SV

PROVIDER: S-EPMC6167151 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Publications

Data Mining for Adverse Drug Events With a Propensity Score-matched Tree-based Scan Statistic.

Wang Shirley V SV Maro Judith C JC Baro Elande E Izem Rima R Dashevsky Inna I Rogers James R JR Nguyen Michael M Gagne Joshua J JJ Patorno Elisabetta E Huybrechts Krista F KF Major Jacqueline M JM Zhou Esther E Reidy Megan M Cosgrove Austin A Schneeweiss Sebastian S Kulldorff Martin M

Epidemiology (Cambridge, Mass.) 20181101 6

The tree-based scan statistic is a statistical data mining tool that has been used for signal detection with a self-controlled design in vaccine safety studies. This disproportionality statistic adjusts for multiple testing in evaluation of thousands of potential adverse events. However, many drug safety questions are not well suited for self-controlled analysis. We propose a method that combines tree-based scan statistics with propensity score-matched analysis of new initiator cohorts, a robust ...[more]

PMID: 30074538

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Project description:BackgroundTyrosine kinase inhibitors (TKIs) have shown long-term survival benefits in patients with chronic myeloid leukemia (CML). Nevertheless, significant concern has been raised regarding long-term TKI-associated vascular adverse events (VAEs). The objective of this retrospective cohort study was to investigate the incidence of VAEs in Taiwanese patients with CML treated with different TKIs (imatinib, nilotinib, and dasatinib) as well as potential risk factors.Materials and methodsWe conducted a retrospective cohort study using the Taiwan Cancer Registry Database and National Health Insurance Research Database. Adult patients diagnosed with CML from 2008 to 2016 were identified and categorized into three groups according to their first-line TKI treatment (imatinib, nilotinib, and dasatinib). Propensity score matching was performed to control for potential confounders. Cox regressions were used to estimate the hazard ratio (HR) of VAEs in different TKI groups.ResultsIn total, 1,111 patients with CML were included in our study. We found that the risk of VAEs in nilotinib users was significantly higher than that in imatinib users, with an HR of 3.13 (95% confidence interval (CI), 1.30-7.51), whereas dasatinib users also showed a nonsignificant trend for developing VAEs, with an HR of 1.71 (95% CI, 0.71-4.26). In multivariable logistic regression analysis, only nilotinib usage, older age, and history of cerebrovascular diseases were identified as significant risk factors. The annual incidence rate of VAEs was highest within the first year after the initiation of TKIs.ConclusionThese findings can support clinicians in making treatment decisions and monitoring VAEs in patients with CML in Taiwan.Implications for practiceThis study found that patients with chronic myeloid leukemia (CML) treated with nilotinib and dasatinib may be exposed to a higher risk of developing vascular adverse events (VAEs) compared with those treated with imatinib. Thus, this study suggests that patients with CML who are older or have a history of cerebrovascular diseases should be under close monitoring of VAEs, particularly within the first year after the initiation of tyrosine kinase inhibitors.

Project description:Pain is common among patients with cirrhosis, yet managing pain in this population is challenging. Opioid analgesics are thought to be particularly high risk in patients with cirrhosis, and their use has been discouraged. We sought to understand patterns of opioid use among inpatients with cirrhosis and the risks of serious opioid-related adverse events in this population. We used the Vizient Clinical Database/Resource Manager, which includes clinical and billing data from hospitalizations at more than 500 academic medical centers. We identified all nonsurgical patients with cirrhosis hospitalized in 2017-2018 as well as a propensity score-matched cohort of patients without cirrhosis. Inpatient prescription records defined patterns of inpatient opioid use. Conditional logistic regression compared rates of use and serious opioid-related adverse events between patients with and without cirrhosis. Of 116,146 nonsurgical inpatients with cirrhosis, 62% received at least one dose of opioids and 34% had regular inpatient opioid use (more than half of hospital days), rates that were significantly higher than in patients without cirrhosis (adjusted odds ratio [AOR] for any use, 1.17; 95% confidence interval [CI], 1.13-1.21; P < 0.001; AOR for regular use, 1.07; 95% CI, 1.02-1.11; P = 0.002). Compared with patients without cirrhosis, patients with cirrhosis more often received tramadol (P < 0.001) and less commonly received opioid/acetaminophen combinations (P < 0.001). Rates of serious opioid-related adverse events were similar in patients with and without cirrhosis (1.6% vs. 1.9%; AOR, 0.96; P = 0.63). Conclusion: Over half of patients with cirrhosis have pain managed with opioids during hospitalization. Patterns of opioid use differ in patients with cirrhosis compared with patients without cirrhosis, although rates of serious adverse events are similar. Future studies should further explore the safety and efficacy of opioids in patients with cirrhosis, with the goal of improving pain management and quality of life in this population.

Dataset Information

Data Mining for Adverse Drug Events With a Propensity Score-matched Tree-based Scan Statistic.

Publications

Data Mining for Adverse Drug Events With a Propensity Score-matched Tree-based Scan Statistic.

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