Dataset Information

A computational method to quantitatively measure pediatric drug safety using electronic medical records.

ABSTRACT:

Background

Drug safety in children is a major concern; however, there is still a lack of methods for quantitatively measuring, let alone to improving, drug safety in children under different clinical conditions. To assess pediatric drug safety under different clinical conditions, a computational method based on Electronic Medical Record (EMR) datasets was proposed.

Methods

In this study, a computational method was designed to extract the significant drug-diagnosis associations (based on a Bonferroni-adjusted hypergeometric P-value ResultsThe comparison between the children's hospital and the general hospital showed unique features of pediatric drug use and identified the drug treatment gap between children and adults. In total, 591 drugs were used in the children's hospital; 18 drug clusters that were associated with certain clinical conditions were generated based on our method; and the quantitative drug safety levels of each drug cluster (under different clinical conditions) were calculated, analyzed, and visualized.

Conclusion

With this method, quantitative drug safety levels under certain clinical conditions in pediatric patients can be evaluated and compared. If there are longitudinal data, improvements can also be measured. This method has the potential to be used in many population-level, health data-based drug safety studies.

SUBMITTER: Yu G

PROVIDER: S-EPMC6961323 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Publications

A computational method to quantitatively measure pediatric drug safety using electronic medical records.

Yu Gang G Zeng Xian X Ni Shaoqing S Jia Zheng Z Chen Weihong W Lu Xudong X An Jiye J Duan Huilong H Shu Qiang Q Li Haomin H

BMC medical research methodology 20200114 1

<h4>Background</h4>Drug safety in children is a major concern; however, there is still a lack of methods for quantitatively measuring, let alone to improving, drug safety in children under different clinical conditions. To assess pediatric drug safety under different clinical conditions, a computational method based on Electronic Medical Record (EMR) datasets was proposed.<h4>Methods</h4>In this study, a computational method was designed to extract the significant drug-diagnosis associations (ba ...[more]

PMID: 31937265

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Project description:Background: Drug-induced coagulopathy (DIC) is a severe adverse reaction and has become a significantly increased clinical problem in children. It is crucial to the detection of the DIC safety signal for drug post-marketing scientific supervision purposes. Therefore, this study aimed to detect potential signals for DIC in children using the routine electronic medical record (EMR) data. Methods: This study extracted EMR data from Beijing Children's Hospital between 2009 and 2020. A two-stage modeling method was developed to detect the signal of DIC. We calculated the crude incidence by mining cases of coagulopathy to select the potential suspected drugs; then, propensity score-matched retrospective cohorts of specific screened drugs from the first stage were constructed and estimated the odds ratio (OR) and 95% confidence interval (CI) using conditional logistic regression models. The current literature evidence was used to assess the novelty of the signal. Results:In the study, from a total of 340 drugs, 22 drugs were initially screened as potentially inducing coagulopathy. In total, we identified 19 positive DIC associations. Of these, potential DIC risk of omeprazole (OR: 2.23, 95% CI: 1.88-2.65), chlorpheniramine (OR:3.04, 95% CI:2.56-3.60), and salbutamol sulfate (OR:1.36, 95% CI:1.07-1.73) were three new DIC signals in both children and adults. Twelve associations between coagulopathy and drugs, meropenem (OR: 3.38, 95% CI: 2.72-4.20), cefoperazone sulbactam (OR: 2.80, 95% CI: 2.30-3.41), fluconazole (OR: 2.11, 95% CI: 1.71-2.59), voriconazole (OR: 2.82, 95% CI: 2.20-3.61), ambroxol hydrochloride (OR: 2.12, 95% CI: 1.74-2.58), furosemide (OR: 2.36, 95% CI: 2.08-2.67), iodixanol (OR: 2.21, 95% CI: 1.72-2.85), cefamandole (OR: 1.82, 95% CI: 1.56-2.13), ceftizoxime (OR: 1.95, 95% CI: 1.44-2.63), ceftriaxone (OR: 1.95, 95% CI: 1.44-2.63), latamoxef sodium (OR: 1.76, 95% CI: 1.49-2.07), and sulfamethoxazole (OR: 1.29, 95% CI: 1.01-1.64), were considered as new signals in children. Conclusion: The two-stage algorithm developed in our study to detect safety signals of DIC found nineteen signals of DIC, including twelve new signals in a pediatric population. However, these safety signals of DIC need to be confirmed by further studies based on population study and mechanism research.

Project description:AimsAssess the relationship between timely treatment intensification and hemoglobin A1C (HbA1C) control quality-of-care performance measures, i.e., HbA1C levels, among patients with uncontrolled type 2 diabetes.Materials and methodsElectronic medical records and diabetes registry data from a large, accountable care organization (ACO) were used to isolate a sample of adult patients with type 2 diabetes who received at least one oral antidiabetes agent and had at least one HbA1C level measurement ≥8.0% (64 mmol/mol; i.e., uncontrolled diabetes) between 7/1/2011 and 6/30/2015. Treatment intensification status was evaluated for each patient during a 120-day treatment intensification window following the index HbA1c measure. Two-level hierarchical generalized linear models, with patients aggregated at the physician level, were used to assess the association between treatment intensification and achieving HbA1C quality performance measures.Results547 patients met study selection criteria and 480 patients had at least one HbA1C test after the treatment intensification window and were used for the statistical analyses. About 40% of patients who had uncontrolled diabetes received treatment intensification during the 120-day window. Greater index HbA1C, greater patient body mass index, and fewer unique pre-index oral antidiabetes agents were significantly associated with greater likelihood of receiving timely treatment intensification. The odds of receiving treatment intensification were about 1.8 times higher (P = 0.0027) among patients with poor index HbA1C control (HbA1c level >9.0% [75 mmol/mol]) compared to other patients (index HbA1c 8.0% - 9.0%). Hispanic patients (compared to White patients) were significantly more likely to exhibit poor control after treatment intensification (odds ratio [OR] 2.91, P = 0.0304), underscoring the difficulty of controlling diabetes in this vulnerable group. In contrast, being male and being treated primarily by an internist (compared to primary treatment by a family medicine specialist) were both significantly associated with achieving superior control (HbA1c level <8.0%) after treatment intensification (OR 0.53 [P = 0.0165]; OR 0.41 [P = 0.0275], respectively).ConclusionsTimely treatment intensification was significantly associated with greater likelihood of patients achieving superior HbA1C control (<8.0%) and better HbA1C control quality performance for the practice. Even in an ACO with resources dedicated to diabetes control, it is incumbent upon clinicians to readily identify and open dialogues with patients who may benefit from closely supervised, individualized attention.

Dataset Information

A computational method to quantitatively measure pediatric drug safety using electronic medical records.

Background

Methods

Conclusion

Publications

A computational method to quantitatively measure pediatric drug safety using electronic medical records.

Similar Datasets

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