Risk of cancer in patients exposed to gabapentin in two electronic medical record systems.
Ontology highlight
ABSTRACT:
SUBMITTER: Irizarry MC
PROVIDER: S-EPMC3314779 | biostudies-literature | 2012 Feb
REPOSITORIES: biostudies-literature
Publications
Risk of cancer in patients exposed to gabapentin in two electronic medical record systems.
Irizarry Michael C MC Webb David J DJ Boudiaf Nada N Logie John J Habel Laurel A LA Udaltsova Natalia N Friedman Gary D GD
Pharmacoepidemiology and drug safety 20111206 2
<h4>Purpose</h4>High doses of gabapentin were associated with pancreatic acinar cell tumors in male Wistar rats, but there is little published epidemiological data regarding gabapentin and carcinogenicity. We explored the association between gabapentin and cancer in a US medical care program and followed up nominally significant associations in a UK primary care database.<h4>Methods</h4>In the US Kaiser Permanente Northern California (KPNC) health system, we performed nested case-control analyse ...[more]
Similar Datasets
Project description:BackgroundElectronic Medical Records (EMRs) are one of a range of digital health solutions that are key enablers of the data revolution transforming the health sector. They offer a wide range of benefits to health professionals, patients, researchers and other key stakeholders. However, effective implementation has proved challenging.MethodsA qualitative methodology was used in the study. Interviews were conducted with 12 clinical and administrative staff of a cancer centre at one-month pre-launch and eight clinical and administrative staff at 12-months post-launch of an EMR. Data from the interviews was collected via audio recording. Audio recordings were transcribed, de-identified and analysed to identify staff experiences with the EMR.ResultsData from the pre-implementation interviews were grouped into four categories: 1) Awareness and understanding of EMR; 2) Engagement in launch process; 3) Standardisation and completeness of data; 4) Effect on workload. Data from the post-launch interviews were grouped into six categories: 1) Standardisation and completeness of data; 2) Effect on workload; 3) Feature completeness and functionality; 4) Interaction with technical support; 5) Learning curve; 6) Buy-in from staff. Two categories: Standardisation and completeness of data and effect on workload were common across pre and post-implementation interviews.ConclusionFindings from this study contribute new knowledge on barriers and enablers to the implementation of EMRs in complex clinical settings. Barriers to successful implementation include lack of technical support once the EMR has launched, health professional perception the EMR increases workload, and the learning curve for staff adequately familiarize themselves with using the EMR.
| S-EPMC7789279 | biostudies-literature
Project description:IntroductionIncreased utilization of electronic health records (EHR) has enriched databases for creating risk models. We used machine learning techniques to develop an EHR-based risk model locally fitted to patients with type 2 diabetes mellitus (T2DM) for predicting cardiovascular disease.MethodsThis retrospective observational study was conducted within Ochsner Health, Louisiana, USA, between 2013-2017. Data analysis included 6245 patients who had two outpatient diagnoses of T2DM recorded on separate days or a diagnosis recorded during an inpatient encounter. Baseline clinical data were limited to 180 days before the index diagnosis. Cardiovascular outcomes were coronary heart disease (CHD), heart failure and stroke. Machine learning approaches were used to select predictor variables into Cox proportional hazards models for each outcome. Locally fit equations were compared to "generalized" risk equations (RECODe, AS-CVD, QRISK3) using model discrimination and calibration.ResultsAmong factors identified in the Ochsner (n = 11), RECODe (n = 14), AS-CVD (n = 15) and QRISK3 (n = 23), only age was common to all four risk equations. The Ochsner model had high internal discrimination for CHD (C-statistics 0.85) and better discrimination than RECODe (C-statistics 0.45), the QRISK3 (C-statistics 0.72) and AS-CVD (C-statistics 0.54).ConclusionsThe Ochsner model overestimated 5-year CHD risk, but had relatively higher calibration than the other models in CHD. Risk equations fitted for local populations improved cardiovascular risk stratification for patients with T2DM. Application of machine learning simplified the models compared to "generalized" risk equations.
| S-EPMC8266923 | biostudies-literature
Project description:BackgroundOncologists use patients' life expectancy to guide decisions and may benefit from a tool that accurately predicts prognosis. Existing prognostic models generally use only a few predictor variables. We used an electronic medical record dataset to train a prognostic model for patients with metastatic cancer.MethodsThe model was trained and tested using 12 588 patients treated for metastatic cancer in the Stanford Health Care system from 2008 to 2017. Data sources included provider note text, labs, vital signs, procedures, medication orders, and diagnosis codes. Patients were divided randomly into a training set used to fit the model coefficients and a test set used to evaluate model performance (80%/20% split). A regularized Cox model with 4126 predictor variables was used. A landmarking approach was used due to the multiple observations per patient, with t0 set to the time of metastatic cancer diagnosis. Performance was also evaluated using 399 palliative radiation courses in test set patients.ResultsThe C-index for overall survival was 0.786 in the test set (averaged across landmark times). For palliative radiation courses, the C-index was 0.745 (95% confidence interval [CI] = 0.715 to 0.775) compared with 0.635 (95% CI = 0.601 to 0.669) for a published model using performance status, primary tumor site, and treated site (two-sided P < .001). Our model's predictions were well-calibrated.ConclusionsThe model showed high predictive performance, which will need to be validated using external data. Because it is fully automated, the model can be used to examine providers' practice patterns and could be deployed in a decision support tool to help improve quality of care.
| S-EPMC6579743 | biostudies-literature
Project description:BackgroundWell-designed randomized trials provide high-quality clinical evidence but are not always feasible or ethical. In their absence, the electronic medical record (EMR) presents a platform to conduct comparative effectiveness research, central to the emerging academic learning health system (aLHS) model. A barrier to realizing this vision is the lack of a process to efficiently generate a reference comparison group for each patient.ObjectiveTo test a multi-step process for the selection of comparators in the EMR.Materials and methodsWe conducted a mixed-methods study within a large aLHS in North Carolina. We (1) created a list of 35 candidate variables; (2) surveyed 270 researchers to assess the importance of candidate variables; and (3) built consensus rankings around survey-identified variables (ie, importance scores >7) across two panels of 7-8 clinical research experts. Prioritized algorithm inputs were collected from the EMR and applied using a greedy matching technique. Feasibility was measured as the percentage of patients with 100 matched comparators and performance was measured via computational time and Euclidean distance.ResultsNine variables were selected: age, sex, race, ethnicity, body mass index, insurance status, smoking status, Charlson Comorbidity Index, and neighborhood percentage in poverty. The final process successfully generated 100 matched comparators for each of 1.8 million candidate patients, executed in less than 100 min for the majority of strata, and had average Euclidean distance 0.043.ConclusionEMR-derived matching is feasible to implement across a diverse patient population and can provide a reproducible, efficient source of comparator data for observational studies, with additional testing in clinical research applications needed.
| S-EPMC10797581 | biostudies-literature
Project description:Several reviews and case reports have described how information derived from the analysis of genomes are currently included in electronic health records (EHRs) for the purposes of supporting clinical decisions. Since the introduction of this new type of information in EHRs is relatively new (for instance, the widespread adoption of EHRs in the United States is just about a decade old), it is not surprising that a myriad of approaches has been attempted, with various degrees of success. EHR systems undergo much customization to fit the needs of health systems; these approaches have been varied and not always generalizable. The intent of this article is to present a high-level view of these approaches, emphasizing the functionality that they are trying to achieve, and not to advocate for specific solutions, which may become obsolete soon after this review is published. We start by broadly defining the end goal of including genomics in EHRs for healthcare and then explaining the various sources of information that need to be linked to arrive at a clinically actionable genomics analysis using a pharmacogenomics example. In addition, we include discussions on open issues and a vision for the next generation systems that integrate whole genome sequencing and EHRs in a seamless fashion.
| S-EPMC5946823 | biostudies-literature
Project description:ImportanceReliance on prescription opioids for postprocedural analgesia has contributed to the opioid epidemic. With the implementation of electronic medical record (EMR) systems, there has been increasing use of computerized order entry systems for medication prescriptions, which is now more common than handwritten prescriptions. The EMR can autopopulate a default number of pills prescribed, and 1 potential method to alter prescriber behavior is to change the default number presented via the EMR system.ObjectiveTo investigate the association of lowering the default number of pills presented when prescribing opioids in an EMR system with the amount of opioid prescribed after procedures.Design, setting, and participantsA prepost intervention study was conducted to compare postprocedural prescribing patterns during the 3 months before the default change (February 18 to May 17, 2017) with the 3 months after the default change (May 18 to August 18, 2017). The setting was a multihospital health care system that uses Epic EMR (Hyperspace 2015 IU2; Epic Systems Corporation). Participants were all patients in the study period undergoing 1 of the 10 most common operations and discharged by postoperative day 1.InterventionThe default number of opioid pills autopopulated in the EMR when prescribing discharge analgesia was lowered from 30 to 12.Main outcomes and measuresLinear regression estimating the change in the median number of opioid pills and the total dose of opioid prescribed was performed. Opioid doses were converted into morphine milligram equivalents (MME) for comparison. The frequency of patients requiring analgesic prescription refills was also evaluated.ResultsThere were 1447 procedures (mean [SD] age, 54.4 [17.3] years; 66.9% female) before the default change and 1463 procedures (mean [SD] age, 54.5 [16.4] years; 67.0% female) after the default change. After the default change, the median number of opioid pills prescribed decreased from 30 (interquartile range, 15-30) to 20 (interquartile range, 12-30) per prescription (P < .001). The percentage of prescriptions written for 30 pills decreased from 39.7% (554 of 1397) before the default change to 12.9% (183 of 1420) after the default change (P < .001), and the percentage of prescriptions written for 12 pills increased from 2.1% (29 of 1397) before the default change to 24.6% (349 of 1420) after the default change (P < .001). Regression analysis demonstrated a decrease of 5.22 (95% CI, -6.12 to -4.32) opioid pills per prescription after the default change, for a total decrease of 34.41 (95% CI, -41.36 to -27.47) MME per prescription. There was no statistical difference in opioid refill rates (3.0% [4 of 135] before the default change vs 1.5% [2 of 135] after the default change, P = .41).Conclusions and relevanceLowering the default number of opioid pills prescribed in an EMR system is a simple, effective, cheap, and potentially scalable intervention to change prescriber behavior and decrease the amount of opioid medication prescribed after procedures.
| S-EPMC6583068 | biostudies-literature
Project description:Background & aimsPatients with cirrhosis have 1-month rates of readmission as high as 35%. Early identification of high-risk patients could permit interventions to reduce readmission. The aim of our study was to construct an automated 30-day readmission risk model for cirrhotic patients using electronic medical record (EMR) data available early during hospitalization.MethodsWe identified patients with cirrhosis admitted to a large safety-net hospital from January 2008 through December 2009. A multiple logistic regression model for 30-day rehospitalization was developed using medical and socioeconomic factors available within 48 hours of admission and tested on a validation cohort. Discrimination was assessed using receiver operator characteristic curve analysis.ResultsWe identified 836 cirrhotic patients with 1291 unique admission encounters. Rehospitalization occurred within 30 days for 27% of patients. Significant predictors of 30-day readmission included the number of address changes in the prior year (odds ratio [OR], 1.13; 95% confidence interval [CI], 1.05-1.21), number of admissions in the prior year (OR, 1.14; 95% CI, 1.05-1.24), Medicaid insurance (OR, 1.53; 95% CI, 1.10-2.13), thrombocytopenia (OR, 0.50; 95% CI, 0.35-0.72), low level of alanine aminotransferase (OR, 2.56; 95% CI, 1.09-6.00), anemia (OR, 1.63; 95% CI, 1.17-2.27), hyponatremia (OR, 1.78; 95% CI, 1.14-2.80), and Model for End-stage Liver Disease score (OR, 1.04; 95% CI, 1.01-1.06). The risk model predicted 30-day readmission, with c-statistics of 0.68 (95% CI, 0.64-0.72) and 0.66 (95% CI, 0.59-0.73) in the derivation and validation cohorts, respectively.ConclusionsClinical and social factors available early during admission and extractable from an EMR predicted 30-day readmission in cirrhotic patients with moderate accuracy. Decision support tools that use EMR-automated data are useful for risk stratification of patients with cirrhosis early during hospitalization.
| S-EPMC3851321 | biostudies-literature
Project description:Genome-wide association studies (GWAS) require high specificity and large numbers of subjects to identify genotype-phenotype correlations accurately. The aim of this study was to identify type 2 diabetes (T2D) cases and controls for a GWAS, using data captured through routine clinical care across five institutions using different electronic medical record (EMR) systems.An algorithm was developed to identify T2D cases and controls based on a combination of diagnoses, medications, and laboratory results. The performance of the algorithm was validated at three of the five participating institutions compared against clinician review. A GWAS was subsequently performed using cases and controls identified by the algorithm, with samples pooled across all five institutions.The algorithm achieved 98% and 100% positive predictive values for the identification of diabetic cases and controls, respectively, as compared against clinician review. By standardizing and applying the algorithm across institutions, 3353 cases and 3352 controls were identified. Subsequent GWAS using data from five institutions replicated the TCF7L2 gene variant (rs7903146) previously associated with T2D.By applying stringent criteria to EMR data collected through routine clinical care, cases and controls for a GWAS were identified that subsequently replicated a known genetic variant. The use of standard terminologies to define data elements enabled pooling of subjects and data across five different institutions to achieve the robust numbers required for GWAS.An algorithm using commonly available data from five different EMR can accurately identify T2D cases and controls for genetic study across multiple institutions.
| S-EPMC3277617 | biostudies-literature
Project description:BackgroundWith the proliferation of electronic medical record (EMR) systems, there is an increasing interest in utilizing EMR data for medical research; yet, there is no quantitative research on EMR data utilization for medical research purposes in China.ObjectiveThis study aimed to understand how and to what extent EMR data are utilized for medical research purposes in a Healthcare Information and Management Systems Society (HIMSS) Analytics Electronic Medical Record Adoption Model (EMRAM) Stage 7 hospital in Beijing, China. Obstacles and issues in the utilization of EMR data were also explored to provide a foundation for the improved utilization of such data.MethodsFor this descriptive cross-sectional study, cluster sampling from Xuanwu Hospital, one of two Stage 7 hospitals in Beijing, was conducted from 2016 to 2019. The utilization of EMR data was described as the number of requests, the proportion of requesters, and the frequency of requests per capita. Comparisons by year, professional title, and age were conducted by double-sided chi-square tests.ResultsFrom 2016 to 2019, EMR data utilization was poor, as the proportion of requesters was 5.8% and the frequency was 0.1 times per person per year. The frequency per capita gradually slowed and older senior-level staff more frequently used EMR data compared with younger staff.ConclusionsThe value of using EMR data for research purposes is not well studied in China. More research is needed to quantify to what extent EMR data are utilized across all hospitals in Beijing and how these systems can enhance future studies. The results of this study also suggest that young doctors may be less exposed or have less reason to access such research methods.
| S-EPMC8371484 | biostudies-literature
Project description:OBJECTIVES: To examine the association between patient panels of underserved populations and adoption of electronic medical records (EMRs) among office-based physicians. DATA SOURCES: Two thousand three hundred and twenty-six office-based physicians who responded and saw patients in the 2005 and 2006 National Ambulatory Medical Care Surveys. STUDY DESIGN: This study used a cross-sectional design. The unit of analysis was the office-based physician. EMR adoption was defined based on functionalities (No EMR, Limited, or Comprehensive). An EMR was considered to have "comprehensive" functionalities if it included computerized orders for prescriptions and tests, test results, and clinical notes by physicians. Patient panels of underserved populations were measured as proportions of racial/ethnic minorities, Medicaid recipients, or self-pay/no charge/charity care patients treated by a physician using the reported sociodemographic characteristics in patient records linked to their treating physicians. Data were analyzed using multivariate regression analyses controlling for other patient-panel characteristics and characteristics of physicians and their practices. PRINCIPAL FINDINGS: We found a negative association between the proportion of Hispanics treated by a physician and physician adoption of EMRs with "comprehensive" functionalities after adjusting for other covariates. CONCLUSIONS: Physicians treating high shares of Hispanic patients may have lower access to EMRs with essential functionalities.
| S-EPMC2910564 | biostudies-other