Risk of cancer in patients exposed to gabapentin in two electronic medical record systems.
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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]
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Project description:BackgroundElectronic medical record (EMR) systems and electronic messages are an increasingly common conduit between physicians and patients. Clear benefits of this type of communication have been established, especially among cancer patients. Studies suggest that patient portals and electronic messaging platforms can help with care coordination between oncology providers and facilitate asynchronous patient-provider communication. Despite the many benefits, there is little research regarding EMR and secure messaging use, particularly among patients with breast cancer.ObjectivesThe objective of this systematic review was to examine the evidence supporting the use of EMR-based messaging systems in patients with breast cancer.MethodsA systematic literature search of Ovid MEDLINE, PubMed, Scopus, Web of Science CINAHL, and Cochrane Library was conducted. Studies were required to be published between 2005 and 2022 and report data on demographic information and electronic messaging between patients and providers. Studies were excluded if they reported insufficient data, did not include breast cancer patients, or were not published in English.ResultsThis study identified 10 articles that met inclusion criteria. The resulting studies investigated topics such as: patterns of messaging and medication adherence, cancer screening, messaging as a predictor of behavior or outcomes, and symptom management. The literature indicates that electronic messaging with providers was associated with clinical benefits for breast cancer patients and improved screening behaviors.ConclusionThis review uncovered multiple areas to focus future research on, including ideal volume of electronic messages sent and their relation to prescription adherence, studies focusing solely on the breast cancer population, racial disparities in electronic messaging, and provider perceptions of electronic messaging. It is vital that more work be done to understand barriers and gaps in EMR usage to ensure that all individuals can access this increasingly essential medical service while minimizing physician workload and burnout.
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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.
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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.
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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.
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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.
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Project description:BackgroundOver the past decade, overdoses involving opioids and benzodiazepines have risen at alarming rates, making reductions in coprescribing of these medications a priority, particularly among patients who may be susceptible to adverse events due to high-risk conditions.ObjectivesThis quality improvement project evaluated the effectiveness of a medication alert designed to reduce opioid and benzodiazepine coprescribing among Veterans with known high-risk conditions (substance use, sleep apnea, suicide-risk, age 65 and above) at 1 Veterans Affairs (VA) health care system.MethodsPrescribers were exposed to the point-of-prescribing alert for 12 months. For each high-risk cohort we used interrupted time series design to examine population trends in coprescribing 12 months after alert launch adjusting for coprescribing 12 months before launch, demographics and clinical covariates. Trends at the alert site were compared with those of a similar VA health care system without the alert. Secondary analyses examined population trends in opioid and benzodiazepine prescribing separately.ResultsOver 12 months, the alert activated for 1332 patients. Proportions of patients with concurrent prescriptions decreased significantly postalert launch among substance use [adjusted odds ratio (AOR)=0.97; 95% confidence interval (CI)=0.96-0.99; 12-month decrease=25.0%], sleep apnea (AOR=0.97, 95% CI=0.95-0.98, 12-month decrease=38.5%), and suicide-risk (AOR=0.94, 95% CI=0.91-0.98, 12-month decrease=61.5%) cohorts at the alert site. Decreases in coprescribing were significantly different from the comparison site among suicide-risk (AOR=0.92, 95% CI=0.86-0.97) and sleep apnea (AOR=0.98, 95% CI=0.96-1.00) cohorts. Significant decreases in benzodiazepine prescribing trends were observed at the alert site only.ConclusionsMedication alerts hold promise as a means of reducing opioid and benzodiazepine coprescribing among certain high-risk groups.
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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.
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Project description:Abstract Older adults with multimorbidities have the highest rate of emergency department (ED) usage. These patients are typically on numerous medications, may have underlying dementia, and often present with falls and delirium. Identifying these high-risk older adults for possible intervention is challenging in the ED setting since available screening methods are manual and resource-intensive. The objective is to study the electronic medical record (EMR) use for identifying high-risk older adults in ED. This feasibility study is conducted in an academic ED with 67 000 total and 24% geriatric (age ≥ 65 years) annual visits, American College of Emergency Physician (ACEP) accredited Level 1 Geriatric Emergency Department with an ED-based geriatric consultation program by incorporating criteria from existing manual geriatric screening instruments and the 4M framework into an automated EMR screen to identify high-risk geriatric patients. ED providers are then alerted by an EMR Best Practice Alert (BPA) if high-risk status is identified. Initial development and impact on geriatric ED consults are reported. During the study period, 7450 patient encounters occurred; 1836 (24.6%) encounters involved patients who were 65 years or older. A total of 1398 (76.1%) high-risk ED encounters resulted in BPA alerts using the EMR automated screen. BPA alerts resulted in 82 (5.9%) geriatric evaluations. We conclude that using the EMR to automate screening for older adults for high-risk geriatric conditions in the ED is feasible. An automated EMR screen with a BPA to ED providers identified a well-defined cohort of older patients appropriate for further ED geriatric evaluation.
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