More than 20% of approximately 35,000 patients filling a diuretic prescription had no potassium blood test recorded within the previous year. A laboratory reporting system used throughout Israel by Maccabi Healthcare Services physicians was modified to provide physician alerts regarding potassium testing. The physicians were experienced users of a computerized medical record (CMR) that provided online laboratory test results. A nightly batch file checked pharmacy diuretic purchases against the patient's potassium blood test status. On-screen computer-generated reminders were sent to physicians of patients lacking a recent potassium test. Reminders to clinicians increased potassium testing by 9.8% (p < 0.001). Physician age and gender played a small part in predicting compliance to the alert, but specialty and practice size did not. The time delay between the date a reminder was sent and the potassium test date decreased steadily during the intervention. The success of this reminder system encourages expansion to include more drug-laboratory interactions. Furthermore, direct alerts to patients at multiple organization/patient contact points are planned.
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PharmGKB (www.pharmgkb.org) and the disease association database (http://geneticassociationdb.nih.gov/). To be considered, a polymorphism had to be associated with disease in two independent populations and it had to have a reported minor allele frequency in a Caucasian population of at least 0.20. The final assay was developed on the Sequenom platform and all individuals within the PMRP cohort were analyzed. Probability of identity for the panel was calculated and each polymorphism was tested for Hardy Weinberg equilibrium. The allele frequencies found in our population of 20,000 were compared to reported allele frequencies. The allele frequencies in the PMRP population were compared between males and females and between different age categories. Results: From a list of 116 potential polymorphisms we developed a single assay of 36 medically relevant somatic polymorphisms with at least one polymorphism on each chromosome and a single sex marker. The probability of identity for the assay was 6.132x10 -15 and the sibling probability of identity was 3.077x10 -8 . Three polymorphisms differed from previously reported allele frequencies by more than 10%. In our population, four polymorphisms were different in males and females and four polymorphisms varied in allele frequencies between age groups. Furthermore, the chosen polymorphisms have been used in two studies and as preliminary data in several other studies. Conclusions: We were able to create a DNA panel of 36 medically relevant polymorphisms that successfully tested DNA quality, increased the knowledge of population wide allele frequencies, and is useful to investigators for candidate gene studies. Background: Research on the genetics of opioid addiction among illicit drug users is difficult given challenges to case ascertainment and selection of an appropriate control group. To address this limitation, we tested a model by studying prescription opioid users. Methods: We undertook a case -control, candidate-gene study of opioid-dependent users and nondependent users among Geisinger Clinic outpatients. Electronic health record (EHR) data were reviewed for patients who were prescribed opioids. Patients were selected for a phone interview if they had five or more prescription orders for opioids in the prior 12 months for non-cancer pain. Cases were identified based on the Diagnostic and Statistical Manual of Mental Disorder, Version IV (DSM-IV) criteria for opioid dependence. A modified Composite International Diagnostic Interview (CIDI) was used to obtain data on dependence status and history. Data were also gathered on detailed drug, psychological, and environmental exposures. Altogether, 703 patients completed the CIDI; 512 of these patients (73%) provided DNA for genotyping and completed the NEO Five-factor Personality Inventory. Findings: Altogether 37.4% (95% CI = 33.8-41.1) of patients met DSM-IV criteria for lifetime opioid dependence and 30.7% (95% CI = 27.3 -34.3) met criteria for current dependence. Significant predictors of opioid dependence incl...
Background/Aims: In preliminary work toward Cancer Research Networkbased studies of lymphoma risk pathways, we undertook an evaluation of the utility of ICD-9 CM diagnostic codes to accurately identify potential risk factor conditions, including rheumatoid arthritis (RA). Methods: Using the enrolled Virtual Data Warehouse (VDW) cohort at Marshfield Clinic Research Foundation, we ascertained a set of potential RA cases diagnosed between 2000 and 2010 by the presence of one or more diagnostic codes for RA (ICD-9 CM 714-714.89). Medical records were abstracted by trained staff on a random sample of 206 cases. Cases were adjudicated into categories of confirmed, probable, documented physician diagnosis only, equivocal, and non-case using a literature-based gold standard definition scheme. Outcome measures included positive predictive value (PPV) and relative sensitivity, with the confirmed, probable and physician diagnosis categories considered valid cases for the main analysis. Results: Upon review, 25 subjects did not have sufficient medical records available for evaluation, leaving 181 subjects for analysis, including 57 with only one RA code and 124 with 2 or more instances of an RA code. Overall PPV for patients with one or more RA codes was 56%. Subjects with only one diagnostic code had very low PPV (12%), while PPV was higher among those with 2 or more (76%). PPV improved to 91% when requiring a rheumatologist diagnosis, but only 40% of the true cases in the set were detectable in this way. The one algorithm that provided acceptably high PPV and relative sensitivity required 2 or more RA diagnostic codes and history of a rheumatoid factor test (PPV 83%, relative sensitivity 82%). Conclusions: A single ICD-9CM diagnostic code for RA was not highly predictive of a true diagnosis, but PPV was enhanced when requiring multiple codes and incorporating other VDW data elements. With one exception, algorithms with higher PPV had strong reductions in relative sensitivity. Limitations include non-systematic collection of RA medication data preventing the use of treatment in the algorithms, and the inability to evaluate absolute sensitivity. Next steps include a joint RA analysis with investigators at Kaiser Permanente Southern California, and possible inclusion of RA treatment data into the Marshfield algorithms.
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