Identification of acute myocardial infarction from electronic healthcare records using different disease coding systems

Objective: To evaluate positive predictive value (PPV) of different disease codes and free text in identifying acute myocardial infarction (AMI) from electronic healthcare records (EHRs). Design: Validation study of cases of AMI identified from general practitioner records and hospital discharge diagnoses using free text and codes from the International Classification of Primary Care (ICPC), International Classification of Diseases 9th revision-clinical modification (ICD9-CM) and ICD-10th revision (ICD-10). Setting: Population-based databases comprising routinely collected data from primary care in Italy and the Netherlands and from secondary care in Denmark from 1996 to 2009. Participants: A total of 4 034 232 individuals with 22 428 883 person-years of follow-up contributed to the data, from which 42 774 potential AMI cases were identified. A random sample of 800 cases was subsequently obtained for validation. Main outcome measures: PPVs were calculated overall and for each code/free text. 'Best-case scenario' and 'worst-case scenario' PPVs were calculated, the latter taking into account non-retrievable/non-assessable cases. We further assessed the effects of AMI misclassification on estimates of risk during drug exposure. Results: Records of 748 cases (93.5% of sample) were retrieved. ICD-10 codes had a 'best-case scenario' PPV of 100% while ICD9-CM codes had a PPV of 96.6% (95% CI 93.2% to 99.9%). ICPC codes had a 'best-case scenario' PPV of 75% (95% CI 67.4% to 82.6%) and free text had PPV ranging from 20% to 60%. Corresponding PPVs in the 'worst-case scenario' all decreased. Use of codes with lower PPV generally resulted in small changes in AMI risk during drug exposure, but codes with higher PPV resulted in attenuation of risk for positive associations. Conclusions: ICD9-CM and ICD-10 codes have good PPV in identifying AMI from EHRs; strategies are necessary to further optimise utility of ICPC codes and free-text search. Use of specific AMI disease codes in estimation of risk during drug exposure may lead to small but significant changes and at the expense of decreased precision

Adverse drug reactions in a primary care population prescribed non-steroidal anti-inflammatory drugs

Objective. To determine how often patients with musculoskeletal (MSK) complaints prescribed a non-steroidal anti-inflammatory drug (NSAID) subsequently consult their general practitioner (GP) with a non-serious adverse drug reaction (ADR). Design. Cohort study. Setting. A healthcare database containing the electronic GP medical records of over 1.5 million patients throughout the Netherlands. Patients. A total of 16 626 adult patients with MSK complaints prescribed an NSAID. Main outcome measures. The patients' medical records were manually assessed for the duration of NSAID use for a maximum of two months, and consultations for complaints predefined as potential ADRs were identified. Subsequently, the likelihood of an association with the NSAID use was assessed and these potential ADRs were categorized as likely, possible, or unlikely ADRs. Results. In total, 961 patients (6%) consulted their GP with 1227 non-serious potential ADRs. In 174 patients (1%) at least one of these was categorized as a likely ADR, and in a further 408 patients (2.5%) at least one was categorized as a possible ADR. Dyspepsia was the most frequent likely ADR, followed by diarrhoea and dyspnoea (respectively 34%, 8%, and 8% of all likely ADRs). Conclusion. Of the patients with MSK complaints prescribed an NSAID, almost one in 30 patients re-consulted their GP with a complaint likely or possibly associated with the use of this drug. The burden of such consultations for non-serious ADRs should be taken into account by GPs when deciding whether treatment with an NSAID is appropriate

Suboptimal gastroprotective coverage of NSAID use and the risk of upper gastrointestinal bleeding and ulcers: An observational study using three European databases

Background: Gastro-protective agents (GPA) are co-prescribed with non-steroidal anti-inflammatory drugs (NSAID) to lower the risk of upper gastrointestinal (UGI) events. It is unknown to what extent the protective effect is influenced by therapy adherence. Aim: To study the association between GPA adherence and UGI events among non-selective (ns) NSAID users. Methods: The General Practice Research Database (UK 1998e2008), the Integrated Primary Care Information database (the Netherlands 1996-2007) and the Health Search/CSD Longitudinal Patient Database (Italy 2000-2007) were used. A nested case-control design was employed within a cohort of nsNSAID users aged ≥50 years, who also used a GPA. UGI event cases (UGI bleeding and/or symptomatic ulcer with/without obstruction/perforation) were matched to event-free members of the cohort for age, sex, database and calendar time. Adherence to GPA was calculated as the proportion of nsNSAID treatment days covered by a GPA prescription. Adjusted OR with 95% CI were calculated. Results: The cohort consisted of 618 684 NSAID users, generating 1 107 266 nsNSAID episodes. Of these, 117 307 (10.6%) were (partly) covered by GPA, 4.9% of which with a GPA coverage 80% (full adherence). 339 patients experienced an event. Among non-adherers, the OR was 2.39 (95% CI 1.66 to 3.44) for all UGI events and 1.89 (95% CI 1.09 to 3.28) for UGI bleeding alone, compared to full adherers. Conclusions: The risk of UGI events was significantly higher in nsNSAID users with GPA non-adherence. This underlines the importance of strategies to improve GPA adherence. Copyright Article author (or their employer) 2011

Extracting information from the text of electronic medical records to improve case detection: a systematic review

Background: Electronic medical records (EMRs) are revolutionizing health-related research. One key issue for study quality is the accurate identification of patients with the condition of interest. Information in EMRs can be entered as structured codes or unstructured free text. The majority of research studies have used only coded parts of EMRs for case-detection, which may bias findings, miss cases, and reduce study quality. This review examines whether incorporating information from text into case-detection algorithms can improve research quality. Methods: A systematic search returned 9659 papers, 67 of which reported on the extraction of information from free text of EMRs with the stated purpose of detecting cases of a named clinical condition. Methods for extracting information from text and the technical accuracy of case-detection algorithms were reviewed. Results: Studies mainly used US hospital-based EMRs, and extracted information from text for 41 conditions using keyword searches, rule-based algorithms, and machine learning methods. There was no clear difference in case-detection algorithm accuracy between rule-based and machine learning methods of extraction. Inclusion of information from text resulted in a significant improvement in algorithm sensitivity and area under the receiver operating characteristic in comparison to codes alone (median sensitivity 78% (codes + text) vs 62% (codes), P = .03; median area under the receiver operating characteristic 95% (codes + text) vs 88% (codes), P = .025). Conclusions: Text in EMRs is accessible, especially with open source information extraction algorithms, and significantly improves case detection when combined with codes. More harmonization of reporting within EMR studies is needed, particularly standardized reporting of algorithm accuracy metrics like positive predictive value (precision) and sensitivity (recall)

Prevalence and incidence rate of hospital admissions related to medication between 2008 and 2013 in The Netherlands

PURPOSE: In 2009 a Dutch guideline was published containing recommendations to reduce Hospital Admissions Related to Medications (HARMs). This study aims to examine time-trends of HARMs and their potential preventability between 2008 and 2013 in The Netherlands. METHODS: A retrospective prevalence study was conducted using the Dutch PHARMO Database Network. A semi-automated pre-selection was used to make a crude identification of possible HARMs of which four samples were selected. These were independently assessed with respect to causality and potential preventability by a physician and pharmacist. The results were stratified by age into 18-64 years and 65 years and older. For these groups the net prevalences and incidence rates of HARMs and potentially preventable HARMs were calculated for the years 2008, 2009, 2011 and 2013. RESULTS: Four samples of 467 (2008), 447 (2009), 446 (2011) and 408 (2013) admissions were assessed. The net prevalence of HARMs in the 18-64 years group was approximately four times smaller compared to the older group with a mean prevalence of 2.7% (95% confidence interval [CI]:2.4%-3.0%) and 10.2% (95%CI: 9.7%-10.7%) respectively. The potential preventability was 25.1% (18.4%-31.8%) and 48.3% (95%CI: 44.8%-51.8%), respectively. The prevalence of HARMs in both groups did not change significantly between 2008 and 2013 with 2.4% (95%CI: 1.9%-3.0%) and 10.0% (95%CI: 9.0%-11.0%) in 2008 and 3.1% (2.7%-3.5%) and 10.4% (95%CI: 9.4%-11.4%) in 2013, respectively. CONCLUSION: Despite efforts to reduce HARMs, the prevalence did not decrease over time. Additional measures are therefore necessary, especially in the elderly population

Bleeding in cardiac patients prescribed antithrombotic drugs: Electronic health record phenotyping algorithms, incidence, trends and prognosis

Background Clinical guidelines and public health authorities lack recommendations on scalable approaches to defining and monitoring the occurrence and severity of bleeding in populations prescribed antithrombotic therapy. Methods We examined linked primary care, hospital admission and death registry electronic health records (CALIBER 1998–2010, England) of patients with newly diagnosed atrial fibrillation, acute myocardial infarction, unstable angina or stable angina with the aim to develop algorithms for bleeding events. Using the developed bleeding phenotypes, Kaplan-Meier plots were used to estimate the incidence of bleeding events and we used Cox regression models to assess the prognosis for all-cause mortality, atherothrombotic events and further bleeding. Results We present electronic health record phenotyping algorithms for bleeding based on bleeding diagnosis in primary or hospital care, symptoms, transfusion, surgical procedures and haemoglobin values. In validation of the phenotype, we estimated a positive predictive value of 0.88 (95% CI 0.64, 0.99) for hospitalised bleeding. Amongst 128,815 patients, 27,259 (21.2%) had at least 1 bleeding event, with 5-year risks of bleeding of 29.1%, 21.9%, 25.3% and 23.4% following diagnoses of atrial fibrillation, acute myocardial infarction, unstable angina and stable angina, respectively. Rates of hospitalised bleeding per 1000 patients more than doubled from 1.02 (95% CI 0.83, 1.22) in January 1998 to 2.68 (95% CI 2.49, 2.88) in December 2009 coinciding with the increased rates of antiplatelet and vitamin K antagonist prescribing. Patients with hospitalised bleeding and primary care bleeding, with or without markers of severity, were at increased risk of all-cause mortality and atherothrombotic events compared to those with no bleeding. For example, the hazard ratio for all-cause mortality was 1.98 (95% CI 1.86, 2.11) for primary care bleeding with markers of severity and 1.99 (95% CI 1.92, 2.05) for hospitalised bleeding without markers of severity, compared to patients with no bleeding. Conclusions Electronic health record bleeding phenotyping algorithms offer a scalable approach to monitoring bleeding in the population. Incidence of bleeding has doubled in incidence since 1998, affects one in four cardiovascular disease patients, and is associated with poor prognosis. Efforts are required to tackle this iatrogenic epidemic

Prevalence and incidence rate of hospital admissions related to medication between 2008 and 2013 in The Netherlands

Purpose: In 2009 a Dutch guideline was published containing recommendations to reduce Hospital Admissions Related to Medications (HARMs). This study aims to examine