Adverse drug events with hyperkalaemia during inpatient stays: evaluation of an automated method for retrospective detection in hospital databases.

BACKGROUND: Adverse drug reactions and adverse drug events (ADEs) are major public health issues. Many different prospective tools for the automated detection of ADEs in hospital databases have been developed and evaluated. The objective of the present study was to evaluate an automated method for the retrospective detection of ADEs with hyperkalaemia during inpatient stays. METHODS: We used a set of complex detection rules to take account of the patient's clinical and biological context and the chronological relationship between the causes and the expected outcome. The dataset consisted of 3,444 inpatient stays in a French general hospital. An automated review was performed for all data and the results were compared with those of an expert chart review. The complex detection rules' analytical quality was evaluated for ADEs. RESULTS: In terms of recall, 89.5% of ADEs with hyperkalaemia "with or without an abnormal symptom" were automatically identified (including all three serious ADEs). In terms of precision, 63.7% of the automatically identified ADEs with hyperkalaemia were true ADEs. CONCLUSIONS: The use of context-sensitive rules appears to improve the automated detection of ADEs with hyperkalaemia. This type of tool may have an important role in pharmacoepidemiology via the routine analysis of large inter-hospital databases.

Supervised analysis of drug prescription sequences.

Hospitals have at their disposal large databases that may be considered for reuse. The objective of this work is to evaluate the impact of a drug on a specific laboratory result by analyzing these data. This analysis first involves building a record of temporal patterns, including medical context, of drug prescriptions. Changes in outcome due to these patterns of drug prescription are assessed using short phases of the inpatient stay compared to monotonous changes in the laboratory result. To illustrate this technique, we investigated potassium chloride supplementation and its impact on kalemia. This method enables us to assess the impact of a drug (in its frequent context of prescription) on a laboratory result. This kind of analysis could play a role in post-marketing studies.

Evaluation of a computerized tool allowing retrospective detection of potential vitamin K antagonist overdoses in complex contexts.

Management of vitamin K antagonists (VKA) is difficult, and overdoses can have dramatic hemorrhagic consequences. These works form part of a European computerized medical data processing project, which aims to develop IT tools for describing adverse drug events (ADEs). Materials and methods A tool enabling retrospective research of potential ADE cases was developed, using complex ADE detection rules taking into account chronological parameters: the ADE scorecards. The rules were applied on 14,748 medical records from a community hospital. We evaluated the predictive positive value of the rules related to INR elevation by an expert review of the detected cases. The severity of the clinical consequences was evaluated. Results 49 cases were detected, among which 11 cases were ADEs. The predictive positive value of the rules is 22.44%, mostly related to antibiotics and amiodarone introduction. The four cases of clinical damage related to a drug were properly designated by the rules. Discussion - Conclusion Our study shows the great potential of developing complex rules for the identification of adverse drug events in large medical databases.

Can F-MTI semantic-mined drug codes be used for adverse drug events detection when no CPOE is available?

BACKGROUND: Adverse Drug Events (ADEs) endanger the patients. Their detection and prevention is essential to improve the patients' safety. In the absence of computerized physician order entry (CPOE), discharge summaries are the only source of information about the drugs prescribed during a hospitalization. The French Multierminology Indexer (F-MTI) can help to extract drug-related information from those records. METHODS: In first and second validation steps, the performance of the F-MTI tool is evaluated to extract ICD10 and ATC codes from free-text documents. In third step, potential ADE detection rules are used and the confidences of those rules are compared in several hospitals: using a CPOE vs. using semantic mining of free-text documents, diagnoses and lab results being available in both cases. RESULTS: The F-MTI tool is able to extract ATC codes from documents. Moreover, the evaluation shows coherent and comparable results between the hospitals with CPOEs and the hospital with drugs information extracted from the reports for potential ADE detection. CONCLUSION: semantic mining using F-MTI can help to identify previous cases of potential ADEs in absence of CPOE.

Nutritional status and growth in pediatric Crohn's disease: a population-based study.

OBJECTIVES: Growth retardation and malnutrition are major features of pediatric Crohn's disease (CD). We examined nutritional and growth parameters from diagnosis to maximal follow-up in a population-based pediatric cohort, and we determined predictive factors. METHODS: A total of 261 patients (156 boys, 105 girls) with onset of CD before the age of 17 were identified from 1988 to 2004 through the EPIMAD registry (Registre des Maladies Inflammatoires Chroniques de l'Intestin) in northern France. Median age at diagnosis was 13 years (11.2-15.4) and median follow-up was 73 months (46-114). Z-scores of height/age, weight/age, and body mass index (BMI)/age were determined. Multivariate stepwise regression analysis identified predictive factors for malnutrition and growth retardation at maximal follow-up. RESULTS: At diagnosis, 25 children (9.5%) showed height less than -2 s.d., 70 (27%) weight less than -2 s.d., and 84 (32%) BMI less than -2 s.d. At maximal follow-up, growth retardation was present in 18 children (6.9%), whereas 40 (15%) had malnutrition. Nutritional status was more severely impaired in children with stricturing disease. Growth and nutritional retardation at diagnosis, young age, male gender, and extraintestinal manifestations at diagnosis were indicators of poor prognosis. A significant compensation was observed for weight and BMI in both genders and for height in girls. No treatment was associated with height, weight, or BMI at maximal follow-up. CONCLUSIONS: In our pediatric population-based study, growth retardation and severe malnutrition were still present at maximal follow-up in 6.9 and 15% of CD children, respectively. Young boys with substantial inflammatory manifestations of CD have a higher risk of subsequent growth failure, especially when growth retardation is present at diagnosis.

Detection of adverse drug events: proposal of a data model.

Our main objective is to detect adverse drug events (ADEs) in former hospital stays. As ADEs are rare, that supposes to screen thousands of electronic health records (EHRs). For that purpose, we need to define a data model that has two main objectives: (1) being able to describe hospital stays from various hospitals (2) being tuned so as to prepare the data mining process: as ADEs are not flagged in the datasets, the data model must be optimized for ADE detection. The article presents the phases of the design and the data model that results from this work. It is compatible with many hospitals. It deals with diagnoses, drug prescriptions, lab results and administrative information. It allows for data mining and ADE detection in EHRs.

Detection of adverse drug events detection: data aggregation and data mining.

Adverse drug events (ADEs) are a public health issue. The objective of this work is to data-mine electronic health records in order to automatically identify ADEs and generate alert rules to prevent those ADEs. The first step of data-mining is to transform native and complex data into a set of binary variables that can be used as causes and effects. The second step is to identify cause-to-effect relationships using statistical methods. After mining 10,500 hospitalizations from Denmark and France, we automatically obtain 250 rules, 75 have been validated till now. The article details the data aggregation and an example of decision tree that allows finding several rules in the field of vitamin K antagonists.

Adverse drug events prevention rules: multi-site evaluation of rules from various sources.

Adverse drug events are a public health issue (98,000 deaths in the USA every year). Some computerized physician order entry (CPOEs) coupled with clinical decision support systems (CDSS) allow to prevent ADEs thanks to decision rules. Those rules can come from many sources: academic knowledge, record reviews, and data mining. Whatever their origin, the rules may induce too numerous alerts of poor accuracy when identically applied in different places. In this work we formalized rules from various sources in XML and enforced their execution on several medical departments to evaluate their local confidence. The article details the process and shows examples of evaluated rules from various sources. Several needs are enlightened to improve confidences: segmentation, contextualization, and evaluation of the rules over time.

Data-mining-based detection of adverse drug events.

Every year adverse drug events (ADEs) are known to be responsible for 98,000 deaths in the USA. Classical methods rely on report statements, expert knowledge, and staff operated record review. One of our objectives, in the PSIP project framework, is to use data mining (e.g., decision trees) to electronically identify situations leading to risk of ADEs. 10,500 hospitalization records from Denmark and France were used. 500 rules were automatically obtained, which are currently being validated by experts. A decision support system to prevent ADEs is then to be developed. The article examines a decision tree and the rules in the field of vitamin K antagonists.