Using computerized individual medication data to detect drug effects on clinical laboratory tests

Dramatyping: a generic algorithm for detecting reasonable temporal correlations between drug administration and lab value alterations

According to the World Health Organization, one of the criteria for the standardized assessment of case causality in adverse drug reactions is the temporal relationship between the intake of a drug and the occurrence of a reaction or a laboratory test abnormality. This article presents and describes an algorithm for the detection of a reasonable temporal correlation between the administration of a drug and the alteration of a laboratory value course. The algorithm is designed to process normalized lab values and is therefore universally applicable. It has a sensitivity of 0.932 for the detection of lab value courses that show changes in temporal correlation with the administration of a drug and it has a specificity of 0.967 for the detection of lab value courses that show no changes. Therefore, the algorithm is appropriate to screen the data of electronic health records and to support human experts in revealing adverse drug reactions. A reference implementation in Python programming language is available.

Towards a Computable Data Corpus of Temporal Correlations between Drug Administration and Lab Value Changes

Background

The analysis of electronic health records for an automated detection of adverse drug reactions is an approach to solve the problems that arise from traditional methods like spontaneous reporting or manual chart review. Algorithms addressing this task should be modeled on the criteria for a standardized case causality assessment defined by the World Health Organization. One of these criteria is the temporal relationship between drug intake and the occurrence of a reaction or a laboratory test abnormality. Appropriate data that would allow for developing or validating related algorithms is not publicly available, though.

Methods

In order to provide such data, retrospective routine data of drug administrations and temporally corresponding laboratory observations from a university clinic were extracted, transformed and evaluated by experts in terms of a reasonable time relationship between drug administration and lab value alteration.

Result

The result is a data corpus of 400 episodes of normalized laboratory parameter values in temporal context with drug administrations. Each episode has been manually classified whether it contains data that might indicate a temporal correlation between the drug administration and the change of the lab value course, whether such a change is not observable or whether a decision between those two options is not possible due to the data. In addition, each episode has been assigned a concordance value which indicates how difficult it is to assess. This is the first open data corpus of a computable ground truth of temporal correlations between drug administration and lab value alterations.

Discussion

The main purpose of this data corpus is the provision of data for further research and the provision of a ground truth which allows for comparing the outcome of other assessments of this data with the outcome of assessments made by human experts. It can serve as a contribution towards systematic, computerized ADR detection in retrospective data. With this lab value curve data as a basis, algorithms for detecting temporal relationships can be developed, and with the classification made by human experts, these algorithms can immediately be validated. Due to the normalization of the lab value data, it allows for a generic approach rather than for specific or solitary drug/lab value combinations.

E-pharmacovigilance: development and implementation of a computable knowledge base to identify adverse drug reactions

AIMS

Computer-assisted signal generation is an important issue for the prevention of adverse drug reactions (ADRs). However, due to poor standardization of patients' medical data and a lack of computable medical drug knowledge the specificity of computerized decision support systems for early ADR detection is too low and thus those systems are not yet implemented in daily clinical practice. We report on a method to formalize knowledge about ADRs based on the Summary of Product Characteristics (SmPCs) and linking them with structured patient data to generate safety signals automatically and with high sensitivity and specificity.

METHODS

A computable ADR knowledge base (ADR-KB) that inherently contains standardized concepts for ADRs (WHO-ART), drugs (ATC) and laboratory test results (LOINC) was built. The system was evaluated in study populations of paediatric and internal medicine inpatients.

RESULTS

A total of 262 different ADR concepts related to laboratory findings were linked to 212 LOINC terms. The ADR knowledge base was retrospectively applied to a study population of 970 admissions (474 internal and 496 paediatric patients), who underwent intensive ADR surveillance. The specificity increased from 7% without ADR-KB up to 73% in internal patients and from 19.6% up to 91% in paediatric inpatients, respectively.

CONCLUSIONS

This study shows that contextual linkage of patients' medication data with laboratory test results is a useful and reasonable instrument for computer-assisted ADR detection and a valuable step towards a systematic drug safety process. The system enables automated detection of ADRs during clinical practice with a quality close to intensive chart review.

A systematic review to evaluate the accuracy of electronic adverse drug event detection

OBJECTIVE

Adverse drug events (ADEs), defined as adverse patient outcomes caused by medications, are common and difficult to detect. Electronic detection of ADEs is a promising method to identify ADEs. We performed this systematic review to characterize established electronic detection systems and their accuracy.

METHODS

We identified studies evaluating electronic ADE detection from the MEDLINE and EMBASE databases. We included studies if they contained original data and involved detection of electronic triggers using information systems. We abstracted data regarding rule characteristics including type, accuracy, and rationale.

RESULTS

Forty-eight studies met our inclusion criteria. Twenty-four (50%) studies reported rule accuracy but only 9 (18.8%) utilized a proper gold standard (chart review in all patients). Rule accuracy was variable and often poor (range of sensitivity: 40%-94%; specificity: 1.4%-89.8%; positive predictive value: 0.9%-64%). 5 (10.4%) studies derived or used detection rules that were defined by clinical need or the underlying ADE prevalence. Detection rules in 8 (16.7%) studies detected specific types of ADEs.

CONCLUSION

Several factors led to inaccurate ADE detection algorithms, including immature underlying information systems, non-standard event definitions, and variable methods for detection rule validation. Few ADE detection algorithms considered clinical priorities. To enhance the utility of electronic detection systems, there is a need to systematically address these factors.

The Proposal for Solution of Interference Problems Caused By Drugs

For long years the laboratories have the problem of endogenous and axogenous interferences. Drugs are responsable for more than 90% of exogenous interference. The aim of the investigation was to find a rather simple and economic way in solution of every day existing problem in clinical biochemistry. Using the appropriate literature the possible existing of interference is determined. Such approach could be interesting for many of laboratories in every day practice.

Linking laboratory and medication data: new opportunities for pharmacoepidemiological research

Transfer of automated laboratory data collected during routine clinical care from the laboratory information system into a database format that enables linkage to other administrative (e.g., patient characteristics) or clinical (e.g., medication, diagnoses, procedures) data provides a valuable tool for clinical epidemiological research. It allows the investigation of biochemical characteristics of diseases, therapeutic effects and diagnostic and/or prognostic markers for disease with easy access and at relatively low cost. To this end, the Utrecht Patient Oriented Database (UPOD), an infrastructure of relational databases comprising data on patient characteristics, laboratory test results, medication orders, hospital discharge diagnoses and medical procedures for all patients treated at the University Medical Centre Utrecht since January 2004, was established. Current research within UPOD is focused on the innovative linkage of laboratory and medication data, which, for example, makes it possible to assess the quality of pharmacotherapy in clinical practice, to investigate interference between laboratory tests and drugs, to study the risk of adverse drug reactions, and to develop diagnostic and prognostic markers or algorithms for adverse drug reactions. Although recently established, we believe that UPOD broadens the opportunities for clinical pharmacoepidemiological research and can contribute to patient care from a laboratory perspective.

Reminders of Drug Effects on Laboratory Test Results

Drug effects on laboratory test results are difficult to take into account without an online decision support system. In this study, drug effects on hormone test results were coded using a drug-laboratory effect (DLE) code. The criteria that trigger the reminders were defined. To issue reminders, it was necessary to write a computer program linking the DLE knowledge base with databases containing individual patient medication and laboratory test results. During the first 10 months, 11% of the results from hormone samples were accompanied by one or more DLE reminders. The most common drugs to trigger reminders were glucocorticoids, furosemide, and metoclopramide. Physicians facing the reminders completed a questionnaire on the usefulness of the reminders. All respondents considered them useful. In addition, DLE reminders had caused 74% of respondents to refrain from additional, usually performed examinations. In conclusion, drug effects on laboratory tests should always be considered when interpreting laboratory results. An online reminder system is useful in displaying potential drug effects alongside test results.

Preanalytical and analytical factors affecting laboratory results

The results of laboratory tests have a substantial role in the diagnostics of diseases. However, laboratory results do not always correspond with the patient's clinical status. They may be unexpected and surprising. On the other hand, an abnormal laboratory result may be accepted as such and interpreted as a sign of a disease. However, an abnormal result may result from several factors other than disease. Conventionally, these interfering factors have been divided into preanalytical and analytical factors and furthermore into factors acting in vivo and in vitro. The list of these factors is long and laborious to bear in mind. In this review we focus on the factors which, in practice, most often affect laboratory results in healthy individuals and which explain an unexpected result.

Computerized survelliance of adverse drug reactions in hospital: pilot study

AIMS

To develop and assess the use of computerized laboratory data as a detection support tool of adverse drug reactions (ADRs) in hospital.

METHODS

This was a retrospective observational study of 153 sequential medical admissions during a 2-month period to the 34-bed medical ward at the Hadassah University Hospital, Jerusalem, Israel. Measurements made were 1) Retrospective chart review for recognized and unrecognized adverse drug reactions (ADRs) and 2) Analysis of computerizied laboratory data according to defined automatic laboratory signals (ALS) for adverse reactions.

RESULTS

Forty ADRs have been detected in 38 out of the 153 hospital admissions (24.8%). Nine reactions were considered severe. Altogether 212 ALS were generated involving 86 admissions. In 25 (65.8%) of the ADR-positive admissions ADRs were detected through automatic signals generated from the laboratory data. ALS were detected in 56 out of the 115 (48.7%) ADR-negative admissions. Twenty-four (60%) of the ADRs were not recognized as such by the attending physicians. Two of these reactions were severe. ALS could have generated an alert for 19 (79.2%) of the unrecognized reactions.

CONCLUSIONS

Application of automatic laboratory signals can increase the rate of recognition of the ADRs and thereby improve medical care. The sensitivity and specificity of the method might be increased by refinement and redefinition of the signals.

Approaches for certification of electronic prescription software

The proper management of drug treatment is essential, since adverse drug reactions are common reasons of hospitalisations. Expenditure on drug therapy has also been growing faster than any other aspect of health care in many countries. Savings and quality improvements in drug treatment could be achieved with computerised prescribing. In this paper, the architecture of an electronic prescription system is described in the light of software certification and registration. An electronic prescription system is an example of a system supporting shared care and therefore it should be person based, integrated, secure and confidential and data should be shared among health care institutions. The system architecture shares the idea of a virtual patient record and a smart card will be used as a key to prescription data located on the network. The certification and registration of medical software is a difficult and costly procedure. Ensuring the quality of software can be based on; certification of development process, voluntary evaluation, and post-market surveillance. Voluntary evaluation practice would be a precious tool for both the customers and software developers, and it would also be an invaluable source of information in terms of developing new software.

Computerized monitoring of potentially interfering medication in thyroid function diagnostics

OBJECTIVE

Many drugs are known to affect the results of laboratory tests. This may cause problems in the interpretation of clinical laboratory data and lead to wrong diagnoses, unnecessary further tests and additional costs. A computerized monitoring system of potential drug effects on laboratory tests was developed in Turku University Central Hospital. In the present study the incidence and nature of potentially interfering drug effects in thyroid function diagnostics was examined in order to ease the clinical implementation of the system.

METHODS

Computerized medication data of 754 hospital in-patients whose thyroid function was tested were combined with a knowledge base of drug effects on laboratory tests. All medications that potentially affected the levels of serum thyrotropin or free thyroxin in study patients were detected.

RESULTS

40% (292 of 735) of the patients tested for thyrotropin and 32% (107 of 333) of the patients tested for free thyroxin received potentially interfering medication during the tests. The most common potentially interfering medication was acetylsalicylic acid, but the daily dose was usually low, 100 mg.

CONCLUSIONS

The coincidence of potentially interfering medication and thyroid function tests was substantial. On-line hints of drug effects on thyroid function tests might offer valuable decision support to clinicians, but further development of the system is needed to regulate the prevalence of warnings into a clinically optimal level.

Effects of ramipril on the hormone concentrations in serum of hypertensive patients

The effects of the angiotensin-converting enzyme inhibitor ramipril on thirteen endocrinological tests were evaluated. These tests comprised serum follitropin, lutropin, prolactin, thyrotropin, free thyroxine, total thyroxine, free triiodothyronine, parathyrin, cortisol, testosterone, sex hormone binding globulin, androstenedione and dehydroepiandrosterone sulphate. Eleven hypertensive outpatients, 9 men and 2 women, treated at the department of internal medicine in Turku University Central Hospital, received 5 mg of ramipril once a day for the study period of four weeks. The above mentioned endocrinological tests were performed before and at the end of the ramipril treatment. Ramipril decreased the value of free thyroxine statistically significantly, p = 0.011, from the mean value of 17.1 pmol/l to the mean value of 16.0 pmol/l when measured with Amerlex-MAB* free thyroxine kit. The mean within-subject difference was -1.10 pmol/l with a 95% confidence interval of -1.87 - -0.33 pmol/l. With the AutoDELFIA free thyroxine kit and with the reference method dialysis+RIA no effect was detected. Other endocrinological tests examined were not affected by ramipril. Since the decreasing effect of ramipril on free thyroxine was detected only with Amerlex-MAB* but neither with AutoDELFIA nor with dialysis+RIA, the effect was concluded to be analytical. The underlying mechanism and the component ultimately interfering with the analysis is unknown.