Dataset Information

Development and performance of electronic acute kidney injury triggers to identify pediatric patients at risk for nephrotoxic medication-associated harm.

ABSTRACT:

Background

Nephrotoxic medication-associated acute kidney injury (NTMx-AKI) is a costly clinical phenomenon and more common than previously recognized. Prior efforts to use technology to identify AKI have focused on detection after renal injury has occurred.

Objectives

Describe an approach and provide a technical framework for the creation of risk-stratifying AKI triggers and the development of an application to manage the AKI trigger data. Report the performance characteristics of those triggers and the refinement process and on the challenges of implementation.

Methods

Initial manual trigger screening guided design of an automated electronic trigger report. A web-based application was designed to alleviate inefficiency and serve as a user interface and central workspace for the project. Performance of the NTMx exposure trigger reports from September 2011 to September 2013 were evaluated using sensitivity (SN), specificity (SP), positive and negative predictive values (PPV, NPV).

Results

Automated reports were created to replace manual screening for NTMx-AKI. The initial performance of the NTMx exposure triggers for SN, SP, PPV, and NPV all were ?0.78, and increased over the study, with all four measures reaching ?0.95 consistently. A web-based application was implemented that simplifies data entry and couriering from the reports, expedites results viewing, and interfaces with an automated data visualization tool. Sociotechnical challenges were logged and reported.

Conclusion

We have built a risk-stratifying system based on electronic triggers that detects patients at-risk for NTMx-AKI before injury occurs. The performance of the NTMx-exposed reports has neared 100% through iterative optimization. The complexity of the trigger logic and clinical workflows surrounding NTMx-AKI led to a challenging implementation, but one that has been successful from technical, clinical, and quality improvement standpoints. This report summarizes the construction of a trigger-based application, the performance of the triggers, and the challenges uncovered during the design, build, and implementation of the system.

SUBMITTER: Kirkendall ES

PROVIDER: S-EPMC4081739 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Development and performance of electronic acute kidney injury triggers to identify pediatric patients at risk for nephrotoxic medication-associated harm.

Kirkendall E S ES Spires W L WL Mottes T A TA Schaffzin J K JK Barclay C C Goldstein S L SL

Applied clinical informatics 20140402 2

<h4>Background</h4>Nephrotoxic medication-associated acute kidney injury (NTMx-AKI) is a costly clinical phenomenon and more common than previously recognized. Prior efforts to use technology to identify AKI have focused on detection after renal injury has occurred.<h4>Objectives</h4>Describe an approach and provide a technical framework for the creation of risk-stratifying AKI triggers and the development of an application to manage the AKI trigger data. Report the performance characteristics o ...[more]

PMID: 25024752

Similar Datasets

Project description:BackgroundSelf-harm occurring within pregnancy and the postnatal year ("perinatal self-harm") is a clinically important yet under-researched topic. Current research likely under-estimates prevalence due to methodological limitations. Electronic healthcare records (EHRs) provide a source of clinically rich data on perinatal self-harm.Aims(1) To create a Natural Language Processing (NLP) tool that can, with acceptable precision and recall, identify mentions of acts of perinatal self-harm within EHRs. (2) To use this tool to identify service-users who have self-harmed perinatally, based on their EHRs.MethodsWe used the Clinical Record Interactive Search system to extract de-identified EHRs of secondary mental healthcare service-users at South London and Maudsley NHS Foundation Trust. We developed a tool that applied several layers of linguistic processing based on the spaCy NLP library for Python. We evaluated mention-level performance in the following domains: span, status, temporality and polarity. Evaluation was done against a manually coded reference standard. Mention-level performance was reported as precision, recall, F-score and Cohen's kappa for each domain. Performance was also assessed at 'service-user' level and explored whether a heuristic rule improved this. We report per-class statistics for service-user performance, as well as likelihood ratios and post-test probabilities.ResultsMention-level performance: micro-averaged F-score, precision and recall for span, polarity and temporality >0.8. Kappa for status 0.68, temporality 0.62, polarity 0.91. Service-user level performance with heuristic: F-score, precision, recall of minority class 0.69, macro-averaged F-score 0.81, positive LR 9.4 (4.8-19), post-test probability 69.0% (53-82%). Considering the task difficulty, the tool performs well, although temporality was the attribute with the lowest level of annotator agreement.ConclusionsIt is feasible to develop an NLP tool that identifies, with acceptable validity, mentions of perinatal self-harm within EHRs, although with limitations regarding temporality. Using a heuristic rule, it can also function at a service-user-level.

Dataset Information

Development and performance of electronic acute kidney injury triggers to identify pediatric patients at risk for nephrotoxic medication-associated harm.

Background

Objectives

Methods

Results

Conclusion

Publications

Development and performance of electronic acute kidney injury triggers to identify pediatric patients at risk for nephrotoxic medication-associated harm.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets