ArticleApplied clinical informatics2015
Retrospective Derivation and Validation of an Automated Electronic Search Algorithm to Identify Post Operative Cardiovascular and Thromboembolic Complications.
Article in Applied clinical informatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review.JMIR medical informatics · 2023Review
- Septic shock definitions and associated outcomes in blood culture positive critically ill patients.Annals of translational medicine · 2023Article
- Standardized Description of the Feature Extraction Process to Transform Raw Data Into Meaningful Information for Enhancing Data Reuse: Consensus Study.JMIR medical informatics · 2022Article
- Rule-Based Cohort Definitions for Acute Respiratory Distress Syndrome: A Computable Phenotyping Strategy Based on the Berlin Definition.Critical care explorations · 2021Article
- Electronic Medical Record-Based Case Phenotyping for the Charlson Conditions: Scoping Review.JMIR medical informatics · 2021Article
- Derivation and Validation of an Automated Search Strategy to Retrospectively Identify Acute Respiratory Distress Patients Per Berlin Definition.Frontiers in medicine · 2021Article
- Article
- Derivation and validation of a computable phenotype for acute decompensated heart failure in hospitalized patients.BMC medical informatics and decision making · 2020Article
- Risk factors and outcomes of postoperative emergency response team activation: a matched case-control study.Critical care and resuscitation : journal of the Australasian Academy of Critical Care Medicine · 2020Article
- Machine learning in data abstraction: A computable phenotype for sepsis and septic shock diagnosis in the intensive care unit.World journal of critical care medicine · 2019Article
- Improving Diagnostic Fidelity: An Approach to Standardizing the Process in Patients With Emerging Critical Illness.Mayo Clinic proceedings. Innovations, quality & outcomes · 2019Article
- Autonomous detection, grading, and reporting of postoperative complications using natural language processing.Surgery · 2018Article
- The Reliability of Electronic Health Record Data Used for Obstetrical Research.Applied clinical informatics · 2018Article
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Authors and funding
7 authors.
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Abstract
backgroundWith increasing numbers of hospitals adopting electronic medical records, electronic search algorithms for identifying postoperative complications can be invaluable tools to expedite data abstraction and clinical research to improve patient outcomes.
objectivesTo derive and validate an electronic search algorithm to identify postoperative thromboembolic and cardiovascular complications such as deep venous thrombosis, pulmonary embolism, or myocardial infarction within 30 days of total hip or knee arthroplasty.
methodsA total of 34 517 patients undergoing total hip or knee arthroplasty between January 1, 1996 and December 31, 2013 were identified. Using a derivation cohort of 418 patients, several iterations of a free-text electronic search were developed and refined for each complication. Subsequently, the automated search algorithm was validated on an independent cohort of 2 857 patients, and the sensitivity and specificities were compared to the results of manual chart review.
resultsIn the final derivation subset, the automated search algorithm achieved a sensitivity of 91% and specificity of 85% for deep vein thrombosis, a sensitivity of 96% and specificity of 100% for pulmonary embolism, and a sensitivity of 100% and specificity of 95% for myocardial infarction. When applied to the validation cohort, the search algorithm achieved a sensitivity of 97% and specificity of 99% for deep vein thrombosis, a sensitivity of 97% and specificity of 100% for pulmonary embolism, and a sensitivity of 100% and specificity of 99% for myocardial infarction.
conclusionsThe derivation and validation of an electronic search strategy can accelerate the data abstraction process for research, quality improvement, and enhancement of patient care, while maintaining superb reliability compared to manual review.
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