Derivation and validation of a computable phenotype for acute decompensated heart failure in hospitalized patients

Kashyap, Rahul; Sarvottam, Kumar; Wilson, Gregory; Jentzer, Jacob C.; Seisa, Mohamed O.; Kashani, Kianoush

doi:10.1186/s12911-020-1092-5

Cited by 16 publications

(14 citation statements)

References 23 publications

(25 reference statements)

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“…Our study demonstrates that informatics approaches combining structured EMR data, such as orders for pathology testing, investigations and diagnostic codes, with symptom and keyword text mining in narrative free-text during the data extraction process in production EMR systems, can create high fidelity clinical-defined patient cohorts. This inclusive approach to EMR data extraction enables subsequent datasets for EMR-derived cohorts to be readily created and updated as new conditions/diseases emerge and clinical definitions are updated [ 28 ], as well as the extraction of clinically-relevant information enabling future validation studies of diagnostic and procedure codes, which are essential for real-time clinical decision support and secondary use of EMR data [ 11 , 29 , 30 ]. This is particularly significant for diagnoses which have a diverse range of presenting problems (e.g.…”

Section: Discussionmentioning

confidence: 99%

“…The gold standard depends on the context of how the EMR data will be used and could be clinician diagnosis if used for clinical decision support, or clinical registries or validation against ICD10 codes as previously performed in other conditions such as heart failure, post-traumatic stress disorder, Charleston Co-morbidity index, etc. [ 30 , 38 , 39 ]. A validation study estimating sensitivity and specificity for acute coronary syndrome comparing clinician diagnosis against ICD10 codes was performed in a 3 month EMR dataset from our current study; those results are currently under review [ 40 ].…”

Section: Discussionmentioning

confidence: 99%

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Combining structured and unstructured data in EMRs to create clinically-defined EMR-derived cohorts

Tam

Gullick

Saavedra

et al. 2021

BMC Med Inform Decis Mak

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Background There have been few studies describing how production EMR systems can be systematically queried to identify clinically-defined populations and limited studies utilising free-text in this process. The aim of this study is to provide a generalisable methodology for constructing clinically-defined EMR-derived patient cohorts using structured and unstructured data in EMRs. Methods Patients with possible acute coronary syndrome (ACS) were used as an exemplar. Cardiologists defined clinical criteria for patients presenting with possible ACS. These were mapped to data tables within the production EMR system creating seven inclusion criteria comprised of structured data fields (orders and investigations, procedures, scanned electrocardiogram (ECG) images, and diagnostic codes) and unstructured clinical documentation. Data were extracted from two local health districts (LHD) in Sydney, Australia. Outcome measures included examination of the relative contribution of individual inclusion criteria to the identification of eligible encounters, comparisons between inclusion criterion and evaluation of consistency of data extracts across years and LHDs. Results Among 802,742 encounters in a 5 year dataset (1/1/13–30/12/17), the presence of an ECG image (54.8% of encounters) and symptoms and keywords in clinical documentation (41.4–64.0%) were used most often to identify presentations of possible ACS. Orders and investigations (27.3%) and procedures (1.4%), were less often present for identified presentations. Relevant ICD-10/SNOMED CT codes were present for 3.7% of identified encounters. Similar trends were seen when the two LHDs were examined separately, and across years. Conclusions Clinically-defined EMR-derived cohorts combining structured and unstructured data during cohort identification is a necessary prerequisite for critical validation work required for development of real-time clinical decision support and learning health systems.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Combining structured and unstructured data in EMRs to create clinically-defined EMR-derived cohorts

Tam

Gullick

Saavedra

et al. 2021

BMC Med Inform Decis Mak

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“…A blind reviewer manually reviewed the two derivative cohorts established from clinical notes and diagnostic searches through ACE and DataMart. A third reviewer resolved the conflicts with access to electronic data, manual review results, and medical charts [ 16 ]. Once conflicts were resolved, sensitivity and specificity were derived (Figure 1 ).…”

Section: Methodsmentioning

confidence: 99%

“…However, an effective method is required to identify, capture, and extract particular data in a short period. Algorithms have been 1 2 3 4 derived and validated previously such as identification of post-operative complications, cognitive impairment, sepsis, continuous renal replacement therapy, mechanical ventilation initiation, emergent intubation, Charlson comorbidities, and extubation failure, but no strategy has been developed for DIC diagnosis identification [9][10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

An Electronic Search Algorithm for Early Disseminated Intravascular Coagulopathy Diagnosis in the Intensive Care Unit: A Derivation and Validation Study

Jawaid

Gangat

Weister

et al. 2020

Cureus

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Aim: We aim to create and validate an electronic search algorithm for accurate detection of disseminated intravascular coagulopathy (DIC) from medical records. Methods: Patients with DIC in Mayo Clinic's intensive care units (ICUs) from Jan 1, 2007, to May 4, 2018, were included in the study. An algorithm was developed based on clinical notes and ICD diagnosis codes. A cohort of 50 patients was included with DIC diagnosis, its variations, and no diagnosis of DIC. Then, the next set of 50 patients was used to refine the algorithm. Results were compared with a manual reviewer and the disagreements were resolved by the third reviewer. The same process was repeated with 'revised clinical note search' for the first and second derivation cohort with additional exclusion terms. The obtained sensitivity and specificity were reported. The generated algorithm was applied to another set of 50 patients for validation. Results: In the first derivation cohort-DIC search by clinical notes and diagnosis codes had 92% sensitivity and 100% specificity. Sensitivity dropped to 71% in the second cohort although specificity remains the same. Therefore, the algorithm was refined to clinical notes search only. The revised search was reapplied to first and second derivation cohorts and results obtained for the first derivation were the same but 91.3% sensitive and 100% specific for the second derivation. The search was locked and applied in the validation cohort with 95.8% sensitivity and 100% specificity, respectively. Conclusion: The revised clinical note based electronic search algorithm was found to be highly sensitive and specific for DIC during the corresponding ICU duration.

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“…Kashyap et al noted sensitivity of only 47.5% and specificity of 96.7% for inpatient acute HF using International Classification of Diseases (ICD)-9 codes for records from 2006 to 2014, highlighting the importance of improved usefulness in the current era for use of ICD plus Current Procedural Terminology (CPT) to create a phenotype. 9 Ontologies such as SNOMED, ICD-9 and -10, and CPT codes have allowed use of big data techniques to initiate registries of patients of interest with various diseases such as diabetes, hypertension, and obesity. 4,[10][11][12] These registries often involve development of a clinical phenotype that defines the patient population.…”

Section: Background and Significancementioning

confidence: 99%

Coronary Artery Disease Phenotype Detection in an Academic Hospital System Setting

et al. 2021

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Background The United States, and especially West Virginia, have a tremendous burden of coronary artery disease (CAD). Undiagnosed familial hypercholesterolemia (FH) is an important factor for CAD in the U.S. Identification of a CAD phenotype is an initial step to find families with FH. Objective We hypothesized that a CAD phenotype detection algorithm that uses discrete data elements from electronic health records (EHRs) can be validated from EHR information housed in a data repository. Methods We developed an algorithm to detect a CAD phenotype which searched through discrete data elements, such as diagnosis, problem lists, medical history, billing, and procedure (International Classification of Diseases [ICD]-9/10 and Current Procedural Terminology [CPT]) codes. The algorithm was applied to two cohorts of 500 patients, each with varying characteristics. The second (younger) cohort consisted of parents from a school child screening program. We then determined which patients had CAD by systematic, blinded review of EHRs. Following this, we revised the algorithm by refining the acceptable diagnoses and procedures. We ran the second algorithm on the same cohorts and determined the accuracy of the modification. Results CAD phenotype Algorithm I was 89.6% accurate, 94.6% sensitive, and 85.6% specific for group 1. After revising the algorithm (denoted CAD Algorithm II) and applying it to the same groups 1 and 2, sensitivity 98.2%, specificity 87.8%, and accuracy 92.4; accuracy 93% for group 2. Group 1 F1 score was 92.4%. Specific ICD-10 and CPT codes such as “coronary angiography through a vein graft” were more useful than generic terms. Conclusion We have created an algorithm, CAD Algorithm II, that detects CAD on a large scale with high accuracy and sensitivity (recall). It has proven useful among varied patient populations. Use of this algorithm can extend to monitor a registry of patients in an EHR and/or to identify a group such as those with likely FH.

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Derivation and validation of a computable phenotype for acute decompensated heart failure in hospitalized patients

Cited by 16 publications

References 23 publications

Combining structured and unstructured data in EMRs to create clinically-defined EMR-derived cohorts

Combining structured and unstructured data in EMRs to create clinically-defined EMR-derived cohorts

An Electronic Search Algorithm for Early Disseminated Intravascular Coagulopathy Diagnosis in the Intensive Care Unit: A Derivation and Validation Study

Coronary Artery Disease Phenotype Detection in an Academic Hospital System Setting

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