Real-Time Use of a Chest Pain Risk Stratification Clinical Decision Rule: A Cautionary Tale

2013 ◽  
Vol 62 (4) ◽  
pp. S112 ◽  
Author(s):  
S.A. Mahler ◽  
R.F. Riley ◽  
G.L. Burke ◽  
B.C. Hiestand ◽  
C.D. Miller
Keyword(s):  
Chest Pain ◽  
Risk Stratification ◽  
Real Time ◽  
Decision Rule ◽  
Time Use ◽  
Clinical Decision ◽  
Clinical Decision Rule ◽  
Cautionary Tale

scholarly journals Early Identification of Patients with Acute Gastrointestinal Bleeding using Electronic Health Record Phenotyping

2020 ◽  
Author(s):  
Dennis Shung ◽  
Cynthia Tsay ◽  
Loren Laine ◽  
Prem Thomas ◽  
Caitlin Partridge ◽  
...  
Keyword(s):  
Risk Stratification ◽  
Gastrointestinal Bleeding ◽  
Real Time ◽  
Decision Rule ◽  
Language Processing ◽  
Clinical Decision Making ◽  
External Validation ◽  
Clinical Decision ◽  
Internal Validation ◽  
Electronic Health

Background and AimGuidelines recommend risk stratification scores in patients presenting with gastrointestinal bleeding (GIB), but such scores are uncommonly employed in practice. Automation and deployment of risk stratification scores in real time within electronic health records (EHRs) would overcome a major impediment. This requires an automated mechanism to accurately identify (“phenotype”) patients with GIB at the time of presentation. The goal is to identify patients with acute GIB by developing and evaluating EHR-based phenotyping algorithms for emergency department (ED) patients.MethodsWe specified criteria using structured data elements to create rules for identifying patients, and also developed a natural-language-processing (NLP)-based algorithm for automated phenotyping of patients, tested them with tenfold cross-validation (n=7144) and external validation (n=2988), and compared them with the standard method for encoding patient conditions in the EHR, Systematized Nomenclature of Medicine (SNOMED). The gold standard for GIB diagnosis was independent dual manual review of medical records. The primary outcome was positive predictive value (PPV).ResultsA decision rule using GIB-specific terms from ED triage and from ED review-of-systems assessment performed better than SNOMED on internal validation (PPV=91% [90%-93%] vs. 74% [71%-76%], P<0.001) and external validation (PPV=85% [84%-87%] vs. 69% [67%-71%], P<0.001). The NLP algorithm (external validation PPV=80% [79-82%]) was not superior to the structured-datafields decision rule.ConclusionsAn automated decision rule employing GIB-specific triage and review-of-systems terms can be used to trigger EHR-based deployment of risk stratification models to guide clinical decision-making in real time for patients with acute GIB presenting to the ED.

Validation of a clinical decision rule: chest X-ray in patients with chest pain and possible acute coronary syndrome

2014 ◽  
Vol 21 (4) ◽  
pp. 367-372 ◽  
Author(s):  
Romi Goldschlager ◽  
Hedley Roth ◽  
Jarryd Solomon ◽  
Scott Robson ◽  
Jessica Green ◽  
...  
Keyword(s):  
Acute Coronary Syndrome ◽  
Chest Pain ◽  
Decision Rule ◽  
Clinical Decision ◽  
Clinical Decision Rule ◽  
X Ray ◽  
Coronary Syndrome ◽  
Chest X Ray

scholarly journals Early health technology assessment of future clinical decision rule aided triage of patients presenting with acute chest pain in primary care

2017 ◽  
Vol 19 (02) ◽  
pp. 176-188 ◽  
Author(s):  
Robert T.A. Willemsen ◽  
Michelle M.A. Kip ◽  
Hendrik Koffijberg ◽  
Ron Kusters ◽  
Frank Buntinx ◽  
...  
Keyword(s):  
Primary Care ◽  
Chest Pain ◽  
The Netherlands ◽  
Decision Rule ◽  
Current Practice ◽  
Clinical Decision ◽  
Sensitivity Analyses ◽  
Pain Patient ◽  
Clinical Decision Rule ◽  
Referral Decisions

The objective of the paper is to estimate the number of patients presenting with chest pain suspected of acute coronary syndrome (ACS) in primary care and to calculate possible cost effects of a future clinical decision rule (CDR) incorporating a point-of-care test (PoCT) as compared with current practice. The annual incidence of chest pain, referrals and ACS in primary care was estimated based on a literature review and on a Dutch and Belgian registration study. A health economic model was developed to calculate the potential impact of a future CDR on costs and effects (ie, correct referral decisions), in several scenarios with varying correct referral decisions. One-way, two-way, and probabilistic sensitivity analyses were performed to test robustness of the model outcome to changes in input parameters. Annually, over one million patient contacts in primary care in the Netherlands concern chest pain. Currently, referral of eventual ACS negative patients (false positives, FPs) is estimated to cost €1,448 per FP patient, with total annual cost exceeding 165 million Euros in the Netherlands. Based on ‘international data’, at least a 29% reduction in FPs is required for the addition of a PoCT as part of a CDR to become cost-saving, and an additional €16 per chest pain patient (ie, 16.4 million Euros annually in the Netherlands) is saved for every further 10% relative decrease in FPs. Sensitivity analyses revealed that the model outcome was robust to changes in model inputs, with costs outcomes mainly driven by costs of FPs and costs of PoCT. If PoCT-aided triage of patients with chest pain in primary care could improve exclusion of ACS, this CDR could lead to a considerable reduction in annual healthcare costs as compared with current practice.

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