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Clinical Decision Rule

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81. JAK2-tree: a simple CBC-based decision rule to guide appropriate JAK2 V617F mutation testing. (PubMed)

JAK2 V617F testing only when most appropriate.To assist with the screening of patients being considered for JAK2 V617F testing, we developed a clinical decision rule, "JAK2-tree", which can be easily applied to basic CBC parameters (haemoglobin, platelet and white blood cell counts).We tested JAK2-tree on two independent datasets, one an unselected population-based sample (the Copenhagen General Population Study) and the other an historical clinical laboratory referral set, with sensitivities (...) JAK2-tree: a simple CBC-based decision rule to guide appropriate JAK2 V617F mutation testing. The JAK2 V617F mutation is highly recurrent in many of the myeloproliferative neoplasms, a molecular variant that can be easily detected using sensitive and minimally invasive techniques. Given the ease of JAK2 V617F testing, this test may be improperly requested for the purposes of patient 'screening' and to optimise laboratory resource utilisation, it behooves clinicians and laboratorians to perform

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2018 Journal of Clinical Pathology

82. Decision support system for triage management: A hybrid approach using rule-based reasoning and fuzzy logic. (PubMed)

engine and predict the triage levels.Fourteen RBR and 27 fuzzy rules were extracted and used in the rule-based engine. The performance of the system was evaluated using three methods with real triage data. The accuracy of the clinical decision support systems (CDSSs; in the test data) was 99.44%. The evaluation of the error rate revealed that, when using the traditional method, 13.4% of the patients were miss-triaged, which is statically significant. The completeness of the documentation also (...) Decision support system for triage management: A hybrid approach using rule-based reasoning and fuzzy logic. Fast and accurate patient triage for the response process is a critical first step in emergency situations. This process is often performed using a paper-based mode, which intensifies workload and difficulty, wastes time, and is at risk of human errors. This study aims to design and evaluate a decision support system (DSS) to determine the triage level.A combination of the Rule-Based

2018 International journal of medical informatics

83. Accuracy of Clinician Practice Compared With Three Head Injury Decision Rules in Children: A Prospective Cohort Study. (PubMed)

Accuracy of Clinician Practice Compared With Three Head Injury Decision Rules in Children: A Prospective Cohort Study. Three clinical decision rules for head injuries in children (Pediatric Emergency Care Applied Research Network [PECARN], Canadian Assessment of Tomography for Childhood Head Injury [CATCH], and Children's Head Injury Algorithm for the Prediction of Important Clinical Events [CHALICE]) have been shown to have high performance accuracy. The utility of any of these in a particular (...) clinician accuracy and a low CT rate, PECARN, CATCH, or CHALICE clinical decision rules have limited potential to increase the accuracy of detecting clinically important traumatic brain injury and may increase the CT rate.Copyright © 2018 American College of Emergency Physicians. Published by Elsevier Inc. All rights reserved.

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2018 Annals of Emergency Medicine

84. Accuracy of NEXUS II head injury decision rule in children: a prospective PREDICT cohort study. (PubMed)

Accuracy of NEXUS II head injury decision rule in children: a prospective PREDICT cohort study. The National Emergency X-Radiography Utilisation Study II (NEXUS II) clinical decision rule (CDR) can be used to optimise the use of CT in children with head trauma. We set out to externally validate this CDR in a large cohort.We performed a prospective observational study of patients aged <18 years presenting with head trauma of any severity to 10 Australian/New Zealand EDs. In a planned secondary (...) analysis, we assessed the accuracy of the NEXUS II CDR (with 95% CI) to detect clinically important intracranial injury (ICI). We also assessed clinician accuracy without the rule.Of 20 137 total patients, we excluded 28 with suspected penetrating injury. Median age was 4.2 years. CTs were obtained in ED for 1962 (9.8%), of whom 377 (19.2%) had ICI as defined by NEXUS II. 74 (19.6% of ICI) patients underwent neurosurgery.Sensitivity for ICI based on the NEXUS II CDR was 379/383 (99.0 (95% CI 97.3

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2018 Emergency Medicine Journal

85. Data-based Decision Rules to Personalize Depression Follow-up (PubMed)

Data-based Decision Rules to Personalize Depression Follow-up Depression is a common mental illness with complex and heterogeneous progression dynamics. Risk grouping of depression treatment population based on their longitudinal patterns has the potential to enable cost-effective monitoring policy design. This paper establishes a rule-based method to identify a set of risk predictive patterns from person-level longitudinal disease measurements by integrating the data transformation, rule (...) discovery and rule evaluation. We further extend the identified rules to create rule-based monitoring strategies to adaptively monitor individuals with different disease severities. We applied the rule-based method on an electronic health record (EHR) dataset of depression treatment population containing person-level longitudinal Patient Health Questionnaire (PHQ)-9 scores for assessing depression severity. 12 risk predictive rules are identified, and the rule-based prognostic model based on identified

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2018 Scientific reports

86. Prospective validation and refinement of a decision rule to obtain CXR in patients with non-traumatic chest pain in the ED. (PubMed)

with a chief complaint of nontraumatic CP. Previously defined high-risk history and examination elements were combined into a refined decision rule and these elements were recorded for each patient by the ED physician. CXR results were reviewed and analyzed to determine the presence of clinically significant findings including pneumonia, pleural effusion, pneumothorax, congestive heart failure, or the presence of a new mass. Odds ratios for each history and examination element were analyzed as well (...) as sensitivity, specificity, and negative predictive value (NPV) of the rule overall.A total of 1,111 patients were enrolled and 1,089 CXRs were analyzed. There were 70 (6.4%) patients with clinically relevant findings on CXR. The refined decision rule had a sensitivity of 92.9% (confidence interval [CI] = 83.4%-97.3%) and specificity of 30.4% (CI = 27.6%-33.4%) to predict clinically relevant findings on CXR, with a NPV of 98.4% (CI = 96.1%-99.4%). Five CXRs with clinically significant findings would have

2018 Academic Emergency Medicine

87. Derivation and validation of a clinical decision rule to identify young children with skull fracture following isolated head trauma (PubMed)

Derivation and validation of a clinical decision rule to identify young children with skull fracture following isolated head trauma There is no clear consensus regarding radiologic evaluation of head trauma in young children without traumatic brain injury. We conducted a study to develop and validate a clinical decision rule to identify skull fracture in young children with head trauma and no immediate need for head tomography.We performed a prospective cohort study in 3 tertiary care emergency (...) was at the physician's discretion. The clinical decision rule was derived using recursive partitioning.A total of 811 patients (49 with skull fracture) were recruited during the derivation phase. The 2 predictors identified through recursive partitioning were parietal or occipital swelling or hematoma and age less than 2 months. The rule had a sensitivity of 94% (95% confidence interval [CI] 83%-99%) and a specificity of 86% (95% CI 84%-89%) in the derivation phase. During the validation phase, 856 participants (44

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2015 EvidenceUpdates

88. Use of mind maps and iterative decision trees to develop a guideline-based clinical decision support system for routine surgical practice: case study in thyroid nodules. (PubMed)

Use of mind maps and iterative decision trees to develop a guideline-based clinical decision support system for routine surgical practice: case study in thyroid nodules. The study sought to develop a clinical decision support system (CDSS) for the treatment of thyroid nodules, using a mind map and iterative decision tree (IDT) approach to the integration of clinical practice guidelines (CPGs).Thyroid nodule CPGs of the American Thyroid Association and Korean Thyroid Association were analyzed (...) by endocrine surgeons (domain experts) and computer scientists. Clinical knowledge from the CPGs was expressed using mind maps. The mind maps were analyzed and converted into IDTs. The final IDT was implemented as a set of candidate rules (3700) for a knowledge-based CDSS. The system was evaluated via a retrospective review of the medical records of 483 patients who had undergone thyroidectomy between January and December 2015 at a single tertiary center (Seoul National University Hospital Bundang, Korea

2019 Journal of the American Medical Informatics Association

89. Clinical decision rules to rule out subarachnoid hemorrhage for acute headache. (PubMed)

Clinical decision rules to rule out subarachnoid hemorrhage for acute headache. Three clinical decision rules were previously derived to identify patients with headache requiring investigations to rule out subarachnoid hemorrhage.To assess the accuracy, reliability, acceptability, and potential refinement (ie, to improve sensitivity or specificity) of these rules in a new cohort of patients with headache.Multicenter cohort study conducted at 10 university-affiliated Canadian tertiary care (...) (6.2%) had subarachnoid hemorrhage. The decision rule including any of age 40 years or older, neck pain or stiffness, witnessed loss of consciousness, or onset during exertion had 98.5% (95% CI, 94.6%-99.6%) sensitivity and 27.5% (95% CI, 25.6%-29.5%) specificity for subarachnoid hemorrhage. Adding "thunderclap headache" (ie, instantly peaking pain) and "limited neck flexion on examination" resulted in the Ottawa SAH Rule, with 100% (95% CI, 97.2%-100.0%) sensitivity and 15.3% (95% CI, 13.8%-16.9

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2013 JAMA

90. BET 1: Can the Manchester Acute Coronary Syndromes and Troponin-only Manchester Acute Coronary Syndromes decision aids rule out acute coronary syndromes in the emergency department? (PubMed)

BET 1: Can the Manchester Acute Coronary Syndromes and Troponin-only Manchester Acute Coronary Syndromes decision aids rule out acute coronary syndromes in the emergency department? A short-cut review was carried out to establish whether the Manchester Acute Coronary Syndromes (MACS) and Troponin-only MACS (T-MACS) decision aids can safely rule out acute coronary syndromes in patients presenting to the ED with suspected cardiac chest pain. Six studies were directly relevant to the question (...) . The author, date and country of publication, patient group studied, study type, relevant outcomes, results and study weaknesses of these papers are tabulated. The clinical bottom line is that both rules have high sensitivity for acute coronary syndromes, including the detection of major adverse cardiac events at 30 days. The original MACS algorithm may have marginally greater sensitivity than T-MACS but has inferior specificity and requires the use of a biomarker assay (for heart-type fatty acid binding

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2017 Emergency Medicine Journal

91. Prospective study of a non-restrictive decision rule for acute aortic syndrome. (PubMed)

Prospective study of a non-restrictive decision rule for acute aortic syndrome. To determine the impact of a non-restrictive clinical decision rule on CT utilization for Emergency Department patients suspected of having an acute aortic syndrome (AAS).We prospectively assessed the performance of a previously described, collaboratively designed, non-restrictive clinical decision rule for AAS. Emergency Department patients with suspected AAS were stratified into low and high-risk groups based (...) on decision rule results, from July 2013-August 2014. Patients with acute trauma, prior AAS or aortic surgery were excluded. CT dose reduction protocols were concurrently implemented as a quality improvement measure. Bivariate analysis was performed to compare the prospective cohort with the historical derivation cohort for CT utilization rates, results of CT, AAS incidence and radiation exposure. The performance of the clinical decision rule was evaluated.Compared with the historic cohort, the study

2017 American Journal of Emergency Medicine

92. Improved rule-out diagnostic gain with a combined aortic dissection detection risk score and D-dimer Bayesian decision support scheme.

Improved rule-out diagnostic gain with a combined aortic dissection detection risk score and D-dimer Bayesian decision support scheme. The objective of this study was to develop a Bayesian clinical decision support mathematical model that can assist in assessing a diagnostic utility integrating the aortic dissection detection risk score (ADD-RS) combined with the diagnostic quality of D-dimer testing.Our method uses the Bayes nomogram. Pretest probability scoring for the ADD-RS was obtained (...) [CI], 0.94-0.99), specificity of 0.56 (95% CI, 0.51-0.60), negative LR of 0.06 (95% CI, 0.03-0.12), and positive LR of 2.43 (95% CI, 1.89-3.12). Bayesian modeling for negative LRs demonstrated posttest probabilities scores of 0.24% for low risk (AADG = 4.06% and RDG=94.42%), 3.4% for intermediate risk (AADG = 33.1% and RDG=90.68%), and 7.9% for high risk (AADG = 51.3% and RDG=86.65%).The integration of the ADD-RS and D-dimer testing in a decision support scheme suggested rule-out diagnostic value

2017 Journal of critical care

93. Evaluating Terminologies to Enable Imaging-Related Decision Rule Sharing (PubMed)

Evaluating Terminologies to Enable Imaging-Related Decision Rule Sharing Purpose: Clinical decision support tools provide recommendations based on decision rules. A fundamental challenge regarding decision rule-sharing involves inadequate expression using standard terminology. We aimed to evaluate the coverage of three standard terminologies for mapping imaging-related decision rules. Methods: 50 decision rules, randomly selected from an existing library, were mapped to Systemized Nomenclature (...) of Medicine (SNOMED CT), Radiology Lexicon (RadLex) and International Classification of Disease (ICD-10-CM). Decision rule attributes and values were mapped to unique concepts, obtaining the best possible coverage with the fewest concepts. Manual and automated mapping using Clinical Text Analysis and Knowledge Extraction System (cTAKES) were performed. Results: Using manual mapping, SNOMED CT provided the greatest concept coverage (83%), compared to RadLex (36%) and ICD-10-CM (8%) (p<0.0001). Combined

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2017 AMIA Annual Symposium Proceedings

94. Quality Improvement in Pediatric Head Trauma with PECARN Rules Implementation as Computerized Decision Support (PubMed)

Quality Improvement in Pediatric Head Trauma with PECARN Rules Implementation as Computerized Decision Support For the 1.4 million emergency department (ED) visits for traumatic brain injury (TBI) annually in the United States, computed tomography (CT) may be over utilized. The Pediatric Emergency Care Applied Research Network developed 2 prediction rules to identify children at very low risk of clinically important TBI. We implemented these prediction rules as decision support within our (...) electronic health record (EHR) to reduce CT.To test EHR decision support implementation in reducing CT rates for head trauma at 2 pediatric EDs.We compared monthly CT rates 1 year before [preimplementation (PRE)] and 1 year after [postimplementation (POST)] decision support implementation. The primary outcome was change in CT use rate over time, measured using statistical process control charts. Secondary analyses included multivariate comparisons of PRE to POST. Balancing measures included ED length

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2017 Pediatric Quality & Safety

95. Can a Clinical Prediction Rule Reliably Predict Pediatric Bacterial Meningitis?

articles. STUDY SELECTION Derivation, validation, or impact studies of a pediatric bacterial meningitis clinical prediction rule were included. Clinical prediction rules were de?ned as decision tools making use of 3 or more predictors and provided probability estimates of meningitis or guided treatment in children younger than 18 years. To be included, studies were required to measure cases of bacterial meningitis by cerebrospinal ?uid culture. Although both prospective and retrospective studies were (...) in children with suspected meningitis. However, the majority of the rules included in this review used chart review, an ultimately unreliable me- thodology for prediction rule development. In general, decision rules accurate and reliable enough to allow pro- viders and parents to forgo testing, empiric treatment, or admission when bacterial meningitis is a considerationshouldbedeveloped and validated prospectively. None of the currently derived and vali- dated clinical prediction rules for bacterial

2015 Annals of Emergency Medicine Systematic Review Snapshots

96. Clinical prediction rules for hemodynamically significant angiographic stenoses: a systematic review

Clinical prediction rules for hemodynamically significant angiographic stenoses: a systematic review Print | PDF PROSPERO This information has been provided by the named contact for this review. CRD has accepted this information in good faith and registered the review in PROSPERO. The registrant confirms that the information supplied for this submission is accurate and complete. CRD bears no responsibility or liability for the content of this registration record, any associated files (...) tumor 4. No control group 5. Combination therapy or contamination 6. Not about analgesics used in the clinic Full text-screening: As above, with the addition of: 7. No relevant outcome measure reported ">Prioritise the exclusion criteria Example: Two reviewers will independently extract data from each article. We first try to extract numerical data from tables, text or figures. If these are not reported, we will extract data from graphs using digital ruler software. In case data are not reported

2019 PROSPERO

97. Prediction rule: Physician practice and PECARN rule outperform CATCH and CHALICE rules based on the detection of traumatic brain injury as defined by PECARN

of significant traumatic intracranial injuries is important and cranial CT is the gold standard for their diagnosis. However, CT bears risks associated with ionising radiation-induced malignancies, in particular in children. Three high-quality clinical decision rules (CDR) have been developed to assist with decision-making on whether or not to use a cranial CT scan in children who sustain a trauma to the head : the Canadian Assessment of Tomography for Childhood Head injury (CATCH) the Children's Head injury (...) study provides a useful guide for clinicians. In settings with experienced clinicians, physicians seem to be able to recognise children with possible ciTBI. In other settings, PECARN appears to provide a good guide. References Lyttle MD, Crowe L, Oakley E, et al . Comparing CATCH, CHALICE, and PECARN Clinical Decision Rules for Paediatric Head Injuries. Emerg Med J 2012;29:785–94. Osmond MH , Klassen TP , Wells GA , et al . CATCH: a clinical decision rule for the use of computed tomography

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2015 Evidence-Based Medicine (Requires free registration)

98. Diagnostic accuracy of a clinical prediction rule (CPR) for identifying patients with recent-onset undifferentiated arthritis who are at a high risk of developing rheumatoid arthritis: a systematic review and meta-analysis

Diagnostic accuracy of a clinical prediction rule (CPR) for identifying patients with recent-onset undifferentiated arthritis who are at a high risk of developing rheumatoid arthritis: a systematic review and meta-analysis Diagnostic accuracy of a clinical prediction rule (CPR) for identifying patients with recent-onset undifferentiated arthritis who are at a high risk of developing rheumatoid arthritis: a systematic review and meta-analysis Diagnostic accuracy of a clinical prediction rule (...) (CPR) for identifying patients with recent-onset undifferentiated arthritis who are at a high risk of developing rheumatoid arthritis: a systematic review and meta-analysis McNally E, Keogh C, Galvin R, Fahey T CRD summary The authors concluded that a cut-off point of ≥9 or ≥10 on the Leiden clinical prediction rule may be optimal in identifying patients with undifferentiated arthritis at high risk of developing rheumatoid arthritis, but the results should be interpreted with caution. Limitations

2014 DARE.

99. Development and implementation of "Check of Medication Appropriateness" (CMA): advanced pharmacotherapy-related clinical rules to support medication surveillance. (PubMed)

Development and implementation of "Check of Medication Appropriateness" (CMA): advanced pharmacotherapy-related clinical rules to support medication surveillance. To improve medication surveillance and provide pharmacotherapeutic support in University Hospitals Leuven, a back-office clinical service, called "Check of Medication Appropriateness" (CMA), was developed, consisting of clinical rule based screening for medication inappropriateness. The aim of this study is twofold: 1) describing (...) the development of CMA and 2) evaluating the preliminary results, more specifically the number of clinical rule alerts, number of actions on the alerts and acceptance rate by physicians.CMA focuses on patients at risk for potentially inappropriate medication and involves the daily checking by a pharmacist of high-risk prescriptions generated by advanced clinical rules integrating patient specific characteristics with details on medication. Pharmacists' actions are performed by adding an electronic note

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2019 Medical Informatics and Decision Making

100. Development and validation of a clinical rule for recognition of early inflammatory arthritis. (PubMed)

Development and validation of a clinical rule for recognition of early inflammatory arthritis. National and international guidelines recommend prompt referral of patients presenting with inflammatory arthritis (IA), but general practitioners (GPs) feel uncertain in their proficiency to detect synovitis through joint examination, the method of choice to identify IA. Our objective was to develop and validate a rule composed of clinical characteristics to assist GPs and other physicians (...) items was derived and validated, yielding an area under the receiver operator characteristic curve (AUC) of 0.74 (95% CI 0.70 to 0.78) in the derivation data set. Validation yielded an AUC of 0.71 (95% CI 0.67 to 0.75). Finally, the model was repeated to study predicted probabilities with a lower prevalence of inflammatory arthritis to simulate performance in primary care settings.Our rule, composed of clinical parameters, had reasonable discriminative ability for IA and could assist physicians

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2019 BMJ open

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