The purpose of this review is to present a guide to help readers critically appraise the methodologic quality of an article or articles describing a clinical decision rule. This guide will also be useful to clinical researchers who wish to answer 1 or more questions detailed in this article. We consider the 6 major stages in the development and testing of a new clinical decision rule and discuss a number of standards within each stage. We use examples from emergency medicine and, in particular, examples from our own research on clinical decisions rules for radiography in trauma.

Iconographies supplémentaires de l'article : Methodologic Standards for the Development of Clinical Decision Rules in Emergency Medicine

Interpretability in machine learning (ML) is crucial for high stakes decisions and troubleshooting. In this work, we provide fundamental principles for interpretable ML, and dispel common misunderstandings that dilute the importance of this crucial topic. We also identify 10 technical challenge areas in interpretable machine learning and provide history and background on each problem. Some of these problems are classically important, and some are recent problems that have arisen in the last few years. These problems are: (1) Optimizing sparse logical models such as decision trees; (2) Optimization of scoring systems; (3) Placing constraints into generalized additive models to encourage sparsity and better interpretability; (4) Modern case-based reasoning, including neural networks and matching for causal inference; (5) Complete supervised disentanglement of neural networks; (6) Complete or even partial unsupervised disentanglement of neural networks; (7) Dimensionality reduction for data visualization; (8) Machine learning models that can incorporate physics and other generative or causal constraints; (9) Characterization of the "Rashomon set" of good models; and (10) Interpretable reinforcement learning. This survey is suitable as a starting point for statisticians and computer scientists interested in working in interpretable machine learning.

Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges

Background: High-sensitivity assays for cardiac troponin T (hs-cTnT) are sometimes used to rapidly rule out acute myocardial infarction (AMI). Purpose: To estimate the ability of a single hs-cTnT concentration below the limit of detection (<0.005 µg/L) and a nonischemic electrocardiogram (ECG) to rule out AMI in adults presenting to the emergency department (ED) with chest pain. Data Sources: EMBASE and MEDLINE without language restrictions (1 January 2008 to 14 December 2016). Study Selection: Cohort studies involving adults presenting to the ED with possible acute coronary syndrome in whom an ECG and hs-cTnT measurements were obtained and AMI outcomes adjudicated during initial hospitalization. Data Extraction: Investigators of studies provided data on the number of low-risk patients (no new ischemia on ECG and hs-cTnT measurements <0.005 µg/L) and the number who had AMI during hospitalization (primary outcome) or a major adverse cardiac event (MACE) or death within 30 days (secondary outcomes), by risk classification (low or not low risk). Two independent epidemiologists rated risk of bias of studies. Data Synthesis: Of 9241 patients in 11 cohort studies, 2825 (30.6%) were classified as low risk. Fourteen (0.5%) low-risk patients had AMI. Sensitivity of the risk classification for AMI ranged from 87.5% to 100% in individual studies. Pooled estimated sensitivity was 98.7% (95% CI, 96.6% to 99.5%). Sensitivity for 30-day MACEs ranged from 87.9% to 100%; pooled sensitivity was 98.0% (CI, 94.7% to 99.3%). No low-risk patients died. Limitation: Few studies, variation in timing and methods of reference standard troponin tests, and heterogeneity of risk and prevalence of AMI across studies. Conclusion: A single hs-cTnT concentration below the limit of detection in combination with a nonischemic ECG may successfully rule out AMI in patients presenting to EDs with possible emergency acute coronary syndrome. Primary Funding Source: Emergency Care Foundation.

Rapid Rule-out of Acute Myocardial Infarction With a Single High-Sensitivity Cardiac Troponin T Measurement Below the Limit of Detection: A Collaborative Meta-analysis

Comparison of the GRACE, HEART and TIMI score to predict major adverse cardiac events in chest pain patients at the emergency department

Importance About 10% of patients with acute chest pain are ultimately diagnosed with acute coronary syndrome (ACS). Early, accurate estimation of the probability of ACS in these patients using the clinical examination could prevent many hospital admissions among low-risk patients and ensure that high-risk patients are promptly treated. Objective To review systematically the accuracy of the initial history, physical examination, electrocardiogram, and risk scores incorporating these elements with the first cardiac-specific troponin. Study Selection MEDLINE and EMBASE were searched (January 1, 1995-July 31, 2015), along with reference lists from retrieved articles, to identify prospective studies of diagnostic test accuracy among patients admitted to the emergency department with symptoms suggesting ACS. Data Extraction and Synthesis We identified 2992 unique articles; 58 met inclusion criteria. Main Outcomes and Measures Sensitivity, specificity, and likelihood ratio (LR) of findings for the diagnosis of ACS. The reference standard for ACS was either a final hospital diagnosis of ACS or occurrence of a cardiovascular event within 6 weeks. Results The clinical findings and risk factors most suggestive of ACS were prior abnormal stress test (specificity, 96%; LR, 3.1 [95% CI, 2.0-4.7]), peripheral arterial disease (specificity, 97%; LR, 2.7 [95% CI, 1.5-4.8]), and pain radiation to both arms (specificity, 96%; LR, 2.6 [95% CI, 1.8-3.7]). The most useful electrocardiogram findings were ST-segment depression (specificity, 95%; LR, 5.3 [95% CI, 2.1-8.6]) and any evidence of ischemia (specificity, 91%; LR, 3.6 [95% CI,1.6-5.7]). Both the History, Electrocardiogram, Age, Risk Factors, Troponin (HEART) and Thrombolysis in Myocardial Infarction (TIMI) risk scores performed well in diagnosing ACS: LR, 13 (95% CI, 7.0-24) for the high-risk range of the HEART score (7-10) and LR, 6.8 (95% CI, 5.2-8.9) for the high-risk range of the TIMI score (5-7). The most useful for identifying patients less likely to have ACS were the low-risk range HEART score (0-3) (LR, 0.20 [95% CI, 0.13-0.30]), low-risk range TIMI score (0-1) (LR, 0.31 [95% CI, 0.23-0.43]), or low to intermediate risk designation by the Heart Foundation of Australia and Cardiac Society of Australia and New Zealand risk algorithm (LR, 0.24 [95% CI, 0.19-0.31]). Conclusions and Relevance Among patients with suspected ACS presenting to emergency departments, the initial history, physical examination, and electrocardiogram alone did not confirm or exclude the diagnosis of ACS. Instead, the HEART or TIMI risk scores, which incorporate the first cardiac troponin, provided more diagnostic information.

https://jamanetwork.com/journals/JAMA/articlepdf/2468896/jrc150002supp1_prod.pdf

Does This Patient With Chest Pain Have Acute Coronary Syndrome?: The Rational Clinical Examination Systematic Review

Objective: Risk scores and accelerated diagnostic protocols can identify chest pain patients with low risk of major adverse cardiac event who could be discharged early from the ED, saving time and costs. We aimed to derive and validate a chest pain score and accelerated diagnostic protocol (ADP) that could safely increase the proportion of patients suitable for early discharge. Methods: Logistic regression identified statistical predictors for major adverse cardiac events in a derivation cohort. Statistical coefficients were converted to whole numbers to create a score. Clinician feedback was used to improve the clinical plausibility and the usability of the final score (Emergency Department Assessment of Chest pain Score [EDACS]). EDACS was combined with electrocardiogram results and troponin results at 0 and 2 h to develop an ADP (EDACS-ADP). The score and EDACS-ADP were validated and tested for reproducibility in separate cohorts of patients. Results: In the derivation (n = 1974) and validation (n = 608) cohorts, the EDACS-ADP classified 42.2% (sensitivity 99.0%, specificity 49.9%) and 51.3% (sensitivity 100.0%, specificity 59.0%) as low risk of major adverse cardiac events, respectively. The intra-class correlation coefficient for categorisation of patients as low risk was 0.87. Conclusion: The EDACS-ADP identified approximately half of the patients presenting to the ED with possible cardiac chest pain as having low risk of short-term major adverse cardiac events, with high sensitivity. This is a significant improvement on similar, previously reported protocols. The EDACS-ADP is reproducible and has the potential to make considerable cost reductions to health systems.

Development and validation of the Emergency Department Assessment of Chest pain Score and 2 h accelerated diagnostic protocol.

Free to read

Objective

Risk scores and accelerated diagnostic protocols can identify chest pain patients with low risk of major adverse cardiac event who could be discharged early from the ED, saving time and costs. We aimed to derive and validate a chest pain score and accelerated diagnostic protocol (ADP) that could safely increase the proportion of patients suitable for early discharge.

Methods

Logistic regression identified statistical predictors for major adverse cardiac events in a derivation cohort. Statistical coefficients were converted to whole numbers to create a score. Clinician feedback was used to improve the clinical plausibility and the usability of the final score (Emergency Department Assessment of Chest pain Score [EDACS]). EDACS was combined with electrocardiogram results and troponin results at 0 and 2 h to develop an ADP (EDACS-ADP). The score and EDACS-ADP were validated and tested for reproducibility in separate cohorts of patients.

Results

In the derivation (n = 1974) and validation (n = 608) cohorts, the EDACS-ADP classified 42.2% (sensitivity 99.0%, specificity 49.9%) and 51.3% (sensitivity 100.0%, specificity 59.0%) as low risk of major adverse cardiac events, respectively. The intra-class correlation coefficient for categorisation of patients as low risk was 0.87.

Conclusion

The EDACS-ADP identified approximately half of the patients presenting to the ED with possible cardiac chest pain as having low risk of short-term major adverse cardiac events, with high sensitivity. This is a significant improvement on similar, previously reported protocols. The EDACS-ADP is reproducible and has the potential to make considerable cost reductions to health systems.

Development and validation of the Emergency Department Assessment of Chest pain Score and 2h accelerated diagnostic protocol

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Development and validation of the Emergency Department Assessment of Chest pain Score and 2 h accelerated diagnostic protocol.

Development and validation of the Emergency Department Assessment of Chest pain Score and 2h accelerated diagnostic protocol