Risk Scores for Clinical Risk Stratification of Emergency Department Patients With Chest Pain but No Acute Myocardial Infarction: A Systematic Review.

BACKGROUND: Chest pain is a common cause for emergency department (ED) presentations. After myocardial infarction (MI) has been ruled out by means of electrocardiography and troponin testing, decisions around anatomic or functional testing may be informed by clinical risk scores. We conducted a systematic review to synthesize evidence of the prognostic performance of chest pain risk scores among ED patients who have had MI ruled out by means of a high-sensitivity troponin assay. METHODS: We queried multiple databases from inception to May 17, 2022. We included studies that quantified risk of 30-day major adverse cardiac events (MACE), at different cutoffs of clinical risk scores, among adult patients who had MI ruled out by means of a high-sensitivity troponin assay. Prognostic performance of each score was synthesized and described, but meta-analysis was not possible. RESULTS: Six studies met inclusion criteria. Short-term MACE risk among patients who had MI ruled out by means of high-sensitivity cardiac troponin assays was very low. The HEART score, with a cutoff of 3 or less, predicted a very low risk of MACE among the greatest proportion of patients. Other scores had lower sensitivity or classified fewer patients as low risk. CONCLUSIONS: The HEART score with a cutoff value of 3 or less accurately identified the greatest number of patients at low risk of 30-day MACE. However, MACE risk among patients who have MI ruled out by means of high-sensitivity troponin testing is sufficiently low that clinical risk stratification or noninvasive testing may be of little additional value in identifying patients with coronary disease.

Reporting of Model Performance and Statistical Methods in Studies That Use Machine Learning to Develop Clinical Prediction Models: Protocol for a Systematic Review.

BACKGROUND: With the growing excitement of the potential benefits of using machine learning and artificial intelligence in medicine, the number of published clinical prediction models that use these approaches has increased. However, there is evidence (albeit limited) that suggests that the reporting of machine learning-specific aspects in these studies is poor. Further, there are no reviews assessing the reporting quality or broadly accepted reporting guidelines for these aspects. OBJECTIVE: This paper presents the protocol for a systematic review that will assess the reporting quality of machine learning-specific aspects in studies that use machine learning to develop clinical prediction models. METHODS: We will include studies that use a supervised machine learning algorithm to develop a prediction model for use in clinical practice (ie, for diagnosis or prognosis of a condition or identification of candidates for health care interventions). We will search MEDLINE for studies published in 2019, pseudorandomly sort the records, and screen until we obtain 100 studies that meet our inclusion criteria. We will assess reporting quality with a novel checklist developed in parallel with this review, which includes content derived from existing reporting guidelines, textbooks, and consultations with experts. The checklist will cover 4 key areas where the reporting of machine learning studies is unique: modelling steps (order and data used for each step), model performance (eg, reporting the performance of each model compared), statistical methods (eg, describing the tuning approach), and presentation of models (eg, specifying the predictors that contributed to the final model). RESULTS: We completed data analysis in August 2021 and are writing the manuscript. We expect to submit the results to a peer-reviewed journal in early 2022. CONCLUSIONS: This review will contribute to more standardized and complete reporting in the field by identifying areas where reporting is poor and can be improved. TRIAL REGISTRATION: PROSPERO International Prospective Register of Systematic Reviews CRD42020206167; https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=206167. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/30956.