Machine learning uses historical data to make predictions about new data. It has been frequently applied in healthcare to optimise diagnostic classification through discovery of hidden patterns in data that may not be obvious to clinicians. Congenital Heart Defect (CHD) machine learning research entails one of the most promising clinical applications, in which timely and accurate diagnosis is essential. The objective of this scoping review is to summarise the application and clinical utility of machine learning techniques used in paediatric cardiology research, specifically focusing on approaches aiming to optimise diagnosis and assessment of underlying CHD. Out of 50 full-text articles identified between 2015 and 2021, 40% focused on optimising the diagnosis and assessment of CHD. Deep learning and support vector machine were the most commonly used algorithms, accounting for an overall diagnostic accuracy > 0.80. Clinical applications primarily focused on the classification of auscultatory heart sounds, transthoracic echocardiograms, and cardiac MRIs. The range of these applications and directions of future research are discussed in this scoping review.

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Patients with occlusion myocardial infarction (OMI) and no ST-elevation on presenting electrocardiogram (ECG) are increasing in numbers. These patients have a poor prognosis and would benefit from immediate reperfusion therapy, but, currently, there are no accurate tools to identify them during initial triage. Here we report, to our knowledge, the first observational cohort study to develop machine learning models for the ECG diagnosis of OMI. Using 7,313 consecutive patients from multiple clinical sites, we derived and externally validated an intelligent model that outperformed practicing clinicians and other widely used commercial interpretation systems, substantially boosting both precision and sensitivity. Our derived OMI risk score provided enhanced rule-in and rule-out accuracy relevant to routine care, and, when combined with the clinical judgment of trained emergency personnel, it helped correctly reclassify one in three patients with chest pain. ECG features driving our models were validated by clinical experts, providing plausible mechanistic links to myocardial injury.

Our project included 1,945 arterial catheters encompassing 7,197 catheter days. During the preintervention period, on average, 3.1 patients per month experienced an arterial catheter-related injury compared with 1.9 patients per month following intervention, a reduction of 38.7% (3.1 vs 1.9; p = 0.01). The rate of injury per 1,000 arterial catheter days was reduced from 16.7 pre intervention to 7.52 post intervention, a 55% overall reduction (16.7 vs 7.52; p = 0.0001). The rate of concerning arterial catheter nursing assessment based on our definition was reduced by 18.0% following our intervention cycles (25.5% vs 20.9%; p = 0.001) CONCLUSIONS:: Implementation of a quality improvement initiative and changing local practices reduced arterial catheter-associated harm in children with congenital and acquired heart disease requiring care in a cardiac ICU.

The purpose of this project was to develop and implement a consistent process for (1) screening adolescents by history for alcohol and substance abuse and (2) providing a motivational interview for change and appropriate referrals as needed. In the 18 months since we implemented the program, 534 patients were eligible for screening. Of these, 442 actually underwent screening and of these, 32 screened positive, thus receiving a brief intervention by social work and referral for further treatment. Use of the electronic medical record was key to the implementation and sustainability of this project.

High-risk low-volume therapies are those therapies that are practiced infrequently and yet carry an increased risk to patients because of their complexity. Staff nurses are required to competently manage these therapies to treat patients' unique needs and optimize outcomes; however, maintaining competence is challenging. This article describes implementation of Just-in-Time Training, which requires validation of minimum competency of bedside nurses managing high-risk low-volume therapies through direct observation of a return-demonstration competency checklist.

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