Pinsky, Michael R.

Background: Paroxysmal atrial fibrillation (AF) is a common arrhythmogenic complication in the intensive care unit (ICU) associated with significant costs and morbidity. AF detection models in the literature have utilized entropic analyses of telemetry to diagnose AF, but they have not been utilized to forecast its onset. Hypothesis: Deep learning techniques applied to high-frequency telemetry data can be utilized to forecast AF with clinically significant lead time. Methods: Telemetry data were collected from 72 ICU monitored beds in a tertiary care center from 4/2018-12/2023. Coefficient of sample entropy (COSEn), a metric of heart rate variability complexity, was calculated on successive 1-minute windows and utilized to identify areas of AF initiation as well as control windows without AF. ECG lead II (256Hz) and beat-to-beat (B2B) measurements were analyzed by the MOMENT time-series foundational model to generate representation embeddings. A binary classification multilayer perceptron (MLP) was trained on these embeddings to forecast AF onset 5m in advance. All results were reported on separate test sets. Group-based trajectory modelling (GBTM) was performed on risk score trajectories from the 20m before AF onset or absence. Results: Over 230,000 hours of B2B data from 2263 patients was analyzed using COSEn to identify 287 transitions to AF from 136 patients. The same number of AF-absent windows were randomly selected. COSEn alone as a forecaster produced an area under the receiver operating characteristic curve (AUC) of 0.65 (95% CI: 0.61-0.69). The MOMENT + MLP model (MMM) achieved an AUC of 0.81 (0.78-0.83) (Fig. 1). COSEn and MMM were subsequently applied to the two hours preceding AF onset or absence. For COSEn, the mean risk trajectory dipped in the baseline period whereas the MMM risk trajectory sustained a high level through the baseline, highlighting its capacity to forecast AF (Fig. 2). For AF patients, GBMT yielded two types of trajectories: one with elevated risk throughout, and another with a rising trend 5 minutes prior to AF onset (Fig. 3). Conclusions: Deep learning techniques can forecast the onset of AF with up to 5 minutes of lead-time and GBTM allowed for the identification of different pre-AF risk trajectory phenotypes.The ability to forecast AF onset has potential to clinically prevent poor outcomes in the ICU, though further refinements to augment the amount of lead time are warranted.

AbstractHow to deliver best care in various clinical settings remains a vexing problem. All pertinent healthcare-related questions have not, cannot, and will not be addressable with costly time- and resource-consuming controlled clinical trials. At present, evidence-based guidelines can address only a small fraction of the types of care that clinicians deliver. Furthermore, underserved areas rarely can access state-of-the-art evidence-based guidelines in real-time, and often lack the wherewithal to implement advanced guidelines. Care providers in such settings frequently do not have sufficient training to undertake advanced guideline implementation. Nevertheless, in advanced modern healthcare delivery environments, use of eActions (validated clinical decision support systems) could help overcome the cognitive limitations of overburdened clinicians. Widespread use of eActions will require surmounting current healthcare technical and cultural barriers and installing clinical evidence/data curation systems. The authors expect that increased numbers of evidence-based guidelines will result from future comparative effectiveness clinical research carried out during routine healthcare delivery within learning healthcare systems.

AbstractClinical decision-making is based on knowledge, expertise, and authority, with clinicians approving almost every intervention—the starting point for delivery of “All the right care, but only the right care,” an unachieved healthcare quality improvement goal. Unaided clinicians suffer from human cognitive limitations and biases when decisions are based only on their training, expertise, and experience. Electronic health records (EHRs) could improve healthcare with robust decision-support tools that reduce unwarranted variation of clinician decisions and actions. Current EHRs, focused on results review, documentation, and accounting, are awkward, time-consuming, and contribute to clinician stress and burnout. Decision-support tools could reduce clinician burden and enable replicable clinician decisions and actions that personalize patient care. Most current clinical decision-support tools or aids lack detail and neither reduce burden nor enable replicable actions. Clinicians must provide subjective interpretation and missing logic, thus introducing personal biases and mindless, unwarranted, variation from evidence-based practice. Replicability occurs when different clinicians, with the same patient information and context, come to the same decision and action. We propose a feasible subset of therapeutic decision-support tools based on credible clinical outcome evidence: computer protocols leading to replicable clinician actions (eActions). eActions enable different clinicians to make consistent decisions and actions when faced with the same patient input data. eActions embrace good everyday decision-making informed by evidence, experience, EHR data, and individual patient status. eActions can reduce unwarranted variation, increase quality of clinical care and research, reduce EHR noise, and could enable a learning healthcare system.