Prospective validation of clinical deterioration predictive models prior to intensive care unit transfer among patients admitted to acute care cardiology wards.

Very few predictive models have been externally validated in a prospective cohort following the implementation of an artificial intelligence analytic system. This type of real-world validation is critically important due to the risk of data drift, or changes in data definitions or clinical practices over time, that could impact model performance in contemporaneous real-world cohorts. In this work, we report the model performance of a predictive analytics tool developed before COVID-19 and demonstrate model performance during the COVID-19 pandemic. The analytic system (CoMETⓇ, Nihon Kohden Digital Health Solutions LLC, Irvine, CA) was implemented in a randomized controlled trial that enrolled 10,422 patient visits in a 1:1 display-on display-off design. The CoMET scores were calculated for all patients but only displayed in the display-on arm. Only the control/display-off group is reported here because the scores could not alter care patterns. Of the 5184 visits in the display-off arm, 311 experienced clinical deterioration and care escalation, resulting in transfer to the intensive care unit (ICU), primarily due to respiratory distress. The model performance of CoMET was assessed based on areas under the receiver operating characteristic curve, which ranged from 0.725 to 0.737. The models were well-calibrated, and there were dynamic increases in the model scores in the hours preceding the clinical deterioration events. A hypothetical alerting strategy based on a rise in score and duration of the rise would have had good performance, with a positive predictive value more than 10-fold the event rate. We conclude that predictive statistical models developed five years before study initiation had good model performance despite the passage of time and the impact of the COVID-19 pandemic. .

Cardiac Magnetic Resonance, Electromechanical Activation, Kidney Function, and Natriuretic Peptides in Cardiac Resynchronization Therapy Upgrades.

As the mechanism for worse prognosis after cardiac resynchronization therapy (CRT) upgrades in heart failure patients with RVP dependence (RVP-HF) has clinical implications for patient selection and CRT implementation approaches, this study's objective was to evaluate prognostic implications of cardiac magnetic resonance (CMR) findings and clinical factors in 102 HF patients (23.5% female, median age 66.5 years old, median follow-up 4.8 years) with and without RVP dependence undergoing upgrade and de novo CRT implants. Compared with other CRT groups, RVP-HF patients had decreased survival (p = 0.02), more anterior late-activated LV pacing sites (p = 0.002) by CMR, more atrial fibrillation (p = 0.0006), and higher creatinine (0.002). CMR activation timing at the LV pacing site predicted post-CRT LV functional improvement (p < 0.05), and mechanical activation onset < 34 ms by CMR at the LVP site was associated with decreased post-CRT survival in a model with higher pre-CRT creatinine and B-type natriuretic peptide (AUC 0.89; p < 0.0001); however, only the higher pre-CRT creatinine partially mediated (37%) the decreased survival in RVP-HF patients. In conclusion, RVP-HF had a distinct CMR phenotype, which has important implications for the selection of LV pacing sites in CRT upgrades, and only chronic kidney disease mediated the decreased survival after CRT in RVP-HF.

Noninvasive Electrical Mapping Compared with the Paced QRS Complex for Optimizing CRT Programmed Settings and Predicting Multidimensional Response.

The aim was to test the hypothesis that left ventricular (LV) and right ventricular (RV) activation from body surface electrical mapping (CardioInsight 252-electrode vest, Medtronic) identifies optimal cardiac resynchronization therapy (CRT) pacing strategies and outcomes in 30 patients. The LV80, RV80, and BIV80 were defined as the times to 80% LV, RV, or biventricular electrical activation. Smaller differences in the LV80 and RV80 (|LV80-RV80|) with synchronized LV pacing predicted better LV function post-CRT (p = 0.0004) than the LV-paced QRS duration (p = 0.32). Likewise, a lower RV80 was associated with a better pre-CRT RV ejection fraction by CMR (r = - 0.40, p = 0.04) and predicted post-CRT improvements in myocardial oxygen uptake (p = 0.01) better than the biventricular-paced QRS (p = 0.38), while a lower LV80 with BIV pacing predicted lower post-CRT B-type natriuretic peptide (BNP) (p = 0.02). RV pacing improved LV function with smaller |LV80-RV80| (p = 0.009). In conclusion, 3-D electrical mapping predicted favorable post-CRT outcomes and informed effective pacing strategies.

First-in-human noninvasive left ventricular ultrasound pacing: A potential screening tool for cardiac resynchronization therapy.

Background: A screening tool to predict response to cardiac resynchronization therapy (CRT) could improve patient selection and outcomes. Objective: The purpose of this study was to investigate the feasibility and safety of noninvasive CRT via transcutaneous ultrasonic left ventricular (LV) pacing applied as a screening test before CRT implants. Methods: P-wave-triggered ultrasound stimuli were delivered during bolus dosing of an echocardiographic contrast agent to simulate CRT noninvasively. Ultrasound pacing was delivered at a variety of LV locations with a range of atrioventricular delays to achieve fusion with intrinsic ventricular activation. Three-dimensional cardiac activation maps were acquired via the Medtronic CardioInsight 252-electrode mapping vest during baseline, ultrasound pacing, and after CRT implantation. A separate control group received only the CRT implants. Results: Ultrasound pacing was achieved in 10 patients with a mean of 81.2 ± 50.8 ultrasound paced beats per patient and up to 20 consecutive beats of ultrasound pacing. QRS width at baseline (168.2 ± 17.8 ms) decreased significantly to 117.3 ± 21.5 ms (P <.001) in the best ultrasound paced beat and to 125.8 ± 13.3 ms (P <.001) in the best CRT beat. Electrical activation patterns were similar between CRT pacing and ultrasound pacing with stimulation from the same area of the LV. Troponin results were similar between the ultrasound pacing and the control groups (P = .96), confirming safety. Conclusion: Noninvasive ultrasound pacing before CRT is safe and feasible, and it estimates the degree of electrical resynchronization achievable with CRT. Further study of this promising technique to guide CRT patient selection is warranted.

Beyond prediction: Off-target uses of artificial intelligence-based predictive analytics in a learning health system.

Introduction: Artificial-intelligence (AI)-based predictive analytics provide new opportunities to leverage rich sources of continuous data to improve patient care through early warning of the risk of clinical deterioration and improved situational awareness.Part of the success of predictive analytic implementation relies on integration of the analytic within complex clinical workflows. Pharmaceutical interventions have off-target uses where a drug indication has not been formally studied for a different indication but has potential for clinical benefit. An analog has not been described in the context of AI-based predictive analytics, that is, when a predictive analytic has been trained on one outcome of interest but is used for additional applications in clinical practice. Methods: In this manuscript we present three clinical vignettes describing off-target use of AI-based predictive analytics that evolved organically through real-world practice. Results: Off-target uses included:real-time feedback about treatment effectiveness, indication of readiness to discharge, and indication of the acuity of a hospital unit. Conclusion: Such practice fits well with the learning health system goals to continuously integrate data and experience to provide.

A novel predictive analytics score reflecting accumulating disease burden-an investigation of the cumulative CoMET score.

OBJECTIVE: Predictive analytics tools variably take into account data from the electronic medical record, lab tests, nursing charted vital signs and continuous cardiorespiratory monitoring to deliver an instantaneous prediction of patient risk or instability. Few, if any, of these tools reflect the risk to a patient accumulated over the course of an entire hospital stay. APPROACH: We have expanded on our instantaneous CoMET predictive analytics score to generate the cumulative CoMET score (cCoMET), which sums all of the instantaneous CoMET scores throughout a hospital admission relative to a baseline expected risk unique to that patient. MAIN RESULTS: We have shown that higher cCoMET scores predict mortality, but not length of stay, and that higher baseline CoMET scores predict higher cCoMET scores at discharge/death. cCoMET scores were higher in males in our cohort, and added information to the final CoMET when it came to the prediction of death. SIGNIFICANCE: We have shown that the inclusion of all repeated measures of risk estimation performed throughout a patients hospital stay adds information to instantaneous predictive analytics, and could improve the ability of clinicians to predict deterioration, and improve patient outcomes in so doing.

Continuous ECG monitoring should be the heart of bedside AI-based predictive analytics monitoring for early detection of clinical deterioration.

The idea that we can detect subacute potentially catastrophic illness earlier by using statistical models trained on clinical data is now well-established. We review evidence that supports the role of continuous cardiorespiratory monitoring in these predictive analytics monitoring tools. In particular, we review how continuous ECG monitoring reflects the patient and not the clinician, is less likely to be biased, is unaffected by changes in practice patterns, captures signatures of illnesses that are interpretable by clinicians, and is an underappreciated and underutilized source of detailed information for new mathematical methods to reveal.

Machine learning for multidimensional response and survival after cardiac resynchronization therapy using features from cardiac magnetic resonance.

Background: Cardiac resynchronization therapy (CRT) response is complex, and better approaches are required to predict survival and need for advanced therapies. Objective: The objective was to use machine learning to characterize multidimensional CRT response and its relationship with long-term survival. Methods: Associations of 39 baseline features (including cardiac magnetic resonance [CMR] findings and clinical parameters such as glomerular filtration rate [GFR]) with a multidimensional CRT response vector (consisting of post-CRT left ventricular end-systolic volume index [LVESVI] fractional change, post-CRT B-type natriuretic peptide, and change in peak VO2) were evaluated. Machine learning generated response clusters, and cross-validation assessed associations of clusters with 4-year survival. Results: Among 200 patients (median age 67.4 years, 27.0% women) with CRT and CMR, associations with more than 1 response parameter were noted for the CMR CURE-SVD dyssynchrony parameter (associated with post-CRT brain natriuretic peptide [BNP] and LVESVI fractional change) and GFR (associated with peak VO2 and post-CRT BNP). Machine learning defined 3 response clusters: cluster 1 (n = 123, 90.2% survival [best]), cluster 2 (n = 45, 60.0% survival [intermediate]), and cluster 3 (n = 32, 34.4% survival [worst]). Adding the 6-month response cluster to baseline features improved the area under the receiver operating characteristic curve for 4-year survival from 0.78 to 0.86 (P = .02). A web-based application was developed for cluster determination in future patients. Conclusion: Machine learning characterizes distinct CRT response clusters influenced by CMR features, kidney function, and other factors. These clusters have a strong and additive influence on long-term survival relative to baseline features.

Cardiac magnetic resonance defines mechanisms of sex-based differences in outcomes following cardiac resynchronization therapy.

Background: Mechanisms of sex-based differences in outcomes following cardiac resynchronization therapy (CRT) are poorly understood. Objective: To use cardiac magnetic resonance (CMR) to define mechanisms of sex-based differences in outcomes after CRT and describe distinct CMR-based phenotypes of CRT candidates based on sex and non-ischemic/ischemic cardiomyopathy type. Materials and methods: In a prospective study, sex-based differences in three short-term CRT response measures [fractional change in left ventricular end-systolic volume index 6 months after CRT (LVESVI-FC), B-type natriuretic peptide (BNP) 6 months after CRT, change in peak VO2 6 months after CRT], and long-term survival were evaluated with respect to 39 baseline parameters from CMR, exercise testing, laboratory testing, electrocardiograms, comorbid conditions, and other sources. CMR was also used to quantify the degree of left-ventricular mechanical dyssynchrony by deriving the circumferential uniformity ratio estimate (CURE-SVD) parameter from displacement encoding with stimulated echoes (DENSE) strain imaging. Statistical methods included multivariable linear regression with evaluation of interaction effects associated with sex and cardiomyopathy type (ischemic and non-ischemic cardiomyopathy) and survival analysis. Results: Among 200 patients, the 54 female patients (27%) pre-CRT had a smaller CMR-based LVEDVI (p = 0.04), more mechanical dyssynchrony based on the validated CMR CURE-SVD parameter (p = 0.04), a lower frequency of both late gadolinium enhancement (LGE) and ischemic cardiomyopathy (p < 0.0001), a greater RVEF (p = 0.02), and a greater frequency of LBBB (p = 0.01). After categorization of patients into four groups based on cardiomyopathy type (ischemic/non-ischemic cardiomyopathy) and sex, female patients with non-ischemic cardiomyopathy had the lowest CURE-SVD (p = 0.003), the lowest pre-CRT BNP levels (p = 0.01), the lowest post-CRT BNP levels (p = 0.05), and the most favorable LVESVI-FC (p = 0.001). Overall, female patients had better 3-year survival before adjustment for cardiomyopathy type (p = 0.007, HR = 0.45) and after adjustment for cardiomyopathy type (p = 0.009, HR = 0.67). Conclusion: CMR identifies distinct phenotypes of female CRT patients with non-ischemic and ischemic cardiomyopathy relative to male patients stratified by cardiomyopathy type. The more favorable short-term response and long-term survival outcomes in female heart failure patients with CRT were associated with lower indexed CMR-based LV volumes, decreased presence of scar associated with prior myocardial infarction and ICM, and greater CMR-based dyssynchrony with the CURE-SVD.

Exercise in the Genetic Arrhythmia Syndromes - A Review.

Provide a brief summary of your article (100-150 words; no references or figures/tables). The synopsis appears only in the table of contents and is often used by indexing services such as PubMed. Genetic arrhythmia syndromes are rare, yet harbor the potential for highly consequential, often unpredictable arrhythmias or sudden death events. There has been historical uncertainty regarding the correct advice to offer to affected patients who are reasonably wanting to participate in sporting and athletic endeavors. In some cases, this had led to abundantly cautious disqualifications, depriving individuals from participation unnecessarily. Societal guidance and expert opinion has evolved significantly over the last decade or 2, along with our understanding of the genetics and natural history of these conditions, and the emphasis has switched toward shared decision making with respect to the decision to participate or not, with patients and families becoming better informed, and willing participants in the decision making process. This review aims to give a brief update of the salient issues for the busy physician concerning these syndromes and to provide a framework for approaching their management in the otherwise aspirational or keen sports participant.

Evaluation of a novel mechanical compression device for hematoma prevention and wound cosmesis after CIED implantation.

BACKGROUND: An important complication of cardiac implantable electronic devices (CIED) implantation is the development of hematoma and device infection. OBJECTIVE: We aimed to evaluate a novel mechanical compression device for hematoma prevention and cosmetic outcomes following CIED implantation. METHODS: An open, prospective, randomized, single-center clinical trial was performed in patients undergoing CIED implantation. Patients were randomized to receive a novel mechanical compression device (PressRite, PR) or to receive the standard of care post device implantation. Skin pliability was measured with a calibrated durometer; the surgical site was evaluated using the Manchester Scar Scale (MSS) by a blinded plastic surgeon and the Patient and Observer Scar Scale (POSAS). Performance of PR was assessed through pressure measurements, standardized scar scales and tolerability. RESULTS: From the total of 114 patients evaluated for enrollment, 105 patients were eligible for analysis. Fifty-one patients were randomized to management group (PR) and 54 to the control group. No patients required early removal or experienced adverse effects from PR application. There were 11 hematomas (14.8% vs. 5.9% in the control and PR group respectively, p = NS). The control group had higher post procedure durometer readings in the surgical site when compared with the PR group (7.50 ± 3.45 vs. 5.37 ± 2.78; p = < .01). There were lower MSS scores in the PR group after 2 weeks (p = .03). CONCLUSION: We have demonstrated the safety of PR application and removal. In addition, PR appears to improve postoperative skin pliability, which could facilitate wound healing.

Continuous cardiorespiratory monitoring is a dominant source of predictive signal in machine learning for risk stratification and clinical decision support.

Beaulieu-Jones and coworkers propose a litmus test for the field of predictive analytics-performance improvements must be demonstrated to be the result of non-clinician-initiated data, otherwise, there should be caution in assuming that predictive models could improve clinical decision-making (Beaulieu-Joneset al2021). They demonstrate substantial prognostic information in unsorted physician orders made before the first midnight of hospital admission, and we are persuaded that it is fair to ask-if the physician thought of it first, what exactly is machine learning for in-patient risk stratification learning about? While we want predictive analytics to represent the leading indicators of a patient's illness, does it instead merely reflect the lagging indicators of clinicians' actions? We propose that continuous cardiorespiratory monitoring-'routine telemetry data,' in Beaulieu-Jones' terms-represents the most valuable non-clinician-initiated predictive signal present in patient data, and the value added to patient care justifies the efforts and expense required. Here, we present a clinical and a physiological point of view to support our contention.

ß-Adrenergic Stimulation Synchronizes a Broad Spectrum of Action Potential Firing Rates of Cardiac Pacemaker Cells toward a Higher Population Average.

The heartbeat is initiated by pacemaker cells residing in the sinoatrial node (SAN). SAN cells generate spontaneous action potentials (APs), i.e., normal automaticity. The sympathetic nervous system increases the heart rate commensurate with the cardiac output demand via stimulation of SAN ß-adrenergic receptors (ßAR). While SAN cells reportedly represent a highly heterogeneous cell population, the current dogma is that, in response to ßAR stimulation, all cells increase their spontaneous AP firing rate in a similar fashion. The aim of the present study was to investigate the cell-to-cell variability in the responses of a large population of SAN cells. We measured the ßAR responses among 166 single SAN cells isolated from 33 guinea pig hearts. In contrast to the current dogma, the SAN cell responses to ßAR stimulation substantially varied. In each cell, changes in the AP cycle length were highly correlated (R2 = 0.97) with the AP cycle length before ßAR stimulation. While, as expected, on average, the cells increased their pacemaker rate, greater responses were observed in cells with slower basal rates, and vice versa: cells with higher basal rates showed smaller responses, no responses, or even decreased their rate. Thus, ßAR stimulation synchronized the operation of the SAN cell population toward a higher average rate, rather than uniformly shifting the rate in each cell, creating a new paradigm of ßAR-driven fight-or-flight responses among individual pacemaker cells.

Cardiac Magnetic Resonance Assessment of Response to Cardiac Resynchronization Therapy and Programming Strategies.

OBJECTIVES: The objective was to determine the feasibility and effectiveness of cardiac magnetic resonance (CMR) cine and strain imaging before and after cardiac resynchronization therapy (CRT) for assessment of response and the optimal resynchronization pacing strategy. BACKGROUND: CMR with cardiac implantable electronic devices can safely provide high-quality right ventricular/left ventricular (LV) ejection fraction (RVEF/LVEF) assessments and strain. METHODS: CMR with cine imaging, displacement encoding with stimulated echoes for the circumferential uniformity ratio estimate with singular value decomposition (CURE-SVD) dyssynchrony parameter, and scar assessment was performed before and after CRT. Whereas the pre-CRT scan constituted a single "imaging set" with complete volumetric, strain, and scar imaging, multiple imaging sets with complete strain and volumetric data were obtained during the post-CRT scan for biventricular pacing (BIVP), LV pacing (LVP), and asynchronous atrial pacing modes by reprogramming the device outside the scanner between imaging sets. RESULTS: 100 CMRs with a total of 162 imaging sets were performed in 50 patients (median age 70 years [IQR: 50-86 years]; 48% female). Reduction in LV end-diastolic volumes (P = 0.002) independent of CRT pacing were more prominent than corresponding reductions in right ventricular end-diastolic volumes (P = 0.16). A clear dependence of the optimal CRT pacing mode (BIVP vs LVP) on the PR interval (P = 0.0006) was demonstrated. The LVEF and RVEF improved more with BIVP than LVP with PR intervals ≥240 milliseconds (P = 0.025 and P = 0.002, respectively); the optimal mode (BIVP vs LVP) was variable with PR intervals <240 milliseconds. A lower pre-CRT displacement encoding with stimulated echoes (DENSE) CURE-SVD was associated with greater improvements in the post-CRT CURE-SVD (r = -0.69; P < 0.001), LV end-systolic volume (r = -0.58; P < 0.001), and LVEF (r = -0.52; P < 0.001). CONCLUSIONS: CMR evaluation with assessment of multiple pacing modes during a single scan after CRT is feasible and provides useful information for patient care with respect to response and the optimal pacing strategy.

Regulation of sinus node pacemaking and atrioventricular node conduction by HCN channels in health and disease.

The funny current, If, was first recorded in the heart 40 or more years ago by Dario DiFrancesco and others. Since then, we have learnt that If plays an important role in pacemaking in the sinus node, the innate pacemaker of the heart, and more recently evidence has accumulated to show that If may play an important role in action potential conduction through the atrioventricular (AV) node. Evidence has also accumulated to show that regulation of the transcription and translation of the underlying Hcn genes plays an important role in the regulation of sinus node pacemaking and AV node conduction under normal physiological conditions - in athletes, during the circadian rhythm, in pregnancy, and during postnatal development - as well as pathological states - ageing, heart failure, pulmonary hypertension, diabetes and atrial fibrillation. There may be yet more pathological conditions involving changes in the expression of the Hcn genes. Here, we review the role of If and the underlying HCN channels in physiological and pathological changes of the sinus and AV nodes and we begin to explore the signalling pathways (microRNAs, transcription factors, GIRK4, the autonomic nervous system and inflammation) involved in this regulation. This review is dedicated to Dario DiFrancesco on his retirement.

Remodeling of the Purkinje Network in Congestive Heart Failure in the Rabbit.

BACKGROUND: Purkinje fibers (PFs) control timing of ventricular conduction and play a key role in arrhythmogenesis in heart failure (HF) patients. We investigated the effects of HF on PFs. METHODS: Echocardiography, electrocardiography, micro-computed tomography, quantitative polymerase chain reaction, immunohistochemistry, volume electron microscopy, and sharp microelectrode electrophysiology were used. RESULTS: Congestive HF was induced in rabbits by left ventricular volume- and pressure-overload producing left ventricular hypertrophy, diminished fractional shortening and ejection fraction, and increased left ventricular dimensions. HF baseline QRS and corrected QT interval were prolonged by 17% and 21% (mean±SEMs: 303±6 ms HF, 249±11 ms control; n=8/7; P=0.0002), suggesting PF dysfunction and impaired ventricular repolarization. Micro-computed tomography imaging showed increased free-running left PF network volume and length in HF. mRNA levels for 40 ion channels, Ca2+-handling proteins, connexins, and proinflammatory and fibrosis markers were assessed: 50% and 35% were dysregulated in left and right PFs respectively, whereas only 12.5% and 7.5% changed in left and right ventricular muscle. Funny channels, Ca2+-channels, and K+-channels were significantly reduced in left PFs. Microelectrode recordings from left PFs revealed more negative resting membrane potential, reduced action potential upstroke velocity, prolonged duration (action potential duration at 90% repolarization: 378±24 ms HF, 249±5 ms control; n=23/38; P<0.0001), and arrhythmic events in HF. Similar electrical remodeling was seen at the left PF-ventricular junction. In the failing left ventricle, upstroke velocity and amplitude were increased, but action potential duration at 90% repolarization was unaffected. CONCLUSIONS: Severe volume- followed by pressure-overload causes rapidly progressing HF with extensive remodeling of PFs. The PF network is central to both arrhythmogenesis and contractile dysfunction and the pathological remodeling may increase the risk of fatal arrhythmias in HF patients.

Predictive Monitoring-Impact in Acute Care Cardiology Trial (PM-IMPACCT): Protocol for a Randomized Controlled Trial.

BACKGROUND: Patients in acute care wards who deteriorate and are emergently transferred to intensive care units (ICUs) have poor outcomes. Early identification of patients who are decompensating might allow for earlier clinical intervention and reduced morbidity and mortality. Advances in bedside continuous predictive analytics monitoring (ie, artificial intelligence [AI]-based risk prediction) have made complex data easily available to health care providers and have provided early warning of potentially catastrophic clinical events. We present a dynamic, visual, predictive analytics monitoring tool that integrates real-time bedside telemetric physiologic data into robust clinical models to estimate and communicate risk of imminent events. This tool, Continuous Monitoring of Event Trajectories (CoMET), has been shown in retrospective observational studies to predict clinical decompensation on the acute care ward. There is a need to more definitively study this advanced predictive analytics or AI monitoring system in a prospective, randomized controlled, clinical trial. OBJECTIVE: The goal of this trial is to determine the impact of an AI-based visual risk analytic, CoMET, on improving patient outcomes related to clinical deterioration, response time to proactive clinical action, and costs to the health care system. METHODS: We propose a cluster randomized controlled trial to test the impact of using the CoMET display in an acute care cardiology and cardiothoracic surgery hospital floor. The number of admissions to a room undergoing cluster randomization was estimated to be 10,424 over the 20-month study period. Cluster randomization based on bed number will occur every 2 months. The intervention cluster will have the CoMET score displayed (along with standard of care), while the usual care group will receive standard of care only. RESULTS: The primary outcome will be hours free from events of clinical deterioration. Hours of acute clinical events are defined as time when one or more of the following occur: emergent ICU transfer, emergent surgery prior to ICU transfer, cardiac arrest prior to ICU transfer, emergent intubation, or death. The clinical trial began randomization in January 2021. CONCLUSIONS: Very few AI-based health analytics have been translated from algorithm to real-world use. This study will use robust, prospective, randomized controlled, clinical trial methodology to assess the effectiveness of an advanced AI predictive analytics monitoring system in incorporating real-time telemetric data for identifying clinical deterioration on acute care wards. This analysis will strengthen the ability of health care organizations to evolve as learning health systems, in which bioinformatics data are applied to improve patient outcomes by incorporating AI into knowledge tools that are successfully integrated into clinical practice by health care providers. TRIAL REGISTRATION: ClinicalTrials.gov NCT04359641; https://clinicaltrials.gov/ct2/show/NCT04359641. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/29631.