Automated Prediction of Cardiorespiratory Deterioration in Patients With Single Ventricle

Rusin, Craig G.; Acosta, Sebastián; Vu, Eric L.; Ahmed, Mubbasheer; Brady, Kennith M.; Penny, Daniel J.

doi:10.1016/j.jacc.2021.04.072

Cited by 30 publications

(14 citation statements)

References 21 publications

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“…Prior studies have demonstrated the utility of predictive analytics algorithms in the identification of patients at risk for instability or a catastrophic event ( 8 , 9 , 11 , 19 ). To the best of our knowledge, this study is one of the first to suggest predictive analytics algorithm such as ID o 2 may be a valuable tool in the de-escalation of patient care.…”

Section: Discussionmentioning

confidence: 99%

“…Predictive analytic algorithms may be able to identify patients with earlier, subclinical signs of instability ( 8 , 9 , 11 , 19 ). Our study suggests that predictive analytic algorithms such as ID o 2 may have utility in identifying patients at risk for deteriorating physiology during the de-escalation period of postoperative management ( 12 , 20 , 21 ).…”

Section: Discussionmentioning

confidence: 99%

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Use of a Risk Analytic Algorithm to Inform Weaning From Vasoactive Medication in Patients Following Pediatric Cardiac Surgery

Goldsmith

Futterman

Gazit

et al. 2021

Critical Care Explorations

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Advanced clinical decision support tools, such as real-time risk analytic algorithms, show promise in assisting clinicians in making more efficient and precise decisions. These algorithms, which calculate the likelihood of a given underlying physiology or future event, have predominantly been used to identify the risk of impending clinical decompensation. There may be broader clinical applications of these models. Using the inadequate delivery of oxygen index, a U.S. Food and Drug Administration-approved risk analytic algorithm predicting the likelihood of low cardiac output state, the primary objective was to evaluate the association of inadequate delivery of oxygen index with success or failure of weaning vasoactive support in postoperative cardiac surgery patients. DESIGN:Multicenter retrospective cohort study. SETTING: Three pediatric cardiac ICUs at tertiary academic children's hospitals. PATIENTS:Infants and children greater than 2 kg and less than 12 years following cardiac surgery, who required vasoactive infusions for greater than 6 hours in the postoperative period. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS:Postoperative patients were identified who successfully weaned off initial vasoactive infusions (n = 2,645) versus those who failed vasoactive wean (required reinitiation of vasoactive, required mechanical circulatory support, renal replacement therapy, suffered cardiac arrest, or died) (n = 516). Inadequate delivery of oxygen index for final 6 hours of vasoactive wean was captured. Inadequate delivery of oxygen index was significantly elevated in patients with failed versus successful weans (inadequate delivery of oxygen index 11.6 [sd 19.0] vs 6.4 [sd 12.6]; p < 0.001). Mean 6-hour inadequate delivery of oxygen index greater than 50 had strongest association with failed vasoactive wean (adjusted odds ratio, 4.0; 95% CI, 2.5-6.6). In patients who failed wean, reinitiation of vasoactive support was associated with concomitant fall in inadequate delivery of oxygen index (11.1 [sd 18] vs 8.9 [sd 16]; p = 0.007). CONCLUSIONS:During the de-escalation phase of postoperative cardiac ICU management, elevation of the real-time risk analytic model, inadequate delivery of oxygen index, was associated with failure to wean off vasoactive infusions. Future studies should prospectively evaluate utility of risk analytic models as clinical decision support tools in de-escalation practices in critically ill patients.KEY WORDS: clinical decision support systems; critical illness; low cardiac output; pediatric intensive care unit; physiologic monitoring; risk assessment C ritically ill pediatric patients with congenital and acquired cardiovascular disease have high rates of inhospital cardiac arrest and death (1). Clinical decision support tools aimed at identifying cardiac patients at high risk of clinical deterioration (2-6) have been described in the literature,

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Use of a Risk Analytic Algorithm to Inform Weaning From Vasoactive Medication in Patients Following Pediatric Cardiac Surgery

Goldsmith

Futterman

Gazit

et al. 2021

Critical Care Explorations

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“…Such technologies, potentially integrated with wearable monitoring devices, could provide early warning that risk of complications is increasing, alert the patient to see a physician, and suggest effective strategies to reduce risk proactively. A number of promising steps toward realizing this idea in the setting of CHD have been reported in the literature (23)(24)(25)(26). For example, Rusin et al (23) demonstrated that cardiorespiratory deterioration during hospitalization in patients with single ventricle could be predicted based on data from electrocardiogram and photoplethysmography.…”

Section: Medicine-based Evidence In Congenital Heart Diseasementioning

confidence: 99%

“…A number of promising steps toward realizing this idea in the setting of CHD have been reported in the literature (23)(24)(25)(26). For example, Rusin et al (23) demonstrated that cardiorespiratory deterioration during hospitalization in patients with single ventricle could be predicted based on data from electrocardiogram and photoplethysmography. Diller et al (24) developed an automatic deep-learning imaging algorithm that predicted death/aborted cardiac arrest or documented ventricular tachycardia in patients with tetralogy of Fallot, and in another publication (25) showed that an automatically derived disease severity score based on clinical and demographic data as well as results from electrocardiogram, cardiopulmonary exercise testing and laboratory markers could accurately predict survival in adults with CHD and effectively augment decision-making.…”

Section: Medicine-based Evidence In Congenital Heart Diseasementioning

confidence: 99%

Medicine-Based Evidence in Congenital Heart Disease: How Artificial Intelligence Can Guide Treatment Decisions for Individual Patients

Eynde

Manlhiot

Bruaene

et al. 2021

Front. Cardiovasc. Med.

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Built on the foundation of the randomized controlled trial (RCT), Evidence Based Medicine (EBM) is at its best when optimizing outcomes for homogeneous cohorts of patients like those participating in an RCT. Its weakness is a failure to resolve a clinical quandary: patients appear for care individually, each may differ in important ways from an RCT cohort, and the physician will wonder each time if following EBM will provide best guidance for this unique patient. In an effort to overcome this weakness, and promote higher quality care through a more personalized approach, a new framework has been proposed: Medicine-Based Evidence (MBE). In this approach, big data and deep learning techniques are embraced to interrogate treatment responses among patients in real-world clinical practice. Such statistical models are then integrated with mechanistic disease models to construct a “digital twin,” which serves as the real-time digital counterpart of a patient. MBE is thereby capable of dynamically modeling the effects of various treatment decisions in the context of an individual's specific characteristics. In this article, we discuss how MBE could benefit patients with congenital heart disease, a field where RCTs are difficult to conduct and often fail to provide definitive solutions because of a small number of subjects, their clinical complexity, and heterogeneity. We will also highlight the challenges that must be addressed before MBE can be embraced in clinical practice and its full potential can be realized.

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“…However, as we realize that outcomes for neonates may have plateaued (12), the future of postoperative monitoring is likely real-time integration of clinical, vital sign, and waveform data and trends rather than a single postoperative value to augment the course of this vulnerable patient population. Integrated health data platforms are emerging that may serve to discern patterns that the clinician cannot see and identify trends before a patient has fallen too far off the curve (13–15). However, to aid the computers and algorithms, it is important to continue to explore what data may help the signal, and what data are just noise.…”

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confidence: 99%

Searching High and Low: The Association of Superior and Inferior Vena Cava Saturations With Outcomes in Neonates After Cardiac Surgery*

Gardner

Naim

2022

Pediatric Critical Care Medicine

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Automated Prediction of Cardiorespiratory Deterioration in Patients With Single Ventricle

Cited by 30 publications

References 21 publications

Use of a Risk Analytic Algorithm to Inform Weaning From Vasoactive Medication in Patients Following Pediatric Cardiac Surgery

Use of a Risk Analytic Algorithm to Inform Weaning From Vasoactive Medication in Patients Following Pediatric Cardiac Surgery

Medicine-Based Evidence in Congenital Heart Disease: How Artificial Intelligence Can Guide Treatment Decisions for Individual Patients

Searching High and Low: The Association of Superior and Inferior Vena Cava Saturations With Outcomes in Neonates After Cardiac Surgery*

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