Current monitoring and innovative predictive modeling to improve care in the pediatric cardiac intensive care unit

Analysis of haemodynamics surrounding blood transfusions after the arterial switch operation: a pilot study utilising real-time telemetry high-frequency data capture

BACKGROUND

Packed red blood cell transfusions occur frequently after congenital heart surgery to augment haemodynamics, with limited understanding of efficacy. The goal of this study was to analyse the hemodynamic response to packed red blood cell transfusions in a single cohort, as "proof-of-concept" utilising high-frequency data capture of real-time telemetry monitoring.

METHODS

Retrospective review of patients after the arterial switch operation receiving packed red blood cell transfusions from 15 July 2020 to 15 July 2021. Hemodynamic parameters were collected from a high-frequency data capture system (SickbayTM) continuously recording vital signs from bedside monitors and analysed in 5-minute intervals up to 6 hours before, 4 hours during, and 6 hours after packed red blood cell transfusions-up to 57,600 vital signs per packed red blood cell transfusions. Variables related to oxygen balance included blood gas co-oximetry, lactate levels, near-infrared spectroscopy, and ventilator settings. Analgesic, sedative, and vasoactive infusions were also collected.

RESULTS

Six patients, at 8.5[IQR:5-22] days old and weighing 3.1[IQR:2.8-3.2]kg, received transfusions following the arterial switch operation. There were 10 packed red blood cell transfusions administered with a median dose of 10[IQR:10-15]mL/kg over 169[IQR:110-190]min; at median post-operative hour 36[IQR:10-40]. Significant increases in systolic and mean arterial blood pressures by 5-12.5% at 3 hours after packed red blood cell transfusions were observed, while renal near-infrared spectroscopy increased by 6.2% post-transfusion. No significant changes in ventilation, vasoactive support, or laboratory values related to oxygen balance were observed.

CONCLUSIONS

Packed red blood cell transfusions given after the arterial switch operation increased arterial blood pressure by 5-12.5% for 3 hours and renal near-infrared spectroscopy by 6.2%. High-frequency data capture systems can be leveraged to provide novel insights into the hemodynamic response to commonly used therapies such as packed red blood cell transfusions after paediatric cardiac surgery.

Role of artificial intelligence in early detection of congenital heart diseases in neonates

In the domain of healthcare, most importantly pediatric healthcare, the role of artificial intelligence (AI) has significantly impacted the medical field. Congenital heart diseases represent a group of heart diseases that are known to be some of the most critical cardiac conditions present at birth. These heart diseases need a swift diagnosis as well as an intervention to ensure the wellbeing of newborns. Fortunately, with the help of AI, including the highly advanced algorithms, analytics and imaging involved, it provides us with a promising era for neonatal care. This article reviewed published data in PubMed, Science Direct, UpToDate, and Google Scholar between the years 2015-2023. To conclude The use of artificial intelligence in detecting congenital heart diseases has shown great promise in improving the accuracy and efficiency of diagnosis. Several studies have demonstrated the efficacy of AI-based approaches for diagnosing congenital heart diseases, with results indicating that the systems can achieve high levels of sensitivity and specificity. In addition, AI can help reduce the workload of healthcare professionals allowing them to focus on other critical aspects of patient care. Despite the potential benefits of using AI, in addition to detecting congenital heart disease, there are still some challenges to overcome, such as the need for large amounts of high-quality data and the requirement for careful validation of the algorithms. Nevertheless, with ongoing research and development, AI is likely to become an increasingly valuable tool for improving the diagnosis and treatment of congenital heart diseases.

Development and Temporal Validation of a Machine Learning Model to Predict Clinical Deterioration

OBJECTIVES

Early warning scores detecting clinical deterioration in pediatric inpatients have wide-ranging performance and use a limited number of clinical features. This study developed a machine learning model leveraging multiple static and dynamic clinical features from the electronic health record to predict the composite outcome of unplanned transfer to the ICU within 24 hours and inpatient mortality within 48 hours in hospitalized children.

METHODS

Using a retrospective development cohort of 17 630 encounters across 10 388 patients, 2 machine learning models (light gradient boosting machine [LGBM] and random forest) were trained on 542 features and compared with our institutional Pediatric Early Warning Score (I-PEWS).

RESULTS

The LGBM model significantly outperformed I-PEWS based on receiver operating characteristic curve (AUROC) for the composite outcome of ICU transfer or mortality for both internal validation and temporal validation cohorts (AUROC 0.785 95% confidence interval [0.780-0.791] vs 0.708 [0.701-0.715] for temporal validation) as well as lead-time before deterioration events (median 11 hours vs 3 hours; P = .004). However, LGBM performance as evaluated by precision recall curve was lesser in the temporal validation cohort with associated decreased positive predictive value (6% vs 29%) and increased number needed to evaluate (17 vs 3) compared with I-PEWS.

CONCLUSIONS

Our electronic health record based machine learning model demonstrated improved AUROC and lead-time in predicting clinical deterioration in pediatric inpatients 24 to 48 hours in advance compared with I-PEWS. Further work is needed to optimize model positive predictive value to allow for integration into clinical practice.

Risk of in-hospital Deterioration for Children with Single Ventricle Physiology

Children with single ventricle physiology (SV) are at high risk of in-hospital morbidity and mortality. Identifying children at risk for deterioration may allow for earlier escalation of care and subsequently decreased mortality.We conducted a retrospective chart review of all admissions to the pediatric cardiology non-ICU service from 2014 to 2018 for children < 18 years old. We defined clinical deterioration as unplanned transfer to the ICU or inpatient mortality. We selected children with SV by diagnosis codes and defined infants as children < 1 year old. We compared demographic, vital sign, and lab values between infants with and without a deterioration event. We evaluated vital sign and medical therapy changes before deterioration events.Among infants with SV (129 deterioration events over 225 admissions, overall 25% with hypoplastic left heart syndrome), those who deteriorated were younger (p = 0.001), had lower baseline oxygen saturation (p = 0.022), and higher baseline respiratory rate (p = 0.022), heart rate (p = 0.023), and hematocrit (p = 0.008). Median Duke Pediatric Early Warning Score increased prior to deterioration (p < 0.001). Deterioration was associated with administration of additional oxygen support (p = 0.012), a fluid bolus (p < 0.001), antibiotics (p < 0.001), vasopressor support (p = 0.009), and red blood cell transfusion (p < 0.001).Infants with SV are at high risk for deterioration. Integrating baseline and dynamic patient data from the electronic health record to identify the highest risk patients may allow for earlier detection and intervention to prevent clinical deterioration.

Perfusion index in newborns with CHD without clinical signs of hypoperfusion and heart failure: comparison with healthy newborns

INTRODUCTION

Peripheral perfusion index has been proposed as a possible method for detecting circulatory impairment. We aimed to determine the normal range of peripheral perfusion index in healthy newborns and compare it with that of newborns with CHD.

METHODS

Right-hand saturation and right-hand peripheral perfusion index levels were recorded, and physical examination and echocardiography were performed in newborns who were 0-28 days old and whom were evaluated in our paediatric cardiology outpatient clinic. The saturation and peripheral perfusion index levels of newborns with normal heart anatomy and function were compared with those of newborns with CHD.

RESULTS

Out of 358 newborns (238 mature and 75 premature) enrolled in the study, 39 had CHD (20 mild CHD, 13 moderate CHD, and 6 severe CHD), of which 29 had CHD with left-to-right shunting, 5 had obstructive CHD, and 5 had cyanotic CHD. No newborn had clinical signs of hypoperfusion or heart failure, such as prolonged capillary refill, weakened pulses, or coldness of extremities. Peripheral perfusion index level was median (interquartile range) 1.7 (0.6) in healthy newborns, 1.8 (0.7) in newborns with mild CHD, and 1.8 (0.4) in newborns with moderate and severe CHD, and there was no significant difference between the groups regarding peripheral perfusion index level.

CONCLUSION

Peripheral perfusion index remains unchanged in newborns with CHD without the clinical signs of hypoperfusion or heart failure. Larger studies with repeated peripheral perfusion index measurements can determine how valuable this method will be in the follow-up of newborns with CHD.

Time series analysis of non-invasive hemodynamic monitoring data in neonates with hypoxic-ischemic encephalopathy

Background and aims

Hemodynamic instability is common in neonates with hypoxic-ischemic encephalopathy (HIE) undergoing therapeutic hypothermia (TH). Rewarming is a critical period and non-invasive circulatory monitoring may help guide cardiovascular supportive therapy. The aim of the study was to provide a comprehensive analysis of cardiac function parameters during TH and its relation to neurodevelopmental outcome.

Methods

In a prospective, observational study, 26 neonates with moderate-severe HIE were enrolled, born between 2016 and 2019. A hemodynamic monitor based on electrical velocimetry (ICON, Osypka Medical GmbH, Berlin, Germany) was used. Heart rate (HR), stroke volume (SV), cardiac output (CO) data were recorded continuously throughout TH and rewarming. Neurological outcome was assessed at 2 years of age using the Bayley Scales of Infant Development II. edition. Favorable outcome was defined as >70 points on both the psychomotor and mental scales. Time-series analysis was used and features of cardiac function were described to perform logistic regression modeling for outcome prediction.

Results

Fourteen (54%) patients had favorable and 12 (46%) had adverse outcome. Data collection started from median [IQR] of 11.8 [7.0; 24.3] hours (h) of life and lasted until 84.0. [81.8; 87.0] h. During TH, the mean HR of the favorable outcome group was significantly lower than that of the adverse outcome group (86 ± 13/min vs. 104 ± 18/min, p = 0.01). During rewarming HR increased similarly in both groups. SV was unaffected by rewarming, and showed a slowly increasing trend. SV of the favorable outcome group was significantly higher compared to the adverse outcome group (1.55 ± 0.23 ml/kg vs. 1.29 ± 0.30 ml/kg, p = 0.035). In line with this, CO was similar in both groups (136 ± 27 ml/kg/min vs. 134 ± 36 ml/kg/min), and a significant 25% increase in CO was observed during rewarming. Based on multiple regression modeling, HR during TH was independently associated with neurological outcome (p = 0.023).

Conclusion

Based on continuous hemodynamic monitoring, patients with adverse outcome have lower SV and higher HR to achieve similar CO to patients with favorable outcome during TH. HR during hypothermia is independently associated with the neurodevelopmental outcome.

Big Data in cardiac surgery: real world and perspectives

Big Data, and the derived analysis techniques, such as artificial intelligence and machine learning, have been considered a revolution in the modern practice of medicine. Big Data comes from multiple sources, encompassing electronic health records, clinical studies, imaging data, registries, administrative databases, patient-reported outcomes and OMICS profiles. The main objective of such analyses is to unveil hidden associations and patterns. In cardiac surgery, the main targets for the use of Big Data are the construction of predictive models to recognize patterns or associations better representing the individual risk or prognosis compared to classical surgical risk scores. The results of these studies contributed to kindle the interest for personalized medicine and contributed to recognize the limitations of randomized controlled trials in representing the real world. However, the main sources of evidence for guidelines and recommendations remain RCTs and meta-analysis. The extent of the revolution of Big Data and new analytical models in cardiac surgery is yet to be determined.

Prediction of extubation failure in the paediatric cardiac ICU using machine learning and high-frequency physiologic data

BACKGROUND

Cardiac intensivists frequently assess patient readiness to wean off mechanical ventilation with an extubation readiness trial despite it being no more effective than clinician judgement alone. We evaluated the utility of high-frequency physiologic data and machine learning for improving the prediction of extubation failure in children with cardiovascular disease.

METHODS

This was a retrospective analysis of clinical registry data and streamed physiologic extubation readiness trial data from one paediatric cardiac ICU (12/2016-3/2018). We analysed patients' final extubation readiness trial. Machine learning methods (classification and regression tree, Boosting, Random Forest) were performed using clinical/demographic data, physiologic data, and both datasets. Extubation failure was defined as reintubation within 48 hrs. Classifier performance was assessed on prediction accuracy and area under the receiver operating characteristic curve.

RESULTS

Of 178 episodes, 11.2% (N = 20) failed extubation. Using clinical/demographic data, our machine learning methods identified variables such as age, weight, height, and ventilation duration as being important in predicting extubation failure. Best classifier performance with this data was Boosting (prediction accuracy: 0.88; area under the receiver operating characteristic curve: 0.74). Using physiologic data, our machine learning methods found oxygen saturation extremes and descriptors of dynamic compliance, central venous pressure, and heart/respiratory rate to be of importance. The best classifier in this setting was Random Forest (prediction accuracy: 0.89; area under the receiver operating characteristic curve: 0.75). Combining both datasets produced classifiers highlighting the importance of physiologic variables in determining extubation failure, though predictive performance was not improved.

CONCLUSION

Physiologic variables not routinely scrutinised during extubation readiness trials were identified as potential extubation failure predictors. Larger analyses are necessary to investigate whether these markers can improve clinical decision-making.

Artificial Intelligence to Improve Health Outcomes in the NICU and PICU: A Systematic Review

CONTEXT

Artificial intelligence (AI) technologies are increasingly used in pediatrics and have the potential to help inpatient physicians provide high-quality care for critically ill children.

OBJECTIVE

We aimed to describe the use of AI to improve any health outcome(s) in neonatal and pediatric intensive care.

DATA SOURCE

PubMed, IEEE Xplore, Cochrane, and Web of Science databases.

STUDY SELECTION

We used peer-reviewed studies published between June 1, 2010, and May 31, 2020, in which researchers described (1) AI, (2) pediatrics, and (3) intensive care. Studies were included if researchers assessed AI use to improve at least 1 health outcome (eg, mortality).

DATA EXTRACTION

Data extraction was conducted independently by 2 researchers. Articles were categorized by direct or indirect impact of AI, defined by the European Institute of Innovation and Technology Health joint report.

RESULTS

Of the 287 publications screened, 32 met inclusion criteria. Approximately 22% (n = 7) of studies revealed a direct impact and improvement in health outcomes after AI implementation. Majority were in prototype testing, and few were deployed into an ICU setting. Among the remaining 78% (n = 25) AI models outperformed standard clinical modalities and may have indirectly influenced patient outcomes. Quantitative assessment of health outcomes using statistical measures, such as area under the receiver operating curve (56%; n = 18) and specificity (38%; n = 12), revealed marked heterogeneity in metrics and standardization.

CONCLUSIONS

Few studies have revealed that AI has directly improved health outcomes for pediatric critical care patients. Further prospective, experimental studies are needed to assess AI's impact by using established implementation frameworks, standardized metrics, and validated outcome measures.

The Compensatory Reserve Index Responds to Acute Hemodynamic Changes in Patients with Congenital Heart Disease: A Proof of Concept Study

Patients with congenital heart disease (CHD) who undergo cardiac procedures may become hemodynamically unstable. Predictive algorithms that utilize dense physiologic data may be useful. The compensatory reserve index (CRI) trends beat-to-beat progression from normovolemia (CRI = 1) to decompensation (CRI = 0) in hemorrhagic shock by continuously analyzing unique sets of features in the changing pulse photoplethysmogram (PPG) waveform. We sought to understand if the CRI accurately reflects changing hemodynamics during and after a cardiac procedure for patients with CHD. A transcatheter pulmonary valve replacement (TcPVR) model was used because left ventricular stroke volume decreases upon sizing balloon occlusion of the right ventricular outflow tract (RVOT) and increases after successful valve placement. A single-center, prospective cohort study was performed. The CRI was continuously measured to determine the change in CRI before and after RVOT occlusion and successful TcPVR. Twenty-six subjects were enrolled with a median age of 19 (interquartile range (IQR) 13-29) years. The mean (± standard deviation) CRI decreased from 0.66 ± 0.15 1-min before balloon inflation to 0.53 ± 0.16 (p = 0.03) 1-min after balloon deflation. The mean CRI increased from a pre-valve mean CRI of 0.63 [95% confidence interval (CI) 0.56-0.70] to 0.77 (95% CI 0.71-0.83) after successful TcPVR. In this study, the CRI accurately reflected acute hemodynamic changes associated with TcPVR. Further research is justified to determine if the CRI can be useful as an early warning tool in patients with CHD at risk for decompensation during and after cardiac procedures.

A primer on artificial intelligence for the paediatric cardiologist

The combination of pediatric cardiology being both a perceptual and a cognitive subspecialty demands a complex decision-making model which makes artificial intelligence a particularly attractive technology with great potential. The prototypical artificial intelligence system would autonomously impute patient data into a collaborative database that stores, syncs, interprets and ultimately classifies the patient's profile to specific disease phenotypes to compare against a large aggregate of shared peer health data and outcomes, the current medical body of literature and ongoing trials to offer morbidity and mortality prediction, drug therapy options targeted to each patient's genetic profile, tailored surgical plans and recommendations for timing of sequential imaging. The focus of this review paper is to offer a primer on artificial intelligence and paediatric cardiology by briefly discussing the history of artificial intelligence in medicine, modern and future applications in adult and paediatric cardiology across selected concentrations, and current barriers to implementation of these technologies.

Augmented intelligence in pediatric anesthesia and pediatric critical care

PURPOSE OF REVIEW

Acute care technologies, including novel monitoring devices, big data, increased computing capabilities, machine-learning algorithms and automation, are converging. This enables the application of augmented intelligence for improved outcome predictions, clinical decision-making, and offers unprecedented opportunities to improve patient outcomes, reduce costs, and improve clinician workflow. This article briefly explores recent work in the areas of automation, artificial intelligence and outcome prediction models in pediatric anesthesia and pediatric critical care.

RECENT FINDINGS

Recent years have yielded little published research into pediatric physiological closed loop control (a type of automation) beyond studies focused on glycemic control for type 1 diabetes. However, there has been a greater range of research in augmented decision-making, leveraging artificial intelligence and machine-learning techniques, in particular, for pediatric ICU outcome prediction.

SUMMARY

Most studies focusing on artificial intelligence demonstrate good performance on prediction or classification, whether they use traditional statistical tools or novel machine-learning approaches. Yet the challenges of implementation, user acceptance, ethics and regulation cannot be underestimated. Areas in which there is easy access to routinely labeled data and robust outcomes, such as those collected through national networks and quality improvement programs, are likely to be at the forefront of the adoption of these advances.

A deep learning model for real-time mortality prediction in critically ill children

Background

The rapid development in big data analytics and the data-rich environment of intensive care units together provide unprecedented opportunities for medical breakthroughs in the field of critical care. We developed and validated a machine learning-based model, the Pediatric Risk of Mortality Prediction Tool (PROMPT), for real-time prediction of all-cause mortality in pediatric intensive care units.

Methods

Utilizing two separate retrospective observational cohorts, we conducted model development and validation using a machine learning algorithm with a convolutional neural network. The development cohort comprised 1445 pediatric patients with 1977 medical encounters admitted to intensive care units from January 2011 to December 2017 at Severance Hospital (Seoul, Korea). The validation cohort included 278 patients with 364 medical encounters admitted to the pediatric intensive care unit from January 2016 to November 2017 at Samsung Medical Center.

Results

Using seven vital signs, along with patient age and body weight on intensive care unit admission, PROMPT achieved an area under the receiver operating characteristic curve in the range of 0.89–0.97 for mortality prediction 6 to 60 h prior to death. Our results demonstrated that PROMPT provided high sensitivity with specificity and outperformed the conventional severity scoring system, the Pediatric Index of Mortality, in predictive ability. Model performance was indistinguishable between the development and validation cohorts.

Conclusions

PROMPT is a deep model-based, data-driven early warning score tool that can predict mortality in critically ill children and may be useful for the timely identification of deteriorating patients.

Electronic supplementary material

The online version of this article (10.1186/s13054-019-2561-z) contains supplementary material, which is available to authorized users.

A framework to characterize the performance of early warning index alarm systems for patient monitoring

In [Scully, C.G., and Daluwatte, C., Evaluating performance of early warning indices to predict physiological instabilities. J Biomed Inform. 75 (2017) 14–21], a framework was presented to characterize the performance of warning indices to provide information on the 1) probability a critical health event will occur when a warning is given (analogous to positive predictive value) and 2) proportion of warned events to all events (analogous to sensitivity). This framework also provides information about the timeliness of the warnings with respect to event occurrence and the warning burden of the system.

•

In the current work, we provide information on how this framework can be used when cases without events are present in a dataset to examine the proportion of warned non-events to all non-events (analogous to false positive rate).

•

Information on steps to apply the method, software, data and results for the case study are also provided to enable implementation of the framework.

•

Application and extension of the framework is demonstrated and discussed by adding non-event records to our previous case study comparing two warning strategies to predict physiologic instabilities.

A Narrative Review of Analytics in Pediatric Cardiac Anesthesia and Critical Care Medicine

Congenital heart disease (CHD) is one of the most common birth anomalies, and the care of children with CHD has improved over the past 4 decades. However, children with CHD who undergo general anesthesia remain at increased risk for morbidity and mortality. The proliferation of electronic health record systems and sophisticated patient monitors affords the opportunity to capture and analyze large amounts of CHD patient data, and the application of novel, effective analytics methods to these data can enable clinicians to enhance their care of pediatric CHD patients. This narrative review covers recent efforts to leverage analytics in pediatric cardiac anesthesia and critical care to improve the care of children with CHD.