Complex decision making in an intensive care environment: Perceived practice versus observed reality.

RATIONALE: Advancing our understanding of how decisions are made in cognitively, socially and technologically complex hospital environments may reveal opportunities to improve healthcare delivery, medical education and the experience of patients, families and clinicians. AIMS AND OBJECTIVES: Explore factors impacting clinician decision making in the Boston Children's Hospital Cardiac Intensive Care Unit. METHODS: A convergent mixed methods design was used. Quantitative and qualitative data sources consisted of a faculty survey, direct observations of clinical rounds in a specific patient population identified by a clinical decision support system (CDSS) and semistructured interviews (SSIs). Deductive and inductive coding was used for qualitative data. Qualitative data were translated into images using social network analysis which illustrate the frequency and connectivity of the codes in each data set. RESULTS: A total of 25 observations of eight faculty-led interprofessional teams were performed between 12 February and 31 March 2021. Individual patient characteristics were noted by faculty in SSIs to be the most important factor in their decision making, yet ethnographic observations suggested faculty cognitive traits, team expertise and value-based decisions were more heavily weighted. The development of expertise was impacted by role modeling. Decisions were perceived to be influenced by the system and environment. CONCLUSIONS: Clinician perception of decision making was not congruent with the observed behaviours in a complicated and dynamic system. This study identifies important considerations in clinical curricula as well as the design and implementation of CDSS. Our method of using social network analysis to visualize components of decision making could be adopted to explore other complex environments.

The truth Hertz-synchronization of electroencephalogram signals with physiological waveforms recorded in an intensive care unit.

Objective.The ability to synchronize continuous electroencephalogram (cEEG) signals with physiological waveforms such as electrocardiogram (ECG), invasive pressures, photoplethysmography and other signals can provide meaningful insights regarding coupling between brain activity and other physiological subsystems. Aligning these datasets is a particularly challenging problem because device clocks handle time differently and synchronization protocols may be undocumented or proprietary.Approach.We used an ensemble-based model to detect the timestamps of heartbeat artefacts from ECG waveforms recorded from inpatient bedside monitors and from cEEG signals acquired using a different device. Vectors of inter-beat intervals were matched between both datasets and robust linear regression was applied to measure the relative time offset between the two datasets as a function of time.Main Results.The timing error between the two unsynchronized datasets ranged between -84 s and +33 s (mean 0.77 s, median 4.31 s, IQR25-4.79 s, IQR75 11.38s). Application of our method improved the relative alignment to within ± 5ms for more than 61% of the dataset. The mean clock drift between the two datasets was 418.3 parts per million (ppm) (median 414.6 ppm, IQR25 411.0 ppm, IQR75 425.6 ppm). A signal quality index was generated that described the quality of alignment for each cEEG study as a function of time.Significance.We developed and tested a method to retrospectively time-align two clinical waveform datasets acquired from different devices using a common signal. The method was applied to 33,911h of signals collected in a paediatric critical care unit over six years, demonstrating that the method can be applied to long-term recordings collected under clinical conditions. The method can account for unknown clock drift rates and the presence of discontinuities caused by clock resynchronization events.

Effects of Unit Census and Patient Acuity Levels on Discussions During Patient Rounds.

OBJECTIVES: PICU teams adapt the duration of patient rounding discussions to accommodate varying contextual factors, such as unit census and patient acuity. Although studies establish that shorter discussions can lead to the omission of critical patient information, little is known about how teams adapt their rounding discussions about essential patient topics (i.e., introduction/history, acute clinical status, care plans) in response to changing contexts. To fill this gap, we examined how census and patient acuity impact time spent discussing essential topics during individual patient encounters. DESIGN: Observational study. SETTING: PICU at a university-affiliated children's hospital, Toronto, ON, Canada. SUBJECTS: Interprofessional morning rounding teams. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We observed 165 individual patient encounters during morning rounds over 10 weeks. Regardless of census or patient acuity, the duration of patient introductions/history did not change. When census was high versus low, acute clinical status discussions significantly decreased for both low acuity patients (00 min:50 s high census; 01 min:39 s low census; -49.5% change) and high acuity patients (01 min:10 s high census; 02 min:02 s low census; -42.6% change). Durations of care plan discussions significantly reduced as a function of census (01 min:19 s high census; 02 min:52 s low census; -54.7% change) for low but not high acuity patients. CONCLUSIONS: Under high census and patient acuity levels, rounding teams disproportionately shorten time spent discussing essential patient topics. Of note, while teams preserved time to plan the care for acute patients, they cut care plan discussions of low acuity patients. This study provides needed detail regarding how rounding teams adapt their discussions of essential topics and establishes a foundation for consideration of varying contextual factors in the design of rounding guidelines. As ICUs are challenged with increasing census and patient acuity levels, it is critical that we turn our attention to these contextual aspects and understand how these adaptations impact clinical outcomes to address them.

Comparison between Del Nido and conventional blood cardioplegia in pediatric open-heart surgery.

BACKGROUND: Del Nido cardioplegia (DNc) was designed for superior myocardial protection during cardiopulmonary bypass (CPB). We conducted a retrospective review to explore if DNc was associated with increase in systemic ventricle dysfunction (sVD) following pediatric CPB. METHODS AND RESULTS: This single-center, retrospective study included 1534 patients undergoing CPB between 2013 and 2016, 997 prior to center-wide conversion to DNc and 537 following. The primary outcome was new postoperative ≥moderate sVD by echocardiogram. Secondary outcomes included sVD of any severity and right ventricular dysfunction. Data was evaluated by interrupted time-series analysis. Groups had similar cardiac diagnoses and surgical complexity. Del Nido cardioplegia was associated with longer median (IQR) CPB [117 (84-158) vs 108 (81-154), p = 0.04], and aortic cross-clamp [83 (55-119) vs 76 (53-106), p = 0.03], and fewer cardioplegia doses [2 (1-2) vs 3 (2-4), p < 0.0001]. Mortality was similar in both groups. Frequency of sVD was unchanged following DNc, including predetermine subgroups (neonates, infants, and prolonged cross-clamp). Logistic regression showed a significant rise in right ventricular dysfunction (OR 5.886 [95% CI: 0.588, 11.185], p = 0.03) but similar slope. CONCLUSIONS: Use of DNc was not associated with increased in reported sVD, and provided similar myocardical protection to the systemic ventricle compared to conventional cardioplegia but may possibly impact right ventricular function. Studies evaluating quantitative systolic and diastolic function are needed.

Decision-centered design of a clinical decision support system for acute management of pediatric congenital heart disease.

Background and Objectives: Children with congenital heart disease (CHD), have fragile hemodynamics and can deteriorate due to common childhood illnesses and the natural progression of their disease. During these acute periods of deterioration, these children often present to their local emergency departments (ED) where expertise in CHD is limited, and appropriate intervention is crucial to their survival. Previous studies identified that determining the appropriate intervention for CHD patients can be difficult for ED physicians, particularly since key components of effective decision making are not being met. Although key components of effective decision making for ED physicians have been identified, they have yet to be transformed into actionable guidance. We used decision centered design (DCD) to translate key components of decision making into decision requirements and associated design concepts, that we subsequently incorporated into a prototype clinical decision support system (CDSS). Methods: Using framework analysis, transcripts from Critical Decision Method interviews of CHD experts and ED physicians were inductively coded to identify key decision requirements for ED physicians that are currently not well supported, and their associated design concepts. A design workshop was held to refine the identified key decision requirements and design concepts as well as to sketch information that would satisfy the identified requirements. These were iteratively incorporated into a prototype CDSS. Results: Three decision requirements: (1) distinguish the patient's unique physiology based on their unique cardiac anatomy, (2) explicitly consider CHD specific differential diagnoses to allow a more structured reflection of diagnosis, and (3) select CHD appropriate interventions for each patient, were identified. These requirements along with design concepts and information needs identified through the design workshop were incorporated into the CDSS prototype. Conclusion: We identified key decision requirements and associated design concepts, that informed the design of a CDSS to provide actionable guidance for ED physicians when managing CHD patients. Meeting ED physicians' decision components with a CDSS requires the translation of their key decision requirements in its design. If not, we risk creating designs that interfere with clinician performance.

Analyzing Continuous Physiologic Data to Find Hemodynamic Signatures Associated With New Brain Injury After Congenital Heart Surgery.

Continuous data capture technology is becoming more common. Establishing analytic approaches for continuous data could aid in understanding the relationship between physiology and clinical outcomes. OBJECTIVES: Our objective was to design a retrospective analysis for continuous physiologic measurements and their relationship with new brain injury over time after cardiac surgery. DESIGN SETTING AND PARTICIPANTS: Retrospective cohort study in the Cardiac Critical Care Unit at the Hospital for Sick Children in patients after repair of transposition of the great arteries (TGA) or single ventricle (SV) lesions. MAIN OUTCOMES AND MEASURES: Continuously acquired physiologic measurements for up to 72 hours after cardiac surgery were analyzed for association with new brain injury by MRI. Distributions of heart rate (HR), systolic blood pressure (BP), and oxygen saturation (Spo2) for SV and TGA were analyzed graphically and with descriptive statistics over postoperative time for data-driven variable selection. Mixed-effects regression analyses characterized relationships between HR, BP, and Spo2 and new brain injury over time while accounting for variation between patients, measurement heterogeneity, and missingness. RESULTS: Seventy-seven patients (60 TGA; 17 SV) were included. New brain injury was seen in 26 (34%). In SV patients, with and without new brain injury, respectively, in the first 24 hours after cardiac surgery, the median (interquartile range) HR was 172.0 beats/min (bpm) (169.7-176.0 bpm) versus 159.6 bpm (145.0-167.0 bpm); systolic BP 74.8 (67.9-78.5 mm Hg) versus 68.9 mm Hg (61.6-70.9 mm Hg). Higher postoperative HR (parameter estimate, 19.4; 95% CI, 7.8-31; p = 0.003 and BP, 8.6; 1.3-15.8; p = 0.024) were associated with new brain injury in SV patients. The strength of this relationship decreased with time. CONCLUSIONS AND RELEVANCE: Retrospective analysis of continuous physiologic measurements can provide insight into changes in postoperative physiology over time and their relationship with new brain injury. This technique could be applied to assess relationships between physiologic data and many patient interventions or outcomes.

Timing errors and temporal uncertainty in clinical databases-A narrative review.

A firm concept of time is essential for establishing causality in a clinical setting. Review of critical incidents and generation of study hypotheses require a robust understanding of the sequence of events but conducting such work can be problematic when timestamps are recorded by independent and unsynchronized clocks. Most clinical models implicitly assume that timestamps have been measured accurately and precisely, but this custom will need to be re-evaluated if our algorithms and models are to make meaningful use of higher frequency physiological data sources. In this narrative review we explore factors that can result in timestamps being erroneously recorded in a clinical setting, with particular focus on systems that may be present in a critical care unit. We discuss how clocks, medical devices, data storage systems, algorithmic effects, human factors, and other external systems may affect the accuracy and precision of recorded timestamps. The concept of temporal uncertainty is introduced, and a holistic approach to timing accuracy, precision, and uncertainty is proposed. This quantitative approach to modeling temporal uncertainty provides a basis to achieve enhanced model generalizability and improved analytical outcomes.

An integration engineering framework for machine learning in healthcare.

Background and Objectives: Machine Learning offers opportunities to improve patient outcomes, team performance, and reduce healthcare costs. Yet only a small fraction of all Machine Learning models for health care have been successfully integrated into the clinical space. There are no current guidelines for clinical model integration, leading to waste, unnecessary costs, patient harm, and decreases in efficiency when improperly implemented. Systems engineering is widely used in industry to achieve an integrated system of systems through an interprofessional collaborative approach to system design, development, and integration. We propose a framework based on systems engineering to guide the development and integration of Machine Learning models in healthcare. Methods: Applied systems engineering, software engineering and health care Machine Learning software development practices were reviewed and critically appraised to establish an understanding of limitations and challenges within these domains. Principles of systems engineering were used to develop solutions to address the identified problems. The framework was then harmonized with the Machine Learning software development process to create a systems engineering-based Machine Learning software development approach in the healthcare domain. Results: We present an integration framework for healthcare Artificial Intelligence that considers the entirety of this system of systems. Our proposed framework utilizes a combined software and integration engineering approach and consists of four phases: (1) Inception, (2) Preparation, (3) Development, and (4) Integration. During each phase, we present specific elements for consideration in each of the three domains of integration: The Human, The Technical System, and The Environment. There are also elements that are considered in the interactions between these domains. Conclusion: Clinical models are technical systems that need to be integrated into the existing system of systems in health care. A systems engineering approach to integration ensures appropriate elements are considered at each stage of model design to facilitate model integration. Our proposed framework is based on principles of systems engineering and can serve as a guide for model development, increasing the likelihood of successful Machine Learning translation and integration.

Creating a health equity and inclusion office in an academic pediatric medical center: priorities addressed and lessons learned.

BACKGROUND: Over the last two decades, medical schools and academic health centers have acknowledged the persistence of health disparities in their patients and the lack of diversity in their faculty, leaders and extended workforce. We established an Office of Health Equity and Inclusion (OHEI) at our pediatric academic medical center after a thorough evaluation of prior diversity initiatives and review of faculty development data. OBJECTIVE: To describe the lessons learned at a pediatric academic medical center in prioritizing and implementing health equity, diversity and inclusion (EDI) initiatives in creating the OHEI. MATERIALS AND METHODS: We reviewed internal administrative data and faculty development data, including data related to faculty who are underrepresented in medicine, to understand the role of our EDI initiatives in the strategic priorities addressed and lessons learned in the creation of the OHEI. RESULTS: The intentional steps taken in our medical center's strategic approach in the creation of this office led to four important lessons to improve pediatric health equity: (1) board, senior executive and institutional prioritization of EDI initiatives; (2) multi-specialty and interprofessional collaboration; (3) academic approach to EDI programmatic development; and (4) intentionality with accountability in all EDI initiatives. CONCLUSION: The key lessons learned during the creation of an Office of Health Equity and Inclusion can provide guidance to other academic health centers committed to implementing institutional priorities that focus their EDI initiatives on the improvement of pediatric health equity.

Understanding Clinician Macrocognition to Inform the Design of a Congenital Heart Disease Clinical Decision Support System.

BACKGROUND AND OBJECTIVES: Children with congenital heart disease (CHD) are at risk of deterioration in the face of common childhood illnesses, and their resuscitation and acute treatment requires guidance of CHD experts. Many children with CHD, however, present to their local emergency departments (ED) with gastrointestinal and respiratory symptoms that closely mimic symptoms of CHD related heart failure. This can lead to incorrect or delayed diagnosis and treatment where CHD expertise is limited. An understanding of the differences in cognitive decision-making processes between CHD experts and ED physicians can inform how best to support ED physicians when treating CHD patients. METHODS: Cardiac intensivists (CHD experts) and pediatric emergency department physicians (ED physicians) in a major academic cardiac center were interviewed using the critical decision method. Interview transcripts were coded deductively based on Schubert and Klein's macrocognitive frameworks and inductively to allow for new or modified characterization of dimensions. RESULTS: In total, 6 CHD experts and 7 ED physicians were interviewed for this study. Although both CHD experts and ED physicians spent a lot of time sensemaking, their approaches to sensemaking differed. CHD experts reported readily recognizing the physiology of complex congenital heart disease and focused primarily on ruling out cardiac causes for the presenting illness. ED physicians reported a delay in attributing the signs and symptoms of the presenting illness to congenital heart disease, because these clinical findings were often non-specific, and thus explored different diagnoses. CHD experts moved quickly to treatment and more time anticipating potential problems and making specific contingency plans, while ED physicians spent more time gathering a range of data prior to arriving at a diagnosis. These findings were then applied to develop a prototype web-based decision support application for patients with CHD. CONCLUSION: There are differences in the cognitive processes used by CHD experts and ED physicians when managing CHD patients. An understanding of differences in the cognitive processes used by CHD experts and ED physicians can inform the development of potential interventions, such as clinical decision support systems and training pathways, to support decision making pertaining to the acute treatment of pediatric CHD patients.

Associations With Extubation Failure and Predictive Value of Risk Analytics Algorithms With Extubation Readiness Tests Following Congenital Cardiac Surgery.

OBJECTIVES: Extubation failure is associated with morbidity and mortality in children following cardiac surgery. Current extubation readiness tests (ERT) do not consider the nonrespiratory support provided by mechanical ventilation (MV) for children with congenital heart disease. We aimed to identify factors associated with extubation failure in children following cardiac surgery and assess the performance of two risk analytics algorithms for patients undergoing an ERT. DESIGN: Retrospective cohort study. SETTING: CICU at a tertiary-care children's hospital. PATIENTS: Children receiving MV greater than 48 hours following cardiac surgery between January 1, 2017, and December 31, 2019. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Six hundred fifty encounters were analyzed with 49 occurrences (8%) of reintubation. Extubation failure occurred most frequently within 6 hours of extubation. On multivariable analysis, younger age (per each 3-mo decrease: odds ratio [OR], 1.06; 95% CI, 1.001-1.12), male sex (OR, 2.02; 95% CI, 1.03-3.97), Society of Thoracic Surgery-European Association for Cardiothoracic Surgery category 5 procedure (p equals to 0.005), and preoperative respiratory support (OR, 2.08; 95% CI, 1.09-3.95) were independently associated with unplanned reintubation. Our institutional ERT had low sensitivity to identify patients at risk for reintubation (23.8%; 95% CI, 9.7-47.6%). The addition of the inadequate delivery of oxygen (IDO2) index to the ERT increased the sensitivity by 19.0% (95% CI, -2.5 to 40.7%; p = 0.05), but the sensitivity remained low and the accuracy of the test dropped by 8.9% (95% CI, 4.7-13.1%; p < 0.01). CONCLUSIONS: Preoperative respiratory support, younger age, and more complex operations are associated with postoperative extubation failure. IDO2 and IVCO2 provide unique cardiorespiratory monitoring parameters during ERTs but require further investigation before being used in clinical evaluation for extubation failure.

Effect of clinical decision support systems on emergency medicine physicians' decision-making: A pilot scenario-based simulation study.

Background and objectives: Children with congenital heart disease (CHD) are predisposed to rapid deterioration in the face of common childhood illnesses. When they present to their local emergency departments (ED) with acute illness, rapid and accurate diagnosis and treatment is crucial to recovery and survival. Previous studies have shown that ED physicians are uncomfortable caring for patients with CHD and there is a lack of actionable guidance to aid in their decision making. To support ED physicians' key decision components (sensemaking, anticipation, and managing complexity) when managing CHD patients, a Clinical Decision Support System (CDSS) was previously designed. This pilot study evaluates the effect of this CDSS on ED physicians' decision making compared to usual care without clinical decision support. Methods: In a pilot scenario-based simulation study with repeated measures, ED physicians managed mock CHD patients with and without the CDSS. We compared ED physicians' CHD-specific and general decision-making processes (e.g., recognizing sepsis, starting antibiotics, and managing symptoms) with and without the use of CDSS. The frequency of participants' utterances related to each key decision components of sensemaking, anticipation, and managing complexity were coded and statistically analyzed for significance. Results: Across all decision-making components, the CDSS significantly increased ED physicians' frequency of "CHD specific utterances" (Mean = 5.43, 95%CI: 3.7-7.2) compared to the without CDSS condition (Mean = 2.05, 95%CI: 0.3-3.8) whereas there was no significant difference in frequencies of "general utterances" when using CDSS (Mean = 4.62, 95%CI: 3.1-6.1) compared to without CDSS (Mean = 5.14 95%CI: 4.4-5.9). Conclusion: A CDSS that integrates key decision-making components (sensemaking, anticipation, and managing complexity) can trigger and enrich communication between clinicians and enhance the clinical management of CHD patients. For patients with complex and subspecialized diseases such as CHD, a well-designed CDSS can become part of a multifaceted solution that includes knowledge translation, broader communication around interpretation of information, and access to additional expertise to support CHD specific decision-making.

Human Factors Analysis of Latent Safety Threats in a Pediatric Critical Care Unit.

OBJECTIVES: To identify unique latent safety threats spanning routine pediatric critical care activities and categorize them according to their underlying work system factors (i.e., "environment, organization, person, task, tools/technology") and associated clinician behavior (i.e., "legal": expected compliance with or "illegal-normal": deviation from and "illegal-illegal": disregard for standard policies and protocols). DESIGN: A prospective observational study with contextual inquiry of clinical activities over a 5-month period. SETTING: Two PICUs (i.e., medical-surgical ICU and cardiac ICU) in an urban free-standing quaternary children's hospital. SUBJECTS: Attending physicians and trainees, nurse practitioners, registered nurses, respiratory therapists, dieticians, pharmacists, and patient services assistants were observed. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Conducted 188 hours of observations to prospectively identify unique latent safety threats. Qualitative observational notes were analyzed by human factors experts using a modified framework analysis methodology to summarize latent safety threats and categorize them based on associated clinical activity, predominant work system factor, and clinician behavior. Two hundred twenty-six unique latent safety threats were observed. The latent safety threats were categorized into 13 clinical activities and attributed to work system factors as follows: "organization" (n = 83; 37%), "task" (n = 52; 23%), "tools/technology" (n = 40; 18%), "person" (n = 32; 14%), and "environment" (n = 19; 8%). Twenty-three percent of latent safety threats were identified when staff complied with policies and protocols (i.e., "legal" behavior) and 77% when staff deviated from policies and protocols (i.e., "illegal-normal" behavior). There was no "illegal-illegal" behavior observed. CONCLUSIONS: Latent safety threats span various pediatric critical care activities and are attributable to many underlying work system factors. Latent safety threats are present both when staff comply with and deviate from policies and protocols, suggesting that simply reinforcing compliance with existing policies and protocols, the common default intervention imposed by healthcare organizations, will be insufficient to mitigate safety threats. Rather, interventions must be designed to address the underlying work system threats. This human factors informed framework analysis of observational data is a useful approach to identifying and understanding latent safety threats and can be used in other clinical work systems.

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

OBJECTIVES: 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.

Shock Index, Coronary Perfusion Pressure, and Rate Pressure Product As Predictors of Adverse Outcome After Pediatric Cardiac Surgery.

OBJECTIVES: To determine whether shock index, coronary perfusion pressure, or rate pressure product in the first 24 hours after congenital heart surgery are independent predictors of subsequent clinically significant adverse outcomes. DESIGN: A retrospective cohort study. SETTING: A tertiary care center. PATIENTS: All patients less than 18 years old who underwent cardiac surgery at Boston Children's Hospital between January 1, 2010, and December 31, 2018. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Shock index (heart rate/systolic blood pressure), coronary perfusion pressure (diastolic blood pressure-right atrial pressure), and rate pressure product (heart rate × systolic blood pressure) were calculated every 5 seconds, and the median value for the first 24 hours of cardiac ICU admission for each was used as a predictor. The composite, primary outcome was the occurrence of any of the following adverse events in the first 7 days following cardiac ICU admission: cardiopulmonary resuscitation, extracorporeal cardiopulmonary resuscitation, mechanical circulatory support, unplanned surgery, heart transplant, or death. The association of each variable of interest with this outcome was tested in a multivariate logistic regression model. Of the 4,161 patients included, 296 (7%) met the outcome within the specified timeframe. In a multivariate regression model adjusted for age, surgical complexity, inotropic and respiratory support, and organ dysfunction, shock index greater than 1.83 was significantly associated with the primary outcome (odds ratio, 6.6; 95% CI, 4.4-10.0), and coronary perfusion pressure greater than 35 mm Hg was protective against the outcome (odds ratio, 0.5; 0.4-0.7). Rate pressure product was not found to be associated with the outcome. However, the predictive ability of the shock index and coronary perfusion pressure models were not superior to their component hemodynamic variables alone. CONCLUSIONS: Both shock index and coronary perfusion pressure may offer predictive value for adverse outcomes following cardiac surgery in children, although they are not superior to the primary hemodynamic variables.

A practical approach to storage and retrieval of high-frequency physiological signals.

OBJECTIVE: Storage of physiological waveform data for retrospective analysis presents significant challenges. Resultant data can be very large, and therefore becomes expensive to store and complicated to manage. Traditional database approaches are not appropriate for large scale storage of physiological waveforms. Our goal was to apply modern time series compression and indexing techniques to the problem of physiological waveform storage and retrieval. APPROACH: We deployed a vendor-agnostic data collection system and developed domain-specific compression approaches that allowed long term storage of physiological waveform data and other associated clinical and medical device data. The database (called AtriumDB) also facilitates rapid retrieval of retrospective data for high-performance computing and machine learning applications. MAIN RESULTS: A prototype system has been recording data in a 42-bed pediatric critical care unit at The Hospital for Sick Children in Toronto, Ontario since February 2016. As of December 2019, the database contains over 720,000 patient-hours of data collected from over 5300 patients, all with complete waveform capture. One year of full resolution physiological waveform storage from this 42-bed unit can be losslessly compressed and stored in less than 300 GB of disk space. Retrospective data can be delivered to analytical applications at a rate of up to 50 million time-value pairs per second. SIGNIFICANCE: Stored data are not pre-processed or filtered. Having access to a large retrospective dataset with realistic artefacts lends itself to the process of anomaly discovery and understanding. Retrospective data can be replayed to simulate a realistic streaming data environment where analytical tools can be rapidly tested at scale.

Inadequate oxygen delivery index dose is associated with cardiac arrest risk in neonates following cardiopulmonary bypass surgery.

AIM: To evaluate the Inadequate oxygen delivery (IDO2) index dose as a predictor of cardiac arrest (CA) in neonates following congenital heart surgery. METHODS: Retrospective cohort study in 3 US pediatric cardiac intensive units (1/2011- 8/2016). Calculated IDO2 index values were blinded to bedside clinicians and generated from data collected up to 30 days postoperatively, or until death or ECMO initiation. Control event data was collected from patients who did not experience CA or require ECMO. IDO2 dose was computed over a 120-min window up to 30 min prior to the CA and control events. A multivariate logistic regression prediction model including the IDO2 dose and presence or absence of a single ventricle (SV) was used. Model performance metrics were the odds ratio for each regression coefficient and receiver operating characteristic area under the curve (ROC AUC). RESULTS: Of 897 patients monitored during the study period, 601 met inclusion criteria: 29 patients had CA (33 events) and 572 patients were used for control events. Seventeen (59%) CA and 125 (26%) control events occurred in SV patients. Median age/weight at surgery and level of monitoring were similar in both groups. Median postoperative event time was 0.73 days [0.05-22.39] in CA patients and 0.82 days [0.08 25.11] in control patients. Odds ratio of the IDO2 dose coefficient was 1.008 (95% CI: 1.006-1.012, p = 0.0445), and 2.952 (95% CI: 2.952-3.258, p = 0.0079) in SV. The ROC AUC using both coefficients was 0.74 (95% CI: 0.73-0.75). These associations of IDO2 dose with CA risk remained robust, even when censored periods prior to arrest were 10 and 20 min. CONCLUSION: In neonates post-CPB surgery, higher IDO2 index dose over a 120-min monitoring period is associated with increased risk of cardiac arrest, even when censoring data 10, 20 or 30 min prior to the CA event.