Search

ObjectiveTo quantify the accuracy of and clinical events associated with a risk alert threshold for impending hypoglycemia during ICU admissions.DesignRetrospective electronic health record review of clinical events occurring ≥1 and ≤12 hours after the hypoglycemia risk alert threshold was met.SettingAdult ICU admissions from June 2020 through April 2021 at the University of Virginia Medical Center.Patients342 critically-ill adults that were 63.5% male with median age 60.8 years, median weight 79.1 kg, and median body mass index of 27.5 kg/m2.InterventionsReal-world testing of our validated predictive model as a clinical decision support tool for ICU hypoglycemia.Measurements and Main ResultsWe retrospectively reviewed 350 hypothetical alerts that met inclusion criteria for analysis. The alerts correctly predicted 48 cases of Level 1 hypoglycemia that occurred ≥1 and ≤12 hours after the alert threshold was met (positive predictive value= 13.7%). Twenty-one of these 48 cases (43.8%) involved Level 2 hypoglycemia. Notably, three myocardial infarctions, one medical emergency team call, two initiations of cardiopulmonary resuscitation, 6 unplanned surgeries, 19 deaths, 20 arrhythmias, and 38 blood or urine cultures were identified or obtained ≥1 and ≤12 hours after an alert threshold was met. Alerts identified 102 total events of hypoglycemia and/or clinical deterioration, yielding a positive predictive value for any event of 29.1%.ConclusionsAlerts generated by a validated ICU hypoglycemia prediction model had positive predictive value of 29.1% for hypoglycemia and other associated adverse clinical events.Key PointsQuestionWhat are the accuracy of and clinical events associated with a risk alert threshold for ICU hypoglycemia?FindingsWe retrospectively reviewed 350 hypothetical alerts that correctly predicted 48 cases of Level 1 hypoglycemia occurring ≥1 and ≤12 hours after the alert threshold was met (positive predictive value= 13.7%). Notably, three myocardial infarctions, one medical emergency team call, two initiations of cardiopulmonary resuscitation, 6 unplanned surgeries, 19 deaths, 20 arrhythmias, and 38 blood or urine cultures were identified or obtained ≥1 and ≤12 hours after an alert threshold was met.MeaningAlerts generated by a validated ICU hypoglycemia prediction model had positive predictive value of 29.1% for hypoglycemia and other associated adverse clinical events.

BackgroundProgressive hypoxemia is the predominant mode of deterioration in COVID-19. Among hypoxemia measures, the ratio of the partial pressure of arterial oxygen to the fraction of inspired oxygen (P/F ratio) has optimal construct validity but poor availability because it requires arterial blood sampling. Pulse oximetry reports oxygenation continuously, but occult hypoxemia can occur in Black patients because the technique is affected by skin color. Oxygen dissociation curves allow non-invasive estimation of P/F ratios (ePFR) but this approach remains unproven.Research QuestionCan ePFRs measure overt and occult hypoxemia?Study Design and methodsWe retrospectively studied COVID-19 hospital encounters (n=5319) at two academic centers (University of Virginia [UVA] and Emory University). We measured primary outcomes (death or ICU transfer within 24 hours), ePFR, conventional hypoxemia measures, baseline predictors (age, sex, race, comorbidity), and acute predictors (National Early Warning Score (NEWS) and Sepsis-3). We updated predictors every 15 minutes. We assessed predictive validity using adjusted odds ratios (AOR) and area under receiver operating characteristics curves (AUROC). We quantified disparities (Black vs non-Black) in empirical cumulative distributions using the Kolmogorov-Smirnov (K-S) two-sample test.ResultsOvert hypoxemia (low ePFR) predicted bad outcomes (AOR for a 100-point ePFR drop: 2.7 [UVA]; 1.7 [Emory]; pInterpretationThe ePFR was a valid measure of overt hypoxemia. In COVID-19, it may outperform multi-organ dysfunction models like NEWS and Sepsis-3. By accounting for biased oximetry as well as clinicians’ real-time responses to it (supplemental oxygen adjustment), ePFRs may enable statistical modelling of racial disparities in outcomes attributable to occult hypoxemia.

Misidentification of illness severity may lead to patients being admitted to a ward bed then unexpectedly transferring to an ICU as their condition deteriorates. Our objective was to develop a predictive analytic tool to identify emergency department (ED) patients that required upgrade to an intensive or intermediate care unit (ICU or IMU) within 24 h after being admitted to an acute care floor. We conducted a single-center retrospective cohort study to identify ED patients that were admitted to an acute care unit and identified cases where the patient was upgraded to ICU or IMU within 24 h. We used data available at the time of admission to build a logistic regression model that predicts early ICU transfer. We found 42,332 patients admitted between January 2012 and December 2016. There were 496 cases (1.2%) of early ICU transfer. Case patients had 18.0-fold higher mortality (11.1% vs. 0.6%, p

Early recognition of patients at risk for sepsis is paramount to improve clinical outcomes. We hypothesized that subtle signatures of illness are present in physiological and biochemical time series of pediatric-intensive care unit (PICU) patients in the early stages of sepsis. We developed multivariate models in a retrospective observational cohort to predict the clinical diagnosis of sepsis in children. We focused on age as a predictor and asked whether random forest models, with their potential for multiple cut points, had better performance than logistic regression. One thousand seven hundred and eleven admissions for 1425 patients admitted to a mixed cardiac and medical/surgical PICU were included. We identified, through individual chart review, 187 sepsis diagnoses that were not within 14 days of a prior sepsis diagnosis. Multivariate models predicted sepsis in the next 24 h: cross-validated C-statistic for logistic regression and random forest were 0.74 (95% confidence interval (CI): 0.71–0.77) and 0.76 (95% CI: 0.73–0.79), respectively. Statistical models based on physiological and biochemical data already available in the PICU identify high-risk patients up to 24 h prior to the clinical diagnosis of sepsis. The random forest model was superior to logistic regression in capturing the context of age.

Patients who deteriorate while on the acute care ward and are emergently transferred to the Intensive Care Unit (ICU) experience high rates of mortality. To date, risk scores for clinical deterioration applied to the acute care wards rely on static or intermittent inputs of vital sign and assessment parameters. We propose the use of continuous predictive analytics monitoring, or data that relies on real-time physiologic monitoring data captured from ECG, documented vital signs, laboratory results, and other clinical assessments to predict clinical deterioration. A necessary step in translation to practice is understanding how an alert threshold would perform if applied to a continuous predictive analytic that was trained to detect clinical deterioration. The purpose of this study was to evaluate the positive predictive value of 'risk spikes', or large abrupt increases in the output of a statistical model of risk predicting clinical deterioration. We studied 8111 consecutive patient admissions to a cardiovascular medicine and surgery ward with continuous ECG data. We first trained a multivariable logistic regression model for emergent ICU transfer in a test set and tested the characteristics of the model in a validation set of 4059 patient admissions. Then, in a nested analysis we identified large, abrupt spikes in risk (increase by three units over the prior 6 h; a unit is the fold-increase in risk of ICU transfer in the next 24 h) and reviewed hospital records of 91 patients for clinical events such as emergent ICU transfer. We compared results to 59 control patients at times when they were matched for baseline risk including the National Warning Score (NEWS). There was a 3.4-fold higher event rate for patients with risk spikes (positive predictive value 24% compared to 7%, p = 0.006). If we were to use risk spikes as an alert, they would fire about once per day on a 73-bed acute care ward. Risk spikes that were primarily driven by respiratory changes (ECG-derived respiration (EDR) or charted respiratory rate) had highest PPV (30-35%) while risk spikes driven by heart rate had the lowest (7%). Alert thresholds derived from continuous predictive analytics monitoring are able to be operationalized as a degree of change from the person's own baseline rather than arbitrary threshold cut-points, which can likely better account for the individual's own inherent acuity levels. Point of care clinicians in the acute care ward settings need tailored alert strategies that promote a balance in recognition of clinical deterioration and assessment of the utility of the alert approach.

BackgroundPatients 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. ObjectiveThe 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. MethodsWe 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. ResultsThe 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. ConclusionsVery 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 RegistrationClinicalTrials.gov NCT04359641; https://clinicaltrials.gov/ct2/show/NCT04359641 International Registered Report Identifier (IRRID)DERR1-10.2196/29631

The goal of predictive analytics monitoring is the early detection of patients at high risk of subacute potentially catastrophic illnesses. An excellent example of a targeted illness is respiratory failure leading to urgent unplanned intubation, where early detection might lead to interventions that improve patient outcomes. Previously, we identified signatures of this illness in the continuous cardiorespiratory monitoring data of Intensive Care Unit patients and devised algorithms to identify patients at rising risk. Here, we externally validated three logistic regression models to estimate the risk of emergency intubation developed in Medical and Surgical ICUs at the University of Virginia. We calculated the model outputs for more than 8000 patients in the University of California – San Francisco ICUs, 240 of whom underwent emergency intubation as determined by individual chart review. We found that the AUC of the models exceeded 0.75 in this external population, and that the risk rose appreciably over the 12 hours before the event. We conclude that there are generalizable physiological signatures of impending respiratory failure in the continuous cardiorespiratory monitoring data.

Background Multi-system inflammatory syndrome in children (MIS-C) is a rare consequence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). MIS-C shares features with common infectious and inflammatory syndromes and differentiation early in the course is difficult. Identification of early features specific to MIS-C may lead to faster diagnosis and treatment. We aimed to determine clinical, laboratory, and cardiac features distinguishing MIS-C patients within the first 24 hours of admission to the hospital from those who present with similar features but ultimately diagnosed with an alternative etiology. Methods We performed retrospective chart reviews of children (0-20 years) who were admitted to Vanderbilt Children’s Hospital and evaluated under our institutional MIS-C algorithm between June 10, 2020-April 8, 2021. Subjects were identified by review of infectious disease (ID) consults during the study period as all children with possible MIS-C require an ID consult per our institutional algorithm. Clinical, lab, and cardiac characteristics were compared between children with and without MIS-C. The diagnosis of MIS-C was determined by the treating team and available consultants. P-values were calculated using two-sample t-tests allowing unequal variances for continuous and Pearson’s chi-squared test for categorical variables, alpha set at < 0.05. Results There were 128 children admitted with concern for MIS-C. Of these, 45 (35.2%) were diagnosed with MIS-C and 83 (64.8%) were not. Patients with MIS-C had significantly higher rates of SARS-CoV-2 exposure, hypotension, conjunctival injection, abdominal pain, and abnormal cardiac exam (Table 1). Laboratory evaluation showed that patients with MIS-C had lower platelet count, lymphocyte count and sodium level, with higher c-reactive protein, fibrinogen, B-type natriuretic peptide, and neutrophil percentage (Table 2). Patients with MIS-C also had lower ejection fraction and were more likely to have abnormal electrocardiogram. Conclusion We identified early features that differed between patients with MIS-C from those without. Development of a diagnostic prediction model based on these early distinguishing features is currently in progress. Disclosures Natasha B. Halasa, MD, MPH, Genentech (Other Financial or Material Support, I receive an honorarium for lectures - it’s a education grant, supported by genetech)Quidel (Grant/Research Support, Other Financial or Material Support, Donation of supplies/kits)Sanofi (Grant/Research Support, Other Financial or Material Support, HAI/NAI testing) Natasha B. Halasa, MD, MPH, Genentech (Individual(s) Involved: Self): I receive an honorarium for lectures - it’s a education grant, supported by genetech, Other Financial or Material Support, Other Financial or Material Support; Sanofi (Individual(s) Involved: Self): Grant/Research Support, Research Grant or Support James A. Connelly, MD, Horizon Therapeutics (Advisor or Review Panel member)X4 Pharmaceuticals (Advisor or Review Panel member)

Objective: The goal of predictive analytics monitoring is the early detection of patients at high risk of subacute potentially catastrophic illnesses. An excellent example of a targeted illness is respiratory failure leading to urgent unplanned intubation, where early detection might lead to interventions that improve patient outcomes. Previously, we identified signatures of this illness in the continuous cardiorespiratory monitoring data of intensive care unit (ICU) patients and devised algorithms to identify patients at rising risk. Here, we externally validated three logistic regression models to estimate the risk of emergency intubation developed in Medical and Surgical ICUs at the University of Virginia. Approach: We calculated the model outputs for more than 8000 patients in the University of California—San Francisco ICUs, 240 of whom underwent emergency intubation as determined by individual chart review. Main results: We found that the AUC of the models exceeded 0.75 in this external population, and that the risk rose appreciably over the 12 h before the event. Significance: We conclude that there are generalizable physiological signatures of impending respiratory failure in the continuous cardiorespiratory monitoring data.

BACKGROUND Patients on acute care wards who deteriorate and are emergently transferred to intensive care units have poor outcomes. Early identification of decompensating patients might allow for earlier clinical intervention and reduced morbidity and mortality. Advances in bedside continuous predictive analytics monitoring (i.e., artificial intelligence (AI)-based risk prediction) make complex data easily available to healthcare providers, and can provide 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, CoMET (Continuous Monitoring of Event Trajectories), 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: (1) improving patient outcomes related to clinical deterioration, (2) response time to proactive clinical action, and (3) costs to the healthcare system. METHODS We propose a cluster randomized controlled trial (NCT04359641) to test the impact of displaying CoMET on an acute care cardiology and cardiothoracic surgery hospital floor. The number of admissions to a room undergoing cluster-randomization is estimated to be 10,424 over the 20-month study period. Cluster randomization based on bed number occurs every 2 months. The intervention cluster will have the CoMET score displayed (along with standard of care), while the usual care group receives 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 are 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 incorporating real-time telemetric data for identifying clinical deterioration on acute care wards. This analysis will strengthen the ability of healthcare organizations to evolve as learning health systems, which apply bioinformatics data to improve patient outcomes by incorporating AI into knowledge tools that are successfully integrated into clinical practice by healthcare providers. CLINICALTRIAL Clinical trials identifier: NCT04359641

Our objective was to determine the effect of sleep on heart rate following a recorded seizure. We prospectively acquired heart rate data in hospitalized epilepsy monitoring unit patients. We analyzed heart rate trends for multiple seizures (n = 101) in patients (n = 42) with electroencephalographically confirmed events. The patient's sleep state was scored for the 5 min preceding each seizure and correlated with the postictal nadir heart rate (PINHR). The depth of sleep during the 5 min before a seizure correlated (correlation coefficient [CC] = -.229, p < .05) with PINHR. This result was more significant and strengthened (CC = -.272, 95% confidence interval = -.392 to -.152, p < .001) when adjusted for covariates of age, generalized tonic-clonic seizures, and baseline heart rate. Sleep depth is an independent predictor of the change in heart rate following a seizure. Diminished heart rate following a seizure in the setting of sleep is likely secondary to non-rapid eye movement sleep's synergistic effect on parasympathetic tone.

In the intensive care unit, clinicians monitor a diverse array of data inputs to detect early signs of impending clinical demise or improvement. Continuous predictive analytics monitoring synthesizes data from a variety of inputs into a risk estimate that clinicians can observe in a streaming environment. For this to be useful, clinicians must engage with the data in a way that makes sense for their clinical workflow in the context of a learning health system (LHS). This article describes the processes needed to evoke clinical action after initiation of continuous predictive analytics monitoring in an LHS.

Background Preventing urgent intubation and upgrade in level of care in patients with subclinical deterioration could be of great utility in hospitalized patients. Early detection should result in decreased mortality, duration of stay, and/or resource use. The goal of this study was to externally validate a previously developed, vital sign-based, intensive care unit, respiratory instability model on a separate population, intermediate care patients. Methods From May 2014 to May 2016, the model calculated relative risk of adverse events every 15 minutes ( n = 373,271 observations) for 2,050 patients in a surgical intermediate care unit. Results We identified 167 upgrades and 57 intubations. The performance of the model for predicting upgrades within 12 hours was highly significant with an area under the curve of 0.693 (95% confidence interval, 0.658–0.724). The model was well calibrated with relative risks in the highest and lowest deciles of 2.99 and 0.45, respectively (a 6.6-fold increase). The model was effective at predicting intubation, with a demonstrated area under the curve within 12 hours of the event of 0.748 (95% confidence interval, 0.685–0.800). The highest and lowest deciles of observed relative risk were 3.91 and 0.39, respectively (a 10.1-fold increase). Univariate analysis of vital signs showed that transfer upgrades were associated, in order of importance, with rising respiration rate, rising heart rate, and falling pulse-oxygen saturation level. Conclusion The respiratory instability model developed previously is valid in intermediate care patients to predict both urgent intubations and requirements for upgrade in level of care to an intensive care unit.

A near-ubiquitous pathology in very low birth weight infants is neonatal apnea, breathing pauses with slowing of the heart and falling blood oxygen. Events of substantial duration occasionally occur after an infant is discharged from the neonatal intensive care unit (NICU). It is not known whether apneas result from a predictable process or from a stochastic process, but the observation that they occur in seemingly random clusters justifies the use of stochastic models. We use a hidden-Markov model to analyze the distribution of durations of apneas and the distribution of times between apneas. The model suggests the presence of four breathing states, ranging from very stable (with an average lifetime of 12 h) to very unstable (with an average lifetime of 10 s). Although the states themselves are not visible, the mathematical analysis gives estimates of the transition rates among these states. We have obtained these transition rates, and shown how they change with post-menstrual age; as expected, the residence time in the more stable breathing states increases with age. We also extrapolated the model to predict the frequency of very prolonged apnea during the first year of life. This paradigm-stochastic modeling of cardiorespiratory control in neonatal infants to estimate risk for severe clinical events-may be a first step toward personalized risk assessment for life threatening apnea events after NICU discharge.

Supplemental Digital Content is available in the text., Objectives: Bloodstream infection is associated with high mortality rates in critically ill patients but is difficult to identify clinically. This results in frequent blood culture testing, exposing patients to additional costs as well as the potential harms of unnecessary antibiotics. The purpose of this study was to assess whether the analysis of bedside physiologic monitoring data could accurately describe a pathophysiologic signature of bloodstream infection in patients admitted to the ICU. Design: Development of a statistical model using physiologic data from a retrospective observational cohort. Setting: University of Virginia Medical Center (Charlottesville, VA), a tertiary-care academic medical center. Patients: Critically ill patients consecutively admitted to either the medical or surgical/trauma ICUs with available physiologic monitoring data between February 2011 and June 2015. Interventions: None. Measurements and Main Results: We analyzed 9,954 ICU admissions with 144 patient-years of vital sign and electrocardiography waveform data, totaling 1.3 million hourly measurements. There were 15,577 blood culture instances, with 1,184 instances of bloodstream infection (8%). The multivariate pathophysiologic signature of bloodstream infection was characterized by abnormalities in 15 different physiologic features. The cross-validated area under the receiver operating characteristic curve was 0.78 (95% CI, 0.69–0.85). We also identified distinct signatures of Gram-negative and fungal bloodstream infections, but not Gram-positive bloodstream infection. Conclusions: Signatures of bloodstream infection can be identified in the routine physiologic monitoring data of critically ill adults. This may assist in identifying infected patients, maximizing diagnostic stewardship, and measuring the effect of new therapeutic modalities for sepsis.

Supplemental Digital Content is available in the text., Objectives: Early detection of subacute potentially catastrophic illnesses using available data is a clinical imperative, and scores that report risk of imminent events in real time abound. Patients deteriorate for a variety of reasons, and it is unlikely that a single predictor such as an abnormal National Early Warning Score will detect all of them equally well. The objective of this study was to test the idea that the diversity of reasons for clinical deterioration leading to ICU transfer mandates multiple targeted predictive models. Design: Individual chart review to determine the clinical reason for ICU transfer; determination of relative risks of individual vital signs, laboratory tests and cardiorespiratory monitoring measures for prediction of each clinical reason for ICU transfer; and logistic regression modeling for the outcome of ICU transfer for a specific clinical reason. Setting: Cardiac medical-surgical ward; tertiary care academic hospital. Patients: Eight-thousand one-hundred eleven adult patients, 457 of whom were transferred to an ICU for clinical deterioration. Interventions: None. Measurements and Main Results: We calculated the contributing relative risks of individual vital signs, laboratory tests and cardiorespiratory monitoring measures for prediction of each clinical reason for ICU transfer, and used logistic regression modeling to calculate receiver operating characteristic areas and relative risks for the outcome of ICU transfer for a specific clinical reason. The reasons for clinical deterioration leading to ICU transfer were varied, as were their predictors. For example, the three most common reasons—respiratory instability, infection and suspected sepsis, and heart failure requiring escalated therapy—had distinct signatures of illness. Statistical models trained to target-specific reasons for ICU transfer performed better than one model targeting combined events. Conclusions: A single predictive model for clinical deterioration does not perform as well as having multiple models trained for the individual specific clinical events leading to ICU transfer.

Early recognition of patients at risk for sepsis is paramount to improve clinical outcomes. We hypothesized that subtle signatures of illness are present in physiological and biochemical time series of pediatric-intensive care unit (PICU) patients in the early stages of sepsis.We developed multivariate models in a retrospective observational cohort to predict the clinical diagnosis of sepsis in children. We focused on age as a predictor and asked whether random forest models, with their potential for multiple cut points, had better performance than logistic regression.One thousand seven hundred and eleven admissions for 1425 patients admitted to a mixed cardiac and medical/surgical PICU were included. We identified, through individual chart review, 187 sepsis diagnoses that were not within 14 days of a prior sepsis diagnosis. Multivariate models predicted sepsis in the next 24 h: cross-validated C-statistic for logistic regression and random forest were 0.74 (95% confidence interval (CI): 0.71-0.77) and 0.76 (95% CI: 0.73-0.79), respectively.Statistical models based on physiological and biochemical data already available in the PICU identify high-risk patients up to 24 h prior to the clinical diagnosis of sepsis. The random forest model was superior to logistic regression in capturing the context of age.

BACKGROUND: Predictive analytics monitoring, the use of patient data to provide continuous risk estimation of deterioration, is a promising new application of Big Data analytical techniques to the care of individual patients. We tested the hypothesis that continuous display of novel electronic risk visualization of respiratory and cardiovascular events would impact ICU patient outcomes. METHODS: In an adult tertiary care Surgical Trauma ICU, we displayed risk estimation visualizations on a large monitor, but in the Medical ICU in the same institution we did not. The risk estimates were based solely on analysis of continuous cardiorespiratory monitoring. We examined 4275 individual patient records within a seven month time period preceding and following data display. We determined cases of septic shock, emergency intubation, hemorrhage, and death to compare rates per patient care pre-and post-implementation. RESULTS: Following implementation, the incidence of septic shock fell by half (p

In the intensive care unit, clinicians monitor a diverse array of data inputs to detect early signs of impending clinical demise or improvement. Continuous predictive analytics monitoring synthesizes data from a variety of inputs into a risk estimate that clinicians can observe in a streaming environment. For this to be useful, clinicians must engage with the data in a way that makes sense for their clinical workflow in the context of a learning health system (LHS). This article describes the processes needed to evoke clinical action after initiation of continuous predictive analytics monitoring in an LHS.

Background Bloodstream infection (BSI) is associated with high mortality rates in critically ill patients but is difficult to identify clinically. This uncertainty results in frequent blood culture testing, which exposes patients to additional costs and the potential harms of unnecessary antibiotics. Accordingly, we aimed to identify signatures in physiological data from critically ill adults that characterize BSI. Methods We reviewed all blood culture, vital sign, laboratory, and cardiorespiratory monitoring (CRM) data from patients admitted to the medical and surgical/trauma ICUs at the University of Virginia Medical Center from February 2011 to June 2015. Blood culture results were categorized as positive, negative, or contaminant. For the BSI population, we included data obtained within 12 hours before or 24 hours after the acquisition of a positive blood culture. The control population included data greater than 12 hours before or 24 hours after the acquisition of a positive blood culture, and all data from patients without BSI. We used multivariable logistic regression to identify the physiological characteristics of BSI. Results We analyzed 9,955 ICU admissions with 144 patient-years of vital sign and CRM data (1.3M hourly measurements). The average age was 59 years; the population was mostly Caucasian (81%) and male (56%). There were 5,671 (57%) admissions with ≥1 blood culture, and 744 (7%) had a BSI. The in-hospital mortality rate for patients with BSI was 28% vs. 12% for all others. The physiological signature of BSI was characterized by abnormalities in 12 parameters (Figure 1)—e.g., BSI was more likely in patients with a higher pulse and lower platelets. Several associations were nonlinear—e.g., temperature and WBC had U-shaped relationships with BSI. The internally validated C-statistic was 0.77. Conclusion Statistical modeling revealed a clinically sensible physiological signature of BSI that could assist with bedside decisions regarding the utility of blood culture testing in critically ill adults. Disclosures All authors: No reported disclosures.

Occult hemorrhage in surgical/trauma intensive care unit (STICU) patients is common and may lead to circulatory collapse. Continuous electrocardiography (ECG) monitoring may allow for early identification and treatment, and could improve outcomes. We studied 4,259 consecutive admissions to the STICU at the University of Virginia Health System. We collected ECG waveform data captured by bedside monitors and calculated linear and non-linear measures of the RR interbeat intervals. We tested the hypothesis that a transfusion requirement of 3 or more PRBC transfusions in a 24 hour period is preceded by dynamical changes in these heart rate measures and performed logistic regression modeling. We identified 308 hemorrhage events. A multivariate model including heart rate, standard deviation of the RR intervals, detrended fluctuation analysis, and local dynamics density had a C-statistic of 0.62. Earlier detection of hemorrhage might improve outcomes by allowing earlier resuscitation in STICU patients.

Periodic breathing (PB), regular cycles of short apneic pauses and breaths, is common in newborn infants. To characterize normal and potentially pathologic PB, we used our automated apnea detection system and developed a novel method for quantifying PB. We identified a preterm infant who died of sudden infant death syndrome (SIDS) and who, on review of her breathing pattern while in the neonatal intensive care unit (NICU), had exaggerated PB.We analyzed the chest impedance signal for short apneic pauses and developed a wavelet transform method to identify repetitive 10-40 second cycles of apnea/breathing. Clinical validation was performed to distinguish PB from apnea clusters and determine the wavelet coefficient cutoff having optimum diagnostic utility. We applied this method to analyze the chest impedance signals throughout the entire NICU stays of all 70 infants born at 32 weeks' gestation admitted over a two-and-a-half year period. This group includes an infant who died of SIDS and her twin.For infants of 32 weeks' gestation, the fraction of time spent in PB peaks 7-14 d after birth at 6.5%. During that time the infant that died of SIDS spent 40% of each day in PB and her twin spent 15% of each day in PB.This wavelet transform method allows quantification of normal and potentially pathologic PB in NICU patients.

The purpose of this paper was to use the revised Reinforcement Sensitivity Theory as a framework to understand loneliness. We expected higher loneliness to be associated with high reward sensitivity and low punishment sensitivity. We tested how reward and punishment sensitivity could affect loneliness by exploring potential mediators including shyness, sociability, communal orientation, and acceptance. We tested 370 participants using an online questionnaire. High punishment sensitivity, but not anxiety, predicted higher loneliness. This association was mediated by higher shyness and lower psychological acceptance. High reward sensitivity was associated with lower loneliness. This association was mediated by lower shyness, higher sociability, higher communal orientation, and higher acceptance. The mediated model with reward and punishment sensitivity accounted for over half the variance in loneliness. Considered in isolation, acceptance predicted over a quarter of the variance in loneliness. These results allow us to identify those at risk of loneliness and, by addressing the mediators, especially acceptance, suggest possible interventions for loneliness.

Background Continuous predictive monitoring has been employed successfully to predict subclinical adverse events. Should low values on these models, however, reassure us that a patient will not have an adverse outcome? Negative predictive values of such models could help predict safe patient discharge. The goal of this study was to validate the negative predictive value of an ensemble model for critical illness (using previously developed models for respiratory instability, hemorrhage, and sepsis) based on bedside monitoring data in the intensive care units and intermediate care unit. Methods We calculated the relative risk of 3 critical illnesses for all patients every 15 minutes (n = 124,588) for 2,924 patients downgraded from the surgical intensive care units and intermediate care unit between May 2014 to May 2016. We constructed an ensemble model to estimate at the time of intensive care units or intermediate care unit discharge the probability of favorable outcome after downgrade. Results Outputs form the ensemble model stratified patients by risk of favorable and bad outcomes in both intensive care units/intermediate care unit; area under the receiver operating characteristic curve = .639/.629 respectively for favorable outcomes and .645/.641 for adverse events. These performance characteristics are commensurate with published models for predicting readmission. The ensemble model remained a statistically significant predictor after adjusting for hospital duration of stay and admitting service. The rate of favorable outcome in the highest and lowest deciles in the intensive care units were 76.2% and 27.3% (2.8-fold decrease) and 88.3% and 33.2% in the intermediate care unit (2.7-fold decrease), respectively. Conclusion An ensemble model for critical illness predicts favorable outcome after downgrade and safe patient discharge (hospital stay

In healthy neonates, connections between the heart and lungs through brain stem chemosensory pathways and the autonomic nervous system result in cardiorespiratory synchronization. This interdependence between cardiac and respiratory dynamics can be difficult to measure because of intermittent signal quality in intensive care settings and variability of heart and breathing rates. We employed a phase-based measure suggested by Schäfer and coworkers (Schäfer C, Rosenblum MG, Kurths J, Abel HH. Nature 392: 239–240, 1998) to obtain a breath-by-breath analysis of cardiorespiratory interaction. This measure of cardiorespiratory interaction does not distinguish between cardiac control of respiration associated with cardioventilatory coupling and respiratory influences on the heart rate associated with respiratory sinus arrhythmia. We calculated, in sliding 4-min windows, the probability density of heartbeats as a function of the concurrent phase of the respiratory cycle. Probability density functions whose Shannon entropy had a

Apnea of prematurity (AOP) is an important and common clinical problem, and is often the rate-limiting process in NICU discharge. Accurate detection of episodes of clinically important neonatal apnea using existing chest impedance monitoring is a clinical imperative. The technique relies on changes in impedance as the lungs fill with air, a high impedance substance. A potential confounder, however, is blood coursing through the heart. Thus the cardiac signal during apnea might be mistaken for breathing. We report here a new filter to remove the cardiac signal from the chest impedance that employs a novel resampling technique optimally suited to remove the heart rate signal, allowing improved apnea detection. We also develop an apnea detection method that employs the chest impedance after cardiac filtering. The method has been applied to a large database of physiological signals, and we prove that, compared to the presently-used monitors, the new method gives substantial improvement in apnea detection.

Objective Predictive analytics monitoring that informs clinicians of the risk for failed extubation would help minimize both the duration of mechanical ventilation and the risk of emergency re-intubation in ICU patients. We hypothesized that dynamic monitoring of cardiorespiratory data, vital signs, and lab test results would add information to standard clinical risk factors. Methods We report model development in a retrospective observational cohort admitted to either the medical or surgical/trauma ICU that were intubated during their ICU stay and had available physiologic monitoring data (n = 1202). The primary outcome was removal of endotracheal intubation (i.e. extubation) followed within 48 h by reintubation or death (i.e. failed extubation). We developed a standard risk marker model based on demographic and clinical data. We also developed a novel risk marker model using dynamic data elements-continuous cardiorespiratory monitoring, vital signs, and lab values. Results Risk estimates from multivariate predictive models in the 24 h preceding extubation were significantly higher for patients that failed. Combined standard and novel risk markers demonstrated good predictive performance in leave-one-out validation: AUC of 0.64 (95% CI: 0.57-0.69) and 1.6 alerts per week to identify 32% of extubations that will fail. Novel risk factors added significantly to the standard model. Conclusion Predictive analytics monitoring models can detect changes in vital signs, continuous cardiorespiratory monitoring, and laboratory measurements in both the hours preceding and following extubation for those patients destined for extubation failure.

Apnea is nearly universal among very low birth weight (VLBW) infants, and the associated bradycardia and desaturation may have detrimental consequences. We describe here very long (>60 s) central apnea events (VLAs) with bradycardia and desaturation, discovered using a computerized detection system applied to our database of over 100 infant years of electronic signals. Eighty-six VLAs occurred in 29 out of 335 VLBW infants. Eighteen of the 29 infants had a clinical event or condition possibly related to the VLA. Most VLAs occurred while infants were on nasal continuous positive airway pressure, supplemental oxygen, and caffeine. Apnea alarms on the bedside monitor activated in 66% of events, on average 28 s after cessation of breathing. Bradycardia alarms activated late, on average 64 s after cessation of breathing. Before VLAs oxygen saturation was unusually high, and during VLAs oxygen saturation and heart rate fell unusually slowly. We give measures of the relative severity of VLAs and theoretical calculations that describe the rate of decrease of oxygen saturation. A clinical conclusion is that very long apnea (VLA) events with bradycardia and desaturation are not rare. Apnea alarms failed to activate for about one-third of VLAs. It appears that neonatal intensive care unit (NICU) personnel respond quickly to bradycardia alarms but not consistently to apnea alarms. We speculate that more reliable apnea detection systems would improve patient safety in the NICU. A physiological conclusion is that the slow decrease of oxygen saturation is consistent with a physiological model based on assumed high values of initial oxygen saturation.

We examined changes in loneliness over time. Study 1 was a cross-temporal meta-analysis of 48 samples of American college students who completed the Revised UCLA Loneliness Scale (total N = 13,041). In Study 1, loneliness declined from 1978 to 2009 ( d = −0.26). Study 2 used a representative sample of high school students from the Monitoring the Future project (total N = 385,153). In Study 2, loneliness declined from 1991 to 2012. Declines were similar among White students ( d = −0.14), Black students ( d = −0.17), male students ( d = −0.11), and female students ( d = −0.11). Different loneliness factors showed diverging trends. Subjective isolation declined ( d = −0.20), whereas social network isolation increased ( d = 0.06). We discuss the declines in loneliness within the context of other cultural changes, including changes to group membership and personality.

Objective In 2006 the apnea of prematurity (AOP) consensus group identified inaccurate counting of apnea episodes as a major barrier to progress in AOP research. We compare nursing records of AOP to events detected by a clinically validated computer algorithm that detects apnea from standard bedside monitors. Study Design Waveform, vital sign, and alarm data were collected continuously from all very low-birth-weight infants admitted over a 25-month period, analyzed for central apnea, bradycardia, and desaturation (ABD) events, and compared with nursing documentation collected from charts. Our algorithm defined apnea as > 10 seconds if accompanied by bradycardia and desaturation. Results Of the 3,019 nurse-recorded events, only 68% had any algorithm-detected ABD event. Of the 5,275 algorithm-detected prolonged apnea events > 30 seconds, only 26% had nurse-recorded documentation within 1 hour. Monitor alarms sounded in only 74% of events of algorithm-detected prolonged apnea events > 10 seconds. There were 8,190,418 monitor alarms of any description throughout the neonatal intensive care unit during the 747 days analyzed, or one alarm every 2 to 3 minutes per nurse. Conclusion An automated computer algorithm for continuous ABD quantitation is a far more reliable tool than the medical record to address the important research questions identified by the 2006 AOP consensus group.

Background Infants admitted to the neonatal intensive care unit (NICU), and especially those born with very low birth weight (VLBW

Background Analysis and modeling of data monitoring vital signs and waveforms in patients in a surgical/trauma intensive care unit (STICU) may allow for early identification and treatment of patients with evolving respiratory failure. Methods Between February 2011 and March 2012, data of vital signs and waveforms for STICU patients were collected. Every-15-minute calculations (n = 172,326) of means and standard deviations of heart rate (HR), respiratory rate (RR), pulse-oxygen saturation (SpO2), cross-correlation coefficients, and cross-sample entropy for HR-RR, RR-SpO2, and HR-SpO2, and cardiorespiratory coupling were calculated. Urgent intubations were recorded. Univariate analyses were performed for the periods

To compare the frequency and severity of apneic events in very low birth weight (VLBW) infants before and after blood transfusions using continuous electronic waveform analysis.We continuously collected waveform, heart rate, and oxygen saturation data from patients in all 45 neonatal intensive care unit beds at the University of Virginia for 120 weeks. Central apneas were detected using continuous computer processing of chest impedance, electrocardiographic, and oximetry signals. Apnea was defined as respiratory pauses of10,20, and30 seconds when accompanied by bradycardia (100 beats per minute) and hypoxemia (80% oxyhemoglobin saturation as detected by pulse oximetry). Times of packed red blood cell transfusions were determined from bedside charts. Two cohorts were analyzed. In the transfusion cohort, waveforms were analyzed for 3 days before and after the transfusion for all VLBW infants who received a blood transfusion while also breathing spontaneously. Mean apnea rates for the previous 12 hours were quantified and differences for 12 hours before and after transfusion were compared. In the hematocrit cohort, 1453 hematocrit values from all VLBW infants admitted and breathing spontaneously during the time period were retrieved, and the association of hematocrit and apnea in the next 12 hours was tested using logistic regression.Sixty-seven infants had 110 blood transfusions during times when complete monitoring data were available. Transfusion was associated with fewer computer-detected apneic events (P.01). Probability of future apnea occurring within 12 hours increased with decreasing hematocrit values (P.001).Blood transfusions are associated with decreased apnea in VLBW infants, and apneas are less frequent at higher hematocrits.

Phase models are a powerful method to quantify the coupled dynamics of nonlinear oscillators from measured data. We use two phase modeling methods to quantify the dynamics of pairs of coupled electrochemical oscillators, based on the phases of the two oscillators independently and the phase difference, respectively. We discuss the benefits of the two-dimensional approach relative to the one-dimensional approach using phase difference. We quantify the dependence of the coupling functions on the coupling magnitude and coupling time delay. We show differences in synchronization predictions of the two models using a toy model. We show that the two-dimensional approach reveals behavior not detected by the one-dimensional model in a driven experimental oscillator. This approach is broadly applicable to quantify interactions between nonlinear oscillators, especially where intrinsic oscillator sensitivity and coupling evolve with time.

We wish to save lives of patients admitted to ICUs. Their mortality is high enough based simply on the severity of the original injury or illness, but is further raised by events during their stay. We target those events that are subacute but potentially catastrophic, such as infection. Sepsis, for example, is a bacterial infection of the bloodstream, that is common in ICU patients and has a >25% risk of death. Logically, early detection and treatment with antibiotics should improve outcomes. Our fundamental precepts are (1) some potentially catastrophic medical and surgical illnesses have subclinical phases during which early diagnosis and treatment might have life-saving effects, (2) these phases are characterized by changes in the normal highly complex but highly adaptive regulation and interaction of the nervous system and other organs such as the heart and lungs, (3) teams of clinicians and quantitative scientists can work together to identify clinically important abnormalities of monitoring data, to develop algorithms that match the clinicians' eye in detecting abnormalities, and to undertake the clinical trials to test their impact on outcomes.

Background Home infestations from Harmonia axyridis (ladybug) occur throughout the United States. IgE-mediated sensitization with allergic disease has been reported. The prevalence of ladybug sensitization has been studied by questionnaire and allergy testing in patients diagnosed as having allergic disease. Cross-reactivity with cockroach exists. Objectives To determine the prevalence of ladybug specific IgE in the general population by specific IgE immunoassay and to examine cross-reactivity to cockroach. Methods An experimental solid phase for use in immunoassay was prepared using a ladybug extract, and performance characteristics were determined. Serum samples from 100 adult blood bank donors were tested using the ladybug specific IgE immunoassay. Known ladybug IgE-positive serum samples obtained from symptomatic patients were used to study cross-reactivity with German cockroach by specific IgE immunoassay inhibition. Results The mean background response of the assay solid phase was 51 fluorescent units with an analytical cutoff of 59 fluorescent units. It was estimated that a response of 88 fluorescent units corresponds to a specific IgE concentration of 0.1 kUa/L. The extinction dilution curve was linear to 0.1 kUa/L. The assay cutoff was set at 0.1 kUa/L. Of the 100 blood donor serum samples, 10 were positive for ladybug specific IgE. Inhibition assays revealed partial cross-reactivity with German cockroach. Conclusion Although an immunoassay solid phase prepared with ladybug whole body extract will identify persons in a general population sensitized to epitopes found in the extract, clinicians performing allergy testing using whole body ladybug extract should be aware that a positive result may or may not indicate that exposure to actual ladybug allergens is causing sensitization.

Background: Heart rate characteristics monitoring using the HeRO score detects reduced variability and transient decelerations that occur prior to clinical signs of neonatal sepsis, and reduces VLBW mortality when displayed to clinicians. Neonatal apnea causes HR decelerations. Aim: We tested the hypothesis that central apneas are the cause of high HeRO scores. Methods: We fashioned an algorithm to detect central neonatal apnea that efficiently removes the cardiac component of the chest impedance signal. We identified 0.9 episodes per day lasting more than 30 sec and accompanied by bradycardia and desaturation in 1837 non-ventilated days in 105 VLBW infants in the University of Virginia NICU. Results: Severe central apneas accounted for only 17% of the variance in HeRO scores but were highly significant predictors of high HeRO score even after birth weight, Apgar, and hospital day were taken into account. Individual records (Figure), though, showed inconsistent temporal association of HeRO score (shade) and the number of severe apneas (line), here in an infant who died of lung disease - some but not all, synchronized with HeRO. Figure Conclusion: Frequent and severe central neonatal apneas can correlate with elevated HeRO scores, and may be partly causative. We speculate that abnormal heart rate control in sepsis may have mechanisms in addition to hypoxemia caused by central apnea.