Cardiorespiratory Monitoring Data to Predict Respiratory Outcomes in Extremely Preterm Infants.

Rationale: Immature control of breathing is associated with apnea, periodic breathing, intermittent hypoxemia, and bradycardia in extremely preterm infants. However, it is not clear if such events independently predict worse respiratory outcome. Objectives: To determine if analysis of cardiorespiratory monitoring data can predict unfavorable respiratory outcomes at 40 weeks postmenstrual age (PMA) and other outcomes, such as bronchopulmonary dysplasia at 36 weeks PMA. Methods: The Prematurity-related Ventilatory Control (Pre-Vent) study was an observational multicenter prospective cohort study including infants born at <29 weeks of gestation with continuous cardiorespiratory monitoring. The primary outcome was either "favorable" (alive and previously discharged or inpatient and off respiratory medications/O2/support at 40 wk PMA) or "unfavorable" (either deceased or inpatient/previously discharged on respiratory medications/O2/support at 40 wk PMA). Measurements and Main Results: A total of 717 infants were evaluated (median birth weight, 850 g; gestation, 26.4 wk), 53.7% of whom had a favorable outcome and 46.3% of whom had an unfavorable outcome. Physiologic data predicted unfavorable outcome, with accuracy improving with advancing age (area under the curve, 0.79 at Day 7, 0.85 at Day 28 and 32 wk PMA). The physiologic variable that contributed most to prediction was intermittent hypoxemia with oxygen saturation as measured by pulse oximetry <90%. Models with clinical data alone or combining physiologic and clinical data also had good accuracy, with areas under the curve of 0.84-0.85 at Days 7 and 14 and 0.86-0.88 at Day 28 and 32 weeks PMA. Intermittent hypoxemia with oxygen saturation as measured by pulse oximetry <80% was the major physiologic predictor of severe bronchopulmonary dysplasia and death or mechanical ventilation at 40 weeks PMA. Conclusions: Physiologic data are independently associated with unfavorable respiratory outcome in extremely preterm infants.

Heart rate fragmentation gives novel insights into non-autonomic mechanisms governing beat-to-beat control of the heart's rhythm.

To demonstrate how heart rate fragmentation gives novel insights into non-autonomic mechanisms of beat-to-beat variability in cycle length, and predicts survival of cardiology clinic patients, over and above traditional clinical risk factors and measures of heart rate variability. Approach: We studied 2893 patients seen by cardiologists with clinical data including 24-hour Holter monitoring. Novel measures of heart rate fragmentation alongside canonical time and frequency domain measures of heart rate variability, as well as an existing local dynamics score were calculated. A proportional hazards model was utilized to relate the results to survival. Main results: The novel heart rate fragmentation measures were validated and characterized with respect to the effects of age, ectopy and atrial fibrillation. Correlations between parameters were determined. Critically, heart rate fragmentation results could not be accounted for by undersampling respiratory sinus arrhythmia. Increased heart rate fragmentation was associated with poorer survival (p ≪ 0.01 in the univariate model). In multivariable analyses, increased heart rate fragmentation and more abnormal local dynamics (p 0.045), along with increased clinical risk factors (age (p ≪ 0.01), tobacco use (p ≪ 0.01) and history of heart failure (p 0.019)) and lower low- to high-frequency ratio (p 0.022) were all independent predictors of 2-year mortality. Significance: Analysis of continuous ECG data with heart rate fragmentation indices yields information regarding non-autonomic control of beat-to-beat variability in cycle length that is independent of and additive to established parameters for investigating heart rate variability, and predicts mortality in concert with measures of local dynamics, frequency content of heart rate, and clinical risk factors.

Correction: Oxygen desaturations in the early neonatal period predict development of bronchopulmonary dysplasia.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Towards development of alert thresholds for clinical deterioration using continuous predictive analytics monitoring.

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.

Oxygen desaturations in the early neonatal period predict development of bronchopulmonary dysplasia.

BACKGROUND: Bradycardia and oxygen desaturation episodes are common among preterm very low birth weight (VLBW) infants in the Neonatal Intensive Care Unit (NICU), and their association with adverse outcomes such as bronchopulmonary dysplasia (BPD) is unclear. METHODS: For 502 VLBW infants we quantified bradycardias (HR < 100 for ≥ 4 s) and desaturations (SpO2 < 80% for ≥ 10 s), combined bradycardia and desaturation (BD) events, and percent time in events in the first 4 weeks after birth (32 infant-years of data). We tested logistic regression models of clinical risks (including a respiratory acuity score incorporating FiO2 and level of respiratory support) to estimate the risks of BPD or death and secondary outcomes. We then tested the additive value of the bradycardia and desaturation metrics for outcomes prediction. RESULTS: BPD occurred in 187 infants (37%). The clinical risk model had ROC area for BPD of 0.874. Measures of desaturation, but not bradycardia, significantly added to the predictive model. Desaturation metrics also added to clinical risks for prediction of severe intraventricular hemorrhage, retinopathy of prematurity and prolonged length of stay in the NICU. CONCLUSIONS: Oxygen desaturations in the first month of the NICU course are associated with risk of BPD and other morbidities in VLBW infants.

Identifying the low risk patient in surgical intensive and intermediate care units using continuous monitoring.

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 <7 days, no readmission, upgrade, or death).

Hemorrhage Prediction Models in Surgical Intensive Care: Bedside Monitoring Data Adds Information to Lab Values.

Hemorrhage is a frequent complication in surgery patients; its identification and management have received increasing attention as a target for quality improvement in patient care in the Intensive Care Unit (ICU). The purposes of this work were 1) to find an early detection model for hemorrhage by exploring the range of data mining methods that are currently available, and 2) to compare prediction models utilizing continuously measured physiological data from bedside monitors to those using commonly obtained laboratory tests. We studied 3766 patients admitted to the University of Virginia Health System Surgical Trauma Burn ICU. Hemorrhage was defined as three or more units of red blood cells transfused within 24 h without red blood cell transfusion in the preceding 24 h. 222 patients (5.9%) experienced a hemorrhage, and multivariate models based on vital signs and their trends showed good results (AUC = 76.1%). The hematocrit, not surprisingly, had excellent performance (AUC = 87.7%). Models that included both continuous monitoring and laboratory tests had the best performance (AUC = 92.2%). The results point to a combined strategy of continuous monitoring and intermittent lab tests as a reasonable clinical approach to the early detection of hemorrhage in the surgical ICU.

External validation in an intermediate unit of a respiratory decompensation model trained in an intensive care unit.

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.

Early Heart Rate Characteristics Predict Death and Morbidities in Preterm Infants.

OBJECTIVES: To determine whether an early heart rate characteristics (HRC) index (HeRO score), measured in the first day and week after birth predicts death and morbidities compared with established illness severity scores. STUDY DESIGN: For all very low birth weight infants in a single neonatal intensive care unit from 2004-2014, the average first day HRC index was calculated within 24 hours of birth (aHRC-24h) and the average first week HRC index within 7 days of birth (aHRC-7d). The Score for Neonatal Acute Physiology (SNAP-II) and Clinical Risk Indicator for Babies (CRIB-II) were calculated when data were available. The aHRC was compared with the SNAP-II and CRIB-II for predicting death, late-onset septicemia, necrotizing enterocolitis, bronchopulmonary dysplasia, severe intraventricular hemorrhage, or severe retinopathy of prematurity. RESULTS: All 4 scores were associated with death and severe intraventricular hemorrhage (P < .01). The OR and 95% CI for every 1-point increase in aHRC for predicting mortality, adjusted for gestational age, was 1.59 (1.25-2.00) for aHRC-24h and 2.61 (1.58-4.33) for aHRC-7d. High aHRC-7d, SNAP-II, and CRIB-II were associated with bronchopulmonary dysplasia (P < .001). High aHRC-7d was associated with late-onset septicemia (P < .05). None of the scores predicted necrotizing enterocolitis or severe retinopathy of prematurity. CONCLUSIONS: HRC assessed in the first day or first week after birth compares favorably to established risk scores to predict death and morbidities in very low birth weight infants.

Infection and other clinical correlates of abnormal heart rate characteristics in preterm infants.

OBJECTIVE: To identify clinical conditions associated with a large increase (spike) in the heart rate characteristics index in very low birth weight (VLBW) infants. STUDY DESIGN: Retrospective medical record review within a day of all large heart rate characteristics index spikes (increase of ≥3 from the previous 5-day average) in VLBW infants at a single center enrolled from 2007 to 2010 in a multicenter trial of heart rate characteristics monitoring. In the trial, infants were randomized to having their heart rate characteristics index displayed to clinicians or not displayed. RESULTS: Of 274 eligible infants, 224 large heart rate characteristics spikes occurred in 105 infants. Thirty-three spikes were associated with surgery or procedures requiring anesthetic or anticholinergic medications, and infection-related conditions were the most common clinical association with the other spikes. Of the first spikes in 47 infants randomized to conventional monitoring (heart rate characteristics index not displayed to clinicians), 53% were associated with suspected or proven infection. Respiratory deterioration without suspected infection occurred with 34%, and no association was identified in 13%. Infants randomized to having their heart rate characteristics index displayed were more likely to have antibiotics initiated around the time of a large heart rate characteristics index spike. CONCLUSIONS: Sepsis, other infectious or systemic inflammatory conditions, respiratory deterioration, and surgical procedures are the most common clinical associations with a large increase in the heart rate characteristics index in VLBW infants. This information may improve use of heart rate characteristics monitors in patients in the neonatal intensive care unit.

Predicting the need for urgent intubation in a surgical/trauma intensive care unit.

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 <24 and ≥24 hours before intubation. Multivariate predictive models for the risk of unplanned intubation were developed and validated internally by subsequent sample and bootstrapping techniques. RESULTS: Fifty unplanned intubations (41 patients) were identified from 798 STICU patients. The optimal multivariate predictive model (HR, RR, and SpO2 means, and RR-SpO2 correlation coefficient) had a receiving operating characteristic (ROC) area of 0.770 (95% confidence interval [CI], 0.712-0.841). For this model, relative risks of intubation in the next 24 hours for the lowest and highest quintiles were 0.20 and 2.95, respectively (15-fold increase, baseline risk 1.46%). Adding age and days since previous extubation to this model increased ROC area to 0.865 (95 % CI, 0.821-0.910). CONCLUSION: Among STICU patients, a multivariate model predicted increases in risk of intubation in the following 24 hours based on vital sign data available currently on bedside monitors. Further refinement could allow for earlier detection of respiratory decompensation and intervention to decrease preventable morbidity and mortality in surgical/trauma patients.

Cytokine screening identifies NICU patients with Gram-negative bacteremia.

INTRODUCTION: Biomarkers and physiomarkers may be useful adjunct tests for sepsis detection in neonatal intensive care unit (NICU) patients. We studied whether measuring plasma cytokines at the time of suspected sepsis could identify patients with bacteremia in centers in which patients were undergoing continuous physiomarker screening using a heart rate characteristics (HRC) index monitor. RESULTS: Six cytokines were higher in Gram-negative bacteremia (GNB) than in Gram-positive bacteremia or candidemia (GPBC). A cytokine score using thresholds for granulocyte colony-stimulating factor (G-CSF), interleukin (IL)-6, IL-8, and tumor necrosis factor (TNF)-α had 100% sensitivity and 69% positive predictive value (PPV) for GNB. A single cytokine marker, IL-6 < 130 pg/ml, had 100% sensitivity and 52% PPV for sepsis ruled out (SRO). The average HRC index was abnormal in this cohort of patients with clinical suspicion of sepsis and did not discriminate between the final sepsis designations. DISCUSSION: In summary, in NICU patients with suspected late-onset sepsis, plasma cytokines can identify those with SRO and those with GNB, potentially aiding in decisions regarding therapy. METHODS: Seven cytokines were measured in 226 plasma samples from patients >3 d old with sepsis suspected based on clinical signs, abnormal HRC index, or both. Cases were classified as SRO, clinical sepsis (CS), GPBC, or GNB.

Endotoxin depresses heart rate variability in mice: cytokine and steroid effects.

Heart rate variability (HRV) falls in humans with sepsis, but the mechanism is not well understood. We utilized a mouse model of endotoxemia to test the hypothesis that cytokines play a role in abnormal HRV during sepsis. Adult male C57BL/6 mice underwent surgical implantation of probes to continuously monitor electrocardiogram and temperature or blood pressure via radiotelemetry. Administration of high-dose LPS (Escherichia coli LPS, 10 mg/kg, n = 10) caused a biphasic response characterized by an early decrease in temperature and heart rate at 1 h in some mice, followed by a prolonged period of depressed HRV in all mice. Further studies showed that LPS doses as low as 0.01 mg/kg evoked a significant decrease in HRV. With high-dose LPS, the initial drops in temperature and HR were temporally correlated with peak expression of TNFalpha 1 h post-LPS, whereas maximal depression in HRV coincided with peak levels of multiple other cytokines 3-9 h post-LPS. Neither hypotension nor hypothermia explained the HRV response. Pretreatment with dexamethasone prior to LPS significantly blunted expression of 7 of the 10 cytokines studied and shortened the duration of depressed HRV by about half. Interestingly, dexamethasone treatment alone caused a dramatic increase in both low- and high-frequency HRV. Administration of recombinant TNFalpha caused a biphasic response in HR and HRV similar to that caused by LPS. Understanding the role of cytokines in abnormal HRV during sepsis could lead to improved strategies for detecting life-threatening nosocomial infections in intensive care unit patients.

Phospholemman overexpression inhibits Na+-K+-ATPase in adult rat cardiac myocytes: relevance to decreased Na+ pump activity in postinfarction myocytes.

Messenger RNA levels of phospholemman (PLM), a member of the FXYD family of small single-span membrane proteins with putative ion-transport regulatory properties, were increased in postmyocardial infarction (MI) rat myocytes. We tested the hypothesis that the previously observed reduction in Na+-K+-ATPase activity in MI rat myocytes was due to PLM overexpression. In rat hearts harvested 3 and 7 days post-MI, PLM protein expression was increased by two- and fourfold, respectively. To simulate increased PLM expression post-MI, PLM was overexpressed in normal adult rat myocytes by adenovirus-mediated gene transfer. PLM overexpression did not affect the relative level of phosphorylation on serine68 of PLM. Na+-K+-ATPase activity was measured as ouabain-sensitive Na+-K+ pump current (Ip). Compared with control myocytes overexpressing green fluorescent protein alone, Ip measured in myocytes overexpressing PLM was significantly (P < 0.0001) lower at similar membrane voltages, pipette Na+ ([Na+]pip) and extracellular K+ ([K+]o) concentrations. From -70 to +60 mV, neither [Na+]pip nor [K+]o required to attain half-maximal Ip was significantly different between control and PLM myocytes. This phenotype of decreased V(max) without appreciable changes in K(m) for Na+ and K+ in PLM-overexpressed myocytes was similar to that observed in MI rat myocytes. Inhibition of Ip by PLM overexpression was not due to decreased Na+-K+-ATPase expression because there were no changes in either protein or messenger RNA levels of either alpha1- or alpha2-isoforms of Na+-K+-ATPase. In native rat cardiac myocytes, PLM coimmunoprecipitated with alpha-subunits of Na+-K+-ATPase. Inhibition of Na+-K+-ATPase by PLM overexpression, in addition to previously reported decrease in Na+-K+-ATPase expression, may explain altered V(max) but not K(m) of Na+-K+-ATPase in postinfarction rat myocytes.