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.

Race, Class, and Place Modify Mortality Rates for the Leading Causes of Death in the United States, 1999-2021.

BACKGROUND: Race and ethnicity, socioeconomic class, and geographic location are well-known social determinants of health in the US. Studies of population mortality often consider two, but not all three of these risk factors. OBJECTIVES: To disarticulate the associations of race (whiteness), class (socioeconomic status), and place (county) with risk of cause-specific death in the US. DESIGN: We conducted a retrospective analysis of death certificate data. Bayesian regression models, adjusted for age and race/ethnicity from the American Community Survey and the county Area Deprivation Index, were used for inference. MAIN MEASURES: County-level mortality for 11 leading causes of death (1999-2019) and COVID-19 (2020-2021). KEY RESULTS: County "whiteness" and socioeconomic status modified death rates; geospatial effects differed by cause of death. Other factors equal, a 20% increase in county whiteness was associated with 5-8% increase in death from three causes and 4-15% reduction in death from others, including COVID-19. Other factors equal, advantaged counties had significantly lower death rates, even when juxtaposed with disadvantaged ones. Patterns of residual risk, measured by spatial county effects, varied by cause of death; for example: cancer and heart disease death rates were better explained by age, socioeconomic status, and county whiteness than were COVID-19 and suicide deaths. CONCLUSIONS: There are important independent contributions from race, class, and geography to risk of death in the US.

Predictive Monitoring-Impact in Acute Care Cardiology Trial (PM-IMPACCT): Protocol for a Randomized Controlled Trial.

BACKGROUND: Patients 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. OBJECTIVE: The 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. METHODS: We 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. 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 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 REGISTRATION: ClinicalTrials.gov NCT04359641; https://clinicaltrials.gov/ct2/show/NCT04359641. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/29631.

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.

Pre-Vent: the prematurity-related ventilatory control study.

BACKGROUND: The increasing incidence of bronchopulmonary dysplasia in premature babies may be due in part to immature ventilatory control, contributing to hypoxemia. The latter responds to ventilation and/or oxygen therapy, treatments associated with adverse sequelae. This is an overview of the Prematurity-Related Ventilatory Control Study which aims to analyze the under-utilized cardiorespiratory continuous waveform monitoring data to delineate mechanisms of immature ventilatory control in preterm infants and identify predictive markers. METHODS: Continuous ECG, heart rate, respiratory, and oxygen saturation data will be collected throughout the NICU stay in 500 infants < 29 wks gestation across 5 centers. Mild permissive hypercapnia, and hyperoxia and/or hypoxia assessments will be conducted in a subcohort of infants along with inpatient questionnaires, urine, serum, and DNA samples. RESULTS: Primary outcomes will be respiratory status at 40 wks and quantitative measures of immature breathing plotted on a standard curve for infants matched at 36-37 wks. Physiologic and/or biologic determinants will be collected to enhance the predictive model linking ventilatory control to outcomes. CONCLUSIONS: By incorporating bedside monitoring variables along with biomarkers that predict respiratory outcomes we aim to elucidate individualized cardiopulmonary phenotypes and mechanisms of ventilatory control contributing to adverse respiratory outcomes in premature infants.

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.

Pathogen-induced heart rate changes associated with cholinergic nervous system activation.

The autonomic nervous system plays a central role in regulation of host defense and in physiological responses to sepsis, including changes in heart rate and heart rate variability. The cholinergic anti-inflammatory response, whereby infection triggers vagal efferent signals that dampen production of proinflammatory cytokines, would be predicted to result in increased vagal signaling to the heart and increased heart rate variability. In fact, decreased heart rate variability is widely described in humans with sepsis. Our studies elucidate this apparent paradox by showing that mice injected with pathogens demonstrate transient bradyarrhythmias of vagal origin in a background of decreased heart rate variability (HRV). Intraperitoneal injection of a large inoculum of Gram-positive or Gram-negative bacteria or Candida albicans rapidly induced bradyarrhythmias of sinus and AV nodal block, characteristic of cardiac vagal firing and dramatically increased short-term HRV. These pathogen-induced bradycardias were immediately terminated by atropine, an antagonist of muscarinic cholinergic receptors, demonstrating the role of vagal efferent signaling in this response. Vagal afferent signaling following pathogen injection was demonstrated by intense nuclear c-Fos activity in neurons of the vagal sensory ganglia and brain stem. Surprisingly, pathogen-induced bradycardia demonstrated rapid and prolonged desensitization and did not recur on repeat injection of the same organism 3 h or 3 days after the initial exposure. After recovery from the initial bradycardia, depressed heart rate variability developed in some mice and was correlated with elevated plasma cytokine levels and mortality. Our findings of decreased HRV and transient heart rate decelerations in infected mice are similar to heart rate changes described by our group in preterm neonates with sepsis. Pathogen sensing and signaling via the vagus nerve, and the desensitization of this response, may account for periods of both increased and decreased heart rate variability in sepsis.

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.

Characterization of the phospholemman knockout mouse heart: depressed left ventricular function with increased Na-K-ATPase activity.

Phospholemman (PLM, FXYD1), abundantly expressed in the heart, is the primary cardiac sarcolemmal substrate for PKA and PKC. Evidence supports the hypothesis that PLM is part of the cardiac Na-K pump complex and provides the link between kinase activity and pump modulation. PLM has also been proposed to modulate Na/Ca exchanger activity and may be involved in cell volume regulation. This study characterized the phenotype of the PLM knockout (KO) mouse heart to further our understanding of PLM function in the heart. PLM KO mice were bred on a congenic C57/BL6 background. In vivo conductance catheter measurements exhibited a mildly depressed cardiac contractile function in PLM KO mice, which was exacerbated when hearts were isolated and Langendorff perfused. There were no significant differences in action potential morphology in paced Langendorff-perfused hearts. Depressed contractile function was associated with a mild cardiac hypertrophy in PLM KO mice. Biochemical analysis of crude ventricular homogenates showed a significant increase in Na-K-ATPase activity in PLM KO hearts compared with wild-type controls. SDS-PAGE and Western blot analysis of ventricular homogenates revealed small, nonsignificant changes in Na- K-ATPase subunit expression, with two-dimensional gel (isoelectric focusing, SDS-PAGE) analysis revealing minimal changes in ventricular protein expression, indicating that deletion of PLM was the primary reason for the observed PLM KO phenotype. These studies demonstrate that PLM plays an important role in the contractile function of the normoxic mouse heart. Data are consistent with the hypothesis that PLM modulates Na-K-ATPase activity, indirectly affecting intracellular Ca and hence contractile function.

Regulation of cardiac Na+/Ca2+ exchanger by phospholemman.

Phospholemman (PLM) is the first sequenced member of the FXYD family of regulators of ion transport. The mature protein has 72 amino acids and consists of an extracellular N terminus containing the signature FXYD motif, a single transmembrane (TM) domain, and a cytoplasmic C-terminal domain containing four potential sites for phosphorylation. PLM and other members of the FXYD family are known to regulate Na+-K+-ATPase. Using adenovirus-mediated gene transfer into adult rat cardiac myocytes, we showed that changes in contractility and intracellular Ca2+ homeostasis associated with PLM overexpression or downregulation are not consistent with the effects expected from inhibition of Na+-K+-ATPase by PLM. Additional studies with heterologous expression of PLM and cardiac Na+/Ca2+ exchanger 1 (NCX1) in HEK293 cells and cardiac myocytes isolated from PLM-deficient mice demonstrated by co-localization, co-immunoprecipitation, and electrophysiological and radioactive tracer uptake techniques that PLM associates with NCX1 in the sarcolemma and transverse tubules and that PLM inhibits NCX1, independent of its effects on Na+-K+-ATPase. Mutational analysis indicates that the cytoplasmic domain of PLM is required for its regulation of NCX1. In addition, experiments using phosphomimetic and phospho-deficient PLM mutants, as well as activators of protein kinases A and C, indicate that PLM phosphorylated at serine68 is the active form that inhibits NCX1. This is in sharp contrast to the finding that the unphosphorylated PLM form inhibits Na+-K+-ATPase. We conclude that PLM regulates cardiac contractility by modulating the activities of NCX and Na+-K+-ATPase.

Phospholemman inhibition of the cardiac Na+/Ca2+ exchanger. Role of phosphorylation.

We have demonstrated previously that phospholemman (PLM), a 15-kDa integral sarcolemmal phosphoprotein, inhibits the cardiac Na+/Ca2+ exchanger (NCX1). In addition, protein kinase A phosphorylates serine 68, whereas protein kinase C phosphorylates both serine 63 and serine 68 of PLM. Using human embryonic kidney 293 cells that are devoid of both endogenous PLM and NCX1, we first demonstrated that the exogenous NCX1 current (I(NaCa)) was increased by phorbol 12-myristate 13-acetate (PMA) but not by forskolin. When co-expressed with NCX1, PLM resulted in: (i) decreases in I(NaCa), (ii) attenuation of the increase in I(NaCa) by PMA, and (iii) additional reduction in I(NaCa) in cells treated with forskolin. Mutating serine 63 to alanine (S63A) preserved the sensitivity of PLM to forskolin in terms of suppression of I(NaCa), whereas mutating serine 68 to alanine (S68A) abolished the inhibitory effect of PLM on I(NaCa). Mutating serine 68 to glutamic acid (phosphomimetic) resulted in additional suppression of I(NaCa) as compared with wild-type PLM. These results suggest that PLM phosphorylated at serine 68 inhibited I(NaCa). The physiological significance of inhibition of NCX1 by phosphorylated PLM was evaluated in PLM-knock-out (KO) mice. When compared with wild-type myocytes, I(NaCa) was significant larger in PLM-KO myocytes. In addition, the PMA-induced increase in I(NaCa) was significantly higher in PLM-KO myocytes. By contrast, forskolin had no effect on I(NaCa) in wild-type myocytes. We conclude that PLM, when phosphorylated at serine 68, inhibits Na+/Ca2+ exchange in the heart.