Predicting neonatal sepsis using features of heart rate variability, respiratory characteristics and ECG-derived estimates of infant motion

This study in preterm infants was designed to characterize the prognostic potential of several features of heart rate variability (HRV), respiration, and (infant) motion for the predictive monitoring of late-onset sepsis (LOS). In a neonatal intensive care setting, the cardiorespiratory waveforms of infants with blood-culture positive LOS were analyzed to characterize the prognostic potential of 22 features for discriminating control from sepsis-state, using the Naïve Bayes algorithm. Historical data of the subjects acquired from a period sufficiently before the clinical suspicion of LOS was used as control state, whereas data from the 24 h preceding the clinical suspicion of LOS were used as sepsis state (test data). The overall prognostic potential of all features was quantified at three-hourly intervals for the period corresponding to test data by calculating the area under the receiver operating characteristics curve. For the 49 infants studied, features of HRV, respiration, and movement showed characteristic changes in the hours leading up to the clinical suspicion of sepsis, namely, an increased propensity toward pathological heart rate decelerations, increased respiratory instability, and a decrease in spontaneous infant activity, i.e., lethargy. While features characterizing HRV and respiration can be used to probe the state of the autonomic nervous system, those characterizing movement probe the state of the motor system-dysregulation of both reflects an increased likelihood of sepsis. By using readily interpretable features derived from cardiorespiratory monitoring, opportunities for pre-emptively identifying and treating LOS can be developed.</p

Study of Early Predictors of Fatality in Mechanically Ventilated Neonates in NICU

Objective: To evaluate the risk factors associated with fatality in mechanically ventilated neonates using multiple regression analysis. Design & settings: Prospective study conducted at Neonatal ICU at New Civil Hospital, Surat – a tertiary care centre, from December, 2007 to May, 2008 for 6 months. Methods: Fifty neonates in NICU consecutively put on mechanical ventilator during study period were enrolled in the study. The pressure limited time cycled ventilator was used. All admitted neonates were subjected to an arterial blood gas analysis along with a set of investigations to look for pulmonary maturity, infections, renal function, hyperbilirubinemia, intraventricular hemorrhage and congenital anomalies. Different investigation facilities were used as and when required during ventilation of neonates. Multiple logistic regression analysis was done to find out the predictors of fatality among these neonates. Results: Various factors suspected as predictors of fatality of mechanically ventilated neonates were assessed. Hypothermia, prolonged capillary refill time (CRT), initial requirement of oxygen fraction (FiO2) >0.6, alveolar to arterial PO2 difference (AaDO2) >250, alveolar to arterial PO2 ratio (a/A) <0.25, & oxygenation index (OI) >10 were found statistically highly significant predictors of mortality among mechanically ventilated neonates. Conclusion: Hypothermia and prolonged capillary refill time were independent predictors of fatality in neonatal mechanical ventilation. Risk of fatality can be identified in mechanically ventilated neonates

Presumed Neuroglycopenia Caused by Severe Hypoglycemia in Horses.

BackgroundNeuroglycopenia refers to a shortage of glucose in the brain resulting in neuronal dysfunction and death if left untreated. Presumed neuroglycopenia has not been described in horses.ObjectiveTo report neurological signs in horses with presumed neuroglycopenia as the result of severe hypoglycemia.AnimalsNinety horses (hours to 28 years of age) diagnosed with hypoglycemia (blood glucose concentration &lt; 75 mg/dL [&lt; 4.2 mmol/L]).MethodsRetrospective study. Electronic medical records were searched. Signalment, history, complaint, clinical signs, laboratory findings including CSF analysis, electroencephalogram, clinical or definitive diagnosis, and outcome were recorded. Kruskal-Wallis analysis of variance and logistic regression were used to investigate association between blood glucose concentration and data extracted. Statistical significance was set at P &lt; 0.05.ResultsThirty-eight and 52 horses had mild (50-74 mg/dL [2.8-4.1 mmol/L]), and severe hypoglycemia (&lt; 50 mg/dL [&lt; 2.8 mmol/L]), respectively. Most common causes of hypoglycemia included liver and gastrointestinal (40%) disease, sepsis (33%), neoplasia (7%), and insulin-induced (4%). Most common neurologic deficits included obtundation (100%), seizures (42%), and disorientation (22%). CSF-glucose was severely low (mean 2.5 mg/dL [0.1 mmol/L], median 0 mg/dL). Paroxysmal discharges in support of seizures were identified in the occipital (visual) and parietal (closest to temporal-auditory) cortical regions upon EEG examination (8/8 horses).Conclusions and clinical importanceNeuroglycopenia is presumed to occur in horses as the result of severe hypoglycemia. Subclinical seizures, and intermittent blindness and deafness of cortical origin can occur. Severe altered state of consciousness and seizures can be observed at a blood glucose cut-off value of &lt; 42 mg/dL (&lt; 2.3 mmol/L)

Predictive monitoring for early detection of subacute potentially catastrophic illnesses in critical care

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 \u3e 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\u27 eye in detecting abnormalities, and to undertake the clinical trials to test their impact on outcomes

Visual assessment of heart rate variability patterns associated with neonatal infection in preterm infants

Early identification of neonatal sepsis may help reduce morbidity. From Heart Rate Variability (HRV) visually assessed in preterm infants, eight of nine recordings in babies with positive blood cultures had low HRV and six infants without positive cultures had normal HRV. Straightforward HRV display could help identify infection in infants

Predictive Monitoring for Respiratory Decompensation Leading to Urgent Unplanned Intubation in the Neonatal Intensive Care Unit

Background: Infants admitted to the neonatal intensive care unit (NICU), and especially those born with very low birth weight (VLBW; \u3c 1,500 g), are at risk for respiratory decompensation requiring endotracheal intubation and mechanical ventilation. I ntubation and mechanical ventilation are associated with increased morbidity, particularly in urgent unplanned cases. Methods: We tested the hypothesis that the systemic response associated with respiratory decombensation can be detected from physiological monitoring and that statistical models of bedside monitoring data can identify infants at increased risk of urgent unplanned intubation. We studied 287 VLBW infants consecutively admitted to our NICU and found 96 events in 51 patients, excluding intUbations occurring within. 12h of a previous extubation. Results: In order of importance in a multivariable statistical model, we found that the characteristics of reduced O-2 satura, tion, especially as heart rate was falling; increased heart rate correlation with respiratory rate; and the amount of apnea were aIF significant independent pr,edictors.\u27 The predictive model, validated internally by bootStrap, had a receiver-operating characteristic area of 0.84 + / - 0.04. Conclusion: We propose that predictive monitoring in the NICU for urgent unplanned intubation may improve outcomes by allowing clinicians to intervene noninvasively before intubation is required

Signatures of Subacute Potentially Catastrophic Illness in the ICU: Model Development and Validation

Objectives: Patients in ICUs are susceptible to subacute potentially catastrophic illnesses such as respiratory failure, sepsis, and hemorrhage that present as severe derangements of vital signs. More subtle physiologic signatures may be present before clinical deterioration, when treatment might be more effective. We performed multivariate statistical analyses of bedside physiologic monitoring data to identify such early subclinical signatures of incipient life-threatening illness. Design: We report a study of model development and validation of a retrospective observational cohort using resampling (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis type 1b internal validation) and a study of model validation using separate data (type 2b internal/external validation). Setting: University of Virginia Health System (Charlottesville), a tertiary-care, academic medical center. Patients: Critically ill patients consecutively admitted between January 2009 and June 2015 to either the neonatal, surgical/trauma/burn, or medical ICUs with available physiologic monitoring data. Interventions: None. Measurements and Main Results: We analyzed 146 patient-years of vital sign and electrocardiography waveform time series from the bedside monitors of 9,232 ICU admissions. Calculations from 30-minute windows of the physiologic monitoring data were made every 15 minutes. Clinicians identified 1,206 episodes of respiratory failure leading to urgent unplanned intubation, sepsis, or hemorrhage leading to multi-unit transfusions from systematic individual chart reviews. Multivariate models to predict events up to 24 hours prior had internally validated C-statistics of 0.61-0.88. In adults, physiologic signatures of respiratory failure and hemorrhage were distinct from each other but externally consistent across ICUs. Sepsis, on the other hand, demonstrated less distinct and inconsistent signatures. Physiologic signatures of all neonatal illnesses were similar. Conclusions: Subacute potentially catastrophic illnesses in three diverse ICU populations have physiologic signatures that are detectable in the hours preceding clinical detection and intervention. Detection of such signatures can draw attention to patients at highest risk, potentially enabling earlier intervention and better outcomes