Impact of sedation and organ failure on continuous heart and respiratory rate variability monitoring in critically ill patients: a pilot study

Caution in the use of sedation and endomyocardial biopsy for the management of pediatric acute heart failure caused by endocardial fibroelastosis

Endocardial fibroelastosis (EFE) is commonly considered to be an inflammatory reactive lesion of hyperplasia and deposition of tissue fibers and collagen in the endocardium and/or subendocardium, which is strongly associated with endocardial sclerosis, ventricular remodeling and acute and chronic heart failure, and is one of the important causes for pediatric heart transplantation. Early diagnosis and treatment are the key factors in determining the prognosis of the children. In this paper, we would like to highlight the potential unintended consequences of the use of sedation and biopsy for pediatric acute heart failure caused by EFE and the comprehensive considerations prior to clinical diagnosis.

Caution in the use of sedation and endomyocardial biopsy for the management of pediatric acute heart failure caused by endocardial fibroelastosis

Endocardial fibroelastosis (EFE) is commonly considered to be an inflammatory reactive lesion of hyperplasia and deposition of tissue fibers and collagen in the endocardium and/or subendocardium, which is strongly associated with endocardial sclerosis, ventricular remodeling and acute and chronic heart failure, and is one of the important causes for pediatric heart transplantation. Early diagnosis and treatment are the key factors in determining the prognosis of the children. In this paper, we would like to highlight the potential unintended consequences of the use of sedation and biopsy for pediatric acute heart failure caused by EFE and the comprehensive considerations prior to clinical diagnosis.

Early prediction of delirium upon intensive care unit admission: Model development, validation, and deployment

STUDY OBJECTIVE

To develop, validate, and deploy models for predicting delirium in critically ill adult patients as early as upon intensive care unit (ICU) admission.

DESIGN

Retrospective cohort study.

SETTING

Single university teaching hospital in Taipei, Taiwan.

PATIENTS

6238 critically ill patients from August 2020 to August 2021.

MEASUREMENTS

Data were extracted, pre-processed, and split into training and testing datasets based on the time period. Eligible variables included demographic characteristics, Glasgow Coma Scale, vital signs parameters, treatments, and laboratory data. The predicted outcome was delirium, defined as any positive result (a score ≥ 4) of the Intensive Care Delirium Screening Checklist that was assessed by primary care nurses in each 8-h shift within 48 h after ICU admission. We trained models to predict delirium upon ICU admission (ADM) and at 24 h (24H) after ICU admission by using logistic regression (LR), gradient boosted trees (GBT), and deep learning (DL) algorithms and compared the models' performance.

MAIN RESULTS

Eight features were extracted from the eligible features to train the ADM models, including age, body mass index, medical history of dementia, postoperative intensive monitoring, elective surgery, pre-ICU hospital stays, and GCS score and initial respiratory rate upon ICU admission. In the ADM testing dataset, the incidence of ICU delirium occurred within 24 h and 48 h was 32.9% and 36.2%, respectively. The area under the receiver operating characteristic curve (AUROC) (0.858, 95% CI 0.835-0.879) and area under the precision-recall curve (AUPRC) (0.814, 95% CI 0.780-0.844) for the ADM GBT model were the highest. The Brier scores of the ADM LR, GBT, and DL models were 0.149, 0.140, and 0.145, respectively. The AUROC (0.931, 95% CI 0.911-0.949) was the highest for the 24H DL model and the AUPRC (0.842, 95% CI 0.792-0.886) was the highest for the 24H LR model.

CONCLUSION

Our early prediction models based on data obtained upon ICU admission could achieve good performance in predicting delirium occurred within 48 h after ICU admission. Our 24-h models can improve delirium prediction for patients discharged >1 day after ICU admission.

Association between postoperative delirium and heart rate variability in the intensive care unit and readmissions and mortality in elderly patients with cardiovascular surgery

This study aimed to investigate the relationship between heart rate variability (HRV), a parameter of the autonomic nervous system activity (ANSA), and postoperative delirium and postoperative events. This retrospective cohort study included elderly patients aged 65 years or older who were admitted to the intensive care unit (ICU) after cardiovascular surgery. ANSA was measured using HRV parameters for 1 h at daytime and 1 h at night-time before ICU discharge. The primary endpoint was the effect of HRV parameters and delirium on mortality and readmission rates within 1 year after discharge, and the secondary endpoint was the association between HRV parameters and delirium. Cox proportional hazards models were used to examine the association between HRV parameters and postoperative events by adjusting for delirium and pre and postoperative information. A total of 71 patients, 39 without delirium and 32 with delirium, met the inclusion criteria. The incidence of death and readmission within 1 year was significantly higher in the delirium group and in the group with higher daytime HF (high frequency power) and r-MSSD (square root of the squared mean of the difference of successive NN intervals), parameters of the parasympathetic nervous system activity (PNSA), than that in other groups. Furthermore, the delirium group had significantly higher HF and r-MSSD than the nondelirium group. Even after adjusting for confounding factors in the multivariate analysis, a trend of higher daytime HF and r-MSSD was observed, indicating a significant effect on the occurrence of combined events within 1 year of discharge. ICU delirium has been associated with higher daytime HF and r-MSSD, parameters of PNSA. ICU delirium was a prognostic factor, and increased daytime PNSA may worsen the prognosis of elderly patients after cardiovascular surgery.

Sevoflurane and Hypercapnia Blunt the Physiological Variability of Spontaneous Breathing: A Comparative Interventional Study

Background: Although spontaneous breathing is known to exhibit substantial physiological fluctuation that contributes to alveolar recruitment, changes in the variability of the respiratory pattern following inhalation of carbon dioxide (CO2) and volatile anesthetics have not been characterized. Therefore, we aimed at comparing the indices of breathing variability under wakefulness, sleep, hypercapnia and sedative and anesthetic concentrations of sevoflurane.

Methods: Spontaneous breathing pattern was recorded on two consecutive days in six rabbits using open whole-body plethysmography under wakefulness and spontaneous sleep and following inhalation of 5% CO2, 2% sevoflurane (0.5 MAC) and 4% (1 MAC) sevoflurane. Tidal volume (VT), respiratory rate (RR), minute ventilation (MV), inspiratory time (TI) and mean inspiratory flow (VT/TI) were calculated from the pressure fluctuations in the plethysmograph. Means and coefficients of variation were calculated for each measured variable. Autoregressive model fitting was applied to estimate the relative contributions of random, correlated, and oscillatory behavior to the total variance.

Results: Physiological sleep decreased MV by lowering RR without affecting VT. Hypercapnia increased MV by elevating VT. Sedative and anesthetic concentrations of sevoflurane increased VT but decreased MV due to a decrease in RR. Compared to the awake stage, CO2 had no effect on VT/TI while sevoflurane depressed significantly the mean inspiratory flow. Compared to wakefulness, the variability in VT, RR, MV, TI and VT/TI were not affected by sleep but were all significantly decreased by CO2 and sevoflurane. The variance of TI originating from correlated behavior was significantly decreased by both concentrations of sevoflurane compared to the awake and asleep conditions.

Conclusions: The variability of spontaneous breathing during physiological sleep and sevoflurane-induced anesthesia differed fundamentally, with the volatile agent diminishing markedly the fluctuations in respiratory volume, inspiratory airflow and breathing frequency. These findings may suggest the increased risk of lung derecruitment during procedures under sevoflurane in which spontaneous breathing is maintained.

Breathing variability during propofol/remifentanil procedural sedation with a single additional dose of midazolam or s-ketamine: a prospective observational study

Purpose

Regulation of spontaneous breathing is highly complex and may be influenced by drugs administered during the perioperative period. Because of their different pharmacological properties we hypothesized that midazolam and s-ketamine exert different effects on the variability of minute ventilation (MV), tidal volume (TV) and respiratory rate (RR).

Methods

Patients undergoing procedural sedation (PSA) with propofol and remifentanil received a single dose of midazolam (1–3 mg, n = 10) or s-ketamine (10–25 mg, n = 10). We used non-invasive impedance-based respiratory volume monitoring to record RR as well as changes in TV and MV. Variability of these three parameters was calculated as coefficients of variation.

Results

TV and MV decreased during PSA to a comparable extent in both groups, whereas there was no significant change in RR. In line with our hypothesis we observed marked differences in breathing variability. The variability of MV (– 47.5% ± 24.8%, p = 0.011), TV (– 42.1% ± 30.2%, p = 0.003), and RR (– 28.5% ± 29.3%, p = 0.011) was significantly reduced in patients receiving midazolam. In contrast, variability remained unchanged in patients receiving s-ketamine (MV + 16% ± 45.2%, p = 0.182; TV +12% ± 47.7%, p = 0.390; RR +39% ± 65.2%, p = 0.129). After termination of PSA breathing variables returned to baseline values.

Conclusions

While midazolam reduces respiratory variability in spontaneously breathing patients undergoing procedural sedation, s-ketamine preserves variability suggesting different effects on the regulation of spontaneous breathing.

Breathing variability-implications for anaesthesiology and intensive care

The respiratory system reacts instantaneously to intrinsic and extrinsic inputs. This adaptability results in significant fluctuations in breathing parameters, such as respiratory rate, tidal volume, and inspiratory flow profiles. Breathing variability is influenced by several conditions, including sleep, various pulmonary diseases, hypoxia, and anxiety disorders. Recent studies have suggested that weaning failure during mechanical ventilation may be predicted by low respiratory variability. This review describes methods for quantifying breathing variability, summarises the conditions and comorbidities that affect breathing variability, and discusses the potential implications of breathing variability for anaesthesia and intensive care.

Variations in respiratory rate do not reflect changes in tidal volume or minute ventilation after major abdominal surgery

Monitoring of postoperative pulmonary function usually includes respiratory rate and oxygen saturation measurements. We hypothesized that changes in postoperative respiratory rate do not correlate with changes in tidal volume or minute ventilation. In addition, we hypothesized that variability of minute ventilation and tidal volume is larger than variability of respiratory rate. Respiratory rate and changes in tidal volume and in minute ventilation were continuously measured in 27 patients during 24 h following elective abdominal surgery, using an impedance-based non-invasive respiratory volume monitor (ExSpiron, Respiratory Motion, Waltham, MA, US). Coefficients of variation were used as a measure for variability of respiratory rate, tidal volume and minute ventilation. Data of 38,149 measurements were analyzed. We found no correlation between respiratory rate and tidal volume or minute ventilation (r² = 0.02 and 0.01). Mean respiratory rate increased within the first 24 h after abdominal surgery from 13.9 ± 2.5 to 16.2 ± 2.4 breaths/min (p = 0.008), while tidal volume and minute ventilation remained unchanged (p = 0.90 and p = 0.18). Of interest, variability of respiratory rate (0.21 ± 0.06) was significantly smaller than variability of tidal volume (0.37 ± 0.12, p < 0.001) and minute ventilation (0.41 ± 0.12, p < 0.001). Changes in postoperative respiratory rate do not allow conclusions about changes in tidal volume or minute ventilation. We suggest that postoperative alveolar hypoventilation may not be recognized by monitoring respiratory rate alone. Variability of respiratory rate is smaller than variability in tidal volume and minute ventilation, suggesting that adaptations of alveolar ventilation to metabolic needs may be predominately achieved by variations in tidal volume.

Early prognostication of neurological outcome by heart rate variability in adult patients with out-of-hospital sudden cardiac arrest

Background

Most deaths of comatose survivors of out-of-hospital sudden cardiac arrest result from withdrawal of life-sustaining treatment (WLST) decisions based on poor neurological prognostication and the family’s intention. Thus, accurate prognostication is crucial to avoid premature WLST decisions. However, targeted temperature management (TTM) with sedation or neuromuscular blockade against shivering significantly affects early prognostication. In this study, we investigated whether heart rate variability (HRV) analysis could prognosticate poor neurological outcome in comatose patients undergoing hypothermic TTM.

Methods

Between January 2015 and December 2017, adult patients with out-of-hospital sudden cardiac arrest, successfully resuscitated in the emergency department and admitted to the intensive care unit of the Niigata University in Japan, were prospectively included. All patients had an initial Glasgow Coma Scale motor score of 1 and received hypothermic TTM (at 34 °C). Twenty HRV-related variables (deceleration capacity; 4 time-, 3 geometric-, and 7 frequency-domain; and 5 complexity variables) were computed based on RR intervals between 0:00 and 8:00 am within 24 h after return of spontaneous circulation (ROSC). Based on Glasgow Outcome Scale (GOS) at 2 weeks after ROSC, patients were divided into good outcome (GOS 1–2) and poor outcome (GOS 3–5) groups.

Results

Seventy-six patients were recruited and allocated to the good (n = 22) or poor (n = 54) outcome groups. Of the 20 HRV-related variables, ln very-low frequency (ln VLF) power, detrended fluctuation analysis (DFA) (α1), and multiscale entropy (MSE) index significantly differed between the groups (p = 0.001), with a statistically significant odds ratio (OR) by univariate logistic regression analysis (p = 0.001). Multivariate logistic regression analysis of the 3 variables identified ln VLF power and DFA (α1) as significant predictors for poor outcome (OR = 0.436, p = 0.006 and OR = 0.709, p = 0.024, respectively). The area under the receiver operating characteristic curve for ln VLF power and DFA (α1) in predicting poor outcome was 0.84 and 0.82, respectively. In addition, the minimum value of ln VLF power or DFA (α1) for the good outcome group predicted poor outcome with sensitivity = 61% and specificity = 100%.

Conclusions

The present data indicate that HRV analysis could be useful for prognostication for comatose patients during hypothermic TTM.

Potential EEG biomarkers of sedation doses in intensive care patients unveiled by using a machine learning approach

OBJECTIVE

Sedation of neurocritically ill patients is one of the most challenging situation in ICUs. Quantitative knowledge on the sedation effect on brain activity in that complex scenario could help to uncover new markers for sedation assessment. Hence, we aim to evaluate the existence of changes of diverse EEG-derived measures in deeply-sedated (RASS-Richmond agitation-sedation scale  -4 and  -5) neurocritically ill patients, and also whether sedation doses are related with those eventual changes.

APPROACH

We performed an observational prospective cohort study in the intensive care unit of the Hospital de la Princesa. Twenty-six adult patients suffered from traumatic brain injury and subarachnoid hemorrhage were included in the present study. Long-term continuous electroencephalographic (EEG) recordings (2141 h) and hourly annotated information were used to determine the relationship between intravenous sedation infusion doses and network and spectral EEG measures. To do that, two different strategies were followed: assessment of the statistical dependence between both variables using the Spearman correlation rank and by performing an automatic classification method based on a machine learning algorithm.

MAIN RESULTS

More than 60% of patients presented a correlation greater than 0.5 in at least one of the calculated EEG measures with the sedation dose. The automatic classification method presented an accuracy of 84.3% in discriminating between different sedation doses. In both cases the nodes' degree was the most relevant measurement.

SIGNIFICANCE

The results presented here provide evidences of brain activity changes during deep sedation linked to sedation doses. Particularly, the capability of network EEG-derived measures in discriminating between different sedation doses could be the framework for the development of accurate methods for sedation levels assessment.

Autonomic Nervous System Dysfunction in Pediatric Sepsis

The autonomic nervous system (ANS) plays a major role in maintaining homeostasis through key adaptive responses to stress, including severe infections and sepsis. The ANS-mediated processes most relevant during sepsis include regulation of cardiac output and vascular tone, control of breathing and airway resistance, inflammation and immune modulation, gastrointestinal motility and digestion, and regulation of body temperature. ANS dysfunction (ANSD) represents an imbalanced or maladaptive response to injury and is prevalent in pediatric sepsis. Most of the evidence on ANSD comes from studies of heart rate variability, which is a marker of ANS function and is inversely correlated with organ dysfunction and mortality. In addition, there is evidence that other measures of ANSD, such as respiratory rate variability, skin thermoregulation, and baroreflex and chemoreflex sensitivity, are associated with outcomes in critical illness. The relevance of understanding ANSD in the context of pediatric sepsis stems from the fact that it might play an important role in the pathophysiology of sepsis, is associated with outcomes, and can be measured continuously and noninvasively. Here we review the physiology and dysfunction of the ANS during critical illness, discuss methods for measuring ANS function in the intensive care unit, and review the diagnostic, prognostic, and therapeutic value of understanding ANSD in pediatric sepsis.

Continuous blood purification ameliorates clinical signs and corrects the plasma phospholipid levels of patients with multiple organ dysfunction syndromes

BACKGROUND

Multiple organ dysfunction syndromes (MODS) is reported as a leading cause of mortality in intensive care units. Recently, continuous blood purification (CBP) has been mostly applied for MODS treatment. Thus, the purpose of this study was to investigate the effects of CBP on plasma phospholipid level in patients with MODS.

METHODS

A total of 126 patients with MODS and 120 healthy people were collected. The serum cytokine levels, blood biochemical parameters, and blood gas indexes were detected, and the correlation among phospholipid compounds with serum cytokine levels, blood biochemical parameters, and blood gas indexes was analyzed.

RESULTS

Before CBP, levels of body temperature, RR, HR, CVP, IL-6, IL-10, TNF-α, BUN, SCr, PaCO₂ , SM747, and LPC540 were obviously higher, and pH, HCO_3- , PaO₂ , SaO₂ , PE750, PI885, PC792, PC826, PC830, PC854, PC802, and PG747 were lower in the MODS group than those in the control group. During CBP, the MODS group had gradually declined RR, CVP, levels of IL-6, IL-10 and TNF-α, BUN, SCr, PaCO₂ , SM747, and LPC540 and increased HCO_3- , PaO₂ and SaO₂ , PE750, PI885, PC792, PC826, PC830, PC854, PC802, and PG747. Besides, levels of PE750, PI885, PC792, PC826, PC830, PC854, PC802, and PG747 had an obvious negative correlation with levels of TNF-α, IL-10, IL-6, BUN, SCr, and PaCO₂ , and a significant positive correlation with levels of HCO_3- , PaO₂ , and SaO₂ .

CONCLUSION

CBP could effectively ameliorate clinical signs of patients with MODS and correct the plasma phospholipid levels.

Vagal withdrawal and psychological distress during ventilator weaning and the related outcomes

OBJECTIVE

This study investigated the associations between changes in autonomic nervous system (ANS) function, psychological status during the mechanical ventilation (MV) weaning process, and weaning outcomes.

METHODS

In this prospective study, we recruited 67 patients receiving MV for >24h at a medical center in northern Taiwan. Patients' ANS function, represented by heart rate variability (HRV), the rapid shallow breathing index (RSBI), anxiety, fear, and dyspnea, was repeatedly measured 10min before and 30min after undergoing a weaning trial. Forty-nine patients capable of sustaining a 2-h weaning trial were successfully weaned.

RESULTS

Compared with the failed group, the success group showed significantly smaller decreases in high-frequency HRV (HRV-HF) and smaller increases in RSBI (per 10 breaths/min/L), fear, dyspnea, and anxiety in response to the weaning trial (odds ratio [OR]=2.19, 0.81, 0.69, 0.66, and 0.77, respectively; p<0.05). Multivariate analyses revealed that low-frequency HRV before weaning (OR=2.32; 95% confidence interval [CI]=1.13-4.78, p=0.02), changes in HRV-HF (OR=3.33; 95% CI=1.18-9.44, p=0.02), and psychological fear during the weaning process (OR=0.50; 95% CI=0.27-0.92, p=0.03) were three independent factors associated with 2-h T-piece weaning success.

CONCLUSIONS

ANS responses and psychological distress during weaning were associated with T-piece weaning outcomes and may reflect the need for future studies to utilize these factors to guide weaning processes and examine their impact on outcomes.

Patient-Specific Classification of ICU Sedation Levels From Heart Rate Variability

OBJECTIVE

To develop a personalizable algorithm to discriminate between sedation levels in ICU patients based on heart rate variability.

DESIGN

Multicenter, pilot study.

SETTING

Several ICUs at Massachusetts General Hospital, Boston, MA.

PATIENTS

We gathered 21,912 hours of routine electrocardiogram recordings from a heterogenous group of 70 adult ICU patients. All patients included in the study were mechanically ventilated and were receiving sedatives.

MEASUREMENTS AND MAIN RESULTS

As "ground truth" for developing our method, we used Richmond Agitation Sedation Scale scores grouped into four levels denoted "comatose" (-5), "deeply sedated" (-4 to -3), "lightly sedated" (-2 to 0), and "agitated" (+1 to +4). We trained a support vector machine learning algorithm to calculate the probability of each sedation level from heart rate variability measures derived from the electrocardiogram. To estimate algorithm performance, we calculated leave-one-subject out cross-validated accuracy. The patient-independent version of the proposed system discriminated between the four sedation levels with an overall accuracy of 59%. Upon personalizing the system supplementing the training data with patient-specific calibration data, consisting of an individual's labeled heart rate variability epochs from the preceding 24 hours, accuracy improved to 67%. The personalized system discriminated between light- and deep-sedation states with an average accuracy of 75%.

CONCLUSIONS

With further refinement, the methodology reported herein could lead to a fully automated system for depth of sedation monitoring. By enabling monitoring to be continuous, such technology may help clinical staff to monitor sedation levels more effectively and to reduce complications related to over- and under sedation.

Heart rate variability in critical care medicine: a systematic review

BACKGROUND

Heart rate variability (HRV) has been used to assess cardiac autonomic activity in critically ill patients, driven by translational and biomarker research agendas. Several clinical and technical factors can interfere with the measurement and/or interpretation of HRV. We systematically evaluated how HRV parameters are acquired/processed in critical care medicine.

METHODS

PubMed, MEDLINE, EMBASE and the Cochrane Central Register of Controlled Trials (1996-2016) were searched for cohort or case-control clinical studies of adult (>18 years) critically ill patients using heart variability analysis. Duplicate independent review and data abstraction. Study quality was assessed using two independent approaches: Newcastle-Ottowa scale and Downs and Black instrument. Conduct of studies was assessed in three categories: (1) study design and objectives, (2) procedures for measurement, processing and reporting of HRV, and (3) reporting of relevant confounding factors.

RESULTS

Our search identified 31/271 eligible studies that enrolled 2090 critically ill patients. A minority of studies (15; 48%) reported both frequency and time domain HRV data, with non-normally distributed, wide ranges of values that were indistinguishable from other (non-critically ill) disease states. Significant heterogeneity in HRV measurement protocols was observed between studies; lack of adjustment for various confounders known to affect cardiac autonomic regulation was common. Comparator groups were often omitted (n = 12; 39%). This precluded meaningful meta-analysis.

CONCLUSIONS

Marked differences in methodology prevent meaningful comparisons of HRV parameters between studies. A standardised set of consensus criteria relevant to critical care medicine are required to exploit advances in translational autonomic physiology.

Systems Biology and Clinical Practice in Respiratory Medicine. The Twain Shall Meet

Respiratory diseases are highly complex, being driven by host-environment interactions and manifested by inflammatory, structural, and functional abnormalities that vary over time. Traditional reductionist approaches have contributed vastly to our knowledge of biological systems in health and disease to date; however, they are insufficient to provide an understanding of the behavior of the system as a whole. In this Pulmonary Perspective, we discuss systems biology approaches, especially but not limited to the study of the lung as a complex system. Such integrative approaches take into account the large number of dynamic subunits and their interactions found in biological systems. Borrowing methods from physics and mathematics, it is possible to study the collective behavior of these systems over time and in a multidimensional manner. We first examine the physiological basis for complexity in the respiratory system and its implications for disease. We then expand on the potential applications of systems biology methods to study complex systems, within the context of diagnosis and monitoring of respiratory diseases including asthma, chronic obstructive pulmonary disease (COPD), and critical illness. We summarize the significant advances made in recent years using systems approaches for disease phenotyping, applied to data ranging from the molecular to clinical level, obtained from large-scale asthma and COPD networks. We describe new studies using temporal complexity patterns to characterize asthma and COPD and predict exacerbations as well as predict adverse outcomes in critical care. We highlight new methods that are emerging with this approach and discuss remaining questions that merit greater attention in the field.

Heart rate variability as a biomarker for sedation depth estimation in ICU patients

An automated patient-specific system to classify the level of sedation in ICU patients using heart rate variability signal is presented in this paper. ECG from 70 mechanically ventilated adult patients with administered sedatives in an ICU setting were used to develop a support vector machine based system for sedation depth monitoring using several heart rate variability measures. A leave-one-subject-out cross validation was used for classifier training and performance evaluations. The proposed patient-specific system provided a sensitivity, specificity and an AUC of 64%, 84.8% and 0.72, respectively. It is hoped that with the help of additional physiological signals the proposed patient-specific sedation level prediction system could lead to a fully automated multimodal system to assist clinical staff in ICUs to interpret the sedation level of the patient.

Automatic Classification of Sedation Levels in ICU Patients Using Heart Rate Variability

OBJECTIVE

To explore the potential value of heart rate variability features for automated monitoring of sedation levels in mechanically ventilated ICU patients.

DESIGN

Multicenter, pilot study.

SETTING

Several ICUs at Massachusetts General Hospital, Boston, MA.

PATIENTS

Electrocardiogram recordings from 40 mechanically ventilated adult patients receiving sedatives in an ICU setting were used to develop and test the proposed automated system.

MEASUREMENTS AND MAIN RESULTS

Richmond Agitation-Sedation Scale scores were acquired prospectively to assess patient sedation levels and were used as ground truth. Richmond Agitation-Sedation Scale scores were grouped into four levels, denoted "unarousable" (Richmond Agitation- Sedation Scale = -5, -4), "sedated" (-3, -2, -1), "awake" (0), "agitated" (+1, +2, +3, +4). A multiclass support vector machine algorithm was used for classification. Classifier training and performance evaluations were carried out using leave-oneout cross validation. An overall accuracy of 69% was achieved for discriminating between the four levels of sedation. The proposed system was able to reliably discriminate (accuracy = 79%) between sedated (Richmond Agitation-Sedation Scale < 0) and nonsedated states (Richmond Agitation-Sedation Scale > 0).

CONCLUSIONS

With further refinement, the methodology reported herein could lead to a fully automated system for depth of sedation monitoring. By enabling monitoring to be continuous, such technology may help clinical staff to monitor sedation levels more effectively and to reduce complications related to over- and undersedation.

Respiratory induced heart rate variability during slow mechanical ventilation : Marker to exclude brain death patients

BACKGROUND

Respiratory induced heart rate variability (rHRV) was analysed in mechanically ventilated patients during two levels of sedation and brain death. Our aim was to determine whether rHRV can distinguish between different levels of sedation and especially between brain death and sedated patients.

METHODS

In this study 30 critically ill and 23 brain death patients were included and four respiratory rates of 15, 12, 8 and 6 breaths per minute, each lasting 5 min were used. Two sedation levels, basal and deep, were performed in the critically ill patients. Heart rate and blood pressure changes induced by ventilation were subsequently detected and analysed.

RESULTS

Significant differences were found in rHRV and rHRV adjusted for tidal volume (rHRV/VT) between critically ill and brain death patients during slow breathing at 6 or 8 breaths per minute. The rHRV at 6 breaths per minute was below 15 ms in all brain death subjects except one. The rHRV/VT was lower than 25 ms/l at both 6 and 8 breaths per minute in all brain death patients and simultaneously at 75% of non-brain death patients was higher (specificity 1, sensitivity 0.24). Differences in rHRV and rHRV/VTs between basal and deep sedation were not significant.

CONCLUSIONS

The main clinical benefit of the study is the finding that rHRV and rHRV/VT during 6 and 8 breaths per minute can differentiate between critically ill and brain death patients. An rHRV/VT exceeding 25 ms/l reliably excludes brain death.

A Novel and Effective Method for Congestive Heart Failure Detection and Quantification Using Dynamic Heart Rate Variability Measurement

Risk assessment of congestive heart failure (CHF) is essential for detection, especially helping patients make informed decisions about medications, devices, transplantation, and end-of-life care. The majority of studies have focused on disease detection between CHF patients and normal subjects using short-/long-term heart rate variability (HRV) measures but not much on quantification. We downloaded 116 nominal 24-hour RR interval records from the MIT/BIH database, including 72 normal people and 44 CHF patients. These records were analyzed under a 4-level risk assessment model: no risk (normal people, N), mild risk (patients with New York Heart Association (NYHA) class I-II, P1), moderate risk (patients with NYHA III, P2), and severe risk (patients with NYHA III-IV, P3). A novel multistage classification approach is proposed for risk assessment and rating CHF using the non-equilibrium decision-tree-based support vector machine classifier. We propose dynamic indices of HRV to capture the dynamics of 5-minute short term HRV measurements for quantifying autonomic activity changes of CHF. We extracted 54 classical measures and 126 dynamic indices and selected from these using backward elimination to detect and quantify CHF patients. Experimental results show that the multistage risk assessment model can realize CHF detection and quantification analysis with total accuracy of 96.61%. The multistage model provides a powerful predictor between predicted and actual ratings, and it could serve as a clinically meaningful outcome providing an early assessment and a prognostic marker for CHF patients.

Initial fractal exponent of heart rate variability is associated with success of early resuscitation in patients with severe sepsis or septic shock: a prospective cohort study

INTRODUCTION

Heart rate variability (HRV) reflects autonomic nervous system tone as well as the overall health of the baroreflex system. We hypothesized that loss of complexity in HRV upon intensive care unit (ICU) admission would be associated with unsuccessful early resuscitation of sepsis.

METHODS

We prospectively enrolled patients admitted to ICUs with severe sepsis or septic shock from 2009 to 2011. We studied 30 minutes of electrocardiogram, sampled at 500 Hz, at ICU admission and calculated heart rate complexity via detrended fluctuation analysis. Primary outcome was vasopressor independence at 24 hours after ICU admission. Secondary outcome was 28-day mortality.

RESULTS

We studied 48 patients, of whom 60% were vasopressor independent at 24 hours. Five (10%) died within 28 days. The ratio of fractal alpha parameters was associated with both vasopressor independence and 28-day mortality (P = .04) after controlling for mean heart rate. In the optimal model, Sequential Organ Failure Assessment score and the long-term fractal α parameter were associated with vasopressor independence.

CONCLUSIONS

Loss of complexity in HRV is associated with worse outcome early in severe sepsis and septic shock. Further work should evaluate whether complexity of HRV could guide treatment in sepsis.