Heart rate variability as a predictor of mechanical ventilation weaning outcomes

Association of heart rate variability index with depressive symptoms and lung function in chronic obstructive pulmonary disease

BACKGROUND

Depression is a common comorbidity in patients with chronic obstructive pulmonary disease (COPD). Research indicates that COPD affects cardiac autonomic control, and heart rate variability (HRV) serves as a simple, non-invasive measure of autonomic nerve activity. However, the relationship between HRV and lung function, as well as the impact of depressive symptoms, remains unclear.

AIM

To investigate the correlation between HRV indicators and depressive symptoms and lung function in patients with COPD.

METHODS

A retrospective cross-sectional study involving 120 COPD patients hospitalized from January 2018 to January 2024 at our institution was conducted. Demographic and clinical characteristics were collected, and depressive symptoms were assessed using the Beck Depression Inventory (BDI). Patients were categorized into a depressed group (BDI ≥ 16) and a non-depressed group (BDI < 16). A control group consisting of 60 healthy volunteers who underwent check-ups at the same institution was also included. Statistical analyses were performed using SPSS 26.0 software. Pearson correlation coefficients were calculated to determine and compare the relationships between HRV parameters, lung function measures, and depressive symptoms across the groups.

RESULTS

Of the 120 patients with COPD, 35.8% (43/120) were diagnosed with depression, compared to 5.0% (3/60) in the control group. The HRV index in COPD patients was significantly lower than that in the control group (P < 0.05), and the value in the depressed group was significantly lower than that in the non-depressed group (P < 0.05). Similarly, the COPD group had a significantly lower pulmonary forced vital capacity (FVC), first-second expiratory volume (FEV1) and FEV1/FVC ratios than the control group (P < 0.05), and the depressed group was significantly lower than that in the non-depressed group (P < 0.05). Pearson correlation analysis revealed that the standard deviation of normal R-R intervals, standard deviation of the mean of 5-minute normal R-R intervals, root mean square of successive differences of normal R-R intervals, percentage of normal R-R intervals greater than 50 ms, high-frequency, and low-frequency indices showed positive correlations with lung function parameters (P < 0.05) and negative correlations with BDI scores (P < 0.05).

CONCLUSION

Compared to patients without COPD, the incidence of depressive symptoms is higher among patients with COPD and is negatively correlated with the patients’ HRV indices. In contrast, HRV indices are positively correlated with the patients’ pulmonary function parameters. Patients and healthcare professionals should enhance their awareness of depression, actively conduct depression assessment screenings, and incorporate HRV indices into disease management. This approach aims to improve the psychological health of patients and ultimately enhance their prognosis and quality of life.

Heart Rate Variability Applications in Medical Specialties: A Narrative Review

HRV is clinically considered to be a surrogate measure of the asymmetrical interplay of the sympathetic and parasympathetic nervous system. While HRV has become an increasingly measured variable through commercially-available wearable devices, HRV is not routinely monitored or utilized in healthcare settings at this time. The purpose of this narrative review is to discuss and evaluate the current research and potential future applications of HRV in several medical specialties, including critical care, cardiology, pulmonology, nephrology, gastroenterology, endocrinology, infectious disease, hematology and oncology, neurology and rehabilitation, sports medicine, surgery and anesthesiology, rheumatology and chronic pain, obstetrics and gynecology, pediatrics, and psychiatry/psychology. A narrative literature review was conducted with search terms including HRV and relevant terminology to the medical specialty in question. While HRV has demonstrated promise for some diagnoses as a non-invasive, easy to use, and cost-effective metric for early disease detection, prognosis and mortality prediction, disease monitoring, and biofeedback therapy, several issues plague the current literature. Substantial heterogeneity exists in the current HRV literature which limits its applicability in clinical practice. However, applications of HRV in psychiatry, critical care, and in specific chronic diseases demonstrate sufficient evidence to warrant clinical application regardless of the surmountable research issues. More data is needed to understand the exact impact of standardizing HRV monitoring and treatment protocols on patient outcomes in each of the clinical contexts discussed in this paper.

Heart Rate Variability During Weaning from Invasive Mechanical Ventilation: A Systematic Review

Background: The role of Heart Rate Variability (HRV) indices in predicting the outcome of the weaning process remains a subject of debate. The aim of this study is to investigate HRV analysis in critically ill adult patients undergoing weaning from invasive mechanical ventilation (IMV). Methods: The protocol of this systematic review was registered with PROSPERO (CRD42024485800). We searched PubMed and Scopus databases from inception till March 2023 to identify randomized controlled trials and observational studies investigating HRV analysis in critically ill adult patients undergoing weaning from invasive mechanical ventilation. Our primary outcome was to investigate HRV changes occurring during the weaning from IMV. Results: Seven studies (n = 342 patients) were included in this review. All studies reported significant changes in at least one HRV parameter. The indices Low Frequency (LF), High Frequency (HF), and LF/HF ratio seem to be the most promising in predicting the outcome of weaning with reliability. Some HRV indices showed modification in response to different ventilator settings or modalities. Conclusions: Available data report HRV modifications during the process of weaning and suggest a promising role of some HRV indices in predicting weaning outcomes in critically ill patients. Point-of-care HRV monitoring systems might help to early detect patients at risk of weaning failure.

Central venous pressure waveform analysis during sleep/rest: a novel approach to enhance intensive care unit post-extubation monitoring of extubation failure

This pilot study aimed to investigate the relation between cardio-respiratory parameters derived from Central Venous Pressure (CVP) waveform and Extubation Failure (EF) in mechanically ventilated ICU patients during post-extubation period. This study also proposes a new methodology for analysing these parameters during rest/sleep periods to try to improve the identification of EF. We conducted a prospective observational study, computing CVP-derived parameters including breathing effort, spectral analyses, and entropy in twenty critically ill patients post-extubation. The Dynamic Warping Index (DWi) was calculated from the respiratory component extracted from the CVP signal to identify rest/sleep states. The obtained parameters from EF patients and patients without EF were compared both during arbitrary periods and during reduced DWi (rest/sleep). We have analysed data from twenty patients of which nine experienced EF. Our findings may suggest significantly increased respiratory effort in EF patients compared to those successfully extubated. Our study also suggests the occurrence of significant change in the frequency dispersion of the cardiac signal component. We also identified a possible improvement in the differentiation between the two groups of patients when assessed during rest/sleep states. Although with caveats regarding the sample size, the results of this pilot study may suggest that CVP-derived cardio-respiratory parameters are valuable for monitoring respiratory failure during post-extubation, which could aid in managing non-invasive interventions and possibly reduce the incidence of EF. Our findings also indicate the possible importance of considering sleep/rest state when assessing cardio-respiratory parameters, which could enhance respiratory failure detection/monitoring.

Use of a continuous single lead electrocardiogram analytic to predict patient deterioration requiring rapid response team activation

Identifying the onset of patient deterioration is challenging despite the potential to respond to patients earlier with better vital sign monitoring and rapid response team (RRT) activation. In this study an ECG based software as a medical device, the Analytic for Hemodynamic Instability Predictive Index (AHI-PI), was compared to the vital signs of heart rate, blood pressure, and respiratory rate, evaluating how early it indicated risk before an RRT activation. A higher proportion of the events had risk indication by AHI-PI (92.71%) than by vital signs (41.67%). AHI-PI indicated risk early, with an average of over a day before RRT events. In events whose risks were indicated by both AHI-PI and vital signs, AHI-PI demonstrated earlier recognition of deterioration compared to vital signs. A case-control study showed that situations requiring RRTs were more likely to have AHI-PI risk indication than those that did not. The study derived several insights in support of AHI-PI's efficacy as a clinical decision support system. The findings demonstrated AHI-PI's potential to serve as a reliable predictor of future RRT events. It could potentially help clinicians recognize early clinical deterioration and respond to those unnoticed by vital signs, thereby helping clinicians improve clinical outcomes.

Using Artificial Intelligence to Predict Mechanical Ventilation Weaning Success in Patients with Respiratory Failure, Including Those with Acute Respiratory Distress Syndrome

The management of mechanical ventilation (MV) remains a challenge in intensive care units (ICUs). The digitalization of healthcare and the implementation of artificial intelligence (AI) and machine learning (ML) has significantly influenced medical decision-making capabilities, potentially enhancing patient outcomes. Acute respiratory distress syndrome, an overwhelming inflammatory lung disease, is common in ICUs. Most patients require MV. Prolonged MV is associated with an increased length of stay, morbidity, and mortality. Shortening the MV duration has both clinical and economic benefits and emphasizes the need for better MV weaning management. AI and ML models can assist the physician in weaning patients from MV by providing predictive tools based on big data. Many ML models have been developed in recent years, dealing with this unmet need. Such models provide an important prediction regarding the success of the individual patient's MV weaning. Some AI models have shown a notable impact on clinical outcomes. However, there are challenges in integrating AI models into clinical practice due to the unfamiliar nature of AI for many physicians and the complexity of some AI models. Our review explores the evolution of weaning methods up to and including AI and ML as weaning aids.

Defining predictors for successful mechanical ventilation weaning, using a data-mining process and artificial intelligence

Mechanical ventilation weaning within intensive care units (ICU) is a difficult process, while crucial when considering its impact on morbidity and mortality. Failed extubation and prolonged mechanical ventilation both carry a significant risk of adverse events. We aimed to determine predictive factors of extubation success using data-mining and artificial intelligence. A prospective physiological and biomedical signal data warehousing project. A 21-beds medical Intensive Care Unit of a University Hospital. Adult patients undergoing weaning from mechanical ventilation. Hemodynamic and respiratory parameters of mechanically ventilated patients were prospectively collected and combined with clinical outcome data. One hundred and eight patients were included, for 135 spontaneous breathing trials (SBT) allowing to identify physiological parameters either measured before or during the trial and considered as predictive for extubation success. The Early-Warning Score Oxygen (EWSO2) enables to discriminate patients deemed to succeed extubation, at 72-h and 7-days. Cut-off values for EWSO2 (AUC = 0.80; Se = 0.75; Sp = 0.76), mean arterial pressure and heart-rate variability parameters were determined. A predictive model for extubation success was established including body-mass index (BMI) on inclusion, occlusion pressure at 0,1 s. (P0.1) and heart-rate analysis parameters (LF/HF) both measured before SBT, and heart rate during SBT (global performance 62%; 83%). The data-mining process enabled to detect independent predictive factors for extubation success and to develop a dynamic predictive model using artificial intelligence. Such predictive tools may help clinicians to better discriminate patients deemed to succeed extubation and thus improve clinical performance.

Prediction of episode of hemodynamic instability using an electrocardiogram based analytic: a retrospective cohort study

BACKGROUND

Predicting the onset of hemodynamic instability before it occurs remains a sought-after goal in acute and critical care medicine. Technologies that allow for this may assist clinicians in preventing episodes of hemodynamic instability (EHI). We tested a novel noninvasive technology, the Analytic for Hemodynamic Instability-Predictive Indicator (AHI-PI), which analyzes a single lead of electrocardiogram (ECG) and extracts heart rate variability and morphologic waveform features to predict an EHI prior to its occurrence.

METHODS

Retrospective cohort study at a quaternary care academic health system using data from hospitalized adult patients between August 2019 and April 2020 undergoing continuous ECG monitoring with intermittent noninvasive blood pressure (NIBP) or with continuous intraarterial pressure (IAP) monitoring.

RESULTS

AHI-PI's low and high-risk indications were compared with the presence of EHI in the future as indicated by vital signs (heart rate > 100 beats/min with a systolic blood pressure < 90 mmHg or a mean arterial blood pressure of < 70 mmHg). 4,633 patients were analyzed (3,961 undergoing NIBP monitoring, 672 with continuous IAP monitoring). 692 patients had an EHI (380 undergoing NIBP, 312 undergoing IAP). For IAP patients, the sensitivity and specificity of AHI-PI to predict EHI was 89.7% and 78.3% with a positive and negative predictive value of 33.7% and 98.4% respectively. For NIBP patients, AHI-PI had a sensitivity and specificity of 86.3% and 80.5% with a positive and negative predictive value of 11.7% and 99.5% respectively. Both groups performed with an AUC of 0.87. AHI-PI predicted EHI in both groups with a median lead time of 1.1 h (average lead time of 3.7 h for IAP group, 2.9 h for NIBP group).

CONCLUSIONS

AHI-PI predicted EHIs with high sensitivity and specificity and within clinically significant time windows that may allow for intervention. Performance was similar in patients undergoing NIBP and IAP monitoring.

Comment on: "heart rate variability as a predictor of mechanical ventilation weaning outcomes": Letter to the Editor

None.