Sympatho-vagal balance, as quantified by ANSindex, predicts post spinal hypotension and vasopressor requirement in parturients undergoing lower segmental cesarean section: a single blinded prospective observational study

Autonomic dysfunction as a predictor of infection in neurocritical care unit: a prospective cohort study

PURPOSE

Infection in the neurocritical care unit ( NCCU) can cause significant mortality and morbidity. Autonomic nervous system plays an important role in defense against infection. Autonomic dysfunction causing inflammatory dysregulation can potentiate infection. We aimed to study the relationship between autonomic dysfunction and occurrence of infection in neurologically ill patients.

METHODS

Fifty one patients who were on mechanical ventilation were prospectively enrolled in this study. Autonomic dysfunction was measured for three consecutive days on admission to NCCU using Ansiscope. Patients were followed up for seven days to see the occurrence of infection. Infection was defined as per centre of disease control definition.

RESULTS

A total of 386 patients were screened for eligibility. 68 patients satisfied the eligibility criteria and 51 patients were finally included in the study. The incidence of infection was 74.5%. The commonest infection was pulmonary infection (38.8%) followed by urinary tract infection (33.3%), blood stream infection(14.8%), central nervous system infection (11.1%) and wound site infection (3.7%). The degree of autonomic dysfunction (AD) percentage was more in infection group (37.7% (25.2-49.7)) compared to non infection group (23.5% (18-33.5)) and maximal on day 3 (P = 0.02). Patients with increasing trend of AD% from day 1 to day 3 had the highest infection rates. The length of NCCU stay (20(10-23) days and mortality (42.1%) was higher in infection group (p < 0.001).

CONCLUSION

AD assessment can be used as a tool to predict development of infection in NCCU. This can help triage and institute early investigation and treatment.

Prevalence and predictors of preoperative cardiac autonomic dysfunction among elective neurosurgical patients: A prospective observational study

Background and Aims

The autonomic nervous system (ANS) is cardinal for systemic homeostasis. Autonomic dysfunction is prevalent in as high as 65% of patients presenting for cardiac surgery in the Indian scenario. Pre-existing cardiac autonomic dysfunction (CAD) in surgical patients can accentuate perioperative haemodynamic fluctuations during stressful intraoperative events, predispose to adverse cardiac events, and contribute to morbidity and mortality. The prevalence and predictors of CAD in the elective neurosurgical population are unknown in the Indian scenario. The current study was conducted to bridge this knowledge gap.

Methods

In this single-centre prospective observational study conducted at a tertiary care neurosciences centre, among 400 consenting adult patients of either gender, between 18 and 80 years of age, undergoing elective neurosurgery, the preoperative ANS function at the bedside was assessed as the primary outcome measure. The ANS status was evaluated using ANSiscope™-derived indices of heart rate variability. The diagnosis of CAD was made when the ANS index exceeded a threshold of 13.5. Data regarding predictors of CAD were collected from patient records as the secondary outcome measure. Statistical analysis was done using the R software. A P-value of <0.05 was considered statistically significant.

Results

The prevalence of preoperative CAD in our study population was 79.7% (319/400 patients). None of the demographic and baseline clinical characteristics we studied predicted CAD in our study.

Conclusion

We observed a significant prevalence of preoperative CAD among elective neurosurgical patients. None of the parameters we evaluated predicted CAD in our study.

Preoperative heart rate variability as a predictor of perioperative outcomes: a systematic review without meta-analysis

Heart rate variability (HRV) is a predictor of mortality and morbidity after non-lethal cardiac ischemia, but the relation between preoperatively measured HRV and intra- and postoperative complications is sparsely studied and most recently reviewed in 2007. We, therefore, reviewed the literature regarding HRV as a predictor for intra- and postoperative complications and outcomes. We carried out a systematic review without meta-analysis. A PICO model was set up, and we searched PubMed, EMBASE, and CENTRAL. The screening was done by one author, but all authors performed detailed review of the included studies. We present data from studies on intraoperative and postoperative complications, which were too heterogeneous to warrant formal meta-analysis, and we provide a pragmatic review of HRV indices to facilitate understanding our findings. The review was registered in PROSPERO (CRD42021230641). We screened 2337 records for eligibility. 131 records went on to full-text assessment, 63 were included. In frequency analysis of HRV, low frequency to high frequency ratio could be a predictor for intraoperative hypotension in spinal anesthesia and lower total power could possibly predict intraoperative hypotension under general anesthesia. Detrended fluctuation analysis of HRV is a promising candidate for predicting postoperative atrial fibrillation. This updated review of the relation between preoperative HRV and surgical outcome suggests a clinically relevant role of HRV but calls for high quality studies due to methodological heterogeneity in the current literature. Areas for future research are suggested.

Performance of the Hypotension Prediction Index With Noninvasive Arterial Pressure Waveforms in Awake Cesarean Delivery Patients Under Spinal Anesthesia

BACKGROUND

Arterial hypotension is common after spinal anesthesia (SA) for cesarean delivery (CD), and to date, there is no definitive method to predict it. The hypotension prediction index (HPI) is an algorithm that uses the arterial waveform to predict early phases of intraoperative hypotension. The aims of this study were to assess the diagnostic ability of HPI working with arterial waveforms detected by ClearSight system in predicting impending hypotension in awake patients, and the agreement of pressure values recorded by ClearSight with conventional noninvasive blood pressure (NIBP) monitoring in patients undergoing CD under SA.

METHODS

In this retrospective analysis of pregnant patients scheduled for elective CD under SA, continuous hemodynamic data measured with the ClearSight monitor until delivery were downloaded from an Edwards Lifesciences HemoSphere platform and analyzed. Receiver operating characteristic (ROC) curves were constructed to evaluate the performance of HPI algorithm working on the ClearSight pressure waveform in predicting hypotensive events, defined as mean arterial pressure (MAP) <65 mm Hg for >1 minute. The sensitivity, specificity, positive predictive value, and negative predictive value were computed at the optimal cutpoint, selected as the value that minimizes the difference between sensitivity and specificity. ClearSight MAP values were compared to NIBP MAP values by linear regression and Bland-Altman analysis corrected for repeated measurements.

RESULTS

Fifty patients undergoing CD were included in the analysis. Hypotension occurred in 23 patients (48%). Among patients experiencing hypotension, the HPI disclosed 71 alerts. The HPI predicted hypotensive events with a sensitivity of 83% (95% confidence interval [CI], 69-97) and specificity of 83% (95% CI, 70-95) at 3 minutes before the event (area under the curve [AUC] 0.913 [95% CI, 0.837-0.99]); with a sensitivity of 97% (95% CI, 92-100) and specificity of 97% (95% CI, 92-100) at 2 minutes before the event (AUC 0.995 [95% CI, 0.979-1.0]); and with a sensitivity of 100% (95% CI, 100-100) and specificity 100% (95% CI, 100-100) 1 minute before the event (AUC 1.0 [95% CI, 1.0-1.0]). A total of 2280 paired NIBP MAP and ClearSight MAP values were assessed. The mean of the differences between the ClearSight and NIBP assessed using Bland-Altman analysis (±standard deviation [SD]; 95% limits of agreement with respective 95% CI) was -0.97 mm Hg (±4.8; -10.5 [-10.8 to -10.1] to 8.5 [8.1-8.8]).

CONCLUSIONS

HPI provides an accurate real time and continuous prediction of impending intraoperative hypotension before its occurrence in awake patients under SA. We found acceptable agreement between ClearSight MAP and NIBP MAP.

Prediction of spinal anesthesia-induced hypotension during elective cesarean section: a systematic review of prospective observational studies

BACKGROUND

Spinal anesthesia is the standard for elective cesarean section but spinal anesthesia-induced hypotension remains an important problem. Accurate prediction of hypotension could enhance clinical decision-making, alter management, and facilitate early intervention. We performed a systematic review of predictors of spinal anesthesia-induced hypotension and their predictive value during cesarean section.

METHODS

PubMed, Embase, Cochrane Library, Google Scholar and Web of Science databases were searched for prospective observational studies assessing the diagnostic accuracy of predictors of spinal anesthesia-induced hypotension in elective cesarean section. The quality of studies was assessed and predictors were grouped in domains based on the type of predictor.

RESULTS

Thirty-eight studies (n=3086 patients) were included. In most studies, patients received 500-1000 mL crystalloid preload or 500-2000 mL crystalloid coload. Vasopressors for post-spinal hypotension were boluses of ephedrine 5-15 mg and/or phenylephrine 25-100 µg in most studies. The hypotension rate varied from 29% to 80% based on the definition. For analysis, >30 predictors were classified into seven domains: demographic characteristics, baseline hemodynamic variables, baseline sympathovagal balance, postural stress testing, peripheral perfusion indices, blood volume and fluid responsiveness indices, and genetic polymorphism.

CONCLUSIONS

Environmental and individual factors increased outcome variability, which restricted the value of the autonomic nervous system and peripheral perfusion indices for prediction of spinal anesthesia-induced hypotension. Supine stress tests may reflect parturients' cardiovascular tolerance during hemodynamic fluctuations and may optimize the predictive value of static state predictors. Future research for predicting spinal anesthesia-induced hypotension should focus on composite and dynamic parameters during the supine stress tests.

Observation of hemodynamic parameters using a non-invasive cardiac output monitor system to identify predictive indicators for post-spinal anesthesia hypotension in parturients undergoing cesarean section

The administration of high-level spinal anesthesia for cesarean section may lead to significant hemodynamic changes. Bioreactance-based non-invasive cardiac output monitoring (NICOM^™) provides an accurate monitoring system for parturients under spinal anesthesia. The present study hypothesized that baseline hemodynamic parameters obtained via the NICOM^™ system could serve as predictive indicators for post-spinal anesthesia hypotension. Therefore, 80 full-term parturients with singleton pregnancies who underwent scheduled cesarean section were enrolled and allocated to either a supine position group or a 15˚ left tilt group. All parturients received standard pre-hydration with 750 ml of 0.9% saline. Baseline cardiac output index (CI), total peripheral resistance index (TPRI) and stroke volume (SV) were recorded using the NICOM^™ system. Subsequently, spinal anesthesia with 2.4 ml of 0.5% hyperbaric bupivacaine, 10 µg of fentanyl and 0.2 mg of morphine was administered. Receiver operating characteristic (ROC) curves and multivariate logistic regression were used to analyze the data. A total of 40 parturients (51.9%) developed hypotension. The areas under the ROC curves were 0.666, 0.594 and 0.622 for the CI, TPRI and SV, respectively. The optimal cut-off value of the CI in predicting hypotension was 3.68 l/min/m² (ROC, sensitivity=85.0%, specificity=48.6%). Furthermore, CI was considered as an independent factor for post-spinal anesthesia hypotension. In conclusion, the baseline CI obtained via the bioreactance-based NICOM^™ system may serve as a predictor of post-spinal anesthesia hypotension in parturients regardless of patient position.

Intraoperative hypotension and its prediction

Intraoperative hypotension (IOH) very commonly accompanies general anaesthesia in patients undergoing major surgical procedures. The development of IOH is unwanted, since it is associated with adverse outcomes such as acute kidney injury and myocardial injury, stroke and mortality. Although the definition of IOH is variable, harm starts to occur below a mean arterial pressure (MAP) threshold of 65 mmHg. The odds of adverse outcome increase for increasing duration and/or magnitude of IOH below this threshold, and even short periods of IOH seem to be associated with adverse outcomes. Therefore, reducing the hypotensive burden by predicting and preventing IOH through proactive appropriate treatment may potentially improve patient outcome. In this review article, we summarise the current state of the prediction of IOH by the use of so-called machine-learning algorithms. Machine-learning algorithms that use high-fidelity data from the arterial pressure waveform, may be used to reveal 'traits' that are unseen by the human eye and are associated with the later development of IOH. These algorithms can use large datasets for 'training', and can subsequently be used by clinicians for haemodynamic monitoring and guiding therapy. A first clinically available application, the hypotension prediction index (HPI), is aimed to predict an impending hypotensive event, and additionally, to guide appropriate treatment by calculated secondary variables to asses preload (dynamic preload variables), contractility (dP/dtmax), and afterload (dynamic arterial elastance, Eadyn). In this narrative review, we summarise the current state of the prediction of hypotension using such novel, automated algorithms and we will highlight HPI and the secondary variables provided to identify the probable origin of the (impending) hypotensive event.

ANSiscope™: Can it be the crystal ball of cardiac anesthesia?

Background

: Autonomic dysfunction (AD) is infrequently evaluated preoperatively despite having profound perioperative implications. The ANSiscope™ is a monitoring device that quantifies AD. This study aims to determine the potential of the device to predict hypotension following anesthetic induction, occurrence of arrhythmias, and inotrope requirement for patients undergoing off-pump coronary artery bypass surgery (OPCAB).

Study Design

: Prospective observational double-blinded study.

Materials and Methodology

Seventy-five patients undergoing OPCAB had their autonomic function assessed by ANSiscope™. They were classified into four groups based on their AD and compared to perioperative adverse events.

Results

Patients with diabetes had a higher ANSindex (P = 0.0263). They had a greater decrease in systolic blood pressure (P = 0.001) and mean arterial pressure (P = 0.004) postinduction, had an increased incidence of arrhythmias (P = 0.009), required higher inotropic support immediately (P = 0.010) and at 24 h after surgery (P = 0.018), and longer duration of postoperative ventilation (P < 0.001). They also had a higher incidence of emergency conversion of OPCAB to on-pump surgery (P = 0.009).

Conclusions

An increased association between AD as quantified by the ANSiscope™ and perioperative adverse outcomes was observed. An increased rate of emergency conversion of OPCAB to on-pump surgery with higher dysfunction was noted. The authors opine that the threshold for conversion must be lower in patients deemed to be at a higher risk. Proper evaluation of the autonomic nervous system empowers the anesthesiologist to anticipate and adequately prepare for complications.