Preventing Postoperative Catheter-Related Bladder Discomfort (CRBD) with Bladder Irrigation Using 0.05% Lidocaine Saline Solution: Monitoring with Analgesia Nociception Index (ANI) after Transurethral Surgery.

(1) Background and Objectives: Catheter-related bladder discomfort (CRBD), a common and distressing consequence of indwelling urinary catheters, can significantly impact postoperative recovery. This study aimed to determine the effectiveness of bladder irrigation with a 0.05% lidocaine normal saline solution for the prevention of CRBD following transurethral surgery. (2) Materials and Methods: In this randomized, double-blind, placebo-controlled trial, patients were assigned to either a control group receiving normal saline or a treatment group receiving 0.05% lidocaine (2% lidocaine 25 mL in 1000 mL saline) for bladder irrigation. Both groups were administered fentanyl (1 µg/kg) for analgesia at the end of the procedure. The primary endpoint was the assessment of the incidence and severity of CRBD upon awakening within the first 6 h postoperatively, using a four-grade scale based on the patients' reports of discomfort. (3) Results: Out of 79 patients completing the study, the incidence of moderate to severe CRBD was significantly lower in the lidocaine group (5.1%, 2/39) compared to the control group (25%, 10/40) at 10 min after waking from anesthesia (p = 0.014). Furthermore, the lidocaine group experienced significantly less CRBD at 1 and 2 h postoperative (2.6% and 0%, respectively) compared to the control group (20% and 10%, respectively) (p = 0.015, p = 0.043), with no significant differences at 6 h (p = 0.317). (4) Conclusions: The results suggest that bladder irrigation with 0.05% lidocaine reduces the occurrence of moderate to severe CRBD by nearly 80% in the initial 2 h postoperative period after transurethral surgery.

High frequency variability index in predicting postoperative pain in video/robotic-assisted thoracoscopic surgery under combined general anesthesia and peripheral nerve block: an observational study.

The high frequency variability index (HFVI)/analgesia nociception index (ANI) is purported to assess the balance between nociception and analgesia in patients under general anesthesia. This observational study investigated whether intraoperative HFVI/ANI correlates with postoperative pain in patients performed with nerve block under general anesthesia in video/robotic-assisted thoracoscopic surgery (VATS/RATS). We investigated whether maximum postoperative pain at rest and postoperative morphine consumption are associated with HFVI/ANI just before extubation, mean HFVI/ANI during anesthesia, the difference in HFVI/ANI between before and 5 min after the start of surgery, and the difference in HFVI/ANI between before and 5 min after the nerve block. Data obtained from 48 patients were analyzed. We found no significant association between HFVI/ANI just before extubation and postoperative Numerical Rating Scale (NRS) score. Receiver operating characteristic curve analysis revealed that moderate (NRS > 3) or severe (NRS > 7) postoperative pain could not be predicted by HFVI/ANI just before extubation. In addition, there were no associations between postoperative morphine consumption and HFVI/ANI at any time points. The present study demonstrated that it is difficult to predict the degree of postoperative pain in patients undergoing VATS/RATS under general anesthesia combined with peripheral nerve block, by using HFVI/ANI obtained at multiple time points during general anesthesia.

Peri-operative multimodal monitoring: a real need or a luxury?

The present case of a patient with several co-morbidities undergoing complex vitrectomy under peribulbar block and sedation with Target Controlled Infusion (TCI of propofol and dexmedetomidine with EEG and Analgesia Nociception Index (ANI) monitoring illustrates the benefits of multimodal monitoring to differentiate the effect of hypnotic and antinociceptive drugs.It is highlighted the delta-alpha electroencephalographic pattern showing adequate sedation, the beta arousal pattern in the EEG concommitant to decrease in the ANI translating insufficient anti-nociception.

Analgesia nociception index and high frequency variability index: promising indicators of relative parasympathetic tone.

At present, there is no objective and absolute measure of nociception, although various monitoring techniques have been developed. One such technique is the Analgesia Nociception Index (ANI), which is calculated from heart rate variability that reflects the relative parasympathetic tone. ANI is expressed on a non-unit scale of 0-100 (100 indicates maximal relative parasympathetic tone). Several studies indicated that ANI-guided anesthesia may help reduce intraoperative opioid use. The usefulness of ANI in the intensive care unit (ICU) and during surgery has also been reported. However, some limitations of ANI have also been reported; for example, ANI is affected by emotions and some drugs. In 2022, a high frequency variability index (HFVI), which was renamed from ANI and uses the same algorithm as ANI, was commercialized; therefore, ANI/HFVI are currently in the spotlight. Unlike ANI, HFVI can be displayed along with other biometric information on the Root® monitor. ANI/HFVI monitoring may affect the prognosis of not only patients in the perioperative period but those in ICU, those who receive home medical care, or outpatients. In this article, we present an updated review on ANI that has been published in the last decade, introduce HFVI, and discuss the outlooks of ANI/HFVI.

Comparison of Deep Learning Algorithms in Predicting Expert Assessments of Pain Scores during Surgical Operations Using Analgesia Nociception Index.

There are many surgical operations performed daily in operation rooms worldwide. Adequate anesthesia is needed during an operation. Besides hypnosis, adequate analgesia is critical to prevent autonomic reactions. Clinical experience and vital signs are usually used to adjust the dosage of analgesics. Analgesia nociception index (ANI), which ranges from 0 to 100, is derived from heart rate variability (HRV) via electrocardiogram (ECG) signals, for pain evaluation in a non-invasive manner. It represents parasympathetic activity. In this study, we compared the performance of multilayer perceptron (MLP) and long short-term memory (LSTM) algorithms in predicting expert assessment of pain score (EAPS) based on patient's HRV during surgery. The objective of this study was to analyze how deep learning models differed from the medical doctors' predictions of EAPS. As the input and output features of the deep learning models, the opposites of ANI and EAPS were used. This study included 80 patients who underwent operations at National Taiwan University Hospital. Using MLP and LSTM, a holdout method was first applied to 60 training patients, 10 validation patients, and 10 testing patients. As compared to the LSTM model, which had a testing mean absolute error (MAE) of 2.633 ± 0.542, the MLP model had a testing MAE of 2.490 ± 0.522, with a more appropriate shape of its prediction curves. The model based on MLP was selected as the best. Using MLP, a seven-fold cross validation method was then applied. The first fold had the lowest testing MAE of 2.460 ± 0.634, while the overall MAE for the seven-fold cross validation method was 2.848 ± 0.308. In conclusion, HRV analysis using MLP algorithm had a good correlation with EAPS; therefore, it can play role as a continuous monitor to predict intraoperative pain levels, to assist physicians in adjusting analgesic agent dosage. Further studies may consider obtaining more input features, such as photoplethysmography (PPG) and other kinds of continuous variable, to improve the prediction performance.