Journal of clinical monitoring and computing
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J Clin Monit Comput · Aug 2022
ReviewPreoperative 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. ⋯ 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.
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J Clin Monit Comput · Jun 2022
ReviewDescription of the validity of the Analgesia Nociception Index (ANI) and Nociception Level Index (NOL) for nociception assessment in anesthetized patients undergoing surgery: a systematized review.
Maintaining optimum analgesia in anesthetized patients is challenging due to the inability to self-report pain or exhibit pain-related behaviours. The Analgesia Nociception Index (ANI) (based on heart rate variability [HRV]) and the Nociception Level Index (NOL) (based on HRV, photoplethysmography, skin conductance, and temperature) both include HRV and provide continuous index monitoring for nociception assessment. The research question was: "What are the validation strategies of the NOL and ANI for nociception assessment in anesthetized patients?". ⋯ Both technologies performed superiorly in detecting nociceptive stimuli compared to individual monitoring of HR and blood pressure. Although the aforementioned validation strategies are deemed appropriate, in the absence of a gold standard, criterion validation findings should be interpreted with caution. Moreover, reliability could be examined using test-retest with consistent ANI/NOL values during a stable time-interval.
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J Clin Monit Comput · Jun 2022
ReviewPower spectrum and spectrogram of EEG analysis during general anesthesia: Python-based computer programming analysis.
The commonly used principle for measuring the depth of anesthesia involves changes in the frequency components of the electroencephalogram (EEG) under general anesthesia. Therefore, it is essential to construct an effective spectrum and spectrogram to analyze the relationship between the depth of anesthesia and the EEG frequency during general anesthesia. This paper reviews the computer programming techniques for analyzing the spectrum and spectrogram derived from a single-channel EEG recorded during general anesthesia. ⋯ Finally, the multitaper method, which can suppress artifacts caused by the edges of the analysis segments, suppress noise, and probabilistically infer values that are close to the real power spectral density, is explained using practical examples of the analysis. All analyses were performed and all graphs plotted using Python under Jupyter Notebook. The analyses demonstrated the effectiveness of Python-based programming under the integrated development environment Jupyter Notebook for constructing an effective spectrum and spectrogram for analyzing the relationship between the depth of anesthesia and EEG frequency analysis in general anesthesia.
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This paper provides a review of a selection of papers published in the Journal of Clinical Monitoring and Computing in 2020 and 2021 highlighting what is new within the field of respiratory monitoring. Selected papers cover work in pulse oximetry monitoring, acoustic monitoring, respiratory system mechanics, monitoring during surgery, electrical impedance tomography, respiratory rate monitoring, lung ultrasound and detection of patient-ventilator asynchrony.
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J Clin Monit Comput · Apr 2022
ReviewEstimation of cardiac output variations induced by hemodynamic interventions using multi-beat analysis of arterial waveform: a comparative off-line study with transesophageal Doppler method during non-cardiac surgery.
Multi-beat analysis (MBA) of the radial arterial pressure (AP) waveform is a new method that may improve cardiac output (CO) estimation via modelling of the confounding arterial wave reflection. We evaluated the precision and accuracy using the trending ability of the MBA method to estimate absolute CO and variations (ΔCO) during hemodynamic challenges. We reviewed the hemodynamic challenges (fluid challenge or vasopressors) performed when intra-operative hypotension occurred during non-cardiac surgery. ⋯ After hemodynamic challenge, the percentage of concordance (PC) with 15% exclusion zone for ΔCO was 93 (CI97.5 = 90 to 97)%. In this retrospective offline analysis, the accuracy, limits of agreements and percentage error between TED and MBA for the absolute estimation of CO were poor, but the MBA could adequately track induced CO variations measured by TED. The MBA needs further evaluation in prospective studies to confirm those results in clinical practice conditions.