To show that quantification of abnormal respiratory sounds by our developed device is useful for predicting respiratory failure and airway problems after extubation. A respiratory sound monitoring system was used to collect respiratory sounds in patients undergoing extubation. The recorded respiratory sounds were subsequently analyzed. We defined the composite poor outcome as requiring any of following medical interventions within 48 h as defined below. This composite outcome includes reintubation, surgical airway management, insertion of airway devices, unscheduled use of noninvasive ventilation or high-flow nasal cannula, unscheduled use of inhaled medications, suctioning of sputum by bronchoscopy and unscheduled imaging studies. The quantitative values (QV) for each abnormal respiratory sound and inspiratory sound volume were compared between composite outcome groups and non-outcome groups. Fifty-seven patients were included in this study. The composite outcome occurred in 18 patients. For neck sounds, the QVs of stridor and rhonchi were significantly higher in the outcome group vs the non-outcome group. For anterior thoracic sounds, the QVs of wheezes, rhonchi, and coarse crackles were significantly higher in the outcome group vs the non-outcome group. For bilateral lateral thoracic sounds, the QV of fine crackles was significantly higher in the outcome group vs the non-outcome group. Cervical inspiratory sounds volume (average of five breaths) immediately after extubation was significantly louder in the outcome group vs non-outcome group (63.3 dB vs 54.3 dB, respectively; p < 0.001). Quantification of abnormal respiratory sounds and respiratory volume may predict respiratory failure and airway problems after extubation.

Smartphones may provide a highly available access to simplified hypertension screening in environments with limited health care resources. Most studies involving smartphone blood pressure (BP) apps have focused on validation in static conditions without taking into account intraindividual BP variations. We report here the first experimental evidence of smartphone-derived BP estimation compared to an arterial catheter in a highly dynamic context such as induction of general anesthesia. We tested a smartphone app (OptiBP) on 121 patients requiring general anesthesia and invasive BP monitoring. For each patient, ten 1-min segments aligned in time with ten smartphone recordings were extracted from the continuous invasive BP. A total of 1152 recordings from 119 patients were analyzed. After exclusion of 2 subjects and rejection of 565 recordings due to BP estimation not generated by the app, we retained 565 recordings from 109 patients (acceptance rate 51.1%). Concordance rate (CR) and angular CR demonstrated values of more than 90% for systolic (SBP), diastolic (DBP) and mean (MBP) BP. Error grid analysis showed that 98% of measurement pairs were in no- or low-risk zones for SBP and MBP, of which more than 89% in the no-risk zone. Evaluation of accuracy and precision [bias ± standard deviation (95% limits of agreement)] between the app and the invasive BP was 0.0 ± 7.5 mmHg [- 14.9, 14.8], 0.1 ± 2.9 mmHg [- 5.5, 5.7], and 0.1 ± 4.2 mmHg [- 8.3, 8.4] for SBP, DBP and MBP respectively. To the best of our knowledge, this is the first time a smartphone app was compared to an invasive BP reference. Its trending ability was investigated in highly dynamic conditions, demonstrating high concordance and accuracy. Our study could lead the way for mobile devices to leverage the measurement of BP and management of hypertension.

Anesthetic agent consumption is often calculated as the product of fresh gas flow (FGF) and vaporizer dial setting (FVAP). Because FVAP of conventional vaporizers is not registered in automated anesthesia records, retrospective agent consumption studies are hampered. The current study examines how FVAP can be retrospectively calculated from the agent's inspired (FIN) and end-expired concentration (FET), FGF, and minute ventilation (MV). Theoretical analysis of agent mass balances in the circle breathing reveals FVAP = [FIN - (dead space fraction * FIN + (1 - dead space fraction) * FET) * (1 - FGF/MV)]/(1-(1 - FGF/MV)). FIN, FET, FGF and MV are routinely monitored, but dead space fraction is unknown. Dead space fraction for sevoflurane, desflurane, and isoflurane was therefore determined empirically from an unpublished data set of 161 patient containing FVAP, FIN, FET, MV and FGF ranging from 0.25 to 8 L/min delivered via an ADU® (GE, Madison, WI, USA). Dead space fraction for each agent was determined empirically by having Excel's solver function calculate the value of dead space fraction that minimized the sum of the squared differences between dialed FVAP and predicted FVAP. With dead space fraction known, the model was then prospectively tested for sevoflurane in O2/air using data collected over the course of two weeks with one FLOW-i (Getinge, Solna, Sweden) and one Zeus workstation (Dräger, Lübeck, Germany). Because both workstations use an electronically controlled vaporizer/injector, the dialed FVAP were available to allow the calculation of median performance error (MDPE) and median absolute performance error (MDAPE). MDPE and MDAP are reported as median and interquartiles. The empirical dead space fraction for isoflurane, sevoflurane, and desflurane were 0.59, 0.49, and 0.66, respectively. For prospective testing, a total of 149.4 h of useful data were collected from 78 patient with the Zeus and Flow-i combined, with FGF ranging from 0.18 to 8 L/min. The model predicted dialed FVAP well, with a MDPE of -1 (-11, 6) % and MDAPE of 8 (4, 17) %. FVAP can be retrospectively calculated from FIN, FET, FGF, and MV plus an agent specific dead space fraction factor with a degree of error that we believe suffices for retrospective sevoflurane consumption analyses. Performance with other agents and N2O awaits further validation.

Intra-abdominal hypertension (IAH) is frequently present in the critically ill and is associated with increased morbidity and mortality. Conventionally, intermittent 'spot-check' manual measurements of bladder pressure in those perceived as high risk are used as surrogates for intra-abdominal pressure (IAP). True patterns of IAH remain unknown. We explored the incidence of IAH in cardiac surgery patients and describe the intra-and postoperative course of IAP using a novel, high frequency, automated bladder pressure measurement system. Sub-analysis of a prospective, multicenter, observational study (NCT04669548) conducted in three large academic medical centers. Continuous urinary output (CUO) and IAP measurements were observed using the Accuryn Monitoring System (Potrero Medical, Hayward, CA). Data collected included demographics, hemodynamic support, and high-frequency IAP and CUO. One Hundred Thirty-Seven cardiac surgery patients were analyzed intraoperatively and followed 48 h postoperatively in the intensive care unit. Median age was 66.4 [58.3, 72.0] years, and 61% were men. Median Foley catheter dwell time was 56.0 [46.8, 77.5] hours, and median baseline IAP was 6.3 [4.0, 8.1] mmHg. 93% (128/137) of patients were in IAH grade I, 82% (113/137) in grade II, 39% (53/137) in grade III, and 5% (7/137) in grade IV for at least 12 cumulative hours. For maximum consecutive duration of IAH, 84% (115/137) of patients spent at least 12 h in grade I, 62% (85/137) in grade II, 18% (25/137) in grade III, and 2% (3/137) in grade IV IAH. During the first 48 h after cardiac surgery, IAH is common and persistent. Improved and automated monitoring of IAP will increase the detection of IAH-which normally would remain undetected using traditional intermittent monitoring methods.

Common physiological time series and waveforms are composed of repeating cardiac and respiratory cycles. Often, the cardiac effect is the primary interest, but for, e.g., fluid responsiveness prediction, the respiratory effect on arterial blood pressure also convey important information. In either case, it is relevant to disentangle the two effects. Generalized additive models (GAMs) allow estimating the effect of predictors as nonlinear, smooth functions. These smooth functions can represent the cardiac and respiratory cycles' effects on a physiological signal. We demonstrate how GAMs allow a decomposition of physiological signals from mechanically ventilated subjects into separate effects of the cardiac and respiratory cycles. Two examples are presented. The first is a model of the respiratory variation in pulse pressure. The second demonstrates how a central venous pressure waveform can be decomposed into a cardiac effect, a respiratory effect and the interaction between the two cycles. Generalized additive models provide an intuitive and flexible approach to modelling the repeating, smooth, patterns common in medical monitoring data.

Using computer simulation we investigated whether machine learning (ML) analysis of selected ICU monitoring data can quantify pulmonary gas exchange in multi-compartment format. A 21 compartment ventilation/perfusion (V/Q) model of pulmonary blood flow processed 34,551 combinations of cardiac output, hemoglobin concentration, standard P50, base excess, VO2 and VCO2 plus three model-defining parameters: shunt, log SD and mean V/Q. From these inputs the model produced paired arterial blood gases, first with the inspired O2 fraction (FiO2) adjusted to arterial saturation (SaO2) = 0.90, and second with FiO2 increased by 0.1. 'Stacked regressor' ML ensembles were trained/validated on 90% of this dataset. The remainder with shunt, log SD, and mean 'held back' formed the test-set. 'Two-Point' ML estimates of shunt, log SD and mean utilized data from both FiO2 settings. 'Single-Point' estimates used only data from SaO2 = 0.90. From 3454 test gas exchange scenarios, two-point shunt, log SD and mean estimates produced linear regression models versus true values with slopes ~ 1.00, intercepts ~ 0.00 and R2 ~ 1.00. Kernel density and Bland-Altman plots confirmed close agreement. Single-point estimates were less accurate: R2 = 0.77-0.89, slope = 0.991-0.993, intercept = 0.009-0.334. ML applications using blood gas, indirect calorimetry, and cardiac output data can quantify pulmonary gas exchange in terms describing a 20 compartment V/Q model of pulmonary blood flow. High fidelity reports require data from two FiO2 settings.

Machine Learning (ML) models have been developed to predict perioperative clinical parameters. The objective of this study was to determine if ML models can serve as decision aids to improve anesthesiologists' prediction of peak intraoperative glucose values and postoperative opioid requirements. A web-based tool was used to present actual surgical case and patient information to 10 practicing anesthesiologists. They were asked to predict peak glucose levels and post-operative opioid requirements for 100 surgical patients with and without presenting ML model estimations of peak glucose and opioid requirements. The accuracies of the anesthesiologists' estimates with and without ML estimates as reference were compared. A questionnaire was also sent to the participating anesthesiologists to obtain their feedback on ML decision support. The accuracy of peak glucose level estimates by the anesthesiologists increased from 79.0 ± 13.7% without ML assistance to 84.7 ± 11.5% (< 0.001) when ML estimates were provided as reference. The accuracy of opioid requirement estimates increased from 18% without ML assistance to 42% (p < 0.001) when ML estimates were provided as reference. When ML estimates were provided, predictions of peak glucose improved for 8 out of the 10 anesthesiologists, while predictions of opioid requirements improved for 7 of the 10 anesthesiologists. Feedback questionnaire responses revealed that the anesthesiologist primarily used the ML estimates as reference to modify their clinical judgement. ML models can improve anesthesiologists' estimation of clinical parameters. ML predictions primarily served as reference information that modified an anesthesiologist's clinical estimate.

To validate the pressure-time index of the inspiratory muscles as a non-invasive index of inspiratory muscle function in spontaneously breathing infants by comparing it against the gold-standard pressure-time index of the diaphragm. Prospective observational cohort study of consecutive infants breathing unsupported in room air in a tertiary neonatal intensive care unit, studied prior to discharge from neonatal care. The invasive pressure-time index of the diaphragm was calculated using a transdiaphragmatic dual-pressure catheter that measured transdiaphragmatic pressure by subtraction of the oesophageal from the gastric pressure. The non-invasive pressure-time index of the inspiratory muscles was calculated using pressure measurements at the level of the mouth via a differential pressure transducer connected to a face mask. Both indices were calculated as the product of the ratio of the mean inspiratory pressure divided by the maximum inspiratory pressure and the ratio of the inspiratory time divided by the total time of a respiratory cycle. One hundred and thirty infants (79 male) were included with a mean (SD) gestational age of 35.2 (3.2) weeks, studied at a median (IQR) postnatal age of 9 (6-20) days. The mean (SD) pressure-time index of the diaphragm was 0.063 (0.019) and the mean (SD) pressure-time index of the inspiratory muscles was 0.065 (0.023). The correlation coefficient for the two indices was 0.509 (p < 0.001). The mean (SD) absolute difference between the pressure-time index of the inspiratory muscles and pressure-time index of the diaphragm was 0.002 (0.021). In convalescent infants, the non-invasive pressure-time index of the inspiratory muscles had a moderate degree of correlation with the invasively derived pressure time index of the diaphragm measured with a transdiaphragmatic catheter.

In vitro studies have thoroughly documented age-dependent impact of storage lesions in packed red blood cells (pRBC) on erythrocyte oxygen carrying capacity. While studies have examined the effect of pRBC age on patient outcome only few data exist on the microcirculation as their primary site of action. In this secondary analysis we examined the relationship between age of pRBC and changes of microcirculatory flow (MCF) in 54 patients based on data from the Basel Bedside assessment Microcirculation Transfusion Limit study (Ba2MiTraL) on effects of pRBC on sublingual MCF. Mean change from pre- to post-transfusion proportion of perfused vessels (∆PPV) was + 8.8% (IQR - 0.5 to 22.5), 5.5% (IQR 0.1 to 10.1), and + 4.7% (IQR - 2.1 to 6.5) after transfusion of fresh (≤ 14 days old), medium (15 to 34 days old), and old (≥ 35 days old) pRBC, respectively. Values for the microcirculatory flow index (MFI) were + 0.22 (IQR - 0.1 to 0.6), + 0.22 (IQR 0.0 to 0.3), and + 0.06 (IQR - 0.1 to 0.3) for the fresh, medium, and old pRBC age groups, respectively. Lower ∆PPV and transfusion of older blood correlated with a higher Sequential Organ Failure Assessment (SOFA) score of patients upon admission to the intensive care unit (ICU) (p = 0.01). However, regression models showed no overall significant correlation between pRBC age and ∆PPV (p = 0.2). Donor or recipient sex had no influence. We detected no significant effect of pRBC on microcirculation. Patients with a higher SOFA score upon ICU admission might experience a negative effect on the ∆PPV after transfusion of older blood.

The Hypotension Prediction Index (HPI) is a validated algorithm developed by applying machine learning for predicting intraoperative arterial hypotension (IOH). We evaluated whether the HPI, combined with a personalized treatment protocol, helps to reduce IOH (depth and duration) and perioperative events in real practice. This was a single-center retrospective study including 104 consecutive adults undergoing urgent or elective non-cardiac surgery with moderate-to-high risk of bleeding, requiring invasive blood pressure and continuous cardiac output monitoring. Depending on the sensor, two comparable groups were identified: patients managed following the institutional protocol of personalized goal-directed fluid therapy (GDFT, n = 52), or this GDFT supported by the HPI (HPI, n = 52). The time-weighted average of hypotension for a mean arterial pressure < 65 mmHg (TWAMAP<65), postoperative complications and length of hospital stay (LOS) were automatically downloaded from medical records and revised by clinicians blinded to the management received by patients. Differences in preoperative variables (i.e. physical status -ASA class-, acute kidney Injury-AKI- risk) and outcomes were analyzed using non-parametric tests with Hodges-Lehmann estimator for the median of differences. ASA class and AKI risk were similar (p = 0.749 and p = 0.837, respectively). Blood loss was also comparable (p = 0.279). HPI patients had a lower TWAMAP<65 [0.09 mmHg (0-0.48 mmHg)] vs [0.23 mmHg (0.01 to 0.97 mmHg)], p = 0.037. Postoperative complications were less prevalent in the HPI patients (0.46 ± 0.98 vs. 0.88 ± 1.20), p = 0.035. Finally, LOS was significantly shorter among HPI patients with a median difference of 2 days (p = 0.019). The HPI combined with a GDFT protocol may help to minimize the severity of IOH during non-cardiac surgery.

Complications of the endotracheal tube (ETT) displacement during head and neck positional changes are related to not only the tip position but also the cuff pressure against the larynx. Here, we evaluated movement of the ETT cuff relative to laryngeal structures as well as tip displacement from the carina.Sixty-two patients scheduled for thyroidectomy were recruited. The distance from the cricoid cartilage to the upper margin of the cuff (CC) and that from the ETT tip to the carina (TC) were measured using ultrasonography and fiberoptic bronchoscopy, respectively, during flexion and extension. The total tracheal length (TTL) was defined as the combination of CC, TC, and the distance from the upper margin of the cuff to the tip.During flexion, the CC and TC were 1.5 ± 0.6 and 2.9 ± 1.0 cm respectively. Seven patients (11.7%) exhibited excessively deep intubation. After adjusting the cuff position under ultrasonography (CC = 0), the tip position was corrected in 96.7%. While the TC increased by 2.1 ± 1.0 cm after the positional change in extension, the CC decreased by 0.6 ± 0.7 cm because the TTL lengthened (1.4 ± 1.1 cm). Four patients (6.7%) exhibited excessive cuff displacement beyond the cricoid cartilage, which could have been corrected under ultrasonography.In conclusion, the ETT cuff displaced toward the larynx in a less degree than the tip did from the carina due to the tracheal lengthening during head and neck extension. Nevertheless, we suggest that ultrasonographic assessment of cuff position may avoid ETT misplacement. Trial registration https://cris.nih.go.kr/ (approval no. KCT0005319); registered on May 14, 2019.

Since the recent editorial 2, we were approached to evaluate another video laryngeal mask airway - the Besdata Video Laryngeal Mask (BD-VLM) TM, which has a different design concept, specifications and characteristics (Figure 1) compared to the other two.

To describe an alternative method of measuring the Epidural Waveform Analysis (EWA), a technique through which anesthesiologists can confirm the position of a needle and/or catheter tip in the epidural space. EWA consists of epidural catheter transduction with a pressure system typically used for invasive arterial blood pressure monitoring which generates a characteristic oscillatory waveform (provided the catheter tip is within the epidural space) in synchrony with the pulsatile epidural circulation. The technique requires a double-male connector, a 3-way stopcock and an arterial pressure extension tubing along with the patient's existing arterial line setup while ensuring a meticulously sterile technique to mitigate the risks of neuraxial infection. The technique described herein has been successfully and routinely applied within our institution to measure EWA with the advantage of being potentially less wasteful. EWA allows anesthesiologists to confirm the correct position of an epidural needle/catheter. We describe a method of successfully measuring EWA while reducing wastefulness.

Closed-loop systems have been designed to assist anesthetists in controlling anesthetic drugs and also maintaining the stability of various physiological variables in the normal range. In the present study, we describe and clinically evaluated a novel closed-loop automated blood pressure control system (CLAPS) in patients undergoing cardiac surgery under cardiopulmonary bypass. Forty ASA II-IV adult patients undergoing elective cardiac surgery were randomly allocated to receive adrenaline, noradrenaline, phenylephrine and nitroglycerine (NTG) adjusted either through CLAPS (CLAPS group) or manually (Manual group). The desired target mean arterial blood pressure (MAP) for each patient in both groups was set by the attending anesthesiologist. The hemodynamic performance was assessed based on the percentage duration of time the MAP remained within 20% of the set target. Automated controller performances were compared using performance error criteria of Varvel (MDPE, MDAPE, Wobble) and Global Score. MAP was maintained a significantly longer proportion of time within 20% of the target in the CLAPS group (79.4% vs. 65.5% p < 0.001, 't' test) as compared to the manual group. Median absolute performance error, wobble, and Global score was significantly lower in the CLAPS group. Hemodynamic stability was achieved with a significantly lower dose of Phenyepherine in the CLAPS group (1870 μg vs. 5400 μg, p < 0.05, 't' test). The dose of NTG was significantly higher in the CLAPS group (3070 μg vs. 1600 μg, p-value < 0.05, 't' test). The cardiac index and left ventricular end-diastolic area were comparable between the groups. Automated infusion of vasoactive drugs using CLAPS is feasible and also better than manual control for controlling hemodynamics during cardiac surgery. Trial registration number and date This trial was registered in the Clinical Trial Registry of India under Registration Number CTRI/2018/01/011487 (Retrospective; registration date; January 23, 2018).

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.

The risk factors, outcomes, and typical patterns of intraoperative hypothermia were studied in neonates to better guide the application of insulation measures in the operating room. This retrospective study enrolled 401 neonates undergoing surgery under general anaesthesia with tracheal intubation, including abdominal surgery, thoracic surgery, brain surgery, and others. The study collected basic characteristics, such as age, sex, weight, birth weight, gestational week, primary diagnosis and American Society of Anaesthesiologists (ASA) grade. Perioperative data included preoperative body temperature, length of hospital stay, length of intensive care unit (ICU) stay, intubation time, postoperative bleeding, postoperative pneumonia, postoperative death, and total cost of hospitalization. Intraoperative data included surgical procedures, anaesthesia duration, operation duration, blood transfusion, fluid or albumin infusion, and application of vasoactive drugs. The incidence of intraoperative hypothermia (< 36 °C) was 81.05%. Compared to normothermic patients, gestational week (OR 0.717; 95% CI 0.577-0.890; P = 0.003), preoperative temperature (OR 0.228; 95% CI 0.091-0.571; P = 0.002), duration of anaesthesia (OR 1.052; 95% CI 1.027-1.077; P < 0.001), and type of surgery (OR 2.725; 95% CI 1.292-5.747; P = 0.008) were associated with the risk of intraoperative hypothermia. Patients with hypothermia had longer length of ICU stay (P = 0.001), longer length of hospital stay (P < 0.001), and higher hospital costs (P < 0.001). But there were no association between clinical outcomes and intraoperative hypothermia in the multivariable regression adjusted analysis. The lowest point of intraoperative body temperature was approximately 1 h 30 min. Then, the body temperature of patients successively entered a short plateau phase and a period of slow ascent. The greatest decrease in body temperatures occurred in preterm babies and neonates with preoperative hypothermia. The lowest core temperatures that occurred in neonates with preoperative hypothermia was lower than 35 °C. This study shows that there is a high incidence of intraoperative hypothermia in the neonate population. The intraoperative body temperature of neonates dropped to the lowest point in 1-1.5 h. The greatest decrease in core temperatures occurred in preterm babies and neonates with lower preoperative temperature.

This study was designed to investigate qCON and qNOX variations during outpatient laparoscopic cholecystectomy using remifentanil and desflurane without muscle relaxants and compare these indices with ANI and MAC. Adult patients undergoing outpatient laparoscopic cholecystectomy were included in this prospective observational study. Maintenance of anesthesia was performed using remifentanil targeted to ANI 50-80 and desflurane targeted to MAC 0.8-1.2 without muscle relaxants. The ANI, qCON and qNOX and desflurane MAC values were collected at different time-points and analyzed using repeated measures ANOVA. The relationship between ANI and qNOX and between qCON and MAC were analyzed by linear regression. The ANI was comprised between 50 and 80 during maintenance of anesthesia. Higher values of qNOX and qCON were observed at induction and extubation than during all other time-points where they were comprised between 40 and 60. A poor but significant negative linear relationship (r2 = 0.07, p < 0.001) was observed between ANI and qNOX. There also was a negative linear relationship between qCON and MAC (r2 = 0.48, p < 0.001) and between qNOX and remifentanil infusion rate (r2 = 0.13, p < 0.001). The linear mixed-effect regression correlation (r2) was 0.65 for ANI-qNOX and 0.96 for qCON-MAC. The qCON and qNOX monitoring seems informative during general anesthesia using desflurane and remifentanil without muscle relaxants in patients undergoing ambulatory laparoscopic cholecystectomy. While qCON correlated with MAC, the correlation of overall qCON and ANI was poor but significant. Additionally, the qNOX weakly correlated with the remifentanil infusion rate. This observational study suggests that the proposed ranges of 40-60 for both indexes may correspond to adequate levels of hypnosis and analgesia during general anesthesia, although this should be confirmed by further research.