Risk factor analysis and clinical experience of treating capsular contracture after prepectoral implant-based breast reconstruction.

Background: Capsular contracture is one of the most common and severe complications after implant-based breast reconstruction. Recently, prepectoral implant-based breast reconstruction using acellular dermal matrix (ADM) has become an alternative to subpectoral implant-based reconstruction. However, risk factors for capsular contracture associated with recent prepectoral reconstruction trends are not well refined yet. Thus, the aim of this study was to determine risk factors for capsular contracture, and share our experience of treating capsular contracture in prepectoral reconstruction. Methods: This retrospective comparative study focused on 110 patients who underwent prepectoral implant-based breast reconstruction with ADM. Risk factors of capsular contracture were analyzed by comparing a capsular contracture group (27 cases) and a non-capsular contracture group (83 cases). Secondary treatment after capsular contracture development was analyzed in capsular contracture group. Results: According to univariate and multivariate analyses of risk factors for capsular contracture, single staged implant-based reconstruction (direct-to-implant), infection, and postoperative radiotherapy were significantly related to the development of capsular contracture. Also, surgical intervention including capsulectomy and capsulotomy with implant change showed a significant higher remission rate than other groups. Conclusions: Our study provides insights into risk factors and treatment choices for capsular contracture after prepectoral implant-based breast reconstruction with ADM. These findings can aid selection of patients, postoperative care and preventative treatment before reconstruction.

Association Between High Preoperative White Blood Cell-to-Hemoglobin Ratio and Postoperative Symptomatic Cerebral Vasospasm in Patients With Aneurysmal Subarachnoid Hemorrhage.

BACKGROUND: Cerebral vasospasm after aneurysmal subarachnoid hemorrhage (ASAH) is a serious complication and has a strong relationship with systemic inflammatory responses. Given previously reported relationships between leukocytosis and anemia with ASAH-related cerebral vasospasm, this study examined the association between the preoperative white blood cell-to-hemoglobin ratio (WHR) and postoperative symptomatic cerebral vasospasm (SCV) in patients with ASAH. METHODS: Demographic, preoperative (comorbidities, ASAH characteristics, laboratory findings), intraoperative (operation and anesthesia), and postoperative (SCV, other neurological complications, clinical course) data were retrospectively analyzed in patients with ASAH who underwent surgical or endovascular treatment of the culprit aneurysm. Patients were divided into high-WHR (n=286) and low-WHR (n=257) groups based on the optimal cutoff value of preoperative WHR (0.74), and stabilized inverse probability weighting was performed between the 2 groups. The predictive power of the WHR and other preoperative systemic inflammatory indices (neutrophil-to-albumin, neutrophil-to-lymphocyte, platelet-to-lymphocyte, platelet-to-neutrophil, platelet-to-white blood cell ratios, and systemic immune-inflammation index) for postoperative SCV was evaluated. RESULTS: Postoperative SCV was more frequent in the high-WHR group than in the low-WHR group before (33.2% vs. 12.8%; P<0.001) and after (29.4% vs. 19.1%; P=0.005) inverse probability weighting. Before weighting, the predictive power for postoperative SCV was the highest for the WHR among the preoperative systematic inflammatory indices investigated (area under receiver operating characteristics curve 0.66, P<0.001). After weighting, preoperative WHR ≥0.74 was independently associated with postoperative SCV (odds ratio 1.76; P=0.006). CONCLUSIONS: High preoperative WHR was an independent predictor of postoperative SCV in patients with ASAH.

INSPIRE, a publicly available research dataset for perioperative medicine.

We present the INSPIRE dataset, a publicly available research dataset in perioperative medicine, which includes approximately 130,000 surgical operations at an academic institution in South Korea over a ten-year period between 2011 and 2020. This comprehensive dataset includes patient characteristics such as age, sex, American Society of Anesthesiologists physical status classification, diagnosis, surgical procedure code, department, and type of anaesthesia. The dataset also includes vital signs in the operating theatre, general wards, and intensive care units (ICUs), laboratory results from six months before admission to six months after discharge, and medication during hospitalisation. Complications include total hospital and ICU length of stay and in-hospital death. We hope this dataset will inspire collaborative research and development in perioperative medicine and serve as a reproducible external validation dataset to improve surgical outcomes.

Non-invasive prediction of massive transfusion during surgery using intraoperative hemodynamic monitoring data.

OBJECTIVE: Failure to receive prompt blood transfusion leads to severe complications if massive bleeding occurs during surgery. For the timely preparation of blood products, predicting the possibility of massive transfusion (MT) is essential to decrease morbidity and mortality. This study aimed to develop a model for predicting MT 10 min in advance using non-invasive bio-signal waveforms that change in real-time. METHODS: In this retrospective study, we developed a deep learning-based algorithm (DLA) to predict intraoperative MT within 10 min. MT was defined as the transfusion of 3 or more units of red blood cells within an hour. The datasets consisted of 18,135 patients who underwent surgery at Seoul National University Hospital (SNUH) for model development and internal validation and 621 patients who underwent surgery at the Boramae Medical Center (BMC) for external validation. We constructed the DLA by using features extracted from plethysmography (collected at 500 Hz) and hematocrit measured during surgery. RESULTS: Among 18,135 patients in SNUH and 621 patients in BMC, 265 patients (1.46%) and 14 patients (2.25%) received MT during surgery, respectively. The area under the receiver operating characteristic curve (AUROC) of DLA predicting intraoperative MT before 10 min was 0.962 (95% confidence interval [CI], 0.948-0.974) in internal validation and 0.922 (95% CI, 0.882-0.959) in external validation, respectively. CONCLUSION: The DLA can successfully predict intraoperative MT using non-invasive bio-signal waveforms.

Effect of hyperbilirubinemia on the accuracy of continuous non-invasive hemoglobin measurements in liver transplantation recipients.

This study evaluated the effect of hyperbilirubinemia on the accuracy of continuous non-invasive hemoglobin (SpHb) measurements in liver transplantation recipients. Overall, 1465 SpHb and laboratory hemoglobin (Hb) measurement pairs (n = 296 patients) were analyzed. Patients were grouped into normal (< 1.2 mg/dL), mild-to-moderate (1.2-3.0 mg/dL), and severe (> 3.0 mg/dL) hyperbilirubinemia groups based on the preoperative serum total bilirubin levels. Bland-Altman analysis showed a bias of 0.20 (95% limit of agreement, LoA: - 2.59 to 3.00) g/dL, 0.98 (95% LoA: - 1.38 to 3.35) g/dL, and 1.23 (95% LoA: - 1.16 to 3.63) g/dL for the normal, mild-to-moderate, and severe groups, respectively. The four-quadrant plot showed reliable trending ability in all groups (concordance rate > 92%). The rates of possible missed transfusion (SpHb > 7.0 g/dL for Hb < 7.0 g/dL) were higher in the hyperbilirubinemia groups (2%, 7%, and 12% for the normal, mild-to-moderate, and severe group, respectively. all P < 0.001). The possible over-transfusion rate was less than 1% in all groups. In conclusion, the use of SpHb in liver transplantation recipients with preoperative hyperbilirubinemia requires caution due to the positive bias and high risk of missed transfusion. However, the reliable trending ability indicated its potential use in clinical settings.

Real-time machine learning model to predict short-term mortality in critically ill patients: development and international validation.

BACKGROUND: A real-time model for predicting short-term mortality in critically ill patients is needed to identify patients at imminent risk. However, the performance of the model needs to be validated in various clinical settings and ethnicities before its clinical application. In this study, we aim to develop an ensemble machine learning model using routinely measured clinical variables at a single academic institution in South Korea. METHODS: We developed an ensemble model using deep learning and light gradient boosting machine models. Internal validation was performed using the last two years of the internal cohort dataset, collected from a single academic hospital in South Korea between 2007 and 2021. External validation was performed using the full Medical Information Mart for Intensive Care (MIMIC), eICU-Collaborative Research Database (eICU-CRD), and Amsterdam University Medical Center database (AmsterdamUMCdb) data. The area under the receiver operating characteristic curve (AUROC) was calculated and compared to that for the National Early Warning Score (NEWS). RESULTS: The developed model (iMORS) demonstrated high predictive performance with an internal AUROC of 0.964 (95% confidence interval [CI] 0.963-0.965) and external AUROCs of 0.890 (95% CI 0.889-0.891) for MIMIC, 0.886 (95% CI 0.885-0.887) for eICU-CRD, and 0.870 (95% CI 0.868-0.873) for AmsterdamUMCdb. The model outperformed the NEWS with higher AUROCs in the internal and external validation (0.866 for the internal, 0.746 for MIMIC, 0.798 for eICU-CRD, and 0.819 for AmsterdamUMCdb; p < 0.001). CONCLUSIONS: Our real-time machine learning model to predict short-term mortality in critically ill patients showed excellent performance in both internal and external validations. This model could be a useful decision-support tool in the intensive care units to assist clinicians.

Multicentre validation of a machine learning model for predicting respiratory failure after noncardiac surgery.

BACKGROUND: Postoperative respiratory failure is a serious complication that could benefit from early accurate identification of high-risk patients. We developed and validated a machine learning model to predict postoperative respiratory failure, defined as prolonged (>48 h) mechanical ventilation or reintubation after surgery. METHODS: Easily extractable electronic health record (EHR) variables that do not require subjective assessment by clinicians were used. From EHR data of 307,333 noncardiac surgical cases, the model, trained with a gradient boosting algorithm, utilised a derivation cohort of 99,025 cases from Seoul National University Hospital (2013-9). External validation was performed using three separate cohorts A-C from different hospitals comprising 208,308 cases. Model performance was assessed by area under the receiver operating characteristic (AUROC) curve and area under the precision-recall curve (AUPRC), a measure of sensitivity and precision at different thresholds. RESULTS: The model included eight variables: serum albumin, age, duration of anaesthesia, serum glucose, prothrombin time, serum creatinine, white blood cell count, and body mass index. Internally, the model achieved an AUROC of 0.912 (95% confidence interval [CI], 0.908-0.915) and AUPRC of 0.113. In external validation cohorts A, B, and C, the model achieved AUROCs of 0.879 (95% CI, 0.876-0.882), 0.872 (95% CI, 0.870-0.874), and 0.931 (95% CI, 0.925-0.936), and AUPRCs of 0.029, 0.083, and 0.124, respectively. CONCLUSIONS: Utilising just eight easily extractable variables, this machine learning model demonstrated excellent discrimination in both internal and external validation for predicting postoperative respiratory failure. The model enables personalised risk stratification and facilitates data-driven clinical decision-making.

Microbiologic Profile of Nipple Swab Culture and its Association With Postoperative Complications in Prosthetic Breast Reconstruction.

BACKGROUND: In breast surgeries, a lactiferous duct leading to lactic glands of breast parenchyma allows direct contamination by normal bacterial flora of the nipple-areola complex. Complete blockage of nipple flora from the intraoperative field is almost impossible. OBJECTIVES: We aimed to analyze the microbiological profile of nipple flora of breast cancer patients who underwent an implant-based immediate breast reconstruction after a total mastectomy, and to evaluate the association of nipple bacterial flora with postoperative complications. METHODS: A retrospective chart review was performed of patients who underwent an implant-based immediate breast reconstruction after a total mastectomy. A nipple swab culture was performed preoperatively. Patient demographics, surgical characteristics, and complications were compared between positive and negative nipple swab culture groups. Microbiological profile data including antibacterial resistance were collected. RESULTS: Among 128 breasts, 60 cases (46.9%) had positive preoperative nipple swab culture results. Staphylococcus epidermidis accounted for 41.4% of microorganisms isolated. A multivariate logistic regression analysis of postoperative complications revealed that the presence of nipple bacterial flora was a risk factor for capsular contracture. Seven cases of postoperative infection were analyzed. In 2 cases (40% of pathogen-proven infection), the causative pathogen matched the patient's nipple bacterial flora, which was methicillin-resistant S. epidermidis in both cases. CONCLUSIONS: Nipple bacterial flora was associated with an increased risk of capsular contracture. Preoperative analysis of nipple bacterial flora can be an informative source for treating clinically diagnosed postoperative infections. More studies are needed to determine the effectiveness of active antibiotic decolonization of the nipple.

Regulation of long-range BMP gradients and embryonic polarity by propagation of local calcium-firing activity.

Many amniote vertebrate species including humans can form identical twins from a single embryo, but this only occurs rarely. It has been suggested that the primitive-streak-forming embryonic region emits signals that inhibit streak formation elsewhere but the signals involved, how they are transmitted and how they act has not been elucidated. Here we show that short tracks of calcium firing activity propagate through extraembryonic tissue via gap junctions and prevent ectopic primitive streak formation in chick embryos. Cross-regulation of calcium activity and an inhibitor of primitive streak formation (Bone Morphogenetic Protein, BMP) via NF-κB and NFAT establishes a long-range BMP gradient spanning the embryo. This mechanism explains how embryos of widely different sizes can maintain positional information that determines embryo polarity. We provide evidence for similar mechanisms in two different human embryo models and in Drosophila, suggesting an ancient evolutionary origin.

High Intraoperative Serum Lactate Level is Associated with Acute Kidney Injury after Brain Tumor Resection.

BACKGROUND: Postoperative acute kidney injury (AKI) is associated with poor clinical outcomes. Identification of risk factors for postoperative AKI is clinically important. Serum lactate can increase in situations of inadequate oxygen delivery and is widely used to assess a patient's clinical course. We investigated the association between intraoperative serum lactate levels and AKI after brain tumor resection. METHODS: Demographics, medical and surgical history, tumor characteristics, surgery, anesthesia, preoperative and intraoperative blood test results, and postoperative clinical outcomes were retrospectively collected from 4131 patients who had undergone brain tumor resection. Patients were divided into high (n=1078) and low (n=3053) lactate groups based on an intraoperative maximum serum lactate level of 3.35 mmol/L. After propensity score matching, 1005 patients were included per group. AKI was diagnosed using the Kidney Disease Improving Global Outcomes criteria, based on serum creatinine levels within 7 days after surgery. RESULTS: Postoperative AKI was observed in 53 (1.3%) patients and was more frequent in those with high lactate both before (3.2% [n=35] vs. 0.6% [n=18]; P < 0.001) and after (3.3% [n=33] vs. 0.6% [n=6]; P < 0.001) propensity score matching. Intraoperative predictors of postoperative AKI were maximum serum lactate levels > 3.35 mmol/L (odds ratio [95% confidence interval], 3.57 [1.45-8.74], P = 0.005), minimum blood pH (odds ratio per 1 unit, 0.01 [0.00-0.24], P = 0.004), minimum hematocrit (odds ratio per 1%, 0.91 [0.84-1.00], P = 0.037), and mean serum glucose levels > 200 mg/dL (odds ratio, 6.22 [1.75-22.16], P = 0.005). CONCLUSION: High intraoperative serum lactate levels were associated with AKI after brain tumor resection.

Continuous cardiac output estimation using a new modified Fick method during off-pump coronary artery bypass graft surgery: a retrospective observational study.

PURPOSE: Several technical aspects of the Fick method limit its use intraoperatively. A data-driven modification of the Fick method may enable its use in intraoperative settings. METHODS: This two-center retrospective observational study included 57 (28 and 29 in each center) patients who underwent off-pump coronary artery bypass graft (OPCAB) surgery. Intraoperative recordings of physiological data were obtained and divided into training and test datasets. The Fick equation was used to calculate cardiac output (CO-Fick) using ventilator-determined variables, intraoperative hemoglobin level, and SvO2, with continuous thermodilution cardiac output (CCO) used as a reference. A modification CO-Fick was derived and validated: CO-Fick-AD, which adjusts the denominator of the original equation. RESULTS: Increased deviation between CO-Fick and CCO was observed when oxygen extraction was low. The root mean square error of CO-Fick was decreased from 6.07 L/min to 0.70 L/min after the modification. CO-Fick-AD showed a mean bias of 0.17 (95% CI 0.00-0.34) L/min, with a 36.4% (95% CI 30.6-44.4%) error. The concordance rates of CO-Fick-AD ranged from 73.3 to 87.1% depending on the time interval and exclusion zone. CONCLUSIONS: The original Fick method is not reliable when oxygen extraction is low, but a modification using data-driven approach could enable continuous estimation of cardiac output during the dynamic intraoperative period with minimal bias. However, further improvements in precision and trending ability are needed.

Real-time machine learning model to predict in-hospital cardiac arrest using heart rate variability in ICU.

Predicting in-hospital cardiac arrest in patients admitted to an intensive care unit (ICU) allows prompt interventions to improve patient outcomes. We developed and validated a machine learning-based real-time model for in-hospital cardiac arrest predictions using electrocardiogram (ECG)-based heart rate variability (HRV) measures. The HRV measures, including time/frequency domains and nonlinear measures, were calculated from 5 min epochs of ECG signals from ICU patients. A light gradient boosting machine (LGBM) algorithm was used to develop the proposed model for predicting in-hospital cardiac arrest within 0.5-24 h. The LGBM model using 33 HRV measures achieved an area under the receiver operating characteristic curve of 0.881 (95% CI: 0.875-0.887) and an area under the precision-recall curve of 0.104 (95% CI: 0.093-0.116). The most important feature was the baseline width of the triangular interpolation of the RR interval histogram. As our model uses only ECG data, it can be easily applied in clinical practice.

Comparison of invasive and non-invasive measurements of cardiac index and systemic vascular resistance in living-donor liver transplantation: a prospective, observational study.

BACKGROUND: Based on the controversy surrounding pulmonary artery catheterization (PAC) in surgical patients, we investigated the interchangeability of cardiac index (CI) and systemic vascular resistance (SVR) measurements between ClearSight™ and PAC during living-donor liver transplantation (LDLT). METHODS: This prospective study included consecutively selected LDLT patients. ClearSight™-based CI and SVR measurements were compared with those from PAC at seven LDLT-stage time points. ClearSight™-based systolic (SAP), mean (MAP), and diastolic (DAP) arterial pressures were also compared with those from femoral arterial catheterization (FAC). For the comparison and analysis of ClearSight™ and the reference method, Bland-Altman analysis was used to analyze accuracy while polar and four-quadrant plots were used to analyze the trending ability. RESULTS: From 27 patients, 189 pairs of ClearSight™ and reference values were analyzed. The CI and SVR performance errors (PEs) exhibited poor accuracy between the two methods (51.52 and 51.73%, respectively) in the Bland-Altman analysis. CI and SVR also exhibited unacceptable trending abilities in both the polar and four-quadrant plot analyses. SAP, MAP, and DAP PEs between the two methods displayed favorable accuracy (24.28, 21.18, and 26.26%, respectively). SAP and MAP exhibited acceptable trending ability in the four-quadrant plot between the two methods, but not in the polar plot analyses. CONCLUSIONS: During LDLT, CI and SVR demonstrated poor interchangeability, while SAP and MAP exhibited acceptable interchangeability between ClearSight™ and FAC.

Automatic segmentation of atrial fibrillation and flutter in single-lead electrocardiograms by self-supervised learning and Transformer architecture.

OBJECTIVES: Automatic detection of atrial fibrillation and flutter (AF/AFL) is a significant concern in preventing stroke and mitigating hemodynamic instability. Herein, we developed a Transformer-based deep learning model for AF/AFL segmentation in single-lead electrocardiograms (ECGs) by self-supervised learning with masked signal modeling (MSM). MATERIALS AND METHODS: We retrieved data from 11 open-source databases on PhysioNet; 7 of these databases included labeled ECGs, while the other 4 were without labels. Each database contained ECG recordings with durations of ≥30 s. A total of 24 intradialytic ECGs with paroxysmal AF/AFL during 4 h of hemodialysis sessions at Seoul National University Hospital were used for external validation. The model was pretrained by predicting masked areas of ECG signals and fine-tuned by predicting AF/AFL areas. Cross-database validation was used for evaluation, and the intersection over union (IOU) was used as a main performance metric in external database validation. RESULTS: In the 7 labeled databases, the areas marked as AF/AFL constituted 41.1% of the total ECG signals, ranging from 0.19% to 51.31%. In the evaluation per ECG segment, the model achieved IOU values of 0.9254 and 0.9477 for AF/AFL segmentation and other segmentation tasks, respectively. When applied to intradialytic ECGs with paroxysmal AF/AFL, the IOUs for the segmentation of AF/AFL and non-AF/AFL were 0.9896 and 0.9650, respectively. Model performance by different training procedure indicated that pretraining with MSM and the application of an appropriate masking ratio both contributed to the model performance. It also showed higher IOUs of AF/AFL labels than in previous studies when training and test databases were matched. CONCLUSION: The present model with self-supervised learning by MSM performs robustly in segmenting AF/AFL.

Ultrasonographic assessment of rectus abdominis muscle adaptation after deep inferior epigastric artery perforator (DIEP) flap surgery: Single institution retrospective study.

The impact of deep inferior epigastric artery perforator (DIEP) flap on abdominal wall integrity has been the topic of an ongoing debate with previous studies having reported conflicting results using various imaging modalities. Ultrasonography is a noninvasive, cost-effective, and readily available method for evaluating the changes to the rectus muscle after DIEP flap surgery. In the present study, we aimed to compare rectus abdominis muscle thickness between the operated and non-operated sides using ultrasound imaging. The muscle thickness was measured at the cross point of the midclavicular line and the level of the umbilicus and anterior superior iliac spine using real-time B-mode ultrasonography. The muscle anteroposterior diameters of the pedicle-dissected side and the control side were compared using paired t test. In total 31 patients with a mean follow-up of 70.18 weeks were included. The mean diameters at the level of the umbilicus of the operated and non-operated sides were 8.16 ±â€…1.83 and 8.14 ±â€…1.43 mm, respectively (P = .94). The mean thicknesses at the anterior superior iliac spine level were 7.74 ±â€…1.85 on the flap harvested side and 8.04 ±â€…1.84 mm on the control side (P = .35). There was no statistically significant difference between the 2 groups. Ultrasonography can be a reliable, inexpensive, and easily usable modality for evaluating donor site complication following DIEP flap. DIEP flap seems to have minimal impact on the abdominal donor site, and it may be safe and versatile to reconstruct the breast after mastectomy.

Open datasets in perioperative medicine: a narrative review.

With the growing interest of researchers in machine learning and artificial intelligence (AI) based on large data, their roles in medical research have become increasingly prominent. Despite the proliferation of predictive models in perioperative medicine, external validation is lacking. Open datasets, defined as publicly available datasets for research, play a crucial role by providing high-quality data, facilitating collaboration, and allowing an objective evaluation of the developed models. Among the available datasets for surgical patients, VitalDB has been the most widely used, with the Medical Informatics Operating Room Vitals and Events Repository recently launched and the Informative Surgical Patient dataset for Innovative Research Environment expected to be released soon. For critically ill patients, the available resources include the Medical Information Mart for Intensive Care, the eICU Collaborative Research Database, the Amsterdam University Medical Centers Database, and the High time Resolution ICU Dataset, with the anticipated release of the Intensive Care Network with Million Patients' information for the AI Clinical decision support system Technology dataset. This review presents a detailed comparison of each to enrich our understanding of these open datasets for data science and AI research in perioperative medicine.

Predicting optimal endotracheal tube size and depth in pediatric patients using demographic data and machine learning techniques.

BACKGROUND: Use of endotracheal tubes (ETTs) with appropriate size and depth can help minimize intubation-related complications in pediatric patients. Existing age-based formulae for selecting the optimal ETT size present several inaccuracies. We developed a machine learning model that predicts the optimal size and depth of ETTs in pediatric patients using demographic data, enabling clinical applications. METHODS: Data from 37,057 patients younger than 12 years who underwent general anesthesia with endotracheal intubation were retrospectively analyzed. Gradient boosted regression tree (GBRT) model was developed and compared with traditional age-based formulae. RESULTS: The GBRT model demonstrated the highest macro-averaged F1 scores of 0.502 (95% CI 0.486, 0.568) and 0.669 (95% CI 0.640, 0.694) for predicting the uncuffed and cuffed ETT size (internal diameter [ID]), outperforming the age-based formulae that yielded 0.163 (95% CI 0.140, 0.196, P < 0.001) and 0.392 (95% CI 0.378, 0.406, P < 0.001), respectively. In predicting the ETT depth (distance from tip to lip corner), the GBRT model showed the lowest mean absolute error (MAE) of 0.71 cm (95% CI 0.69, 0.72) and 0.72 cm (95% CI 0.70, 0.74) compared to the age-based formulae that showed an error of 1.18 cm (95% CI 1.16, 1.20, P < 0.001) and 1.34 cm (95% CI 1.31, 1.38, P < 0.001) for uncuffed and cuffed ETT, respectively. CONCLUSIONS: The GBRT model using only demographic data accurately predicted the ETT size and depth. If these results are validated, the model may be practical for predicting optimal ETT size and depth for pediatric patients.

Pleth variability index during preoxygenation could predict anesthesia-induced hypotension: A prospective, observational study.

STUDY OBJECTIVE: To determine whether changes in the pleth variability index (PVi) during preoxygenation with forced ventilation for 1 min could predict anesthesia-induced hypotension. DESIGN: Prospective, observational study. SETTING: A tertiary teaching hospital. PATIENTS: Ninety-six patients who underwent general anesthesia using total intravenous anesthesia were enrolled. INTERVENTIONS: Upon the patient's arrival at the preoperative waiting area, a PVi sensor was affixed to their fourth fingertip. For preoxygenation, forced ventilation of 8 breaths/min in a 1:2 inspiratory-expiratory ratio was conducted using the guidance of an audio file. One minute after preoxygenation, anesthetic administration was initiated. Blood pressure was measured for the next 15 min. MEASUREMENTS: We calculated the difference (dPVi) and percentage of change (%PVi) between the PVi values immediately before and after forced ventilation. Anesthesia-induced hypotension was defined as a mean arterial pressure of <60 mmHg within 15 min after the infusion of anesthetics. MAIN RESULTS: Overall, 87 patients were included in the final analysis. Anesthesia-induced hypotension occurred in 31 (35.6%) of the 87 patients. Receiver operating characteristic curve analyses identified a cut-off value of -2 for dPVi, with an area under the curve of 0.691 (95% confidence interval [CI], 0.564-0.818; P < 0.001) and a cut-off value of -7.6% for %PVi, with an area under the curve of 0.711 (95% CI, 0.589-0.832; P < 0.001). Further, multivariate logistic regression analysis showed that a low %PVi with an odds ratio of 9.856 (95% CI, 3.131-31.032; P < 0.001) was a significant determinant of anesthesia-induced hypotension. CONCLUSIONS: Hypotension frequently occurs during general anesthesia induction and can impact outcomes. Additionally, the percentage change in the PVi before and after preoxygenation using deep breathing can be used to predict anesthesia-induced hypotension.

Development and validation of a reinforcement learning model for ventilation control during emergence from general anesthesia.

Ventilation should be assisted without asynchrony or cardiorespiratory instability during anesthesia emergence until sufficient spontaneous ventilation is recovered. In this multicenter cohort study, we develop and validate a reinforcement learning-based Artificial Intelligence model for Ventilation control during Emergence (AIVE) from general anesthesia. Ventilatory and hemodynamic parameters from 14,306 surgical cases at an academic hospital between 2016 and 2019 are used for training and internal testing of the model. The model's performance is also evaluated on the external validation cohort, which includes 406 cases from another academic hospital in 2022. The estimated reward of the model's policy is higher than that of the clinicians' policy in the internal (0.185, the 95% lower bound for best AIVE policy vs. -0.406, the 95% upper bound for clinicians' policy) and external validation (0.506, the 95% lower bound for best AIVE policy vs. 0.154, the 95% upper bound for clinicians' policy). Cardiorespiratory instability is minimized as the clinicians' ventilation matches the model's ventilation. Regarding feature importance, airway pressure is the most critical factor for ventilation control. In conclusion, the AIVE model achieves higher estimated rewards with fewer complications than clinicians' ventilation control policy during anesthesia emergence.