Liver transplantation (LT) is closely associated with decreased immune function, a contributor to herpes zoster (HZ). However, risk factors for HZ in living donor LT (LDLT) remain unknown. Neutrophil-lymphocyte ratio (NLR) and immune system function are reportedly correlated. This study investigated the association between NLR and HZ in 1688 patients who underwent LDLT between January 2010 and July 2020 and evaluated risk factors for HZ and postherpetic neuralgia (PHN). The predictive power of NLR was assessed through the concordance index and an integrated discrimination improvement (IDI) analysis. Of the total cohort, 138 (8.2%) had HZ. The incidence of HZ after LT was 11.2 per 1000 person-years and 0.1%, 1.3%, 2.9%, and 13.5% at 1, 3, 5, and 10 years, respectively. In the Cox regression analysis, preoperative NLR was significantly associated with HZ (adjusted hazard ratio [HR], 1.05; 95% confidence interval [CI], 1.02–1.09; p = 0.005) and PHN (HR, 1.08; 95% CI, 1.03–1.13; p = 0.001). Age, sex, mycophenolate mofetil use, and hepatitis B virus infection were risk factors for HZ versus age and sex for PHN. In the IDI analysis, NLR was discriminative for HZ and PHN (p = 0.020 and p = 0.047, respectively). Preoperative NLR might predict HZ and PHN in LDLT recipients.

The prognostic nutritional index (PNI) has been reported to be associated with postoperative complications and prognosis in cancer surgery. However, few studies have evaluated the association between preoperative PNI and postoperative acute kidney injury (AKI) in colorectal cancer patients. This study evaluated association of preoperative PNI and postoperative AKI in patients who underwent colorectal cancer surgery. This study retrospectively analyzed 3543 patients who underwent colorectal cancer surgery between June 2008 and February 2012. The patients were classified into four groups by the quartile of PNI: Q1 (≤43.79), Q2 (43.79–47.79), Q3 (47.79–51.62), and Q4 (≥51.62). Multivariate regression analysis was performed to assess the risk factors for AKI and 1-year mortality. AKI was defined according to Kidney Disease Improving Global Outcomes classification (KDIGO) criteria. Additionally, we assessed surgical outcomes such as hospital stay, ICU admission, and postoperative complications. The incidence of postoperative AKI tended to increase in the Q1 group (13.4%, 9.2%, 9.4%, 8.8%). In the multivariate analysis, high preoperative PNI was significantly associated with low risk of postoperative AKI (adjusted odds ratio [OR]: 0.96, 95% confidence interval [CI]: 0.93–0.99, p = 0.003) and low 1-year mortality (OR: 0.92, 95% CI: 0.86–0.98, p = 0.011). Male sex, body mass index, diabetes mellitus, and hypertension were risk factors for AKI. The Q1 (≤43.79) group had poor surgical outcomes, such as postoperative AKI (OR: 1.52, 95% CI: 1.18–1.95, p = 0.001), higher rates of ICU admission (OR: 3.13, 95% CI: 1.82–5.39, p < 0.001) and higher overall mortality (OR: 3.81, 95% CI: 1.86–7.79, p < 0.001). In conclusion, low preoperative PNI levels, especially in the Q1 (≤43.79), were significantly associated with postoperative AKI and surgical outcomes, such as hospital stay, postoperative ICU admission, and mortality.

Difficult tracheal intubation is the third most common respiratory-related adverse co-morbid episode and can lead to death or brain damage. Since difficult tracheal intubation is less frequent, trainees have fewer opportunities to perform difficult tracheal intubation; this leads to the need to practice with a hyper-realistic intubation simulator. However, conventional simulators are expensive, relatively stiffer than the human airway, and have a lack of diversity in terms of disease variations and anatomic reproducibility. Therefore, we proposed the development of a patient-specific and hyper-realistic difficult tracheal intubation simulator using three-dimensional printing technology and silicone moulding and to test the feasibility of patient-specific and hyper-realistic difficult intubation simulation using 3D phantom for the trainee. This difficult tracheal intubation phantom can provide a realistic simulation experience of managing various difficult tracheal intubation cases to trainees, which could minimise unexpected tissue damage before anaesthesia. To achieve a more realistic simulation, a patient-specific phantom was fabricated to mimic human tissue with realistic mouth opening and accurate difficult airway shape. This has great potential for the medical education and training field.

Although the stroke volume (SV) estimation by arterial blood pressure has been widely used in clinical practice, its accuracy is questionable, especially during periods of hemodynamic instability. We aimed to create novel SV estimating model based on deep-learning (DL) method. A convolutional neural network was applied to estimate SV from arterial blood pressure waveform data recorded from liver transplantation (LT) surgeries. The model was trained using a gold standard referential SV measured via pulmonary artery thermodilution method. Merging a gold standard SV and corresponding 10.24 seconds of arterial blood pressure waveform as an input/output data set with 2-senconds of sliding overlap, 484,384 data sets from 34 LT surgeries were used for training and validation of DL model. The performance of DL model was evaluated by correlation and concordance analyses in another 491,353 data sets from 31 LT surgeries. We also evaluated the performance of pre-existing commercialized model (EV1000), and the performance results of DL model and EV1000 were compared. The DL model provided an acceptable performance throughout the surgery (r = 0.813, concordance rate = 74.15%). During the reperfusion phase, where the most severe hemodynamic instability occurred, DL model showed superior correlation (0.861; 95% Confidence Interval, (CI), 0.855–0.866 vs. 0.570; 95% CI, 0.556–0.584, P < 0.001) and higher concordance rate (90.6% vs. 75.8%) over EV1000. In conclusion, the DL-based model was superior for estimating intraoperative SV and thus might guide physicians to precise intraoperative hemodynamic management. Moreover, the DL model seems to be particularly promising because it outperformed EV1000 in circumstance of rapid hemodynamic changes where physicians need most help.

The monitoring of arterial blood pressure (ABP) in anesthetized patients is crucial for preventing hypotension, which can lead to adverse clinical outcomes. Several efforts have been devoted to develop artificial intelligence-based hypotension prediction indices. However, the use of such indices is limited because they may not provide a compelling interpretation of the association between the predictors and hypotension. Herein, an interpretable deep learning model is developed that forecasts hypotension occurrence 10 min before a given 90-s ABP record. Internal and external validations of the model performance show the area under the receiver operating characteristic curves of 0.9145 and 0.9035, respectively. Furthermore, the hypotension prediction mechanism can be physiologically interpreted using the predictors automatically generated from the proposed model for representing ABP trends. Finally, the applicability of a deep learning model with high accuracy is demonstrated, thus providing an interpretation of the association between ABP trends and hypotension in clinical practice.