Machine learning-based prediction of intraoperative hypoxemia for pediatric patients.

BACKGROUND: Reducing the duration of intraoperative hypoxemia in pediatric patients by means of rapid detection and early intervention is considered crucial by clinicians. We aimed to develop and validate a machine learning model that can predict intraoperative hypoxemia events 1 min ahead in children undergoing general anesthesia. METHODS: This retrospective study used prospectively collected intraoperative vital signs and parameters from the anesthesia ventilator machine extracted every 2 s in pediatric patients undergoing surgery under general anesthesia between January 2019 and October 2020 in a tertiary academic hospital. Intraoperative hypoxemia was defined as oxygen saturation <95% at any point during surgery. Three common machine learning techniques were employed to develop models using the training dataset: gradient-boosting machine (GBM), long short-term memory (LSTM), and transformer. The performances of the models were compared using the area under the receiver operating characteristics curve using randomly assigned internal testing dataset. We also validated the developed models using temporal holdout dataset. Pediatric patient surgery cases between November 2020 and January 2021 were used. The performances of the models were compared using the area under the receiver operating characteristic curve (AUROC). RESULTS: In total, 1,540 (11.73%) patients with intraoperative hypoxemia out of 13,130 patients' records with 2,367 episodes were included for developing the model dataset. After model development, 200 (13.25%) of the 1,510 patients' records with 289 episodes were used for holdout validation. Among the models developed, the GBM had the highest AUROC of 0.904 (95% confidence interval [CI] 0.902 to 0.906), which was significantly higher than that of the LSTM (0.843, 95% CI 0.840 to 0.846 P < .001) and the transformer model (0.885, 95% CI, 0.882-0.887, P < .001). In holdout validation, GBM also demonstrated best performance with an AUROC of 0.939 (95% CI 0.936 to 0.941) which was better than LSTM (0.904, 95% CI 0.900 to 0.907, P < .001) and the transformer model (0.929, 95% CI 0.926 to 0.932, P < .001). CONCLUSIONS: Machine learning models can be used to predict upcoming intraoperative hypoxemia in real-time based on the biosignals acquired by patient monitors, which can be useful for clinicians for prediction and proactive treatment of hypoxemia in an intraoperative setting.

Evaluation and optimization of a conventional SPCE for FMD post-vaccination monitoring.

BACKGROUND: Foot-and-mouth disease (FMD) can be controlled by either stamping out or vaccination, a choice which depends on both the economic importance of the livestock sector as well as the disease status. In FMD-free countries with vaccination, such as Korea, vaccination programs should guarantee prevention against transmission of FMD. Monitoring of vaccination programs is also essential for ensuring sufficient coverage that will limit the transmission of FMDV. There are several methods to screen FMD virus (FMDV) structural protein (SP) antibodies including SPCE (Solid-phase competitive ELISA), LPBE (Liquid-phase blocking ELISA), and VNT (Virus neutralization test). Among these, SPCE is widely used for serological monitoring since VNT-the gold standard method-has certain practical limitations, such as high costs in terms of time and labor. However, whether SPCE can ensure the vaccination status of individual animals and whole farms is unclear. In this study, SPCE, LPBE and VNT were compared with respect to correlation with each other and sensitivity at commercial pig farms. RESULTS: The positive results obtained by PrioCHECK SPCE differed from those obtained by LPBE and VNT. The sensitivity of SPCE relative to those of the other tests was fairly low. The raw data of SPCE were most highly correlated with those of VNT with XJ strain, while their positivity and negativity were most highly correlated with LPBE. The results of ROC analysis proposed new cut-off for PrioCHECK SPCE higher than the previous 50% inhibition. CONCLUSIONS: The high false positive rate of PrioCHECK SPCE suggested that high seropositivity by SPCE may not guarantee a true vaccination coverage. Adjusting the cut-off percentage (%) inhibition value for SPCE is needed to address this problem, and it is highly recommended that routine FMDV serological monitoring programs using PrioCHECK SPCE should be combined with alternative methods such as LPBE or VNT.