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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.
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Background@#Many studies have examined the risk factors for postoperative acute kidney injury (AKI), but few have focused on intraoperative peripheral perfusion index (PPI) that has recently been shown to be associated with postoperative morbidity and mortality. Therefore, this study aimed to evaluate the relationship between intraoperative PPI and postoperative AKI under the hypothesis that lower intraoperative PPI is associated with AKI occurrence. @*Methods@#We retrospectively searched electronic medical records to identify patients who underwent surgery at the general surgery department from May 2021 to November 2021. Patient baseline characteristics, pre- and post-operative laboratory test results, comorbidities, intraoperative vital signs, and discharge profiles were obtained from the Institutional Clinical Data Warehouse and VitalDB. Intraoperative PPI was the primary exposure variable, and the primary outcome was postoperative AKI. @*Results@#Overall, 2,554 patients were identified and 1,586 patients were included in our analysis. According to Kidney Disease Improving Global Outcomes (KDIGO) criteria, postoperative AKI occurred in 123 (7.8%) patients. We found that risks of postoperative AKI increased (odds ratio: 2.00, 95% CI [1.16, 3.44], P = 0.012) when PPI was less than 0.5 for more than 10% of surgery time. Other risk factors for AKI occurrence were male sex, older age, higher American Society of Anesthesiologists physical status, obesity, underlying renal disease, prolonged operation time, transfusion, and emergent operation. @*Conclusions@#Low intraoperative PPI was independently associated with postoperative AKI.
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Recent advancements in artificial intelligence (AI) techniques have enabled the development of accurate prediction models using clinical big data. AI models for perioperative risk stratification, intraoperative event prediction, biosignal analyses, and intensive care medicine have been developed in the field of perioperative medicine. Some of these models have been validated using external datasets and randomized controlled trials. Once these models are implemented in electronic health record systems or software medical devices, they could help anesthesiologists improve clinical outcomes by accurately predicting complications and suggesting optimal treatment strategies in real-time. This review provides an overview of the AI techniques used in perioperative medicine and a summary of the studies that have been published using these techniques. Understanding these techniques will aid in their appropriate application in clinical practice.
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Numerous methods for human body fluid identification using microbiological markers specific to different human body parts are well-established in forensic science. However, method for vaginal fluid screening have not been standardized yet. Therefore, in this study, a real-time polymerase chain reaction based assay for vaginal fluid identification was devised using bacteria residing in human vagina. This method employed three markers, namely Lactobacillus iners, Lactobacillus crispatus, and Bacteroides fragilis. L. iners and L. crispatus were chosen due to their high abundance in the vagina, whereas B. fragilis resides in the rectum. To examine the suitability of the new method for forensic microbial applications, a study of the distribution of vaginal flora in 143 Korean women was performed, along with characterization of the specificity, and performance of the new assay. Additionally, a casework study based on 130, 21, 20 and 17 DNA samples collected from the vagina, anus, saliva, blood, respectively, was carried out. L. iners (80.4%) and L. crispatus (55.2%) were detected with high abundance in the vagina of Korean women. The specificity of these markers was verified using microbial DNA from 23 species. This method could detect at least 1,000 copies/µL of microorganisms for all markers, thereby highlighting its robust sensitivity for vaginal fluid identification. The casework study confirmed these findings, with 89.2% (116/130) detection of vaginal fluid-derived DNA samples, and no false positives identified from the other sources studied. In conclusion, the developed method is expected to be efficient for preliminary microbiological analysis of vaginal samples in forensics.
ABSTRACT
BACKGROUND: The high cost of intrathecal morphine pump (ITMP) implantation may be the main obstacle to its use. Since July 2014, the Korean national health insurance (NHI) program began paying 50% of the ITMP implantation cost in select refractory chronic pain patients. The aims of this study were to investigate the financial break-even point and patients' satisfaction in patients with ITMP treatment after the initiation of the NHI reimbursement. METHODS: We collected data retrospectively or via direct phone calls to patients who underwent ITMP implantation at a single university-based tertiary hospital between July 2014 and May 2016. Pain severity, changes in the morphine equivalent daily dosage (MEDD), any adverse events, and patients' satisfaction were determined. We calculated the financial break-even point of ITMP implantation via investigating the patient's actual medical costs and insurance information. RESULTS: During the studied period, 23 patients received ITMP implantation, and 20 patients were included in our study. Scores on an 11-point numeric rating scale (NRS) for pain were significantly reduced compared to the baseline value (P < 0.001). The MEDD before ITMP implantation was 0.59 [IQR: 0.55–0.82]. The total MEDD increased steadily to 0.77 [IQR: 0.53–1.08] at 1 year, which was 126% of the baseline (P < 0.001). More than a half (60%) responded that the ITMP therapy was somewhat satisfying. The financial break-even point was 28 months for ITMP treatment after the NHI reimbursement policy. CONCLUSIONS: ITMP provided effective chronic pain management with improved satisfaction and reasonable financial break-even point of 28 months with 50% financial coverage by NHI program.