Prospective, multicenter validation of the deep learning-based cardiac arrest risk management system for predicting in-hospital cardiac arrest or unplanned intensive care unit transfer in patients admitted to general wards.

BACKGROUND: Retrospective studies have demonstrated that the deep learning-based cardiac arrest risk management system (DeepCARS™) is superior to the conventional methods in predicting in-hospital cardiac arrest (IHCA). This prospective study aimed to investigate the predictive accuracy of the DeepCARS™ for IHCA or unplanned intensive care unit transfer (UIT) among general ward patients, compared with that of conventional methods in real-world practice. METHODS: This prospective, multicenter cohort study was conducted at four teaching hospitals in South Korea. All adult patients admitted to general wards during the 3-month study period were included. The primary outcome was predictive accuracy for the occurrence of IHCA or UIT within 24 h of the alarm being triggered. Area under the receiver operating characteristic curve (AUROC) values were used to compare the DeepCARS™ with the modified early warning score (MEWS), national early warning Score (NEWS), and single-parameter track-and-trigger systems. RESULTS: Among 55,083 patients, the incidence rates of IHCA and UIT were 0.90 and 6.44 per 1,000 admissions, respectively. In terms of the composite outcome, the AUROC for the DeepCARS™ was superior to those for the MEWS and NEWS (0.869 vs. 0.756/0.767). At the same sensitivity level of the cutoff values, the mean alarm counts per day per 1,000 beds were significantly reduced for the DeepCARS™, and the rate of appropriate alarms was higher when using the DeepCARS™ than when using conventional systems. CONCLUSION: The DeepCARS™ predicts IHCA and UIT more accurately and efficiently than conventional methods. Thus, the DeepCARS™ may be an effective screening tool for detecting clinical deterioration in real-world clinical practice. Trial registration This study was registered at ClinicalTrials.gov ( NCT04951973 ) on June 30, 2021.

Multicenter validation of a deep-learning-based pediatric early-warning system for prediction of deterioration events.

BACKGROUND: Early recognition of deterioration events is crucial to improve clinical outcomes. For this purpose, we developed a deep-learning-based pediatric early-warning system (pDEWS) and aimed to validate its clinical performance. METHODS: This is a retrospective multicenter cohort study including five tertiary-care academic children's hospitals. All pediatric patients younger than 19 years admitted to the general ward from January 2019 to December 2019 were included. Using patient electronic medical records, we evaluated the clinical performance of the pDEWS for identifying deterioration events defined as in-hospital cardiac arrest (IHCA) and unexpected general ward-to-pediatric intensive care unit transfer (UIT) within 24 hours before event occurrence. We also compared pDEWS performance to those of the modified pediatric early-warning score (PEWS) and prediction models using logistic regression (LR) and random forest (RF). RESULTS: The study population consisted of 28,758 patients with 34 cases of IHCA and 291 cases of UIT. pDEWS showed better performance for predicting deterioration events with a larger area under the receiver operating characteristic curve, fewer false alarms, a lower mean alarm count per day, and a smaller number of cases needed to examine than the modified PEWS, LR, or RF models regardless of site, event occurrence time, age group, or sex. CONCLUSIONS: The pDEWS outperformed modified PEWS, LR, and RF models for early and accurate prediction of deterioration events regardless of clinical situation. This study demonstrated the potential of pDEWS as an efficient screening tool for efferent operation of rapid response teams.

Effects of predrilling on the osseointegration potential of mini-implants.

OBJECTIVES: To determine a reliable method of drilling a pilot hole when using a self-tapping surface-treated mini-implant and to evaluate stability after placement. MATERIALS AND METHODS: Implant sites were predrilled in 12 rabbits with two devices: a conventional motor-driven handpiece and a newly developed hand drill. Mini-implants were then inserted in a complete random block design. Samples were divided into 1-week and 6-week groups to investigate osseointegration capacity in relation to the two time intervals. Mechanical and histomorphometric assessments were performed. RESULTS: Mechanical analysis revealed no difference in maximum removal torque or total removal energy between the motor-driven predrilling group and the hand-drilling group. No difference was found between the 1-week group and the 6-week group. Histomorphometric evaluation showed no difference in the bone-implant contact (BIC) ratio or the bone volume (BV) area. For the time interval, a statistically significant increase in BIC and BV area was found in the 6-week group when compared to the 1-week group. CONCLUSIONS: The osseointegration potential of the motor-driven predrilling method was not different from that of the manual predrilling method with the newly developed hand drill. Hand drilling may be an attractive predrilling method in preference to the conventional motor-driven pilot drilling.