Background: Multidisciplinary rounds (MDRs) are scheduled, patient-focused communication mechanisms among multidisciplinary providers in the intensive care unit (ICU). Objective: i-Dashboard is a custom-developed visualization dashboard that supports (1) key information retrieval and reorganization, (2) time-series data, and (3) display on large touch screens during MDRs. This study aimed to evaluate the performance, including the efficiency of prerounding data gathering, communication accuracy, and information exchange, and clinical satisfaction of integrating i-Dashboard as a platform to facilitate MDRs. Methods: A cluster-randomized controlled trial was performed in 2 surgical ICUs at a university hospital. Study participants included all multidisciplinary care team members. The performance and clinical satisfaction of i-Dashboard during MDRs were compared with those of the established electronic medical record (EMR) through direct observation and questionnaire surveys. Results: Between April 26 and July 18, 2021, a total of 78 and 91 MDRs were performed with the established EMR and i-Dashboard, respectively. For prerounding data gathering, the median time was 10.4 (IQR 9.1-11.8) and 4.6 (IQR 3.5-5.8) minutes using the established EMR and i-Dashboard (P<.001), respectively. During MDRs, data misrepresentations were significantly less frequent with i-Dashboard (median 0, IQR 0-0) than with the established EMR (4, IQR 3-5; P<.001). Further, effective recommendations were significantly more frequent with i-Dashboard than with the established EMR (P<.001). The questionnaire results revealed that participants favored using i-Dashboard in association with the enhancement of care plan development and team participation during MDRs. Conclusions: i-Dashboard increases efficiency in data gathering. Displaying i-Dashboard on large touch screens in MDRs may enhance communication accuracy, information exchange, and clinical satisfaction. The design concepts of i-Dashboard may help develop visualization dashboards that are more applicable for ICU MDRs. Trial Registration: ClinicalTrials.gov NCT04845698; https://clinicaltrials.gov/ct2/show/NCT04845698

Besides the traditional indices such as biochemistry, arterial blood gas, rapid shallow breathing index (RSBI), acute physiology and chronic health evaluation (APACHE) II score, this study suggests a data science framework for extubation prediction in the surgical intensive care unit (SICU) and investigates the value of the information our prediction model provides. A data science framework including variable selection (e.g., multivariate adaptive regression splines, stepwise logistic regression and random forest), prediction models (e.g., support vector machine, boosting logistic regression and backpropagation neural network (BPN)) and decision analysis (e.g., Bayesian method) is proposed to identify the important variables and support the extubation decision. An empirical study of a leading hospital in Taiwan in 2015–2016 is conducted to validate the proposed framework. The results show that APACHE II and white blood cells (WBC) are the two most critical variables, and then the priority sequence is eye opening, heart rate, glucose, sodium and hematocrit. BPN with selected variables shows better prediction performance (sensitivity: 0.830; specificity: 0.890; accuracy 0.860) than that with APACHE II or RSBI. The value of information is further investigated and shows that the expected value of experimentation (EVE), 0.652 days (patient staying in the ICU), is saved when comparing with current clinical experience. Furthermore, the maximal value of information occurs in a failure rate around 7.1% and it reveals the “best applicable condition” of the proposed prediction model. The results validate the decision quality and useful information provided by our predicted model.

Background: Improper endotracheal tube (ETT) positioning is frequently observed and potentially hazardous in the intensive care unit. The authors developed a deep learning–based automatic detection algorithm detecting the ETT tip and carina on portable supine chest radiographs to measure the ETT–carina distance. This study investigated the hypothesis that the algorithm might be more accurate than frontline critical care clinicians in ETT tip detection, carina detection, and ETT–carina distance measurement. Methods: A deep learning–based automatic detection algorithm was developed using 1,842 portable supine chest radiographs of 1,842 adult intubated patients, where two board-certified intensivists worked together to annotate the distal ETT end and tracheal bifurcation. The performance of the deep learning–based algorithm was assessed in 4-fold cross-validation (1,842 radiographs), external validation (216 radiographs), and an observer performance test (462 radiographs) involving 11 critical care clinicians. The performance metrics included the errors from the ground truth in ETT tip detection, carina detection, and ETT–carina distance measurement. Results: During 4-fold cross-validation and external validation, the median errors (interquartile range) of the algorithm in ETT–carina distance measurement were 3.9 (1.8 to 7.1) mm and 4.2 (1.7 to 7.8) mm, respectively. During the observer performance test, the median errors (interquartile range) of the algorithm were 2.6 (1.6 to 4.8) mm, 3.6 (2.1 to 5.9) mm, and 4.0 (1.7 to 7.2) mm in ETT tip detection, carina detection, and ETT–carina distance measurement, significantly superior to that of 6, 10, and 7 clinicians (all P < 0.05), respectively. The algorithm outperformed 7, 3, and 0, 9, 6, and 4, and 5, 5, and 3 clinicians (all P < 0.005) regarding the proportions of chest radiographs within 5 mm, 10 mm, and 15 mm error in ETT tip detection, carina detection, and ETT–carina distance measurement, respectively. No clinician was significantly more accurate than the algorithm in any comparison. Conclusions: A deep learning–based algorithm can match or even outperform frontline critical care clinicians in ETT tip detection, carina detection, and ETT–carina distance measurement. Editor’s Perspective: What We Already Know about This Topic: What This Article Tells Us That Is New:

In intensive care units (ICUs), after endotracheal intubation, the position of the endotracheal tube (ETT) should be checked to avoid complications. The malposition can be detected by the distance between the ETT tip and the Carina (ETT–Carina distance). However, it struggles with a limited performance for two major problems, i.e., occlusion by external machine, and the posture and machine of taking chest radiographs. While previous studies addressed these problems, they always suffered from the requirements of manual intervention. Therefore, the purpose of this paper is to locate the ETT tip and the Carina more accurately for detecting the malposition without manual intervention. The proposed architecture is composed of FCOS: Fully Convolutional One-Stage Object Detection, an attention mechanism named Coarse-to-Fine Attention (CTFA), and a segmentation branch. Moreover, a post-process algorithm is adopted to select the final location of the ETT tip and the Carina. Three metrics were used to evaluate the performance of the proposed method. With the dataset provided by National Cheng Kung University Hospital, the accuracy of the malposition detected by the proposed method achieves $88.82\%$ and the ETT–Carina distance errors are less than $5.333\pm 6.240$ mm.