ObjectiveTo systematically evaluate the clinical value of machine learning (ML) for predicting the neurological outcome of out-of-hospital cardiac arrest (OHCA), and to develop a prediction model. MethodsWe searched the PubMed, Web of Science, EMbase, CNKI, Wanfang database from January 1, 2011 to November 24, 2021. Studies on ML for predicting neurological outcomes in OHCA pateints were collected. Two researchers independently screened the literature, extracted the data and evaluated the bias of the included literature, evaluated the accuracy of different models and compared the area under the receiver operating characteristic curve (AUC). ResultsA total of 20 studies were included. Eleven of the studies were from open source databases and nine were from retrospective studies. Sixteen studies directly predicted OHCA neurological outcomes, and four predicted OHCA neurological outcomes after target temperature management. A total of seven ML algorithms were used, among which neural network was the ML algorithm with the highest frequency (n=5), followed by support vector machine and random forest (n=4). Three papers used multiple algorithms. The most frequently used input characteristic was age (n=19), followed by heart rate (n=17) and gender (n=13). A total of 4 studies compared the predictive value of ML with other classical statistical models, and the AUC value of ML model was higher than that of classical statistical models. ConclusionExisting evidence suggests that ML can more accurately predict OHCA nervous system outcomes, and the predictive performance of ML is superior to traditional statistical models in certain situations.
Wearable monitoring, which has the advantages of continuous monitoring for a long time with low physiological and psychological load, represents a future development direction of monitoring technology. Based on wearable physiological monitoring technology, combined with Internet of Things (IoT) and artificial intelligence technology, this paper has developed an intelligent monitoring system, including wearable hardware, ward Internet of Things platform, continuous physiological data analysis algorithm and software. We explored the clinical value of continuous physiological data using this system through a lot of clinical practices. And four value points were given, namely, real-time monitoring, disease assessment, prediction and early warning, and rehabilitation training. Depending on the real clinical environment, we explored the mode of applying wearable technology in general ward monitoring, cardiopulmonary rehabilitation, and integrated monitoring inside and outside the hospital. The research results show that this monitoring system can be effectively used for monitoring of patients in hospital, evaluation and training of patients’ cardiopulmonary function, and management of patients outside hospital.
Objective To explore the source and distribution of patients with multidrug resistant organisms (MDROs) acquired (infections/colonizations) outside the hospital and to provide a reference for guiding proactive interventions for nosocomial transmission of MDROs. Methods Bacterial culture results and clinical data of patients newly admitted to Beijing Anzhen Nanchong Hospital of Capital Medical University & Nanchong Central Hospita1 were retrospectively investigated between January 1st 2022 and December 31st 2023. The types of MDROs infections/colonizations, patient sources, and triple distributions of patients with nosocomial acquisition of MDROs were analyzed. Results A total of 293 patients with 308 infections/colonizations were investigated in the extranocomial infection of MDROs, 198110 newly admitted patients during the same period, and the total case rate of extranocomial infection of MDROs was 0.155% (308/198110). Among them, the case rate of extranocomial infection of methicillin-resistant Staphylococcus aureus (0.062%) and carbapenem resistant Acinetobacter baumannii (0.044%) were higher than those of other types of bacteria. The case rate of extranocomial infection of MDROs was statistically significant in terms of the distribution of the route of admission, gender of the patient, age of the patient, department of admission, and time of admission (P<0.001); The distribution of patients with extranocomial infection of various types of MDROs was correlated with admission route, patient age, and admission department (P<0.001), and the associations with patient gender and admission time were not statistically significant (P>0.05). Conclusions The total case rate of extranocomial infection of MDROs in the institution was at a relatively low level, and conducting large-scale active screening has certain limitations. Active screening factors should be considered in a comprehensive manner to capture differences in epidemiological characteristics of patients with extranocomial infection of MDROs, and targeted prevention and intervention should be carried out to achieve a reduction in infections from MDROs in hospitals.