ABSTRACT
Cloud technology and the Internet of things ( IOT) technology can be used in building the patient-centered critical care medical information platform.The platform could inenable medical staff to efficiently obtain critical care medical information, and raise critical patients′ cure rates. The authors introduced the critical process of rapidly building and improving the urgent care medical information integration platform in a campus of the First Affiliated Hospital of Zhejiang University Medical School.The IOT platform could effectively interconnect a variety of critical care medical equipments, and features such functions as vital signs monitoring and life support, bedside treatment data retrieval, remote diagnosis and treatment, and cloud sharing. The platform featured the advantages of complete information integration, fast construction, and satisfactory system extensibility. It could improve the medical work efficiency, improve the effect of critical care, and reduce the frequency of contact with patients, hence providing references for rapidly establishing a vital care treatment platform for public health events.
ABSTRACT
Objective:To analyze the risk factors of Intensive Care Unit-Acquired Weakness, and to develop and verify the model.Methods:A total of 247 patients admitted to ICU patients from November 2018 to October 2019 were selected, and risk factors between ICU acquired weakness group ( n=106) and non-ICU acquired weakness group( n=141)were compared using logistic regression for model construction.The Hosmer-Lemeshow test was used to verify the goodness of fit of the model. The area under the ROC curve was used to test the model to predict the effects. From November 2019 to May 2020, 106 patients were recruited for application of the model. Results:The incidence of ICU acquired weakness in this study was 42.91%(106/247), and 44.34%(47/106),the study finally included age ( OR=1.043) ,mechanical ventilation time ( OR=1.140) , APACHE II score ( OR=1.081) , blood sugar ( OR=1.117) , lactic acid( OR=1.459) ,and neuromuscular blockers ( OR=3.499) to construct the risk prediction. The model formula was P=1/1+exp (- Z) =1/1+exp (8.808-0.042×age -1.252×neuromuscular blockers-0.078×APACHE II score -0.110×blood sugar -0.378×lactic acid -0.131×mechanical ventilation time. The area under the ROC curve of this model was 0.896 (95% CI: 0.824-0.914) , the maximum value of the Youden index was 0.577, and the corresponding sensitivity was 0.754,the specificity was 0.823,the cutoff value was 0.503. The model verification results the sensibility of 70.2%, the specificity of 88.1%, and the accuracy of 80.2%. Conclusion:The predictic model of ICU acquired weakness couducted in this study has satisfactory prediction effect, which can provide a reference for clinical screening of high-risk patients.