Objective To construct a key nursing technology system for the treatment of patients exposed to nuclear radiation in hospitals，and provide technical guidance and support for emergency nursing rescue in hospitals of nuclear radiation accidents. Methods A research group was composed of a team with rich experience in nuclear radiation accidents. Based on 4 scenarios of nuclear radiation accidents（including external irradiation，internal irradiation，external contamination，internal contamination），the literature search was conducted to form the first draft of the system. Delphi method was used to complete 2 rounds of expert letter consultation，and the final draft of the key nursing technology system for hospital treatment of patients with nuclear radiation exposure was constructed according to the revised opinions of experts. Results A total of 16 experts completed 2 rounds of correspondence. The effective recovery rates were 100% and 80%；the recommendation rates were 65% and 50%；the authority coefficients（Cr） were 0.778 and 0.797；the coefficient of variation（CV） of the 2 rounds of expert letter consultation was≤0.25. Finally，a key nursing technology system for in-hospital treatment of patients with nuclear radiation exposure was formed，including 5 first-level indicators，26 second-level indicators and 74 third-level indicators. Conclusion The constructed key nursing technology system for hospital treatment of patients with nuclear radiation exposure is highly practical and scientific，and it is conducive to the formation of standardized nuclear radiation exposure treatment procedures，and provides a theoretical basis for the training and evaluation of nursing staff related to nuclear radiation exposure.

Objective To investigate the current status of fall risk in hospitalized patients with acute coronary syndrome（ACS） based on Morse Score Scale，and to analyze the influencing factors. Methods A single-center，prospective cohort study was carried out for investigation. Between June 2015 to January 2020，consecutive ACS patients hospitalized at a tertiary A general hospital in Beijing were enrolled. All patients underwent portable sleep breathing monitoring，and they were then divided into a low-risk fall group（score<25） and a medium/high-risk fall group（score≥25） by Morse Fall Score administered by trained nurses in charge. Multivariate stepwise Logistic regression was used to analyze the influencing factors. Results A total of 1 732 ACS patients were enrolled. Medium/high-risk fall was present in 1 576 patients （91.0%），and 156 patients（9.0%） were in the low risk fall group. The proportion of patients with medium/high-risk of fall combined with hypoglycemic therapy，cardiovascular related drugs（anti-platelet，anti-hypertensive，lipid-lowering） was higher than the low-risk group（P<0.05）. The difference of mean SaO₂ between the 2 groups was statistically significant（P=0.019）. Compared with the medium/high risk fall group，the proportion of patients receiving PCI，coronary rotational atherectomy and multiple PCI was higher in the low-risk fall group（P<0.05）. Multiple linear regression analysis showed that mean SaO₂（RR=1.035， 95%CI 1.020~1.048，P=0.043），age（RR=1.040，95%CI 1.021~1.059，P<0.001），prior hypertension（RR=3.177，95%CI 2.215~4.557，P<0.001），prior myocardial infarction（RR=1.751，95%CI 1.009~3.037，P=0.046），diabetes mellitus （RR=1.633，95%CI 1.073~2.579，P=0.046），and body mass index（RR=1.064，95%CI 1.011~1.120，P=0.018） were the risk factors for medium/high risk falls. Conclusion Among hospitalized ACS patients，the incidence of medium/high risk falls was as high as 91.0%. The risk of falls was higher in patients with older age，higher body mass index，history of hypertension，diabetes，myocardial infarction，and lower the average oxygen saturation at night.

Objective To construct a risk predictive model of intraoperative hypothermia for patients undergoing laparoscopic surgery and to verify the predictive effect of the model. Methods 1043 patients who underwent laparoscopic surgery and met the inclusion and exclusion criteria were selected in our hospital from June to October 2020,using the convenience sampling method. They were randomly assigned to a modeling group and a verification group at a ratio of 7 ∶ 3. The influencing factors of patients with intraoperative hypothermia（n=407） and patients without intraoperative hypothermia（n=323） in the modeling group were compared,which is conducive to the random forest algorithm to sort the influencing factors and build the prediction model. Results The incidence of intraoperative hypothermia was 55.75% in the modeling group and 54.95% in the validation group. In the importance score of random forest algorithm variables,basic body temperature,operating room temperature,BMI,operation time and other indicators have a high contribution to the model classification,with clinical significance. The area under the receiver operating characteristic curve of the predictive model is 0.797;the sensitivity is 78.74%;the specificity is 64.03%;the accuracy is 72.20%. Conclusion The prediction model based on random forest algorithm is effective,which is of great significance to identify the key factors of intraoperative hypothermia in patients undergoing laparoscopic surgery and intervene timely and effectively.