[Establishment of risk prediction model for pneumonia infection in elderly severe patients and analysis of prevention effect of 1M3S nursing plan under early warning mode].

To construct a risk prediction model for elderly severe patients with pneumonia infection, and analyze the prevention effect of 1M3S nursing plan under early warning mode. Firstly, 180 elderly severe patients admitted to the department of intensive care unit (ICU) of the Second Affiliated Hospital of Xingtai Medical College from September 2020 to September 2021 were enrolled. Their clinical data were collected and retrospectively analyzed, and they were divided into infected group and non-infected group according to whether they developed severe pneumonia. The risk factors affecting severe pneumonia in elderly severe patients were screened by univariate and multifactorial analysis methods, and the risk prediction model was constructed. The predictive efficiency of the model was analyzed by receiver operator characteristic curve (ROC curve). Then the risk prediction model was applied to prospectively include 60 high-risk elderly patients with severe pneumonia admitted from December 2021 to August 2022. The patients were randomly divided into study group and control group by envelope method, with 30 cases in each group. Both groups were given routine nursing. On this basis, the study group adopted 1M3S nursing scheme [standardized nursing management (1M), improving nursing skills (S1), optimizing nursing service (S2), ensuring nursing safety (S3)] in the early warning mode for intervention. Acute physiology and chronic health evaluation II (APACHE II) and Murray lung injury score were compared between the two groups before intervention and 7 days after intervention. Among 180 elderly severe patients, 34 cases were infected with pneumonia (18.89%). The proportion of patients with Glasgow coma scale (GCS) ≤ 8, duration of mechanical ventilation > 7 days, use of antibiotics, poor oral hygiene, hospital stay > 15 days and albumin ≤ 30 g/L in the infected group were significantly higher than those in the non-infected group. Multivariate Logistic regression analysis showed that duration of mechanical ventilation > 7 days, use of antibiotics, GCS score≤ 8, hospital stay > 15 days, albumin ≤ 30 g/L and poor oral hygiene were all independent risk factors for severe pneumonia in elderly severe patients. The odds ratio (OR) values were 3.180, 3.394, 1.108, 1.881, 1.517 and 2.512 (all P < 0.05). ROC curve analysis showed that the area under the ROC curve (AUC) of the prediction model to predict severe pneumonia in elderly severe patients was 0.838, 95% confidence interval was 0.748-0.927, sensitivity and specificity were 81.25% and 72.57%, respectively, and the Youden index was 0.538. (2) There was no significantly difference in general data between the study group and the control group, which was comparable. After intervention, the APACHE II score and Murray lung injury score of the two groups were significantly decreased, and the APACHE II score and Murray lung injury score of the study group were significantly lower than those of the control group (APACHE II score: 3.15±1.02 vs. 3.81±0.25, Murray lung injury score: 5.01±1.12 vs. 6.55±0.21, both P < 0.01). There are many risk factors affecting the development of severe pneumonia in elderly severe patients. The risk prediction model based on duration of mechanical ventilation > 7 days, hospital stay > 15 days, GCS score≤ 8, albumin ≤ 30 g/L, poor oral hygiene and history of combined antibacterial use has high predictive efficacy. The intervention of 1M3S nursing scheme in the early warning mode can effectively reduce the risk of severe pneumonia in elderly severe patients, and significantly improve the pathophysiological status.