基于深度学习模型优化的急救护理对急性左心衰竭患者治疗依从性及疗效影响的回顾性研究

An retrospective study on the effects of deep learning model-based optimization emergency nursing on treatment compliance and curative effect of patients with acute left heart failure.

作者信息

Dai Qian, Huang Jing, Huang Hui, Song Lin

机构信息

The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, 221000, China.

出版信息

BMC Emerg Med. 2024 Dec 31;24(1):240. doi: 10.1186/s12873-024-01156-x.

DOI:10.1186/s12873-024-01156-x

PMID:39736523

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11686966/

Abstract

BACKGROUND

Based on explainable DenseNet model, the therapeutic effects of optimization nursing on patients with acute left heart failure (ALHF) and its application values were discussed.

METHOD

In this study, 96 patients with ALHF in the emergency department of the Affiliated Hospital of Xuzhou Medical University were selected. According to different nursing methods, they were divided into conventional group and optimization group. Activity of daily living (ADL) scale was used to evaluate ADL of patients 6 months after discharge. Self-rating anxiety scale (SAS) and self-rating depression scale (SDS) were employed to assess patients' psychological state. 45 min improvement rate, 60 min show efficiency, rescue success rate, and transfer rate were used to assess the effect of first aid. Likert 5-level scoring method was adopted to evaluate nursing satisfaction.

RESULTS

The optimization group showed shorter durations for first aid, hospitalization, electrocardiography, vein channel establishment, and blood collection compared to the conventional group. However, their SBP, DBP, and HR were inferior. On the other hand, LVEF and FS were significantly better in the optimization group. After nursing intervention, SAS and SDS scores were lower in the optimization group. Additionally, the optimization group had higher 45-minute improvement rates, 60-minute show efficiency, rescue success, and transfer rates. They also performed better in 6-minute walking distance and ADL scores 6 months post-discharge. The optimization group had better compliance, total effective rates, and satisfaction than the conventional group.

CONCLUSION

It was demonstrated that explainable DenseNet model had application values in the diagnosis of ALHF. Optimization emergency method could effectively shorten the duration of first aid, relieve anxiety, and other adverse emotions, and improve rescue success rate and short-term efficacy. Nursing intervention has a positive impact on the total effective efficiency and patient satisfaction.

摘要

背景

基于可解释的密集连接网络（DenseNet）模型，探讨优化护理对急性左心衰竭（ALHF）患者的治疗效果及其应用价值。

方法

本研究选取徐州医科大学附属医院急诊科96例ALHF患者。根据护理方法不同，将其分为常规组和优化组。采用日常生活活动能力（ADL）量表评估患者出院6个月后的ADL。采用自评焦虑量表（SAS）和自评抑郁量表（SDS）评估患者的心理状态。用45分钟改善率、60分钟显效率、抢救成功率及转出率评估急救效果。采用Likert 5级评分法评估护理满意度。

结果

与常规组相比，优化组的急救、住院、心电图检查、静脉通道建立及采血时间更短。然而，其收缩压、舒张压和心率较差。另一方面，优化组的左心室射血分数（LVEF）和左心室短轴缩短率（FS）明显更好。护理干预后，优化组的SAS和SDS评分更低。此外，优化组的45分钟改善率、60分钟显效率、抢救成功率及转出率更高。出院6个月时，优化组在6分钟步行距离和ADL评分方面表现也更好。优化组的依从性、总有效率和满意度均优于常规组。