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.

IF 2.3 3区 医学 Q1 EMERGENCY MEDICINE BMC Emergency Medicine Pub Date : 2024-12-31 DOI:10.1186/s12873-024-01156-x
Qian Dai, Jing Huang, Hui Huang, Lin Song
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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.

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基于深度学习模型的优化急诊护理对急性左心衰患者治疗依从性及疗效影响的回顾性研究
背景:基于可解释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:选取徐州医科大学附属医院急诊科收治的96例ALHF患者为研究对象。根据护理方法的不同分为常规组和优化组。采用日常生活活动(ADL)量表评估患者出院后6个月的ADL。采用焦虑自评量表(SAS)和抑郁自评量表(SDS)评估患者的心理状态。采用45 min改善率、60 min显示效率、抢救成功率和转移率评价急救效果。护理满意度采用Likert 5级计分法。结果:优化组急救时间、住院时间、心电图时间、静脉通道建立时间、采血时间均短于常规组。然而,他们的收缩压、舒张压和心率较差。另一方面,优化组的LVEF和FS明显更好。护理干预后,优化组SAS、SDS评分均低于对照组。此外,优化组具有更高的45分钟改善率、60分钟显示效率、救援成功率和转移率。出院后6个月的6分钟步行距离和ADL评分均优于对照组。优化组的依从性、总有效率和满意度均高于常规组。结论:可解释DenseNet模型在ALHF诊断中具有一定的应用价值。优化应急方法可有效缩短急救时间,缓解焦虑等不良情绪,提高抢救成功率和短期疗效。护理干预对总有效效率和患者满意度有正向影响。
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来源期刊
BMC Emergency Medicine
BMC Emergency Medicine Medicine-Emergency Medicine
CiteScore
3.50
自引率
8.00%
发文量
178
审稿时长
29 weeks
期刊介绍: BMC Emergency Medicine is an open access, peer-reviewed journal that considers articles on all urgent and emergency aspects of medicine, in both practice and basic research. In addition, the journal covers aspects of disaster medicine and medicine in special locations, such as conflict areas and military medicine, together with articles concerning healthcare services in the emergency departments.
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