研究方案:对医院获得性肺炎患者进行回顾性观察分析。

NIHR open research Pub Date : 2025-09-15 eCollection Date: 2025-01-01 DOI:10.3310/nihropenres.13853.2
Samuel Quarton, Mohammed Baragilly, Elizabeth Sapey
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引用次数: 0

摘要

背景:医院获得性肺炎(hospital -acquired pneumonia, HAP)是一种重要的住院并发症,发病率高,后果严重。然而,我们对致病生物和预后因素的了解是有限的。尽管呼吸机相关性肺炎(VAP)是HAP的一个重要子集,已被广泛研究,但对非通气病例的了解较少,因此需要对这一组进行重点研究。这项回顾性观察队列研究旨在通过比较通气和非通气病例来确定治疗为HAP的患者群体。它旨在阐明微生物学诊断的频率,经常发现的微生物,以及这是否对结果有相关影响。还将评估HAP患者放射学阳性变化的相对影响。方法:数据将从英国健康数据研究急性护理中心获得,“先锋”病例符合编码标准或临床监测定义的5年期间的HAP将被提取。人口统计学、临床和微生物学变量将首先进行描述性分析,随后进行多重逻辑回归分析,以调查影响微生物学诊断的因素。主要结局变量为住院死亡率、30天死亡率和1年内死亡率以及1年内全因再入院率。次要结局包括医院感染,如艰难梭菌。Kaplan-Meier曲线和Cox比例风险回归模型将用于调查结果和比较亚组。一个关键的比较是鉴定出假定病原体的人与完全凭经验治疗的人之间的比较。为此,我们还将使用治疗加权逆概率分析来比较结果。此外,我们将探索使用自然语言处理识别胸部成像报告中的实变,以考虑这可能对死亡率和再入院率产生的相对影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Study Protocol: A retrospective observational analysis of patients treated for hospital-acquired pneumonia.

Background: Hospital-acquired pneumonia (HAP) is an important complication of hospital admission, with both high incidence and consequences for patients. However, our understanding of causative organisms and prognostic factors is limited. Although ventilator-associated pneumonia (VAP,) an important subset of HAP,has been extensively investigated, less is known about non-ventilated cases, leading to calls for focused research in this group. This retrospective observational cohort study aims to define a population of patients treated as HAP by comparing ventilated and non-ventilated cases. It aims to clarify how often a microbiological diagnosis is reached, what organisms are frequently identified, and whether this has a relevant impact on the outcomes. The relative impact of positive radiographic changes among patients treated for HAP will also be assessed.

Methods: Data will be obtained from the Health Data Research UK acute care hub, 'PIONEER' Cases meeting coding criteria or a clinical surveillance definition of HAP over a 5-year period will be extracted. Demographic, clinical, and microbiological variables will be analysed initially descriptively, and subsequently, with multiple logistic regression analysis to investigate factors affecting microbiological diagnosis. Key outcome variables are in-hospital, 30-day and 1 year mortality, as well as all-cause readmissions within 1 year. Secondary outcomes include nosocomial infections, such as C. difficile. Kaplan-Meier curves and a Cox proportional hazards regression model will be used to investigate outcomes and compare subgroups. A key comparison is between those in whom a putative pathogen is identified and those treated entirely empirically. For this purpose, we will also compare outcomes using an inverse probability of treatment weighting analysis. Additionally, we will explore identifying consolidation on chest imaging reports using natural language processing to allow consideration of the relative impact this may have on mortality and readmission rates.

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