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Predictors for length of hospital stay in patients with community-acquired Pneumonia: Results from a Swiss Multicenter study

机译:社区获得性肺炎患者住院时间的预测指标:瑞士多中心研究的结果

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Background Length of hospital stay (LOS) in patients with community-acquired pneumonia (CAP) is variable and directly related to medical costs. Accurate estimation of LOS on admission and during follow-up may result in earlier and more efficient discharge strategies. Methods This is a prospective multicenter study including patients in emergency departments of 6 tertiary care hospitals in Switzerland between October 2006 and March 2008. Medical history, clinical data at presentation and health care insurance class were collected. We calculated univariate and multivariate cox regression models to assess the association of different characteristics with LOS. In a split sample analysis, we created two LOS prediction rules, first including only admission data, and second including also additional inpatient information. Results The mean LOS in the 875 included CAP patients was 9.8?days (95%CI 9.3-10.4). Older age, respiratory rate >20?pm, nursing home residence, chronic pulmonary disease, diabetes, multilobar CAP and the pneumonia severity index class were independently associated with longer LOS in the admission prediction model. When also considering follow-up information, low albumin levels, ICU transfer and development of CAP-associated complications were additional independent risk factors for prolonged LOS. Both weighted clinical prediction rules based on these factors showed a high separation of patients in Kaplan Meier Curves (p logrank ) and a good calibration when comparing predicted and observed results. Conclusions Within this study we identified different baseline and follow-up characteristics to be strong and independent predictors for LOS. If validated in future studies, these factors may help to optimize discharge strategies and thus shorten LOS in CAP patients.
机译:背景社区获得性肺炎(CAP)患者的住院时间(LOS)是可变的,并且与医疗费用直接相关。入院时和随访期间对LOS的准确估计可能会导致更早,更有效的出院策略。方法这是一项前瞻性多中心研究,对象是2006年10月至2008年3月在瑞士6家三级医院的急诊科就诊的患者。收集了病史,就诊时的临床数据和医疗保险类别。我们计算了单变量和多变量Cox回归模型,以评估LOS与不同特征之间的关联。在拆分样本分析中,我们创建了两个LOS预测规则,第一个仅包含入院数据,第二个还包含其他住院信息。结果875名包括CAP的患者的平均LOS为9.8天(95%CI 9.3-10.4)。在入院预测模型中,年龄较大,呼吸频率> 20?pm,疗养院住所,慢性肺部疾病,糖尿病,多叶CAP和肺炎严重程度指数类别与更长的LOS独立相关。当还考虑随访信息时,低白蛋白水平,ICU转移和CAP相关并发症的发生是延长LOS的其他独立危险因素。两种基于这些因素的加权临床预测规则均显示,在比较预测结果和观察结果时,患者的Kaplan Meier曲线高度分离(p logrank),并且校准良好。结论在本研究中,我们确定了不同的基线和随访特征是LOS的强而独立的预测因素。如果在未来的研究中得到证实,这些因素可能有助于优化出院策略,从而缩短CAP患者的LOS。

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