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肺结核预测模型的推导与验证

Derivation and validation of a pulmonary tuberculosis prediction model.

作者信息

Mylotte J M, Rodgers J, Fassl M, Seibel K, Vacanti A

机构信息

Department of Medicine, School of Medicine and Biomedical Sciences, State University of New York at Buffalo, USA.

出版信息

Infect Control Hosp Epidemiol. 1997 Aug;18(8):554-60. doi: 10.1086/647671.

Abstract

OBJECTIVE

To describe the derivation and validation of a pulmonary tuberculosis (TB) prediction model that would enable early discontinuation of unnecessary respiratory isolation.

DESIGN

Patients placed in isolation for suspected pulmonary TB were studied retrospectively (derivation cohort) and prospectively (validation cohort). Independent predictors of pulmonary TB in the derivation cohort (January 1992-March 1994) were identified by retrospective analysis. Predictors in the model were assigned weights on the basis of the results of the multivariate analysis in order to quantitate the risk of TB in an individual patient. The prospective validation consisted of application of the model to patients placed in isolation during the period April 1994 to June 1995. The predictability of the model in the derivation and validation cohorts was evaluated using receiver operating characteristics (ROC), curve analysis, and calculation of the area under the ROC curve (AUC).

SETTING

A university-affiliated, urban, public hospital with a large population of prison inmates and patients with human immunodeficiency virus infection.

INTERVENTIONS

Prospective application of the prediction model to patients placed in isolation during the validation period.

RESULTS

Four factors were found to be independent predictors of pulmonary TB among 296 isolation episodes in the derivation cohort; positive acid-fast sputum smear (odds ratio [OR], 5.8; 95% confidence interval [CI95], 3.0-11.0; weight = 3 points), localized chest radiograph findings (OR, 2.5; CI95, 1.3-4.9; weight = 2 points), residence in a correctional facility (OR, 2.3; CI95, 1.2-4.4; weight = 2 points), and history of weight loss (OR, 1.8; CI95, 1.0-3.2; weight = 1 points). Infection control practitioners applied the model prospectively to 220 isolation episodes. The mean (+/-SE) AUCs of the ROC curve for the derivation and validation cohorts were not significantly different (.86 +/- .04 vs .86 +/- .07; P = .90). There was a significant decline in the mean duration of isolation from the onset of an automatic TB isolation policy in August 1992 to the end of the study (P = .045 by analysis of variance).

CONCLUSIONS

A pulmonary TB prediction model was derived and validated prospectively in a hospital with a moderately high prevalence of TB. The model quantitated the risk of TB in an individual patient and aided infection control practitioners and primary-care physicians in their decisions to discontinue isolation during the validation period. Utilization of the model was responsible, in part, for a decrease in the mean duration of isolation during the study period. Although the model may not have general applicability due to the uniqueness of the patient population studied, this study illustrates how prediction models can be developed and used effectively to deal with a clinical problem.

摘要

目的

描述一种肺结核(TB)预测模型的推导与验证过程,该模型能够使不必要的呼吸道隔离得以早期解除。

设计

对因疑似肺结核而被隔离的患者进行回顾性研究(推导队列)和前瞻性研究(验证队列)。通过回顾性分析确定推导队列(1992年1月至1994年3月)中肺结核的独立预测因素。根据多变量分析结果为模型中的预测因素赋予权重,以量化个体患者患肺结核的风险。前瞻性验证包括将该模型应用于1994年4月至1995年6月期间被隔离的患者。使用受试者工作特征(ROC)曲线分析和计算ROC曲线下面积(AUC)来评估该模型在推导队列和验证队列中的预测能力。

地点

一家附属于大学的城市公立医院,有大量监狱囚犯和人类免疫缺陷病毒感染患者。

干预措施

在验证期内将预测模型前瞻性地应用于被隔离的患者。

结果

在推导队列的296次隔离事件中,发现有四个因素是肺结核的独立预测因素;痰涂片抗酸染色阳性(比值比[OR],5.8;95%置信区间[CI95],3.0 - 11.0;权重 = 3分)、胸部X线片局限性表现(OR,2.5;CI95,1.3 - 4.9;权重 = 2分)、居住在惩教机构(OR,2.3;CI95,1.2 - 4.4;权重 = 2分)以及体重减轻史(OR,1.8;CI95,1.0 - 3.2;权重 = 1分)。感染控制从业者将该模型前瞻性地应用于220次隔离事件。推导队列和验证队列的ROC曲线平均(±标准误)AUC无显著差异(.86 ±.04 对.86 ±.07;P =.90)。从1992年8月自动肺结核隔离政策开始实施到研究结束,隔离的平均持续时间显著下降(方差分析P =.045)。

结论

在一家肺结核患病率中等偏高的医院中推导并前瞻性验证了一种肺结核预测模型。该模型量化了个体患者患肺结核的风险,并在验证期内帮助感染控制从业者和初级保健医生做出解除隔离的决策。在研究期间,模型的应用在一定程度上导致了隔离平均持续时间的减少。尽管由于所研究患者群体的独特性,该模型可能不具有普遍适用性,但本研究说明了如何有效开发和使用预测模型来处理临床问题。

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