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. 2013:2013:504136.
doi: 10.1155/2013/504136. Epub 2013 Oct 21.

Risk prediction models for mortality in community-acquired pneumonia: a systematic review

Chun Shing Kwok  1 , Yoon K Loke, Kenneth Woo, Phyo Kyaw Myint
Affiliations

Risk prediction models for mortality in community-acquired pneumonia: a systematic review

Chun Shing Kwok et al. Biomed Res Int. 2013.

Abstract

Background: Several models have been developed to predict the risk of mortality in community-acquired pneumonia (CAP). This study aims to systematically identify and evaluate the performance of published risk prediction models for CAP.

Methods: We searched MEDLINE, EMBASE, and Cochrane library in November 2011 for initial derivation and validation studies for models which predict pneumonia mortality. We aimed to present the comparative usefulness of their mortality prediction.

Results: We identified 20 different published risk prediction models for mortality in CAP. Four models relied on clinical variables that could be assessed in community settings, with the two validated models BTS1 and CRB-65 showing fairly similar balanced accuracy levels (0.77 and 0.72, resp.), while CRB-65 had AUROC of 0.78. Nine models required laboratory tests in addition to clinical variables, and the best performance levels amongst the validated models were those of CURB and CURB-65 (balanced accuracy 0.73 and 0.71, resp.), with CURB-65 having an AUROC of 0.79. The PSI (AUROC 0.82) was the only validated model with good discriminative ability among the four that relied on clinical, laboratorial, and radiological variables.

Conclusions: There is no convincing evidence that other risk prediction models improve upon the well-established CURB-65 and PSI models.

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Figures

Figure 1
Figure 1
Search results and study selection.
Figure 2
Figure 2
Balanced accuracy and area under ROC of pneumonia severity scores versus number of variables.
Figure 3
Figure 3
Sensitivity and specificity of pneumonia severity scores by a number of variables.
See this image and copyright information in PMC

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