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A prediction rule for elderly primary-care patients with lower respiratory tract infections

¹ Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, ² Center for Biostatistics, Utrecht University, ³ Netherlands Institute for Health Services Research (NIVEL), Utrecht, and ⁴ Department of General Practice / EMGO Institute, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.

CORRESPONDENCE: J. Bont, Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, PO Box 85060, 3508 AB Utrecht, The Netherlands. Fax: 31 302539028. E-mail: j.bont{at}umcutrecht.nl

Keywords: Aged, clinical prediction rule, primary healthcare, prognosis, respiratory tract infections

Received: October 4, 2006
Accepted December 20, 2006

Prognostic scores for lower respiratory tract infections (LRTI) have been mainly derived in a hospital setting. The current authors have developed and validated a prediction rule for the prognosis of acute LRTI in elderly primary-care patients.

Data including demographics, medication use, healthcare use and comorbid conditions from 3,166 episodes of patients aged 65 yrs visiting the general practitioner (GP) with LRTI were collected. Multiple logistic regression analysis was used to construct a predictive model. The main outcome measure was 30-day hospitalisation or death. The Second Dutch Survey of GPs was used for validation.

The following were independent predictors of 30-day hospitalisation or death: increasing age; previous hospitalisation; heart failure; diabetes; use of oral glucocorticoids; previous use of antibiotics; a diagnosis of pneumonia; and exacerbation of chronic obstructive pulmonary disease. A prediction rule based on these variables showed that the outcome increased directly with increasing scores: 3, 10 and 31% for scores of <2 points, 3–6 and 7 points, respectively. Corresponding figures for the validation cohort were 3, 11 and 26%, respectively.

This simple prediction rule can help the primary-care physician to differentiate between high- and low-risk patients. As a possible consequence, low-risk patients may be suitable for home treatment, whereas high-risk patients might be monitored more closely in a homecare or hospital setting. Future studies should assess whether information on signs and symptoms can further improve this prediction rule.

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