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

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

^* To whom correspondence should be addressed. E-mail: j.bont{at}umcutrecht.nl.

	Abstract

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

Data, including demographics, medication use, health care use and comorbid conditions, from 3,166 episodes of patients aged 65 years 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.

Increasing age, male gender, previous hospitalisation, heart failure, diabetes, use of oral glucocorticoids, previous use of antibiotics and a diagnosis of pneumonia or exacerbation of Chronic Obstructive Pulmonary Disease were independent predictors of 30-day hospitalisation or death. 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, 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. Further studies should assess whether information on signs and symptoms can further improve this prediction rule.

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