Prediction model for pneumonia in primary care patients with an acute respiratory tract infection: role of symptoms, signs, and biomarkers
Abstract Background Diagnosing pneumonia can be challenging in general practice but is essential to distinguish from other respiratory tract infections because of treatment choice and outcome prediction. We determined predictive signs, symptoms and biomarkers for the presence of pneumonia in patient...
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| Format: | Article |
| Language: | English |
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BMC
2019-11-01
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| Series: | BMC Infectious Diseases |
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| Online Access: | http://link.springer.com/article/10.1186/s12879-019-4611-1 |
| _version_ | 1818436660195491840 |
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| author | G. H. Groeneveld J. W. van ’t Wout N. J. Aarts C. J. van Rooden T. J. M. Verheij C. M. Cobbaert E. J. Kuijper J. J. C. de Vries J. T. van Dissel |
| author_facet | G. H. Groeneveld J. W. van ’t Wout N. J. Aarts C. J. van Rooden T. J. M. Verheij C. M. Cobbaert E. J. Kuijper J. J. C. de Vries J. T. van Dissel |
| author_sort | G. H. Groeneveld |
| collection | DOAJ |
| description | Abstract Background Diagnosing pneumonia can be challenging in general practice but is essential to distinguish from other respiratory tract infections because of treatment choice and outcome prediction. We determined predictive signs, symptoms and biomarkers for the presence of pneumonia in patients with acute respiratory tract infection in primary care. Methods From March 2012 until May 2016 we did a prospective observational cohort study in three radiology departments in the Leiden-The Hague area, The Netherlands. From adult patients we collected clinical characteristics and biomarkers, chest X ray results and outcome. To assess the predictive value of C-reactive protein (CRP), procalcitonin and midregional pro-adrenomedullin for pneumonia, univariate and multivariate binary logistic regression were used to determine risk factors and to develop a prediction model. Results Two hundred forty-nine patients were included of whom 30 (12%) displayed a consolidation on chest X ray. Absence of runny nose and whether or not a patient felt ill were independent predictors for pneumonia. CRP predicts pneumonia better than the other biomarkers but adding CRP to the clinical model did not improve classification (− 4%); however, CRP helped guidance of the decision which patients should be given antibiotics. Conclusions Adding CRP measurements to a clinical model in selected patients with an acute respiratory infection does not improve prediction of pneumonia, but does help in giving guidance on which patients to treat with antibiotics. Our findings put the use of biomarkers and chest X ray in diagnosing pneumonia and for treatment decisions into some perspective for general practitioners. |
| first_indexed | 2024-12-14T17:12:18Z |
| format | Article |
| id | doaj.art-cdc94ad1e9a3420db277cb290985d5c6 |
| institution | Directory Open Access Journal |
| issn | 1471-2334 |
| language | English |
| last_indexed | 2024-12-14T17:12:18Z |
| publishDate | 2019-11-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Infectious Diseases |
| spelling | doaj.art-cdc94ad1e9a3420db277cb290985d5c62022-12-21T22:53:32ZengBMCBMC Infectious Diseases1471-23342019-11-011911910.1186/s12879-019-4611-1Prediction model for pneumonia in primary care patients with an acute respiratory tract infection: role of symptoms, signs, and biomarkersG. H. Groeneveld0J. W. van ’t Wout1N. J. Aarts2C. J. van Rooden3T. J. M. Verheij4C. M. Cobbaert5E. J. Kuijper6J. J. C. de Vries7J. T. van Dissel8Department of Internal Medicine and Infectious Diseases, Leiden University Medical CenterDepartment of Internal Medicine and Infectious Diseases, Leiden University Medical CenterDepartment of Radiology, HMC BronovoDepartment of Radiology, HAGA hospitalJulius Center for Health Sciences and Primary Care, University Medical Center UtrechtDepartment of Clinical Chemistry and Laboratory Medicine, Leiden University Medical CenterDepartment of Medical Microbiology, Leiden University Medical Center, LeidenDepartment of Medical Microbiology, Leiden University Medical Center, LeidenCentre for Infectious Disease Control, National Institute for Public Health and the Environment (Rijksinstituut voor Volksgezondheid en Milieu, RIVM)Abstract Background Diagnosing pneumonia can be challenging in general practice but is essential to distinguish from other respiratory tract infections because of treatment choice and outcome prediction. We determined predictive signs, symptoms and biomarkers for the presence of pneumonia in patients with acute respiratory tract infection in primary care. Methods From March 2012 until May 2016 we did a prospective observational cohort study in three radiology departments in the Leiden-The Hague area, The Netherlands. From adult patients we collected clinical characteristics and biomarkers, chest X ray results and outcome. To assess the predictive value of C-reactive protein (CRP), procalcitonin and midregional pro-adrenomedullin for pneumonia, univariate and multivariate binary logistic regression were used to determine risk factors and to develop a prediction model. Results Two hundred forty-nine patients were included of whom 30 (12%) displayed a consolidation on chest X ray. Absence of runny nose and whether or not a patient felt ill were independent predictors for pneumonia. CRP predicts pneumonia better than the other biomarkers but adding CRP to the clinical model did not improve classification (− 4%); however, CRP helped guidance of the decision which patients should be given antibiotics. Conclusions Adding CRP measurements to a clinical model in selected patients with an acute respiratory infection does not improve prediction of pneumonia, but does help in giving guidance on which patients to treat with antibiotics. Our findings put the use of biomarkers and chest X ray in diagnosing pneumonia and for treatment decisions into some perspective for general practitioners.http://link.springer.com/article/10.1186/s12879-019-4611-1Respiratory tract infectionPneumoniaPrimary careBiomarkersPrediction modelCRP |
| spellingShingle | G. H. Groeneveld J. W. van ’t Wout N. J. Aarts C. J. van Rooden T. J. M. Verheij C. M. Cobbaert E. J. Kuijper J. J. C. de Vries J. T. van Dissel Prediction model for pneumonia in primary care patients with an acute respiratory tract infection: role of symptoms, signs, and biomarkers BMC Infectious Diseases Respiratory tract infection Pneumonia Primary care Biomarkers Prediction model CRP |
| title | Prediction model for pneumonia in primary care patients with an acute respiratory tract infection: role of symptoms, signs, and biomarkers |
| title_full | Prediction model for pneumonia in primary care patients with an acute respiratory tract infection: role of symptoms, signs, and biomarkers |
| title_fullStr | Prediction model for pneumonia in primary care patients with an acute respiratory tract infection: role of symptoms, signs, and biomarkers |
| title_full_unstemmed | Prediction model for pneumonia in primary care patients with an acute respiratory tract infection: role of symptoms, signs, and biomarkers |
| title_short | Prediction model for pneumonia in primary care patients with an acute respiratory tract infection: role of symptoms, signs, and biomarkers |
| title_sort | prediction model for pneumonia in primary care patients with an acute respiratory tract infection role of symptoms signs and biomarkers |
| topic | Respiratory tract infection Pneumonia Primary care Biomarkers Prediction model CRP |
| url | http://link.springer.com/article/10.1186/s12879-019-4611-1 |
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