Dataset Information

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 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.

SUBMITTER: Groeneveld GH

PROVIDER: S-EPMC6865035 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Prediction model for pneumonia in primary care patients with an acute respiratory tract infection: role of symptoms, signs, and biomarkers.

Groeneveld G H GH van 't Wout J W JW Aarts N J NJ van Rooden C J CJ Verheij T J M TJM Cobbaert C M CM Kuijper E J EJ de Vries J J C JJC van Dissel J T JT

BMC infectious diseases 20191120 1

<h4>Background</h4>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.<h4>Methods</h4>From March 2012 until May 2016 we did a prospective observational cohort study in three radiology departments in the Leiden-The ...[more]

PMID: 31747890

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Project description:BackgroundKnowledge of predominant pathogens and their association with outcome are of importance for the management of lower respiratory tract infection (LRTI). As antibiotic therapy is indicated in pneumonia and not in acute bronchitis, a predictor of pneumonia is needed.AimTo describe the aetiology and outcome of LRTI in adults with pneumonic and adults with non-pneumonic LRTI treated in general practice and to identify predictors of radiographic pneumonia.Design of studyProspective, observational study.SettingForty-two general practices and an outpatient clinic at the Department of Infectious Diseases, Odense University Hospital, Denmark.MethodA total of 364 adults diagnosed with community-acquired LRTI by their GP were studied with chest radiography, vital signs, biochemical markers of inflammation (C-reactive protein [CRP] and leukocyte count), and microbiological examinations. Primary outcome measure was hospitalisation within 4 weeks.ResultsPneumonia was radiographically verified in 48 of 364 patients (13%). Bacterial infection was seen more often in patients with pneumonia (33% versus 17%, P<0.001), and viral infection more often in non-pneumonic patients (26% versus 13%, P<0.05). Hospitalisation was more common in patients with pneumonia compared to non-pneumonic patients (19 versus 3%, P<0.001); and in patients with pneumococcal infection compared with patients without pneumococcal infection (26 versus 4%, P = 0.001). The positive predictive value of GPs' diagnosis of pneumonia was low (0.23), but the vital signs, CRP, and leukocyte count had comparably low positive predictive values (0.23-0.30).ConclusionStreptococcus pneumoniae was the most common bacterial pathogen. The risk of hospitalisation was highest among patients with pneumonia or pneumococcal infection; this emphasises the importance of coverage of S. pneumoniae when treatment is indicated. CRP should not be introduced for diagnosis of radiographic pneumonia in general practice before its use has been investigated in prospective, controlled intervention trials using CRP-guided treatment algorithms.

Project description:BackgroundPneumonia remains difficult to diagnose in primary care. Prediction models based on signs and symptoms (S&S) serve to minimize the diagnostic uncertainty. External validation of these models is essential before implementation into routine practice. In this study all published S&S models for prediction of pneumonia in primary care were externally validated in the individual patient data (IPD) of previously performed diagnostic studies.Methods and findingsS&S models for diagnosing pneumonia in adults presenting to primary care with lower respiratory tract infection and IPD for validation were identified through a systematical search. Six prediction models and IPD of eight diagnostic studies (N total = 5308, prevalence pneumonia 12%) were included. Models were assessed on discrimination and calibration. Discrimination was measured using the pooled Area Under the Curve (AUC) and delta AUC, representing the performance of an individual model relative to the average dataset performance. Prediction models by van Vugt et al. and Heckerling et al. demonstrated the highest pooled AUC of 0.79 (95% CI 0.74-0.85) and 0.72 (0.68-0.76), respectively. Other models by Diehr et al., Singal et al., Melbye et al., and Hopstaken et al. demonstrated pooled AUCs of 0.65 (0.61-0.68), 0.64 (0.61-0.67), 0.56 (0.49-0.63) and 0.53 (0.5-0.56), respectively. A similar ranking was present based on the delta AUCs of the models. Calibration demonstrated close agreement of observed and predicted probabilities in the models by van Vugt et al. and Singal et al., other models lacked such correspondence. The absence of predictors in the IPD on dataset level hampered a systematical comparison of model performance and could be a limitation to the study.ConclusionsThe model by van Vugt et al. demonstrated the highest discriminative accuracy coupled with reasonable to good calibration across the IPD of different study populations. This model is therefore the main candidate for primary care use.

Project description:ObjectivesTo quantify the diagnostic accuracy of selected inflammatory markers in addition to symptoms and signs for predicting pneumonia and to derive a diagnostic tool.DesignDiagnostic study performed between 2007 and 2010. Participants had their history taken, underwent physical examination and measurement of C reactive protein (CRP) and procalcitonin in venous blood on the day they first consulted, and underwent chest radiography within seven days.SettingPrimary care centres in 12 European countries.ParticipantsAdults presenting with acute cough.Main outcome measuresPneumonia as determined by radiologists, who were blind to all other information when they judged chest radiographs.ResultsOf 3106 eligible patients, 286 were excluded because of missing or inadequate chest radiographs, leaving 2820 patients (mean age 50, 40% men) of whom 140 (5%) had pneumonia. Re-assessment of a subset of 1675 chest radiographs showed agreement in 94% (κ 0.45, 95% confidence interval 0.36 to 0.54). Six published "symptoms and signs models" varied in their discrimination (area under receiver operating characteristics curve (ROC) ranged from 0.55 (95% confidence interval 0.50 to 0.61) to 0.71 (0.66 to 0.76)). The optimal combination of clinical prediction items derived from our patients included absence of runny nose and presence of breathlessness, crackles and diminished breath sounds on auscultation, tachycardia, and fever, with an ROC area of 0.70 (0.65 to 0.75). Addition of CRP at the optimal cut off of >30 mg/L increased the ROC area to 0.77 (0.73 to 0.81) and improved the diagnostic classification (net reclassification improvement 28%). In the 1556 patients classified according to symptoms, signs, and CRP >30 mg/L as "low risk" (<2.5%) for pneumonia, the prevalence of pneumonia was 2%. In the 132 patients classified as "high risk" (>20%), the prevalence of pneumonia was 31%. The positive likelihood ratio of low, intermediate, and high risk for pneumonia was 0.4, 1.2, and 8.6 respectively. Measurement of procalcitonin added no relevant additional diagnostic information. A simplified diagnostic score based on symptoms, signs, and CRP >30 mg/L resulted in proportions of pneumonia of 0.7%, 3.8%, and 18.2% in the low, intermediate, and high risk group respectively.ConclusionsA clinical rule based on symptoms and signs to predict pneumonia in patients presenting to primary care with acute cough performed best in patients with mild or severe clinical presentation. Addition of CRP concentration at the optimal cut off of >30 mg/L improved diagnostic information, but measurement of procalcitonin concentration did not add clinically relevant information in this group.

Project description:ObjectiveTo identify the prevalence of respiratory syncytial virus (RSV) in a cohort of children under 5 years of age with World Health Organization (WHO)-defined pneumonia and the factors associated with developing severe RSV-associated community-acquired pneumonia (CAP) in primary care in a single centre in Northern Malawi.MethodsThe BIOmarkers TO diagnose PnEumonia (BIOTOPE) study was a prospective cohort study conducted from March to June 2016 that took place in a primary care centre in Northern Malawi. Data from this study was used to identify the characteristics of children under 5 years of age who presented with RSV and WHO-defined CAP. Means, standard deviations, medians and ranges were calculated for continuous variables. A univariate logistic regression was performed to examine the potential predictor variables.ResultsFour hundred and ninety-four infants presented with CAP and were eligible for inclusion in the study; RSV infection was detected in 205 (41.6%) of the infants. Eight factors were associated with increased risk for RSV CAP in the univariate model: age, born at term, presenting for care in June, crowded living environment, not being exclusively breastfed, not having received zinc or vitamin A supplementation in the last six months. Infants with RSV were more likely to have an oxygen saturation ≤92% compared to infants with other causes of pneumonia and more likely to have severe pneumonia as defined by the WHO.ConclusionThis study supports that RSV-associated CAP is linked to modifiable and non-modifiable risk factors; further research is indicated to determine which interventions would be most impactful. Developing and implementing an infant or maternal vaccine could be a cost-effective way to prevent RSV-associated CAP and mortality in developing nations. More research is needed to understand seasonal patterns of CAP and research over extended periods can offer valuable insights on host, environmental and pathogen-specific factors that contribute to RSV-associated CAP.

Project description:ImportancePrimary care is an important part of the care pathway for patients with psychosis; therefore, primary care physicians need to be able to accurately identify those at clinical high risk of psychosis. The difficulty of this task is increased because clinical high-risk symptoms are frequently nonspecific to psychosis.ObjectiveTo determine whether the consultation patterns for a prespecified set of symptoms can be used to identify primary care patients who later developed a psychotic illness.Design, setting, and participantsThis nested case-control study used primary care consultation data collected from 530 primary care practices in 13 UK regions from January 1, 2000, through September 30, 2009. Participants included 11 690 adults with a diagnosis of psychosis and 81 793 control participants who did not have a diagnosis of psychosis individually matched by age group, sex, and primary care practice. Data were analyzed from July 1, 2015, through June 2, 2017.ExposuresPrespecified symptoms selected from literature included attention-deficit/hyperactivity disorder-like symptoms, bizarre behavior, blunted affect, problems associated with cannabis, depressive symptoms, role functioning problems, social isolation, symptoms of mania, obsessive-compulsive disorder-like symptoms, disordered personal hygiene, sleep disturbance, problems associated with cigarette smoking, and suicidal behavior (including self-harm).Main outcomes and measuresCase (diagnosis of psychosis) or control (no diagnosis of psychosis) status. Conditional logistic regression was used to investigate the association between symptoms and case-control status in the 5 years before diagnosis. Positive predictive values (PPVs) were calculated using the Bayes theorem for symptoms stratified by age group and sex. Repeated-measures Poisson regression was used to investigate symptom consultation rate.ResultsOf the total sample of 93 483 participants, 57.4% were female and 40.0% were older than 60 years (mean [SD] age, 51.34 [21.75] years). Twelve symptoms were associated with a later psychotic diagnosis (all prespecified symptoms except disordered personal hygiene). The strongest association was with suicidal behavior (odds ratio [OR], 19.06; 95% CI, 16.55-21.95). Positive predictive values were heterogeneous across age and sex. The highest PPVs were for suicidal behavior (33.0% in men 24 years or younger [95% CI, 24.2%-43.2%] and 19.6% in women aged 25-34 years [95% CI, 13.7%-27.2%]). Pairs of symptoms were associated with an increase in PPV. Consultation rates were higher in cases and increased 3 months before diagnosis.Conclusions and relevanceMost of the preselected nonspecific symptoms were associated with a later psychotic diagnosis, particularly among young men consulting for suicidal behavior, especially if consulting with increasing frequency. These symptoms should alert physicians to patients who may benefit from a further assessment of psychotic symptoms.

Project description:BACKGROUND: During an influenza epidemic prompt diagnosis of influenza is important. This diagnosis however is still essentially based on the interpretation of symptoms and signs by general practitioners. No single symptom is specific enough to be useful in differentiating influenza from other respiratory infections. Our objective is to formulate prediction rules for the diagnosis of influenza with the best diagnostic performance, combining symptoms, signs and context among patients with influenza-like illness. METHODS: During five consecutive winter periods (2002-2007) 138 sentinel general practitioners sampled (naso- and oropharyngeal swabs) 4597 patients with an influenza-like illness (ILI) and registered their symptoms and signs, general characteristics and contextual information. The samples were analysed by a DirectigenFlu-A&B and RT-PCR tests. 4584 records were useful for further analysis.Starting from the most relevant variables in a Generalized Estimating Equations (GEE) model, we calculated the area under the Receiver Operating Characteristic curve (ROC AUC), sensitivity, specificity and likelihood ratios for positive (LR+) and negative test results (LR-) of single and combined signs, symptoms and context taking into account pre-test and post-test odds. RESULTS: In total 52.6% (2409/4584) of the samples were positive for influenza virus: 64% (2066/3212) during and 25% (343/1372) pre/post an influenza epidemic. During and pre/post an influenza epidemic the LR+ of 'previous flu-like contacts', 'coughing', 'expectoration on the first day of illness' and 'body temperature above 37.8°C' is 3.35 (95%CI 2.67-4.03) and 1.34 (95%CI 0.97-1.72), respectively. During and pre/post an influenza epidemic the LR- of 'coughing' and 'a body temperature above 37.8°C' is 0.34 (95%CI 0.27-0.41) and 0.07 (95%CI 0.05-0.08), respectively. CONCLUSIONS: Ruling out influenza using clinical and contextual information is easier than ruling it in. Outside an influenza epidemic the absence of cough and fever (> 37,8°C) makes influenza 14 times less likely in ILI patients. During an epidemic the presence of 'previous flu-like contacts', cough, 'expectoration on the first day of illness' and fever (>37,8°C) increases the likelihood for influenza threefold. The additional diagnostic value of rapid point of care tests especially for confirming influenza still has to be established.

Dataset Information

Prediction model for pneumonia in primary care patients with an acute respiratory tract infection: role of symptoms, signs, and biomarkers.

Background

Methods

Results

Conclusions

Publications

Prediction model for pneumonia in primary care patients with an acute respiratory tract infection: role of symptoms, signs, and biomarkers.

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