Influence of the practice setting on diagnostic prediction rules using FENO measurement in combination with clinical signs and symptoms of asthma

Deriving and validating an asthma diagnosis prediction model for children and young people in primary care

Introduction: Accurately diagnosing asthma can be challenging. We aimed to derive and validate a prediction model to support primary care clinicians assess the probability of an asthma diagnosis in children and young people. Methods: The derivation dataset was created from the Avon Longitudinal Study of Parents and Children (ALSPAC) linked to electronic health records. Participants with at least three inhaled corticosteroid prescriptions in 12-months and a coded asthma diagnosis were designated as having asthma. Demographics, symptoms, past medical/family history, exposures, investigations, and prescriptions were considered as candidate predictors. Potential candidate predictors were included if data were available in ≥60% of participants. Multiple imputation was used to handle remaining missing data. The prediction model was derived using logistic regression. Internal validation was completed using bootstrap re-sampling. External validation was conducted using health records from the Optimum Patient Care Research Database (OPCRD). Results: Predictors included in the final model were wheeze, cough, breathlessness, hay-fever, eczema, food allergy, social class, maternal asthma, childhood exposure to cigarette smoke, prescription of a short acting beta agonist and the past recording of lung function/reversibility testing. In the derivation dataset, which comprised 11,972 participants aged <25 years (49% female, 8% asthma), model performance as indicated by the C-statistic and calibration slope was 0.86, 95% confidence interval (CI) 0.85-0.87 and 1.00, 95% CI 0.95-1.05 respectively. In the external validation dataset, which included 2,670 participants aged <25 years (50% female, 10% asthma), the C-statistic was 0.85, 95% CI 0.83-0.88, and calibration slope 1.22, 95% CI 1.09-1.35. Conclusions: We derived and validated a prediction model for clinicians to calculate the probability of asthma diagnosis for a child or young person up to 25 years of age presenting to primary care. Following further evaluation of clinical effectiveness, the prediction model could be implemented as a decision support software.

Deriving and validating an asthma diagnosis prediction model for children and young people in primary care

Introduction: Accurately diagnosing asthma can be challenging. We aimed to derive and validate a prediction model to support primary care clinicians assess the probability of an asthma diagnosis in children and young people. Methods: The derivation dataset was created from the Avon Longitudinal Study of Parents and Children (ALSPAC) linked to electronic health records. Participants with at least three inhaled corticosteroid prescriptions in 12-months and a coded asthma diagnosis were designated as having asthma. Demographics, symptoms, past medical/family history, exposures, investigations, and prescriptions were considered as candidate predictors. Potential candidate predictors were included if data were available in ≥60% of participants. Multiple imputation was used to handle remaining missing data. The prediction model was derived using logistic regression. Internal validation was completed using bootstrap re-sampling. External validation was conducted using health records from the Optimum Patient Care Research Database (OPCRD). Results: Predictors included in the final model were wheeze, cough, breathlessness, hay-fever, eczema, food allergy, social class, maternal asthma, childhood exposure to cigarette smoke, prescription of a short acting beta agonist and the past recording of lung function/reversibility testing. In the derivation dataset, which comprised 11,972 participants aged <25 years (49% female, 8% asthma), model performance as indicated by the C-statistic and calibration slope was 0.86, 95% confidence interval (CI) 0.85-0.87 and 1.00, 95% CI 0.95-1.05 respectively. In the external validation dataset, which included 2,670 participants aged <25 years (50% female, 10% asthma), the C-statistic was 0.85, 95% CI 0.83-0.88, and calibration slope 1.22, 95% CI 1.09-1.35. Conclusions: We derived and validated a prediction model for clinicians to calculate the probability of asthma diagnosis for a child or young person up to 25 years of age presenting to primary care. Following further evaluation of clinical effectiveness, the prediction model could be implemented as a decision support software.

Development of a diagnostic score using FeNO and symptoms to predict asthma

BACKGROUND

Fractional exhaled nitric oxide (FeNO) is known as effective for ruling-in asthma. The diagnostic value might be increased in combination with clinical signs and symptoms (CSS). The aim was to develop a new model for ruling-in and ruling-out asthma.

METHODS

Diagnostic multi-centre study in three practices of pneumologists in Germany. Whole-body plethysmography was combined with bronchodilation tests or bronchial provocation as diagnostic reference standard. Follow-up was performed after 3 months. An expert committee evaluated test results, symptoms, and course of disease for the final diagnosis. Relevant CSS known from guidelines were used to enable combinatorial development of decision rules. Outcomes of multiple logistic regression modeling were translated into a diagnostic score and internally validated by ten-fold cross validation.

RESULTS

308 patients with complete follow-up were included. 186 (60.4%) were female, average age was 44.7 years and 161 (52.5%) had asthma. The average area under the receiver operating curve (AUC) of the diagnostic score was 0.755 (interquartile range 0.721-0.814). Allergic rhinitis, wheezing, dyspnea on exertion, coughing attacks at night, and awakening by shortness of breath were leading symptoms for ruling-in asthma. Frequent coughing and frequent respiratory infections were leading symptoms for ruling-out. The combination of FeNO and CSS allowed ruling-in asthma with a probability of up to 99%, and ruling-out with a post-test probability down to 9%.

CONCLUSION

The diagnostic scoring model increased the diagnostic value of FeNO in combination with CSS. The new decision rule allowed to rule-in asthma with high certainty, and also to rule-out with acceptable certainty.

Diagnostic accuracy of FeNO in asthma and predictive value for inhaled corticosteroid responsiveness: A prospective, multicentre study

BACKGROUND

Fractional exhaled nitric oxide (FeNO) is promising for diagnosing asthma and could replace bronchial provocation (BP). To date, cut-off values have been derived by post hoc analysis only. The aim was to validate the diagnostic accuracy for predefined FeNO cut-off values and the predictive value for responsiveness to inhaled corticosteroids (ICS).

METHODS

We conducted a prospective, diagnostic, multicentre study with patients attending three private practices of pneumologists in Upper Bavaria, Germany, from July 3, 2020 to Jan 21, 2022. Index test was FENO measurement. Reference standard was Tiffeneau ratio (FEV₁/VC) or airway resistance as assessed by whole body plethysmography, with additional BP or bronchodilation test. Follow-up was performed after 12 weeks. Analyses of Receiver Operating Characteristics curves were conducted to determine the diagnostic accuracy and predictive value of FeNO.

FINDINGS

308 patients with complete follow-up were recruited, 186 (60·4%) were female, average age was 44·7 years, 161 (52·3%) had asthma. Regarding diagnostic accuracy, the area under the curve (AUC) was 0·718 (95% CI 0·661-0·775; p < 0·001). Sensitivity at FeNO >50 ppb was 0·24 (95% CI 0·18-0·32), specificity 0·99 (0·95-1·0), positive predictive value (PPV) 0·95 (0·84-0·99), negative predictive value (NPV) 0·54 (0·48-0·60). In 66 patients with ´wheezing´ and ´allergic rhinitis´, the sensitivity at FeNO >33 ppb was 0·49 (0·34-0·64), specificity 0·88 (0·64-0·99), PPV 0·92 (0·75-0·99), NPV 0·38 (0·23-0·54). In 68 patients with ICS medication, responsiveness was predicted at the cut-off >43 ppb, with a sensitivity of 0·55 (95%CI 0·36-0·74), specificity 0·82 (0·66-0·92), PPV 0·70 (0·47-0·87), NPV 0·71 (0·56-0·84).

INTERPRETATION

FeNO measurement allows a valid ruling-in of an asthma diagnosis, whereas ruling-out of asthma is not possible. Enhanced probability of ICS responsiveness is also given with increased FeNO values.

FUNDING

Circassia Germany gave 25% discount on the purchase of three NIOX VERO devices.

A dynamic nomogram for predicting the risk of asthma: Development and validation in a database study

Background

Asthma remains a serious health problem with increasing prevalence and incidence. This study was to develop and validate a dynamic nomogram for predicting asthma risk.

Methods

Totally 597 subjects whose age ≥18 years old with asthma, an accurate age at first cigarette, and clear smoking status were selected from the National Health and Nutrition Examination Survey (NHANES) database (2013–2018). The dataset was randomly split into the training set and the testing set at a ratio of 4:6. Simple and multiple logistic regressions were used for identifying independent predictors. Then the nomogram was developed and internally validated using data from the testing set. The receiver operator characteristic (ROC) curve was used for assessing the performance of the nomogram.

Results

According to the simple and multiple logistic regressions, smoking ≥40 years, female gender, the age for the first smoking, having close relative with asthma were independently associated with the risk of an asthma attack. The nomogram was thereby developed with the link of https://yanglifen.shinyapps.io/Dynamic_Nomogram_for_Asthma/. The ROC analyses showed an AUC of 0.726 (0.724–0.728) with a sensitivity of 0.887 (0.847–0.928) in the training set, and an AUC of 0.702 (0.700–0.703) with a sensitivity of 0.860 (0.804–0.916) in the testing set, fitting well in calibration curves. Decision curve analysis further confirmed the clinical usefulness of the nomogram.

Conclusion

Our dynamic nomogram could help clinicians to assess the individual probability of asthma attack, which was helpful for improving the treatment and prognosis of asthma.

Fractional exhaled nitric oxide as a determinant for the clinical course of asthma: a systematic review

Background: Precision medicine means linking the right patient to the right management strategy including best possible pharmacological therapy, considering the individual variability of the disease characteristics, type of inflammation, genes, environment, and lifestyle. For heterogenous diseases such as asthma, reliable biomarkers are needed to facilitate the best possible disease control and reduce the risk of side effects. The present review examines fractional exhaled nitric oxide (FeNO) as a guide for the management strategy of asthma and predictor of its clinical course.

Method: The literature included was identified by searching the PubMed database using specific key words and MeSH terms. Studies were not excluded based on their design alone. The search resulted in 212 hits, of which 35 articles were included in this review.

Results: Several studies support a potential role for high FeNO levels as a prognostic biomarker for accelerated lung function decline in adults with newly diagnosed asthma. Furthermore, studies report an association between high FeNO levels and excess decline in FEV₁ in adults with long-standing moderate to severe asthma despite optimised therapy, whereas the findings for patients with less severe disease are conflicting. Applying a FeNO-based management algorithm reduces the exacerbation rate in adults with asthma. Similar observations are seen in children, though based on fewer studies. The available studies provide evidence that the level of FeNO may be useful as a predictor of subsequent loss of asthma control in adults, though the evidence is somewhat conflicting in children and young adults.

Conclusion: The present review provides evidence of the prognostic value of FeNO as a surrogate biomarker for type 2 inflammation in the airways. FeNO is likely to emerge as an important biomarker in monitoring and tailoring modern asthma treatment, either alone or in combination with other biomarkers.

Evaluation of the diagnostic accuracy of fractional exhaled nitric oxide (FeNO) in patients with suspected asthma: study protocol for a prospective diagnostic study

INTRODUCTION

The measurement of fractional exhaled nitric oxide (FeNO) is promising for diagnosing asthma and might substitute for bronchial provocation (BP) tests. To evaluate the diagnostic accuracy of FeNO within a confirmatory study, the following hypotheses will be tested: (1) A FeNO cut-off >50 ppb (parts per billion) is suitable for diagnosing asthma (sensitivity 35%, specificity 95%); (2) If the clinical symptoms 'allergic rhinitis' and 'wheezing' are present, asthma can be diagnosed at FeNO >33 ppb with a positive predictive value (PPV) >70% and (3) A FeNO >33 ppb can predict responsiveness to inhaled corticosteroid (ICS) with a PPV >70%.

METHODS AND ANALYSIS

A prospective diagnostic study will be conducted in three practices of pneumologists in Germany. 300 patients suspected of suffering from asthma will be included. As an index test, patients perform FeNO measurement with the device NIOX VERO. As reference a test, patients are examined with whole bodyplethysmography and BP, if necessary. After 3 months, patients with an asthma diagnosis will be examined again to verify the diagnosis and evaluate ICS responsiveness. Patients who did not receive an asthma diagnosis at the initial examination will be phoned after 3 months and asked about persistent respiratory symptoms to exclude false negative findings. As a primary target, sensitivity and specificity of FeNO >50 ppb will be determined. As a secondary target the PPV for asthma at FeNO >33 ppb, when the symptoms 'allergic rhinitis' and 'wheezing' are present, will be calculated. Regarding ICS responsiveness, the PPV of FeNO >33 ppb will be determined.

ETHICS AND DISSEMINATION

The study was approved by the Ethical Committee of the Technical University of Munich (Reference number 122/20 S). The major results will be published in peer-reviewed academic journals and disseminated through conferences.

TRIAL REGISTRATION NUMBER

DRKS00021125.

The use of a direct bronchial challenge test in primary care to diagnose asthma

Many asthmatics in primary care have mild symptoms and lack airflow obstruction. If variable expiratory airflow limitation cannot be determined by spirometry or peak expiratory flow, despite a history of respiratory symptoms, a positive bronchial challenge test (BCT) can confirm the diagnosis of asthma. However, BCT is traditionally performed in secondary care. In this observational real-life study, we retrospectively analyze 5-year data of a primary care diagnostic center carrying out BCT by histamine provocation. In total, 998 primary care patients aged ≥16 years underwent BCT, without any adverse events reported. To explore diagnostic accuracy, we examine 584 patients with a high pretest probability of asthma. Fifty-seven percent of these patients have a positive BCT result and can be accurately diagnosed with asthma. Our real-life data show BCT is safe and feasible in a suitably equipped primary care diagnostic center. Furthermore, it could potentially reduce diagnostic referrals to secondary care.

Diagnostic accuracy of FeNO [fractional exhaled nitric oxide] and asthma symptoms increased when evaluated with a superior reference standard

OBJECTIVES

The objective of the study is to determine the impact of changing reference standards (RS), namely spirometry vs. whole-body plethysmography (WBP), on estimation of the diagnostic accuracy of fractional exhaled nitric oxide (FeNO) and clinical signs and symptoms (CSS) as index tests regarding asthma diagnosis.

STUDY DESIGN AND SETTING

This was a diagnostic study conducted in 393 patients attending a private practice of pneumologists with complaints suspicious of asthma. First, the index tests were compared with the diagnostic results of spirometry in terms of forced expiratory volume in the first second (FEV₁) responsiveness. Second, the index tests were compared with the results of WBP in terms of specific airway resistance and FEV₁ responsiveness. Areas under the curve (AUC) were compared with a generalized estimating equation approach based on binary logistic regression.

RESULTS

FeNO values and CSS 'wheezing' and 'allergic rhinitis' showed higher specificities (P < 0.001) and sensitivities (not significant) when evaluated with WBP; also, Youden indices increased in these CSS (P < 0.05). AUC of FeNO in combination with 'wheezing' and 'allergic rhinitis' when WBP was used as RS (AUC = 0.724; 95% confidence interval 0.672 to 0.776) was higher compared with spirometry as RS (AUC = 0.654; 95% confidence interval 0.585 to 0.722) (P < 0.001).

CONCLUSION

In case of asthma, superior RS led to more favorable assessment of index tests. FeNO measurement might have been underestimated in some previous studies.

Capnovolumetry in combination with clinical history for the diagnosis of asthma and COPD

Capnovolumetry performed during resting ventilation is an easily applicable diagnostic tool sensitive to airway obstruction. In the present analysis, we investigated in which way capnovolumetric parameters can be combined with basic anamnestic information to support the diagnosis of asthma and COPD. Among 1400 patients of a previous diagnostic study, we selected 1057 patients with a diagnosis of asthma (n = 433), COPD (n = 260), or without respiratory disease (n = 364). Besides performing capnovolumetry, patients answered questions on symptoms and smoking status. Logistic regression analysis, single decision trees (CHAID), and ensembles of trees (random forest) were used to identify diagnostic patterns of asthma and COPD. In the random forest approach, area/volume of phase 3, dyspnea upon strong exertion, s3/s2, and current smoking were identified as relevant parameters for COPD vs control. For asthma vs control, they were wheezing, volume of phase 2, current smoking, and dyspnea at strong exertion. For COPD vs asthma, s3/s2 was the primary criterion, followed by current smoking and smoking history. These parameters were also identified as relevant in single decision trees. Regarding the diagnosis of asthma vs control, COPD vs control, and COPD vs asthma, the area under the curve was 0.623, 0.875, and 0.880, respectively, in the random forest approach. Our results indicate that for the diagnosis of asthma and COPD capnovolumetry can be combined with basic anamnestic information in a simple, intuitive, and efficient manner. As capnovolumetry requires less cooperation from the patient than spirometry, this approach might be helpful for clinical practice.

A Randomized Trial of Comparing a Combination of Montelukast and Budesonide With Budesonide in Allergic Rhinitis

OBJECTIVES/HYPOTHESIS

It is not unequivocally proven whether a combination of an intranasal corticosteroids (INSs) and a cysteinyl leukotriene receptor antagonist has greater efficacy than INSs in the treatment of seasonal allergic rhinitis (SAR).

STUDY DESIGN

Single-center, randomized, open-label study.

METHODS

Study subjects included 46 participants with SAR. Participants were randomized to receive budesonide (BD; 256 μg) plus montelukast (MNT; 10 mg) (BD + MNT) or BD alone (256 μg) for 2 weeks. Visual analog scale scores for five major symptoms of SAR, nasal cavity volume (NCV), nasal airway resistance (NAR), and fractional exhaled nitric oxide (FeNO) were assessed before and at the end of treatments.

RESULTS

Both treatments significantly improved the five main SAR symptoms from baseline; however, BD + MNT produced significantly greater improvements in nasal blockage and nasal itching compared to BD alone. At baseline, the nasal blockage score was significantly correlated with NCV and NAR (r = -0.473, P = .002 and r = -0.383, P = .013, respectively). After 2 weeks of treatment, BD + MNT significantly improved NCV, but not NAR, to a greater level than BD. The number of patients with FeNO concentration ≥ 30 ppb at baseline was significantly decreased after BD + MNT treatment, but not after BD treatment. Similarly, BD + MNT treatment led to a significantly greater decrease in FeNO concentration than BD treatment.

CONCLUSIONS

BD + MNT treatment may have an overall superior efficacy than BD monotherapy for patients with SAR, especially in improvement of nasal blockage, itching, and subclinical lower airway inflammation. Also, NCV and NAR could be used to assess nasal blockage more accurately.

LEVEL OF EVIDENCE

1b Laryngoscope, 131:E1054-E1061, 2021.

Systematic review of clinical prediction models to support the diagnosis of asthma in primary care

Diagnosing asthma is challenging. Misdiagnosis can lead to untreated symptoms, incorrect treatment and avoidable deaths. The best combination of clinical features and tests to achieve a diagnosis of asthma is unclear. As asthma is usually diagnosed in non-specialist settings, a clinical prediction model to aid the assessment of the probability of asthma in primary care may improve diagnostic accuracy. We aimed to identify and describe existing prediction models to support the diagnosis of asthma in children and adults in primary care. We searched Medline, Embase, CINAHL, TRIP and US National Guidelines Clearinghouse databases from 1 January 1990 to 23 November 17. We included prediction models designed for use in primary care or equivalent settings to aid the diagnostic decision-making of clinicians assessing patients with symptoms suggesting asthma. Two reviewers independently screened titles, abstracts and full texts for eligibility, extracted data and assessed risk of bias. From 13,798 records, 53 full-text articles were reviewed. We included seven modelling studies; all were at high risk of bias. Model performance varied, and the area under the receiving operating characteristic curve ranged from 0.61 to 0.82. Patient-reported wheeze, symptom variability and history of allergy or allergic rhinitis were associated with asthma. In conclusion, clinical prediction models may support the diagnosis of asthma in primary care, but existing models are at high risk of bias and thus unreliable for informing practice. Future studies should adhere to recognised standards, conduct model validation and include a broader range of clinical data to derive a prediction model of value for clinicians.

Metastatic risk stratification of clear cell renal cell carcinoma patients based on genomic aberrations

Prognostic markers for the definition of the individual metastatic risk in renal cell carcinoma are still missing. The aim of our study was to establish a total number of specific aberrations (TNSA) genetic score as a new prognostic test for metastatic risk evaluation. Fluorescence in situ hybridization (FISH) was performed on isolated cell nuclei of 100 ccRCCs (50 M1/50 M0) and 100 FFPE sections (second cohort, 32 M1/68 M0). For each chromosomal region (1q21.3, 7q36.3, 9p21.3p24.1, 20q11.21q13.32) cut-off values were determined by receiver-operator curve (ROC)-curve analysis. TNSA was calculated based on the dichotomized specific CNVs. The prognostic significance of CNVs was proven by Cox and logistic regression. TNSA was the best predictor of metastasis and recurrence free survival in both cohorts. We derived an algorithm for risk stratification by combining TNSA and T-category, which increased the prognostic accuracy to 87% (specificity = 86%, sensitivity = 88%). This model divides patients into two risk groups with significantly different RFS, CSS, and OS (P = 3.8×10^-5 , P = 5×10^-6 and P = 3.57×10^-8 respectively). The genetic risk model was superior to Leibovich score and was able to identify patients with metachronous metastatic spread which were incorrectly classified as "low" or "intermediate risk." We present a new tool for individual risk stratification by combining genetic alterations with clinico-pathologic parameters. Interphase FISH proves to be a dependable method for prognostic evaluation in primary tumor tissue on isolated cell nuclei as well as on FFPE sections. Especially in organ-confined tumors the genetic score seems to be an important tool to identify patients at high risk for metastatic disease.

Establishment of Predictive Models for Nonocclusive Mesenteric Ischemia Comparing 8,296 Control with 452 Study Patients

OBJECTIVE

The aim of this study was to develop clinical preoperative, intraoperative, and postoperative scores for early identification of patients who are at risk of nonocclusive mesenteric ischemia (NOMI).

DESIGN

A retrospective analysis.

SETTING

Single center.

PARTICIPANTS

From January 2008 to December 2014, all patients from the Department of Thoracic and Cardiovascular Surgery were included on the basis of the hospital database.

INTERVENTIONS

All mesenteric angiographically identified NOMI patients were compared with non-NOMI patients.

MEASUREMENTS AND MAIN RESULTS

The study population of 8,748 patients was randomized into a cohort for developing the scores (non-NOMI 4,214 and NOMI 235) and a cohort for control (non-NOMI 4,082 and NOMI 217). Risk factors were identified using forward and backward Wald test and were included in the predictive scores for the occurrence of NOMI. C statistic showed that the scores had a high discrimination for the prediction of NOMI preoperatively (C statistic 0.79; p < 0.001), intraoperatively (C statistic 0.68; p < 0.001), and postoperatively (C statistic 0.85; p < 0.001). A combination of the preoperative, intraoperative, and postoperative risk scores demonstrated the highest discrimination (C statistic 0.87; p < 0.001). The combined score included the following risk factors: renal insufficiency (preoperative); use of cardiopulmonary bypass and intra-aortic balloon pump support (intraoperative); and reexploration for bleeding, renal replacement therapy, and packed red blood cells ≥ 4 units (postoperative). The results were similar in the control group.

CONCLUSIONS

These scores could be useful to identify patients at risk for NOMI and promote a rapid diagnosis and therapy.

A novel statistical model for analyzing data of a systematic review generates optimal cutoff values for fractional exhaled nitric oxide for asthma diagnosis

OBJECTIVES

Measurement of fractional exhaled nitric oxide (FE_NO) might substitute bronchial provocation for diagnosing asthma. However, optimal FE_NO thresholds for diagnosing asthma remain unclear. We reanalyzed data collected for a systematic review investigating the diagnostic accuracy of FE_NO measurement to exploit all available thresholds under consideration of pretest probabilities using a newly developed statistical model.

STUDY DESIGN AND SETTING

One hundred and fifty data sets for a total of 53 different cutoffs extracted from 26 studies with 4,518 participants were analyzed with the multiple thresholds model. This model allows identifying thresholds at which the test is likely to perform best.

RESULTS

Diagnosing asthma might only be possible in a meaningful manner when the pretest probability of asthma is at least 30%. In that case, FE_NO > 50 ppb leads to a positive predictive value of 0.76 [95% confidence interval (CI): 0.29-0.96]. Excluding asthma might only be possible, when the pretest probability of asthma is 30% at maximum. Then, FE_NO < 20 ppb leads to a negative predictive value of 0.86 (95% CI 0.66-0.95).

CONCLUSION

The multiple thresholds model generates a more comprehensive and more clinically useful picture of the effects of different thresholds, which facilitates the determination of optimal thresholds for diagnosing or excluding asthma with FE_NO measurement.

[Clinical significance of fractional exhaled nitric oxide combined with in vitro allergen test in identifying children at a high risk of asthma among those with recurrent wheezing]

OBJECTIVE

To investigate the clinical value of combined determination of in vitro allergens and fractional exhaled nitric oxide (FeNO) in indentifying children at a high risk of asthma among those with recurrent wheezing.

METHODS

A total of 148 children with recurrent wheezing (0.5-6 years old) were enrolled as study subjects, and 80 healthy children who underwent physical examination were enrolled as the control group. Pharmacia UniCAP immunoassay analyzer was used to measure specific immunoglobulin E (sIgE). Nano Coulomb Nitric Oxide Analyzer was used to measure FeNO. The asthma predictive index (API) was evaluated.

RESULTS

The recurrent wheezing group had a significantly higher proportion of children with positive sIgE than the control group [68.9% (102/148) vs 11.3% (9/80); P<0.05]. The recurrent wheezing group also had significantly higher levels and positive rate of FeNO than the control group (P<0.05). The overall positive rate of API in children with wheezing was 32.4%, and the API-positive children had a significantly higher FeNO value than the API-negative children (51±6 ppb vs 13±5 ppb; P<0.05). The detection rate of API was 40.2% (41/102) in positive-sIgE children and 50.1% (38/73) in FeNO-positive children, and there was no significant difference between these two groups. The children with positive sIgE and FeNO had a significantly higher detection rate of API (81.4%) than those with positive sIgE or FeNO (P<0.05).

CONCLUSIONS

Combined determination of FeNO and in vitro allergens is more sensitive in detecting children at a high risk of asthma than FeNO or in vitro allergens determination alone and provides a good method for early identification, diagnosis, and intervention of asthma in children.

Use of electronic medical records and biomarkers to manage risk and resource efficiencies

The migration from paper to electronic medical records (EMRs) was motivated by the administrative need to record, retrieve and process increasing amounts of clinical data in the 1980s. In the intervening period, there has been growing recognition of the potential of such records for achieving care efficiencies, informing clinical decision making and real-life research. EMRs can be used to characterise patient groups, management approaches and differential outcomes. Characterisation can also help with identification of potential biomarkers for future risk determination and likely treatment response. The future heralds even greater opportunities through integration of clinical records and a range of technology-based solutions within a more complete electronic health record (EHR). Through application of algorithms based on identified risk predictors and disease determinants, clinical records could also be used to enable risk stratification of patients to optimise targeted interventions, conserving resources to achieve individual patient and system-wide benefit. In this review, we reflect on the evolution of the EMR and EHR and discuss current and emerging opportunities, particularly with respect to biomarkers and targeting of innovative biologic interventions. We also consider some of the critical issues associated with realising the potential of the EHR as a clinical aid and research tool in an age of emerging technologies.​​​​.

Clinical Utility of Fractional exhaled Nitric Oxide (FeNO) as a Biomarker to Predict Severity of Disease and Response to Inhaled Corticosteroid (ICS) in Asthma Patients

INTRODUCTION

Bronchial asthma is a common chronic inflammatory airway disease diagnosed and is based on symptomatic history and Pulmonary Function Tests (PFT). Fractional exhaled Nitric Oxide (FeNO) is exclusively a non-invasive biomarker of on-going eosinophilic airway inflammation which remains unpredictable only with PFTs. FeNO measurement is recommended in predicting asthma severity and Inhaled Corticosteroid (ICS) response but further research is required to understand its clinical utility and agreement with current recommendations in a specific population.

AIM

To estimate FeNO levels in Tamilian patients with mild-to-moderate persistent asthma and to correlate with disease severity and ICS response.

MATERIALS AND METHODS

The study was a prospective cohort with a single group of 102 persistent asthma patients under standard ICS regimen for 8 weeks (follow-up period). PFT and FeNO were measured using portable spirometry and chemiluminescence based exhaled breath analyser, at baseline and during follow-up visits. Based on PFT and FeNO parameters, the study population was sub-grouped with respect to asthma severity (as mild, moderate and moderately severe), FeNO cut-off (> or < 50ppb) and ICS response classification (good vs poor ICS responders).

RESULTS

Significant decrease in mean FeNO levels were found in mild, moderate and moderately severe asthmatic groups following ICS treatment (90.15±27.36, 75.74±31.98 and 77.18±32.79 ppb) compared to similar baseline FeNO levels (103.03±34.08, 91.38±37.60 and 97.90±43.84 ppb) in all the above groups. Similarly, significant decrease in mean FeNO levels was found - FeNO>50ppb, good and poor ICS responders groups, in post- ICS treatment (89.63±24.04, 77.90±31.12 and 86.49±32.57 ppb) compared to baseline levels (110.183±1.23, 97.12±42.04 and 99.68±34.71 ppb).

CONCLUSION

The observed baseline FeNO values in all groups as stated above did not show significant difference to differentiate asthma severity or ICS responders groups. The present study results do not support the predictive association of baseline FeNO levels with asthma severity and future ICS response, but the decrements in FeNO levels on ICS treatment, supports its clinical utility in monitoring of ongoing airway inflammation and understanding treatment response rate.

Accuracy of FENO for diagnosing asthma: a systematic review

BACKGROUND

Measurement of FE_NO might substitute bronchial provocation for diagnosing asthma. We aimed to investigate the diagnostic accuracy of FE_NO measurement compared with established reference standard.

METHODS

Systematic review and diagnostic meta-analysis. Data sources were Medline, Embase and Scopus up to 29 November 2015. Sensitivity and specificity were estimated using a bivariate model. Additionally, summary receiver-operating characteristic curves were estimated.

RESULTS

26 studies with 4518 participants (median 113) were included. Risk of bias was considered low for six of seven items in five studies and for five items in seven studies. The overall sensitivity in the meta-analysis was 0.65 (95% CI 0.58 to 0.72), the overall specificity 0.82 (0.76 to 0.86), the diagnostic OR 9.23 (6.55 to 13.01) and the area under the curve 0.80 (0.77 to 0.85). In meta-regression analyses, higher cut-off values were associated with increasing specificity (OR 1.46 per 10 ppb increase in cut-off) while there was no association with sensitivity. Sensitivities varied significantly within the different FE_NO devices, but not specificities. Neither prevalence, age, use of bronchoprovocation in >90% of participants or as exclusive reference standard test, nor risk of bias were significantly associated with diagnostic accuracy.

CONCLUSIONS

There appears to be a fair accuracy of FE_NO for making the diagnosis of asthma. The overall specificity was higher than sensitivity, which indicates a higher diagnostic potential for ruling in than for ruling out the diagnosis of asthma.