Can diagnostic triage by general practitioners or rheumatology nurses improve the positive predictive value of referrals to early arthritis clinics?

Objectives: To determine whether diagnostic triage by general practitioners (GPs) or rheumatology nurses (RNs) can improve the positive predictive value of referrals to early arthritis clinics (EACs).

Methods: Four GPs and two RNs were trained in the assessment of early in?ammatory arthritis (IA) by four visits to an EAC supervised by hospital rheumatologists. Patients referred to one of three EACs were recruited for study and assessed independently by a GP, an RN and one of six rheumatologists. Each assessor was asked to record their clinical ?ndings and whether they considered the patient to have IA. Each was then asked to judge the appropriateness of the referral according to predetermined guidelines. The rheumatologists had been shown previously to have a satisfactory level of agreement in the assessment of IA.

Results: Ninety-six patients were approached and all consented to take part in the study. In 49 cases (51%), the rheumatologist judged that the patient had IA and that the referral was appropriate. The assessments of GPs and RNs were compared with those of the rheumatologists. Levels of agreement were measured using the kappa value, where 1.0 represents total unanimity. The kappa value was
0.77 for the GPs when compared with the rheumatologists and 0.79 for the RNs. Signi?cant stiffness in the morning or after rest and objective joint swelling were the most important clinical features enabling the GPs and RNs to discriminate between IA and non-IA conditions.

Conclusion: Diagnostic triage by GPs or RNs improved the positive predictive value of referrals to an EAC with a degree of accuracy approaching that of a group of experienced rheumatologists.

Collectives of diagnostic biomarkers identify high-risk subpopulations of hematuria patients: exploiting heterogeneity in large-scale biomarker data

Background: Ineffective risk stratification can delay diagnosis of serious disease in patients with hematuria. We applied a systems biology approach to analyze clinical, demographic and biomarker measurements (n = 29) collected from 157 hematuric patients: 80 urothelial cancer (UC) and 77 controls with confounding pathologies.

Methods: On the basis of biomarkers, we conducted agglomerative hierarchical clustering to identify patient and biomarker clusters. We then explored the relationship between the patient clusters and clinical characteristics using Chi-square analyses. We determined classification errors and areas under the receiver operating curve of Random Forest Classifiers (RFC) for patient subpopulations using the biomarker clusters to reduce the dimensionality of the data.

Results: Agglomerative clustering identified five patient clusters and seven biomarker clusters. Final diagnoses categories were non-randomly distributed across the five patient clusters. In addition, two of the patient clusters were enriched with patients with ‘low cancer-risk’ characteristics. The biomarkers which contributed to the diagnostic classifiers for these two patient clusters were similar. In contrast, three of the patient clusters were significantly enriched with patients harboring ‘high cancer-risk” characteristics including proteinuria, aggressive pathological stage and grade, and malignant cytology. Patients in these three clusters included controls, that is, patients with other serious disease and patients with cancers other than UC. Biomarkers which contributed to the diagnostic classifiers for the largest ‘high cancer- risk’ cluster were different than those contributing to the classifiers for the ‘low cancer-risk’ clusters. Biomarkers which contributed to subpopulations that were split according to smoking status, gender and medication were different.

Conclusions: The systems biology approach applied in this study allowed the hematuric patients to cluster naturally on the basis of the heterogeneity within their biomarker data, into five distinct risk subpopulations. Our findings highlight an approach with the promise to unlock the potential of biomarkers. This will be especially valuable in the field of diagnostic bladder cancer where biomarkers are urgently required. Clinicians could interpret risk classification scores in the context of clinical parameters at the time of triage. This could reduce cystoscopies and enable priority diagnosis of aggressive diseases, leading to improved patient outcomes at reduced costs. © 2013 Emmert-Streib et al; licensee BioMed Central Ltd.

Combined Analysis of Gamma-H2AX/53BP1 Foci and Caspase Activation in Lymphocyte Subsets Detects Recent and More Remote Radiation Exposures

Analysis of gamma-H2AX foci in blood lymphocytes is a promising approach for rapid dose estimation to support patient triage after a radiation accident but has one major drawback: the rapid decline of foci levels post-exposure cause major uncertainties in situations where the exact timing between exposure and blood sampling is unknown. To address this issue, radiation-induced apoptosis (RIA) in lymphocytes was investigated using fluorogenic inhibitors of caspases (FLICA) as an independent biomarker for radiation exposure, which may complement the gamma-H2AX assay. Ex vivo X-irradiated peripheral blood lymphocytes from 17 volunteers showed dose-and time-dependent increases in radiation-induced apoptosis over the first 3 days after exposure, albeit with considerable interindividual variation. Comparison with gamma-H2AX and 53BP1 foci counts suggested an inverse correlation between numbers of residual foci and radiation-induced apoptosis in lymphocytes at 24 h postirradiation (P = 0.007). In T-helper (CD4), T-cytotoxic (CD8) and B-cells (CD19), some significant differences in radiation induced DSBs or apoptosis were observed, however no correlation between foci and apoptosis in lymphocyte subsets was observed at 24 h postirradiation. While gamma-H2AX and 53BP1 foci were rapidly induced and then repaired after exposure, radiation-induced apoptosis did not become apparent until 24 h after exposure. Data from six volunteers with different ex vivo doses and post-exposure times were used to test the capability of the combined assay. Results show that simultaneous analysis of gamma-H2AX and radiation-induced apoptosis may provide a rapid and more accurate triage tool in situations where the delay between exposure and blood sampling is unknown compared to gamma-H2AX alone. This combined approach may improve the accuracy of dose estimations in cases where blood sampling is performed days after the radiation exposure.