Retreatment with Hyaluronic Acid Viscosupplementation in Knee Osteoarthritis: Agreement between EUROVISCO Guidelines and Current Medical Practice

OBJECTIVES

This work studied if and how current clinical practice agrees with European Viscosupplementation Consensus Group (EUROVISCO) recommendations and how this agreement might be different according to physician's specialization. In addition, this work aimed to identify key decision factors that practitioners consider in their decision to retreat or not a patient with hyaluronic acid viscosupplementation.

METHODS

Practitioners have been invited by e-mail to participate in an online exercise on viscosupplementation retreatment. They received a fictional patient case at random among a set of predefined fictional cases. The platform asked the practitioner if he/she would retreat the patient with viscosupplementation or not. To take a decision, the practitioner could select questions among a list of predefined questions. Among them, some were related to criteria used in the EUROVISCO decision tree and others served as confounding factors.

RESULTS

A total of 506 practitioners participated to the exercise, of which 399 gave their decision about the case assigned to them by the platform. The observed agreement between practitioner decisions and EUROVISCO recommendations was 58.89 ± 4.95% (95% confidence interval [CI]). Overall, the decision to retreat was taken in 47.87% of the cases, while the EUROVISCO guidelines follow-up would have led to 55.89% retreatment for the same cases (P = 0.03).

CONCLUSIONS

In current practice, physicians tended to reinject their patients less than recommended, although EUROVISCO guidelines for viscosupplementation retreatment consider decision criteria that clearly correspond to those of practitioners in real life. These include the patients' willingness to be treated or the patients' perception of the effectiveness of the treatment.

Assessment of the Most Impactful Combination of Factors Associated with Nocturia and to Define Nocturnal Polyuria by Multivariate Modelling

BACKGROUND

Nocturia is common and associated with multiple disease states. Many potential mechanisms have been proposed for nocturia, which also remains challenging to manage.

PURPOSE

To use multivariate analysis to determine which combinations of factors can accurately discriminate clinically significant nocturia in patients to facilitate clinical management and treatment decisions.

PATIENTS AND METHODS

Data analysis was based on frequency volume charts from three randomized controlled trials. There were 1479 patients included, of which 215 patients had no/mild nocturia and 1264 had clinically significant nocturia with at least two voids per night. Factors studied that may influence nocturia were demographics, sleep duration, functional bladder capacity, 24 h urine volume and literature-suggested definitions of nocturnal polyuria. We used univariate analysis and cross-validated multivariate modelling to assess association between factors and nocturia status, redundancy between factors and whether the combined use of factors could explain patients' nocturia status.

RESULTS

The multivariate analyses showed that the most useful definitions of nocturia are 'Nocturia Index' (NI) and 'Nocturnal Urine Production per hour' (NUPh) in combination with functional bladder capacity and sleep duration. Published definitions providing binary nocturnal polyuria outcomes had lower performance than continuous indices. These analyses also showed that NI was not specific to nocturnal polyuria as it also captured nocturia due to low functional bladder capacity. By contrast, NUPh was demonstrated to be specific to nocturnal polyuria.

CONCLUSION

NUPh has previously been shown among elderly males to be essential in nocturia and a very valid measure of nocturnal polyuria. However, the current, large and independent dataset now confirms that it can be applied in an adult population with a complaint of nocturia covering both males and females.

Development of a multivariate prediction model for nocturia, based on urinary tract etiologies

PURPOSE

The main objective of our study was to determine which combination of modifiable and non-modifiable parameters that could discriminate patients with nocturia from those without nocturia. This was a post-hoc analysis of 3 prospective, observational studies conducted in Ghent University. Participants completed frequency volume chart (FVC) to compare characteristics between patients with and without nocturia.

METHOD

This was a post hoc analysis of three prospective, observational studies conducted in Ghent University. Participants completed frequency volume chart (FVC) to compare characteristics between adults with and without nocturia. Study 1: adults with and without nocturia (n = 148); Study 2: patients ≥65 years with and without nocturnal LUTS (n = 54); Study 3: menopausal women before and after hormone replacement therapy (n = 43). All eligible patients (n = 183) completed a FVC during 24 hours (n = 13), 48 hours (n = 30) or 72 hours (n = 140). The combination of algorithms and number of determinants obtaining the best average area under the receiver operating curve (AUC-ROC) led to the final model. Differences between groups were assessed using the AUC-ROC and Mann- Whitney-Wilcoxon tests. Holm corrections were applied for multiple statistical testing. Also, the stability of the feature selection was evaluated.

RESULTS

The best discrimination was obtained when 13 determinants were included. However, a logistic regression model based on seven determinants selected with random forest had comparable discrimination including an optimal signature stability. It was able to discriminate almost perfectly between nights with and without nocturia.

CONCLUSION

Relevant information to accomplish the excellent predictability of the model is; functional bladder capacity, 24 hours urine output, nocturnal output, age, BMI. The multivariate model used in this analysis provides new insights into combination therapy as it allows simulating the effect of different available treatment modalities and its combinations.

Discovery and validation of a serum microRNA signature to characterize oligo- and polymetastatic prostate cancer: not ready for prime time

PURPOSE

Patients with oligometastatic prostate cancer (PC) may benefit from metastasis-directed therapy (MDT), delaying disease progression and the start of palliative systemic treatment. However, a significant proportion of oligometastatic PC patients progress to polymetastatic PC within a year following MDT, suggesting an underestimation of the metastatic load by current staging modalities. Molecular markers could help to identify true oligometastatic patients eligible for MDT.

METHODS

Patients with asymptomatic biochemical recurrence following primary PC treatment were classified as oligo- or polymetastatic based on 18^F-choline PET/CT imaging. Oligometastatic patients had up to three metastases at baseline and did not progress to more than three lesions following MDT or surveillance within 1 year of diagnosis of metastases. Polymetastatic patients had > 3 metastases at baseline or developed > 3 metastases within 1 year following imaging. A model aiming to prospectively distinguish oligo- and polymetastatic PC patients was trained using clinicopathological parameters and serum-derived microRNA expression profiles from a discovery cohort of 20 oligometastatic and 20 polymetastatic PC patients. To confirm the models predictive performance, it was applied on biomarker data obtained from an independent validation cohort of 44 patients with oligometastatic and 39 patients with polymetastatic disease.

RESULTS

Oligometastatic PC patients had a more favorable prognosis compared to polymetastatic ones, as defined by a significantly longer median CRPC-free survival (not reached versus 38 months; 95% confidence interval 31-45 months with P < 0.001). Despite the good performance of a predictive model trained on the discovery cohort, with an AUC of 0.833 (0.693-0.973; 95% CI) and a sensitivity of 0.894 (0.714-1.000; 95% CI) for oligometastatic disease, none of the miRNA targets were found to be differentially expressed between oligo- and polymetastatic PC patients in the signature validation cohort. The multivariate model had an AUC of 0.393 (0.534 after cross-validation) and therefore, no predictive ability.

CONCLUSIONS

Although PC patients with oligometastatic disease had a more favorable prognosis, no serum-derived biomarkers allowing for prospective discrimination of oligo- and polymetastatic prostate cancer patients could be identified.

Heterogeneity of synovial molecular patterns in patients with arthritis

Objectives

Early diagnosis of rheumatoid arthritis (RA) is an unmet medical need in the field of rheumatology. Previously, we performed high-density transcriptomic studies on synovial biopsies from patients with arthritis, and found that synovial gene expression profiles were significantly different according to the underlying disorder. Here, we wanted to further explore the consistency of the gene expression signals in synovial biopsies of patients with arthritis, using low-density platforms.

Methods

Low-density assays (cDNA microarray and microfluidics qPCR) were designed, based on the results of the high-density microarray data. Knee synovial biopsies were obtained from patients with RA, spondyloarthropathies (SA) or osteoarthritis (OA) (n = 39), and also from patients with initial undifferentiated arthritis (UA) (n = 49).

Results

According to high-density microarray data, several molecular pathways are differentially expressed in patients with RA, SA and OA: T and B cell activation, chromatin remodelling, RAS GTPase activation and extracellular matrix regulation. Strikingly, disease activity (DAS28-CRP) has a significant influence on gene expression patterns in RA samples. Using the low-density assays, samples from patients with OA are easily discriminated from RA and SA samples. However, overlapping molecular patterns are found, in particular between RA and SA biopsies. Therefore, prediction of the clinical diagnosis based on gene expression data results in a diagnostic accuracy of 56.8%, which is increased up to 98.6% by the addition of specific clinical symptoms in the prediction algorithm. Similar observations are made in initial UA samples, in which overlapping molecular patterns also impact the accuracy of the diagnostic algorithm. When clinical symptoms are added, the diagnostic accuracy is strongly improved.

Conclusions

Gene expression signatures are overall different in patients with OA, RA and SA, but overlapping molecular signatures are found in patients with these conditions. Therefore, an accurate diagnosis in patients with UA requires a combination of gene expression and clinical data.