Combined serum biomarker analysis shows no benefit in the diagnosis of periprosthetic joint infection.

PURPOSE: In many cases, the diagnosis of a periprosthetic joint infection (PJI) consisting of the clinical appearance, laboratory tests, and other diagnostic tools remains a difficult task. Single serum biomarkers are easy to collect, are suitable for periodical assessment, and are a crucial tool in PJI diagnosis, but limited sensitivity or specificity is reported in literature. The aim of this study was to combine the best-performing single serum biomarkers into a multi-biomarker model aiming to improve the diagnostic properties. METHODS: Within a 27-month period, 124 surgical procedures (aseptic or septic revision total knee arthroplasty (TKA) or total hip arthroplasty (THA)) were prospectively included. The serum leukocyte count, C-reactive protein (CRP), interleukin-6, procalcitonin, interferon alpha, and fibrinogen were assessed 1 day prior to surgery. Logistic regression with lasso-regularization was used for the biomarkers and all their ratios. After randomly splitting the data into a training (75%) and a test set (25%), the multi-biomarker model was calculated and validated in a cross-validation approach. RESULTS: CRP (AUC 0.91, specificity 0.67, sensitivity 0.90, p value 0.03) and fibrinogen (AUC 0.93, specificity 0.73, sensitivity 0.94, p value 0.02) had the best single-biomarker performances. The multi-biomarker model including fibrinogen, CRP, the ratio of fibrinogen to CRP, and the ratio of serum thrombocytes to CRP showed a similar performance (AUC 0.95, specificity 0.91, sensitivity 0.72, p value 0.01). CONCLUSION: In this study, multiple biomarkers were tested for their diagnostic performance, with CRP and fibrinogen showing the best results regarding the AUC, accuracy, sensitivity, and specificity. It was not possible to further increase the diagnostic accuracy by combining multiple biomarkers using sophisticated statistical methods.

Critical evaluation of platelet size as a prognostic biomarker in colorectal cancer across multiple treatment settings: a retrospective cohort study.

PURPOSE: The role of mean platelet volume (MPV) as a predictor of outcomes in various cancer entities including colorectal cancer (CRC) has already been analyzed. However, data on the prognostic and predictive value of MPV in CRC over multiple lines of systemic therapy are missing. METHODS: In this retrospective single-center cohort study, 690 patients with UICC stage II, III or IV CRC receiving adjuvant and/or palliative chemotherapy were included. Primary endpoints in the adjuvant, palliative and best supportive care (BSC) setting were 3-year recurrence-free survival (RFS), 6-months progression-free survival (PFS), and 6-months overall survival (OS), respectively. Kaplan-Meier estimators, log-rank tests, and uni- and multivariable Cox models were used to analyze RFS, PFS and OS. A cut-off defining patients with low MPV was chosen empirically at the 25th percentile of the MPV distribution in the respective treatment setting. RESULTS: Three-year RFS was 76%. Median 6-month PFS estimates in 1st, 2nd and 3rd line therapy were 59, 37 and 27%, respectively. Median 6-month OS in BSC was 31%. Small platelets as indicated by low MPV did not predict for shorter RFS. In the first 3 palliative treatment lines a consistent association between low MPV and decreased 6-month PFS was not observed. In the BSC setting, patients with low MPV had numerically but not significantly shorter OS. Higher MPV levels did not consistently predict for ORR or DCR across the first 3 palliative treatment lines. CONCLUSION: Small platelets are not predicting CRC outcomes, and thus are hardly useful for influencing clinical decision making.

An Easy-to-Use Prognostic Model for Survival Estimation for Patients with Symptomatic Long Bone Metastases.

BACKGROUND: A survival estimation for patients with symptomatic long bone metastases (LBM) is crucial to prevent overtreatment and undertreatment. This study analyzed prognostic factors for overall survival and developed a simple, easy-to-use prognostic model. METHODS: A multicenter retrospective study of 1,520 patients treated for symptomatic LBM between 2000 and 2013 at the radiation therapy and/or orthopaedic departments was performed. Primary tumors were categorized into 3 clinical profiles (favorable, moderate, or unfavorable) according to an existing classification system. Associations between prognostic variables and overall survival were investigated using the Kaplan-Meier method and multivariate Cox regression models. The discriminatory ability of the developed model was assessed with the Harrell C-statistic. The observed and expected survival for each survival category were compared on the basis of an external cohort. RESULTS: Median overall survival was 7.4 months (95% confidence interval [CI], 6.7 to 8.1 months). On the basis of the independent prognostic factors, namely the clinical profile, Karnofsky Performance Score, and presence of visceral and/or brain metastases, 12 prognostic categories were created. The Harrell C-statistic was 0.70. A flowchart was developed to easily stratify patients. Using cutoff points for clinical decision-making, the 12 categories were narrowed down to 4 categories with clinical consequences. Median survival was 21.9 months (95% CI, 18.7 to 25.1 months), 10.5 months (95% CI, 7.9 to 13.1 months), 4.6 months (95% CI, 3.9 to 5.3 months), and 2.2 months (95% CI, 1.8 to 2.6 months) for the 4 categories. CONCLUSIONS: This study presents a model to easily stratify patients with symptomatic LBM according to their expected survival. The simplicity and clarity of the model facilitate and encourage its use in the routine care of patients with LBM, to provide the most appropriate treatment for each individual patient. LEVEL OF EVIDENCE: Prognostic Level IV. See Instructions for Authors for a complete description of levels of evidence.

The width of resection margins influences local recurrence in soft tissue sarcoma patients.

BACKGROUND: Patients with soft tissue sarcoma (STS) being treated following the standardized guidelines can still not be guaranteed to remain free from local recurrence (LR). A complete tumour resection has been accepted as a major prognostic factor for LR. This retrospective study was designed to analyse the influence of two different classifications of resection margins (R-classification and UICC-classification) on LR in STS patients. MATERIALS AND METHODS: Of 411 patients treated at our institution for STS, 265 were eligible for statistical analysis. Kaplan-Meier curves and Cox regression models were used to assess the impact of an R0 resection according to the R-classification (resection margin clear but allowing <1 mm) and according to the UICC-classification (minimal resection margin ≥1 mm) on LR. RESULTS: Survival curves showed a lower LR rate for R0 resections in the UICC-classification, namely 1.3%, 12% and 12% as compared to 2.1%, 9.5% and 16.5% for the R-classification. In multivariate analysis calculated separately for each classification, R1 resection as defined by the R-classification (HR: 11.214; 95%CI: 2.394-52.517; p = 0.002) as well as by UICC-classification (HR: 15.634; 95%CI: 2.493-98.029; p = 0.003) remained significant. CONCLUSION: In our study, margin status according to both classifications represents an independent prognostic factor for LR in patients with STS following curative surgery. Local control rates were superior after a minimal resection margin of 1 mm (R0 by UICC-classification) compared to R0 resections after the R-classification.

Evaluating the British sarcoma referral form.

INTRODUCTION: This study investigated the accuracy of general practitioner (GP) referrals under the two-week wait pathway for soft tissue sarcomas and whether the current National Institute for Health and Care Excellence criteria should be refined. METHODS: All patients referred under the two-week wait system to one centre over the course of one year were reviewed. Comparison was made between the criteria identified by the GP and those confirmed by the centre to assess the accuracy of the referrals, and to identify what criteria predicted malignancy. RESULTS: Overall, 135 patients were referred to our unit with a mean age of 56.4 years. Of these, 45 (33%) were found to have a malignant tumour. Factors identified by the GP were accurate in 74% of cases. The best predictor of malignancy was 'size >5cm' (76% sensitivity) while 'pain' was the least useful (27% sensitivity). Lowering the threshold for concern to a size of >4cm increased sensitivity to 89%. Although 106 patients had undergone some form of imaging prior to referral, this did not increase the likelihood of malignancy being detected. The combination of factors most likely to predict malignancy was a size of >5cm, increase in size, deep location and no pain (10 out of 13 referrals, 77% accuracy). CONCLUSIONS: Based on the results of this study, we recommend an adaption of the existing features for concern. The new feature for concern should be 'size >4cm' and the factor 'pain' should be removed from the urgent referral form.