Matteo Gelati

The ROMA (Risk of Ovarian Malignancy Algorithm) for estimating the risk of epithelial ovarian cancer in women presenting with pelvic mass: is it really useful?

Abstract Background: The study is aimed at evaluating the performance of the predictive model ROMA (Risk of Ovarian Malignancy Algorithm), which utilizes the combination of human epididymis protein 4 (HE4) and CA125 values to assess the risk of epithelial ovarian cancer (EOC) in women with a pelvic mass. Methods: One hundred and four women diagnosed with a pelvic mass (55 EOC and 49 benign cases) and scheduled to have surgery were enrolled, along with 49 healthy females. Preoperative serum concentrations of HE4 and CA125 were measured. Separate logistic regression algorithms ROMA for pre-menopausal and post-menopausal women were used to categorize patients into low- and high-risk groups for EOC. The area under the curve (AUC), sensitivity and specificity were calculated for HE4, CA125 and ROMA for the diagnosis of ovarian cancer using receiver operating characteristic (ROC) analysis. Results: The median CA125 and HE4 serum concentrations were significantly higher among EOC patients than in healthy females (both p<0.05) and those with a benign mass (both p<0.05). The pre-menopausal group included 36 benign cases (29 of which were classified by ROMA as low-risk with a specificity of 80.6%; 95% CI: 64.0%–91.8%), and 15 EOC (eight of which were classified by ROMA as high-risk, with a sensitivity of 53.3%; 95% CI: 26.6%–78.7%). The post-menopausal group enclosed 13 benign cases (11 of which were classified by ROMA as low-risk with a specificity of 84.6%; 95% CI: 54.6%–98.0%), and 40 EOC (33 of which were classified by ROMA as high-risk with a sensitivity of 82.5%; 95% CI: 67.2%–92.7%). In the pre-menopausal group, the AUC was 0.64 (p=0.12, 95% CI: 0.44–0.83) for CA125, 0.77 (p=0.003, 95% CI: 0.62–0.92) for HE4 and 0.77 (p=0.002, 95% CI: 0.63–0.92) for ROMA. In the post-menopausal group, the AUC was 0.84 (p=0.0003, 95% CI: 0.73–0.94) for CA125, 0.94 (p<0.0001, 95% CI: 0.88–0.99) for HE4 and 0.92 (p<0.0001, 95% CI: 0.85–0.99) for ROMA. Conclusions: The ROMA is a simple scoring system which shows excellent diagnostic performance for the detection of EOC in post-menopausal women, but not in pre-menopausal women. Moreover, the dual marker combination of HE4 and CA125 (ROMA) does not show better performance than HE4 alone.

Acute variation of biochemical markers of muscle damage following a 21-km, half-marathon run.

Objective. Although there is information on biochemical markers of muscle and cardiac damage following strenuous exercise, little is known about the kinetics of these markers in athletes performing sub‐maximal exercise. Material and methods. Fifteen healthy, trained, Caucasian males took part in a 21‐km run. Blood samples were collected before the run, immediately after (post), and 3 h, 6 h and 24 h thereafter. Biochemical markers of muscle and cardiac damage were evaluated on the Modular System, employing proprietary reagents. In no case did the concentration of troponin T increase by >0.03 ng/mL. The values of aspartate aminotransferase (AST), creatine kinase (CK), CK MB, lactate dehydrogenase (LDH) and myoglobin increased significantly immediately after the run and remained elevated 24 h thereafter. Results. The number of subjects with values above the upper limit of the relative reference ranges did not vary throughout the study period for AST and LDH, while it increased significantly for CK, CK MB and myoglobin. The major variation over the pre‐run value was recorded for myoglobin (3‐fold increment), whereas AST and LDH increased 1.1 and 1.3‐fold, respectively. Conclusions. The results suggest the hypothesis that sub‐maximal exercise influences the concentration of several biomarkers of muscle damage for up to 24 h with no biochemical signs of myocardial damage.

Influence of a light meal on routine haematological tests.

INTRODUCTION Patient-related variables, such as physical exercise, stress and fasting status are important sources of variability in laboratory testing. However, no clear indications about fasting requirements exist for routine haematological tests, nor has the influence of meals been assessed. METHODS We studied 17 healthy volunteers who consumed a light meal containing a standardized amount of carbohydrates, protein and lipids. Blood was taken for routine haematological tests before the meal and 1, 2 and 4 hours thereafter. RESULTS One hour after the meal, neutrophil count and mean corpuscular haemoglobin (MHC) increased significantly, whereas lymphocyte and monocyte counts, red blood cell distribution width, haematocrit, and mean corpuscular volume decreased significantly. A clinically significant variation was only observed for lymphocytes. Two hours after the meal, a significant increase was observed for neutrophils and MCH, whereas lymphocytes, eosinophils, haemoglobin and haematocrit decreased significantly. Clinically significant variations were recorded for lymphocytes, red blood cells (RBC), haemoglobin, haematocrit and MCH. Four hours after the meal MCH was significantly increased, while lymphocytes, eosinophils, RBC, haemoglobin and haematocrit were significantly decreased. Clinically significant variations were recorded for neutrophils, eosinophils, RBC, hematocrit and MCH. CONCLUSION The significant variation of several haematological parameters after a light meal demonstrates that the fasting time needs to be carefully considered in order to interpret the results of haematological tests correctly.

The role of resistin in colorectal cancer

BACKGROUND To date the role of resistin in colorectal cancer (CRC) is far from being elucidated. The aim of this study was to investigate the association between serum resistin levels and CRC in relation to known risk/protective factors including anthropometric, metabolic, inflammatory parameters as well as lifestyle individual characteristics. METHODS 40 CRC patients and 40 controls were enrolled. Body weight, height, waist circumference and blood pressure were recorded. Fasting plasma glucose, lipids, C-reactive protein (CRP) and resistin levels were measured. Metabolic Syndrome (MS) was defined according to the harmonized definition. RESULTS Resistin levels were significantly higher in CRC patients than in controls (p=0.028) and gradually increased with tumor stage progression (p=0.042). A high resistin level was statistically significant determinant of CRC after adjusting for age, sex, body mass index and lifestyle parameters (p=0.029). Resistin showed a strong association with CRP levels (p ≤ 0.0001). In stepwise regression analysis CRP remained the only independent predictor of both resistin levels (p=0.001) and CRC risk (p=0.021). CONCLUSIONS These results clarify the nature of the association between resistin and CRC risk suggesting that the proinflammatory state of cancer, rather than the clinical diagnosis of CRC itself or its link with obesity and MS, may govern this association.

Measurement of morning saliva cortisol in athletes.

OBJECTIVES To evaluate feasibility and reliability of measuring saliva cortisol in athletes. DESIGN AND METHODS Saliva cortisol was measured in 25 soccer players, and compared with serum cortisol measured with two commercial immunoassays. RESULTS A highly significant correlation was observed between saliva and serum cortisol. The percentage of saliva and serum values above the upper limit of the reference range was nearly identical. CONCLUSIONS Salivary measurement is a suitable approach for monitoring cortisol in athletes.

Acute Variation of Estimated Glomerular Filtration Rate Following a Half-Marathon Run

The accurate estimation of glomerular filtration rate (GFR) is pivotal in sports medicine. However, there is controversial information on the acute influence of physical exercise on kidney function in healthy athletes. The estimated GFR (EGFR) was assessed by the recommended Modification of Diet in Renal Disease (MDRD) equation before a 21-km half-marathon, at the end, and 3, 6, 24 hrs thereafter on 17 trained, middle-aged males. Results were corrected for plasma volume changes. The mean EGFR at the baseline was 76 mL/min/1.73 m (2); it decreased at the end of the run (62 mL/min/1.73 m (2)) and for the following 3 hrs (68 mL/min/1.73 m (2)) and 6 hrs (70 mL/min/1.73 m (2)), though statistical significance was only achieved immediately after the run (mean decrease 16 %, p < 0.01). The frequency of athletes with EGFR below the normal threshold was higher than the baseline immediately after the race and for the following 6 hrs. Twenty-four hours after the run, the EGFR had returned to values similar and nonsignificantly different from those recorded at the baseline. These results attest that medium to high strains of running in healthy, middle-aged, trained individuals do not cause renal damage, but a limited and temporary decline in renal function.

Influence of a Regular, Standardized Meal on Clinical Chemistry Analytes

Background Preanalytical variability, including biological variability and patient preparation, is an important source of variability in laboratory testing. In this study, we assessed whether a regular light meal might bias the results of routine clinical chemistry testing. Methods We studied 17 healthy volunteers who consumed light meals containing a standardized amount of carbohydrates, proteins, and lipids. We collected blood for routine clinical chemistry tests before the meal and 1, 2, and 4 hr thereafter. Results One hour after the meal, triglycerides (TG), albumin (ALB), uric acid (UA), phosphatase (ALP), Ca, Fe, and Na levels significantly increased, whereas blood urea nitrogen (BUN) and P levels decreased. TG, ALB, Ca, Na, P, and total protein (TP) levels varied significantly. Two hours after the meal, TG, ALB, Ca, Fe, and Na levels remained significantly high, whereas BUN, P, UA, and total bilirubin (BT) levels decreased. Clinically significant variations were recorded for TG, ALB, ALT, Ca, Fe, Na, P, BT, and direct bilirubin (BD) levels. Four hours after the meal, TG, ALB, Ca, Fe, Na, lactate dehydrogenase (LDH), P, Mg, and K levels significantly increased, whereas UA and BT levels decreased. Clinically significant variations were observed for TG, ALB, ALT, Ca, Na, Mg, K, C-reactive protein (CRP), AST, UA, and BT levels. Conclusions A significant variation in the clinical chemistry parameters after a regular meal shows that fasting time needs to be carefully considered when performing tests to prevent spurious results and reduce laboratory errors, especially in an emergency setting.

Right or wrong sample received for coagulation testing? Tentative algorithms for detection of an incorrect type of sample

Inappropriate blood collection potentially comprises the major pre‐analytical problem for coagulation testing. Inappropriate samples are most difficult to detect when received as secondary aliquots, common for referred tests. This study aimed to identify a simple, quick and inexpensive process to help laboratories distinguish the type of sample, should there be suspicion of inappropriate collection. Samples from 15 patients [selected on the basis that four different primary tubes were available: serum, citrated plasma, ethylene diamine tetraacetic acid (EDTA) plasma, lithium‐heparin plasma], were tested for common electrolytes that might substantially differ according to the type of sample. In citrated plasma, potassium, chloride, calcium and magnesium were significantly decreased compared with serum and lithium‐heparin plasma, while sodium was markedly increased. In EDTA plasma, sodium and chloride were significantly decreased compared with both serum and lithium‐heparin plasma, potassium was always >14 mmol/l, whereas magnesium and calcium were virtually undetectable. These data allowed development of two algorithms for differential identification of citrated plasma vs. other samples with 100% sensitivity and specificity, the former based on the sequential measurement of potassium, calcium and sodium, the latter on potassium and sodium. These simple assays can supplement classical coagulation test methods to identify most inappropriate blood collections and validate sample rejection.

Hemolysis, lipaemia and icterus in specimens for arterial blood gas analysis.

OBJECTIVE To assess prevalence of interference from hemolysis, lipaemia, icterus in arterial blood gas analysis (ABG). DESIGN Serum indices (SI) were assessed in the plasma of ABG samples over a 2-month period. RESULTS Out of a total of 478 ABG specimens, we identified 17 hemolyzed samples (4%), 52 (11%) with lipaemia, and 63 (13%) with icterus. CONCLUSION Test results on a considerable number of ABG specimens might be unreliable due to presence of interference.

Reference range of hemolysis index in serum and lithium-heparin plasma measured with two analytical platforms in a population of unselected outpatients.

BACKGROUND The hemolysis index (HI) is now available in several laboratory analyzers, but doubts remain about the thresholds for suppressing test results, the degree of standardization among different instrumentations and the use of different reference ranges in different biological matrices. This study was hence planned to establish the reference ranges of HI in serum and lithium-heparin plasma in a population of unselected outpatients, using two analytical platforms. MATERIALS AND METHODS We analyzed the HI in serum and lithium-heparin samples collected from 135 unselected outpatients, and we also defined the relative reference ranges according to Clinical and Laboratory Standards Institute (CLSI) recommendations. Samples were collected in the morning by expert nurses, using straight needle venipuncture. The HI in serum and lithium-heparin plasma was assessed with Roche Cobas c501 and Siemens Dimension Vista 1500. RESULTS The median concentration of cell-free hemoglobin was significantly higher in serum than in lithium-heparin plasma when measured with Cobas c501, but not with Dimension Vista 1500. After categorizing values according to cell-free hemoglobin thresholds, the agreement between instruments was 0.75 (p<0.01) for serum and 0.95 (p<0.01) for lithium-heparin plasma. The upper limits calculated according to CLSI document C28-A3 were 0.22 g/L for Roche Cobas c501 and 0.25 g/L for Siemens Dimension Vista 1500 in serum, whereas they were 0.13 g/L for Cobas c501 and 0.10 g/L for Dimension Vista 1500 in lithium-heparin plasma. CONCLUSIONS According to our data, different thresholds of cell-free hemoglobin should be used between serum and lithium-heparin plasma for monitoring phlebotomy practice.