Optimization of the Preanalytical Phase by Estimating Serum Indices Using an Automated Classifier.

BACKGROUND: Most laboratory errors occur in the preanalytical phase. Among the most common preanalytical errors are interferences due to hemolysis, lipemia, and icterus. Our objective was to evaluate a serum interference estimation methodology by the RSD classifier, to identify other biochemical parameters affected by preanalytical interferences, and to determine the economic impact of its implementation. METHODS: The serum indices of 65,529 requests measured by the RSD system and by the analytical determination on the Cobas 711 or Cobas 8000 platforms were collected. We proceeded to the search for association patterns between the serum indices and laboratory analytical tests using data mining techniques. The influence of the preanalytical interferences was evaluated in 91 laboratory tests that include biochemistry, immunoassay, and coagulation. The savings estimation after the implementation of this methodology was made by time series models. RESULTS: The evaluation of the generated model showed compatibilities between the methods used (94.4% accuracy). The implementation of a protocol for serum indices estimation by the RSD would avoid the unnecessary analysis of the tests which are affected by interferences, achieving an estimated annual savings of €10,561. In addition, it allowed the estimation of bilirubin values which would add an annual savings of €4,900 in our laboratory. On the other hand, data mining techniques have allowed us to identify the following laboratory tests affected by hemolysis which are not usually considered in laboratories: iron, transferrin, fibrinogen, and alkaline phosphatase. CONCLUSIONS: The RSD classifier is an efficient estimation method of serum indices and it allows the estimation of bilirubin values. The implementation of this methodology in our laboratory could lead to an estimated annual savings of more than €15,000 without increasing response times. Iron, alkaline phosphatase, transferrin, and fibrinogen should be included in the evaluated procedure.

[URISCAM project: Multicenter evaluation of the UF-Series cytometer in the urinary tract infections screening]. / Proyecto URISCAM: Evaluación multicéntrica del citómetro UF-Series en el despistaje de infecciones urinarias.

OBJECTIVE: Urine culture, the gold standard to confirm the presence of urinary tract infection (UTI), is the most requested assay in the microbiology department. Our objective was to determine the diagnostic yield of the UF-Series cytometer as a screening method for UTI. METHODS: All the urine samples sent to the six Microbiology Laboratories participating in a period of 5 working days were analyzed. We collected demographic variables, apart from those variables related to urine samples: source and sample type (midstream, catheterized or nephrostomy urines), collection with/without boric acid, cytometer parameters (leukocyturia, bacteriuria, bacteria morphology and epithelial cells) and urine culture results. ROC curves were plotted to determine predictive capacity of the cytometer. RESULTS: A sample of 2,468 patients with average age of 53 years were processed (ratio women:men 2:1). Urine culture detected 23% of positive urine samples. The predictor variables of UTI were: morphology of bacilli, bacteriuria ≥21 bacteria/µL, age ≥65 years, samples collected in the emergency service and hospitalization and preserving conditions. With 21 bacteria/µL as a cut-off point, we obtained a sensitivity of 93.3% and 94.5% negative predictive value, then reducing the samples to be cultured by 28.9% with 1.6% false negatives. CONCLUSIONS: We consider that the UF-Series is a valid and accurate tool for the detection of UTI. Therefore, it could be used as screening method in the clinical practice prior to the urine culture, reducing culture requirement by approximately 30%, with a low false negative rate.

Sysmex UF-1000i flow cytometer to screen urinary tract infections: the URISCAM multicentre study.

The new Sysmex UF-1000i analyzer - which incorporates bacteria morphology distinction - allows to automatically screen samples to be cultured at microbiology laboratories. We have evaluated the feasibility and accuracy of Sysmex UF-1000i to screen urinary tract infections (UTIs). A total amount of 2468 urine samples from six Spanish hospitals were analysed. Demographic and clinical data such as age, gender, source and sample type, preserving conditions, cytometer parameters (bacteria, leucocytes and bacteria morphology) as well as urine culture results (gold standard) were recorded. After applying data mining techniques, the variables of age, bacteria count and rod morphology were defined as predictive variables of UTIs. By using the UF-1000i in combination with a predictive algorithm of three decision rules, we could identify 94·9 and 47·4% positive and negative urine samples, respectively, with a negative predictive value of 97 and only 1·17% diagnostic error. This error was reduced down to 0·4% when contaminated samples were excluded. Our results show that flow cytometry parameters together with age, by means of a predictive algorithm model, can be used to screen UTIs. Its implementation would avoid culturing 38% of urine samples, and therefore, would reduce time to diagnosis with a discrete false negative ratio. SIGNIFICANCE AND IMPACT OF THE STUDY: Fluorescent flow cytometry performance has recently spread for urine screening. However, controversy about cytometer results can be drawn from medical literature. This study shows the diagnosis accuracy of Sysmex UF-1000i analyzer by means of a group of decision rules encompassing both demographic variables (age) and cytometer parameters (bacteria, leucocytes and bacteria morphology). After applying the predictive algorithm, the UF-1000i could optimally identify 95% urinary tract infections with high negative predictive value and low diagnostic error. Implementation of UF-1000i would avoid culturing almost 38% of urine samples, thus reducing time to diagnosis, unnecessary antibiotic treatments and consequently improving cost-effectiveness.

[Cystatin C as estimator of glomerular filtration rate in patients with advanced chronic renal disease]. / Cistatina C como estimador de la función renal en estadios avanzados de enfermedad renal crónica.

Serum cystatin C (CysC) has been shown to be more accurate than serum creatinine (Cr) in estimating renal function, especially in patients with mildmoderate chronic renal failure. However, it is unknown whether CysC provides or not any advantage over Cr in severe chronic renal failure. The aim of this study was to establish the accuracy of CysC in estimating the glomerular filtration rate (GFR) in advanced chronic renal failure patients. The study group consisted of 94 patients (57 females, mean age 61 +/- 16 years) with advanced chronic renal failure. None of them had thyroid dysfunction or was on corticoid treatment. CFR was measured by TC99mDTPA, and simultaneously, serum CysC (particle enhanced immunonephelometry) and Cr (modified Jaffe's kinetic reaction) were also determined. Serum Cr and CysC levels were correlated with GFR, and the influences of age, sex and diabetes on these correlations were analyzed. The predictive value of CysC and Cr to estimate a GFR less than 15 ml/min/1.73 m2 was analyzed by measuring the crea-under-the-curve (AUC) with Receiver-Operating Characteristics (ROC) plots. The mean CFR was 16.49 +/- 4.65 ml/min/1.73 m2. The mean concentrations of Cr and CysC were 4.19 +/- 1.19 mg/L and 3.44 +/- 0.73 mg/L, respectively. Both Cr and CysC correlated significantly with GFR (R = 0.49, p < 0.0001 and R = 0.52, p < 0.0001, respectively). Age and sex influenced on the correlation between Cr and GFR, but these demographic characteristics did not influence on the correlation between CysC and GFR. The AUC for the prediction of a GFR less than 15 ml/min/1.73 m2 with serum Cr was 0.675 (p = 0.004), while with CysC was 0.633 (p = 0.030). In conclusion, both serum Cr and CysC are highly inaccurate markers of renal function in advanced chronic renal failure patients.