Rapid syndromic PCR testing in patients with respiratory tract infections reduces time to results and improves microbial yield.

Lack of rapid and comprehensive microbiological diagnosis in patients with community acquired pneumonia (CAP) hampers appropriate antimicrobial therapy. This study evaluates the real-world performance of the BioFire FilmArray Pneumonia panel plus (FAP plus) and explores the feasibility of evaluation in a randomised controlled trial. Patients presenting to hospital with suspected CAP were recruited in a prospective feasibility study. An induced sputum or an endotracheal aspirate was obtained from all participants. The FAP plus turnaround time (TAT) and microbiological yield were compared with standard diagnostic methods (SDs). 96/104 (92%) enrolled patients had a respiratory tract infection (RTI); 72 CAP and 24 other RTIs. Median TAT was shorter for the FAP plus, compared with in-house PCR (2.6 vs 24.1 h, p < 0.001) and sputum cultures (2.6 vs 57.5 h, p < 0.001). The total microbiological yield by the FAP plus was higher compared to SDs (91% (162/179) vs 55% (99/179), p < 0.0001). Haemophilus influenzae, Streptococcus pneumoniae and influenza A virus were the most frequent pathogens. In conclusion, molecular panel testing in adults with CAP was associated with a significant reduction in time to actionable results and increased microbiological yield. The impact on antibiotic use and patient outcome should be assessed in randomised controlled trials.

Hypertensive nephrosclerosis: wider kidney biopsy indications may be needed to improve diagnostics.

BACKGROUND: Hypertensive nephrosclerosis is the presumed underlying cause in many end-stage kidney disease (ESKD) patients, but the diagnosis is disputed and based on clinical criteria with low diagnostic accuracy. OBJECTIVE: To evaluate and improve the diagnostic process for nephrosclerosis patients. METHODS: We included adults from the population-based HUNT study (n = 50 552), Norwegian CKD patients referred for kidney biopsy 1988-2012 (n = 7261), and unselected nephrology clinic patients (n = 193) used for matching. Decision tree analysis and ROC curve-based methods of optimal cut-offs were used to improve clinical nephrosclerosis criteria. RESULTS: Nephrosclerosis prevalence was 2.7% in the general population, and eGFR decline and risk for kidney-related hospital admissions and ESKD were comparable to patients with diabetic kidney disease. In the biopsy cohort, current clinical criteria had very low sensitivity (0.13) but high specificity (0.94) for biopsy-verified arterionephrosclerosis. A new optimized diagnostic algorithm based on proteinuria (<0.75 g d-1 ), systolic blood pressure (>155 mm Hg) and age (>75 years) only marginally improved diagnostic accuracy (sensitivity 0.19, specificity 0.96). Likewise, there were still false-positive cases with treatable diagnoses like glomerulonephritis, interstitial nephritis and others (40% of all test positive). Decision curve analysis showed that the new criteria can lead to higher clinical utility, especially for patients considering the potential harms to be close to the potential benefits, while the more risk-tolerant ones (harm:benefit ratio < 1:4) should consider kidney biopsy. CONCLUSION: Further improvements of the current clinical criteria seem difficult, so risks and benefits of kidney biopsy could be more actively discussed with selected patients to reduce misclassification and direct treatment.

Outcome in biopsy-proven Lupus nephritis: Evaluation of biopsies from the Norwegian Kidney Biopsy Registry.

Objective To evaluate long-term mortality and end-stage renal disease (ESRD) in a cohort of Norwegian patients with biopsy-proven lupus nephritis (LN). Methods Renal biopsies were obtained from 178 patients with LN from 1988 until 2007. Mortality rate and death causes were provided by Statistics Norway and ESRD data were provided by the Norwegian Renal Registry. Risk factors for all-cause mortality were evaluated by Cox regression. Standardized mortality ratio (SMR) was compared to observed deaths in a matched general population sample. Results Mean age was 37.6 (±14.4) years, and median time of follow-up was 8.5 years (0-26.2). Thirty-six patients (20.2%) died during follow-up. The SMR for all-cause mortality was 5.6 (Confidence interval [CI] 3.7-7.5). In an adjusted multivariate analysis proliferative glomerulonephritis (LN class IV) was independently associated with all-cause mortality; hazard ratio (HR) 2.6 (Confidence interval [CI] 1.2-5.7 p = 0.017). Main causes of death were infections (47.2%) and cardiovascular events 8 (22.2%). Thirty-six patients (20.2%) reached ESRD. Conclusions Biopsy-proven LN is associated with increased mortality compared to the general population. LN class IV is associated with all-cause mortality. Infections and cardiovascular events were the most common causes of death. Patients with LN have a high incidence of ESRD.

Patient classification and outcome prediction in IgA nephropathy.

OBJECTIVE: IgA Nephropathy (IgAN) is a common kidney disease which may entail renal failure, known as End Stage Kidney Disease (ESKD). One of the major difficulties dealing with this disease is to predict the time of the long-term prognosis for a patient at the time of diagnosis. In fact, the progression of IgAN to ESKD depends on an intricate interrelationship between clinical and laboratory findings. Therefore, the objective of this work has been the selection of the best data mining tool to build a model able to predict (I) if a patient with a biopsy proven IgAN will reach ESKD and (II) if a patient will reach the ESKD before or after 5 years. MATERIAL AND METHODS: The largest available cohort study worldwide on IgAN has been used to design and compare several data-driven models. The complete dataset was composed of 1174 records collected from Italian, Norwegian, and Japanese IgAN patients, in the last 30 years. The data mining tools considered in this work were artificial neural networks (ANNs), neuro fuzzy systems (NFSs), support vector machines (SVMs), and decision trees (DTs). A 10-fold cross validation was used to evaluate unbiased performances for all the models. RESULTS: An extensive model comparison based on accuracy, precision, recall, and f-measure was provided. Overall, the results indicate that ANNs can provide superior performance compared to the other models. The ANN for time-to-ESKD prediction is characterized by accuracy, precision, recall, and f-measure greater than 90%. The ANN for ESKD prediction has accuracy greater than 90% as well as precision, recall, and f-measure for the class of patients not reaching ESKD, while precision, recall, and f-measure for the class of patients reaching ESKD are slightly lower. The obtained model has been implemented in a Web-based decision support system (DSS). CONCLUSIONS: The extraction of novel knowledge from clinical data and the definition of predictive models to support diagnosis, prognosis, and therapy is becoming an essential tool for researchers and clinical practitioners in medicine. The proposed comparative study of several data mining models for the outcome prediction in IgAN patients, using a large dataset of clinical records from three different countries, provides an insight into the relative prediction ability of the considered methods applied to such a disease.