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Frendl, Gyorgy

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Frendl

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Gyorgy

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Frendl, Gyorgy
Author's Publications: search.filters.isAuthorOfPublication.09de3560-2955-4496-babf-286c81c4038b

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    Diagnostic potential for a serum miRNA neural network for detection of ovarian cancer
    (eLife Sciences Publications, Ltd, 2017) Elias, Kevin; Fendler, Wojciech; Stawiski, Konrad; Fiascone, Stephen; Vitonis, Allison F; Berkowitz, Ross; Frendl, Gyorgy; Konstantinopoulos, Panagiotis; Crum, Christopher; Kedzierska, Magdalena; Cramer, Daniel; Chowdhury, Dipanjan
    Recent studies posit a role for non-coding RNAs in epithelial ovarian cancer (EOC). Combining small RNA sequencing from 179 human serum samples with a neural network analysis produced a miRNA algorithm for diagnosis of EOC (AUC 0.90; 95% CI: 0.81–0.99). The model significantly outperformed CA125 and functioned well regardless of patient age, histology, or stage. Among 454 patients with various diagnoses, the miRNA neural network had 100% specificity for ovarian cancer. After using 325 samples to adapt the neural network to qPCR measurements, the model was validated using 51 independent clinical samples, with a positive predictive value of 91.3% (95% CI: 73.3–97.6%) and negative predictive value of 78.6% (95% CI: 64.2–88.2%). Finally, biologic relevance was tested using in situ hybridization on 30 pre-metastatic lesions, showing intratumoral concentration of relevant miRNAs. These data suggest circulating miRNAs have potential to develop a non-invasive diagnostic test for ovarian cancer.
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    Lung Injury Prediction Score for the Emergency Department: First Step Towards Prevention in Patients at Risk
    (Springer, 2012) Elie-Turenne, Marie-Carmelle; Hou, Peter; Mitani, Aya; Barry, Jonathan M; Kao, Erica Y; Cohen, Jason E; Frendl, Gyorgy; Gajic, Ognjen; Gentile, Nina T
    Background: Early identification of patients at risk of developing acute lung injury (ALI) is critical for potential preventive strategies. We aimed to derive and validate an acute lung injury prediction score (EDLIPS) in a multicenter sample of emergency department (ED) patients. Methods: We performed a subgroup analysis of 4,361 ED patients enrolled in the previously reported multicenter observational study. ED risk factors and conditions associated with subsequent ALI development were identified and included in the EDLIPS model. Scores were derived and validated using logistic regression analyses. The model was assessed with the area under the receiver-operating curve (AUC) and compared to the original LIPS model (derived from a population of elective high-risk surgical and ED patients) and the Acute Physiology and Chronic Health Evaluation (APACHE II) score. Results: The incidence of ALI was 7.0% (303/4361). EDLIPS discriminated patients who developed ALI from those who did not with an AUC of 0.78 (95% CI 0.75, 0.82), better than the APACHE II AUC 0.70 (p ≤ 0.001) and similar to the original LIPS score AUC 0.80 (p = 0.07). At an EDLIPS cutoff of 5 (range −0.5, 15) positive and negative likelihood ratios (95% CI) for ALI development were 2.74 (2.43, 3.07) and 0.39 (0.30, 0.49), respectively, with a sensitivity 0.72(0.64, 0.78), specificity 0.74 (0.72, 0.76), and positive and negative predictive value of 0.18 (0.15, 0.21) and 0.97 (0.96, 0.98). Conclusion: EDLIPS may help identify patients at risk for ALI development early in the course of their ED presentation. This novel model may detect at-risk patients for treatment optimization and identify potential patients for ALI prevention trials.
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    Towards Prevention of Acute Lung Injury: Frequency and Outcomes of Emergency Department Patients At-Risk: A Multicenter Cohort Study
    (Springer, 2012) Hou, Peter; Elie-Turenne, Marie-Carmelle; Mitani, Aya; Barry, Jonathan M; Kao, Erica Y; Cohen, Jason E; Frendl, Gyorgy; Gajic, Ognjen; Gentile, Nina T
    Background: Few emergency department (ED) evaluations on acute lung injury (ALI) have been carried out; hence, we sought to describe a cohort of hospitalized ED patients at risk for ALI development. Methods: Patients presenting to the ED with at least one predisposing condition to ALI were included in this study, a subgroup analysis of a multicenter observational cohort study (USCIITG-LIPS 1). Patients who met ALI criteria within 6 h of initial ED assessment, received end-of-life care, or were readmitted during the study period were excluded. Primary outcome was frequency of ALI development; secondary outcomes were ICU and hospital mortality. Results: Twenty-two hospitals enrolled 4,361 patients who were followed from the ED to hospital discharge. ALI developed in 303 (7.0 %) patients at a median onset of 2 days (IQR 2–5). Of the predisposing conditions, frequency of ALI development was highest in patients who had aortic surgery (43 %) and lowest in patients with pancreatitis (2.8 %). Compared to patients who did not develop ALI, those who did had higher ICU (24 % vs. 3.0 %, p < 0.001) and hospital (28 % vs. 4.6 %, p < 0.001) mortality, and longer hospital length of stay (16 vs. 5 days, p < 0.001). Among the 22 study sites, frequency of ALI development varied from less than 1 % to more than 12 % after adjustment for APACHE II. Conclusions: Seven percent of hospitalized ED patients with at least one predisposing condition developed ALI. The frequency of ALI development varied significantly according to predisposing conditions and across institutions. Further research is warranted to determine the factors contributing to ALI development.