1,721,198 research outputs found
The search for better prognostic factors for men treated for localized prostate cancer continues
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Reply to Ian Beckley and Masood A. Khan's letter to the editor Re: Felix K.-H. Chun, Thomas Steuber, Andreas Erbersdobler, et al. development and internal validation of a nomogram predicting the probability of prostate cancer gleason sum upgrading between biopsy and radical prostatectomy pathology. Eur Urol 2006;49 : 820-26
No full textComparison of nomograms with other methods for predicting outcomes in prostate cancer: A critical analysis of the literature
No full textPurpose: Accurate estimates of risk are essential for physicians if they are to recommend a specific management to patients with prostate cancer, Accurate risk estimates are also required for clinical trial design, to ensure homogeneous patient groups. Because there is more than one model available for prediction of most outcomes, model comparisons are necessary for selection of the best model. We describe the criteria based on which to judge predictive tools, describe the limitations of current predictive tools, and compare the different predictive methodologies that have been used in the prostate cancer literature. Experimental Design: Using MEDLINE, a literature search was done on prostate cancer decision aids from January 1966 to July 2007. Results: The decision aids consist of nomograms, risk groupings, artificial neural networks, probability tables, and classification and regression tree analyses. The following considerations need to be applied when the qualities of predictive models are assessed: predictive accuracy (internal or ideally external validation), calibration (i.e., performance according to risk level or in specific patient subgroups), generalizability (reproducibility and transportability), and level of complexity relative to established models, to assess whether the new model offers advantages relative to available alternatives. Studies comparing decision aids have shown that nomograms outperform the other methodologies. Conclusions: Nomograms provide superior individualized disease-related risk estimations that facilitate management-related decisions. Of currently available prediction tools, the nomograms have the highest accuracy and the best discriminating characteristics for predicting outcomes in prostate cancer patients
Family physicians could help in predicting life expectancy without prostate cancer - Reply
No full textRebuttal from Authors re: Urs E. Studer, Laurence Collette. Morbidity from Pelvic Lymphadenectomy in Men Undergoing Radical Prostatectomy.
No full textPrediction of delayed graft function after renal transplantation
No full textIntroduction: Delayed graft function (DGF), defined as the need for dialysis during the first week after renal transplantation, is an important adverse clinical outcome. A previous model relied on 16 variables to quantify the risk of DGF, thereby undermining its clinical usefulness. We explored the possibility of developing a simpler, equally accurate and more user-friendly paradigm for renal transplant recipients from deceased donors. Methods: Logistic regression analyses addressed the occurrence of DGF in 532 renal transplant recipients from deceased donors. Predictors consisted of recipient age, gender, race, weight, number of HLA-A, HLA-B and HLA-DR mismatches, maximum and last titre of panel reactive antibodies, donor age and cold ischemia time. Accuracy was quantified with the area under the curve. Two hundred bootstrap resamples were used for internal validation. Results: Delayed graft function occurred in 103 patients (19.4%). Recipient weight (p < 0.001), panel of reactive antibodies (p < 0.001), donor age (p < 0.001), cold ischemia time (p = 0.005) and HLA-DR mismatches (p = 0.05) represented independent predictors. The multivariable nomogram relying on 6 predictors was 74.3% accurate in predicting the probability of DGF. Conclusion: Our simple and user-friendly model requires 6 variables and is at least equally accurate (74%) to the previous nomogram (71 %). We demonstrate that DGF can be accurately predicted in different populations with this new model
Predictive and Prognostic Models in Radical Prostatectomy Candidates: A Critical Analysis of the Literature
No full textContext: Numerous predictive and prognostic tools have recently been developed for risk stratification of prostate cancer (PCa) patients who are candidates for or have been treated with radical prostatectomy (RP). Objective: To critically review the currently available predictive and prognostic tools for RP patients and to describe the criteria that should be applied in selecting the most accurate and appropriate tool for a given clinical scenario. Evidence acquisition: A review of the literature was performed using the Medline, Scopus, and Web of Science databases. Relevant reports published between 1996 and January 2010 identified using the keywords prostate cancer, radical prostatectomy, predictive tools, predictive models, and nomograms were critically reviewed and summarised. Evidence synthesis: We identified 16 predictive and 22 prognostic validated tools that address a variety of end points related to RP. The majority of tools are prediction models, while a few consist of risk-stratification schemes. Regardless of their format, the tools can be distinguished as preoperative or postoperative. Preoperative tools focus on either predicting pathologic tumour characteristics or assessing the probability of biochemical recurrence (BCR) after RP. Postoperative tools focus on cancer control outcomes (BCR, metastatic progression, PCa-specific mortality [PCSM], overall mortality). Finally, a novel category of tools focuses on functional outcomes. Prediction tools have shown better performance in outcome prediction than the opinions of expert clinicians. The use of these tools in clinical decision-making provides more accurate and highly reproducible estimates of the outcome of interest. Efforts are still needed to improve the available tools' accuracy and to provide more evidence to further justify their routine use in clinical practice. In addition, prediction tools should be externally validated in independent cohorts before they are applied to different patient populations. Conclusions: Predictive and prognostic tools represent valuable aids that are meant to consistently and accurately provide most evidence-based estimates of the end points of interest. More accurate, flexible, and easily accessible tools are needed to simplify the practical task of prediction. (C) 2010 European Association of Urology. Published by Elsevier B.V. All rights reserved
Neoadjuvant sutent induction therapy may effectively down-stage renal cell carcinoma atrial thrombi
No full textA 75-yr-old previously healthy woman presented with gross hematuria, European Cooperative Oncology Group 0, and an 11-cm renal mass with right atrial thrombus. The patient refused the sternotomy. She was offered two cycles of sunitinib maleate (Sutent) induction therapy to down-stage the thrombus and to reduce the extent of the surgery. (C) 2007 European Association of Urology. Published by Elsevier B.V. All rights reserved
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