Luis M. Esteban

Genetic predisposition to early recurrence in clinically localized prostate cancer.

Currently available nomograms to predict preoperative risk of early biochemical recurrence (EBCR) after radical prostatectomy are solely based on classic clinicopathological variables. Despite providing useful predictions, these models are not perfect. Indeed, most researchers agree that nomograms can be improved by incorporating novel biomarkers. In the last few years, several single nucleotide polymorphisms (SNPs) have been associated with prostate cancer, but little is known about their impact on disease recurrence. We have identified four SNPs associated with EBCR. The addition of SNPs to classic nomograms resulted in a significant improvement in terms of discrimination and calibration. The new nomogram, which combines clinicopathological and genetic variables, will help to improve prediction of prostate cancer recurrence.

Implementing the use of nomograms by choosing threshold points in predictive models: 2012 updated Partin Tables vs a European predictive nomogram for organ-confined disease in prostate cancer.

To implement the use of nomograms in clinical practice showing how to choose thresholds in nomograms’ predictions to select risk groups. To validate and compare the predictive ability and clinical utility of the Hospital Universitario ‘Miguel Servet’ (HUMS) and the updated Partin Tables 2012 (PT‐2012) nomograms to predict organ‐confined disease (OCD) after radical prostatectomy (RP).

Urología y recursos predictivos en la Web

d q e t m c e d La medicina actual persigue una especialización profesional y una individualización terapéutica bajo el concepto de medicina individualizada que no es sino una medicina genómica personalizada. Sin embargo, nuestra práctica clínica dista de poder disponer de un análisis genómico individualizado de cada paciente y con la suficiente evidencia como para ofrecer consejo terapéutico personalizado1. Las limitaciones actuales a esta medicina personalizada de base genética son el gran objetivo a vencer en este siglo. Sin embargo, indirectamente sí disponemos de recursos que permiten ofrecer un consejo médico individualizado a nuestros pacientes. Estos recursos provienen del análisis estadístico-matemático de amplias series de pacientes, sus características, su evolución y sus resultados objetivables. No estamos hablando sino de complejos análisis multivariantes provenientes de técnicas más o menos convencionales, como regresión logística o regresión de riesgos proporcionales de Cox, o más avanzadas como modelos basados en inteligencia artificial2. Estos análisis tienden a presentarse en el universo científico y clínico en forma de gráficos que se conocen como nomogramas. Sin embargo, estos nomogramas todavía resultan incómodos de aplicar en la práctica clínica y ello ha dificultado su implementación. Sería ideal disponer de una herramienta capaz de facilitarnos el acceso y uso de estos recursos predictivos, de modo que una vez introducidas las características de nuestro paciente nos ofrecería su predicción individualizada de que le ocurriera un determinado evento. En realidad esta herramienta existe, o mejor dicho «existen», pues un buen número de ellas están disponibles on-line y atañen a ámbitos de la urología, como la posible evolución de un paciente y su hiperplasia benigna de próstata, la supervivencia de un injerto postrasplante, o las posibilidades de padecer un determinado tumor urológico, su extensión o supervivencia global, cáncer-específica y/o libre de progresión. En este trabajo hemos realizado un esfuerzo para identificar los recursos predictivos disponibles on-line relacionados p m d b

A step-by-step algorithm for combining diagnostic tests

Combining data of several tests or markers for the classification of patients according to their health status for assigning better treatments is a major issue in the study of diseases such as cancer. In order to tackle this problem, several approaches have been proposed in the literature. In this paper, a step-by-step algorithm for estimating the parameters of a linear classifier that combines several measures is considered. The optimization criterion is to maximize the area under the receiver operating characteristic curve. The algorithm is applied to different simulated data sets and its performance is evaluated. Finally, the method is illustrated with a prostate cancer staging database.

Comparison of fetal weight distribution improved by paternal height by Spanish standard versus Intergrowth 21st standard

Abstract Objective: Our main objective was to study the influence on birth and ultrasound fetal weight of traditional factors in combination with non-traditionally explored predictors such as paternal height to provide a new customized in utero growth model. We also have compared it in our population with other customized and non-customized models. Methods: We collected 5243 cases of singleton pregnancies. An integrated study of the different variables was performed in a multivariate model to predict the fetus birthweight and customized growth curves were created following the Gardosi procedure. Results: Gestational age (P<0.001), parity (P<0.001), maternal age (P<0.001), maternal body mass index (P<0.001), maternal height (P<0.001), parental height (P<0.001), pregnancy-associated plasma protein A (PAPP-A) (P<0.001), free-beta human chorionic gonadotropin (FBHCG) (P<0.013), single umbilical artery (SUA) (P<0.009), region of origin (P<0.001), fetal sex (P<0.001), smoking (P<0.001) and pre-gestational diabetes (P<0.001) showed statistical significance. We created two growth customized models (simple and advance) that have shown good performance in predicting fetal weight at delivery and estimated by ultrasounds. The percentage of small for gestational age (SGA) cases (P10) predicted by the two models at birth were 9.9% and 9%, and for large gestational ages (LGA) (P90) we obtained values of 90.1% and 90.3%. Also, using the fetal weights measured by ultrasounds, we obtained P10 adjusted predictions, 9.2% and 9.4%, for the simpler and advance models, respectively, which were more adjusted than the 0.4, 4.6 and 10.6 obtained using the other compared models. For an easy use of models an app and a nomogram is provided. Conclusion: Using new predictor variables we implemented new growth in utero model, with predictions more adjusted to our population than Spanish customized or Intergrowth 21st models with better performance for birth and ultrasound fetal weights. We propose using a prediction model that includes parental height.

Role of the 4Kscore test as a predictor of reclassification in prostate cancer active surveillance

BackgroundManagement of active surveillance (AS) in low-risk prostate cancer (PCa) patients could be improved with new biomarkers, such as the 4Kscore test. We analyze its ability to predict tumor reclassification by upgrading at the confirmatory biopsy at 6 months.MethodsObservational, prospective, blinded, and non-randomized study, within the Spanish National Registry on AS (AEU/PIEM/2014/0001; NCT02865330) with 181 patients included after initial Bx and inclusion criteria: PSA ≤10 ng/mL, cT1c-T2a, Grade group 1, ≤2 cores, and ≤5 mm/50% length core involved. Central pathological review of initial and confirmatory Bx was performed on all biopsy specimens. Plasma was collected 6 months after initial Bx and just before confirmatory Bx to determine 4Kscore result. In order to predict reclassification defined as Grade group ≥2, we analyzed 4Kscore, percent free to total (%f/t) PSA ratio, prostate volume, PSA density, family history, body mass index, initial Bx, total cores, initial Bx positive cores, initial Bx % of positive cores, initial Bx maximum cancer core length and initial Bx cancer % involvement. Wilcoxon rank-sum test, non-parametric trend test or Fisher’s exact test, as appropriate established differences between groups of reclassification.ResultsA total of 137 patients met inclusion criteria. Eighteen patients (13.1%) were reclassified at confirmatory Bx. The %f/t PSA ratio and 4Kscore showed differences between the groups of reclassification (Yes/No). Using 7.5% as cutoff for the 4Kscore, we found a sensitivity of 89% and a specificity of 29%, with no reclassifications to Grade group 3 for patients with 4Kscore below 7.5% and 2 (6%) missed Grade group 2 reclassified patients. Using this threshold value there is a biopsy reduction of 27%. Additionally, 4Kscore was also associated with changes in tumor volume.ConclusionsOur preliminary findings suggest that the 4Kscore may be a useful tool in the decision-making process to perform a confirmatory Bx in active surveillance management.

Maternal morbidity after implementation of a postpartum hemorrhage protocol including use of misoprostol

To compare maternal morbidity before and after implementation of a postpartum hemorrhage (PPH) protocol that included misoprostol.

MP53-05 TESTOSTERONE RECOVERY AFTER LONG TIME DEPRIVATION THERAPY: PREDICITIVE FACTORS AND MODELS (NOMOGRAMS)

was 27.9 months (range: 3.3-114.6). Median PSA prior to initiation of ADT was 18 ng/mL (range: 0.61-2940). 72.3% of patients achieved a 1year mean T < 20 ng/dl; 18.6% achieved 20-32 ng/dl; 5.4% achieved 32-50 ng/dl; and 3.6% achieved > 50 ng/dl. There was no statistically significant difference in progression-free survival between patients with different levels of 1-year mean testosterone values (log-rank p1⁄40.813). CONCLUSIONS: The results suggest that there may not be a significant impact of strict testosterone control beyond what is considered the traditional castrate-level testosterone. However, only a small proportion of patients had 1-year testosterone > 32 ng/dl (9.0%). A larger study may reveal a beneficial role of strict testosterone reduction in the management of advanced prostate cancer.

The management of active surveillance in prostate cancer: validation of the Canary Prostate Active Surveillance Study risk calculator with the Spanish Urological Association Registry

The follow up of patients on active surveillance requires to repeat prostate biopsies. Predictive models that identify patients at low risk of progression or reclassification are essential to reduce the number of unnecessary biopsies. The aim of this study is to validate the Prostate Active Surveillance Study risk calculator (PASS-RC) in the multicentric Spanish Urological Association Registry of patients on active surveillance (AS), from common clinical practice. Results We find significant differences in age, PSA and clinical stage between our validation cohort and the PASS-RC generation cohort (p < .0001), with a reclassification rate of 10–22% on the follow-up Bx, no cancer was found in 43% of the first follow-up Bx. The calibration curve shows underestimation of real appearance of reclassification. The AUC is 0.65 (C.I.95%: 0.60–0.71). PDF and CUC do not suggest a specific cut-off point of clinical use. Methods We select 498 patients on AS with a minimum of one follow-up biopsy (Bx) from the 1,024 males registered by 36 Spanish centers recruiting patients on the Spanish Urological Association Registry on AS. PASS-RC external validation is carried by means of calibration curve and area under de ROC-curve (AUC), identifying cut-offs of clinical utility by probability density functions (PDF) and clinical utility curves (CUC). Conclusions In our first external validation of the PASS-RC we have obtained a moderate discrimination ability, although we cannot recommend cut-off points of clinical use. We suggest the exploration of new biomarkers and/or morpho-functional parameters from multiparametric magnetic resonance image, to improve those necessary tools on AS.