Testosterone recovery after androgen deprivation therapy in prostate cancer: building a predictive model

Purpose: To analyze the variability, associated actors, and the design of nomograms for individualized testosterone recovery after cessation of androgen deprivation therapy (ADT). Materials and Methods: A longitudinal study was carried out with 208 patients in the period 2003 to 2019. Castrated and normogonadic testosterone levels were defined as 0.5 and 3.5 ng/mL, respectively. The cumulative incidence curve described the recovery of testosterone. Univariate and multivariate analyzes were performed to predict testosterone recovery with candidate prognostic factors prostate-specific antigen at diagnosis, clinical stage, Gleason score from biopsy, age at cessation of ADT, duration of ADT, primary therapy and use of LHRH (luteinizing hormone-releasing hormone) agonists. Results: The median follow-up duration in the study was 80 months (interquartile range, 49–99 mo). Twenty-five percent and 81% of patients did not recover the castrate and normogonadic levels, respectively. Duration of ADT and age at ADT cessation were significant predictors of testosterone recovery. We built two nomograms for testosterone recovery at 12, 24, 36, and 60 months. The castration recovery model had good calibration. The C-index was 0.677, with area under the receiver operating characteristic curve (AUC-ROC) of 0.736, 0.783, 0.782, and 0.780 at 12, 24, 36, and 60 months, respectively. The normogonadic recovery model overestimated the higher values of probability of recovery. The Cindex was 0.683, with AUC values of 0.812, 0.711, 0.708 and 0.693 at 12, 24, 36, and 60 months, respectively. Conclusions: Depending on the age of the patient and the length of treatment, clinicians may stop ADT and the castrated testosterone level will be maintained or, if the course of treatment has been short, we can estimate if it will return to normogonadic levels

Active surveillance in prostate cancer: role of available biomarkers in daily practice

Prostate cancer (PCa) is the most commonly diagnosed cancer in men. The diagnosis is currently based on PSA levels, which are associated with overdiagnosis and overtreatment. Moreover, most PCas are localized tumours; hence, many patients with low-/very low-risk PCa could benefit from active surveillance (AS) programs instead of more aggressive, active treatments. Heterogeneity within inclusion criteria and follow-up strategies are the main controversial issues that AS presently faces. Many biomarkers are currently under investigation in this setting; however, none has yet demonstrated enough diagnostic ability as an independent predictor of pathological or clinical progression. This work aims to review the currently available literature on tissue, blood and urine biomarkers validated in clinical practice for the management of AS patients

A multistate model and its standalone tool to predict hospital and ICU occupancy by patients with COVID-19

Objective: This study aims to build a multistate model and describe a predictive tool for estimating the daily number of intensive care unit (ICU) and hospital beds occupied by patients with coronavirus 2019 disease (COVID-19). Material and methods: The estimation is based on the simulation of patient trajectories using a multistate model where the transition probabilities between states are estimated via competing risks and cure models. The input to the tool includes the dates of COVID-19 diagnosis, admission to hospital, admission to ICU, discharge from ICU and discharge from hospital or death of positive cases from a selected initial date to the current moment. Our tool is validated using 98,496 cases positive for severe acute respiratory coronavirus 2 extracted from the Aragón Healthcare Records Database from July 1, 2020 to February 28, 2021. Results: The tool demonstrates good performance for the 7- and 14-days forecasts using the actual positive cases, and shows good accuracy among three scenarios corresponding to different stages of the pandemic: 1) up-scenario, 2) peak-scenario and 3) down-scenario. Long term predictions (two months) also show good accuracy, while those using Holt-Winters positive case estimates revealed acceptable accuracy to day 14 onwards, with relative errors of 8.8%. Discussion: In the era of the COVID-19 pandemic, hospitals must evolve in a dynamic way. Our prediction tool is designed to predict hospital occupancy to improve healthcare resource management without information about clinical history of patients. Conclusions: Our easy-to-use and freely accessible tool (https://github.com/peterman65) shows good performance and accuracy for forecasting the daily number of hospital and ICU beds required for patients with COVID-19

Predictors Of Positivity Of [F-18]F-Choline PET-CT In Prostate Cancer Recurrence. Preliminary Results

EP-173 Aim/Introduction: To analyze the validity of [18F]F-Choline PET-CT results in prostate cancer recurrence in our daily practice, based on theoretical cut-off points of prostatespecific antigen (PSA), its kinetic, and PSA doubling time (PSADT), to identify predictors of positivity and modify the indication criteria. Materials and Methods: Prior to the validity analysis, a descriptive, prospective analysis of consecutive patients with prostate cancer treated with curative intent by radical prostatectomy (RP) or radiotherapy (RT), who underwent PET-CT scan with recurrence criteria: PSA =1 or PSA 0.4-1 with PSADT Nadir + 2 after RT, was performed. Results: From April to December 2019, 69 patients were included, 40 were treated with RP (58%) and 29 with RT (42%). In 45 patients (65%) PET-CT was able to identify recurrence of the disease (positive PET) and in 24 it was not (negative PET). Of patients treated with RP, 82, 5% (33/40) had PSA>1, and of those, 61% were positive PET. 17, 5% (7/40) had PSA6months (28/69), in 71% if PSADT6 months, in 61% and 92% if PSADT<6 months and in 77% and 100% if PSADT<3 months. Conclusion: Preliminarily and awaiting validation, it seems that PSA>1 after RP or Nadir +2 after RT is an indicator of PET-CT. There seems to be a tendency that shows that PSA<1 after RP is an indicator of PET-CT if PSADT<3 months. PSADT <3 or <6 months could be the best predictor of positivity of PET-CT with [18F]F-Choline in recurrent prostate cancer

Optimizing the clinical utility of PCA3 to diagnose prostate cancer in initial prostate biopsy

Background: PCA3 has been included in a nomogram outperforming previous clinical models for the prediction of any prostate cancer (PCa) and high grade PCa (HGPCa) at the initial prostate biopsy (IBx). Our objective is to validate such IBx-specific PCA3-based nomogram. We also aim to optimize the use of this nomogram in clinical practice through the definition of risk groups. Methods: Independent external validation. Clinical and biopsy data from a contemporary cohort of 401 men with the same inclusion criteria to those used to build up the reference’s nomogram in IBx. The predictive value of the nomogram was assessed by means of calibration curves and discrimination ability through the area under the curve (AUC). Clinical utility of the nomogram was analyzed by choosing thresholds points that minimize the overlapping between probability density functions (PDF) in PCa and no PCa and HGPCa and no HGPCa groups, and net benefit was assessed by decision curves. Results: We detect 28 % of PCa and 11 % of HGPCa in IBx, contrasting to the 46 and 20 % at the reference series. Due to this, there is an overestimation of the nomogram probabilities shown in the calibration curve for PCa. The AUC values are 0.736 for PCa (C.I.95 %:0.68–0.79) and 0.786 for HGPCa (C.I.95 %:0.71–0.87) showing an adequate discrimination ability. PDF show differences in the distributions of nomogram probabilities in PCa and not PCa patient groups. A minimization of the overlapping between these curves confirms the threshold probability of harboring PCa >30 % proposed by Hansen is useful to indicate a IBx, but a cut-off > 40 % could be better in series of opportunistic screening like ours. Similar results appear in HGPCa analysis. The decision curve also shows a net benefit of 6.31 % for the threshold probability of 40 %. Conclusions: PCA3 is an useful tool to select patients for IBx. Patients with a calculated probability of having PCa over 40 % should be counseled to undergo an IBx if opportunistic screening is required

Relación entre la grasa corporal y la expresión de ira en personas que realizan ejercicio regularmente

La actividad física proporciona benecios, tanto a la población sana como enferma, pero también puede derivar en problemas psicológicos y emocionales como respuesta al estrés. Además, aquellos atletas con menor peso corporal presentan indicadores más elevados de depresión e ira. Se plantea determinar la relación de la grasa corporal sobre la expresión de ira y entender la relación entre distintos comportamientos psicológicos, en personas físicamente activas. 264 sujetos cumplimentaron el Inventario de Expresión de Ira Estado-Rasgo, versión 2, para el estudio sobre las características de la ira y sus efectos en la salud mental y física. Se tomaron medidas antropométricas (peso, talla, IMC, porcentaje de grasa corporal, masa libre de grasa). Se calcularon distintos percentiles en función del género y edad, clasicando a los participantes en tres grupos: percentil &lt;45, entre 45 y 55 y &gt;55 de grasa corporal. Se analizaron distintos comportamientos en relación al STAXI-2 y a los distintos percentiles, pero sin encontrar diferencias significativas entre la ira y los tres grupos.

4kscore test, prostate cancer prevention trial-risk calculator y european research screening prostate-risk calculator en la predicción del cáncer de próstata de alto grado; estudio preliminar

Introducción: Frente al sobrediagnóstico y al sobretratamiento en cáncer de próstata (CaP) se establecen estrategias terapéuticas como la vigilancia activa o la terapia focal, o métodos para precisar el diagnóstico del CaP de alto grado (CaP-AG), Gleason = 7, como la resonancia magnética multiparamétrica o nuevos marcadores como el 4Kscore Test (4KsT).: Es nuestro propósito testar mediante un estudio piloto la capacidad del 4KsT como identificador de CaP-AG (suma de Gleason = 7) en biopsia de próstata (Bx) y compararlo con otros modelos pronósticos multivariantes disponibles, como el Prostate Cancer Prevention Trial-Risk Calculator 2.0 (PCPTRC 2.0) y el European Research Screening Prostate Cancer-Risk Calculator 4 (ERSPC-RC 4). Material y métodos: Cincuenta y un pacientes sometidos a BxP según práctica clínica habitual, con un mínimo de 10 cilindros. Diagnóstico de CaP-AG consensuado por 4 uropatólogos. Comparación de las predicciones ofrecidas por los diferentes modelos mediante prueba U Mann-Whitney, áreas bajo la curva ROC (AUC) (test de DeLong), funciones de densidad de probabilidad, diagramas de caja y curvas de utilidad clínica (CUC). Resultados: Un 43% presentaron CaP y un 23,5% CaP-AG. Las medianas de probabilidad de 4KsT, PCPTRC 2.0 y ERSPC-RC 4 fueron significativamente diferentes entre los pacientes con CaP-AG y no CaP-AG (p = 0,022), siendo más diferenciadas en el caso de 4KsT (mediana en CaP-AG: 51,5% [percentil 25-75: 25-80,5%], frente a 16% [P 25-75: 8-26,5%] en no CaP-AG [p = 0,002]). Todos los modelos mostraron AUC por encima de 0,7 sin diferencias significativas entre ninguno de ellos y 4KsT (p = 0,20). Las funciones de densidad de probabilidad y diagramas de caja muestran una buena capacidad discriminativa, especialmente en los modelos de ERSPC-RC 4 y 4KsT. Las CUC muestran como un punto de corte del 9% de 4KsT identifica a todos los CaP-AG y permite un ahorro del 22% de biopsias, similar a lo que ocurre con los modelos de ERSPC-RC 4 y un punto de corte del 3%. Conclusiones: Los modelos predictivos evaluados ofrecen una buena capacidad de discriminación del CaP-AG en Bx. 4KsT es un buen modelo clasificatorio en su conjunto, seguido de ERSPC-RC 4 y PCPTRC 2.0. Las CUC permiten sugerir puntos de corte de decisión clínica: 9% para 4KsT y 3% en ERSPC-RC 4. Este estudio preliminar debe ser interpretado con cautela por su limitado tamaño muestral. Introduction: To prevent the overdiagnosis and overtreatment of prostate cancer (PC), therapeutic strategies have been established such as active surveillance and focal therapy, as well as methods for clarifying the diagnosis of high-grade prostate cancer (HGPC) (defined as a Gleason score =7), such as multiparametric magnetic resonance imaging and new markers such as the 4Kscore test (4. KsT).By means of a pilot study, we aim to test the ability of the 4. KsT to identify HGPC in prostate biopsies (Bx) and compare the test with other multivariate prognostic models such as the Prostate Cancer Prevention Trial Risk Calculator 2.0 (PCPTRC 2.0) and the European Research Screening Prostate Cancer Risk Calculator 4 (ERSPC-RC 4). Material and methods: Fifty-one patients underwent a prostate Bx according to standard clinical practice, with a minimum of 10 cores. The diagnosis of HGPC was agreed upon by 4 uropathologists. We compared the predictions from the various models by using the Mann-Whitney U test, area under the ROC curve (AUC) (DeLong test), probability density function (PDF), box plots and clinical utility curves. Results: Forty-three percent of the patients had PC, and 23.5% had HGPC. The medians of probability for the 4. KsT, PCPTRC 2.0 and ERSPC-RC 4 were significantly different between the patients with HGPC and those without HGPC (p=.022) and were more differentiated in the case of 4. KsT (51.5% for HGPC 25-75 percentile: 25-80.5%] vs. 16% P 25-75: 8-26.5%] for non-HGPC; p = 002). All models presented AUCs above 0.7, with no significant differences between any of them and 4. KsT (p=.20). The PDF and box plots showed good discriminative ability, especially in the ERSPC-RC 4 and 4. KsT models. The utility curves showed how a cutoff of 9% for 4. KsT identified all cases of HGPC and provided a 22% savings in biopsies, which is similar to what occurs with the ERSPC-RC 4 models and a cutoff of 3%. Conclusions: The assessed predictive models offer good discriminative ability for HGPCs in Bx. The 4. KsT is a good classification model as a whole, followed by ERSPC-RC 4 and PCPTRC 2.0. The clinical utility curves help suggest cutoff points for clinical decisions: 9% for 4. KsT and 3% for ERSPC-RC 4. This preliminary study should be interpreted with caution due to its limited sample size

Automated Turbidimetry of Serum Lipoprotein(a)

We describe a simple iminunoturbidimetric method for quantifying lipoprotein(a) in serum based on latex-enhanced particle agglutination technology. Carboxylated latex particles (diameter 240 nm) covalently coated with F(ab')2 fragments of anti-lipoprotein(a) antibodies are incubated with the sample for 5 min at 37°C, and the resulting agglutination is quantified by measuring the change of turbidity produced at 700 nm. The assay is rapid, precise and fully automated on the Hitachi 911 analyser. The assay range is about 0.03—0.9 g/l. Average analytical recovery was 97.8%. Precision (CV) ranged from 1.9 to 3.1% at different lipoprotein(a) values. There was no interference from bilirubin, Intfalipid®, haemoglobin, plasminogen or apolipoprotein B. Comparisons with a latex nephelometric assay carried out on the Behring nephelometer analyser, and with three commercially available methods, a radioimmunoassay and two ELISA assays, gave good correlations (r > 0.95), although a large among-method variation in lipoprotein(a) values was found. We conclude that the proposed latex turbidimetric immunoassay method is suitable for routine use in clinical laboratories.Peer Reviewe

Automated Turbidimetry of Rheumatoid Factor without Heat Inactivation of Serum

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