BACKGROUND: Prostate Imaging Reporting and Data System (PI-RADS) 3 lesions, identified through multiparametric magnetic resonance imaging (mpMRI), present a clinical challenge due to their equivocal nature in predicting clinically significant prostate cancer (csPCa). Aim of the study is to improve risk stratification of patients with PI-RADS 3 lesions and candidates for prostate biopsy. METHODS: A cohort of 4841 consecutive patients who underwent MRI and subsequent MRI-targeted and systematic biopsies between January 2016 and April 2023 were retrospectively identified from independent prospectively maintained database. Only patients who have PI-RADS 3 lesions were included in the final analysis. A multivariable logistic regression analysis was performed to identify covariables associated with csPCa defined as International Society of Urological Pathology (ISUP) grade group ≥2. Performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC), calibration, and net benefit. Significant predictors were then selected for further exploration using a Chi-squared Automatic Interaction Detection (CHAID) analysis. RESULTS: Overall, 790 patients had PI-RADS 3 lesions and 151 (19%) had csPCa. Significant associations were observed for age (OR: 1.1 [1.0-1.1]; p = 0.01) and PSA density (OR: 1643 [2717-41,997]; p < 0.01). The CHAID analysis identified PSAd as the sole significant factor influencing the decision tree. Cut-offs for PSAd were 0.13 ng/ml/cc (csPCa detection rate of 1% vs. 18%) for the two-nodes model and 0.09 ng/ml/cc and 0.16 ng/ml/cc for the three-nodes model (csPCa detection rate of 0.5% vs. 2% vs. 17%). CONCLUSIONS: For individuals with PI-RADS 3 lesions on prostate mpMRI and a PSAd below 0.13, especially below 0.09, prostate biopsy can be omitted, in order to avoid unnecessary biopsy and overdiagnosis of non-csPCa.

• MeSH
• hodnocení rizik metody   MeSH
• lidé středního věku MeSH
• lidé MeSH
• multiparametrická magnetická rezonance * metody   MeSH
• nádory prostaty * patologie   diagnostické zobrazování   diagnóza   krev   MeSH
• prostata patologie   diagnostické zobrazování   MeSH
• prostatický specifický antigen * krev   MeSH
• retrospektivní studie MeSH
• ROC křivka MeSH
• senioři MeSH
• stupeň nádoru MeSH
• ultrazvukem navigovaná biopsie metody   MeSH
• Check Tag
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři MeSH
• Publikační typ
• časopisecké články MeSH

BACKGROUND AND AIMS: Risk stratification of sudden cardiac death after myocardial infarction and prevention by defibrillator rely on left ventricular ejection fraction (LVEF). Improved risk stratification across the whole LVEF range is required for decision-making on defibrillator implantation. METHODS: The analysis pooled 20 data sets with 140 204 post-myocardial infarction patients containing information on demographics, medical history, clinical characteristics, biomarkers, electrocardiography, echocardiography, and cardiac magnetic resonance imaging. Separate analyses were performed in patients (i) carrying a primary prevention cardioverter-defibrillator with LVEF ≤ 35% [implantable cardioverter-defibrillator (ICD) patients], (ii) without cardioverter-defibrillator with LVEF ≤ 35% (non-ICD patients ≤ 35%), and (iii) without cardioverter-defibrillator with LVEF > 35% (non-ICD patients >35%). Primary outcome was sudden cardiac death or, in defibrillator carriers, appropriate defibrillator therapy. Using a competing risk framework and systematic internal-external cross-validation, a model using LVEF only, a multivariable flexible parametric survival model, and a multivariable random forest survival model were developed and externally validated. Predictive performance was assessed by random effect meta-analysis. RESULTS: There were 1326 primary outcomes in 7543 ICD patients, 1193 in 25 058 non-ICD patients ≤35%, and 1567 in 107 603 non-ICD patients >35% during mean follow-up of 30.0, 46.5, and 57.6 months, respectively. In these three subgroups, LVEF poorly predicted sudden cardiac death (c-statistics between 0.50 and 0.56). Considering additional parameters did not improve calibration and discrimination, and model generalizability was poor. CONCLUSIONS: More accurate risk stratification for sudden cardiac death and identification of low-risk individuals with severely reduced LVEF or of high-risk individuals with preserved LVEF was not feasible, neither using LVEF nor using other predictors.

• MeSH
• defibrilátory implantabilní * MeSH
• elektrokardiografie MeSH
• hodnocení rizik metody   MeSH
• infarkt myokardu * mortalita   komplikace   MeSH
• lidé MeSH
• náhlá srdeční smrt * prevence a kontrola   epidemiologie   etiologie   MeSH
• tepový objem * fyziologie   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• metaanalýza MeSH

BACKGROUND: Prediction of side-specific extraprostatic extension (EPE) is crucial in selecting patients for nerve-sparing radical prostatectomy (RP). Multiple nomograms, which include magnetic resonance imaging (MRI) information, are available predict side-specific EPE. It is crucial that the accuracy of these nomograms is assessed with external validation to ensure they can be used in clinical practice to support medical decision-making. METHODS: Data of prostate cancer (PCa) patients that underwent robot-assisted RP (RARP) from 2017 to 2021 at four European tertiary referral centers were collected retrospectively. Four previously developed nomograms for the prediction of side-specific EPE were identified and externally validated. Discrimination (area under the curve [AUC]), calibration and net benefit of four nomograms were assessed. To assess the strongest predictor among the MRI features included in all nomograms, we evaluated their association with side-specific EPE using multivariate regression analysis and Akaike Information Criterion (AIC). RESULTS: This study involved 773 patients with a total of 1546 prostate lobes. EPE was found in 338 (22%) lobes. The AUCs of the models predicting EPE ranged from 72.2% (95% CI 69.1-72.3%) (Wibmer) to 75.5% (95% CI 72.5-78.5%) (Nyarangi-Dix). The nomogram with the highest AUC varied across the cohorts. The Soeterik, Nyarangi-Dix, and Martini nomograms demonstrated fair to good calibration for clinically most relevant thresholds between 5 and 30%. In contrast, the Wibmer nomogram showed substantial overestimation of EPE risk for thresholds above 25%. The Nyarangi-Dix nomogram demonstrated a higher net benefit for risk thresholds between 20 and 30% when compared to the other three nomograms. Of all MRI features, the European Society of Urogenital Radiology score and tumor capsule contact length showed the highest AUCs and lowest AIC. CONCLUSION: The Nyarangi-Dix, Martini and Soeterik nomograms resulted in accurate EPE prediction and are therefore suitable to support medical decision-making.

• MeSH
• lidé středního věku MeSH
• lidé MeSH
• magnetická rezonanční tomografie * metody   MeSH
• nádory prostaty * diagnostické zobrazování   patologie   chirurgie   MeSH
• nomogramy * MeSH
• prognóza MeSH
• prostata diagnostické zobrazování   patologie   chirurgie   MeSH
• prostatektomie * metody   MeSH
• retrospektivní studie MeSH
• roboticky asistované výkony metody   MeSH
• senioři MeSH
• Check Tag
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři MeSH
• Publikační typ
• časopisecké články MeSH
• validační studie MeSH

Pulse pressure amplification (PPA) is the brachial-to-aortic pulse pressure ratio and decreases with age and cardiovascular risk factors. This individual-participant meta-analysis of population studies aimed to define an outcome-driven threshold for PPA. Incidence rates and standardized multivariable-adjusted hazard ratios (HRs) of cardiovascular and coronary endpoints associated with PPA, as assessed by the SphygmoCor software, were evaluated in the International Database of Central Arterial Properties for Risk Stratification (n = 5608). Model refinement was assessed by the integrated discrimination (IDI) and net reclassification (NRI) improvement. Age ranged from 30 to 96 years (median 53.6). Over 4.1 years (median), 255 and 109 participants experienced a cardiovascular or coronary endpoint. In a randomly defined discovery subset of 3945 individuals, the rounded risk-carrying PPA thresholds converged at 1.3. The HRs for cardiovascular and coronary endpoints contrasting PPA < 1.3 vs ≥1.3 were 1.54 (95% confidence interval [CI]: 1.00-2.36) and 2.45 (CI: 1.20-5.01), respectively. Models were well calibrated, findings were replicated in the remaining 1663 individuals analyzed as test dataset, and NRI was significant for both endpoints. The HRs associating cardiovascular and coronary endpoints per PPA threshold in individuals <60 vs ≥60 years were 3.86 vs 1.19 and 6.21 vs 1.77, respectively. The proportion of high-risk women (PPA < 1.3) was higher at younger age (<60 vs ≥60 years: 67.7% vs 61.5%; P < 0.001). In conclusion, over and beyond common risk factors, a brachial-to-central PP ratio of <1.3 is a forerunner of cardiovascular coronary complications and is an underestimated risk factor in women aged 30-60 years. Our study supports pulse wave analysis for risk stratification.

• MeSH
• analýza pulzové vlny MeSH
• arteria brachialis fyziologie   MeSH
• dospělí MeSH
• kardiovaskulární nemoci * patofyziologie   MeSH
• krevní tlak * fyziologie   MeSH
• lidé středního věku MeSH
• lidé MeSH
• rizikové faktory kardiovaskulárních chorob MeSH
• rizikové faktory MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• metaanalýza MeSH

BACKGROUND: Head and neck cancer (HNC) incidence is on the rise, often diagnosed at late stage and associated with poor prognoses. Risk prediction tools have a potential role in prevention and early detection. METHODS: The IARC-ARCAGE European case-control study was used as the model development dataset. A clinical HNC risk prediction model using behavioral and demographic predictors was developed via multivariable logistic regression analyses. The model was then externally validated in the UK Biobank cohort. Model performance was tested using discrimination and calibration metrics. RESULTS: 1926 HNC cases and 2043 controls were used for the development of the model. The development dataset model including sociodemographic, smoking, and alcohol variables had moderate discrimination, with an area under curve (AUC) value of 0.75 (95% CI, 0.74-0.77); the calibration slope (0.75) and tests were suggestive of good calibration. 384 616 UK Biobank participants (with 1177 HNC cases) were available for external validation of the model. Upon external validation, the model had an AUC of 0.62 (95% CI, 0.61-0.64). CONCLUSION: We developed and externally validated a HNC risk prediction model using the ARCAGE and UK Biobank studies, respectively. This model had moderate performance in the development population and acceptable performance in the validation dataset. Demographics and risk behaviors are strong predictors of HNC, and this model may be a helpful tool in primary dental care settings to promote prevention and determine recall intervals for dental examination. Future addition of HPV serology or genetic factors could further enhance individual risk prediction.

• MeSH
• dospělí MeSH
• hodnocení rizik MeSH
• lidé středního věku MeSH
• lidé MeSH
• logistické modely MeSH
• nádory hlavy a krku * epidemiologie   MeSH
• rizikové faktory MeSH
• senioři MeSH
• studie případů a kontrol MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• validační studie MeSH
• Geografické názvy
• Evropa MeSH
• Spojené království MeSH

BACKGROUND: Aortic pulse wave velocity (PWV) predicts cardiovascular events (CVEs) and total mortality (TM), but previous studies proposing actionable PWV thresholds have limited generalizability. This individual-participant meta-analysis is aimed at defining, testing calibration, and validating an outcome-driven threshold for PWV, using 2 populations studies, respectively, for derivation IDCARS (International Database of Central Arterial Properties for Risk Stratification) and replication MONICA (Monitoring of Trends and Determinants in Cardiovascular Disease Health Survey - Copenhagen). METHODS: A risk-carrying PWV threshold for CVE and TM was defined by multivariable Cox regression, using stepwise increasing PWV thresholds and by determining the threshold yielding a 5-year risk equivalent with systolic blood pressure of 140 mm Hg. The predictive performance of the PWV threshold was assessed by computing the integrated discrimination improvement and the net reclassification improvement. RESULTS: In well-calibrated models in IDCARS, the risk-carrying PWV thresholds converged at 9 m/s (10 m/s considering the anatomic pulse wave travel distance). With full adjustments applied, the threshold predicted CVE (hazard ratio [CI]: 1.68 [1.15-2.45]) and TM (1.61 [1.01-2.55]) in IDCARS and in MONICA (1.40 [1.09-1.79] and 1.55 [1.23-1.95]). In IDCARS and MONICA, the predictive accuracy of the threshold for both end points was ≈0.75. Integrated discrimination improvement was significant for TM in IDCARS and for both TM and CVE in MONICA, whereas net reclassification improvement was not for any outcome. CONCLUSIONS: PWV integrates multiple risk factors into a single variable and might replace a large panel of traditional risk factors. Exceeding the outcome-driven PWV threshold should motivate clinicians to stringent management of risk factors, in particular hypertension, which over a person's lifetime causes stiffening of the elastic arteries as waypoint to CVE and death.

• MeSH
• analýza pulzové vlny škodlivé účinky   MeSH
• aorta MeSH
• arterie MeSH
• hypertenze * diagnóza   epidemiologie   komplikace   MeSH
• kardiovaskulární nemoci * diagnóza   epidemiologie   etiologie   MeSH
• lidé MeSH
• rizikové faktory MeSH
• tuhost cévní stěny * fyziologie   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• metaanalýza MeSH
• práce podpořená grantem MeSH

BACKGROUND: We tested whether a model identifying prostate cancer (PCa) patients at risk of pT3-4/pN1 can be developed for use during COVID19 pandemic, in order to guarantee appropriate treatment to patients harboring advanced disease patients without compromising sustainability of care delivery. METHODS: Within the Surveillance, Epidemiology and End Results database 2010-2016, we identified 27,529 patients with localized PCa and treated with radical prostatectomy. A multivariable logistic regression model predicting presence of pT3-4/pN1 disease was fitted within a development cohort (n=13,977, 50.8%). Subsequently, external validation (n=13,552, 49.2%) and head-to-head comparison with NCCN risk group stratification was performed. RESULTS: In model development, age, PSA, biopsy Gleason Grade Group (GGG) and percentage of positive biopsy cores were independent predictors of pT3-4/pN1 stage. In external validation, prediction of pT3-4/pN1 with novel nomogram was 74% accurate versus 68% for NCCN risk group stratification. Nomogram achieved better calibration and showed net-benefit over NCCN risk group stratification in decision curve analyses. The use of nomogram cut-off of 49% resulted in pT3-4/pN1 rate of 65%, instead of the average 35%. CONCLUSION: The newly developed, externally validated nomogram predicts presence of pT3-4/pN1 better than NCCN risk group stratification and allows to focus radical prostatectomy treatment on individuals at highest risk of pT3-4/pN1.

• Publikační typ
• časopisecké články MeSH

INTRODUCTION: This study aimed to determine the prognostic value of a panel of SIR-biomarkers, relative to standard clinicopathological variables, to improve mRCC patient selection for cytoreductive nephrectomy (CN). MATERIAL AND METHODS: A panel of preoperative SIR-biomarkers, including the albumin-globulin ratio (AGR), De Ritis ratio (DRR), and systemic immune-inflammation index (SII), was assessed in 613 patients treated with CN for mRCC. Patients were randomly divided into training and testing cohorts (65/35%). A machine learning-based variable selection approach (LASSO regression) was used for the fitting of the most informative, yet parsimonious multivariable models with respect to prognosis of cancer-specific survival (CSS). The discriminatory ability of the model was quantified using the C-index. After validation and calibration of the model, a nomogram was created, and decision curve analysis (DCA) was used to evaluate the clinical net benefit. RESULTS: SIR-biomarkers were selected by the machine-learning process to be of high discriminatory power during the fitting of the model. Low AGR remained significantly associated with CSS in both training (HR 1.40, 95% CI 1.07-1.82, p = 0.01) and testing (HR 1.78, 95% CI 1.26-2.51, p = 0.01) cohorts. High levels of SII (HR 1.51, 95% CI 1.10-2.08, p = 0.01) and DRR (HR 1.41, 95% CI 1.01-1.96, p = 0.04) were associated with CSS only in the testing cohort. The exclusion of the SIR-biomarkers for the prognosis of CSS did not result in a significant decrease in C-index (- 0.9%) for the training cohort, while the exclusion of SIR-biomarkers led to a reduction in C-index in the testing cohort (- 5.8%). However, SIR-biomarkers only marginally increased the discriminatory ability of the respective model in comparison to the standard model. CONCLUSION: Despite the high discriminatory ability during the fitting of the model with machine-learning approach, the panel of readily available blood-based SIR-biomarkers failed to add a clinical benefit beyond the standard model.

• MeSH
• biologické markery MeSH
• cytoredukční chirurgie MeSH
• karcinom z renálních buněk * patologie   MeSH
• lidé MeSH
• nádory ledvin * patologie   MeSH
• nefrektomie metody   MeSH
• retrospektivní studie MeSH
• strojové učení MeSH
• syndrom systémové zánětlivé reakce MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• randomizované kontrolované studie MeSH

BACKGROUND: Some high-risk prostate cancer (PCa) patients may show more favorable Gleason pattern at radical prostatectomy (RP) than at biopsy. OBJECTIVE: To test whether downgrading could be predicted accurately. DESIGN, SETTING, AND PARTICIPANTS: Within the Surveillance, Epidemiology and End Results database (2010-2016), 6690 National Comprehensive Cancer Network (NCCN) high-risk PCa patients were identified. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSES: We randomly split the overall cohort between development and validation cohorts (both n = 3345, 50%). Multivariable logistic regression models used biopsy Gleason, prostate-specific antigen, number of positive prostate biopsy cores, and cT stage to predict downgrading. Accuracy, calibration, and decision curve analysis (DCA) tested the model in the external validation cohort. RESULTS AND LIMITATIONS: Of 6690 patients, 50.3% were downgraded at RP, and of 2315 patients with any biopsy pattern 5, 44.1% were downgraded to RP Gleason pattern ≤4 + 4. Downgrading rates were highest in biopsy Gleason pattern 5 + 5 (84.1%) and lowest in 3 + 4 (4.0%). In the validation cohort, the logistic regression model-derived nomogram predicted downgrading with 71.0% accuracy, with marginal departures (±3.3%) from ideal predictions in calibration. In DCA, a net benefit throughout all threshold probabilities was recorded, relative to treat-all or treat-none strategies and an algorithm based on an average downgrading rate of 50.3%. All steps were repeated in the subgroup with any biopsy Gleason pattern 5, to predict RP Gleason pattern ≤4 + 4. Here, a second nomogram (n = 2315) yielded 68.0% accuracy, maximal departures from ideal prediction of ±5.7%, and virtually the same DCA pattern as the main nomogram. CONCLUSIONS: Downgrading affects half of all high-risk PCa patients. Its presence may be predicted accurately and may help with better treatment planning. PATIENT SUMMARY: Downgrading occurs in every second high-risk prostate cancer patients. The nomograms developed by us can predict these probabilities accurately.

• MeSH
• lidé MeSH
• nádory prostaty * chirurgie   patologie   MeSH
• nomogramy * MeSH
• prostata patologie   MeSH
• prostatektomie metody   MeSH
• stupeň nádoru MeSH
• Check Tag
• lidé MeSH
• mužské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

BACKGROUND: Contemporary seminal vesicle invasion (SVI) rates in National Cancer Comprehensive Network (NCCN) high-risk prostate cancer (PCa) patients are not well known but essential for treatment planning. We examined SVI rates according to individual patient characteristics for purpose of treatment planning. MATERIALS AND METHODS: Within Surveillance, Epidemiology, and End Results (SEER) database (2010-2015), 4975 NCCN high-risk patients were identified. In the development cohort (SEER geographic region of residence: South, North-East, Mid-West, n = 2456), we fitted a multivariable logistic regression model predicting SVI. Its accuracy, calibration, and decision curve analyses (DCAs) were then tested versus previous models within the external validation cohort (SEER geographic region of residence: West, n = 2519). RESULTS: Out of 4975 patients, 28% had SVI. SVI rate ranged from 8% to 89% according to clinical T stage, prostate-specific antigen (PSA), biopsy Gleason Grade Group and percentage of positive biopsy cores. In the development cohort, these variables were independent predictors of SVI. In the external validation cohort, the current model achieved 77.6% accuracy vs 73.7% for Memorial Sloan Kettering Cancer Centre (MSKCC) vs 68.6% for Gallina et al. Calibration was better than for the two alternatives: departures from ideal predictions were 6.0% for the current model vs 9.8% for MSKCC vs 38.5% for Gallina et al. In DCAs, the current model outperformed both alternatives. Finally, different nomogram cutoffs allowed to discriminate between low versus high SVI risk patients. CONCLUSIONS: More than a quarter of NCCN high-risk PCa patients harbored SVI. Since SVI positivity rate varies from 8% to 89%, the currently developed model offers a valuable approach to distinguish between low and high SVI risk patients.

• MeSH
• biopsie MeSH
• invazivní růst nádoru patologie   MeSH
• lidé MeSH
• nádory prostaty * patologie   MeSH
• nomogramy MeSH
• prostatektomie * metody   MeSH
• prostatický specifický antigen MeSH
• semenné váčky patologie   MeSH
• staging nádorů MeSH
• Check Tag
• lidé MeSH
• mužské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH