Importance: Sudden cardiac death (SCD) is the most common mode of death in childhood hypertrophic cardiomyopathy (HCM), but there is no validated algorithm to identify those at highest risk. Objective: To develop and validate an SCD risk prediction model that provides individualized risk estimates. Design, Setting, and Participants: A prognostic model was developed from a retrospective, multicenter, longitudinal cohort study of 1024 consecutively evaluated patients aged 16 years or younger with HCM. The study was conducted from January 1, 1970, to December 31, 2017. Exposures: The model was developed using preselected predictor variables (unexplained syncope, maximal left-ventricular wall thickness, left atrial diameter, left-ventricular outflow tract gradient, and nonsustained ventricular tachycardia) identified from the literature and internally validated using bootstrapping. Main Outcomes and Measures: A composite outcome of SCD or an equivalent event (aborted cardiac arrest, appropriate implantable cardioverter defibrillator therapy, or sustained ventricular tachycardia associated with hemodynamic compromise). Results: Of the 1024 patients included in the study, 699 were boys (68.3%); mean (interquartile range [IQR]) age was 11 (7-14) years. Over a median follow-up of 5.3 years (IQR, 2.6-8.3; total patient years, 5984), 89 patients (8.7%) died suddenly or had an equivalent event (annual event rate, 1.49; 95% CI, 1.15-1.92). The pediatric model was developed using preselected variables to predict the risk of SCD. The model's ability to predict risk at 5 years was validated; the C statistic was 0.69 (95% CI, 0.66-0.72), and the calibration slope was 0.98 (95% CI, 0.59-1.38). For every 10 implantable cardioverter defibrillators implanted in patients with 6% or more of a 5-year SCD risk, 1 patient may potentially be saved from SCD at 5 years. Conclusions and Relevance: This new, validated risk stratification model for SCD in childhood HCM may provide individualized estimates of risk at 5 years using readily obtained clinical risk factors. External validation studies are required to demonstrate the accuracy of this model's predictions in diverse patient populations.

• MeSH
• dítě MeSH
• hodnocení rizik metody   MeSH
• hypertrofická kardiomyopatie komplikace   mortalita   MeSH
• incidence MeSH
• lidé MeSH
• míra přežití trendy   MeSH
• mladiství MeSH
• náhlá srdeční smrt epidemiologie   etiologie   MeSH
• následné studie MeSH
• prognóza MeSH
• retrospektivní studie MeSH
• rizikové faktory MeSH
• Check Tag
• dítě MeSH
• lidé MeSH
• mladiství MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• multicentrická studie MeSH
• práce podpořená grantem MeSH
• Geografické názvy
• Evropa MeSH

BACKGROUND: Young people with psychosis are at high risk of developing cardiometabolic disorders; however, there is no suitable cardiometabolic risk prediction algorithm for this group. We aimed to develop and externally validate a cardiometabolic risk prediction algorithm for young people with psychosis. METHODS: We developed the Psychosis Metabolic Risk Calculator (PsyMetRiC) to predict up to 6-year risk of incident metabolic syndrome in young people (aged 16-35 years) with psychosis from commonly recorded information at baseline. We developed two PsyMetRiC versions using the forced entry method: a full model (including age, sex, ethnicity, body-mass index, smoking status, prescription of a metabolically active antipsychotic medication, HDL concentration, and triglyceride concentration) and a partial model excluding biochemical results. PsyMetRiC was developed using data from two UK psychosis early intervention services (Jan 1, 2013, to Nov 4, 2020) and externally validated in another UK early intervention service (Jan 1, 2012, to June 3, 2020). A sensitivity analysis was done in UK birth cohort participants (aged 18 years) who were at risk of developing psychosis. Algorithm performance was assessed primarily via discrimination (C statistic) and calibration (calibration plots). We did a decision curve analysis and produced an online data-visualisation app. FINDINGS: 651 patients were included in the development samples, 510 in the validation sample, and 505 in the sensitivity analysis sample. PsyMetRiC performed well at internal (full model: C 0·80, 95% CI 0·74-0·86; partial model: 0·79, 0·73-0·84) and external validation (full model: 0·75, 0·69-0·80; and partial model: 0·74, 0·67-0·79). Calibration of the full model was good, but there was evidence of slight miscalibration of the partial model. At a cutoff score of 0·18, in the full model PsyMetRiC improved net benefit by 7·95% (sensitivity 75%, 95% CI 66-82; specificity 74%, 71-78), equivalent to detecting an additional 47% of metabolic syndrome cases. INTERPRETATION: We have developed an age-appropriate algorithm to predict the risk of incident metabolic syndrome, a precursor of cardiometabolic morbidity and mortality, in young people with psychosis. PsyMetRiC has the potential to become a valuable resource for early intervention service clinicians and could enable personalised, informed health-care decisions regarding choice of antipsychotic medication and lifestyle interventions. FUNDING: National Institute for Health Research and Wellcome Trust.

• MeSH
• algoritmy * MeSH
• dospělí MeSH
• kardiometabolické riziko * MeSH
• lidé MeSH
• metabolický syndrom diagnóza   MeSH
• mladiství MeSH
• mladý dospělý MeSH
• psychotické poruchy * diagnóza   MeSH
• reprodukovatelnost výsledků MeSH
• Check Tag
• dospělí MeSH
• lidé MeSH
• mladiství MeSH
• mladý dospělý MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• validační studie MeSH

Cíl: Protože kardiovaskulární onemocnění jsou příčinou závažné morbidity a mortality, je třeba zjišťovat jejich přítomnost a začít je včas léčit. Proto byla pro stanovení rizika vypracována řada stupnic, v současnosti se však rutinně nepoužívá žádný biochemický marker pro stanovení kardiovaskulárního rizika. Ateroskleróza je nejzávažnější příčinou rozvoje kardiovaskulárních onemocnění a v patofyziologii aterosklerózy hraje jistou úlohu zánět cév. Řada studií prokázala, že mnoho kroků v procesu rozvoje tohoto zánětu ovlivňují hodnoty YKL-40 v séru. Evropská kardiologická společnost používá pro stanovení desetiletého kardiovaskulárního rizika skórovací systém SCORE2. V naší studii jsme zkoumali vztah mezi algoritmem pro toto riziko a hodnotou biochemického markeru YKL-40 v séru. Materiál a metody: Do studie bylo zařazeno 87 dobrovolníků ve věku 40–70 let, kteří se dostavili na naši kliniku, v minulosti neprodělali kardiovaskulární příhodu, měli však rizikové faktory pro rozvoj kardiovaskulárního onemocnění. Pomocí predikčního modelu SCORE2 bylo stanoveno jejich kardiovaskulární riziko a současně změřeny hodnoty YKL-40 v séru. Tyto hodnoty se mění s věkem bez ohledu na přítomnost či nepřítomnost nemoci, což platilo pro naši studii stejně jako pro jiné studie. Abychom eliminovali toto paradigma, hodnotili jsme hodnoty YKL-40 v séru pomocí statistického modelu společně s věkem. Výsledky: Naše základní analýza nezjistila významný vztah mezi hodnotami YKL-40 a všemi parametry v algoritmu predikčního modelu SCORE2. Nicméně po porovnání výsledku analýzy s výsledkem statistického modelu s použitím věku se ukázalo se, že hodnota YKL-40 představuje biochemický marker, který lze použít, podobně jako systém SCORE2, při stanovování kardiovaskulárního rizika (R2 : 0,72; p < 0,001).

Objective: Since cardiovascular diseases are a cause of serious morbidity and mortality, it is important to detect and treat them in advance. For this reason, many risk scales have been created, but there is currently no biochemical marker in routine use to estimate cardiovascular risk. Atherosclerosis is the most important reason for the development of cardiovascular disease, and vascular inflammation plays a role in the pathophysiology of atherosclerosis. It has been observed in many studies that the serum YKL-40 level has an effect on many steps in the development process of this inflammation. SCORE2 are used by the European Society of Cardiology to estimate 10-year cardiovascular risk. In our study, we investigated the relationship between this risk algorithm and the serum YKL-40 level, which is a biochemical marker. Material and methods: 87 volunteers between the ages of 40-70 who applied to our clinic, who had not yet experienced a cardiovascular event but had risk factors for cardiovascular diseases, were included in the study. SCORE2 cardiovascular disease risk was calculated for the patients and serum YKL-40 levels were gaged. Serum YKL-40 levels change with age, regardless of the disease, in our study as in many studies. In order to eliminate this paradigm, we examined serum YKL-40 levels with a statistical model that evaluates them jointly with age. Results: We could not detect a significant relationship with YKL-40 levels in the basal analysis performed by considering all parameters of SCORE2 algorithm. However, result of the statistical model that we evaluated with age, we found that the YKL-40 level is a biochemical parameter that can be used like SCORE2 in cardiovascular disease risk estimation (R2 : 0.72, p < 0.001).

• MeSH
• biologické markery krev   MeSH
• dospělí MeSH
• kardiovaskulární nemoci krev   prevence a kontrola   MeSH
• lidé středního věku MeSH
• lidé MeSH
• prognóza MeSH
• protein CHI3L1 * krev   MeSH
• rizikové faktory kardiovaskulárních chorob * MeSH
• senioři MeSH
• statistika jako téma MeSH
• ukazatele zdravotního stavu MeSH
• věkové faktory MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• klinická studie MeSH

BACKGROUND: Prediction of susceptibility to multiple sclerosis (MS) might have important clinical applications, either as part of a diagnostic algorithm or as a means to identify high-risk individuals for prospective studies. We investigated the usefulness of an aggregate measure of risk of MS that is based on genetic susceptibility loci. We also assessed the added effect of environmental risk factors that are associated with susceptibility for MS. METHODS: We created a weighted genetic risk score (wGRS) that includes 16 MS susceptibility loci. We tested our model with data from 2215 individuals with MS and 2189 controls (derivation samples), a validation set of 1340 individuals with MS and 1109 controls taken from several MS therapeutic trials (TT cohort), and a second validation set of 143 individuals with MS and 281 controls from the US Nurses' Health Studies I and II (NHS/NHS II), for whom we also have data on smoking and immune response to Epstein-Barr virus (EBV). FINDINGS: Individuals with a wGRS that was more than 1.25 SD from the mean had a significantly higher odds of MS in all datasets. In the derivation sample, the mean (SD) wGRS was 3.5 (0.7) for individuals with MS and 3.0 (0.6) for controls (p<0.0001); in the TT validation sample, the mean wGRS was 3.4 (0.7) for individuals with MS versus 3.1 (0.7) for controls (p<0.0001); and in the NHS/NHS II dataset, the mean wGRS was 3.4 (0.8) for individuals with MS versus 3.0 (0.7) for controls (p<0.0001). In the derivation cohort, the area under the receiver operating characteristic curve (C statistic; a measure of the ability of a model to discriminate between individuals with MS and controls) for the genetic-only model was 0.70 and for the genetics plus sex model was 0.74 (p<0.0001). In the TT and NHS cohorts, the C statistics for the genetic-only model were both 0.64; adding sex to the TT model increased the C statistic to 0.72 (p<0.0001), whereas adding smoking and immune response to EBV to the NHS model increased the C statistic to 0.68 (p=0.02). However, the wGRS does not seem to be correlated with the conversion of clinically isolated syndrome to MS. INTERPRETATION: The inclusion of 16 susceptibility alleles into a wGRS can modestly predict MS risk, shows consistent discriminatory ability in independent samples, and is enhanced by the inclusion of non-genetic risk factors into the algorithm. Future iterations of the wGRS might therefore make a contribution to algorithms that can predict a diagnosis of MS in a clinical or research setting.

• MeSH
• alely MeSH
• algoritmy * MeSH
• dítě MeSH
• dospělí MeSH
• genotyp MeSH
• hodnocení rizik MeSH
• jednonukleotidový polymorfismus genetika   MeSH
• kohortové studie MeSH
• lidé středního věku MeSH
• lidé MeSH
• lokus kvantitativního znaku MeSH
• mladiství MeSH
• odds ratio MeSH
• prediktivní hodnota testů MeSH
• předškolní dítě MeSH
• rizikové faktory MeSH
• roztroušená skleróza * epidemiologie   genetika   MeSH
• senioři MeSH
• životní prostředí MeSH
• Check Tag
• dítě MeSH
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladiství MeSH
• mužské pohlaví MeSH
• předškolní dítě MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• práce podpořená grantem MeSH
• Research Support, N.I.H., Extramural MeSH

BACKGROUND: The European Society of Cardiology recommends a 0/1-hour algorithm for rapid rule-out and rule-in of non-ST-segment elevation myocardial infarction using high-sensitivity cardiac troponin (hs-cTn) concentrations irrespective of renal function. Because patients with renal dysfunction (RD) frequently present with increased hs-cTn concentrations even in the absence of non-ST-segment elevation myocardial infarction, concern has been raised regarding the performance of the 0/1-hour algorithm in RD. METHODS: In a prospective multicenter diagnostic study enrolling unselected patients presenting with suspected non-ST-segment elevation myocardial infarction to the emergency department, we assessed the diagnostic performance of the European Society of Cardiology 0/1-hour algorithm using hs-cTnT and hs-cTnI in patients with RD, defined as an estimated glomerular filtration rate <60 mL/min/1.73 m2, and compared it to patients with normal renal function. The final diagnosis was centrally adjudicated by 2 independent cardiologists using all available information, including cardiac imaging. Safety was quantified as sensitivity in the rule-out zone, accuracy as the specificity in the rule-in zone, and efficacy as the proportion of the overall cohort assigned to either rule-out or rule-in based on the 0- and 1-hour sample. RESULTS: Among 3254 patients, RD was present in 487 patients (15%). The prevalence of non-ST-segment elevation myocardial infarction was substantially higher in patients with RD compared with patients with normal renal function (31% versus 13%, P<0.001). Using hs-cTnT, patients with RD had comparable sensitivity of rule-out (100.0% [95% confidence interval {CI}, 97.6-100.0] versus 99.2% [95% CI, 97.6-99.8]; P=0.559), lower specificity of rule-in (88.7% [95% CI, 84.8-91.9] versus 96.5% [95% CI, 95.7-97.2]; P<0.001), and lower overall efficacy (51% versus 81%, P<0.001), mainly driven by a much lower percentage of patients eligible for rule-out (18% versus 68%, P<0.001) compared with patients with normal renal function. Using hs-cTnI, patients with RD had comparable sensitivity of rule-out (98.6% [95% CI, 95.0-99.8] versus 98.5% [95% CI, 96.5-99.5]; P=1.0), lower specificity of rule-in (84.4% [95% CI, 79.9-88.3] versus 91.7% [95% CI, 90.5-92.9]; P<0.001), and lower overall efficacy (54% versus 76%, P<0.001; proportion ruled out, 18% versus 58%, P<0.001) compared with patients with normal renal function. CONCLUSIONS: In patients with RD, the safety of the European Society of Cardiology 0/1-hour algorithm is high, but specificity of rule-in and overall efficacy are decreased. Modifications of the rule-in and rule-out thresholds did not improve the safety or overall efficacy of the 0/1-hour algorithm. CLINICAL TRIAL REGISTRATION: URL: https://www.clinicaltrials.gov. Unique identifier: NCT00470587.

• MeSH
• algoritmy * MeSH
• biologické markery krev   MeSH
• časové faktory MeSH
• hodnocení rizik MeSH
• hodnoty glomerulární filtrace * MeSH
• infarkt myokardu bez ST elevací krev   diagnóza   epidemiologie   MeSH
• kreatinin krev   MeSH
• ledviny patofyziologie   MeSH
• lidé středního věku MeSH
• lidé MeSH
• metody pro podporu rozhodování * MeSH
• nemoci ledvin krev   diagnóza   epidemiologie   patofyziologie   MeSH
• prediktivní hodnota testů MeSH
• prevalence MeSH
• prognóza MeSH
• prospektivní studie MeSH
• reprodukovatelnost výsledků MeSH
• rizikové faktory MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• třídění pacientů * MeSH
• troponin krev   MeSH
• upregulace MeSH
• Check Tag
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• multicentrická studie MeSH
• práce podpořená grantem MeSH
• Geografické názvy
• Evropa MeSH

Identification of risk factors for transient ischemic attack (TIA) is crucial for patients with atrial fibrillation (AF). However, identifying risk factors in young patients with low-risk AF is difficult, because the incidence of TIA in such patients is very low, which would result in traditional multiple logistic regression not being able to successfully identify the risk factors in such patients. Therefore, a novel algorithm for identifying risk factors for TIA is necessary. We thus propose a novel algorithm, which combines multiple correspondence analysis and hierarchical cluster analysis and uses the Taiwan National Health Insurance Research Database, a population-based database, to determine risk factors in these patients. The results of this study can help clinicians or patients with AF in preventing TIA or stroke events as early as possible.

• MeSH
• algoritmy MeSH
• fibrilace síní epidemiologie   komplikace   MeSH
• hodnocení rizik statistika a číselné údaje   MeSH
• lidé MeSH
• rizikové faktory MeSH
• tranzitorní ischemická ataka epidemiologie   etiologie   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• práce podpořená grantem MeSH

OBJECTIVES AND DESIGN: A novel risk stratification algorithm estimating risk of death in patients with relapsed multiple myeloma starting second-line treatment was recently developed using multivariable Cox regression of data from a Czech registry. It uses 16 parameters routinely collected in medical practice to stratify patients into four distinct risk groups in terms of survival expectation. To provide insight into generalisability of the risk stratification algorithm, the study aimed to validate the risk stratification algorithm using real-world data from specifically designed retrospective chart audits from three European countries. PARTICIPANTS AND SETTING: Physicians collected data from 998 patients (France, 386; Germany, 344; UK, 268) and applied the risk stratification algorithm. METHODS: The performance of the Cox regression model for predicting risk of death was assessed by Nagelkerke's R2, goodness of fit and the C-index. The risk stratification algorithm's ability to discriminate overall survival across four risk groups was evaluated using Kaplan-Meier curves and HRs. RESULTS: Consistent with the Czech registry, the stratification performance of the risk stratification algorithm demonstrated clear differentiation in risk of death between the four groups. As risk groups increased, risk of death doubled. The C-index was 0.715 (95% CI 0.690 to 0.734). CONCLUSIONS: Validation of the novel risk stratification algorithm in an independent 'real-world' dataset demonstrated that it stratifies patients in four subgroups according to survival expectation.

• MeSH
• algoritmy MeSH
• hodnocení rizik MeSH
• lidé MeSH
• mnohočetný myelom * MeSH
• retrospektivní studie MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Geografické názvy
• Evropa MeSH
• Francie MeSH
• Německo MeSH

BACKGROUND: Current strategies for risk stratification and prediction of neonatal early-onset sepsis (EOS) are inefficient and lack diagnostic performance. The aim of this study was to use machine learning to analyze the diagnostic accuracy of risk factors (RFs), clinical signs and biomarkers and to develop a prediction model for culture-proven EOS. We hypothesized that the contribution to diagnostic accuracy of biomarkers is higher than of RFs or clinical signs. STUDY DESIGN: Secondary analysis of the prospective international multicenter NeoPInS study. Neonates born after completed 34 weeks of gestation with antibiotic therapy due to suspected EOS within the first 72 hours of life participated. Primary outcome was defined as predictive performance for culture-proven EOS with variables known at the start of antibiotic therapy. Machine learning was used in form of a random forest classifier. RESULTS: One thousand six hundred eighty-five neonates treated for suspected infection were analyzed. Biomarkers were superior to clinical signs and RFs for prediction of culture-proven EOS. C-reactive protein and white blood cells were most important for the prediction of the culture result. Our full model achieved an area-under-the-receiver-operating-characteristic-curve of 83.41% (±8.8%) and an area-under-the-precision-recall-curve of 28.42% (±11.5%). The predictive performance of the model with RFs alone was comparable with random. CONCLUSIONS: Biomarkers have to be considered in algorithms for the management of neonates suspected of EOS. A 2-step approach with a screening tool for all neonates in combination with our model in the preselected population with an increased risk for EOS may have the potential to reduce the start of unnecessary antibiotics.

• MeSH
• antibakteriální látky terapeutické užití   MeSH
• biologické markery krev   MeSH
• C-reaktivní protein analýza   MeSH
• kojenec MeSH
• lidé MeSH
• novorozenec MeSH
• novorozenecká sepse diagnóza   farmakoterapie   MeSH
• prospektivní studie MeSH
• rizikové faktory MeSH
• ROC křivka MeSH
• strojové učení * MeSH
• Check Tag
• kojenec MeSH
• lidé MeSH
• mužské pohlaví MeSH
• novorozenec MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

BACKGROUND: The alert-based HeartInsight algorithm predicts risk of worsening heart failure hospitalisations (WHFHs) by evaluating temporal trends of seven physiologic parameters obtained through automatic daily remote monitoring of implantable cardioverter-defibrillators. The aim of the present study was to evaluate the predictive performance of HeartInsight in a larger and more heterogeneous meta-cohort of patients, incorporating newer device generations and including patients managed with the most recent guideline-directed medical therapy (GDMT). METHODS: The HeartInsight algorithm was retrospectively applied to data from four clinical trials in which WHFH events were adjudicated by independent external boards and remote monitoring was activated to provide relevant parameter trends. The analysis comprised 1352 patients with New York Heart Association (NYHA) class II/III, and no long-standing atrial fibrillation. RESULTS: During a median follow-up of 599 days, 110 patients (median age 68 years (IQR, 61-75), 75.7% male) had a total of 165 WHFHs. The estimated sensitivity of WHFH prediction, as determined by generalised estimating equations, was 51.5% (95% CI 43.0% to 59.9%). The false alert rate was 0.85 per patient-year, the median alerting time was 34 days (IQR, 16-78) and the specificity was 81.4% (95% CI 80.4 to 82.4%). The results were verified in the multivariable analysis with two adjusting covariates (newer/older device generation and quadruple/other GDMT) and in the univariable analysis of prespecified patient subgroups according to NYHA class, aetiology and sex, showing no significant differences. CONCLUSIONS: Study results underscore the robustness of the predictive algorithm in a heterogeneous and contemporarily managed heart failure population.

• MeSH
• algoritmy * MeSH
• časové faktory MeSH
• defibrilátory implantabilní * MeSH
• elektrická defibrilace * přístrojové vybavení   škodlivé účinky   MeSH
• hodnocení rizik MeSH
• hospitalizace * MeSH
• lidé středního věku MeSH
• lidé MeSH
• prediktivní hodnota testů MeSH
• retrospektivní studie MeSH
• rizikové faktory MeSH
• senioři MeSH
• srdeční selhání * terapie   diagnóza   patofyziologie   MeSH
• Check Tag
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• metaanalýza MeSH

Limited evidence exists about preserving neurovascular bundles during radical prostatectomy (RP) for high-risk prostate cancer (HRPCa) patients. Hence, we validated an existing algorithm predicting contralateral extraprostatic extension (cEPE) risk in unilateral high-risk cases. This algorithm aims to assist in determining the suitability of unilateral nerve-sparing RP. Among 264 patients, 48 (18%) had cEPE. The risk of cECE varied: 8%, 17.2%, and 30.8% for the low, intermediate, and high-risk groups, respectively. Despite a higher risk of cECE among individuals classified as low-risk in the development group compared to the validation group, our algorithm's superiority over always/never nerve-sparing RP was reaffirmed by decision curve analysis. Therefore, we conclude that bilateral excision may not always be justified in men with unilateral HRPCa. Instead, decisions can be based on our suggested nomogram.

• MeSH
• algoritmy * MeSH
• hodnocení rizik metody   MeSH
• léčba šetřící orgány * metody   MeSH
• lidé středního věku MeSH
• lidé MeSH
• nádory prostaty * chirurgie   patologie   MeSH
• nomogramy MeSH
• prostata chirurgie   inervace   patologie   MeSH
• prostatektomie * metody   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