Psychiatrie posledních let se stává medicínským oborem založeným na důkazech. Stále se nám však nedostává spolehlivých možností predikce, které by nám pomohly předpovídat riziko vzniku, průběh a výsledky léčby u závažných duševních nemocí, jako je schizofrenie Stávající modely predikce v psychiatrii lze rozdělit na modely tautologické, heuristické, irelevantní a logické. Logické modely predikce schizofrenie se soustřeďují na studie genetické, zobrazovacích metod (morfologické a funkční) a neuroendokrinní testy. Genetické studie hledají zejména příčiny onemocnění a farmakogenetické prediktory odpovědi na léčbu. Jedním z kandidátských genů je gen pro COMT s dobře známým polymorfizmem, popsaná je také geneticky podmíněná terapeutická odpověď na clozapin. Strukturální a funkční abnormity jsou obvykle spojovány s horším průběhem a nepříznivou odpovědí na léčbu. Neuroendokrinní studie sledují přímo či nepřímo pomocí stimulačních testů funkční stav neurotransmiterových systémů. Příkladem jsou prezentované výsledky d-fenfluraminového testu, který měří vztah mezi aktivitou serotoninergního systému a zlepšením po léčbě antipsychotiky u akutně nemocných se schizofrenií. Uvedené výsledky několika konkrétních studií jsou příkladem úspěšného modelu logické predikce v psychiatrii. Ukazuje se, že na teoretickém podkladě lze postavit hypotézu a následně ji testovat v experimentálních podmínkách. Aplikací zobrazovacích metod, genetických analýz a neuroendokrinních testů v klinické praxi můžeme individualizovat diagnostický a terapeutický přístup k pacientům.

Recently, psychiatry has become an evidence-based field of medicine. However, there is a lack of reliable predictions of risk of onset, course, and outcome for major psychiatric disorders, e.g. schizophrenia. Available prediction models in psychiatry are tautological, heuristic, logical, and irrelevant. Logical models of prediction in schizophrenia are based on genetic, neuroimaging (structural and functional), and neuroendocrine studies. Genetic research searches the causes of illness and pharmacogenetic predictors of treatment response. There are numerous studies of candidate gene encoding COMT polymorphism, or pharmacogenetics of response to clozapine. Neuroanatomical and functional abnormalities are generally associated with unfavorable course, outcome and treatment failure. Neuroendocrine studies measure directly or indirectly, using challenge tests, functional state of neurotransmitter systems. Results of d-fenfluramine challenge test investigating relationship between 5-HT system reactivity and response to antipsychotic treatment in young acute schizophrenia patients are presented. Examples of genetic, neuroimaging, and neuroendocrine studies represent models of successful logical prediction in psychiatry based on the solid theoretical background. Implementation of neuroimaging methods, genetic analyses, and neuroendocrine tests into the clinical practice, may help to individualize diagnostics and treatment of schizophrenia.

• MeSH
• diagnostické zobrazování MeSH
• finanční podpora výzkumu jako téma MeSH
• lidé MeSH
• neuroendokrinologie metody   MeSH
• předpověď metody   MeSH
• přehledová literatura jako téma MeSH
• schizofrenie genetika   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• hodnotící studie MeSH
• srovnávací studie MeSH

PURPOSE: Magnetic resonance imaging (MRI) is a promising tool for risk assessment, potentially reducing the burden of unnecessary prostate biopsies. Risk prediction models that incorporate MRI data have gained attention, but their external validation and comparison are essential for guiding clinical practice. The aim is to externally validate and compare risk prediction models for the diagnosis of clinically significant prostate cancer (csPCa). METHODS: A cohort of 4606 patients across fifteen European tertiary referral centers were identified from a prospective maintained database between January 2016 and April 2023. Transrectal or transperineal image-fusion MRI-targeted and systematic biopsies for PI-RADS score of ≥ 3 or ≥ 2 depending on patient characteristics and physician preferences. Probabilities for csPCa, defined as International Society of Urological Pathology (ISUP) grade ≥ 2, were calculated for each patients using eight models. Performance was characterized by area under the receiver operating characteristic curve (AUC), calibration, and net benefit. Subgroup analyses were performed across various clinically relevant subgroups. RESULTS: Overall, csPCa was detected in 2154 (47%) patients. The models exhibited satisfactory performance, demonstrating good discrimination (AUC ranging from 0.75 to 0.78, p < 0.001), adequate calibration, and high net benefit. The model described by Alberts showed the highest clinical utility for threshold probabilities between 10 and 20%. Subgroup analyses highlighted variations in models' performance, particularly when stratified according to PSA level, biopsy technique and PI-RADS version. CONCLUSIONS: We report a comprehensive external validation of risk prediction models for csPCa diagnosis in patients who underwent MRI-targeted and systematic biopsies. The model by Alberts demonstrated superior clinical utility and should be favored when determining the need for a prostate biopsy.

• MeSH
• hodnocení rizik metody   MeSH
• lidé středního věku MeSH
• lidé MeSH
• magnetická rezonanční tomografie * metody   MeSH
• nádory prostaty * patologie   diagnostické zobrazování   MeSH
• prediktivní hodnota testů MeSH
• prostata * patologie   diagnostické zobrazování   MeSH
• senioři MeSH
• ultrazvukem navigovaná biopsie metody   MeSH
• Check Tag
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři MeSH
• Publikační typ
• časopisecké články MeSH
• multicentrická studie MeSH
• srovnávací studie MeSH
• validační studie MeSH

OBJECTIVES: To develop and validate strategies, using new ultrasound-based mathematical models, for the prediction of high-risk endometrial cancer and compare them with strategies using previously developed models or the use of preoperative grading only. METHODS: Women with endometrial cancer were prospectively examined using two-dimensional (2D) and three-dimensional (3D) gray-scale and color Doppler ultrasound imaging. More than 25 ultrasound, demographic and histological variables were analyzed. Two logistic regression models were developed: one 'objective' model using mainly objective variables; and one 'subjective' model including subjective variables (i.e. subjective impression of myometrial and cervical invasion, preoperative grade and demographic variables). The following strategies were validated: a one-step strategy using only preoperative grading and two-step strategies using preoperative grading as the first step and one of the new models, subjective assessment or previously developed models as a second step. RESULTS: One-hundred and twenty-five patients were included in the development set and 211 were included in the validation set. The 'objective' model retained preoperative grade and minimal tumor-free myometrium as variables. The 'subjective' model retained preoperative grade and subjective assessment of myometrial invasion. On external validation, the performance of the new models was similar to that on the development set. Sensitivity for the two-step strategy with the 'objective' model was 78% (95% CI, 69-84%) at a cut-off of 0.50, 82% (95% CI, 74-88%) for the strategy with the 'subjective' model and 83% (95% CI, 75-88%) for that with subjective assessment. Specificity was 68% (95% CI, 58-77%), 72% (95% CI, 62-80%) and 71% (95% CI, 61-79%) respectively. The two-step strategies detected up to twice as many high-risk cases as preoperative grading only. The new models had a significantly higher sensitivity than did previously developed models, at the same specificity. CONCLUSION: Two-step strategies with 'new' ultrasound-based models predict high-risk endometrial cancers with good accuracy and do this better than do previously developed models.Copyright © 2013 ISUOG. Published by John Wiley & Sons Ltd.

• MeSH
• časná detekce nádoru MeSH
• cervix uteri * diagnostické zobrazování   patologie   MeSH
• dospělí MeSH
• hodnocení rizik MeSH
• invazivní růst nádoru MeSH
• lidé středního věku MeSH
• lidé MeSH
• myometrium * diagnostické zobrazování   patologie   MeSH
• nádory endometria * diagnostické zobrazování   patologie   MeSH
• prospektivní studie MeSH
• reprodukovatelnost výsledků MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• senzitivita a specificita MeSH
• staging nádorů MeSH
• teoretické modely MeSH
• ultrasonografie dopplerovská barevná * MeSH
• ultrazvuk * trendy   MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• ženské pohlaví MeSH

Polygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk stratification. Joint estimation of Single Nucleotide Polymorphism (SNP) effects in models could improve predictive performance over standard approaches of PRS construction. Here, we implemented computationally efficient, penalized, logistic regression models (lasso, elastic net, stepwise) to individual level genotype data and a Bayesian framework with continuous shrinkage, "select and shrink for summary statistics" (S4), to summary level data for epithelial non-mucinous ovarian cancer risk prediction. We developed the models in a dataset consisting of 23,564 non-mucinous EOC cases and 40,138 controls participating in the Ovarian Cancer Association Consortium (OCAC) and validated the best models in three populations of different ancestries: prospective data from 198,101 women of European ancestries; 7,669 women of East Asian ancestries; 1,072 women of African ancestries, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestries. In the external validation data, the model with the strongest association for non-mucinous EOC risk derived from the OCAC model development data was the S4 model (27,240 SNPs) with odds ratios (OR) of 1.38 (95% CI: 1.28-1.48, AUC: 0.588) per unit standard deviation, in women of European ancestries; 1.14 (95% CI: 1.08-1.19, AUC: 0.538) in women of East Asian ancestries; 1.38 (95% CI: 1.21-1.58, AUC: 0.593) in women of African ancestries; hazard ratios of 1.36 (95% CI: 1.29-1.43, AUC: 0.592) in BRCA1 pathogenic variant carriers and 1.49 (95% CI: 1.35-1.64, AUC: 0.624) in BRCA2 pathogenic variant carriers. Incorporation of the S4 PRS in risk prediction models for ovarian cancer may have clinical utility in ovarian cancer prevention programs.

• MeSH
• Bayesova věta MeSH
• epiteliální ovariální karcinom genetika   MeSH
• genetická predispozice k nemoci MeSH
• jednonukleotidový polymorfismus MeSH
• lidé MeSH
• nádory prsu * MeSH
• nádory vaječníků * epidemiologie   genetika   MeSH
• prospektivní studie MeSH
• rizikové faktory MeSH
• Check Tag
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

AIMS: Cardiovascular disease (CVD) risk prediction models are used in Western European countries, but less so in Eastern European countries where rates of CVD can be two to four times higher. We recalibrated the SCORE prediction model for three Eastern European countries and evaluated the impact of adding seven behavioural and psychosocial risk factors to the model. METHODS AND RESULTS: We developed and validated models using data from the prospective HAPIEE cohort study with 14 598 participants from Russia, Poland, and the Czech Republic (derivation cohort, median follow-up 7.2 years, 338 fatal CVD cases) and Estonian Biobank data with 4632 participants (validation cohort, median follow-up 8.3 years, 91 fatal CVD cases). The first model (recalibrated SCORE) used the same risk factors as in the SCORE model. The second model (HAPIEE SCORE) added education, employment, marital status, depression, body mass index, physical inactivity, and antihypertensive use. Discrimination of the original SCORE model (C-statistic 0.78 in the derivation and 0.83 in the validation cohorts) was improved in recalibrated SCORE (0.82 and 0.85) and HAPIEE SCORE (0.84 and 0.87) models. After dichotomizing risk at the clinically meaningful threshold of 5%, and when comparing the final HAPIEE SCORE model against the original SCORE model, the net reclassification improvement was 0.07 [95% confidence interval (CI) 0.02-0.11] in the derivation cohort and 0.14 (95% CI 0.04-0.25) in the validation cohort. CONCLUSION: Our recalibrated SCORE may be more appropriate than the conventional SCORE for some Eastern European populations. The addition of seven quick, non-invasive, and cheap predictors further improved prediction accuracy.

• MeSH
• hodnocení rizik MeSH
• kardiovaskulární nemoci * epidemiologie   MeSH
• kohortové studie MeSH
• lidé MeSH
• prospektivní studie MeSH
• rizikové faktory kardiovaskulárních chorob MeSH
• rizikové faktory MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Research Support, N.I.H., Extramural MeSH
• Geografické názvy
• Česká republika MeSH
• Polsko MeSH
• Rusko MeSH

AIMS: To develop and validate a clinically useful risk prediction tool for patients with adult congenital heart disease (ACHD). METHODS AND RESULTS: A risk model was developed in a prospective cohort of 602 patients with moderate/complex ACHD who routinely visited the outpatient clinic of a tertiary care centre in the Netherlands (2011-2013). This model was externally validated in a retrospective cohort of 402 ACHD patients (Czech Republic, 2004-2013). The primary endpoint was the 4-year risk of death, heart failure, or arrhythmia, which occurred in 135 of 602 patients (22%). Model development was performed using multivariable logistic regression. Model performance was assessed with C-statistics and calibration plots. Of the 14 variables that were selected by an expert panel, the final prediction model included age (OR 1.02, 95%CI 1.00-1.03, p = 0.031), congenital diagnosis (OR 1.52, 95%CI 1.03-2.23, p = 0.034), NYHA class (OR 1.74, 95%CI 1.07-2.84, p = 0.026), cardiac medication (OR 2.27, 95%CI 1.56-3.31, p < 0.001), re-intervention (OR 1.41, 95%CI 0.99-2.01, p = 0.060), BMI (OR 1.03, 95%CI 0.99-1.07, p = 0.123), and NT-proBNP (OR 1.63, 95%CI 1.45-1.84, p < 0.001). Calibration-in-the-large was suboptimal, reflected by a lower observed event rate in the validation cohort (17%) than predicted (36%), likely explained by heterogeneity and different treatment strategies. The externally validated C-statistic was 0.78 (95%CI 0.72-0.83), indicating good discriminative ability. CONCLUSION: The proposed ACHD risk score combines six readily available clinical characteristics and NT-proBNP. This tool is easy to use and can aid in distinguishing high- and low-risk patients, which could further streamline counselling, location of care, and treatment in ACHD.

• MeSH
• dospělí MeSH
• hodnocení rizik metody   normy   MeSH
• kohortové studie MeSH
• lidé MeSH
• následné studie MeSH
• prediktivní hodnota testů MeSH
• prospektivní studie MeSH
• reprodukovatelnost výsledků MeSH
• teoretické modely * MeSH
• vrozené srdeční vady diagnostické zobrazování   epidemiologie   MeSH
• Check Tag
• dospělí MeSH
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• Geografické názvy
• Nizozemsko 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

Although genetic variation at chromosome 9p21.3 is associated with incident cardiovascular disease, it is unclear whether screening for this polymorphism improves risk prediction. OBJECTIVE: To determine whether knowledge of variation at chromosome 9p21.3 provides predictive information beyond that from other readily available risk factors. DESIGN: Prospective cohort study. SETTING: United States. PATIENTS: 22 129 female white health professionals participating in the Women's Genome Health Study, initially without any major chronic disease, who were prospectively followed over a median of 10.2 years for incident cardiovascular disease. MEASUREMENTS: Polymorphism at rs10757274 in chromosome 9p21.3 and additional cardiovascular disease risk factors (blood pressure, smoking status, diabetes, blood levels of cholesterol, high-sensitivity C-reactive protein, and family history of premature myocardial infarction). RESULTS: Polymorphism at rs10757274 was associated with an adjusted hazard ratio for incident cardiovascular disease of 1.25 (95% CI, 1.04 to 1.51) for the AG genotype and 1.32 (CI, 1.07 to 1.63) for the GG genotype. However, the addition of the genotype to a prediction model based on traditional risk factors, high-sensitivity C-reactive protein, and family history of premature myocardial infarction had no effect on model discrimination as measured by the c-index (0.807 to 0.809) and did not improve the Net Reclassification Improvement score (-0.2%; P = 0.59) or the Integrated Discrimination Improvement score (0.0; P = 0.18). Limitation: Study participants were all white women. CONCLUSION: In this large prospective cohort of white women, genetic variation in chromosome 9p21.3 was associated with incident cardiovascular disease but did not improve on the discrimination or classification of predicted risk achieved with traditional risk factors, high-sensitivity C-reactive protein, and family history of premature myocardial infarction.

• MeSH
• financování organizované MeSH
• genotyp MeSH
• jednonukleotidový polymorfismus MeSH
• Kaplanův-Meierův odhad MeSH
• kardiovaskulární nemoci epidemiologie   genetika   MeSH
• lidé středního věku MeSH
• lidé MeSH
• lidské chromozomy, pár 9 genetika   MeSH
• následné studie MeSH
• pravděpodobnost MeSH
• proporcionální rizikové modely MeSH
• prospektivní studie MeSH
• rizikové faktory MeSH
• Check Tag
• lidé středního věku MeSH
• lidé MeSH
• ženské pohlaví MeSH

Environmental fate and exposure models are a powerful means to integrate information on chemicals, their partitioning and degradation behaviour, the environmental scenario and the emissions in order to compile a picture of chemical distribution and fluxes in the multimedia environment. A 1995 pioneering book, resulting from a series of workshops among model developers and users, reported the main advantages and identified needs for research in the field of multimedia fate models. Considerable efforts were devoted to their improvement in the past 25 years and many aspects were refined; notably the inclusion of nanomaterials among the modelled substances, the development of models at different spatial and temporal scales, the estimation of chemical properties and emission data, the incorporation of additional environmental media and processes, the integration of sensitivity and uncertainty analysis in the simulations. However, some challenging issues remain and require research efforts and attention: the need of methods to estimate partition coefficients for polar and ionizable chemical in the environment, a better description of bioavailability in different environments as well as the requirement of injecting more ecological realism in exposure predictions to account for the diversity of ecosystem structures and functions in risk assessment. Finally, to transfer new scientific developments into the realm of regulatory risk assessment, we propose the formation of expert groups that compare, discuss and recommend model modifications and updates and help develop practical tools for risk assessment.

• MeSH
• hodnocení rizik MeSH
• látky znečišťující životní prostředí analýza   MeSH
• monitorování životního prostředí metody   MeSH
• nejistota MeSH
• teoretické modely * MeSH
• vystavení vlivu životního prostředí analýza   MeSH
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH

PURPOSE: Determining the frequency and distribution of pathogenic germline variants (PGVs) in Austrian prostate cancer (PCa) patients and to assess the accuracy of different clinical risk scores to correctly predict PGVs. METHODS: This cross-sectional study included 313 men with advanced PCa. A comprehensive personal and family history was obtained based on predefined questionnaires. Germline DNA sequencing was performed between 2019 and 2021 irrespective of family history, metastatic or castration status or age at diagnosis. Clinical risk scores for hereditary cancer syndromes were evaluated and a PCa-specific score was developed to assess the presence of PGVs. RESULTS: PGV presence was associated with metastasis (p = 0.047) and castration resistance (p = 0.011), but not with personal cancer history or with relatives with any type of cancer. Clinical risk scores (Manchester score, PREMM5 score, Amsterdam II criteria or Johns Hopkins criteria) showed low sensitivities (3.3-20%) for assessing the probability of PGV presence. A score specifically designed for PCa patients stratifying patients into low- or high-risk regarding PGV probability, correctly classified all PGV carriers as high-risk, whereas a third of PCa patients without PGVs was classified as low risk of the presence of PGVs. CONCLUSION: Application of common clinical risk scores based on family history are not suitable to identify PCa patients with high PGV probabilities. A PCa-specific score stratified PCa patients into low- or high-risk of PGV presence with sufficient accuracy, and germline DNA sequencing may be omitted in patients with a low score. Further studies are needed to evaluate the score.

• MeSH
• genetická predispozice k nemoci MeSH
• lidé MeSH
• nádory prostaty * genetika   patologie   MeSH
• průřezové studie MeSH
• rizikové faktory MeSH
• zárodečné buňky patologie   MeSH
• Check Tag
• lidé MeSH
• mužské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• Geografické názvy
• Rakousko MeSH