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
• Algorithms * MeSH
• Adult MeSH
• Cardiometabolic Risk Factors * MeSH
• Humans MeSH
• Metabolic Syndrome diagnosis   MeSH
• Adolescent MeSH
• Young Adult MeSH
• Psychotic Disorders * diagnosis   MeSH
• Reproducibility of Results MeSH
• Check Tag
• Adult MeSH
• Humans MeSH
• Adolescent MeSH
• Young Adult MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Validation Study 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
• Alleles MeSH
• Algorithms * MeSH
• Child MeSH
• Adult MeSH
• Genotype MeSH
• Risk Assessment MeSH
• Polymorphism, Single Nucleotide genetics   MeSH
• Cohort Studies MeSH
• Middle Aged MeSH
• Humans MeSH
• Quantitative Trait Loci MeSH
• Adolescent MeSH
• Odds Ratio MeSH
• Predictive Value of Tests MeSH
• Child, Preschool MeSH
• Risk Factors MeSH
• Multiple Sclerosis * epidemiology   genetics   MeSH
• Aged MeSH
• Environment MeSH
• Check Tag
• Child MeSH
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Adolescent MeSH
• Male MeSH
• Child, Preschool MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Research Support, Non-U.S. Gov't MeSH
• Research Support, N.I.H., Extramural 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
• Algorithms MeSH
• Atrial Fibrillation epidemiology   complications   MeSH
• Risk Assessment statistics & numerical data   MeSH
• Humans MeSH
• Risk Factors MeSH
• Ischemic Attack, Transient epidemiology   etiology   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Research Support, Non-U.S. Gov't 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
• Algorithms MeSH
• Risk Assessment MeSH
• Humans MeSH
• Multiple Myeloma * MeSH
• Retrospective Studies MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Geographicals
• Europe MeSH
• France MeSH
• Germany MeSH

DNA-based tests for assessment of genetic predisposition to coronary heart disease need to provide information over and above that of conventional risk factors. The efficacy of selected 'candidate' gene loci in risk algorithms, to improve the predictive accuracy for coronary heart disease, remains to be demonstrated. RECENT FINDINGS: Although many candidate genes for coronary heart disease have been tested, the optimal set of risk genotypes has yet to be identified. There is only a relatively modest risk to be expected in association with any single genotype, published estimates are in the range of 1.12-1.73. Thus the risk associated with any one genotype is modest, but, in combination, selected genotypes may be associated with a clinically significant risk. Since the allele frequency for many of these variants is high, many individuals will carry several 'risk alleles'. A small number of selected single nucleotide polymorphisms should complement the conventional risk factors to identify high-risk individuals in whom correction of 'modifiable risk factors' through lifestyle interventions or medication would be most beneficial. SUMMARY: As our understanding of how genetic variation impacts on common diseases advances, the novel loci identified by genome-wide association scans associated with disease risk will rapidly improve these risk algorithms.

• MeSH
• Alleles MeSH
• Algorithms MeSH
• Polymorphism, Single Nucleotide MeSH
• Cardiovascular Diseases genetics   MeSH
• Humans MeSH
• Risk Factors MeSH
• Check Tag
• Humans MeSH

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
• Child MeSH
• Risk Assessment methods   MeSH
• Cardiomyopathy, Hypertrophic complications   mortality   MeSH
• Incidence MeSH
• Humans MeSH
• Survival Rate trends   MeSH
• Adolescent MeSH
• Death, Sudden, Cardiac epidemiology   etiology   MeSH
• Follow-Up Studies MeSH
• Prognosis MeSH
• Retrospective Studies MeSH
• Risk Factors MeSH
• Check Tag
• Child MeSH
• Humans MeSH
• Adolescent MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Multicenter Study MeSH
• Research Support, Non-U.S. Gov't MeSH
• Geographicals
• Europe 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
• Biomarkers blood   MeSH
• Adult MeSH
• Cardiovascular Diseases blood   prevention & control   MeSH
• Middle Aged MeSH
• Humans MeSH
• Prognosis MeSH
• Chitinase-3-Like Protein 1 * blood   MeSH
• Heart Disease Risk Factors * MeSH
• Aged MeSH
• Statistics as Topic MeSH
• Health Status Indicators MeSH
• Age Factors MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Clinical Study MeSH

Úvod: Bolesti na hrudi patří mezi nejčastější důvody pro akutní vyšetření. Pacienti ve vysokém věku se často prezentují atypickými symptomy a nejednoznačnými laboratorními a elektrokardiografickými nálezy, což znesnadňuje rychlou diagnostiku. K efektivní rizikové stratifikaci byly vyvinuty diagnostické algoritmy, které mohou dále nasměrovat další vyšetřovací a léčebný postup.

Introduction: Chest pain is one of the leading causes for visit at the emergency department. Atypical symptoms, ambiguous laboratory and electrocardiographical findings in elderly make the rapid diagnostics difficult. Diagnostic algorithms were developed for effective risk stratification and they can direct us towards the right diagnosis and correct treatment. Objective: The aim of this study is to evaluate the effectiveness of T-MACS algorithm in very old patients presenting with acute chest pain. Methods: Retrospective analysis of 104 patients older than 80 years which were examined at emergency department for acute chest pain. Primary composite endpoint was combination of acute myocardial infarction, percutaneous coronary intervention (PCI) and all-cause death in 30 day and 12 months follow-up. Results: Mean age of study population is 84.9 years. Risk stratification according to T-MACS model: very low risk 1 %, low risk 24 %, intermediate risk 69.2 % and high risk 5.8 % patients. In 30 days follow-up, the incidence of primary composite endpoint (MACE) was 26.9 %, acute myocardial infarction 26 %, PCI 7.7% and all-cause mortality was 1.9 %. Estimated risk of major adverse cardiac events in 30 days was 28 % (average T-MACS score). T-MACS < 2 % has 100 % sensitivity and 100 % negative predictive value for absence of MACE, T-MACS > 95 % has 98.7 % specificity and 83.3 % positive predictive value for occurrence of MACE respectively. Patients with MACE had significantly different T-MACS score (p value < 0.01) compared to patients without MACE, difference in levels of hs-TnT was not statistically significance (p value > 0.05). Conclusion: We found good correlation between estimated and real incidence of selected cardiac events in our population. For the prediction of MACE the single value of hs-TnT is not good enough, more convenient is to use combination of more parameters. T-MACS has very high sensitivity and negative predictive value for absence of MACE and can be used in real world practice even in population of very old patients.

• Keywords
• T-MACS,
• MeSH
• Algorithms * MeSH
• Chest Pain diagnosis   etiology   MeSH
• Cardiovascular Diseases * diagnosis   epidemiology   MeSH
• Humans MeSH
• Retrospective Studies MeSH
• Risk MeSH
• Heart Disease Risk Factors MeSH
• Aged, 80 and over MeSH
• Statistics as Topic MeSH
• Check Tag
• Humans MeSH
• Aged, 80 and over MeSH
• Publication type
• Research Support, Non-U.S. Gov't 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
• Algorithms * MeSH
• Biomarkers blood   MeSH
• Time Factors MeSH
• Risk Assessment MeSH
• Glomerular Filtration Rate * MeSH
• Non-ST Elevated Myocardial Infarction blood   diagnosis   epidemiology   MeSH
• Creatinine blood   MeSH
• Kidney physiopathology   MeSH
• Middle Aged MeSH
• Humans MeSH
• Decision Support Techniques * MeSH
• Kidney Diseases blood   diagnosis   epidemiology   physiopathology   MeSH
• Predictive Value of Tests MeSH
• Prevalence MeSH
• Prognosis MeSH
• Prospective Studies MeSH
• Reproducibility of Results MeSH
• Risk Factors MeSH
• Aged, 80 and over MeSH
• Aged MeSH
• Triage * MeSH
• Troponin blood   MeSH
• Up-Regulation MeSH
• Check Tag
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Aged, 80 and over MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Multicenter Study MeSH
• Research Support, Non-U.S. Gov't MeSH
• Geographicals
• Europe MeSH

Tzv. syndrom „hvízdavého dítěte“, typický pro kojenecký a batolecí věk, se nejčastěji objevuje při akutním virovém respiračním onemocnění. U některých dětí se však může jednat i o první projev bronchiálního astmatu. Dosud se nepodařilo objasnit hlediska, podle kterých by bylo možné určit, zda se u „hvízdavého“ dítěte jedná o budoucí asthma bronchiale. Pro stanovení diagnózy bronchiální astmatu zůstávají stále nejdůležitější kritéria klinická.

„Wheezy baby syndrom“ is a typical, although poorly defined, disease entity occurring frequently mainly during acute viral respiratory illnesses in infants. Some wheezy children will develop true bronchial asthma while others will „grow out“ of the problem. There are still no clearly defined risk factors reliably predicting the development of bronchial asthma in these children. The clinical evaluation and close follow-up still remain the most important factors for diagnosing asthma.

• MeSH
• Algorithms MeSH
• Asthma diagnosis   etiology   immunology   MeSH
• Bronchitis complications   MeSH
• Cytokines physiology   blood   MeSH
• Child MeSH
• Dyspnea MeSH
• Infant MeSH
• Humans MeSH
• Infant, Newborn MeSH
• Respiratory Sounds MeSH
• Risk MeSH
• Check Tag
• Child MeSH
• Infant MeSH
• Humans MeSH
• Infant, Newborn MeSH
• Publication type
• Review MeSH