Risk Score Methods Research Articles

Identifying genetic factors that contribute to the increased risk of congenital heart defects in infants with Down syndrome

Atrioventricular septal defects (AVSD) are a severe congenital heart defect present in individuals with Down syndrome (DS) at a > 2000-fold increased prevalence compared to the general population. This study aimed to identify risk-associated genes and pathways and to examine a potential polygenic contribution to AVSD in DS. We analyzed a total cohort of 702 individuals with DS with or without AVSD, with genomic data from whole exome sequencing, whole genome sequencing, and/or array-based imputation. We utilized sequence kernel association testing and polygenic risk score (PRS) methods to examine rare and common variants. Our findings suggest that the Notch pathway, particularly NOTCH4, as well as genes involved in the ciliome including CEP290 may play a role in AVSD in DS. These pathways have also been implicated in DS-associated AVSD in prior studies. A polygenic component for AVSD in DS has not been examined previously. Using weights based on the largest genome-wide association study of congenital heart defects available (2594 cases and 5159 controls; all general population samples), we found PRS to be associated with AVSD with odds ratios ranging from 1.2 to 1.3 per standard deviation increase in PRS and corresponding liability r2 values of approximately 1%, suggesting at least a small polygenic contribution to DS-associated AVSD. Future studies with larger sample sizes will improve identification and quantification of genetic contributions to AVSD in DS.

Influence of electrolyte balance on the prognosis of long-term cardiovascular events after acute coronary syndrome

Aim. To assess the effect of electrolyte changes on the prognosis of long-term cardiovascular events after acute coronary syndrome (ACS).Material and methods. The study included 105 patients with ACS who underwent coronary angiography (CA) with coronary stenting. At the study inclusion (before CA with coronary stenting), we collected data on traditional risk factors, analyzed levels of urinary sodium and potassium, kaliuresis and natriuresis. Free water clearance (FWC) and electrolyte free water clearance (EFWC), as well as fluid balance using bioelectrical impedance analysis were determined. Study endpoints (fatal and nonfatal cardiovascular events) were determined 6,2±0,2 months after CA with coronary stenting.Results. It was found that a decrease in urinary sodium (χ2=5,64, p=0,02, Constanta B0 =-0,62, Estimate =-16,5) and natriuresis (χ2=4,1, р=0,044, Constanta B0 =-1,38, Estimate =-5,2) increase the death risk. Urinary sodium of 0,2 mol/L and natriuresis of 0,5 mol are threshold levels of increased risk of death. Urinary potassium decrease was associated with an increase in death risk (threshold level — 0,5 mol/L, χ2=4,99, р=0,025, Constanta B0 =-0,63, Estimate =-70,4) and acute myocardial infarction (threshold level — 0,06 mol/L, χ2=3,93, р=0,04, Constanta B0 =-0,99, Estimate =-58,0) in the long-term period. Increase in EFWC increased the likelihood of long-term transient ischemic attack after ACS (χ2=4,61, р=0,03, Constanta B0 =-2,95, Estimate =-1,0). There were no significant relationships in the matter of FWC (p>0,05). However, with a decrease in intracellular fluid volume compared to normal values and a decrease in FWC or an increase in EFWC, the likelihood of longterm composite endpoints after ACS increases.Conclusion. As a result of the study, risk markers for long-term fatal and non-fatal cardiovascular events after ACS were established: decrease in urinary sodium <0,2 mol/l and potassium <0,5 mol/l; decrease in FWC and increase in EFWC with or without cellular dehydration. The established markers can complement the current cardiovascular risk score methods in patients with ACS.

Selection of priority pesticides in Japanese drinking water quality regulation: Validity, limitations, and evolution of a risk prediction method

Several risk scoring and ranking methods have been applied for the prioritization of micropollutants, including pesticides, and in the selection of pesticides to be regulated regionally and nationally. However, the effectiveness of these methods has not been evaluated in Japan. We developed a risk prediction method to select pesticides that have a high probability of being detected in drinking water sources where no monitoring data is available. The risk prediction method was used to select new pesticides for the 2013 Primary List in the Japanese Drinking Water Quality Guidelines. Here, we examined the effectiveness of the method on the basis of the results of water quality examinations conducted by water supply authorities across Japan, and studied ways to improve the risk prediction method. Of the 120 pesticides in the 2013 Primary List, 80 were detected in drinking water sources (raw water entering water treatment plants). The rates of detection of the newly selected pesticides and previously listed pesticides were not significantly different: 64% and 68%, respectively. When the risk predictor was revised to incorporate degradability of dry-field pesticides and current pesticide sales data, the rate of detection of pesticides selected as having a high risk of detection improved from 72% to 88%. We prepared regional versions of the Primary List using the revised risk predictors and verified their utility. The number of listed pesticides varied greatly by region, ranging from 32 to 73; all regional lists were much shorter than the national Primary List. In addition, 55% to 100% of the pesticides detected in each region were included in a Regional Primary List. This work verifies the ability of the risk prediction method to screen pesticides and select those with a high risk of detection.

Identification of miRNA-mRNA Regulatory Network and Construction of Prognostic Signature in Cervical Cancer

Cervical cancer (CC) remains a most prevalent female cancer worldwide, but there are few biomarkers used in diagnosis and prognosis of CC. The aim of this study is to find reliable and effective biomarkers regarding CC development. Microarray datasets were downloaded from the Gene Expression Omnibus (GEO) database to search potential miRNA-mRNA in CC. The gene ontology term enrichment and Kyoto encyclopedia of genes and genomes (KEGG) pathway analyses were conducted to reveal the underlying functions and pathways of differently expressed genes (DEGs). Univariate Cox, multivariate Cox, and risk scoring methods were performed to identify a prognostic model. A total of 209 DEGs of CC were identified. In the protein-protein interaction network, hub module, and hub genes were recognized. Based on DEGs, three small molecules (thioguanosine, apigenin, and trichostatin A) were screened out as potential drugs. Two miRNAs (hsa-mir-101-3p and hsa-mir-6507-5p) and some transcription factors were found to be associated with prognosis of CC. A five-candidate gene signature (APOBEC3B, DSG2, CXCL8, ABCA8, and PLAGL1) was constructed to stratify risk subgroups for patients with CC. The risk score of the prognostic model was also found to be associated with immune cells infiltration, including mast cell activation, natural killer cells resting, dendritic cells resting, T cells regulatory (Tregs), and T cells follicular helper. The miRNA-mRNA regulatory network and the prognostic model are of great clinical significance in promoting prognosis prediction and treatment of CC.

Cannabis use, depression and self-harm: phenotypic and genetic relationships.

The use of cannabis has previously been linked to both depression and self-harm; however, the role of genetics in this relationship is unclear. This study aimed to estimate the phenotypic and genetic associations between cannabis use and depression and self-harm. Cross-sectional data collected through UK Biobank were used to test the phenotypic association between cannabis use, depression and self-harm. UK Biobank genetic data were then combined with consortia genome-wide association study summary statistics to further test the genetic relationships between these traits using LD score regression, polygenic risk scoring and Mendelian randomization methods. United Kingdom, with additional international consortia data. A total of 126 291 British adults aged between 40 and 70years, recruited into UK Biobank. Phenotypic outcomes were life-time history of cannabis use (including initial and continued cannabis use), depression (including single-episode and recurrent depression) and self-harm. Genome-wide genetic data were used and assessment centre, batch and the first six principal components were included as key covariates when handling genetic data. In UK Biobank, cannabis use is associated with an increased likelihood of depression [odds ratio (OR)=1.64, 95% confidence interval (CI)=1.59-1.70] and self-harm (OR=2.85, 95% CI=2.69-3.01). The strength of this phenotypic association is stronger when more severe trait definitions of cannabis use and depression are considered. Using consortia genome-wide summary statistics, significant genetic correlations are seen between cannabis use and depression [rg=0.289, standard error (SE)=0.036]. Polygenic risk scores for cannabis use and depression explain a small but significant proportion of variance in cannabis use, depression and self-harm within a UK Biobank target sample. However, two-sample Mendelian randomization analyses were not significant. Cannabis use appeared to be both phenotypically and genetically associated with depression and self-harm. Limitations in statistical power mean that conclusions could not be made on the direction of causality between these traits.

Broad‐ and narrow‐sense validity performance of three polygenic risk score methods for prostate cancer risk assessment

Several polygenic risk score (PRS) methods are available for measuring the cumulative effect of multiple risk-associated single nucleotide polymorphisms (SNPs). Their performance in predicting risk at the individual level has not been well studied. We compared the performance of three PRS methods for prostate cancer risk assessment in a clinical trial cohort, including genetic risk score (GRS), pruning and thresholding (P + T), and linkage disequilibrium prediction (LDpred). Performance was evaluated for score deciles (broad-sense validity) and score values (narrow-sense validity). A training process was required to identify the best P + T model (397 SNPs) and LDpred model (3 011 362 SNPs). In contrast, GRS was directly calculated based on 110 established risk-associated SNPs. For broad-sense validity in the testing population, higher deciles were significantly associated with higher observed risk; Ptrend was 7.40 × 10-11 , 7.64 × 10-13 , and 7.51 × 10-10 for GRS, P + T, and LDpred, respectively. For narrow-sense validity, the calibration slope (1 is best) was 1.03, 0.77, and 0.87, and mean bias score (0 is best) was 0.09, 0.21, and 0.10 for GRS, P + T, and LDpred, respectively. The performance of GRS was better than P + T and LDpred. Fewer and well-established SNPs of GRS also make it more feasible and interpretable for genetic testing at the individual level.

Confounder adjustment in observational comparative effectiveness researches: (1) statistical adjustment approaches for measured confounder

Observational comparative effectiveness studies have been widely conducted to provide evidence on additional effectiveness and to support randomized controlled findings in research. Although this type of study becomes more important over time, challenges related to the common biases which stemmed from confounders, are difficult to control. This manuscript summarizes some statistical methods used on adjusting measured confounders that often noticed in research, regarding the observational comparative effectiveness. Useful traditional methods would include stratified analysis, paired analysis, covariate model and multivariable model, etc.. Unconventional adjustment approaches such as propensity score and disease risk score methods may also be used in studies, for matching, stratification and adjustment. A good study design should be able to control confounders. The limitations of all the post hoc statistical adjustment methods should also be fully understood before being appropriately applied in practical events.

Scoring colorectal cancer risk with an artificial neural network based on self-reportable personal health data.

Colorectal cancer (CRC) is third in prevalence and mortality among all cancers in the US. Currently, the United States Preventative Services Task Force (USPSTF) recommends anyone ages 50–75 and/or with a family history to be screened for CRC. To improve screening specificity and sensitivity, we have built an artificial neural network (ANN) trained on 12 to 14 categories of personal health data from the National Health Interview Survey (NHIS). Years 1997–2016 of the NHIS contain 583,770 respondents who had never received a diagnosis of any cancer and 1409 who had received a diagnosis of CRC within 4 years of taking the survey. The trained ANN has sensitivity of 0.57 ± 0.03, specificity of 0.89 ± 0.02, positive predictive value of 0.0075 ± 0.0003, negative predictive value of 0.999 ± 0.001, and concordance of 0.80 ± 0.05 per the guidelines of Transparent Reporting of Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) level 2a, comparable to current risk-scoring methods. To demonstrate clinical applicability, both USPSTF guidelines and the trained ANN are used to stratify respondents to the 2017 NHIS into low-, medium- and high-risk categories (TRIPOD levels 4 and 2b, respectively). The number of CRC respondents misclassified as low risk is decreased from 35% by screening guidelines to 5% by ANN (in 60 cases). The number of non-CRC respondents misclassified as high risk is decreased from 53% by screening guidelines to 6% by ANN (in 25,457 cases). Our results demonstrate a robustly-tested method of stratifying CRC risk that is non-invasive, cost-effective, and easy to implement publicly.

Prediction model for the efficacy of folic acid therapy on hyperhomocysteinaemia based on genetic risk score methods.

No risk assessment tools for the efficacy of folic acid treatment for hyperhomocysteinaemia (HHcy) have been developed. We aimed to use two common genetic risk score (GRS) methods to construct prediction models for the efficacy of folic acid therapy on HHcy, and the best gene-environment prediction model was screened out. A prospective cohort study enrolling 638 HHcy patients was performed. We used a logistic regression model to estimate the associations of two GRS methods with the efficacy. Performances were compared using area under the receiver operating characteristic curve (AUC). The simple count genetic risk score (SC-GRS) and weighted genetic risk score (wGRS) were found to be independently associated with the efficacy of folic acid treatment for HHcy. Using the SC-GRS, per risk allele increased with a 1·46-fold increased failure risk (P < 0·001) after adjustment for traditional risk factors, including age, sex, BMI, smoking, alcohol consumption, history of diabetes, history of hypertension, history of hyperlipidaemia, history of stroke and history of CHD. When used the wGRS, the association was strengthened (OR = 2·08, P < 0·001). Addition of the SC-GRS and wGRS to the traditional risk model significantly improved the predictive ability by AUC (0·859). A precise gene-environment predictive model with good performance was developed for predicting the treatment failure rate of folic acid therapy for HHcy.

The Relationship Between Polygenic Risk Scores and Cognition in Schizophrenia.

BackgroundCognitive impairment is a clinically important feature of schizophrenia. Polygenic risk score (PRS) methods have demonstrated genetic overlap between schizophrenia, bipolar disorder (BD), major depressive disorder (MDD), educational attainment (EA), and IQ, but very few studies have examined associations between these PRS and cognitive phenotypes within schizophrenia cases.MethodsWe combined genetic and cognitive data in 3034 schizophrenia cases from 11 samples using the general intelligence factor g as the primary measure of cognition. We used linear regression to examine the association between cognition and PRS for EA, IQ, schizophrenia, BD, and MDD. The results were then meta-analyzed across all samples. A genome-wide association studies (GWAS) of cognition was conducted in schizophrenia cases. ResultsPRS for both population IQ (P = 4.39 × 10–28) and EA (P = 1.27 × 10–26) were positively correlated with cognition in those with schizophrenia. In contrast, there was no association between cognition in schizophrenia cases and PRS for schizophrenia (P = .39), BD (P = .51), or MDD (P = .49). No individual variant approached genome-wide significance in the GWAS. ConclusionsCognition in schizophrenia cases is more strongly associated with PRS that index cognitive traits in the general population than PRS for neuropsychiatric disorders. This suggests the mechanisms of cognitive variation within schizophrenia are at least partly independent from those that predispose to schizophrenia diagnosis itself. Our findings indicate that this cognitive variation arises at least in part due to genetic factors shared with cognitive performance in populations and is not solely due to illness or treatment-related factors, although our findings are consistent with important contributions from these factors.

The Use of Fuzzy Logic Elements for the Risk Analysis in Aviation

Abstract The paper presents the possibility of using fuzzy logic in aviation provided in an example of estimating the risk of a glider pilot. The results of expert questionnaires were used to define the input data concerning categories of loss and the probability of undesirable events occurrence. A model of inference and fuzzy sets concerning input and output data were defined. The risk analysis was performed in accordance with the standard fuzzy regulator scheme. Moreover, the results obtained were verified with the use of Risk Matrix and Risk Score methods.

Treatment-Resistant Schizophrenia: Insights From Genetic Studies and Machine Learning Approaches.

Schizophrenia (SCZ) is a severe psychiatric disorder affecting approximately 23 million people worldwide. It is considered the eighth leading cause of disability according to the World Health Organization and is associated with a significant reduction in life expectancy. Antipsychotics represent the first-choice treatment in SCZ, but approximately 30% of patients fail to respond to acute treatment. These patients are generally defined as treatment-resistant and are eligible for clozapine treatment. Treatment-resistant patients show a more severe course of the disease, but it has been suggested that treatment-resistant schizophrenia (TRS) may constitute a distinct phenotype that is more than just a more severe form of SCZ. TRS is heritable, and genetics has been shown to play an important role in modulating response to antipsychotics. Important efforts have been put into place in order to better understand the genetic architecture of TRS, with the main goal of identifying reliable predictive markers that might improve the management and quality of life of TRS patients. However, the number of candidate gene and genome-wide association studies specifically focused on TRS is limited, and to date, findings do not allow the disentanglement of its polygenic nature. More recent studies implemented polygenic risk score, gene-based and machine learning methods to explore the genetics of TRS, reporting promising findings. In this review, we present an overview on the genetics of TRS, particularly focusing our discussion on studies implementing polygenic approaches.

Association between polygenic risk for tobacco or alcohol consumption and liability to licit and illicit substance use in young Australian adults

BackgroundCo-morbid substance use is very common. Despite a historical focus using genetic epidemiology to investigate comorbid substance use and misuse, few studies have examined substance-substance associations using polygenic risk score (PRS) methods. MethodsUsing summary statistics from the largest substance use GWAS to date (258,797- 632,802 subjects), GWAS and Sequencing Consortium of Alcohol and Nicotine use (GSCAN), we constructed PRSs for smoking initiation (PRS-SI), age of initiation of regular smoking (PRS-AI), cigarettes per day (PRS-CPD), smoking cessation (PRS-SC), and drinks per week (PRS-DPW). We then estimated the fixed effect of individual PRSs on 22 lifetime substance use and substance use disorder phenotypes collected in an independent sample of 2463 young Australian adults using genetic restricted maximal likelihood (GREML) in Genome-wide Complex Trait Analysis (GCTA), separately in females, males and both sexes together. ResultsAfter accounting for multiple testing, PRS-SI significantly explained variation in the risk of cocaine (0.67%), amphetamine (1.54%), hallucinogens (0.72%), ecstasy (1.66%) and cannabis initiation (0.97%), as well as DSM-5 alcohol use disorder (0.72%). PRS-DPW explained 0.75%, 0.59% and 0.90% of the variation of cocaine, amphetamine and ecstasy initiation respectively. None of the 22 phenotypes including emergent classes of substance use were significantly predicted by PRS-AI, PRS-CPD, and PRS-SC. ConclusionsTo our knowledge, this is the first study to report significant genetic overlap between the polygenic risks for smoking initiation and alcohol consumption and the risk of initiating major classes of illicit substances. PRSs constructed from large discovery GWASs allows the detection of novel genetic associations.

A novel model for predicting non-responsiveness to intravenous immunoglobulins in Kawasaki disease: The Singapore experience.

We aimed to assess the utility of four published risk-scoring methods in predicting intravenous immunoglobulins (IVIG) non-responsiveness in Kawasaki disease (KD) patients from Singapore and develop a new predictive model. We reviewed the medical records of 215 KD children. The performance of existing scoring methods in identifying non-responsive cases based on sensitivities (SN) and specificities (SP) was evaluated in 122 Singaporean Chinese. From our dataset, a model involving six predictors was built. The following respective SN (%) and SP (%) were obtained: Egami: 26%, 68%; Kobayashi: 21%, 62%; Sano: 13%, 86% and Fukunishi: 46%, 71%. These results indicated that the existing scoring methods performed poorly compared to those reported in their respective original publications, which ranged between 68 and 87%. The new predictive model was derived with an improved SN (80%) and SP (80%). Currently available risk-scoring methods have less applicability in the Singaporean Chinese population. The proposed new risk-scoring predictive model derived based on data from Chinese cohort demonstrated much better SN and SP.

Risk scores to guide referral decisions for people with suspected ovarian cancer in secondary care: a systematic review and cost-effectiveness analysis.

Ovarian cancer is the sixth most common cancer in UK women and can be difficult to diagnose, particularly in the early stages. Risk-scoring can help to guide referral to specialist centres. To assess the clinical and cost-effectiveness of risk scores to guide referral decisions for women with suspected ovarian cancer in secondary care. Twenty-one databases, including MEDLINE and EMBASE, were searched from inception to November 2016. Review methods followed published guidelines. The meta-analysis using weighted averages and random-effects modelling was used to estimate summary sensitivity and specificity with 95% confidence intervals (CIs). The cost-effectiveness analysis considered the long-term costs and quality-adjusted life-years (QALYs) associated with different risk-scoring methods, and subsequent care pathways. Modelling comprised a decision tree and a Markov model. The decision tree was used to model short-term outcomes and the Markov model was used to estimate the long-term costs and QALYs associated with treatment and progression. Fifty-one diagnostic cohort studies were included in the systematic review. The Risk of Ovarian Malignancy Algorithm (ROMA) score did not offer any advantage over the Risk of Malignancy Index 1 (RMI 1). Patients with borderline tumours or non-ovarian primaries appeared to account for disproportionately high numbers of false-negative, low-risk ROMA scores. (Confidential information has been removed.) To achieve similar levels of sensitivity to the Assessment of Different NEoplasias in the adneXa (ADNEX) model and the International Ovarian Tumour Analysis (IOTA) group's simple ultrasound rules, a very low RMI 1 decision threshold (25) would be needed; the summary sensitivity and specificity estimates for the RMI 1 at this threshold were 94.9% (95% CI 91.5% to 97.2%) and 51.1% (95% CI 47.0% to 55.2%), respectively. In the base-case analysis, RMI 1 (threshold of 250) was the least effective [16.926 life-years (LYs), 13.820 QALYs] and the second cheapest (£5669). The IOTA group's simple ultrasound rules (inconclusive, assumed to be malignant) were the cheapest (£5667) and the second most effective [16.954 LYs, 13.841 QALYs], dominating RMI 1. The ADNEX model (threshold of 10%), costing £5699, was the most effective (16.957 LYs, 13.843 QALYs), and compared with the IOTA group's simple ultrasound rules, resulted in an incremental cost-effectiveness ratio of £15,304 per QALY gained. At thresholds of up to £15,304 per QALY gained, the IOTA group's simple ultrasound rules are cost-effective; the ADNEX model (threshold of 10%) is cost-effective for higher thresholds. Information on the downstream clinical consequences of risk-scoring was limited. Both the ADNEX model and the IOTA group's simple ultrasound rules may offer increased sensitivity relative to current practice (RMI 1); that is, more women with malignant tumours would be referred to a specialist multidisciplinary team, although more women with benign tumours would also be referred. The cost-effectiveness model supports prioritisation of sensitivity over specificity. Further research is needed on the clinical consequences of risk-scoring. This study is registered as PROSPERO CRD42016053326. The National Institute for Health Research Health Technology Assessment programme.

Japanese scoring systems to predict resistance to intravenous immunoglobulin in Kawasaki disease were unreliable for Caucasian Israeli children.

This study assessed the validity of using established Japanese risk scoring methods to predict intravenous immunoglobulin (IVIG) resistance to Kawasaki disease in Israeli children. We reviewed the medical records of 282 patients (70% male) with Kawasaki disease from six Israeli medical centres between 2004 and 2013. Their mean age was 2.5 years. The risk scores were calculated using the Kobayashi, Sano and Egami scoring methods and analysed to determine whether a higher risk score predicted IVIG resistance in this population. Factors that predicted a lack of response to the initial IVIG dose were identified. We found that 18% did not respond to the first IVIG dose. The three scoring methods were unable to reliably predict IVIG resistance, with sensitivities of 23%-32% and specificities of 67%-87%. Calculating a predictive score that was specific for this population was also unsuccessful. The factors that predicted a lacked of response to the first IVIG dose included low albumin, elevated total bilirubin and ethnicity. The established risk scoring methods created for Japanese populations with Kawasaki disease were not suitable for predicting IVIG resistance in Caucasian Israeli children, and we were unable to create a specific scoring method that was able to do this.

Personalized survival predictions via Trees of Predictors: An application to cardiac transplantation.

BackgroundRisk prediction is crucial in many areas of medical practice, such as cardiac transplantation, but existing clinical risk-scoring methods have suboptimal performance. We develop a novel risk prediction algorithm and test its performance on the database of all patients who were registered for cardiac transplantation in the United States during 1985-2015.Methods and findingsWe develop a new, interpretable, methodology (ToPs: Trees of Predictors) built on the principle that specific predictive (survival) models should be used for specific clusters within the patient population. ToPs discovers these specific clusters and the specific predictive model that performs best for each cluster. In comparison with existing clinical risk scoring methods and state-of-the-art machine learning methods, our method provides significant improvements in survival predictions, both post- and pre-cardiac transplantation. For instance: in terms of 3-month survival post-transplantation, our method achieves AUC of 0.660; the best clinical risk scoring method (RSS) achieves 0.587. In terms of 3-year survival/mortality predictions post-transplantation (in comparison to RSS), holding specificity at 80.0%, our algorithm correctly predicts survival for 2,442 (14.0%) more patients (of 17,441 who actually survived); holding sensitivity at 80.0%, our algorithm correctly predicts mortality for 694 (13.0%) more patients (of 5,339 who did not survive). ToPs achieves similar improvements for other time horizons and for predictions pre-transplantation. ToPs discovers the most relevant features (covariates), uses available features to best advantage, and can adapt to changes in clinical practice.ConclusionsWe show that, in comparison with existing clinical risk-scoring methods and other machine learning methods, ToPs significantly improves survival predictions both post- and pre-cardiac transplantation. ToPs provides a more accurate, personalized approach to survival prediction that can benefit patients, clinicians, and policymakers in making clinical decisions and setting clinical policy. Because survival prediction is widely used in clinical decision-making across diseases and clinical specialties, the implications of our methods are far-reaching.

Adjusting for Confounding in Early Postlaunch Settings

Postlaunch data on medical treatments can be analyzed to explore adverse events or relative effectiveness in real-life settings. These analyses are often complicated by the number of potential confounders and the possibility of model misspecification. We conducted a simulation study to compare the performance of logistic regression, propensity score, disease risk score, and stabilized inverse probability weighting methods to adjust for confounding. Model misspecification was induced in the independent derivation dataset. We evaluated performance using relative bias confidence interval coverage of the true effect, among other metrics. At low events per coefficient (1.0 and 0.5), the logistic regression estimates had a large relative bias (greater than -100%). Bias of the disease risk score estimates was at most 13.48% and 18.83%. For the propensity score model, this was 8.74% and >100%, respectively. At events per coefficient of 1.0 and 0.5, inverse probability weighting frequently failed or reduced to a crude regression, resulting in biases of -8.49% and 24.55%. Coverage of logistic regression estimates became less than the nominal level at events per coefficient ≤5. For the disease risk score, inverse probability weighting, and propensity score, coverage became less than nominal at events per coefficient ≤2.5, ≤1.0, and ≤1.0, respectively. Bias of misspecified disease risk score models was 16.55%. In settings with low events/exposed subjects per coefficient, disease risk score methods can be useful alternatives to logistic regression models, especially when propensity score models cannot be used. Despite better performance of disease risk score methods than logistic regression and propensity score models in small events per coefficient settings, bias, and coverage still deviated from nominal.

Comparative characteristics of diabetes risk scores

The worldwide prevalence of diabetes among adults (aged 20?79 years) was 8.35% in 2013, and this is expected to increase by 55% (592 million adults) by 2035. To avoid the increase in the prevalence of diabetes, primary prevention and early diagnosis of prediabetes are required. It is important to identify individuals at a high risk of hyperglycaemia using inexpensive and available methods. At present, risk score is an alternative to identify the risk of developing diabetes. There are approximately 10 types of risk scores in the world, and further research for the development and adaptation of risk scores for various populations are being conducted. The use of risk score methods for prediction allows the setting of the level of total risk, identification of high-risk patients and prescription of necessary preventive measures. Actual validation of existing diabetes risk score for the Russian population is being conducted. Assessment of the risk of diabetes is simple, fast, inexpensive, non-invasive and reliable.

From the Editors

From the Editors