Statin drugs are highly effective in lowering blood concentrations of LDL-cholesterol, with concomitant reduction in risk of major cardiovascular events. Although statins are generally regarded as ...safe and well-tolerated, some users develop muscle symptoms that are mostly mild but in rare cases can lead to life-threatening rhabdomyolysis. The SEARCH genome-wide association study, which has been independently replicated, found a significant association between the rs4149056 (c.521T>C) single-nucleotide polymorphism (SNP) in the SLCO1B1 gene, and myopathy in individuals taking 80 mg simvastatin per day, with an odds ratio of 4.5 per rs4149056 C allele. The purpose of this paper is to assemble evidence relating to the analytical validity, clinical validity and clinical utility of using SLCO1B1 rs4149056 genotyping to inform choice and dose of statin treatment, with the aim of minimising statin-induced myopathy and increasing adherence to therapy. Genotyping assays for the rs4149056 SNP appear to be robust and accurate, though direct evidence for the performance of array-based platforms in genotyping individual SNPs was not found. Using data from the SEARCH study, calculated values for the clinical sensitivity, specificity, positive- and negative-predictive values of a test for the C allele to predict definite or incipient myopathy during 5 years of 80 mg/day simvastatin use were 70.4%, 73.7%, 4.1% and 99.4% respectively. There is a need for studies comparing the clinical validity of SLCO1B1 rs4149056 genotyping with risk scores for myopathy based on other factors such as racial background, statin type and dose, gender, body mass index, co-medications and co-morbidities. No direct evidence was found for clinical utility of statin prescription guided by SLCO1B1 genotype.
Machine learning has shown utility in detecting patterns within large, unstructured, and complex datasets. One of the promising applications of machine learning is in precision medicine, where ...disease risk is predicted using patient genetic data. However, creating an accurate prediction model based on genotype data remains challenging due to the so-called “curse of dimensionality” (i.e., extensively larger number of features compared to the number of samples). Therefore, the generalizability of machine learning models benefits from feature selection, which aims to extract only the most “informative” features and remove noisy “non-informative,” irrelevant and redundant features. In this article, we provide a general overview of the different feature selection methods, their advantages, disadvantages, and use cases, focusing on the detection of relevant features (i.e., SNPs) for disease risk prediction.
Risk prediction tools for colorectal cancer (CRC) have potential to improve the efficiency of population-based screening by facilitating risk-adapted strategies. However, such an applicable tool has ...yet to be established in the Chinese population. In this study, a risk score was created using data from the China Kadoorie Biobank (CKB), a nationwide cohort study of 409,854 eligible participants. Diagnostic performance of the risk score was evaluated in an independent CRC screening programme, which included 91,575 participants who accepted colonoscopy at designed hospitals in Zhejiang Province, China. Over a median follow-up of 11.1 years, 3136 CRC cases were documented in the CKB. A risk score was created based on nine questionnaire-derived variables, showing moderate discrimination for 10-year CRC risk (C-statistic = 0.68, 95 % CI: 0.67–0.69). In the CRC screening programme, the detection rates of CRC were 0.25 %, 0.82 %, and 1.93 % in low-risk (score <6), intermediate-risk (score: 6–19), and high-risk (score >19) groups, respectively. The newly developed score exhibited a C-statistic of 0.65 (95 % CI: 0.63–0.66), surpassing the widely adopted tools such as the Asia-Pacific Colorectal Screening (APCS), modified APCS, and Korean Colorectal Screening scores (all C-statistics = 0.60). In conclusion, we developed a novel risk prediction tool that is useful to identify individuals at high risk of CRC. A user-friendly online calculator was also constructed to encourage broader adoption of the tool.
•We developed a new risk score that effectively stratified individuals based on their CRC risk.•The novel risk score outperformed commonly used models in identifying individuals at risk for CRC.•We made the tool easily accessible online, allowing the general population to actively engage in CRC prevention.
This study sought to evaluate whether frailty improves mortality prediction in combination with the conventional scores.
European System for Cardiac Operative Risk Evaluation (EuroSCORE) or Society ...of Thoracic Surgeons (STS) score have not been evaluated in combined models with frailty for mortality prediction after transcatheter aortic valve replacement (TAVR).
This prospective cohort comprised 330 consecutive TAVR patients ≥70 years of age. Conventional scores and a frailty index (based on assessment of cognition, mobility, nutrition, and activities of daily living) were evaluated to predict 1-year all-cause mortality using Cox proportional hazards regression (providing hazard ratios HRs with confidence intervals CIs) and measures of test performance (providing likelihood ratio LR chi-square test statistic and C-statistic CS).
All risk scores were predictive of the outcome (EuroSCORE, HR: 1.90 95% CI: 1.45 to 2.48, LR chi-square test statistic 19.29, C-statistic 0.67; STS score, HR: 1.51 95% CI: 1.21 to 1.88, LR chi-square test statistic 11.05, C-statistic 0.64; frailty index, HR: 3.29 95% CI: 1.98 to 5.47, LR chi-square test statistic 22.28, C-statistic 0.66). A combination of the frailty index with either EuroSCORE (LR chi-square test statistic 38.27, C-statistic 0.72) or STS score (LR chi-square test statistic 28.71, C-statistic 0.68) improved mortality prediction. The frailty index accounted for 58.2% and 77.6% of the predictive information in the combined model with EuroSCORE and STS score, respectively. Net reclassification improvement and integrated discrimination improvement confirmed that the added frailty index improved risk prediction.
This is the first study showing that the assessment of frailty significantly enhances prediction of 1-year mortality after TAVR in combined risk models with conventional risk scores and relevantly contributes to this improvement.
Display omitted
Acute kidney injury (AKI) is a serious postoperative complication associated with increased morbidity and mortality. Identifying patients at risk for AKI is important for risk stratification and ...management. This study aimed to develop an AKI risk prediction model for colectomy and determine if the operative approach (laparoscopic versus open) alters the influence of predictive factors through an interaction term analysis.
The American College of Surgeons National Surgical Quality Improvement Program database was analyzed from 2005 to 2019. Patients undergoing laparoscopic and open colectomy were identified and propensity score matched. Multivariable logistic regression identified significant preoperative demographic, comorbidity, and laboratory value predictors of AKI. The predictive ability of a baseline model consisting of these variables was compared to a proposed model incorporating interaction terms between operative approach and predictor variables using the likelihood ratio test, c-statistic, and Brier score. Shapley Additive Explanations values assessed relative importance of significant predictors.
252,372 patients were included in the analysis. Significant AKI predictors were hypertension, age, sex, race, body mass index, smoking, diabetes, preoperative sepsis, Congestive heart failure, preoperative creatinine, preoperative albumin, and operative approach (P < 0.001). The proposed model with interaction terms had improved predictive ability per the likelihood ratio test (P < 0.05) but had no statistically significant interaction terms. C-statistic and Brier scores did not improve. Shapley Additive Explanations analysis showed hypertension had the highest importance. The importance of age and diabetes showed some variation between operative approaches.
While the inclusion of interaction terms collectively improved AKI prediction, no individual operative approach interaction terms were significant. Including operative approach interactions may enhance predictive ability of AKI risk models for colectomy.
EXABS-171-MDS Update on CHIP and CCUS Weeks, Lachelle D.
Clinical lymphoma, myeloma and leukemia,
September 2023, 2023-09-00, Volume:
23
Journal Article
Risk prediction following ST-Elevation Myocardial Infarction (STEMI) in resource limited countries is critical to identify patients at an increased risk of mortality who might benefit from intensive ...management.
North India ST-Elevation Myocardial Infarction (NORIN-STEMI) is an ongoing registry that has prospectively enrolled 3,635 STEMI patients. Of these, 3191 patients with first STEMI were included. Patients were divided into two groups: development (n=2668) and validation (unseen) dataset (n=523). Various ML strategies were used to train and tune the model based on validation dataset results that included 31 clinical characteristics. These models were compared in sensitivity, specificity, F1-score, receiver operating characteristic area under the curve (AUC), and overall accuracy to predict mortality at 30 days. ML model decision making was analyzed using the Shapley Additive exPlanations (ShAP) summary plot.
At 30 days, the mortality was 7.7%. On the validation dataset, Extra Tree ML model had the best predictive ability with sensitivity: 85%, AUC: 79.7%, and Accuracy: 75%. ShAP interpretable summary plot determined delay in time to revascularization, baseline cardiogenic shock, left ventricular ejection fraction <30%, age, serum creatinine, heart failure on presentation, female sex, and moderate-severe mitral regurgitation to be major predictors of all-cause mortality at 30 days (P<0.001 for all).
ML models lead to an improved mortality prediction following STEMI. ShAP summary plot for the interpretability of the AI model helps to understand the model’s decision in identifying high-risk individuals who may benefit from intensified follow-up and close monitoring.
•Risk stratification is an integral component of ST-Elevation Myocardial Infarction (STEMI) care.•Limitations of logistic regression-based risk scores include statistical assumption of linear relationship between variables•Scores not well validated in South-East Asian population with diverse ethnic group and different and risk factor profile.•Machine Learning (ML) model overcomes all the limitations of the traditional logistic regression based models.•ML model helps in identifying high-risk individuals requiring intensified treatment regime and a proper follow-up.
PDF
