METHODS AND ANALYSIS: Hip fracture Accelerated surgical TreaTment And Care tracK (HIP ATTACK) is a multicentre, international, parallel-group randomised controlled trial (RCT). Patients who suffer a hip fracture are randomly allocated to either accelerated medical assessment and surgical repair with a goal of surgery within 6 hours of diagnosis or standard care where a repair typically occurs 24 to 48 hours after diagnosis. The primary outcome of this substudy is the development of AKI within 7 days of randomisation. We anticipate at least 1998 patients will participate in this substudy.
ETHICS AND DISSEMINATION: We obtained ethics approval for additional serum creatinine recordings in consecutive patients enrolled at 70 participating centres. All patients provide consent before randomisation. We anticipate reporting substudy results by 2021.
TRIAL REGISTRATION NUMBER: NCT02027896; Pre-results.
OBJECTIVES: The objectives of this study were to evaluate whether a 1-time measurement of non-HDL-C or LDL-C in a young adult can predict cumulative exposure to these lipids during early adulthood, and to quantify the association between cumulative exposure to non-HDL-C or LDL-C during early adulthood and the risk of ASCVD after age 40 years.
METHODS: We included CARDIA (Coronary Artery Risk Development in Young Adults Study) participants who were free of cardiovascular disease before age 40 years, were not taking lipid-lowering medications, and had ≥3 measurements of LDL-C and non-HDL-C before age 40 years. First, we assessed the ability of a 1-time measurement of LDL-C or non-HDL-C obtained between age 18 and 30 years to predict the quartile of cumulative lipid exposure from ages 18 to 40 years. Second, we assessed the associations between quartiles of cumulative lipid exposure from ages 18 to 40 years with ASCVD events (fatal and nonfatal myocardial infarction and stroke) after age 40 years.
RESULTS: Of 4,104 CARDIA participants who had multiple lipid measurements before and after age 30 years, 3,995 participants met our inclusion criteria and were in the final analysis set. A 1-time measure of non-HDL-C and LDL-C had excellent discrimination for predicting membership in the top or bottom quartiles of cumulative exposure (AUC: 0.93 for the 4 models). The absolute values of non-HDL-C and LDL-C that predicted membership in the top quartiles with the highest simultaneous sensitivity and specificity (highest Youden's Index) were >135 mg/dL for non-HDL-C and >118 mg/dL for LDL-C; the values that predicted membership in the bottom quartiles were <107 mg/dL for non-HDL-C and <96 mg/dL for LDL-C. Individuals in the top quartile of non-HDL-C and LDL-C exposure had demographic-adjusted HRs of 4.6 (95% CI: 2.84-7.29) and 4.0 (95% CI: 2.50-6.33) for ASCVD events after age 40 years, respectively, when compared with each bottom quartile.
CONCLUSIONS: Single measures of non-HDL-C and LDL-C obtained between ages 18 and 30 years are highly predictive of cumulative exposure before age 40 years, which in turn strongly predicts later-life ASCVD events.
METHODS: We collected data from 7954 asymptomatic subjects (age, 50-75 y) who received screening colonoscopy examinations at 14 sites in Asia. We randomly assigned 5303 subjects to the derivation cohort and the remaining 2651 to the validation cohort. We collected data from the derivation cohort on age, sex, family history of colorectal cancer, smoking, drinking, body mass index, medical conditions, and use of nonsteroidal anti-inflammatory drugs or aspirin. Associations between the colonoscopic findings of APN and each risk factor were examined using the Pearson χ2 test, and we assigned each participant a risk score (0-15), with scores of 0 to 3 as average risk and scores of 4 or higher as high risk. The scoring system was tested in the validation cohort. We used the Cochran-Armitage test of trend to compare the prevalence of APN among subjects in each group.
RESULTS: In the validation cohort, 79.5% of patients were classified as average risk and 20.5% were classified as high risk. The prevalence of APN in the average-risk group was 1.9% and in the high-risk group was 9.4% (adjusted relative risk, 5.08; 95% CI, 3.38-7.62; P < .001). The score included age (61-70 y, 3; ≥70 y, 4), smoking habits (current/past, 2), family history of colorectal cancer (present in a first-degree relative, 2), and the presence of neoplasia in the distal colorectum (nonadvanced adenoma 5-9 mm, 2; advanced neoplasia, 7). The c-statistic of the score was 0.74 (95% CI, 0.68-0.79), and for distal findings alone was 0.67 (95% CI, 0.60-0.74). The Hosmer-Lemeshow goodness-of-fit test statistic was greater than 0.05, indicating the reliability of the validation set. The number needed to refer was 11 (95% CI, 10-13), and the number needed to screen was 15 (95% CI, 12-17).
CONCLUSIONS: We developed and validated a scoring system to identify persons at risk for APN. Screening participants who undergo flexible sigmoidoscopy screening with a score of 4 points or higher should undergo colonoscopy evaluation.
Methods: Based on discouragement and organizational control theory, this research examined the effects of organizational external factors and rules and regulations on construction risk management among 238 employees operating in construction companies in Abuja and Lagos, Nigeria. A personally administered questionnaire was used to acquire the data. The data were analyzed using partial least squares structural equation modeling.
Results: A significant positive relationship between organizational external factors and construction risk management was asserted. This study also found a significant positive relationship between rules and regulations and construction risk management. As anticipated, rules and regulations were found to moderate the relationship between organizational external factors and construction risk management, with a significant positive result. Similarly, a significant interaction effect was also found between rules and regulations and organizational external factors. Implications of the research from a Nigerian point of view have also been discussed.
Conclusion: Political, economy, and technology factors helped the construction companies to reduce the chance of risk occurrence during the construction activities. Rules and regulations also helped to lessen the rate of accidents involving construction workers as well as the duration of the projects. Similarly, the influence of the organizational external factors with rules and regulations on construction risk management has proven that most of the construction companies that implement the aforementioned factors have the chance to deliver their projects within the stipulated time, cost, and qualities, which can be used as a yardstick to measure a good project.
METHODS: In total, 299 SNPs previously associated with prostate cancer were evaluated for inclusion in a new PHS, using a LASSO-regularized Cox proportional hazards model in a training dataset of 72,181 men from the PRACTICAL Consortium. The PHS model was evaluated in four testing datasets: African ancestry, Asian ancestry, and two of European Ancestry-the Cohort of Swedish Men (COSM) and the ProtecT study. Hazard ratios (HRs) were estimated to compare men with high versus low PHS for association with clinically significant, with any, and with fatal prostate cancer. The impact of genetic risk stratification on the positive predictive value (PPV) of PSA testing for clinically significant prostate cancer was also measured.
RESULTS: The final model (PHS290) had 290 SNPs with non-zero coefficients. Comparing, for example, the highest and lowest quintiles of PHS290, the hazard ratios (HRs) for clinically significant prostate cancer were 13.73 [95% CI: 12.43-15.16] in ProtecT, 7.07 [6.58-7.60] in African ancestry, 10.31 [9.58-11.11] in Asian ancestry, and 11.18 [10.34-12.09] in COSM. Similar results were seen for association with any and fatal prostate cancer. Without PHS stratification, the PPV of PSA testing for clinically significant prostate cancer in ProtecT was 0.12 (0.11-0.14). For the top 20% and top 5% of PHS290, the PPV of PSA testing was 0.19 (0.15-0.22) and 0.26 (0.19-0.33), respectively.
CONCLUSIONS: We demonstrate better genetic risk stratification for clinically significant prostate cancer than prior versions of PHS in multi-ancestry datasets. This is promising for implementing precision-medicine approaches to prostate cancer screening decisions in diverse populations.
OBJECTIVE: To derive a single algorithm using deep learning and machine learning for the prediction and identification of factors associated with in-hospital mortality in Asian patients with ACS and to compare performance to a conventional risk score.
METHODS: The Malaysian National Cardiovascular Disease Database (NCVD) registry, is a multi-ethnic, heterogeneous database spanning from 2006-2017. It was used for in-hospital mortality model development with 54 variables considered for patients with STEMI and Non-STEMI (NSTEMI). Mortality prediction was analyzed using feature selection methods with machine learning algorithms. Deep learning algorithm using features selected from machine learning was compared to Thrombolysis in Myocardial Infarction (TIMI) score.
RESULTS: A total of 68528 patients were included in the analysis. Deep learning models constructed using all features and selected features from machine learning resulted in higher performance than machine learning and TIMI risk score (p < 0.0001 for all). The best model in this study is the combination of features selected from the SVM algorithm with a deep learning classifier. The DL (SVM selected var) algorithm demonstrated the highest predictive performance with the least number of predictors (14 predictors) for in-hospital prediction of STEMI patients (AUC = 0.96, 95% CI: 0.95-0.96). In NSTEMI in-hospital prediction, DL (RF selected var) (AUC = 0.96, 95% CI: 0.95-0.96, reported slightly higher AUC compared to DL (SVM selected var) (AUC = 0.95, 95% CI: 0.94-0.95). There was no significant difference between DL (SVM selected var) algorithm and DL (RF selected var) algorithm (p = 0.5). When compared to the DL (SVM selected var) model, the TIMI score underestimates patients' risk of mortality. TIMI risk score correctly identified 13.08% of the high-risk patient's non-survival vs 24.7% for the DL model and 4.65% vs 19.7% of the high-risk patient's non-survival for NSTEMI. Age, heart rate, Killip class, cardiac catheterization, oral hypoglycemia use and antiarrhythmic agent were found to be common predictors of in-hospital mortality across all ML feature selection models in this study. The final algorithm was converted into an online tool with a database for continuous data archiving for prospective validation.
CONCLUSIONS: ACS patients were better classified using a combination of machine learning and deep learning in a multi-ethnic Asian population when compared to TIMI scoring. Machine learning enables the identification of distinct factors in individual Asian populations to improve mortality prediction. Continuous testing and validation will allow for better risk stratification in the future, potentially altering management and outcomes.
METHODS: A systematic literature search guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement was performed using the EBSCOHost® platform, ScienceDirect, Scopus and Google Scholar between July and August 2021. Studies from January 2010 to January 2021 were eligible for review. Nine articles were eligible and included in this systematic review. The risk of bias assessment used the National Institutes of Health quality assessment tool for observational cohort and cross-sectional studies. The WHO-ICF helped to guide the categorization of fall risk factors.
RESULTS: Seven screening tools adequately predicted fall risk among community-dwelling older adults. Six screening tools covered most of the components of the WHO-ICF, and three screening tools omitted the environmental factors. The modified 18-item Stay Independent Brochure demonstrated most of the predictive values in predicting fall risk. All tools are brief and easy to use in community or outpatient settings.
CONCLUSION: The review explores the literature evaluating fall risk screening tools for nurses and other healthcare providers to assess fall risk among independent community-dwelling older adults. A fall risk screening tool consisting of risk factors alone might be able to predict fall risk. However, further refinements and validations of the tools before use are recommended.