MATERIALS AND METHOD: 180 SNPs, shown to be previously associated with prostate cancer, were used to develop a PHS model in men with European ancestry. A machine-learning approach, LASSO-regularized Cox regression, was used to select SNPs and to estimate their coefficients in the training set (75,596 men). Performance of the resulting model was evaluated in the testing/validation set (6,411 men) with two metrics: (1) hazard ratios (HRs) and (2) positive predictive value (PPV) of prostate-specific antigen (PSA) testing. HRs were estimated between individuals with PHS in the top 5% to those in the middle 40% (HR95/50), top 20% to bottom 20% (HR80/20), and bottom 20% to middle 40% (HR20/50). PPV was calculated for the top 20% (PPV80) and top 5% (PPV95) of PHS as the fraction of individuals with elevated PSA that were diagnosed with clinically significant prostate cancer on biopsy.
RESULTS: 166 SNPs had non-zero coefficients in the Cox model (PHS166). All HR metrics showed significant improvements for PHS166 compared to PHS46: HR95/50 increased from 3.72 to 5.09, HR80/20 increased from 6.12 to 9.45, and HR20/50 decreased from 0.41 to 0.34. By contrast, no significant differences were observed in PPV of PSA testing for clinically significant prostate cancer.
CONCLUSIONS: Incorporating 120 additional SNPs (PHS166 vs PHS46) significantly improved HRs for prostate cancer, while PPV of PSA testing remained the same.
METHODS: A prospective pre- and post-intervention study was conducted among medical inpatients in a Malaysian secondary care hospital. DVT and bleeding risks were stratified using validated Padua Risk Assessment Model (RAM) and International Medical Prevention Registry on Venous Thromboembolism (IMPROVE) Bleeding Risk Assessment Model. Pharmacist-driven DRAT was developed and implemented post-interventional phase. DVT prophylaxis use was determined and its appropriateness was compared between pre and post study using multivariate logistic regression with IBM SPSS software version 21.0.
RESULTS: Overall, 286 patients (n=142 pre-intervention versus n=144 post-intervention) were conveniently recruited. The prevalence of DVT prophylaxis use was 10.8%. Appropriate thromboprophylaxis prescribing increased from 64.8% to 68.1% post-DRAT implementation. Of note, among high DVT risk patients, DRAT intervention was observed to be a significant predictor of appropriate thromboprophylaxis use (14.3% versus 31.3%; adjusted odds ratio=2.80; 95% CI 1.01 to 7.80; p<0.05).
CONCLUSION: The appropriateness of DVT prophylaxis use was suboptimal but doubled after implementation of DRAT intervention. Thus, an integrated risk stratification checklist is an effective approach for the improvement of rational DVT prophylaxis use.
PATIENTS AND METHODS: A total of 7476 patients with routine health check-up data who underwent prostate biopsies from January 2008 to December 2021 in eight referral centres in Asia were screened. After data pre-processing and cleaning, 5037 patients and 117 features were analyzed. Seven AI-based algorithms were tested for feature selection and seven AI-based algorithms were tested for classification, with the best combination applied for model construction. The APAC score was established in the CH cohort and validated in a multi-centre cohort and in each validation cohort to evaluate its generalizability in different Asian regions. The performance of the models was evaluated using area under the receiver operating characteristic curve (ROC), calibration plot, and decision curve analyses.
RESULTS: Eighteen features were involved in the APCA score predicting HGPCa, with some of these markers not previously used in prostate cancer diagnosis. The area under the curve (AUC) was 0.76 (95% CI:0.74-0.78) in the multi-centre validation cohort and the increment of AUC (APCA vs. PSA) was 0.16 (95% CI:0.13-0.20). The calibration plots yielded a high degree of coherence and the decision curve analysis yielded a higher net clinical benefit. Applying the APCA score could reduce unnecessary biopsies by 20.2% and 38.4%, at the risk of missing 5.0% and 10.0% of HGPCa cases in the multi-centre validation cohort, respectively.
CONCLUSIONS: The APCA score based on routine health check-ups could reduce unnecessary prostate biopsies without additional examinations in Asian populations. Further prospective population-based studies are warranted to confirm these results.
METHODS AND ANALYSIS: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Protocols statement was used as a template for this protocol. A systematic search of Medline, Embase and Global Health from database inception to present will be conducted to identify prospective studies reporting on the associations between major measures of body composition (body mass index, waist circumference, waist-hip ratio, total body fat, visceral adiposity tissue and lean mass) and risk of heart failure. Article screening and selection will be performed by two reviewers independently, and disagreements will be adjudicated by consensus or by a third reviewer. Data from eligible articles will be extracted, and article quality will be assessed using the Newcastle-Ottawa Scale. Relative risks (and 95% CIs) will be pooled in a fixed effect meta-analysis, if there is no prohibitive heterogeneity of studies as assessed using the Cochrane Q statistic and I2 statistic. Subgroup analyses will be by age, sex, ethnicity and heart failure subtypes. Publication bias in the meta-analysis will be assessed using Egger's test and funnel plots.
ETHICS AND DISSEMINATION: This work is secondary analyses on published data and ethical approval is not required. We plan to publish results in an open-access peer-reviewed journal, present it at international and national conferences, and share the findings on social media.
PROSPERO REGISTRATION NUMBER: CRD42020224584.
OBJECTIVE: To investigate the effect of hand position and lower limb length measurement method on LQ-YBT scores and their interpretation.
DESIGN: Cross-sectional study.
SETTING: National Sports Institute of Malaysia.
PATIENTS OR OTHER PARTICIPANTS: A total of 46 volunteers, consisting of 23 men (age = 25.7 ± 4.6 years, height = 1.70 ± 0.05 m, mass = 69.3 ± 9.2 kg) and 23 women (age = 23.5 ± 2.5 years, height = 1.59 ± 0.07 m, mass = 55.7 ± 10.6 kg).
INTERVENTION(S): Participants performed the LQ-YBT with hands on hips and hands free to move on both lower limbs.
MAIN OUTCOME MEASURE(S): In a single-legged stance, participants reached with the contralateral limb in each of the anterior, posteromedial, and posterolateral directions 3 times. Maximal reach distances in each direction were normalized to lower limb length measured from the anterior-superior iliac spine to the lateral and medial malleoli. Composite scores (average of the 3 normalized reach distances) and anterior-reach differences (in raw units) were extracted and used to identify participants at risk for injury (ie, anterior-reach difference ≥4 cm or composite score ≤94%). Data were analyzed using paired t tests, Fisher exact tests, and magnitude-based inferences (effect size [ES], ±90% confidence limits [CLs]).
RESULTS: Differences between hand positions in normalized anterior-reach distances were trivial (t91 = -2.075, P = .041; ES = 0.12, 90% CL = ±0.10). In contrast, reach distances were greater when the hands moved freely for the normalized posteromedial (t91 = -6.404, P < .001; ES = 0.42, 90% CL = ±0.11), posterolateral (t91 = -6.052, P < .001; ES = 0.58, 90% CL = ±0.16), and composite (t91 = -7.296, P < .001; ES = 0.47, 90% CL = ±0.11) scores. A similar proportion of the cohort was classified as at risk with the hands on the hips (35% [n = 16]) and the hands free to move (43% [n = 20]; P = .52). However, the participants classified as at risk with the hands on the hips were not all categorized as at risk with the hands free to move and vice versa. The lower limb length measurement method exerted trivial effects on LQ-YBT outcomes.
CONCLUSIONS: Hand position exerted nontrivial effects on LQ-YBT outcomes and interpretation, whereas the lower limb length measurement method had trivial effects.