BACKGROUND: Low-density lipoprotein cholesterol (LDL-C) and lipoprotein(a) (Lp[a]) levels are independently associated with atherosclerotic cardiovascular disease (ASCVD). However, the relationship between Lp(a) level, LDL-C level, and ASCVD risk at different thresholds is not well defined. METHODS: A participant-level meta-analysis of 27 658 participants enrolled in 6 placebo-controlled statin trials was performed to assess the association of LDL-C and Lp(a) levels with risk of fatal or nonfatal coronary heart disease events, stroke, or any coronary or carotid revascularization (ASCVD). The multivariable-adjusted association between baseline Lp(a) level and ASCVD risk was modeled continuously using generalized additive models, and the association between baseline LDL-C level and ASCVD risk by baseline Lp(a) level by Cox proportional hazards models with random effects. The joint association between Lp(a) level and statin-achieved LDL-C level with ASCVD risk was evaluated using Cox proportional hazards models. RESULTS: Compared with an Lp(a) level of 5 mg/dL, increasing levels of Lp(a) were log-linearly associated with ASCVD risk in statin- and placebo-treated patients. Among statin-treated individuals, those with Lp(a) level >50 mg/dL (≈125 nmol/L) had increased risk across all quartiles of achieved LDL-C level and absolute change in LDL-C level. Even among those with the lowest quartile of achieved LDL-C level (3.1–77.0 mg/dL), those with Lp(a) level >50 mg/dL had greater ASCVD risk (hazard ratio, 1.38 [95% CI, 1.06–1.79]) than those with Lp(a) level ≤50 mg/dL. The greatest risk was observed with both Lp(a) level >50 mg/dL and LDL-C level in the fourth quartile (hazard ratio, 1.90 [95% CI, 1.46–2.48]). CONCLUSIONS: These findings demonstrate the independent and additive nature of Lp(a) and LDL-C levels for ASCVD risk, and that LDL-C lowering does not fully offset Lp(a)-mediated risk.

In Western countries, the current standard of care for resectable gastric cancer is perioperative chemotherapy. Preoperative chemoradiotherapy has been considered, but data are limited regarding this treatment as compared with perioperative chemotherapy alone. We conducted an international, phase 3 trial in which patients with resectable adenocarcinoma of the stomach or gastroesophageal junction were randomly assigned to receive preoperative chemoradiotherapy plus perioperative chemotherapy or perioperative chemotherapy alone (control). In both groups, patients received either epirubicin, cisplatin, and fluorouracil or fluorouracil, leucovorin, oxaliplatin, and docetaxel both before and after surgery; the preoperative-chemoradiotherapy group also received chemoradiotherapy (45 Gy in 25 fractions of radiation, plus fluorouracil infusion). The primary end point was overall survival, and secondary end points included progression-free survival, pathological complete response, toxic effects, and quality of life. A total of 574 patients underwent randomization at 70 sites in Australasia, Canada, and Europe: 286 to the preoperative-chemoradiotherapy group and 288 to the perioperative-chemotherapy group. A higher percentage of patients in the preoperative-chemoradiotherapy group than in the perioperative-chemotherapy group had a pathological complete response (17% vs. 8%) and greater tumor downstaging after resection. At a median follow-up of 67 months, no significant between-group differences in overall survival or progression-free survival were noted. The median overall survival was 46 months with preoperative chemoradiotherapy and 49 months with perioperative chemotherapy (hazard ratio for death, 1.05; 95% confidence interval, 0.83 to 1.31), and the median progression-free survival was 31 months and 32 months, respectively. Treatment-related toxic effects were similar in the two groups. The addition of preoperative chemoradiotherapy to perioperative chemotherapy did not improve overall survival as compared with perioperative chemotherapy alone among patients with resectable gastric and gastroesophageal junction adenocarcinoma. (Funded by the National Health and Medical Research Council and others; TOPGEAR ClinicalTrials.gov number, NCT01924819.).

Randomized controlled trials (RCTs) form a cornerstone of oncology research by generating evidence about the efficacy of therapies in selected patient populations. However, their implementation is often resource- and cost-intensive, and their generalisability to patients treated in routine practice may be limited. Real-world evidence leverages data collected about patients receiving clinical care in routine practice outside of clinical trial settings and provides opportunities to identify and address gaps in clinical trial evidence. This review outlines the strengths and limitations of real-world and RCT evidence and proposes a framework for the complementary use of the two bodies of evidence to advance cancer research. There are challenges to the implementation of real-world research in oncology, including heterogeneity of data sources, timely access to high-quality data, and concerns about the quality of methods leveraging real-world data, particularly causal inference. Improved understanding of the strengths and limitations of real-world data and ongoing efforts to optimise the conduct of real-world evidence research will improve its reliability, understanding and acceptance, and enable the full potential of real-world evidence to be realised in oncology practice.

Background: Kidney failure prevalence is increasing worldwide. Haemodialysis, peritoneal dialysis or kidney transplantation are undertaken to extend life with kidney failure. People receiving haemodialysis commonly experience fatigue, pain, nausea, cramping, itching, sleeping difficulties, anxiety and depression. This symptom burden contributes to poor health-related quality of life (QOL) and is a major reason for treatment withdrawal and death. The Symptom monitoring WIth Feedback Trial (SWIFT) will test the hypothesis that regular symptom monitoring with feedback to people receiving haemodialysis and their treating clinical team can improve QOL. Methods: We are conducting an Australia and New Zealand Dialysis and Transplant (ANZDATA) registry-based cluster randomised controlled trial to determine the clinical- and cost-effectiveness at 12 months, of 3-monthly symptom monitoring using the Integrated Palliative Outcome Scale-Renal (IPOS-Renal) survey with clinician feedback, compared with usual care among adults treated with haemodialysis. Participants complete symptom scoring using a tablet, which are provided to participants and to clinicians. The trial aims to recruit 143 satellite haemodialysis centres, (up to 2400 participants). The primary outcome is change in health-related QOL, as measured by EuroQol 5-Dimension, 5-Level (EQ-5D-5L) instrument. Secondary outcomes include overall survival, symptom severity (including haemodialysis-associated fatigue), healthcare utilisation and cost-effectiveness. Discussion: SWIFT is the first registry-based trial in the Australian haemodialysis population to investigate whether regular symptom monitoring with feedback to participants and clinicians improves QOL. SWIFT is embedded in the ANZDATA Registry facilitating pragmatic recruitment from public and private dialysis clinics, throughout Australia. SWIFT will inform future collection, storage and reporting of patient-reported outcome measures (PROMs) within a clinical quality registry. As the first trial to rigorously estimate the efficacy and cost-effectiveness of routine PROMs collection and reporting in haemodialysis units, SWIFT will provide invaluable information to health services, clinicians and researchers working to improve the lives of those with kidney failure. Trial registration: Australian New Zealand Clinical Trials Registry ACTRN12620001061921. Registered on 16 October 2020

Statin therapy is a highly successful and cost-effective strategy for the prevention and treatment of cardiovascular diseases (CVD). Adjusting for statin usage is crucial when exploring the association of the lipidome with CVD to avoid erroneous conclusions. However, practical challenges arise in real-world scenarios due to the frequent absence of statin usage information. To address this limitation, we demonstrate that statin usage can be accurately predicted using lipidomic data. Using three large population datasets and a longitudinal clinical study, we show that lipidomic-based statin prediction models exhibit high prediction accuracy in external validation. Furthermore, we introduce a re-weighted model, designed to overcome a ubiquitous limitation of prediction models, namely the need for predictor alignment between training and target data. We demonstrated that the re-weighted model achieves comparable prediction accuracy to ad hoc models which use the aligned predictor between training and target data. This innovation holds promise for significantly enhancing the transferability of 'omics prediction models, especially in situations where predictor alignment is challenging.