Salt intake and gastric cancer: a pooled analysis within the Stomach cancer Pooling (StoP) Project

Purpose: Previous studies show that consuming foods preserved by salting increases the risk of gastric cancer, while results on the association between total salt or added salt and gastric cancer are less consistent and vary with the exposure considered. This study aimed to quantify the association between dietary salt exposure and gastric cancer, using an individual participant data meta-analysis of studies participating in the Stomach cancer Pooling (StoP) Project. Methods: Data from 25 studies (10,283 cases and 24,643 controls) from the StoP Project with information on salt taste preference (tasteless, normal, salty), use of table salt (never, sometimes, always), total sodium intake (tertiles of grams/day), and high-salt and salt-preserved foods intake (tertiles of grams/day) were used. A two-stage approach based on random-effects models was used to pool study-specific adjusted (sex, age, and gastric cancer risk factors) odds ratios (aORs), and the corresponding 95% confidence intervals (95% CI). Results: Gastric cancer risk was higher for salty taste preference (aOR 1.59, 95% CI 1.25–2.03), always using table salt (aOR 1.33, 95% CI 1.16–1.54), and for the highest tertile of high-salt and salt-preserved foods intake (aOR 1.24, 95% CI 1.01–1.51) vs. the lowest tertile. No significant association was observed for the highest vs. the lowest tertile of total sodium intake (aOR 1.08, 95% CI 0.82–1.43). The results obtained were consistent across anatomic sites, strata of Helicobacter pylori infection, and sociodemographic, lifestyle and study characteristics. Conclusion: Salty taste preference, always using table salt, and a greater high-salt and salt-preserved foods intake increased the risk of gastric cancer, though the association was less robust with total sodium intake. © 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG

How well do covariates perform when adjusting for sampling bias in online COVID-19 research? Insights from multiverse analyses

COVID-19 research has relied heavily on convenience-based samples, which—though often necessary—are susceptible to important sampling biases. We begin with a theoretical overview and introduction to the dynamics that underlie sampling bias. We then empirically examine sampling bias in online COVID-19 surveys and evaluate the degree to which common statistical adjustments for demographic covariates successfully attenuate such bias. This registered study analysed responses to identical questions from three convenience and three largely representative samples (total N = 13,731) collected online in Canada within the International COVID-19 Awareness and Responses Evaluation Study (www.icarestudy.com). We compared samples on 11 behavioural and psychological outcomes (e.g., adherence to COVID-19 prevention measures, vaccine intentions) across three time points and employed multiverse-style analyses to examine how 512 combinations of demographic covariates (e.g., sex, age, education, income, ethnicity) impacted sampling discrepancies on these outcomes. Significant discrepancies emerged between samples on 73% of outcomes. Participants in the convenience samples held more positive thoughts towards and engaged in more COVID-19 prevention behaviours. Covariates attenuated sampling differences in only 55% of cases and increased differences in 45%. No covariate performed reliably well. Our results suggest that online convenience samples may display more positive dispositions towards COVID-19 prevention behaviours being studied than would samples drawn using more representative means. Adjusting results for demographic covariates frequently increased rather than decreased bias, suggesting that researchers should be cautious when interpreting adjusted findings. Using multiverse-style analyses as extended sensitivity analyses is recommended

Determinants of hospitalization in a cohort of chronic dialysis patients in central Italy

Background: Few studies linking hospital discharge records with the population register of chronic dialysis (CD) patients are available. This study aimed to evaluate the frequency and the determinants of hospitalization, taking into account the demographic, clinical and biochemical data. Methods: We conducted a retrospective cohort study in 3411 patients starting dialysis from 1996-2000, reported to the Lazio Dialysis Registry (RDL) (Italy). These patients were linked with the hospital information system from 1996-2002. Hospital admission probability was calculated using the Kaplan-Meier method. To evaluate the determinants of hospitalization risk we used Cox's proportional hazards for the first admission and a marginal model considering competitive effect of mortality, the Wei-Lin-Weissfeld model, for any admission. Results: We found 7530 hospital admissions, referring to 1711 patients (50.7%), with a rate of 63/100 person-years. The most prevalent diagnoses were "diseases of the genitourinary system", (37.4%), and "diseases of the circulatory system", among secondary diagnoses (46.6%). Hospitalization probability was 34.4% at 1 yr after starting dialysis. The risk of first and any hospital admission was higher (p<0.05) for patients having more than one comorbid disease, hematocrit (Hct) level <30%, serum albumin level <3.5 g/dL, and a low degree of self-sufficiency. Conclusions: Hospitalization frequency, mainly during the first months of dialysis, suggests the need to improve the early management of chronic renal failure and indicates the importance of preventing complications and maximizing functional status among the dialysis population

At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods

By September 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mortality risk estimators are often, however, developed with small unrepresentative samples and with methodological limitations. It is highly important to develop predictive tools for pulmonary embolism (PE) in COVID-19 patients as one of the most severe preventable complications of COVID-19. Early recognition can help provide life-saving targeted anti-coagulation therapy right at admission. Using a dataset of more than 800,000 COVID-19 patients from an international cohort, we propose a cost-sensitive gradient-boosted machine learning model that predicts occurrence of PE and death at admission. Logistic regression, Cox proportional hazards models, and Shapley values were used to identify key predictors for PE and death. Our prediction model had a test AUROC of 75.9% and 74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortality respectively on a highly diverse and held-out test set. The PE prediction model was also evaluated on patients in UK and Spain separately with test results of 74.5% AUROC, 63.5% sensitivity and 78.9% AUROC, 95.7% sensitivity. Age, sex, region of admission, comorbidities (chronic cardiac and pulmonary disease, dementia, diabetes, hypertension, cancer, obesity, smoking), and symptoms (any, confusion, chest pain, fatigue, headache, fever, muscle or joint pain, shortness of breath) were the most important clinical predictors at admission. Age, overall presence of symptoms, shortness of breath, and hypertension were found to be key predictors for PE using our extreme gradient boosted model. This analysis based on the, until now, largest global dataset for this set of problems can inform hospital prioritisation policy and guide long term clinical research and decision-making for COVID-19 patients globally. Our machine learning model developed from an international cohort can serve to better regulate hospital risk prioritisation of at-risk patients. © The Author(s) 2024

Evaluation of a quality improvement intervention to reduce anastomotic leak following right colectomy (EAGLE): pragmatic, batched stepped-wedge, cluster-randomized trial in 64 countries

Background: Anastomotic leak affects 8 per cent of patients after right colectomy with a 10-fold increased risk of postoperative death. The EAGLE study aimed to develop and test whether an international, standardized quality improvement intervention could reduce anastomotic leaks. Methods: The internationally intended protocol, iteratively co-developed by a multistage Delphi process, comprised an online educational module introducing risk stratification, an intraoperative checklist, and harmonized surgical techniques. Clusters (hospital teams) were randomized to one of three arms with varied sequences of intervention/data collection by a derived stepped-wedge batch design (at least 18 hospital teams per batch). Patients were blinded to the study allocation. Low- and middle-income country enrolment was encouraged. The primary outcome (assessed by intention to treat) was anastomotic leak rate, and subgroup analyses by module completion (at least 80 per cent of surgeons, high engagement; less than 50 per cent, low engagement) were preplanned. Results: A total 355 hospital teams registered, with 332 from 64 countries (39.2 per cent low and middle income) included in the final analysis. The online modules were completed by half of the surgeons (2143 of 4411). The primary analysis included 3039 of the 3268 patients recruited (206 patients had no anastomosis and 23 were lost to follow-up), with anastomotic leaks arising before and after the intervention in 10.1 and 9.6 per cent respectively (adjusted OR 0.87, 95 per cent c.i. 0.59 to 1.30; P = 0.498). The proportion of surgeons completing the educational modules was an influence: the leak rate decreased from 12.2 per cent (61 of 500) before intervention to 5.1 per cent (24 of 473) after intervention in high-engagement centres (adjusted OR 0.36, 0.20 to 0.64; P < 0.001), but this was not observed in low-engagement hospitals (8.3 per cent (59 of 714) and 13.8 per cent (61 of 443) respectively; adjusted OR 2.09, 1.31 to 3.31). Conclusion: Completion of globally available digital training by engaged teams can alter anastomotic leak rates. Registration number: NCT04270721 (http://www.clinicaltrials.gov)