Co-design, co-learning, and co-production of an app for pancreatic cancer patients—the “Pancreas Plus” study protocol

Background: Pancreatic cancer is a malignant and complex tumor that often leads to an adverse prognosis. Patients need to face a challenging treatment path, which involves highly-specialized multidisciplinary professionals. The complexity of the disease requires the development of dedicated tools to support patients in their care journey. Co-production stands as a valuable strategy in oncological care to engage patients in understanding their care journey and behaving accordingly to get the best possible clinical outcome. Methods: The non-profit association Unipancreas, active in promoting the latest advances in pancreatic cancer care and in supporting pancreatic cancer patients, has partnered with a multidisciplinary group of professionals to conceive the brand new program “Pancreas Plus” to employ a co-design, co-learning, and co-production path to design an app devoted to pancreatic cancer patients to assist them during their treatment and follow-up journey. The app, which is the outcome of a multi-stakeholder engagement project, offers health information and medical advice specifically tailored on the pancreatic cancer disease. The article reports the research protocol, which may be replicated for the design of other e-health tools focusing on different conditions. Discussion: The study’s output will be an app that sees the pancreatic cancer patient as the main beneficiary but which can gather and address the interests and needs of all meaningful stakeholders, including clinicians, researchers, healthcare and educational institutions, and

Long-Term Effect of Physical Exercise on the Risk for Hospitalization and Death in Dialysis Patients. A Post-Trial Long-Term Observational Study

Background and objectives In the EXerCise Introduction to Enhance Performance in Dialysis (EXCITE) trial, a simple, personalized 6-month walking exercise program at home during the day off of dialysis improved the functional status and the risk for hospitalization in patients with kidney failure. In this post-trial observational study, we tested whether the same intervention was associated with a lower long-term risk of death or hospitalization (combined end point) during a follow-up extended up to 36 months. Design, setting, participants, & measurements In total, 227 patients (exercise, n5104; control, n5123) completed the 6-month trial and entered the post-trial observational study. Data were analyzed by unadjusted and adjusted Cox regression analyses and Bayesian analysis. Results In the long-term observation (up to 36 months), 134 events were recorded (eight deaths not preceded by hospitalization and 126 hospitalizations, which were followed by death in 38 cases). The long-term risk for hospitalization or death was 29% lower (hazard ratio, 0.71; 95% confidence interval, 0.50 to 1.00), and in an analysis stratified by adherence to the walking exercise program during the 6-month trial, the subgroup with high adherence (.60% of prescribed sessions) had a 45% lower risk as compared with the control group (hazard ratio, 0.55; 95% confidence interval, 0.35 to 0.87). A Bayesian analysis showed that the posterior probability of a hazard ratio of 0.71 (95% confidence interval, 0.50 to 1.00) for the risk of the composite outcome observed in the post-trial observational study was 93% under the conservative prior and 97% under the optimistic prior. Sensitivity analyses restricted to the risk of hospitalization only or censoring patients at the time of transplantation fully confirmed these findings. Conclusions A simple, personalized, home-based, low-intensity exercise program was associated with a lower risk of hospitalization

Summer Distribution, Relative Abundance and Encounter Rates of Cetaceans in the Mediterranean Waters off Southern Italy (Western Ionian Sea and Southern Tyrrhenian Sea)

In summer 2010 and summer 2011, weekly cetacean surveys were undertaken in "passing mode", using ferries as platform of opportunity, along the "fixed line transect" between Catania and Civitavecchia (Southern Italy). Of the 20 species of cetaceans confirmed for the Mediterranean sea, 8 were sighted within the survey period: 7 species represented by Mediterranean subpopulations (Balaenoptera physalus, Physeter macrocephalus, Stenella coeruleoalba, Delphinus delphis, Grampus griseus, Tursiops truncatus and Ziphius cavirostris) and one considered visitor (Steno bredanensis). We had a total of 220 sightings during the 2010 and a total of 240 sightings in the 2011. The most frequent species was S. coeruleoalba. By the comparison of the data from the two sampling seasons, a significant increase of D. delphis sightings and a decrease of sightings of B. physalus and P. macrocephalus was observed from 2010 to 2011. While all the other species were observed in both sampling seasons, Z. cavirostris and Steno bredanensis were observed only during 2011. The presence of mixed groups of odontocetes was documented too: we sighted groups composed by S. coeruleoalba and D. delphis, by S. coeruleoalba and T. truncatus, and by S. coeruleoalba and G. griseus. The results of this research add useful information on cetacean species in a very poorly known area and highlight the need to standardize large scale and long term monitoring programs in order to detect variation in presence, abundance and distribution of cetaceans populations and understand the effect of anthropogenic factors

External validation of a deep-learning model to predict severe acute kidney injury based on urine output changes in critically ill patients

Objectives: The purpose of this study was to externally validate algorithms (previously developed and trained in two United States populations) aimed at early detection of severe oliguric AKI (stage 2/3 KDIGO) in intensive care units patients. Methods: The independent cohort was composed of 10'596 patients from the university hospital ICU of Amsterdam (the “AmsterdamUMC database”) admitted to their intensive care units. In this cohort, we analysed the accuracy of algorithms based on logistic regression and deep learning methods. The accuracy of investigated algorithms had previously been tested with electronic intensive care unit (eICU) and MIMIC-III patients. Results: The deep learning model had an area under the ROC curve (AUC) of 0,907 (± 0,007SE) with a sensitivity and specificity of 80% and 89%, respectively, for identifying oliguric AKI episodes. Logistic regression models had an AUC of 0,877 (± 0,005SE) with a sensitivity and specificity of 80% and 81%, respectively. These results were comparable to those obtained in the two US populations upon which the algorithms were previously developed and trained. Conclusion: External validation on the European sample confirmed the accuracy of the algorithms, previously investigated in the US population. The models show high accuracy in both the European and the American databases even though the two cohorts differ in a range of demographic and clinical characteristics, further underlining the validity and the generalizability of the two analytical approaches. Graphical abstract: [Figure not available: see fulltext.

Plasma cytokines, glomerular filtration rate and adipose tissue cytokines gene expression in chronic kidney disease (CKD) patients

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Increased risk of bone fractures in hemodialysis patients treated with proton pump inhibitors in real world: results from the Dialysis Outcomes and Practice Patterns Study (DOPPS)

Long-term treatment with Proton Pump Inhibitors (PPIs) is associated with an increased risk of fractures in the general population. PPIs are widely prescribed to dialysis patients but to date no study specifically tested, by state-of-art statistical methods, the relationship between PPIs use and fractures in this patient-population. This study aimed to assess whether PPIs use is associated with bone fractures (i.e. hip fractures and fractures other than hip fractures) in a large international cohort of hemodialysis patients. We considered an observational prospective cohort of 27097 hemodialysis patients from the DOPPS study. Data analysis was performed by the Fine & Gray method, considering the competitive risk of mortality, as well as by a cause-specific hazards Cox model dealing death as a censoring event and matching patients according to the prescription time. Out of 27,097 hemodialysis patients, 13,283 patients (49%) were on PPI treatment. Across the follow-up (median:19\u2009months), 3.8 bone fractures x 100 person-years and 1.2 hip fractures x 100 person-years occurred. In multiple Cox models, considering the competitive risk of mortality, the incidence rate of bone (SHR: 1.22, 95% CI: 1.10-1.36, P\u2009<\u20090.001) and hip fractures (SHR: 1.35, 95% CI: 1.13-1.62, P = 0.001) was significantly higher in PPI treated than in PPI untreated patients. These findings held true also in multiple, cause-specific, hazards Cox models matching patients according to the prescription time (bone fractures, HR: 1.47, 95% CI: 1.23-1.76, P\u2009<\u20090.001, hip fractures (HR: 1.85, 95% CI: 1.37-2.50, P\u2009<\u20090.001). The use of PPIs requires caution and a careful evaluation of risks/benefits ratio in hemodialysis patients

An introduction to joint models-applications in nephrology

In nephrology, a great deal of information is measured repeatedly in patients over time, often alongside data on events of clinical interest. In this introductory article we discuss how these two types of data can be simultaneously analysed using the joint model (JM) framework, illustrated by clinical examples from nephrology. As classical survival analysis and linear mixed models form the two main components of the JM framework, we will also briefly revisit these techniques