Zobrazení zdroje stránky Kiilerichmullins6344

izing HDL function in the right patients at the optimal time in their disease course. We provide a framework to help the research and clinical communities, as well as funding agencies and stakeholders, obtain insights into current thinking on these topics, and what we predict will be an exciting future for research and development on HDLs.<br /><br /> Evidence suggests that adolescents and young adults (AYAs) with cancer (defined as age 15-39 years) receive high-intensity (HI) medical care at the end-of-life (EOL). Previous population-level studies are limited and lack information on the impact of palliative care (PC) provision. We evaluated prevalence and predictors of HI-EOL care in AYAs with cancer in Ontario, Canada. A secondary aim was to evaluate the impact of PC physicians on the intensity of EOL care in AYAs.<br /><br /> A retrospective decedent cohort of AYAs with cancer who died between 2000 and 2017 in Ontario, Canada, was assembled using a provincial registry and linked to population-based health care data. On the basis of previous studies, the primary composite measure HI-EOL care included any of the following intravenous chemotherapy &lt; 14 days from death, more than one emergency department visit, and more than one hospitalization or intensive care unit admission &lt; 30 days from death. Secondary measures included the most invasive (MI) EOL care (eg, mechanical ventilation &lt; 14 days from death) and PC physician involvement. We determined predictors of outcomes using appropriate regression models.<br /><br /> Of 7,122 AYAs, 43.8% experienced HI-EOL care. PC physician involvement (odds ratio [OR], 0.57; 95% CI, 0.51 to 0.63) and older age at death (OR, 0.60; 95% CI, 0.48 to 0.74) were associated with a lower risk of HI-EOL care. AYAs with hematologic malignancies were at highest risk for HI and MI-EOL care. PC physician involvement substantially reduced the odds of mechanical ventilation at EOL (OR, 0.36; 95% CI, 0.30 to 0.43).<br /><br /> A large proportion of AYAs with cancer experience HI-EOL care. Our study provides strong evidence that PC physician involvement can help mitigate the risk of HI and MI-EOL care in AYAs with cancer.<br /><br />A large proportion of AYAs with cancer experience HI-EOL care. Our study provides strong evidence that PC physician involvement can help mitigate the risk of HI and MI-EOL care in AYAs with cancer.<br /><br /> Population-based cancer incidence rates of bladder cancer may be underestimated. Accurate estimates are needed for understanding the burden of bladder cancer in the United States. We developed and evaluated the feasibility of a machine learning-based classifier to identify bladder cancer cases missed by cancer registries, and estimated the rate of bladder cancer cases potentially missed.<br /><br /> Data were from population-based cohort of 37,940 bladder cancer cases 65 years of age and older in the SEER cancer registries linked with Medicare claims (2007-2013). Cases with other urologic cancers, abdominal cancers, and unrelated cancers were included as control groups. A cohort of cancer-free controls was also selected using the Medicare 5% random sample. We used five supervised machine learning methods classification and regression trees, random forest, logic regression, support vector machines, and logistic regression, for predicting bladder cancer.<br /><br /> Registry linkages yielded 37,940 bladder cancer cases and 766,303 cancer-free controls. Using health insurance claims, classification and regression trees distinguished bladder cancer cases from noncancer controls with very high accuracy (95%). Bacille Calmette-Guerin, cystectomy, and mitomycin were the most important predictors for identifying bladder cancer. From 2007 to 2013, we estimated that up to 3,300 bladder cancer cases in the United States may have been missed by the SEER registries. This would result in an average of 3.5% increase in the reported incidence rate.<br /><br /> SEER cancer registries may potentially miss bladder cancer cases during routine reporting. These missed cases can be identified leveraging Medicare claims and data analytics, leading to more accurate estimates of bladder cancer incidence.<br /><br />SEER cancer registries may potentially miss bladder cancer cases during routine reporting. These missed cases can be identified leveraging Medicare claims and data analytics, leading to more accurate estimates of bladder cancer incidence.<br /><br /> Oral chemotherapy challenges providers' abilities to safely monitor patients' symptoms, adherence, and financial toxicity. COVID-19 has increased the urgency of caring for patients remotely. Collection of electronic patient-reported outcomes (ePROs) has demonstrated efficacy for patients on intravenous chemotherapy, but limited data support their use in oral chemotherapy. We undertook a pilot project to assess the feasibility of implementing an ePRO system for patients starting oral chemotherapy at our cancer center, which includes both an academic site and a community site.<br /><br /> Patients initiating oral chemotherapy were asked to participate. [https://www.selleckchem.com/JAK.html learn more] A five-question tool was built in REDCap. Concerning responses triggered outreach within one business day. The primary outcome was time to first symptom assessment. For comparison, we used a historical cohort of patients who had been prescribed oral chemotherapies by providers in the same disease groups at the cancer center.<br /><br /> Twenty-five of 62 (40%) patients completed es time to symptom assessment. Further investigation is needed to improve patient engagement with ePROs and evaluate the long-term impacts for patients on oral chemotherapy.<br /><br /> To inform precision oncology, methods are needed to use electronic health records (EHRs) to identify patients with cancer who are experiencing clinical inflection points, consistent with worsening prognosis or a high propensity to change treatment, at specific time points. Such patients might benefit from real-time screening for clinical trials.<br /><br /> Using serial unstructured imaging reports for patients with solid tumors or lymphoma participating in a single-institution precision medicine study, we trained a deep neural network natural language processing (NLP) model to dynamically predict patients' prognoses and propensity to start new palliative-intent systemic therapy within 30 days. Model performance was evaluated using Harrell's c-index (for prognosis) and the area under the receiver operating characteristic curve (AUC; for new treatment and new clinical trial enrollment). Associations between model outputs and manual annotations of cancer progression were also evaluated using the AUC.<br /><br /> A deep NLP model was trained and evaluated using 302,688 imaging reports for 16,780 patients.