Short‐term outcomes after vascular resection for pancreatic tumors: Lessons learned from 72 cases from a single Brazilian Cancer Center

2019 ◽  
Author(s):  
Silvio Melo Torres ◽  
Diego Greatti Vaz da Silva ◽  
Héber S. C. Ribeiro ◽  
Alessandro L. Diniz ◽  
Matheus Melo Lobo ◽  
...  
Keyword(s):  
Lessons Learned ◽  
Vascular Resection ◽  
Pancreatic Tumors ◽  
Cancer Center ◽  
Short Term

scholarly journals A machine learning approach to predicting short-term mortality risk in patients starting chemotherapy

2017 ◽  
Author(s):  
Aymen A. Elfiky ◽  
Maximilian J. Pany ◽  
Ravi B. Parikh ◽  
Ziad Obermeyer
Keyword(s):  
Machine Learning ◽  
Mortality Risk ◽  
Palliative Chemotherapy ◽  
Learning Algorithm ◽  
Cancer Center ◽  
Short Term ◽  
Machine Learning Model ◽  
Machine Learning Approach ◽  
Short Term Mortality ◽  
And Performance

ABSTRACTBackgroundCancer patients who die soon after starting chemotherapy incur costs of treatment without benefits. Accurately predicting mortality risk from chemotherapy is important, but few patient data-driven tools exist. We sought to create and validate a machine learning model predicting mortality for patients starting new chemotherapy.MethodsWe obtained electronic health records for patients treated at a large cancer center (26,946 patients; 51,774 new regimens) over 2004-14, linked to Social Security data for date of death. The model was derived using 2004-11 data, and performance measured on non-overlapping 2012-14 data.Findings30-day mortality from chemotherapy start was 2.1%. Common cancers included breast (21.1%), colorectal (19.3%), and lung (18.0%). Model predictions were accurate for all patients (AUC 0.94). Predictions for patients starting palliative chemotherapy (46.6% of regimens), for whom prognosis is particularly important, remained highly accurate (AUC 0.92). To illustrate model discrimination, we ranked patients initiating palliative chemotherapy by model-predicted mortality risk, and calculated observed mortality by risk decile. 30-day mortality in the highest-risk decile was 22.6%; in the lowest-risk decile, no patients died. Predictions remained accurate across all primary cancers, stages, and chemotherapies—even for clinical trial regimens that first appeared in years after the model was trained (AUC 0.94). The model also performed well for prediction of 180-day mortality (AUC 0.87; mortality 74.8% in the highest risk decile vs. 0.2% in the lowest). Predictions were more accurate than data from randomized trials of individual chemotherapies, or SEER estimates.InterpretationA machine learning algorithm accurately predicted short-term mortality in patients starting chemotherapy using EHR data. Further research is necessary to determine generalizability and the feasibility of applying this algorithm in clinical settings.

Meaningful Language and Cultural Experiences for Future Teachers in Puerto Rico

2016 ◽  
pp. 160-176
Author(s):  
Judith Cruzado-Guerrero ◽  
Gilda Martinez-Alba
Keyword(s):  
Puerto Rico ◽  
Study Abroad ◽  
Implementation Strategies ◽  
Lessons Learned ◽  
Teacher Preparation Program ◽  
Short Term ◽  
Short Term Study ◽  
Puerto Rican Culture ◽  
Travel Abroad ◽  
Study Abroad Program

The authors describe a faculty led study abroad program implemented in Puerto Rico. The short-term study abroad model highlights both design and implementation strategies for travel abroad. This chapter also focuses on the unique cultural and linguistic experiences in Puerto Rico which were planned for college students in an early childhood education teacher preparation program. The chapter addresses the strategies used to facilitate learning about Puerto Rican culture and languages, methods to support students learning dual languages and strategies for working with families, communities, and other professionals. The chapter concludes with lessons learned from this experience and emphasizes both issues and recommendations for faculty who are developing future short-term travel experiences.

scholarly journals Taiwan's budgetary responses to COVID-19: the use of special budgets

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Wei-Jie Liao ◽  
Nai-Ling Kuo ◽  
Shih-Hsien Chuang
Keyword(s):  
Lessons Learned ◽  
Fiscal Sustainability ◽  
Short Term ◽  
Content Type ◽  
The Great Recession ◽  
News Reports ◽  
Economic Stimulus ◽  
Fiscal Implications ◽  
Taiwanese Government

PurposeThe authors examine the Taiwanese government's budgetary responses to COVID-19, with a focus on the special budgets created for containing the virus, undertaking bailouts and providing economic stimulus. The authors assess the short-term and long-term fiscal implications of the budgetary measures and discuss how Taiwan's experiences could provide lessons for other countries for future emergencies.Design/methodology/approachThe authors collect data from Taiwan's official documents and news reports and compare the special budgets proposed by the Taiwanese government during the Great Recession and the COVID-19 pandemic. The authors discuss lessons learned from the 2008–09 special budget and possible concerns of the 2020 special budgets. In the conclusions, the authors discuss potential long-term implications for Taiwan's budgetary system as well as possible lessons for other countries based on Taiwan's experiencesFindingsThe authors found that the 2008–09 special budgets focused only on economic stimulus, whereas the 2020 special budgets covered COVID-19 treatments, bailouts and economic stimulus. In 2020, the Taiwanese government devised targeted bailout plans for industries and individuals most affected by the pandemic and created the Triple Stimulus Vouchers to boost the economy. Since the special budgets were largely funded through borrowing, the authors pointed out concerns for fiscal sustainability and intergenerational equity.Originality/valueCOVID-19 has changed how the world functions massively. This work adds to the literature on COVID-19 by providing Taiwan's budgetary responses to the pandemic. This work also identifies ways for Taiwan to improve the existing budgetary system and discusses lessons for other countries.

Psycho-oncology via telemedicine: A model to provide services to a rural population.

2014 ◽  
Vol 32 (31_suppl) ◽  
pp. 236-236
Author(s):  
Susan Krigel ◽  
Eve-Lynn Nelson ◽  
Ashley Spaulding ◽  
Hope Krebill ◽  
Melanie Leepers ◽  
...  
Keyword(s):  
Lessons Learned ◽  
Rural Site ◽  
Cancer Center ◽  
Medical Illness ◽  
Psychosocial Needs ◽  
Distress Screening ◽  
Oncology Service ◽  
Management Procedures ◽  
Rural Sites ◽  
Evidence Based Care

236 Background: The Midwest Cancer Alliance (MCA), the outreach arm of the University of Kansas Cancer Center, developed psycho-oncology services over videoconferencing in partnership with its rural members. The collaborative goal is to address patient-identified psychosocial needs that are not currently being met in the communities. Methods: Following telemental health best practices, MCA personnel, rural site leaders, local oncology and behavioral health teams, and telemedicine staff collaborated closely to establish the service. They developed detailed protocols related to: referral and scheduling; technical training and support; paperwork; videoconferencing sessions; post-visit follow-up/documentation; and emergency management procedures. Attention was given to: confidential space; equipment to meet therapy needs; the telemedicine coordinator’s role; and completion of professional requirements (e.g., licensing, credentialing, malpractice coverage). Results: Credentialing, site implementation, and patient recruitment took longer than anticipated. To date, 15 patient visits have occurred through the psycho-oncology service across two rural sites. Worsening illness has impacted referrals as well as retention in therapy. Videoconferencing services have approximated onsite psycho-oncology strategies, with focus on evidence-based care. In general, patients have had advanced medical illness as well as significant premorbid psychosocial difficulties, such as mood disorders, substance abuse history, and relationship problems. Basic challenges to care have remained, including lack of consistent transportation. The telepsychologist’s role in relation to the rural medical team expectations is still evolving. Conclusions: The early lessons learned will continue to strengthen the psycho-oncology service as videoconferencing services expand in order to increase access. Ongoing, site-specific needs assessment remains essential, as “one size does not fit all.” Future plans include improving recruitment by coordinating referrals with distress screening. In order to provide sites with maximum flexibility, new methods of communication will be offered as they become available.

Interdisciplinary care rounds in the community: Changing the paradigm of supportive service involvement in cancer care.

2016 ◽  
Vol 34 (7_suppl) ◽  
pp. 83-83
Author(s):  
Robert D. Siegel ◽  
Hal E. Crosswell ◽  
Terra Dillard ◽  
Jennifer Bayne ◽  
Tina Redenz ◽  
...  
Keyword(s):  
Emergency Room ◽  
Cancer Patients ◽  
Hospital Admissions ◽  
Academic Community ◽  
Adolescent And Young Adult ◽  
Cancer Center ◽  
Interdisciplinary Care ◽  
Supportive Services ◽  
Short Term ◽  
Tumor Boards

83 Background: Although cancer centers have focused on optimizing seamless Multidisciplinary Care (MDC) at tumor boards and/or clinics, there has been little published on effective ways to involve supportive services into the management of cancer patients. Historically, supportive services have been initiated when there is an active need rather than in anticipation of that need. As an alternative to pursuing such "crisis management" in our patients, Bon Secours St. Francis Cancer Center (BSSF) initiated Interdisciplinary Care (IDC) Rounds in an effort to anticipate patient needs, enhance quality of life (QoL), and potentially limit avoidable emergency room and hospital admissions. Methods: We initiated IDC Rounds with participants from the following disciplines: medical oncology, navigation, clinic nursing, palliative medicine, financial counseling, psychology, nutrition, clinical research, adolescent and young adult, and oncology rehabilitation/survivorship (ORS). A database was created to track new patients with malignancies within three weeks of presentation and the subsequent recommendations made by the IDC team. Those recommendations are then forwarded to the primary medical oncologist who has the ability to agree to those recommendations in full or in part before they are actuated. Results: BSSF is a non-academic, community-based cancer program and receives over 1,300 referrals annually from a referral population of 1.32 million in 10 counties. Short term metrics demonstrate a 57% and 100% increase in referrals to ORS and palliative care, respectively. Successes and challenges including sustainability, cost and measurable impact will be discussed. Conclusions: We have shown that it is feasible in the community setting to create a process that will allow early integration of supportive services into the full service care of cancer patients. Results demonstrate an increase in short-term metrics such as referrals to supportive services. Our ultimate goal is that formalized IDC results not only in earlier involvement by needed services but enhanced QoL for our patients with fewer emergency room and hospital admissions. Those data will be compiled as the program matures.

Detection of circulating tumor DNA in pancreas cancer.

2020 ◽  
Vol 38 (4_suppl) ◽  
pp. 645-645
Author(s):  
Daniel King ◽  
Ash A. Alizadeh ◽  
George A. Fisher
Keyword(s):  
Pancreas Cancer ◽  
Residual Disease ◽  
Circulating Tumor Dna ◽  
Pancreatic Tumors ◽  
Cancer Center ◽  
Blood Collection ◽  
Early Assessment ◽  
Response To Chemotherapy ◽  
Investigation Methods ◽  
Tumor Dna

645 Background: Pancreas cancer remains a leading cause of cancer-related death. Improved detection of early relapse or early failure of chemotherapy also has the potential to further improve outcomes. Exploring circulating tumor DNA (ctDNA) in this setting is an area of active investigation. Methods: We previously developed an approach, CAPP-Seq, combining high-depth sequencing with several strategies of error-suppression to identify minute amounts of circulating tumor DNA. We then trained and validated a new capture panel for pancreas cancer from 640 tumors from three data sources (TCGA, ICGC, UTSW), targeting 265 kb of the genome. We enrolled two cohorts of patients with pancreatic cancers at Stanford Cancer Center: (1) patients with localized tumors undergoing resection with curative intent, and (2) patients with unresectable or metastatic disease undergoing systemic therapy. Results: As of August 2019, we recruited 131 patients with at least one blood collection, with 63% having resectable disease and 27% having advanced disease; 59 patients had 2 or more blood collections. Stage distribution included 34% stage I, 33% stage II, 18% III, 16% IV disease. Approximately 15% had normal CA19-9 levels. Deep sequencing (4,000x unique depth) of an initial set of resected pancreatic tumors and matched germline specimens identified 1-6 non-synonymous coding mutations per case (median=3, n=14), with the most frequently mutated genes involving KRAS (79%), TP53 (50%), SMAD4 (29%). Among newly diagnosed treatment-naïve patients with resectable adenocarcinoma (n=9), we detected ctDNA in 4 patients (44%) prior to surgery including with AFs ranging from 0.27% - 0.88%. Subsequent sequencing will compare patients with and without neoadjuvant therapy prior to resection, selection of unresectable patients across a larger range of tumor burden and across multiple timepoints, and integration of large-scale copy number variant detection using low-pass whole-genome sequencing. Conclusions: Circulating tumor DNA monitoring with CAPP-Seq shows promise for improved detection of PDAC. Two key applications include early detection of minimal residual disease after resection and early assessment of response to chemotherapy.

Key lessons learned from short-term treatment trials of cholinesterase inhibitors for amnestic MCI

2008 ◽  
Vol 20 (1) ◽  
pp. 40-46 ◽  
Author(s):  
Stephen Salloway ◽  
Stephen Correia ◽  
Sharon Richardson
Keyword(s):  
Cognitive Impairment ◽  
Outcome Measures ◽  
Primary Outcome ◽  
Rating Scales ◽  
Controlled Trial ◽  
Term Treatment ◽  
Lessons Learned ◽  
Secondary Outcome ◽  
Short Term ◽  
Short Term Treatment

ABSTRACTObjective: This paper reviews the key lessons learned from the first published short-term, placebo-controlled trial of a cholinesterase inhibitor for treatment of mild cognitive impairment (MCI).Methods: The study was a 24-week placebo-controlled trial designed to evaluate the efficacy and safety of donepezil HCl (donepezil) in the treatment of cognitive impairment in subjects with MCI. Primary outcome measures were the NYU Paragraphs Test and the ADCS Clinicians Global-Impression of Change in the intent-to-treat last-observation-carried-forward group.Results: There was no benefit of donepezil treatment on primary outcome measures (NYU Paragraphs and ADCS CGI-C) in the ITT-LOCF group but positive findings were seen on NYU Paragraphs in the fully evaluable group and in certain secondary outcome measures across both groups.Conclusions: The results highlight the need for the use of primary cognitive and functional measures that are reliable and sensitive to change in patients with MCI. Measures of episodic memory, psychomotor speed and complex attention were most sensitive in this study. Functional rating scales are needed that measure change in individual subjects' key areas of functional deficit, which typically involve executive aspects of instrumental ADLs. Tolerability can be increased by use of flexible dosing and efficacy is likely to be enhanced by increasing the length of the trial from six to 12 months and by enriching the sample with subjects more likely to decline during the trial.

scholarly journals A machine learning approach to predicting short-term mortality risk for patients starting chemotherapy.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 6538-6538
Author(s):  
Ravi Bharat Parikh ◽  
Aymen Elfiky ◽  
Maximilian J. Pany ◽  
Ziad Obermeyer
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Characteristic Curve ◽  
Predictive Performance ◽  
Cancer Center ◽  
Short Term ◽  
Model Predictions ◽  
Short Term Mortality ◽  
Chemotherapy Initiation ◽  
Predicted Risk

6538 Background: Patients who die soon after starting chemotherapy incur symptoms and financial costs without survival benefit. Prognostic uncertainty may contribute to increasing chemotherapy use near the end of life, but few prognostic aids exist to guide physicians and patients in the decision to initiate chemotherapy. Methods: We obtained all electronic health record (EHR) data from 2004-14 from a large national cancer center, linked to Social Security data to determine date of death. Using EHR data before treatment initiation, we created a machine learning (ML) model to predict 180-day mortality from the start of chemotherapy. We derived the model using data from 2004-11 and report predictive performance on data from 2012-14. Results: 26,946 patients initiated 51,774 discrete chemotherapy regimens over the study period; 49% received multiple lines of chemotherapy. The most common cancers were breast (23.6%), colorectal (17.6%), and lung (16.6%). 18.4% of patients died within 180 days after chemotherapy initiation. Model predictions were used to rank patients in the validation cohort by predicted risk. Patients in the highest decile of predicted risk had a 180-day mortality of 74.8%, vs. 0.2% in the lowest decile (area under the receiver-operating characteristic curve [AUC] 0.87). Predictions were accurate for patients with metastatic disease (AUC 0.85) and for individual primary cancers and chemotherapy regimens—including experimental regimens not present in the derivation sample. Model predictions were valid for 30- and 90-day mortality (AUC 0.94 and 0.89, respectively). ML predictions outperformed regimen-based mortality estimates from randomized trials (RT) (AUC 0.77 [ML] vs. 0.56 [RT]), and National Cancer Institute Surveillance, Epidemiology, and End Results Program (SEER) estimates (AUC 0.81 [ML] vs. 0.40 [SEER]). Conclusions: Using EHR data from a single cancer center, we derived a machine learning algorithm that accurately predicted short-term mortality after chemotherapy initiation. Further research is necessary to determine applications of this algorithm in clinical settings and whether this tool can improve shared decision making leading up to chemotherapy initiation.

scholarly journals Creating an Authentic International Development Learning Opportunity

2020 ◽  
Vol 13 ◽  
pp. 76-85
Author(s):  
Lori Beckstead
Keyword(s):  
International Development ◽  
Learning Experience ◽  
Time Frame ◽  
Lessons Learned ◽  
Faculty Members ◽  
Short Term ◽  
Learning Opportunity ◽  
International Partner ◽  
International Learning ◽  
Specific Learning

This paper draws from the author’s experience of developing a short-term intensive international learning experience within the framework of a one-semester course. The paper is aimed at faculty members who are interested in implementing a short but effective and authentic international learning opportunity, but who may not have expertise in issues surrounding international development and learning abroad. It addresses some of the challenges, successes, and lessons learned, such as working with an appropriate international partner, overcoming barriers to student participation, ensuring discipline-specific learning, and providing the appropriate context of international development issues within the time-frame of a single semester.   Nous nous fondons ici sur notre propre expérience d’élaboration d’une situation d’apprentissage internationale, intensive et à court terme, dans le cadre d’un cours d’un seul semestre. Cet article est destiné aux enseignants qui, sans nécessairement posséder une expertise en matière de développement internationale et d’apprentissage à l’étranger, souhaiteraient mettre en œuvre une expérience d’apprentissage internationale aussi brève qu’efficace et authentique. Nous abordons les difficultés, les réussites et les leçons tirées de l’expérience, comme la nécessité d’un partenaire international adéquat, le dépassement des obstacles à la participation des étudiants, l’apprentissage adapté à des disciplines en particulier, et l’établissement d’un contexte approprié pour les questions de développement international dans le cadre temporel d’un semestre unique.

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