scholarly journals Incidence of and Risk Factors for Tuberculosis among Cancer Patients in Endemic Area: A Regional Cohort Study

2020 ◽  
Vol 21 (9) ◽  
pp. 2715-2721
Author(s):  
Sirinya Nanthanangkul ◽  
Supannee Promthet ◽  
Krittika Suwanrungruang ◽  
Chalongpon Santong ◽  
Patravoot Vatanasapt
2020 ◽  
Author(s):  
Nicolas Waespe ◽  
Fabien Naomi Belle ◽  
Shelagh Redmond ◽  
Christina Schindera ◽  
Ben Daniel Spycher ◽  
...  

Background: Childhood cancer patients are at increased risk of second primary neoplasms (SPNs). We assessed incidence and risk factors for early SPNs with a focus on cancer predisposition syndromes (CPSs). Patients and methods: This cohort study used data from the Swiss Childhood Cancer Registry. We included patients with first primary neoplasms (FPN) until age 21 years from 1986 to 2015 and identified SPNs occurring before age 21. We calculated standardized incidence ratios (SIR) and absolute excess risks (AER) using Swiss population cancer incidence data and cumulative incidence of SPNs. We calculated hazard ratios (HR) of risk factors for SPNs using Fine and Gray competing risk regression. Results: Among 8,074 childhood cancer patients, 304 (4%) were diagnosed with a CPS and 94 (1%) developed early SPNs. The incidence of SPNs was more than 10-fold increased in childhood cancer patients compared to neoplasms in the general population (SIR 10.6, 95%-confidence interval [CI] 8.7-13.1) and the AER was 179/100,000 person-years (CI 139-219). Cumulative incidence of SPNs 20 years after FPN diagnosis was 23% in patients with CPSs and 3% in those without. Risk factors for SPNs were CPSs (HR 7.8, CI 4.8-12.7), chemotherapy (HR 2.2, CI 1.1-4.6), radiotherapy (HR 1.9, CI 1.2-2.9), hematopoietic stem cell transplantation (HR 1.8, CI 1-3.3), and older age (15-20 years) at FPN diagnosis (HR 1.9, CI 1.1-3.2). Conclusion: CPSs are associated with a high risk of SPNs before age 21 years. Identification of CPSs is important for appropriate cancer surveillance and targeted screening.


PLoS ONE ◽  
2017 ◽  
Vol 12 (1) ◽  
pp. e0168277 ◽  
Author(s):  
Inge T. A. Peters ◽  
Erik W. van Zwet ◽  
Vincent T. H. B. M. Smit ◽  
Gerrit Jan Liefers ◽  
Peter J. K. Kuppen ◽  
...  

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 529-529
Author(s):  
Rushad Patell ◽  
Alejandra Gutierrez ◽  
Lisa Rybicki ◽  
Alok A. Khorana

Abstract Background: Bleeding and thrombosis are both major complications of hospitalization in cancer patients. We have previously shown that rate of inpatient venous thromboembolism (VTE) is 4% in a general oncology population (Patell et al, ASCO 2016). Concern regarding bleeding risk may reduce compliance with thromboprophylaxis. A better understanding of predictors of bleeding could optimize use of prophylaxis but there remain major knowledge gaps regarding risk factors for in-hospital bleeding in cancer patients. We assessed major and clinically relevant bleeding incidence and identified risk factors at admission associated with subsequent in-hospital bleeding risk in a cohort of hospitalized cancer patients. Methods: We conducted a retrospective cohort study of consecutive adults admitted to general oncology floor at Cleveland Clinic from 2013-14 (n= 3466). Patients were excluded for bleeding on admission (108). Data collected included demographics, body mass index (BMI), cancer type, length of stay (LOS), use of anticoagulants and baseline laboratory values (+48 hours). Bleeding was assessed using the ISTH definitions of major bleeding and clinically relevant non-major bleeding [Schulman 2005 and Decosus 2011]. Data were collected using an electronic query system of electronic health records. Reason for admission and all bleeding events were confirmed by manual chart review. Univariate risk factors were identified with logistic regression analysis. Multivariable risk factors were identified with stepwise logistic regression and confirmed with bootstrap analysis. Results are summarized as odds ratio (OR) and 95% confidence interval (CI). Results: The study population comprised 3,358 patients of whom 69 (2.1%) developed major and clinically relevant non-major bleeding during hospitalization. Median age was 62 (range, 19-98) years and 56% were male. Median length of stay (LOS) was 5 (range, 0-152) days. The majority of bleeding events were either gastrointestinal (N=30, 43%) or intracranial (N= 13, 19%). In univariate analysis, luminal gastrointestinal (GI) cancers (OR 4.2, CI 2.4-7.5, P<0.001), anemia as reason for admission (OR 9.1, CI 5.1-16.4, P<0.001), thrombocytopenia (OR 1.6, CI 1.0-2.6, P=0.046), leukocytosis (OR 2.1, CI 1.2-3.7,P=0.005), low hemoglobin (OR 3.2, CI 1.4-7.1 P=0.003), BMI ≥ 40 kg/m2 (OR 2.6, CI 1.1-5.94, P=0.018) and anticoagulant use on admission (OR 0.4, CI 0.3-0.8, P=0.004) were significantly associated with bleeding. In multivariable analysis, anemia as the reason for admission, primary cancer site, BMI>40, thrombocytopenia and low hemoglobin on admission remained predictive of bleeding (table 1). Conclusion: The incidence of major and clinically relevant bleeding in a large population of hospitalized cancer patients was about 2%, compared to incidence of inpatient VTE in a similar population of 4%. Risk factors at admission included type of cancer and morbid obesity. Improved prediction of bleeding risk can assist physicians in optimizing selection of thromboprophylaxis in this population that is also at increased risk of VTE. Disclosures Khorana: Sanofi: Consultancy, Honoraria; Leo: Consultancy, Honoraria, Research Funding; Amgen: Consultancy, Honoraria, Research Funding; Bayer: Consultancy, Honoraria; Halozyme: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria; Roche: Consultancy, Honoraria; Janssen Scientific Affairs, LLC: Consultancy, Honoraria, Research Funding.


2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e21537-e21537
Author(s):  
Jurema Telles O Lima ◽  
Anke Bergmann ◽  
Maria Julia Gonçalves Mello ◽  
Zilda Cavalcanti ◽  
Mirella Rebello Bezerra ◽  
...  

e21537 Background: Components of the comprehensive geriatric assessment (CGA) correlate with risk of early mortality in elderly cancer patients (ECP). However, its complexity and the time required for its administration. The aim of this study was to determine the impact of each CGA domain on overall survival(OS) and to first step for the development of a prognostic scoring system to stratify ECP. Methods: a prospective cohort study. Participants with a recent diagnosis of cancer were from eight hospitals and one cancer center in Brazil and were recruited during their first medical appointment at the outpatient oncologic clinic. A basal CGA was done before the care decision (ADL, Charlson Comorbidity Index- CCI, Karnofsky Performance status – KPS, GDS15, IPAQ, MMSE, MNA, MNA-SF, PS, PPS, Polipharmacy, QLQc30, TUG). During the follow up of six months, information about the treatments performed and early death was collected. OS was estimated using the Kaplan–Meier method, and survival curves were compared using the Log rank test for categorical variables. A multivariate Cox proportional hazards model was used to select early death risk factors. A clinical score considering the number of risk variables was created. Results: From 2015-2016, 608 ECP, mean age 71.9 (SD ±7.4; range 60-96), 50.7% male, were enrolled. 100 (16.4%) ECP died in less than six months of follow-up. In our multivariate model, controlled by age, site of cancer and cancer stage, the remaining significant risk factors were malnutrition/nonutrition determined by MNA (HR 3.3, 95%CI 1.81-5.99, p < 0.001), KPS < 50% (HR 2.44, CI 1.56-3.81, p < 0.001) and CCI > 2 (HR 1.6, CI 1.09-2.52, p = 0.018). The risk for early death according to the number of risk variables: three (HR 12.99, CI 5.69-29.60, p < 0.001), two (HR 5.65, CI 2.61-12.24, p < 0.001) or one (HR 2.7, CI 1.28-5.87, p = 0.009). Conclusions: a practical clinical score using three instruments of the CGA (MNA, KPS and CCI) can predict independent the risk for an early death in ECP. The development of a practical system for risk scoring, incorporating few clinical prognostic factors, helps to stratify patients into risk groups and to plan a personalized care.


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