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Adjunctive ECT can be efficacious in clozapine nonresponders suffering from schizophrenia.

Background Few studies have examined the impact of treatment-related morbidity on long-term, cause-specific mortality in Hodgkin lymphoma (HL) patients. Methods This multicenter cohort included 4919 HL patients, treated before age 51 years between 1965 and 2000, with a median follow-up of 20.2 years. Standardized mortality ratios, absolute excess mortality (AEM) per 10 000 person-years, and cause-specific cumulative mortality by stage and primary treatment, accounting for competing risks, were calculated. Results HL patients experienced a 5.1-fold (AEM = 123 excess deaths per 10 000 person-years) higher risk of death due to causes other than HL. This risk remained increased in 40-year survivors (standardized mortality ratio = 5.2, 95% confidence interval [CI] = 4.2 to 6.5, AEM = 619). At age 54 years, HL survivors experienced similar cumulative mortality (20.0%) from causes other than HL to 71-year-old individuals from the general population. Whereas HL mortality statistically significantly decreased over the calendar period (P < .001), solid tumor mortality did not change in the most recent treatment era. Patients treated in 1989-2000 had lower 25-year cardiovascular disease mortality than patients treated in 1965-1976 (4.3% vs 5.7%; subdistribution hazard ratio = 0.65, 95% CI = 0.46 to 0.93). Infectious disease mortality was not only increased after splenectomy but also after spleen irradiation (hazard ratio = 2.81, 95% CI = 1.55 to 5.07). For stage I-II, primary treatment with chemotherapy (CT) alone was associated with statistically significantly higher HL mortality (P < .001 for CT vs radiotherapy [RT]; P = .04 for CT vs RT+CT) but lower 30-year mortality from causes other than HL (15.8%, 95% CI = 9.7% to 23.3%) compared with RT alone (36.9%, 95% CI = 34.0% to 39.8%, P = .001) and RT and CT combined (29.8%, 95% CI = 26.8% to 32.9%, P = .02). Conclusions Compared with the general population, HL survivors have a substantially reduced life expectancy. Optimal selection of patients for primary CT is crucial, weighing risks of HL relapse and long-term toxicity.

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BackgroundWhile wild chimpanzees are experiencing drastic population declines, their numbers at African rescue and rehabilitation projects are growing rapidly. Chimpanzees follow complex routes to these refuges; and their geographic origins are often unclear. Identifying areas where hunting occurs can help law enforcement authorities focus scarce resources for wildlife protection planning. Efficiently focusing these resources is particularly important in Cameroon because this country is a key transportation waypoint for international wildlife crime syndicates. Furthermore, Cameroon is home to two chimpanzee subspecies, which makes ascertaining the origins of these chimpanzees important for reintroduction planning and for scientific investigations involving these chimpanzees.ResultsWe estimated geographic origins of 46 chimpanzees from the Limbe Wildlife Centre (LWC) in Cameroon. Using Bayesian approximation methods, we determined their origins using mtDNA sequences and microsatellite (STRP) genotypes compared to a spatial map of georeferenced chimpanzee samples from 10 locations spanning Cameroon and Nigeria. The LWC chimpanzees come from multiple regions of Cameroon or forested areas straddling the Cameroon-Nigeria border. The LWC chimpanzees were partitioned further as originating from one of three biogeographically important zones occurring in Cameroon, but we were unable to refine these origin estimates to more specific areas within these three zones.ConclusionsOur findings suggest that chimpanzee hunting is widespread across Cameroon. Live animal smuggling appears to occur locally within Cameroon, despite the existence of local wildlife cartels that operate internationally. This pattern varies from the illegal wildlife trade patterns observed in other commercially valuable species, such as elephants, where specific populations are targeted for exploitation. A broader sample of rescued chimpanzees compared against a more comprehensive grid of georeferenced samples may reveal 'hotspots' of chimpanzee hunting and live animal transport routes in Cameroon. These results illustrate also that clarifying the origins of refuge chimpanzees is an important tool for designing reintroduction programs. Finally, chimpanzees at refuges are frequently used in scientific investigations, such as studies investigating the history of zoonotic diseases. Our results provide important new information for interpreting these studies within a precise geographical framework.

Purpose To develop a prognostic model to predict 30-day mortality following CRC surgery using the Surveillance, Epidemiology, and End Results (SEER)-Medicare linked data, and to assess whether race/ethnicity, neighborhood, and hospital characteristics influence model performance. Methods We included patients aged 66 years and older from the linked 2000–2005 SEER-Medicare database. Outcome included 30-day mortality, both in-hospital and following discharge. Potential prognostic factors included tumor, treatment, sociodemographic, hospital, and neighborhood characteristics (census-tract-poverty rate). We performed a multilevel logistic regression analysis to account for nesting of CRC patients within hospitals. Model performance was assessed using the area under the receiver operating characteristic curve (AUC) for discrimination and the Hosmer-Lemeshow goodness-of-fit test for calibration. Results In a model that included all prognostic factors, important predictors of 30-day mortality included age at diagnosis, cancer stage and mode of presentation. Race/ethnicity, census-tract-poverty rate and hospital characteristics were independently associated with 30-day mortality, but they did not influence model performance. Our SEER-Medicare model achieved moderate discrimination (AUC=0.76), despite suboptimal calibration. Conclusions We developed a prognostic model that included tumor, treatment, sociodemographic, hospital, and neighborhood predictors. Race/ethnicity, neighborhood, and hospital characteristics did not improve model performance compared with previously developed models.

PURPOSE Women treated with chest radiation for childhood cancer have one of the highest risks of breast cancer. Models producing personalized breast cancer risk estimates applicable to this population do not exist. We sought to develop and validate a breast cancer risk prediction model for childhood cancer survivors treated with chest radiation incorporating treatment-related factors, family history, and reproductive factors. METHODS Analyses were based on multinational cohorts of female 5-year survivors of cancer diagnosed younger than age 21 years and treated with chest radiation. Model derivation was based on 1,120 participants in the Childhood Cancer Survivor Study diagnosed between 1970 and 1986, with median attained age 42 years (range 20-64) and 242 with breast cancer. Model validation included 1,027 participants from three cohorts, with median age 32 years (range 20-66) and 105 with breast cancer. RESULTS The model included current age, chest radiation field, whether chest radiation was delivered within 1 year of menarche, anthracycline exposure, age at menopause, and history of a first-degree relative with breast cancer. Ten-year risk estimates ranged from 2% to 23% for 30-year-old women (area under the curve, 0.63; 95% CI, 0.50 to 0.73) and from 5% to 34% for 40-year-old women (area under the curve, 0.67; 95% CI, 0.54 to 0.84). The highest risks were among premenopausal women older than age 40 years treated with mantle field radiation within a year of menarche who had a first-degree relative with breast cancer. It showed good calibration with an expected-to-observed ratio of the number of breast cancers of 0.92 (95% CI, 0.74 to 1.16). CONCLUSION Breast cancer risk varies among childhood cancer survivors treated with chest radiation. Accurate risk prediction may aid in refining surveillance, counseling, and preventive strategies in this population.

ObjectiveObtaining accurate data about causes of death may be difficult in patients with a complicated disease history, including cancer survivors. This study compared causes of death derived from medical records (CODMR) with causes of death derived from death certificates (CODDC) as processed by Statistics Netherlands of patients primarily treated for Hodgkin lymphoma (HL) or breast cancer (BC).MethodsTwo hospital-based cohorts comprising 1,215 HL patients who died in the period 1980–2013 and 714 BC patients who died in the period 2000–2013 were linked with cause-of-death statistics files. The level of agreement was assessed for common underlying causes of death using Cohen’s kappa, and original death certificates were reviewed when CODDC and CODMR showed discrepancies. We examined the influence of using CODDC or CODMR on standardized mortality ratio (SMR) estimates.ResultsAgreement for the most common causes of death, including selected malignant neoplasms and circulatory and respiratory diseases, was 81% for HL patients and 97% for BC patients. HL was more often reported as CODDC (CODDC=33.1% vs. CODMR=23.2%), whereas circulatory disease (CODDC=15.6% vs. CODMR=20.9%) or other diseases potentially related to HL treatment were more often reported as CODMR. Compared to SMRs based on CODDC, SMRs based on CODMR complemented with CODDC were lower for HL and higher for circulatory disease.ConclusionOverall, we observed high levels of agreement between CODMR and CODDC for common causes of death in HL and BC patients. Observed discrepancies between CODMR and CODDC frequently occurred in the presence of late effects of treatment for HL.