The impact of surgical delay on resectability of colorectal cancer: An international prospective cohort study

AIM: The SARS-CoV-2 pandemic has provided a unique opportunity to explore the impact of surgical delays on cancer resectability. This study aimed to compare resectability for colorectal cancer patients undergoing delayed versus non-delayed surgery. METHODS: This was an international prospective cohort study of consecutive colorectal cancer patients with a decision for curative surgery (January-April 2020). Surgical delay was defined as an operation taking place more than 4 weeks after treatment decision, in a patient who did not receive neoadjuvant therapy. A subgroup analysis explored the effects of delay in elective patients only. The impact of longer delays was explored in a sensitivity analysis. The primary outcome was complete resection, defined as curative resection with an R0 margin. RESULTS: Overall, 5453 patients from 304 hospitals in 47 countries were included, of whom 6.6% (358/5453) did not receive their planned operation. Of the 4304 operated patients without neoadjuvant therapy, 40.5% (1744/4304) were delayed beyond 4 weeks. Delayed patients were more likely to be older, men, more comorbid, have higher body mass index and have rectal cancer and early stage disease. Delayed patients had higher unadjusted rates of complete resection (93.7% vs. 91.9%, P = 0.032) and lower rates of emergency surgery (4.5% vs. 22.5%, P < 0.001). After adjustment, delay was not associated with a lower rate of complete resection (OR 1.18, 95% CI 0.90-1.55, P = 0.224), which was consistent in elective patients only (OR 0.94, 95% CI 0.69-1.27, P = 0.672). Longer delays were not associated with poorer outcomes. CONCLUSION: One in 15 colorectal cancer patients did not receive their planned operation during the first wave of COVID-19. Surgical delay did not appear to compromise resectability, raising the hypothesis that any reduction in long-term survival attributable to delays is likely to be due to micro-metastatic disease

Classificação de microáreas de risco com uso de mineraçãode dados Clasificación de microáreas de riesgo con uso de mineración de datos Classification of risk micro-areas using data mining

OBJETIVO: Identificar, com o auxílio de técnicas computacionais, regras referentes às condições do ambiente físico para a classificação de microáreas de risco. MÉTODOS: Pesquisa exploratória, desenvolvida na cidade de Curitiba, PR, em 2007, dividida em três etapas: identificação de atributos para classificar uma microárea; construção de uma base de dados; e aplicação do processo de descoberta de conhecimento em base de dados, por meio da aplicação de mineração de dados. O conjunto de atributos envolveu as condições de infra- estrutura, hidrografia, solo, área de lazer, características da comunidade e existência de vetores. A base de dados foi construída com dados obtidos em entrevistas com agentes comunitários de saúde, sendo utilizado um questionário com questões fechadas, elaborado com os atributos essenciais, selecionados por especialistas. RESULTADOS: Foram identificados 49 atributos, sendo 41 essenciais e oito irrelevantes. Foram obtidas 68 regras com a mineração de dados, as quais foram analisadas sob a perspectiva de desempenho e qualidade e divididas em dois conjuntos: as inconsistentes e as que confirmam o conhecimento de especialistas. A comparação entre os conjuntos mostrou que as regras que confirmavam o conhecimento, apesar de terem desempenho computacional inferior, foram consideradas mais interessantes. CONCLUSÕES: A mineração de dados ofereceu um conjunto de regras úteis e compreensíveis, capazes de caracterizar microáreas, classificando-as quanto ao grau do risco, com base em características do ambiente físico. A utilização das regras propostas permite que a classificação de uma microárea possa ser realizada de forma mais rápida, menos subjetiva, mantendo um padrão entre as equipes de saúde, superando a influência da percepção particular de cada componente da equipe.<br>OBJETIVO: Identificar, con auxilio de técnicas computacionales, reglas relacionadas con las condiciones del ambiente físico para la clasificación de microáreas de riesgo. MÉTODOS: Investigación exploratoria, desarrollada en la ciudad de Curitiba, Sur de Brasil, en 2007, dividida en tres etapas: identificación de atributos para clasificar una microárea; construcción de una base de datos; y aplicación del proceso de descubrimiento de conocimiento en base de datos, por medio de la aplicación de mineración de datos. El conjunto de atributos involucró las condiciones de infraestructura, hidrografía, suelo, área de diversión, características de la comunidad y existencia de vectores. La base de datos fue construida con datos obtenidos en entrevistas con agentes comunitarios de salud, siendo utilizado un cuestionario con respuestas cerradas, elaborado con los atributos esenciales, seleccionados por especialistas. RESULTADOS: Fueron identificados 49 atributos, siendo 41 esenciales y ocho irrelevantes. Fueron obtenidas 68 reglas con la mineración de datos, las cuales fueron analizadas bajo la perspectiva de desempeño y calidad y divididas en dos conjuntos: las inconsistentes y las que confirman el conocimiento de especialistas. La comparación entre los conjuntos mostró que las reglas que confirmaban el conocimiento, a pesar de tener desempeño computacional inferior, fueron consideradas más interesantes. CONCLUSIONES: La mineración de datos ofreció un conjunto de reglas útiles y comprensibles, capaces de caracterizar microáreas, clasificándolas con respecto al grado de riesgo, con base en características del ambiente físico. La utilización de las reglas propuestas permite que la clasificación de una microárea pueda ser realizada de forma más rápida, menos subjetiva, manteniendo un patrón entre los equipos de salud, superando la influencia de la percepción particular de cada componente del equipo.<br>OBJECTIVE: To identify, with the assistance of computational techniques, rules concerning the conditions of the physical environment for the classification of risk micro-areas. METHODS: Exploratory research carried out in Curitiba, Southern Brazil, in 2007. It was divided into three phases: the identification of attributes to classify a micro-area; the construction of a database; and the process of discovering knowledge in a database through the use of data mining. The set of attributes included the conditions of infrastructure; hydrography; soil; recreation area; community characteristics; and existence of vectors. The database was constructed with data obtained in interviews by community health workers using questionnaires with closed-ended questions, developed with the essential attributes selected by specialists. RESULTS: There were 49 attributes identified, 41 of which were essential and eight irrelevant. There were 68 rules obtained in the data mining, which were analyzed through the perspectives of performance and quality and divided into two sets: the inconsistent rules and the rules that confirm the knowledge of experts. The comparison between the groups showed that the rules that confirm the knowledge, despite having lower computational performance, were considered more interesting. CONCLUSIONS: The data mining provided a set of useful and understandable rules capable of characterizing risk areas based on the characteristics of the physical environment. The use of the proposed rules allows a faster and less subjective area classification, maintaining a standard between the health teams and overcoming the influence of individual perception by each team member

Determinantes contextuais da mortalidade neonatal no Rio Grande do Sul por dois modelos de análise

OBJETIVO: Analisar os determinantes da mortalidade neonatal, segundo modelo de regressão logística multinível e modelo hierárquico clássico. MÉTODOS: Estudo de coorte com 138.407 nascidos vivos com declaração de nascimento e 1.134 óbitos neonatais registrados em 2003 no estado do Rio Grande do Sul. Foram vinculados os registros do Sistema de Informações sobre Nascidos Vivos e Mortalidade para o levantamento das informações sobre exposição no nível individual. As variáveis independentes incluíram características da criança ao nascer, da gestação, da assistência à saúde e fatores sociodemográficos. Fatores associados foram estimados e comparados por meio da análise de regressão logística clássica e multinível. RESULTADOS: O coeficiente de mortalidade neonatal foi 8,19 por mil nascidos vivos. As variáveis que se mostraram associadas ao óbito neonatal no modelo hierárquico foram: baixo peso ao nascer, Apgar no 1º e 5º minutos inferiores a oito, presença de anomalia congênita, prematuridade e perda fetal anterior. Cesariana apresentou efeito protetor. No modelo multinível, a perda fetal anterior não se manteve significativa, mas a inclusão da variável contextual (taxa de pobreza) indicou que 15% da variação da mortalidade neonatal podem ser explicados pela variabilidade nas taxas de pobreza em cada microrregião. CONCLUSÕES: O uso de modelos multiníveis foi capaz de mostrar pequeno efeito dos determinantes contextuais na mortalidade neonatal. Foi observada associação positiva com a taxa de pobreza, no modelo geral, e com o percentual de domicílios com abastecimento de água entre os nascidos pré-termos.OBJETIVO: Analizar los determinantes de la mortalidad neonatal, según modelo de regresión logística multinivel y modelo jerárquico clásico. MÉTODOS: Estudio de cohorte con 138.407 nacidos vivos con declaración de nacimiento y 1.134 óbitos neonatales registrados en 2003 en Rio Grande do Sul, Sur de Brasil. Se vincularon los registros del Sistema de Informaciones sobre Nacidos Vivos y Mortalidad para el levantamiento de las informaciones sobre exposición en el nivel individual. Las variables independientes incluyeron características del niño al nacer, de la gestación y asistencia a la salud, y factores sociodemográficos. Factores asociados fueron estimados y comparados por medio del análisis de regresión logística clásica y multinivel. RESULTADOS: El coeficiente de mortalidad neonatal fue 8,19 por mil nacidos vivos. Las variables que se mostraron asociadas al óbito neonatal en el modelo jerárquico fueron: bajo peso al nacer, Apgar en el 1º y 5º minutos inferiores a ocho, presencia de anomalía congénita, prematuridad y pérdida fetal anterior. La cesárea presentó efecto protector. En el modelo multinivel, la pérdida fetal anterior no se mantuvo significativa, pero la inclusión de la variable contextual (tasa de pobreza) indicó que 15% de la variación de la mortalidad neonatal pueden ser explicados por la variabilidad en las tasas de pobreza en cada microrregión. CONCLUSIONES: El uso de modelos multiniveles fue capaz de mostrar pequeño efecto de los determinantes contextuales en la mortalidad neonatal. Se observó asociación positiva con la tasa de pobreza, en el modelo general, y con el porcentual de residencias con abastecimiento de agua, entre los prematuros.OBJECTIVE: To analyze neonatal mortality determinants using multilevel logistic regression and classic hierarchical models. METHODS: Cohort study including 138,407 live births with birth certificates and 1,134 neonatal deaths recorded in 2003, in the state of Rio Grande do Sul, Southern Brazil. The Information System on Live Births and mortality records were linked for gathering information on individual-level exposures. Sociodemographic data and information on the pregnancy, childbirth care and characteristics of the children at birth were collected. The associated factors were estimated and compared by traditional and multilevel logistic regression analysis. RESULTS: The neonatal mortality rate was 8.19 deaths per 1,000 live births. Low birth weight, 1- and 5-minute Apgar score below eight, congenital malformation, pre-term birth and previous fetal loss were associated with neonatal death in the traditional model. Elective cesarean section had a protective effect. Previous fetal loss did not remain significant in the multilevel model, but the inclusion of a contextual variable (poverty rate) showed that 15% of neonatal mortality variation can be explained by varying poverty rates in the microregions. CONCLUSIONS: The use of multilevel models showed a small effect of contextual determinants on the neonatal mortality rate. There was found a positive association with the poverty rate in the general model, and the proportion of households with water supply among preterm newborns

Common genetic variations in the LEP and LEPR genes, obesity and breast cancer incidence and survival

OBJECTIVE: Obesity is a strong risk factor for breast cancer in postmenopausal women and adverse prognostic indicator regardless of menopausal status. Leptin is an important regulator of adipose tissue mass and has been associated with tumor cell growth. Leptin exerts its effects through interaction with the leptin receptor (LEPR). We investigated whether genetic variations in the leptin (LEP) and LEPR genes are associated with risk of breast cancer, or once diagnosed, with survival. METHODS: The polymorphisms LEP G-2548A and LEPR Q223R were characterized in population-based study consisting of mostly European-American women. The study examined 1,065 women diagnosed with first, primary invasive breast cancer between 1996 and 1997. Controls were 1,108 women frequency matched to the cases by 5-year age group. RESULTS: A modest increase in risk of developing breast cancer was associated with the LEP -2548AA genotype when compared to the LEP -2548GG genotype (age-adjusted OR=1.30; 95% CI=1.01–1.66). This association was stronger among postmenopausal women who were obese (OR=1.86; 95% CI=0.95–3.64) although the interaction was of borderline statistical significance (P=0.07). We found no evidence of an association with polymorphisms of either LEP or LEPR in relation to all-cause or breast cancer-specific mortality among women with breast cancer (mean follow-up time=66.7 months). The effects of these genotypes on breast cancer risk and mortality did not vary significantly when stratified by menopausal status. CONCLUSIONS: In summary, our results show that a common variant in LEP may be associated with the risk of developing breast cancer supporting the hypothesis that leptin is involved in breast carcinogenesis