Sonali Kaushik1, Luke Hounsome2, Catherine Blinman3, Robert Gornall3, Julia Verne4. 1. Royal Sussex County Hospital, Brighton. 2. Public Health England National Cancer Registration and Analysis Service, Bristol. 3. Cheltenham General Hospital, Cheltenham. 4. End of Life Care Public Health England, Bristol, England.
Abstract
OBJECTIVE: The aim of this study was to develop a predictive model for risk of death in hospital for gynecological cancer patients specifically examining the impact of sociodemographic factors and emergency admissions to inform patient choice in place of death. METHODS: The model was based on data from 71,269 women with gynecological cancer as underlying cause of death in England, January 1, 2000, to July 1, 2012, in a national Hospital Episode Statistics-Office for National Statistics database. Two thousand eight hundred eight deaths were used for validation of the model. Logistic regression identified independent predictors of a hospital death: adjusting for year of death, age group, income deprivation quintile, Strategic Health Authority, gynecological cancer site, and number of elective and emergency hospital admissions and respective total durations of stay. RESULTS: Forty-three percent of deaths from gynecological cancer occurred in hospital. The variables significantly predicting death in hospital were less recent year of death (odds ratio [OR], 0.93; P < 0.001), increasing age (OR, 1.17; P < 0.001), increasing deprivation (OR, 1.06; P < 0. 001), increasing frequency and length of elective and emergency admissions (P < 0.001). The model correctly identified 73% of hospital deaths with a sensitivity of 75% and a specificity of 72%. The areas under the receiver operating curve were 0.78 for the predictive model and 0.71 for the validation data set. Each subsequent emergency admission in the last month of life increased the odds of death in hospital by 2.4 times (OR, 2.38; P < 0.001). Hospital deaths were significantly lower in all other regions compared with London. The model predicted a 16% reduction of deaths in hospital if 50% of emergency hospital admissions in the last month of life could be avoided by better community care. CONCLUSIONS: Our findings could enable identification of patients at risk of dying in hospital to ensure greater patient choice for place of death.
OBJECTIVE: The aim of this study was to develop a predictive model for risk of death in hospital for gynecological cancerpatients specifically examining the impact of sociodemographic factors and emergency admissions to inform patient choice in place of death. METHODS: The model was based on data from 71,269 women with gynecological cancer as underlying cause of death in England, January 1, 2000, to July 1, 2012, in a national Hospital Episode Statistics-Office for National Statistics database. Two thousand eight hundred eight deaths were used for validation of the model. Logistic regression identified independent predictors of a hospital death: adjusting for year of death, age group, income deprivation quintile, Strategic Health Authority, gynecological cancer site, and number of elective and emergency hospital admissions and respective total durations of stay. RESULTS: Forty-three percent of deaths from gynecological cancer occurred in hospital. The variables significantly predicting death in hospital were less recent year of death (odds ratio [OR], 0.93; P < 0.001), increasing age (OR, 1.17; P < 0.001), increasing deprivation (OR, 1.06; P < 0. 001), increasing frequency and length of elective and emergency admissions (P < 0.001). The model correctly identified 73% of hospital deaths with a sensitivity of 75% and a specificity of 72%. The areas under the receiver operating curve were 0.78 for the predictive model and 0.71 for the validation data set. Each subsequent emergency admission in the last month of life increased the odds of death in hospital by 2.4 times (OR, 2.38; P < 0.001). Hospital deaths were significantly lower in all other regions compared with London. The model predicted a 16% reduction of deaths in hospital if 50% of emergency hospital admissions in the last month of life could be avoided by better community care. CONCLUSIONS: Our findings could enable identification of patients at risk of dying in hospital to ensure greater patient choice for place of death.