社会人口因素和急诊入院对英国妇科癌症患者死亡地点的影响：全国死亡率-医院病例统计链接数据集的分析。

The Impact of Sociodemographic Factors and Emergency Admissions on the Place of Death of Gynecological Cancer Patients in England: An Analysis of a National Mortality-Hospital Episode Statistics-Linked Data Set.

机构信息

Royal Sussex County Hospital, Brighton.

Public Health England National Cancer Registration and Analysis Service, Bristol.

出版信息

Int J Gynecol Cancer. 2018 Nov;28(9):1714-1721. doi: 10.1097/IGC.0000000000001373.

DOI:10.1097/IGC.0000000000001373

PMID:30358705

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.

摘要

目的

本研究旨在建立一个预测妇科癌症患者住院死亡风险的模型，特别考察社会人口因素和急诊入院对患者选择死亡地点的影响。

方法

该模型基于 2000 年 1 月 1 日至 2012 年 7 月 1 日期间英格兰国家医院入院统计数据-国家统计局数据库中 71269 名因妇科癌症为根本死因的女性数据。使用 2808 例死亡病例对模型进行验证。采用 logistic 回归分析确定与医院死亡相关的独立预测因素：调整死亡年份、年龄组、收入剥夺五分位数、战略卫生署、妇科癌症部位、择期和急诊入院次数以及各自的住院总时长。

结果

43%的妇科癌症死亡发生在医院。显著预测医院死亡的变量为较晚的死亡年份（比值比[OR]，0.93；P < 0.001）、年龄增加（OR，1.17；P < 0.001）、贫困程度增加（OR，1.06；P < 0.001）、择期和急诊入院次数和时长增加（P < 0.001）。该模型正确识别了 73%的医院死亡病例，其敏感性为 75%，特异性为 72%。预测模型和验证数据集的受试者工作特征曲线下面积分别为 0.78 和 0.71。生命最后一个月的每次急诊入院都会使在医院死亡的几率增加 2.4 倍（OR，2.38；P < 0.001）。与伦敦相比，所有其他地区的医院死亡人数明显较低。如果能通过更好的社区护理避免生命最后一个月 50%的急诊入院，该模型预测医院死亡人数将减少 16%。