Williams Nick
The Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health.
Arch Intern Med Res. 2023;6(4):74-85. doi: 10.26502/aimr.0151. Epub 2023 Oct 30.
SARS-CoV-2 infections co-occurred with other diverse pre-existing clinical conditions in mortality cases. We use encounter level health data to evaluate the impact of non-Covid-19 diagnostic events on all-cause mortality observed among Covid-19 positive cases billing Medicare. We further investigate prior diagnostic codes which occur in pre-pandemic study years among cases presenting to Medicare clinically with Covid-19 and cases with Covid-19 who experience all-cause mortality to inform patient population management.
We aggregated encounter level records sourced from all Medicare beneficiaries from 1999-2021. Odds ratios were constructed using diagnostic history, age decile, study year and survival status.We used Generalized Linear Model (GLM) to predict the Decedent Observation Odds Ratio (DOOR) from study year, case observation odds ratio, age decile, non-covid conditions within counts of distinct covid-ever cases and their decedents. Odds ratios are relative to covid-never cases, or cases who did not present with Covid-19 clinically.
High explanatory DOOR measures are observed for diagnostic codes commonly associated with inpatient Covid-19 mortality. High DOOR measures are also observed for individuals living with specific kinds of cancers, experiencing cardiac arrest or acute tubular necrosis.
Covid-ever mortality is influenced by primary infection itself and exacerbations of pre-existing conditions. Consequences of primary infection are observable in GLM, as well as meaningful prior clinical risk factors such as cancer, diabetes, cardiac and respiratory disease. Long-covid conditions require surviving Covid-19 clinical presentation and are predictable from GLM models.
在死亡病例中,SARS-CoV-2感染与其他多种预先存在的临床病症同时出现。我们使用就诊层面的健康数据来评估非新冠诊断事件对医保计费的新冠阳性病例中全因死亡率的影响。我们进一步调查在大流行前研究年份中,向医保机构临床就诊的新冠病例以及经历全因死亡的新冠病例中出现的先前诊断代码,以为患者群体管理提供信息。
我们汇总了1999年至2021年所有医保受益人的就诊层面记录。使用诊断史、年龄十分位数、研究年份和生存状态构建比值比。我们使用广义线性模型(GLM)从研究年份、病例观察比值比、年龄十分位数、不同新冠确诊病例及其死亡病例中的非新冠病症来预测死亡观察比值比(DOOR)。比值比相对于从未感染新冠的病例,即临床上未出现新冠感染的病例。
对于通常与住院新冠死亡相关的诊断代码,观察到较高的解释性DOOR指标。对于患有特定类型癌症、经历心脏骤停或急性肾小管坏死的个体,也观察到较高的DOOR指标。
感染过新冠后的死亡率受初次感染本身以及原有病症的加重影响。初次感染的后果在GLM中是可观察到的,同时还有诸如癌症、糖尿病、心脏和呼吸系统疾病等有意义的先前临床风险因素。长期新冠病症需要在新冠临床表现后存活下来,并且可以从GLM模型中预测。