PDF(Pubmed)
Abstract：
OBJECTIVE: In order to predict at hospital admission the prognosis of patients with serious and life-threatening COVID-19 pneumonia, we sought to understand the clinical characteristics of hospitalised patients at admission as the SARS-CoV-2 pandemic progressed, document their changing response to the virus and its variants over time, and identify factors most importantly associated with mortality after hospital admission.
METHODS: Observational study using a prospective hospital systemwide COVID-19 database.
METHODS: 15-hospital US health system.
METHODS: 26 872 patients admitted with COVID-19 to our Northeast Ohio and Florida hospitals from 1 March 2020 to 1 June 2022.
METHODS: 60-day mortality (highest risk period) after hospital admission analysed by random survival forests machine learning using demographics, medical history, and COVID-19 vaccination status, and viral variant, symptoms, and routine laboratory test results obtained at hospital admission.
RESULTS: Hospital mortality fell from 11% in March 2020 to 3.7% in March 2022, a 66% decrease (p<0.0001); 60-day mortality fell from 17% in May 2020 to 4.7% in May 2022, a 72% decrease (p<0.0001). Advanced age was the strongest predictor of 60-day mortality, followed by admission laboratory test results. Risk-adjusted 60-day mortality had all patients been admitted in March 2020 was 15% (CI 3.0% to 28%), and had they all been admitted in May 2022, 12% (CI 2.2% to 23%), a 20% decrease (p<0.0001). Dissociation between observed and predicted decrease in mortality was related to temporal change in admission patient profile, particularly in laboratory test results, but not vaccination status or viral variant.
CONCLUSIONS: Hospital mortality from COVID-19 decreased substantially as the pandemic evolved but persisted after hospital discharge, eclipsing hospital mortality by 50% or more. However, after accounting for the many, even subtle, changes across the pandemic in patients\' demographics, medical history and particularly admission laboratory results, a patient admitted early in the pandemic and predicted to be at high risk would remain at high risk of mortality if admitted tomorrow.
METHODS: Observational study using a prospective hospital systemwide COVID-19 database.
METHODS: 15-hospital US health system.
METHODS: 26 872 patients admitted with COVID-19 to our Northeast Ohio and Florida hospitals from 1 March 2020 to 1 June 2022.
METHODS: 60-day mortality (highest risk period) after hospital admission analysed by random survival forests machine learning using demographics, medical history, and COVID-19 vaccination status, and viral variant, symptoms, and routine laboratory test results obtained at hospital admission.
RESULTS: Hospital mortality fell from 11% in March 2020 to 3.7% in March 2022, a 66% decrease (p<0.0001); 60-day mortality fell from 17% in May 2020 to 4.7% in May 2022, a 72% decrease (p<0.0001). Advanced age was the strongest predictor of 60-day mortality, followed by admission laboratory test results. Risk-adjusted 60-day mortality had all patients been admitted in March 2020 was 15% (CI 3.0% to 28%), and had they all been admitted in May 2022, 12% (CI 2.2% to 23%), a 20% decrease (p<0.0001). Dissociation between observed and predicted decrease in mortality was related to temporal change in admission patient profile, particularly in laboratory test results, but not vaccination status or viral variant.
CONCLUSIONS: Hospital mortality from COVID-19 decreased substantially as the pandemic evolved but persisted after hospital discharge, eclipsing hospital mortality by 50% or more. However, after accounting for the many, even subtle, changes across the pandemic in patients\' demographics, medical history and particularly admission laboratory results, a patient admitted early in the pandemic and predicted to be at high risk would remain at high risk of mortality if admitted tomorrow.
摘要：
目的：为了在入院时预测严重和危及生命的COVID-19肺炎患者的预后，我们试图了解SARS-CoV-2大流行进展时住院患者的临床特征，记录他们对病毒及其变种的反应随着时间的推移，并确定与入院后死亡率最重要的相关因素。
方法：使用前瞻性医院全系统COVID-19数据库进行观察性研究。
方法：美国15医院卫生系统。
方法：2020年3月1日至2022年6月1日，我们俄亥俄州东北部和佛罗里达州医院收治了26872例COVID-19患者。
方法：入院后60天死亡率（最高危险期）通过随机生存森林机器学习使用人口统计学分析，病史,和COVID-19疫苗接种状况，和病毒变体，症状,以及入院时获得的常规实验室检查结果。
结果：医院死亡率从2020年3月的11%下降到2022年3月的3.7%，下降66%（p<0.0001）；60天死亡率从2020年5月的17%下降到2022年5月的4.7%，下降72%（p<0.0001）。高龄是60天死亡率的最强预测指标，其次是入院实验室检查结果。在2020年3月所有患者入院的情况下，风险调整后的60天死亡率为15%(CI3.0%至28%)，如果他们都在2022年5月被录取，12%(CI2.2%至23%)，下降20%(p<0.0001)。观察到的死亡率下降与预测的死亡率下降之间的分离与入院患者概况的时间变化有关，特别是在实验室测试结果中，但不是疫苗接种状态或病毒变异。
结论：随着疫情的发展，COVID-19的医院死亡率大幅下降，但在出院后持续存在，使医院死亡率下降50%或更多。然而,在考虑了很多之后，甚至微妙，大流行患者的人口统计学变化，病史，特别是入院实验室结果，一名在大流行早期入院并预测处于高风险的患者如果明天入院，仍将处于高死亡风险。
方法：使用前瞻性医院全系统COVID-19数据库进行观察性研究。
方法：美国15医院卫生系统。
方法：2020年3月1日至2022年6月1日，我们俄亥俄州东北部和佛罗里达州医院收治了26872例COVID-19患者。
方法：入院后60天死亡率（最高危险期）通过随机生存森林机器学习使用人口统计学分析，病史,和COVID-19疫苗接种状况，和病毒变体，症状,以及入院时获得的常规实验室检查结果。
结果：医院死亡率从2020年3月的11%下降到2022年3月的3.7%，下降66%（p<0.0001）；60天死亡率从2020年5月的17%下降到2022年5月的4.7%，下降72%（p<0.0001）。高龄是60天死亡率的最强预测指标，其次是入院实验室检查结果。在2020年3月所有患者入院的情况下，风险调整后的60天死亡率为15%(CI3.0%至28%)，如果他们都在2022年5月被录取，12%(CI2.2%至23%)，下降20%(p<0.0001)。观察到的死亡率下降与预测的死亡率下降之间的分离与入院患者概况的时间变化有关，特别是在实验室测试结果中，但不是疫苗接种状态或病毒变异。
结论：随着疫情的发展，COVID-19的医院死亡率大幅下降，但在出院后持续存在，使医院死亡率下降50%或更多。然而,在考虑了很多之后，甚至微妙，大流行患者的人口统计学变化，病史，特别是入院实验室结果，一名在大流行早期入院并预测处于高风险的患者如果明天入院，仍将处于高死亡风险。
