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COVID-19 发病后透析患者短期和长期死亡率的预测因素:拉丁美洲和北美国家中机器学习模型的并行使用。

Predictors of shorter- and longer-term mortality after COVID-19 presentation among dialysis patients: parallel use of machine learning models in Latin and North American countries.

机构信息

Fresenius Medical Care Latin America, Rio de Janeiro, Brazil.

Fresenius Medical Care, Global Medical Office, 920 Winter Street, Waltham, MA, 02451, USA.

出版信息

BMC Nephrol. 2022 Oct 22;23(1):340. doi: 10.1186/s12882-022-02961-x.

Abstract

BACKGROUND

We developed machine learning models to understand the predictors of shorter-, intermediate-, and longer-term mortality among hemodialysis (HD) patients affected by COVID-19 in four countries in the Americas.

METHODS

We used data from adult HD patients treated at regional institutions of a global provider in Latin America (LatAm) and North America who contracted COVID-19 in 2020 before SARS-CoV-2 vaccines were available. Using 93 commonly captured variables, we developed machine learning models that predicted the likelihood of death overall, as well as during 0-14, 15-30, > 30 days after COVID-19 presentation and identified the importance of predictors. XGBoost models were built in parallel using the same programming with a 60%:20%:20% random split for training, validation, & testing data for the datasets from LatAm (Argentina, Columbia, Ecuador) and North America (United States) countries.

RESULTS

Among HD patients with COVID-19, 28.8% (1,001/3,473) died in LatAm and 20.5% (4,426/21,624) died in North America. Mortality occurred earlier in LatAm versus North America; 15.0% and 7.3% of patients died within 0-14 days, 7.9% and 4.6% of patients died within 15-30 days, and 5.9% and 8.6% of patients died > 30 days after COVID-19 presentation, respectively. Area under curve ranged from 0.73 to 0.83 across prediction models in both regions. Top predictors of death after COVID-19 consistently included older age, longer vintage, markers of poor nutrition and more inflammation in both regions at all timepoints. Unique patient attributes (higher BMI, male sex) were top predictors of mortality during 0-14 and 15-30 days after COVID-19, yet not mortality > 30 days after presentation.

CONCLUSIONS

Findings showed distinct profiles of mortality in COVID-19 in LatAm and North America throughout 2020. Mortality rate was higher within 0-14 and 15-30 days after COVID-19 in LatAm, while mortality rate was higher in North America > 30 days after presentation. Nonetheless, a remarkable proportion of HD patients died > 30 days after COVID-19 presentation in both regions. We were able to develop a series of suitable prognostic prediction models and establish the top predictors of death in COVID-19 during shorter-, intermediate-, and longer-term follow up periods.

摘要

背景

我们开发了机器学习模型,以了解四个美洲国家接受血液透析(HD)治疗的 COVID-19 患者的短期、中期和长期死亡率的预测因素。

方法

我们使用了在拉丁美洲(LatAm)和北美地区的一家全球供应商的区域机构接受治疗的 COVID-19 成年 HD 患者的数据,这些患者在 SARS-CoV-2 疫苗问世之前于 2020 年感染了 COVID-19。我们使用 93 个常用变量开发了机器学习模型,这些模型预测了总体死亡率,以及 COVID-19 发病后 0-14、15-30 和>30 天的死亡率,并确定了预测因素的重要性。XGBoost 模型使用相同的编程在同一时间并行构建,使用 60%:20%:20%的随机分割对 LatAm(阿根廷、哥伦比亚、厄瓜多尔)和北美(美国)数据集进行训练、验证和测试。

结果

在 COVID-19 的 HD 患者中,28.8%(1001/3473)在 LatAm 死亡,20.5%(4426/21624)在北美死亡。LatAm 的死亡率比北美更早;0-14 天内,15.0%和 7.3%的患者死亡,15-30 天内,7.9%和 4.6%的患者死亡,COVID-19 发病后>30 天,5.9%和 8.6%的患者死亡。在两个地区,所有预测模型的曲线下面积均在 0.73 至 0.83 之间。在所有时间点,年龄较大、使用时间较长、营养不良和炎症标志物等较差的营养状况一直是 COVID-19 后死亡的主要预测因素。在 COVID-19 发病后 0-14 和 15-30 天内,独特的患者特征(较高的 BMI、男性)是死亡率的主要预测因素,但在 COVID-19 发病后 30 天以上,并非死亡率的主要预测因素。

结论

研究结果表明,2020 年拉丁美洲和北美的 COVID-19 死亡率存在明显差异。LatAm 在 COVID-19 发病后 0-14 和 15-30 天内的死亡率较高,而北美在 COVID-19 发病后 30 天以上的死亡率较高。尽管如此,两个地区都有相当比例的 HD 患者在 COVID-19 发病后 30 天以上死亡。我们能够开发一系列合适的预后预测模型,并确定 COVID-19 期间短期、中期和长期随访期间死亡的主要预测因素。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d458/9587666/bb6bb50dff70/12882_2022_2961_Fig1_HTML.jpg

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