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基于 Shapley 值的机器学习对 COVID-19 血液灌流影响的调查。

Surveying haemoperfusion impact on COVID-19 from machine learning using Shapley values.

机构信息

Nephrology and Urology Research Center, Clinical Science Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran.

Health Research Center, Life Style Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran.

出版信息

Inflammopharmacology. 2024 Aug;32(4):2285-2294. doi: 10.1007/s10787-024-01494-z. Epub 2024 May 19.

Abstract

BACKGROUND

Haemoperfusion (HP) is an innovative extracorporeal therapy that utilizes special cartridges to filter the blood, effectively removing pro-inflammatory cytokines, toxins, and pathogens in COVID-19 patients. This retrospective cohort study aimed to assess the clinical benefits of HP for severe COVID-19 cases using Shapley values for machine learning models.

METHODS

The research involved 578 inpatients (≥ 20 years old) admitted to Baqiyatallah hospital (Tehran, Iran). The control group (359 patients) received standard treatment, including high doses of corticosteroids (a single 500 mg methylprednisolone pulse, followed by 250 mg for 2 days), categorized as regimen (I). On the other hand, the HP group (219 patients) received regimen II, consisting of the same corticosteroid treatment (regimen I) along with haemoperfusion using Cytosorb H300. The frequency of haemoperfusion sessions varied based on the type of lung involvement determined by chest CT scans. In addition, the value function defines the Shapley value of the th feature for the query point , where the input matrix features represent individual characteristics, drugs, and history and clinical conditions of the patient.

RESULTS

Our data showed a favorable clinical response in the HP group compared to the control group. Notably, one-to-three sessions of HP using the CytoSorb 300 cartridge led to reduced ventilation requirements and mortality rates in severe COVID-19 patients. Shapley values were calculated to evaluate the contribution of haemoperfusion among other factors, such as side effects, medications, and individual characteristics, to COVID-19 patient outcomes. In addition, there is a significant difference between the two groups among the treatments and medications used remdesivir, adalimumab, tocilizumab, favipiravir, Interferon beta-1a, enoxaparin prophylaxis, enoxaparin full dose, heparin prophylaxis, and heparin full dose (P < 0.05). It seems that haemoperfusion has a positive impact on the reduction of inflammation markers and renal functional such as ferritin and creatinine, respectively, as well as D-dimer and WBC levels in the HP group were significantly lower than the control group.

CONCLUSION

The findings indicated that haemoperfusion played a crucial role in predicting patient survival, making it a significant feature in classifying patients' prognoses.

摘要

背景

血液灌流(HP)是一种创新的体外治疗方法,利用特殊的试剂盒过滤血液,有效清除 COVID-19 患者体内的促炎细胞因子、毒素和病原体。本回顾性队列研究旨在使用机器学习模型的 Shapley 值评估 HP 对重症 COVID-19 病例的临床获益。

方法

该研究纳入了 578 名(≥20 岁)入住巴奇亚塔拉拉医院(伊朗德黑兰)的住院患者。对照组(359 例)接受标准治疗,包括大剂量皮质类固醇(单次 500mg 甲基强的松龙冲击,随后 2 天 250mg),归类为方案 I。另一方面,HP 组(219 例)接受方案 II,包括相同的皮质类固醇治疗(方案 I),同时使用 Cytosorb H300 进行血液灌流。血液灌流的次数取决于胸部 CT 扫描确定的肺部受累类型。此外,值函数定义了查询点的第 th 个特征的 Shapley 值,其中输入矩阵特征代表个体特征、药物和患者的病史和临床情况。

结果

与对照组相比,HP 组的临床反应良好。值得注意的是,使用 CytoSorb 300 试剂盒进行 1-3 次血液灌流可降低重症 COVID-19 患者的通气需求和死亡率。计算 Shapley 值以评估血液灌流与其他因素(如副作用、药物和个体特征)对 COVID-19 患者结局的贡献。此外,两组在使用的治疗和药物方面存在显著差异,包括瑞德西韦、阿达木单抗、托珠单抗、法匹拉韦、干扰素β-1a、依诺肝素预防剂量、依诺肝素全剂量、肝素预防剂量和肝素全剂量(P<0.05)。似乎血液灌流对降低炎症标志物和肾脏功能有积极影响,分别为铁蛋白和肌酐,以及 HP 组的 D-二聚体和白细胞计数均明显低于对照组。

结论

研究结果表明,血液灌流在预测患者生存方面发挥了关键作用,是分类患者预后的重要特征。

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