Evidence map›Paper›PMID 38762840›Full record

ArticleInflammopharmacology2024

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

Behzad Einollahi, Mohammad Javanbakht, Mehrdad Ebrahimi, Mohammad Ahmadi, Morteza Izadi, Sholeh Ghasemi, Zahra Einollahi, Bentolhoda Beyram, Abolfazl Mirani, Ehsan Kianfar

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Article in Inflammopharmacology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Behzad EinollahiNephrology and Urology Research Center, Clinical Science Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran. behzad.einollahi@gmail.com.
Mohammad JavanbakhtNephrology and Urology Research Center, Clinical Science Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran.
Mehrdad EbrahimiNephrology and Urology Research Center, Clinical Science Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran.
Mohammad AhmadiNephrology and Urology Research Center, Clinical Science Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran.
Morteza IzadiHealth Research Center, Life Style Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran.
Sholeh GhasemiDepartment of Nephrology, Shahid Hasheminejad Kidney Center, School of Medicine, Iran University of Medical Sciences, Tehran, Iran.
Zahra EinollahiNephrology and Urology Research Center, Clinical Science Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran.
Bentolhoda BeyramNephrology and Urology Research Center, Clinical Science Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran.
Abolfazl MiraniBiomedical Engineering Research Center, Clinical Science Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran. rsr.mirani@bmsu.ac.ir.
Ehsan KianfarBiomedical Engineering Research Center, Clinical Science Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran. ehsan_kianfar2010@yahoo.com.ORCID http://orcid.org/0000-0002-7213-3772

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHaemoperfusion (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.

methodsThe 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

resultsOur 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

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

Indexed as

COVID-19HemoperfusionMachine LearningAdrenal Cortex HormonesAdultAgedCOVID-19 Drug TreatmentFemaleHumansIranMaleMethylprednisoloneMiddle AgedRetrospective StudiesTreatment OutcomeAdrenal Cortex HormonesMethylprednisoloneCorticosteroidHaemoperfusionMortalityShapley valuesVentilation

Identifiers

PMID38762840

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.