Evaluating the Effect of Artificial Liver Support on Acute-on-Chronic Liver Failure Using the Quantitative Difference Algorithm: Retrospective Study
BackgroundLiver failure, including acute-on-chronic liver failure (ACLF), occurs mainly in young adults and is associated with high mortality and resource costs. The prognosis evaluation is a crucial part of the ACLF treatment process and should run through the entire diagnos...
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
JMIR Publications
2023-10-01
|
| Series: | JMIR Formative Research |
| Online Access: | https://formative.jmir.org/2023/1/e45395 |
| _version_ | 1797652086934994944 |
|---|---|
| author | Tinghuai Huang Jianwei Huang Timon Cheng-Yi Liu Meng Li Rui She Liyu Liu Hongguang Qu Fei Liang Yuanjing Cao Yuanzheng Chen Lu Tang |
| author_facet | Tinghuai Huang Jianwei Huang Timon Cheng-Yi Liu Meng Li Rui She Liyu Liu Hongguang Qu Fei Liang Yuanjing Cao Yuanzheng Chen Lu Tang |
| author_sort | Tinghuai Huang |
| collection | DOAJ |
| description |
BackgroundLiver failure, including acute-on-chronic liver failure (ACLF), occurs mainly in young adults and is associated with high mortality and resource costs. The prognosis evaluation is a crucial part of the ACLF treatment process and should run through the entire diagnosis process. As a recently proposed novel algorithm, the quantitative difference (QD) algorithm holds promise for enhancing the prognosis evaluation of ACLF.
ObjectiveThis study aims to examine whether the QD algorithm exhibits comparable or superior performance compared to the Model for End-Stage Liver Disease (MELD) in the context of prognosis evaluation.
MethodsA total of 27 patients with ACLF were categorized into 2 groups based on their treatment preferences: the conventional treatment (n=12) and the double plasma molecular absorption system (DPMAS) with conventional treatment (n=15) groups. The prognosis evaluation was performed by the MELD and QD scoring systems.
ResultsA significant reduction was observed in alanine aminotransferase (P=.02), aspartate aminotransferase (P<.001), and conjugated bilirubin (P=.002), both in P values and QD value (Lτ>1.69). A significant decrease in hemoglobin (P=.01), red blood cell count (P=.01), and total bilirubin (P=.02) was observed in the DPMAS group, but this decrease was not observed in QD (Lτ≤1.69). Furthermore, there was a significant association between MELD and QD values (P<.001). Significant differences were observed between groups based on patients’ treatment outcomes. Additionally, the QD algorithm can also demonstrate improvements in patient fatigue. DPMAS can reduce alanine aminotransferase, aspartate aminotransferase, and unconjugated bilirubin.
ConclusionsAs a dynamic algorithm, the QD scoring system can evaluate the therapeutic effects in patients with ACLF, similar to MELD. Nevertheless, the QD scoring system surpasses the MELD by incorporating a broader range of indicators and considering patient variability. |
| first_indexed | 2024-03-11T16:24:58Z |
| format | Article |
| id | doaj.art-409fc25bc718462cb277dbc17f11d687 |
| institution | Directory Open Access Journal |
| issn | 2561-326X |
| language | English |
| last_indexed | 2024-03-11T16:24:58Z |
| publishDate | 2023-10-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR Formative Research |
| spelling | doaj.art-409fc25bc718462cb277dbc17f11d6872023-10-24T13:00:40ZengJMIR PublicationsJMIR Formative Research2561-326X2023-10-017e4539510.2196/45395Evaluating the Effect of Artificial Liver Support on Acute-on-Chronic Liver Failure Using the Quantitative Difference Algorithm: Retrospective StudyTinghuai Huanghttps://orcid.org/0000-0003-1028-0598Jianwei Huanghttps://orcid.org/0009-0007-3184-2437Timon Cheng-Yi Liuhttps://orcid.org/0000-0002-6585-1776Meng Lihttps://orcid.org/0009-0000-8503-1874Rui Shehttps://orcid.org/0009-0004-1018-6555Liyu Liuhttps://orcid.org/0009-0006-7677-1540Hongguang Quhttps://orcid.org/0009-0004-2445-4530Fei Lianghttps://orcid.org/0009-0009-7094-2419Yuanjing Caohttps://orcid.org/0009-0009-9860-9378Yuanzheng Chenhttps://orcid.org/0000-0002-8238-6482Lu Tanghttps://orcid.org/0000-0002-2682-6578 BackgroundLiver failure, including acute-on-chronic liver failure (ACLF), occurs mainly in young adults and is associated with high mortality and resource costs. The prognosis evaluation is a crucial part of the ACLF treatment process and should run through the entire diagnosis process. As a recently proposed novel algorithm, the quantitative difference (QD) algorithm holds promise for enhancing the prognosis evaluation of ACLF. ObjectiveThis study aims to examine whether the QD algorithm exhibits comparable or superior performance compared to the Model for End-Stage Liver Disease (MELD) in the context of prognosis evaluation. MethodsA total of 27 patients with ACLF were categorized into 2 groups based on their treatment preferences: the conventional treatment (n=12) and the double plasma molecular absorption system (DPMAS) with conventional treatment (n=15) groups. The prognosis evaluation was performed by the MELD and QD scoring systems. ResultsA significant reduction was observed in alanine aminotransferase (P=.02), aspartate aminotransferase (P<.001), and conjugated bilirubin (P=.002), both in P values and QD value (Lτ>1.69). A significant decrease in hemoglobin (P=.01), red blood cell count (P=.01), and total bilirubin (P=.02) was observed in the DPMAS group, but this decrease was not observed in QD (Lτ≤1.69). Furthermore, there was a significant association between MELD and QD values (P<.001). Significant differences were observed between groups based on patients’ treatment outcomes. Additionally, the QD algorithm can also demonstrate improvements in patient fatigue. DPMAS can reduce alanine aminotransferase, aspartate aminotransferase, and unconjugated bilirubin. ConclusionsAs a dynamic algorithm, the QD scoring system can evaluate the therapeutic effects in patients with ACLF, similar to MELD. Nevertheless, the QD scoring system surpasses the MELD by incorporating a broader range of indicators and considering patient variability.https://formative.jmir.org/2023/1/e45395 |
| spellingShingle | Tinghuai Huang Jianwei Huang Timon Cheng-Yi Liu Meng Li Rui She Liyu Liu Hongguang Qu Fei Liang Yuanjing Cao Yuanzheng Chen Lu Tang Evaluating the Effect of Artificial Liver Support on Acute-on-Chronic Liver Failure Using the Quantitative Difference Algorithm: Retrospective Study JMIR Formative Research |
| title | Evaluating the Effect of Artificial Liver Support on Acute-on-Chronic Liver Failure Using the Quantitative Difference Algorithm: Retrospective Study |
| title_full | Evaluating the Effect of Artificial Liver Support on Acute-on-Chronic Liver Failure Using the Quantitative Difference Algorithm: Retrospective Study |
| title_fullStr | Evaluating the Effect of Artificial Liver Support on Acute-on-Chronic Liver Failure Using the Quantitative Difference Algorithm: Retrospective Study |
| title_full_unstemmed | Evaluating the Effect of Artificial Liver Support on Acute-on-Chronic Liver Failure Using the Quantitative Difference Algorithm: Retrospective Study |
| title_short | Evaluating the Effect of Artificial Liver Support on Acute-on-Chronic Liver Failure Using the Quantitative Difference Algorithm: Retrospective Study |
| title_sort | evaluating the effect of artificial liver support on acute on chronic liver failure using the quantitative difference algorithm retrospective study |
| url | https://formative.jmir.org/2023/1/e45395 |
| work_keys_str_mv | AT tinghuaihuang evaluatingtheeffectofartificialliversupportonacuteonchronicliverfailureusingthequantitativedifferencealgorithmretrospectivestudy AT jianweihuang evaluatingtheeffectofartificialliversupportonacuteonchronicliverfailureusingthequantitativedifferencealgorithmretrospectivestudy AT timonchengyiliu evaluatingtheeffectofartificialliversupportonacuteonchronicliverfailureusingthequantitativedifferencealgorithmretrospectivestudy AT mengli evaluatingtheeffectofartificialliversupportonacuteonchronicliverfailureusingthequantitativedifferencealgorithmretrospectivestudy AT ruishe evaluatingtheeffectofartificialliversupportonacuteonchronicliverfailureusingthequantitativedifferencealgorithmretrospectivestudy AT liyuliu evaluatingtheeffectofartificialliversupportonacuteonchronicliverfailureusingthequantitativedifferencealgorithmretrospectivestudy AT hongguangqu evaluatingtheeffectofartificialliversupportonacuteonchronicliverfailureusingthequantitativedifferencealgorithmretrospectivestudy AT feiliang evaluatingtheeffectofartificialliversupportonacuteonchronicliverfailureusingthequantitativedifferencealgorithmretrospectivestudy AT yuanjingcao evaluatingtheeffectofartificialliversupportonacuteonchronicliverfailureusingthequantitativedifferencealgorithmretrospectivestudy AT yuanzhengchen evaluatingtheeffectofartificialliversupportonacuteonchronicliverfailureusingthequantitativedifferencealgorithmretrospectivestudy AT lutang evaluatingtheeffectofartificialliversupportonacuteonchronicliverfailureusingthequantitativedifferencealgorithmretrospectivestudy |