Editorial: Assessing the Prognosis of Patients With HBV and ACLF—Comorbidities Matter. Authors' Reply

IF 6.6 1区 医学 Q1 GASTROENTEROLOGY & HEPATOLOGY
Jiong Yu, Xinyi Chen, Guoqiang Cao, Qiaoling Pan, Chenjie Huang, Rui Luo, Xiaoqing Lu, Xiaoxiao Chen, Tan Li, Haijun Huang, Jian Wu, Lanjuan Li, Hongcui Cao
{"title":"Editorial: Assessing the Prognosis of Patients With HBV and ACLF—Comorbidities Matter. Authors' Reply","authors":"Jiong Yu, Xinyi Chen, Guoqiang Cao, Qiaoling Pan, Chenjie Huang, Rui Luo, Xiaoqing Lu, Xiaoxiao Chen, Tan Li, Haijun Huang, Jian Wu, Lanjuan Li, Hongcui Cao","doi":"10.1111/apt.18392","DOIUrl":null,"url":null,"abstract":"<p>We extend our sincere gratitude to Dr. Francesco Paolo Russo and Alberto Ferrarese for their thorough evaluation and professional insights on our study [<span>1</span>]. We are gratified by their recognition of the potential of the age-adjusted Charlson Comorbidity Index for Hepatitis B Virus-Related Acute-on-Chronic Liver Failure (aCCI-HBV-ACLF) score in enhancing the accuracy of short-term and medium-term prognostic predictions, particularly in integrating comorbidity factors and reducing variability among clinicians [<span>2</span>].</p>\n<p>Previous research has established that multiple comorbidities are strongly associated with poor prognosis, with extrahepatic complications such as chronic renal failure and diabetes significantly elevating the mortality risk in patients with liver disease [<span>3-5</span>]. However, the relatively low incidence of these comorbidities presents challenges in fully incorporating them into prognostic models. Although the aCCI was initially designed for long-term prognostic evaluation, study has underscored its relevance in evaluating the prognosis of liver disease patients [<span>6</span>]. Similarly, our further analysis demonstrated that in the short-term prognosis of patients with HBV-related ACLF, nearly all comorbidities included in the aCCI are significantly correlated with short-term survival outcomes (Table 1). For instance, cardiovascular diseases were associated with a 287% increase in the 28-day mortality risk and a 267% increase in the 90-day mortality risk. Additionally, patients with chronic obstructive pulmonary disease, connective tissue diseases, diabetes, moderate to severe renal disease, tumours and haematological diseases exhibited substantially increased mortality risks. In contrast, although peptic ulcer disease showed a certain increase in risk, it did not reach statistical significance (<i>p</i> &gt; 0.05).</p>\n<div>\n<header><span>TABLE 1. </span>Relationships between comorbidity and 28-day mortality and 90-day mortality in patients with HBV-ACLF.</header>\n<div tabindex=\"0\">\n<table>\n<thead>\n<tr>\n<th rowspan=\"2\">Variables</th>\n<th rowspan=\"2\">Total (<i>n</i>)</th>\n<th colspan=\"3\">28-day mortality</th>\n<th colspan=\"3\">90-day mortality</th>\n</tr>\n<tr>\n<th style=\"top: 41px;\">Events (%)</th>\n<th style=\"top: 41px;\">HR (95% CI)</th>\n<th style=\"top: 41px;\"><i>p</i> value</th>\n<th style=\"top: 41px;\">Events (%)</th>\n<th style=\"top: 41px;\">HR (95% CI)</th>\n<th style=\"top: 41px;\"><i>p</i> value</th>\n</tr>\n</thead>\n<tbody>\n<tr>\n<td>Total</td>\n<td>1238</td>\n<td>295 (23.8)</td>\n<td></td>\n<td></td>\n<td>397 (32.1)</td>\n<td></td>\n<td></td>\n</tr>\n<tr>\n<td colspan=\"8\">Cardiovascular diseases<sup>a</sup></td>\n</tr>\n<tr>\n<td style=\"padding-left:2em;\">Yes</td>\n<td>31</td>\n<td>22 (71.0)</td>\n<td rowspan=\"2\">3.87 (2.51, 5.98)</td>\n<td rowspan=\"2\">&lt; 0.001</td>\n<td>24 (77.4)</td>\n<td rowspan=\"2\">3.67 (2.42, 5.56)</td>\n<td rowspan=\"2\">&lt; 0.001</td>\n</tr>\n<tr>\n<td style=\"padding-left:2em;\">No</td>\n<td>1207</td>\n<td>273 (22.6)</td>\n<td>373 (30.9)</td>\n</tr>\n<tr>\n<td colspan=\"8\">COPD</td>\n</tr>\n<tr>\n<td style=\"padding-left:2em;\">Yes</td>\n<td>22</td>\n<td>15 (68.2)</td>\n<td rowspan=\"2\">4.02 (2.39, 6.76)</td>\n<td rowspan=\"2\">&lt; 0.001</td>\n<td>15 (68.2)</td>\n<td rowspan=\"2\">3.29 (1.96, 5.52)</td>\n<td rowspan=\"2\">&lt; 0.001</td>\n</tr>\n<tr>\n<td style=\"padding-left:2em;\">No</td>\n<td>1216</td>\n<td>280 (23.0)</td>\n<td>382 (31.4)</td>\n</tr>\n<tr>\n<td colspan=\"8\">Connective tissue disease</td>\n</tr>\n<tr>\n<td style=\"padding-left:2em;\">Yes</td>\n<td>25</td>\n<td>13 (52.0)</td>\n<td rowspan=\"2\">2.45 (1.41, 4.28)</td>\n<td rowspan=\"2\">&lt; 0.001</td>\n<td>17 (68.0)</td>\n<td rowspan=\"2\">2.66 (1.64, 4.33)</td>\n<td rowspan=\"2\">&lt; 0.001</td>\n</tr>\n<tr>\n<td style=\"padding-left:2em;\">No</td>\n<td>1213</td>\n<td>282 (23.2)</td>\n<td>380 (31.3)</td>\n</tr>\n<tr>\n<td colspan=\"8\">Ulcer disease</td>\n</tr>\n<tr>\n<td style=\"padding-left:2em;\">Yes</td>\n<td>27</td>\n<td>10 (37.0)</td>\n<td rowspan=\"2\">1.64 (0.87, 3.08)</td>\n<td rowspan=\"2\">0.124</td>\n<td>13 (48.2)</td>\n<td rowspan=\"2\">1.64 (0.94, 2.85)</td>\n<td rowspan=\"2\">0.079</td>\n</tr>\n<tr>\n<td style=\"padding-left:2em;\">No</td>\n<td>1211</td>\n<td>285 (23.5)</td>\n<td>384 (31.7)</td>\n</tr>\n<tr>\n<td colspan=\"8\">Diabetes</td>\n</tr>\n<tr>\n<td style=\"padding-left:2em;\">Yes</td>\n<td>116</td>\n<td>44 (37.9)</td>\n<td rowspan=\"2\">1.85 (1.34, 2.55)</td>\n<td rowspan=\"2\">&lt; 0.001</td>\n<td>58 (50.0)</td>\n<td rowspan=\"2\">1.90 (1.43, 2.51)</td>\n<td rowspan=\"2\">&lt; 0.001</td>\n</tr>\n<tr>\n<td style=\"padding-left:2em;\">No</td>\n<td>1122</td>\n<td>251 (22.4)</td>\n<td>339 (30.2)</td>\n</tr>\n<tr>\n<td colspan=\"8\">Moderate to severe kidney disease</td>\n</tr>\n<tr>\n<td style=\"padding-left:2em;\">Yes</td>\n<td>60</td>\n<td>38 (63.3)</td>\n<td rowspan=\"2\">4.54 (3.22, 6.39)</td>\n<td rowspan=\"2\">&lt; 0.001</td>\n<td>45 (75.0)</td>\n<td rowspan=\"2\">4.32 (3.16, 5.90)</td>\n<td rowspan=\"2\">&lt; 0.001</td>\n</tr>\n<tr>\n<td style=\"padding-left:2em;\">No</td>\n<td>1178</td>\n<td>257 (21.8)</td>\n<td>352 (29.9)</td>\n</tr>\n<tr>\n<td colspan=\"8\">Tumours and haematological diseases<sup>a</sup></td>\n</tr>\n<tr>\n<td style=\"padding-left:2em;\">Yes</td>\n<td>43</td>\n<td>22 (51.2)</td>\n<td rowspan=\"2\">2.57 (1.67, 3.97)</td>\n<td rowspan=\"2\">&lt; 0.001</td>\n<td>24 (55.8)</td>\n<td rowspan=\"2\">2.21 (1.46, 3.34)</td>\n<td rowspan=\"2\">&lt; 0.001</td>\n</tr>\n<tr>\n<td style=\"padding-left:2em;\">No</td>\n<td>1195</td>\n<td>273 (22.9)</td>\n<td>373 (31.2)</td>\n</tr>\n</tbody>\n</table>\n</div>\n<div>\n<ul>\n<li>\n<i>Note:</i> Tumours and haematological diseases, including leukaemia, lymphoma, metastatic solid tumour and any tumour. </li>\n<li> Abbreviations: CI, confidence interval; COPD, chronic obstructive pulmonary disease; HBV-ACLF, hepatitis B virus-related acute-on-chronic liver failure; HR, hazard ratio. </li>\n<li title=\"Footnote 1\"><span>\n<sup>a</sup>\n</span> Cardiovascular diseases, including myocardial infarction, congestive heart failure, peripheral vascular disease and cerebrovascular disease. </li>\n</ul>\n</div>\n<div></div>\n</div>\n<p>We fully concur with the concern regarding the exclusion of liver transplant patients from the study population. Liver transplantation is a crucial therapeutic option for ACLF, demonstrating significant improvements in patient outcomes [<span>7, 8</span>]. Studies have demonstrated that liver transplantation markedly enhances survival rates compared to patients not receiving transplantation, with only a small proportion succumbing to surgical complications or postoperative issues [<span>9</span>]. Including liver transplant recipients in this study could disproportionately elevate the incidence of end-point events, potentially leading to an overestimation of associated risks. Moreover, differences in medical expertise and resources across centres can significantly influence transplant success rates and post-transplant survival outcomes. Critical determinants of transplantation outcomes include the quality of donor organs, the overall health status of recipients and the expertise of the surgical team [<span>10</span>]. Not all patients awaiting transplantation are eligible, and therefore, including liver transplant recipients might compromise the predictive accuracy of the model when applied to other medical centres. Consequently, liver transplant recipients were excluded from the model's development.</p>\n<p>We also acknowledge the potential influence of regional and racial variations on the generalisability of the aCCI-HBV-ACLF score. To address this, we aim to expand our data collection in future studies to validate the score in diverse populations. Once again, we are grateful for their in-depth review and constructive suggestions on our study. Their feedback has further illuminated the applicability and avenues for optimisation of the aCCI-HBV-ACLF score in different populations. We firmly believe that the aCCI-HBV-ACLF score can provide a more accurate prognostic assessment tool for the treatment and management of patients with ACLF in clinical practice.</p>","PeriodicalId":121,"journal":{"name":"Alimentary Pharmacology & Therapeutics","volume":"46 1","pages":""},"PeriodicalIF":6.6000,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Alimentary Pharmacology & Therapeutics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1111/apt.18392","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GASTROENTEROLOGY & HEPATOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

We extend our sincere gratitude to Dr. Francesco Paolo Russo and Alberto Ferrarese for their thorough evaluation and professional insights on our study [1]. We are gratified by their recognition of the potential of the age-adjusted Charlson Comorbidity Index for Hepatitis B Virus-Related Acute-on-Chronic Liver Failure (aCCI-HBV-ACLF) score in enhancing the accuracy of short-term and medium-term prognostic predictions, particularly in integrating comorbidity factors and reducing variability among clinicians [2].

Previous research has established that multiple comorbidities are strongly associated with poor prognosis, with extrahepatic complications such as chronic renal failure and diabetes significantly elevating the mortality risk in patients with liver disease [3-5]. However, the relatively low incidence of these comorbidities presents challenges in fully incorporating them into prognostic models. Although the aCCI was initially designed for long-term prognostic evaluation, study has underscored its relevance in evaluating the prognosis of liver disease patients [6]. Similarly, our further analysis demonstrated that in the short-term prognosis of patients with HBV-related ACLF, nearly all comorbidities included in the aCCI are significantly correlated with short-term survival outcomes (Table 1). For instance, cardiovascular diseases were associated with a 287% increase in the 28-day mortality risk and a 267% increase in the 90-day mortality risk. Additionally, patients with chronic obstructive pulmonary disease, connective tissue diseases, diabetes, moderate to severe renal disease, tumours and haematological diseases exhibited substantially increased mortality risks. In contrast, although peptic ulcer disease showed a certain increase in risk, it did not reach statistical significance (p > 0.05).

TABLE 1. Relationships between comorbidity and 28-day mortality and 90-day mortality in patients with HBV-ACLF.
Variables Total (n) 28-day mortality 90-day mortality
Events (%) HR (95% CI) p value Events (%) HR (95% CI) p value
Total 1238 295 (23.8) 397 (32.1)
Cardiovascular diseasesa
Yes 31 22 (71.0) 3.87 (2.51, 5.98) < 0.001 24 (77.4) 3.67 (2.42, 5.56) < 0.001
No 1207 273 (22.6) 373 (30.9)
COPD
Yes 22 15 (68.2) 4.02 (2.39, 6.76) < 0.001 15 (68.2) 3.29 (1.96, 5.52) < 0.001
No 1216 280 (23.0) 382 (31.4)
Connective tissue disease
Yes 25 13 (52.0) 2.45 (1.41, 4.28) < 0.001 17 (68.0) 2.66 (1.64, 4.33) < 0.001
No 1213 282 (23.2) 380 (31.3)
Ulcer disease
Yes 27 10 (37.0) 1.64 (0.87, 3.08) 0.124 13 (48.2) 1.64 (0.94, 2.85) 0.079
No 1211 285 (23.5) 384 (31.7)
Diabetes
Yes 116 44 (37.9) 1.85 (1.34, 2.55) < 0.001 58 (50.0) 1.90 (1.43, 2.51) < 0.001
No 1122 251 (22.4) 339 (30.2)
Moderate to severe kidney disease
Yes 60 38 (63.3) 4.54 (3.22, 6.39) < 0.001 45 (75.0) 4.32 (3.16, 5.90) < 0.001
No 1178 257 (21.8) 352 (29.9)
Tumours and haematological diseasesa
Yes 43 22 (51.2) 2.57 (1.67, 3.97) < 0.001 24 (55.8) 2.21 (1.46, 3.34) < 0.001
No 1195 273 (22.9) 373 (31.2)
  • Note: Tumours and haematological diseases, including leukaemia, lymphoma, metastatic solid tumour and any tumour.
  • Abbreviations: CI, confidence interval; COPD, chronic obstructive pulmonary disease; HBV-ACLF, hepatitis B virus-related acute-on-chronic liver failure; HR, hazard ratio.
  • a Cardiovascular diseases, including myocardial infarction, congestive heart failure, peripheral vascular disease and cerebrovascular disease.

We fully concur with the concern regarding the exclusion of liver transplant patients from the study population. Liver transplantation is a crucial therapeutic option for ACLF, demonstrating significant improvements in patient outcomes [7, 8]. Studies have demonstrated that liver transplantation markedly enhances survival rates compared to patients not receiving transplantation, with only a small proportion succumbing to surgical complications or postoperative issues [9]. Including liver transplant recipients in this study could disproportionately elevate the incidence of end-point events, potentially leading to an overestimation of associated risks. Moreover, differences in medical expertise and resources across centres can significantly influence transplant success rates and post-transplant survival outcomes. Critical determinants of transplantation outcomes include the quality of donor organs, the overall health status of recipients and the expertise of the surgical team [10]. Not all patients awaiting transplantation are eligible, and therefore, including liver transplant recipients might compromise the predictive accuracy of the model when applied to other medical centres. Consequently, liver transplant recipients were excluded from the model's development.

We also acknowledge the potential influence of regional and racial variations on the generalisability of the aCCI-HBV-ACLF score. To address this, we aim to expand our data collection in future studies to validate the score in diverse populations. Once again, we are grateful for their in-depth review and constructive suggestions on our study. Their feedback has further illuminated the applicability and avenues for optimisation of the aCCI-HBV-ACLF score in different populations. We firmly believe that the aCCI-HBV-ACLF score can provide a more accurate prognostic assessment tool for the treatment and management of patients with ACLF in clinical practice.

社论:评估 HBV 和 ACLF 患者的预后--合并症很重要。作者回复
他们的反馈进一步阐明了 aCCI-HBV-ACLF 评分在不同人群中的适用性和优化途径。我们坚信,aCCI-HBV-ACLF 评分能为临床实践中 ACLF 患者的治疗和管理提供更准确的预后评估工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
15.60
自引率
7.90%
发文量
527
审稿时长
3-6 weeks
期刊介绍: Alimentary Pharmacology & Therapeutics is a global pharmacology journal focused on the impact of drugs on the human gastrointestinal and hepato-biliary systems. It covers a diverse range of topics, often with immediate clinical relevance to its readership.
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