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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Aging Neurosci.</journal-id>
<journal-title>Frontiers in Aging Neuroscience</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Aging Neurosci.</abbrev-journal-title>
<issn pub-type="epub">1663-4365</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fnagi.2023.1259668</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Aging Neuroscience</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Hemoglobin to red cell distribution width ratio as a prognostic marker for ischemic stroke after mechanical thrombectomy</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author"><name><surname>Feng</surname> <given-names>Xianrong</given-names></name><xref rid="aff1" ref-type="aff"><sup>1</sup></xref>
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</contrib>
<contrib contrib-type="author"><name><surname>Zhang</surname> <given-names>Yaodan</given-names></name><xref rid="aff1" ref-type="aff"><sup>1</sup></xref>
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<contrib contrib-type="author"><name><surname>Li</surname> <given-names>Qizheng</given-names></name><xref rid="aff1" ref-type="aff"><sup>1</sup></xref>
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</contrib>
<contrib contrib-type="author"><name><surname>Wang</surname> <given-names>Baojia</given-names></name><xref rid="aff1" ref-type="aff"><sup>1</sup></xref>
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<contrib contrib-type="author" corresp="yes"><name><surname>Shen</surname> <given-names>Jie</given-names></name><xref rid="aff2" ref-type="aff"><sup>2</sup></xref><xref rid="c001" ref-type="corresp"><sup>&#x002A;</sup></xref>
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<aff id="aff1"><sup>1</sup><institution>Department of Neurology, Hospital of Chengdu University of Traditional Chinese Medicine</institution>, <addr-line>Chengdu, Sichuan</addr-line>, <country>China</country></aff>
<aff id="aff2"><sup>2</sup><institution>Department of Neurology, Chengdu Second People&#x2019;s Hospital</institution>, <addr-line>Chengdu, Sichuan</addr-line>, <country>China</country></aff>
<author-notes>
<fn fn-type="edited-by" id="fn0001">
<p>Edited by: Stefano Tarantini, University of Oklahoma Health Sciences Center, United States</p>
</fn>
<fn fn-type="edited-by" id="fn0002">
<p>Reviewed by: Andreia Morais, Harvard Medical School, United States; Kai Wang, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, China</p>
</fn>
<corresp id="c001">&#x002A;Correspondence: Jie Shen, <email>370375606@qq.com</email></corresp>
</author-notes>
<pub-date pub-type="epub">
<day>21</day>
<month>11</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="collection">
<year>2023</year>
</pub-date>
<volume>15</volume>
<elocation-id>1259668</elocation-id>
<history>
<date date-type="received">
<day>31</day>
<month>07</month>
<year>2023</year>
</date>
<date date-type="accepted">
<day>16</day>
<month>10</month>
<year>2023</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x00A9; 2023 Feng, Zhang, Li, Wang and Shen.</copyright-statement>
<copyright-year>2023</copyright-year>
<copyright-holder>Feng, Zhang, Li, Wang and Shen</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p>
</license>
</permissions>
<abstract>
<sec id="sec1">
<title>Background</title>
<p>The hemoglobin to red cell distribution width ratio (HRR) has been experimentally associated with the prognosis of acute ischemic stroke (AIS). However, its relationship with mechanical thrombectomy (MT) for AIS remains unclear. Therefore, this study aimed to investigate the relationship between HRR at admission, follow-up HRR, and clinical outcomes in patients undergoing MT.</p>
</sec>
<sec id="sec2">
<title>Methods</title>
<p>Acute ischemic stroke patients undergoing MT were consecutively enrolled from January 2017 to December 2022. Demographic, clinical, and laboratory information were collected. HRR was measured by dividing hemoglobin (Hb) by red cell distribution width (RDW) at admission and after 24&#x2009;h of MT. Clinical outcomes after 3&#x2009;months were evaluated using the modified Rankin Scale (mRS). The primary outcome was poor prognosis (mRS&#x2009;&#x003E;&#x2009;2) at 3&#x2009;months, while the secondary outcome was death within 3&#x2009;months.</p>
</sec>
<sec id="sec3">
<title>Results</title>
<p>A total of 310 patients were analyzed, of whom 216 patients (69.7%) had poor prognosis, and 92 patients (29.6%) died. Patients with a poor prognosis and death had significantly lower HRR levels at admission and after 24&#x2009;h. HRR at admission was not associated with clinical outcomes according to multivariable logistic regression analysis. However, HRR after 24&#x2009;h was significantly associated with poor prognosis (adjusted odds ratio [OR]: 0.646, 95% confidence interval [CI]: 0.520&#x2013;0.803, <italic>p</italic> &#x003C;&#x2009;0.001) and death (adjusted OR: 0.615, 95% CI: 0.508&#x2013;0.744, <italic>p</italic> &#x003C;&#x2009;0.001). Receiver-operating characteristic curve analysis demonstrated the predictive ability of HRR after 24&#x2009;h, with areas under the curves of 0.790 for poor prognosis and 0.771 for death.</p>
</sec>
<sec id="sec4">
<title>Conclusion</title>
<p>Rapidly measurable HRR levels are an independent marker of outcome after MT in AIS patients. This may provide a reliable auxiliary outcome measure for clinical routine and interventional therapy.</p>
</sec>
</abstract>
<kwd-group>
<kwd>acute ischemic stroke</kwd>
<kwd>mechanical thrombectomy</kwd>
<kwd>hemoglobin to red cell distribution width ratio</kwd>
<kwd>poor prognosis</kwd>
<kwd>death</kwd>
<kwd>marker</kwd>
</kwd-group>
<contract-num rid="cn1">2018YFC1311400</contract-num>
<contract-sponsor id="cn1">Chronic Disease Prevention and Control Technology in Southwest China</contract-sponsor>
<counts>
<fig-count count="3"/>
<table-count count="3"/>
<equation-count count="0"/>
<ref-count count="56"/>
<page-count count="9"/>
<word-count count="6612"/>
</counts>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Neuroinflammation and Neuropathy</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec sec-type="intro" id="sec5">
<label>1</label>
<title>Introduction</title>
<p>The Global Burden of Disease Report identifies stroke as the second leading cause of worldwide mortality and disability (<xref ref-type="bibr" rid="ref23">Krishnamurthi et al., 2020</xref>). Mechanical thrombectomy (MT) has emerged as one of the most efficacious therapies for acute ischemic stroke (AIS; <xref ref-type="bibr" rid="ref35">Powers et al., 2018</xref>, <xref ref-type="bibr" rid="ref36">2019</xref>). Timely and effective thrombectomy significantly improves the prognosis for AIS patients. However, complications such as cerebral hemorrhage, vascular re-occlusion, and cerebral edema may occur after MT, and the limited treatment time window presents challenges to treatment efficacy (<xref ref-type="bibr" rid="ref21">Kimberly et al., 2018</xref>; <xref ref-type="bibr" rid="ref26">Luby et al., 2023</xref>; <xref ref-type="bibr" rid="ref46">Sarraj et al., 2023</xref>). Hence, it is crucial to investigate simple, convenient, and effective clinical indicators that can predict the prognosis of AIS, guide clinical decision-making, and enhance treatment outcomes.</p>
<p>Hemoglobin (Hb) and red cell distribution width (RDW) are conventional blood test parameters. Hb levels determine the oxygen-carrying capacity, while RDW quantifies the variation in red blood cell size (<xref ref-type="bibr" rid="ref25">Lippi and Plebani, 2014</xref>; <xref ref-type="bibr" rid="ref45">Salvagno et al., 2015</xref>). These parameters not only reflect the balance between hematopoietic function and red blood cell survival but also play a critical role in inflammation, oxidative stress, and the vascular innate immune system (<xref ref-type="bibr" rid="ref7">Emans et al., 2011</xref>; <xref ref-type="bibr" rid="ref31">Patel et al., 2013</xref>; <xref ref-type="bibr" rid="ref19">Kawabata, 2020</xref>). For instance, anemia has been shown to induce the release of interleukin-6 and tumor necrosis factor-&#x03B1; (<xref ref-type="bibr" rid="ref11">Feret et al., 2022</xref>), and RDW has been linked to inflammatory markers and oxidative stress (<xref ref-type="bibr" rid="ref12">F&#x00F6;rh&#x00E9;cz et al., 2009</xref>). Inflammation and oxidative stress can worsen cerebral edema and hemorrhage, delay cerebral ischemia, and contribute to poor prognosis (<xref ref-type="bibr" rid="ref56">Zhao et al., 2017</xref>; <xref ref-type="bibr" rid="ref8">Esposito et al., 2022</xref>; <xref ref-type="bibr" rid="ref49">Westendorp et al., 2022</xref>). These findings suggest that both Hb and RDW, as essential markers of underlying inflammatory processes, may be correlated with the clinical outcome of ischemic stroke. Hb levels have been identified as significant predictors of AIS and coronary heart disease (<xref ref-type="bibr" rid="ref24">Kwok et al., 2016</xref>; <xref ref-type="bibr" rid="ref4">Chang et al., 2020</xref>; <xref ref-type="bibr" rid="ref54">Zhang et al., 2021</xref>). Research has also demonstrated that RDW can predict adverse outcomes in patients with acute myocardial infarction receiving percutaneous coronary intervention (PCI) and in patients with AIS (<xref ref-type="bibr" rid="ref10">Feng et al., 2017</xref>; <xref ref-type="bibr" rid="ref30">Parizadeh et al., 2019</xref>; <xref ref-type="bibr" rid="ref50">Xiao et al., 2022</xref>). Furthermore, RDW, regardless of anemia status, has been associated with stroke severity and adverse outcomes in AIS, thereby improving stroke prediction accuracy (<xref ref-type="bibr" rid="ref44">Saliba et al., 2015</xref>; <xref ref-type="bibr" rid="ref52">Xue et al., 2022</xref>).</p>
<p>Recently, the hemoglobin-to-red cell distribution width ratio (HRR) has emerged as a novel biomarker for cardiovascular diseases. It is calculated from Hb and RDW without additional costs (<xref ref-type="bibr" rid="ref39">Rahamim et al., 2022</xref>; <xref ref-type="bibr" rid="ref51">Xiu et al., 2022</xref>). HRR has been studied in relation to myocardial infarction stenting and ischemic stroke associated with atrial fibrillation (<xref ref-type="bibr" rid="ref37">Qin et al., 2022</xref>; <xref ref-type="bibr" rid="ref48">Sun et al., 2022</xref>). However, further investigation is needed to determine whether HRR can effectively predict the prognosis of AIS patients undergoing thrombectomy. Additionally, various components of the immune system undergo dynamic changes after AIS, which may have varying effects depending on the stage of stroke development. Therefore, our study aims to investigate the association between HRR and the prognosis of AIS patients undergoing thrombectomy, as well as explore the optimal time point at which HRR functions as a prognostic marker.</p>
</sec>
<sec sec-type="materials|methods" id="sec6">
<label>2</label>
<title>Materials and methods</title>
<sec id="sec7">
<label>2.1</label>
<title>Study population</title>
<p>From January 2017 to December 2022, this retrospective study included consecutive patients who underwent MT at Chengdu Second People&#x2019;s Hospital, based on a prospective database. The MT selection criteria and time window strictly adhered to the current guidelines of the American Heart Association/American Stroke Association for the Early Management of Acute Ischemic Stroke Patients (<xref ref-type="bibr" rid="ref34">Powers et al., 2015</xref>, <xref ref-type="bibr" rid="ref35">2018</xref>; <xref ref-type="bibr" rid="ref14">Greenberg et al., 2022</xref>). The choice of materials and thrombectomy approach, whether stent-retriever, aspiration, or a combined technique, was determined by the neuro-interventionalist. Follow-up computed tomography (CT) scans were performed approximately 24&#x2009;h after MT to assess any intracranial hemorrhage.</p>
<p>The exclusion criteria for this study were as follows: (1) acute or chronic infection; (2) severe systemic illnesses such as malignancy, hematological disorders, severe heart failure, liver, or renal dysfunction; (3) incomplete clinical data; (4) modified Rankin Scale (mRS) score&#x2009;&#x003E;&#x2009;2 prior to the onset of stroke; and (5) patients who were lost to follow-up. 378 AIS patients who underwent MT were initially screened, and 37 patients were excluded based on the inclusion and exclusion criteria. Additionally, 21 patients were lost during the follow-up period. After excluding 10 patients who lacked baseline Hb and RDW values, the final sample size for the analysis was 310 patients (<xref rid="fig1" ref-type="fig">Figure 1</xref>). The retrospective study obtained approval from the ethics committee of Chengdu Second People&#x2019;s Hospital, and patients or their families provided written informed consent.</p>
<fig position="float" id="fig1">
<label>Figure 1</label>
<caption>
<p>Study flow chart. AIS, Acute ischemic stroke; MT, Mechanical thrombectomy; mRS, Modified Rankin scale.</p>
</caption>
<graphic xlink:href="fnagi-15-1259668-g001.tif"/>
</fig>
</sec>
<sec id="sec8">
<label>2.2</label>
<title>Data collection</title>
<p>Demographic information, past medical history, vascular risk factors, National Institutes of Health Stroke Scale (NIHSS) scores at admission, pre-admission modified Rankin Scale (mRS) scores, results of computed tomography angiography or digital subtraction angiography, pre-treatment magnetic resonance imaging, and information regarding whether intravenous thrombolysis was administered before MT and post-MT recanalization rates were obtained from medical institution databases for analysis. Stroke etiology was classified according to the Trial of Org 10172 in Acute Stroke Treatment (TOAST) criteria (<xref ref-type="bibr" rid="ref1">Adams et al., 1993</xref>). Blood samples were collected at admission and 24&#x2009;h after MT to measure Hb and RDW levels. The HRR was calculated as the ratio of Hb to RDW. CT scans were conducted 24&#x2009;h after MT to evaluate symptomatic intracranial hemorrhage (sICH). Follow-up data on current mRS scores were obtained through standardized telephone interviews from symptom onset to 3&#x2009;months later.</p>
</sec>
<sec id="sec9">
<label>2.3</label>
<title>Clinical outcomes</title>
<p>Successful reperfusion was defined as the modified Thrombolysis in Cerebral Infarction (mTICI)&#x2009;&#x2265;&#x2009;2b (<xref ref-type="bibr" rid="ref28">Mokin et al., 2014</xref>; <xref ref-type="bibr" rid="ref18">Kaesmacher et al., 2018</xref>). sICH was defined as intracranial hemorrhage with an increase of at least four points on the NIHSS scale (<xref ref-type="bibr" rid="ref15">Hacke et al., 1998</xref>). The modified Rankin Scale (mRS) was used to assess the primary outcome at 3&#x2009;months; good prognosis was defined as mRS&#x2009;&#x2264;&#x2009;2, while poor prognosis was defined as mRS&#x2009;&#x003E;&#x2009;2 (<xref ref-type="bibr" rid="ref16">Hill et al., 2020</xref>; <xref ref-type="bibr" rid="ref40">Rebchuk et al., 2020</xref>). The secondary outcome was death within 3&#x2009;months of MT. Death was defined as an all-cause passing resulting from a stroke.</p>
</sec>
<sec id="sec10">
<label>2.4</label>
<title>Statistical analysis</title>
<p>All data were analyzed using SPSS version 22.0 (SPSS Inc., Chicago, IL, United States), GraphPad Prism version 8.0 (GraphPad Software, San Diego, California, United States), and MedCalc version 22.0 (MedCalc Software, Ostend, Belgium). The Kolmogorov&#x2013;Smirnov test assessed the distributional normality. Categorical variables were presented as counts and percentages, and compared using the chi-squared test, while continuous variables were presented as mean&#x2009;&#x00B1;&#x2009;standard deviation or median with interquartile range (IQR). Multivariate logistic regression was used to examine the impact of HRR on outcomes, adjusted for variables selected through the forward selection method, calculating odds ratios (ORs), and 95% confidence intervals (CIs). The Pearson correlation test was utilized to analyze the correlation between baseline laboratory data and data collected after 24&#x2009;h. The discrimination of Hb, RDW, and HRR for outcomes was analyzed using the area under the receiver operating characteristic curve (AUC-ROC). Cut-off values for each biomarker were determined by Youden index. The generalized linear model was obtained by binary logistic regression analysis combining different parameters. Prediction probability was calculated from the regression equation as an additional parameter, which was further evaluated by ROC analysis. <italic>p</italic>&#x2009;&#x003C;&#x2009;0.05 was regarded as statistically significant.</p>
</sec>
</sec>
<sec sec-type="results" id="sec11">
<label>3</label>
<title>Results</title>
<sec id="sec12">
<label>3.1</label>
<title>Basic characteristics of study patients</title>
<p>The study included a total of 310 patients who underwent MT (<xref rid="fig1" ref-type="fig">Figure 1</xref>). The detailed characteristics of the patients are shown in <xref rid="tab1" ref-type="table">Table 1</xref>. The median age of the patients was 72 (60&#x2013;79) years, with 139 (44.8%) being female. The median NIHSS score at baseline was 15 (12&#x2013;20). Prior to MT, 86 patients (27.7%) received intravenous thrombolysis. Successful reperfusion (mTICI&#x2009;&#x2265;&#x2009;2b) was achieved in 82.3% of cases following MT. After MT, sICH was observed in 39 patients (12.6%). At 3&#x2009;months, 216 patients (69.7%) had a poor prognosis, and 92 patients (29.6%) died. In addition, significant statistical differences were found between patients with good and poor prognosis concerning age, sex, atrial fibrillation, baseline NIHSS score at admission, NIHSS score after 24&#x2009;h, sICH, and successful reperfusion (<italic>p</italic>&#x2009;&#x003C;&#x2009;0.05). Those who died within 3&#x2009;months were more likely to be female, had older age, a higher NIHSS score at admission and after 24&#x2009;h, a higher incidence of sICH, and a lower success rate of reperfusion (<italic>p</italic>&#x2009;&#x003C;&#x2009;0.05).</p>
<table-wrap position="float" id="tab1">
<label>Table 1</label>
<caption>
<p>Basic characteristics of study population according to 3-month prognosis and occurrence of death.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Variables</th>
<th align="center" valign="top">Total (<italic>N</italic>&#x2009;=&#x2009;310)</th>
<th align="center" valign="top">Good prognosis (<italic>N</italic>&#x2009;=&#x2009;94)</th>
<th align="center" valign="top">Poor prognosis (<italic>N</italic>&#x2009;=&#x2009;216)</th>
<th align="center" valign="top">
<italic>p</italic>
</th>
<th align="center" valign="top">Alive (<italic>N</italic>&#x2009;=&#x2009;218)</th>
<th align="center" valign="top">Dead (<italic>N</italic>&#x2009;=&#x2009;92)</th>
<th align="center" valign="top">
<italic>p</italic>
</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="middle" colspan="8">Demographic data</td>
</tr>
<tr>
<td align="left" valign="middle">Age, years, median (IQR)</td>
<td align="center" valign="middle">72 (60&#x2013;79)</td>
<td align="center" valign="middle">64 (54&#x2013;75)</td>
<td align="center" valign="middle">74 (63&#x2013;81)</td>
<td align="center" valign="middle">&#x003C;0.001</td>
<td align="center" valign="middle">70 (58&#x2013;78)</td>
<td align="center" valign="middle">76 (66&#x2013;81)</td>
<td align="center" valign="middle">0.003</td>
</tr>
<tr>
<td align="left" valign="middle">Sex (female), <italic>n</italic> (%)</td>
<td align="center" valign="middle">139 (44.8)</td>
<td align="center" valign="middle">29 (30.9)</td>
<td align="center" valign="middle">110 (50.9)</td>
<td align="center" valign="middle">0.001</td>
<td align="center" valign="middle">88 (40.4)</td>
<td align="center" valign="middle">51 (55.4)</td>
<td align="center" valign="middle">0.018</td>
</tr>
<tr>
<td align="left" valign="middle" colspan="8">Stroke risk factors, <italic>n</italic> (%)</td>
</tr>
<tr>
<td align="left" valign="middle">Hypertension</td>
<td align="center" valign="middle">166 (53.5)</td>
<td align="center" valign="middle">49 (52.1)</td>
<td align="center" valign="middle">117 (54.2)</td>
<td align="center" valign="middle">0.805</td>
<td align="center" valign="middle">123 (56.4)</td>
<td align="center" valign="middle">43 (46.7)</td>
<td align="center" valign="middle">0.135</td>
</tr>
<tr>
<td align="left" valign="middle">Diabetes mellitus</td>
<td align="center" valign="middle">55 (17.7)</td>
<td align="center" valign="middle">14 (14.9)</td>
<td align="center" valign="middle">41 (19.0)</td>
<td align="center" valign="middle">0.423</td>
<td align="center" valign="middle">35 (16.1)</td>
<td align="center" valign="middle">20 (21.7)</td>
<td align="center" valign="middle">0.256</td>
</tr>
<tr>
<td align="left" valign="middle">History of stroke</td>
<td align="center" valign="middle">39 (12.6)</td>
<td align="center" valign="middle">11 (11.7)</td>
<td align="center" valign="middle">28 (13.0)</td>
<td align="center" valign="middle">0.853</td>
<td align="center" valign="middle">27 (12.4)</td>
<td align="center" valign="middle">12 (13.0)</td>
<td align="center" valign="middle">0.853</td>
</tr>
<tr>
<td align="left" valign="middle">Hyperlipidemia</td>
<td align="center" valign="middle">38 (12.3)</td>
<td align="center" valign="middle">12 (12.8)</td>
<td align="center" valign="middle">26 (12.0)</td>
<td align="center" valign="middle">0.852</td>
<td align="center" valign="middle">25 (11.5)</td>
<td align="center" valign="middle">13 (14.1)</td>
<td align="center" valign="middle">0.570</td>
</tr>
<tr>
<td align="left" valign="middle">Atrial fibrillation</td>
<td align="center" valign="middle">160 (51.6)</td>
<td align="center" valign="middle">33 (35.1)</td>
<td align="center" valign="middle">127 (58.8)</td>
<td align="center" valign="middle">&#x003C;0.001</td>
<td align="center" valign="middle">109 (50.0)</td>
<td align="center" valign="middle">51 (55.4)</td>
<td align="center" valign="middle">0.387</td>
</tr>
<tr>
<td align="left" valign="middle">Smoking</td>
<td align="center" valign="middle">95 (30.6)</td>
<td align="center" valign="middle">35 (37.2)</td>
<td align="center" valign="middle">60 (27.8)</td>
<td align="center" valign="middle">0.108</td>
<td align="center" valign="middle">70 (32.1)</td>
<td align="center" valign="middle">25 (27.2)</td>
<td align="center" valign="middle">0.421</td>
</tr>
<tr>
<td align="left" valign="middle">Drinking</td>
<td align="center" valign="middle">59 (19.0)</td>
<td align="center" valign="middle">23 (24.5)</td>
<td align="center" valign="middle">36 (16.7)</td>
<td align="center" valign="middle">0.117</td>
<td align="center" valign="middle">46 (21.1)</td>
<td align="center" valign="middle">13 (14.1)</td>
<td align="center" valign="middle">0.205</td>
</tr>
<tr>
<td align="left" valign="middle">Antiplatelet agents</td>
<td align="center" valign="middle">30 (9.7)</td>
<td align="center" valign="middle">11 (11.7)</td>
<td align="center" valign="middle">19 (8.8)</td>
<td align="center" valign="middle">0.412</td>
<td align="center" valign="middle">23 (10.6)</td>
<td align="center" valign="middle">7 (7.6)</td>
<td align="center" valign="middle">0.530</td>
</tr>
<tr>
<td align="left" valign="middle">Anticoagulant</td>
<td align="center" valign="middle">27 (8.7)</td>
<td align="center" valign="middle">5 (5.3)</td>
<td align="center" valign="middle">22 (10.2)</td>
<td align="center" valign="middle">0.193</td>
<td align="center" valign="middle">18 (8.3)</td>
<td align="center" valign="middle">9 (9.8)</td>
<td align="center" valign="middle">0.663</td>
</tr>
<tr>
<td align="left" valign="middle" colspan="8">Stroke evaluation</td>
</tr>
<tr>
<td align="left" valign="middle">Baseline NIHSS score, median (IQR)</td>
<td align="center" valign="middle">15 (12&#x2013;20)</td>
<td align="center" valign="middle">13 (9&#x2013;16)</td>
<td align="center" valign="middle">17 (13&#x2013;20)</td>
<td align="center" valign="middle">&#x003C;0.001</td>
<td align="center" valign="middle">14 (11&#x2013;18)</td>
<td align="center" valign="middle">17 (14&#x2013;22)</td>
<td align="center" valign="middle">&#x003C;0.001</td>
</tr>
<tr>
<td align="left" valign="middle">Intravenous thrombolysis, <italic>n</italic> (%)</td>
<td align="center" valign="middle">86 (27.7)</td>
<td align="center" valign="middle">23 (24.5)</td>
<td align="center" valign="middle">63 (29.2)</td>
<td align="center" valign="middle">0.412</td>
<td align="center" valign="middle">60 (27.5)</td>
<td align="center" valign="middle">26 (28.3)</td>
<td align="center" valign="middle">0.890</td>
</tr>
<tr>
<td align="left" valign="middle">Location of stroke, <italic>n</italic> (%)</td>
<td/>
<td/>
<td/>
<td align="center" valign="middle">0.196</td>
<td/>
<td/>
<td align="center" valign="middle">0.712</td>
</tr>
<tr>
<td align="left" valign="middle">Anterior circulation</td>
<td align="center" valign="middle">270 (87.1)</td>
<td align="center" valign="middle">78 (83.0)</td>
<td align="center" valign="middle">192 (88.9)</td>
<td/>
<td align="center" valign="middle">191 (87.6)</td>
<td align="center" valign="middle">79 (85.9)</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">Posterior circulation</td>
<td align="center" valign="middle">40 (12.9)</td>
<td align="center" valign="middle">16 (17.0)</td>
<td align="center" valign="middle">24 (11.1)</td>
<td/>
<td align="center" valign="middle">27 (12.4)</td>
<td align="center" valign="middle">13 (14.1)</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">Symptom onset to treatment time, min</td>
<td align="center" valign="middle">254 (187&#x2013;304)</td>
<td align="center" valign="middle">251 (174&#x2013;301)</td>
<td align="center" valign="middle">256 (189&#x2013;306)</td>
<td align="center" valign="middle">0.636</td>
<td align="center" valign="middle">243 (182&#x2013;302)</td>
<td align="center" valign="middle">261 (194&#x2013;316)</td>
<td align="center" valign="middle">0.091</td>
</tr>
<tr>
<td align="left" valign="middle">mTICI &#x2265;2b, <italic>n</italic> (%)</td>
<td align="center" valign="middle">255 (82.3)</td>
<td align="center" valign="middle">90 (95.7)</td>
<td align="center" valign="middle">165 (76.4)</td>
<td align="center" valign="middle">&#x003C;0.001</td>
<td align="center" valign="middle">191 (87.6)</td>
<td align="center" valign="middle">64 (69.6)</td>
<td align="center" valign="middle">&#x003C;0.001</td>
</tr>
<tr>
<td align="left" valign="middle">Pre-mRS</td>
<td align="center" valign="middle">0 (0&#x2013;0)</td>
<td align="center" valign="middle">0 (0&#x2013;0)</td>
<td align="center" valign="middle">0 (0&#x2013;0)</td>
<td align="center" valign="middle">0.648</td>
<td align="center" valign="middle">0 (0&#x2013;0)</td>
<td align="center" valign="middle">0 (0&#x2013;0)</td>
<td align="center" valign="middle">0.935</td>
</tr>
<tr>
<td align="left" valign="middle">TOAST, <italic>n</italic> (%)</td>
<td/>
<td/>
<td/>
<td align="center" valign="middle">0.402</td>
<td/>
<td/>
<td align="center" valign="middle">0.491</td>
</tr>
<tr>
<td align="left" valign="middle">Large-artery atherosclerosis</td>
<td align="center" valign="middle">173 (55.8)</td>
<td align="center" valign="middle">47 (50.0)</td>
<td align="center" valign="middle">126 (58.3)</td>
<td/>
<td align="center" valign="middle">122 (56.0)</td>
<td align="center" valign="middle">51 (55.4)</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">Small-vessel occlusion</td>
<td align="center" valign="middle">109 (35.2)</td>
<td align="center" valign="middle">39 (41.5)</td>
<td align="center" valign="middle">70 (32.4)</td>
<td/>
<td align="center" valign="middle">78 (35.8)</td>
<td align="center" valign="middle">31 (33.7)</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">Cardioembolism</td>
<td align="center" valign="middle">10 (3.2)</td>
<td align="center" valign="middle">2 (2.1)</td>
<td align="center" valign="middle">8 (3.7)</td>
<td/>
<td align="center" valign="middle">8 (3.7)</td>
<td align="center" valign="middle">2 (2.2)</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">Other determined/undetermined</td>
<td align="center" valign="middle">18 (5.8)</td>
<td align="center" valign="middle">6 (6.4)</td>
<td align="center" valign="middle">12 (5.6)</td>
<td/>
<td align="center" valign="middle">10 (4.6)</td>
<td align="center" valign="middle">8 (8.7)</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">NIHSS score after 24&#x2009;h, median (IQR)</td>
<td align="center" valign="middle">15 (8&#x2013;21)</td>
<td align="center" valign="middle">6 (3&#x2013;11)</td>
<td align="center" valign="middle">18 (12&#x2013;25)</td>
<td align="center" valign="middle">&#x003C;0.001</td>
<td align="center" valign="middle">12 (6&#x2013;18)</td>
<td align="center" valign="middle">23 (16&#x2013;36)</td>
<td align="center" valign="middle">&#x003C;0.001</td>
</tr>
<tr>
<td align="left" valign="middle" colspan="8">Outcomes, <italic>n</italic> (%)</td>
</tr>
<tr>
<td align="left" valign="middle">90-day mRS</td>
<td align="center" valign="middle">4 (2&#x2013;6)</td>
<td align="center" valign="middle">1 (0&#x2013;1)</td>
<td align="center" valign="middle">5 (4&#x2013;6)</td>
<td align="center" valign="middle">&#x003C;0.001</td>
<td align="center" valign="middle">3 (1&#x2013;4)</td>
<td align="center" valign="middle">6 (6&#x2013;6)</td>
<td align="center" valign="middle">&#x003C;0.001</td>
</tr>
<tr>
<td align="left" valign="middle">sICH</td>
<td align="center" valign="middle">39 (12.6)</td>
<td align="center" valign="middle">1 (1.1)</td>
<td align="center" valign="middle">38 (17.6)</td>
<td align="center" valign="middle">&#x003C;0.001</td>
<td align="center" valign="middle">17 (7.8)</td>
<td align="center" valign="middle">22 (23.9)</td>
<td align="center" valign="middle">&#x003C;0.001</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>Continuous and categorical variables were compared using the Mann&#x2013;Whitney U-test and chi-squared test. NIHSS, National Institutes of Health Stroke Scale; mRS, Modified Rankin Scale; mTICI, Modified Thrombolysis in Cerebral Infarction; sICH, Symptomatic intracranial hemorrhage; and IQR, Interquartile range.</p>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="sec13">
<label>3.2</label>
<title>Association of lower HRR with poor prognosis</title>
<p>At admission, no differences in Hb and RDW levels were observed between patients with good prognosis and those without. However, patients with poor prognosis had lower HRR levels compared to those with a good prognosis [10.22 (8.97&#x2013;12.05) vs. 11.18 (10.09&#x2013;12.06); <italic>p</italic> =&#x2009;0.016; <xref rid="tab2" ref-type="table">Table 2</xref>; <xref rid="fig2" ref-type="fig">Figure 2A</xref>]. Nevertheless, multivariate analysis indicated that HRR at admission showed no correlation with the prognosis after 3&#x2009;months in AIS patients undergoing MT (OR&#x2009;=&#x2009;1.045; 95% CI: 0.886&#x2013;1.232; <italic>p</italic> =&#x2009;0.605; <xref rid="tab3" ref-type="table">Table 3</xref>).</p>
<table-wrap position="float" id="tab2">
<label>Table 2</label>
<caption>
<p>Comparison of HRR laboratory data according to different outcomes.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Variables</th>
<th align="center" valign="top">Good prognosis</th>
<th align="center" valign="top">Poor prognosis</th>
<th align="center" valign="top">
<italic>p</italic>
</th>
<th align="center" valign="top">Alive</th>
<th align="center" valign="top">Dead</th>
<th align="center" valign="top">
<italic>p</italic>
</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="middle" colspan="7">At admission</td>
</tr>
<tr>
<td align="left" valign="middle">Hb</td>
<td align="center" valign="middle">121 (97&#x2013;141)</td>
<td align="center" valign="middle">119 (96&#x2013;145)</td>
<td align="center" valign="middle">0.810</td>
<td align="center" valign="middle">121 (97&#x2013;145)</td>
<td align="center" valign="middle">118 (93&#x2013;143)</td>
<td align="center" valign="middle">0.279</td>
</tr>
<tr>
<td align="left" valign="middle">RDW</td>
<td align="center" valign="middle">11.1 (9.0&#x2013;13.3)</td>
<td align="center" valign="middle">11.4 (9.6&#x2013;13.5)</td>
<td align="center" valign="middle">0.249</td>
<td align="center" valign="middle">11.2 (9.3&#x2013;13.6)</td>
<td align="center" valign="middle">11.3 (9.8&#x2013;13.0)</td>
<td align="center" valign="middle">0.667</td>
</tr>
<tr>
<td align="left" valign="middle">HRR</td>
<td align="center" valign="middle">11.18 (10.09&#x2013;12.06)</td>
<td align="center" valign="middle">10.22 (8.97&#x2013;12.05)</td>
<td align="center" valign="middle">0.016</td>
<td align="center" valign="middle">10.82 (9.37&#x2013;12.04)</td>
<td align="center" valign="middle">9.87 (8.21&#x2013;12.27)</td>
<td align="center" valign="middle">0.035</td>
</tr>
<tr>
<td align="left" valign="middle" colspan="7">After 24&#x2009;h</td>
</tr>
<tr>
<td align="left" valign="middle">Hb</td>
<td align="center" valign="middle">121 (97&#x2013;140)</td>
<td align="center" valign="middle">119 (93&#x2013;143)</td>
<td align="center" valign="middle">0.460</td>
<td align="center" valign="middle">120 (95&#x2013;143)</td>
<td align="center" valign="middle">118 (88&#x2013;139)</td>
<td align="center" valign="middle">0.120</td>
</tr>
<tr>
<td align="left" valign="middle">RDW</td>
<td align="center" valign="middle">10.8 (9.0&#x2013;12.9)</td>
<td align="center" valign="middle">13.0 (10.9&#x2013;14.9)</td>
<td align="center" valign="middle">&#x003C;0.001</td>
<td align="center" valign="middle">11.8 (9.4&#x2013;13.9)</td>
<td align="center" valign="middle">13.6 (11.9&#x2013;15.8)</td>
<td align="center" valign="middle">&#x003C;0.001</td>
</tr>
<tr>
<td align="left" valign="middle">HRR</td>
<td align="center" valign="middle">11.24 (10.60&#x2013;12.40)</td>
<td align="center" valign="middle">9.18 (7.95&#x2013;10.70)</td>
<td align="center" valign="middle">&#x003C;0.001</td>
<td align="center" valign="middle">10.67 (9.01&#x2013;11.71)</td>
<td align="center" valign="middle">8.57 (6.83&#x2013;9.70)</td>
<td align="center" valign="middle">&#x003C;0.001</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>Variables were compared using the Mann&#x2013;Whitney U-test. Values are presented as median (IQR). Hb, Hemoglobin; RDW, Red cell distribution width; HRR, Hemoglobin to red cell distribution width ratio.</p>
</table-wrap-foot>
</table-wrap>
<fig position="float" id="fig2">
<label>Figure 2</label>
<caption>
<p>Comparison of HRR levels between different outcomes. <bold>(A,B)</bold> Comparison of hemoglobin to red cell distribution width ratio (HRR) at admission according to different outcomes. <bold>(C,D)</bold> Comparison of HRR after 24&#x2009;h according to different outcomes.</p>
</caption>
<graphic xlink:href="fnagi-15-1259668-g002.tif"/>
</fig>
<table-wrap position="float" id="tab3">
<label>Table 3</label>
<caption>
<p>Multivariable analysis of HRR data in predicting clinical outcomes.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Variable</th>
<th align="center" valign="top">&#x03B2;</th>
<th align="center" valign="top">SE</th>
<th align="center" valign="top">Adjusted OR</th>
<th align="center" valign="top">(95% CI)</th>
<th align="center" valign="top">
<italic>p</italic>
</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="middle">Poor prognosis<sup>a</sup></td>
<td/>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="middle">HRR at admission</td>
<td align="center" valign="middle">0.044</td>
<td align="center" valign="middle">0.084</td>
<td align="center" valign="middle">1.045</td>
<td align="center" valign="middle">0.886&#x2013;1.232</td>
<td align="center" valign="middle">0.605</td>
</tr>
<tr>
<td align="left" valign="middle">HRR after 24&#x2009;h</td>
<td align="center" valign="middle">&#x2212;0.437</td>
<td align="center" valign="middle">0.111</td>
<td align="center" valign="middle">0.646</td>
<td align="center" valign="middle">0.520&#x2013;0.803</td>
<td align="center" valign="middle">&#x003C;0.001</td>
</tr>
<tr>
<td align="left" valign="middle">Occurrence of death<sup>b</sup></td>
<td/>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="middle">HRR at admission</td>
<td align="center" valign="middle">0.022</td>
<td align="center" valign="middle">0.062</td>
<td align="center" valign="middle">1.022</td>
<td align="center" valign="middle">0.905&#x2013;1.154</td>
<td align="center" valign="middle">0.728</td>
</tr>
<tr>
<td align="left" valign="middle">HRR after 24&#x2009;h</td>
<td align="center" valign="middle">&#x2212;0.487</td>
<td align="center" valign="middle">0.097</td>
<td align="center" valign="middle">0.615</td>
<td align="center" valign="middle">0.508&#x2013;0.744</td>
<td align="center" valign="middle">&#x003C;0.001</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>Multivariate analysis was performed using logistic regression analysis. <sup>a</sup>HRR after 24&#x2009;h: adjusted age, NIHSS score after 24&#x2009;h, mTICI&#x2009;&#x2265;&#x2009;2b. HRR at admission: sICH was additionally adjusted, along with above factors. <sup>b</sup>HRR after 24&#x2009;h: adjusted age, hypertension, NIHSS score after 24&#x2009;h, and antiplatelet agents. HRR at admission: adjusted age, hypertension, NIHSS score after 24&#x2009;h, and mTICI&#x2009;&#x2265;&#x2009;2b.</p>
</table-wrap-foot>
</table-wrap>
<p>After 24&#x2009;h of MT, a significant difference in RDW was observed [13 (10.9&#x2013;14.9) vs. 10.8 (9.0&#x2013;12.9); <italic>p</italic>&#x2009;&#x003C;&#x2009;0.001; <xref rid="tab2" ref-type="table">Table 2</xref>], while there was no difference in Hb levels between patients with good and poor prognosis. And HRR levels were significantly lower in patients with poor prognosis [9.18 (7.95&#x2013;10.70) vs. 11.24 (10.60&#x2013;12.40); <italic>p</italic>&#x2009;&#x003C;&#x2009;0.001; <xref rid="tab2" ref-type="table">Table 2</xref>; <xref rid="fig2" ref-type="fig">Figure 2C</xref>]. In multivariate logistic regression, lower HRR was independently associated with a higher risk of poor prognosis (OR&#x2009;=&#x2009;0.646; 95% CI: 0.520&#x2013;0.803; <italic>p</italic> &#x003C;&#x2009;0.001; <xref rid="tab3" ref-type="table">Table 3</xref>). Furthermore, the multivariate analysis revealed that older age, higher NIHSS score after 24&#x2009;h, and lower successful reperfusion rate were risk factors for poor prognosis (<xref ref-type="supplementary-material" rid="SM1">Supplementary Table 1</xref>).</p>
<p>In addition, we observed strong and statistically significant positive correlations between Hb at admission and after 24&#x2009;h (<italic>r</italic>&#x2009;=&#x2009;0.983, <italic>p</italic>&#x2009;&#x003C;&#x2009;0.001), between RDW at admission and after 24&#x2009;h (<italic>r</italic>&#x2009;=&#x2009;0.703, <italic>p</italic>&#x2009;&#x003C;&#x2009;0.001), as well as a moderate and positive correlation between HRR at admission and after 24&#x2009;h (<italic>r</italic>&#x2009;=&#x2009;0.507, <italic>p</italic>&#x2009;&#x003C;&#x2009;0.001).</p>
</sec>
<sec id="sec14">
<label>3.3</label>
<title>Association of lower HRR with death</title>
<p>Similarly, the HRR levels exhibited a significant difference between dead and alive patients at admission [9.87 (8.21&#x2013;12.27) vs. 10.82 (9.37&#x2013;12.04); <italic>p</italic>&#x2009;=&#x2009;0.035; <xref rid="tab2" ref-type="table">Table 2</xref>; <xref rid="fig2" ref-type="fig">Figure 2B</xref>], as well as after 24&#x2009;h [8.57 (6.83&#x2013;9.70) vs. 10.67 (9.01&#x2013;11.71); <italic>p</italic>&#x2009;&#x003C;&#x2009;0.001; <xref rid="tab2" ref-type="table">Table 2</xref>; <xref rid="fig2" ref-type="fig">Figure 2D</xref>]. After adjusting for potential confounding variables, HRR after 24&#x2009;h was found to be an independent predictor of death (OR 0.615; 95% CI 0.508&#x2013;0.744; <italic>p</italic> &#x003C;&#x2009;0.001; <xref rid="tab3" ref-type="table">Table 3</xref>). However, HRR at admission was not associated with death (OR&#x2009;=&#x2009;1.022; 95% CI: 0.905&#x2013;1.154; <italic>p</italic> =&#x2009;0.728; <xref rid="tab3" ref-type="table">Table 3</xref>). Other risk factors for death were showed in <xref ref-type="supplementary-material" rid="SM1">Supplementary Table 2</xref>.</p>
</sec>
<sec id="sec15">
<label>3.4</label>
<title>ROC curve analysis of HRR as a prognostic marker</title>
<p>Receiver operating characteristic analysis showed that HRR after 24&#x2009;h was more discriminative of clinical outcome (poor prognosis and death) than HRR at admission (<xref ref-type="supplementary-material" rid="SM1">Supplementary Figure 1</xref>). Given that the results showed significant differences in HRR at 24&#x2009;h among patients with poor prognosis, as well as the association between Hb, RDW, and HRR, we used ROC curves to evaluate their roles as prognostic markers (<xref rid="fig3" ref-type="fig">Figure 3A</xref>). Among all patients, RDW had good diagnostic accuracy in predicting poor prognosis, with an AUC of 0.697 (95% CI: 0.642&#x2013;0.747, <italic>p</italic>&#x2009;&#x003C;&#x2009;0.001), sensitivity of 66.67%, and specificity of 63.83%. Notably, as a composite predictor calculated from Hb and RDW, HRR demonstrated even stronger discriminative ability, with an AUC of 0.790 (95% CI: 0.741&#x2013;0.834, <italic>p</italic>&#x2009;&#x003C;&#x2009;0.001), sensitivity of 73.15%, and specificity of 76.60%. Furthermore, when combined with HRR, the AUC value of current NIHSS score for predicting poor prognosis increased from 0.859 (95% CI: 0.815&#x2013;0.896) to 0.885 (95% CI: 0.845&#x2013;0.919) (<italic>p</italic>&#x2009;=&#x2009;0.025), with a sensitivity of 86.57% and specificity of 77.66%.</p>
<fig position="float" id="fig3">
<label>Figure 3</label>
<caption>
<p>Prognostic accuracies of HRR as a predictor of <bold>(A)</bold> a poor prognosis or <bold>(B)</bold> an occurrence of death at 3 months after stroke.</p>
</caption>
<graphic xlink:href="fnagi-15-1259668-g003.tif"/>
</fig>
<p>Similar results were found when evaluating the predictive value of HRR for death (<xref rid="fig3" ref-type="fig">Figure 3B</xref>).</p>
</sec>
</sec>
<sec sec-type="discussion" id="sec16">
<label>4</label>
<title>Discussion</title>
<p>The purpose of this study was to investigate the association between the novel biomarker HRR and the outcomes of AIS patients who underwent MT. Our findings suggested that HRR at admission and after 24&#x2009;h may be significantly lower in patients with poor prognosis and death. However, after adjusting for cerebrovascular risk factors, HRR after 24&#x2009;h remained an independent risk factor for both poor prognosis and death, regardless of whether intravenous thrombolysis was performed prior to MT.</p>
<p>Anemia is a prevalent condition among AIS patients, and its pathogenesis involves various mechanisms such as reduced oxygen-carrying capacity, inflammation, energy imbalance, and hypercoagulation (<xref ref-type="bibr" rid="ref4">Chang et al., 2020</xref>). Decreased levels of Hb imply a compromised oxygen-carrying capacity, leading to limited oxygen supply and an energy imbalance in the ischemic penumbra (<xref ref-type="bibr" rid="ref22">Kimberly et al., 2013</xref>; <xref ref-type="bibr" rid="ref5">Desai et al., 2023</xref>). Moreover, anemia can trigger the release of inflammatory factors like tumor necrosis factor-&#x03B1; (<xref ref-type="bibr" rid="ref11">Feret et al., 2022</xref>). Recently, the role of inflammatory reactions in the progression of AIS and its association with poor prognosis has been widely recognized (<xref ref-type="bibr" rid="ref20">Kehrel and Fender, 2016</xref>; <xref ref-type="bibr" rid="ref56">Zhao et al., 2017</xref>; <xref ref-type="bibr" rid="ref47">Shi et al., 2019</xref>; <xref ref-type="bibr" rid="ref8">Esposito et al., 2022</xref>). Studies have indicated that overall poor nutritional status and weakened immune response may contribute to a worse prognosis (<xref ref-type="bibr" rid="ref3">Bullock et al., 2020</xref>). Furthermore, anemic patients may experience hypercoagulability, further increasing the risk of ischemic events. Evidence suggests that severe anemia substantially raises the likelihood of stent thrombosis (<xref ref-type="bibr" rid="ref32">Pilgrim et al., 2012</xref>), and lower Hb level was a strong predictor of post-PCI in-stent restenosis (<xref ref-type="bibr" rid="ref17">Hu et al., 2021</xref>). Several studies have highlighted baseline Hb as a significant predictor of mortality and ischemic events in PCI (<xref ref-type="bibr" rid="ref42">Reinecke, 2003</xref>; <xref ref-type="bibr" rid="ref6">Dutsch et al., 2022</xref>). Additionally, a U-shaped association has been found between Hb levels and poor prognosis, all-cause mortality, and stroke severity in ischemic stroke (<xref ref-type="bibr" rid="ref4">Chang et al., 2020</xref>; <xref ref-type="bibr" rid="ref54">Zhang et al., 2021</xref>). Another study reported that lower Hb levels and variability were linked to mortality at 3&#x2009;months in AIS patients undergoing MT (<xref ref-type="bibr" rid="ref29">Nisar et al., 2021</xref>). In our investigation, AIS patients undergoing MT with poor prognosis and death had lower Hb levels compared to those with good prognosis and survival, although the difference was not statistically significant.</p>
<p>The value of RDW in cardiovascular disease reflects various mechanisms in the pathophysiological process. Firstly, an increased RDW indicates an underlying inflammatory state and impaired maturation of red blood cells (<xref ref-type="bibr" rid="ref43">Ridker et al., 2001</xref>). RDW has been associated with inflammatory markers such as C-reactive protein with high sensitivity, interleukin-6, and fibrinogen levels (<xref ref-type="bibr" rid="ref12">F&#x00F6;rh&#x00E9;cz et al., 2009</xref>). These inflammatory factors can disrupt red blood cell maturation by affecting the homeostasis of red blood cells, impairing iron metabolism, and inhibiting erythropoietin production (<xref ref-type="bibr" rid="ref41">Rechavi and Rivella, 2008</xref>; <xref ref-type="bibr" rid="ref45">Salvagno et al., 2015</xref>). Secondly, oxidative stress and microcirculatory damage play a significant role. Red blood cells possess substantial antioxidant capacity, and oxidative damage can lead to decreased cell survival rates (<xref ref-type="bibr" rid="ref7">Emans et al., 2011</xref>; <xref ref-type="bibr" rid="ref25">Lippi and Plebani, 2014</xref>). Changes in the morphology of red blood cells are associated with oxidative stress (<xref ref-type="bibr" rid="ref13">Friedman et al., 2004</xref>). In addition, reduced red blood cell deformability can impede blood flow through the microcirculation, exacerbating ischemic conditions (<xref ref-type="bibr" rid="ref31">Patel et al., 2013</xref>). Several studies have independently linked higher baseline RDW levels to mortality and adverse cardiovascular events in myocardial infarction patients undergoing PCI (<xref ref-type="bibr" rid="ref9">Fatemi et al., 2013</xref>; <xref ref-type="bibr" rid="ref2">Bujak et al., 2015</xref>; <xref ref-type="bibr" rid="ref55">Zhao et al., 2015</xref>; <xref ref-type="bibr" rid="ref50">Xiao et al., 2022</xref>). Research has also demonstrated that RDW may serve as a significant prognostic factor in AIS (<xref ref-type="bibr" rid="ref10">Feng et al., 2017</xref>; <xref ref-type="bibr" rid="ref27">Mohindra et al., 2020</xref>). Baseline RDW has been proposed as a prospective marker of mortality in AIS patients undergoing intravenous thrombolysis (<xref ref-type="bibr" rid="ref33">Pinho et al., 2018</xref>; <xref ref-type="bibr" rid="ref53">Ye et al., 2020</xref>). Similarly, in our study, AIS patients with poor prognosis and mortality tended to have elevated RDW levels 24&#x2009;h after MT.</p>
<p>Although Hb and RDW have shown prognostic value in AIS patients, these two parameters can be influenced by various factors as mentioned above. Since HRR is calculated from Hb and RDW, it may provide a more effective and stable assessment compared to individual Hb or RDW measurements, objectively reflecting inflammatory and microcirculatory status, thus potentially serving as a superior biomarker. HRR has recently emerged as a crucial indicator for predicting cardiovascular disease mortality and prognosis (<xref ref-type="bibr" rid="ref39">Rahamim et al., 2022</xref>). Lower HRR is associated with a higher risk of frailty and adverse outcomes in hospitalized older patients with coronary heart disease (<xref ref-type="bibr" rid="ref38">Qu et al., 2021</xref>). Moreover, HRR may serve as a reliable indicator of mortality risk in patients with coronary artery disease after PCI (<xref ref-type="bibr" rid="ref48">Sun et al., 2022</xref>; <xref ref-type="bibr" rid="ref51">Xiu et al., 2022</xref>). Lower HRR levels have been found to increase the risk of death from all causes in AIS patients with atrial fibrillation (<xref ref-type="bibr" rid="ref37">Qin et al., 2022</xref>). However, no studies have investigated the predictive significance of HRR for clinical outcomes in AIS patients undergoing MT. By excluding the influence of cardiovascular diseases, malignancies, infectious diseases, serious liver and kidney dysfunction, and adjusting for multivariate confounders, our study suggests that lower HRR significantly increases the risk of poor prognosis and death in AIS patients undergoing MT. Additionally, HRR demonstrates superior discriminative accuracy compared to the single parameter RDW. Globally, the NIHSS score is recognized as a valuable prognostic indicator for AIS, and incorporating HRR into the NIHSS enhances its predictive value.</p>
<p>While our study investigated the association between HRR and outcomes in AIS patients undergoing MT, we acknowledge certain limitations. Firstly, we only collected HRR data at admission and after 24&#x2009;h of MT, precluding analysis of HRR changes throughout the entire stroke process. Secondly, there was a loss to follow-up of 21 patients, potentially introducing bias into our results. Thirdly, this was a retrospective and single-center design study, raising the possibility of selection bias and limiting the generalizability of our findings. It is important to consider these limitations when interpreting the results, and future research should address these issues to provide more robust evidence on the relationship between HRR and outcomes in AIS patients undergoing MT.</p>
</sec>
<sec sec-type="conclusions" id="sec17">
<label>5</label>
<title>Conclusion</title>
<p>Our study indicates that HRR levels after 24&#x2009;h of MT, as a simple, novel, cost-effective, and valuable biomarker, are an independent predictor of poor prognosis and death for AIS patients undergoing MT. However, further research is necessary to elucidate the underlying biological mechanisms and confirm the clinical utility of HRR.</p>
</sec>
<sec sec-type="data-availability" id="sec18">
<title>Data availability statement</title>
<p>The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.</p>
</sec>
<sec sec-type="ethics-statement" id="sec19">
<title>Ethics statement</title>
<p>The studies involving humans were approved by the ethics committee of Chengdu Second People&#x2019;s Hospital. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.</p>
</sec>
<sec sec-type="author-contributions" id="sec20">
<title>Author contributions</title>
<p>XF: Methodology, Data curation, Investigation, Writing &#x2013; original draft, Funding acquisition. YZ: Investigation, Formal Analysis, Writing &#x2013; review &#x0026; editing. QL: Formal Analysis, Writing &#x2013; review &#x0026; editing, Software, Validation. BW: Writing &#x2013; review &#x0026; editing, Validation, Formal Analysis, Software. JS: Validation, Writing &#x2013; review &#x0026; editing, Project administration, Conceptualization, Methodology, Resources.</p>
</sec>
</body>
<back>
<sec sec-type="funding-information" id="sec21">
<title>Funding</title>
<p>The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was supported by the comprehensive demonstration study on Chronic Disease Prevention and Control Technology in Southwest China (grant no. 2018YFC1311400).</p>
</sec>
<ack>
<p>The patients and their families are all sincerely appreciated for their cooperation, and we would like to thank the nurses for their contributions and assistance.</p>
</ack>
<sec sec-type="COI-statement" id="sec22">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec id="sec100" sec-type="disclaimer">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
<sec sec-type="supplementary-material" id="sec23">
<title>Supplementary material</title>
<p>The Supplementary material for this article can be found online at: <ext-link xlink:href="https://www.frontiersin.org/articles/10.3389/fnagi.2023.1259668/full#supplementary-material" ext-link-type="uri">https://www.frontiersin.org/articles/10.3389/fnagi.2023.1259668/full#supplementary-material</ext-link></p>
<supplementary-material xlink:href="Data_Sheet_1.PDF" id="SM1" mimetype="application/pdf" xmlns:xlink="http://www.w3.org/1999/xlink"/>
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