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Lactate dehydrogenase to albumin ratio and poor prognosis after thrombolysis in ischemic stroke patients: developing a novel nomogram

BMC Medical Informatics and Decision Making volume 25, Article number: 166 (2025) Cite this article

Abstract

Background

Ischemic stroke (IS) is associated with high disability and mortality. This study aimed to identify the prognostic predictors and develop a nomogram for a prediction model for ischemic stroke patients after thrombolysis.

Methods

We retrospectively analyzed data from 359 IS patients who underwent thrombolysis. Clinical characteristics, laboratory parameters, and prognosis data were collected. One-third of the subjects were randomly selected as a validation set (n = 108) for internal validation. Logistic regression analysis was used to derive independent risk indicators. A nomogram was constructed using these indicators, and the performance of the nomogram was assessed by the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC). The agreement of the model predictions with actual observations was assessed via calibration curves, and the clinical utility of the nomogram was assessed via decision curve analysis.

Results

Multivariate logistic regression analysis showed that age, leukocytes, Lactate Dehydrogenase to Albumin Ratio (LAR) and NIHSS were independent predictors of three-month post-thrombolysis prognosis in IS patients. We created a nomogram based on the weighting coefficients of these factors. The AUC curves showed that our model including age, leukocytes, LAR and NIHSS was more accurate in predicting prognosis than a single factor. The calibration curves showed a good fit between actual and predicted probabilities in both the training and validation groups.

Conclusion

LAR has a good predictive power for the prognosis of IS patients 3 months after thrombolytic therapy and can be used as a new clinical indicator to establish a practical nomogram.

Peer Review reports

Introduction

Ischemic stroke (IS) is the second leading cause of disability and death worldwide [1]. It refers to the general term for the necrosis of brain tissue caused by stenosis or occlusion of the blood supply arteries (carotid artery and vertebral artery) to the brain, resulting in insufficient blood supply to the brain. For IS, thrombolytic therapy is currently the most important measure to restore blood flow, and efficient reperfusion therapy is widely used. Whether patients can seek medical attention within 6 h after onset and adopt direct and effective measures such as intravenous thrombolytic therapy or mechanical thrombus extraction as soon as possible if conditions permit is the main factor affecting prognosis [2,3,4]. In recent years, with the deepening of clinical research, a variety of prognostic treatment methods and markers have been proposed and applied in clinical practice. For example, serum enolase, as a potential prognostic indicator, has been confirmed to have certain sensitivity and specificity, which can provide an important reference for the diagnosis and prognosis of ischemia-related diseases [5, 6]. However, existing studies have also found that the processing problems of clinical samples such as hemolysis may lead to a false increase in the concentration of enolase, resulting in false positive results. This not only affects the reliability of the marker but also highlights the shortcomings of existing clinical prediction models in addressing complex clinical situations. Therefore, exploring a specific and sensitive index to establish an effective nomogram to predict the prognosis of patients with IS after thrombolytic therapy is of great significance for improving the clinical management of patients with ischemic stroke.

Serum lactate dehydrogenase (LDH) is a class of nicotinamide adenine dinucleotide (NAD)-dependent kinases, a glycolytic enzyme widely found in human tissues, and one of the key enzyme families of glycolysis. It is a major player in glucose metabolism, and it is released from cells when cell membranes are damaged [7]. What’s more, LDH is also of wide interest as a common biomarker in many diseases. Higher LDH is indicative of many diseases to varying degrees. For example, the prognostic relationship between serum LDH and osteosarcoma [8], and the significance of LDH as a prognostic indicator for brain metastases [9]. LDH has also been shown to be a prognostic factor in IS, for example, high levels of LDH are an independent predictor of poor prognosis and risk of all-cause mortality in acute ischemic stroke (AIS) and are suggestive of initial severity and unfavorable functional outcome in AIS patients [10]. Another study mentioned that hemorrhagic transformation (HT) is a serious but common complication of AIS. High LDH levels in patients with AIS are associated with an increased risk of HT [11].However, LDH is present in almost all body cells and is generally highly active in human tissues, an increase in serum LDH is not specific for any single tissue or organ, and therefore a single indicator of LDH may not be able to accurately predict the prognosis of IS with any degree of precision.

Albumin is an essential protein that regulates blood osmotic pressure and affects the physiological functions of the circulatory system [12]. It also has anti-inflammatory, antioxidant, platelet-inhibiting and anti-thrombotic properties [13,14,15]. In recent years, studies have also confirmed that low serum albumin levels are significantly associated with an increased risk of adverse outcomes in patients with AIS or transient ischemic attack [16]. A large-scale survey study in the United States found that low albumin levels can independently predict mortality in patients with acute stroke. Hypoalbuminemia can reduce patients’ cellular immune function and increase the risk of infection and pressure ulcers [17]. However, serum albumin levels are influenced by kidney disease or nutritional status, limiting its value in predicting the outcomes of ischemic stroke [18, 19]. In recent years, a more important, lactate dehydrogenase to albumin ratio (LAR) calculated from the ratio of lactate dehydrogenase to albumin in human serum has been widely used as a prognostic indicator in a variety of critically ill patients (e.g., the critically ill) [20,21,22] and its potential in acute pancreatitis, severe pneumonia, and traumatic brain injury has been discussed in several studies Predictive value [23,24,25,26]. However, the impact of LAR on the prognosis of IS after thrombolytic therapy remains unknown.

Therefore, this study aimed to analyze and study the effect of LAR on the prognosis of IS patients 3 months after thrombolysis. We collected and compiled data from 359 patients 3 months after IS thrombolysis in our clinical work after analyzing the data, and then created a nomogram, to provide a reference basis for the prediction of the prognosis of such patients.

Methods

Data collection and ethical statements

We collected data retrospectively from July 2021 to March 2023. We provide comprehensive data on all records of patients during their hospitalization, including admission time, laboratory results, surgical procedures, postoperative follow-up records, and imaging data. To protect patient privacy, these data were anonymized and random codes were used instead of personal information. This retrospective research experiment was conducted in accordance with the ethical guidelines of the World Medical Association (Declaration of Helsinki) and was approved by the Ethics Committee of the First Affiliated Hospital of Fujian Medical University. The ethical number was 20,241,121. The study was conducted according to the approved methodology. All patients were informed and signed an informed consent form before the experiment.

Selection criteria

We selected patients who underwent intravenous thrombolysis after the diagnosis of IS at our institution, all of whom underwent intravenous thrombolysis within 4.5 h of symptom onset. All patients who underwent thrombolysis were followed up 3 months after the procedure to assess their functional recovery and prognosis. As shown in Fig. 1, we excluded (1) patients who did not undergo intravenous thrombolysis. (2) patients with missing baseline data, where blood LDH and serum albumin data were not recorded within 24 h of admission, and for patients with multiple hospitalizations we only included data from their first hospitalization.3. Patients with an mRS score greater than 1 on admission were also excluded. Ultimately, 359 patients who met the criteria were enrolled in this study.

Fig. 1
figure 1

Study design and study flowchart. IS: ischemic stroke. mRS: modified rankin scale. FJMU FAH: the First Affiliated Hospital of Fujian Medical University

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Data processing and preprocessing

We selected LAR as the main variable of interest. We calculated the LAR by the following formula: LAR = LDH (U/L)/serum albumin (g/L). We used the first measurement of serum LDH and serum albumin levels after admission to minimize the effect of subsequent treatment on this value and to make the results more accurate. Potential confounders were extracted, including demographics (age, sex), vital signs (systolic blood pressure, diastolic blood pressure), medical history (number of atrial fibrillations, hypertension, diabetes mellitus, cigarette smoking, transient ischemic attack/cerebral infarction), laboratory indices (leukocytes, creatinine, glucose, etc.), and the National Institutes of Health Stroke Scale (NIHSS).

Primary outcome

For the purposes of this study, the primary outcome was defined as the patient’s mRS score at 3 months after thrombolysis. mRS scores were used to assess the patient’s neurologic recovery and ranged from 0 (asymptomatic) to 6 (death). The time point for assessment of the primary outcome was at 3 months after intravenous thrombolysis. Assessment of the primary outcome was completed by a neurologist trained to ensure accuracy and consistency of assessment.

Statistical analysis

Statistical analysis was performed using SPSS 23.0 and STATA 17.0. We used all subject data as the training cohort (n = 359), in addition, we internally validated the model by randomly selecting one-third of all patients as the validation cohort (n = 108). Predictive models were developed using the data in the training cohort and then the developed models were validated using the validation cohort. Baseline characteristics of all patients were classified according to their survival status. Continuous variables were presented as mean ± standard deviation (if normal) or median with interquartile range (if non-normal). Categorical variables were presented as percentages. Differences between groups for continuous variables were tested using t-tests or Wilcoxon rank-sum tests, and for categorical variables, chi-square tests were used. Variables with a p-value less than 0.001 were included in a binary logistic regression model to assess the relationship between patients’ clinical characteristics and prognosis. Univariate (unadjusted) and multivariate (adjusted) binary logistic regression models were used to evaluate the relationship between patients’ clinical characteristics and prognosis. Variables with a p-value less than 0.001 in the multivariate logistic regression model were used to construct the final prediction model. A nomogram was constructed based on the logistic prediction model, and the ROC curve analysis was used to evaluate the performance of the prediction model in both the training and test groups. The AUC curve was used to summarize diagnostic accuracy (AUC = 0.5 for no discrimination and AUC = 1 for perfect discrimination), and accuracy, sensitivity, specificity, negative predictive value, and positive predictive value were also calculated. Additionally, decision curve analysis (DCA) was employed to evaluate the clinical utility of the model.

Result

Baseline characteristics of study participants

Table 1 shows the baseline characteristics of the participants. Of the 359 patients who met the inclusion criteria, 239 (66.57%) were male and the median age was 69 years. Of these, 242 (67.41%) had a good prognosis (i.e., mRS score of 0–2) at 3 months postoperatively. Those with a poorer prognosis were older (P < 0.001), had a mRS score of 3–6, higher leukocyte levels (P < 0.001), higher LDH levels (P < 0.001), and higher LAR [5.54 (1.91)] (P < 0.001). There were no significant differences between groups for the other covariates (P > 0.001). Table S1 shows the baseline characteristics of the participants in the validation cohort.

Table 1 Baseline characteristics of study participants
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Age, WBC, LAR, and NIHSS are independent risk indicators for 3-month prognosis after IS thrombolysis

Univariate and multivariate logistic regression analyses of factors associated with poor functional prognosis (Table 2). In the univariate analysis of factors associated with poor functional prognosis, a total of five variables (age, WBC, LAR, NIHSS, and TOAST) were statistically significant (P < 0.001) in the univariate logistic regression analysis. Then, the variables were included in multivariate logistic regression analysis for further analysis. In the multivariate regression analysis, the results showed that age [odds ratio (OR), 1.035; 95% confidence interval (95% CI), 1.006–1.064; P = 0.017], WBC (OR, 1.213; 95% CI, 1.100-1.337; P < 0.001), LAR (OR, 1.898; 95% CI, 1.481–2.434; P < 0.001), and NIHSS (OR, 4.417; 95% CI, 2.311–8.441; P < 0.001) were the independent risk indicators for the prognosis at 3 months after thrombolysis.

Table 2 Univariate and multivariate logistic regression analysis of factors associated with poor functional outcome. ***P<0.001
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LAR is an independent risk indicator for 3-month prognosis after IS thrombolysis

In univariate and multivariate logistic analyses of the association between LAR and poor functional prognosis (Table 3), the unadjusted LAR (model 1) correlated with poor prognosis in patients 3 months after IS thrombolysis (unadjusted OR, 4.545; 95% CI, 2.838–7.280; P < 0.001), and after adjusting for age and gender factors (model 2), LAR was a prognostically independent predictor of risk [adjusted OR, 2.97; 95% CI, 1.788–4.934; P < 0.001]. In addition, based on model 2, with additional adjustments for history of atrial fibrillation, smoking, hypertension, diabetes mellitus, cerebral infarction or transient ischemic attack, National Institutes of Health Stroke Scale, leukocytes, blood glucose, triglycerides, high-density lipoprotein, low-density lipoprotein, and total cholesterol, it was moreover demonstrated that the LAR was an independent risk indicator influencing the prognosis of the 3-months after IS thrombolysis in model 3 [adjusted OR,2.881; 95% CI, 1.623–5.113; P < 0.001]. We also did restricted cubic spline curves (RCS, Fig. 2) for LAR to visualize the relationship between LAR and the predicted three-month prognosis after IS thrombolysis. It can be seen that the probability of poor prognosis increases with increasing LAR after a value of LAR greater than 5.

Table 3 Univariate and multivariate logistic analyses of the association between LAR and poor functional outcome. ***P<0.001
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Fig. 2
figure 2

Restricted cubic spline curve for the association of LAR and the predicted three-month prognosis after IS thrombolysis

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Development the predictive nomogram

A nomogram (Fig. 3) was constructed to assess prognosis based on the results of multivariate logistic regression analysis. The following four variables were included in the prognostic graph (age, WBC, LAR, NIHSS). Each patient will receive a total score plus the scores of the four prognostic variables, which corresponds to a predicted prognosis at 3 months after IS thrombolysis. In order to compare the agreement between the prognosis predicted by the nomogram model and the actual results, calibration curves were plotted. As shown (Fig. 4), the calibration curves for the nomogram were close to the standard curves in the training and validation cohorts, which suggests that the nomogram has a great deal of consistency.

Fig. 3
figure 3

Establishing a prognostic nomogram for ischemic stroke

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Fig. 4
figure 4

The calibration curves for the nomogram in the training cohorts and the standard curves in the validation cohorts. a n = 359, b n = 108

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Assessment of the nomogram’s predictive performance and clinical utility

In order to compare the predictive effect of different metrics, we analyzed the performance of different metrics with ROC curves (Fig. 5). As shown, the model group had the largest area under the curve and had optimal specificity and sensitivity (Tables S2-S3). These results indicated that LAR had good predictive ability for the prognosis of patients at 3 months after IS thrombolysis, and the new model combining age, WBC, LAR, and NIHSS had better predictive ability compared with NIHSS alone. We further constructed a DCA curve for the reliability of the nomogram (Fig. 6). The DCA curve showed that the nomogram had a higher clinical benefit than NIHSS alone in terms of patient prognosis at 3 months after IS thrombolysis.

Fig. 5
figure 5

The receiver operating characteristic (ROC) curves of different indices on the prognosis of patients in the training cohorts and validation cohorts. AUC: area under the curve. LAR: lactate dehydrogenase to albumin ratio. a n = 359, b n = 108. The points on each curve in the graph and the values they show are the Jordon indexes

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Fig. 6
figure 6

The Decision Curve (DCA) was plotted for the reliability of the nomograms in the training cohorts and the validation cohorts

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Discussion

IS is the second leading cause of disability and death worldwide and is characterized by high disability, high morbidity [27,28,29]. Therefore, early prediction of a patient’s prognosis is of great clinical value in determining improvement. For this reason, it is necessary to develop a nomogram to assess early prognosis in order to better predict the prognosis of patients and to prevent and treat adverse outcomes in advance. In this study, we investigated the clinical use of LAR, which is the first study to investigate the prognostic relevance of LAR in patients three months after IS thrombolysis. The results showed that a higher LAR was associated with a poorer clinical prognosis in patients with IS after thrombolysis. In univariate and multivariate regression analyses, LAR was shown to be an independent risk factor for prognosis in patients three months after thrombolysis after excluding confounding factors. Furthermore, our study confirms that the predictive accuracy of LAR is superior to that of lactate and albumin alone, and that a nomogram built on this basis possesses better clinical utility.

LDH is one of the most important enzymes for anaerobic degradation of sugar and gluconeogenesis, and is widely present in human tissues. α-HBDH is an isoenzyme of LDH, which can reflect the overall level of LDH1 and LDH2 in the serum of the organism. In ischemic stroke patients, after brain tissue damage, neuronal cells and other necrotic lesions occur, causing leakage of damaged cellular enzymes from the brain tissue, which breaks the blood-brain barrier and enters the bloodstream, leading to elevated levels of α-HBDH, which responds to the increase in LDH in the organism [30,31,32]. Numerous data demonstrate that blood LDH concentration has an impact on the prognosis of IS patients, which is similar to our results that serum LDH is an independent predictor of prognosis in IS patients [10, 33]. However, since systemic LDH is influenced by many other factors, such as in patients with cerebral ischemia, by the effect of blood glucose on it, the prognostic predictive role of lactate remains somewhat controversial.

Albumin has anti-inflammatory properties, as it binds and transports inflammatory substances and mediators and promotes the inflammatory response [34, 35]. In the present study, we also used albumin as a parameter in the model. The results suggest that albumin has a prognostic benefit in patients with IS after thrombolysis, which is similar to the results of previous studies.

More importantly, since LDH and albumin are produced in different organs and are affected by multiple mechanisms [36], a single index cannot accurately predict prognosis, whereas the composite index of LAR can minimize the influence of a single factor on the regulatory mechanisms. It has been shown that higher LAR is independently associated with the development of early cognitive dysfunction after acute ischemic stroke [37]. The prognostic value of LAR at 3 months after IS thrombolysis has not been reported so far. To our knowledge, the current study is the first to investigate the prognostic value of LAR in the 3 months after IS thrombolysis. The results of the present study suggest that higher LAR can be used as an independent indicator of poor prognosis in patients 3 months after IS thrombolysis. As confirmed by our results, the predictive value of LAR is superior to the prognostic predictive value of LDH or albumin alone. Currently, NIHSS is commonly used in the prognostic assessment of IS. In this study, we found that LAR alone was similar to the direction of NIHSS prediction in predicting IS clinical outcomes. LAR provides an earlier and more accurate clinical tool for prognostic assessment of stroke patients after thrombolysis.

In addition, on the basis of multivariate analysis, we constructed a new prognostic prediction score chart that included independent factors such as age, WBC, LAR, and NIHSS. Our data suggest that the new prediction model combining LAR and other independent factors is more clinically valuable in assessing the prognosis of patients with IS at 3 months after thrombolytic therapy than the APSIII scoring system alone, allowing for a more detailed diagnostic and therapeutic plan to address the patient’s poor prognosis. This approach using this composite index including LAR this reduces the influence of single factors on regulatory mechanisms, provides a more comprehensive prognostic assessment, and allows for a more detailed treatment plan to be developed in response to the patient’s poor prognosis.

There are several limitations to our study. First, these data were from a single center, and our model was only internally validated, which may underestimate the model error rate and produce overly optimistic results, so our conclusions will require multicenter data in the future to further confirm their clinical value, and second, although we performed a detailed analysis of the short-term prognosis in our study, we acknowledge that these findings may not be fully predictive of long-term clinical outcomes. Due to the limitations of our study design and available resources, our data focused primarily on short-term prognosis, and there was insufficient long-term follow-up data to determine the long-term health and survival of patients. Future studies require longer follow-up to collect and analyze data on long-term prognosis to better guide clinical practice and patient management.

Conclusion

In our study, LAR can be used as an independent influencing factor in the prediction of prognosis of IS patients after thrombolytic therapy, which is superior to the prediction of a single LDH or albumin alone and not inferior to the prediction of NIHSS, which goes in the same direction. This will provide medical practitioners with a more valuable indicator in predicting the prognosis of patients with IS after thrombolytic therapy and provide predictive value for better interventions and clinical planning for poor prognosis. More multicenter prospective studies are still needed for LAR as a more readily available objective biomarker.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Abbreviations

IS:

Ischemic stroke

LDH:

Lactate dehydrogenase

NAD:

Nicotinamide adenine dinucleotide

AIS:

Acute ischemic stroke

HT:

Hemorrhagic transformation

LAR:

Lactate dehydrogenase to albumin ratio

References

  1. Katan M, Luft A. Global burden of stroke. Semin Neurol. 2018. https://doiorg.publicaciones.saludcastillayleon.es/10.1055/s-0038-1649503.

    Article  PubMed  Google Scholar 

  2. Herpich F, Rincon F. Management of acute ischemic stroke. Crit Care Med. 2020. https://doiorg.publicaciones.saludcastillayleon.es/10.1097/ccm.0000000000004597.

  3. Jolugbo P, Ariëns RAS. Thrombus composition and efficacy of thrombolysis and thrombectomy in acute ischemic stroke. Stroke. 2021. https://doiorg.publicaciones.saludcastillayleon.es/10.1161/strokeaha.120.032810.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Saini V, Guada L, Yavagal DR. Global epidemiology of stroke and access to acute ischemic stroke interventions. Neurology. 2021. https://doiorg.publicaciones.saludcastillayleon.es/10.1212/wnl.0000000000012781.

  5. A AM, A A-AF G MM, Moselhy SS. Biochemical markers as diagnostic/prognostic indicators for ischemic disease. Afr Health Sci. 2018. https://doiorg.publicaciones.saludcastillayleon.es/10.4314/ahs.v18i2.13.

  6. Montellano FA, Ungethüm K, Ramiro L, Nacu A, Hellwig S, Fluri F, Whiteley WN, Bustamante A, Montaner J, Heuschmann PU. Role of blood-based biomarkers in ischemic stroke prognosis. Stroke. 2021. https://doiorg.publicaciones.saludcastillayleon.es/10.1161/strokeaha.120.029232.

  7. Jurisic V, Radenkovic S, Konjevic G. The actual role of LDH as tumor marker, biochemical and clinical aspects. Adv Cancer Biomarkers. 2015:115–24.

  8. Fu Y, Lan T, Cai H, Lu A, Yu W. Meta-analysis of serum lactate dehydrogenase and prognosis for osteosarcoma. Med. 2018. https://doiorg.publicaciones.saludcastillayleon.es/10.1097/md.0000000000010741.

  9. Pei Y-Y, Zhang Y, Liu Z-R, He Y, Yu J-Y, Zhang R-J, Wang J-J, Peng X-C, Fang F. Lactate dehydrogenase as promising marker for prognosis of brain metastasis. J Neuro-Oncology. 2022. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s11060-022-04070-z.

  10. Tu W-J, Jin X-X, Fang M-D, Hu L-L, Yuan Y, Xu J-F, Lu G-G, Li T. Elevated lactate dehydrogenase predicts poor prognosis of acute ischemic stroke. Plos One. 2022. https://doiorg.publicaciones.saludcastillayleon.es/10.1371/journal.pone.0275651.

  11. Chen L, Xu M, Huang Q, Wen C, Ren W. Lactate dehydrogenase predicts hemorrhagic transformation in patients with acute ischemic stroke. Gerontology. 2023. https://doiorg.publicaciones.saludcastillayleon.es/10.1159/000528951.

    Article  PubMed  Google Scholar 

  12. Joannidis M, Wiedermann CJ, Ostermann M. Ten myths about albumin. Intensive Care Med. 2022. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s00134-022-06655-8.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Belinskaia DA, Voronina PA, Shmurak VI, Jenkins RO, Goncharov NV. Serum albumin in health and disease: esterase, antioxidant, transporting and signaling properties. Int J Mol Sci. 2021. https://doiorg.publicaciones.saludcastillayleon.es/10.3390/ijms221910318.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Luo L, Chen Z, Gong T, Ye Q, Li H, Guo Y, Wen J, Hu Y, Wu J. Cytosolic perfluorocarbon delivery to platelets via albumin for antithrombotic therapy. J Controlled Release. 2023. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.jconrel.2023.01.036.

    Article  Google Scholar 

  15. Luo Xq, Luo J, Wang Cj L, Zx T, Dy X, Xh. Clinical features of severe Mycoplasma pneumoniae pneumonia with pulmonary complications in childhood: A retrospective study. Pediatr Pulmonol. 2023. https://doiorg.publicaciones.saludcastillayleon.es/10.1002/ppul.26593.

    Article  PubMed  Google Scholar 

  16. Zhou H, Wang A, Meng X, Lin J, Jiang Y, Jing J, Zuo Y, Wang Y, Zhao X, Li H, et al. Low serum albumin levels predict poor outcome in patients with acute ischaemic stroke or transient ischaemic attack. Stroke Vasc Neurol. 2021. https://doiorg.publicaciones.saludcastillayleon.es/10.1136/svn-2020-000676.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Mo L, Chen Y, Li Z, Xu L, Dong W, Zhang L, Liang H, Li R, Tao Y, Shi W, et al. Red blood cell distribution width as a marker of cerebral infarction in hemodialysis patients. Ren Fail. 2017. https://doiorg.publicaciones.saludcastillayleon.es/10.1080/0886022x.2017.1398664.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Manolis AA, Manolis TA, Melita H, Mikhailidis DP, Manolis AS. Low serum albumin: A neglected predictor in patients with cardiovascular disease. Eur J Intern Med. 2022. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.ejim.2022.05.004.

  19. Suri DJ, Sombié OO, Zeba AN, Nama GM, Bekele TH, Woldeyohannes M, van Stuijvenberg ME, Dhansay MA, Urio EM, Loechl CU, et al. Association between biomarkers of inflammation and total liver vitamin A reserves estimated by 13 C-Retinol isotope dilution among preschool children in 5 African countries. J Nutr. 2023. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.tjnut.2022.10.012.

    Article  PubMed  Google Scholar 

  20. Liang M, Ren X, Huang D, Ruan Z, Chen X, Qiu Z. The association between lactate dehydrogenase to serum albumin ratio and the 28-day mortality in patients with sepsis-associated acute kidney injury in intensive care: a retrospective cohort study. Ren Fail. 2023. https://doiorg.publicaciones.saludcastillayleon.es/10.1080/0886022x.2023.2212080.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Mazaherpour H, Soofian M, Farahani E, Saebbian A, Etebari M, Mazaherpour S, Ashrafian F, Ramezani A, Saleem A. Evaluating the factors affecting COVID-19 patients’ mortality in Arak in 2020. Can Respir J. 2022. https://doiorg.publicaciones.saludcastillayleon.es/10.1155/2022/9594931.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Shu X-P, Xiang Y-C, Liu F, Cheng Y, Zhang W, Peng D. Effect of serum lactate dehydrogenase-to-albumin ratio (LAR) on the short-term outcomes and long-term prognosis of colorectal cancer after radical surgery. BMC Cancer. 2023. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12885-023-11446-5.

  23. Liu Q, Zheng H-L, Wu M-M, Wang Q-Z, Yan S-J, Wang M, Yu J-J, Li D-P. Association between lactate-to-albumin ratio and 28-days all-cause mortality in patients with acute pancreatitis: A retrospective analysis of the MIMIC-IV database. Front Immunol. 2022. https://doiorg.publicaciones.saludcastillayleon.es/10.3389/fimmu.2022.1076121.

  24. Lee JH. The association between lactate to albumin ratio and outcomes at early phase in patients with traumatic brain injury. Turkish J Trauma Emerg Surg. 2023. https://doiorg.publicaciones.saludcastillayleon.es/10.14744/tjtes.2023.40033.

    Article  Google Scholar 

  25. Wang R, He M, Qu F, Zhang J, Xu J. Lactate albumin ratio is associated with mortality in patients with moderate to severe traumatic brain injury. Front Neurol. 2022. https://doiorg.publicaciones.saludcastillayleon.es/10.3389/fneur.2022.662385.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Patet C, Suys T, Carteron L, Oddo M. Cerebral lactate metabolism after traumatic brain injury. Curr Neurol Neurosci Rep. 2016. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s11910-016-0638-5.

    Article  PubMed  Google Scholar 

  27. Chen J, Or CK, Chen T. Effectiveness of using virtual Reality–Supported exercise therapy for upper extremity motor rehabilitation in patients with stroke: systematic review and meta-analysis of randomized controlled trials. J Med Internet Res. 2022. https://doiorg.publicaciones.saludcastillayleon.es/10.2196/24111.

  28. Feigin VL, Nichols E, Alam T, Bannick MS, Beghi E, Blake N, Culpepper WJ, Dorsey ER, Elbaz A, Ellenbogen RG, et al. Global, regional, and national burden of neurological disorders, 1990–2016: a systematic analysis for the global burden of disease study 2016. Lancet Neurol. 2019. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/s1474-4422(18)30499-x.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Veltkamp R, Pearce LA, Korompoki E, Sharma M, Kasner SE, Toni D, Ameriso SF, Mundl H, Tatlisumak T, Hankey GJ, et al. Characteristics of recurrent ischemic stroke after embolic stroke of undetermined source. JAMA Neurol. 2020. https://doiorg.publicaciones.saludcastillayleon.es/10.1001/jamaneurol.2020.1995.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Ding J, Karp JE, Emadi A. Elevated lactate dehydrogenase (LDH) can be a marker of immune suppression in cancer: interplay between hematologic and solid neoplastic clones and their microenvironments. Cancer Biomarkers. 2017. https://doiorg.publicaciones.saludcastillayleon.es/10.3233/cbm-160336.

    Article  PubMed  Google Scholar 

  31. Wang K, Zhou W, Wen L, Jin X, Meng T, Li S, Hong Y, Xu Y, Yuan H, Hu F. The protective effects of axitinib on blood-brain barrier dysfunction and ischemia-reperfusion injury in acute ischemic stroke. Exp Neurol. 2024. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.expneurol.2024.114870.

    Article  PubMed  PubMed Central  Google Scholar 

  32. A. P. S. Narang, R. S. Greval, H. Chopra, and C. S. Kalra. The role of two enzymes (LDH and PHI) and a tumour marker (CEA) in the prognostic evalution of head and neck malignancy. Indian J Otolaryngol Head Neck Surg. 2001. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/BF02910990.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Dong F, Wang X, Li J, Zhao D, Li J. Causal relationship between lactate dehydrogenase and risk of developing ischemic stroke: A Mendelian randomized study. Brain Behav. 2023. https://doiorg.publicaciones.saludcastillayleon.es/10.1002/brb3.3352.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Arroyo V, García-Martinez R, Salvatella X. Human serum albumin, systemic inflammation, and cirrhosis. J Hepatol. 2014. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.jhep.2014.04.012.

    Article  PubMed  Google Scholar 

  35. Esparza GA, Teghanemt A, Zhang D, Gioannini TL, Weiss JP. Endotoxin·albumin complexes transfer endotoxin monomers to MD-2 resulting in activation of TLR4. Innate Immun. 2011. https://doiorg.publicaciones.saludcastillayleon.es/10.1177/1753425911422723.

    Article  PubMed  Google Scholar 

  36. Perez R, de Garibay A, Kellum JA, Honigschnabel J, Kreymann B. Respiratory and metabolic acidosis correction with the advanced organ support system. Intensive Care Med Experimental. 2019. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s40635-019-0269-7.

  37. Xu M, Wu Z, Wu B, Hu Y, Duan Q, Wang H, He J. Lactate dehydrogenase-to albumin ratio (LAR) is associated with early-onset cognitive impairment after acute ischemic stroke. J Clin Neurosci. 2022. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.jocn.2022.10.004.

    Article  PubMed  Google Scholar 

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Acknowledgements

We would like to express our gratitude for the general support received during the conduct of this study.

Funding

This work was supported by the High-level Talents Research Start-up Project of the First Affiliated Hospital, Fujian Medical University (YJRC4200) and the Postdoctoral Nursery Project of the Second Affiliated Hospital of Fujian Medical University (2024BSH01).

Author information

Author notes

  1. Xiao-Dan Zhang, Zong-Yong Zhang and Ming-Pei Zhao contributed equally to this work and shared first authorship.

Authors and Affiliations

  1. Neurosurgery Department, The 2nd Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China

    Xiao-Dan Zhang, Hong-Zhi Gao & Zong-Qing Zheng

  2. Clinic Center of Molecular Diagnosis and Therapy of the 2nd Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China

    Xiao-Dan Zhang, Hong-Zhi Gao & Zong-Qing Zheng

  3. Department of Neurosurgery, Neurosurgery Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China

    Xiao-Dan Zhang, Zong-Yong Zhang, Ming-Pei Zhao, Xiang-Tao Zhang, Neng Wang, Yuan-Xiang Lin & Zong-Qing Zheng

  4. Department of Neurosurgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China

    Xiao-Dan Zhang, Zong-Yong Zhang, Ming-Pei Zhao, Xiang-Tao Zhang, Neng Wang, Yuan-Xiang Lin & Zong-Qing Zheng

  5. Fujian Provincial Institutes of Brain Disorders and Brain Sciences, First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China

    Xiao-Dan Zhang, Zong-Yong Zhang, Ming-Pei Zhao, Xiang-Tao Zhang, Neng Wang, Yuan-Xiang Lin & Zong-Qing Zheng

  6. Fujian Provincial Clinical Research Center for Neurological Diseases, First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China

    Xiao-Dan Zhang, Zong-Yong Zhang, Ming-Pei Zhao, Xiang-Tao Zhang, Neng Wang, Yuan-Xiang Lin & Zong-Qing Zheng

Authors
  1. Xiao-Dan Zhang
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  2. Zong-Yong Zhang
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  3. Ming-Pei Zhao
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  4. Xiang-Tao Zhang
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  5. Neng Wang
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  7. Yuan-Xiang Lin
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  8. Zong-Qing Zheng
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Contributions

The authorship requirements have been met, and the final manuscript was approved by all authors. Conceptualization, ZQ.Z and XD.Z; methodology, MP.Z; formal analysis, ZY.Z, XD.Z and ZQ.Z; data curation, XD.Z and XT.Z; writing—original draft preparation, XD.Z and ZQ.Z; writing—review and editing, XD.Z, N.W, HZ.G, ZQ.Z. and YX.L; supervision, ZQ.Z. and YX.L.

Corresponding authors

Correspondence to Yuan-Xiang Lin or Zong-Qing Zheng.

Ethics declarations

Ethical approval

This retrospective research experiment was conducted in accordance with the ethical guidelines of the World Medical Association (Declaration of Helsinki) and was approved by the Ethics Committee of the First Affiliated Hospital of Fujian Medical University with the ethics number 20241121. It was conducted according to the approved methods. All patients were informed and signed an informed consent form before the experiment.

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The authors declare no competing interests.

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Zhang, XD., Zhang, ZY., Zhao, MP. et al. Lactate dehydrogenase to albumin ratio and poor prognosis after thrombolysis in ischemic stroke patients: developing a novel nomogram. BMC Med Inform Decis Mak 25, 166 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12911-025-02991-z

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Keywords

BMC Medical Informatics and Decision Making

ISSN: 1472-6947

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