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ORIGINAL ARTICLE
Year : 2022  |  Volume : 18  |  Issue : 3  |  Page : 725-732

The hemoglobin, albumin, lymphocyte, and platelet (HALP) score is a novel prognostic factor for patients with diffuse large B-cell lymphoma


1 Faculty of Medicine Osijek, University Josip Juraj Strossmayer Osijek; University Hospital Centre Osijek, Department of Hematology, Osijek, Croatia
2 Clinical Hospital Dubrava Zagreb, Department of Hematology, Zagreb, Croatia
3 Faculty of Medicine Osijek, University Josip Juraj Strossmayer Osijek, Osijek, Croatia

Date of Submission29-Jan-2021
Date of Decision19-Feb-2021
Date of Acceptance25-Mar-2021
Date of Web Publication25-Apr-2022

Correspondence Address:
Perisa Vlatka
Faculty of Medicine Osijek, University Josip Juraj Strossmayer Osijek, Osijek and University Hospital Centre Osijek, Department of Hematology, Josipa Huttlera 4, Osijek
Croatia
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jcrt.jcrt_174_21

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 > Abstract 


Context: The hemoglobin, albumin, lymphocyte, and platelet (HALP) score is a prognostic marker in several types of malignant tumors. The prognostic value of HALP score in diffuse large B-cell lymphoma (DLBCL) remains unknown.
Aim: We aimed to determine the prognostic value of baseline HALP score in DLBCL patients.
Subjects and Methods: We retrospectively analyzed data from 153 newly diagnosed DLBCL patients treated with R-CHOP or R-CHOP-like regimens at our university hospital center. We evaluated the significance of HALP score as a predictor of response to treatment, overall survival (OS), and event-free survival (EFS).
Results: The median follow-up time for all patients was 40 months. Lower HALP score was found in patients with advanced stages of disease (P = 0.005) and in those with poor response to therapy (P = 0.004). Patients with a HALP score ≤20.8 had significantly worse 5-year OS (47.3% vs. 79.5%, P < 0.001) and 5-year EFS (40.6% vs. 76.7%, P < 0.001). These observations remained statistically significant in the multivariate Cox regression models independently of International Prognostic Index (IPI) and age.
Conclusion: Lower HALP is associated with unfavorable clinicopathological characteristics of DLBCL and seems to be an IPI independent negative prognostic factor. HALP score can be easily and inexpensively applied to timely recognize DLBCL patients under higher risk of unwanted outcomes in everyday clinical practice.

Keywords: Albumin, diffuse large B-cell lymphoma, hemoglobin, lymphocyte, platelet, prognosis


How to cite this article:
Vlatka P, Marko L, Stefan M, Dorian L. The hemoglobin, albumin, lymphocyte, and platelet (HALP) score is a novel prognostic factor for patients with diffuse large B-cell lymphoma. J Can Res Ther 2022;18:725-32

How to cite this URL:
Vlatka P, Marko L, Stefan M, Dorian L. The hemoglobin, albumin, lymphocyte, and platelet (HALP) score is a novel prognostic factor for patients with diffuse large B-cell lymphoma. J Can Res Ther [serial online] 2022 [cited 2022 Aug 10];18:725-32. Available from: https://www.cancerjournal.net/text.asp?2022/18/3/725/343914




 > Introduction Top


Diffuse large B-cell lymphoma (DLBCL) is a pathologically and clinically heterogeneous group of aggressive B-cell lymphomas.[1] It is the most common histologic subtype of lymphoma which represents approximately 25% of non-Hodgkin's lymphomas (NHLs).[1],[2],[3] The International Prognostic Index (IPI) and its variants are the main prognostic implements used in aggressive NHLs and retained their prognostic value in the era of novel therapies.[4],[5] However, the treatment for some patients who have favorable prognostic factors is not successful and vice versa, so more precise prognostic markers are needed. In the era of rituximab therapy, the efficacy of the IPI has decreased. To improve the traditional IPI various prognostic scoring systems were formed. The R-IPI classified three different prognostic groups instead of four groups, with the same parameters but different grouping criteria as the standard IPI does.[5] The improved IPI (NCCN-IPI) further clarified the categorization of age and normalized lactate dehydrogenase (LDH) and established four risk groups.[6] Both the R-IPI and the NCCN-IPI are more efficacious than the IPI for predicting survival in the rituximab era. However, it seems apparent that all the prognostic models are inadequate in identifying a risk group with less than a 50% chance of survival.[7]

Certain prognostically significant molecular biomarkers and genetic signatures in DLBCL have been identified, but their cost and unavailability before treatment make their everyday clinical application impractical.[8],[9] For that reason, finding new and readily available prognostic markers could make an important contribution to an improved determination of individual risk assessment.

Immune response and inflammatory cells have been identified as crucial parameters in cancer survival rates.[10],[11] Several recent studies have reported that peripheral blood cells, involving absolute lymphocyte count (ALC), monocytes, and platelets, have been related to higher mortality rates in DLBCL patients.[12],[13],[14] Based on that evidence, many combinations of inflammatory parameters such as neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and lymphocyte-to-monocyte ratio have been used to foresee the prognosis of DLBCL patients.[15],[16] The nutritional status of DLBCL patients (including hemoglobin and albumin levels) has also been marked as a crucial factor influencing survival outcomes.[17],[18],[19] Previous studies have recognized a new inflammatory index called HALP, consisting of hemoglobin, albumin, lymphocytes, and platelets, which has been shown to bear good prognostic properties in patients with renal, bladder, gastric, small cell lung, and prostate cancer.[20],[21],[22],[23],[24]

So far, the significance of the HALP score in DLBCL patients has not been reported. The main goal of our study was to determine the significance of baseline HALP score in predicting response to treatment, overall survival (OS), and event-free survival (EFS) in patients who suffer from DLBCL.


 > Subjects and Methods Top


Subjects

Retrospective data were collected from 153 consecutive patients with newly diagnosed DLBCL between November 2006 and June 2019 in our university hospital center and treated with R-CHOP or R-CHOP-like regimens. Patients with disease stages (Lugano modification of the Ann Arbor [AA] staging system) II-IV, IE (i.e., localized involvement of a single extranodal organ or site in the absence of any lymph node involvement), or I bulky (i.e., measurable tumor mass >10 cm in diameter or a mediastinal mass >1/3 of the chest diameter) who were supposed to go through at least 4 cycles of immunochemotherapy were included in the study. Patients were excluded from the study in case of transformed indolent lymphoma, human immunodeficiency virus, which is an associated DLBCL, primary central nervous system lymphoma, posttransplant DLBCL, with evidence of an infection and/or active chronic inflammatory disease (e.g., arthritis, asthma, inflammatory bowel disease, and psoriasis), or missing necessary laboratory and clinical data at diagnosis.

Ethical approval was obtained from the ethics committee of our hospital and the study was designed consistent with the Declaration of Helsinki. This research was carried out in accordance with the ethical approval, and informed consent was obtained from all patients.

Clinical assessment and laboratory data

Basic clinical and laboratory parameters were obtained from medical records including age, gender, disease stage, IPI score, presence of B symptoms, Eastern Cooperative Oncology Group performance status (ECOG-PS), red blood cell count, white blood cell count, platelet count, absolute neutrophil count, ALC, serum LDH levels, C-reactive protein levels (CRP), serum albumin concentration, and hemoglobin concentration.

The HALP score was calculated according to the following formula: Hemoglobin (g/L) × albumin (g/L) × lymphocytes (/L)/platelets (/L).[22] Initial HALP score and laboratory parameters were characterized as values collected within 2 weeks before the beginning of the treatment.

Outcome

The outcomes which were analyzed were response to treatment, EFS, and OS. Response to treatment was completed according to the International Working Group criteria.[25] The EFS was measured from the date of diagnosis to the date of one of the events such as disease progression, relapse, death irrespective of cause, or last control. The OS was measured from the date of the diagnosis to the last visit or death due to any cause.

Statistical analysis

The normality of numerical data distribution was assessed using the Kolmogorov–Smirnov test. Numerical variables with normal distribution were presented as mean and standard deviation and were compared between groups using the t-test or the one-way analysis of variance (ANOVA). Numerical variables which were nonnormally distributed were presented as the median and interquartile range (IQR) and were compared between groups using the nonparametric Kruskal–Wallis ANOVA and Mann–Whitney U-test. Categorical variables were expressed as relative and absolute frequencies and were compared using the Fisher's exact test or the Chi-square test where suitable. Univariate and multivariate logistic regression analyses were performed to investigate the significant predictors of response to treatment. The receiver operating characteristic (ROC) curve analysis using survival status as a classification variable was performed for determining an optimal HALP cutoff value for survival based on the Youden index (sensitivity + specificity – 1).[26] The maximum value of the Youden index was considered as the optimal cutoff point. Survival curves were constructed using the Kaplan–Meier method and differences in survival were assessed using the log-rank test. Cox regression analysis was used to investigate multivariate associations with survival. P < 0.05 was considered statistically significant.

ROC curve calculation was performed using the MedCalc statistical software version 11.4.2.3 (Ostend, Belgium), but all other statistical analyses were performed with SPSS version 15.0 (Chicago, IL, USA).


 > Results Top


Basic characteristics of diffuse large B-cell lymphoma patients

Overall, 88 (57.5%) female and 65 (42.5%) male patients with DLBCL were included in the study cohort. The median age of all patients was 64 years (IQR: 54–72 years) [Table 1]. The AA stage was defined as Stage I or II in 44 (28.8%) patients and Stage III or IV in 109 (71.2%) patients. Regarding the IPI, 45 (29.4%) had a low, 41 (26.8%) had a low-intermediate, 37 (24.2%) had a high-intermediate and 30 (19.6%) patients were classified as a high IPI. One hundred and twenty-six (82.4%) patients received R-CHOP-21 immunochemotherapy consisting of rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone, while the rest of 27 (17.4%) received R-CHOP-like regimens. The median value of the HALP score was 29.44 (IQR: 17.21–46.58).
Table 1: Hemoglobin albumin lymphocyte and platelet score concerning treatment outcome and patients' characteristics

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Hemoglobin, albumin, lymphocyte, and platelet score and stage of disease and International Prognostic Index

Patients with advanced disease (AA Stage III and IV) had lower values of HALP score compared to patients with localized disease (AA Stage I and II) [26.55 (IQR: 15.06–42.7) vs. 41.38 (IQR: 24.19–56.56), P = 0.005, [Figure 1]a]. Patients with higher IPI score also had lower values of HALP score (16.98 [IQR: 9.72–27.25] vs. 28.04 [IQR: 17.12–42.35] vs. 32.03 [IQR: 17.98–48.82] vs. 38.85 [IQR: 26.37–54.04], P = 0.001) for patients with low, low-intermediate, high-intermediate, and high IPI, respectively [Figure 1]b.
Figure 1: Baseline hemoglobin, albumin, lymphocyte, and platelet score in diffuse large B-cell lymphoma patients (N = 153) according to (a) Ann Arbor clinical staging, (b) International Prognostic Index, and (c) response to treatment

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Hemoglobin, albumin, lymphocyte, and platelet score category and pathological clinical/laboratory features

According to ROC analysis, patients were divided into two groups, based on optimal HALP cutoff values for predicting OS [data shown in Figure Supplementary Material 1]. The optimal cutoff value was identified to be >20.8. Ninety-nine patients had HALP >20.8, while 54 patients had HALP ≤20.8. A group of patients with HALP ≤20.8 had significantly more advanced clinical stages (AA Stage III and IV) (P = 0.001), worse ECOG PS (P = 0.001), higher IPI (P < 0.001), increased CRP (P < 0.001), and increased LDH (P < 0.001). Furthermore, low HALP group had more frequent expressed B symptoms (P = 0.017), bone marrow infiltration (P < 0.001), and worse response to treatment (P < 0.001) [Table 1].

Hemoglobin, albumin, lymphocyte, and platelet score and treatment outcome

HALP scores varied significantly between groups according to treatment outcome. Patients with higher HALP score had better response to treatment (32.56 [IQR: 23.07–48.37) vs. 19.13 [IQR: 16.95–40] vs. 17.45 [IQR: 9.7–40.7], P = 0.004) for complete remission, partial remission, no response, or progression (NR), respectively [Figure 1]c.

Logistic regression analysis of potential response predictive markers is shown in [Table 2]. HALP ≤20.8 turned out to be an independent predictive factor for response to treatment in addition to IPI and B symptoms. Patients with HALP ≤20.8 had a higher risk of treatment failure. The odds ratio was 3.696 (95% confidence interval [CI]: 1.229–11.118, P = 0.02) compared to patients with HALP >20.8.
Table 2: Univariate and multivariate logistic regression analysis of prognostic factors for response to treatment in diffuse large B-cell lymphoma patients (n=153)

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Prognosis analysis

The median follow-up of our cohort was 40 months (1–151). A total of 50 patients died, and 53 experienced one of the events (disease progression, relapse, or death due to any cause). The 5-year OS and EFS were 71.5% and 63.9% for all patients, respectively. Kaplan–Meier analysis and log-rank test showed a significant difference in OS and EFS between the two groups (HALP ≤20.8 vs. HALP >20.8). The 5-year OS and 5-year EFS were significantly lower in those with HALP ≤20.8 (47.3% vs. 79.5% for OS, P < 0.001) [Figure 2]a (40.6% vs. 76.7% for EFS, P < 0.001) [Figure 2]b in comparison to patients with HALP >20.8.
Figure 2: Kaplan–Meier curves for (a) overall survival in regard to baseline hemoglobin, albumin, lymphocyte, and platelet score (≤20.8, >20.8) in diffuse large B-cell lymphoma patients (N = 153); (b) event-free survival in regard to hemoglobin, albumin, lymphocyte, and platelet score (≤20.8, >20.8) in diffuse large B-cell lymphoma patients (N = 153)

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HALP ≤20.8 was shown to be essentially associated with shorter OS (hazard ratio [HR]: 2.509, 95% CI: 1.369–4.598, P = 0.003) in the multivariate Cox regression model adjusted for IPI (HR: 4.823, 95% CI: 2.212–10.514, P < 0.001), age (HR: 1.029, 95% CI: 1.001–1.058, P = 0.048), and gender (HR: 1.707, 95% CI: 0.962–3.026, P = 0.067). Similarly, HALP ≤20.8 was shown to be significantly associated with shorter EFS (HR: 2.532, 95% CI: 1.419–6.024, P = 0.002) independently of IPI (HR: 3.260, 95% CI: 1.645–6.461, P < 0.001), age (HR: 1.035, 95% CI: 1.008–1.063, P = 0.011), and gender (HR: 3.476, 95% CI: 0.948–2.851, P = 0.077) as well.

Thus, HALP ≤20.8 bears IPI independent negative prognostic properties for both OS and EFS and might help in the recognition of patients under high risk of unwanted clinical outcomes.

Low values of hemoglobin, albumin, lymphocyte, and platelet predicted poor overall survival and event-free survival irrespective of patient gender

Since male patients had higher baseline HALP levels in comparison to females, mainly due to higher hemoglobin levels, we decided to additionally investigate the prognostic properties of HALP separately in female and male patients. Patients who had a low level of HALP score had a shorter 5-year OS [54.2% vs. 84.9%, P = 0.002, for females; and 38.3% vs. 72.3%, P < 0.001, for males; [Figure 3]a and [Figure 3]b] and 5-year EFS [50.7% vs. 79.6%, P < 0.001, for females; and 26.8% vs. 73.2%, P = 0.001, for males; [Figure 3]c and [Figure 3]d] in comparison to patients with high level of HALP score irrespective of patient gender.
Figure 3: Kaplan–Meier curves for (a) overall survival regarding to baseline hemoglobin, albumin, lymphocyte, and platelet score (≤20.8, >20.8) in diffuse large B-cell lymphoma male patients (n = 65); (b) overall survival according to hemoglobin, albumin, lymphocyte, and platelet score (≤20.8, >20.8) in diffuse large B-cell lymphoma female patients (n = 88): Kaplan–Meier curves for (c) event-free survival according to hemoglobin, albumin, lymphocyte, and platelet score (≤20.8, >20.8) in diffuse large B-cell lymphoma male patients (n = 65); (d) event-free survival according to baseline hemoglobin, albumin, lymphocyte, and platelet score (≤20.8, >20.8) in diffuse large B-cell lymphoma female patients (n = 88)

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 > Discussion Top


To the best of our knowledge, our study first aims to evaluate clinical associations and prognostic relevance of the HALP score in DLBCL patients. In this study, we demonstrated that a low level of HALP was an IPI independent risk factor of response to treatment, EFS, and OS in DLBCL patients. Due to higher hemoglobin levels, male patients had a high level of HALP more often than female patients. Subgroup analysis based on gender showed that a low level of HALP was associated with shorter survival in both male and female patients. Hence, HALP can be used for the prediction of prognosis regardless of gender.

Systemic inflammation and nutrition status are recognized as important components of cancer. The novel HALP index, consisting of hemoglobin, albumin, lymphocytes, and platelets, is considered as a marker to reflect both, host systemic inflammation and nutrition. The HALP index incorporates the factors of malnutrition (hemoglobin and albumin) with factors of the inflammatory response (lymphocyte and platelet count).

Our study suggests that HALP is a reflection of systemic inflammation in DLBCL patients. HALP correlated negatively with CRP. Lower HALP is associated with unfavorable clinical characteristics (poorer ECOG PS, bone marrow involvement, advanced disease stage, high IPI score, and presence of B symptoms). These unfavorable indicators could reflect tumor burden and might explain why the low HALP group tended to have a poorer prognosis than the high HALP group.

Other possible explanations of mechanisms of the association between low HALP and poor prognosis include: (i) hypoalbuminemia, which may indicate malnutrition in patients who may have poor quality of life, worse response to treatment, and an increased risk of toxicity induced by chemotherapy; (ii) reduced production of albumin, lymphocyte, and hemoglobin which may be caused by an increase in tumor release of cytokines, such as interleukin 6 (IL-6) and tumor necrosis factor-alpha, which is a characteristic of more aggressive form of disease; (iii) platelet activation in the vascular system contributes to the metastasis of tumors through growth factors that activated platelets secrete and consequently protect tumor cells from attack by immune system, and enhances tumor motility and growth; and (iv) IL-6 is a multifunctional cytokine that causes synthesis of hepcidin and CRP to happen but inhibits production of hepcidin and albumin, which stops the release of iron from enterocytes and macrophages, which then interferes with an adequate supply of iron for erythropoiesis, and later causes anemia.[27],[28],[29],[30],[31],[32],[33],[34],[35],[36] By now, many conflicting results on platelet counts have been published, and certain studies even suggest that thrombocytopenia is an inadequate prognostic factor in DLBCL[37] and others which suggest that thrombocytopenia is linked to a positive prognosis.[14] It was also reported by some studies that patients who suffer from lymphoma with thrombocytopenia showed poor survival if the patients had bone marrow involvement.[38],[39] Nevertheless, it has been discovered that the prognostic effect of platelet counts was inconsistent. Platelet count was not prognostic in our cohort of patients (data not shown). Formation of new blood vessels that provide oxygen and nutrients for persisted lesion growth is one of the most important requirements for the development of tumors. Platelets release vascular endothelial growth factor upon their activation, thus promoting angiogenesis.[40] Lysophosphatidic acid derived from platelets improves bone metastatic growth and progression, and platelet-released factors may play a part in tumor progression.[31] For that reason, these results serve as evidence that platelet counts may serve as inflammatory biomarkers and could help the prognostication process. In our study, patients with bone marrow involvement did not have lower values of platelets.

Our findings are limited by single-center experience, retrospective study design, and small sample size restraining statistical power for some of the presented analyses. Furthermore, we could not properly investigate the HALP relationship with protein expression and molecular mutation patterns. Nevertheless, our results strengthen the previous knowledge on the significance between HALP and cancer prognosis and establish HALP as a significant IPI independent predictor of DLBCL prognosis. We need future studies to confirm our results in independent cohorts of patients, as well as to establish potential pathogenetic mechanisms behind observed associations. In our opinion, special focus should be put on the relationship between HALP and inflammation and the response to cancer treatment.


 > Conclusions Top


Lower HALP is associated with unfavorable clinicopathological characteristics of DLBCL and seems to be IPI independent negative prognostic factor. HALP scores can be easily and inexpensively applied to timely recognize DLBCL patients under higher risk of unwanted outcomes in everyday clinical practice.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.


 > Supplementary Material Top






 
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    Figures

  [Figure 1], [Figure 2], [Figure 3]
 
 
    Tables

  [Table 1], [Table 2]



 

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