The association between acute appendicitis complications and hematological parameters-based indices

Abstract

Aim Recent studies have focused on more simple and easily accessible biomarkers for the discrimination of complicated appendicitis (CA) and non-complicated appendicitis (NCA). In the present study, we aimed to investigate the value of hematological parameters-based indices measured on admission for CA and NCA discrimination since the main purpose is to recognize those with a poor prognosis among patients with AA who present to the emergency department (ED). Methods A total of 699 patients who underwent surgery with the diagnosis of acute appendicitis (AA) between 2018 and 2022 were retrospectively analyzed. Patients were allocated to NCA and CA groups according to operation and pathology results. Two groups were compared for systemic immune-inflammation index (SII), systemic inflammatory response index (SIRI), prognostic nutritional index (PNI), neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), and monocyte/lymphocyte ratio (MLR). Results There was a significant difference between groups with regard to SII, SIRI, PNI, NLR, PLR, and MLR (p<0.001 for all). For CA estimation, the SII cut-off value reached a maximum when >5703.30 with a sensitivity of 99.2%, specificity of 99.5%, and AUC of 0.999 (p<0.001). According to logistic regression analysis, these parameters were specified as the risk factors, which discriminate between CA and NCA. Conclusion SII, SIRI, PNI, NLR, PLR, and MLR levels measured on admission to the ED are predictors that may be used for CA and NCA discrimination.

Keywords

Introduction

Acute appendicitis (AA), which usually results in an operation, is one of the most common causes of acute abdomen in the emergency department (ED).¹ In the literature, appendicitis (A) is classified as simple (non-complicated A) (NCA) and complicated A. Complicated appendicitis (CA) is associated with high morbidity and mortality in the postoperative period.² Despite all these diagnostic methods, literature data show that the ratio of negative appendectomies and perforations may reach up to 20 - 30%.³ Recent studies have focused on more simple diagnostic biomarkers for the discrimination of CA and NCA.²
Various laboratory parameters have been used for the diagnosis of AA and the detection of its clinical severity. Pehlivanli et al. have reported that the platelet/lymphocyte ratio (PLR) is a useful prognostic biomarker for NCA and CA discrimination.⁴ Another study has reported that neutrophil/lymphocyte ratio (NLR) could potentially predict the diagnosis and severity of AA.³ It has been reported that the systemic immune-inflammation index (SII) and the systemic inflammatory response index (SIRI), which are inflammation-related indices based on peripheral hematological parameters, can be used as suitable markers in the detection of complications of AA.⁵
Serum albumin is another inflammation marker that can be used for all inflammatory diseases. The prognostic nutritional index (PNI) obtained from serum albumin and lymphocyte values is also an indicator reflecting the immune-nutritional and inflammatory status of patients.⁶ Kalaycı T et al. also emphasized that PNI and albumin values could be used as prognostic factors in patients with AA due to their high sensitivity and specificity.¹
The present study has investigated the effect of SII, SIRI, PNI, NLR, PLR, and monocyte/lymphocyte ratio (MLR) levels in differentiating NCA and CA since the main purpose is to recognize those with a poor prognosis among patients with AA who come to the ED.

Materials and Methods

Patients and Study DesignA total of 699 patients who underwent surgery with the diagnosis of AA between January 01, 2018, and November 01, 2022, were retrospectively analyzed. Patients older than 18 years of age, male or female, whose all clinical and laboratory information could be accessed from the hospital registration system and whose diagnosis of AA was confirmed according to history, clinical and physical examination findings, laboratory values, imaging results, surgical and pathology reports were included in the study. Patients under 18 years of age, patients with a positive polymerase chain reaction (PCR) test, pregnant women, patients with a history of acute/chronic inflammatory, hematologic, rheumatologic diseases, heart, kidney, and liver failure, cancers, autoimmune or immunosuppressive patients, patients with a history of trauma/operation within the last 1 month, patients with a normal appendix in the surgery/pathology report, patients followed up with medical treatment (non-surgical), and patients whose information could not be accessed from the electronic registry system were excluded from the study. All patients were divided into two groups: NCA (phlegmonous, catarrhal, suppurative appendicitis) and CA (gangrenous, plastron, perforated appendicitis) according to the results of surgery and pathology.^1,7
The NLR was calculated by dividing neutrophil count by lymphocyte count, PLR was calculated by dividing platelet count by lymphocyte count, and MLR was calculated by dividing monocyte count by lymphocyte count. PNI = (10 × serum albumin [g/dL]) + (0.005 × lymphocytes/μL), SIRI = (neutrophil × monocyte/lymphocyte), and SII = (platelet × neutrophil/lymphocyte). Two groups were compared with regard to SII, SIRI, PNI, NLR, PLR, and MLR levels. The correlation of these parameters with the length of hospital stay was also evaluated. The study was approved by Necmettin Erbakan University Faculty of Medicine Ethics Committee on 17/06/2022 with the number 2022/3846 (10422).Data Collection and Laboratory TestsAge, gender, medical history, white blood cell (WBC), neutrophil, lymphocyte, monocyte, platelet (PLT), red blood cell distribution width (RDW), C-reactive protein (CRP), and albumin values obtained from routine blood analysis at the time of admission to the ED, PCR results, abdominal ultrasonography and/or tomography results, surgery and pathology reports, length of hospital stay, and clinical outcomes (discharge/in-hospital death) were obtained retrospectively from patient epicrises and hospital electronic record system. Complete blood count (CBC) was measured using Mindray auto hematology analyzer BC-6800 (Shenzhen, China). Biochemical parameters were obtained using Mindray chemistry analyzer BS-2000M.Ethical ApprovalEthics Committee approval for the study was obtained.Statistical AnalysisStatistical analyses were performed using SPSS 21.0 program (IBM Inc, Chicago, IL, USA). Numerical parameters were expressed as median (IQR) and categorical variables as frequency and percentage (%). The Kolmogorov-Smirnov test, histogram analysis, and skewness/kurtosis data were used to evaluate the conformity of numerical variables to normal distribution. In two independent group comparisons of numerical parameters, the Mann-Whitney U test was used for those that did not show a normal distribution, and an independent t-test was used for those that showed a normal distribution. Spearman’s correlation analysis was used to test the correlation between numerical parameters. Binary Logistic Regression analysis was performed to determine the predictive factors. According to the results of the Logistic Regression analysis, the appropriate parameters were subjected to ROC analysis and diagnostic data were presented. In the whole study, the Type-I error rate was taken as 5% and p=0.05 was accepted as statistically significant.

Results

Of the total 699 patients, 677 (96.9%) were discharged and 22 (3.1%) died. Of all patients, 455 (65.1%) were male, and the mean age was 29 (11-75) (years). Among all patients, 570 (81.5%) had NCA and 129 (18.5%) had CA. The mean duration of hospital stay for all patients was 3 (1-12) (days). The mean NLR, PLR, MLR, PNI, SII, and SIRI values of all patients were 4.21 (0.38-1727.0), 137.93 (31.43-44800.0), 0.288 (0.01-160.0), 450.0 (190.0-550.0), 1081.8 (60.8-773635.1), 2.11 (0.05-2763.0), respectively. Comparison of the CA and NCA patient groups with regard to demographic and laboratory findings is shown in Table 1. When CA and NCA groups were compared for age and gender, no significant difference was found between the two groups (p=0.27, p=0.91). WBC, RDW (%), neutrophil, monocyte, PLT, CRP, NLR, PLR, MLR, SII and SIRI levels were significantly higher in the CA group (p<0.001 for all). Similarly, lymphocyte, albumin, PNI values, and discharge rate were lower, and the duration of hospital stay was longer in the CA group (p<0.001 for all). All in-hospital deaths were seen in the CA group. According to logistic regression analysis, WBC, neutrophil, monocyte, lymphocyte, PLT, CRP, albumin, NLR, PLR, MLR, PNI, SII and SIRI levels were determined as risk factors differentiating CA from NCA in Table 2. The predictive values of CA according to ROC analysis and cut-off values of these parameters are presented in Table 3. Accordingly, SII had the highest and WBC had the lowest AUC values (0.999 and 0.813, respectively) compared to other parameters (p<0.001 for all). Besides, among all parameters, SII cut-off value >5703.30 reached the highest values with 99.2% sensitivity and 99.5% specificity. There was a moderate negative correlation between the length of hospital stay and lymphocyte, albumin, and PNI levels (respectively, the correlation coefficient: -0.464, -0.436, -0.438) (for all p<0.001). A moderate positive correlation was detected between the length of hospital stay and WBC, RDW (%), neutrophils, monocytes, PLT, CRP, NLR, PLR, MLR, SII and SIRI (respectively, the correlation coefficient: 0.341, 0.200, 0.481, 0.403, 0.443, 0.426, 0.501, 0.462, 0.451, 0.498, 0.483) (for all p<0.001).

Discussion

Morbidity and mortality significantly decrease in AA when diagnosed early. Although imaging studies increase the likelihood of early diagnosis, it is of debate due to difficulties to reach sources and radiation exposure. So far, no classification, biomarker, imaging method, or scoring system has been proven to be sufficiently effective in differentiating NCA and CA.⁸ Therefore, safe and easily accessible markers and indices that can support the differential diagnosis are still being investigated in the literature.²
Complete blood count (CBC) is inexpensive, commonly used, and easily reached parameter in clinical laboratories. The physiologic reaction of WBCs against stress leads to an increase in neutrophil count and a decrease in lymphocyte count.⁹ Changes in platelet indices have also been reported to play a role in inflammatory processes.¹⁰ NLR and PLR that are estimated from the ratio of these parameters are being used as inflammation parameters in many conditions today.^7,9,11 AA develops as a result of the inflammation caused by the obliteration of the appendix lumen. Thus, NLR may be a valuable tool to determine the diagnosis and the severity of AA.^2-3 Çelik B et al. have emphasized that elevated NLR and PLR levels could be useful to determine the risky patients for CA development.⁹ In another study, sensitivity was found to be 64.3% and specificity was 67.5% for differentiating CA and NCA when the PLR cut-off value was ≥163.27 (AUC=0.660, p=0.041).⁴ Monocyte count-containing ratios like MLR have also been shown to have a useful potential for the detection of CA.¹² In our study, NLR, PLR, and MLR levels were significantly higher in patients with CA compared to those with NCA (p<0.001). Moreover, all three parameters had high sensitivity and specificity in differentiating CA from NCA. Therefore, high NLR, PLR, and MLR values may be useful markers to predict patients with AA who are more likely to develop complications, especially in ED.
NLR, PLR, and MLR can serve as reliable diagnostic tools to identify CA cases.¹² It is mainly accepted that neutrophils and monocytes are involved in the inflammatory process in non-specific and lymphocytes in specific pathways. However, a single inflammation indicator is not sufficient to estimate the severity of inflammation. Therefore, SIRI, a composite index based on neutrophils, monocytes, and lymphocytes, is less affected by the absolute number of a single index and has a higher ability to predict the severity of inflammation.¹³ In recent years, studies are available in the literature showing a close relationship between SIRI and mortality in stroke, vasculitis, and ischemic heart disease.^14-15-16 In the study by Biyik M et al., SIRI was found to be higher in patients with severe acute pancreatitis compared to those with mild/moderate pancreatitis. The authors also emphasized that SIRI is a valuable marker in monitoring the severity and consequences of inflammation.¹⁷ SIRI levels were higher in the CA group compared to the NCA group also in our study (p<0.001). To our knowledge, few studies are available in the literature investigating the relationship between SIRI and CA. Similar to the study by Cakcak IE et al.⁵ our study supports that SIRI has a high potential to predict CA (AUC: 0.998, 98.4% sensitivity, and 98.4% specificity). Therefore, our study should be supported by further studies with SIRI and AA, a new and emerging index of inflammatory events.
SII, which is another index including peripheral neutrophils, lymphocytes, and platelets, has been reported to be elevated as an inflammatory marker in some studies.^18-19 SII is a newly defined, simple, accessible, and inexpensive index reflecting the balance between inflammatory and immune responses.¹¹ Gönüllü E et al. reported that SII levels in AA patients were found to be higher compared to the control group and the SII index may be a valuable marker for the prediction of AA.²⁰ In a study by Kart Y et al. conducted with 162 children with the diagnosis of AA, the authors showed that SII was higher in the perforated appendicitis group than in the non-perforated group, but no statistically significant difference was found between the groups (p=0.879).²¹ Finally, Cakcak IE et al. have also found that SIRI and SII values were significantly higher in Group I (with a higher complication rate) than those in Group II (with a lower complication rate) (p<0.001). Thus, they have reported that SII and SIRI could be used as markers for the prediction of AA complications.⁵ In our study, SII levels were found to be significantly higher in the CA group compared to the NCA group (p<0.001). In addition, SII showed the highest AUC, sensitivity, and specificity compared to other parameters in CA prediction (0.999, 99.2, 99.5, respectively). Thus, using SII alone seems to be a more potent marker for the prediction of CA compared to using other CBC parameters separately.
Severe inflammation is related to hypoalbuminemia, and the likelihood of low albumin levels is higher among patients with CA.¹ Low PNI levels, including albumin and lymphocyte counts, reflect hypoalbuminemia and lymphocytopenia.²² Since AA is a bacterial infection, the neutrophil count increases and the lymphocyte count decreases. Therefore, PNI levels will decrease as the severity of AA increases.¹ While PNI was previously used for prognostic evaluation of patients with cancer,²³ it is now thought to better reflect the severity of inflammation.^6,22,24 In the study of Kalayci T et al., albumin and PNI showed a statistically significant difference between the two groups with a diagnosis of AA. Both albumin and PNI values were found to be lower in the group with morbidity compared to that without morbidity (p=0.006 and p=0.017, respectively). Besides, the sensitivity of PNI was found to be 94.4% and specificity was found to be 71.4% when the cut-off value of PNI was 38 (AUC=0.810, p=0.018).¹ PNI levels in the patient group with CA were statistically different compared to the group with NCA (p<0.001) also in our study. In addition, PNI levels were found to have a stronger predictive value than albumin and lymphocyte in differentiating CA from NCA (AUC: 0.991, 0.989, 0.981, respectively). Therefore, PNI can be used as an additional tool for the early prediction of AA complications.

Limitations

The results of our study should be supported by future multi-center and prospective studies conducted with larger populations as it is a single-center and retrospective study conducted with a small number of patients. Secondly, we could only study hematological parameters at the time of admission to the ED, so we could not evaluate the changes in these markers and indices with time and their effect on the results. Third, since this was a retrospective study, we do not know what medications the patients were taking before admission and the time between the onset of symptoms and admission, which may have affected the levels of inflammatory markers.
Conclusion
According to the results of our study, SII, SIRI, PNI, NLR, PLR, and MLR levels measured at the time of admission are associated with disease severity and complications in patients with AA. These inflammatory indices, which can be easily estimated from CBC simply and inexpensively are the predictors that may be used for discrimination of high-risk patients for CA.

References

1. Kalayci T, Kartal M. Significance of neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, serum albumin, and prognostic nutritional index as predictors of morbidity in super-elderly patients operated on for acute appendicitis. Eur Rev Med Pharmacol Sci. 2022;26(3):820-827. doi:10.26355/eurrev_202202_27990
   Article · PubMed · Google Scholar
2. Sengul S, Guler Y, Calis H, Karabulut Z. The role of serum laboratory biomarkers for complicated and uncomplicated appendicitis in adolescents. J Coll Physicians Surg Pak. 2020;30(4):420-424. doi:10.29271/jcpsp.2020.04.420
   Article · PubMed · Google Scholar
3. Hajibandeh S, Hajibandeh S, Hobbs N, Mansour M. Neutrophil-to-lymphocyte ratio predicts acute appendicitis and distinguishes between complicated and uncomplicated appendicitis: a systematic review and meta-analysis. Am J Surg. 2020;219(1):154-163. doi:10.1016/j.amjsurg.2019.04.018
   Article · PubMed · Google Scholar
4. Pehlivanlı F, Aydin O. Role of platelet-to-lymphocyte ratio as a biomedical marker for the preoperative diagnosis of acute appendicitis. Surg Infect (Larchmt). 2019;20(8):631-636. doi:10.1089/sur.2019.042
   Article · PubMed · Google Scholar
5. Cakcak İE, Türkyılmaz Z, Demirel T. Relationship between SIRI, SII values, and Alvarado score with complications of acute appendicitis during the COVID-19 pandemic. Ulus Travma Acil Cerrahi Derg. 2022;28(6):751-755. doi:10.14744/tjtes.2021.94580
   Article · PubMed · Google Scholar
6. Nalbant A, Demirci T, Kaya T, Aydın A, Altındiş M, Güçlü E. Can prognostic nutritional index and systemic immune-inflammatory index predict disease severity in COVID-19? Int J Clin Pract. 2021;75(10). doi:10.1111/ijcp.14544
   Article · PubMed · Google Scholar
7. Ünal Y. A new and early marker in the diagnosis of acute complicated appendicitis: immature granulocytes. Ulus Travma Acil Cerrahi Derg. 2018;24(5):434-439. doi:10.5505/tjtes.2018.91661
   Article · PubMed · Google Scholar
8. Skjold-Ødegaard B, Søreide K. The diagnostic differentiation challenge in acute appendicitis: how to distinguish between uncomplicated and complicated appendicitis in adults. Diagnostics (Basel). 2022;12(7):1724. doi:10.3390/diagnostics12071724
   Article · PubMed · Google Scholar
9. Çelik B, Nalçacıoğlu H, Özçatal M, Altuner Torun Y. Role of neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio in identifying complicated appendicitis in the pediatric emergency department. Ulus Travma Acil Cerrahi Derg. 2019;25(3):222-228. doi:10.5505/tjtes.2018.06709
   Article · PubMed · Google Scholar
10. Krishnan N, Anand S, Pakkasjärvi N, Bajpai M, Dhua AK, Yadav DK. Mean platelet volume in the diagnosis of acute appendicitis in the pediatric population: a systematic review and meta-analysis. Diagnostics (Basel). 2022;12(7):1596. doi:10.3390/diagnostics12071596
    Article · PubMed · Google Scholar
11. Liu X, Guan G, Cui X, Liu Y, Liu Y, Luo F. Systemic immune-inflammation index can be an early indicator for predicting the severity of acute pancreatitis: a retrospective study. Int J Gen Med. 2021;14:9483-9489. doi:10.2147/ijgm.s343110
    Article · PubMed · Google Scholar
12. Halaseh SA, Kostalas M, Kopec CA, Nimer A. Single-center retrospective analysis of neutrophil, monocyte, and platelet-to-lymphocyte ratios as predictors of complicated appendicitis. Cureus. 2022;14(9). doi:10.7759/cureus.29177
    Article · PubMed · Google Scholar
13. Wang X, Ni Q, Wang J, Wu S, Chen P, Xing D. Systemic inflammation response index is a promising prognostic marker in elderly patients with heart failure: a retrospective cohort study. Front Cardiovasc Med. 2022;9:871031. doi:10.3389/fcvm.2022.871031
    Article · PubMed · Google Scholar
14. Zhang Y, Xing Z, Zhou K, Jiang S. The predictive role of systemic inflammation response index in the prognosis of stroke patients. Clin Interv Aging. 2021;16:1997-2007. doi:10.2147/cia.s339221
    Article · PubMed · Google Scholar
15. Lee LE, Pyo JY, Ahn SS, Song JJ, Park YB, Lee SW. Systemic inflammation response index predicts all-cause mortality in patients with antineutrophil cytoplasmic antibody-associated vasculitis. Int Urol Nephrol. 2021;53(8):1631-1638. doi:10.1007/s11255-020-02777-4
    Article · PubMed · Google Scholar
16. Dziedzic EA, Gąsior JS, Tuzimek A, Dąbrowski M, Jankowski P. The association between serum vitamin D concentration and new inflammatory biomarkers-systemic inflammatory index and systemic inflammatory response-in patients with ischemic heart disease. Nutrients. 2022;14(19):4212. doi:10.3390/nu14194212
    Article · PubMed · Google Scholar
17. Biyik M, Biyik Z, Asil M, Keskin M. Systemic inflammation response index and systemic immune inflammation index are associated with clinical outcomes in patients with acute pancreatitis. J Invest Surg. 2022;35(8):1613-1620. doi:10.1080/08941939.2022.2084187
    Article · PubMed · Google Scholar
18. Tanacan E, Dincer D, Erdogan FG, Gurler A. A cutoff value for the systemic immune-inflammation index in determining activity of Behçet disease. Clin Exp Dermatol. 2021;46(2):286-291. doi:10.1111/ced.14432
    Article · PubMed · Google Scholar
19. Erdogan T. Role of systemic immune-inflammation index in asthma and NSAID-exacerbated respiratory disease. Clin Respir J. 2021;15(4):400-405. doi:10.1111/crj.13314
    Article · PubMed · Google Scholar
20. Gönüllü E, Harmantepe AT, İbrahim ZB, Küçük IF, Çapoğlu R, Dikiciler E. Comparison of the systemic immune inflammatory index with the neutrophil lymphocyte and platelet lymphocyte ratio in terms of success in predicting appendicitis: single-center experience. Kocaeli Med J. 2022;11(2):130-137. doi:10.5505/ktd.2022.21957
    Article · PubMed · Google Scholar
21. Kart Y, Uğur C. Evaluation of systemic immune-inflammation index as novel marker in the diagnosis of acute appendicitis in children. J Contemp Med. 2022;12(5):593-597. doi:10.16899/jcm.1106843
    Article · PubMed · Google Scholar
22. Wu H, Zhou C, Kong W, Zhang Y, Pan D. Prognostic nutrition index is associated with the all-cause mortality in sepsis patients: a retrospective cohort study. J Clin Lab Anal. 2022;36(4). doi:10.1002/jcla.24297
    Article · PubMed · Google Scholar
23. Nogueiro J, Santos-Sousa H, Pereira A, et al. The impact of the prognostic nutritional index in gastric cancer. Langenbecks Arch Surg. 2022;407(7):2703-2714. doi:10.1007/s00423-022-02627-0
    Article · PubMed · Google Scholar
24. Keskin HA, Kurtul A, Esenboğa K, Çiçek CM, Katırcıoğlu SF. Prognostic nutritional index predicts in-hospital mortality in patients with acute Stanford type A aortic dissection. Perfusion. 2021;36(7):710-716. doi:10.1177/0267659120961937
    Article · PubMed · Google Scholar

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