The Predictive Role of Systemic Inflammatory Markers in the Development of Acute Kidney Failure and Mortality in Patients with Abdominal Trauma
by
, , ,
Vlad Vunvulea
1,2,3,†,
Ovidiu Aurelian Budișcă
4,†,
Emil Marian Arbănași
1,5,6,7,
Adrian Vasile Mureșan
5,6,
Eliza Mihaela Arbănași
8,
Klara Brînzaniuc
3,
Raluca Niculescu
1,9,
Iuliu Gabriel Cocuz
1,9,
Adrian Dumitru Ivănescu
3
,
Ioana Hălmaciu
2,3,*,
Lucian Mărginean
2,
Réka Kaller
1,5,6,
Eliza Russu
5,6 and
Bogdan Andrei Suciu
3 1
Doctoral School of Medicine and Pharmacy, George Emil Palade University of Medicine, Pharmacy, Sciences and Technology of Targu Mures, 540142 Targu Mures, Romania
2
Department of Radiology, Mures County Emergency Hospital, 540136 Targu Mures, Romania
3
Department of Anatomy, George Emil Palade University of Medicine, Pharmacy, Science and Technology of Targu Mures, 540139 Targu Mures, Romania
4
Department of General Surgery, George Emil Palade University of Medicine, Pharmacy, Science and Technology of Targu Mures, 540139 Targu Mures, Romania
5
Department of Vascular Surgery, George Emil Palade University of Medicine, Pharmacy, Science and Technology of Targu Mures, 540139 Targu Mures, Romania
6
Clinic of Vascular Surgery, Mures County Emergency Hospital, 540136 Targu Mures, Romania
7
Center for Advanced Medical and Pharmaceutical Research, George Emil Palade University of Medicine, Pharmacy, Sciences and Technology of Targu Mures, 540139 Targu Mures, Romania
8
Faculty of Pharmacy, George Emil Palade University of Medicine, Pharmacy, Science and Technology of Targu Mures, 540139 Targu Mures, Romania
9
Department of Pathophysiology, George Emil Palade University of Medicine, Pharmacy, Science and Technology of Targu Mures, 540139 Targu Mures, Romania
*
Author to whom correspondence should be addressed.
†
These authors have contributed equally to this work.
J. Pers. Med. 2022, 12(12), 2045; https://doi.org/10.3390/jpm12122045
Submission received: 12 November 2022
/
Revised: 6 December 2022
/
Accepted: 7 December 2022
/
Published: 10 December 2022
(This article belongs to the Collection Advances of Emergency and Intensive Care)
Abstract
:Background: Abdominal trauma is defined as a variety of injuries to the abdominal wall, solid or hollow intra-abdominal organs, and various intra-abdominal vessels. Recently, there has been a significant amount of interest in the establishment of a reliable biomarker that can predict the outcome in patients with an abdominal injury. The purpose of this study is to confirm the predictive role of inflammatory biomarkers and underlying risk factors and the risk of acute kidney insufficiency (AKI) developing and mortality in abdominal trauma patients; Materials and methods: The current study was intended as an observational, analytical, retrospective cohort study and included all patients over 18 years of age with a diagnosis of abdominal trauma confirmed through a CT scan admitted to the County Emergency Clinical Hospital of Targu-Mureș, Romania between January 2017, and December 2021; Results: Non-survivor patients had a greater age (p = 0.02), as well as a higher prevalence of ischemic heart disease (IHD) (p = 0.007), history of myocardial infarction (MI) (p = 0.002), peripheral arterial disease (PAD) (p = 0.01), chronic kidney disease (CKD) (p = 0.01), and all risk factors (p = 0.0004 and p < 0.0001). In terms of injured organs, we have in the second group a higher incidence of kidney injury (p = 0.003) and hemoperitoneum (p = 0.008). Multivariate analysis showed a high baseline value for all inflammatory biomarkers that are independent predictors of adverse outcomes for all recruited patients. Furthermore, for all hospitalized patients, the history of MI (p = 0.03; p = 0.001; and p = 0.003), PAD (p = 0.01; p = 0.01; and p = 0.002), obesity (for all p < 0.0001), CKD (p < 0.001; p = 0.01; and p = 0.001), and kidney injury (p = 0.02; p = 0.004; and p = 0.01) were independent predictors of all outcomes. Moreover, IHD (p = 0.008 and p = 0.02), tobacco (p < 0.0001 and p = 0.02), and hemoperitoneum (p = 0.009 and p = 0.01) were predictors of mortality and composite endpoint, but not for AKI risk, as well as atrial fibrillation [AF] (p = 0.04) as predictors of the composite endpoint Conclusions: Higher monocyte to lymphocyte ratio (MLR), platelets to lymphocyte ratio (PLR), systemic inflammatory index (SII), neutrophil to lymphocyte ratios (NLR), aggregate inflammatory systemic index (AISI), and systemic inflammatory response index (SIRI) levels at admission, according to our data, highly predict AKI risk and death.
1. Introduction
Abdominal trauma is defined as a variety of injuries to the abdominal wall, solid or hollow intra-abdominal organs, and various intra-abdominal vessels [1]. Depending on the mechanism of injury, abdominal trauma can be classified into blunt and penetrating trauma. Nevertheless, abdominal trauma is associated with high morbidity and mortality rates, the abdomen being the third most affected body region in trauma [2]. According to recent studies, abdominal trauma mortality rates are reported to range from 1 to 20% globally, which is largely due to population diversity [3,4,5,6,7,8]. Recent literature reports the spleen, liver, and kidney as being the most commonly affected organs [9,10,11].
We classify acute kidney insufficiency (AKI) as one of the most dangerous post-traumatic complications, with an incidence of up to 10% [12,13,14,15,16], and it can occur secondary to rhabdomyolysis in the case of crushing [17], or more commonly by impaired kidney perfusion [18].
Recently, there has been a significant amount of interest in the establishment of a reliable biomarker that can predict the outcome in patients with an abdominal injury. One of the most accessible biomarkers is the neutrophil-to-lymphocyte ratio (NLR). The ratio has been proven to be a valid predictor for the outcome of patients with COVID-19 infection [19,20,21,22], breast cancer [23], cardiovascular disease [24,25,26,27,28], and kidney disease [21,29]. Additionally, the platelet-to-lymphocyte ratio (PLR) is another widely researched biomarker, found to have great prediction power in the outcome of patients in the fields of oncology [30], orthopedy [25,31], and trauma care [32]. Moreover, the other hematological ratios, monocyte-to-lymphocyte ratio (MLR), systemic inflammatory index (SII), systemic inflammatory response index (SIRI), and aggregate inflammatory systemic index (AISI) have proved their prediction regarding the poor outcome in numerously chronic and acute pathologies [19,20,21,25,28]. The role of neutrophils, lymphocytes, and platelets in the modulation of inflammatory processes has been extensively described in the literature [33,34].
In works published by Bi et al. [35], Guangging et al. [36], Tang et al. [37], and Ntalouka et al. [38], the predictive role of NLR and PLR in the risk of AKI occurrence as an adverse event following gastrointestinal and hepatobiliary surgery, on-pump coronary artery bypass, and non-cardiac surgery patients, respectively, in the case of the endovascular treatment of aortic aneurysm, was demonstrated abdominally.
This study aims to verify the predictive role of inflammatory biomarkers and underlying risk factors and the risk of acute kidney insufficiency (AKI) developing and mortality in abdominal trauma patients.
2. Materials and Methods
2.1. Study Design
The current investigation was intended as an observational, analytical, and retrospective cohort study that included all patients over the age of 18 who had been diagnosed with abdominal trauma confirmed through a CT scan admitted to the County Emergency Clinical Hospital of Targu-Mureș, Romania between January 2017 and December 2021. Exclusion criteria were as follows: patients who died in the first 24 h, patients with bone fractures who required hospitalization in orthopedics, and patients with septic shock, hematological diseases, or thromboembolic events in the last two months.
Patients in the research were initially classified as “survivors” or “non-survivors” based on their bad prognosis during their hospitalization. To determine the risk of AKI, mortality, and a composite endpoint of AKI and mortality, the optimal cut-off values for NLR, MLR, PLR, SII, SIRI, and AISI were employed.
2.2. Data Collection
The patient’s age, sex, cardiovascular disease [atrial fibrillation (AF), arterial hypertension (AH), chronic heart failure (CHF), ischemic heart disease (IHD), history of myocardial infarction (MI), and peripheral arterial disease (PAD)], chronic kidney disease (CKD), diabetes mellitus (DM), chronic obstructive pulmonary disease (COPD), obesity (body mass index > 30), tobacco, and length of hospital stay (LOS) were extracted from the hospital’s electronic database. Moreover, the first blood test result extracted hemoglobin level, hematocrit, neutrophil, monocyte, lymphocyte, and platelet count, sodium, potassium, glomerular filtration rate (GFR), blood urea nitrogen (BUN), creatinine, and uric acid.
In terms of abdominal trauma at computed tomography (CT) scan, we recorded the presence of injury at the level of the following organs: liver, spleen, pancreas, kidney, small bowel, large bowel, and the presence of hemoperitoneum.
2.3. Systemic Inflammatory Markers
The first blood test result was used to determine the systemic inflammatory biomarkers, as follows:
- −
- MLR = monocytes/lymphocytes
- −
- NLR = neutrophils/lymphocytes
- −
- PLR = platelets/lymphocytes
- −
- SII = (neutrophils × platelets)/lymphocytes
- −
- SIRI = (monocytes × platelets)/lymphocytes
- −
- AISI = (neutrophils × monocytes × platelets)/lymphocytes
2.4. Study Outcomes
The primary endpoints were the risk of AKI, in-hospital mortality rate, and a composite endpoint of AKI and mortality. Outcomes were stratified for all optimal inflammatory biomarkers of cut-off values at baseline.
In terms of AKI classification, we used the kidney disease improving global outcomes (KDIGO) guidelines, based on the increased serum creatinine level or urine output range from stage I to III [39].
2.5. Statistical Analysis
SPSS for Mac OS version 28.0.1.0 was used for statistical analysis (SPSS, Inc., Chicago, IL, USA). Chi-square tests were used to assess the associations of all systemic inflammatory markers with category factors, while Student t-tests or Mann–Whitney tests were used to assess differences in continuous variables. To assess the predictive power and establish the cut-off of inflammatory markers, the receiver operating characteristic (ROC) curve analysis was utilized. The ROC curve analysis was used to determine the appropriate NLR, MLR, PLR, SII, SIRI, and AISI cut-off values based on the Youden index (Youden Index = Sensitivity + Specificity − 1, ranging from 0 to 1). A multivariate logistic regression analysis with variables with p < 0.1 was performed to find the independent predictors of the AKI risk, mortality, and a composite endpoint of the AKI and mortality.
3. Results
During the study period, 364 patients diagnosed with abdominal trauma met the inclusion criteria and followed up during hospitalization. The mean age was 42.83 ± 18.24 (18–89), and 258 patients were male (70.88%) (Table 1). During the hospitalization, 84 patients (23.07%) developed AKI, 81 patients died (22.25%), and 57 patients (15.65%) developed AKI and deceased later, respectively. In terms of AKI staging, 26 patients (7.14%) were stage I KDIGO, 31 patients (8.51%) were stage II, and 27 patients (7.41%) were stage III.
After we divided the patients according to survival status, we had a higher age in the non-survivor group (p = 0.02), as well as a higher incidence of IHD (p = 0.007), MI (p = 0.002), PAD (p = 0.01), CKD (p = 0.01), and all risk factors (p = 0.0004 and p < 0.0001). In terms of injured organs, we have in the second group a higher incidence of kidney injury (p = 0.003) and hemoperitoneum (p = 0.008). Moreover, severe variables from laboratory data were associated with poor outcomes: non-survivors had lower hemoglobin and hematocrit levels (p < 0.0001), lower GFR (p = 0.004) and lymphocyte levels (p < 0.0001), and higher neutrophils (p < 0.0001), monocyte (p < 0.0001), glucose (p = 0.02), PLT (p = 0.001), BUN (p < 0.0001), creatinine (p < 0.0001), uric acid (p = 0.02), and all hematological ratios (p < 0.0001). Additionally, the non-survivor patients had a higher incidence of AKI (p < 0.0001), composite endpoint (p < 0.0001), a long hospital stay (p < 0.0001), and stage II (p < 0.0001) and III (p < 0.0001) KDIGO. In contrast, there was a higher incidence of stage I KDIGO (p < 0.0001) in survivor patients. The rest of the comorbidities and laboratory data are presented in Table 1.
To evaluate if the baseline of these indicators was predictive of AKI risk, death, and common endpoints in patients with abdominal injuries, receiver operating characteristic curves of all hematological ratios were generated (Figure 1, Figure 2 and Figure 3). Table 2 shows the ideal cut-off value determined by Youden’s index, the areas under the curve (AUC), and the prediction accuracy of the markers.
The results were subsequently evaluated after separating the patients into paired groups based on the optimal cut-off value of MLR, PLR, NLR, SII, AISI, and SIRI, according to the ROC. As seen in Table 3, there was a greater incidence of all poor outcomes for all inflammatory biomarkers studied.
A high baseline value for all of the studied markers was an independent prognostic factor of unfavorable outcomes for all enrolled patients, according to multivariate analysis (all p < 0.0001). Furthermore, for all hospitalized patients, the history of myocardial infarction (p = 0.03; p = 0.001; and p = 0.003), PAD (p = 0.01; p = 0.01; and p = 0.002), obesity (for all p < 0.0001), CKD (p < 0.001; p = 0.01; and p = 0.001), and kidney injury (p = 0.02; p = 0.004; and p = 0.01) were independent predictors of all outcomes. Moreover, IHD (p = 0.008 and p = 0.02), tobacco (p < 0.0001 and p = 0.02), and hemoperitoneum (p = 0.009 and p = 0.01) were predictors of mortality and composite endpoint, but not for AKI risk, as well as AF (p = 0.04) as predictors of the composite endpoint (Table 4).
4. Discussion
This research included 364 patients diagnosed with abdominal trauma. We identified the inflammatory biomarkers in all patients’ first admission blood test results and monitored the development of AKI, mortality rate, and a composite outcome of AKI and mortality. Our study’s most important outcome is that a high baseline value for NLR, MLR, PLR, AISI, SII, SIRI, cardiovascular disease (MI and PAD), and CKD are strong predictors of all outcomes. Additionally, the presence of a CT scan of kidney injury and obesity can predict all the outcomes. To the best of our knowledge, this is the first study to demonstrate that patients with high hematological ratios had a higher risk of AKI and intra-hospital mortality.
AKI is a well-known concern in polytraumatized patients, with an incidence of up to 36% [40,41,42,43,44,45], leading to a significant increase in mortality rate [46,47,48]. In the work published by Younan et al. [49], in which the dynamic evolution of NLR values in critically ill male trauma patients was analyzed, it was demonstrated that an increase in NLR in the first 48 h is associated with organ failure among male trauma patients. Moreover, Rau et al. [32], demonstrated that the low values of the total number of lymphocytes (OR: 1.1; p = 0.04) are a predictor of mortality in the case of 479 adult patients with polytrauma. Additionally, Ke et al. [50], demonstrated in univariate and multivariate analysis, that PLR (p < 0.001 and p = 0.02) is associated with an increased risk of mortality in the case of a group of 2854 adult trauma patients admitted to the intensive care unit.
According to the literature, the predictive values of hematological reports in polytraumatized patients have increasingly been studied, but with inconsistent findings. Additionally, the demand for prognostic tools in the negative evolution and decompensation of polytrauma patients has recently increased. High NLR values, according to Duchesne et al. [51], are related to early mortality in patients with severe post-traumatic hemorrhage who required a massive transfusion protocol. In contrast, Qiu et al. [52] discovered an association between NLR and the length of stay in the ICU and the duration of invasive mechanical ventilation, but not with mortality.
Regarding PLR, Li et al. [53] analyzed the predictive role of this marker in the case of 170 patients with traumatic brain injury, and in the multivariate analysis, they demonstrated that PLR (HR: 1.52; p = 0.009) is an independent predictor of short-term mortality.
Abu Alfeilat et al. [54], demonstrated that a value of NLR > 5.5, in the case of 294 patients who presented themselves to the emergency department, is a predictive factor in the case of the development of AKI (OR: 6.423; p = 0.031) in the multivariate analysis. Additionally, de Hond et al. [55], demonstrated that hematological ratios are associated with an increased risk of AKI and mortality in the case of a group of 1889 patients who presented to the emergency department with suspected infectious disease. Furthermore, Guangging et al. [36] observed high values of NLR in the group of patients who developed AKI after on-pump coronary artery bypass (2.63 vs. 2.06; p = 0.002).
In the current study, in the multivariate analysis, the high values of NLR (OR:7.09; p < 0.001 and OR:11.06; p < 0.001), MLR (OR:5.78; p < 0.001 and OR:11.14; p < 0.001), PLR (OR:5.89; p < 0.001 and OR:18.72; p < 0.001), SII (OR:6.76; p < 0.001 and OR:18.04; p < 0.001), SIRI (OR:6.25; p < 0.001 and OR:9.64; p < 0.001), and AISI (OR:6.08; p < 0.001 and OR:14.60; p < 0.001) are independent factors both for predicting the risk of developing AKI and for mortality during hospitalization. Moreover, the presence of PAD (OR:5.84; p = 0.01 and OR:6.14; p = 0.001), MI (OR:3.51; p = 0.03 and OR:7.64; p = 0.001), and obesity (OR:9.16; p < 0.001 and OR:9.67; p < 0.001) are associated with all recorded outcomes.
Although our findings are statistically significant, the study has a few limitations. Firstly, it is a retrospective and monocentric study with patient follow-up only throughout hospitalization. Future prospective multicenter studies with extended follow-ups are advised. Secondly, because the study was retrospective, we were unable to acquire information on chronic treatments used prior to admission (corticosteroids or anti-inflammatories meds). As a result, we were unable to determine how different drugs influence inflammatory biomarkers. Furthermore, there is no available information on the surgery, and we monitored the patients while they were in the hospital, but we are unsure of how many of them required chronic dialysis. In addition, more investigations are required to confirm our conclusions.
5. Conclusions
Higher levels of systemic inflammatory biomarkers upon admission, according to our data, greatly predict AKI risk and fatality. Additionally, myocardial infarction, obesity, renal damage, PAD, and CKD, were independent predictors of all outcomes in all hospitalized patients. Additionally, IHD, tobacco use, and hemoperitoneum have a predictive role in mortality and the composite endpoints, but not in AKI risk, while AF has a predictive role in the composite endpoint. Considering the simplicity of use and the low cost of these ratios, as well as the high risk of AKI development and mortality in trauma patients, they can be used to classify admission risk groups, improve patient treatment, and create predictive patterns.
Author Contributions
Conceptualization, methodology, writing—original draft preparation, V.V. and O.A.B.; software, E.M.A. (Emil Marian Arbănași), A.V.M., and A.D.I.; formal analysis, investigation, R.K. and I.G.C.; resources, R.N. and I.H.; writing—review and editing, E.M.A. (Eliza Mihaela Arbănași); data curation, project administration, visualization, supervision, K.B., B.A.S., L.M., and E.R.; validation, all authors. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Targu Mures Emergency County Hospital, Romania (protocol code 8524, on 5 April 2022).
Informed Consent Statement
Informed consent was obtained from all subjects involved in the study.
Data Availability Statement
Not applicable.
Acknowledgments
This paper was published with the support of the George Emil Palade University of Medicine, Pharmacy, Sciences and Technology of Targu Mures and is part of a Ph.D. thesis from the Doctoral School of Medicine and Pharmacy within the George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures, with the title “The study of the importance of pro-inflammatory factors as prognostic factors in the evolution of polytrauma studies”, which will be presented by Vlad Vunvulea, having the approval of all authors.
Conflicts of Interest
The authors declare no conflict of interest.
References
- Ki, Y.-J.; Jo, Y.-G.; Park, Y.-C.; Kang, W.-S. The Efficacy and Safety of Laparoscopy for Blunt Abdominal Trauma: A Systematic Review and Meta-Analysis. J. Clin. Med. 2021, 10, 1853. [Google Scholar] [CrossRef]
- Ntundu, S.H.; Herman, A.M.; Kishe, A.; Babu, H.; Jahanpour, O.F.; Msuya, D.; Chugulu, S.G.; Chilonga, K. Patterns and Outcomes of Patients with Abdominal Trauma on Operative Management from Northern Tanzania: A Prospective Single Centre Observational Study. BMC Surg. 2019, 19, 69. [Google Scholar] [CrossRef] [PubMed]
- Gad, M.A.; Saber, A.; Farrag, S.; Shams, M.E.; Ellabban, G.M. Incidence, Patterns, and Factors Predicting Mortality of Abdominal Injuries in Trauma Patients. N. Am. J. Med. Sci. 2012, 4, 129. [Google Scholar] [CrossRef]
- Gönültaş, F.; Kutlutürk, K.; Gok, A.F.K.; Barut, B.; Sahin, T.T.; Yilmaz, S. Analysis of Risk Factors of Mortality in Abdominal Trauma. Turk. J. Trauma Emerg. Surg. 2020, 26, 43–49. [Google Scholar] [CrossRef]
- Parra-Romero, G.; Contreras-Cantero, G.; Orozco-Guibaldo, D.; Domínguez-Estrada, A.; de Mercado-Martín del Campo, J.J.; Bravo-Cuéllar, L. Trauma Abdominal: Experiencia de 4961 Casos En El Occidente de México. Cir. Cir. 2019, 87, 183–189. [Google Scholar] [CrossRef]
- Pekkari, P.; Bylund, P.-O.; Lindgren, H.; Öman, M. Abdominal Injuries in a Low Trauma Volume Hospital—A Descriptive Study from Northern Sweden. Scand. J. Trauma Resusc. Emerg. Med. 2014, 22, 48. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Ferrah, N.; Cameron, P.; Gabbe, B.; Fitzgerald, M.; Martin, K.; Beck, B. Trends in the Nature and Management of Serious Abdominal Trauma. World J. Surg. 2019, 43, 1216–1225. [Google Scholar] [CrossRef] [PubMed]
- Smith, J.; Caldwell, E.; D’Amours, S.; Jalaludin, B.; Sugrue, M. Abdominal Trauma: A Disease in Evolution. ANZ J. Surg. 2005, 75, 790–794. [Google Scholar] [CrossRef] [PubMed]
- Raza, M.; Abbas, Y.; Devi, V.; Prasad, K.V.S.; Rizk, K.N.; Nair, P.P. Non Operative Management of Abdominal Trauma—A 10 Years Review. World J. Emerg. Surg. 2013, 8, 14. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- El-Menyar, A.; Abdelrahman, H.; Al-Hassani, A.; Peralta, R.; AbdelAziz, H.; Latifi, R.; Al-Thani, H. Single Versus Multiple Solid Organ Injuries Following Blunt Abdominal Trauma. World J. Surg. 2017, 41, 2689–2696. [Google Scholar] [CrossRef]
- García, I.C.; Villalba, J.S.; Iovino, D.; Franchi, C.; Iori, V.; Pettinato, G.; Inversini, D.; Amico, F.; Ietto, G. Liver Trauma: Until When We Have to Delay Surgery? A Review. Life 2022, 12, 694. [Google Scholar] [CrossRef] [PubMed]
- Qureshi, A.I.; Huang, W.; Lobanova, I.; Hanley, D.F.; Hsu, C.Y.; Malhotra, K.; Steiner, T.; Suarez, J.I.; Toyoda, K.; Yamamoto, H.; et al. Systolic Blood Pressure Reduction and Acute Kidney Injury in Intracerebral Hemorrhage. Stroke 2020, 51, 3030–3038. [Google Scholar] [CrossRef] [PubMed]
- Jalal, D.I.; Chonchol, M.; Targher, G. Disorders of Hemostasis Associated with Chronic Kidney Disease. Semin. Thromb. Hemost. 2010, 36, 34–40. [Google Scholar] [CrossRef] [PubMed]
- Hoste, E.A.J.; Schurgers, M. Epidemiology of Acute Kidney Injury: How Big Is the Problem? Crit. Care Med. 2008, 36, S146–S151. [Google Scholar] [CrossRef] [PubMed]
- Awad, A.S.; Rouse, M.; Huang, L.; Vergis, A.L.; Reutershan, J.; Cathro, H.P.; Linden, J.; Okusa, M.D. Compartmentalization of Neutrophils in the Kidney and Lung Following Acute Ischemic Kidney Injury. Kidney Int. 2009, 75, 689–698. [Google Scholar] [CrossRef] [Green Version]
- Ávila Martínez, R.J.; Hernández Voth, A.; Marrón Fernández, C.; Hermoso Alarza, F.; Martínez Serna, I.; Mariscal de Alba, A.; Zuluaga Bedoya, M.; Trujillo, M.D.; Meneses Pardo, J.C.; Díaz Hellin, V.; et al. Evolution and Complications of Chest Trauma. Arch. Bronconeumol. 2013, 49, 177–180. [Google Scholar] [CrossRef] [PubMed]
- Letteri, J.M. Post Traumatic Acute Renal Failure. In Acute Renal Failure: Clinical and Experimental; Amerio, A., Coratelli, P., Campese, V.M., Massry, S.G., Eds.; Advances in Experimental Medicine and Biology; Springer: Boston, MA, USA, 1987; pp. 211–218. ISBN 978-1-4684-8240-9. [Google Scholar]
- Stene, J.K. Renal Failure in the Trauma Patient. Crit. Care Clin. 1990, 6, 111–119. [Google Scholar] [CrossRef] [PubMed]
- Halmaciu, I.; Arbănași, E.M.; Kaller, R.; Mureșan, A.V.; Arbănași, E.M.; Bacalbasa, N.; Suciu, B.A.; Cojocaru, I.I.; Runcan, A.I.; Grosu, F.; et al. Chest CT Severity Score and Systemic Inflammatory Biomarkers as Predictors of the Need for Invasive Mechanical Ventilation and of COVID-19 Patients’ Mortality. Diagnostics 2022, 12, 2089. [Google Scholar] [CrossRef]
- Arbănași, E.M.; Halmaciu, I.; Kaller, R.; Mureșan, A.V.; Arbănași, E.M.; Suciu, B.A.; Coșarcă, C.M.; Cojocaru, I.I.; Melinte, R.M.; Russu, E. Systemic Inflammatory Biomarkers and Chest CT Findings as Predictors of Acute Limb Ischemia Risk, Intensive Care Unit Admission, and Mortality in COVID-19 Patients. Diagnostics 2022, 12, 2379. [Google Scholar] [CrossRef]
- Kaller, R.; Arbănași, E.M.; Mureșan, A.V.; Voidăzan, S.; Arbănași, E.M.; Horváth, E.; Suciu, B.A.; Hosu, I.; Halmaciu, I.; Brinzaniuc, K.; et al. The Predictive Value of Systemic Inflammatory Markers, the Prognostic Nutritional Index, and Measured Vessels’ Diameters in Arteriovenous Fistula Maturation Failure. Life 2022, 12, 1447. [Google Scholar] [CrossRef]
- Mureșan, A.V.; Hălmaciu, I.; Arbănași, E.M.; Kaller, R.; Arbănași, E.M.; Budișcă, O.A.; Melinte, R.M.; Vunvulea, V.; Filep, R.C.; Mărginean, L.; et al. Prognostic Nutritional Index, Controlling Nutritional Status (CONUT) Score, and Inflammatory Biomarkers as Predictors of Deep Vein Thrombosis, Acute Pulmonary Embolism, and Mortality in COVID-19 Patients. Diagnostics 2022, 12, 2757. [Google Scholar] [CrossRef] [PubMed]
- Shao, B.; Liu, X.; Li, H.; Song, G.; Di, L.; Jiang, H.; Yan, Y.; Zhang, R.; Ran, R.; Zhang, J.; et al. Prognostic Value of Pretreatment Neutrophil-to-Lymphocyte Ratio in HER2-Positive Metastatic Breast Cancer. Curr. Oncol. 2022, 29, 6154–6166. [Google Scholar] [CrossRef] [PubMed]
- Arbănași, E.M.; Mureșan, A.V.; Coșarcă, C.M.; Kaller, R.; Bud, T.I.; Hosu, I.; Voidăzan, S.T.; Arbănași, E.M.; Russu, E. Neutrophil-to-Lymphocyte Ratio and Platelet-to-Lymphocyte Ratio Impact on Predicting Outcomes in Patients with Acute Limb Ischemia. Life 2022, 12, 822. [Google Scholar] [CrossRef] [PubMed]
- Melinte, R.M.; Arbănași, E.M.; Blesneac, A.; Zolog, D.N.; Kaller, R.; Mureșan, A.V.; Arbănași, E.M.; Melinte, I.M.; Niculescu, R.; Russu, E. Inflammatory Biomarkers as Prognostic Factors of Acute Deep Vein Thrombosis Following the Total Knee Arthroplasty. Medicina 2022, 58, 1502. [Google Scholar] [CrossRef] [PubMed]
- Russu, E.; Mureșan, A.V.; Arbănași, E.M.; Kaller, R.; Hosu, I.; Voidăzan, S.; Arbănași, E.M.; Coșarcă, C.M. The Predictive Role of NLR and PLR in Outcome and Patency of Lower Limb Revascularization in Patients with Femoropopliteal Disease. J. Clin. Med. 2022, 11, 2620. [Google Scholar] [CrossRef] [PubMed]
- Arbănași, E.M.; Mureșan, A.V.; Arbănași, E.M.; Kaller, R.; Cojocaru, I.I.; Coșarcă, C.M.; Russu, E. The Neutrophil-to-Lymphocyte Ratio’s Predictive Utility in Acute Pulmonary Embolism: Systematic Review. J. Cardiovasc. Emergencies 2022, 8, 25–30. [Google Scholar] [CrossRef]
- Niculescu, R.; Russu, E.; Arbănași, E.M.; Kaller, R.; Arbănași, E.M.; Melinte, R.M.; Coșarcă, C.M.; Cocuz, I.G.; Sabău, A.H.; Tinca, A.C.; et al. Carotid Plaque Features and Inflammatory Biomarkers as Predictors of Restenosis and Mortality Following Carotid Endarterectomy. Int. J. Environ. Res. Public. Health 2022, 19, 13934. [Google Scholar] [CrossRef]
- Mureșan, A.V.; Russu, E.; Arbănași, E.M.; Kaller, R.; Hosu, I.; Arbănași, E.M.; Voidăzan, S.T. The Predictive Value of NLR, MLR, and PLR in the Outcome of End-Stage Kidney Disease Patients. Biomedicines 2022, 10, 1272. [Google Scholar] [CrossRef]
- Halmaciu, I.; Suciu, B.A.; Vunvulea, V.; Muresan, M.G.; Scarlat, F.C.; Molnar, C.; Bacalbasa, N.; Brinzaniuc, K.; Voidazan, S. The Importance of Neutrophil to Lymphocyte Ratio and Platelet to Lymphocyte Ratio in Predicting the Appearance of the Rectal Stenosis or Rectal Obstruction in Patients with Rectal Adenocarcinomas. Rev. Chim. 2020, 71, 239–247. [Google Scholar] [CrossRef]
- Festa, E.; Ascione, T.; Bernasconi, A.; Di Gennaro, D.; Basso, M.A.; Guarino, A.; Balato, G. Diagnostic Performance of Neutrophil to Lymphocyte Ratio, Monocyte to Lymphocyte Ratio, Platelet to Lymphocyte Ratio, and Platelet to Mean Platelet Volume Ratio in Periprosthetic Hip and Knee Infections: A Systematic Review and Meta-Analysis. Diagnostics 2022, 12, 2033. [Google Scholar] [CrossRef]
- Rau, C.-S.; Wu, S.-C.; Tsai, C.-H.; Chou, S.-E.; Su, W.-T.; Hsu, S.-Y.; Hsieh, C.-H. Association of White Blood Cell Subtypes and Derived Ratios with a Mortality Outcome in Adult Patients with Polytrauma. Healthcare 2022, 10, 1384. [Google Scholar] [CrossRef] [PubMed]
- Soehnlein, O. Multiple Roles for Neutrophils in Atherosclerosis. Circ. Res. 2012, 110, 875–888. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- MacIsaac, R.J.; Ekinci, E.I.; Jerums, G. Markers of and Risk Factors for the Development and Progression of Diabetic Kidney Disease. Am. J. Kidney Dis. 2014, 63, S39–S62. [Google Scholar] [CrossRef]
- Bi, J.-B.; Zhang, J.; Ren, Y.-F.; Du, Z.-Q.; Wu, Z.; Lv, Y.; Wu, R.-Q. Neutrophil-to-Lymphocyte Ratio Predicts Acute Kidney Injury Occurrence after Gastrointestinal and Hepatobiliary Surgery. World J. Gastrointest. Surg. 2020, 12, 326–335. [Google Scholar] [CrossRef] [PubMed]
- Guangqing, Z.; Liwei, C.; Fei, L.; Jianshe, Z.; Guang, Z.; Yan, Z.; Jianjun, C.; Ming, T.; Hao, C.; Wei, L. Predictive Value of Neutrophil to Lymphocyte Ratio on Acute Kidney Injury after On-Pump Coronary Artery Bypass: A Retrospective, Single-Center Study. Gen. Thorac. Cardiovasc. Surg. 2022, 70, 624–633. [Google Scholar] [CrossRef] [PubMed]
- Tang, Y.; Chen, L.; Li, B.; Yang, L.; Ouyang, W.; Li, D. Preoperative Neutrophil-Lymphocyte Ratio for Predicting Surgery-Related Acute Kidney Injury in Non-Cardiac Surgery Patients under General Anaesthesia: A Retrospective Cohort Study. PLoS ONE 2022, 17, e0270066. [Google Scholar] [CrossRef]
- Ntalouka, M.P.; Nana, P.; Kouvelos, G.N.; Stamoulis, K.; Spanos, K.; Giannoukas, A.; Matsagkas, M.; Arnaoutoglou, E. Association of Neutrophil–Lymphocyte and Platelet–Lymphocyte Ratio with Adverse Events in Endovascular Repair for Abdominal Aortic Aneurysm. J. Clin. Med. 2021, 10, 1083. [Google Scholar] [CrossRef]
- Khwaja, A. KDIGO Clinical Practice Guidelines for Acute Kidney Injury. Nephron Clin. Pract. 2012, 120, c179–c184. [Google Scholar] [CrossRef]
- Søvik, S.; Isachsen, M.S.; Nordhuus, K.M.; Tveiten, C.K.; Eken, T.; Sunde, K.; Brurberg, K.G.; Beitland, S. Acute Kidney Injury in Trauma Patients Admitted to the ICU: A Systematic Review and Meta-Analysis. Intensive Care Med. 2019, 45, 407–419. [Google Scholar] [CrossRef] [Green Version]
- Haines, R.W.; Fowler, A.J.; Kirwan, C.J.; Prowle, J.R. The Incidence and Associations of Acute Kidney Injury in Trauma Patients Admitted to Critical Care: A Systematic Review and Meta-Analysis. J. Trauma Acute Care Surg. 2019, 86, 141–147. [Google Scholar] [CrossRef]
- Ishani, A.; Nelson, D.; Clothier, B.; Schult, T.; Nugent, S.; Greer, N.; Slinin, Y.; Ensrud, K.E. The Magnitude of Acute Serum Creatinine Increase after Cardiac Surgery and the Risk of Chronic Kidney Disease, Progression of Kidney Disease, and Death. Arch. Intern. Med. 2011, 171, 226–233. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Bihorac, A.; Delano, M.J.; Schold, J.D.; Lopez, M.C.; Nathens, A.B.; Maier, R.V.; Layon, A.J.; Baker, H.V.; Moldawer, L.L. Incidence, Clinical Predictors, Genomics, and Outcome of Acute Kidney Injury among Trauma Patients. Ann. Surg. 2010, 252, 158–165. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Shaw, A. Update on Acute Kidney Injury after Cardiac Surgery. J. Thorac. Cardiovasc. Surg. 2012, 143, 676–681. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Grams, M.E.; Sang, Y.; Coresh, J.; Ballew, S.; Matsushita, K.; Molnar, M.Z.; Szabo, Z.; Kalantar-Zadeh, K.; Kovesdy, C.P. Acute Kidney Injury After Major Surgery: A Retrospective Analysis of Veterans Health Administration Data. Am. J. Kidney Dis. Off. J. Natl. Kidney Found. 2016, 67, 872–880. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Borthwick, E.; Ferguson, A. Perioperative Acute Kidney Injury: Risk Factors, Recognition, Management, and Outcomes. BMJ 2010, 341, c3365. [Google Scholar] [CrossRef] [Green Version]
- Bihorac, A.; Yavas, S.; Subbiah, S.; Hobson, C.E.; Schold, J.D.; Gabrielli, A.; Layon, A.J.; Segal, M.S. Long-Term Risk of Mortality and Acute Kidney Injury during Hospitalization after Major Surgery. Ann. Surg. 2009, 249, 851–858. [Google Scholar] [CrossRef]
- Hobson, C.E.; Yavas, S.; Segal, M.S.; Schold, J.D.; Tribble, C.G.; Layon, A.J.; Bihorac, A. Acute Kidney Injury Is Associated with Increased Long-Term Mortality after Cardiothoracic Surgery. Circulation 2009, 119, 2444–2453. [Google Scholar] [CrossRef] [Green Version]
- Younan, D.; Richman, J.; Zaky, A.; Pittet, J.-F. An Increasing Neutrophil-to-Lymphocyte Ratio Trajectory Predicts Organ Failure in Critically-Ill Male Trauma Patients. An Exploratory Study. Healthcare 2019, 7, 42. [Google Scholar] [CrossRef] [Green Version]
- Ke, R.-T.; Rau, C.-S.; Hsieh, T.-M.; Chou, S.-E.; Su, W.-T.; Hsu, S.-Y.; Hsieh, C.-H.; Liu, H.-T. Association of Platelets and White Blood Cells Subtypes with Trauma Patients’ Mortality Outcome in the Intensive Care Unit. Healthcare 2021, 9, 942. [Google Scholar] [CrossRef]
- Duchesne, J.C.; Tatum, D.; Jones, G.; Davis, B.; Robledo, R.; DeMoya, M.; O’Keeffe, T.; Ferrada, P.; Jacome, T.; Schroll, R.; et al. Multi-Institutional Analysis of Neutrophil-to-Lymphocyte Ratio (NLR) in Patients with Severe Hemorrhage: A New Mortality Predictor Value. J. Trauma Acute Care Surg. 2017, 83, 888–893. [Google Scholar] [CrossRef]
- Qiu, Y.; Fitzgerald, M.; Mitra, B. Association of the Neutrophil–Lymphocyte Ratio to Patient Outcomes after Trauma: A Systematic Review. Trauma 2022, 24, 195–203. [Google Scholar] [CrossRef]
- Li, W.; Deng, W. Platelet-to-Lymphocyte Ratio Predicts Short-Term Mortality in Patients with Moderate to Severe Traumatic Brain Injury. Sci. Rep. 2022, 12, 13976. [Google Scholar] [CrossRef] [PubMed]
- Abu Alfeilat, M.; Slotki, I.; Shavit, L. Single Emergency Room Measurement of Neutrophil/Lymphocyte Ratio for Early Detection of Acute Kidney Injury (AKI). Intern. Emerg. Med. 2018, 13, 717–725. [Google Scholar] [CrossRef] [PubMed]
- de Hond, T.A.P.; Ocak, G.; Groeneweg, L.; Oosterheert, J.J.; Haitjema, S.; Khairoun, M.; Kaasjager, K.A.H. Hematological Ratios Are Associated with Acute Kidney Injury and Mortality in Patients That Present with Suspected Infection at the Emergency Department. J. Clin. Med. 2022, 11, 1017. [Google Scholar] [CrossRef] [PubMed]
Figure 1.
ROC curve analysis concerning the AKI risk (A) NLR (AUC: 0.777; p < 0.0001), (B) MLR (AUC: 0.744; p < 0.0001), (C) PLR (AUC: 0.751; p < 0.0001), (D) SII (AUC: 0.796; p < 0.0001), (E) SIRI (AUC: 0.790; p < 0.0001), and (F) AISI (AUC: 0.802; p < 0.0001); blue line – ROC curve; green line – diagonal line.
Figure 1.
ROC curve analysis concerning the AKI risk (A) NLR (AUC: 0.777; p < 0.0001), (B) MLR (AUC: 0.744; p < 0.0001), (C) PLR (AUC: 0.751; p < 0.0001), (D) SII (AUC: 0.796; p < 0.0001), (E) SIRI (AUC: 0.790; p < 0.0001), and (F) AISI (AUC: 0.802; p < 0.0001); blue line – ROC curve; green line – diagonal line.
Figure 2.
ROC curve analysis concerning the mortality (A) NLR (AUC: 0.870; p < 0.0001), (B) MLR (AUC: 0.800; p < 0.0001), (C) PLR (AUC: 0.865; p < 0.0001), (D) SII (AUC: 0.893; p < 0.0001), (E) SIRI (AUC: 0.846; p < 0.0001), and (F) AISI (AUC: 0.859; p < 0.0001); blue line – ROC curve; green line – diagonal line.
Figure 2.
ROC curve analysis concerning the mortality (A) NLR (AUC: 0.870; p < 0.0001), (B) MLR (AUC: 0.800; p < 0.0001), (C) PLR (AUC: 0.865; p < 0.0001), (D) SII (AUC: 0.893; p < 0.0001), (E) SIRI (AUC: 0.846; p < 0.0001), and (F) AISI (AUC: 0.859; p < 0.0001); blue line – ROC curve; green line – diagonal line.
Figure 3.
ROC curve analysis concerning the composite endpoint (A) NLR (AUC: 0.835; p < 0.0001), (B) MLR (AUC: 0.817; p < 0.0001), (C) PLR (AUC: 0.841; p < 0.0001), (D) SII (AUC: 0.862; p < 0.0001), (E) SIRI (AUC: 0.855; p < 0.0001), and (F) AISI (AUC: 0.873; p < 0.0001); blue line – ROC curve; green line – diagonal line.
Figure 3.
ROC curve analysis concerning the composite endpoint (A) NLR (AUC: 0.835; p < 0.0001), (B) MLR (AUC: 0.817; p < 0.0001), (C) PLR (AUC: 0.841; p < 0.0001), (D) SII (AUC: 0.862; p < 0.0001), (E) SIRI (AUC: 0.855; p < 0.0001), and (F) AISI (AUC: 0.873; p < 0.0001); blue line – ROC curve; green line – diagonal line.
Table 1.
Demographic information, comorbidities, risk factors, damaged organs, laboratory results, and outcomes were collected for all patients, and the two categories were separated based on poor outcomes.
Table 1.
Demographic information, comorbidities, risk factors, damaged organs, laboratory results, and outcomes were collected for all patients, and the two categories were separated based on poor outcomes.
| Variables | All Patients N = 364 | Survivors N = 283 | Non-Survivors N = 81 | p Value (OR; CI 95%) |
|---|---|---|---|---|
| Age mean ± SD (MIN–MAX) | 42.83 ± 18.24 (18–89) | 41.60 ± 17.61 (18–89) | 45.80 ± 20.53 (20–88) | 0.02 |
| Male/Female SEX NO. (%) | 258 (70.88%) 106 (29.12%) | 198 (69.96%) 85 (30.04%) | 60 (74.07%) 21 (25.93%) | 0.47 (1.22; 0.70–2.14) |
| Comorbidities and Risk Factors | ||||
| AH, no. (%) | 64 (17.58%) | 47 (16.60%) | 17 (20.98%) | 0.36 (1.33; 0.71–2.7) |
| IHD, no. (%) | 48 (13.18%) | 30 (10.60%) | 18 (22.22%) | 0.007 (2.40; 1.26–4.59) |
| AF, no. (%) | 14 (3.84%) | 8 (2.82%) | 6 (7.40%) | 0.06 (2.75; 0.92–8.17) |
| CHF, no. (%) | 24 (6.59%) | 17 (6.007%) | 7 (8.64%) | 0.40 (148; 0.59–3.70) |
| MI, no. (%) | 12 (3.29%) | 4 (1.41%) | 8 (9.87%) | 0.002 (6.11; 1.94–19.24) |
| DM, no. (%) | 38 (10.43%) | 27 (9.54%) | 11 (13.58%) | 0.29 (1.48; 0.70–3.15) |
| COPD, no. (%) | 10 (2.74%) | 8 (2.82%) | 2 (2.46%) | 0.86 (0.87; 0.18–4.18) |
| PAD, no. (%) | 8 (2.19%) | 3 (1.06%) | 5 (6.12%) | 0.01 (6.14; 1.43–26.27) |
| CKD, no. (%) | 20 (5.49%) | 11 (3.88%) | 9 (11.11%) | 0.01 (2.55; 1.46–4.46) |
| Tobacco, no. (%) | 16 (4.39%) | 6 (2.12%) | 10 (12.34%) | 0.0004 (6.50; 2.28–18.49) |
| Obesity, no. (%) | 17 (4.67%) | 5 (1.76%) | 12 (14.81%) | <0.0001 (9.66; 3.29–28.36) |
| Injured Organs | ||||
| Liver, no. (%) | 130 (35.71%) | 103 (36.39%) | 27 (33.33%) | 0.61 (0.87; 0.51–1.47) |
| Spleen, no. (%) | 201 (55.21%) | 160 (56.53%) | 41 (50.61%) | 0.34 (0.78; 0.48–1.29) |
| Pancreas, no. (%) | 18 (4.94%) | 14 (4.94%) | 4 (4.93%) | 0.99 (0.99; 0.31–3.12) |
| Large bowel, no. (%) | 23 (6.31%) | 17 (6.007%) | 6 (7.40%) | 0.64 (1.25; 0.47–3.28) |
| Small bowel, no. (%) | 25 (6.86%) | 20 (7.06%) | 5 (6.17%) | 0.77 (0.86; 0.31–2.38) |
| Kidney, no. (%) | 23 (6.31%) | 12 (4.24%) | 11 (13.58%) | 0.003 (3.54; 1.50–8.38) |
| Hemoperitoneum, no. (%) | 191 (52.47%) | 138 (48.76%) | 53 (65.43%) | 0.008 (1.98; 1.18–3.32) |
| Laboratory Data | ||||
| Hemoglobin g/dL median (Q1–Q3) | 11.91 (10.47–13.36) | 12.2 (10.5–13.55) | 11.50 (10.4–12.7) | 0.03 |
| Hematocrit % median (Q1–Q3) | 35.9 (31.4–39.98) | 36.6 (31.36–40.66) | 33.86 (31.6–37.2) | 0.01 |
| Glucose mg/dL median (Q1–Q3) | 109 (93–140.75) | 105 (92.5–132.9) | 143 (104.25–170.5) | 0.02 |
| Sodium median (Q1–Q3) | 138 (135–141) | 138 (135–141) | 138 (135–140.7) | 0.35 |
| Potassium median (Q1–Q3) | 4.25 (3.74–5.0) | 4.21 (3.72–5.19) | 4.36 (3.9–4.74) | 0.37 |
| Uric acid median (Q1–Q3) | 6.45 (5.1–8.2) | 6.2 (5.0–7.95) | 6.90 (5.5–8.6) | 0.02 |
| Bun mg/dL median (Q1–Q3) | 67 (38.47–194.42) | 56.5 (35.85–163.22) | 145.5 (51–247.98) | <0.0001 |
| Creatinine mg/dL median (Q1–Q3) | 1.55 (0.86–6.1) | 1.22 (0.84–5.68) | 4.41 (1.36–9.57) | <0.0001 |
| GFR (mL/min/1.73 m2) median (Q1–Q3) | 74.17 (56.02–90.5) | 76.72 (57.84–92.56) | 66.52 (54.74–85.15) | 0.004 |
| Neutrophils ×10³/µL median (Q1–Q3) | 8.24 (5.58–12.98) | 7.43 (5.25–11.06) | 12.94 (8.03–17.25) | <0.0001 |
| Lymphocytes ×10³/µL median (Q1–Q3) | 1.96 (1.47–2.66) | 2.13 (1.68–2.88) | 1.26 (0.98–1.89) | <0.0001 |
| Monocyte ×10³/µL median (Q1–Q3) | 0.9 (0.61–1.62) | 0.8 (0.59–1.38) | 1.29 (0.73–2.23) | <0.0001 |
| PLT ×10³/µL median (Q1–Q3) | 245.5 (200.6–303.85) | 238.7 (193.15–300.4) | 272 (222.7–316.1) | 0.001 |
| MLR, median (Q1–Q3) | 4.15 (2.39–7.31) | 3.36 (2.17–5.62) | 9.79 (6.06–13.96) | <0.0001 |
| NLR, median (Q1–Q3) | 0.47 (0.30–0.94) | 0.40 (0.28–0.68) | 0.98 (0.67–1.80) | <0.0001 |
| PLR, median (Q1–Q3) | 120.03 (91.08–168.92) | 110.66 (82.79–147.27) | 224.05 (166.67–288.37) | <0.0001 |
| SII, median (Q1–Q3) | 1013.22 (583.19–1750.97) | 757.46 (532.12–1392.7) | 2725.15 (1697.4–3840) | <0.0001 |
| SIRI, median (Q1–Q3) | 4.96 (1.89–11.32) | 3.44 (1.62–7.54) | 15.56 (7.92–23.74) | <0.0001 |
| AISI, median (Q1–Q3) | 1163.85 (461.48–2874.06) | 849.73 (377.96–1767.38) | 3956.66 (2561.89–6192.3) | <0.0001 |
| Outcomes | ||||
| AKI, no. (%) | 84 (23.07%) | 27 (9.54%) | 57 (70.37%) | <0.0001 (5.57; 2.77–11.22) |
| AKI + Mortality, no. (%) | 57 (15.65%) | 0 | 57 (70.37%) | <0.0001 (5.57; 2.77–11.22) |
| Length of hospital stay, MEAN ± SD | 9 (7–13) | 9 (6–12) | 10 (7–16) | <0.0001 |
| Length of ICU stay, mean ± SD | 7 (5–8.25) | 6 (5–8) | 8 (7–11) | <0.0001 |
| AKI stage KDIGO | ||||
| 0, no. (%) | 280 (76.92%) | 256 (90.45%) | 24 (29.62%) | <0.0001 |
| I, no. (%) | 26 (7.14%) | 18 (6.36%) | 8 (9.87%) | 0.26 |
| II, no. (%) | 31 (8.51%) | 7 (2.47%) | 24 (29.62%) | <0.0001 |
| III, no. (%) | 27 (7.41%) | 2 (0.7%) | 25 (30.86%) | <0.0001 |
AH = arterial hypertension; GFR = glomerular filtration rate; IHD = ischemic heart disease; AF = atrial fibrillation; BUN = blood urea nitrogen; CHF = chronic heart failure; MI = myocardial infarction; COPD = chronic obstructive pulmonary disease; PAD = peripheral arterial disease; CKD = chronic kidney disease; MLR = monocyte to lymphocyte ratio; PLR = platelets to lymphocyte ratio; NLR = neutrophil to lymphocyte ratio; AISI = aggregate index of systemic inflammation; SIRI = systemic inflammation response index; SII = systemic inflammatory index; AKI = acute kidney insufficiency; ICU = Intensive Care Unit; SD = standard deviation.
Table 2.
ROC curves, ideal cut-off value, AUC, and prediction accuracy of inflammatory indicators in terms of outcomes.
Table 2.
ROC curves, ideal cut-off value, AUC, and prediction accuracy of inflammatory indicators in terms of outcomes.
| Variables | Cut-off | AUC | Std. Error | 95% CI | Sensitivity | Specificity | p-Value |
|---|---|---|---|---|---|---|---|
| AKI | |||||||
| NLR | 4.40 | 0.777 | 0.028 | 0.722–0.831 | 79.8% | 64.6% | <0.0001 |
| MLR | 0.51 | 0.744 | 0.030 | 0.685–0.803 | 77.4% | 62.9% | <0.0001 |
| PLR | 158.82 | 0.751 | 0.032 | 0.689–0.813 | 60.7% | 78.9% | <0.0001 |
| SII | 1295.99 | 0.796 | 0.027 | 0.744–0.849 | 75.0% | 69.3% | <0.0001 |
| SIRI | 5.57 | 0.790 | 0.027 | 0.738–0.843 | 78.6% | 64.3% | <0.0001 |
| AISI | 1657.92 | 0.802 | 0.026 | 0.750–0.853 | 72.6% | 69.6% | <0.0001 |
| Mortality | |||||||
| NLR | 4.98 | 0.870 | 0.021 | 0.828–0.911 | 80.2% | 73.1% | <0.0001 |
| MLR | 0.57 | 0.800 | 0.025 | 0.752–0.848 | 82.7% | 70.3% | <0.0001 |
| PLR | 161.07 | 0.865 | 0.025 | 0.816–0.914 | 76.5% | 85.2% | <0.0001 |
| SII | 1559.39 | 0.893 | 0.020 | 0.853–0.933 | 80.2% | 81.6% | <0.0001 |
| SIRI | 7.85 | 0.846 | 0.024 | 0.798–0.894 | 75.3% | 76% | <0.0001 |
| AISI | 2131.74 | 0.859 | 0.023 | 0.814–0.905 | 79% | 79.5% | <0.0001 |
| AKI and Mortality | |||||||
| NLR | 4.49 | 0.835 | 0.026 | 0.783–0.886 | 89.5% | 63.8% | <0.0001 |
| MLR | 0.67 | 0.817 | 0.025 | 0.768–0.865 | 80.7% | 71% | <0.0001 |
| PLR | 176.14 | 0.841 | 0.029 | 0.783–0.898 | 75.4% | 86.6% | <0.0001 |
| SII | 1559.39 | 0.862 | 0.025 | 0.813–0.911 | 80.7% | 76.9% | <0.0001 |
| SIRI | 10.08 | 0.855 | 0.026 | 0.804–0.906 | 78.9% | 78.8% | <0.0001 |
| AISI | 2530.35 | 0.873 | 0.024 | 0.827–0.920 | 80.7% | 78.5% | <0.0001 |
AISI = aggregate index of systemic inflammation; MLR = monocyte to lymphocyte ratio; PLR = platelets to lymphocyte ratio; NLR = neutrophil to lymphocyte ratio; SIRI = systemic inflammation response index; SII = systemic inflammatory index.
Table 3.
Univariate analysis of all inflammatory biomarkers and adverse event occurrences in all patients over the study period.
Table 3.
Univariate analysis of all inflammatory biomarkers and adverse event occurrences in all patients over the study period.
| AKI | Mortality | AKI and Mortality | |
|---|---|---|---|
| Low-NLR vs. high-NLR | 17/197 (8.63%) vs. 67/167 (40.12%) p < 0.0001 | 16/223 (7.17%) vs. 65/141 (46.10%) p < 0.0001 | 6/202 (2.97%) vs. 51/162 (31.48%) p < 0.0001 |
| Low-MLR vs. high-MLR | 19/195 (9.74%) vs. 65/169 (38.46%) p < 0.0001 | 14/212 (6.60%) vs. 67/152 (44.08%) p < 0.0001 | 11/228 (4.82%) vs. 46/136 (33.82%) p < 0.0001 |
| Low-PLR vs. high-PLR | 33/254 (12.9%) vs. 51/110 (46.36%) p < 0.0001 | 19/260 (7.31%) vs. 62/104 (59.62%) p < 0.0001 | 14/280 (5.00%) vs. 43/84 (51.19%) p < 0.0001 |
| Low-SII vs. high-SII | 21/215 (9.77%) vs. 63/149 (42.28%) p < 0.0001 | 16/247 (6.48%) vs. 65/117 (55.56%) p < 0.0001 | 16/247 (6.48%) vs. 65/117 (55.56%) p < 0.0001 |
| Low-SIRI vs. high-SIRI | 19/200 (9.50%) vs. 65/164 (39.63%) p < 0.0001 | 20/235 (8.51%) vs. 61/129 (47.29%) p < 0.0001 | 12/254 (4.72%) vs. 45/110 (40.91%) p < 0.0001 |
| Low-AISI vs. high-AISI | 23/218 (10.5%) vs. 61/146 (41.78%) p < 0.0001 | 17/242 (7.02%) vs. 64/122 (52.46%) p < 0.0001 | 11/252 (4.37%) vs. 46/112 (41.07%) p < 0.0001 |
MLR = monocyte to lymphocyte ratio; PLR = platelets to lymphocyte ratio; NLR = neutrophil to lymphocyte ratio; AISI = aggregate index of systemic inflammation; SIRI = systemic inflammation response index; SII = systemic inflammatory index.
Table 4.
Multivariate analysis of new adverse events occurred throughout the course of the research.
Table 4.
Multivariate analysis of new adverse events occurred throughout the course of the research.
| AKI | Mortality | AKI AND Mortality | |||||||
|---|---|---|---|---|---|---|---|---|---|
| OR | 95% CI | p Value | OR | 95% CI | p Value | OR | 95% CI | p Value | |
| Age > 45 | 1.25 | 0.76–2.05 | 0.36 | 1.55 | 0.94–2.55 | 0.08 | 1.21 | 0.68–2.15 | 0.50 |
| IHD | 1.62 | 0.83–3.16 | 0.15 | 2.41 | 1.26–4.59 | 0.008 | 2.29 | 1.12–4.67 | 0.02 |
| AF | 2.61 | 0.88–7.76 | 0.08 | 2.75 | 0.92–8.17 | 0.06 | 3.18 | 1.02–9.87 | 0.04 |
| MI | 3.51 | 1.10–11.19 | 0.03 | 7.64 | 2.24–26.08 | 0.001 | 5.90 | 1.83–19.01 | 0.003 |
| PAD | 5.84 | 1.36–24.98 | 0.01 | 6.14 | 1.43–26.27 | 0.01 | 9.74 | 2.26–24.09 | 0.002 |
| CKD | 2.55 | 1.46–4.46 | <0.001 | 3.09 | 1.23–7.74 | 0.01 | 3.21 | 1.97–6.50 | 0.001 |
| Tobacco | 2.73 | 0.98–7.58 | 0.053 | 6.50 | 2.28–18.49 | <0.001 | 3.49 | 1.21–10.03 | 0.02 |
| Obesity | 9.16 | 3.12–26.86 | <0.001 | 9.67 | 3.29–28.36 | <0.001 | 9.11 | 3.30–25.12 | <0.001 |
| Kidney injury | 2.77 | 1.17–5.68 | 0.02 | 3.54 | 1.50–8.38 | 0.004 | 3.17 | 1.28–7.89 | 0.01 |
| Hemoperitoneum | 1.27 | 0.78–2.08 | 0.32 | 1.98 | 1.19–3.32 | 0.009 | 2.20 | 1.21–4.03 | 0.01 |
| high-NLR | 7.09 | 3.94–12.74 | <0.001 | 11.06 | 6.03–20.30 | <0.001 | 15.09 | 6.24–36.09 | <0.001 |
| high-MLR | 5.78 | 3.28–10.19 | <0.001 | 11.14 | 5.94–20.92 | <0.001 | 10.08 | 4.99–20.35 | <0.001 |
| high-PLR | 5.89 | 3.42–9.77 | <0.001 | 18.72 | 10.17–34.44 | <0.001 | 19.92 | 10.02–39.60 | <0.001 |
| high-SII | 6.76 | 3.88–11.79 | <0.001 | 18.04 | 9.66–33.69 | <0.001 | 13.90 | 6.83–28.25 | <0.001 |
| high-SIRI | 6.25 | 3.54–11.02 | <0.001 | 9.64 | 5.43–17.11 | <0.001 | 13.96 | 6.98–27.92 | <0.001 |
| high-AISI | 6.08 | 3.53–10.47 | <0.001 | 14.60 | 7.95–25.81 | <0.001 | 15.27 | 7.49–31.12 | <0.001 |
AF = atrial fibrillation; IHD = ischemic heart disease; PAD = peripheral arterial disease; MI = myocardial infarction; CKD = chronic kidney disease; MLR = monocyte to lymphocyte ratio; PLR = platelets to lymphocyte ratio; NLR = neutrophil to lymphocyte ratio; AISI = aggregate index of systemic inflammation; SII = systemic inflammatory index; SIRI = systemic inflammation response index.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
MDPI and ACS Style
Vunvulea, V.; Budișcă, O.A.; Arbănași, E.M.; Mureșan, A.V.; Arbănași, E.M.; Brînzaniuc, K.; Niculescu, R.; Cocuz, I.G.; Ivănescu, A.D.; Hălmaciu, I.; et al. The Predictive Role of Systemic Inflammatory Markers in the Development of Acute Kidney Failure and Mortality in Patients with Abdominal Trauma. J. Pers. Med. 2022, 12, 2045. https://doi.org/10.3390/jpm12122045
AMA Style
Vunvulea V, Budișcă OA, Arbănași EM, Mureșan AV, Arbănași EM, Brînzaniuc K, Niculescu R, Cocuz IG, Ivănescu AD, Hălmaciu I, et al. The Predictive Role of Systemic Inflammatory Markers in the Development of Acute Kidney Failure and Mortality in Patients with Abdominal Trauma. Journal of Personalized Medicine. 2022; 12(12):2045. https://doi.org/10.3390/jpm12122045
Chicago/Turabian StyleVunvulea, Vlad, Ovidiu Aurelian Budișcă, Emil Marian Arbănași, Adrian Vasile Mureșan, Eliza Mihaela Arbănași, Klara Brînzaniuc, Raluca Niculescu, Iuliu Gabriel Cocuz, Adrian Dumitru Ivănescu, Ioana Hălmaciu, and et al. 2022. "The Predictive Role of Systemic Inflammatory Markers in the Development of Acute Kidney Failure and Mortality in Patients with Abdominal Trauma" Journal of Personalized Medicine 12, no. 12: 2045. https://doi.org/10.3390/jpm12122045
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.
