Next Article in Journal
Thrombosis in Chronic Kidney Disease in Children
Previous Article in Journal
The Role of Echocardiography in the Diagnosis of Cardiac Involvement in a Rare Systemic Condition: The Carcinoid Heart Disease: A Case Report and Review of Literature
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Diagnostics
Volume 12
Issue 12
10.3390/diagnostics12122930
diagnostics-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Using Second Measurement of De Ritis Ratio to Improve Mortality Prediction in Adult Trauma Patients in Intensive Care Unit

by
Wei-Ti Su
1,
Cheng-Shyuan Rau
2,
Sheng-En Chou
1,
Ching-Hua Tsai
1,
Peng-Chen Chien
1 and
Ching-Hua Hsieh
1,*
1
Department of Trauma Surgery, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung 83301, Taiwan
2
Department of Neurosurgery, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung 83301, Taiwan
*
Author to whom correspondence should be addressed.
Diagnostics 2022, 12(12), 2930; https://doi.org/10.3390/diagnostics12122930
Submission received: 13 October 2022 / Revised: 21 November 2022 / Accepted: 22 November 2022 / Published: 24 November 2022
(This article belongs to the Section Pathology and Molecular Diagnostics)
Browse Figures
Versions Notes

Abstract

:
The De Ritis ratio (DRR), the ratio of serum levels of aspartate aminotransferase/alanine aminotransferase, has been reported to be a valuable biomarker in risk stratification for many liver and non-liver diseases. This study aimed to explore whether the inclusion of DRR at the date of intensive care unit (ICU) admission or days after ICU admission improves the predictive performance of various prognosis prediction models. This study reviewed 888 adult trauma patients (74 deaths and 814 survivors) in the trauma registered database between 1 January 2009, and 31 December 2020. Medical information with AST and ALT levels and derived DRR at the date of ICU admission (1st DRR) and 3–7 day after ICU admission (2nd DRR) was retrieved. Logistic regression was used to build new probability models for mortality prediction using additional DRR variables in various mortality prediction models. There was no significant difference in the 1st DRR between the death and survival patients; however, there was a significantly higher 2nd DRR in the death patients than the survival patients. This study showed that the inclusion of the additional DRR variable, measured 3–7 days after ICU admission, significantly increased the prediction performance in all studied prognosis prediction models.

1. Introduction

The serum ratio of aspartate aminotransferase (AST)/alanine aminotransferase (ALT), the so-called De Ritis ratio (DRR)”, has been demonstrated to be a valuable tool in risk stratification for different kinds of liver diseases [1,2,3]. ALT is found predominantly in the cytosol of hepatocytes, while AST is found in the cytosol and mitochondria of hepatocytes as well as in the cells in the brain, kidney, heart, and skeletal muscle [3]; Therefore, the increase in the serum level of ALT indicates parenchymal liver illness with liver-specific dysfunction, while the increase in the serum level of AST suggests systemic involvement other than liver dysfunction. Ischemia-reperfusion injury, oxidative stress, and metabolic disorders can increase serum levels of AST [4,5,6]. Therefore, DRR has also been proposed to be valuable in the diagnosis and risk stratification of many illnesses other than liver diseases, including cancers other than hepatoma [1,7,8,9,10,11,12,13], acute kidney injury [14,15,16], heart diseases [17,18,19], sepsis [20], and coronavirus disease 2019 (COVID-19) [21,22,23,24].
Prognosis prediction models are broadly used in intensive care units (ICU) for risk stratification, quality control, and scientific research [25,26,27,28]. The Trauma Score and Injury Severity Score (TRISS) [29] is generally recommended for trauma patients; however, for patients with critical illness in the ICU, some other prognosis prediction models have been proposed and reviewed in the literature [30,31,32]. Most of these models, such as the Acute Physiology and Chronic Health Evaluation (APACHE) [33], Simplified Acute Physiology Score (SAPS) [34], and Mortality Prediction Model (MPM) [35], are based on data collected on the first day of admission to the ICU. Other models collect data every day throughout the stay in the ICU or for the first 3 days in the ICU, including MPM II at 24 h (MPM24 II), MPM II at 48 h (MPM48 II), MPM II at 72 h (MPM72 II) [36,37], Logistic Organ Dysfunction System (LODS) [38], Multiple Organ Dysfunction Score (MODS) [39], 24 h ICU point system [40], Sequential Organ Failure Assessment (SOFA) [41], and Three-Day Recalibrating ICU Outcomes (TRIOS) [29].
To improve the accuracy of outcome prediction for trauma patients in the ICU, this study was designed to explore whether the inclusion of DRR at the date of admission or days after ICU admission as a variable in these various prognosis prediction models could improve predictive performance. In this study, we found that the inclusion of the 2nd DRR, a value of AST/ALT measured between days 3 and 7 after ICU admission, as an additional variable in all prognosis prediction models, can build models with better predictive performance for mortality.

2. Materials and Methods

2.1. Study Population and Data Collection

Of 43,114 hospitalized trauma patients by all trauma causes enrolled in the trauma registered database of the Chang Gung Memorial hospital [42,43,44] between 1 January 2009, and 31 December 2020 (Figure 1), 2491 patients aged ≥20 years admitted to the ICU were included. After excluding patients with hepatocellular carcinoma (n = 18), pre-existing decompensated cirrhosis (n = 169), and those who lacked AST or ALT data (n = 1416), we finally included 888 adult trauma patients with critical illness in the study population. Decompensated cirrhosis was defined as the presence of at least one pre-existing complication, including jaundice, ascites, variceal bleeding, or hepatic encephalopathy [45]. Patients’ medical information, which was recorded upon arrival at the emergency department, was retrieved from the registered trauma database, including age, sex, body mass index (BMI), pre-existing comorbidities, vital signs, Glasgow coma scale (GCS) score, abbreviated injury scale (AIS) in different body regions, injury severity score (ISS), and in-hospital mortality. Blood-drawn laboratory data at admission to the ICU included glucose, bicarbonate (HCO3), sodium (Na), potassium (K), red blood cell count (RBC), white blood cell count (WBC), neutrophil (%), hemoglobin (Hb), hematocrit (Hct), platelets, international normalized ratio (INR), blood urine nitrogen (BUN), creatinine (Cr), albumin, bilirubin, aspartate aminotransferase (AST), and alanine aminotransferase (ALT). The pre-existing comorbidities recorded included hypertension (HTN), coronary artery disease (CAD), end-stage renal disease (ESRD), cerebrovascular accident (CVA), and diabetes mellitus (DM). The levels of AST and ALT detected upon admission to the ICU were defined as the first measurement of the liver enzymes (1st AST and 1st ALT, respectively), while the levels of AST and ALT detected between days 3 and 7 were defined as the second measurement of the liver enzymes (2nd AST and 2nd ALT, respectively). Therefore, the AST/ALT ratio of the first measurement produced 1st DRR, whereas the AST/ALT ratio of the second measurement indicated the 2nd DRR.

2.2. Statistical Analyses

In this study, we used the commercial software SPSS Windows (version 23.0; IBM Inc., Chicago, IL, USA) for all statistical analyses. Categorical data were compared using two-sided Fisher’s exact test or Pearson’s χ2 test. Normalization of the distributed continuous data was analyzed using the Kolmogorov–Smirnov test. Non-normally distributed continuous data were compared using the Mann–Whitney U test, with the data expressed as median with interquartile range (IQR) between Q1 and Q3. The predictive performance of 1st and 2nd DDR on patient mortality was determined based on the area under the receiver operating characteristic curve (AUCROC). The score, as well as the variables for different mortality prediction models, including TRISS, MPM II, MPM24 II, MPM48 II, MPM72 II, APACHE II, SAPS II, LODS, MODS, 24 h ICU point system, SOFA, and TRIOS, were retrieved from the trauma-registered database. Logistic regression was used to reconstruct new probability models for mortality prediction with the addition of the 2nd DRR to the variables of different mortality prediction models. A two-tailed p-value < 0.05 was considered statistically significant.

3. Results

3.1. The Patient and Injury Characteristics of the Death and Survival Patients

This study included 74 deaths and 814 surviving patients. There were no significant differences in sex between the death and survival groups (Table 1). Significantly higher rates of pre-existing comorbidities of HTN, CAD, and ESRD were found in patients who died than in those who survived. The death and survival patients presented significant differences in AIS of injuries in the head and abdominal regions but not in other body regions. As shown in Table 2, the patients who died were significantly older than those who survived (p < 0.001). The death patients had significantly lower GCS (median [IQR, Q1–Q3], 6 [3–11] vs. 11 [8–15], p < 0.001) but higher ISS (25 [19–29] vs. 20 [16–25], p < 0.001) than the survival patients. The death patients had significantly higher levels of glucose, BUN, and Cr, but lower HCO3, INR, and albumin levels than the survival patients. Notably, there was no significant difference in the bilirubin levels of the death and survival patients (0.8 [0.5–1.4] vs. 0.8 [0.6–1.1], p = 0.826).
Regarding liver enzymes, the day to check the 2nd measurement was at median 5 day (IQR: 4.3–5.9 day). there were no significant differences in the levels of 1st AST, 1st ALT, and 2nd ALT between the death and survival groups. However, there was a significantly higher level of 2nd AST in patients in the death group than those in the survival group (57 [32–107] vs. 39 [26–63], p < 0.001). Therefore, there was no significant difference in the derived 1st DRR between the death and survival patients; however, there was a significantly higher 2nd DRR in the death patients than in the survival patients (1.92 [1.24–3.28] vs. 1.17 [0.83–1.61], p < 0.001). These results showed that a higher 2nd DRR was associated with higher mortality risk. This elevated DRR was mainly attributed to an elevated AST level in the second measurement of the patients who died.
Regarding the scores of various prognosis prediction models (Table 3), the dead patients presented significant differences in the scores of all prognosis prediction models compared to the survival patients (all p < 0.001).

3.2. Analysis of the Plotted ROC Curve

According to the ROC curve analysis (Figure 2), the 1st DRR of 1.4 and a 2nd DRR of 1.7 were identified as the cutoff points with the highest AUC of 63.2% (47.4–74.3%) and 73.8% (66.2–77.3%), respectively. In addition, the 2nd AST of 57.5 was identified as the cutoff point to predict the mortality (AUC 63.4%, 50.0–70.5%) (Figure 2). The inclusion of the 2nd DRR as an additional variable in the prognosis prediction models via logistic regression generated a significantly higher AUC for predicting mortality in all models (Figure 3), including TRISS (AUC, 72.6–80.4%), MPM II (82.2–86.5%), MPM24 II (86.1–89.2%), MPM48 II (85.6–88.2%), MPM72 II (86.0–87.4%), APACHE II (77.5–82.4%), SAPS II (82.1–86.1%), LODS (80.1–85.3%), MODS (71.5–80.7%), 24-h ICU point system (74.1–81.5%), SOFA (62.3–79.1%), and TRIOS (80.1–85.2%) (all p < 0.001). The inclusion of the 2nd AST as an additional variable in the prognosis prediction models generated a significantly higher AUC for predicting mortality in all models (Figure 3), but the AUCs of all prognosis prediction models were lower than those with inclusion of the 2nd DRR as an additional variable. The inclusion of 2nd DRR into MPM24 II had the highest AUC (89.2%), followed by MPM48 II (88.2%), and MPM72 II (87.4%).

4. Discussion

This study revealed that a higher 2nd DRR was associated with a higher mortality risk for trauma patients in the ICU, and the inclusion of the 2nd DRR as an additional variable in those prognosis prediction models generated significantly better prediction performance in all models. The inclusion of 2nd DRR into MPM24 II had the highest AUC (89.2%). The MPM II uses data on heath condition (medical or unscheduled surgical admission), pre-existing illness (such as metastatic neoplasm and cirrhosis), acute diagnosis (such as infection, intracranial mass effect, and coma), physiological variables (such as Cr levels, urine output, and partial pressure of oxygen), laboratory data (prothrombin time), and some other variables (such as mechanical ventilation and use of vasoactive drugs) [35,37]. Although MPM II had already considered the cirrhosis condition of the patients, the inclusion of 2nd DRR still can increase the AUC of prediction from 86.1% to 89.2%, indicating the relative change of AST and ALT help to assess the outcome of the patients with major trauma. In addition, the elevated DRR was mainly attributed to an elevated AST level in the second measurement of the death patients. Notably, although both AST and ALT are involved in aerobic glycolysis, catalyzing nucleotide and nonessential amino acids [46,47,48], an isolated elevation of AST values indicates a non-hepatic source of AST from the injury to non-liver cells, particularly those cells containing mitochondria [3]. Elevated AST levels, but not ALT, led to a higher DRR and indicated mitochondrial dysfunction upon oxidative stress [3,49,50]. Therefore, it has also been reported that in many cancers utilizing glucose, DRR is related to the metabolism of malignancies [51].
How high the DRR in a single measurement would indicate a worse outcome may vary greatly, depending on the illness studied. For example, a DRR ≥ 1.2 specify a higher mortality risk for patients with acute myocardial infarction [17]. A DRR ≥ 1.5 provide a significant postoperative prognostic factor for patients with renal cell carcinoma [52]. In patients with peripheral arterial occlusive disease, a DRR > 1.67 was associated with two-fold odds of risk for critical limb ischemia [53]. For patients with distal cholangiocarcinoma, a DRR > 2.0 was identified as a prognostic indicator [54]. In this study, a 2nd DRR of 1.7 was identified as the cutoff point to stratify the mortality risk of the patients.
Theoretically, the variables input into the mortality prediction model includes four classifications: age, acute diagnosis, pre-existing comorbidities, and physiological changes. The studied models of SAPS II, LODS, MODS, SOFA, and TRIOS took the bilirubin level into the algorithm for outcome prediction, while the MPM II model used cirrhosis as a weighted score for outcome prediction. TRISS, APACHE II, and 24-h ICU point system did not include any variable regarding liver function in the outcome prediction. However, in this study, the inclusion of the 2nd DRR as an additional variable in the prognosis prediction models generated significantly better prediction performance in all models, implying that liver function did matter in influencing the mortality outcome and the 2nd DRR, which was measured days after admission into the ICU, may be more sensitive than the bilirubin level in the mortality prediction, particularly considering that there was no significant difference in the bilirubin level of the death and survival patients at admission into the ICU.
This study had some limitations. First, this retrospective study may have led to selection bias in the outcome assessment. Second, some selection bias may exist in the study because the primary outcome measured in-hospital mortality but did not include death declared on arrival at the emergency room and death in long-term mortality. Moreover, those who died within 3 days after admission to the ICU did not have a second measurement of liver enzymes. Third, the extent of muscle injury and the interventions or management, such as surgery, massive blood transfusion and resuscitation, may have led to a bias in the measurement of liver function; however, the influence was unknown. Fourth, in the presence of undetected liver diseases or drug use, the serum levels of AST and ALT, as well as the derived DRR, may be disturbed, leading to bias in the outcome assessment. Various pre-existed comorbidity conditions may also result in the bias in the outcome assessment. Finally, the study population was limited to a single urban trauma center without confirmation in other regions.

5. Conclusions

This study revealed that the inclusion of the additional variable of DRR, which was measured from day 3 to 7 after ICU admission, significantly increased the prediction performance in all the studied prognosis prediction models.

Author Contributions

Writing—original draft preparation, W.-T.S.; funding acquisition, W.-T.S.; writing—review and editing, C.-S.R.; resources, S.-E.C.; validation, C.-H.T.; formal analysis, P.-C.C.; Conceptualization, C.-H.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by a grant from CMRPG8L1251 by Chang Gung Memorial Hospital to WTS.

Institutional Review Board Statement

The protocol was approved by the Institutional Review Board (IRB) of Chang Gung Memorial Hospital before implementation of the study. The approval number for this study was 202100842B0 on 9 June 2021. The requirement for patient consent was waived owing to the retrospective design of the study based on the registered database.

Informed Consent Statement

Patient consent was waived due to retrospective study.

Data Availability Statement

Not applicable.

Acknowledgments

We appreciate the assistance of the Biostatistics Center, Kaohsiung Chang Gung Memorial Hospital, for statistical analyses.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Mo, Q.; Liu, Y.; Zhou, Z.; Li, R.; Gong, W.; Xiang, B.; Tang, W.; Yu, H. Prognostic Value of Aspartate Transaminase/Alanine Transaminase Ratio in Patients with Hepatitis B Virus-Related Hepatocellular Carcinoma Undergoing Hepatectomy. Front. Oncol. 2022, 12, 876900. [Google Scholar] [CrossRef] [PubMed]
  2. Darstein, F.; Häuser, F.; Straub, B.K.; Wenzel, J.J.; Conradi, R.; Mittler, J.; Lang, H.; Galle, P.R.; Zimmermann, T. Hepatitis E virus genotype 3 is a common finding in liver-transplanted patients undergoing liver biopsy for elevated liver enzymes with a low De Ritis ratio and suspected acute rejection: A real-world cohort. Clin. Transpl. 2018, 32, e13411. [Google Scholar] [CrossRef] [PubMed]
  3. Botros, M.; Sikaris, K.A. The de ritis ratio: The test of time. Clin. Biochem. Rev. 2013, 34, 117–130. [Google Scholar] [PubMed]
  4. Sookoian, S.; Pirola, C.J. Liver enzymes, metabolomics and genome-wide association studies: From systems biology to the personalized medicine. World J. Gastroenterol. 2015, 21, 711–725. [Google Scholar] [CrossRef]
  5. Sookoian, S.; Pirola, C.J. Alanine and aspartate aminotransferase and glutamine-cycling pathway: Their roles in pathogenesis of metabolic syndrome. World J. Gastroenterol. 2012, 18, 3775–3781. [Google Scholar] [CrossRef] [PubMed]
  6. Cichoż-Lach, H.; Michalak, A. Oxidative stress as a crucial factor in liver diseases. World J. Gastroenterol. 2014, 20, 8082–8091. [Google Scholar] [CrossRef]
  7. Quhal, F.; Abufaraj, M.; Janisch, F.; Mori, K.; Lysenko, I.; Mostafaei, H.; D’Andrea, D.; Mathieu, R.; Enikeev, D.V.; Fajkovic, H.; et al. The significance of De Ritis ratio in patients with radiation-recurrent prostate cancer undergoing salvage radical prostatectomy. Arab J. Urol. 2020, 18, 213–218. [Google Scholar] [CrossRef]
  8. Li, J.; Cao, D.; Peng, L.; Meng, C.; Xia, Z.; Li, Y.; Wei, Q. Potential Clinical Value of Pretreatment De Ritis Ratio as a Prognostic Biomarker for Renal Cell Carcinoma. Front. Oncol. 2021, 11, 780906. [Google Scholar] [CrossRef] [PubMed]
  9. Fukui-Kawaura, S.; Kawahara, T.; Araki, Y.; Nishimura, R.; Uemura, K.; Namura, K.; Mizuno, N.; Yao, M.; Uemura, H.; Ikeda, I. A higher De Ritis ratio (AST/ALT) is a risk factor for progression in high-risk non-muscle invasive bladder cancer. Oncotarget 2021, 12, 917–922. [Google Scholar] [CrossRef]
  10. Batur, A.F.; Aydogan, M.F.; Kilic, O.; Korez, M.K.; Gul, M.; Kaynar, M.; Goktas, S.; Akand, M. Comparison of De Ritis Ratio and other systemic inflammatory parameters for the prediction of prognosis of patients with transitional cell bladder cancer. Int. J. Clin. Pract. 2021, 75, e13743. [Google Scholar] [CrossRef]
  11. Uleri, A.; Hurle, R.; Contieri, R.; Diana, P.; Buffi, N.; Lazzeri, M.; Saita, A.; Casale, P.; Guazzoni, G.; Lughezzani, G. Combination of AST to ALT and neutrophils to lymphocytes ratios as predictors of locally advanced disease in patients with bladder cancer subjected to radical cystectomy: Results from a single-institutional series. Urologia 2022, 89, 363–370. [Google Scholar] [CrossRef] [PubMed]
  12. Knittelfelder, O.; Delago, D.; Jakse, G.; Reinisch, S.; Partl, R.; Stranzl-Lawatsch, H.; Renner, W.; Langsenlehner, T. The AST/ALT (De Ritis) Ratio Predicts Survival in Patients with Oral and Oropharyngeal Cancer. Diagnostics 2020, 10, 973. [Google Scholar] [CrossRef] [PubMed]
  13. Ghahari, M.; Salari, A.; Ghafoori Yazdi, M.; Nowroozi, A.; Fotovat, A.; Momeni, S.A.; Nowroozi, M.R.; Amini, E. Association Between Preoperative De Ritis (AST/ALT) Ratio and Oncological Outcomes Following Radical Cystectomy in Patients with Urothelial Bladder Cancer. Clin. Genitourin Cancer 2022, 20, e89–e93. [Google Scholar] [CrossRef] [PubMed]
  14. Pilarczyk, K.; Carstens, H.; Heckmann, J.; Canbay, A.; Koch, A.; Pizanis, N.; Jakob, H.; Kamler, M. The aspartate transaminase/alanine transaminase (DeRitis) ratio predicts mid-term mortality and renal and respiratory dysfunction after left ventricular assist device implantation. Eur. J. Cardiothorac Surg. 2017, 52, 781–788. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  15. Park, J.Y.; Yu, J.; Hong, J.H.; Lim, B.; Kim, Y.; Hwang, J.H.; Kim, Y.K. Elevated De Ritis Ratio as a Predictor for Acute Kidney Injury after Radical Retropubic Prostatectomy. J. Pers. Med. 2021, 11, 836. [Google Scholar] [CrossRef] [PubMed]
  16. He, H.M.; He, C.; Zhang, S.C.; You, Z.B.; Lin, X.Q.; Luo, M.Q.; Lin, M.Q.; Guo, Y.S.; Zheng, W.P.; Lin, K.Y. Predictive value of aspartate aminotransferase-to-alanine aminotransferase ratio for contrast-associated acute kidney injury in patients undergoing elective percutaneous coronary intervention. J. Cardiol. 2022, 79, 618–625. [Google Scholar] [CrossRef]
  17. Steininger, M.; Winter, M.P.; Reiberger, T.; Koller, L.; El-Hamid, F.; Forster, S.; Schnaubelt, S.; Hengstenberg, C.; Distelmaier, K.; Goliasch, G.; et al. De-Ritis Ratio Improves Long-Term Risk Prediction after Acute Myocardial Infarction. J. Clin. Med. 2018, 7, 474. [Google Scholar] [CrossRef] [Green Version]
  18. Jasiewicz, M.; Siedlaczek, M.; Kasprzak, M.; Gorog, D.A.; Jilma, B.; Siller-Matula, J.; Obońska, K.; Dobosiewicz, R.; Pstrągowski, K.; Kubica, J. Elevated serum transaminases in patients with acute coronary syndromes: Do we need a revision of exclusion criteria for clinical trials? Cardiol. J. 2021; ahead of print. [Google Scholar] [CrossRef]
  19. Djakpo, D.K.; Wang, Z.Q.; Shrestha, M. The significance of transaminase ratio (AST/ALT) in acute myocardial infarction. Arch Med. Sci. Atheroscler Dis. 2020, 5, e279–e283. [Google Scholar] [CrossRef]
  20. Zhao, P.Y.; Yao, R.Q.; Ren, C.; Li, S.Y.; Li, Y.X.; Zhu, S.Y.; Yao, Y.M.; Du, X.H. De Ritis Ratio as a Significant Prognostic Factor in Patients with Sepsis: A Retrospective Analysis. J. Surg. Res. 2021, 264, 375–385. [Google Scholar] [CrossRef] [PubMed]
  21. Zinellu, A.; Arru, F.; De Vito, A.; Sassu, A.; Valdes, G.; Scano, V.; Zinellu, E.; Perra, R.; Madeddu, G.; Carru, C.; et al. The De Ritis ratio as prognostic biomarker of in-hospital mortality in COVID-19 patients. Eur. J. Clin. Investig. 2021, 51, e13427. [Google Scholar] [CrossRef]
  22. Pranata, R.; Huang, I.; Lim, M.A.; Yonas, E.; Vania, R.; Lukito, A.A.; Nasution, S.A.; Siswanto, B.B.; Kuswardhani, R.A.T. Elevated De Ritis Ratio Is Associated with Poor Prognosis in COVID-19: A Systematic Review and Meta-Analysis. Front. Med. 2021, 8, 676581. [Google Scholar] [CrossRef] [PubMed]
  23. Guzey-Aras, Y.; Yazar, H.; Acar, T.; Kayacan, Y.; Acar, B.A.; Boncuk, S.; Eryilmaz, H.A. The Role of De Ritis Ratio as a Clinical Prognostic Parameter in COVID 19 Patients. Clin. Lab 2021, 67, 114–119. [Google Scholar] [CrossRef] [PubMed]
  24. Yashashwini, A.; Vedavathi, R. The Study of De Ritis (Ast/ Alt) Ratio in Comparision with Other Parameters for Predicting Poor Prognosis in Covid 19 Patients. J. Assoc. Physicians India 2022, 70, 11–12. [Google Scholar]
  25. Bulgarelli, L.; Deliberato, R.O.; Johnson, A.E.W. Prediction on critically ill patients: The role of “big data”. J. Crit. Care 2020, 60, 64–68. [Google Scholar] [CrossRef]
  26. Fu, L.H.; Schwartz, J.; Moy, A.; Knaplund, C.; Kang, M.J.; Schnock, K.O.; Garcia, J.P.; Jia, H.; Dykes, P.C.; Cato, K.; et al. Development and validation of early warning score system: A systematic literature review. J. Biomed. Inf. 2020, 105, 103410. [Google Scholar] [CrossRef] [PubMed]
  27. Keuning, B.E.; Kaufmann, T.; Wiersema, R.; Granholm, A.; Pettilä, V.; Møller, M.H.; Christiansen, C.F.; Castela Forte, J.; Snieder, H.; Keus, F.; et al. Mortality prediction models in the adult critically ill: A scoping review. Acta Anaesthesiol. Scand 2020, 64, 424–442. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  28. Sinuff, T.; Adhikari, N.K.; Cook, D.J.; Schünemann, H.J.; Griffith, L.E.; Rocker, G.; Walter, S.D. Mortality predictions in the intensive care unit: Comparing physicians with scoring systems. Crit. Care Med. 2006, 34, 878–885. [Google Scholar] [CrossRef]
  29. Timsit, J.F.; Fosse, J.P.; Troché, G.; De Lassence, A.; Alberti, C.; Garrouste-Orgeas, M.; Azoulay, E.; Chevret, S.; Moine, P.; Cohen, Y. Accuracy of a composite score using daily SAPS II and LOD scores for predicting hospital mortality in ICU patients hospitalized for more than 72 h. Intensive Care Med. 2001, 27, 1012–1021. [Google Scholar] [CrossRef]
  30. Breslow, M.J.; Badawi, O. Severity scoring in the critically ill: Part 1--interpretation and accuracy of outcome prediction scoring systems. Chest 2012, 141, 245–252. [Google Scholar] [CrossRef]
  31. Lemeshow, S.; Le Gall, J.R. Modeling the severity of illness of ICU patients. A systems update. JAMA 1994, 272, 1049–1055. [Google Scholar] [CrossRef] [PubMed]
  32. Le Gall, J.R. The use of severity scores in the intensive care unit. Intensive Care Med. 2005, 31, 1618–1623. [Google Scholar] [CrossRef] [PubMed]
  33. Knaus, W.A.; Draper, E.A.; Wagner, D.P.; Zimmerman, J.E. APACHE II: A severity of disease classification system. Crit. Care Med. 1985, 13, 818–829. [Google Scholar] [CrossRef]
  34. Le Gall, J.R.; Lemeshow, S.; Saulnier, F. A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study. Jama 1993, 270, 2957–2963. [Google Scholar] [CrossRef] [PubMed]
  35. Lemeshow, S.; Klar, J.; Teres, D.; Avrunin, J.S.; Gehlbach, S.H.; Rapoport, J.; Rué, M. Mortality probability models for patients in the intensive care unit for 48 or 72 h: A prospective, multicenter study. Crit. Care Med. 1994, 22, 1351–1358. [Google Scholar] [CrossRef] [PubMed]
  36. Sekulic, A.D.; Trpkovic, S.V.; Pavlovic, A.P.; Marinkovic, O.M.; Ilic, A.N. Scoring Systems in Assessing Survival of Critically Ill ICU Patients. Med. Sci. Monit 2015, 21, 2621–2629. [Google Scholar] [CrossRef] [Green Version]
  37. Wu, S.C.; Chou, S.E.; Liu, H.T.; Hsieh, T.M.; Su, W.T.; Chien, P.C.; Hsieh, C.H. Performance of Prognostic Scoring Systems in Trauma Patients in the Intensive Care Unit of a Trauma Center. Int. J. Env. Res. Public Health 2020, 17, 7226. [Google Scholar] [CrossRef]
  38. Le Gall, J.R.; Klar, J.; Lemeshow, S.; Saulnier, F.; Alberti, C.; Artigas, A.; Teres, D. The Logistic Organ Dysfunction system. A new way to assess organ dysfunction in the intensive care unit. ICU Scoring Group. JAMA 1996, 276, 802–810. [Google Scholar] [CrossRef]
  39. Marshall, J.C.; Cook, D.J.; Christou, N.V.; Bernard, G.R.; Sprung, C.L.; Sibbald, W.J. Multiple organ dysfunction score: A reliable descriptor of a complex clinical outcome. Crit. Care Med. 1995, 23, 1638–1652. [Google Scholar] [CrossRef]
  40. Vassar, M.J.; Wilkerson, C.L.; Duran, P.J.; Perry, C.A.; Holcroft, J.W. Comparison of APACHE II, TRISS, and a proposed 24-h ICU point system for prediction of outcome in ICU trauma patients. J. Trauma 1992, 32, 490–499. [Google Scholar] [CrossRef] [PubMed]
  41. Vincent, J.L.; de Mendonça, A.; Cantraine, F.; Moreno, R.; Takala, J.; Suter, P.M.; Sprung, C.L.; Colardyn, F.; Blecher, S. Use of the SOFA score to assess the incidence of organ dysfunction/failure in intensive care units: Results of a multicenter, prospective study. Working group on “sepsis-related problems” of the European Society of Intensive Care Medicine. Crit. Care Med. 1998, 26, 1793–1800. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  42. Hsieh, C.H.; Hsu, S.Y.; Hsieh, H.Y.; Chen, Y.C. Differences between the sexes in motorcycle-related injuries and fatalities at a Taiwanese level I trauma center. Biomed. J. 2017, 40, 113–120. [Google Scholar] [CrossRef]
  43. Hsieh, C.H.; Liu, H.T.; Hsu, S.Y.; Hsieh, H.Y.; Chen, Y.C. Motorcycle-related hospitalizations of the elderly. Biomed. J. 2017, 40, 121–128. [Google Scholar] [CrossRef]
  44. Hsieh, C.H.; Chen, Y.C.; Hsu, S.Y.; Hsieh, H.Y.; Chien, P.C. Defining polytrauma by abbreviated injury scale >/= 3 for a least two body regions is insufficient in terms of short-term outcome: A cross-sectional study at a level I trauma center. Biomed. J. 2018, 41, 321–327. [Google Scholar] [CrossRef]
  45. Crismale, J.F.; Friedman, S.L. Acute Liver Injury and Decompensated Cirrhosis. Med. Clin. N. Am. 2020, 104, 647–662. [Google Scholar] [CrossRef]
  46. Elf, S.E.; Chen, J. Targeting glucose metabolism in patients with cancer. Cancer 2014, 120, 774–780. [Google Scholar] [CrossRef] [Green Version]
  47. Lin, L.Y.; Hsu, C.Y.; Chiou, H.Y.; Lee, H.A.; Hsu, L.M.; Chang, P.Y.; Kurniawan, A.L.; Chao, J.C. Association between Dietary Patterns and Serum Hepatic Enzyme Levels in Adults with Dyslipidemia and Impaired Fasting Plasma Glucose. Nutrients 2021, 13, 987. [Google Scholar] [CrossRef]
  48. Colomba, J.; Netedu, S.R.; Lehoux-Dubois, C.; Coriati, A.; Boudreau, V.; Tremblay, F.; Cusi, K.; Rabasa-Lhoret, R.; Leey, J.A. Hepatic enzyme ALT as a marker of glucose abnormality in men with cystic fibrosis. PLoS ONE 2019, 14, e0219855. [Google Scholar] [CrossRef] [Green Version]
  49. Zoppini, G.; Cacciatori, V.; Negri, C.; Stoico, V.; Lippi, G.; Targher, G.; Bonora, E. The aspartate aminotransferase-to-alanine aminotransferase ratio predicts all-cause and cardiovascular mortality in patients with type 2 diabetes. Medicine 2016, 95, e4821. [Google Scholar] [CrossRef]
  50. Sripradha, R.; Sridhar, M.G.; Agrawal, A. Can protein carbonyl/glutathione ratio be used as a potential biomarker to assess oxidative stress in alcoholic hepatitis? Indian J. Med. Sci. 2010, 64, 476–483. [Google Scholar] [PubMed]
  51. Tai, Y.S.; Chen, C.H.; Huang, C.Y.; Tai, H.C.; Wang, S.M.; Pu, Y.S. Diabetes mellitus with poor glycemic control increases bladder cancer recurrence risk in patients with upper urinary tract urothelial carcinoma. Diabetes Metab. Res. Rev. 2015, 31, 307–314. [Google Scholar] [CrossRef]
  52. Lee, H.; Lee, S.E.; Byun, S.S.; Kim, H.H.; Kwak, C.; Hong, S.K. De Ritis ratio (aspartate transaminase/alanine transaminase ratio) as a significant prognostic factor after surgical treatment in patients with clear-cell localized renal cell carcinoma: A propensity score-matched study. BJU Int. 2017, 119, 261–267. [Google Scholar] [CrossRef] [Green Version]
  53. Rief, P.; Pichler, M.; Raggam, R.; Hafner, F.; Gerger, A.; Eller, P.; Brodmann, M.; Gary, T. The AST/ALT (De-Ritis) ratio: A novel marker for critical limb ischemia in peripheral arterial occlusive disease patients. Medicine 2016, 95, e3843. [Google Scholar] [CrossRef]
  54. Tan, X.; Xiao, K.; Liu, W.; Chang, S.; Zhang, T.; Tang, H. Prognostic factors of distal cholangiocarcinoma after curative surgery: A series of 84 cases. Hepatogastroenterology 2013, 60, 1892–1895. [Google Scholar] [PubMed]
Diagnostics 12 02930 g001 550
Figure 1. Flowchart demonstrating the inclusion of hospitalized adult trauma patients admitted into the intensive care unit. After the exclusion of patients who had hepatocellular carcinoma, pre-existed decompensated cirrhosis, and lacked AST or ALT data, the study population was grouped into the death (n = 74) and survival groups (n = 814).
Figure 1. Flowchart demonstrating the inclusion of hospitalized adult trauma patients admitted into the intensive care unit. After the exclusion of patients who had hepatocellular carcinoma, pre-existed decompensated cirrhosis, and lacked AST or ALT data, the study population was grouped into the death (n = 74) and survival groups (n = 814).
Diagnostics 12 02930 g001
Diagnostics 12 02930 g002 550
Figure 2. The receiver operating characteristic curves and the area under the curve (AUCROC) of the 1st and 2nd DRR to predict the mortality of the adult trauma patients in the intensive care unit.
Figure 2. The receiver operating characteristic curves and the area under the curve (AUCROC) of the 1st and 2nd DRR to predict the mortality of the adult trauma patients in the intensive care unit.
Diagnostics 12 02930 g002
Diagnostics 12 02930 g003 550
Figure 3. The receiver operating characteristic curves and the area under the curve (AUCROC) of the prognosis prediction models in predicting mortality (left above figure) and those curves with inclusion of the 2nd DRR (right above figure) or 2nd AST (left lower figure) as an additional variable in the mortality outcome prediction.
Figure 3. The receiver operating characteristic curves and the area under the curve (AUCROC) of the prognosis prediction models in predicting mortality (left above figure) and those curves with inclusion of the 2nd DRR (right above figure) or 2nd AST (left lower figure) as an additional variable in the mortality outcome prediction.
Diagnostics 12 02930 g003
Table
Table 1. Categorical variables of patient and injury characteristics of the death and survival adult trauma patients who were admitted into the intensive care unit.
Table 1. Categorical variables of patient and injury characteristics of the death and survival adult trauma patients who were admitted into the intensive care unit.
VariablesTotalMortalityp-Value
(n = 888)No (n = 814)Yes (n = 74)
SexFemale289 (32.6%)265 (32.6%)24 (32.4%)>0.999
Male599 (67.5%)549 (67.4%)50 (67.6%)
HTNNo597 (67.2%)558 (68.6%)39 (52.7%)0.007
Yes291 (32.8%)256 (31.5%)35 (47.3%)
CADNo817 (92.0%)754 (92.6%)63 (85.1%)0.040
Yes71 (8.0%)60 (7.4%)11 (14.9%)
ESRDNo868 (97.8%)802 (98.5%)66 (89.2%)<0.001
Yes20 (2.3%)12 (1.5%)8 (10.8%)
CVANo854 (96.2%)785 (96.4%)69 (93.2%)0.194
Yes34 (3.8%)29 (3.6%)5 (6.8%)
DMNo718 (80.9%)663 (81.5%)55 (74.3%)0.163
Yes170 (19.1%)151 (18.6%)19 (25.7%)
AIS (Head)0225 (25.3%)214 (26.3%)11 (14.9%)<0.001
18 (0.9%)8 (1.0%)0 (0.0%)
218 (2.0%)17 (2.1%)1 (1.4%)
376 (8.6%)72 (8.9%)4 (5.4%)
4400 (45.1%)376 (46.2%)24 (32.4%)
5160 (18.0%)127 (15.6%)33 (44.6%)
61 (0.1%)0 (0.0%)1 (1.4%)
AIS (Face)0715 (80.5%)651 (80.0%)64 (86.5%)0.357
120 (2.3%)20 (2.5%)0 (0.0%)
2146 (16.4%)136 (16.7%)10 (13.5%)
37 (0.8%)7 (0.9%)0 (0.0%)
AIS (Thorax)0569 (64.1%)524 (64.4%)45 (60.8%)0.523
125 (2.8%)25 (3.1%)0 (0.0%)
256 (6.3%)51 (6.3%)5 (6.8%)
3134 (15.1%)122 (15.0%)12 (16.2%)
491 (10.3%)81 (10.0%)10 (13.5%)
513 (1.5%)11 (1.4%)2 (2.7%)
AIS (Abdomen)0668 (75.2%)605 (74.3%)63 (85.1%)0.040
277 (8.7%)75 (9.2%)2 (2.7%)
380 (9.0%)78 (9.6%)2 (2.7%)
443 (4.8%)37 (4.6%)6 (8.1%)
520 (2.3%)19 (2.3%)1 (1.4%)
AIS (Extremity)0523 (58.9%)472 (58.0%)51 (68.9%)0.528
15 (0.6%)5 (0.6%)0 (0.0%)
2214 (24.1%)202 (24.8%)12 (16.2%)
3129 (14.5%)119 (14.6%)10 (13.5%)
416 (1.8%)15 (1.8%)1 (1.4%)
51 (0.1%)1 (0.1%)0 (0.0%)
AIS (External)0848 (95.5%)779 (95.7%)69 (93.2%)0.667
128 (3.2%)25 (3.1%)3 (4.1%)
27 (0.8%)6 (0.7%)1 (1.4%)
31 (0.1%)1 (0.1%)0 (0.0%)
41 (0.1%)1 (0.1%)0 (0.0%)
53 (0.3%)2 (0.3%)1 (1.4%)
AIS = abbreviated injury scale; CAD = coronary artery disease; CVA = cerebral vascular accident; DM = diabetes mellitus; ESRD = end-stage renal disease; HTN = hypertension.
Table
Table 2. Continuous variables of patient and injury characteristics of the death and survival adult trauma patients who were admitted into the intensive care unit.
Table 2. Continuous variables of patient and injury characteristics of the death and survival adult trauma patients who were admitted into the intensive care unit.
VariablesTotalMortalityp-Value
(n = 888)No (n = 814)Yes (n = 74)
Age (years)59 [40, 70]58 [39, 69]71 [58, 78]<0.001
BMI23.9 [21.4, 27.1]23.9 [21.4, 27.1]23.8 [21.4, 28.0]0.861
Temperature (°C)36.5 [36.1, 37.0]36.5 [36.1, 37.0]36.2 [36.0, 36.7]0.001
HR (beats/min)92 [80, 106]92 [80, 106]98 [86, 112]0.030
SBP (mmHg)140 [122, 156]139 [123, 156]141 [116, 158]0.675
RR (times/min)19 [18, 20]19 [18, 20]20 [17, 22]0.932
GCS11 [7, 15]11 [8, 15]6 [3, 11]<0.001
ISS20 [16, 25]20 [16, 25]25 [19, 29]<0.001
Glucose (mg/dL)159 [133, 205]158 [132, 198]197 [157, 250]<0.001
HCO3 (meq/L)21.7 [19.3, 23.6]21.8 [19.4, 23.7]20.4 [18.5, 22.5]0.003
Na (mEq/L)138 [136, 140]138 [136, 140]139 [136, 141]0.599
K (mEq/L)3.6 [3.2, 3.9]3.6 [3.3, 3.9]3.7 [3.1, 4.2]0.742
RBC (106/μL)4.4 [3.9, 4.9]4.4 [3.9, 4.9]4.4 [3.9, 4.8]0.864
WBC (103/μL)11.6 [8.4, 15.7]11.6 [8.6, 15.5]11.4 [7.5, 16.0]0.572
Neutrophil (%)79.5 [68.1, 85.5]79.3 [68.0, 85.5]81.1 [69.5, 85.2]0.702
Hb (g/dL)13.1 [11.5, 14.4]13.1 [11.6, 14.4]13.0 [10.9, 14.6]0.665
Hct (%)39.2 [34.8, 42.9]39.2 [35.1, 42.9]39.7 [33.7, 42.6]0.594
Platelets (103/μL)215 [169, 266]215 [169, 266]215 [172, 265]0.729
INR1.05 [1.01, 1.12]1.05 [1.00, 1.12]1.09 [1.03, 1.20]<0.001
BUN (mg/dL)15 [11, 19]14 [11, 19]19 [13, 26]<0.001
Cr (mg/dL)0.96 [0.76, 1.21]0.94 [0.75, 1.19]1.19 [0.95, 2.06]<0.001
BUN/Cr14.97 [11.39, 19.23]15.04 [11.46, 19.25]13.69 [10.72, 19.09]0.192
Albumin (g/dL)3.3 [2.9, 3.7]3.4 [2.9, 3.8]2.9 [2.5, 3.6]<0.001
Bilirubin (mg/dL)0.8 [0.6, 1.1]0.8 [0.6, 1.1]0.8 [0.5, 1.4]0.826
1st AST (U/L) 53 [33, 128]53 [33, 128]54 [34, 118]0.768
1st ALT (U/L)37 [22, 88]37 [22, 92]29 [19, 61]0.050
2nd AST (U/L)40 [26, 66]39 [26, 63]57 [32, 107]<0.001
2nd ALT (U/L)34 [20, 62]34 [20, 62]25 [16, 56]0.066
1st DRR1.49 [1.19, 1.88]1.46 [1.18, 1.85]1.66 [1.41, 2.26]<0.001
2nd DRR1.22 [0.85, 1.69]1.17 [0.83, 1.61]1.92 [1.24, 3.28]<0.001
ALT = alanine aminotransferase; AST = Aspartate transaminase; BMI = body mass index; BUN = blood urea nitrogen; Cr = creatinine; DRR = De Ritis ratio; HCO3 = bicarbonate; HR = heart rate; GCS = Glasgow coma scale; Hb = hemoglobin; Hct = hematocrit; INR = international normalized ratio; K = potassium; Na = sodium; ISS = injury severity score; RBC = red blood cells; RR = respiratory rate; SBP = systolic blood pressure; WBC = white blood cells. These continuous data was expressed with median and interquartile range.
Table
Table 3. The scores of various prognosis prediction algorithms for the death and survival adult trauma patients who were admitted into the intensive care unit.
Table 3. The scores of various prognosis prediction algorithms for the death and survival adult trauma patients who were admitted into the intensive care unit.
VariablesTotalMortalityp-Value
(n = 888)No (n = 814)Yes (n = 74)
TRISS0.93 [0.79, 0.97]0.93 [0.82, 0.97]0.79 [0.27, 0.93]<0.001
MPM II11.9 [7.1, 22.5]11.4 [6.9, 19.2]44.4 [23.5, 69.9]<0.001
MPM24 II7.8 [4.4, 14.1]7.2 [4.1, 12.5]29.4 [16.7, 49.9]<0.001
MPM48 II10.2 [5.6, 17.8]9.4 [5.2, 15.7]34.9 [19.5, 56.4]<0.001
MPM72 II12.1 [6.6, 21.3]10.8 [6.1, 18.2]40.6 [22.7, 60.2]<0.001
APACHE II13 [8, 18]12 [8, 18]21 [16, 26]<0.001
SAPS II28 [19, 38]27 [18, 37]49 [35, 56]<0.001
LODS2 [1, 4]2 [1, 4]6 [4, 8]<0.001
MODS3 [1, 5]2 [1, 4]5 [4, 6]<0.001
24 h ICU point system1 [0, 4]1 [0, 3]4 [2, 6]<0.001
SOFA3 [1, 5]3 [1, 5]5 [4, 8]<0.001
TRIOS8.7 [4.3, 17.7]7.6 [4.2, 15.0]23.6 [14.8, 41.5]<0.001
APACHE = the Acute Physiology and Chronic Health Evaluation; ICU = intensive care unit; LODS = Logistic Organ Dysfunction System; MODS = Multiple Organs Dysfunction Score; MPM = Mortality Prediction Model; SAPS = Simplified Acute Physiology Score; SOFA = Sequential Organ Failure Assessment; TRIOS = Three-Day Recalibrating ICU Outcomes; TRISS = The Trauma Score and Injury Severity Score.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Su, W.-T.; Rau, C.-S.; Chou, S.-E.; Tsai, C.-H.; Chien, P.-C.; Hsieh, C.-H. Using Second Measurement of De Ritis Ratio to Improve Mortality Prediction in Adult Trauma Patients in Intensive Care Unit. Diagnostics 2022, 12, 2930. https://doi.org/10.3390/diagnostics12122930

AMA Style

Su W-T, Rau C-S, Chou S-E, Tsai C-H, Chien P-C, Hsieh C-H. Using Second Measurement of De Ritis Ratio to Improve Mortality Prediction in Adult Trauma Patients in Intensive Care Unit. Diagnostics. 2022; 12(12):2930. https://doi.org/10.3390/diagnostics12122930

Chicago/Turabian Style

Su, Wei-Ti, Cheng-Shyuan Rau, Sheng-En Chou, Ching-Hua Tsai, Peng-Chen Chien, and Ching-Hua Hsieh. 2022. "Using Second Measurement of De Ritis Ratio to Improve Mortality Prediction in Adult Trauma Patients in Intensive Care Unit" Diagnostics 12, no. 12: 2930. https://doi.org/10.3390/diagnostics12122930

APA Style

Su, W.-T., Rau, C.-S., Chou, S.-E., Tsai, C.-H., Chien, P.-C., & Hsieh, C.-H. (2022). Using Second Measurement of De Ritis Ratio to Improve Mortality Prediction in Adult Trauma Patients in Intensive Care Unit. Diagnostics, 12(12), 2930. https://doi.org/10.3390/diagnostics12122930

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop