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Comparison of different intensive care scoring systems and Glasgow Aneurysm score for aortic aneurysm in predicting 28-day mortality: a retrospective cohort study from MIMIC-IV database

BMC Cardiovascular Disorders volume 24, Article number: 513 (2024) Cite this article

Abstract

Objective

This study aims to assess the performance of various scoring systems in predicting the 28-day mortality of patients with aortic aneurysms (AA) admitted to the intensive care unit (ICU).

Methods

We utilized data from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) to perform a comparative analysis of various predictive systems, including the Glasgow Aneurysm Score (GAS), Simplified Acute Physiology Score (SAPS) III, SAPS II, Logical Organ Dysfunction System (LODS), Sequential Organ Failure Assessment (SOFA), Systemic Inflammatory Response Syndrome (SIRS), and The Oxford Acute Illness Severity Score (OASIS). The discrimination abilities of these systems were compared using the area under the receiver operating characteristic curve (AUROC). Additionally, a 4-knotted restricted cubic spline regression was employed to evaluate the association between the different scoring systems and the risk of 28-day mortality. Finally, we conducted a subgroup analysis focusing on patients with abdominal aortic aneurysms (AAA).

Results

This study enrolled 586 patients with AA (68.39% male). Among them, 26 patients (4.4%) died within 28 days. Comparative analysis revealed higher SAPS II, SAPS III, SOFA, LODS, OASIS, and SIRS scores in the deceased group, while no statistically significant difference was observed in GAS scores between the survivor and deceased groups (P = 0.148). The SAPS III system exhibited superior predictive value for the 28-day mortality rate (AUROC 0.805) compared to the LODS system (AUROC 0.771), SOFA (AUROC 0.757), SAPS II (AUROC 0.759), OASIS (AUROC 0.742), SIRS (AUROC 0.638), and GAS (AUROC 0.586) systems. The results of the univariate and multivariate logistic analyses showed that SAPS III was statistically significant for both 28-day and 1-year mortality. Subgroup analyses yielded results consistent with the overall findings. No nonlinear relationship was identified between these scoring systems and 28-day all-cause mortality (P for nonlinear > 0.05).

Conclusion

The SAPS III system demonstrated superior discriminatory ability for both 28-day and 1-year mortality compared to the GAS, SAPS II SIRS, SOFA, and OASIS systems among patients with AA.

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Introduction

Aortic aneurysms (AA) are characterized by a localized dilation exceeding at least 50% of the normal diameter of the aorta. AA mainly encompasses thoracic aortic aneurysm (TAA), abdominal aortic aneurysm (AAA), and thoracoabdominal aortic aneurysm (TAAA). Following atherosclerosis, AA stands as the most prevalent arterial disease and ranks as the ninth leading cause of death globally [1,2,3]. The primary cause of death in AA is rupture, accounting for an approximate community mortality rate of 85%, with a substantial number of patients succumbing before reaching the hospital [4, 5].

Given its elevated mortality rate, the development of accurate and efficient scoring systems based on patient-related baseline vital signs is imperative for the early identification of high-risk patients and the timely initiation of treatment. Such systems not only facilitate the swift administration of medical interventions but also contribute to the judicious utilization of healthcare resources. Presently, recommended prediction scoring systems for patients with ruptured AA encompass the Glasgow Aneurysm Score (GAS), the Hardman Index, and the Physiological and Operative Severity Score for the Enumeration of Mortality and Morbidity (POSSUM) [6,7,8]. However, there exists notable controversy surrounding their predictive efficacy [9, 10]. Furthermore, many of these systems are tailored specifically for predicting outcomes in ruptured AA patients. To the best of our knowledge, there is currently no available research focusing on scoring and predicting the prognosis of unruptured AA.

For patients with abdominal aortic aneurysm (AA), whether undergoing open surgical repair (OSR) or endovascular aortic repair (EVAR), a notably high admission rate to the Intensive Care Unit (ICU) is observed. Currently, several scoring systems have been developed to predict the mortality rate of patients in the ICU. The systemic inflammatory response syndrome (SIRS) scoring system was initially employed [11], and Oxford acute severity of illness score (OASIS), developed by Johnson at el [12]. through machine learning algorithms, stands as another predictive system. Additionally, scoring systems such as Simplified Acute Physiology Score (SAPS) II, SAPS III, Logistic Organ Dysfunction System (LODS), Sequential Organ Failure Assessment (SOFA), and others have been established based on clinical variables and conditions [13,14,15]. These systems stratify patients based on the severity of their illness and predict the mortality rate of critically ill patients. Traditional AA prediction systems were established several decades ago. As time progresses, it is well-known that the discriminatory power and calibration accuracy of prediction systems often deteriorate. In comparison to these ICU scoring indicators, the predictive performance of traditional AA systems remains uncertain.

Hence, this study aims to evaluate the predictive efficacy of the GAS, SIRS, SOFA, OASIS, LODS, SAPS II, and SAPS III systems for both 28-day and 1-year mortality rates of unruptured AA. The assessment will be conducted through the analysis of data from the fourth edition of the Medical Information Mart for Intensive Care (MIMIC IV) database.

Methods and material

Data source and study population

This study utilized data from the electronic database of Medical Information Mart for Intensive Care (MIMIC-IV) version 2.2, a collaborative project between the Massachusetts Institute of Technology (MIT) and Beth Israel Deaconess Medical Center (BIDMC). The database includes relevant information on patients admitted to BIDMC from 2008 to 2019. Access to this platform is granted upon completion of the online course provided by the National Institutes of Health (NIH) [16]. Since all patient records in the MIMIC-IV database are completely de-identified, the Institutional Review Board at BIDMC waived the need for informed consent and approved the sharing of this research resource.

The MIMIC-IV database encompasses a total of 523,740 recorded hospital admissions, with 76,540 cases involving ICU stays. Admission information for patients with AA was extracted based on International Classification of Diseases, 9th Revision (ICD-9), and 10th Revision (ICD-10) codes, resulting in a total of 3,331 entries, of which 1,732 patients were admitted to the ICU. Inclusion criteria for extraction were as follows: (1) age less than 18 years at the time of the first admission; (2) for patients with multiple ICU admissions due to AA, only data from the first admission were extracted; (3) patients with severe conditions such as end-stage renal failure, cirrhosis, cancer, etc.; (4) patients with ICU stays less than 3 h; (5) patients who did not undergo surgical treatment. Ultimately, 587 patients were enrolled in this study (Fig. 1).

Fig. 1
figure 1

Flowchart of participant selection from Medical Information Mart for Intensive Care (MIMIC) IV database

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Data collection

The extraction of information was conducted using PostgresSQL (version 13.7.2) and Navicat Premium (version 16), employing Structured Query Language (SQL). Patient characteristics were collected, encompassing: (1) Demographic data, including age, gender, weight, height, and BMI. (2) Comorbidities, such as atrial fibrillation, hypertension, congestive heart failure, diabetes, carotid artery disease, peripheral vascular disease, etc. (3) Laboratory indicators, covering red blood cells (RBC), white blood cells (WBC), neutrophils, lymphocytes, hemoglobin, platelets, serum creatinine, fasting blood glucose (FBG), and triglycerides (TG), etc. (4) APSIII, SAPS-II, OASIS, SIRS, LODS, and SOFA The GAS for each patient was calculated using the formula: GAS = age (year) + 17 (for shock) + 7 (for myocardial disease) + 10 (for cerebrovascular disease) + 14 (for renal disease) [17]. In cases where laboratory indicators were tested multiple times within the first 24 hours of admission, the results from the initial test were utilized. Given the notable prevalence of missing values for laboratory indicators in the MIMIC-IV database, the percentage of missing values for each continuous variable was computed. To address potential bias arising from sample exclusion, variables with more than 20% missing values were excluded. Variables with less than 20% missing data underwent multiple imputation using the random forest algorithm, as implemented in the ‘mice’ package of the R software [18, 19].

Outcomes

The primary endpoint of this study was 28-day all-cause mortality, with the secondary endpoint being 1-year all-cause mortality. The MIMIC database records mortality data for patients during their hospitalization at Beth Israel Deaconess Medical Center (BIDMC). Each patient’s hospitalization record includes detailed documentation of any death events that occurred during their stay. The MIMIC database also matches with the Social Security Death Index (SSDI) to obtain data on post-discharge mortality.

Statistical analysis

The normal distribution of continuous variables was assessed using the Shapiro–Wilk test. For normally distributed continuous variables, Student t-tests were employed, and mean values ± standard deviations (SD) were reported. Non-normally distributed continuous variables were analyzed using the Mann-Whitney U test, with median values and interquartile ranges (IQR) presented. Categorical variables, expressed as frequencies and percentages (%), underwent assessment using the chi-square test and Fisher’s exact test. Receiver Operating Characteristic (ROC) curve analysis was conducted to identify the optimal cutoff level for risk scores. The performance of the test was evaluated based on the Area Under the ROC Curve (AUROC), and sensitivity and specificity for the identified cutoff values were reported. Potential nonlinear correlations between various scoring systems and outcomes were explored using restricted cubic splines (RCS). In addition, we conducted univariate and multivariate logistic analyses on different intensive care scoring systems with 28-day and 1-year mortality to explore the relationship between various scores and mortality rates. Throughout the analysis, two-tailed P values < 0.05 were considered statistically significant. All statistical analyses were performed using R software (version 4.3.1) and SPSS 27.0 (IBM SPSS Statistics, Armonk, NY, USA).

Results

Table 1 displays the baseline characteristics of 586 patients meeting inclusion criteria, categorized into admission 28-day survival and non-survival groups. Among them, 191 (32.6%) were female, and 395 (67.4%) were male, with a median age of 59.0 (67.5 ± 12.5) years. The 28-day mortality rate after admission was 4.4%. Comparing the non-survival group with AA to the 28-day survival group revealed a higher proportion of females (P = 0.018), elevated body temperature (P = 0.016), and a higher incidence of shock (P < 0.001) in the former. Concerning laboratory indicators, the 28-day mortality group exhibited significantly lower albumin levels than the survival group (P = 0.004). Conversely, glucose levels in the mortality group were significantly higher than those in the survival group (P = 0.01), with no statistical difference in the prevalence of diabetes between the two groups (P = 0.614). Among various scoring systems, including LODS, OASIS, SAPS II, SAPS III, SIRS, and SOFA, the 28-day mortality group demonstrated significantly higher scores than the survival group (P < 0.01). However, the remaining covariates showed no significant differences between the two groups (P > 0.05).

Table 1 Baseline clinical data of the included patients with thoracoabdominal aortic aneurysm
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The AUROC values for the GAS, SIRS, SOFA, OASIS, SAPS II, LODS, and SAPS III systems were calculated to assess their predictive performance for the overall 28-day mortality rate in the total patient population (N = 586). The SAPS III system (AUROC 0.805, 95% CI 0.718–0.892) demonstrated superior predictive value compared to the LODS system (AUROC 0.771, 95% CI 0.656–0.886), SOFA (AUROC 0.757, 95% CI 0.674–0.846), SAPS II (AUROC 0.759, 95% CI 0.641–0.876), OASIS (AUROC 0.742, 95% CI 0.616–0.868), SIRS (AUROC 0.638, 95% CI 0.519–0.756), and GAS (AUROC 0.586, 95% CI 0.459–0.713) systems. The SAPS III score’s AUROC showed the best discrimination (sensitivity = 0.769, specificity = 0.768, AUROC = 0.805), significantly outperforming other scoring systems (Table 2; Fig. 2). The Youden index for the SAPS III system was 0.537, also higher than other scoring systems. Among these six systems, GAS and SIRS exhibited the poorest discriminatory ability compared to the others (Table 2). Additionally, predictions for 1-year all-cause mortality were conducted using various scoring systems for AA. Consistent with our short-term predictive model, the SAPS III scoring system exhibited superior predictive performance (AUROC = 0.739) (Fig. 2; Table 3).

Table 2 Performance of the scoring systems for predicting 28-day mortality rate
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Fig. 2
figure 2

ROC curves of different scoring systems of predict all-cause mortality of AA. (A):28-day all-cause mortality; (B) 1-year all-cause mortality. AA, Aortic aneurysms; LODS, Logistic Organ Dysfunction System; OASIS, Oxford Acute Severity of Illness Score; SAPS II, Simplified Acute Physiology Score II; SAPS III, Simplified Acute Physiology Score III; SIRS, systemic inflammatory response syndrome; SOFA, Sequential Organ Failure Assessment; GAS, Glasgow

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Table 3 Performance of the scoring systems for predicting one year mortality rate
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Subsequently, we performed a subgroup analysis focusing on patients with the highest incidence of AAA. ROC curve analysis for 28-day and 1-year all-cause mortality in AAA patients revealed that the SAPS III scoring system exhibited the best performance (Fig. 3). However, due to the limited number of patients with TAA and TAAA, no further subgroup analysis was conducted.

Fig. 3
figure 3

ROC curves of different scoring systems predict the all-cause mortality of AAA. (A):28-day all-cause mortality; (B) 1-year all-cause mortality. AAA, abdominal aortic aneurysm; LODS, Logistic Organ Dysfunction System; OASIS, Oxford Acute Severity of Illness Score; SAPS II, Simplified Acute Physiology Score II; SAPS III, Simplified Acute Physiology Score III; SIRS, systemic inflammatory response syndrome; SOFA, Sequential Organ Failure Assessment; GAS, Glasgow

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In addition, we employed RCS curves, with four knots set at the 5th, 35th, 65th, and 95th percentiles, to explore potential nonlinear relationships between different scoring systems and 28-day all-cause mortality (Fig. 4). The RCS analysis for the GAS scoring system yielded an overall P-value of 0.08, accompanied by a non-linear P-value of 0.108. Consequently, this system is considered statistically insignificant, suggesting no significant relationship between the GAS score and 28-day all-cause mortality, whether in terms of linear or non-linear associations. On the other hand, the overall P-values for LODS, OASIS, SAPS III, SIRS, and SOFA are all less than 0.05, indicating a statistically significant relationship. However, there is no evidence of a non-linear relationship between LODS, OASIS, SAPS III, SOFA, and SIRS and 28-day all-cause mortality (P for nonlinear > 0.05). Multivariate logistic analysis for 28-day and 1-year mortality showed that in the univariate logistic analysis, except for GAS, which had an OR value of 1.02 for 28-day mortality (95% CI 0.99 ~ 1.05, P = 0.148), all other scores were statistically significant for both 28-day and 1-year mortality. The multivariate analysis results indicated that SAPS III was an independent risk factor for 28-day mortality. For 1-year mortality, OASIS, GAS, and SAPS were all statistically significant, with SAPS III showing the strongest association (Table 4).

Fig. 4
figure 4

Association between different scoring systems and 28-day all-cause mortality with the RCS function. Model with 4 knots located at 5th, 35th, 65th, and 90th percentiles. LODS, Logistic Organ Dysfunction System; OASIS, Oxford Acute Severity of Illness Score; SAPS II, Simplified Acute Physiology Score II; SAPS III, Simplified Acute Physiology Score III; SIRS, systemic inflammatory response syndrome; SOFA, Sequential Organ Failure Assessment; GAS, Glasgow; 95% CI, 95% Confidence Interval; OR, Odds Ratio

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Table 4 Univariate and Multivariate Logistic Regression Analysis of different ICU Scoring systems for 28-Day and 1-Year mortality
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Discussion

AA is considered a medical emergency, emphasizing the importance of early and aggressive intervention. The perioperative mortality rate for AA is particularly elevated, especially among patients with a ruptured AA. Following EVAR, the mortality rate stands at approximately 55.2%, while OSR can result in a mortality rate as high as 62% [20, 21]. Therefore, it is crucial to conduct a systematic assessment early on for patients with AA to determine the severity of clinical symptoms, aiming to reduce perioperative mortality rates. Many predictive factors for perioperative mortality in ruptured AA have been reported and are included in various scoring systems. Samy et al. [17] employed the GAS to identify for perioperative mortality. Furthermore, other indices such as the Hardman Index have been utilized for similar purposes. However, opinions on the accuracy of their predictive efficacy vary Furthermore, these evaluations primarily focus on ruptured AA [22,23,24]. This study not only compares various scoring systems in the ICU with the traditional GAS but also focuses on non-ruptured AA.

The findings presented in this study suggest the effectiveness of SAPS III in predicting not only the 28-day all-cause mortality but also the one-year mortality rates among AA patients in the ICU. Furthermore, outcomes from RCS analysis indicate a positive correlation, showing that as SAPS III scores increase, the mortality rate for AA patients similarly rises. The SAPS III scoring system comprises several components, including age, comorbidities, temperature, heart rate, blood pressure, hydrogen ion concentration, platelets, albumin, total bilirubin, oxygenation index, and specific ICU-related factors [25, 26]. Serving as a revision of SAPS II, SAPS III has demonstrated improved predictive accuracy over time. This enhanced performance has been validated in our ROC curves, reaffirming its superiority compared to SAPS II [27]. It is not surprising to find the results, since the SAPS III scoring system has more complicated parameters than the other systems, thus SAPS III proves effective in accurately evaluating the severity of patients’ condition. This thorough evaluation facilitates more precise clinical attention and interventions, fostering a proactive approach to patient treat. In addition, studies validating the efficacy of the SAPS III system have predominantly concentrated on specific patient populations, such as transplant recipients, elderly individuals, and those with conditions like cancer, acute coronary syndrome, or acute kidney injury [28,29,30]. Nevertheless, certain studies have suggested that SAPS III might overestimate the risk of mortality and may not be optimally suited for comprehensive risk assessment [31,32,33]. Addressing these concerns requires further validation through large-scale clinical randomized controlled trials.

Moreover, the OASIS is another commonly employed scoring system for predicting the risk of mortality in ICU patients. Notably, OASIS incorporates fewer evaluation variables when compared to SAPS II and SAPS III [12]. A retrospective cohort study conducted by Chen et al. [34] implies a need for caution when applying OASIS to critically ill patients. Nevertheless, owing to the complexity of calculations involved in other scoring systems, OASIS is also recommended as a predictive alternative. SIRS and LODS are commonly utilized scoring systems for the evaluation of septic shock and multiple organ dysfunction [35]. Conversely, the SOFA score is recognized as a tool to delineate the onset of various organ dysfunctions and to predict outcomes [36]. LODS and SOFA, in predicting the all-cause mortality of AA, closely align with SAPS III but encompass fewer variables than SAPS and OASIS. This reduction in variables may be largely attributed to the inclusion of platelets and prothrombin time. Platelet activation and coagulation factor generation are deemed pivotal in forecasting the prognosis of AA patients [37, 38].

In our study, the GAS scoring system demonstrated the lowest performance, contrasting with findings from some previous research. Furthermore, the RCS curves we generated reveal a positive linear correlation between the 28-day all-cause mortality rate and all scoring systems except for GAS. Several factors may contribute to this observation: Firstly, a retrospective study by Biancari et al. [39] reported an AUROC = 0.634 for ICU patients, which was lower than other scoring systems in our investigation. Secondly, while GAS has been considered effective for predicting mortality in patients with ruptured AA, our focus was on non-ruptured patients admitted to the ICU, including those after OSR and EVAR. Another retrospective study highlighted the limited predictive value of GAS for post-EVAR mortality and morbidity, particularly underscoring its constrained utility in clinical decision-making for high-risk patients [40, 41].

Nevertheless, it is crucial to recognize the limitations inherent in this study. Firstly, given its a single-center retrospective nature, the study cannot establish definitive causal relationships, and clinical outcomes may be influenced by certain confounding factors. In the future, multicenter studies are needed to further validate the generalizability of these findings across different populations. Secondly, the study encompasses a range of surgical methods, including both EVAR and OSR. Owing to the limited number of patients undergoing OSR, a subgroup analysis specific to this category was not conducted. Thirdly, since the MIMIC database only contains detailed information during patient hospitalization, we are unable to obtain specific follow-up time data for patients, and thus cannot perform Kaplan-Meier survival curves and Cox regression analysis. Lastly, and of utmost importance, our utilization of propensity score matching during data collection aimed to minimize reductions in the study sample size. However, this approach may have led to a less precise match of the data to the actual clinical scenario, introducing confounding bias. Furthermore, the relatively small number of deaths in our sample may limit the statistical power and robustness of the conclusions. Despite this, appropriate statistical methods were employed to maximize the reliability of the results, our system exhibited satisfactory performance in predicting short-term mortality rates in an external validation cohort.

Conclusion

The discriminatory ability of the SAPS III systems for 28-day mortality outperformed that of the GAS, SIRS, SOFA, OASIS, and SAPS II systems. Notably, the SAPS III system demonstrated the highest discrimination capacity for 28-day mortality when compared with the other systems in patients with AA.

Data availability

Data availabilityThe datasets used and/or analyzed during the current study are available froDm the MIMIC-III and MIMIC-IV database, https://www.physionet.org/content/ mimiciii/1.4/, https://www.physionet.org/content/mimiciv/2.2/.

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Acknowledgements

We sincerely thank the researchers and participants of MIMIC-IV for data collection and management of data resources.

Funding

This research was funded by the National Key Research and Development Program of China (2021YFC2500500).

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Author notes

  1. Hui Wang and Sensen Wu have made equal contributions to this article and are both listed as the first authors.

    *Yongquan Gu and Zichuan Liu are the co-corresponding authors of this article.

Authors and Affiliations

  1. Department of Vascular Surgery, Xuanwu Hospital, Capital Medical University, No. 45, Changchun Street, Beijing, 100053, China

    Hui Wang, Sensen Wu, Dikang Pan, Yachan Ning, Yang Li, Jianming Guo & Yongquan Gu

  2. Tianjin University and Health-Biotech United Group Joint Laboratory of Innovative Drug Development and Translational Medicine, Tianjin University, Tianjin, 300072, China

    Chunjing Feng

  3. School of Pharmaceutical Science and Technology, Tianjin University, Tianjin, 300072, China

    Zichuan Liu

Authors
  1. Hui Wang
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  2. Sensen Wu
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  4. Yachan Ning
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  7. Jianming Guo
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  8. Zichuan Liu
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Contributions

H.W: Conceptualization, Methodology, Software, Writing - original draft. SS. W: Methodology, Software, Writing - review & editing. DK. P: Data curation, Writing - review & editing. YC. N: Methodology, Software, Writing - review & editing. CJ. F: Writing - review & editing. Y.L: Data curation. JM. G: Methodology, Software. ZC. L: Writing - review & editing, Supervision, Methodology. YQ. G: Writing - review & editing, Supervision, Conceptualization.

Corresponding authors

Correspondence to Zichuan Liu or Yongquan Gu.

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Competing interests

The authors declare no competing interests.

Ethical approval and participant consent

The MIMIC-IV project obtained approval from the Institutional Review Board at the Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center. Patient information is anonymized, and as such, this study does not require obtaining informed consent from the patients.

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Wang, H., Wu, S., Pan, D. et al. Comparison of different intensive care scoring systems and Glasgow Aneurysm score for aortic aneurysm in predicting 28-day mortality: a retrospective cohort study from MIMIC-IV database. BMC Cardiovasc Disord 24, 513 (2024). https://doi.org/10.1186/s12872-024-04184-4

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  • DOI: https://doi.org/10.1186/s12872-024-04184-4

Keywords

BMC Cardiovascular Disorders

ISSN: 1471-2261

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