Abstract
The Triglyceride glucose (TyG) index is a dependable indicator of IR, with numerous studies underscoring its influence on Cardiovascular disease. Nevertheless, the connection between the TyG index and prognosis in AMI patients after PCI is still uncertain. This investigation aims to explore the link in individuals who have received PCI for AMI. Upon admission, data regarding the patients’ age, sex, concurrent diseases, TyG index, and laboratory findings were meticulously documented. To discern the link between the TyG index and the 30-day and 12-month ACM, we employed a multivariate Cox proportional hazard regression model and K-M survival curve. The concordance evaluation was also enhanced by subgroup analysis. The investigation encompassed data from 1410 AMI patients who received PCI. The 30-day ACM rate was observed to be 15.1% (214/1410), while the rate at 12 months escalated to 26.0% (368/1410). Upon adjusting for potential confounders, multivariate analysis delineated a dramatic link between high TyG index and heightened mortality risk at both 30 days (HR 1.233, 95% CI 1.086–1.399) and 12 months (HR 1.127, 95% CI 0.963–1.318). According to K-M survival curve, patients presenting with higher TyG indexes demonstrated a noticeably higher probability of ACM within both the 30-day and 12-month. In AMI patients after PCI, the TyG index demonstrates a substantial link with ACM at 30-day and 12-month marks. This finding suggests the effectiveness of the TyG index in detecting AMI patients who are at a higher risk of mortality after undergoing PCI.
Keywords: Triglyceride-glucose index, Acute myocardial infarction, Percutaneous coronary intervention, All-cause mortality MIMIC-IV database
Subject terms: Cardiovascular diseases, Acute coronary syndromes, Myocardial infarction
Introduction
Cardiovascular disease (CVD) remains a pivotal contributor to the global health burden, with an escalating number of patients dying from it1. The most severe manifestations of CVD is AMI, which is categorically differentiated into ST-elevation or non-ST elevation MI2. Despite substantial breakthroughs in medical innovations, especially coronary revascularization, the mortality associated with AMI continues to be a dramatic concern3. AMI patients are still at risk after PCI. However, scant research has been conducted to evaluate the prognosis of AMI patients after PCI. Therefore, there is a pressing demand for a more convenient and effective predictor to swiftly recognize individuals at a heightened risk post-PCI for AMI, aiming to improve their prognosis.
IR, which is defined by a decreased insulin efficiency in glucose absorption and use, is a principal feature of metabolic syndrome4. This dysregulation sets the stage for a cascade of molecular events25, including its involvement in the pathogenesis of atherosclerosis, hypertension, macrophage accumulation, and vascular function5,6. IR notably influences the development and progression of CVD, while also being crucial in the pathogenesis of MetS and T2DM5,7. The hyperinsulinaemic-euglycaemic clamp, which provides a direct assessment of insulin sensitivity, is infrequently adopted in clinical settings owing to its difficult, costly, labor-intensive, and intrusive process.
The TyG index, an accessible, dependable, and straightforward alternative, has demonstrated excellence in evaluating IR. It is computed based on fasting TG and BG8. This study explored the predictive value of the TyG index regarding mortality in AMI individuals who experienced PCI, aiming to provide insights for improved risk stratification.
Methods
Data source
The Medical Information Mart for Intensive Care (MIMIC-IV) (version 2), a large public access database, served for the retrospective cohort study.
The Massachusetts Institute of Technology and the Institutional Review Board (IRB) of Beth Israel Deaconess Medical Center (BIDMC, Boston, MA, USA) has approved the usage of the database. This study received authorization from the IRB of MIT and BIDMC and conducted using data from BIDMC patients admitted between 2008 and 2019. Author Zilun Huang completed the online course (Record ID: 50160408) to access the database for data extraction. To preserve patients’ privacy, data de-identification was done. Therefore, the ethical committee of the Beth Israel Deaconess Medical Center waived patients’ informed consent. The investigation adhered to the Strengthening the Reporting of Observational studies in Epidemiology (STROBE) and the Declaration of Helsinki in reporting.
Population selection
Utilizing the MIMIC-IV diagnosis codes (version 9,10) for the International Classification of Diseases, we categorized adult patients diagnosed with AMI following PCI at hospital admission. Exclusion criteria: (I) Under eighteen years old; (II) Not being in their first ICU stay; (III) Lacking TG data on the initial day of hospitalization. Accordingly, this investigation encompassed 1410 subjects (Fig. 1). Our team pinpointed the primary exposure factor and variable of interest to be the TG values of patients immediately after their admission.
Fig. 1.
Selection of study population from MIMIC-IV database.
Data were meticulously retrieved from the MIMIC-IV using PostgreSQL and SQL. This included demographic information; comorbidities were identified through diagnosis codes (ICD9, ICD10) from the database for associated analysis; laboratory variables were obtained at the initial test upon admission comprised hemoglobin, platelet count, serum, anion gap, etc.
Outcomes
The primary outcome measured was ACM, encompassing both 30-day and 12-month mortality.
Statistical analysis
Continuous variables were reported as means ± SD or median, and categorical variables as percentages. The Mann–Whitney H test was utilized for TyG index quartiles comparisons: Q1 (6.60–8.67), Q2 (8.67–9.06), Q3 (9.06–9.54), and Q4 (9.54–12.29). The TyG index was not linearly associated with 30-day or 12-month ACM in AMI patients after PCI, as shown by restricted cubic spline analysis. This association was assessed using multivariate Cox proportional risk models, adjusting for confounders selected based on more than 10% change in effect estimates or clinical significance. Model I adjustments included gender and age. Model II further included BMI, diabetes, heart failure, and other diseases such as WBC, BUN, creatinine, serum anion gap, sodium, potassium, HDL, and LDL. The relationships were depicted using the K-M curve, aiding in risk stratification. Outcomes were reported as HR with 95% CI, deeming P < 0.05 as dramatic.
Results
Baseline characteristics of subjects
This investigation comprised 1410 AMI patients after PCI, including 915 men and 495 women, aged 57–76 with an average age of 66 (Table 1). The mortality rates observed were 15.1% at 30 days and 26.0% at 12 months after PCI.
Table 1.
.
| Categories | Overall | Q1 | Q2 | Q3 | Q4 | P-value |
|---|---|---|---|---|---|---|
| (N = 1410) | (N = 358) | (N = 353) | (N = 348) | (N = 351) | ||
| Age, years |
66 (57–76) |
66 (57–76) |
66 (57–76) |
66 (57–75) |
68 (59–76) |
0.618 |
| Male, n (%) |
915 (64.8%) |
241 (67.3%) |
228 (64.5%) |
227 (65.2%) |
219 (62.3%) |
0.590 |
| BMI, kg/m2 | 27.9(24.6–31.8) | 28.1(24.8–32.7) | 27.4(23.9–31.7) | 27.9(25.1–31.3) | 28.1(24.8–32.2) | 0.583 |
| Comorbidities, n (%) | ||||||
| Heart failure |
552 (39.1%) |
127 (35.4%) |
142 (40.2%) |
158 (45.4%) |
125 (35.6%) |
0.028 |
| Peripheral vascular disease |
165 (11.7%) |
48 (13.4%) |
39 (11.0%) |
38 (10.9%) |
40 (11.3%) |
0.708 |
| Chronic pulmonary disease |
268 (19%) |
69 (19.2%) |
68 (19.2%) |
68 (19.5%) |
63 (13.9%) |
0.950 |
|
Cerebrovasculr disease |
200 (14.1%) |
53 (14.8%) |
51 (14.4%) |
47 (13.5%) |
49 (13.9%) |
0.964 |
| Diabetes |
541 (38.3%) |
132 (36.8%) |
131 (37.1%) |
129 (37.0%) |
149 (42.4%) |
0.348 |
| Renal disease |
339 (23.9%) |
76 (21.2%) |
93 (26.3%) |
85 (24.4%) |
84 (23.9%) |
0.456 |
| Laboratory tests | ||||||
| WBC(10⁹mmo/L) |
11.5 (8.8–14.7) |
10.5 (8.1–14.2) |
11.2 (8.9–13.9) |
11.9 (9.5–15.0) |
12.2 (9.1–15.8) |
< 0.001 |
| Hemoglobin (g/dL) |
11.5 (9.6–13.3) |
11.5 (9.8–13.1) |
11.7 (9.6–13.4) |
11.3 (9.4–13.4) |
11.6 (9.6–13.5) |
0.661 |
| Platelet (10⁹mmo/L) |
204 (160–259) |
199 (155–247) |
207 (164–257) |
211 (163–264) |
200 (160–259) |
0.271 |
| Serum anion gap (mmol/L) |
15 (13–18) |
14 (13–17) |
15 (13–17) |
16 (13–18) |
17 (14–20) |
< 0.001 |
| Sodium (mmol/L) |
138 (136–140) |
138 (136–140) |
138 (136–140) |
138 (136–140) |
138 (136–140) |
0.953 |
| Potassium (mmol/L) |
4.3 (3.9–4.6) |
4.3 (3.9–4.6) |
4.3 (3.9–4.7) |
4.3 (3.9–4.6) |
4.2 (3.9–4.6) |
0.282 |
| Chloride (mmol/L) |
103 (100–106) |
104 (101–107) |
103 (101–106) |
103 (100–106) |
102 (99–105) |
< 0.001 |
| Creatinine (mg/dL) |
1.1 (0.9–1.6) |
1.0 (0.8–1.4) |
1.0 (0.8–1.4) |
1.2 (0.9–1.9) |
1.2 (0.9–2.0) |
< 0.001 |
| Bicarbonate (mmol/L) |
23 (20.5–25) |
23.5 (21–25) |
23 (21–25) |
22.5 (20–24.5) |
22 (18.5–24) |
< 0.001 |
| Bun(mg/dL) |
20 (14.5–33.0) |
17.8 (12–28.4) |
19.5 (15–30) |
20.5 (14.5–35.1) |
23.5 (16.5–39) |
< 0.001 |
| HDL(mg/dL) |
44 (36–55) |
44 (37–56) |
43 (36–55) |
45 (35–56) |
44 (36–54) |
0.769 |
| LDL(mg/dL) |
85 (61–113) |
85 (65–115) |
86 (62–113) |
88 (61–115) |
82 (58–112) |
0.493 |
| Events | ||||||
| LOS ICU, days |
2.0 (1.2–3.9) |
1.8 (1.1–3.2) |
2.2 (1.1–4.1) |
2.2 (1.3–4.9) |
2.1 (1.2–4.2) |
0.001 |
| LOS Hospital, days |
6.7 (3.3–12.0) |
5.6 (3.1–10.9) |
6.4 (3.2–12.7) |
7.7 (3.8–13.2) |
6.1 (3.2–11.7) |
0.016 |
| 30 days mortality, n (%) |
214 (15.1%) |
34 (9.4%) |
49 (13.8%) |
54 (15.5%) |
77 (21.9%) |
< 0.001 |
| 12 months mortality, n (%) |
368 (26.0%) |
72 (20.1%) |
93 (26.3%) |
92 (26.4%) |
111 (31.6%) |
0.007 |
TyG index: Q1 (6.60–8.67), Q2 (8.67–9.06), Q3 (9.06–9.54), Q4 (9.54–12.29).
WBC White blood cell, BUN Blood urea nitrogen, BMI Body mass index, LDL Low-density lipoprotein, HDL High-density lipoprotein, TyG Triglyceride glucose index.
Correlation of TyG index and ACM in AMI individuals after PCI
RCS analysis revealed that ACM escalated in correlation with the TyG index (Fig. 2). Cox regression showed a notable association between the TyG index and mortality at 30 days and 12 months in both crude and adjusted models (Table 2). In the crude analysis, the hazard ratios for 30-day and 12-month mortality were 1.339 (95% CI 1.184–1.515, P < 0.001) and 1.164 (95% CI 1.062–1.276, P < 0.001). This relationship persisted after adjustments, with the 30-day mortality hazard ratio being 1.336 (95% CI 1.181–1.512, P < 0.001) in Adjusted Model I and 1.233 (95% CI 1.086–1.399, P = 0.001) in Adjusted Model II. For 12-month mortality, the hazard ratios were 1.158 (95% CI 1.057–1.270, P < 0.001) in Adjusted Model I and 1.071 (95% CI 0.974–1.177, P = 0.029) in Adjusted Model II. Furthermore, the K-M survival curve revealed that individuals with an elevated index (Q4) had a decreased survival rate (Fig. 3).
Fig. 2.
Cubic spline plot of the relation between TyG index and risk of 30 days mortality. (a) The model is fitted using restricted cubic splines with four knots in the generalized additive model. Shaded areas around the curves depict 95% confidence intervals. Restricted cubic spline for hospital mortality (b). HR, hazard ratio; CI, confdence interval, TyG, triglyceride-glucose.
Table 2.
Multivariable cox regression models evaluating the association between TyG index and all-cause mortality.
| Crude | Adjust I | Adjust II | ||||
|---|---|---|---|---|---|---|
| HR (95% CI) | P-value | HR (95% CI) | P-value | HR (95% CI) | P-value | |
| 30-day mortality | ||||||
| TyG index | ||||||
| Continuous | 1.339 (1.184, 1.515) | < 0.001 | 1.336 (1.181, 1.512) | < 0.001 | 1.233 (1.086, 1.399) | 0.001 |
| Quartiles | ||||||
| Q1 | 1 (Ref) | 1 (Ref) | 1 (Ref) | |||
| Q2 | 1.515 (0.978, 2.347) | 0.063 | 1.514 (0.977, 2.347) | 0.063 | 1.559 (1.001, 2.247) | 0.028 |
| Q3 | 1.696 (1.104, 2.605) | 0.016 | 1.693 (1.102, 2.600) | 0.016 | 1.651 (1.054, 2.587) | 0.050 |
| Q4 | 2.548 (1.702, 3.816) | < 0.001 | 2.532 (1.691, 3.791) | < 0.001 | 2.105 (1.383, 3.203) | 0.001 |
| 12-month mortality | ||||||
| TyG index | ||||||
| Continuous | 1.275 (1.094, 1.485) | 0.002 | 1.262(1.084, 1.471) | 0.003 | 1.127 (0.963, 1.318) | 0.138 |
| Quartilies | ||||||
| Q1 | 1 (Ref) | 1 (Ref) | 1 (Ref) | |||
| Q2 | 1.352 (0.994, 1.839) | 0.055 | 1.345 (0.989, 1.830) | 0.059 | 1.317 (0.963, 1.799) | 0.084 |
| Q3 | 1.361 (1.000, 1.853) | 0.050 | 1.351 (0.992, 1.839) | 0.056 | 1.103 (0.804, 1.513) | 0.544 |
| Q4 | 1.672 (1.242, 2.249) | 0.001 | 1.645 (1.223, 2.214) | 0.001 | 1.310 (0.966, 1.775) | 0.082 |
TyG index: Q1 (6.60–8.67), Q2 (8.67–9.06), Q3 (9.06–9.54), Q4(9.54–12.29).
Crude model: adjusted for none.
Model I: adjusted for age, sex.
Model II: adjusted according to Model I + BMI, heart failure, diabetes, renal disease, chronic pulmonary disease, peripheral vascular disease, cerebrovascular disease, renal disease, WBC, BUN, creatinine, serum anion gap, sodium, potassium, HDL, LDL.
Fig. 3.
Kaplan–Meier survival analysis curves for all-cause mortality. Footnote TyG index quartiles: Q1 (6.60–8.67), Q2 (8.67–9.06), Q3 (9.06–9.54), Q4(9.54–12.29). Kaplan–Meier curves showing cumulative probability of all-cause mortality according to groups at 30 days (a), and 12 months (b).
Subgroup analyses
The relationship between ACD and the TyG index was thoroughly examined across a range of possible moderating factors, such as sex, age, BMI, LDL, serum anion gap, cardiac failure, diabetic conditions, kidney disorders, cerebrovasculr issues, and chronic pulmonary disease (Fig. 4).
Fig. 4.
Forest plots of hazard ratios for the 30 days mortality in different subgroups. HR, hazard ratio; CI, confidence interval; BMI, body mass index;
In subgroups of females [HR 1.757, 95% CI 1.278–2.415], age ≤ 65 [HR 1.872, 95% CI 1.369–2.561], BMI > 30 kg/m2 [HR 1.861, 95% CI 1.251–2.769], LDL > 130 mg/dL [HR 1.832, 95% CI 1.220–2.751], serum anion gap ≤ 16 mmol/L [HR 1.289, 95% CI 0.883–1.880], cerebrovascular issue [HR 2.033, 95% CI 1.120–3.585], without heart failure [HR 1.646, 95% CI 1.269–2.314], without renal disorder [HR 1.707, 95% CI 1.350–2.158], and chronic pulmonary disease [HR 1.766, 95% CI 1.155–2.701] (all P < 0.05). Individuals with a serum anion gap ≤ 16 mmol/L [HR 1.289, 95% CI 0.883–1.880) vs. serum anion gap > 16 mmol/L [HR 1.239, 95% CI 0.992–1.548], P for interaction < 0.001, seemed to possess a greater predictive significance for the index. Similarly, in stratified studies of 12-month mortality, a notable link was discovered between the TyG index and a higher 12-month mortality risk in female subgroups [HR 1.288, 95% CI 1.015–1.635], age ≤ 65 [HR 1.311, 95% CI 1.031–1.667], BMI > 30 kg/m2 [HR 1.288, 95% CI 0.951–1.744], LDL > 130 mg/dL [HR 1.338, 95% CI 0.936–1.913], serum anion gap > 16 mmol/L [HR 1.066, 95% CI 0.882–1.289], without diabetes [HR 1.272, 95% CI 1.037–1.560], cerebrovascular disease [HR 1.407, 95% CI 0.934–2.118], without heart failure [HR 1.286, 95% CI 1.051–1.573], without renal disorders [HR 1.288, 95% CI 1.074–1.546], and chronic lung disorders [HR 1.420, 95% CI 1.006–2.006]. It’s interesting to observe that female [HR 1.288, 95% CI 1.015–1.635] demonstrated greater predictive significance for the index in comparison to male patients [HR 1.253, 95% CI 1.026–1.532], P for interaction = 0.019. Additionally, there was a significant interaction (P = 0.001) with serum anion gap, where patients with a serum anion gap ≤ 16 mmol/L [HR 1.025, 95% CI 0.788–1.333] exhibited a different predictive pattern compared to those with a serum anion gap > 16 mmol/L [HR 1.066, 95% CI 0.882–1.289] (Fig. 5).
Fig. 5.
Forest plots of hazard ratios for the 12 months mortality in different subgroups. HR, hazard ratio; CI, confidence interval; BMI, body mass index;
Discussion
This investigation marks the initial exploration of the TyG index’s link with 30-day and 12-month post-PCI mortality in AMI individuals. Our findings reveal that patients with elevated TyG indices after PCI for AMI experienced higher mortality rates. Additionally, even after adjusting for other influencing factors, a substantial correlation persisted. Identifying high-risk AMI individuals post-PCI is crucial for improving treatment and reducing future cardiovascular events. In this scenario, the TyG index is a potentially valuable standalone risk factor and a useful tool for clinicians in decision-making for AMI individuals post-PCI.
The TyG index has been explored in prior clinical research for its correlation with CVD illness and death rates across different patient populations and in the general public. It was identified as a highly dependable marker of hospital death rates among severe CVD individuals13. Adverse cardiovascular events showed a favorable link with the index among patients with stable CVD14. The accurate biological causes for the link are unknown, but IR might be a major influencer. IR is known to increase the risk and advancement of CVD in the public and individuals with diabetes. Moreover, it serves as a prognostic indicator for CVD15.
Individuals with IR often face metabolic issues that are tightly linked to worse outcomes of CVD16. IR-induced chronic hyperglycemia and dyslipidemia can lead to increased production of foam cells, exacerbate inflammatory responses, and boost muscle cell growth. Furthermore, hyperinsulinemia can exacerbate renal salt retention and increase sympathetic nervous system activity. Chronic IR can lead to renal and vascular damage, elevated heart rate, and increased blood pressure17. These pathophysiological changes can contribute to a poor prognosis, further exacerbating the onset and progression of coronary artery disease. Studies have indicated that fasting TG reflects IR mostly from adipocytes, while FBG reflects IR predominantly from liver. Consequently, the TyG index is tightly related to IR and may serve as a representative measure of IR from both perspectives18.
Studies on cardiovascular risk in women have been more prevalent, suggesting the potential for worse clinical results among women and indicating gender disparities in the prognosis and clinical characteristics of CAD individuals19,20. A longitudinal study spanning 11 years found the correlation among the overall population, with women exhibiting a significantly higher risk compared to men21. Another study identified gender disparities in various IR assessment parameters and the development of atherosclerosis 22. In our subgroup analysis, among female AMI patients undergoing PCI, the TyG index was associated with an increased hazard of 30-day and 12-month mortality. This association seemed more pronounced in 12-month females ([HR 1.288, 95% CI 1.015–1.635)] compared to males [HR 1.253, 95% CI 1.026–1.532)], with a significant interaction (P = 0.019). Notably, approximately 90% of the women in one of these trials were in the transitional or postmenopausal stage, a period marked by heightened vulnerability to endothelial dysfunction, increased belly obesity, dyslipidemia, and IR, coupled with a diminished protective impact of oestrogen23,24. As a result, female patients exhibited greater variability in the TyG index, potentially elucidating the gender subgroup findings. Additional research is essential to substantiate this link and to unravel the mechanisms involved.Recent trials suggesting that the use of diabetes medications such as SGLT2i, GLPi, and Icosp ten ethyl ester reduces the prognosis of cardiovascular disease may change this ratio.Doing some prospective studies on these drugs and patients with a high glucose glycerol index is warranted.
Our research holds significant ramifications for patient care and professional practice. We indicated an association between increased TyG levels and higher mortality rates within 30 days and 12 months. This finding suggests that risk categorization and management of this high-risk patient group may benefit from incorporating the TyG index. A thorough approach to risk management is required in order to handle the elevated risk that is linked to elevated TyG levels. This entails managing risk factors such as hypertension, cholesterol, and quitting smoking. It’s vital to regularly monitor and promptly intervene in those with increased TyG levels to reduce the risk of poor outcomes.
The study emphasizes the necessity for comprehensive risk management in this patient cohort, using the TyG index as an effective instrument. We explored the link between TyG index and 30-day and 12-month mortality in AMI individuals following PCI. However, there are several limitations to consider. Clinically meaningful information may have been missing from the databases utilized for the investigation, and the effect of excluding a substantial number of individuals because of unavailable data and the scarcity of CVD may have been understated.
For instance, the article lacks information regarding the frequency of procedures undergone by the patients, the number of stents implanted, and the extent of stenosis. Secondly, alterations in the TyG index during follow-up were not monitored, and the usage of lipid-lowering or glucose-lowering drugs was not documented, potentially influencing TG and FBG levels. Thirdly, the model did not account for other variables like exercise habits, dietary information, or cardiorespiratory fitness. To validate our results, increased sample sizes, extended follow-up times, and multicentered trials are required.
Limitation
The database does not evaluate differences in lipid-lowering therapy in this cohort post-acute MI. This can be a confounder in the study results.The study does not evaluate A1C values and glycemic control in this cohort, which may also be a confounder. Furthermore, the study is unable to evaluate if the increased events in the group with a higher triglyceride-glucose index is associated with poor glycemic control.
Conclusion
Our study demonstrates that the TyG index predicts 30-day and 12-month mortality post-PCI in AMI individuals. It could be a technique for risk management and classification in these populations. Additional investigations are required to confirm these findings and to probe into other mechanisms inking TyG to mortality in AMI patients after PCI.
Acknowledgements
The authors thank all contributors and personnel of the MIMIC-IV database for their contributions.
Abbreviations
- TyG
Triglyceride glucose
- ACM
All-cause mortality
- PCI
Percutaneous coronary intervention
- CI
Confidence interval
- IR
Insulin resistance
- KM
Kaplan–Meier
- AMI
Acute myocardial infarction
Author contributions
S.Y. and G.Y., Z.H. planned the investigation and assembled the data. G.Y., Z.H., S.W. assessed the results. Z.H. prepared the draft, which was then refined by S.Y. and G.Y. Every contributor agreed on the ultimate version.
Funding
No funding.
Data availability
The data in the study was provided by the Medical Information Mart for Intensive Care IV (MIMIC-IV), and the following licenses/restrictions apply: To obtain access to these files, you are required to be a credentialed user, finish necessary training as well as sign the project data use agreement.The datasets used and analysed during the current study available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
Beth Israel Deaconess Medical Center (BIDMC) managed study approvals with human participants, anonymizing patient data for privacy. Ethical Committee of the Beth Israel Deaconess Medical Center exempted informed consent requirements.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Guang Yang, Zilun Huang and Shanjie Wang both authors contributed equally to this article.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data in the study was provided by the Medical Information Mart for Intensive Care IV (MIMIC-IV), and the following licenses/restrictions apply: To obtain access to these files, you are required to be a credentialed user, finish necessary training as well as sign the project data use agreement.The datasets used and analysed during the current study available from the corresponding author on reasonable request.