Correlation between albumin-corrected calcium levels and prognosis in non-ST-elevation myocardial infarction patients: a retrospective cohort study using the MIMIC-IV database

Introduction

In the field of cardiovascular disease (CVD) research, non-ST-segment elevation myocardial infarction (NSTEMI) is regarded as a serious manifestation of coronary artery disease (CAD) and has received continuous attention due to its high morbidity and mortality. The incidence and hospitalization rate of NSTEMI have increased in recent years (1). Although the management strategy of NSTEMI has been significantly improved with the progress of biomarker detection techniques and the popularization of cardiovascular interventional therapy, the prognosis among patients is still significantly different.

Numerous prognostic factors for NSTEMI have been identified, including age, renal function, left ventricular ejection fraction, and GRACE score (2). These indicators are widely used in routine clinical practice for risk stratification and predicting adverse outcomes. Nonetheless, most evidence is derived from unselected NSTEMI patients. The predictive accuracy of these indicators may be reduced in patients with severe NSTEMI, who typically present with more extensive myocardial damage, impaired haemodynamics, and multiple organ dysfunction (3). Given these differences, traditional markers are less applicable, and it is necessary to explore more specific prognostic indicators in this high-risk group. The sample size of current research on prognostic assessment for severe NSTEMI is small, and validated biomarkers suitable for this population are lacking. Addressing these gaps is crucial for improving risk stratification and guiding timely interventions.

Serum albumin, as an indicator reflecting the nutritional status and inflammatory response of the body (4), is closely associated with the prognosis of CVD patients. Abnormal calcium metabolism, particularly in the context of acute myocardial infarction (AMI), is also regarded as a significant factor influencing the prognosis of patients (5). Although serum calcium levels are impacted by various factors, albumin-corrected calcium (ACC) can accurately assess the body’s true calcium status, thereby providing more reliable prognostic information for cardiovascular events (6). Previous studies have suggested its prognostic value in acute coronary syndrome (ACS); however, evidence for severe NSTEMI remains scarce. Therefore, the present study focuses on patients with severe NSTEMI and aims to systematically assess the predictive accuracy of ACC for the prognosis with the data from the MIMIC-IV database and seeks to establish a theoretical foundation for the clinical interventions and nursing care of patients with severe NSTEMI. We present this article in accordance with the TRIPOD reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1170/rc).

Methods

Study design and data source

A retrospective cohort study was conducted using data from all eligible adult patients sourced from the MIMIC-IV, version 2.2. The institutional review committee of Beth Israel Deaconess Medical Center reviewed the collection of patient information and the creation of study resources. The committee approved the exemption of informed consent, as well as the data sharing initiative. Following this approval, authors were granted access to the database (ID number: 62406486). The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

From this database, we identified adult patients (≥18 years) diagnosed with NSTEMI (based on International Classification of Diseases codes) who were admitted to the intensive care unit (ICU). Patients were screened according to predefined inclusion and exclusion criteria to ensure the cohort’s representativeness and validity. For each eligible patient, baseline demographic and clinical characteristics at ICU admission were extracted from the database, including age, sex, vital signs, laboratory parameters, and comorbidities. Patients were followed up using outcome data extracted from MIMIC-IV. We systematically collected data on exposure and outcome to assess prognostic factors associated with adverse outcomes in critically ill patients with NSTEMI.

Population and sampling

All adult patients aged 18 years or older diagnosed with NSTEMI who were admitted to the ICU for the first time during hospitalization were identified. To minimize bias, we only considered data on the first ICU admission of each patient. Patients were excluded if they met any of the following criteria: (I) ICU stay duration less than 24 hours; (II) missing data on essential baseline laboratory indicators (including albumin and serum calcium levels measured at admission); or (III) missing data on demographic information (marital status or ethnicity).

Data collection and variables

PostgreSQL (version 16.3) and structured query language were leveraged for data extraction from the MIMIC-IV database. The collected data included the following categories: (I) basic information: age, gender, race, marital status, BMI; (II) the extracted laboratory results are the results of examination on the first day after admission, including serum calcium, albumin, creatinine, glucose, bilirubin, serum potassium, hemoglobin A1c (HbA1c), alanine aminotransferase (ALT), aspartate aminotransferase (AST), white blood cells, lymphocytes, anion gap, and hemoglobin; (III) vital signs: blood pressure, respiratory rate, body temperature, and heart rate; (IV) complications include diabetes, hypertension, stroke, anemia, congestive heart failure (CHF), malignant tumor, ventricular fibrillation (VF), and atrial fibrillation (AF); (V) drugs: aspirin, angiotensin-converting enzyme inhibitor (ACEI), angiotensin receptor blocker (ARB), beta-blocker, statins, dobutamine, and norepinephrine; (VI) score: Glasgow Coma Scale (GCS), Simplified Acute Physiology Score II (SAPS II), Sequential Organ Failure Assessment (SOFA), Systemic Inflammatory Response Syndrome (SIRS), Oxford Acute Severity of Illness Score (OASIS), and Acute Physiology Score III (APS III); (VII) operations: mechanical ventilation (MV) and continuous renal replacement therapy (CRRT).

Previous studies have established the following formula for the calculation of ACC: ACC = serum total calcium (mg/dL) + 0.8 × [4.0 − serum albumin (g/dL)].

Missing values and outliers processing

Variables with more than 20% missing values were excluded from subsequent analysis (HbA1c, body temperature). For variables with missing values below 20%, multiple imputation was performed using the mice package in R. Specifically, five imputed datasets were generated (m=5) by applying the random forest method (method = “rf”) to account for complex interactions and nonlinear relationships among variables. The final imputed dataset was then selected from these imputed datasets for subsequent analysis. This approach could help reduce potential biases introduced by missing data and preserve the inherent variability within the dataset. Subsequently, the winsorization method was used to deal with outliers, and the critical points were set to 1% and 99%.

Follow-up and outcome assessment

Patients’ survival status and dates of death were obtained from the MIMIC-IV database, which integrates hospital records and data from the Social Security Administration Death Master File (SSA DMF). All patients were followed up from the date of ICU admission until death or the end of the follow-up period. The primary outcome measure was all-cause mortality within 1 year of ICU admission, and the secondary outcome measure was all-cause mortality within 90 days of ICU admission.

Statistical analysis

In the baseline information table, continuous variables were assessed for normality using the Shapiro-Wilk test. Normally distributed variables are expressed as mean ± standard deviation (SD), while non-normally distributed variables are presented as median [interquartile range (IQR)]. Comparisons of continuous variables between groups were performed using either Student’s t-test or Mann-Whitney test based on the distribution of the data. Categorical variables were summarized as frequencies and percentages. Significant differences were analyzed by means of either Pearson’s chi-squared test or Fisher’s exact test.

Restricted cubic splines (RCS) analysis was used to check the nonlinear relationship between ACC and mortality risk, and finally the cutoff value of grouping was obtained. Patients were then divided into high-risk group and low-risk group based on the cutoff value. Least absolute shrinkage and selection operator (LASSO) and ten-fold cross-validation regression were implemented to further identify variables associated with 1-year and 90-day prognosis. The variables identified through LASSO analysis were incorporated into Cox regression models to investigate the correlation between ACC and mortality at both 1 year and 90 days. Model I made no adjustments. Model II was adjusted for the following covariates: marital status, age, and respiratory rate. Based on Model II, Model III was adjusted for the following covariates: hemoglobin, AF, anemia, CHF, dobutamine, norepinephrine, beta-blocker, APS III score, and SIRS score. In Kaplan-Meier survival curve plotting, the log-rank test was applied to compare survival differences among different ACC level groups. In addition, subgroup analysis and interaction tests were carried out to evaluate the stability of the association between ACC and mortality, considering marital status (single, divorced/widowed, married), race (White, non-White), gender (male, female), AF (no, yes), anemia (no, yes), CHF (no, yes), hypertension (no, yes), diabetes (no, yes), VF (no, yes), aspirin (no, yes), dobutamine (no, yes), norepinephrine (no, yes), statins (no, yes), ARB (no, yes), beta-blocker (no, yes), ACEI (no, yes), CRRT (no, yes), and MV (no, yes). All statistical analysis was carried out with R (version 4.4.0) software. All tests were two-tailed, and statistical significance was defined as P<0.05.

Results

Baseline characteristics of patients with NSTEMI

Ultimately, the study included 2,107 eligible patients, as depicted in Figure 1. Patients diagnosed with NSTEMI had a median age of 70.0 (IQR: 60.0, 78.0) years, comprising 1,265 males (60.0%) and 842 females (40.0%). The median value of ACC was 9.18 (IQR: 8.78, 9.60). All participants were divided into two cohorts on the basis of their ACC results: the low ACC group (<9.19) and the high ACC group (≥9.19). The basic properties of these cohorts are shown in Table 1. According to the study findings, patients classified in the high ACC group exhibited notably higher rates of all-cause mortality at both 1-year and 90-day compared to those in the low ACC group. Among the laboratory indices, patients in the low ACC group demonstrated significantly elevated levels of ALT, AST, and albumin relative to those in the high ACC group (P<0.05). Conversely, serum calcium and lymphocyte counts were significantly lower in the low ACC group compared to the high ACC group (P<0.05). Regarding medication use, a higher proportion of patients in the high ACC group were prescribed norepinephrine, ACEI, and beta-blocker (P<0.05). In terms of comorbidities, patients with severe NSTEMI in the high ACC group were more likely to have CHF compared to their counterparts in the low ACC group. Additionally, patients in the high ACC group exhibited significantly lower SIRS scores than those in the low ACC group (Table 1).

Figure 1 Participant selection process. ACC, albumin-corrected calcium; ICU, intensive care unit; NSTEMI, non-ST-elevation myocardial infarction.

Cutoff value and RCS analysis

In the RCS analysis, the cutoff value for ACC was determined as 9.19 based on a hazard ratio (HR) of 1 for mortality risk. The patients were then categorized into groups with low ACC (<9.19, n=1,055) and high ACC (≥9.19, n=1,052). The results of the RCS analysis, as depicted in Figure 2, revealed a linear relationship between ACC and the risk of all-cause death at 1-year and 90-day (P for nonlinear =0.561). Furthermore, it was noted that an ACC value exceeding 9.19 was associated with an elevated risk of 1-year all-cause death among the patients studied. A comparable trend was also identified between ACC and the risk of all-cause death at 90 days in NSTEMI patients (P for nonlinear =0.688).

Figure 2 RCS analysis of the relationship between albumin-corrected calcium and the risk of all-cause death in patients with non-ST-elevation myocardial infarction at 1-year (A) and 90-day (B). CI, confidence interval; HR, hazard ratio; RCS, restricted cubic splines.

ACC and all-cause mortality

The baseline characteristics outlined in Table 1 were subjected to LASSO regression analysis and ten-fold cross-validation, yielding a λ value of 0.021 (Figure 3). Thirteen covariates related to prognosis were ultimately selected, including marital status, age, respiratory rate, hemoglobin, AF, anemia, CHF, dobutamine, norepinephrine, beta-blocker, CRRT, APS III score, and SIRS score. Cox regression analysis indicated a positive correlation between ACC levels and 1-year all-cause mortality across models: unadjusted (HR, 1.32; 95% CI: 1.21–1.45), partially adjusted (HR, 1.29; 95% CI: 1.18–1.41), and fully adjusted (HR, 1.22; 95% CI: 1.12–1.34) (all P<0.05, Table 2). Furthermore, patients classified in the high ACC group exhibited a progressively higher risk of 1-year all-cause mortality compared to those in the low ACC group. This correlation was consistent across different models: unadjusted [low ACC vs. high ACC: HR, 1.29 (95% CI: 1.13–1.46)], partially adjusted [low ACC vs. high ACC: HR, 1.23 (95% CI: 1.10–1.38)], and fully adjusted [low ACC vs. high ACC: HR, 1.20 (95% CI: 1.07–1.34)] models (all P<0.05). Multivariate Cox proportional hazards analysis showed that ACC and 90-day mortality had similar results.

Figure 3 The process of selecting variables by least absolute shrinkage and selection operator regression (A). The process of selecting the optimal lambda value parameter through cross-validation (B).

Kaplan-Meier survival curve

The Kaplan-Meier survival curves depicted in Figure 4 revealed that patients with NSTEMI in the high ACC (≥9.19) group exhibited lower cumulative 1-year and 90-day survival rates compared to those in the low ACC (<9.19) group (P<0.001, P=0.01).

Figure 4 Kaplan-Meier survival curves of 1-year (A) and 90-day (B) cumulative survival rates with different albumin-corrected calcium levels. ACC, albumin-corrected calcium.

Subgroup analysis

We examined and evaluated the relationship between ACC and 1-year and 90-day all-cause mortality in NSTEMI patients and conducted subgroup analysis based on marital status, race, gender, AF, anemia, CHF, hypertension, stroke, diabetes, VF, aspirin, dobutamine, norepinephrine, statins, ARB, beta-blocker, ACEI, CRRT, and MV (Figure 5). The results showed that, except for the interaction between ACC and AF, anemia, CHF, diabetes, and beta-blocker in 1-year mortality (P value of the interaction was <0.05), no interaction was observed in other variables. The subgroup analysis results indicated significant correlations in all subgroups except for those who were divorced, had VF, did not use aspirin, used dobutamine, did not use statins, did not use beta-blocker, or underwent renal replacement therapy. These findings emphasize the robustness of the observed correlations. The correlation between ACC and 90-day mortality only interacted with race, anemia, stroke, diabetes, and beta-blocker.

Figure 5 Subgroup analysis chart of 1-year and 90-day. ACEI, angiotensin-converting enzyme inhibitors; AF, atrial fibrillation; ARB, angiotensin receptor blocker; CHF, congestive heart failure; CI, confidence interval; CRRT, continuous renal replacement therapy; HR, hazard ratio; MV, mechanical ventilation; VF, ventricular fibrillation.

Discussion

NSTEMI is a common myocardial ischemic disease with complex pathogenesis and clinical manifestations. Therefore, in the diagnosis and treatment process, it is not only necessary to pay attention to common indicators such as myocardial enzyme spectrum, but also to comprehensively consider some potential predictive factors to diagnose, treat, and evaluate the prognosis of patients more accurately. This study provided evidence of the significant impact of ACC as an independent determinant of 1-year and 90-day mortality in NSTEMI patients, and established a cutoff value (9.19) for risk stratification in these patients. The results showed that with the increase of ACC, the mortality would continue to increase. Furthermore, as ACC levels increased, the relationship between ACC and the risk of death within both 1 year and 90 days became more evident, showing a positive correlation between ACC and mortality risk.

The measurement of total calcium is affected by the body’s pH and serum albumin levels, which can compromise its accuracy, whereas measuring ionized calcium is more complex and influenced by numerous factors (7). Hence, ACC is often used to evaluate ionized calcium levels in clinical practice, and many studies have explored it in recent years. A study has shown that a decrease and an increase in serum corrected calcium levels (including in the “normal” range) are related to a higher risk of hospitalization and long-term death in patients with AMI (8). Madhu et al. found that ACC measured in the first 24 hours is a useful predictor of acute pancreatitis, and its predictive sensitivity is better than the traditional scale (9). In addition, a study has found that higher serum ACC levels are independently related to the revascularization and poor prognosis of stroke patients after mechanical thrombectomy (10), indicating that ACC can be used as an important predictor in the early stage, critical stage, and postoperative period of some diseases, and may be superior to some traditional measurement schemes. However, there are few ACC-related studies on severe NSTEMI patients. Therefore, we conducted a retrospective analysis using MIMIC-IV.

Calcium metabolism disorders are relatively common in the ICU. A large-scale, nationally representative cohort of ICU patients was studied, and the findings indicated that ACC abnormalities, particularly mild hypocalcemia or hypercalcemia, were linked to an elevated risk of 30-day in-hospital mortality (11,12). Stable blood calcium levels can ensure the stability of cell function and internal milieu. A previous study has suggested that both hypocalcemia and hypercalcemia have a U-shaped relationship with the increase of short-term and long-term mortality (13). The association between higher and lower serum calcium levels and mortality can be attributed to the complex physiological effects of calcium. The mechanisms behind adverse cardiovascular events caused by hypercalcemia are largely associated with vascular calcification. A study has found a close relationship between coronary artery calcification and coronary atherosclerosis, which has been a major pathogenic factor in NSTEMI. A study in vitro indicated that elevated calcium levels can promote vascular smooth muscle cell calcification by binding to calcium-sensing receptors, thereby leading to receptor downregulation and enhanced mineralization (14,15). Moreover, elevated calcium levels are an important feature of primary hyperparathyroidism (PHPT). When PHPT occurs, parathyroid hormone (PTH) levels may increase or become abnormal. Elbuken et al. found that an increase in PTH has an impact on both myocardial cells and vascular smooth muscle cells, which is closely related to the occurrence of CVD (16). Hypocalcemia is also associated with a poor prognosis in patients with CVD, such as ACS, including higher mortality and higher incidence of cardiovascular events (17). Calcium is a vital component in the electrophysiological stability of excitable cell membranes, and low serum calcium levels can increase the risk of AF (15), which is a risk factor for NSTEMI, consistent with our subgroup analysis results (P<0.05). Furthermore, low calcium levels represent lower levels of vitamin D in the body, and a recent study suggested that individuals with vitamin D deficiency have significantly higher CVD mortality (18).

As an important indicator for evaluating body nutrients, albumin levels are closely related to adverse clinical outcomes in emergencies. A meta-analysis indicated that hypoalbuminemia is an effective independent predictor of adverse outcomes in acute illnesses, including increased complications and higher mortality. Furthermore, achieving serum albumin levels exceeding 30 g/L during albumin therapy may reduce the incidence of complications (19). Meanwhile, a number of studies have demonstrated that albumin can serve as a valuable predictor of risk in patients with CAD (20). Myocardial injury and ischemia caused by myocardial infarction may trigger systemic inflammatory and oxidative stress responses, leading to decreased synthesis and increased consumption of albumin. Therefore, a decrease in albumin levels may reflect the activity of the patient’s inflammatory response. Inflammatory response is a significant factor influencing cardiovascular function. Moreover, a related mechanism study indicated chronic inflammation as a key process in the pathogenesis of atherosclerosis. An experimental study has shown that there is a complex interaction between inflammation and thrombosis in cardiovascular pathology, known as thromboinflammation, and the rupture of unstable coronary artery plaques and thrombosis are the most important pathological foundations of ACS (21,22).

Notably, even after adjusting for confounding factors in our model, the relationship remained statistically significant in the study. Subgroup analysis reinforced our findings, demonstrating significant interactions between ACC and AF, anemia, CHF, diabetes, and beta-blocker over 1 year. These findings are consistent with prior studies. Numerous studies have shown that both anemia and diabetes are risk factors for NSTEMI (23,24), with anemia being a long-term influencing factor. Our findings align with this, as the interaction P values for both the 1-year and 90-day periods were less than 0.05. Hypocalcemia can lead to decreased neuromuscular excitability and weakened myocardial contractility, leading to NSTEMI, while beta-blocker can enhance myocardial contractility and have a beneficial effect on ACS (25). These findings are robust, and we hope they can provide assistance to clinicians in practice.

Limitations

The findings of this study indicate that ACC is an independent predictor of 1-year and 90-day mortality in patients with NSTEMI. Nevertheless, the following limitations must be acknowledged. Firstly, despite the application of subgroup analyses and multivariable adjustment, residual confounding factors may introduce bias into the data. Secondly, due to the MIMIC-IV database’s inherent limitations, baseline characteristics and diagnoses at admission of NSTEMI patients, as well as the socioeconomic status of study participants, were not considered. This may have introduced bias into the study results. Finally, this is a single-center retrospective study, and rigorous prospective and multicenter studies are needed to confirm the findings.

Conclusions

In summary, this study established the correlation between ACC and 1-year and 90-day mortality in a cohort of severe NSTEMI patients. The results of our study indicated that elevated ACC can serve as an independent prognostic indicator for overall mortality in patients with severe NSTEMI. Even after taking potential confounding factors into account, the relationship remains statistically significant. Further analysis is recommended to confirm the association between ACC and all-cause mortality in severe NSTEMI patients.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1170/rc

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1170/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1170/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Georgiopoulos G, Kraler S, Mueller-Hennessen M, et al. Modification of the GRACE Risk Score for Risk Prediction in Patients With Acute Coronary Syndromes. JAMA Cardiol 2023;8:946-56. [Crossref] [PubMed]
2. Ni W, Pan ZZ, Zhou H. A Nomogram Incorporating Inflammation and Nutrition Indexes for Predicting Outcomes in Patients with Acute Coronary Syndrome and Chronic Kidney Disease. J Inflamm Res 2024;17:8181-98. [Crossref] [PubMed]
3. Tsai ML, Hsieh MJ, Chen CC, et al. Prognosis of patients with cardiogenic shock following acute myocardial infarction: The difference between ST-segment elevation myocardial infarction and non-ST-segment elevation myocardial infarction. Medicine (Baltimore) 2022;101:e30426. [Crossref] [PubMed]
4. Eckart A, Struja T, Kutz A, et al. Relationship of Nutritional Status, Inflammation, and Serum Albumin Levels During Acute Illness: A Prospective Study. Am J Med 2020;133:713-722.e7. [Crossref] [PubMed]
5. Fang D, Chen H. Association between serum calcium level and in-hospital mortality in patients with acute myocardial infarction: a retrospective cohort study. Sci Rep 2022;12:19954. [Crossref] [PubMed]
6. Reid IR, Gamble GD, Bolland MJ. Circulating calcium concentrations, vascular disease and mortality: a systematic review. J Intern Med 2016;279:524-40. [Crossref] [PubMed]
7. Zhong L, Lu J, Sun X, et al. The association between albumin-corrected calcium and prognosis in patients with cardiac arrest: a retrospective study based on the MIMIC-IV database. Eur J Med Res 2024;29:251. [Crossref] [PubMed]
8. Shiyovich A, Plakht Y, Gilutz H. Serum calcium levels independently predict in-hospital mortality in patients with acute myocardial infarction. Nutr Metab Cardiovasc Dis 2018;28:510-6. [Crossref] [PubMed]
9. Gutiérrez-Jiménez AA, Castro-Jiménez E, Lagunes-Córdoba R. Total serum calcium and corrected calcium as severity predictors in acute pancreatitis. Rev Gastroenterol Mex 2014;79:13-21. [Crossref] [PubMed]
10. He X, Lin B, Tao T, et al. Higher serum albumin-corrected calcium levels are associated with revascularization and poor outcome after mechanical thrombectomy. BMC Neurol 2022;22:330. [Crossref] [PubMed]
11. Sauter TC, Lindner G, Ahmad SS, et al. Calcium Disorders in the Emergency Department: Independent Risk Factors for Mortality. PLoS One 2015;10:e0132788. [Crossref] [PubMed]
12. Qin X, Cen J, Hu H, et al. Non-linear relationship between albumin-corrected calcium and 30-day in-hospital mortality in ICU patients: A multicenter retrospective cohort study. Front Endocrinol (Lausanne) 2022;13:1059201. [Crossref] [PubMed]
13. Thongprayoon C, Cheungpasitporn W, Chewcharat A, et al. Hospital mortality and long-term mortality among hospitalized patients with various admission serum ionized calcium levels. Postgrad Med 2020;132:385-90. [Crossref] [PubMed]
14. Budoff MJ, Young R, Burke G, et al. Ten-year association of coronary artery calcium with atherosclerotic cardiovascular disease (ASCVD) events: the multi-ethnic study of atherosclerosis (MESA). Eur Heart J 2018;39:2401-8. [Crossref] [PubMed]
15. Ding C, Bi C, Lin T, et al. Association between serum calcium levels and first stroke: A community-based nested case-control study. Front Neurol 2022;13:938794. [Crossref] [PubMed]
16. Elbuken G, Aydin C, Ozturk BO, et al. B-mode ultrasound assessment of carotid artery structural features in patients with normocalcaemic hyperparathyroidism. Endokrynol Pol 2023;74:67-73. [Crossref] [PubMed]
17. Yan SD, Liu XJ, Peng Y, et al. Admission Serum Calcium Levels Improve the GRACE Risk Score Prediction of Hospital Mortality in Patients With Acute Coronary Syndrome. Clin Cardiol 2016;39:516-23. [Crossref] [PubMed]
18. Herrmann M, Keppel MH, Zelzer S, et al. The role of functional vitamin D deficiency and low vitamin D reservoirs in relation to cardiovascular health and mortality. Clin Chem Lab Med 2025;63:208-19. [Crossref] [PubMed]
19. Vincent JL, Dubois MJ, Navickis RJ, et al. Hypoalbuminemia in acute illness: is there a rationale for intervention? A meta-analysis of cohort studies and controlled trials. Ann Surg 2003;237:319-34. [Crossref] [PubMed]
20. Seidu S, Kunutsor SK, Khunti K. Serum albumin, cardiometabolic and other adverse outcomes: systematic review and meta-analyses of 48 published observational cohort studies involving 1,492,237 participants. Scand Cardiovasc J 2020;54:280-93. [Crossref] [PubMed]
21. Ridker PM, Hennekens CH, Buring JE, et al. C-reactive protein and other markers of inflammation in the prediction of cardiovascular disease in women. N Engl J Med 2000;342:836-43. [Crossref] [PubMed]
22. Yan K, Li J, Li Y, et al. Inflammation modifies the platelet reactivity among thrombocytopenia patients undergoing percutaneous coronary intervention. Platelets 2024;35:2327835. [Crossref] [PubMed]
23. Zhu Z, Zhou X. Association between anaemia and long-term prognosis in patients with non-ST segment elevation myocardial infarction. Acta Cardiol 2024;79:179-86. [Crossref] [PubMed]
24. Yılmaz AS, Kahraman F, Ersoy İ, et al. Baseline Characteristics of a Patient Cohort and Predictors of In-hospital MORtality in CORonary Care Units (MORCOR-TURK) Trial in Türkiye. Turk Kardiyol Dern Ars 2024;52:175-81. [Crossref] [PubMed]
25. Wang Y, Ma H, Hao X, et al. Low serum calcium is associated with left ventricular systolic dysfunction in a Chinese population with coronary artery disease. Sci Rep 2016;6:22283. [Crossref] [PubMed]