The Prognostic Value of Calcium in Post-Cardiovascular Surgery Patients in the Intensive Care Unit

Investigating Potential Correlations between Calcium Metabolism Biomarkers and Periprocedural Clinical Events in Major Cardiovascular Surgeries: An Exploratory Study

Background: There is emerging but conflicting evidence regarding the association between calcium biomarkers, more specifically ionized calcium and the prognosis of intensive care unit (ICU) postoperative cardiac patients. Methods: Our study investigated the relationship between ionized calcium, vitamin D, and periprocedural clinical events such as cardiac, neurologic and renal complications, major bleeding, vasoactive-inotropic score (VIS), and length of ICU and hospitalization. Results: Our study included 83 consecutive subjects undergoing elective major cardiac surgery requiring cardiopulmonary bypass. The mean age of the participants was 64.9 ± 8.5 years. The majority of procedures comprised isolated CABG (N = 26, 31.3%), aortic valve procedures (N = 26, 31.3%), and mitral valve procedures (N = 12, 14.5%). A difference in calcium levels across all time points (p < 0.001) was observed, with preoperative calcium being directly associated with intraoperative VIS (r = 0.26, p = 0.016). On day 1, calcium levels were inversely associated with the duration of mechanical ventilation (r = -0.30, p = 0.007) and the length of hospital stay (r = -0.22, p = 0.049). At discharge, calcium was inversely associated with length of hospital stay (r = -0.22, p = 0.044). All calcium levels tended to be lower in those who died during the 1-year follow-up (p = 0.054). Preoperative vitamin D levels were significantly higher in those who experienced AKI during hospitalization (median 17.5, IQR 14.5-17.7, versus median 15.3, IQR 15.6-20.5, p = 0.048) Conclusion: Fluctuations in calcium levels and vitamin D may be associated with the clinical course of patients undergoing cardiac surgery. In our study, hypocalcemic patients exhibited a greater severity of illness, as evidenced by elevated VIS scores, and experienced prolonged mechanical ventilation time and hospital stays. Additional larger-scale studies are required to gain a deeper understanding of their impact on cardiac performance and the process of weaning from cardiopulmonary bypass, as well as to distinguish between causal and associative relationships.

Analysis of serum calcium change trajectories and prognostic factors in patients with acute type A aortic dissection

OBJECTIVES

This study aimed to analyze the correlation between serum calcium changes and short-term prognosis of patients with acute type A aortic dissection.

METHODS

Patients who underwent acute type A aortic dissection surgery at Fujian Heart Medical Center between June 2019 and June 2021 were retrospectively analyzed.

RESULTS

A total of 383 patients were enrolled. According to the changing track of serum calcium in patients after acute type A aortic dissection, three potential category tracks were determined: high-level (n = 85), medium-level (n = 259), and continuous low-level groups (n = 39). Using the medium-level group as the control, regression analysis showed that poor prognosis risk was increased in the group with continuous low serum calcium (odds ratio = 2.454, P < 0.05) and in the group with continuous low serum calcium > 48 h (odds ratio = 3.595, P < 0.05). Age (odds ratio = 1.063, P < 0.001), body mass index (odds ratio = 1.138, P < 0.05), hypertension (odds ratio = 3.697, P < 0.05), and the highest lactic acid within 72 h after surgery(odds ratio = 1.093, P < 0.05) were independent risk factors for poor prognosis after aortic dissection.

CONCLUSION

Continuous low serum calcium was an independent predictor of poor prognosis in patients with acute type A aortic dissection.

Development and Validation of a Personalized Model With Transfer Learning for Acute Kidney Injury Risk Estimation Using Electronic Health Records

IMPORTANCE

Acute kidney injury (AKI) is a heterogeneous syndrome prevalent among hospitalized patients. Personalized risk estimation and risk factor identification may allow effective intervention and improved outcomes.

OBJECTIVE

To develop and validate personalized AKI risk estimation models using electronic health records (EHRs), examine whether personalized models were beneficial in comparison with global and subgroup models, and assess the heterogeneity of risk factors and their outcomes in different subpopulations.

DESIGN, SETTING, AND PARTICIPANTS

This diagnostic study analyzed EHR data from 1 tertiary care hospital and used machine learning and logistic regression to develop and validate global, subgroup, and personalized risk estimation models. Transfer learning was implemented to enhance the personalized model. Predictor outcomes across subpopulations were analyzed, and metaregression was used to explore predictor interactions. Adults who were hospitalized for 2 or more days from November 1, 2007, to December 31, 2016, were included in the analysis. Patients with moderate or severe kidney dysfunction at admission were excluded. Data were analyzed between August 28, 2019, and May 8, 2022.

EXPOSURES

Clinical and laboratory variables in the EHR.

MAIN OUTCOMES AND MEASURES

The main outcome was AKI of any severity, and AKI was defined using the Kidney Disease: Improving Global Outcomes serum creatinine criteria. Performance of the models was measured with area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve, and calibration.

RESULTS

The study cohort comprised 76 957 inpatient encounters. Patients had a mean (SD) age of 55.5 (17.4) years and included 42 159 men (54.8%). The personalized model with transfer learning outperformed the global model for AKI estimation in terms of AUROC among general inpatients (0.78 [95% CI, 0.77-0.79] vs 0.76 [95% CI, 0.75-0.76]; P < .001) and across the high-risk subgroups (0.79 [95% CI, 0.78-0.80] vs 0.75 [95% CI, 0.74-0.77]; P < .001) and low-risk subgroups (0.74 [95% CI, 0.73-0.75] vs 0.71 [95% CI, 0.70-0.72]; P < .001). The AUROC improvement reached 0.13 for the high-risk subgroups, such as those undergoing liver transplant and cardiac surgery. Moreover, the personalized model with transfer learning performed better than or comparably with the best published models in well-studied AKI subgroups. Predictor outcomes varied significantly between patients, and interaction analysis uncovered modifiers of the predictor outcomes.

CONCLUSIONS AND RELEVANCE

Results of this study demonstrated that a personalized modeling with transfer learning is an improved AKI risk estimation approach that can be used across diverse patient subgroups. Risk factor heterogeneity and interactions at the individual level highlighted the need for agile, personalized care.