Association between acute kidney injury and prognoses of cardiac surgery patients: Analysis of the MIMIC-III database

Impact of prognostic scores on acute kidney injury assessment in the postoperative period of myocardial revascularization

OBJECTIVE

To determine the impact of the Simplified Acute Physiology Score 3 and the Sequential Organ Failure Assessment in assessing the severity of acute kidney injury in patients after cardiopulmonary bypass.

METHOD

A retrospective cohort study with a non-probabilistic sample. Inferential analysis was performed using Pearson's chi-square, Fisher's exact and Mann-Whitney tests, with a significance level of 5%.

RESULTS

The prevalence of acute kidney injury was 31.4%. The Simplified Acute Physiology Score 3 and the Sequential Organ Failure Assessment showed higher scores in patients with kidney injury (58 (48-64) versus 48 (37-57), p = 0.02; 7 (6-9) versus 6 (5-7), p = 0.003), in addition to a longer stay in intensive care, 8 (6-16) versus 6 (5-8) (p = 0.02) days, respectively.

CONCLUSION

Patients with acute kidney injury remained in intensive care longer, and the Simplified Acute Physiology Score 3 and the Sequential Organ Failure Assessment showed good performance, evidencing greater severity among patients with acute kidney injury in the postoperative period of coronary artery bypass grafting.

Association between platelet count and neonatal acute kidney injury: a cohort study using the medical information mart for intensive care III database

OBJECTIVE

A decrease in platelet count has been reported to be associated with several neonatal inflammatory diseases, including sepsis and necrotizing enterocolitis; while its association with neonatal acute kidney injury (AKI) has not been reported. This study aims to explore the association between platelet count and neonatal AKI.

METHODS

This was a retrospective cohort study based on the Medical Information Mart for Intensive Care III (MIMIC-III) database. Data were extracted based on baseline characteristics, comorbidities, vital signs, laboratory parameters, and intervention measures. Logistic regression analysis was used to assess the association between platelet count and AKI, and results were shown as odds ratios (OR) with 95% confidence intervals (CI).

RESULTS

A total of 1,576 neonates were finally included in the analysis. After adjusting birth weight, sepsis, patent ductus arteriosus, hematocrit, percentage of neutrophils, and vasopressor use, we found that platelet count in the lowest quartile (Q1) was significantly associated with the higher odds of AKI than platelet count in the highest quartile (Q4) (OR = 1.70, 95% CI: 1.01-2.87).

CONCLUSIONS

Low platelet count was associated with the high odds of AKI in the neonatal intensive care unit (NICU), indicating that platelet count might be a biomarker for neonatal AKI. Large-scale multicenter studies should be performed to verify the results.

Forecasting acute kidney injury and resource utilization in ICU patients using longitudinal, multimodal models

BACKGROUND

Advances in artificial intelligence (AI) have realized the potential of revolutionizing healthcare, such as predicting disease progression via longitudinal inspection of Electronic Health Records (EHRs) and lab tests from patients admitted to Intensive Care Units (ICU). Although substantial literature exists addressing broad subjects, including the prediction of mortality, length-of-stay, and readmission, studies focusing on forecasting Acute Kidney Injury (AKI), specifically dialysis anticipation like Continuous Renal Replacement Therapy (CRRT) are scarce. The technicality of how to implement AI remains elusive.

OBJECTIVE

This study aims to elucidate the important factors and methods that are required to develop effective predictive models of AKI and CRRT for patients admitted to ICU, using EHRs in the Medical Information Mart for Intensive Care (MIMIC) database.

METHODS

We conducted a comprehensive comparative analysis of established predictive models, considering both time-series measurements and clinical notes from MIMIC-IV databases. Subsequently, we proposed a novel multi-modal model which integrates embeddings of top-performing unimodal models, including Long Short-Term Memory (LSTM) and BioMedBERT, and leverages both unstructured clinical notes and structured time series measurements derived from EHRs to enable the early prediction of AKI and CRRT.

RESULTS

Our multimodal model achieved a lead time of at least 12 h ahead of clinical manifestation, with an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.888 for AKI and 0.997 for CRRT, as well as an Area Under the Precision Recall Curve (AUPRC) of 0.727 for AKI and 0.840 for CRRT, respectively, which significantly outperformed the baseline models. Additionally, we performed a SHapley Additive exPlanation (SHAP) analysis using the expected gradients algorithm, which highlighted important, previously underappreciated predictive features for AKI and CRRT.

CONCLUSION

Our study revealed the importance and the technicality of applying longitudinal, multimodal modeling to improve early prediction of AKI and CRRT, offering insights for timely interventions. The performance and interpretability of our model indicate its potential for further assessment towards clinical applications, to ultimately optimize AKI management and enhance patient outcomes.

Forecasting Acute Kidney Injury and Resource Utilization in ICU patients using longitudinal, multimodal models

Background

Advances in artificial intelligence (AI) have realized the potential of revolutionizing healthcare, such as predicting disease progression via longitudinal inspection of Electronic Health Records (EHRs) and lab tests from patients admitted to Intensive Care Units (ICU). Although substantial literature exists addressing broad subjects, including the prediction of mortality, length-of-stay, and readmission, studies focusing on forecasting Acute Kidney Injury (AKI), specifically dialysis anticipation like Continuous Renal Replacement Therapy (CRRT) are scarce. The technicality of how to implement AI remains elusive.

Objective

This study aims to elucidate the important factors and methods that are required to develop effective predictive models of AKI and CRRT for patients admitted to ICU, using EHRs in the Medical Information Mart for Intensive Care (MIMIC) database.

Methods

We conducted a comprehensive comparative analysis of established predictive models, considering both time-series measurements and clinical notes from MIMIC-IV databases. Subsequently, we proposed a novel multi-modal model which integrates embeddings of top-performing unimodal models, including Long Short-Term Memory (LSTM) and BioMedBERT, and leverages both unstructured clinical notes and structured time series measurements derived from EHRs to enable the early prediction of AKI and CRRT.

Results

Our multimodal model achieved a lead time of at least 12 hours ahead of clinical manifestation, with an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.888 for AKI and 0.997 for CRRT, as well as an Area Under the Precision Recall Curve (AUPRC) of 0.727 for AKI and 0.840 for CRRT, respectively, which significantly outperformed the baseline models. Additionally, we performed a SHapley Additive exPlanation (SHAP) analysis using the expected gradients algorithm, which highlighted important, previously underappreciated predictive features for AKI and CRRT.

Conclusion

Our study revealed the importance and the technicality of applying longitudinal, multimodal modeling to improve early prediction of AKI and CRRT, offering insights for timely interventions. The performance and interpretability of our model indicate its potential for further assessment towards clinical applications, to ultimately optimize AKI management and enhance patient outcomes.

Alterations to Kidney Physiology during Cardiopulmonary Bypass-A Narrative Review of the Literature and Practical Remarks

INTRODUCTION

According to different authors, cardiac surgery-associated acute kidney injury (CSA-AKI) incidence can be as high as 20-50%. This complication increases postoperative morbidity and mortality and impairs long-term kidney function in some patients. This review aims to summarize current knowledge regarding alterations to renal physiology during cardiopulmonary bypass (CPB) and to discuss possible nephroprotective strategies for cardiac surgeries. Relevant sections: Systemic and renal circulation, Vasoactive drugs, Fluid balance and Osmotic regulation and Inflammatory response.

CONCLUSIONS

Considering the available scientific evidence, it is concluded that adequate kidney perfusion and fluid balance are the most critical factors determining postoperative kidney function. By adequate perfusion, one should understand perfusion with proper oxygen delivery and sufficient perfusion pressure. Maintaining the fluid balance is imperative for a normal kidney filtration process, which is essential for preserving the intra- and postoperative kidney function.

FUTURE DIRECTIONS

The review of the available literature regarding kidney function during cardiac surgery revealed a need for a more holistic approach to this subject.