Assessment of acute kidney injury risk using a machine-learning guided generalized structural equation model: a cohort study.

BACKGROUND: Acute kidney injury is common in the surgical intensive care unit (ICU). It is associated with poor patient outcomes and high healthcare resource usage. This study's primary objective is to help identify which ICU patients are at high risk for acute kidney injury. Its secondary objective is to examine the effect of acute kidney injury on a patient's prognosis during and after the ICU admission. METHODS: A retrospective cohort of patients admitted to a Singaporean surgical ICU between 2015 to 2017 was collated. Patients undergoing chronic dialysis were excluded. The outcomes were occurrence of ICU acute kidney injury, hospital mortality and one-year mortality. Predictors were identified using decision tree algorithms. Confirmatory analysis was performed using a generalized structural equation model. RESULTS: A total of 201/940 (21.4%) patients suffered acute kidney injury in the ICU. Low ICU haemoglobin levels, low ICU bicarbonate levels, ICU sepsis, low pre-ICU estimated glomerular filtration rate (eGFR) and congestive heart failure was associated with the occurrence of ICU acute kidney injury. Acute kidney injury, together with old age (> 70 years), and low pre-ICU eGFR, was associated with hospital mortality, and one-year mortality. ICU haemoglobin level was discretized into 3 risk categories for acute kidney injury: high risk (haemoglobin ≤9.7 g/dL), moderate risk (haemoglobin between 9.8-12 g/dL), and low risk (haemoglobin > 12 g/dL). CONCLUSION: The occurrence of acute kidney injury is common in the surgical ICU. It is associated with a higher risk for hospital and one-year mortality. These results, in particular the identified haemoglobin thresholds, are relevant for stratifying a patient's acute kidney injury risk.

Association of genetic polymorphisms with acute kidney injury after cardiac surgery in a Southeast Asian population.

INTRODUCTION: Genetic polymorphisms are important in explaining the wide interpatient variability that exists in the development of acute kidney injury (AKI) post cardiac surgery. We hypothesised that polymorphisms in 4 candidate genes, namely angiotensin-converting enzyme (ACE), apolipoprotein-E (ApoE), interleukin-6 (IL-6), and tumour necrosis factor-alpha (TNF-α) are associated with AKI. METHODS: 870 patients who underwent cardiac surgery in Singapore were analysed. All patients who fulfilled stage 1 KDIGO criteria and above were considered to have AKI. This was investigated against various demographic, clinical and genetic factors. RESULTS: Increased age, history of hypertension, anaemia and renal impairment remained important preoperative risk factors for AKI. Intraoperatively, longer cardiopulmonary bypass (CPB) time and the use of intra-aortic balloon pump (IABP) were shown to be associated with AKI. Among the genetic factors, ACE-D allele was associated with an increased risk of AKI while IL6-572C allele was associated with a decreased risk of AKI. CONCLUSION: ACE-D allele was associated with the development of AKI similar to other studies. On the other hand, IL6-572C was shown to have a protective role against the development of AKI, contradictory to studies done in the Caucasian population. This contradictory effect of IL6-572C is a result of a complex interplay between the gene and population specific modulating factors. Our findings further underscored the necessity of taking into account population specific differences when developing prediction models for AKI.

Automatic identification of blood vessel cross-section for central venous catheter placement using a cascading classifier.

This paper presents a system that is able to automatically identify, segment, and track the cross-section of the internal jugular vein (IJV) and the common carotid artery (CCA) in an ultrasound image feed during a central venous catheter (CVC) placement procedure. The goal is to provide assistance to the practitioner in order to decrease the probability of complications stemming from inadvertent punctures of the CCA during the procedure. In the system, a modified Star algorithm is implemented to segment and track the blood vessel throughout an ultrasound video feed. A novel algorithm based on a cascading classifier is used to identify the location of the IJV and the CCA for two main tasks: (1) selecting the initial seed point at the start of tracking and (2) validating the segmentation results at each subsequent frame. The classifier uses shape features (vessel area, ellipse fitting error, vessel depth, vessel eccentricity) and pixel-based features (pixel intensity and the histogram of oriented gradients descriptor) to differentiate between vessel and non-vessel structures and also differentiate between the IJV and the CCA. Evaluated on a database of 800 ultrasound images containing the cross-section of both vessels, the cascading classifier was able to identify the IJV and the CCA in 92.25% and 85.13% of the images respectively without any initialization from the user at a maximum processing rate of 40.65 frames per second. This allows identification to be conducted in real-time with existing ultrasound machines.