[Comparison of machine learning method and logistic regression model in prediction of acute kidney injury in severely burned patients]

Machine learning model for predicting acute kidney injury progression in critically ill patients

Background

Acute kidney injury (AKI) is a serve and harmful syndrome in the intensive care unit. Comparing to the patients with AKI stage 1/2, the patients with AKI stage 3 have higher in-hospital mortality and risk of progression to chronic kidney disease. The purpose of this study is to develop a prediction model that predict whether patients with AKI stage 1/2 will progress to AKI stage 3.

Methods

Patients with AKI stage 1/2, when they were first diagnosed with AKI in the Medical Information Mart for Intensive Care, were included. We used the Logistic regression and machine learning extreme gradient boosting (XGBoost) to build two models which can predict patients who will progress to AKI stage 3. Established models were evaluated by cross-validation, receiver operating characteristic curve, and precision–recall curves.

Results

We included 25,711 patients, of whom 2130 (8.3%) progressed to AKI stage 3. Creatinine, multiple organ failure syndromes were the most important in AKI progression prediction. The XGBoost model has a better performance than the Logistic regression model on predicting AKI stage 3 progression. Thus, we build a software based on our data which can predict AKI progression in real time.

Conclusions

The XGboost model can better identify patients with AKI progression than Logistic regression model. Machine learning techniques may improve predictive modeling in medical research.

Meta-Analysis of Renal Replacement Therapy for Burn Patients: Incidence Rate, Mortality, and Renal Outcome

Background: Renal replacement therapy (RRT) was often needed by some severe burn patients with acute kidney injury (AKI). The primary aim of this study was to review incidence rate and mortality of RRT in severe burn patients. Second aims were to review RRT complications and renal outcome.

Methods: We searched multiple databases for studies published between 1 January 1960 and 31 December 2019. Studies about adult populations with burn injury, providing epidemiologic data on prevalence or mortality of RRT, were included.

Results: A total of selected 57 studies, including 27,437 patients were enrolled in our analysis. The prevalence rates of RRT were 8.34% (95% CI 7.18–9.5%) in all burn patients and 37.05% (95% CI 29.85–44.24%) in AKI patients. The mortality of all burn patients with RRT was 65.52% (95% CI 58.41–72.64%). The prevalence rates of RRT in sample size≥100 group were 6.86% (95% CI 5.70–8.03%), which was lower than that of <100 group (17.61%, 95% CI 13.39–21.82%). With the increase of TBSA, the prevalence of RRT may have the increasing trend. The prevalence rates of RRT in Asian group was 12.75% (95% CI 9.50–16.00%), which was higher than that of European (10.45%, 95% CI 7.30–13.61%) and North America group (5.61%, 95% CI 4.27–6.95%). The prevalence rates of RRT in 2010–2019 group was 12.22% (95% CI 10.09–14.35%), which was higher than that of 2009–2000 group (5.17%, 95% CI 2.88–7.46%). The prevalence rates of RRT in 1989 and before group was the lowest, which was 1.56% (95% CI 0–3.68%). However, there was no significant correlation between the year of publication and the mortality of burn patients with RRT. Dialysis-requiring AKI in burn patients could increases the risk of chronic kidney disease progression and end-stage renal disease. About 35% of RRT patients need to maintain haemodialysis temporarily, even if they survive and leave hospital.

Conclusions: The prevalence rate of RRT is about 6–8%; approximately, one-third of burn patients with AKI need RRT. The prevalence rate of RRT increased over time, but the mortality did not change. The prevalence rates of RRT in Asian group was higher than that of European and North America group.

Machine Learning Demonstrates High Accuracy for Disease Diagnosis and Prognosis in Plastic Surgery

INTRODUCTION

Machine learning (ML) is a set of models and methods that can detect patterns in vast amounts of data and use this information to perform various kinds of decision-making under uncertain conditions. This review explores the current role of this technology in plastic surgery by outlining the applications in clinical practice, diagnostic and prognostic accuracies, and proposed future direction for clinical applications and research.

METHODS

EMBASE, MEDLINE, CENTRAL and ClinicalTrials.gov were searched from 1990 to 2020. Any clinical studies (including case reports) which present the diagnostic and prognostic accuracies of machine learning models in the clinical setting of plastic surgery were included. Data collected were clinical indication, model utilised, reported accuracies, and comparison with clinical evaluation.

RESULTS

The database identified 1181 articles, of which 51 articles were included in this review. The clinical utility of these algorithms was to assist clinicians in diagnosis prediction (n=22), outcome prediction (n=21) and pre-operative planning (n=8). The mean accuracy is 88.80%, 86.11% and 80.28% respectively. The most commonly used models were neural networks (n=31), support vector machines (n=13), decision trees/random forests (n=10) and logistic regression (n=9).

CONCLUSIONS

ML has demonstrated high accuracies in diagnosis and prognostication of burn patients, congenital or acquired facial deformities, and in cosmetic surgery. There are no studies comparing ML to clinician's performance. Future research can be enhanced using larger datasets or utilising data augmentation, employing novel deep learning models, and applying these to other subspecialties of plastic surgery.

Predicting Acute Kidney Injury after Cardiac Surgery by Machine Learning Approaches

Cardiac surgery-associated AKI (CSA-AKI) is common after cardiac surgery and has an adverse impact on short- and long-term mortality. Early identification of patients at high risk of CSA-AKI by applying risk prediction models allows clinicians to closely monitor these patients and initiate effective preventive and therapeutic approaches to lessen the incidence of AKI. Several risk prediction models and risk assessment scores have been developed for CSA-AKI. However, the definition of AKI and the variables utilized in these risk scores differ, making general utility complex. Recently, the utility of artificial intelligence coupled with machine learning, has generated much interest and many studies in clinical medicine, including CSA-AKI. In this article, we discussed the evolution of models established by machine learning approaches to predict CSA-AKI.

Role of Artificial Intelligence in Kidney Disease

Artificial intelligence (AI), as an advanced science technology, has been widely used in medical fields to promote medical development, mainly applied to early detections, disease diagnoses, and management. Owing to the huge number of patients, kidney disease remains a global health problem. Challenges remain in its diagnosis and treatment. AI could take individual conditions into account, produce suitable decisions and promise to make great strides in kidney disease management. Here, we review the current studies of AI applications in kidney disease in alerting systems, diagnostic assistance, guiding treatment and evaluating prognosis. Although the number of studies related to AI applications in kidney disease is small, the potential of AI in the management of kidney disease is well recognized by clinicians; AI will greatly enhance clinicians' capacity in their clinical practice in the future.