Impact of a computerized decision support tool deployed in two intensive care units on acute kidney injury progression and guideline compliance: a prospective observational study.

BACKGROUND: Acute kidney injury (AKI) affects a large proportion of the critically ill and is associated with worse patient outcomes. Early identification of AKI can lead to earlier initiation of supportive therapy and better management. In this study, we evaluate the impact of computerized AKI decision support tool integrated with the critical care clinical information system (CCIS) on patient outcomes. Specifically, we hypothesize that integration of AKI guidelines into CCIS will decrease the proportion of patients with Stage 1 AKI deteriorating into higher stages of AKI. METHODS: The study was conducted in two intensive care units (ICUs) at University Hospitals Bristol, UK, in a before (control) and after (intervention) format. The intervention consisted of the AKIN guidelines and AKI care bundle which included guidance for medication usage, AKI advisory and dashboard with AKI score. Clinical data and patient outcomes were collected from all patients admitted to the units. AKI stage was calculated using the Acute Kidney Injury Network (AKIN) guidelines. Maximum AKI stage per admission, change in AKI stage and other metrics were calculated for the cohort. Adherence to eGFR-based enoxaparin dosing guidelines was evaluated as a proxy for clinician awareness of AKI. RESULTS: Each phase of the study lasted a year, and a total of 5044 admissions were included for analysis with equal numbers of patients for the control and intervention stages. The proportion of patients worsening from Stage 1 AKI decreased from 42% (control) to 33.5% (intervention), p = 0.002. The proportion of incorrect enoxaparin doses decreased from 1.72% (control) to 0.6% (intervention), p < 0.001. The prevalence of any AKI decreased from 43.1% (control) to 37.5% (intervention), p < 0.05. CONCLUSIONS: This observational study demonstrated a significant reduction in AKI progression from Stage 1 and a reduction in overall development of AKI. In addition, a reduction in incorrect enoxaparin dosing was also observed, indicating increased clinical awareness. This study demonstrates that AKI guidelines coupled with a newly designed AKI care bundle integrated into CCIS can impact patient outcomes positively.

Utility of adding Radiomics to clinical features in predicting the outcomes of radiotherapy for head and neck cancer using machine learning.

BACKGROUND: Radiomics involves the extraction of quantitative information from annotated Computed-Tomography (CT) images, and has been used to predict outcomes in Head and Neck Squamous Cell Carcinoma (HNSCC). Subjecting combined Radiomics and Clinical features to Machine Learning (ML) could offer better predictions of clinical outcomes. This study is a comparative performance analysis of ML models with Clinical, Radiomics, and Clinico-Radiomic datasets for predicting four outcomes of HNSCC treated with Curative Radiation Therapy (RT): Distant Metastases, Locoregional Recurrence, New Primary, and Residual Disease. METHODOLOGY: The study used retrospective data of 311 HNSCC patients treated with radiotherapy between 2013-2018 at our centre. Binary prediction models were developed for the four outcomes with Clinical-only, Clinico-Radiomic, and Radiomics-only datasets, using three different ML classification algorithms namely, Random Forest (RF), Kernel Support Vector Machine (KSVM), and XGBoost. The best-performing ML algorithms of the three dataset groups was then compared. RESULTS: The Clinico-Radiomic dataset using KSVM classifier provided the best prediction. Predicted mean testing accuracy for Distant Metastases, Locoregional Recurrence, New Primary, and Residual Disease was 97%, 72%, 99%, and 96%, respectively. The mean area under the receiver operating curve (AUC) was calculated and displayed for all the models using three dataset groups. CONCLUSION: Clinico-Radiomic dataset improved the predictive ability of ML models over clinical features alone, while models built using Radiomics performed poorly. Radiomics data could therefore effectively supplement clinical data in predicting outcomes.

Descriptive study of differences in acute kidney injury progression patterns in General and Cardiac Intensive Care Units.

BACKGROUND: Acute kidney injury is common in critically ill patients with detrimental effects on mortality, length of stay and post-discharge outcomes. The Acute Kidney Injury Network developed guidelines based on urine output and serum creatinine to classify patients into stages of acute kidney injury. METHODS: In this analysis we utilize the Acute Kidney Injury Network guidelines to evaluate the acute kidney injury stage in patients admitted to general and cardiac intensive care units over a period of 18 months. Acute kidney injury stage was calculated in real time hourly based on the guidelines and using these temporal stage scores calculated for the population; the prevalence and progression of acute kidney injury stage was compared between the two units. We hypothesized that the prevalence and progression of acute kidney injury stage between the two units may be different. RESULTS: More cardiac intensive care unit patients had no acute kidney injury (stage <1) during their intensive care unit stay but more cardiac intensive care unit patients developed acute kidney injury (stage >1), compared to the General Intensive Care Unit. Both at intensive care unit admission and discharge, more General Intensive Care Unit patients had acute kidney injury; however, the number of cardiac intensive care unit patients with acute kidney injury was three times higher at discharge than admission. Acute kidney injury developed in a different pattern in the two intensive care units over five days of intensive care unit stay. In the General Intensive Care Unit, acute kidney injury was most prevalent on second day of intensive care unit stay and in cardiac intensive care unit acute kidney injury was most prevalent on the third day of intensive care unit stay. We observed the biggest increase in new acute kidney injury in the first day of General Intensive Care Unit and second day of the cardiac intensive care unit stay. CONCLUSIONS: The study demonstrates the different trends of acute kidney injury pattern in general and cardiac intensive care unit patient populations highlighting the earlier development of acute kidney injury on General Intensive Care Unit and more prevalence of acute kidney injury on discharge from cardiac intensive care unit.