Urinary chitinase 3-like protein 1 for early diagnosis of acute kidney injury : a prospective cohort study in adult critically ill patients

Background: Acute kidney injury (AKI) occurs frequently and adversely affects patient and kidney outcomes, especially when its severity increases from stage 1 to stages 2 or 3. Early interventions may counteract such deterioration, but this requires early detection. Our aim was to evaluate whether the novel renal damage biomarker urinary chitinase 3-like protein 1 (UCHI3L1) can detect AKI stage >= 2 more early than serum creatinine and urine output, using the respective Kidney Disease vertical bar Improving Global Outcomes (KDIGO) criteria for definition and classification of AKI, and compare this to urinary neutrophil gelatinase-associated lipocalin (UNGAL). Methods: This was a translational single-center, prospective cohort study at the 22-bed surgical and 14-bed medical intensive care units (ICU) of Ghent University Hospital. We enrolled 181 severely ill adult patients who did not yet have AKI stage >= 2 based on the KDIGO criteria at time of enrollment. The concentration of creatinine (serum, urine) and CHI3L1 (serum, urine) was measured at least daily, and urine output hourly, in the period from enrollment till ICU discharge with a maximum of 7 ICU-days. The concentration of UNGAL was measured at enrollment. The primary endpoint was the development of AKI stage >= 2 within 12 h after enrollment. Results: After enrollment, 21 (12 %) patients developed AKI stage >= 2 within the next 7 days, with 6 (3 %) of them reaching this condition within the first 12 h. The enrollment concentration of UCHI3L1 predicted the occurrence of AKI stage >= 2 within the next 12 h with a good AUC-ROC of 0.792 (95 % CI: 0.726-0.849). This performance was similar to that of UNGAL (AUC-ROC of 0.748 (95 % CI: 0.678-0.810)). Also, the samples collected in the 24-h time frame preceding diagnosis of the 1st episode of AKI stage >= 2 had a 2.0 times higher (95 % CI: 1.3-3.1) estimated marginal mean of UCHI3L1 than controls. We further found that increasing UCHI3L1 concentrations were associated with increasing AKI severity. Conclusions: In this pilot study we found that UCHI3L1 was a good biomarker for prediction of AKI stage >= 2 in adult ICU patients

Preimplantation biopsy predicts delayed graft function, glomerular filtration rate and long-term graft survival of transplanted kidneys

Background The predictive value of preimplantation biopsies for long-term graft function is often limited by conflicting results. The aim of this study was to evaluate the influence of time-zero graft biopsy histological scores on early and late graft function, graft survival and patient survival, at different time points. Methods We retrospectively analyzed 284 preimplantation biopsies at a single center, in a cohort of recipients with grafts from live and deceased donors (standard and nonstandard), and their impact in posttransplant renal function after a mean follow-up of 7 years (range 1–16). Implantation biopsy score (IBS), a combination score derived from 4 histopathological aspects, was determined from each sample. The correlation with incidence of delayed graft function (DGF), creatinine clearance (1st, 3rd and 5th posttransplant year) and graft and patient survival at 1 and 5 years were evaluated. Results Preimplantation biopsies provided somewhat of a prognostic index of early function and outcome of the transplanted kidney in the short and long term. In the immediate posttransplantation period, the degree of arteriolosclerosis and interstitial fibrosis correlated better with the presence of DGF. IBS values between 4 and 6 were predictive of worst renal function at 1st and 3rd years posttransplant and 5-year graft survival. The most important histological finding, in effectively transplanted grafts, was the grade of interstitial fibrosis. Patient survival was not influenced by IBS. Conclusions Higher preimplantation biopsy scores predicted an increased risk of early graft losses, especially primary nonfunction. Graft survival (at 1st and 5th years after transplant) but not patient survival was predicted by IBS

Machine Learning with Abstention for Automated Liver Disease Diagnosis

This paper presents a novel approach for detection of liver abnormalities in an automated manner using ultrasound images. For this purpose, we have implemented a machine learning model that can not only generate labels (normal and abnormal) for a given ultrasound image but it can also detect when its prediction is likely to be incorrect. The proposed model abstains from generating the label of a test example if it is not confident about its prediction. Such behavior is commonly practiced by medical doctors who, when given insufficient information or a difficult case, can chose to carry out further clinical or diagnostic tests before generating a diagnosis. However, existing machine learning models are designed in a way to always generate a label for a given example even when the confidence of their prediction is low. We have proposed a novel stochastic gradient based solver for the learning with abstention paradigm and use it to make a practical, state of the art method for liver disease classification. The proposed method has been benchmarked on a data set of approximately 100 patients from MINAR, Multan, Pakistan and our results show that the proposed scheme offers state of the art classification performance.Comment: Preprint version before submission for publication. complete version published in proc. 15th International Conference on Frontiers of Information Technology (FIT 2017), December 18-20, 2017, Islamabad, Pakistan. http://ieeexplore.ieee.org/document/8261064

Sensor-AssistedWeighted Average Ensemble Model for Detecting Major Depressive Disorder

The present methods of diagnosing depression are entirely dependent on self-report ratings or clinical interviews. Those traditional methods are subjective, where the individual may or may not be answering genuinely to questions. In this paper, the data has been collected using self-report ratings and also using electronic smartwatches. This study aims to develop a weighted average ensemble machine learning model to predict major depressive disorder (MDD) with superior accuracy. The data has been pre-processed and the essential features have been selected using a correlation-based feature selection method. With the selected features, machine learning approaches such as Logistic Regression, Random Forest, and the proposedWeighted Average Ensemble Model are applied. Further, for assessing the performance of the proposed model, the Area under the Receiver Optimization Characteristic Curves has been used. The results demonstrate that the proposed Weighted Average Ensemble model performs with better accuracy than the Logistic Regression and the Random Forest approaches