Data mining of routine laboratory tests can predict liver disease progression in Egyptian diabetic patients with hepatitis C virus (G4) infection: a cohort study of 71 806 patients

Association of Gender, Diagnosis, and Obesity With Retention Rate of Secukinumab in Spondyloarthropathies: Results Form a Multicenter Real-World Study

Background: Secukinumab has been shown effective for psoriatic arthritis (PsA) and axial spondylarthritis (AxSpA) in randomized trials. The aim of this study was to analyze baseline patient and disease characteristics associated with a better retention rate of secukinumab under real-world conditions.

Patients and Methods: Real-life, prospective multicenter observational study involving 138 patients, 61 PsA and 77 AxSpA, who were analyzed at baseline, 6, 12 months and subsequently every year after starting secukinumab regardless of the line of treatment. Demographics and disease characteristics, measures of activity, secukinumab use, and adverse events were collected. Drug survival was analyzed using Kaplan-Meier curves and factors associated with discontinuation were evaluated using Cox regression. The machine-learning J48 decision tree classifier was also applied.

Results: During the 1st year of treatment, 75% of patients persisted with secukinumab, but accrued 71% (n = 32) in total losses (n = 45). The backward stepwise (Wald) method selected diagnosis, obesity, and gender as relevant variables, the latter when analyzing the interactions. At 1 year of follow-up, the Cox model showed the best retention rate in the groups of AxSpa women (95%, 95% CI 93–97%) and PsA men (89%, 95% CI 84–93%), with the worst retention in PsA women (66%, 95% CI 54–79%). The J48 predicted secukinumab retention with an accuracy of 77.2%. No unexpected safety issues were observed.

Conclusions: Secukinumab shows the best retention rate at 1 year of treatment in AxSpA women and in PsA men, independently of factors such as the time of disease evolution, the line of treatment or the initial dose of the drug.

Chronic kidney disease diagnosis using decision tree algorithms

BACKGROUND

Chronic Kidney Disease (CKD), i.e., gradual decrease in the renal function spanning over a duration of several months to years without any major symptoms, is a life-threatening disease. It progresses in six stages according to the severity level. It is categorized into various stages based on the Glomerular Filtration Rate (GFR), which in turn utilizes several attributes, like age, sex, race and Serum Creatinine. Among multiple available models for estimating GFR value, Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI), which is a linear model, has been found to be quite efficient because it allows detecting all CKD stages.

METHODS

Early detection and cure of CKD is extremely desirable as it can lead to the prevention of unwanted consequences. Machine learning methods are being extensively advocated for early detection of symptoms and diagnosis of several diseases recently. With the same motivation, the aim of this study is to predict the various stages of CKD using machine learning classification algorithms on the dataset obtained from the medical records of affected people. Specifically, we have used the Random Forest and J48 algorithms to obtain a sustainable and practicable model to detect various stages of CKD with comprehensive medical accuracy.

RESULTS

Comparative analysis of the results revealed that J48 predicted CKD in all stages better than random forest with an accuracy of 85.5%. The study also showed that J48 shows improved performance over Random Forest.

CONCLUSIONS

The study concluded that it may be used to build an automated system for the detection of severity of CKD.

Potential value and impact of data mining and machine learning in clinical diagnostics

Data mining involves the use of mathematical sciences, statistics, artificial intelligence, and machine learning to determine the relationships between variables from a large sample of data. It has previously been shown that data mining can improve the prediction and diagnostic precision of type 2 diabetes mellitus. A few studies have applied machine learning to assess hypertension and metabolic syndrome-related biomarkers, as well as refine the assessment of cardiovascular disease risk. Machine learning methods have also been applied to assess new biomarkers and survival outcomes in patients with renal diseases to predict the development of chronic kidney disease, disease progression, and renal graft survival. In the latter, random forest methods were found to be the best for the prediction of chronic kidney disease. Some studies have investigated the prognosis of nonalcoholic fatty liver disease and acute liver failure, as well as therapy response prediction in patients with viral disorders, using decision tree models. Machine learning techniques, such as Sparse High-Order Interaction Model with Rejection Option, have been used for diagnosing Alzheimer's disease. Data mining techniques have also been applied to identify the risk factors for serious mental illness, such as depression and dementia, and help to diagnose and predict the quality of life of such patients. In relation to child health, some studies have determined the best algorithms for predicting obesity and malnutrition. Machine learning has determined the important risk factors for preterm birth and low birth weight. Published studies of patients with cancer and bacterial diseases are limited and should perhaps be addressed more comprehensively in future studies. Herein, we provide an in-depth review of studies in which biochemical biomarker data were analyzed using machine learning methods to assess the risk of several common diseases, in order to summarize the potential applications of data mining methods in clinical diagnosis. Data mining techniques have now been increasingly applied to clinical diagnostics, and they have the potential to support this field.

Risk Factors Contributing to the Occurrence and Recurrence of Hepatocellular Carcinoma in Hepatitis C Virus Patients Treated with Direct-Acting Antivirals

Although hepatitis C virus (HCV) RNA may be eliminated from blood circulation by direct-acting antivirals (DAA) therapy as assessed by real-time polymerase chain reaction (PCR), HCV RNA can still be present in liver tissue, and this is known as occult HCV. There has been a lot of controversy surrounding the recurrence of hepatocellular carcinoma (HCC) after DAA treatment of hepatic cells infected with chronic HCV. One of the main risk factors that leads to de novo HCC is the chronicity of HCV in hepatic cells. There are many studies regarding the progression of HCV-infected hepatic cells to HCC. However, there is a lack of research on the different molecular mechanisms that lead to the progression of chronic HCV infection to HCC, as well as on the effect of HCV on the alteration of DNA ploidy, which eventually leads to a recurrence of HCC after DAA treatment. In this review article, we will address some risk factors that could lead to the development/recurrence of HCC after treatment of HCV with DAA therapy, such as the role of liver cirrhosis, the alteration of DNA ploidy, the reactivation of hepatitis B virus (HBV), the role of cytokines and the alteration of the immune system, concomitant non- alcoholic fatty liver disease (NAFLD), obesity, alcohol consumption and also occult HCV infection/co-infection. Clinicians should be cautious considering that full eradication of hepatocarcinogenesis cannot be successfully accomplished by anti-HCV treatment alone.

Risk Factors Contributing to the Occurrence and Recurrence of Hepatocellular Carcinoma in Hepatitis C Virus Patients Treated with Direct-Acting Antivirals

Although hepatitis C virus (HCV) RNA may be eliminated from blood circulation by direct-acting antivirals (DAA) therapy as assessed by real-time polymerase chain reaction (PCR), HCV RNA can still be present in liver tissue, and this is known as occult HCV. There has been a lot of controversy surrounding the recurrence of hepatocellular carcinoma (HCC) after DAA treatment of hepatic cells infected with chronic HCV. One of the main risk factors that leads to de novo HCC is the chronicity of HCV in hepatic cells. There are many studies regarding the progression of HCV-infected hepatic cells to HCC. However, there is a lack of research on the different molecular mechanisms that lead to the progression of chronic HCV infection to HCC, as well as on the effect of HCV on the alteration of DNA ploidy, which eventually leads to a recurrence of HCC after DAA treatment. In this review article, we will address some risk factors that could lead to the development/recurrence of HCC after treatment of HCV with DAA therapy, such as the role of liver cirrhosis, the alteration of DNA ploidy, the reactivation of hepatitis B virus (HBV), the role of cytokines and the alteration of the immune system, concomitant non- alcoholic fatty liver disease (NAFLD), obesity, alcohol consumption and also occult HCV infection/co-infection. Clinicians should be cautious considering that full eradication of hepatocarcinogenesis cannot be successfully accomplished by anti-HCV treatment alone.