Interpretable Stratification for Chronic Kidney Disease Progression Based on Time to Event Analysis.

In Chronic Kidney Disease (CKD), kidneys are damaged and lose their ability to filter blood, leading to a plethora of health consequences that end up in dialysis. Despite its prevalence, CKD goes often undetected at early stages. In order to better understand disease progression, we stratified patients with CKD by considering the time to dialysis from diagnosis of early CKD (stages 1 or 2). To achieve this, we first reduced the number of clinical features in a predictive time-to-dialysis model and identified the top important features on a cohort of ∼ 40, 000 CKD patients. The extracted features were used to stratify a subpopulation of 3, 522 patients that showed anemia and were prescribed for cardiovascular-related drugs and progressed faster to dialysis. On the other side, clustering patients using conventional clustering methods based on their clinical features did not allow such clear interpretation to identify the main factors for leading fast progression to dialysis. To our knowledge this is the first study extracting interpretable features for stratifying a cohort of early CKD patients using time-to-event analysis which could help prevention and the development of new treatments.

Cross-Border Transmissions of the Delta Substrain AY.29 During Tokyo Olympic and Paralympic Games.

Tokyo Olympic and Paralympic Games, postponed for the COVID-19 pandemic, were finally held in the summer of 2021. Just before the games, the Alpha variant was being replaced with the more contagious Delta variant. AY.4 substrain AY.29, which harbors two additional characteristic mutations of 5239C > T (NSP3 Y840Y) and 5514T > C (NSP3 V932A), emerged in Japan and became dominant in Tokyo by the time of the Olympic Games. Variants of SARS-CoV-2 genomes were performed to extract AY.29 Delta substrain samples with 5239C > T and 5514T > C. Phylogenetic analysis was performed to illustrate how AY.29 strains evolved and were introduced into countries abroad. Simultaneously, ancestral searches were performed for the overseas AY.29 samples to identify their origins in Japan using the maximum variant approach. As of January 10, 2022, 118 samples were identified in 20 countries. Phylogenetic analysis and ancestral searches identified 55 distinct introductions into those countries. The United States had 50 samples with 10 distinct introductions, and the United Kingdom had 13 distinct strains introduced in 18 samples. Other countries or regions with multiple introductions were Canada, Germany, South Korea, Hong Kong, Thailand, and the Philippines. Among the 20 countries, most European and North American countries have vaccination rates over 50% and sufficient genomic surveillances are conducted; transmissions seem contained. However, propagation to unvaccinated regions might have caused unfathomable damages. Since samples in those unvaccinated countries are also undersampled with a longer lead time for data sharing, it will take longer to grasp the whole picture. More rigorous departure screenings for the participants from the unvaccinated countries might have been necessary.

Development of a machine learning-based prediction model for extremely rapid decline in estimated glomerular filtration rate in patients with chronic kidney disease: a retrospective cohort study using a large data set from a hospital in Japan.

OBJECTIVES: Trajectories of estimated glomerular filtration rate (eGFR) decline vary highly among patients with chronic kidney disease (CKD). It is clinically important to identify patients who have high risk for eGFR decline. We aimed to identify clusters of patients with extremely rapid eGFR decline and develop a prediction model using a machine learning approach. DESIGN: Retrospective single-centre cohort study. SETTINGS: Tertiary referral university hospital in Toyoake city, Japan. PARTICIPANTS: A total of 5657 patients with CKD with baseline eGFR of 30 mL/min/1.73 m2 and eGFR decline of ≥30% within 2 years. PRIMARY OUTCOME: Our main outcome was extremely rapid eGFR decline. To study-complicated eGFR behaviours, we first applied a variation of group-based trajectory model, which can find trajectory clusters according to the slope of eGFR decline. Our model identified high-level trajectory groups according to baseline eGFR values and simultaneous trajectory clusters. For each group, we developed prediction models that classified the steepest eGFR decline, defined as extremely rapid eGFR decline compared with others in the same group, where we used the random forest algorithm with clinical parameters. RESULTS: Our clustering model first identified three high-level groups according to the baseline eGFR (G1, high GFR, 99.7±19.0; G2, intermediate GFR, 62.9±10.3 and G3, low GFR, 43.7±7.8); our model simultaneously found three eGFR trajectory clusters for each group, resulting in nine clusters with different slopes of eGFR decline. The areas under the curve for classifying the extremely rapid eGFR declines in the G1, G2 and G3 groups were 0.69 (95% CI, 0.63 to 0.76), 0.71 (95% CI 0.69 to 0.74) and 0.79 (95% CI 0.75 to 0.83), respectively. The random forest model identified haemoglobin, albumin and C reactive protein as important characteristics. CONCLUSIONS: The random forest model could be useful in identifying patients with extremely rapid eGFR decline. TRIAL REGISTRATION: UMIN 000037476; This study was registered with the UMIN Clinical Trials Registry.

Prescription rate of erythropoietin-stimulating agents is low for patients with renal impairment under non-nephrology care in a tertiary-level academic medical center in Japan.

BACKGROUND: Erythropoiesis-stimulating agents (ESAs) and iron supplements may be prescribed appropriately under nephrology care. However, there are few reports detailing the differences in prescription rates of these therapies among clinical departments. METHODS: A total of 39,585 patients with renal impairment were enrolled from a database of 914,280 patients. Patients were selected based on an estimated glomerular filtration rate (eGFR) less than 60 ml/min/1.73 m2. There were eight clinical departments from internal medicine, including nephrology. We defined a hemoglobin level less than 11.0 g/dL as anemia and set 20% of transferrin saturation and 100 ng/mL of serum ferritin as cutoff points. We compared the prescription rates of ESAs and iron supplementation based on the hemoglobin level and iron status among the patients seen across the eight clinical departments. RESULTS: The lower the eGFR, the more the number of patients seen under nephrology care. The rates of patients with no prescription were 52.3, 39.9, 45.9, and 54.3% among those with hemoglobin levels of < 8, 8 ≤ < 9, 9 ≤ < 10, and 10 ≤ < 11 g/dL, respectively. Of the patients with less than 11.0 g/dL of hemoglobin, 77.3% were prescribed ESAs under nephrology care. Meanwhile, only 18.5 and 8.2% of patients were prescribed ESAs in clinical departments of internal medicine, other than nephrology, and non-internal medicine care, respectively. CONCLUSION: Treatment for anemia has not been sufficiently performed in patients with renal impairment under non-nephrology care in a real-world clinical setting.

Evasion of vaccine-induced humoral immunity by emerging sub-variants of SARS-CoV-2.

Background: Emergence of vaccine-escaping SARS-CoV-2 variants is a serious problem for global public health. The currently rampant Omicron has been shown to possess remarkable vaccine escape; however, the selection pressure exerted by vaccines might pave the way for other escape mutants in the near future. Materials & methods: For detection of neutralizing antibodies, the authors used the recently developed HiBiT-based virus-like particle neutralization test system. Sera after vaccination (two doses of Pfizer/BioNTech mRNA vaccine) were used to evaluate the neutralizing activity against various strains of SARS-CoV-2. Results: Beta+R346K, which was identified in the Philippines in August 2021, exhibited the highest vaccine resistance among the tested mutants. Surprisingly, Mu+K417N mutant exhibited almost no decrease in neutralization. Imdevimab retained efficacy against these strains. Conclusions: Mutations outside the receptor-binding domain contributed to vaccine escape. Both genomic surveillance and phenotypic analysis synergistically accelerate identifications of vaccine-escaping strains.

Prior to the Omicron variant, the SARS-CoV-2 Beta sub-variant found in the Philippines in August 2021 exhibited remarkable vaccine-escaping capacity. Although Omicron is, at the time of writing, causing most of the infections globally, both genomic surveillance and phenotypic analysis should be reinforced to accelerate the identification of newly emerging vaccine-escaping SARS-CoV-2 variants.

Molecular and Epidemiological Characterization of Emerging Immune-Escape Variants of SARS-CoV-2.

The successive emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants has presented a major challenge in the management of the coronavirus disease (COVID-19) pandemic. There are growing concerns regarding the emerging variants escaping vaccines or therapeutic neutralizing antibodies. In this study, we conducted an epidemiological survey to identify SARS-CoV-2 variants that are sporadically proliferating in vaccine-advanced countries. Subsequently, we created HiBiT-tagged virus-like particles displaying spike proteins derived from the variants to analyze the neutralizing efficacy of the BNT162b2 mRNA vaccine and several therapeutic antibodies. We found that the Mu variant and a derivative of the Delta strain with E484K and N501Y mutations significantly evaded vaccine-elicited neutralizing antibodies. This trend was also observed in the Beta and Gamma variants, although they are currently not prevalent. Although 95.2% of the vaccinees exhibited prominent neutralizing activity against the prototype strain, only 73.8 and 78.6% of the vaccinees exhibited neutralizing activity against the Mu and the Delta derivative variants, respectively. A long-term analysis showed that 88.8% of the vaccinees initially exhibited strong neutralizing activity against the currently circulating Delta strain; the number decreased to 31.6% for the individuals at 6 months after vaccination. Notably, these variants were shown to be resistant to several therapeutic antibodies. Our findings demonstrate the differential neutralization efficacy of the COVID-19 vaccine and monoclonal antibodies against circulating variants, suggesting the need for pandemic alerts and booster vaccinations against the currently prevalent variants.

Increasing tendency of urine protein is a risk factor for rapid eGFR decline in patients with CKD: A machine learning-based prediction model by using a big database.

Artificial intelligence is increasingly being adopted in medical fields to predict various outcomes. In particular, chronic kidney disease (CKD) is problematic because it often progresses to end-stage kidney disease. However, the trajectories of kidney function depend on individual patients. In this study, we propose a machine learning-based model to predict the rapid decline in kidney function among CKD patients by using a big hospital database constructed from the information of 118,584 patients derived from the electronic medical records system. The database included the estimated glomerular filtration rate (eGFR) of each patient, recorded at least twice over a period of 90 days. The data of 19,894 patients (16.8%) were observed to satisfy the CKD criteria. We characterized the rapid decline of kidney function by a decline of 30% or more in the eGFR within a period of two years and classified the available patients into two groups-those exhibiting rapid eGFR decline and those exhibiting non-rapid eGFR decline. Following this, we constructed predictive models based on two machine learning algorithms. Longitudinal laboratory data including urine protein, blood pressure, and hemoglobin were used as covariates. We used longitudinal statistics with a baseline corresponding to 90-, 180-, and 360-day windows prior to the baseline point. The longitudinal statistics included the exponentially smoothed average (ESA), where the weight was defined to be 0.9*(t/b), where t denotes the number of days prior to the baseline point and b denotes the decay parameter. In this study, b was taken to be 7 (7-day ESA). We used logistic regression (LR) and random forest (RF) algorithms based on Python code with scikit-learn library (https://scikit-learn.org/) for model creation. The areas under the curve for LR and RF were 0.71 and 0.73, respectively. The 7-day ESA of urine protein ranked within the first two places in terms of importance according to both models. Further, other features related to urine protein were likely to rank higher than the rest. The LR and RF models revealed that the degree of urine protein, especially if it exhibited an increasing tendency, served as a prominent risk factor associated with rapid eGFR decline.

Artificial intelligence predicts the progression of diabetic kidney disease using big data machine learning.

Artificial intelligence (AI) is expected to support clinical judgement in medicine. We constructed a new predictive model for diabetic kidney diseases (DKD) using AI, processing natural language and longitudinal data with big data machine learning, based on the electronic medical records (EMR) of 64,059 diabetes patients. AI extracted raw features from the previous 6 months as the reference period and selected 24 factors to find time series patterns relating to 6-month DKD aggravation, using a convolutional autoencoder. AI constructed the predictive model with 3,073 features, including time series data using logistic regression analysis. AI could predict DKD aggravation with 71% accuracy. Furthermore, the group with DKD aggravation had a significantly higher incidence of hemodialysis than the non-aggravation group, over 10 years (N = 2,900). The new predictive model by AI could detect progression of DKD and may contribute to more effective and accurate intervention to reduce hemodialysis.

Risk Prediction of Diabetic Nephropathy via Interpretable Feature Extraction from EHR Using Convolutional Autoencoder.

This paper describes a technology for predicting the aggravation of diabetic nephropathy from electronic health record (EHR). For the prediction, we used features extracted from event sequence of lab tests in EHR with a stacked convolutional autoencoder which can extract both local and global temporal information. The extracted features can be interpreted as similarities to a small number of typical sequences of lab tests, that may help us to understand the disease courses and to provide detailed health guidance. In our experiments on real-world EHRs, we confirmed that our approach performed better than baseline methods and that the extracted features were promising for understanding the disease.