Enabling scalable clinical interpretation of ML-based phenotypes using real world data

The availability of large and deep electronic healthcare records (EHR) datasets has the potential to enable a better understanding of real-world patient journeys, and to identify novel subgroups of patients. ML-based aggregation of EHR data is mostly tool-driven, i.e., building on available or newly developed methods. However, these methods, their input requirements, and, importantly, resulting output are frequently difficult to interpret, especially without in-depth data science or statistical training. This endangers the final step of analysis where an actionable and clinically meaningful interpretation is needed.This study investigates approaches to perform patient stratification analysis at scale using large EHR datasets and multiple clustering methods for clinical research. We have developed several tools to facilitate the clinical evaluation and interpretation of unsupervised patient stratification results, namely pattern screening, meta clustering, surrogate modeling, and curation. These tools can be used at different stages within the analysis. As compared to a standard analysis approach, we demonstrate the ability to condense results and optimize analysis time. In the case of meta clustering, we demonstrate that the number of patient clusters can be reduced from 72 to 3 in one example. In another stratification result, by using surrogate models, we could quickly identify that heart failure patients were stratified if blood sodium measurements were available. As this is a routine measurement performed for all patients with heart failure, this indicated a data bias. By using further cohort and feature curation, these patients and other irrelevant features could be removed to increase the clinical meaningfulness. These examples show the effectiveness of the proposed methods and we hope to encourage further research in this field.Comment: 27 pages, 14 figure

International genome-wide meta-analysis identifies new primary biliary cirrhosis risk loci and targetable pathogenic pathways.

Primary biliary cirrhosis (PBC) is a classical autoimmune liver disease for which effective immunomodulatory therapy is lacking. Here we perform meta-analyses of discovery data sets from genome-wide association studies of European subjects (n=2,764 cases and 10,475 controls) followed by validation genotyping in an independent cohort (n=3,716 cases and 4,261 controls). We discover and validate six previously unknown risk loci for PBC (Pcombined<5 × 10(-8)) and used pathway analysis to identify JAK-STAT/IL12/IL27 signalling and cytokine-cytokine pathways, for which relevant therapies exist

Genetic mechanisms of critical illness in COVID-19.

Host-mediated lung inflammation is present1, and drives mortality2, in the critical illness caused by coronavirus disease 2019 (COVID-19). Host genetic variants associated with critical illness may identify mechanistic targets for therapeutic development3. Here we report the results of the GenOMICC (Genetics Of Mortality In Critical Care) genome-wide association study in 2,244 critically ill patients with COVID-19 from 208 UK intensive care units. We have identified and replicated the following new genome-wide significant associations: on chromosome 12q24.13 (rs10735079, P = 1.65 × 10-8) in a gene cluster that encodes antiviral restriction enzyme activators (OAS1, OAS2 and OAS3); on chromosome 19p13.2 (rs74956615, P = 2.3 × 10-8) near the gene that encodes tyrosine kinase 2 (TYK2); on chromosome 19p13.3 (rs2109069, P = 3.98 ×  10-12) within the gene that encodes dipeptidyl peptidase 9 (DPP9); and on chromosome 21q22.1 (rs2236757, P = 4.99 × 10-8) in the interferon receptor gene IFNAR2. We identified potential targets for repurposing of licensed medications: using Mendelian randomization, we found evidence that low expression of IFNAR2, or high expression of TYK2, are associated with life-threatening disease; and transcriptome-wide association in lung tissue revealed that high expression of the monocyte-macrophage chemotactic receptor CCR2 is associated with severe COVID-19. Our results identify robust genetic signals relating to key host antiviral defence mechanisms and mediators of inflammatory organ damage in COVID-19. Both mechanisms may be amenable to targeted treatment with existing drugs. However, large-scale randomized clinical trials will be essential before any change to clinical practice

Genomic investigations of unexplained acute hepatitis in children

Since its first identification in Scotland, over 1,000 cases of unexplained paediatric hepatitis in children have been reported worldwide, including 278 cases in the UK1. Here we report an investigation of 38 cases, 66 age-matched immunocompetent controls and 21 immunocompromised comparator participants, using a combination of genomic, transcriptomic, proteomic and immunohistochemical methods. We detected high levels of adeno-associated virus 2 (AAV2) DNA in the liver, blood, plasma or stool from 27 of 28 cases. We found low levels of adenovirus (HAdV) and human herpesvirus 6B (HHV-6B) in 23 of 31 and 16 of 23, respectively, of the cases tested. By contrast, AAV2 was infrequently detected and at low titre in the blood or the liver from control children with HAdV, even when profoundly immunosuppressed. AAV2, HAdV and HHV-6 phylogeny excluded the emergence of novel strains in cases. Histological analyses of explanted livers showed enrichment for T cells and B lineage cells. Proteomic comparison of liver tissue from cases and healthy controls identified increased expression of HLA class 2, immunoglobulin variable regions and complement proteins. HAdV and AAV2 proteins were not detected in the livers. Instead, we identified AAV2 DNA complexes reflecting both HAdV-mediated and HHV-6B-mediated replication. We hypothesize that high levels of abnormal AAV2 replication products aided by HAdV and, in severe cases, HHV-6B may have triggered immune-mediated hepatic disease in genetically and immunologically predisposed children

One in a million, or one in thousand: What is the morbidity of rabies in India?

Rabies is the 10th biggest cause of death due to infectious diseases worldwide. The annual death toll is around 50 000–60 000, with 99% occurring in tropical developing countries. Based on available evidence, a fair estimate of rabies burden in India is 2.74 rabies cases per 100 000 people annually

Pretreatment prediction of response to ursodeoxycholic acid in primary biliary cholangitis: development and validation of the UDCA Response Score

Background: Treatment guidelines recommend a stepwise approach to primary biliary cholangitis: all patients begin treatment with ursodeoxycholic acid (UDCA) monotherapy and those with an inadequate biochemical response after 12 months are subsequently considered for second-line therapies. However, as a result, patients at the highest risk can wait the longest for effective treatment. We determined whether UDCA response can be accurately predicted using pretreatment clinical parameters. Methods: We did logistic regression analysis of pretreatment variables in a discovery cohort of patients in the UK with primary biliary cholangitis to derive the best-fitting model of UDCA response, defined as alkaline phosphatase less than 1·67 times the upper limit of normal (ULN), measured after 12 months of treatment with UDCA. We validated the model in an external cohort of patients with primary biliary cholangitis and treated with UDCA in Italy. Additionally, we assessed correlations between model predictions and key histological features, such as biliary injury and fibrosis, on liver biopsy samples. Findings: 2703 participants diagnosed with primary biliary cholangitis between Jan 1, 1998, and May 31, 2015, were included in the UK-PBC cohort for derivation of the model. The following pretreatment parameters were associated with lower probability of UDCA response: higher alkaline phosphatase concentration (p&lt;0·0001), higher total bilirubin concentration (p=0·0003), lower aminotransferase concentration (p=0·0012), younger age (p&lt;0·0001), longer interval from diagnosis to the start of UDCA treatment (treatment time lag, p&lt;0·0001), and worsening of alkaline phosphatase concentration from diagnosis (p&lt;0·0001). Based on these variables, we derived a predictive score of UDCA response. In the external validation cohort, 460 patients diagnosed with primary biliary cholangitis were treated with UDCA, with follow-up data until May 31, 2016. In this validation cohort, the area under the receiver operating characteristic curve for the score was 0·83 (95% CI 0·79–0·87). In 20 liver biopsy samples from patients with primary biliary cholangitis, the UDCA response score was associated with ductular reaction (r=–0·556, p=0·0130) and intermediate hepatocytes (probability of response was 0·90 if intermediate hepatocytes were absent vs 0·51 if present). Interpretation: We have derived and externally validated a model based on pretreatment variables that accurately predicts UDCA response. Association with histological features provides face validity. This model provides a basis to explore alternative approaches to treatment stratification in patients with primary biliary cholangitis. Funding: UK Medical Research Council and University of Milan-Bicocca

Pretreatment prediction of response to ursodeoxycholic acid in primary biliary cholangitis: development and validation of the UDCA Response Score.

BACKGROUND: Treatment guidelines recommend a stepwise approach to primary biliary cholangitis: all patients begin treatment with ursodeoxycholic acid (UDCA) monotherapy and those with an inadequate biochemical response after 12 months are subsequently considered for second-line therapies. However, as a result, patients at the highest risk can wait the longest for effective treatment. We determined whether UDCA response can be accurately predicted using pretreatment clinical parameters. METHODS: We did logistic regression analysis of pretreatment variables in a discovery cohort of patients in the UK with primary biliary cholangitis to derive the best-fitting model of UDCA response, defined as alkaline phosphatase less than 1·67 times the upper limit of normal (ULN), measured after 12 months of treatment with UDCA. We validated the model in an external cohort of patients with primary biliary cholangitis and treated with UDCA in Italy. Additionally, we assessed correlations between model predictions and key histological features, such as biliary injury and fibrosis, on liver biopsy samples. FINDINGS: 2703 participants diagnosed with primary biliary cholangitis between Jan 1, 1998, and May 31, 2015, were included in the UK-PBC cohort for derivation of the model. The following pretreatment parameters were associated with lower probability of UDCA response: higher alkaline phosphatase concentration (p<0·0001), higher total bilirubin concentration (p=0·0003), lower aminotransferase concentration (p=0·0012), younger age (p<0·0001), longer interval from diagnosis to the start of UDCA treatment (treatment time lag, p<0·0001), and worsening of alkaline phosphatase concentration from diagnosis (p<0·0001). Based on these variables, we derived a predictive score of UDCA response. In the external validation cohort, 460 patients diagnosed with primary biliary cholangitis were treated with UDCA, with follow-up data until May 31, 2016. In this validation cohort, the area under the receiver operating characteristic curve for the score was 0·83 (95% CI 0·79-0·87). In 20 liver biopsy samples from patients with primary biliary cholangitis, the UDCA response score was associated with ductular reaction (r=-0·556, p=0·0130) and intermediate hepatocytes (probability of response was 0·90 if intermediate hepatocytes were absent vs 0·51 if present). INTERPRETATION: We have derived and externally validated a model based on pretreatment variables that accurately predicts UDCA response. Association with histological features provides face validity. This model provides a basis to explore alternative approaches to treatment stratification in patients with primary biliary cholangitis. FUNDING: UK Medical Research Council and University of Milan-Bicocca