Chemotherapy drug-induced nephrotoxicity limits clinical applications for treating cancers. Pyroptosis, a newly discovered programmed cell death, was recently reported to be associated with kidney ...diseases. However, the role of pyroptosis in chemotherapeutic drug-induced nephrotoxicity has not been fully clarified. Herein, we demonstrate that the chemotherapeutic drug cisplatin or doxorubicin, induces the cleavage of gasdermin E (GSDME) in cultured human renal tubular epithelial cells, in a time- and concentration-dependent manner. Morphologically, cisplatin- or doxorubicin-treated renal tubular epithelial cells exhibit large bubbles emerging from the cell membrane. Furthermore, activation of caspase 3, not caspase 9, is associated with GSDME cleavage in cisplatin- or doxorubicin-treated renal tubular epithelial cells. Meanwhile, silencing GSDME alleviates cisplatin- or doxorubicin-induced HK-2 cell pyroptosis by increasing cell viability and decreasing LDH release. In addition, treatment with Ac-DMLD-CMK, a polypeptide targeting mouse caspase 3-Gsdme signaling, inhibits caspase 3 and Gsdme activation, alleviates the deterioration of kidney function, attenuates renal tubular epithelial cell injury, and reduces inflammatory cytokine secretion in vivo. Specifically, GSDME cleavage depends on ERK and JNK signaling. NAC, a reactive oxygen species (ROS) inhibitor, reduces GSDME cleavage through JNK signaling in human renal tubular epithelial cells. Thus, we speculate that renal tubular epithelial cell pyroptosis induced by chemotherapy drugs is mediated by ROS-JNK-caspase 3-GSDME signaling, implying that therapies targeting GSDME may prove efficacious in overcoming chemotherapeutic drug-induced nephrotoxicity.
Randomized controlled trials (RCTs) are regarded as the most reputable source of evidence. In some studies, factors beyond the intervention itself may contribute to the measured effect, an occurrence ...known as heterogeneity of treatment effect (HTE). If the RCT population differs from the real-world population on factors that induce HTE, the trials effect will not replicate. The RCTs eligibility criteria should identify the sub-population in which its evidence will replicate. However, the extent to which the eligibility criteria identify the appropriate population is unknown, which raises concerns for generalizability. We compared reported data from RCTs with real-world data from the electronic health records of a large, academic medical center that was curated according to RCT eligibility criteria. Our results show fundamental differences between the RCT population and our observational cohorts, which suggests that eligibility criteria may be insufficient for identifying the applicable real-world population in which RCT evidence will replicate.
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•The completeness of EHR data is dependent upon the definition of completeness being used.•We present four definitions of EHR completeness: documentation, breadth, density, and ...predictive.•Each definition results in a different set of complete patient records.•Researchers reusing EHR data should report completeness limitations and findings.
We demonstrate the importance of explicit definitions of electronic health record (EHR) data completeness and how different conceptualizations of completeness may impact findings from EHR-derived datasets. This study has important repercussions for researchers and clinicians engaged in the secondary use of EHR data. We describe four prototypical definitions of EHR completeness: documentation, breadth, density, and predictive completeness. Each definition dictates a different approach to the measurement of completeness. These measures were applied to representative data from NewYork–Presbyterian Hospital’s clinical data warehouse. We found that according to any definition, the number of complete records in our clinical database is far lower than the nominal total. The proportion that meets criteria for completeness is heavily dependent on the definition of completeness used, and the different definitions generate different subsets of records. We conclude that the concept of completeness in EHR is contextual. We urge data consumers to be explicit in how they define a complete record and transparent about the limitations of their data.
We present Doc2Hpo, an interactive web application that enables interactive and efficient phenotype concept curation from clinical text with automated concept normalization using the Human Phenotype ...Ontology (HPO). Users can edit the HPO concepts automatically extracted by Doc2Hpo in real time, and export the extracted HPO concepts into gene prioritization tools. Our evaluation showed that Doc2Hpo significantly reduced manual effort while achieving high accuracy in HPO concept curation. Doc2Hpo is freely available at https://impact2.dbmi.columbia.edu/doc2hpo/. The source code is available at https://github.com/stormliucong/doc2hpo for local installation for protected health data.
Abstract
Background
Clinical trial protocols are the foundation for advancing medical sciences, however, the extraction of accurate and meaningful information from the original clinical trials is ...very challenging due to the complex and unstructured texts of such documents. Named entity recognition (NER) is a fundamental and necessary step to process and standardize the unstructured text in clinical trials using Natural Language Processing (NLP) techniques.
Methods
In this study we fine-tuned pre-trained language models to support the NER task on clinical trial eligibility criteria. We systematically investigated four pre-trained contextual embedding models for the biomedical domain (i.e., BioBERT, BlueBERT, PubMedBERT, and SciBERT) and two models for the open domains (BERT and SpanBERT), for NER tasks using three existing clinical trial eligibility criteria corpora. In addition, we also investigated the feasibility of data augmentation approaches and evaluated their performance.
Results
Our evaluation results using tenfold cross-validation show that domain-specific transformer models achieved better performance than the general transformer models, with the best performance obtained by the PubMedBERT model (F1-scores of 0.715, 0.836, and 0.622 for the three corpora respectively). The data augmentation results show that it is feasible to leverage additional corpora to improve NER performance.
Conclusions
Findings from this study not only demonstrate the importance of contextual embeddings trained from domain-specific corpora, but also shed lights on the benefits of leveraging multiple data sources for the challenging NER task in clinical trial eligibility criteria text.
Recent advances in large language models (LLMs) have demonstrated remarkable successes in zero- and few-shot performance on various downstream tasks, paving the way for applications in high-stakes ...domains. In this study, we systematically examine the capabilities and limitations of LLMs, specifically GPT-3.5 and ChatGPT, in performing zero-shot medical evidence summarization across six clinical domains. We conduct both automatic and human evaluations, covering several dimensions of summary quality. Our study demonstrates that automatic metrics often do not strongly correlate with the quality of summaries. Furthermore, informed by our human evaluations, we define a terminology of error types for medical evidence summarization. Our findings reveal that LLMs could be susceptible to generating factually inconsistent summaries and making overly convincing or uncertain statements, leading to potential harm due to misinformation. Moreover, we find that models struggle to identify the salient information and are more error-prone when summarizing over longer textual contexts.
Integration of detailed phenotype information with genetic data is well established to facilitate accurate diagnosis of hereditary disorders. As a rich source of phenotype information, electronic ...health records (EHRs) promise to empower diagnostic variant interpretation. However, how to accurately and efficiently extract phenotypes from heterogeneous EHR narratives remains a challenge. Here, we present EHR-Phenolyzer, a high-throughput EHR framework for extracting and analyzing phenotypes. EHR-Phenolyzer extracts and normalizes Human Phenotype Ontology (HPO) concepts from EHR narratives and then prioritizes genes with causal variants on the basis of the HPO-coded phenotype manifestations. We assessed EHR-Phenolyzer on 28 pediatric individuals with confirmed diagnoses of monogenic diseases and found that the genes with causal variants were ranked among the top 100 genes selected by EHR-Phenolyzer for 16/28 individuals (p < 2.2 × 10−16), supporting the value of phenotype-driven gene prioritization in diagnostic sequence interpretation. To assess the generalizability, we replicated this finding on an independent EHR dataset of ten individuals with a positive diagnosis from a different institution. We then assessed the broader utility by examining two additional EHR datasets, including 31 individuals who were suspected of having a Mendelian disease and underwent different types of genetic testing and 20 individuals with positive diagnoses of specific Mendelian etiologies of chronic kidney disease from exome sequencing. Finally, through several retrospective case studies, we demonstrated how combined analyses of genotype data and deep phenotype data from EHRs can expedite genetic diagnoses. In summary, EHR-Phenolyzer leverages EHR narratives to automate phenotype-driven analysis of clinical exomes or genomes, facilitating the broader implementation of genomic medicine.
Purpose
Prescribing guideline-recommended anti-emetics is an effective strategy to prevent CINV. However, the rate of guideline-concordant care is not well-understood. The purpose of this study was ...to describe the proportion of pediatric, adolescent, and young adult patients receiving HEC or MEC who received guideline-concordant antiemetic prophylaxis for acute CINV and to identify potential predictors of guideline-concordant antiemetic prophylaxis.
Methods
Using electronic health record data from 2016 through 2018, a retrospective single-institution cohort study was conducted to investigate how often patients less than 26 years of age receiving moderately or highly emetogenic chemotherapy receive guideline-concordant prophylaxis for acute CINV. Guideline-concordant care was defined according to guidelines from the Pediatric Oncology Group of Ontario for patients < 18 years and the American Society of Clinical Oncology for those ≥ 18 years. Independent variables included: sex, age, insurance status, race, ethnicity, cancer type, chemotherapy regimen, clinical setting, chemotherapy emetogenicity, and patient location. Predictors of receiving guideline-concordant care were determined using multiple logistic regression.
Results
Of 180 eligible patients, 65 (36.1%) received guideline-concordant care. In multivariable analysis, being treated in adult oncology setting (aOR 14.3, CI
95
5.3–38.6), with a cisplatin-based regimen (aOR 3.5, CI
95
1.4–9.0), solid tumor diagnosis (aOR 2.2, CI
95
1.0–4.8), and commercial insurance (aOR 2.4, CI
95
1.1–5.2) were associated with significantly higher likelihood of receiving guideline-concordant care.
Conclusions
Multi-level factors were associated with receiving guideline concordant care for prevention of CINV in children, adolescents, and young adults receiving emetogenic chemotherapy. These findings can inform current efforts to optimize implementation strategies for supportive care guidelines.
Chronic kidney disease (CKD) is a common complex condition associated with high morbidity and mortality. Polygenic prediction could enhance CKD screening and prevention; however, this approach has ...not been optimized for ancestrally diverse populations. By combining APOL1 risk genotypes with genome-wide association studies (GWAS) of kidney function, we designed, optimized and validated a genome-wide polygenic score (GPS) for CKD. The new GPS was tested in 15 independent cohorts, including 3 cohorts of European ancestry (n = 97,050), 6 cohorts of African ancestry (n = 14,544), 4 cohorts of Asian ancestry (n = 8,625) and 2 admixed Latinx cohorts (n = 3,625). We demonstrated score transferability with reproducible performance across all tested cohorts. The top 2% of the GPS was associated with nearly threefold increased risk of CKD across ancestries. In African ancestry cohorts, the APOL1 risk genotype and polygenic component of the GPS had additive effects on the risk of CKD.
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