Impact of two waves of Sars-Cov2 outbreak on the number, clinical presentation, care trajectories and survival of patients newly referred for a colorectal cancer: A French multicentric cohort study from a large group of university hospitals.

The SARS-Cov2 may have impaired care trajectories, patient overall survival (OS), tumor stage at initial presentation for new colorectal cancer (CRC) cases. This study aimed at assessing those indicators before and after the beginning of the pandemic in France. In this retrospective cohort study, we collected prospectively the clinical data of the 11.4 million of patients referred to the Greater Paris University Hospitals (AP-HP). We identified new CRC cases between 1 January 2018 and 31 December 2020, and compared indicators for 2018-2019 to 2020. pTNM tumor stage was extracted from postoperative pathology reports for localized colon cancer, and metastatic status was extracted from CT-scan baseline text reports. Between 2018 and 2020, 3602 and 1083 new colon and rectal cancers were referred to the AP-HP, respectively. The 1-year OS rates reached 94%, 93% and 76% for new CRC patients undergoing a resection of the primary tumor, in 2018-2019, in 2020 without any Sars-Cov2 infection and in 2020 with a Sars-Cov2 infection, respectively (HR 3.78, 95% CI 2.1-7.1). For patients undergoing other kind of anticancer treatment, the percentages are 64%, 66% and 27% (HR 2.1, 95% CI 1.4-3.3). Tumor stage at initial presentation, emergency level of primary tumor resection, delays between the first multidisciplinary meeting and the first anticancer treatment did not differ over time. The SARS-Cov2 pandemic has been associated with less newly diagnosed CRC patients and worse 1-year OS rates attributable to the infection itself rather than to its impact on hospital care delivery or tumor stage at initial presentation.

What level of D-dimers can safely exclude pulmonary embolism in COVID-19 patients presenting to the emergency department?

OBJECTIVES: To identify which level of D-dimer would allow the safe exclusion of pulmonary embolism (PE) in COVID-19 patients presenting to the emergency department (ED). METHODS: This retrospective study was conducted on the COVID database of Assistance Publique - Hôpitaux de Paris (AP-HP). COVID-19 patients who presented at the ED of AP-HP hospitals between March 1 and May 15, 2020, and had CTPA following D-dimer dosage within 48h of presentation were included. The D-dimer sensitivity, specificity, and positive and negative predictive values were calculated for different D-dimer thresholds, as well as the false-negative and failure rates, and the number of CTPAs potentially avoided. RESULTS: A total of 781 patients (mean age 62.0 years, 53.8% men) with positive RT-PCR for SARS-Cov-2 were included and 60 of them (7.7%) had CTPA-confirmed PE. Their median D-dimer level was significantly higher than that of patients without PE (4,013 vs 1,198 ng·mL-1, p < 0.001). Using 500 ng·mL-1, or an age-adjusted cut-off for patients > 50 years, the sensitivity and the NPV were above 90%. With these thresholds, 17.1% and 31.5% of CTPAs could have been avoided, respectively. Four of the 178 patients who had a D-dimer below the age-adjusted cutoff had PE, leading to an acceptable failure rate of 2.2%. Using higher D-dimer cut-offs could have avoided more CTPAs, but would have lowered the sensitivity and increased the failure rate. CONCLUSION: The same D-Dimer thresholds as those validated in non-COVID outpatients should be used to safely rule out PE. KEY POINTS: • The median D-dimer level was significantly higher in COVID-19 patients with PE as compared to those without PE (4,013 ng·mL-1 vs 1,198 ng·mL-1 respectively, p < 0.001). • Using 500 ng·mL-1, or an age-adjusted D-dimer cut-off to exclude pulmonary embolism, the sensitivity and negative predictive value were above 90%. • Higher cut-offs would lead to a reduction in the sensitivity below 85% and an increase in the failure rate, especially for patients under 50 years.

Association of Antihypertensive Agents with the Risk of In-Hospital Death in Patients with Covid-19.

PURPOSE: The role of angiotensin receptor blockers (ARB), angiotensin-converting enzyme inhibitors (ACEi), or other antihypertensive agents in the case of Covid-19 remains controversial. We aimed to investigate the association between antihypertensive agent exposure and in-hospital mortality in patients with Covid-19. METHODS: We performed a retrospective multicenter cohort study on patients hospitalized between February 1 and May 15, 2020. All patients had been followed up for at least 30 days. RESULTS: Of the 8078 hospitalized patients for Covid-19, 3686 (45.6%) had hypertension and were included in the study. In this population, the median age was 75.4 (IQR, 21.5) years and 57.1% were male. Overall in-hospital 30-day mortality was 23.1%. The main antihypertensive pharmacological classes used were calcium channel blockers (CCB) (n=1624, 44.1%), beta-blockers (n=1389, 37.7%), ARB (n=1154, 31.3%), and ACEi (n=998, 27.1%). The risk of mortality was lower in CCB (aOR, 0.83 [0.70-0.99]) and beta-blockers (aOR, 0.80 [0.67-0.95]) users and non-significant in ARB (aOR, 0.88 [0.72-1.06]) and ACEi (aOR, 0.83 [0.68-1.02]) users, compared to non-users. These results remain consistent for patients receiving CCB, beta-blocker, or ARB as monotherapies. CONCLUSION: This large multicenter retrospective of Covid-19 patients with hypertension found a reduced mortality among CCB and beta-blockers users, suggesting a putative protective effect. Our findings did not show any association between the use of renin-angiotensin-aldosterone system inhibitors and the risk of in-hospital death. Although they need to be confirmed in further studies, these results support the continuation of antihypertensive agents in patients with Covid-19, in line with the current guidelines.

Privacy-preserving mimic models for clinical named entity recognition in French.

A vast amount of crucial information about patients resides solely in unstructured clinical narrative notes. There has been a growing interest in clinical Named Entity Recognition (NER) task using deep learning models. Such approaches require sufficient annotated data. However, there is little publicly available annotated corpora in the medical field due to the sensitive nature of the clinical text. In this paper, we tackle this problem by building privacy-preserving shareable models for French clinical Named Entity Recognition using the mimic learning approach to enable the knowledge transfer through a teacher model trained on a private corpus to a student model. This student model could be publicly shared without any access to the original sensitive data. We evaluated three privacy-preserving models using three medical corpora and compared the performance of our models to those of baseline models such as dictionary-based models. An overall macro F-measure of 70.6% could be achieved by a student model trained using silver annotations produced by the teacher model, compared to 85.7% for the original private teacher model. Our results revealed that these privacy-preserving mimic learning models offer a good compromise between performance and data privacy preservation.

The Epidemiology of Patients' Email Addresses in a French University Hospital: Case-Control Study.

BACKGROUND: Health care professionals are caught between the wish of patients to speed up health-related communication via emails and the need for protecting health information. OBJECTIVE: We aimed to analyze the demographic characteristics of patients providing an email, and study the distribution of emails' domain names. METHODS: We used the information system of the European Hospital Georges Pompidou (HEGP) to identify patients who provided an email address. We used a 1:1 matching strategy to study the demographic characteristics of the patients associated with the presence of an email, and described the characteristics of the emails used (in terms of types of emails-free, business, and personal). RESULTS: Overall, 4.22% (41,004/971,822) of the total population of patients provided an email address. The year of last contact with the patient is the strongest driver of the presence of an email address (odds ratio [OR] 20.8, 95% CI 18.9-22.9). Patients more likely to provide an email address were treated for chronic conditions and were more likely born between 1950 and 1969 (taking patients born before 1950 as reference [OR 1.60, 95% CI 1.54-1.67], and compared to those born after 1990 [OR 0.56, 95% CI 0.53-0.59]). Of the 41,004 email addresses collected, 37,779 were associated with known email providers, 31,005 email addresses were associated with Google, Microsoft, Orange, and Yahoo!, 2878 with business emails addresses, and 347 email addresses with personalized domain names. CONCLUSIONS: Emails have been collected only recently in our institution. The importance of the year of last contact probably reflects this recent change in contact information collection policy. The demographic characteristics and especially the age distribution are likely the result of a population bias in the hospital: patients providing email are more likely to be treated for chronic diseases. A risk analysis of the use of email revealed several situations that could constitute a breach of privacy that is both likely and with major consequences. Patients treated for chronic diseases are more likely to provide an email address, and are also more at risk in case of privacy breach. Several common situations could expose their private information. We recommend a very restrictive use of the emails for health communication.

A framework for validating AI in precision medicine: considerations from the European ITFoC consortium.

BACKGROUND: Artificial intelligence (AI) has the potential to transform our healthcare systems significantly. New AI technologies based on machine learning approaches should play a key role in clinical decision-making in the future. However, their implementation in health care settings remains limited, mostly due to a lack of robust validation procedures. There is a need to develop reliable assessment frameworks for the clinical validation of AI. We present here an approach for assessing AI for predicting treatment response in triple-negative breast cancer (TNBC), using real-world data and molecular -omics data from clinical data warehouses and biobanks. METHODS: The European "ITFoC (Information Technology for the Future Of Cancer)" consortium designed a framework for the clinical validation of AI technologies for predicting treatment response in oncology. RESULTS: This framework is based on seven key steps specifying: (1) the intended use of AI, (2) the target population, (3) the timing of AI evaluation, (4) the datasets used for evaluation, (5) the procedures used for ensuring data safety (including data quality, privacy and security), (6) the metrics used for measuring performance, and (7) the procedures used to ensure that the AI is explainable. This framework forms the basis of a validation platform that we are building for the "ITFoC Challenge". This community-wide competition will make it possible to assess and compare AI algorithms for predicting the response to TNBC treatments with external real-world datasets. CONCLUSIONS: The predictive performance and safety of AI technologies must be assessed in a robust, unbiased and transparent manner before their implementation in healthcare settings. We believe that the consideration of the ITFoC consortium will contribute to the safe transfer and implementation of AI in clinical settings, in the context of precision oncology and personalized care.

Impact of obesity on surgical and oncologic outcomes in patients with endometrial cancer treated with a robotic approach.

AIM: The surgical treatment of endometrial cancer (EC) can be more complicated in obese patients. Robotic surgery could simplify the surgical approach in these patients. The aim of our study was to compare the outcomes of robotic surgery in obese (body mass index ≥30 kg/m2 ) and nonobese patients. METHODS: We performed a retrospective study on patients with EC benefitting from a robotic approach in our institution. The primary outcome was the 5-year overall survival (OS). We also assessed the 5-year recurrence-free survival (RFS), type of surgery, laparotomy conversion rate, adjuvant treatment and postoperative morbidity. RESULTS: We analyzed 175 consecutive patients with EC who underwent robotic surgery, 42 patients with obesity and 133 patients without. The median follow-up length was 37 months [1-120]. The OS rate was 97% in the whole population and the RFS was 74%. Obesity did not impact prognosis. Laparotomy conversion rate was low in both groups (5% in patients with obesity vs 3%, P = 0.619). There were no significant differences in terms of postoperative complications (5 vs 9%, P = 0.738). There were significantly less pelvic lymphadenectomies in patients with obesity (5 vs 12%, P = 0.005). In the subgroup of patients with high-risk EC, rate of lymphadenectomy and of adjuvant treatments did not differ between patients with or without obesity. CONCLUSION: Obese patients with EC can be safely treated with a robotic approach, with a low complication rate and similar oncological outcomes compared to nonobese patients.

HPV circulating tumoral DNA quantification by droplet-based digital PCR: A promising predictive and prognostic biomarker for HPV-associated oropharyngeal cancers.

We aimed to determine whether pretherapeutic assessment of HPV circulating tumoral DNA (HPV ctDNA) by droplet-based digital PCR (ddPCR) could constitute a predictive and prognostic biomarker for HPV-associated oropharyngeal squamous cell carcinoma (OPSCC). A mono-institutional prospective biomarker study on 66 patients with p16+/HPV16-positive oropharyngeal squamous cell carcinoma (OPSCC) was conducted in European Georges Pompidou Hospital, Paris, France. Blood samples were collected at the time of diagnosis before any treatment. Optimized digital PCR assays were used to quantify HPV16 ctDNA. Forty-seven (71%) patients showed a positive pretherapeutic HPV ctDNA at time of diagnosis. Interestingly, the quantity of HPV16 ctDNA at baseline, as assessed by ddPCR, was significantly correlated with the T/N/M status or OPSCC stages according to the 2018 new staging criteria for high-risk human papillomavirus (HR HPV) related OPSCC from American Joint Committee on Cancer (AJCC). Moreover, all recurrences and the majority (83%) of death reported events occurred in patients with positive HPV16 ctDNA at baseline. Finally, when posttreatment blood samples were available (n = 6), the kinetic of pretreatment/posttreatment HPV16 ctDNA was clearly associated with treatment success or failure. HPV ctDNA monitoring by ddPCR could constitute a useful and noninvasive dynamic biomarker to select HR HPV-related OPSCC patients eligible for potential treatment de-escalation and to monitor treatment response.

Identification of TP53 mutated group using a molecular and immunohistochemical classification of endometrial carcinoma to improve prognostic evaluation for adjuvant treatments.

INTRODUCTION: Molecular classification of endometrial carcinoma has been proposed to predict survival. However, its role in patient management remains to be determined. We aimed to identify whether a molecular and immunohistochemical classification of endometrial carcinoma could improve decision-making for adjuvant therapy. METHODS: All consecutive patients treated for endometrial carcinoma between 2010 and 2017 at Cochin University Hospital were included. Clinical risk of relapse was based on European Society for Medical Oncology-European Society of Gynaecological Oncology-European SocieTy for Radiotherapy & Oncology (ESMO-ESGO-ESTRO) consensus. The clinical event of interest was event-free survival. Formalin-fixed paraffin-embedded tissue samples were processed for histopathological analysis and DNA extraction. The nuclear expression of mismatch repair and TP53 proteins was analyzed by immunohistochemistry. Next-generation sequencing of a panel of 15 genes including TP53 and POLE was performed using Ampliseq panels on Ion Torrent PGM (ThermoFisher). Tumors were allocated into four molecular groups using a sequential method based on next-generation sequencing and immunohistochemistry data: (1) POLE/ultramutated-like; (2) MSI/hypermutated-like (mismatch repair-deficient); (3) TP53-mutated (without POLE mutations or mismatch repair deficiency); (4) not otherwise specified (the remaining tumors). RESULTS: 159 patients were included; 125 tumors were available for molecular characterization and distributed as follows: (1) POLE/ultramutated-like: n=4 (3%); (2) MSI/hypermutated-like: n=35 (30%); (3) TP53-mutated: n=30 (25%); and (4) not otherwise specified: n=49 (42%). Assessing the TP53 status by immunohistochemistry only rather than next-generation sequencing would have misclassified 6 tumors (5%). TP53-mutated tumors were associated with poor prognosis, independently of International Federation of Gynecology and Obstetrics (FIGO) stage and histological grade (Cox-based adjusted hazard ratio (aHR) 5.54, 95% CI 2.30 to 13.4), and independently of clinical risk of relapse (aHR 3.92, 95% CI 1.59 to 9.64). Among patients with FIGO stage I-II tumors, 6 (38%) TP53-mutated tumors had low/intermediate clinical risk of relapse and did not receive adjuvant chemotherapy or radiotherapy. CONCLUSION: Endometrial carcinoma molecular classification identified potentially under-treated patients with poor molecular prognosis despite being at low/intermediate clinical risk of relapse. Consideration of molecular classification in adjuvant therapeutic decisions should be evaluated in prospective trials.

Natural Language Processing for Rapid Response to Emergent Diseases: Case Study of Calcium Channel Blockers and Hypertension in the COVID-19 Pandemic.

BACKGROUND: A novel disease poses special challenges for informatics solutions. Biomedical informatics relies for the most part on structured data, which require a preexisting data or knowledge model; however, novel diseases do not have preexisting knowledge models. In an emergent epidemic, language processing can enable rapid conversion of unstructured text to a novel knowledge model. However, although this idea has often been suggested, no opportunity has arisen to actually test it in real time. The current coronavirus disease (COVID-19) pandemic presents such an opportunity. OBJECTIVE: The aim of this study was to evaluate the added value of information from clinical text in response to emergent diseases using natural language processing (NLP). METHODS: We explored the effects of long-term treatment by calcium channel blockers on the outcomes of COVID-19 infection in patients with high blood pressure during in-patient hospital stays using two sources of information: data available strictly from structured electronic health records (EHRs) and data available through structured EHRs and text mining. RESULTS: In this multicenter study involving 39 hospitals, text mining increased the statistical power sufficiently to change a negative result for an adjusted hazard ratio to a positive one. Compared to the baseline structured data, the number of patients available for inclusion in the study increased by 2.95 times, the amount of available information on medications increased by 7.2 times, and the amount of additional phenotypic information increased by 11.9 times. CONCLUSIONS: In our study, use of calcium channel blockers was associated with decreased in-hospital mortality in patients with COVID-19 infection. This finding was obtained by quickly adapting an NLP pipeline to the domain of the novel disease; the adapted pipeline still performed sufficiently to extract useful information. When that information was used to supplement existing structured data, the sample size could be increased sufficiently to see treatment effects that were not previously statistically detectable.

Exploring and visualizing multidimensional data in translational research platforms.

The unprecedented advances in technology and scientific research over the past few years have provided the scientific community with new and more complex forms of data. Large data sets collected from single groups or cross-institution consortiums containing hundreds of omic and clinical variables corresponding to thousands of patients are becoming increasingly commonplace in the research setting. Before any core analyses are performed, visualization often plays a key role in the initial phases of research, especially for projects where no initial hypotheses are dominant. Proper visualization of data at a high level facilitates researcher's abilities to find trends, identify outliers and perform quality checks. In addition, research has uncovered the important role of visualization in data analysis and its implied benefits facilitating our understanding of disease and ultimately improving patient care. In this work, we present a review of the current landscape of existing tools designed to facilitate the visualization of multidimensional data in translational research platforms. Specifically, we reviewed the biomedical literature for translational platforms allowing the visualization and exploration of clinical and omics data, and identified 11 platforms: cBioPortal, interactive genomics patient stratification explorer, Igloo-Plot, The Georgetown Database of Cancer Plus, tranSMART, an unnamed data-cube-based model supporting heterogeneous data, Papilio, Caleydo Domino, Qlucore Omics, Oracle Health Sciences Translational Research Center and OmicsOffice® powered by TIBCO Spotfire. In a health sector continuously witnessing an increase in data from multifarious sources, visualization tools used to better grasp these data will grow in their importance, and we believe our work will be useful in guiding investigators in similar situations.

A clinician friendly data warehouse oriented toward narrative reports: Dr. Warehouse.

INTRODUCTION: Clinical data warehouses are often oriented toward integration and exploration of coded data. However narrative reports are of crucial importance for translational research. This paper describes Dr. Warehouse®, an open source data warehouse oriented toward clinical narrative reports and designed to support clinicians' day-to-day use. METHOD: Dr. Warehouse relies on an original database model to focus on documents in addition to facts. Besides classical querying functionalities, the system provides an advanced search engine and Graphical User Interfaces adapted to the exploration of text. Dr. Warehouse is dedicated to translational research with cohort recruitment capabilities, high throughput phenotyping and patient centric views (including similarity metrics among patients). These features leverage Natural Language Processing based on the extraction of UMLS® concepts, as well as negation and family history detection. RESULTS: A survey conducted after 6 months of use at the Necker Children's Hospital shows a high rate of satisfaction among the users (96.6%). During this period, 122 users performed 2837 queries, accessed 4,267 patients' records and included 36,632 patients in 131 cohorts. The source code is available at this github link https://github.com/imagine-bdd/DRWH. A demonstration based on PubMed abstracts is available at https://imagine-plateforme-bdd.fr/dwh_pubmed/.

Translational research platforms integrating clinical and omics data: a review of publicly available solutions.

The rise of personalized medicine and the availability of high-throughput molecular analyses in the context of clinical care have increased the need for adequate tools for translational researchers to manage and explore these data. We reviewed the biomedical literature for translational platforms allowing the management and exploration of clinical and omics data, and identified seven platforms: BRISK, caTRIP, cBio Cancer Portal, G-DOC, iCOD, iDASH and tranSMART. We analyzed these platforms along seven major axes. (1) The community axis regrouped information regarding initiators and funders of the project, as well as availability status and references. (2) We regrouped under the information content axis the nature of the clinical and omics data handled by each system. (3) The privacy management environment axis encompassed functionalities allowing control over data privacy. (4) In the analysis support axis, we detailed the analytical and statistical tools provided by the platforms. We also explored (5) interoperability support and (6) system requirements. The final axis (7) platform support listed the availability of documentation and installation procedures. A large heterogeneity was observed in regard to the capability to manage phenotype information in addition to omics data, their security and interoperability features. The analytical and visualization features strongly depend on the considered platform. Similarly, the availability of the systems is variable. This review aims at providing the reader with the background to choose the platform best suited to their needs. To conclude, we discuss the desiderata for optimal translational research platforms, in terms of privacy, interoperability and technical features.

Leveraging the EHR4CR platform to support patient inclusion in academic studies: challenges and lessons learned.

BACKGROUND: The development of Electronic Health Records (EHRs) in hospitals offers the ability to reuse data from patient care activities for clinical research. EHR4CR is a European public-private partnership aiming to develop a computerized platform that enables the re-use of data collected from EHRs over its network. However, the reproducibility of queries may depend on attributes of the local data. Our objective was 1/ to describe the different steps that were achieved in order to use the EHR4CR platform and 2/ to identify the specific issues that could impact the final performance of the platform. METHODS: We selected three institutional studies covering various medical domains. The studies included a total of 67 inclusion and exclusion criteria and ran in two University Hospitals. We described the steps required to use the EHR4CR platform for a feasibility study. We also defined metrics to assess each of the steps (including criteria complexity, normalization quality, and data completeness of EHRs). RESULTS: We identified 114 distinct medical concepts from a total of 67 eligibility criteria Among the 114 concepts: 23 (20.2%) corresponded to non-structured data (i.e. for which transformation is needed before analysis), 92 (81%) could be mapped to terminologies used in EHR4CR, and 86 (75%) could be mapped to local terminologies. We identified 51 computable criteria following the normalization process. The normalization was considered by experts to be satisfactory or higher for 64.2% (43/67) of the computable criteria. All of the computable criteria could be expressed using the EHR4CR platform. CONCLUSIONS: We identified a set of issues that could affect the future results of the platform: (a) the normalization of free-text criteria, (b) the translation into computer-friendly criteria and (c) issues related to the execution of the query to clinical data warehouses. We developed and evaluated metrics to better describe the platforms and their result. These metrics could be used for future reports of Clinical Trial Recruitment Support Systems assessment studies, and provide experts and readers with tools to insure the quality of constructed dataset.

A novel data-driven workflow combining literature and electronic health records to estimate comorbidities burden for a specific disease: a case study on autoimmune comorbidities in patients with celiac disease.

BACKGROUND: Data collected in EHRs have been widely used to identifying specific conditions; however there is still a need for methods to define comorbidities and sources to identify comorbidities burden. We propose an approach to assess comorbidities burden for a specific disease using the literature and EHR data sources in the case of autoimmune diseases in celiac disease (CD). METHODS: We generated a restricted set of comorbidities using the literature (via the MeSH® co-occurrence file). We extracted the 15 most co-occurring autoimmune diseases of the CD. We used mappings of the comorbidities to EHR terminologies: ICD-10 (billing codes), ATC (drugs) and UMLS (clinical reports). Finally, we extracted the concepts from the different data sources. We evaluated our approach using the correlation between prevalence estimates in our cohort and co-occurrence ranking in the literature. RESULTS: We retrieved the comorbidities for 741 patients with CD. 18.1% of patients had at least one of the 15 studied autoimmune disorders. Overall, 79.3% of the mapped concepts were detected only in text, 5.3% only in ICD codes and/or drugs prescriptions, and 15.4% could be found in both sources. Prevalence in our cohort were correlated with literature (Spearman's coefficient 0.789, p = 0.0005). The three most prevalent comorbidities were thyroiditis 12.6% (95% CI 10.1-14.9), type 1 diabetes 2.3% (95% CI 1.2-3.4) and dermatitis herpetiformis 2.0% (95% CI 1.0-3.0). CONCLUSION: We introduced a process that leveraged the MeSH terminology to identify relevant autoimmune comorbidities of the CD and several data sources from EHRs to phenotype a large population of CD patients. We achieved prevalence estimates comparable to the literature.

Chemotherapy-induced ileitis associated or not with colitis in digestive oncology patients: An AGEO multicentre study.

BACKGROUND: The characteristics and management of ileitis induced by chemotherapy in cancer patients are poorly described in the literature. METHODS: This retrospective multicentre study enroled patients hospitalized in a digestive oncology unit for a symptomatic chemotherapy-induced ileitis. RESULTS: Forty-three patients were included, with a regimen based on fluoropyrimidine and/or irinotecan in 95% of cases. Five patients were excluded due to the diagnosis of infectious ileitis (Clostridium difficile in 3 patients, Campylobacter jejuni in 1 patient and cytomegalovirus in 1 patient). The most frequently described symptoms were diarrhoea (77% including 54% of grade 3-4 diarrhoea), abdominal pain (58%), fever (51%) and vomiting (56%). An ileo-colonoscopy was performed in 35% of patients and did not show any specific results or severity criteria. The ileitis was complicated by bowel perforation and/or obstruction in 3 patients. Disease progression was favourable in 1-2 weeks in the vast majority of cases, on symptomatic treatment, allowing resumption of the chemotherapy regimen involved in 67% of patients. CONCLUSION: Chemotherapy-induced ileitis is a rare complication that most often involves fluoropyri-midine- and/or irinotecan-based regimens. In most cases, endoscopic examinations were not contributory and do not seem useful in the event of non-severe symptomatology which most often develops favourably on symptomatic therapy, allowing resumption of the chemotherapy involved.

How to Improve Cancer Patients ENrollment in Clinical Trials From rEal-Life Databases Using the Observational Medical Outcomes Partnership Oncology Extension: Results of the PENELOPE Initiative in Urologic Cancers.

PURPOSE: To compare the computability of Observational Medical Outcomes Partnership (OMOP)-based queries related to prescreening of patients using two versions of the OMOP common data model (CDM; v5.3 and v5.4) and to assess the performance of the Greater Paris University Hospital (APHP) prescreening tool. MATERIALS AND METHODS: We identified the prescreening information items being relevant for prescreening of patients with cancer. We randomly selected 15 academic and industry-sponsored urology phase I-IV clinical trials (CTs) launched at APHP between 2016 and 2021. The computability of the related prescreening criteria (PC) was defined by their translation rate in OMOP-compliant queries and by their execution rate on the APHP clinical data warehouse (CDW) containing data of 205,977 patients with cancer. The overall performance of the prescreening tool was assessed by the rate of true- and false-positive cases of three randomly selected CTs. RESULTS: We defined a list of 15 minimal information items being relevant for patients' prescreening. We identified 83 PC of the 534 eligibility criteria from the 15 CTs. We translated 33 and 62 PC in queries on the basis of OMOP CDM v5.3 and v5.4, respectively (translation rates of 40% and 75%, respectively). Of the 33 PC translated in the v5.3 of the OMOP CDM, 19 could be executed on the APHP CDW (execution rate of 58%). Of 83 PC, the computability rate on the APHP CDW reached 23%. On the basis of three CTs, we identified 17, 32, and 63 patients as being potentially eligible for inclusion in those CTs, resulting in positive predictive values of 53%, 41%, and 21%, respectively. CONCLUSION: We showed that PC could be formalized according to the OMOP CDM and that the oncology extension increased their translation rate through better representation of cancer natural history.

Longitudinal Study of Advanced Non-Small Cell Lung Cancer with Initial Durable Clinical Benefit to Immunotherapy: Strategies for Anti-PD-1/PD-L1 Continuation beyond Progression.

BACKGROUND AND AIM: A better understanding of resistance to checkpoint inhibitors is essential to define subsequent treatments in advanced non-small cell lung cancer. By characterizing clinical and radiological features of progression after anti-programmed death-1/programmed death ligand-1 (anti-PD-1/PD-L1), we aimed to define therapeutic strategies in patients with initial durable clinical benefit. PATIENTS AND METHODS: This monocentric, retrospective study included patients who presented progressive disease (PD) according to RECIST 1.1 criteria after anti-PD-1/PD-L1 monotherapy. Patients were classified into two groups, "primary resistance" and "Progressive Disease (PD) after Durable Clinical Benefit (DCB)", according to the Society of Immunotherapy of Cancer classification. We compared the post-progression survival (PPS) of both groups and analyzed the patterns of progression. An exploratory analysis was performed using the tumor growth rate (TGR) to assess the global growth kinetics of cancer and the persistent benefit of immunotherapy beyond PD after DCB. RESULTS: A total of 148 patients were included; 105 of them presented "primary resistance" and 43 "PD after DCB". The median PPS was 5.2 months (95% CI: 2.6-6.5) for primary resistance (p < 0.0001) vs. 21.3 months (95% CI: 18.5-36.3) for "PD after DCB", and the multivariable hazard ratio was 0.14 (95% CI: 0.07-0.30). The oligoprogression pattern was frequent in the "PD after DCB" group (76.7%) and occurred mostly in pre-existing lesions (72.1%). TGR deceleration suggested a persistent benefit of PD-1/PD-L1 blockade in 44.2% of cases. CONCLUSIONS: PD after DCB is an independent factor of longer post-progression survival with specific patterns that prompt to contemplate loco-regional treatments. TGR is a promising tool to assess the residual benefit of immunotherapy and justify the continuation of immunotherapy in addition to radiotherapy or surgery.

A mutation-centric approach to identifying pharmacogenomic relations in text.

OBJECTIVES: To explore the notion of mutation-centric pharmacogenomic relation extraction and to evaluate our approach against reference pharmacogenomic relations. METHODS: From a corpus of MEDLINE abstracts relevant to genetic variation, we identify co-occurrences between drug mentions extracted using MetaMap and RxNorm, and genetic variants extracted by EMU. The recall of our approach is evaluated against reference relations curated manually in PharmGKB. We also reviewed a random sample of 180 relations in order to evaluate its precision. RESULTS: One crucial aspect of our strategy is the use of biological knowledge for identifying specific genetic variants in text, not simply gene mentions. On the 104 reference abstracts from PharmGKB, the recall of our mutation-centric approach is 33-46%. Applied to 282,000 abstracts from MEDLINE, our approach identifies pharmacogenomic relations in 4534 abstracts, with a precision of 65%. CONCLUSIONS: Compared to a relation-centric approach, our mutation-centric approach shows similar recall, but slightly lower precision. We show that both approaches have limited overlap in their results, but are complementary and can be used in combination. Rather than a solution for the automatic curation of pharmacogenomic knowledge, we see these high-throughput approaches as tools to assist biocurators in the identification of pharmacogenomic relations of interest from the published literature. This investigation also identified three challenging aspects of the extraction of pharmacogenomic relations, namely processing full-text articles, sequence validation of DNA variants and resolution of genetic variants to reference databases, such as dbSNP.