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.

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.

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.

Association of Lipoprotein(a) Levels With Incidence of Major Adverse Limb Events.

Importance: High lipoprotein(a) (Lp[a]) levels are involved in the development of cardiovascular events, particularly in myocardial infarction, stroke, and peripheral artery disease. Studies assessing the Lp(a) levels associated with adverse lower-limb events are lacking. Objective: To assess the association between Lp(a) levels and incidence of major adverse limb events in unselected hospitalized patients. Design, Setting, and Participants: This large retrospective monocentric cohort study was conducted from January 1, 2000, to December 31, 2020. Data were derived from the clinical information system of the Hôpital Européen Georges-Pompidou, a Paris-based university hospital. Patients who underwent at least 1 Lp(a) measurement at the center during the study period were included. Patients who had no follow-up data or who had the first Lp(a) measurement after the study outcome had occurred were excluded. Data analyses were performed from May 2021 to January 2022. Main Outcomes and Measures: The primary outcome was the first inpatient major adverse limb event, defined as a major amputation, peripheral endovascular revascularization, or peripheral surgical revascularization, during follow-up. Secondary outcomes included individual components of the primary outcome. Lipoprotein(a) levels were categorized as follows: normal (<50 mg/dL), high (50 to <134 mg/dL), and very high (≥134 mg/dL); to convert Lp(a) values to milligrams per liter, multiply by 0.1. Results: A total of 16 513 patients (median [IQR] age, 58.2 [49.0-66.7] years; 9774 men [59.2%]) were included in the cohort. The median (IQR) Lp(a) level was 24 (10.0-60.0) mg/dL. The 1-year incidence of major adverse limb event was 2.44% in the overall population and 4.54% among patients with very high Lp(a) levels. High (adjusted accelerated failure time [AFT] exponential estimate: 0.43; 95% CI, 0.24-0.78; Benjamini-Hochberg-corrected P = .01) and very high (adjusted AFT exponential estimate: 0.17; 95% CI, 0.07-0.40; Benjamini-Hochberg-corrected P < .001) Lp(a) levels were independently associated with an increased risk of major adverse limb event. Conclusions and Relevance: Results of this study showed that higher Lp(a) levels were independently associated with an increased risk of a major adverse limb event in hospitalized patients. The Lp(a) measurement needs to be taken into account to improve lower-limb vascular risk assessment.

Using an Ontological Representation of Chemotherapy Toxicities for Guiding Information Extraction and Integration from EHRs.

INTRODUCTION: Chemotherapies against cancers are often interrupted due to severe drug toxicities, reducing treatment opportunities. For this reason, the detection of toxicities and their severity from EHRs is of importance for many downstream applications. However toxicity information is dispersed in various sources in the EHRs, making its extraction challenging. METHODS: We introduce OntoTox, an ontology designed to represent chemotherapy toxicities, its attributes and provenance. We illustrated the interest of OntoTox by integrating toxicities and grading information extracted from three heterogeneous sources: EHR questionnaires, semi-structured tables, and free-text. RESULTS: We instantiated 53,510, 2,366 and 54,420 toxicities from questionnaires, tables and free-text respectively, and compared the complementarity and redundancy of the three sources. DISCUSSION: We illustrated with this preliminary study the potential of OntoTox to guide the integration of multiple sources, and identified that the three sources are only moderately overlapping, stressing the need for a common representation.

Extraction of Tumor Response Criteria in Semi-Structured Imaging Report.

In this study, we extracted information from 6,376 french CT scan semi-structured text reports evaluating the cancer treatment response using the RECIST methodology. We evaluated the performance against manual annotation of 100 reports and measured the evolution of the presence of information over time. The results show high performances of the extraction as well as trends.

viroCapt: A Bioinformatics Pipeline for Identifying Viral Insertion in Human Host Genome.

INTRODUCTION: The implication of viruses in human cancers, as well as the emergence of next generation sequencing has permitted to investigate further their role and pathophysiology in the development of this disease. One such mechanism is the integration of portions of viral genomes in the human genome, as well as the specific action of viral oncogenes.inding integration sites and preserved oncogenes is still relying on heavy manual intervention. METHODS: We developed an analysis and interpretation pipeline to determine viral insertions. Using data from directed viral capture, the pipeline conducts a crude genotyping phase to select reference viral genomes, identifies chimeric reads, extracts the putative human sequences to locate in the human reference genome, scores and ranks candidate junctions, and exports tabular and visual results. RESULTS: We leverage common bioinformatics tools (bowtie2, samtools, blat), and a dedicated filtering and ranking algorithm, implemented in R, to infer candidate junctions and insertions. Static results (tables, figures) are produced, as well as an interactive interpretation tool developed as a shiny web app. DISCUSSION: We validated this pipeline against published results of HPV, HBV, and AAV2 insertions and show good information retrieval.

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.

Correction: Mining Electronic Health Records for Drugs Associated With 28-day Mortality in COVID-19: Pharmacopoeia-wide Association Study (PharmWAS).

[This corrects the article DOI: 10.2196/35190.].

Mining Electronic Health Records for Drugs Associated With 28-day Mortality in COVID-19: Pharmacopoeia-wide Association Study (PharmWAS).

BACKGROUND: Patients hospitalized for a given condition may be receiving other treatments for other contemporary conditions or comorbidities. The use of such observational clinical data for pharmacological hypothesis generation is appealing in the context of an emerging disease but particularly challenging due to the presence of drug indication bias. OBJECTIVE: With this study, our main objective was the development and validation of a fully data-driven pipeline that would address this challenge. Our secondary objective was to generate pharmacological hypotheses in patients with COVID-19 and demonstrate the clinical relevance of the pipeline. METHODS: We developed a pharmacopeia-wide association study (PharmWAS) pipeline inspired from the PheWAS methodology, which systematically screens for associations between the whole pharmacopeia and a clinical phenotype. First, a fully data-driven procedure based on adaptive least absolute shrinkage and selection operator (LASSO) determined drug-specific adjustment sets. Second, we computed several measures of association, including robust methods based on propensity scores (PSs) to control indication bias. Finally, we applied the Benjamini and Hochberg procedure of the false discovery rate (FDR). We applied this method in a multicenter retrospective cohort study using electronic medical records from 16 university hospitals of the Greater Paris area. We included all adult patients between 18 and 95 years old hospitalized in conventional wards for COVID-19 between February 1, 2020, and June 15, 2021. We investigated the association between drug prescription within 48 hours from admission and 28-day mortality. We validated our data-driven pipeline against a knowledge-based pipeline on 3 treatments of reference, for which experts agreed on the expected association with mortality. We then demonstrated its clinical relevance by screening all drugs prescribed in more than 100 patients to generate pharmacological hypotheses. RESULTS: A total of 5783 patients were included in the analysis. The median age at admission was 69.2 (IQR 56.7-81.1) years, and 3390 (58.62%) of the patients were male. The performance of our automated pipeline was comparable or better for controlling bias than the knowledge-based adjustment set for 3 reference drugs: dexamethasone, phloroglucinol, and paracetamol. After correction for multiple testing, 4 drugs were associated with increased in-hospital mortality. Among these, diazepam and tramadol were the only ones not discarded by automated diagnostics, with adjusted odds ratios of 2.51 (95% CI 1.52-4.16, Q=.1) and 1.94 (95% CI 1.32-2.85, Q=.02), respectively. CONCLUSIONS: Our innovative approach proved useful in generating pharmacological hypotheses in an outbreak setting, without requiring a priori knowledge of the disease. Our systematic analysis of early prescribed treatments from patients hospitalized for COVID-19 showed that diazepam and tramadol are associated with increased 28-day mortality. Whether these drugs could worsen COVID-19 needs to be further assessed.

Overall Survival and Prognostic Factors among Older Patients with Metastatic Pancreatic Cancer: A Retrospective Analysis Using a Hospital Database.

Pre-therapeutic factors associated with overall survival (OS) among older patients ≥70 years with metastatic pancreatic cancer (mPC) are not known. This was a retrospective single-centre cohort study in Paris including 159 consecutive older patients with mPC between 2000 and 2018. Alongside geriatric parameters, specific comorbidities, cancer-related data and chemotherapy regimens were retrieved. Cox multivariate models were run to assess predictors for OS. The median age was 80 years, 52% were women, 21.5% had diabetes, and 48% had pancreatic head cancer and 72% liver metastases. 62% of the patients (n = 99) received chemotherapy, among which the gemcitabine + nab-paclitaxel (GnP) regimen was the most frequent (72%). Median OS [95%CI] was 7.40 [5.60-10.0] and 1.40 [0.90-2.20] months respectively for patients with and without chemotherapy. The GnP regimen (aHR [95%CI] = 0.47 [0.25-0.89], p = 0.02) and diabetes (aHR = 0.44 [0.24-0.77], p = 0.004) (or anti-diabetic therapy) were multivariate protective factors for death, while ECOG-PS, liver metastases, and the neutrophil cell count were multivariate risk factors for death. In the chemotherapy group, ECOG-PS, number of metastatic sites and the GnP remained significantly associated with OS. Our study confirms the feasibility and efficacy of chemotherapy and the protective effects of diabetes among older patients with mPC.

Adjusting D-dimer to Lung Disease Extent to Exclude Pulmonary Embolism in COVID-19 Patients (Co-LEAD).

OBJECTIVE: D-dimer measurement is a safe tool to exclude pulmonary embolism (PE), but its specificity decreases in coronavirus disease 2019 (COVID-19) patients. Our aim was to derive a new algorithm with a specific D-dimer threshold for COVID-19 patients. METHODS: We conducted a French multicenter, retrospective cohort study among 774 COVID-19 patients with suspected PE. D-dimer threshold adjusted to extent of lung damage found on computed tomography (CT) was derived in a patient set (n = 337), and its safety assessed in an independent validation set (n = 337). RESULTS: According to receiver operating characteristic curves, in the derivation set, D-dimer safely excluded PE, with one false negative, when using a 900 ng/mL threshold when lung damage extent was <50% and 1,700 ng/mL when lung damage extent was ≥50%. In the derivation set, the algorithm sensitivity was 98.2% (95% confidence interval [CI]: 94.7-100.0) and its specificity 28.4% (95% CI: 24.1-32.3). The negative likelihood ratio (NLR) was 0.06 (95% CI: 0.01-0.44) and the area under the curve (AUC) was 0.63 (95% CI: 0.60-0.67). In the validation set, sensitivity and specificity were 96.7% (95% CI: 88.7-99.6) and 39.2% (95% CI: 32.2-46.1), respectively. The NLR was 0.08 (95% CI; 0.02-0.33), and the AUC did not differ from that of the derivation set (0.68, 95% CI: 0.64-0.72, p = 0.097). Using the Co-LEAD algorithm, 76 among 250 (30.4%) COVID-19 patients with suspected PE could have been managed without CT pulmonary angiography (CTPA) and 88 patients would have required two CTs. CONCLUSION: The Co-LEAD algorithm could safely exclude PE, and could reduce the use of CTPA in COVID-19 patients. Further prospective studies need to validate this strategy.

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.

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.

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.

Next-Generation Sequencing Targeted Panel in Routine Care for Metastatic Colon Cancers.

In digestive oncology, the clinical impact of targeted next-generation sequencing (NGS) in routine practice should be addressed. In this work, we studied the impact of a 22-gene NGS amplicon-based panel with Ion Torrent Proton Sequencing, prospectively performed in routine practice. We analyzed the results of extended molecular testing, beyond RAS and BRAF, in metastatic colorectal cancer (mCRC) patients in a single-center, retrospective, observational study of consecutive mCRC patients followed up at the Georges Pompidou European Hospital between January 2016 and December 2018. Overall, 210 patients with mCRC were included. Median follow-up was 25.4 months (IQR: 14.9-39.5). The three most frequently mutated genes were: TP53 (63%), KRAS (41%) and PIK3CA (19%). A positive association was found between overall survival and performance status (PS) ≥ 2 (HR: 4.91 (1.84-13.1); p = 0.001) and differentiation (HR: 4.70 (1.51-14.6); p = 0.007) in multivariate analysis. The NGS panel enabled five patients to access a targeted therapy not currently registered for CRC. In conclusion, targeted NGS panels in mCRC are feasible in routine practice, but need to be regularly updated and in-depth studies are needed to better analyze the prognostic factors.

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.

Low-income neighbourhood was a key determinant of severe COVID-19 incidence during the first wave of the epidemic in Paris.

BACKGROUND: Previous studies have demonstrated that socioeconomic factors are associated with COVID-19 incidence. In this study, we analysed a broad range of socioeconomic indicators in relation to hospitalised cases in the Paris area. METHODS: We extracted 303 socioeconomic indicators from French census data for 855 residential units in Paris and assessed their association with COVID-19 hospitalisation risk. FINDINGS: The indicators most associated with hospitalisation risk were the third decile of population income (OR=9.10, 95% CI 4.98 to 18.39), followed by the primary residence rate (OR=5.87, 95% CI 3.46 to 10.61), rate of active workers in unskilled occupations (OR=5.04, 95% CI 3.03 to 8.85) and rate of women over 15 years old with no diploma (OR=5.04, 95% CI 3.03 to 8.85). Of note, population demographics were considerably less associated with hospitalisation risk. Among these indicators, the rate of women aged between 45 and 59 years (OR=2.17, 95% CI 1.40 to 3.44) exhibited the greatest level of association, whereas population density was not associated. Overall, 86% of COVID-19 hospitalised cases occurred within the 45% most deprived areas. INTERPRETATION: Studying a broad range of socioeconomic indicators using census data and hospitalisation data as a readily available and large resource can provide real-time indirect information on populations with a high incidence of COVID-19.

Differential association between inflammatory cytokines and multiorgan dysfunction in COVID-19 patients with obesity.

To investigate the mechanisms underlying the SARS-CoV-2 infection severity observed in patients with obesity, we performed a prospective study of 51 patients evaluating the impact of multiple immune parameters during 2 weeks after admission, on vital organs' functions according to body mass index (BMI) categories. High-dimensional flow cytometric characterization of immune cell subsets was performed at admission, 30 systemic cytokines/chemokines levels were sequentially measured, thirteen endothelial markers were determined at admission and at the zenith of the cytokines. Computed tomography scans on admission were quantified for lung damage and hepatic steatosis (n = 23). Abnormal BMI (> 25) observed in 72.6% of patients, was associated with a higher rate of intensive care unit hospitalization (p = 0.044). SARS-CoV-2 RNAaemia, peripheral immune cell subsets and cytokines/chemokines were similar among BMI groups. A significant association between inflammatory cytokines and liver, renal, and endothelial dysfunctions was observed only in patients with obesity (BMI > 30). In contrast, early signs of lung damage (ground-glass opacity) correlated with Th1/M1/inflammatory cytokines only in normal weight patients. Later lesions of pulmonary consolidation correlated with BMI but were independent of cytokine levels. Our study reveals distinct physiopathological mechanisms associated with SARS-CoV-2 infection in patients with obesity that may have important clinical implications.