Antimicrobial Resistance as Risk Factor for Recurrent Bacteremia after Staphylococcus aureus, Escherichia coli, or Klebsiella spp. Community-Onset Bacteremia.

We investigated links between antimicrobial resistance in community-onset bacteremia and 1-year bacteremia recurrence by using the clinical data warehouse of Europe's largest university hospital group in France. We included adult patients hospitalized with an incident community-onset Staphylococcus aureus, Escherichia coli, or Klebsiella spp. bacteremia during 2017-2019. We assessed risk factors of 1-year recurrence using Fine-Gray regression models. Of the 3,617 patients included, 291 (8.0%) had >1 recurrence episode. Third-generation cephalosporin (3GC)-resistance was significantly associated with increased recurrence risk after incident Klebsiella spp. (hazard ratio 3.91 [95% CI 2.32-6.59]) or E. coli (hazard ratio 2.35 [95% CI 1.50-3.68]) bacteremia. Methicillin resistance in S. aureus bacteremia had no effect on recurrence risk. Although several underlying conditions and infection sources increased recurrence risk, 3GC-resistant Klebsiella spp. was associated with the greatest increase. These results demonstrate a new facet to illness induced by 3GC-resistant Klebsiella spp. and E. coli in the community setting.

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.

Antimicrobial Resistance and Mortality in Hospitalized Patients with Bacteremia in the Greater Paris Area from 2016 to 2019.

Purpose: Antibiotic-resistant bacteremia is a leading global cause of infectious disease morbidity and mortality. Clinical data warehouses (CDWs) allow for the secure, real-time coupling of diverse data sources from real-world clinical settings, including care-based medical-administrative data and laboratory-based microbiological data. The main purpose of this study was to assess the contribution of CDWs in the epidemiological study of antibiotic resistance by constructing a database of bacteremia patients, BactHub, and describing their main clinico-microbiological features and outcomes. Patients and Methods: Adult patients with bacteremia hospitalized between January 1, 2016 and December 31, 2019 in 14 acute care university hospitals from the Greater Paris area were identified; their first bacteremia episode was included. Data describing patients, episodes of bacteremia, bacterial isolates, and antimicrobial resistance were structured. Results: Among 29,228 patients with bacteremia, 41% of episodes were community-onset (CO) and 59% were hospital-acquired (HA). Thirty-day and ninety-day mortality rates were 15% and 20% in CO episodes, and 18% and 36% in HA episodes. Overall resistance rates were high, including third-generation cephalosporin resistance among Klebsiella pneumoniae (CO 21%, HA 37%) and Escherichia coli (CO 13%, HA 17%), and methicillin resistance among Staphylococcus aureus (CO 11%, HA 14%). Annual incidence rates increased significantly from 2017 to 2019, from 20.0 to 20.9 to 22.1 stays with bacteremia per 1000 stays (p < 0.0001). Conclusion: The Bacthub database provides accurate clinico-microbiological data describing bacteremia across France's largest hospital group. Data from Bacthub may inform surveillance and the clinical decision-making process for bacteremia patients, including choice of antimicrobial therapy. The database also offers opportunities for research, including analysis of hospital care pathways and significant patient outcomes such as mortality and recurrence of infection.

AP-HP Health Data Space (AHDS) to the Test of the Covid-19 Pandemic.

Sharing observational and interventional health data within a common data space enables university hospitals to leverage such data for biomedical discovery and moving towards a learning health system. OBJECTIVE: To describe the AP-HP Health Data Space (AHDS) and the IT services supporting piloting, research, innovation and patient care. METHODS: Built on three pillars - governance and ethics, technology and valorization - the AHDS and its major component, the Clinical Data Warehouse (CDW) have been developed since 2015. RESULTS: The AP-HP CDW has been made available at scale to AP-HP both healthcare professionals and public or private partners in January 2017. Supported by an institutional secured and high-performance cloud and an ecosystem of tools, mostly open source, the AHDS integrates a large amount of massive healthcare data collected during care and research activities. As of December 2021, the AHDS operates the electronic data capture for almost +840 clinical trials sponsored by AP-HP, the CDW is enabling the processing of health data from more than 11 million patients and generated +200 secondary data marts from IRB authorized research projects. During the Covid-19 pandemic, AHDS has had to evolve quickly to support administrative professionals and caregivers heavily involved in the reorganization of both patient care and biomedical research. CONCLUSION: The AP-HP Data Space is a key facilitator for data-driven evidence generation and making the health system more efficient and personalized.

Fully automated opportunistic screening of vertebral fractures and osteoporosis on more than 150 000 routine computed tomography scans.

OBJECTIVE: Osteoporosis is underdiagnosed and undertreated, although severe complications of osteoporotic fractures, including vertebral fractures, are well known. This study sought to assess the feasibility and results of an opportunistic screening of vertebral fractures and osteoporosis in a large database of lumbar or abdominal CT scans. MATERIAL AND METHODS: Data were analysed from CT scans obtained in 35 hospitals from patients aged 60 years or older and stored in a Picture Archiving and Communication System in Assistance-Publique-Hôpitaux de Paris, from 2007 to 2013. Dedicated software was used to analyse the presence or absence of at least 1 vertebral fracture (VF), and the radiodensity of the lumbar vertebrae was measured Hounsfield Units (HUs). A simulated T-score was calculated. RESULTS: Data were analysed from 152 268 patients [mean age (S.D.) = 73.2 (9.07) years]. Success rates for VF assessment and HUs measurements were 82 and 87%, respectively. The prevalence of VFs was 24.5% and increased with age. Areas under the receiver operating characteristic curves for the detection of VFs were 0.61 and 0.62 for the mean HUs of the lumbar vertebrae and the L1 HUs, respectively. In patients without VFs, HUs decreased with age, similarly in males and females. The prevalence of osteoporosis (sT-score ≤ -2.5) was 23.8% and 36.5% in patients without and with VFs, respectively. CONCLUSION: It is feasible on a large scale to screen for VFs and osteoporosis during opportunistic screening in patients 60 years or older having lumbar or abdominal CT.

New cancer cases at the time of SARS-Cov2 pandemic and related public health policies: A persistent and concerning decrease long after the end of the national lockdown.

INTRODUCTION: The dissemination of SARS-Cov2 may have delayed the diagnosis of new cancers. This study aimed at assessing the number of new cancers during and after the lockdown. METHODS: We prospectively collected the clinical data of the 11.4 million patients referred to the Assistance Publique Hôpitaux de Paris Teaching Hospital. We identified new cancer cases between 1st January 2018 and 31st September 2020 and compared indicators for 2018 and 2019 to 2020 with a focus on the French lockdown (17th March to 11th May 2020) across cancer types and patient age classes. RESULTS: Between January and September, 28,348, 27,272 and 23,734 new cancer cases were identified in 2018, 2019 and 2020, respectively. The monthly median number of new cases reached 3168 (interquartile range, IQR, 3027; 3282), 3054 (IQR 2945; 3127) and 2723 (IQR 2085; 2,863) in 2018, 2019 and 2020, respectively. From March 1st to May 31st, new cancer decreased by 30% in 2020 compared to the 2018-19 average; then by 9% from 1st June to 31st September. This evolution was consistent across all tumour types: -30% and -9% for colon, -27% and -6% for lung, -29% and -14% for breast, -33% and -12% for prostate cancers, respectively. For patients aged <70 years, the decrease of colorectal and breast new cancers in April between 2018 and 2019 average and 2020 reached 41% and 39%, respectively. CONCLUSION: The SARS-Cov2 pandemic led to a substantial decrease in new cancer cases. Delays in cancer diagnoses may affect clinical outcomes in the coming years.

Initializing a hospital-wide data quality program. The AP-HP experience.

BACKGROUND AND OBJECTIVES: Data Quality (DQ) programs are recognized as a critical aspect of new-generation research platforms using electronic health record (EHR) data for building Learning Healthcare Systems. The AP-HP Clinical Data Repository aggregates EHR data from 37 hospitals to enable large-scale research and secondary data analysis. This paper describes the DQ program currently in place at AP-HP and the lessons learned from two DQ campaigns initiated in 2017. MATERIALS AND METHODS: As part of the AP-HP DQ program, two domains - patient identification (PI) and healthcare services (HS) - were selected for conducting DQ campaigns consisting of 5 phases: defining the scope, measuring, analyzing, improving and controlling DQ. Semi-automated DQ profiling was conducted in two data sets - the PI data set containing 8.8 M patients and the HS data set containing 13,099 consultation agendas and 2122 care units. Seventeen DQ measures were defined and DQ issues were classified using a unified DQ reporting framework. For each domain, actions plans were defined for improving and monitoring prioritized DQ issues. RESULTS: Eleven identified DQ issues (8 for the PI data set and 3 for the HS data set) were categorized into completeness (n = 6), conformance (n = 3) and plausibility (n = 2) DQ issues. DQ issues were caused by errors from data originators, ETL issues or limitations of the EHR data entry tool. The action plans included sixteen actions (9 for the PI domain and 7 for the HS domain). Though only partial implementation, the DQ campaigns already resulted in significant improvement of DQ measures. CONCLUSION: DQ assessments of hospital information systems are largely unpublished. The preliminary results of two DQ campaigns conducted at AP-HP illustrate the benefit of the engagement into a DQ program. The adoption of a unified DQ reporting framework enables the communication of DQ findings in a well-defined manner with a shared vocabulary. Dedicated tooling is needed to automate and extend the scope of the generic DQ program. Specific DQ checks will be additionally defined on a per-study basis to evaluate whether EHR data fits for specific uses.