Racial Disparities in Invasive ICU Treatments Among Septic Patients: High Resolution Electronic Health Records Analysis from MIMIC-IV.

Background: Low-resolution administrative databases can give biased results, whereas high-resolution, time-stamped variables from clinical databases like MIMIC-IV might provide nuanced insights. We evaluated racial-ethnic disparities in life-sustaining ICU-treatments (Invasive Mechanical Ventilation (IMV), Renal Replacement Therapy (RRT), and Vasopressors (VP)) among patients with sepsis. Methods: In this observational retrospective cohort study, patients fulfilling sepsis-3 criteria were categorized by treatment assignment within the first 4 days. The outcomes were treatment allocations. The likelihood of receiving treatment was calculated by race-ethnicity (Racial-ethnic group (REG) or White group (WG)) using 5-fold sub-sampling nested logistic regression and XGBoost. Results: In 23,914 admissions, 82% were White, 42% were women. REG were less likely to receive IMV across all eligibility days (day 1 odds ratio (OR) 0.87, 95% confidence interval (CI) 0.83-0.94, day 4 OR 0.80, 95% CI 0.72 - 0.87). There were no differences in RRT (day 1 OR 1.00, 95% CI 0.96-1.09, day 4 OR 1.00, 95% CI 0.94-1.06). REG were also less likely to be treated with VP at days 1 to 3 (day 1 OR 0.87, 95% CI 0.76-0.94), but not at day 4 (OR 0.95, 95% CI 0.87-1.01). These findings remained robust when relaxing eligibility criteria for treatment allocation. Conclusion: Our findings reveal significant disparities in the use of invasive life-saving ICU treatments among septic patients from racial and ethnic minority backgrounds, particularly with respect to IMV and VP use. These disparities underscore not only the need to address inequality in critical care settings, but also highlight the importance of high-resolution data.

Correction: Good practices for clinical data warehouse implementation: A case study in France.

[This corrects the article DOI: 10.1371/journal.pdig.0000298.].

Good practices for clinical data warehouse implementation: A case study in France.

Real-world data (RWD) bears great promises to improve the quality of care. However, specific infrastructures and methodologies are required to derive robust knowledge and brings innovations to the patient. Drawing upon the national case study of the 32 French regional and university hospitals governance, we highlight key aspects of modern clinical data warehouses (CDWs): governance, transparency, types of data, data reuse, technical tools, documentation, and data quality control processes. Semi-structured interviews as well as a review of reported studies on French CDWs were conducted in a semi-structured manner from March to November 2022. Out of 32 regional and university hospitals in France, 14 have a CDW in production, 5 are experimenting, 5 have a prospective CDW project, 8 did not have any CDW project at the time of writing. The implementation of CDW in France dates from 2011 and accelerated in the late 2020. From this case study, we draw some general guidelines for CDWs. The actual orientation of CDWs towards research requires efforts in governance stabilization, standardization of data schema, and development in data quality and data documentation. Particular attention must be paid to the sustainability of the warehouse teams and to the multilevel governance. The transparency of the studies and the tools of transformation of the data must improve to allow successful multicentric data reuses as well as innovations in routine care.

Standardized Description of the Feature Extraction Process to Transform Raw Data Into Meaningful Information for Enhancing Data Reuse: Consensus Study.

BACKGROUND: Despite the many opportunities data reuse offers, its implementation presents many difficulties, and raw data cannot be reused directly. Information is not always directly available in the source database and needs to be computed afterwards with raw data for defining an algorithm. OBJECTIVE: The main purpose of this article is to present a standardized description of the steps and transformations required during the feature extraction process when conducting retrospective observational studies. A secondary objective is to identify how the features could be stored in the schema of a data warehouse. METHODS: This study involved the following 3 main steps: (1) the collection of relevant study cases related to feature extraction and based on the automatic and secondary use of data; (2) the standardized description of raw data, steps, and transformations, which were common to the study cases; and (3) the identification of an appropriate table to store the features in the Observation Medical Outcomes Partnership (OMOP) common data model (CDM). RESULTS: We interviewed 10 researchers from 3 French university hospitals and a national institution, who were involved in 8 retrospective and observational studies. Based on these studies, 2 states (track and feature) and 2 transformations (track definition and track aggregation) emerged. "Track" is a time-dependent signal or period of interest, defined by a statistical unit, a value, and 2 milestones (a start event and an end event). "Feature" is time-independent high-level information with dimensionality identical to the statistical unit of the study, defined by a label and a value. The time dimension has become implicit in the value or name of the variable. We propose the 2 tables "TRACK" and "FEATURE" to store variables obtained in feature extraction and extend the OMOP CDM. CONCLUSIONS: We propose a standardized description of the feature extraction process. The process combined the 2 steps of track definition and track aggregation. By dividing the feature extraction into these 2 steps, difficulty was managed during track definition. The standardization of tracks requires great expertise with regard to the data, but allows the application of an infinite number of complex transformations. On the contrary, track aggregation is a very simple operation with a finite number of possibilities. A complete description of these steps could enhance the reproducibility of retrospective studies.

Transforming French Electronic Health Records into the Observational Medical Outcome Partnership's Common Data Model: A Feasibility Study.

BACKGROUND: Common data models (CDMs) enable data to be standardized, and facilitate data exchange, sharing, and storage, particularly when the data have been collected via distinct, heterogeneous systems. Moreover, CDMs provide tools for data quality assessment, integration into models, visualization, and analysis. The observational medical outcome partnership (OMOP) provides a CDM for organizing and standardizing databases. Common data models not only facilitate data integration but also (and especially for the OMOP model) extends the range of available statistical analyses. OBJECTIVE: This study aimed to evaluate the feasibility of implementing French national electronic health records in the OMOP CDM. METHODS: The OMOP's specifications were used to audit the source data, specify the transformation into the OMOP CDM, implement an extract-transform-load process to feed data from the French health care system into the OMOP CDM, and evaluate the final database. RESULTS: Seventeen vocabularies corresponding to the French context were added to the OMOP CDM's concepts. Three French terminologies were automatically mapped to standardized vocabularies. We loaded nine tables from the OMOP CDM's "standardized clinical data" section, and three tables from the "standardized health system data" section. Outpatient and inpatient data from 38,730 individuals were integrated. The median (interquartile range) number of outpatient and inpatient stays per patient was 160 (19-364). CONCLUSION: Our results demonstrated that data from the French national health care system can be integrated into the OMOP CDM. One of the main challenges was the use of international OMOP concepts to annotate data recorded in a French context. The use of local terminologies was an obstacle to conceptual mapping; with the exception of an adaptation of the International Classification of Diseases 10th Revision, the French health care system does not use international terminologies. It would be interesting to extend our present findings to the 65 million people registered in the French health care system.