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.

Type 2 diabetics followed up by family physicians: Treatment sequences and changes over time in weight and glycated hemoglobin.

INTRODUCTION: The treatment of type 2 diabetes mellitus (T2DM) is based on preventive hygiene and dietary measures (HDM), oral antidiabetic drugs (OADs), and insulin. The objective of the present study was to reuse general practice data from electronic health records and describe changes over time among patients with T2DM in primary care. METHODS: We analyzed data on patients with T2DM collected by three family physicians in Tourcoing (France) from 2006 to 2018. RESULTS: 403 patients, 1030 treatment sequences, 39,042 appointments, 2440 glycated hemoglobin (HbA1c) measurements, and 9722 wt measurements were included. On inclusion, the mean age was 57.0, the mean weight was 84.4 kg, the mean body mass index was 30.3 kg/m2, and the median HbA1c level was 6.8 % (51 mmol/mol). The patients were following appropriate HDM (40.7 %) and/or were being treated with OADs (54.1 %) or insulin (5.2 %). The median length of follow-up was 3.51 years. Overall, bodyweight was stable for two years during HDM and then increased. The HbA1c level decreased and then increased during HDM, was stable on OADs, and then decreased on insulin. DISCUSSION/CONCLUSION: The present descriptive results may be of value in helping to predict changes over time in bodyweight and HbA1c in T2DM.

"Book Music" Representation for Temporal Data, as a Part of the Feature Extraction Process: A Novel Approach to Improve the Handling of Time-Dependent Data in Secondary Use of Healthcare Structured Data.

Book music is extensively used in street organs. It consists of thick cardboard, containing perforated holes specifying the musical notes. We propose to represent clinical time-dependent data in a tabular form inspired from this principle. The sheet represents a statistical individual, each row represents a binary time-dependent variable, and each hole denotes the "true" value. Data from electronic health records or nationwide medical-administrative databases can then be represented: demographics, patient flow, drugs, laboratory results, diagnoses, and procedures. This data representation is suitable for survival analysis (e.g., Cox model with repeated outcomes and changing covariates) and different types of temporal association rules. Quantitative continuous variables can be discretized, as in clinical studies. The "book music" approach could become an intermediary step in feature extraction from structured data. It would enable to better account for time in analyses, notably for historical cohort analyses based on healthcare data reuse.

Mortality risk and antibiotic use for COVID-19 in hospitalized patients over 80.

INTRODUCTION: Patients over 80 years of age are more prone to develop severe symptoms and die from COVID-19. Antibiotics were massively prescribed in the first days of the pandemic without evidence of super infection. Antibiotics may increase the risk of mortality in cases of viral pneumonia. With age and antibiotic use, the microbiota becomes altered and less protective effect against lethal viral pneumonia. Thus we assessed whether it is safe to prescribe antibiotics for COVID-19 pneumonia to patients over 80 years of age. METHOD: We conducted a retrospective monocentric study in a 1240-bed university hospital. Our inclusion criteria were patients aged ≥ 80 years, hospitalized in a COVID-19 unit, with either a positive SARS-CoV-2 RT-PCR from a nasopharyngeal swab or a CT scan within 72 h after or prior to hospitalization in the unit suggestive of infection. RESULTS: We included 101 patients who received antibiotics and 48 who did not. The demographics in the two groups were similar. Overall mortality was higher for the group that received antibiotics than for the other group (36.6% vs 14.6%,). According to univariate COX analysis, the risk of mortality was higher (HR = 1.98 [0.926; 4.23]) but non-significantly for the antibiotic group. In multivariate analysis, independent risk factors of mortality were an increased leukocyte count and decreased oxygen saturation (HR = 1.097 [1.022; 1.178] and HR = 0.927 [0.891; 0.964], respectively). CONCLUSION: This study raises questions about the interest of antibiotic therapy, its efficacy, and its effect on COVID-19 and encourages further research.

Use of a hospital administrative database to identify and characterize community-acquired, hospital-acquired and drug-induced acute kidney injury.

BACKGROUND: Acute kidney injury (AKI) has serious short- and long-term consequences. The objective of the present study of a cohort of hospitalized patients with AKI was to (i) evaluate the proportion of patients with hospital-acquired (HA) AKI and community-acquired (CA) AKI, the characteristics of these patients and the AKIs, and the short-term outcomes, and (ii) determine the performance of several ICD-10 codes for identifying AKI (both CA and HA) and drug-induced AKI. METHODS: A cohort of hospitalized patients with AKI was constituted by screening hospital's electronic medical records (EMRs) for cases of AKI. We distinguished between and compared CA-AKI and HA-AKI and evaluated the proportion of AKIs that were drug-induced. The EMR data were merged with hospital billing codes (according to the International Classification of Diseases, 10th Edition (ICD-10)) for each hospital stay. The ability of ICD-10 codes to identify AKIs (depending on the type of injury) was determined by calculating the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Lastly, we sought to validate specific ICD-10 codes for drug-induced AKI. RESULTS: Of the 2473 patients included, 1557 experienced an AKI (HA-AKI: 59.3%; CA-AKI: 40.7%). Patients with CA-AKI had a better short-term outcome and a lower death rate (7.6%, vs. 20% for HA-AKI). One AKI in three was drug-induced. The combination of AKI codes had a very high specificity (94.8%), a high PPV (94.9%), a moderate NPV (56.7%) and moderate sensitivity (57.4%). The sensitivity was higher for CA-AKI (72.2%, vs. 47.2% for HA-AKI), for more severe AKI (82.8% for grade 3 AKI vs. 43.7% for grade 1 AKI), and for patients with CKD. Use of a specific ICD-10 code for drug-induced AKI (N14x) alone gave a very low sensitivity (1.8%), whereas combining codes for adverse drug reactions with AKI-specific codes increased the sensitivity. CONCLUSION: Our results show that the combination of an EMR-based analysis with ICD-10-based hospital billing codes gives a comprehensive "real-life" picture of AKI in hospital settings. We expect that this approach will enable researchers to study AKI in more depth.

Heimdall, a Computer Program for Electronic Health Records Data Visualization.

INTRODUCTION: Electronic health records (EHR) comprehend structured and unstructured data, that are usually time dependent, enabling the use of timelines. However, it is often difficult to display all data without inducing information overload. In both clinical usual care and medical research, users should be able to quickly find relevant information, with minimal cognitive overhead. Our goal was to devise simple visualization techniques for handling medical data in both contexts. METHODS: An abstraction layer for structured EHR data was devised after an informal literature review and discussions between authors. The "Heimdall" prototype was developed. Two experts evaluated the tool by answering 5 questions on 24 clinical cases. RESULTS: Temporal data was abstracted in three simple types: events, states and measures, with appropriate visual representations for each type. Heimdall can load and display complex heterogeneous structured temporal data in a straightforward way. The main view can display events, states and measures along a shared timeline. Users can summarize data using temporal, hierarchical compression and filters. Default and custom views can be used to work in problem- oriented ways. The evaluation found conclusive results. CONCLUSION: The "Heimdall" prototype provides a comprehensive and efficient graphical interface for EHR data visualization. It is open source, can be used with an R package, and is available at https://koromix.dev/files/R.

Polypharmacy in older patients: identifying the need for support by a community pharmacist.

BACKGROUND: The community pharmacist is a key player in medication reviews of older outpatients. However, it is not always clear which individuals require a medication review. The objective of the present study was to identify high-priority older patients for intervention by a community pharmacist. METHODS: As part of their final-year placement in a community pharmacy, pharmacy students conducted 10 interviews each with older adults (aged 65 or over) taking at least five medications daily. The student interviewer also offered to examine the patient's home medicine cabinet. An interview guide was developed by an expert group to assess the difficulties in managing and taking medications encountered by older patients. RESULTS: The 141 students interviewed a total of 1370 patients (mean age: 81.5; mean number of medications taken daily: 9.3). Of the 1370 interviews, 743 (54.2%) were performed in the patient's home, and thus also included an examination of the home medicine cabinet. Adverse events were reported by 566 (42.0%) patients. A total of 378 patients (27.6%) reported difficulties in preparing, administering and/or swallowing medications. The inspections of medicine cabinets identified a variety of shortcomings: poorly located cabinets (in 15.0% of inspections), medication storage problems (21.7%), expired medications (40.7%), potentially inappropriate medications (15.0%), several different generic versions of the same drug (19.9%), and redundant medications (20.4%). CONCLUSIONS: In a community pharmacy setting, high-priority older patients for intervention by a community pharmacist can be identified by asking simple questions about difficulties in managing, administering, taking or storing medications.

What Is a Chronic Disease? A Contribution Based on the Secondary Use of 161 Million Discharge Records.

Several definitions of chronic diseases exist. The objective is to reuse a nationwide medical-administrative database (PMSI) to estimate the lifespan of diagnostic codes, hence the chronicity of the corresponding diseases. We analyzed 162 million inpatient stays from 2008 to 2014, and estimate the lifespan of every ICD-10 code for every patient, identified by a unique imprint. We calculated 200 indicators for different time and survival values, and selected the ones that maximized the area under the ROC curve (AUC) drawn by comparison against 4 chronic disease classifications: CCI, ALD, result from the analysis of ICD-10 labels, and a handmade list. The best indicator was the time to reach a survival of 4.5%. It enables to get the following AUC: 78.9% compared with CCI, 90.3% compared with ALD, 75.1% compared with labels analysis, and 91.5% compared with the handmade list. This indicator enables to classify 23,349 ICD-10 codes from "most chronic" to "most acute". The 100 most chronic codes are listed.

Enhancing Nationwide Medico-Administrative Databases Analysis with SAF4SUHAD: A Statistical Analysis Framework for Secondary Use of Healthcare Administrative Databases.

Many epidemiological studies now rely on the reuse of large healthcare administrative databases. In those studies, most of the time is consumed in managing data and performing basic statistical analyses and is not available anymore for complex statistical and medical analysis, therefore the potential of such databases is sometimes underexploited. The objective of this work is to build SAF4SUHAD, a statistical analysis framework for secondary use of healthcare administrative databases, using literature-based specifications. A literature review was performed on PubMed in four different medical domains: caesarian deliveries, cholecystectomies, hip replacement surgeries and bariatric surgeries. We identified 22 papers relating analyses of large databases. They reported epidemiological indicators (e.g. mean age), that were abstracted to features (e.g. univariate description of a quantitative variable), and then were implemented through 32 functions available for the user in R programming language. For instance, a function will draw a histogram, compute the mean with confidence interval, quantiles, etc. Those functions comprehend 4 functions for data management, 9 for univariate analysis, 8 for bivariate analysis, 11 for multivariate analysis, and many other intermediate functions. Those functions were successfully used to analyze a French database of 250 million discharge summaries. The set of R ready-to-use functions defined in this work could enable to secure repetitive tasks, and to refocus efforts on expert analysis.

Elderly Surgical Patients: Automated Computation of Healthcare Quality Indicators by Data Reuse of EHR.

UNLABELLED: The objective of the work is to implement and evaluate the automated computation of 9 healthcare quality indicators, by data reuse of electronic health records, in the field of elderly surgical patients. METHODS: Data are extracted from EHR, including administrative data, ICD10 diagnoses, laboratory results, procedures, administered drugs, and free-text letters. The indicators are implemented by a medical data reuse specialist. The conformity rate is automatically computed (3.5 minutes for 15,000 inpatient stays and 9 indicators). A medical expert reviews 45 stays per indicator. The precision is the proportion of non-conform inpatient stays among the cases detected as non-conform by the algorithms. RESULTS: the paper describes the implemented algorithms, the conformity rates and the precisions. Two indicators have a precision of 0%, 3 indicators have a precision of 40 to 60%, and four indicators have a precision from 80 to 100% (for 2 of them, the conformity rate is lower than 2.5%!). This demonstrates that automated quality screening is possible and enables to detect threatening situations. The implementation of the indicators requires special skills in medicine, medical information sciences, and algorithmics. Failures of precision are mainly due to defaults of information quality (missing codes), and could benefit from text analysis.