Elderly Outcomes After Hospitalization: The Hospital Frailty Risk Score Applied on the French Health Data Hub

Abstract

Objectives: To demonstrate the association between the Hospital Frailty Risk Score (HFRS) and 30-day mortality, 30-day hospital readmission and length of stay (LOS) in France. Methods: Logistic regressions were performed using data recorded in the French national health data system (SNDS) for elderly patients (≥75 years old) hospitalized in France in 2017. Results: Over the 1,111,090 patients included, 30-day mortality was associated with the HFRS: adjusted OR (aOR) for an intermediate HFRS (5–15 points) was 1.91 [95% confidence interval (95% IC); 1.87–1.95] and aOR 2.57 [95% IC; 2.50–2.64] for high HFRS (>15 points), as compared to low HFRS (<5 points). LOS >10 days increased with the HFRS (aOR = 1.36 [95% IC; 1.34–1.38] for an intermediate HFRS and aOR 1.51 [95% IC; 1.48–1.54] for a high HFRS). A high HFRS was associated with 30-day hospital readmission (aOR = 1.06 [95% IC; 1.04–1.08]). Discussion: This real-life analysis of in- and out-patient healthcare pathways confirmed the HFRS’s ability to predict adverse outcomes, after adjustment on social deprivation.

Keywords

frailty elderly epidemiology hospitals

Introduction

Due to demographic aging, the proportion of frail older people is expected to increase. According to a recent meta-analysis of studies of community-dwelling people aged 60 or over in 28 countries, the estimated incidence rate [95% confidence interval (CI)] for frailty was 43.4 [37.3–50.4] per 1000 person-years (Ofori-Asenso et al., 2019). Consequently, the concept of frailty has been implemented in health policies (Kurnat-Thoma et al., 2022; Muscedere et al., 2016; Ntanasi et al., 2020; Puts et al., 2017). Frail elderly people are at risk of adverse events (Kojima, 2016; Kojima et al., 2018). However, frailty is potentially reversible (Rodríguez-Mañas et al., 2013). As the population ages, the challenge could be therefore one of identifying frail people and offering early interventions. A variety of tools have been developed for the identification and assessment of frailty and its various domains (Frailty assessment instruments: Systematic characterization of the uses and contexts of highly-cited instruments – PubMed, s. d.; Sutton et al., 2016; Van Damme et al., 2021; Xie et al., 2017). Although these tools would be useful for detecting elderly people at risk of adverse events, they were not easy to implement in routine clinical practice – especially in acute care settings (Kurnat-Thoma et al., 2022; Lim et al., 2019; Pugh et al., 2018).

Most countries have health information systems for epidemiological monitoring of the population. There is growing interest in the use of health claims data to screen frail elderly people. A number of tools have been developed for detecting frail individuals on the basis of claims data in general (Clegg et al., 2016; Gilbert et al., 2018; Kim et al., 2018; Orkaby et al., 2019; Segal et al., 2017; Wen et al., 2017) and hospital databases in particular (Gilbert et al., 2018; Mak et al., 2022). The Hospital Frailty Risk Score (HFRS) was developed using administrative data (‘hospital episode statistics’) from hospitals in England (Gilbert et al., 2018). The HFRS was based on 109 diagnosis codes from the International Classification of Diseases, 10th Revision (ICD-10), identified through a cluster analysis (Gilbert et al., 2018). Points were awarded for each ICD-10 code that were proportional to how strongly they predicted membership of the cluster, calculated using regression coefficients from a logistic regression model. The final HFRS score was broken down into three categories: low, (HFRS <5), intermediate (HFRS (5–15), and high (HFRS >15). The HFRS was first externally validated in a population of elderly patients (aged 75 or over) admitted to hospital through the emergency department. The validation results showed that the HFRS was associated with 30-day mortality, 30-day emergency hospital readmission, and a length of stay (LOS) of more than 10 days (Gilbert et al., 2018). Since then, the HFRS has been validated in several countries (Eckart et al., 2019; Hoffmann et al., 2019; McAlister & van Walraven, 2019a; Shebeshi et al., 2021; Walraven et al., 2019), including France (Gilbert et al., 2022); all these studies confirmed that the HFRS was predictive of 30-day inpatient mortality and LOS >10 days for inpatients admitted via the emergency room. Even though the association between the HFRS and mortality was confirmed, one can nevertheless suspect that its strength had been underestimated because out-patient mortality was not taken into account. Moreover, current French healthcare policies encourage the direct admission of older people (Ministère en charge de la Santé, s. d.; synthese_atelier_10_hopital_et_personne_agee_14_fev_2018_3_.docx.pdf, s. d.; _urgences_dp_septembre_2019.pdf, s. d.), via specific systems, such as phone hotlines for the elderly (Chaussinand et al., 2021; Martinez et al., 2020). Given that the HFRS was initially developed in a population of patients admitted through the emergency department, broader assessments of the score’s predictive ability must take account of direct admissions and post-discharge outcomes. Hence, the primary objective of the present study of elderly people (aged 75 or over) in France was to evaluate the associations between the HFRS on one hand and 30-day mortality, 30-day hospital readmission, and a long LOS on the other.

Methods

We performed a nationwide, retrospective, observational study using the French national health data system (Système National des Données de Santé, SNDS). The SNDS database includes claims data from all public- and private-sector healthcare facilities, together with information on vital status and social deprivation for around 99% of the French population (Tuppin et al., 2017). All the data on an individual can be linked in an encrypted, deidentified manner through a unique patient identifier.

Population

Elderly patients (aged 75 or over) having spent at least one night in an acute care facility in mainland France between January 1st and December 31st, 2017, were included in the study. For each patient, the first hospital admission during the study period was defined as the index admission and was considered with regard to inclusion. The inclusion criteria are detailed in Supplementary Data S1.

Frailty Definition

For each patient, frailty was calculated using the HFRS by considering only the index admission. For the main analyses, the hospitalization discharge summaries over the 2 years before the index admission were checked against the 109 ICD-10 codes from Gilbert et al. (Gilbert et al., 2018, 2022), such as dementia (F00), fractures (S codes) or urinary incontinence (R32). In the sensitivity analyses, the calculation of the HFRS also took account of information from the index admission. The population was then stratified into three frailty risk categories: low (HFRS <5), intermediate (HFRS = 5–15), and high (HFRS >15).

Outcomes

The primary outcome was 30-day mortality, defined as in-hospital or out-of-hospital deaths during the 30-day following the index admission. The secondary outcomes were (i) LOS >10 days for the index admission, and (ii) unscheduled hospital readmissions within 30 days of discharge from the index admission. The hospital admissions were selected by applying a modified version of an algorithm developed by the French Agency for Information on Hospital Care (Agence Technique de l’Information sur l’Hospitalisation, ATIH). We also considered unscheduled hospital readmissions after the exclusion of those involving patients who died in hospital or outside hospital within 30 days of the index admission (Supplementary Data S1).

Variables of Interest

The potentially confounding variables were age, sex, the number of hospital admissions during the 2 years preceding the index admission, admission in an acute care setting (including admission to the emergency department and direct admission to a hospital’s intensive care unit or a neurovascular unit), and social deprivation. Due to the risk of under-coding social information (ICD-10 Z), social deprivation was gauged through specific ICD-10 Z codes recorded during the index admission and through hospital stays during the previous 2 years (Supplementary data S2), but also, through the presence of specific dedicated health insurance coverage (i.e., state medical aid, health insurance vouchers, universal health insurance coverage or supplementary universal health insurance coverage). In France, indeed, depending on economic conditions, patients could be covered by a specific social health insurance dedicated to unemployed and/or nonresident people. Eventually social deprivation was estimated by a proxy between its specific ICD-10 codes, rarely used because without financial incentives, and the insurance type dedicated to socially deprived individuals. Comorbidities were used in the adjusted models using ICD-10 adapted Charlson comorbidity coding algorithm, identified by the presence of the 17 conditions (Charlson et al., 1994; Charlson et al., 1987; Grammatico-Grammatico-Guillon et al., 2017; Quan et al., 2005) (i.e., the specifications to identify each condition) from significant associated diagnosis.

Statistical Analysis

First, we performed a descriptive analysis: continuous variables were described using the mean ± standard deviation (SD). Qualitative variables were expressed as frequency and percent. Second, we performed a bivariate analysis (using all the above-mentioned potential confounding variables) for each outcome. In a third step, logistic regression models were built for each outcome. The models included all the variables with p < .2 in the bivariate analyses, together with variables considered to be clinically relevant. Last, a descending stepwise process was used to select the final logistic regression model, including all the statistically significant variables. Independent variables were estimated at p < .05 in the final models.

Four sensitivity analyses were performed for the primary outcome (30-day mortality): (model (1) the HFRS was calculated using information in the discharge summary from the index admission (excluded in the main analysis) as well as from the other discharge summaries over the previous 2 years, (model (2) the model was additionally adjusted for the Charlson Comorbidity Index and with the same variables of interest used in the main analysis (age, sex, number of hospital admissions in the previous 2 years, admission to the emergency department/direct admission to a hospital’s intensive care unit or neurovascular unit and social deprivation), (model (3) only patients whose index admission began with admission via an emergency department were considered, and (model (4) only patients admitted to hospital directly were considered. C-statistic was used to measure of the predictive accuracy of the logistic regression models performed in the sensitive analyses.

All analyses were performed using SAS Enterprise Guide 71 64-bit software (SAS Institute Inc., Cary, NC, USA), that is, the version available on the SNDS Website at the time of the analyses.

Results

Descriptive Analyses

1,111,090 patients were included over the 1,671,940 patients aged 75 or over screened (66.5%). The main exclusion criteria were: being transferred after the index admission (64.0% of the excluded patients), twins or patients with unlinkable data (17.7%). The mean ± SD age was 84.0 ± 5.8, and the sex ratio was .78 (Table 1). The mean ± SD HFRS was 3.5 ± 6.4. There were 180,766 patients (16.3%) in the intermediate-risk HFRS group and 76,103 (6.8%) in the high-risk group. Patients with an intermediate or high HFRS were older and more likely to have been admitted to hospital via the emergency room (p < .001). Patients with a high HFRS were less frequently discharged to home than patients with low HFRS. Although the prevalence of social deprivation was 11.2% in the overall study population, the value in the high HFRS group was 25.5%. A total of 63,484 patients died within 30 days of the index admission (5.7%). In a bivariate analysis, the 30-day mortality rate, the 30-day readmission rate and the proportion of LOS >10 days were positively and significantly associated with the HFRS (Table 1). The populations’ sociodemographic characteristics are summarized as a function of the main outcomes in Supplementary Data S3.

Table 1.

Characteristics of Elderly Patients (Aged 75 or Over) Hospitalized in Mainland France in 2017, According to the Main Outcomes.

Overall		30-Day Mortality			30-Day Hospital Readmission			LOS >10 days
Overall		Yes	No	p Value	Yes	No	p Value	Yes	No	p Value
Number of patients, N	1,111,090	63,484	1,047,606		172,126	888,539		180,475	930,615
Male	485,639 (43.7%)	28,373 (44.7%)	457,266 (43.6%)	<.001	85,334 (49.6%)	377,412 (42.5%)	<.001	74,160 (41.1%)	411,479 (44.2%)	<.001
Age	84.0 ± 5.8	87.6 ± 6.0	83.8 ± 5.7	<.001	83.8 ± 5.6	83.8 ± 5.7	NS	85.5 ± 5.8	83.7 ± 5.7	<.001
HFRS	3.5 ± 6.4	5.8 ± 8.3	3.3 ± 6.3	<.001	4.1 ± 6.9	3.3 ± 6.2	<.001	4.8 ± 7.5	3.2 ± 6.1	<.001
LOS index admission, mean (SD)	6.1 ± 6.4	7.0 ± 6.4	6.1 ± 6.4	<.001	6.7 ± 6.8	5.9 ± 6.0	<.001	17.1 ± 8.4	4.0 ± 2.7	<.001
Emergency department admission	566,214 (51.0%)	49,164 (77.4%)	517,050 (49.4%)	<.001	90,612 (52.6%)	435,790 (49.0%)	<.001	124,379 (68.9%)	441,835 (47.5%)	<.001
Days to readmission	157.2 ± 158.0	8.7 ± 15.7	158.4 ± 158.1	<.001	13.3 ± 8.6	207.0 ± 154.7	<.001	134.7 ± 147.0	161.7 ± 159.7	<.001
Home discharge	1,060,665 (95.5%)	15,601 (24.6%)	1,045,064 (99.8%)	<.001	172,126 (100.0%)	888,539 (100.0%)	<.001	166,858 (92.5%)	893,807 (96.0%)	<.001
Outcomes
30-day mortality	63,484 (5.7%)				5590 (3.2%)	10,011 (1.1%)		14,980 (8.3%)	48,504 (5.2%)
30-day readmission	172,126 (15.5%)	5591 (8.8%)	166,535 (15.9%)					33,876 (18.8%)	138,250 (14.9%)
LOS >10 days	180,475 (16.2%)	8.7 ± 15.7	158.4 ± 158.1		33,875 (19.7%)	132,983 (15.0%)
Confounding variables
Admissions in the previous 2 years	1.4 ± 1.9	.1 ± 0.3	1.5 ± 2.0	<.001	2.9 ± 2.6	1.2 ± 1.7	<.001	1.5 ± 2.0	1.4 ± 1.9	<.001
Emergency/direct intensive care unit admission^a	620,524 (55.8%)	53,506 (84.3%)	567,018 (54.1%)	<.001	99,446 (57.8%)	477,058 (53.7%)	<.001	135,805 (75.2%)	484,719 (52.1%)	<.001
Social deprivation	124,134 (11.2%)	6300 (9.9%)	117,834 (11.2%)	<.001	24,295 (14.1%)	94,943 (10.7%)	<.001	29,064 (16.1%)	95,070 (10.2%)	<.001

Data are quoted as the mean ± SD or n (%), as appropriate.

^aIncluding admission to the emergency department and direct admission to a hospital’s intensive care unit or neurovascular unit.

Factors Predicting the Main Outcomes

Age was significantly associated with all outcomes. The prevalence of 30-day mortality and 30-day hospital readmission were higher in men (Table 2 and Supplementary Data S4). Intensive care unit or emergency department admission increased the risk of 30-day mortality, 30-day hospital readmission, and LOS >10 days. The number of admissions in the previous 2 years was negatively associated with the 30-day mortality risk and positively associated with 30-day hospital readmission and LOS. The same trend was found for social deprivation.

Table 2.

Factors Associated With the Mortality Rate, the Readmission Rate and LOS >10 days, in Multivariate Logistic Regressions.

Adjusted Odds ratio [95% confidence interval]	30-day mortality	30-day hospital readmission	LOS >10 days
Adjusted Odds ratio [95% confidence interval]	(n = 1,111,090)	(n = 1,060,665)	(n = 1,111,090)
Low HFRS (<5)	Ref	Ref	Ref
Intermediate HFRS (5-15)	1.91 [1.87–1.95]	1.01 [1.00–1.03]	1.36 [1.34–1.38]
High HFRS (>15)	2.57 [2.50–2.64]	1.06 [1.04–1.08]	1.51 [1.48–1.54]
Age	1.09 [1.09–1.09]	1.01 [1.01–1.01]	1.03 [1.03–1.04]
Males	1.65 [1.62–1.68]	1.19 [1.18–1.21]	1.01 [1.00–1.02]
Number of hospital admissions in the previous 2 years	0.12 [.12–.12]	1.46 [1.45–1.46]	1.02 [1.02–1.03]
Emergency/direct intensive care unit admission^a	3.60 [3.52–3.68]	1.19 [1.18–1.20]	2.47 [2.44–2.50]
Social deprivation	0.81 [.79–.84]	1.18 [1.16–1.20]	1.39 [1.37–1.41]
C-statistic	0.88	0.78	0.66

HFRS: hospital frailty risk score; LOS: length of stay.

The 30-day readmission rate was calculated after excluding patients who died in hospital or outside hospital within 30 days of the index admission date.

The ORs were adjusted for age, sex, number of hospital admissions in the previous 2 years, admission to the emergency department/direct admission to a hospital’s intensive care unit or neurovascular unit and social deprivation.

^aIncluding admission to the emergency department and direct admission to a hospital’s intensive care unit or neurovascular unit.

After adjustment, the 30-day mortality was positively associated with the HFRS: the adjusted OR (aOR) [95% CI] was 1.91 [1.87–1.95] for an intermediate HFRS and 2.57 [2.50–2.64] for a high HFRS, relative to a low HFRS (c-statistic: .88). For the 1,060,665 patients (95.5%) who survived for at least 30 days after discharge from the index stay, the 30-day hospital readmission was significantly associated with a high HFRS (aOR = 1.06 [1.04–1.08]), relative to a lower HFRS. LOS>10 days was positively associated with the HFRS (aOR = 1.36 [1.34–1.38] for an intermediate HFRS and 1.51 [1.48–1.54] for a high HFRS). Box plots are also reported on Supplementary data S4.

Sensitivity Analyses

The results of our sensitivity analyses showed that the association between 30-day mortality and HFRS was robust (Table 3). Model 1 (based on the inclusion hospital data and the previous 2 years) confirmed the HFRS’s predictive power of HFRS, with an aOR of 2.69 [2.64–2.75] for the intermediate-risk group and 3.79 [3.70–3.89] for the high-risk group. After adjustment for the Charlson comorbidity index, the association remained significant (Model 2: aOR = 1.30 [1.27–1.33] for the intermediate-risk group and 1.37 [1.33–1.41] for the high-risk group). Model 3 (applied to elderly patients admitted via the emergency department) evidenced a higher risk of 30-day mortality (aOR = 1.74 [1.70–1.79] for the intermediate-risk group and 2.29 [2.22–2.36] for the high-risk group). An even stronger association was found for elderly patients admitted directly, with an aOR of 2.55 for the intermediate-risk group [2.44–2.67] and 4.03 [3.79–4.28] for the high-risk group (Model 4).

Table 3.

Association Between the 30-Day Mortality Rate and the HFRS, in Various Models.

30-day mortality aOR [95% CI]	Model 1	Model 2	Model 3	Model 4
30-day mortality aOR [95% CI]	Including the Index Admission	Including Charlson Comorbidity	Emergency Department Admissions Only	Direct Admissions Only
Low HFRS	Ref	Ref	Ref	Ref
Intermediate HFRS	2.69 [2.64–2.75]	1.30 [1.27–1.33]	1.74 [1.70–1.79]	2.55 [2.44–2.67]
High HFRS	3.79 [3.70–3.89]	1.37 [1.33–1.41]	2.29 [2.22–2.36]	4.03 [3.79–4.28]
N	1,111,090	1,111,090	566,214	544,876
C-statistic	0.89	0.9	0.85	0.89

HFRS: hospital frailty risk score; OR: odds ratio.

Discussion

Our present results demonstrated that the HFRS is predictive of 30-day mortality, long (>10-day) hospital stays, and 30-day readmission (for the high-risk HFRS group only) among elderly patients discharged from acute care facilities in France. The association between 30-day mortality and HFRS was robust in our sensitivity analyses.

The present real-life, observational study exhaustively analyzed outcomes for elderly patients hospitalized in mainland France. Our use of automated tools showed that the care pathways and mortality rates varied according to the patient’s HFRS and enabled us to build predictive models. The study’s results highlighted the association between the HFRS and the main adverse outcomes (including in-hospital and post-discharge data) and gave an overview of HFRS’s ability to predict mortality, readmission, and LOS. For example, our estimate of 30-day mortality was based on in-hospital and out-of-hospital deaths.

One of the strengths of our study was its consideration of both emergency department admissions and direct hospital admissions for the index admission; our sensitivity analyses confirmed that the HFRS could predict harmful outcomes after both types of admission. The study results were even more robust (giving greater predictive power for 30-day mortality) when information from the index admission were also taken into account. Our main analysis highlighted a significant association between the HFRS and 30-day mortality (aOR about Table 2).

Logistic regressions were also adjusted for individual social deprivation; this is not easily performed with the hospital discharge database alone because the latter only includes an area-based index of social deprivation, rather than an individual index (Rey et al., 2009). Indeed, the available social information (ICD-10 Z codes) is sparse, and the lack of financial incentives leads to under-coding. However, our use of the SNDS provided individual information on health insurance coverage (universal health insurance or state medical aid). In the present study, social deprivation was positively associated with 30-day hospital readmission and LOS >10 days but negative associated with 30-day mortality. The recent literature data on France show that social deprivation is related to a higher mortality rate (Labbe et al., 2015)and a higher risk of premature death (before the age of 65) (Melchior et al., 2006). Consequently, we were surprised to observe a negative association. In view of the older age of our study population, one possible explanation relates to survival/selection bias. Given that social deprivation is linked to higher premature mortality, elderly people who survive beyond at the age of 75 might be more robust than individuals in the general population.

Our results for mortality and LOS were consistent with the findings of the initial study conducted in the United Kingdom (Gilbert et al., 2018), international studies and the first French study to have assessed the French national hospital discharge database (Programme de médicalisation des systèmes d’information) (Bonjour et al., 2021; Gilbert et al., 2022; McAlister & van Walraven, 2019a). In our study, 30-day hospital readmission was associated with a high HFRS but not with an intermediate HFRS. This is in line with some (but not all) of the literature data. For example, Gilbert et al. also highlighted an association between the HFRS and 30-day readmission to an emergency department. However, an association was not found in an earlier French study of all emergency department or unplanned hospital readmissions (Gilbert et al., 2018, 2022). In contrast, McAlister et al. found a negative (but weak) association between the HFRS and 30-day readmissions or emergency department visits (McAlister & van Walraven, 2019b). Differences in study design means that it is difficult to compare results for outcomes like readmissions. However, in our study, the results should be interpreted cautiously due to potential bias inherent to very large population size.

Indeed, our study also had some limitations. First, our use of hospital administrative data created inherent bias. The major source of bias relates to over- or under-representation of the ICD-10 codes included in the HFRS. Nonetheless, we demonstrated a significant association between the HFRS and the main adverse outcomes. It is important to bear in mind that although the HFRS is a proxy of frailty (which lack an ICD-10 code), it constitutes an easy-to-use epidemiological tool for exhaustive, large-scale application (as demonstrated previously (Guillon et al., 2020; Guillon, Laurent, Duclos, et al., 2021; Guillon, Laurent, Godillon, et al., 2021). Moreover, the use of big data tools for research purposes presents some bias regarding the size of the population and the risk of misclassification of the variables of interest due to the extra-large study population. One must keep in mind that we considered the study population as the exhaustive elderly population hospitalized (coding report is mandatory for each hospital stay), but if we have likened it to an extra-large sample, the statistical power of our results could be very high, exposing the model to type 2 error.

However, the study is based on a complete database of healthcare medical reports, and the limits linked to the sample size are out of the spectrum of in the Health data hub we have the completeness of the data, and the p value is in fact out of interest because we are in real-life settings. Hence, there were no p > .2 except the variables without enough observations, giving a risk of false results.

Using the national claims data included in the SNDS is of great value because innovative ‘big data’ tools could be used to build predictive models and improve care pathways for elderly patients with a high HFRS. Thanks to an algorithm that could be implemented automatically in hospitals, it might be possible to use the HFRS for epidemiological, surveillance and health policy guidance purposes at the population level.

Conclusion

Our analysis of SNDS data confirmed that HFRS was associated with 30-day mortality, 30-day hospital readmission and LOS >10 days among elderly people (aged 75 or over) and hospitalized in Metropolitan France. Screening, multidisciplinary assessment and the appropriate management of frailty in elderly are needed to decrease the frequency of hospitalization and the associated poor outcomes. Further research (especially longitudinal research) on care pathways for frail elderly people and the impact of prevention programs on frailty trajectories is needed.

Supplemental Material

Supplemental Material - Elderly Outcomes After Hospitalization: The Hospital Frailty Risk Score Applied on the French Health Data Hub

Supplemental Material for Elderly Outcomes After Hospitalization: The Hospital Frailty Risk Score Applied on the French Health Data Hub by Sophie Dubnitskiy-Robin, Emeline Laurent, Julien Herbert, Bertrand Fougère and Leslie Guillon-Grammatico in Journal of Aging and Health

Footnotes

Author Contributions

SDR, EL, BF and LGG conceived the study and drafted the study protocol. JH analyzed data. SDR, EL, LGG helped to interpret data. SDR, EL, JH, BF and LGG drafted the manuscript. The final manuscript was read and approved by all authors.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This project was supported by the ‘Fondation de l’Avenir’, Paris, France (2020 year, project number [AP-RM-20-027]) and the ‘Region Centre-Val de Loire’, France (2020 year, regional call for project with no grant number).

Ethics

Access to linked, deidentified SNDS data was approved by the French National Data Protection Commission (Commission Nationale de l’Informatique et des Libertés, Paris, France; reference: DR-2020-016). In line with the French legislation on analyses of deidentified data, there was no requirement to provide the patients with study information or to receive the patients’ consent to publication of their anonymized personal data.

Supplemental Material

Supplemental material for this article is available online.

ORCID iD

Leslie Guillon-Grammatico

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