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      P neumocystis pneumonia in French intensive care units in 2013–2019: mortality and immunocompromised conditions

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          Abstract

          Purpose

          The recent epidemiology of Pneumocystis pneumonia (PCP) requiring intensive care unit (ICU) admission and the associated spectrum of immunocompromising conditions are poorly described.

          Methods

          We analyzed all adult PCP cases admitted to French ICUs via the French medical database system (PMSI), over the period from 2013 to 2019.

          Results

          French ICUs admitted a total of 4055 adult patients with PCP. Among all hospitalized PCP cases, the proportion requiring ICU admission increased from 17.8 in 2014 to 21.3% in 2019 (P < 0.001). The incidence of severe PCP rose from 0.85 in 2013 to 1.32/100,000 adult inhabitants in 2019 (P < 0.0001), primarily due to the proportion of HIV-negative patients that increased from 60.6% to 74.4% (P < 0.0001). Meanwhile, the annual number of severe PCP cases among patients with HIV infection remained stable over the years. In-hospital mortality of severe PCP cases was 28.5% in patients with HIV infection and 49.7% in patients without.

          Multivariable logistic analysis showed that patients with HIV infection had a lower adjusted risk of death than patients without HIV infection (Odds Ratio [OR]: 0.30, 95% confidence interval [95CI]: 0.17–0.55). Comorbidities or conditions strongly associated with hospital mortality included the patient’s age, Simplified Acute Physiologic Score II, congestive heart failure, coagulopathy, solid organ cancer, and cirrhosis. A vast array of autoimmune inflammatory diseases affected 19.9% of HIV-negative patients.

          Conclusions

          The number of PCP cases requiring ICU admission in France has risen sharply. While the yearly count of severe PCP cases in HIV-infected patients has remained steady, this rise predominantly affects cancer patients, with a recent surge observed in patients with autoimmune inflammatory diseases, affecting one in five individuals.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s13613-024-01309-y.

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          The REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) Statement

          Eric Benchimol, Liam Smeeth, Astrid Guttmann … (2015)
          Routinely collected health data, obtained for administrative and clinical purposes without specific a priori research goals, are increasingly used for research. The rapid evolution and availability of these data have revealed issues not addressed by existing reporting guidelines, such as Strengthening the Reporting of Observational Studies in Epidemiology (STROBE). The REporting of studies Conducted using Observational Routinely collected health Data (RECORD) statement was created to fill these gaps. RECORD was created as an extension to the STROBE statement to address reporting items specific to observational studies using routinely collected health data. RECORD consists of a checklist of 13 items related to the title, abstract, introduction, methods, results, and discussion section of articles, and other information required for inclusion in such research reports. This document contains the checklist and explanatory and elaboration information to enhance the use of the checklist. Examples of good reporting for each RECORD checklist item are also included herein. This document, as well as the accompanying website and message board (http://www.record-statement.org), will enhance the implementation and understanding of RECORD. Through implementation of RECORD, authors, journals editors, and peer reviewers can encourage transparency of research reporting.
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            Global and Multi-National Prevalence of Fungal Diseases—Estimate Precision

            Felix Bongomin, Sara Gago, Rita Oladele … (2017)
            Fungal diseases kill more than 1.5 million and affect over a billion people. However, they are still a neglected topic by public health authorities even though most deaths from fungal diseases are avoidable. Serious fungal infections occur as a consequence of other health problems including asthma, AIDS, cancer, organ transplantation and corticosteroid therapies. Early accurate diagnosis allows prompt antifungal therapy; however this is often delayed or unavailable leading to death, serious chronic illness or blindness. Recent global estimates have found 3,000,000 cases of chronic pulmonary aspergillosis, ~223,100 cases of cryptococcal meningitis complicating HIV/AIDS, ~700,000 cases of invasive candidiasis, ~500,000 cases of Pneumocystis jirovecii pneumonia, ~250,000 cases of invasive aspergillosis, ~100,000 cases of disseminated histoplasmosis, over 10,000,000 cases of fungal asthma and ~1,000,000 cases of fungal keratitis occur annually. Since 2013, the Leading International Fungal Education (LIFE) portal has facilitated the estimation of the burden of serious fungal infections country by country for over 5.7 billion people (>80% of the world’s population). These studies have shown differences in the global burden between countries, within regions of the same country and between at risk populations. Here we interrogate the accuracy of these fungal infection burden estimates in the 43 published papers within the LIFE initiative.
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              Variable selection – A review and recommendations for the practicing statistician

              Georg Heinze, Christine Wallisch, Daniela Dunkler (2018)
              Abstract Statistical models support medical research by facilitating individualized outcome prognostication conditional on independent variables or by estimating effects of risk factors adjusted for covariates. Theory of statistical models is well‐established if the set of independent variables to consider is fixed and small. Hence, we can assume that effect estimates are unbiased and the usual methods for confidence interval estimation are valid. In routine work, however, it is not known a priori which covariates should be included in a model, and often we are confronted with the number of candidate variables in the range 10–30. This number is often too large to be considered in a statistical model. We provide an overview of various available variable selection methods that are based on significance or information criteria, penalized likelihood, the change‐in‐estimate criterion, background knowledge, or combinations thereof. These methods were usually developed in the context of a linear regression model and then transferred to more generalized linear models or models for censored survival data. Variable selection, in particular if used in explanatory modeling where effect estimates are of central interest, can compromise stability of a final model, unbiasedness of regression coefficients, and validity of p‐values or confidence intervals. Therefore, we give pragmatic recommendations for the practicing statistician on application of variable selection methods in general (low‐dimensional) modeling problems and on performing stability investigations and inference. We also propose some quantities based on resampling the entire variable selection process to be routinely reported by software packages offering automated variable selection algorithms.
              Article 0 comments Cited 370 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Thierry Boulain: thierry.boulain@chu-orleans.fr
                Journal
                Journal ID (nlm-ta): Ann Intensive Care
                Journal ID (iso-abbrev): Ann Intensive Care
                Title: Annals of Intensive Care
                Publisher: Springer International Publishing (Cham )
                ISSN (Electronic): 2110-5820
                Publication date (Electronic): 22 May 2024
                Publication date PMC-release: 22 May 2024
                Publication date Collection: 2024
                Volume: 14
                Electronic Location Identifier: 80
                Affiliations
                GRID grid.112485.b, ISNI 0000 0001 0217 6921, Service de Médecine Intensive Réanimation, , Centre Hospitalier Universitaire d’Orléans, ; 14 Avenue de L’Hôpital CS 86709, 45067 Orleans Cedex 2, France
                Article
                Publisher ID: 1309
                DOI: 10.1186/s13613-024-01309-y
                PMC ID: 11111439
                PubMed ID: 38776012
                SO-VID: 73da9926-8771-49f1-be46-03bfb92a95b9
                Copyright © © The Author(s) 2024
                License:

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                Date received : 5 February 2024
                Date accepted : 6 May 2024
                Categories
                Subject: Research
                Custom metadata
                issue-copyright-statement © La Société de Réanimation de Langue Francaise = The French Society of Intensive Care (SRLF) 2024

                ScienceOpen disciplines: Emergency medicine & Trauma
                Keywords: pneumocystis pneumonia,epidemiology,intensive care unit,immunodeficiency
                Data availability:
                ScienceOpen disciplines: Emergency medicine & Trauma
                Keywords: pneumocystis pneumonia, epidemiology, intensive care unit, immunodeficiency

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