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Imoto W, Ihara Y, Imai T, Kawai R, Yamada K, Kaneko Y, Shintani A, Kakeya H. Incidence and risk factors for coronavirus disease 2019-associated pulmonary aspergillosis using administrative claims data. Mycoses 2024; 67:e13773. [PMID: 39090076 DOI: 10.1111/myc.13773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 06/26/2024] [Accepted: 07/19/2024] [Indexed: 08/04/2024]
Abstract
BACKGROUND Coronavirus disease 2019 (COVID-19)-associated pulmonary aspergillosis (CAPA) is one of the noticeable complications of COVID-19 and its incidence varies widely. In Japan, research on the incidence, risk factors and mortality associated with CAPA is limited. OBJECTIVES This study aimed to explore the incidence and potential risk factors for CAPA in patients with severe or critical COVID-19 and evaluate the relationship between CAPA and mortality of patients with severe or critical COVID-19. METHODS We investigated the incidence of CAPA in patients with severe and critical COVID-19 using administrative claims data from acute care hospitals in Japan. We employed multivariable regression models to explore potential risk factors for CAPA and their contribution to mortality in patients with severe and critical COVID-19. RESULTS The incidence of CAPA was 0.4%-2.7% in 33,136 patients with severe to critical COVID-19. Age, male sex, chronic lung disease, steroids, immunosuppressants, intensive care unit admission, blood transfusion and dialysis were potential risk factors for CAPA in patients with severe to critical COVID-19. CAPA was an independent factor associated with mortality. CONCLUSIONS CAPA is a serious complication in patients with severe and critical COVID-19 and may increase mortality.
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Affiliation(s)
- Waki Imoto
- Department of Infection Control Science, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan
- Department of Infectious Disease Medicine, Osaka Metropolitan University Hospital, Osaka, Japan
- Department of Infection Control and Prevention, Osaka Metropolitan University Hospital, Osaka, Japan
- Research Center for Infectious Disease Sciences (RCIDS), Osaka Metropolitan University Graduate School of Medicine, Osaka, Japan
- Osaka International Research for Infectious Diseases (OIRCID), Osaka Metropolitan University, Osaka, Japan
| | - Yasutaka Ihara
- Department of Medical Statistics, Osaka Metropolitan University Graduate School of Medicine, Osaka, Japan
- Data Intelligence Department, Global DX, Daiichi Sankyo Co., Ltd, Tokyo, Japan
| | - Takumi Imai
- Department of Medical Statistics, Osaka Metropolitan University Graduate School of Medicine, Osaka, Japan
| | - Ryota Kawai
- Department of Medical Statistics, Osaka Metropolitan University Graduate School of Medicine, Osaka, Japan
| | - Koichi Yamada
- Department of Infection Control Science, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan
- Department of Infectious Disease Medicine, Osaka Metropolitan University Hospital, Osaka, Japan
- Department of Infection Control and Prevention, Osaka Metropolitan University Hospital, Osaka, Japan
- Research Center for Infectious Disease Sciences (RCIDS), Osaka Metropolitan University Graduate School of Medicine, Osaka, Japan
- Osaka International Research for Infectious Diseases (OIRCID), Osaka Metropolitan University, Osaka, Japan
| | - Yukihiro Kaneko
- Research Center for Infectious Disease Sciences (RCIDS), Osaka Metropolitan University Graduate School of Medicine, Osaka, Japan
- Osaka International Research for Infectious Diseases (OIRCID), Osaka Metropolitan University, Osaka, Japan
- Department of Bacteriology, Osaka Metropolitan University Graduate School of Medicine, Osaka, Japan
| | - Ayumi Shintani
- Department of Medical Statistics, Osaka Metropolitan University Graduate School of Medicine, Osaka, Japan
| | - Hiroshi Kakeya
- Department of Infection Control Science, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan
- Department of Infectious Disease Medicine, Osaka Metropolitan University Hospital, Osaka, Japan
- Department of Infection Control and Prevention, Osaka Metropolitan University Hospital, Osaka, Japan
- Research Center for Infectious Disease Sciences (RCIDS), Osaka Metropolitan University Graduate School of Medicine, Osaka, Japan
- Osaka International Research for Infectious Diseases (OIRCID), Osaka Metropolitan University, Osaka, Japan
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Imoto W, Imai T, Kawai R, Ihara Y, Nonomiya Y, Namikawa H, Yamada K, Yoshida H, Kaneko Y, Shintani A, Kakeya H. Incidence and potential risk factors of human cytomegalovirus infection in patients with severe and critical coronavirus disease 2019. J Infect Chemother 2024:S1341-321X(24)00171-5. [PMID: 38944381 DOI: 10.1016/j.jiac.2024.06.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Revised: 05/07/2024] [Accepted: 06/21/2024] [Indexed: 07/01/2024]
Abstract
BACKGROUND Human cytomegalovirus (HCMV) infection occurs in immunosuppressed individuals and is known to increase mortality. Patients with coronavirus disease 2019 (COVID-19) are often treated with steroids, require intensive care unit (ICU) treatment, and may therefore be at risk for HCMV infection. However, which factors predispose severely ill patients with COVID-19 to HCMV infection and the prognostic value of such infections remain largely unexplored. This study aimed to examine the incidence and potential risk factors of HCMV infection in patients with severe or critical COVID-19 and evaluate the relationship between HCMV infection and mortality. METHODS AND FINDINGS We used administrative claims data from advanced treatment hospitals in Japan to identify and analyze patients with severe or critical COVID-19. We explored potential risk factors for HCMV infection using multivariable regression models and its contribution to mortality in patients with COVID-19. Overall, 33,151 patients who progressed to severe or critical COVID-19 illness were identified. The incidence of HCMV infection was 0.3-1.7 % depending on the definition of HCMV infection. Steroids, immunosuppressants, ICU admission, and blood transfusion were strongly associated with HCMV infection. Furthermore, HCMV infection was associated with patient mortality independent of the observed risk factors for death. CONCLUSIONS HCMV infection is a notable complication in patients with severe or critical COVID-19 who are admitted to the ICU or receive steroids, immunosuppressants, and blood transfusion and can significantly increase mortality risk.
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Affiliation(s)
- Waki Imoto
- Department of Infection Control Science, Osaka Metropolitan University Graduate School of Medicine, 1-4-3, Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan; Department of Infectious Disease Medicine, Osaka Metropolitan University Hospital, 1-5-7 Asahi-machi, Abeno-ku, Osaka, 545-8586, Japan; Department of Infection Control and Prevention, Osaka Metropolitan University Hospital, 1-5-7 Asahi-machi, Abeno-ku, Osaka, 545-8586, Japan; Research Center for Infectious Disease Sciences (RCIDS), Osaka Metropolitan University Graduate School of Medicine, 1-4-3, Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan; Osaka International Research for Infectious Diseases (OIRCID), Osaka Metropolitan University, 1-2-7-601, Asahi-machi, Abeno-ku, Osaka, 545-0051, Japan.
| | - Takumi Imai
- Department of Medical Statistics, Osaka Metropolitan University Graduate School of Medicine, 1-4-3, Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan.
| | - Ryota Kawai
- Department of Medical Statistics, Osaka Metropolitan University Graduate School of Medicine, 1-4-3, Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan.
| | - Yasutaka Ihara
- Department of Medical Statistics, Osaka Metropolitan University Graduate School of Medicine, 1-4-3, Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan.
| | - Yuta Nonomiya
- Department of Medical Statistics, Osaka Metropolitan University Graduate School of Medicine, 1-4-3, Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan.
| | - Hiroki Namikawa
- Department of Medical Education and General Practice, Osaka Metropolitan University Graduate School of Medicine, 1-4-3, Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan.
| | - Koichi Yamada
- Department of Infection Control Science, Osaka Metropolitan University Graduate School of Medicine, 1-4-3, Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan; Department of Infectious Disease Medicine, Osaka Metropolitan University Hospital, 1-5-7 Asahi-machi, Abeno-ku, Osaka, 545-8586, Japan; Department of Infection Control and Prevention, Osaka Metropolitan University Hospital, 1-5-7 Asahi-machi, Abeno-ku, Osaka, 545-8586, Japan; Research Center for Infectious Disease Sciences (RCIDS), Osaka Metropolitan University Graduate School of Medicine, 1-4-3, Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan; Osaka International Research for Infectious Diseases (OIRCID), Osaka Metropolitan University, 1-2-7-601, Asahi-machi, Abeno-ku, Osaka, 545-0051, Japan.
| | - Hisako Yoshida
- Department of Medical Statistics, Osaka Metropolitan University Graduate School of Medicine, 1-4-3, Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan.
| | - Yukihiro Kaneko
- Research Center for Infectious Disease Sciences (RCIDS), Osaka Metropolitan University Graduate School of Medicine, 1-4-3, Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan; Osaka International Research for Infectious Diseases (OIRCID), Osaka Metropolitan University, 1-2-7-601, Asahi-machi, Abeno-ku, Osaka, 545-0051, Japan; Department of Bacteriology, Osaka Metropolitan University Graduate School of Medicine, 1-4-3, Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan.
| | - Ayumi Shintani
- Department of Medical Statistics, Osaka Metropolitan University Graduate School of Medicine, 1-4-3, Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan.
| | - Hiroshi Kakeya
- Department of Infection Control Science, Osaka Metropolitan University Graduate School of Medicine, 1-4-3, Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan; Department of Infectious Disease Medicine, Osaka Metropolitan University Hospital, 1-5-7 Asahi-machi, Abeno-ku, Osaka, 545-8586, Japan; Department of Infection Control and Prevention, Osaka Metropolitan University Hospital, 1-5-7 Asahi-machi, Abeno-ku, Osaka, 545-8586, Japan; Research Center for Infectious Disease Sciences (RCIDS), Osaka Metropolitan University Graduate School of Medicine, 1-4-3, Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan; Osaka International Research for Infectious Diseases (OIRCID), Osaka Metropolitan University, 1-2-7-601, Asahi-machi, Abeno-ku, Osaka, 545-0051, Japan.
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Buis DTP, van der Vaart TW, Mohan A, Prins JM, van der Meer JTM, Bonten MJM, Jakulj L, van Werkhoven CH, Sigaloff KCE. Acute kidney injury in Staphylococcus aureus bacteraemia: a recurrent events analysis. Clin Microbiol Infect 2024:S1198-743X(24)00297-0. [PMID: 38925460 DOI: 10.1016/j.cmi.2024.06.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 05/09/2024] [Accepted: 06/18/2024] [Indexed: 06/28/2024]
Abstract
OBJECTIVES To estimate risk factors for acute kidney injury (AKI) and the effect of AKI on mortality in Staphylococcus aureus bacteraemia, while taking into account recurrent AKI episodes, competing risks, time-varying variables, and time-varying effects. METHODS We performed an unplanned analysis using data from a multicentre cohort study of patients with Staphylococcus aureus bacteraemia (SAB). The primary outcome was cumulative incidence of AKI, according to Kidney Disease Improving Global Outcomes definitions. RESULTS We included 453 patients in this study of whom 194 (43%) patients experienced one or more AKI episodes. Age (hazard ratio (HR) 1.013, 95% CI 1.001-1.024), Charlson comorbidity index (HR 1.07, 95% CI 1.01-1.14), prior chronic kidney disease (HR 1.76, 95% CI 1.28-2.42), septic shock (HR 3.28, 95% CI 2.31-4.66), persistent bacteraemia (HR 1.53, 95% CI 1.08-2.17), and vancomycin therapy (HR 1.80, 95% CI 1.05-3.09) were independently associated with AKI, but flucloxacillin, cefazolin, rifampicin, and aminoglycoside therapy were not. After adjustment for confounders and immortal time bias, AKI was associated with an increased risk of 90-day mortality (HR 4.26, 95% CI 2.91-6.23). DISCUSSION The incidence of AKI in SAB is high and a substantial proportion of patients develop recurrent episodes of AKI after recovery. AKI is specifically linked to the use of vancomycin and not to anti-staphylococcal penicillins. The clinical outcome of patients with SAB complicated by AKI is worse than previously estimated.
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Affiliation(s)
- David T P Buis
- Department of Internal Medicine, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Amsterdam Institute for Infection and Immunity, Amsterdam, the Netherlands.
| | - Thomas W van der Vaart
- Amsterdam Institute for Infection and Immunity, Amsterdam, the Netherlands; Department of Internal Medicine, Amsterdam UMC Location Universiteit van Amsterdam, Amsterdam, the Netherlands; Julius Center for Health Sciences and Primary Care, UMC Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Asna Mohan
- Department of Internal Medicine, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Amsterdam Institute for Infection and Immunity, Amsterdam, the Netherlands
| | - Jan M Prins
- Department of Internal Medicine, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Jan T M van der Meer
- Amsterdam Institute for Infection and Immunity, Amsterdam, the Netherlands; Department of Internal Medicine, Amsterdam UMC Location Universiteit van Amsterdam, Amsterdam, the Netherlands
| | - Marc J M Bonten
- Julius Center for Health Sciences and Primary Care, UMC Utrecht, Utrecht University, Utrecht, the Netherlands; European Clinical Research Alliance on Infectious Diseases, Utrecht, the Netherlands
| | - Lily Jakulj
- Department of Nephrology, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands; Dianet Dialysis Center Amsterdam, the Netherlands
| | - Cornelis H van Werkhoven
- Julius Center for Health Sciences and Primary Care, UMC Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Kim C E Sigaloff
- Department of Internal Medicine, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Amsterdam Institute for Infection and Immunity, Amsterdam, the Netherlands
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Zhong X, Wang DL, Xiao LH, Liu Y, Yang SW, Mo LF, Wu QF, Lin M, He LF, Luo XF. Investigation of multiple nosocomial infections using a semi-Markov multi-state model. Antimicrob Resist Infect Control 2024; 13:58. [PMID: 38845037 PMCID: PMC11157730 DOI: 10.1186/s13756-024-01421-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 06/04/2024] [Indexed: 06/09/2024] Open
Abstract
BACKGROUND The prevalence of multiple nosocomial infections (MNIs) is on the rise, however, there remains a limited comprehension regarding the associated risk factors, cumulative risk, probability of occurrence, and impact on length of stay (LOS). METHOD This multicenter study includes all hospitalized patients from 2020 to July 2023 in two sub-hospitals of a tertiary hospital in Guangming District, Shenzhen. The semi-Markov multi-state model (MSM) was utilized to analyze risk factors and cumulative risk of MNI, predict its occurrence probability, and calculate the extra LOS of nosocomial infection (NI). RESULTS The risk factors for MNI include age, community infection at admission, surgery, and combined use of antibiotics. However, the cumulative risk of MNI is lower than that of single nosocomial infection (SNI). MNI is most likely to occur within 14 days after admission. Additionally, SNI prolongs LOS by an average of 7.48 days (95% Confidence Interval, CI: 6.06-8.68 days), while MNI prolongs LOS by an average of 15.94 days (95% CI: 14.03-18.17 days). Furthermore, the more sites of infection there are, the longer the extra LOS will be. CONCLUSION The longer LOS and increased treatment difficulty of MNI result in a heavier disease burden for patients, necessitating targeted prevention and control measures.
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Affiliation(s)
- Xiao Zhong
- Infection Management Department, Shenzhen Guangming District People's Hospital, Shenzhen, Guangdong, 518106, China.
| | - Dong-Li Wang
- Testing Centre, Guangming District Centre for Disease Control and Prevention, Shenzhen, Guangdong, China
| | - Li-Hua Xiao
- Infection Management Department, Shenzhen Guangming District People's Hospital, Shenzhen, Guangdong, 518106, China
| | - Yan Liu
- Infection Management Department, Shenzhen Guangming District People's Hospital, Shenzhen, Guangdong, 518106, China
| | - Shan-Wen Yang
- Infection Management Department, Shenzhen Guangming District People's Hospital, Shenzhen, Guangdong, 518106, China
| | - Lan-Fang Mo
- Infection Management Department, Shenzhen Guangming District People's Hospital, Shenzhen, Guangdong, 518106, China
| | - Qin-Fei Wu
- Infection Management Department, Shenzhen Guangming District People's Hospital, Shenzhen, Guangdong, 518106, China
| | - Mei Lin
- Infection Management Department, Shenzhen Guangming District People's Hospital, Shenzhen, Guangdong, 518106, China
| | - Lan-Fang He
- Infection Management Department, Shenzhen Guangming District People's Hospital, Shenzhen, Guangdong, 518106, China
| | - Xiao-Feng Luo
- Infection Management Department, Shenzhen Guangming District People's Hospital, Shenzhen, Guangdong, 518106, China
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Grodd M, Weber S, Wolkewitz M. Stacked probability plots of the extended illness-death model using constant transition hazards - an easy to use shiny app. BMC Med Res Methodol 2024; 24:116. [PMID: 38762731 PMCID: PMC11102298 DOI: 10.1186/s12874-024-02240-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 05/06/2024] [Indexed: 05/20/2024] Open
Abstract
BACKGROUND Extended illness-death models (a specific class of multistate models) are a useful tool to analyse situations like hospital-acquired infections, ventilation-associated pneumonia, and transfers between hospitals. The main components of these models are hazard rates and transition probabilities. Calculation of different measures and their interpretation can be challenging due to their complexity. METHODS By assuming time-constant hazards, the complexity of these models becomes manageable and closed mathematical forms for transition probabilities can be derived. Using these forms, we created a tool in R to visualize transition probabilities via stacked probability plots. RESULTS In this article, we present this tool and give some insights into its theoretical background. Using published examples, we give guidelines on how this tool can be used. Our goal is to provide an instrument that helps obtain a deeper understanding of a complex multistate setting. CONCLUSION While multistate models (in particular extended illness-death models), can be highly complex, this tool can be used in studies to both understand assumptions, which have been made during planning and as a first step in analysing complex data structures. An online version of this tool can be found at https://eidm.imbi.uni-freiburg.de/ .
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Affiliation(s)
- Marlon Grodd
- Institute of Medical Biometry and Statistics, Medical Center - University of Freiburg, Faculty of Medicine, Freiburg, Germany.
- Freiburg Center for Data Analysis and Modeling, University of Freiburg, Freiburg, Germany.
| | - Susanne Weber
- Institute of Medical Biometry and Statistics, Medical Center - University of Freiburg, Faculty of Medicine, Freiburg, Germany
- Freiburg Center for Data Analysis and Modeling, University of Freiburg, Freiburg, Germany
| | - Martin Wolkewitz
- Institute of Medical Biometry and Statistics, Medical Center - University of Freiburg, Faculty of Medicine, Freiburg, Germany
- Freiburg Center for Data Analysis and Modeling, University of Freiburg, Freiburg, Germany
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Petrie JG, Moore R, Lauring AS, Kaye KS. Incidence and outcomes of hospital-associated respiratory virus infections by viral species. Infect Control Hosp Epidemiol 2024; 45:618-629. [PMID: 38073596 PMCID: PMC11031349 DOI: 10.1017/ice.2023.263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/19/2024]
Abstract
BACKGROUND Although the incidence of hospital-associated respiratory virus infection (HARVI) is well recognized, the risk factors for infection and impact on patient outcomes are not well characterized. METHODS We identified a cohort of all inpatient admissions ≥24 hours duration at a single academic medical center from 2017 to 2020. HARVI were defined as respiratory virus detected in a test ordered after the 95th percentile of the virus-specific incubation period. Risk factors for HARVI were assessed using Cox proportional hazards models of the competing outcomes of HARVI and discharge. The associations between time-varying HARVI status and the rates of ICU admission, discharge, and in-hospital death were estimated using Cox-proportional hazards models in a competing risk framework. RESULTS HARVI incidences were 8.8 and 3.0 per 10,000 admission days for pediatric and adult patients, respectively. For adults, congestive heart failure, renal disease, and cancer increased HARVI risk independent of their associations with length of stay. HARVI risk was also elevated for patients admitted in September-June relative to July admissions. For pediatric patients, cardiovascular and respiratory conditions, cancer, medical device dependence, and admission in December increased HARVI risk. Lengths of stay were longer for adults with HARVI compared to those without, and hospital-associated influenza A was associated with increased risk of death. Rates of ICU admission were increased in the 5 days after HARVI identification for adult and pediatric patients. HARVI was not associated with length of stay or death among pediatric patients. CONCLUSIONS HARVI is associated chronic health conditions and increases morbidity and mortality.
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Affiliation(s)
- Joshua G. Petrie
- Center for Clinical Epidemiology and Population Health, Marshfield Clinic Research Institute, Marshfield, Wisconsin
| | - Riley Moore
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, Michigan
| | - Adam S. Lauring
- Department of Microbiology and Immunology and Division of Infectious Diseases, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan
| | - Keith S. Kaye
- Division of Allergy, Immunology and Infectious Diseases, Department of Medicine, Rutgers Robert Wood Johnson School of Medicine, New Brunswick, New Jersey
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Huo X, Liu P. An agent-based model on antimicrobial de-escalation in intensive care units: Implications on clinical trial design. PLoS One 2024; 19:e0301944. [PMID: 38626111 PMCID: PMC11020418 DOI: 10.1371/journal.pone.0301944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Accepted: 03/21/2024] [Indexed: 04/18/2024] Open
Abstract
Antimicrobial de-escalation refers to reducing the spectrum of antibiotics used in treating bacterial infections. This strategy is widely recommended in many antimicrobial stewardship programs and is believed to reduce patients' exposure to broad-spectrum antibiotics and prevent resistance. However, the ecological benefits of de-escalation have not been universally observed in clinical studies. This paper conducts computer simulations to assess the ecological effects of de-escalation on the resistance prevalence of Pseudomonas aeruginosa-a frequent pathogen causing nosocomial infections. Synthetic data produced by the models are then used to estimate the sample size and study period needed to observe the predicted effects in clinical trials. Our results show that de-escalation can reduce colonization and infections caused by bacterial strains resistant to the empiric antibiotic, limit the use of broad-spectrum antibiotics, and avoid inappropriate empiric therapies. Further, we show that de-escalation could reduce the overall super-infection incidence, and this benefit becomes more evident under good compliance with hand hygiene protocols among health care workers. Finally, we find that any clinical study aiming to observe the essential effects of de-escalation should involve at least ten arms and last for four years-a size never attained in prior studies. This study explains the controversial findings of de-escalation in previous clinical studies and illustrates how mathematical models can inform outcome expectations and guide the design of clinical studies.
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Affiliation(s)
- Xi Huo
- Department of Mathematics, University of Miami, Coral Gables, FL, United States of Ameica
| | - Ping Liu
- LinkedIn Corporation, Mountain View, CA, United States of Ameica
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Steen J, Morzywołek P, Van Biesen W, Decruyenaere J, Vansteelandt S. Dealing with time-dependent exposures and confounding when defining and estimating attributable fractions-Revisiting estimands and estimators. Stat Med 2024; 43:912-934. [PMID: 38122818 DOI: 10.1002/sim.9988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 10/26/2023] [Accepted: 11/30/2023] [Indexed: 12/23/2023]
Abstract
The population-attributable fraction (PAF) is commonly interpreted as the proportion of events that can be ascribed to a certain exposure in a certain population. Its estimation is sensitive to common forms of time-dependent bias in the face of a time-dependent exposure. Predominant estimation approaches based on multistate modeling fail to fully eliminate such bias and, as a result, do not permit a causal interpretation, even in the absence of confounding. While recently proposed multistate modeling approaches can successfully eliminate residual time-dependent bias, and moreover succeed to adjust for time-dependent confounding by means of inverse probability of censoring weighting, inadequate application, and misinterpretation prevails in the medical literature. In this paper, we therefore revisit recent work on previously proposed PAF estimands and estimators in settings with time-dependent exposures and competing events and extend this work in several ways. First, we critically revisit the interpretation and applied terminology of these estimands. Second, we further formalize the assumptions under which a causally interpretable PAF estimand can be identified and provide analogous weighting-based representations of the identifying functionals of other proposed estimands. This representation aims to enhance the applied statistician's understanding of different sources of bias that may arise when the aim is to obtain a valid estimate of a causally interpretable PAF. To illustrate and compare these representations, we present a real-life application to observational data from the Ghent University Hospital ICUs to estimate the fraction of ICU deaths attributable to hospital-acquired infections.
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Affiliation(s)
- Johan Steen
- Department of Internal Medicine and Pediatrics, Ghent University, Ghent, Belgium
- Renal Division, Ghent University Hospital, Ghent, Belgium
- Department of Intensive Care Medicine, Ghent University Hospital, Ghent, Belgium
| | - Paweł Morzywołek
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
- Department of Statistics, University of Washington, Seattle, Washington, USA
| | - Wim Van Biesen
- Department of Internal Medicine and Pediatrics, Ghent University, Ghent, Belgium
- Renal Division, Ghent University Hospital, Ghent, Belgium
| | - Johan Decruyenaere
- Department of Internal Medicine and Pediatrics, Ghent University, Ghent, Belgium
- Department of Intensive Care Medicine, Ghent University Hospital, Ghent, Belgium
| | - Stijn Vansteelandt
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
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Wang L, Teng Z, Huo X, Wang K, Feng X. A stochastic dynamical model for nosocomial infections with co-circulation of sensitive and resistant bacterial strains. J Math Biol 2023; 87:41. [PMID: 37561222 DOI: 10.1007/s00285-023-01968-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 06/22/2023] [Accepted: 07/12/2023] [Indexed: 08/11/2023]
Abstract
Nosocomial infections (hospital-acquired) has been an important public health problem, which may make those patients with infections or involved visitors and hospital personnel at higher risk of worse clinical outcomes or infection, and then consume more healthcare resources. Taking into account the stochasticity of the death and discharge rate of patients staying in hospitals, in this paper, we propose a stochastic dynamical model describing the transmission of nosocomial pathogens among patients admitted for hospital stays. The stochastic terms of the model are incorporated to capture the randomness arising from death and discharge processes of patients. Firstly, a sufficient condition is established for the stochastic extinction of disease. It shows that introducing randomness in the model will result in lower potential of nosocomial outbreaks. Further, we establish a threshold criterion on the existence of stationary distribution and ergodicity for any positive solution of the model. Particularly, the spectral radius form of stochastic threshold value is calculated in the special case. Moreover, the numerical simulations are conducted to both validate the theoretical results and investigate the effect of prevention and control strategies on the prevalence of nosocomial infection. We show that enhancing hygiene, targeting colonized and infected patients, improving antibiotic treatment accuracy, shortening treatment periods are all crucial factors to contain nosocomial infections.
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Affiliation(s)
- Lei Wang
- Department of Medical Engineering and Technology, Xinjiang Medical University, Ürümqi, 830017, Xinjiang, People's Republic of China
| | - Zhidong Teng
- Department of Medical Engineering and Technology, Xinjiang Medical University, Ürümqi, 830017, Xinjiang, People's Republic of China
| | - Xi Huo
- Department of Mathematics, University of Miami, Coral Gables, FL, 33146, USA
| | - Kai Wang
- Department of Medical Engineering and Technology, Xinjiang Medical University, Ürümqi, 830017, Xinjiang, People's Republic of China
| | - Xiaomei Feng
- College of Science, Xi'an University of Science and Technology, Xi'an, 710054, Shaanxi, People's Republic of China.
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von Cube M, Schumacher M, Timsit JF, Decruyenaere J, Steen J. The population-attributable fraction for time-to-event data. Int J Epidemiol 2022:6839850. [DOI: 10.1093/ije/dyac217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 11/03/2022] [Indexed: 11/23/2022] Open
Abstract
Abstract
Background
Even though the population-attributable fraction (PAF) is a well-established metric, it is often incorrectly estimated or interpreted not only in clinical application, but also in statistical research articles. The risk of bias is especially high in more complex time-to-event data settings.
Methods
We explain how the PAF can be defined, identified and estimated in time-to-event settings with competing risks and time-dependent exposures. By using multi-state methodology and inverse probability weighting, we demonstrate how to reduce or completely avoid severe types of biases including competing risks bias, immortal time bias and confounding due to both baseline and time-varying patient characteristics.
Results
The method is exemplarily applied to a real data set. Moreover, we estimate the number of deaths that were attributable to ventilator-associated pneumonia in France in the year 2016. The example demonstrates how, under certain simplifying assumptions, PAF estimates can be extrapolated to a target population of interest.
Conclusions
Defining and estimating the PAF in advanced time-to-event settings within a framework that unifies causal and multi-state modelling enables to tackle common sources of bias and allows straightforward implementation with standard software packages.
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Affiliation(s)
- Maja von Cube
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg , Freiburg, Germany
| | - Martin Schumacher
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg , Freiburg, Germany
| | - Jean Francois Timsit
- University of Paris, IAME, INSERM , Paris, France
- AP-HP, Bichat Hospital, Medical and Infectious Diseases ICU (MI2) , Paris, France
| | - Johan Decruyenaere
- Faculty of Medicine and Health Sciences, Department of Internal Medicine and Pediatrics, Ghent University Hospital , Ghent, Belgium
- Department of Intensive Care Medicine, Ghent University Hospital , Ghent, Belgium
| | - Johan Steen
- Faculty of Medicine and Health Sciences, Department of Internal Medicine and Pediatrics, Ghent University Hospital , Ghent, Belgium
- Department of Intensive Care Medicine, Ghent University Hospital , Ghent, Belgium
- Renal Division, Ghent University Hospital , Ghent, Belgium
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11
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Wozniak TM, Dyda A, Merlo G, Hall L. Disease burden, associated mortality and economic impact of antimicrobial resistant infections in Australia. THE LANCET REGIONAL HEALTH. WESTERN PACIFIC 2022; 27:100521. [PMID: 35832237 PMCID: PMC9271974 DOI: 10.1016/j.lanwpc.2022.100521] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
BACKGROUND The growing spread of antimicrobial resistance (AMR) is accepted as a threat to humans, animals and the environment. This threat is considered to be both country specific and global, with bacteria resistant to antibiotic treatment geographically dispersed. Despite this, we have very few Australian estimates available that use national surveillance data supplemented with measures of risk, to generate reliable and actionable measures of AMR impact. These data are essential to direct policies and programs and support equitable healthcare resource utilisation. Importantly, such data can lead to implementation of programs to improved morbidity and mortality of patients with a resistant infection. METHODS Using data from a previous case-cohort study, we estimated the AMR-associated health and economic impact caused by five hospital-associated AMR pathogens (Enterococcus spp., E. coli, K. pneumoniae, P. aeruginosa and S. aureus) in patients with a bloodstream, urinary tract, or respiratory tract infection in Australia in 2020. We estimated disease burden based on the counterfactual scenario in which all AMR infections were replaced by no infection.We used a population-level simulation model to compute AMR-associated mortality, loss of quality-adjusted life years and costs. FINDINGS In 2020, there were 1,031 AMR-associated deaths (95% uncertainty interval [UI] 294, 2,615) from the five resistant hospital-associated infections in Australia. The greatest odds of dying were from respiratory infections (ceftazidime-resistant P. aeruginosa) and bloodstream infections, both resulting in high hospital and premature death costs. MRSA bacteraemia contributed the most to hospital costs (measured as bed-days) as patients with this infection resulted in additional 12,818 (95% UI 7246, 19966) hospital bed-days and cost the hospitals an extra $24,366,741 (95%UI $13,774,548, $37,954,686) per year. However, the cost of premature death from five resistant pathogens was $438,543,052, which was by far greater than the total hospital cost ($71,988,858). We estimate a loss of 27,705 quality-adjusted life years due to the five AMR pathogens. INTERPRETATION These are the first Australian estimates of AMR-associated health and economic impact. Country-level estimates of AMR impact are needed to provide local evidence to better inform programs and health policies to reduce morbidity and mortality associated with infection. The burden in hospital is likely an underestimate of the impact of AMR due to community-associated infections where data are limited, and the AMR burden is high. This should now be the focus of future study in this area. FUNDING TMW was supported by the Australian Partnership for Preparedness Research on Infectious Disease Emergencies (APPRISE) (grant number GNT1116530) Fellowship.
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Affiliation(s)
- Teresa M. Wozniak
- Australian e-Health Research Centre CSIRO, Brisbane, Queensland, Australia
- Menzies School of Health Research, Darwin, Northern Territory, Australia
| | - Amalie Dyda
- School of Public Health, University of Queensland, Brisbane, Queensland, Australia
| | - Greg Merlo
- School of Public Health, University of Queensland, Brisbane, Queensland, Australia
| | - Lisa Hall
- School of Public Health, University of Queensland, Brisbane, Queensland, Australia
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12
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Mortensen VH, Søgaard M, Mygind LH, Wolkewitz M, Kristensen B, Schønheyder HC. Incidence and mortality of hospital-acquired bacteraemia: A population-based cohort study applying a multi-state model approach. Clin Microbiol Infect 2021; 28:879.e9-879.e15. [PMID: 34929409 DOI: 10.1016/j.cmi.2021.12.011] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 12/09/2021] [Accepted: 12/11/2021] [Indexed: 11/03/2022]
Abstract
OBJECTIVES The effect of hospital-acquired bacteraemia on mortality is sparsely investigated. We investigated the incidence and hospital-acquired bacteraemia impact on mortality. METHODS We conducted a 13-year population-based cohort study using The North Denmark Bacteraemia Research Database and Danish health registries. The population comprised all adult patients with a hospital admission lasting ≥48 hours. We used Poisson regression to estimate trends in incidence. The 30-day mortality of hospital-acquired bacteraemia was estimated using an illness-death multi-state model with recovery using the population at risk of hospital-acquired bacteraemia as reference. RESULTS We identified 3,588 episodes of hospital-acquired bacteraemia in 484,264 admissions. The incidence increased proportionally by 1.02 episodes yearly (95% CI 1.01 - 1.03) between 2006 and 2018. Hospital-acquired bacteraemia was associated with increased mortality (adjusted hazard ratio (aHR) 4.32, 95% CI 3.95 - 4.72), especially hospital-acquired bacteraemia with unknown source (aHR 6.42 (95% CI 5.67 - 7.26), 'thoracic incl. pneumonia' (aHR 5.89, 95% CI 3.45 - 10.12), and abdominal source (aHR 4.33, 95% CI 3.27 - 5.74)95% CI95% CI. The relative impact on mortality diminished with age (aHR 5.66, 95% CI 2.00 - 16.01 in 18-40 years old vs. 3.69, 95% CI 3.14 - 4.32 in 81-105 years old) and comorbidity (aHR 5.75, 95% CI 4.45 - 7.42 in low vs. 3.55, 95% CI 3.16 -3.98 in high comorbidity), and was higher in elective admissions (aHR 9.09, 95% CI 7.14 - 11.57 vs. aHR of 4.03, 95% CI 3.67 - 4.42). CONCLUSIONS Hospital-acquired bacteraemia is associated with high mortality, especially when the source is unknown or originating from the thoracic cavity.
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Affiliation(s)
- Viggo Holten Mortensen
- Department of Clinical Microbiology, Aalborg University Hospital, Aalborg, Denmark; Department of Clinical Medicine, Faculty of Health, Aalborg University, Aalborg, Denmark.
| | - Mette Søgaard
- Department of Cardiology, Aalborg University Hospital, Aalborg, Denmark; Aalborg Thrombosis Research Unit, Department of Clinical Medicine, Faculty of Health, Aalborg University, Aalborg, Denmark
| | - Lone Hagens Mygind
- Department of Clinical Medicine, Faculty of Health, Aalborg University, Aalborg, Denmark; Department of Infectious Diseases, Aalborg University Hospital, Aalborg, Denmark
| | - Martin Wolkewitz
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Centre, University of Freiburg, Freiburg, Germany
| | - Brian Kristensen
- Infectious Disease Epidemiology & Prevention, National Centre for Infection Control, Statens Serum Institut, Copenhagen, Denmark
| | - Henrik Carl Schønheyder
- Department of Clinical Microbiology, Aalborg University Hospital, Aalborg, Denmark; Department of Clinical Medicine, Faculty of Health, Aalborg University, Aalborg, Denmark
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13
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Rendtel U, Liebig S, Meister R, Wagner GG, Zinn S. Die Erforschung der Dynamik der Corona-Pandemie in Deutschland: Survey-Konzepte und eine exemplarische Umsetzung mit dem Sozio-oekonomischen Panel (SOEP). ASTA WIRTSCHAFTS- UND SOZIALSTATISTISCHES ARCHIV 2021. [PMCID: PMC8655718 DOI: 10.1007/s11943-021-00296-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Die Weltgesundheitsorganisation (WHO) hat im Frühjahr 2020 Richtlinien für Bevölkerungsstichproben veröffentlicht, die Basisdaten für gesundheitspolitische Entscheidungen im Pandemiefall liefern können. Diese Richtlinien umzusetzen ist keineswegs trivial. In diesem Beitrag schildern wir die Herausforderungen einer entsprechenden statistischen Erfassung der Corona Pandemie. Hierbei gehen wir im ersten Teil auf die Erfassung der Dunkelziffer bei der Meldung von Corona Infektionen, die Messung von Krankheitsverläufen im außerklinischen Bereich, die Messung von Risikomerkmalen sowie die Erfassung von zeitlichen und regionalen Veränderungen der Pandemie-Intensität ein. Wir diskutieren verschiedene Möglichkeiten, aber auch praktische Grenzen der Survey-Statistik, den vielfältigen Herausforderungen durch eine geeignete Anlage der Stichprobe und des Survey-Designs zu begegnen. Ein zentraler Punkt ist die schwierige Koppelung medizinischer Tests mit bevölkerungsrepräsentativen Umfragen, wobei bei einer personalisierten Rückmeldung der Testergebnisse das Statistik-Geheimnis eine besondere Herausforderung darstellt. Im zweiten Teil berichten wir wie eine der großen Wiederholungsbefragungen in Deutschland, das Sozio-oekonomische Panel (SOEP), für eine WHO-konforme Covid-19-Erhebung genutzt wird, die im Rahmen einer Kooperation des Robert-Koch-Instituts (RKI) mit dem SOEP als „RKI-SOEP Stichprobe“ im September 2020 gestartet wurde. Erste Ergebnisse zum Rücklauf dieser Studie, die ab Oktober 2021 mit einer zweiten Erhebungswelle bei denselben Personen fortgesetzt werden wird, werden vorgestellt. Es zeigt sich, dass knapp fünf Prozent der bereits in der Vergangenheit erfolgreich Befragten aufgrund der Anfrage zwei Tests zu machen die weitere Teilnahme an der SOEP-Studie verweigern. Berücksichtigt man alle in der Studie erhobenen Informationen (IgG-Antikörper-Tests, PCR-Tests und Fragebögen) ergibt eine erste Schätzung, dass sich bis November 2020 nur etwa zwei Prozent der in Privathaushalten lebenden Erwachsenen in Deutschland mit SARS-CoV‑2 infiziert hatten. Damit war die Zahl der Infektionen etwa doppelt so hoch wie die offiziell gemeldeten Infektionszahlen.
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Affiliation(s)
| | - Stefan Liebig
- Freie Universität Berlin, Berlin, Deutschland
- Sozio-oekonomisches Panel (SOEP), Berlin, Deutschland
| | | | - Gert G. Wagner
- Sozio-oekonomisches Panel (SOEP), Berlin, Deutschland
- Max PIanck Institut für Bildungsforschung, Berlin, Deutschland
| | - Sabine Zinn
- Sozio-oekonomisches Panel (SOEP), Berlin, Deutschland
- Humboldt Universität, Berlin, Deutschland
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14
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Pitocco C, Lewis HF, Liu J. Performance Measurement Outcomes: An Analysis of Health Care-Associated Infections in New York State. Qual Manag Health Care 2021; 30:219-225. [PMID: 34048377 DOI: 10.1097/qmh.0000000000000300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
BACKGROUND AND OBJECTIVES Comparing outcome measures in health care is a relatively common practice. Reports are designed to compare hospital infection rates in an accurate and fair manner. The current methodology used by New York State (NYS) has some limitations and flaws. This research provides a methodology that overcomes these limitations and flaws. METHODS The methodology is a replication study using data from NYS and includes the use of the binomial and Poisson distribution to calculate upper tail (UTP) and lower tail probabilities (LTP). The UTP is used to screen for poor performers, and the LTP is used to screen for good performers. RESULTS The results we obtained using the tail probability method compared with NYS's confidence interval approach are similar across all health care-associated infection (HAI) categories but have the benefit of allowing for the analysis of any hospital regardless of the number of procedures, number of central line-days, or number of patient-days. In addition, we provide an evaluation of a hospital's performance over time where we identified hospitals that were consistently performing poorly and others consistently performing well. CONCLUSION Identifying hospitals that are consistently performing poorly and hospitals consistently performing well will allow administrators and clinicians to focus their efforts including budgetary to where improvements are needed. Patient care and the reduction of HAIs are a priority for health care institutions. While the results are similar to those reported by NYS, this approach can be used more comprehensively and can be interpreted more easily by administrators and practitioners. Health care administrators and clinicians may find the information useful to address infection rates. Hospitals consistently performing well may be used as a benchmark.
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Affiliation(s)
- Christine Pitocco
- College of Business, Stony Brook University, New York City, New York
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15
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Abstract
Rationale: Estimating the impact of ventilator-associated pneumonia (VAP) from routinely collected intensive care unit (ICU) data is methodologically challenging. Objectives: We aim to replicate earlier findings of limited VAP-attributable ICU mortality in an independent cohort. By refining statistical analyses, we gradually tackle different sources of bias. Methods: Records of 2,720 adult patients admitted to Ghent University Hospital ICUs (2013–2017) and receiving mechanical ventilation within 48 hours after admission were extracted from linked Intensive Care Information System and Computer-based Surveillance and Alerting of Nosocomial Infections, Antimicrobial Resistance, and Antibiotic Consumption in the ICU databases. The VAP-attributable fraction of ICU mortality was estimated using a competing risk analysis that is restricted to VAP-free patients (approach 1), accounts for VAP onset by treating it as either a competing (approach 2) or censoring event (approach 3), or additionally adjusts for time-dependent confounding via inverse probability weighting (approach 4). Results: A total of 210 patients (7.7%) acquired VAP. Based on benchmark approach 4, we estimated that (compared with current preventive measures) hypothetical eradication of VAP would lead to a relative ICU mortality reduction of 1.7% (95% confidence interval, −1.3 to 4.6) by Day 10 and of 3.6% (95% confidence interval, 0.7 to 6.5) by Day 60. Approaches 1–3 produced estimates ranging from −0.7% to 2.5% by Day 10 and from 5.2% to 5.5% by Day 60. Conclusions: In line with previous studies using appropriate methodology, we found limited VAP-attributable ICU mortality given current state-of-the-art VAP prevention measures. Our study illustrates that inappropriate accounting of the time dependency of exposure and confounding of its effects may misleadingly suggest protective effects of early-onset VAP and systematically overestimate attributable mortality.
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Stewart S, Robertson C, Pan J, Kennedy S, Haahr L, Manoukian S, Mason H, Kavanagh K, Graves N, Dancer SJ, Cook B, Reilly J. Impact of healthcare-associated infection on length of stay. J Hosp Infect 2021; 114:23-31. [PMID: 34301393 DOI: 10.1016/j.jhin.2021.02.026] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 02/23/2021] [Accepted: 02/23/2021] [Indexed: 02/06/2023]
Abstract
BACKGROUND Increased length of stay (LOS) for patients is an important measure of the burden of healthcare-associated infection (HAI). AIM To estimate the excess LOS attributable to HAI. METHODS This was a one-year prospective incidence study of HAI observed in one teaching hospital and one general hospital in NHS Scotland as part of the Evaluation of Cost of Nosocomial Infection (ECONI) study. All adult inpatients with an overnight stay were included. HAI was diagnosed using European Centres for Disease Prevention and Control definitions. A multi-state model was used to account for the time-varying nature of HAI and the competing risks of death and discharge. FINDINGS The excess LOS attributable to HAI was 7.8 days (95% confidence interval (CI): 5.7-9.9). Median LOS for HAI patients was 30 days and for non-HAI patients was 3 days. Using a simple comparison of duration of hospital stay for HAI cases and non-cases would overestimate the excess LOS by 3.5 times (27 days compared with 7.8 days). The greatest impact on LOS was due to pneumonia (16.3 days; 95% CI: 7.5-25.2), bloodstream infections (11.4 days; 5.8-17.0) and surgical site infection (SSI) (9.8 days; 4.5-15.0). It is estimated that 58,000 bed-days are occupied due to HAI annually. CONCLUSION A reduction of 10% in HAI incidence could make 5800 bed-days available. These could be used to treat 1706 elective patients in Scotland annually and help reduce the number of patients awaiting planned treatment. This study has important implications for investment decisions in infection prevention and control interventions locally, nationally, and internationally.
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Affiliation(s)
- S Stewart
- Safeguarding Health through Infection Prevention Research Group, Research Centre for Health (ReaCH), Glasgow Caledonian University, Glasgow, UK.
| | - C Robertson
- Department of Mathematics and Statistics, University of Strathclyde, Glasgow, UK
| | - J Pan
- Department of Mathematics and Statistics, University of Strathclyde, Glasgow, UK
| | - S Kennedy
- HPS Stats Support, Public Health Scotland, Glasgow, UK
| | - L Haahr
- Safeguarding Health through Infection Prevention Research Group, Research Centre for Health (ReaCH), Glasgow Caledonian University, Glasgow, UK
| | - S Manoukian
- Yunus Centre for Social Business and Health, Glasgow Caledonian University, Glasgow, UK
| | - H Mason
- Yunus Centre for Social Business and Health, Glasgow Caledonian University, Glasgow, UK
| | - K Kavanagh
- Department of Mathematics and Statistics, University of Strathclyde, Glasgow, UK
| | - N Graves
- Duke-NUS Medical School, Singapore
| | - S J Dancer
- Department of Microbiology, Hairmyres Hospital, NHS Lanarkshire, UK; School of Applied Science, Edinburgh Napier University, Edinburgh, UK
| | - B Cook
- Departments of Anaesthesia and Critical Care, Royal Infirmary of Edinburgh, Edinburgh, UK
| | - J Reilly
- Safeguarding Health through Infection Prevention Research Group, Research Centre for Health (ReaCH), Glasgow Caledonian University, Glasgow, UK; National Services Scotland (NSS), UK
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van der Kooi T, Lepape A, Astagneau P, Suetens C, Nicolaie MA, de Greeff S, Lozoraitiene I, Czepiel J, Patyi M, Plachouras D. Mortality review as a tool to assess the contribution of healthcare-associated infections to death: results of a multicentre validity and reproducibility study, 11 European Union countries, 2017 to 2018. Euro Surveill 2021; 26:2000052. [PMID: 34114542 PMCID: PMC8193992 DOI: 10.2807/1560-7917.es.2021.26.23.2000052] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Accepted: 12/18/2020] [Indexed: 12/16/2022] Open
Abstract
IntroductionThe contribution of healthcare-associated infections (HAI) to mortality can be estimated using statistical methods, but mortality review (MR) is better suited for routine use in clinical settings. The European Centre for Disease Prevention and Control recently introduced MR into its HAI surveillance.AimWe evaluate validity and reproducibility of three MR measures.MethodsThe on-site investigator, usually an infection prevention and control doctor, and the clinician in charge of the patient independently reviewed records of deceased patients with bloodstream infection (BSI), pneumonia, Clostridioides difficile infection (CDI) or surgical site infection (SSI), and assessed the contribution to death using 3CAT: definitely/possibly/no contribution to death; WHOCAT: sole cause/part of causal sequence but not sufficient on its own/contributory cause but unrelated to condition causing death/no contribution, based on the World Health Organization's death certificate; QUANT: Likert scale: 0 (no contribution) to 10 (definitely cause of death). Inter-rater reliability was assessed with weighted kappa (wk) and intra-cluster correlation coefficient (ICC). Reviewers rated the fit of the measures.ResultsFrom 2017 to 2018, 24 hospitals (11 countries) recorded 291 cases: 87 BSI, 113 pneumonia , 71 CDI and 20 SSI. The inter-rater reliability was: 3CAT wk 0.68 (95% confidence interval (CI): 0.61-0.75); WHOCAT wk 0.65 (95% CI: 0.58-0.73); QUANT ICC 0.76 (95% CI: 0.71-0.81). Inter-rater reliability ranged from 0.72 for pneumonia to 0.52 for CDI. All three measures fitted 'reasonably' or 'well' in > 88%.ConclusionFeasibility, validity and reproducibility of these MR measures was acceptable for use in HAI surveillance.
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Affiliation(s)
- Tjallie van der Kooi
- National Institute for Public Health and the Environment, Bilthoven, the Netherlands
| | - Alain Lepape
- These authors contributed equally to this work
- Clinical research unit, Critical care, Lyon Sud University Hospital, Lyon, France
| | - Pascal Astagneau
- These authors contributed equally to this work
- Assistance Publique - Hôpitaux de Paris, Paris, France
| | - Carl Suetens
- European Centre for Disease Prevention and Control, Solna, Sweden
| | - Mioara Alina Nicolaie
- National Institute for Public Health and the Environment, Bilthoven, the Netherlands
| | - Sabine de Greeff
- National Institute for Public Health and the Environment, Bilthoven, the Netherlands
| | - Ilma Lozoraitiene
- Vilnius University Hospital Santariskiu Klinikos, Vilnius, Lithuania
| | - Jacek Czepiel
- Department of Infectious and Tropical Diseases, Jagiellonian University Medical College, Kraków, Poland
| | - Márta Patyi
- Bács-Kiskun County Teaching Hospital, Kecskemét, Hungary
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18
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Hazard D, von Cube M, Kaier K, Wolkewitz M. Predicting Potential Prevention Effects on Hospital Burden of Nosocomial Infections: A Multistate Modeling Approach. VALUE IN HEALTH : THE JOURNAL OF THE INTERNATIONAL SOCIETY FOR PHARMACOECONOMICS AND OUTCOMES RESEARCH 2021; 24:830-838. [PMID: 34119081 DOI: 10.1016/j.jval.2021.02.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 01/15/2021] [Accepted: 02/07/2021] [Indexed: 06/12/2023]
Abstract
OBJECTIVES Hospital-acquired infections (HAIs) place a substantial burden on health systems. Tools are required to quantify the change in this burden as a result of a preventive intervention. We aim to estimate how much a reduction in the rate of hospital-acquired infections translates into a change in hospital mortality and length of stay. METHODS Using multistate modelling and competing risks methodology, we created a tool to estimate the reduction in burden after the introduction of a preventive effect on the infection rate. The tool requires as inputs the patients' length of hospital stay, patients' infection information (status, time), patients' final outcome (discharged alive, dead), and a preventive effect. We demonstrated the methods on both simulated data and 3 published data sets from Germany, France, and Spain. RESULTS A hypothetical prevention that cuts the infection rate in half would result in 21 lives and 2212 patient-days saved in French ventilator-associated pneumonia data, 61 lives and 3125 patient-days saved in Spanish nosocomial infection data, and 20 lives and 1585 patient-days saved in German nosocomial pneumonia data. CONCLUSIONS Our tool provides a quick and easy means of acquiring an impression of the impact a preventive measure would have on the burden of an infection. The tool requires quantities routinely collected and computation can be done with a calculator. R code is provided for researchers to determine the burden in various settings with various effects. Furthermore, cost data can be used to get the financial benefit of the reduction in burden.
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Affiliation(s)
- Derek Hazard
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany.
| | - Maja von Cube
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
| | - Klaus Kaier
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
| | - Martin Wolkewitz
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
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Activity of β-Lactam Antibiotics against Metallo-β-Lactamase-Producing Enterobacterales in Animal Infection Models: a Current State of Affairs. Antimicrob Agents Chemother 2021; 65:AAC.02271-20. [PMID: 33782001 DOI: 10.1128/aac.02271-20] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Metallo-β-lactamases (MBLs) result in resistance to nearly all β-lactam antimicrobial agents, as determined by currently employed susceptibility testing methods. However, recently reported data demonstrate that variable and supraphysiologic zinc concentrations in conventional susceptibility testing media compared with physiologic (bioactive) zinc concentrations may be mediating discordant in vitro-in vivo MBL resistance. While treatment outcomes in patients appear suggestive of this discordance, these limited data are confounded by comorbidities and combination therapy. To that end, the goal of this review is to evaluate the extent of β-lactam activity against MBL-harboring Enterobacterales in published animal infection model studies and provide contemporary considerations to facilitate the optimization of current antimicrobials and development of novel therapeutics.
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Iskandar K, Roques C, Hallit S, Husni-Samaha R, Dirani N, Rizk R, Abdo R, Yared Y, Matta M, Mostafa I, Matta R, Salameh P, Molinier L. The healthcare costs of antimicrobial resistance in Lebanon: a multi-centre prospective cohort study from the payer perspective. BMC Infect Dis 2021; 21:404. [PMID: 33933013 PMCID: PMC8088567 DOI: 10.1186/s12879-021-06084-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Accepted: 04/16/2021] [Indexed: 12/03/2022] Open
Abstract
Background Our aim was to examine whether the length of stay, hospital charges and in-hospital mortality attributable to healthcare- and community-associated infections due to antimicrobial-resistant bacteria were higher compared with those due to susceptible bacteria in the Lebanese healthcare settings using different methodology of analysis from the payer perspective . Methods We performed a multi-centre prospective cohort study in ten hospitals across Lebanon. The sample size consisted of 1289 patients with documented healthcare-associated infection (HAI) or community-associated infection (CAI). We conducted three separate analysis to adjust for confounders and time-dependent bias: (1) Post-HAIs in which we included the excess LOS and hospital charges incurred after infection and (2) Matched cohort, in which we matched the patients based on propensity score estimates (3) The conventional method, in which we considered the entire hospital stay and allocated charges attributable to CAI. The linear regression models accounted for multiple confounders. Results HAIs and CAIs with resistant versus susceptible bacteria were associated with a significant excess length of hospital stay (2.69 days [95% CI,1.5–3.9]; p < 0.001) and (2.2 days [95% CI,1.2–3.3]; p < 0.001) and resulted in additional hospital charges ($1807 [95% CI, 1046–2569]; p < 0.001) and ($889 [95% CI, 378–1400]; p = 0.001) respectively. Compared with the post-HAIs analysis, the matched cohort method showed a reduction by 26 and 13% in hospital charges and LOS estimates respectively. Infections with resistant bacteria did not decrease the time to in-hospital mortality, for both healthcare- or community-associated infections. Resistant cases in the post-HAIs analysis showed a significantly higher risk of in-hospital mortality (odds ratio, 0.517 [95% CI, 0.327–0.820]; p = 0.05). Conclusion This is the first nationwide study that quantifies the healthcare costs of antimicrobial resistance in Lebanon. For cases with HAIs, matched cohort analysis showed more conservative estimates compared with post-HAIs method. The differences in estimates highlight the need for a unified methodology to estimate the burden of antimicrobial resistance in order to accurately advise health policy makers and prioritize resources expenditure.
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Affiliation(s)
- Katia Iskandar
- Department of Mathématiques Informatique et Télécommunications, Université Toulouse III, Paul Sabatier, INSERM, UMR 1295, F-31000, Toulouse, France. .,INSPECT-LB: Institut National de Santé Publique, d'Épidémiologie Clinique et de Toxicologie-Liban, Beirut, Lebanon. .,Department of Pharmacy, Lebanese University, Mount Lebanon, Beirut, Lebanon.
| | - Christine Roques
- Department of Bioprocédés et Systèmes Microbiens, Laboratoire de Génie Chimique, Université Paul Sabatier Toulouse III, UMR 5503, Toulouse, France.,Department of Bactériologie-Hygiène, Centre Hospitalier Universitaire, Toulouse, Hôpital Purpan, Toulouse, France
| | - Souheil Hallit
- INSPECT-LB: Institut National de Santé Publique, d'Épidémiologie Clinique et de Toxicologie-Liban, Beirut, Lebanon.,Faculty of Medicine and Medical Sciences, Holy Spirit University of Kaslik (USEK), Jounieh, Lebanon
| | - Rola Husni-Samaha
- Department of Medicine, Lebanese American University, Byblos, Lebanon.,Department of Infection Control, Lebanese American University Medical Center, Beirut, Lebanon
| | - Natalia Dirani
- Department of Infectious Diseases, Dar El Amal University Hospital, Baalbeck, Lebanon
| | - Rana Rizk
- INSPECT-LB: Institut National de Santé Publique, d'Épidémiologie Clinique et de Toxicologie-Liban, Beirut, Lebanon.,Department of Health Services Research, School CAPHRI, Care and Public Health Research Institute, Maastricht University, 6200, MD, Maastricht, The Netherlands
| | - Rachel Abdo
- INSPECT-LB: Institut National de Santé Publique, d'Épidémiologie Clinique et de Toxicologie-Liban, Beirut, Lebanon.,Medical School, University of Nicosia, Nicosia, Cyprus
| | - Yasmina Yared
- Department of Clinical Pharmacy, Geitaoui Hospital, Beirut, Lebanon
| | - Matta Matta
- Department of Medicine, St Joseph University, Beirut, Lebanon
| | - Inas Mostafa
- Department of Quality and Safety, Nabatieh Governmental Hospital, Nabatieh, Lebanon
| | - Roula Matta
- Department of Pharmacy, Lebanese University, Mount Lebanon, Beirut, Lebanon
| | - Pascale Salameh
- INSPECT-LB: Institut National de Santé Publique, d'Épidémiologie Clinique et de Toxicologie-Liban, Beirut, Lebanon.,Department of Pharmacy, Lebanese University, Mount Lebanon, Beirut, Lebanon.,Medical School, University of Nicosia, Nicosia, Cyprus
| | - Laurent Molinier
- Department of Medical Information, Centre Hospitalier Universitaire, INSERM, UMR 1027, Université Paul Sabatier Toulouse III, F-31000, Toulouse, France
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21
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von Cube M, Grodd M, Wolkewitz M, Hazard D, Wengenmayer T, Canet E, Lambert J. Harmonizing Heterogeneous Endpoints in Coronavirus Disease 2019 Trials Without Loss of Information. Crit Care Med 2021; 49:e11-e19. [PMID: 33148952 PMCID: PMC7737851 DOI: 10.1097/ccm.0000000000004741] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVES Many trials investigate potential effects of treatments for coronavirus disease 2019. To provide sufficient information for all involveddecision-makers (clinicians, public health authorities, and drug regulatory agencies), a multiplicity of endpoints must be considered. The objectives are to provide hands-on statistical guidelines for harmonizing heterogeneous endpoints in coronavirus disease 2019 clinical trials. DESIGN Randomized controlled trials for patients infected with coronavirus disease 2019. SETTING General methods that apply to any randomized controlled trial for patients infected with coronavirus disease 2019. PATIENTS Coronavirus disease 2019 positive individuals. INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS We develop a multistate model that is based on hospitalization, mechanical ventilation, death, and discharge. These events are both categories of the ordinal endpoint recommended by the World Health Organization and also within the core outcome set of the Core Outcome Measures in Effectiveness Trials initiative for coronavirus disease 2019 trials. To support our choice of states in the multistate model, we also perform a brief review of registered coronavirus disease 2019 clinical trials. Based on the multistate model, we give recommendation for compact, informative illustration of time-dynamic treatment effects and explorative statistical analysis. A majority of coronavirus disease 2019 clinical trials collect information on mechanical ventilation, hospitalization, and death. Using reconstructed and real data of coronavirus disease 2019 trials, we show how a stacked probability plot provides a detailed understanding of treatment effects on the patients' course of hospital stay. It contributes to harmonizing multiple endpoints and differing lengths of follow-up both within and between trials. CONCLUSIONS All ongoing clinical trials should include a stacked probability plot in their statistical analysis plan as descriptive analysis. While primary analysis should be on an early endpoint with appropriate capability to be a surrogate (parameter), our multistate model provides additional detailed descriptive information and links results within and between coronavirus disease 2019 trials.
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Affiliation(s)
- Maja von Cube
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
- Freiburg Center for Data Analysis and Modeling, University of Freiburg, Freiburg, Germany
| | - Marlon Grodd
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
- Freiburg Center for Data Analysis and Modeling, University of Freiburg, Freiburg, Germany
| | - Martin Wolkewitz
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
- Freiburg Center for Data Analysis and Modeling, University of Freiburg, Freiburg, Germany
| | - Derek Hazard
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
- Freiburg Center for Data Analysis and Modeling, University of Freiburg, Freiburg, Germany
| | - Tobias Wengenmayer
- Department of Medicine III (Interdisciplinary Medical Intensive Care), Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Emmanuel Canet
- Medical Intensive Care Unit, Nantes University Hospital, University of Nantes, Nantes, France
| | - Jêrome Lambert
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
- INSERM U1153 CRESS, Epidemiology and Clinical Statistics for Tumor, Respiratory, and Resuscitation Assessments (ECSTRRA) Team, Paris, France
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22
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Lee XJ, Blythe R, Choudhury AAK, Simmons T, Graves N, Kularatna S. Review of methods and study designs of evaluations related to clinical pathways. AUST HEALTH REV 2020; 43:448-456. [PMID: 30089529 DOI: 10.1071/ah17276] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2017] [Accepted: 05/19/2018] [Indexed: 11/23/2022]
Abstract
Objective The HealthPathways program is an online information portal that helps clinicians provide consistent and integrated patient care within a local health system through localised pathways for diagnosis, treatment and management of various health conditions. These pathways are consistent with the definition of clinical pathways. Evaluations of HealthPathways programs have thus far focused primarily on website utilisation and clinical users' experience and satisfaction, with limited evidence on changes to patient outcomes. This lack motivated a literature review of the effects of clinical pathways on patient and economic outcomes to inform a subsequent HealthPathways evaluation. Methods A systematic review was performed to summarise the analytical methods, study designs and results of studies evaluating clinical pathways with an economic outcome component published between 1 January 2000 and 31 August 2017 in four academic literature databases. Results Fifty-five relevant articles were identified for inclusion in this review. The practical pre-post study design with retrospective baseline data extraction and prospective intervention data collection was most commonly used in the evaluations identified. Straightforward statistical methods for comparing outcomes, such as the t-test or χ2 test, were frequently used. Only four of the 55 articles performed a cost-effectiveness analysis. Clinical pathways were generally associated with improved patient outcomes and positive economic outcomes in hospital settings. Conclusions Clinical pathways evaluations commonly use pragmatic study designs, straightforward statistical tests and cost-consequence analyses. More HealthPathways program evaluations focused on patient and economic outcomes, clinical pathway evaluations in a primary care setting and cost-effectiveness analyses of clinical pathways are needed. What is known about the topic? HealthPathways is a web-based program that originated from Canterbury, New Zealand, and has seen uptake elsewhere in New Zealand, Australia and the UK. The HealthPathways program aims to assist the provision of consistent and integrated health services through dedicated, localised pathways for various health conditions specific to the health region. Evaluations of HealthPathways program focused on patient and economic outcomes have been limited. What does this paper add? This review synthesises the academic literature of clinical pathways evaluations in order to inform a subsequent HealthPathways evaluation. The focus of the synthesis was on the analytical methods and study designs used in the previous evaluations. The previous clinical pathway evaluations have been pragmatic in nature with relatively straightforward study designs and analysis. What are the implications for practitioners? There is a need for more economic and patient outcome evaluations for HealthPathways programs. More sophisticated statistical analyses and economic evaluations could add value to these evaluations, where appropriate and taking into consideration the data limitations.
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Affiliation(s)
- Xing Ju Lee
- Institute of Health and Biomedical Innovations, School of Public Health and Social Work, Queensland University of Technology, Brisbane, 60 Musk Avenue, Kelvin Grove, Qld 4059, Australia.
| | - Robin Blythe
- Institute of Health and Biomedical Innovations, School of Public Health and Social Work, Queensland University of Technology, Brisbane, 60 Musk Avenue, Kelvin Grove, Qld 4059, Australia.
| | - Adnan Ali Khan Choudhury
- Northern Queensland Primary Health Network, James Cook University, Building 500, 1 James Cook Drive, Douglas, Qld 4811, Australia. Email
| | - Toni Simmons
- Mackay Hospital and Health Service, Mackay, 475 Bridge Road, Mackay, Qld 4740, Australia. Email
| | - Nicholas Graves
- Institute of Health and Biomedical Innovations, School of Public Health and Social Work, Queensland University of Technology, Brisbane, 60 Musk Avenue, Kelvin Grove, Qld 4059, Australia.
| | - Sanjeewa Kularatna
- Institute of Health and Biomedical Innovations, School of Public Health and Social Work, Queensland University of Technology, Brisbane, 60 Musk Avenue, Kelvin Grove, Qld 4059, Australia.
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23
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Wolkewitz M, Lambert J, von Cube M, Bugiera L, Grodd M, Hazard D, White N, Barnett A, Kaier K. Statistical Analysis of Clinical COVID-19 Data: A Concise Overview of Lessons Learned, Common Errors and How to Avoid Them. Clin Epidemiol 2020; 12:925-928. [PMID: 32943941 PMCID: PMC7478365 DOI: 10.2147/clep.s256735] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Accepted: 06/19/2020] [Indexed: 01/16/2023] Open
Abstract
By definition, in-hospital patient data are restricted to the time between hospital admission and discharge (alive or dead). For hospitalised cases of COVID-19, a number of events during hospitalization are of interest regarding the influence of risk factors on the likelihood of experiencing these events. The same is true for predicting times from hospital admission of COVID-19 patients to intensive care or from start of ventilation (invasive or non-invasive) to extubation. This logical restriction of the data to the period of hospitalisation is associated with a substantial risk that inappropriate methods are used for analysis. Here, we briefly discuss the most common types of bias which can occur when analysing in-hospital COVID-19 data.
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Affiliation(s)
- Martin Wolkewitz
- Institute of Medical Biometry and Statistics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Jerome Lambert
- Institute of Medical Biometry and Statistics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Maja von Cube
- Institute of Medical Biometry and Statistics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Lars Bugiera
- Institute of Medical Biometry and Statistics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Marlon Grodd
- Institute of Medical Biometry and Statistics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Derek Hazard
- Institute of Medical Biometry and Statistics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Nicole White
- School of Public Health and Social Work, Queensland University of Technology, Brisbane, QLD, Australia
| | - Adrian Barnett
- School of Public Health and Social Work, Queensland University of Technology, Brisbane, QLD, Australia
| | - Klaus Kaier
- Institute of Medical Biometry and Statistics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
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24
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Zhang Y, Du M, Johnston JM, Andres EB, Suo J, Yao H, Huo R, Liu Y, Fu Q. Estimating length of stay and inpatient charges attributable to hospital-acquired bloodstream infections. Antimicrob Resist Infect Control 2020; 9:137. [PMID: 32811557 PMCID: PMC7431751 DOI: 10.1186/s13756-020-00796-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Accepted: 08/05/2020] [Indexed: 01/27/2023] Open
Abstract
Background Hospital-acquired bloodstream infection (BSI) is associated with high morbidity and mortality and increases patients’ length of stay (LOS) and hospital charges. Our goals were to calculate LOS and charges attributable to BSI and compare results among different models. Methods A retrospective observational cohort study was conducted in 2017 in a large general hospital, in Beijing. Using patient-level data, we compared the attributable LOS and charges of BSI with three models: 1) conventional non-matching, 2) propensity score matching controlling for the impact of potential confounding variables, and 3) risk set matching controlling for time-varying covariates and matching based on propensity score and infection time. Results The study included 118,600 patient admissions, 557 (0.47%) with BSI. Six hundred fourteen microorganisms were cultured from patients with BSI. Escherichia coli was the most common bacteria (106, 17.26%). Among multi-drug resistant bacteria, carbapenem-resistant Acinetobacter baumannii (CRAB) was the most common (42, 38.53%). In the conventional non-matching model, the excess LOS and charges associated with BSI were 25.06 days (P < 0.05) and US$22041.73 (P < 0.05), respectively. After matching, the mean LOS and charges attributable to BSI both decreased. When infection time was incorporated into the risk set matching model, the excess LOS and charges were 16.86 days (P < 0.05) and US$15909.21 (P < 0.05), respectively. Conclusion This is the first study to consider time-dependent bias in estimating excess LOS and charges attributable to BSI in a Chinese hospital setting. We found matching on infection time can reduce bias.
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Affiliation(s)
- Yuzheng Zhang
- School of Public Health, The University of Hong Kong, Patrick Manson Building, 7 Sassoon Road, Pokfulam, Hong Kong, China
| | - Mingmei Du
- Department of Infection Management and Disease Control, Chinese PLA General Hospital, No. 28 Fuxing Road, Haidian District, Beijing, China
| | - Janice Mary Johnston
- School of Public Health, The University of Hong Kong, Patrick Manson Building, 7 Sassoon Road, Pokfulam, Hong Kong, China
| | - Ellie Bostwick Andres
- School of Public Health, The University of Hong Kong, Patrick Manson Building, 7 Sassoon Road, Pokfulam, Hong Kong, China
| | - Jijiang Suo
- Department of Infection Management and Disease Control, Chinese PLA General Hospital, No. 28 Fuxing Road, Haidian District, Beijing, China
| | - Hongwu Yao
- Department of Infection Management and Disease Control, Chinese PLA General Hospital, No. 28 Fuxing Road, Haidian District, Beijing, China
| | - Rui Huo
- XingLin Information Technology Company, No. 57 Jianger Road, Binjiang District, Zhejiang, Hangzhou, China
| | - Yunxi Liu
- Department of Infection Management and Disease Control, Chinese PLA General Hospital, No. 28 Fuxing Road, Haidian District, Beijing, China.
| | - Qiang Fu
- China National Health Development Research Center, No.9 Chegongzhuang Street, Xicheng District, Beijing, China. .,National Center for Healthcare Associated Infection Prevention and Control, Beijing, China.
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25
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Borjas-Howard JF, Bhoelan S, van Miert J, Eck R, Kooistra HAM, Meijer K, Tichelaar VYIG. Beware overestimation of thrombosis in ICU: Mortality is not the only competing risk! Thromb Res 2020; 193:78. [PMID: 32526544 DOI: 10.1016/j.thromres.2020.05.019] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 05/12/2020] [Accepted: 05/13/2020] [Indexed: 01/14/2023]
Affiliation(s)
- Jaime F Borjas-Howard
- Department of Hematology, Division of Thrombosis and Hemostasis, University Medical Centre Groningen, University of Groningen, the Netherlands.
| | - Soerajja Bhoelan
- Department of Hematology, Division of Thrombosis and Hemostasis, University Medical Centre Groningen, University of Groningen, the Netherlands
| | - Jasper van Miert
- Department of Hematology, Division of Thrombosis and Hemostasis, University Medical Centre Groningen, University of Groningen, the Netherlands
| | - Ruben Eck
- Department of Internal Medicine, University Medical Centre Groningen, University of Groningen, the Netherlands
| | - Hilde A M Kooistra
- Department of Hematology, Division of Thrombosis and Hemostasis, University Medical Centre Groningen, University of Groningen, the Netherlands
| | - Karina Meijer
- Department of Hematology, Division of Thrombosis and Hemostasis, University Medical Centre Groningen, University of Groningen, the Netherlands
| | - Vladimir Y I G Tichelaar
- Department of Hematology, Division of Thrombosis and Hemostasis, University Medical Centre Groningen, University of Groningen, the Netherlands
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26
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Kaier K, Mutters NT, Wolkewitz M. Measuring the Financial Burden of Resistance: What Should Be Compared? Clin Infect Dis 2020; 69:1082. [PMID: 30753374 DOI: 10.1093/cid/ciz096] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Accepted: 01/28/2019] [Indexed: 11/14/2022] Open
Affiliation(s)
- Klaus Kaier
- Institute of Medical Biometry and Statistics, University of Freiburg, Germany
| | - Nico T Mutters
- Institute for Infection Prevention and Hospital Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Germany
| | - Martin Wolkewitz
- Institute of Medical Biometry and Statistics, University of Freiburg, Germany
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27
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Nosocomial Infections During Extracorporeal Membrane Oxygenation in Neonatal, Pediatric, and Adult Patients: A Comprehensive Narrative Review. Pediatr Crit Care Med 2020; 21:283-290. [PMID: 31688809 DOI: 10.1097/pcc.0000000000002190] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
OBJECTIVES Extracorporeal membrane oxygenation is increasingly used in critically ill patients with refractory cardiopulmonary failure. Nosocomial infection acquired during extracorporeal membrane oxygenation represents one of the most frequent complications but the available evidence on the risk of infection and its association with outcomes has not been comprehensively analyzed. We performed a narrative review examining the epidemiology of nosocomial infection during extracorporeal membrane oxygenation, association with clinical outcomes, and preventive strategies. DATA SOURCES We searched PubMed, Web of Science, EMBASE, and the Cochrane Library between 1972 and June 2018. STUDY SELECTION We included any article which detailed nosocomial infection during extracorporeal membrane oxygenation. Articles were excluded if they were not written in English, detailed extracorporeal membrane oxygenation use for infections acquired prior to extracorporeal membrane oxygenation, or used other forms of extracorporeal support such as ventricular assist devices. DATA EXTRACTION Two reviewers independently assessed eligibility and extracted data. We screened 984 abstracts and included 59 articles in the final review. DATA SYNTHESIS The reported risk of nosocomial infection among patients receiving extracorporeal membrane oxygenation ranged from 3.5% to 64% per extracorporeal membrane oxygenation run, while the incidence of infection ranged from 10.1 to 116.2/1,000 extracorporeal membrane oxygenation days. Nosocomial infections during extracorporeal membrane oxygenation were consistently associated with longer duration of extracorporeal membrane oxygenation and, in several large multicenter studies, with increased mortality. Risk factors for nosocomial infection included duration of extracorporeal membrane oxygenation, mechanical and hemorrhagic complications on extracorporeal membrane oxygenation, and use of venoarterial and central extracorporeal membrane oxygenation. Biomarkers had low specificity for infection in this population. Few studies examined strategies on how to prevent nosocomial infection on extracorporeal membrane oxygenation. CONCLUSIONS Nosocomial infections in extracorporeal membrane oxygenation patients are common and associated with worse outcomes. There is substantial variation in the rates of reported infection, and thus, it is possible that some may be preventable. The evidence for current diagnostic, preventive, and therapeutic strategies for infection during extracorporeal membrane oxygenation is limited and requires further investigation.
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28
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Kaier K, Wolkewitz M, Hehn P, Mutters NT, Heister T. The impact of hospital-acquired infections on the patient-level reimbursement-cost relationship in a DRG-based hospital payment system. INTERNATIONAL JOURNAL OF HEALTH ECONOMICS AND MANAGEMENT 2020; 20:1-11. [PMID: 31165960 DOI: 10.1007/s10754-019-09267-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2018] [Accepted: 05/28/2019] [Indexed: 06/09/2023]
Abstract
Hospital-acquired infections (HAIs) are a common complication in inpatient care. We investigate the incentives to prevent HAIs under the German DRG-based reimbursement system. We analyze the relationship between resource use and reimbursements for HAI in 188,731 patient records from the University Medical Center Freiburg (2011-2014), comparing cases to appropriate non-HAI controls. Resource use is approximated using national standardized costing system data. Reimbursements are the actual payments to hospitals under the G-DRG system. Timing of HAI exposure, cost-clustering within main diagnoses and risk-adjustment are considered. The reimbursement-cost difference of HAI patients is negative (approximately - €4000). While controls on average also have a negative reimbursement-cost difference (approximately - €2000), HAI significantly increase this difference after controlling for confounding and timing of infection (- 1500, p < 0.01). HAIs caused by vancomycin-resistant Enterococci have the most unfavorable reimbursement-cost difference (- €10,800), significantly higher (- €9100, p < 0.05) than controls. Among infection types, pneumonia is associated with highest losses (- €8400 and - €5700 compared with controls, p < 0.05), while cost-reimbursement relationship for Clostridium difficile-associated diarrhea is comparatively balanced (- €3200 and - €500 compared to controls, p = 0.198). From the hospital administration's perspective, it is not the additional costs of HAIs, but rather the cost-reimbursement relationship which guides decisions. Costs exceeding reimbursements for HAI may increase infection prevention and control efforts and can be used to show their cost-effectiveness from the hospital perspective.
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Affiliation(s)
- Klaus Kaier
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of Freiburg, Stefan-Meier-Str. 26, 79104, Freiburg, Germany.
| | - Martin Wolkewitz
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of Freiburg, Stefan-Meier-Str. 26, 79104, Freiburg, Germany
| | - Philip Hehn
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of Freiburg, Stefan-Meier-Str. 26, 79104, Freiburg, Germany
| | - Nico T Mutters
- Institute for Infection Prevention and Hospital Epidemiology, Faculty of Medicine, Medical Center - University of Freiburg, Breisacher Straße 115 b, 79106, Freiburg, Germany
| | - Thomas Heister
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of Freiburg, Stefan-Meier-Str. 26, 79104, Freiburg, Germany
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29
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Voidazan S, Albu S, Toth R, Grigorescu B, Rachita A, Moldovan I. Healthcare Associated Infections-A New Pathology in Medical Practice? INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:E760. [PMID: 31991722 PMCID: PMC7036829 DOI: 10.3390/ijerph17030760] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Revised: 01/18/2020] [Accepted: 01/23/2020] [Indexed: 11/24/2022]
Abstract
Background: Hospital-acquired infections (HAI) contribute to the emotional stress and functional disorders of the patient and in some cases, can lead to a state of disability that reduces quality of life. Often, HAI are one of the factors that lead to death. The purpose of this study was to analyze the cases of HAI identified in public hospitals at the county level, through case report sheets, as they are reported according to the Romanian legislation. Methods: We performed a cross sectional study design based on the case law of the data reported to the Mures Public Health Directorate, by all the public hospitals belonging to this county. We tracked hospital-acquired infections reported for 2017-2018, respectively, a number of 1024 cases, which implies a prevalence rate of 0.44%, 1024/228,782 cases discharged from these hospitals during the studied period. Results: The most frequent HAIs were reported by the intensive care units (48.4%), the most common infections being the following: bronchopneumonia (25.3%), enterocolitis with Clostridioidesdifficile (23.3%), sepsis, surgical wound infections and urinary tract infections. At the basis of HAI were 22 pathogens, but the five most common germs were Clostridioidesdifficile, Acinetobacterbaumannii, Klebsiella pneumoniae, Pseudomonas aeruginosa and Staphylococcus aureus. Bronchopneumonia have been most frequently reported in intensive care units, the most common being identified the Acinetobacterbaumannii agent. Sepsis and central catheter infections also appeared predominantly in intensive care units, more often with Klebsiella pneumoniae. The enterocolitis with Clostridioidesdifficile, were the apanage of the medical sections. Infections with Staphylococcus aureus have been identified predominantly in the surgical sections at the level of the surgical wounds. Urinary infections had a similar distribution in the intensive care units, the medical and surgical sections, with Klebsiella pneumoniae being the most commonly incriminated agent. Conclusions: We showed a clear correspondence between the medical units and the type of HAI: what recommends the rapid, vigilant and oriented application of the prevention and control strategies of the HAI.
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Affiliation(s)
- Septimiu Voidazan
- Department of Epidemiology, University of Medicine, Pharmacy, Sciences and Technology George Emil Palade of Tîrgu Mureș, 540141 Tîrgu Mureș, Romania;
| | - Sorin Albu
- Department of Physiology, University of Medicine, Pharmacy, Sciences and Technology George Emil Palade of Tîrgu Mureș, 540141 Tîrgu Mureș, Romania
| | - Réka Toth
- Department of Quality Management in Healthcare Services, County Emergency Clinical Hospital of Tîrgu Mureș, 540141 Tîrgu Mureș, Romania;
| | - Bianca Grigorescu
- Department of Pathophysiology, University of Medicine, Pharmacy, Sciences and Technology George Emil Palade of Targu-Mures, 540141 Tîrgu Mureș, Romania;
| | - Anca Rachita
- University of Medicine, Pharmacy, Sciences and Technology George Emil Palade of Targu-Mures, 540141 Tîrgu Mureș, Romania;
| | - Iuliu Moldovan
- Discipline of public health and health management University of Medicine, Pharmacy, Science and Technology George Emil Palade of Targu-Mures, 540141 Tîrgu Mureș, Romania;
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Impact of mechanical ventilation on the daily costs of ICU care: a systematic review and meta regression. Epidemiol Infect 2019; 147:e314. [PMID: 31802726 PMCID: PMC7003623 DOI: 10.1017/s0950268819001900] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
The impact of mechanical ventilation on the daily costs of intensive care unit (ICU) care is largely unknown. We thus conducted a systematic search for studies measuring the daily costs of ICU stays for general populations of adults (age ≥18 years) and the added costs of mechanical ventilation. The relative increase in the daily costs was estimated using random effects meta regression. The results of the analyses were applied to a recent study calculating the excess length-of-stay associated with ICU-acquired (ventilator-associated) pneumonia, a major complication of mechanical ventilation. The search identified five eligible studies including a total of 54 766 patients and ~238 037 patient days in the ICU. Overall, mechanical ventilation was associated with a 25.8% (95% CI 4.7%–51.2%) increase in the daily costs of ICU care. A combination of these estimates with standardised unit costs results in approximate daily costs of a single ventilated ICU day of €1654 and €1580 in France and Germany, respectively. Mechanical ventilation is a major driver of ICU costs and should be taken into account when measuring the financial burden of adverse events in ICU settings.
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Kaier K, Heister T, Götting T, Wolkewitz M, Mutters NT. Measuring the in-hospital costs of Pseudomonas aeruginosa pneumonia: methodology and results from a German teaching hospital. BMC Infect Dis 2019; 19:1028. [PMID: 31795953 PMCID: PMC6888947 DOI: 10.1186/s12879-019-4660-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Accepted: 11/25/2019] [Indexed: 11/16/2022] Open
Abstract
Background Pseudomonas aeruginosa-related pneumonia is an ongoing healthcare challenge. Estimating its financial burden is complicated by the time-dependent nature of the disease. Methods Two hundred thirty-six cases of Pseudomonas aeruginosa-related pneumonia were recorded at a 2000 bed German teaching hospital between 2011 and 2014. Thirty-five cases (15%) were multidrug-resistant (MDR) Pseudomonas aeruginosa. Hospital- and community-acquired cases were distinguished by main diagnoses and exposure time. The impact of Pseudomonas aeruginosa-related pneumonia on the three endpoints cost, reimbursement, and length of stay was analyzed, taking into account (1) the time-dependent nature of exposure, (2) clustering of costs within diagnostic groups, and (3) additional confounders. Results Pseudomonas aeruginosa pneumonia is associated with substantial additional costs that are not fully reimbursed. Costs are highest for hospital-acquired cases (€19,000 increase over uninfected controls). However, community-acquired cases are also associated with a substantial burden (€8400 when Pseudomonas aeruginosa pneumonia is the main reason for hospitalization, and €6700 when not). Sensitivity analyses for hospital-acquired cases showed that ignoring or incorrectly adjusting for time-dependency substantially biases results. Furthermore, multidrug-resistance was rare and only showed a measurable impact on the cost of community-acquired cases. Conclusions Pseudomonas aeruginosa pneumonia creates a substantial financial burden for hospitals. This is particularly the case for nosocomial infections. Infection control interventions could yield significant cost reductions. However, to evaluate the potential effectiveness of different interventions, the time-dependent aspects of incremental costs must be considered to avoid introduction of bias.
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Affiliation(s)
- Klaus Kaier
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany.
| | - Thomas Heister
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Tim Götting
- Institute for Infection Prevention and Hospital Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Martin Wolkewitz
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Nico T Mutters
- Institute for Infection Prevention and Hospital Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
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Improving the State of Use and Understanding of Multistate Models in Critical Care. Crit Care Med 2019; 46:1191-1192. [PMID: 29912101 DOI: 10.1097/ccm.0000000000003162] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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33
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Heister T, Wolkewitz M, Hehn P, Wolff J, Dettenkofer M, Grundmann H, Kaier K. Costs of hospital-acquired Clostridium difficile infections: an analysis on the effect of time-dependent exposures using routine and surveillance data. COST EFFECTIVENESS AND RESOURCE ALLOCATION 2019; 17:16. [PMID: 31388335 PMCID: PMC6670202 DOI: 10.1186/s12962-019-0184-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Accepted: 07/25/2019] [Indexed: 12/18/2022] Open
Abstract
Background Hospital-acquired infections have not only gained increasing attention clinically, but also methodologically, as a time-varying exposure. While methods to appropriately estimate extra length of stay (LOS) have been established and are increasingly used in the literature, proper estimation of cost figures has lagged behind. Methods Analysing the additional costs and reimbursements of Clostridium difficile-infections (CDI), we use a within-main-diagnosis-time-to-exposure stratification approach to incorporate time-varying exposures in a regression model, while at the same time accounting for cost clustering within diagnosis groups. Results We find that CDI is associated with €9000 of extra costs, €7800 of higher reimbursements, and 6.4 days extra length of stay. Using a conventional method, which suffers from time-dependent bias, we derive estimates more than three times as high (€23,000, €8000, 21 days respectively). We discuss our method in the context of recent methodological advances in the estimation of the costs of hospital-acquired infections. Conclusions CDI is associated with sizeable in-hospital costs. Neglecting the methodological particularities of hospital-acquired infections can however substantially bias results. As the data needed for an appropriate analysis are collected routinely in most hospitals, we recommend our approach as a feasible way for estimating the economic impact of time-varying adverse events during hospital stay.
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Affiliation(s)
- Thomas Heister
- 1Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Stefan-Meier-Str. 26, 79104 Freiburg, Germany
| | - Martin Wolkewitz
- 1Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Stefan-Meier-Str. 26, 79104 Freiburg, Germany
| | - Philip Hehn
- 1Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Stefan-Meier-Str. 26, 79104 Freiburg, Germany
| | - Jan Wolff
- 2Department of Psychiatry and Psychotherapy, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Markus Dettenkofer
- Institute for Hospital Hygiene and Infection Prevention, Gesundheitsverbund Landkreis Konstanz, Radolfzell, Germany
| | - Hajo Grundmann
- 4Division of Infection Control and Hospital Epidemiology, University Medical Center Freiburg, Freiburg, Germany
| | - Klaus Kaier
- 1Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Stefan-Meier-Str. 26, 79104 Freiburg, Germany
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Walker AS, Budgell E, Laskawiec-Szkonter M, Sivyer K, Wordsworth S, Quaddy J, Santillo M, Krusche A, Roope LSJ, Bright N, Mowbray F, Jones N, Hand K, Rahman N, Dobson M, Hedley E, Crook D, Sharland M, Roseveare C, Hobbs FDR, Butler C, Vaughan L, Hopkins S, Yardley L, Peto TEA, Llewelyn MJ. Antibiotic Review Kit for Hospitals (ARK-Hospital): study protocol for a stepped-wedge cluster-randomised controlled trial. Trials 2019; 20:421. [PMID: 31296255 PMCID: PMC6625068 DOI: 10.1186/s13063-019-3497-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Accepted: 06/05/2019] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND To ensure patients continue to get early access to antibiotics at admission, while also safely reducing antibiotic use in hospitals, one needs to target the continued need for antibiotics as more diagnostic information becomes available. UK Department of Health guidance promotes an initiative called 'Start Smart then Focus': early effective antibiotics followed by active 'review and revision' 24-72 h later. However in 2017, < 10% of antibiotic prescriptions were discontinued at review, despite studies suggesting that 20-30% of prescriptions could be stopped safely. METHODS/DESIGN Antibiotic Review Kit for Hospitals (ARK-Hospital) is a complex 'review and revise' behavioural intervention targeting healthcare professionals involved in antibiotic prescribing or administration in inpatients admitted to acute/general medicine (the largest consumers of non-prophylactic antibiotics in hospitals). The primary study objective is to evaluate whether ARK-Hospital can safely reduce the total antibiotic burden in acute/general medical inpatients by at least 15%. The primary hypotheses are therefore that the introduction of the behavioural intervention will be non-inferior in terms of 30-day mortality post-admission (relative margin 5%) for an acute/general medical inpatient, and superior in terms of defined daily doses of antibiotics per acute/general medical admission (co-primary outcomes). The unit of observation is a hospital organisation, a single hospital or group of hospitals organised with one executive board and governance framework (National Health Service trusts in England; health boards in Northern Ireland, Wales and Scotland). The study comprises a feasibility study in one organisation (phase I), an internal pilot trial in three organisations (phase II) and a cluster (organisation)-randomised stepped-wedge trial (phase III) targeting a minimum of 36 organisations in total. Randomisation will occur over 18 months from November 2017 with a further 12 months follow-up to assess sustainability. The behavioural intervention will be delivered to healthcare professionals involved in antibiotic prescribing or administration in adult inpatients admitted to acute/general medicine. Outcomes will be assessed in adult inpatients admitted to acute/general medicine, collected through routine electronic health records in all patients. DISCUSSION ARK-Hospital aims to provide a feasible, sustainable and generalisable mechanism for increasing antibiotic stopping in patients who no longer need to receive them at 'review and revise'. TRIAL REGISTRATION ISRCTN Current Controlled Trials, ISRCTN12674243 . Registered on 10 April 2017.
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Affiliation(s)
- Ann Sarah Walker
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Eric Budgell
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Magda Laskawiec-Szkonter
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Oxford Respiratory Trials Unit, University of Oxford, Oxford, UK
| | - Katy Sivyer
- Centre for Clinical and Community Applications of Health Psychology, University of Southampton, Southampton, UK
| | - Sarah Wordsworth
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Jack Quaddy
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Oxford Respiratory Trials Unit, University of Oxford, Oxford, UK
| | - Marta Santillo
- Centre for Clinical and Community Applications of Health Psychology, University of Southampton, Southampton, UK
| | - Adele Krusche
- Centre for Clinical and Community Applications of Health Psychology, University of Southampton, Southampton, UK
| | - Laurence S. J. Roope
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Nicole Bright
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Fiona Mowbray
- Centre for Clinical and Community Applications of Health Psychology, University of Southampton, Southampton, UK
| | - Nicola Jones
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Kieran Hand
- University of Southampton, Southampton, UK
- University Hospital Southampton NHS Trust, Southampton, UK
| | - Najib Rahman
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Oxford Respiratory Trials Unit, University of Oxford, Oxford, UK
| | - Melissa Dobson
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Oxford Respiratory Trials Unit, University of Oxford, Oxford, UK
| | - Emma Hedley
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Oxford Respiratory Trials Unit, University of Oxford, Oxford, UK
| | - Derrick Crook
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | | | | | - F. D. Richard Hobbs
- Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Chris Butler
- Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | | | - Susan Hopkins
- Royal Free London NHS Foundation Trust, London, UK
- National Infection Service, Public Health England, London, UK
| | - Lucy Yardley
- Centre for Clinical and Community Applications of Health Psychology, University of Southampton, Southampton, UK
- School of Psychological Science, University of Bristol, Clifton, UK
| | - Timothy E. A. Peto
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | | | - on behalf of the ARK trial team
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Oxford Respiratory Trials Unit, University of Oxford, Oxford, UK
- Centre for Clinical and Community Applications of Health Psychology, University of Southampton, Southampton, UK
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- University of Southampton, Southampton, UK
- University Hospital Southampton NHS Trust, Southampton, UK
- St George’s, University of London, London, UK
- Southern Health NHS Foundation Trust, Southampton, UK
- Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
- The Nuffield Trust, London, UK
- Royal Free London NHS Foundation Trust, London, UK
- School of Psychological Science, University of Bristol, Clifton, UK
- National Infection Service, Public Health England, London, UK
- Brighton and Sussex Medical School, Brighton, UK
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von Cube M, Schumacher M, Putter H, Timsit JF, van de Velde C, Wolkewitz M. The population-attributable fraction for time-dependent exposures using dynamic prediction and landmarking. Biom J 2019; 62:583-597. [PMID: 31216103 DOI: 10.1002/bimj.201800252] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2018] [Revised: 04/05/2019] [Accepted: 04/24/2019] [Indexed: 11/11/2022]
Abstract
The public health impact of a harmful exposure can be quantified by the population-attributable fraction (PAF). The PAF describes the attributable risk due to an exposure and is often interpreted as the proportion of preventable cases if the exposure was extinct. Difficulties in the definition and interpretation of the PAF arise when the exposure of interest depends on time. Then, the definition of exposed and unexposed individuals is not straightforward. We propose dynamic prediction and landmarking to define and estimate a PAF in this data situation. Two estimands are discussed which are based on two hypothetical interventions that could prevent the exposure in different ways. Considering the first estimand, at each landmark the estimation problem is reduced to a time-independent setting. Then, estimation is simply performed by using a generalized-linear model accounting for the current exposure state and further (time-varying) covariates. The second estimand is based on counterfactual outcomes, estimation can be performed using pseudo-values or inverse-probability weights. The approach is explored in a simulation study and applied on two data examples. First, we study a large French database of intensive care unit patients to estimate the population-benefit of a pathogen-specific intervention that could prevent ventilator-associated pneumonia caused by the pathogen Pseudomonas aeruginosa. Moreover, we quantify the population-attributable burden of locoregional and distant recurrence in breast cancer patients.
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Affiliation(s)
- Maja von Cube
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany.,Freiburg Center for Data Analysis and Modeling, University of Freiburg, Freiburg, Germany
| | - Martin Schumacher
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany.,Freiburg Center for Data Analysis and Modeling, University of Freiburg, Freiburg, Germany
| | - Hein Putter
- Department of Medical Statistics and Bioinformatics, Leiden University Medical Centre, Leiden, The Netherlands
| | - Jéan-François Timsit
- UMR 1137 IAME Inserm/Université Paris Diderot, Paris, France.,APHP Medical and Infectious Diseases ICU, Bichat Hospital, Paris, France
| | | | - Martin Wolkewitz
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany.,Freiburg Center for Data Analysis and Modeling, University of Freiburg, Freiburg, Germany
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36
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Persoon MC, Voor In 't Holt AF, van Meer MPA, Bokhoven KC, Gommers D, Vos MC, Severin JA. Mortality related to Verona Integron-encoded Metallo-β-lactamase-positive Pseudomonas aeruginosa: assessment by a novel clinical tool. Antimicrob Resist Infect Control 2019; 8:107. [PMID: 31244998 PMCID: PMC6582487 DOI: 10.1186/s13756-019-0556-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Accepted: 06/10/2019] [Indexed: 11/10/2022] Open
Abstract
Background Verona Integron-encoded Metallo-β-lactamase-positive Pseudomonas aeruginosa (VIM-PA) can cause nosocomial infections and may be responsible for increased mortality. Multidrug resistance in VIM-PA complicates treatment. We aimed to assess the contribution of VIM-PA to mortality in patients in a large tertiary care hospital in the Netherlands. Methods A focus group of five members created a scheme to define related mortality based on clinical and diagnostic findings. Contribution to mortality was categorized as “definitely”, “probably”, “possibly”, or “not” related to infection with VIM-PA, or as “unknown”. Patients were included when infected with or carrier of VIM-PA between January 2008 and January 2016. Patient-related data and specific data on VIM-PA cultures were retrieved from the electronic laboratory information system. For patients who died in our hospital, medical records were independently reviewed and thereafter discussed by three physicians. Results A total of 198 patients with any positive culture with VIM-PA were identified, of whom 95 (48.0%) died. Sixty-seven patients died in our hospital and could be included in the analysis. The death of 15 patients (22.4%) was judged by all reviewers to be definitely related to infection with VIM-PA. In 17 additional patients (25.4%), death was probably or possibly related to an infection with VIM-PA. The level of agreement was 65.7% after the first evaluation, and 98.5% after one session of discussion. Conclusion Using our assessment tool, infections with VIM-PA were shown to have an important influence on mortality in our complex and severely ill patients. The tool may be used for other (resistant) bacteria as well but this needs further exploration. Electronic supplementary material The online version of this article (10.1186/s13756-019-0556-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Marjolein C Persoon
- 1Department of Medical Microbiology and Infectious Diseases, Erasmus MC University Medical Center Rotterdam, Dr. Molewaterplein 40, 3015 GD Rotterdam, The Netherlands
| | - Anne F Voor In 't Holt
- 1Department of Medical Microbiology and Infectious Diseases, Erasmus MC University Medical Center Rotterdam, Dr. Molewaterplein 40, 3015 GD Rotterdam, The Netherlands
| | - Maurits P A van Meer
- 1Department of Medical Microbiology and Infectious Diseases, Erasmus MC University Medical Center Rotterdam, Dr. Molewaterplein 40, 3015 GD Rotterdam, The Netherlands
| | - Karen C Bokhoven
- 2Department of Adult Intensive Care, Erasmus MC University Medical Center Rotterdam, Doctor Molewaterplein, 40 3015 GD Rotterdam, The Netherlands
| | - Diederik Gommers
- 2Department of Adult Intensive Care, Erasmus MC University Medical Center Rotterdam, Doctor Molewaterplein, 40 3015 GD Rotterdam, The Netherlands
| | - Margreet C Vos
- 1Department of Medical Microbiology and Infectious Diseases, Erasmus MC University Medical Center Rotterdam, Dr. Molewaterplein 40, 3015 GD Rotterdam, The Netherlands
| | - Juliëtte A Severin
- 1Department of Medical Microbiology and Infectious Diseases, Erasmus MC University Medical Center Rotterdam, Dr. Molewaterplein 40, 3015 GD Rotterdam, The Netherlands
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von Cube M, Schumacher M, Bailly S, Timsit JF, Lepape A, Savey A, Machut A, Wolkewitz M. The population-attributable fraction for time-dependent exposures and competing risks-A discussion on estimands. Stat Med 2019; 38:3880-3895. [PMID: 31162706 DOI: 10.1002/sim.8208] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2018] [Revised: 05/02/2019] [Accepted: 05/03/2019] [Indexed: 11/09/2022]
Abstract
The population-attributable fraction (PAF) quantifies the public health impact of a harmful exposure. Despite being a measure of significant importance, an estimand accommodating complicated time-to-event data is not clearly defined. We discuss current estimands of the PAF used to quantify the public health impact of an internal time-dependent exposure for data subject to competing outcomes. To overcome some limitations, we proposed a novel estimand that is based on dynamic prediction by landmarking. In a profound simulation study, we discuss interpretation and performance of the various estimands and their estimators. The methods are applied to a large French database to estimate the health impact of ventilator-associated pneumonia for patients in intensive care.
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Affiliation(s)
- Maja von Cube
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany.,Freiburg Center for Data Analysis and Modelling, University of Freiburg, Freiburg, Germany
| | - Martin Schumacher
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany.,Freiburg Center for Data Analysis and Modelling, University of Freiburg, Freiburg, Germany
| | - Sébastien Bailly
- HP2 Laboratory, University of Grenoble Alpes, Grenoble, France.,Department of Physiology and Sleep, Grenoble Alpes University Hospital, Grenoble, France
| | - Jean-François Timsit
- UMR 1137 IAME Inserm, Université Paris Diderot, Paris, France.,APHP Medical and Infectious Diseases ICU, Bichat Hospital, Paris, France
| | - Alain Lepape
- Clinical Research Unit, Critical Care, Lyon Sud University Hospital, Hospices Civils de Lyon, Lyon, France.,Laboratory of Emerging Pathogens, International Center for Infectiology Research (CIRI), Inserm U1111, CNRS UMR5308, ENS de Lyon, UCBL1, Lyon, France
| | - Anne Savey
- CPIAS Auvergne-Rhône-Alpes, Hospices Civils de Lyon, Lyon, France.,Laboratory of Emerging Pathogens, International Center for Infectiology Research (CIRI), Inserm U1111, CNRS UMR5308, ENS de Lyon, UCBL1, Lyon, France
| | - Anais Machut
- CPIAS Auvergne-Rhône-Alpes, Hospices Civils de Lyon, Lyon, France
| | - Martin Wolkewitz
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany.,Freiburg Center for Data Analysis and Modelling, University of Freiburg, Freiburg, Germany
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Sabetian G, Nikandish R, Zand F, Faghihi H, Masjedi M, Maghsoudi B, Vazin A, Ghorbani M, Asadpour E. Comparing the ventilator-associated pneumonia incidence when pantoprazole or ranitidine is used for stress ulcer prophylaxis in critically ill adult patients. INTERNATIONAL ARCHIVES OF HEALTH SCIENCES 2019. [DOI: 10.4103/iahs.iahs_16_19] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
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Kamal R, Shah D, Sharma S, Janani MK, Kar A, Saurabh K, Roy R, Madhavan HNR. Response to comment on: Culture-positive unilateral panophthalmitis in a serology-2 positive case of dengue hemorrhagic fever. Indian J Ophthalmol 2018; 66:1661. [PMID: 30355904 PMCID: PMC6213666 DOI: 10.4103/ijo.ijo_1373_18] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Affiliation(s)
- Richa Kamal
- Department of Vitreoretinal Services, Aditya Birla Sankara Nethralaya, Kolkata, West Bengal, India
| | - Dhaivat Shah
- Department of Vitreoretinal Services, Aditya Birla Sankara Nethralaya, Kolkata, West Bengal, India
| | - Satish Sharma
- Department of Vitreoretinal Services, Aditya Birla Sankara Nethralaya, Kolkata, West Bengal, India
| | | | - Arindam Kar
- Department of Molecular Microbiology, Sankara Nethralaya Referral Laboratory, Chennai, Tamil Nadu, India
| | - Kumar Saurabh
- Department of Vitreoretinal Services, Aditya Birla Sankara Nethralaya, Kolkata, West Bengal, India
| | - Rupak Roy
- Department of Vitreoretinal Services, Aditya Birla Sankara Nethralaya, Kolkata, West Bengal, India
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Manoukian S, Stewart S, Dancer S, Graves N, Mason H, McFarland A, Robertson C, Reilly J. Estimating excess length of stay due to healthcare-associated infections: a systematic review and meta-analysis of statistical methodology. J Hosp Infect 2018; 100:222-235. [PMID: 29902486 DOI: 10.1016/j.jhin.2018.06.003] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2018] [Accepted: 06/05/2018] [Indexed: 02/09/2023]
Abstract
BACKGROUND Healthcare-associated infection (HCAI) affects millions of patients worldwide. HCAI is associated with increased healthcare costs, owing primarily to increased hospital length of stay (LOS) but calculating these costs is complicated due to time-dependent bias. Accurate estimation of excess LOS due to HCAI is essential to ensure that we invest in cost-effective infection prevention and control (IPC) measures. AIM To identify and review the main statistical methods that have been employed to estimate differential LOS between patients with, and without, HCAI; to highlight and discuss potential biases of all statistical approaches. METHODS A systematic review from 1997 to April 2017 was conducted in PubMed, CINAHL, ProQuest and EconLit databases. Studies were quality-assessed using an adapted Newcastle-Ottawa Scale (NOS). Methods were categorized as time-fixed or time-varying, with the former exhibiting time-dependent bias. Two examples of meta-analysis were used to illustrate how estimates of excess LOS differ between different studies. FINDINGS Ninety-two studies with estimates on excess LOS were identified. The majority of articles employed time-fixed methods (75%). Studies using time-varying methods are of higher quality according to NOS. Studies using time-fixed methods overestimate additional LOS attributable to HCAI. Undertaking meta-analysis is challenging due to a variety of study designs and reporting styles. Study differences are further magnified by heterogeneous populations, case definitions, causative organisms, and susceptibilities. CONCLUSION Methodologies have evolved over the last 20 years but there is still a significant body of evidence reliant upon time-fixed methods. Robust estimates are required to inform investment in cost-effective IPC interventions.
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Affiliation(s)
- S Manoukian
- Yunus Centre for Social Business and Health, Glasgow Caledonian University, Cowcaddens Road, Glasgow, UK.
| | - S Stewart
- School of Health and Life Sciences, Glasgow Caledonian University, Cowcaddens Road, Glasgow, UK
| | - S Dancer
- Department of Microbiology, Hairmyres Hospital, NHS Lanarkshire, UK
| | - N Graves
- Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia
| | - H Mason
- Yunus Centre for Social Business and Health, Glasgow Caledonian University, Cowcaddens Road, Glasgow, UK
| | - A McFarland
- School of Health and Life Sciences, Glasgow Caledonian University, Cowcaddens Road, Glasgow, UK
| | - C Robertson
- Department of Mathematics and Statistics, University of Strathclyde, Glasgow, UK
| | - J Reilly
- School of Health and Life Sciences, Glasgow Caledonian University, Cowcaddens Road, Glasgow, UK
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Effect of methicillin-resistant Staphylococcus aureus in Japan. Am J Infect Control 2018; 46:1142-1147. [PMID: 29784441 DOI: 10.1016/j.ajic.2018.04.214] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2018] [Revised: 04/13/2018] [Accepted: 04/13/2018] [Indexed: 11/20/2022]
Abstract
BACKGROUND Methicillin-resistant Staphylococcus aureus (MRSA) is the most common antimicrobial-resistant organism identified in Japanese health care facilities. This study analyzed the clinical and economic burdens attributable to methicillin resistance in S aureus in Japanese hospitals. METHODS We retrospectively investigated data from 14,905 inpatients of 57 hospitals combined with data from nosocomial infection surveillance and administrative claim databases. The participants were inpatients with admission from April 1, 2014, to discharge on March 31, 2016. The outcomes were evaluated according to length of stay, hospital charges, and in-hospital mortality. We compared the disease burden of MRSA infections with methicillin-susceptible S aureus (MSSA) infections based on patients' characteristics and onset periods. RESULTS We categorized 7,188 and 7,717 patients into MRSA and MSSA groups, respectively. The adjusted effects of the MRSA group were 1.03-fold (95% confidence interval [CI] 1.01-1.05) and 1.04-fold (95% CI, 1.01-1.06), respectively, with an odds ratio of 1.14 (95% CI, 1.02-1.27). CONCLUSIONS The results of this study found that patient severity and onset delays were positively associated with both MRSA and burden and that the effect of methicillin resistance remained significant after adjustment.
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Bluhmki T, Allignol A, Ruckly S, Timsit JF, Wolkewitz M, Beyersmann J. Estimation of adjusted expected excess length-of-stay associated with ventilation-acquired pneumonia in intensive care: A multistate approach accounting for time-dependent mechanical ventilation. Biom J 2018; 60:1135-1150. [DOI: 10.1002/bimj.201700242] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2017] [Revised: 06/28/2018] [Accepted: 08/06/2018] [Indexed: 12/29/2022]
Affiliation(s)
| | | | | | - Jean-Francois Timsit
- UMR 1137 IAME Inserm/University Paris Diderot; Paris France
- APHP; Bichat Hospital; Intensive Care Unit; Paris France
| | - Martin Wolkewitz
- Institute for Medical Biometry and Statistics; Faculty of Medicine and Medical Center-University of Freiburg; Freiburg Germany
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Improving nested case-control studies to conduct a full competing-risks analysis for nosocomial infections. Infect Control Hosp Epidemiol 2018; 39:1196-1201. [PMID: 30157989 DOI: 10.1017/ice.2018.186] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
OBJECTIVE Competing risks are a necessary consideration when analyzing risk factors for nosocomial infections (NIs). In this article, we identify additional information that a competing risks analysis provides in a hospital setting. Furthermore, we improve on established methods for nested case-control designs to acquire this information. METHODS Using data from 2 Spanish intensive care units and model simulations, we show how controls selected by time-dynamic sampling for NI can be weighted to perform risk-factor analysis for death or discharge without infection. This extension not only enables hazard rate analysis for the competing risk, it also enables prediction analysis for NI. RESULTS The estimates acquired from the extension were in good agreement with the results from the full (real and simulated) cohort dataset. The reduced dataset results averted any false interpretation common in a competing-risks setting. CONCLUSIONS Using additional information that is routinely collected in a hospital setting, a nested case-control design can be successfully adapted to avoid a competing risks bias. Furthermore, this adapted method can be used to reanalyze past nested case-control studies to enhance their findings.
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Estimation of Extra Length of Stay Attributable to Hospital-Acquired Infections in Adult ICUs Using a Time-Dependent Multistate Model*. Crit Care Med 2018; 46:1093-1098. [DOI: 10.1097/ccm.0000000000003131] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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45
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Kaier K, Wolkewitz M, Heister T. Estimating the attributable costs of hospital-acquired infections requires a distinct categorization of cases based on time of infection. Am J Infect Control 2018; 46:729. [PMID: 29655667 DOI: 10.1016/j.ajic.2018.02.014] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2018] [Accepted: 02/06/2018] [Indexed: 11/18/2022]
Affiliation(s)
- Klaus Kaier
- Division Methods in Clinical Epidemiology, Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center-University of Freiburg, Freiburg, Baden-Württemberg, Germany.
| | - Martin Wolkewitz
- Division Methods in Clinical Epidemiology, Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center-University of Freiburg, Freiburg, Baden-Württemberg, Germany
| | - Thomas Heister
- Division Methods in Clinical Epidemiology, Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center-University of Freiburg, Freiburg, Baden-Württemberg, Germany
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46
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Rahman S, von Cube M, Schumacher M, Wolkewitz M. Bias due to censoring of deaths when calculating extra length of stay for patients acquiring a hospital infection. BMC Med Res Methodol 2018; 18:49. [PMID: 29843610 PMCID: PMC5975458 DOI: 10.1186/s12874-018-0500-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2017] [Accepted: 04/30/2018] [Indexed: 11/22/2022] Open
Abstract
Background In many studies the information of patients who are dying in the hospital is censored when examining the change in length of hospital stay (cLOS) due to hospital-acquired infections (HIs). While appropriate estimators of cLOS are available in literature, the existence of the bias due to censoring of deaths was neither mentioned nor discussed by the according authors. Methods Using multi-state models, we systematically evaluate the bias when estimating cLOS in such a way. We first evaluate the bias in a mathematically closed form assuming a setting with constant hazards. To estimate the cLOS due to HIs non-parametrically, we relax the assumption of constant hazards and consider a time-inhomogeneous Markov model. Results In our analytical evaluation we are able to discuss challenging effects of the bias on cLOS. These are in regard to direct and indirect differential mortality. Moreover, we can make statements about the magnitude and direction of the bias. For real-world relevance, we illustrate the bias on a publicly available prospective cohort study on hospital-acquired pneumonia in intensive-care. Conclusion Based on our findings, we can conclude that censoring the death cases in the hospital and considering only patients discharged alive should be avoided when estimating cLOS. Moreover, we found that the closed mathematical form can be used to describe the bias for settings with constant hazards. Electronic supplementary material The online version of this article (10.1186/s12874-018-0500-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Shahina Rahman
- Department of Statistics, Texas A&M University, 3143 TAMU, 77843-3143, College Station, Texas, USA
| | - Maja von Cube
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany. .,Freiburg Center of Data Analysis and Modelling, University of Freiburg, Eckerstr. 1, Freiburg, 79104, Germany.
| | - Martin Schumacher
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany.,Freiburg Center of Data Analysis and Modelling, University of Freiburg, Eckerstr. 1, Freiburg, 79104, Germany
| | - Martin Wolkewitz
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany.,Freiburg Center of Data Analysis and Modelling, University of Freiburg, Eckerstr. 1, Freiburg, 79104, Germany
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Determining the Attributable Costs of Clostridium difficile Infections When Exposure Time Is Lacking: Be Wary of "Conditioning on the Future". Infect Control Hosp Epidemiol 2018; 39:759-760. [PMID: 29587890 DOI: 10.1017/ice.2018.42] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Heister T, Wolkewitz M, Kaier K. Estimating the additional costs of surgical site infections: length bias, time-dependent bias, and conditioning on the future. J Hosp Infect 2018; 99:103-104. [PMID: 29458064 DOI: 10.1016/j.jhin.2018.02.012] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2018] [Accepted: 02/12/2018] [Indexed: 11/16/2022]
Affiliation(s)
- T Heister
- Division Methods in Clinical Epidemiology, Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - M Wolkewitz
- Division Methods in Clinical Epidemiology, Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - K Kaier
- Division Methods in Clinical Epidemiology, Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany.
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Sommer H, Bluhmki T, Beyersmann J, Schumacher M. Assessing Noninferiority in Treatment Trials for Severe Infectious Diseases: an Extension to the Entire Follow-Up Period Using a Cure-Death Multistate Model. Antimicrob Agents Chemother 2018; 62:e01691-17. [PMID: 29061757 PMCID: PMC5740315 DOI: 10.1128/aac.01691-17] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2017] [Accepted: 10/17/2017] [Indexed: 12/18/2022] Open
Abstract
In current and former clinical trials for the development of antibacterial drugs, various primary endpoints have been used, and treatment effects are evaluated mostly in noninferiority analyses at the end of follow-up, which varies between studies. A more convincing and highly patient-relevant statement would be a noninferiority assessment over the entire follow-up period with cure and death as coprimary endpoints, while preserving the desired alpha level for statistical testing. To account for the time-dynamic pattern of cure and death, we apply a cure-death multistate model. The endpoint of interest is "get cured and stay alive over time." Noninferiority between treatments over the entire follow-up period is studied by means of one-sided confidence bands provided by a flexible resampling technique. We illustrate the technique by applying it to a recently published study and establish noninferiority in being cured and alive over a time frame of interest for the entire population, patients with hospital-acquired pneumonia, but not for the subset of patients with ventilator-associated pneumonia. Our analysis improves the original results in the sense that our endpoint is more patient benefiting, a stronger noninferiority statement is demonstrated, and the time dependency of cure and death, competing events, and different follow-up times is captured. Multistate methodology combined with confidence bands adds a valuable statistical tool for clinical trials in the context of infection control. The framework is not restricted to the cure-death model but can be adapted to more complex multistate endpoints and equivalence or superiority analyses.
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Affiliation(s)
- Harriet Sommer
- Institute for Medical Biometry and Statistics, Faculty of Medicine and Medical Center-University of Freiburg, Freiburg, Germany
| | | | | | - Martin Schumacher
- Institute for Medical Biometry and Statistics, Faculty of Medicine and Medical Center-University of Freiburg, Freiburg, Germany
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Intensive care unit (ICU)-acquired bacteraemia and ICU mortality and discharge: addressing time-varying confounding using appropriate methodology. J Hosp Infect 2017; 99:42-47. [PMID: 29175434 DOI: 10.1016/j.jhin.2017.11.011] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2017] [Accepted: 11/17/2017] [Indexed: 02/01/2023]
Abstract
BACKGROUND Studies often ignore time-varying confounding or may use inappropriate methodology to adjust for time-varying confounding. AIM To estimate the effect of intensive care unit (ICU)-acquired bacteraemia on ICU mortality and discharge using appropriate methodology. METHODS Marginal structural models with inverse probability weighting were used to estimate the ICU mortality and discharge associated with ICU-acquired bacteraemia among patients who stayed more than two days at the general ICU of a London teaching hospital and remained bacteraemia-free during those first two days. For comparison, the same associations were evaluated with (i) a conventional Cox model, adjusting only for baseline confounders and (ii) a Cox model adjusting for baseline and time-varying confounders. FINDINGS Using the marginal structural model with inverse probability weighting, bacteraemia was associated with an increase in ICU mortality (cause-specific hazard ratio (CSHR): 1.29; 95% confidence interval (CI): 1.02-1.63) and a decrease in discharge (CSHR: 0.52; 95% CI: 0.45-0.60). By 60 days, among patients still in the ICU after two days and without prior bacteraemia, 8.0% of ICU deaths could be prevented by preventing all ICU-acquired bacteraemia cases. The conventional Cox model adjusting for time-varying confounders gave substantially different results [for ICU mortality, CSHR: 1.08 (95% CI: 0.88-1.32); for discharge, CSHR: 0.68 (95% CI: 0.60-0.77)]. CONCLUSION In this study, even after adjusting for the timing of acquiring bacteraemia and time-varying confounding using inverse probability weighting for marginal structural models, ICU-acquired bacteraemia was associated with a decreased daily ICU discharge risk and an increased risk of ICU mortality.
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