1
|
Mengesha MG, Rajasekaran S, Ramachandran K, Sengodan VC, Yasin NF, Williams LM, Laubscher M, Watanabe K, Dastagir O, Akinmadr A, Fisseha HK, Aziz A, Yurac R, Gebrehana E, AlSaifi M, Pathinathan K, Sudhir G, Shokri AA, Chan Kim Y, Jonayed SA, Kido GR, Ignacio JM, Mohammed MS, Abubakar K, Hakim J, Duwal Shrestha SK, Al Mamun Choudhury A, Diallo M, Molina M, Patwardhan S, Hai Y, Ramat AM, Kawai M, Cho JH, Shah Kalawar RP, Choi SW, Zarate-Kalfopulos B, Guiroy A, Astur N, Buunaaim A, Human AL, Zaman AU. Orthopedic postoperative infection profile and antibiotic sensitivity of 2038 patients across 24 countries - Call for region and institution specific surgical antimicrobial prophylaxis. J Orthop 2024; 55:97-104. [PMID: 38681829 PMCID: PMC11047196 DOI: 10.1016/j.jor.2024.04.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Accepted: 04/14/2024] [Indexed: 05/01/2024] Open
Abstract
Purpose Improper utilization of surgical antimicrobial prophylaxis frequently leads to increased risks of morbidity and mortality.This study aims to understand the common causative organism of postoperative orthopedic infection and document the surgical antimicrobial prophylaxis protocol across various institutions in to order to strengthen surgical antimicrobial prophylaxis practice and provide higher-quality surgical care. Methods This multicentric multinational retrospective study, includes 24 countries from five different regions (Asia Pacific, South Eastern Africa, Western Africa, Latin America, and Middle East). Patients who developed orthopedic surgical site infection between January 2021 and December 2022 were included. Demographic details, bacterial profile of surgical site infection, and antibiotic sensitivity pattern were documented. Results 2038 patients from 24 countries were included. Among them 69.7 % were male patients and 64.1 % were between 20 and 60 years. 70.3 % patients underwent trauma surgery and instrumentation was used in 93.5 %. Ceftriaxone was the most common preferred in 53.4 %. Early SSI was seen in 55.2 % and deep SSI in 59.7 %. Western Africa (76 %) and Asia-Pacific (52.8 %) reported a higher number of gram-negative infections whereas gram-positive organisms were predominant in other regions. Most common gram positive organism was Staphylococcus aureus (35 %) and gram-negative was Klebsiella (17.2 %). Majority of the organisms showed variable sensitivity to broad-spectrum antibiotics. Conclusion Our study strongly proves that every institution has to analyse their surgical site infection microbiological profile and antibiotic sensitivity of the organisms and plan their surgical antimicrobial prophylaxis accordingly. This will help to decrease the rate of surgical site infection, prevent the emergence of multidrug resistance and reduce the economic burden of treatment.
Collapse
Affiliation(s)
| | - Shanmuganathan Rajasekaran
- Department of Orthopedics and Spine Surgery, Ganga Medical Centre and Hospital Pvt. Ltd., Mettupalayam Road, Coimbatore, India
| | - Karthik Ramachandran
- Department of Orthopedics and Spine Surgery, Ganga Medical Centre and Hospital Pvt. Ltd., Mettupalayam Road, Coimbatore, India
| | | | - Nor Faissal Yasin
- Natioal Orthopaedic Centre of Excellence for Research and Learning (NOCERAL), Orthopaedic Surgery Department, Faculty of Medicine, Universiti Malaya, Malaysia
| | | | - Maritz Laubscher
- Orthopaedic Research Unit (ORU), University of Cape Town, South Africa
| | - Kota Watanabe
- Department of Orthopaedic Surgery, Keio University School of Medicine, Japan
| | - O.Z.M. Dastagir
- National Institute of Traumatology and Orthopaedic Rehabilitation, Dhaka, Bangladesh
| | | | | | - Amer Aziz
- Orthopaedic & Spine Unit at Lahore Medical & Dental College / Ghurki Trust Teaching Hospital, Lahore, Pakistan
| | - Ratko Yurac
- Department of Orthopedics and Traumatology, Universidad Del Desarrollo (UDD). Clinica Alemana de Santiago, Chile
| | - Ephrem Gebrehana
- Hawassa University College of Medicine and Health Sciences, Ethiopia
| | | | | | - G. Sudhir
- Sri Ramachandra Institute of Higher Education and Research, India
| | | | - Yong Chan Kim
- Department of Orthopaedic Surgery, College of Medicine, Kyung Hee University Hospital at Gangdong, Kyung Hee University, Seol, South Korea
| | - Sharif Ahmed Jonayed
- National Institute of Traumatology and Orthopaedic Rehabilitation, Dhaka, Bangladesh
| | - Gonzalo R. Kido
- Orthopaedic and Traumatology Department, Institute of Orthopedics “Carlos E. Ottolenghiâ€, Hospital Italiano de Buenos Aires, Argentina
| | - Jose Manuel Ignacio
- Department of Orthopedics, University of the Philippines Manila, Manila, Philippines
| | | | | | - Jonaed Hakim
- BIRDEM General Hospital & Ibrahim Medical College, Bangladesh
| | | | | | | | - Marcelo Molina
- Instituto Traumatológico de Santiago, Universidad Finis Terrae, Chile
| | - Sandeep Patwardhan
- Dept. of Orthopaedics, Sancheti Institute of Orthopaedics and Rehabilitation, Pune, Maharashtra, India
| | - Yong Hai
- Beijing Chaoyang Hospital, Capital Medical University, China
| | - Ali M. Ramat
- University of Maiduguri Teaching Hospital, Nigeria
| | - Momotaro Kawai
- Department of Orthopaedic Surgery, Spine Center, Kitasato Institute Hospital, Japan
| | - Jae Hwan Cho
- University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
| | | | - Sung-Woo Choi
- Department of Orthopaedic Surgery, Soonchunhyang University College of Medicine, Seoul, South Korea
| | | | | | - Nelson Astur
- Hospital Israelita Albert Einstein, Sao Paulo, Brazil
| | | | | | - Atiq Uz Zaman
- Ghurki Trust Teaching Hospital/Lahore Medical and Dental College, Pakistan
| |
Collapse
|
2
|
Sim JXY, Pinto S, van Mourik MSM. Comparing automated surveillance systems for detection of pathogen-related clusters in healthcare settings. Antimicrob Resist Infect Control 2024; 13:69. [PMID: 38926895 PMCID: PMC11210035 DOI: 10.1186/s13756-024-01413-5] [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: 02/19/2024] [Accepted: 05/23/2024] [Indexed: 06/28/2024] Open
Abstract
BACKGROUND Detection of pathogen-related clusters within a hospital is key to early intervention to prevent onward transmission. Various automated surveillance methods for outbreak detection have been implemented in hospital settings. However, direct comparison is difficult due to heterogenicity of data sources and methodologies. In the hospital setting, we assess the performance of three different methods for identifying microbiological clusters when applied to various pathogens with distinct occurrence patterns. METHODS In this retrospective cohort study we use WHONET-SaTScan, CLAR (CLuster AleRt system) and our currently used percentile-based system (P75) for the means of cluster detection. The three methods are applied to the same data curated from 1st January 2014 to 31st December 2021 from a tertiary care hospital. We show the results for the following case studies: the introduction of a new pathogen with subsequent endemicity, an endemic species, rising levels of an endemic organism, and a sporadically occurring species. RESULTS All three cluster detection methods showed congruence only in endemic organisms. However, there was a paucity of alerts from WHONET-SaTScan (n = 9) compared to CLAR (n = 319) and the P75 system (n = 472). WHONET-SaTScan did not pick up smaller variations in baseline numbers of endemic organisms as well as sporadic organisms as compared to CLAR and the P75 system. CLAR and the P75 system revealed congruence in alerts for both endemic and sporadic organisms. CONCLUSIONS Use of statistically based automated cluster alert systems (such as CLAR and WHONET-Satscan) are comparable to rule-based alert systems only for endemic pathogens. For sporadic pathogens WHONET-SaTScan returned fewer alerts compared to rule-based alert systems. Further work is required regarding clinical relevance, timelines of cluster alerts and implementation.
Collapse
Affiliation(s)
- Jean Xiang Ying Sim
- Department of Infectious Diseases, Singapore General Hospital, Singapore, Singapore.
- Department of Infection Prevention & Epidemiology, Singapore General Hospital, Singapore, Singapore.
| | - Susanne Pinto
- Department of Medical Microbiology and Infection Control, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Maaike S M van Mourik
- Department of Medical Microbiology and Infection Control, University Medical Center Utrecht, Utrecht, The Netherlands
| |
Collapse
|
3
|
Winroth A, Andersson M, Fjällström P, Johansson AF, Lind A. Automated surveillance of antimicrobial consumption in intensive care, northern Sweden: an observational case study. Antimicrob Resist Infect Control 2024; 13:67. [PMID: 38890711 PMCID: PMC11186282 DOI: 10.1186/s13756-024-01424-2] [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: 02/02/2024] [Accepted: 06/13/2024] [Indexed: 06/20/2024] Open
Abstract
BACKGROUND The digitalization of information systems allows automatic measurement of antimicrobial consumption (AMC), helping address antibiotic resistance from inappropriate drug use without compromising patient safety. OBJECTIVES Describe and characterize a new automated AMC surveillance service for intensive care units (ICUs), with data stratified by referral clinic and linked with individual patient risk factors, disease severity, and mortality. METHODS An automated service collecting data from the electronic medical record was developed, implemented, and validated in a healthcare region in northern Sweden. We performed an observational study from January 1, 2018, to December 31, 2021, encompassing general ICU care for all ≥18-years-olds in a catchment population of 270000 in secondary care and 900000 in tertiary care. We used descriptive analyses to associate ICU population characteristics with AMC outcomes over time, including days of therapy (DOT), length of therapy, defined daily doses, and mortality. RESULTS There were 5608 admissions among 5190 patients with a median age of 65 (IQR 48-75) years, 41.2% females. The 30-day mortality was 18.3%. Total AMC was 1177 DOTs in secondary and 1261 DOTs per 1000 patient days and tertiary care. AMC varied significantly among referral clinics, with the highest total among 810 general surgery admissions in tertiary care at 1486 DOTs per 1000 patient days. Case-mix effects on the AMC were apparent during COVID-19 waves highlighting the need to account for case-mix. Patients exposed to more than three antimicrobial drug classes (N = 242) had a 30-day mortality rate of 40.6%, with significant variability in their expected rates based on admission scores. CONCLUSION We introduce a new service and instructions for automating local ICU-AMC data collection. The versatile long-term ICU-AMC metrics presented, covering patient factors, referral clinics and mortality outcomes, are expected to be beneficial in refining antimicrobial drug use.
Collapse
Affiliation(s)
- Andreas Winroth
- Department of Clinical Microbiology, Umeå University, SE-90187, Umeå, Sweden.
| | - Mattias Andersson
- Center for Intensive Care (IT unit), Norrlands universitetssjukhus, 90185, Umeå, SE, Sweden
| | - Peter Fjällström
- Department of Clinical Microbiology, Umeå University, SE-90187, Umeå, Sweden
- Department of Infection Prevention and Control Region Västerbotten, Norrlands universitetssjukhus, SE-90185, Umeå, Sweden
| | - Anders F Johansson
- Department of Clinical Microbiology, Umeå University, SE-90187, Umeå, Sweden
| | - Alicia Lind
- Department of Diagnostics and Intervention, Umeå University, SE-90187, Umeå, Sweden
| |
Collapse
|
4
|
Brekelmans M, Hopmans T, van Mourik M, de Greeff S, Swillens J, van Rooden S. Evaluation of a multifaceted implementation strategy for semi-automated surveillance of surgical site infections after total hip or knee arthroplasty: a multicentre pilot study in the Netherlands. Antimicrob Resist Infect Control 2024; 13:63. [PMID: 38872201 PMCID: PMC11170835 DOI: 10.1186/s13756-024-01418-0] [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: 04/24/2024] [Accepted: 06/01/2024] [Indexed: 06/15/2024] Open
Abstract
INTRODUCTION To promote the nation-wide implementation of semi-automated surveillance (AS) of surgical site infection after hip and knee arthroplasty, the Dutch National Institute for Public Health and the Environment (RIVM) deployed a decentralised multifaceted implementation strategy. This strategy consisted of a protocol specifying minimum requirements for an AS system, supported by a user manual, education module, individual guidance for hospitals and user-group meetings. This study describes an effect evaluation and process evaluation of the implementation strategy for AS in five frontrunner hospitals. METHODS To evaluate the effect of the implementation strategy, the achieved phase of implementation was determined in each frontrunner hospital at the end of the study period. The process evaluation consisted of (1) an evaluation of the feasibility of strategy elements, (2) an evaluation of barriers and facilitators for implementation and (3) an evaluation of the workload for implementation. Interviews were performed as a basis for a subsequent survey quantifying the results regarding the feasibility as well as barriers and facilitators. Workload was self-monitored per profession. Qualitative data were analysed using a framework analysis, whereas quantitative data were analysed descriptively. RESULTS One hospital finished the complete implementation process in 240 person-hours. Overall, the elements of the implementation strategy were often used, positively received and overall, the strategy was rated effective and feasible. During the implementation process, participants perceived the relative advantage of AS and had sufficient knowledge about AS. However, barriers regarding complexity of AS data extraction, data-infrastructure, and validation, lack of capacity and motivation at the IT department, and difficulties with assigning roles and responsibilities were experienced. CONCLUSION A decentralised multifaceted implementation strategy is suitable for the implementation of AS in hospitals. Effective local project management, including clear project leadership and ownership, obtaining commitment of higher management levels, active involvement of stakeholders, and appropriate allocation of roles and responsibilities is important for successful implementation and should be facilitated by the implementation strategy. Sufficient knowledge about AS, its requirements and the implementation process should be available among stakeholders by e.g. an education module. Furthermore, exchange of knowledge and experiences between hospitals should be encouraged in user-group meetings.
Collapse
Affiliation(s)
- Manon Brekelmans
- Centre for Infectious Diseases Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands.
- Department of Medical Microbiology and Infection Control, University Medical Centre Utrecht, Utrecht, the Netherlands.
| | - Titia Hopmans
- Centre for Infectious Diseases Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands
| | - Maaike van Mourik
- Department of Medical Microbiology and Infection Control, University Medical Centre Utrecht, Utrecht, the Netherlands
| | - Sabine de Greeff
- Centre for Infectious Diseases Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands
| | - Julie Swillens
- Scientific Centre for Quality of Healthcare (IQ Healthcare), Radboud Institute for Health Sciences (RIHS), Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Stephanie van Rooden
- Centre for Infectious Diseases Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands
| |
Collapse
|
5
|
Schults J, Henderson B, Hall L, Havers S. Designing for transparency and trust: Next steps for healthcare associated infection surveillance in Queensland. Infect Dis Health 2024:S2468-0451(24)00030-0. [PMID: 38866603 DOI: 10.1016/j.idh.2024.05.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2024] [Revised: 05/23/2024] [Accepted: 05/26/2024] [Indexed: 06/14/2024]
Affiliation(s)
- Jessica Schults
- Metro North Health, Herston Infectious Diseases Institute, Queensland, Australia; The University of Queensland School of Nursing Midwifery and Social Work, Queensland, Australia.
| | - Belinda Henderson
- Queensland Infection Prevention and Control Unit, Queensland Health, Queensland, Australia
| | - Lisa Hall
- Metro North Health, Herston Infectious Diseases Institute, Queensland, Australia; The University of Queensland, School of Public Health, Queensland, Australia
| | - Sally Havers
- Metro North Health, Herston Infectious Diseases Institute, Queensland, Australia; The University of Queensland School of Nursing Midwifery and Social Work, Queensland, Australia; Darling Downs Health, Queensland, Australia
| |
Collapse
|
6
|
Russo PL, Cheng AC, Asghari-Jafarabadi M, Bucknall T. Comparison of an algorithm, and coding data, with traditional surveillance to identify surgical site infections in Australia: a retrospective multi-centred cohort study. J Hosp Infect 2024; 148:112-118. [PMID: 38615718 DOI: 10.1016/j.jhin.2024.04.001] [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: 02/14/2024] [Revised: 03/13/2024] [Accepted: 04/01/2024] [Indexed: 04/16/2024]
Abstract
BACKGROUND Surveillance of healthcare-associated infections (HAIs) in Australia is disparate, resource intensive, unsustainable, and provides limited information. Traditional HAI surveillance is time intensive and agreement levels between clinicians have been shown to be variable. AIM To compare two methods: a semi-automated algorithm, and coding data, against traditional surgical site infection (SSI) surveillance methods. METHODS This retrospective multi-centre cohort study included all patients undergoing a hip (HPRO) or knee (KPRO) prosthesis and coronary artery bypass graft (CABG) surgery during a two-year period at two large metropolitan hospitals. Routine SSI data were obtained via the infection prevention and control (IPC) team, a previously developed algorithm was applied to all patient records, and the ICD-10-AM data were searched for those categorized as having an SSI. FINDINGS Overall, 1447, 1416, and 1026 patients who underwent HPRO, KPRO, and CABG, respectively, were included. The highest sensitivity values were generated by the algorithm: HPRO deep or organ-space (D/O) 0.87 (95% confidence interval: 0.66-0.96), CABG 0.86 (0.64-0.96), and HPRO all SSI 0.77 (0.57-89); the lowest sensitivity was Code CABG D/O 0.03 (0.00-0.21). The highest PPV values were generated by the algorithm: HPRO D/O 0.97 (0.77-0.99), CABG D/O 0.97 (0.76-0.99), and the Code HPRO D/O 0.9 (0.66-0.99). Both the algorithm and coding data resulted in a substantial reduction in the number of medical records required to review. CONCLUSION The application of algorithms to enhance SSI surveillance demonstrates high accuracy in identifying patient records that require review by IPC teams to determine the presence of an SSI. Coding data alone should not be used to identify SSIs.
Collapse
Affiliation(s)
- P L Russo
- School of Nursing and Midwifery, Monash University, Clayton, Victoria, Australia; Cabrini Health, Malvern, Victoria, Australia.
| | - A C Cheng
- Infectious Diseases, Monash Health, Clayton, Victoria, Australia; School of Clinical Sciences, Monash University, Prahran, Australia
| | - M Asghari-Jafarabadi
- Cabrini Health, Malvern, Victoria, Australia; School of Public Health and Preventive Medicine, Monash University, Prahran, Australia; School of Clinical Sciences, Monash University, Clayton, Victoria, Australia
| | - T Bucknall
- School of Public Health and Preventive Medicine, Monash University, Prahran, Australia; Centre for Quality and Patient Safety Research - Alfred Health Partnership, Melbourne, Victoria, Australia; School of Nursing and Midwifery, Deakin University, Geelong, Victoria, Australia
| |
Collapse
|
7
|
Marschollek M, Marquet M, Reinoso Schiller N, Naim J, Aghdassi SJS, Behnke M, Ehrenberg S, von Landesberger T, Misailovski M, Prasser F, Scherag A, Schlueter D, Wulff A, Pletz M, Scheithauer S. [Automated surveillance and risk prediction with the aim of risk-stratified infection control and prevention (RISK PRINCIPE)]. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2024; 67:685-692. [PMID: 38753020 PMCID: PMC11166781 DOI: 10.1007/s00103-024-03882-w] [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: 12/01/2023] [Accepted: 04/15/2024] [Indexed: 06/12/2024]
Abstract
Healthcare-associated infections (HCAIs) represent an enormous burden for patients, healthcare workers, relatives and society worldwide, including Germany. The central tasks of infection prevention are recording and evaluating infections with the aim of identifying prevention potential and risk factors, taking appropriate measures and finally evaluating them. From an infection prevention perspective, it would be of great value if (i) the recording of infection cases was automated and (ii) if it were possible to identify particularly vulnerable patients and patient groups in advance, who would benefit from specific and/or additional interventions.To achieve this risk-adapted, individualized infection prevention, the RISK PRINCIPE research project develops algorithms and computer-based applications based on standardised, large datasets and incorporates expertise in the field of infection prevention.The project has two objectives: a) to develop and validate a semi-automated surveillance system for hospital-acquired bloodstream infections, prototypically for HCAI, and b) to use comprehensive patient data from different sources to create an individual or group-specific infection risk profile.RISK PRINCIPE is based on bringing together the expertise of medical informatics and infection medicine with a focus on hygiene and draws on information and experience from two consortia (HiGHmed and SMITH) of the German Medical Informatics Initiative (MII), which have been working on use cases in infection medicine for more than five years.
Collapse
Affiliation(s)
- Michael Marschollek
- Peter L. Reichertz Institut für Medizinische Informatik der TU Braunschweig und der Medizinischen Hochschule Hannover, Medizinische Hochschule Hannover, Hannover, Deutschland
| | - Mike Marquet
- Institut für Infektionsmedizin und Krankenhaushygiene, Universitätsklinikum Jena, Jena, Deutschland
| | - Nicolás Reinoso Schiller
- Institut für Krankenhaushygiene und Infektiologie der Universitätsmedizin Göttingen, Robert-Koch-Straße 40, 37075, Göttingen, Deutschland
| | - Joëlle Naim
- Peter L. Reichertz Institut für Medizinische Informatik der TU Braunschweig und der Medizinischen Hochschule Hannover, Medizinische Hochschule Hannover, Hannover, Deutschland
| | | | - Michael Behnke
- Institut für Hygiene und Umweltmedizin der Charité Berlin, Berlin, Deutschland
| | - Sandra Ehrenberg
- Institut für Infektionsmedizin und Krankenhaushygiene, Universitätsklinikum Jena, Jena, Deutschland
| | | | - Martin Misailovski
- Institut für Krankenhaushygiene und Infektiologie der Universitätsmedizin Göttingen, Robert-Koch-Straße 40, 37075, Göttingen, Deutschland
| | - Fabian Prasser
- Medizininformatik, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Deutschland
| | - André Scherag
- Institut für Medizinische Statistik, Informatik und Datenwissenschaften (IMSID), Universitätsklinikum Jena, Jena, Deutschland
| | - Dirk Schlueter
- Institut für Medizinische Mikrobiologie und Krankenhaushygiene, Medizinische Hochschule Hannover, Hannover, Deutschland
| | - Antje Wulff
- Big Data in der Medizin, Department für Versorgungsforschung, Fakultät VI Medizin und Gesundheitswissenschaften, Carl von Ossietzky Universität Oldenburg, Oldenburg, Deutschland
| | - Mathias Pletz
- Institut für Infektionsmedizin und Krankenhaushygiene, Universitätsklinikum Jena, Jena, Deutschland
| | - Simone Scheithauer
- Institut für Krankenhaushygiene und Infektiologie der Universitätsmedizin Göttingen, Robert-Koch-Straße 40, 37075, Göttingen, Deutschland.
| |
Collapse
|
8
|
Leekha S, Robinson GL, Jacob JT, Fridkin S, Shane A, Sick-Samuels A, Milstone AM, Nair R, Perencevich E, Puig-Asensio M, Kobayashi T, Mayer J, Lewis J, Bleasdale S, Wenzler E, Mena Lora AJ, Baghdadi J, Schrank GM, Wilber E, Aldredge AA, Sharp J, Dyer KE, Kendrick L, Ambalam V, Borgetti S, Carmack A, Gushiken A, Patel A, Reddy S, Brown CH, Dantes RB, Harris AD. Evaluation of hospital-onset bacteraemia and fungaemia in the USA as a potential healthcare quality measure: a cross-sectional study. BMJ Qual Saf 2024:bmjqs-2023-016831. [PMID: 38782579 DOI: 10.1136/bmjqs-2023-016831] [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: 10/19/2023] [Accepted: 03/01/2024] [Indexed: 05/25/2024]
Abstract
BACKGROUND Hospital-onset bacteraemia and fungaemia (HOB) is being explored as a surveillance and quality metric. The objectives of the current study were to determine sources and preventability of HOB in hospitalised patients in the USA and to identify factors associated with perceived preventability. METHODS We conducted a cross-sectional study of HOB events at 10 academic and three community hospitals using structured chart review. HOB was defined as a blood culture on or after hospital day 4 with growth of one or more bacterial or fungal organisms. HOB events were stratified by commensal and non-commensal organisms. Medical resident physicians, infectious disease fellows or infection preventionists reviewed charts to determine HOB source, and infectious disease physicians with training in infection prevention/hospital epidemiology rated preventability from 1 to 6 (1=definitely preventable to 6=definitely not preventable) using a structured guide. Ratings of 1-3 were collectively considered 'potentially preventable' and 4-6 'potentially not preventable'. RESULTS Among 1789 HOB events with non-commensal organisms, gastrointestinal (including neutropenic translocation) (35%) and endovascular (32%) were the most common sources. Overall, 636/1789 (36%) non-commensal and 238/320 (74%) commensal HOB events were rated potentially preventable. In logistic regression analysis among non-commensal HOB events, events attributed to intravascular catheter-related infection, indwelling urinary catheter-related infection and surgical site infection had higher odds of being rated preventable while events with neutropenia, immunosuppression, gastrointestinal sources, polymicrobial cultures and previous positive blood culture in the same admission had lower odds of being rated preventable, compared with events without those attributes. Of 636 potentially preventable non-commensal HOB events, 47% were endovascular in origin, followed by gastrointestinal, respiratory and urinary sources; approximately 40% of those events would not be captured through existing healthcare-associated infection surveillance. DISCUSSION Factors identified as associated with higher or lower preventability should be used to guide inclusion, exclusion and risk adjustment for an HOB-related quality metric.
Collapse
Affiliation(s)
- Surbhi Leekha
- Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Gwen L Robinson
- Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Jesse T Jacob
- Department of Medicine, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Scott Fridkin
- Department of Medicine, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Andi Shane
- Department of Pediatrics, Emory University, Atlanta, Georgia, USA
| | - Anna Sick-Samuels
- Department of Pediatrics, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Aaron M Milstone
- Department of Pediatrics, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Rajeshwari Nair
- Department of Internal Medicine, University of Iowa Hospitals and Clinics, Iowa City, Iowa, USA
| | - Eli Perencevich
- Department of Internal Medicine, University of Iowa Hospitals and Clinics, Iowa City, Iowa, USA
| | - Mireia Puig-Asensio
- Department of Internal Medicine, University of Iowa Hospitals and Clinics, Iowa City, Iowa, USA
| | - Takaaki Kobayashi
- Department of Internal Medicine, University of Iowa Hospitals and Clinics, Iowa City, Iowa, USA
| | - Jeanmarie Mayer
- Department of Internal Medicine, University of Utah Health, Salt Lake City, Utah, USA
| | - Julia Lewis
- Department of Internal Medicine, University of Utah Health, Salt Lake City, Utah, USA
| | - Susan Bleasdale
- Department of Medicine, University of Illinois College of Medicine, Chicago, Illinois, USA
| | - Eric Wenzler
- Department of Medicine, University of Illinois College of Medicine, Chicago, Illinois, USA
| | - Alfredo J Mena Lora
- Department of Medicine, University of Illinois College of Medicine, Chicago, Illinois, USA
| | - Jonathan Baghdadi
- Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Gregory M Schrank
- Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Eli Wilber
- Department of Medicine, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Amalia A Aldredge
- Department of Medicine, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Joseph Sharp
- Department of Medicine, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Kelly E Dyer
- Department of Medicine, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Lea Kendrick
- Children's Healthcare of Atlanta, Atlanta, Georgia, USA
| | - Viraj Ambalam
- Department of Pediatrics, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Scott Borgetti
- Department of Medicine, University of Illinois College of Medicine, Chicago, Illinois, USA
| | - Anna Carmack
- Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Alexis Gushiken
- Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Ashka Patel
- Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Sujan Reddy
- Divison of Healthcare Quality Promotion, Nationation Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Clayton H Brown
- Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Raymund B Dantes
- Department of Medicine, Emory University School of Medicine, Atlanta, Georgia, USA
- Divison of Healthcare Quality Promotion, Nationation Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Anthony D Harris
- Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland, USA
| |
Collapse
|
9
|
Catho G, Fortchantre L, Teixeira D, Galas-Haddad M, Boroli F, Chraïti MN, Abbas M, Harbarth S, Buetti N. Surveillance of catheter-associated bloodstream infections: development and validation of a fully automated algorithm. Antimicrob Resist Infect Control 2024; 13:38. [PMID: 38600526 PMCID: PMC11007875 DOI: 10.1186/s13756-024-01395-4] [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: 11/26/2023] [Accepted: 04/01/2024] [Indexed: 04/12/2024] Open
Abstract
BACKGROUND Most surveillance systems for catheter-related bloodstream infections (CRBSI) and central line-associated bloodstream infections (CLABSI) are based on manual chart review. Our objective was to validate a fully automated algorithm for CRBSI and CLABSI surveillance in intensive care units (ICU). METHODS We developed a fully automated algorithm to detect CRBSI, CLABSI and ICU-onset bloodstream infections (ICU-BSI) in patients admitted to the ICU of a tertiary care hospital in Switzerland. The parameters included in the algorithm were based on a recently performed systematic review. Structured data on demographics, administrative data, central vascular catheter and microbiological results (blood cultures and other clinical cultures) obtained from the hospital's data warehouse were processed by the algorithm. Validation for CRBSI was performed by comparing results with prospective manual BSI surveillance data over a 6-year period. CLABSI were retrospectively assessed over a 2-year period. RESULTS From January 2016 to December 2021, 854 positive blood cultures were identified in 346 ICU patients. The median age was 61.7 years [IQR 50-70]; 205 (24%) positive samples were collected from female patients. The algorithm detected 5 CRBSI, 109 CLABSI and 280 ICU-BSI. The overall CRBSI and CLABSI incidence rates determined by automated surveillance for the period 2016 to 2021 were 0.18/1000 catheter-days (95% CI 0.06-0.41) and 3.86/1000 catheter days (95% CI: 3.17-4.65). The sensitivity, specificity, positive predictive and negative predictive values of the algorithm for CRBSI, were 83% (95% CI 43.7-96.9), 100% (95% CI 99.5-100), 100% (95% CI 56.5-100), and 99.9% (95% CI 99.2-100), respectively. One CRBSI was misclassified as an ICU-BSI by the algorithm because the same bacterium was identified in the blood culture and in a lower respiratory tract specimen. Manual review of CLABSI from January 2020 to December 2021 (n = 51) did not identify any errors in the algorithm. CONCLUSIONS A fully automated algorithm for CRBSI and CLABSI detection in critically-ill patients using only structured data provided valid results. The next step will be to assess the feasibility and external validity of implementing it in several hospitals with different electronic health record systems.
Collapse
Affiliation(s)
- Gaud Catho
- Infection Control Programme and World Health Organization Collaborating Centre, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland.
- Infectious Diseases Division, Central Institute, Valais Hospital, Sion, Switzerland.
| | - Loïc Fortchantre
- Infection Control Programme and World Health Organization Collaborating Centre, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland
| | - Daniel Teixeira
- Infection Control Programme and World Health Organization Collaborating Centre, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland
| | - Murielle Galas-Haddad
- Infection Control Programme and World Health Organization Collaborating Centre, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland
| | - Filippo Boroli
- Intensive Care Unit Division, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland
| | - Marie-Noëlle Chraïti
- Infection Control Programme and World Health Organization Collaborating Centre, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland
| | - Mohamed Abbas
- Infection Control Programme and World Health Organization Collaborating Centre, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK
| | - Stephan Harbarth
- Infection Control Programme and World Health Organization Collaborating Centre, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland
| | - Niccolò Buetti
- Infection Control Programme and World Health Organization Collaborating Centre, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland
- INSERM, IAME, Université Paris-Cité, Paris, 75006, France
| |
Collapse
|
10
|
Silva AR, Hoffmann NG, Fernandez-Llimos F, Lima EC. Data quality review of the Brazilian nosocomial infections surveillance system. J Infect Public Health 2024; 17:687-695. [PMID: 38471259 DOI: 10.1016/j.jiph.2024.02.013] [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: 08/07/2023] [Revised: 01/29/2024] [Accepted: 02/21/2024] [Indexed: 03/14/2024] Open
Abstract
BACKGROUND Surveillance of healthcare-associated infections (HAIs) is an essential component of hospital infection prevention and control systems. We aimed to assess the quality of the data compiled by the Brazilian HAI Surveillance System from pediatric (PICUs) and neonatal intensive care units (NICUs), between 2012 and 2021. METHODS Data Quality Review, including adherence, completeness, internal consistency, consistency over time, and consistency of population trend, were computed at both national and state levels based on quality metrics from World Health Organization Toolkit. Incidence rates (or incidence density) of ventilator-associated pneumonia (VAP) and central line-associated bloodstream infection (CLABSI) were obtained from the Brazilian National Nosocomial Infections Surveillance (NNIS) system. Data on sepsis-related mortality, spanning the period from 2012 to 2021, were extracted from the Brazilian National Health Service database (DATASUS). Additionally, correlations between sepsis-related mortality and incidence rates of VAP or CLABSI were calculated. RESULTS Throughout the majority of the study period, adherence to VAP reporting remained below 75%, exhibiting a positive trend post-2016. Widespread outliers, as well as inconsistencies over time and in population trends, were evident across all 27 states. Only four states maintained consistent adherence levels above 75% for more than 8 years regarding HAI incidence rates. Notably, CLABSI in NICUs boasted the highest reporting adherence among all HAIs, with 148 periods out of 270 (54.8%) exhibiting reporting adherence surpassing 75%. Three states achieved commendable metrics for CLABSI in PICUs, while five states demonstrated favorable results for CLABSI in NICUs. CONCLUSIONS While adherence to HAI report is improving among Brazilian states, an important room for improvement in the Brazilian NNIS exists. Additional efforts should be made by the Brazilian government to improve the reliability of HAI data, which could serve as valuable guidance for hospital infection prevention and control policies.
Collapse
Affiliation(s)
- Alice Ramos Silva
- Pharmacy School, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.
| | | | - Fernando Fernandez-Llimos
- Applied Molecular Biosciences Unit (UCIBIO), Laboratory of Pharmacology, Faculty of Pharmacy, University of Porto, Porto, Portugal.
| | | |
Collapse
|
11
|
Wolfensberger A, Scherrer AU, Sax H. Automated surveillance of non-ventilator-associated hospital-acquired pneumonia (nvHAP): a systematic literature review. Antimicrob Resist Infect Control 2024; 13:30. [PMID: 38449045 PMCID: PMC10918924 DOI: 10.1186/s13756-024-01375-8] [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: 11/01/2023] [Accepted: 01/31/2024] [Indexed: 03/08/2024] Open
Abstract
BACKGROUND Hospital-acquired pneumonia (HAP) and its specific subset, non-ventilator hospital-acquired pneumonia (nvHAP) are significant contributors to patient morbidity and mortality. Automated surveillance systems for these healthcare-associated infections have emerged as a potentially beneficial replacement for manual surveillance. This systematic review aims to synthesise the existing literature on the characteristics and performance of automated nvHAP and HAP surveillance systems. METHODS We conducted a systematic search of publications describing automated surveillance of nvHAP and HAP. Our inclusion criteria covered articles that described fully and semi-automated systems without limitations on patient demographics or healthcare settings. We detailed the algorithms in each study and reported the performance characteristics of automated systems that were validated against specific reference methods. Two published metrics were employed to assess the quality of the included studies. RESULTS Our review identified 12 eligible studies that collectively describe 24 distinct candidate definitions, 23 for fully automated systems and one for a semi-automated system. These systems were employed exclusively in high-income countries and the majority were published after 2018. The algorithms commonly included radiology, leukocyte counts, temperature, antibiotic administration, and microbiology results. Validated surveillance systems' performance varied, with sensitivities for fully automated systems ranging from 40 to 99%, specificities from 58 and 98%, and positive predictive values from 8 to 71%. Validation was often carried out on small, pre-selected patient populations. CONCLUSIONS Recent years have seen a steep increase in publications on automated surveillance systems for nvHAP and HAP, which increase efficiency and reduce manual workload. However, the performance of fully automated surveillance remains moderate when compared to manual surveillance. The considerable heterogeneity in candidate surveillance definitions and reference standards, as well as validation on small or pre-selected samples, limits the generalisability of the findings. Further research, involving larger and broader patient populations is required to better understand the performance and applicability of automated nvHAP surveillance.
Collapse
Affiliation(s)
- Aline Wolfensberger
- Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, Rämistrasse 100, 8091, Zurich, Switzerland.
- Institute for Implementation Science in Healthcare, University of Zurich, Zurich, Switzerland.
| | - Alexandra U Scherrer
- Department of Infectious Diseases, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Hugo Sax
- Department of Infectious Diseases, Bern University Hospital and University of Bern, Bern, Switzerland
| |
Collapse
|
12
|
Lotfinejad N, Januel JM, Tschudin-Sutter S, Schreiber PW, Grandbastien B, Damonti L, Lo Priore E, Scherrer A, Harbarth S, Catho G, Buetti N. Systematic scoping review of automated systems for the surveillance of healthcare-associated bloodstream infections related to intravascular catheters. Antimicrob Resist Infect Control 2024; 13:25. [PMID: 38419046 PMCID: PMC10903068 DOI: 10.1186/s13756-024-01380-x] [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: 11/27/2023] [Accepted: 02/08/2024] [Indexed: 03/02/2024] Open
Abstract
INTRODUCTION Intravascular catheters are crucial devices in medical practice that increase the risk of healthcare-associated infections (HAIs), and related health-economic adverse outcomes. This scoping review aims to provide a comprehensive overview of published automated algorithms for surveillance of catheter-related bloodstream infections (CRBSI) and central line-associated bloodstream infections (CLABSI). METHODS We performed a scoping review based on a systematic search of the literature in PubMed and EMBASE from 1 January 2000 to 31 December 2021. Studies were included if they evaluated predictive performance of automated surveillance algorithms for CLABSI/CRBSI detection and used manually collected surveillance data as reference. We assessed the design of the automated systems, including the definitions used to develop algorithms (CLABSI versus CRBSI), the datasets and denominators used, and the algorithms evaluated in each of the studies. RESULTS We screened 586 studies based on title and abstract, and 99 were assessed based on full text. Nine studies were included in the scoping review. Most studies were monocentric (n = 5), and they identified CLABSI (n = 7) as an outcome. The majority of the studies used administrative and microbiological data (n = 9) and five studies included the presence of a vascular central line in their automated system. Six studies explained the denominator they selected, five of which chose central line-days. The most common rules and steps used in the algorithms were categorized as hospital-acquired rules, infection rules (infection versus contamination), deduplication, episode grouping, secondary BSI rules (secondary versus primary BSI), and catheter-associated rules. CONCLUSION The automated surveillance systems that we identified were heterogeneous in terms of definitions, datasets and denominators used, with a combination of rules in each algorithm. Further guidelines and studies are needed to develop and implement algorithms to detect CLABSI/CRBSI, with standardized definitions, appropriate data sources and suitable denominators.
Collapse
Affiliation(s)
- Nasim Lotfinejad
- Infection Control Program and WHO Collaborating Centre, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland.
| | - Jean-Marie Januel
- Infection Control Program and WHO Collaborating Centre, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland
| | - Sarah Tschudin-Sutter
- Division of Infectious Diseases & Hospital Epidemiology, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Peter W Schreiber
- Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Bruno Grandbastien
- Infection Prevention and Control Unit, Service of Infectious Disease, Lausanne University Hospital, Lausanne, Switzerland
| | - Lauro Damonti
- Department of Infectious Diseases, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Elia Lo Priore
- Department of Infectious Diseases and Hospital Epidemiology, EOC Regional Hospital of Lugano, Lugano, Switzerland
| | | | - Stephan Harbarth
- Infection Control Program and WHO Collaborating Centre, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland
| | - Gaud Catho
- Infection Control Program and WHO Collaborating Centre, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland
- Division of Infectious Diseases, Central Institute, Valais Hospital, Sion, Switzerland
| | - Niccolò Buetti
- Infection Control Program and WHO Collaborating Centre, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland
- Université Paris-Cité, INSERM, IAME UMR 1137 , Paris, 75018, France
| |
Collapse
|
13
|
Karmefors Idvall M, Tanushi H, Berge A, Nauclér P, van der Werff SD. The accuracy of fully-automated algorithms for the surveillance of central venous catheter-related bloodstream infection in hospitalised patients. Antimicrob Resist Infect Control 2024; 13:15. [PMID: 38317207 PMCID: PMC10840273 DOI: 10.1186/s13756-024-01373-w] [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: 11/13/2023] [Accepted: 01/26/2024] [Indexed: 02/07/2024] Open
Abstract
BACKGROUND Continuous surveillance for healthcare-associated infections such as central venous catheter-related bloodstream infections (CVC-BSI) is crucial for prevention. However, traditional surveillance methods are resource-intensive and prone to bias. This study aimed to develop and validate fully-automated surveillance algorithms for CVC-BSI. METHODS Two algorithms were developed using electronic health record data from 1000 admissions with a positive blood culture (BCx) at Karolinska University Hospital from 2017: (1) Combining microbiological findings in BCx and CVC cultures with BSI symptoms; (2) Only using microbiological findings. These algorithms were validated in 5170 potential CVC-BSI-episodes from all admissions in 2018-2019, and results extrapolated to all potential CVC-BSI-episodes within this period (n = 181,354). The reference standard was manual record review according to ECDC's definition of microbiologically confirmed CVC-BSI (CRI3-CVC). RESULTS In the potential CVC-BSI-episodes, 51 fulfilled ECDC's definition and the algorithms identified 47 and 49 episodes as CVC-BSI, respectively. Both algorithms performed well in assessing CVC-BSI. Overall, algorithm 2 performed slightly better with in the total period a sensitivity of 0.880 (95%-CI 0.783-0.959), specificity of 1.000 (95%-CI 0.999-1.000), PPV of 0.918 (95%-CI 0.833-0.981) and NPV of 1.000 (95%-CI 0.999-1.000). Incidence according to the reference and algorithm 2 was 0.33 and 0.31 per 1000 in-patient hospital-days, respectively. CONCLUSIONS Both fully-automated surveillance algorithms for CVC-BSI performed well and could effectively replace manual surveillance. The simpler algorithm, using only microbiology data, is suitable when BCx testing adheres to recommendations, otherwise the algorithm using symptom data might be required. Further validation in other settings is necessary to assess the algorithms' generalisability.
Collapse
Affiliation(s)
- Moa Karmefors Idvall
- Department of Medicine Solna, Division of Infectious Diseases, Karolinska Institutet, 171 77, Stockholm, Sweden
| | - Hideyuki Tanushi
- Department of Medicine Solna, Division of Infectious Diseases, Karolinska Institutet, 171 77, Stockholm, Sweden
- Department of Data Processing and Analysis, Karolinska University Hospital, Stockholm, Sweden
| | - Andreas Berge
- Department of Medicine Solna, Division of Infectious Diseases, Karolinska Institutet, 171 77, Stockholm, Sweden
- Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden
| | - Pontus Nauclér
- Department of Medicine Solna, Division of Infectious Diseases, Karolinska Institutet, 171 77, Stockholm, Sweden
- Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden
| | - Suzanne Desirée van der Werff
- Department of Medicine Solna, Division of Infectious Diseases, Karolinska Institutet, 171 77, Stockholm, Sweden.
- Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden.
| |
Collapse
|
14
|
Halonen K, van der Kooi T, Hertogh C, Haenen A, de Greeff SC. Prevalence of healthcare-associated infections in Dutch long-term care facilities from 2009 to 2019. J Hosp Infect 2024; 143:150-159. [PMID: 37321412 DOI: 10.1016/j.jhin.2023.06.008] [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: 02/24/2023] [Revised: 06/02/2023] [Accepted: 06/04/2023] [Indexed: 06/17/2023]
Abstract
OBJECTIVE We assessed trends in the prevalence of healthcare-associated infections (HCAIs) and associated resident and facility characteristics in a national network of long-term care facilities (LTCFs) in the Netherlands from 2009 to 2019. METHODS Participating LTCFs registered the prevalence of urinary tract infection (UTI), lower respiratory tract infection (LRTI), gastrointestinal infection (GI), bacterial conjunctivitis, sepsis and skin infection, using standardized definitions, in biannual point-prevalence surveys (PPSs). In addition, resident and LTCF characteristics were collected. Multi-level analyses were performed to study changes in the HCAI prevalence over time and to identify resident and LTCF-related risk factors. Analyses were performed for HCAIs overall and for UTI, LRTI and GI combined as these were recorded throughout the period. RESULTS Overall, 1353 HCAIs were registered in 44,551 residents with a prevalence of 3.0% (95% confidence interval: 2.8-3.1; range between years 2.3-5.1%). When including only UTI, LRTI and GI the prevalence decreased from 5.0% in 2009 to 2.1% in 2019. Multi-variable regression analyses for UTI, LRTI and GI combined indicated that both prolonged participation and calendar time were independently associated with HCAI prevalence; in LTCFs that participated ≥4 years, the HCAI risk was decreased (OR 0.72 (0.57-0.92)) compared with the first year, and the OR per calendar year was 0.93 (0.88-0.97). CONCLUSIONS Over 11 years of PPS in LTCFs the HCAI prevalence decreased over time. Prolonged participation further reduced the HCAI prevalence, in particular UTIs, despite the increasing age and associated frailty of the LTCF population, illustrating the potential value of surveillance.
Collapse
Affiliation(s)
- K Halonen
- National Institute for Public Health and the Environment, Centre for Infectious Disease Research, Epidemiology and Surveillance, Bilthoven, the Netherlands.
| | - T van der Kooi
- National Institute for Public Health and the Environment, Centre for Infectious Disease Research, Epidemiology and Surveillance, Bilthoven, the Netherlands
| | - C Hertogh
- Department of Medicine for Older People, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, the Netherlands; Amsterdam Public Health Research Institute, Aging & Later Life, Amsterdam, the Netherlands
| | - A Haenen
- National Institute for Public Health and the Environment, Centre for Infectious Disease Research, Epidemiology and Surveillance, Bilthoven, the Netherlands
| | - S C de Greeff
- National Institute for Public Health and the Environment, Centre for Infectious Disease Research, Epidemiology and Surveillance, Bilthoven, the Netherlands
| |
Collapse
|
15
|
Verberk JDM, van der Werff SD, Weegar R, Henriksson A, Richir MC, Buchli C, van Mourik MSM, Nauclér P. The augmented value of using clinical notes in semi-automated surveillance of deep surgical site infections after colorectal surgery. Antimicrob Resist Infect Control 2023; 12:117. [PMID: 37884948 PMCID: PMC10604406 DOI: 10.1186/s13756-023-01316-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 09/25/2023] [Indexed: 10/28/2023] Open
Abstract
BACKGROUND In patients who underwent colorectal surgery, an existing semi-automated surveillance algorithm based on structured data achieves high sensitivity in detecting deep surgical site infections (SSI), however, generates a significant number of false positives. The inclusion of unstructured, clinical narratives to the algorithm may decrease the number of patients requiring manual chart review. The aim of this study was to investigate the performance of this semi-automated surveillance algorithm augmented with a natural language processing (NLP) component to improve positive predictive value (PPV) and thus workload reduction (WR). METHODS Retrospective, observational cohort study in patients who underwent colorectal surgery from January 1, 2015, through September 30, 2020. NLP was used to detect keyword counts in clinical notes. Several NLP-algorithms were developed with different count input types and classifiers, and added as component to the original semi-automated algorithm. Traditional manual surveillance was compared with the NLP-augmented surveillance algorithms and sensitivity, specificity, PPV and WR were calculated. RESULTS From the NLP-augmented models, the decision tree models with discretized counts or binary counts had the best performance (sensitivity 95.1% (95%CI 83.5-99.4%), WR 60.9%) and improved PPV and WR by only 2.6% and 3.6%, respectively, compared to the original algorithm. CONCLUSIONS The addition of an NLP component to the existing algorithm had modest effect on WR (decrease of 1.4-12.5%), at the cost of sensitivity. For future implementation it will be a trade-off between optimal case-finding techniques versus practical considerations such as acceptability and availability of resources.
Collapse
Affiliation(s)
- Janneke D M Verberk
- Department of Medical Microbiology and Infection Prevention, University Medical Centre Utrecht, Utrecht, the Netherlands
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, the Netherlands
- Department of Epidemiology and Surveillance, Centre for Infectious Diseases Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands
| | - Suzanne D van der Werff
- Department of Medicine Solna, Division of Infectious Diseases, Karolinska Institutet, Stockholm, Sweden.
- Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden.
| | - Rebecka Weegar
- Department of Computer and Systems Sciences, Stockholm University, Stockholm, Sweden
| | - Aron Henriksson
- Department of Computer and Systems Sciences, Stockholm University, Stockholm, Sweden
| | - Milan C Richir
- Department of Surgery, Cancer Centre, University Medical Centre Utrecht, Utrecht, the Netherlands
| | - Christian Buchli
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
- Department of Pelvic Cancer, GI Oncology and Colorectal Surgery Unit, Karolinska University Hospital, Stockholm, Sweden
| | - Maaike S M van Mourik
- Department of Medical Microbiology and Infection Prevention, University Medical Centre Utrecht, Utrecht, the Netherlands
| | - Pontus Nauclér
- Department of Medicine Solna, Division of Infectious Diseases, Karolinska Institutet, Stockholm, Sweden
- Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden
| |
Collapse
|
16
|
El-Saed A, Othman F, AlMohrij S, Abanmi M, Tannous E, Alshamrani MM. Coverage and methods of surveillance of healthcare-associated infections in Middle Eastern and North African countries. Am J Infect Control 2023; 51:1151-1156. [PMID: 36931506 DOI: 10.1016/j.ajic.2023.03.004] [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: 12/29/2022] [Revised: 03/05/2023] [Accepted: 03/06/2023] [Indexed: 03/17/2023]
Abstract
BACKGROUND Surveillance of healthcare-associated infections (HAIs) is a cornerstone for effective infection prevention and control (IPC) programs. The objective was to evaluate the coverage and methods of HAI surveillance in Middle Eastern and North African (MENA) countries. METHODS A cross-sectional study targeted IPC staff working in MENA countries using the Infection Control Network electronic database of the Arab countries. The study focused on self-reported surveillance-related characteristics of IPC staff, facilities, and the IPC program. RESULTS A total of 269 IPC staff were included. They were mainly females (68%), nurses (63%), and working in GCC countries (83%). Approximately 69% of covered facilities had surveillance activities. Hand hygiene, multidrug-resistant organisms, central line-associated bloodstream infections, and catheter-associated urinary tract infections were the most common surveillance activities (>90%). The surveillance workload consumed 27% of the average weekly working time. The scores of performing multiple surveillance, with appropriate methods and tools, were 83%, 67%, and 61% (respectively). Appropriate surveillance methods and/or tools were linked to GCC region, CBIC qualifications, surveillance training, specific setting (acute care and long term), staff-to-bed ratio, presence and active function of IPC committee, presence of IPC annual plan, communications with health care workers, and leadership support. CONCLUSIONS While most health care facilities in the MENA region perform multiple surveillance, surveillance methods and tools are still suboptimal and their optimization should be a priority.
Collapse
Affiliation(s)
- Aiman El-Saed
- Infection Prevention and Control Department, King Abdulaziz Medical City, Riyadh, Saudi Arabia; Community Medicine Department, Faculty of Medicine, Mansoura University, Mansoura, Egypt; The Arab Countries Infection Control Network (AcicN), Abu Dhabi, United Arab Emirates
| | - Fatmah Othman
- College of Medicine, King Saud Bin Abdul Aziz University for Health Science, Riyadh, Saudi Arabia; King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
| | - Saud AlMohrij
- College of Medicine, University of Almaarefa, Riyadh, Saudi Arabia
| | - Mohammed Abanmi
- College of Medicine, King Saud Bin Abdul Aziz University for Health Science, Riyadh, Saudi Arabia
| | - Elias Tannous
- The Arab Countries Infection Control Network (AcicN), Abu Dhabi, United Arab Emirates; Infection Prevention and Control, Cleveland Clinic Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - Majid M Alshamrani
- Infection Prevention and Control Department, King Abdulaziz Medical City, Riyadh, Saudi Arabia; College of Medicine, King Saud Bin Abdul Aziz University for Health Science, Riyadh, Saudi Arabia.
| |
Collapse
|
17
|
van der Werff SD, Verberk JDM, Buchli C, van Mourik MSM, Nauclér P. External validation of semi-automated surveillance algorithms for deep surgical site infections after colorectal surgery in an independent country. Antimicrob Resist Infect Control 2023; 12:96. [PMID: 37679824 PMCID: PMC10485951 DOI: 10.1186/s13756-023-01288-y] [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: 11/28/2022] [Accepted: 08/12/2023] [Indexed: 09/09/2023] Open
Abstract
BACKGROUND Automated surveillance methods that re-use electronic health record data are considered an attractive alternative to traditional manual surveillance. However, surveillance algorithms need to be thoroughly validated before being implemented in a clinical setting. With semi-automated surveillance patients are classified as low or high probability of having developed infection, and only high probability patients subsequently undergo manual record review. The aim of this study was to externally validate two existing semi-automated surveillance algorithms for deep SSI after colorectal surgery, developed on Spanish and Dutch data, in a Swedish setting. METHODS The algorithms were validated in 225 randomly selected surgeries from Karolinska University Hospital from the period January 1, 2015 until August 31, 2020. Both algorithms were based on (re)admission and discharge data, mortality, reoperations, radiology orders, and antibiotic prescriptions, while one additionally used microbiology cultures. SSI was based on ECDC definitions. Sensitivity, specificity, positive predictive value, negative predictive value, and workload reduction were assessed compared to manual surveillance. RESULTS Both algorithms performed well, yet the algorithm not relying on microbiological culture data had highest sensitivity (97.6, 95%CI: 87.4-99.6), which was comparable to previously published results. The latter algorithm aligned best with clinical practice and would lead to 57% records less to review. CONCLUSIONS The results highlight the importance of thorough validation before implementation in other clinical settings than in which algorithms were originally developed: the algorithm excluding microbiology cultures had highest sensitivity in this new setting and has the potential to support large-scale semi-automated surveillance of SSI after colorectal surgery.
Collapse
Affiliation(s)
- Suzanne D van der Werff
- Department of Medicine Solna, Division of Infectious Diseases, Karolinska Institutet, Stockholm, 171 77, Sweden.
- Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden.
| | - Janneke D M Verberk
- Department of Medical Microbiology and Infection Prevention, University Medical Centre Utrecht, Utrecht, the Netherlands
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, the Netherlands
- Department of Epidemiology and Surveillance, Centre for Infectious Diseases Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands
| | - Christian Buchli
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
- Department of Pelvic Cancer, GI Oncology and Colorectal Surgery Unit, Karolinska University Hospital, Stockholm, Sweden
| | - Maaike S M van Mourik
- Department of Medical Microbiology and Infection Prevention, University Medical Centre Utrecht, Utrecht, the Netherlands
| | - Pontus Nauclér
- Department of Medicine Solna, Division of Infectious Diseases, Karolinska Institutet, Stockholm, 171 77, Sweden
- Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden
| |
Collapse
|
18
|
Gravningen K, Nymark P, Wyller TB, Kacelnik O. A new automated national register-based surveillance system for outbreaks in long-term care facilities in Norway detected three times more severe acute respiratory coronavirus virus 2 (SARS-CoV-2) clusters than traditional methods. Infect Control Hosp Epidemiol 2023; 44:1451-1457. [PMID: 36524319 PMCID: PMC10507514 DOI: 10.1017/ice.2022.297] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 11/04/2022] [Accepted: 11/09/2022] [Indexed: 12/23/2022]
Abstract
OBJECTIVE To develop and test a new automated surveillance system that can detect, define and characterize infection clusters, including coronavirus disease 2019 (COVID-19), in long-term care facilities (LTCFs) in Norway by combining existing national register data. BACKGROUND The numerous outbreaks in LTCFs during the COVID-19 pandemic highlighted the need for accurate and timely outbreak surveillance. As traditional methods were inadequate, we used severe acute respiratory coronavirus virus 2 (SARS-CoV-2) as a model to test automated surveillance. METHODS We conducted a nationwide study using data from the Norwegian preparedness register (Beredt C19) and defined the study population as an open cohort from January 2020 to December 2021. We analyzed clusters (≥3 individuals with positive SARS-CoV-2 test ≤14 days) by 4-month periods including cluster size, duration and composition, and residents' mortality associated with clusters. RESULTS The study population included 173,907 individuals; 78% employees and 22% residents. Clusters were detected in 427 (43%) of 993 LTCFs. The median cluster size was 4-8 individuals (maximum, 50) by 4-month periods, with a median duration of 9-17 days. Employees represented 60%-82% of cases in clusters and were index cases in 60%-90%. In the last 4-month period of 2020, we detected 107 clusters (915 cases) versus 428 clusters (2,998 cases) in the last period of 2021. The 14-day all-cause mortality rate was higher in resident cases from the clusters. Varying the cluster definitions changed the number of clusters. CONCLUSION Automated national surveillance for SARS-CoV-2 clusters in LTCFs is possible based on existing data sources and provides near real-time detailed information on size, duration, and composition of clusters. Thus, this system can assist in early outbreak detection and improve surveillance.
Collapse
Affiliation(s)
- Kirsten Gravningen
- Department of Infection Prevention and Preparedness, Norwegian Institute of Public Health (NIPH), Oslo, Norway
- Department of Microbiology and Infection Control, Akershus University Hospital, Nordbyhagen, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Petter Nymark
- Department of Infection Prevention and Preparedness, Norwegian Institute of Public Health (NIPH), Oslo, Norway
| | - Torgeir B. Wyller
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway
| | - Oliver Kacelnik
- Department of Infection Prevention and Preparedness, Norwegian Institute of Public Health (NIPH), Oslo, Norway
| |
Collapse
|
19
|
Januel JM, Lotfinejad N, Grant R, Tschudin-Sutter S, Schreiber PW, Grandbastien B, Jent P, Lo Priore E, Scherrer A, Harbarth S, Catho G, Buetti N. Predictive performance of automated surveillance algorithms for intravascular catheter bloodstream infections: a systematic review and meta-analysis. Antimicrob Resist Infect Control 2023; 12:87. [PMID: 37653559 PMCID: PMC10468855 DOI: 10.1186/s13756-023-01286-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 08/09/2023] [Indexed: 09/02/2023] Open
Abstract
BACKGROUND Intravascular catheter infections are associated with adverse clinical outcomes. However, a significant proportion of these infections are preventable. Evaluations of the performance of automated surveillance systems for adequate monitoring of central-line associated bloodstream infection (CLABSI) or catheter-related bloodstream infection (CRBSI) are limited. OBJECTIVES We evaluated the predictive performance of automated algorithms for CLABSI/CRBSI detection, and investigated which parameters included in automated algorithms provide the greatest accuracy for CLABSI/CRBSI detection. METHODS We performed a meta-analysis based on a systematic search of published studies in PubMed and EMBASE from 1 January 2000 to 31 December 2021. We included studies that evaluated predictive performance of automated surveillance algorithms for CLABSI/CRBSI detection and used manually collected surveillance data as reference. We estimated the pooled sensitivity and specificity of algorithms for accuracy and performed a univariable meta-regression of the different parameters used across algorithms. RESULTS The search identified five full text studies and 32 different algorithms or study populations were included in the meta-analysis. All studies analysed central venous catheters and identified CLABSI or CRBSI as an outcome. Pooled sensitivity and specificity of automated surveillance algorithm were 0.88 [95%CI 0.84-0.91] and 0.86 [95%CI 0.79-0.92] with significant heterogeneity (I2 = 91.9, p < 0.001 and I2 = 99.2, p < 0.001, respectively). In meta-regression, algorithms that include results of microbiological cultures from specific specimens (respiratory, urine and wound) to exclude non-CRBSI had higher specificity estimates (0.92, 95%CI 0.88-0.96) than algorithms that include results of microbiological cultures from any other body sites (0.88, 95% CI 0.81-0.95). The addition of clinical signs as a predictor did not improve performance of these algorithms with similar specificity estimates (0.92, 95%CI 0.88-0.96). CONCLUSIONS Performance of automated algorithms for detection of intravascular catheter infections in comparison to manual surveillance seems encouraging. The development of automated algorithms should consider the inclusion of results of microbiological cultures from specific specimens to exclude non-CRBSI, while the inclusion of clinical data may not have an added-value. Trail Registration Prospectively registered with International prospective register of systematic reviews (PROSPERO ID CRD42022299641; January 21, 2022). https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022299641.
Collapse
Affiliation(s)
- Jean-Marie Januel
- Infection Control Program and WHO Collaborating Centre, Geneva University Hospitals and Faculty of Medicine, Service PCI, Rue Gabrielle-Perret-Gentil 4, 1205, Geneve, Switzerland.
| | - Nasim Lotfinejad
- Infection Control Program and WHO Collaborating Centre, Geneva University Hospitals and Faculty of Medicine, Service PCI, Rue Gabrielle-Perret-Gentil 4, 1205, Geneve, Switzerland
| | - Rebecca Grant
- Infection Control Program and WHO Collaborating Centre, Geneva University Hospitals and Faculty of Medicine, Service PCI, Rue Gabrielle-Perret-Gentil 4, 1205, Geneve, Switzerland
| | - Sarah Tschudin-Sutter
- Division of Infectious Diseases & Hospital Epidemiology, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Peter W Schreiber
- Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Bruno Grandbastien
- Service of Hospital Preventive Medicine, Lausanne University Hospital, Lausanne, Switzerland
| | - Philipp Jent
- Department of Infectious Diseases, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Elia Lo Priore
- Department of Infectious Diseases and Hospital Epidemiology, EOC Regional Hospital of Lugano, Lugano, Switzerland
| | | | - Stephan Harbarth
- Infection Control Program and WHO Collaborating Centre, Geneva University Hospitals and Faculty of Medicine, Service PCI, Rue Gabrielle-Perret-Gentil 4, 1205, Geneve, Switzerland
| | - Gaud Catho
- Infection Control Program and WHO Collaborating Centre, Geneva University Hospitals and Faculty of Medicine, Service PCI, Rue Gabrielle-Perret-Gentil 4, 1205, Geneve, Switzerland
- Division of Infectious Diseases, Central Institute, Valais Hospital, Sion, Switzerland
| | - Niccolò Buetti
- Infection Control Program and WHO Collaborating Centre, Geneva University Hospitals and Faculty of Medicine, Service PCI, Rue Gabrielle-Perret-Gentil 4, 1205, Geneve, Switzerland
- Université de Paris, INSERM, IAME UMR 1137, 75018, Paris, France
| |
Collapse
|
20
|
Zheng F, Wang K, Wang Q, Yu T, Wang L, Zhang X, Wu X, Zhou Q, Tan L. Factors Influencing Clinicians' Use of Hospital Information Systems for Infection Prevention and Control: Cross-Sectional Study Based on the Extended DeLone and McLean Model. J Med Internet Res 2023; 25:e44900. [PMID: 37347523 PMCID: PMC10337337 DOI: 10.2196/44900] [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: 12/08/2022] [Revised: 05/18/2023] [Accepted: 05/30/2023] [Indexed: 06/23/2023] Open
Abstract
BACKGROUND Healthcare-associated infections have become a serious public health problem. Various types of information systems have begun to be applied in hospital infection prevention and control (IPC) practice. Clinicians are the key users of these systems, but few studies have assessed the use of infection prevention and control information systems (IPCISs) from their perspective. OBJECTIVE This study aimed to (1) apply the extended DeLone and McLean Information Systems Success model (D&M model) that incorporates IPC culture to examine how technical factors like information quality, system quality, and service quality, as well as organizational culture factors affect clinicians' use intention, satisfaction, and perceived net benefits, and (2) identify which factors are the most important for clinicians' use intention. METHODS A total of 12,317 clinicians from secondary and tertiary hospitals were surveyed online. Data were analyzed using partial least squares-structural equation modeling and the importance-performance matrix analysis. RESULTS Among the technical factors, system quality (β=.089-.252; P<.001), information quality (β=.294-.102; P<.001), and service quality (β=.126-.411; P<.001) were significantly related to user satisfaction (R2=0.833), use intention (R2=0.821), and perceived net benefits (communication benefits [R2=0.676], decision-making benefits [R2=0.624], and organizational benefits [R2=0.656]). IPC culture had an effect on use intention (β=.059; P<.001), and it also indirectly affected perceived net benefits (β=.461-.474; P<.001). In the importance-performance matrix analysis, the attributes of service quality (providing user training) and information quality (readability) were present in the fourth quadrant, indicating their high importance and low performance. CONCLUSIONS This study provides valuable insights into IPCIS usage among clinicians from the perspectives of technology and organization culture factors. It found that technical factors (system quality, information quality, and service quality) and hospital IPC culture have an impact on the successful use of IPCISs after evaluating the application of IPCISs based on the extended D&M model. Furthermore, service quality and information quality showed higher importance and lower performance for use intention. These findings provide empirical evidence and specific practical directions for further improving the construction of IPCISs.
Collapse
Affiliation(s)
- Feiyang Zheng
- School of Medicine and Health Management, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China
| | - Kang Wang
- School of Nursing, Hong Kong Polytechnic University, Kowloon, Hong Kong
| | - Qianning Wang
- School of Medicine and Health Management, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China
| | - Tiantian Yu
- School of Medicine and Health Management, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China
| | - Lu Wang
- School of Medicine and Health Management, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China
| | - Xinping Zhang
- School of Medicine and Health Management, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China
| | - Xiang Wu
- School of Medicine and Health Management, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China
| | - Qian Zhou
- Department of Hospital Infection Management, Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Li Tan
- Tongji Hospital, Tongji Medical College of Huazhong University of Science & Technology, Wuhan, China
| |
Collapse
|
21
|
Aghdassi SJS, Goodarzi H, Gropmann A, Clausmeyer J, Geffers C, Piening B, Gastmeier P, Behnke M. Surgical site infection surveillance in German hospitals: a national survey to determine the status quo of digitalization. Antimicrob Resist Infect Control 2023; 12:49. [PMID: 37208780 PMCID: PMC10197484 DOI: 10.1186/s13756-023-01253-9] [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: 02/21/2023] [Accepted: 05/11/2023] [Indexed: 05/21/2023] Open
Abstract
BACKGROUND Surveillance of surgical site infections (SSI) relies on access to data from various sources. Insights into the practices of German hospitals conducting SSI surveillance and their information technology (IT) infrastructures are scarce. The aim of this study was to evaluate current SSI surveillance practices in German hospitals with a focus on employed IT infrastructures. METHODS German surgical departments actively participating in the national SSI surveillance module "OP-KISS" were invited in August 2020 to participate in a questionnaire-based online survey. Depending on whether departments entered all data manually or used an existing feature to import denominator data into the national surveillance database, departments were separated into different groups. Selected survey questions differed between groups. RESULTS Of 1,346 invited departments, 821 participated in the survey (response rate: 61%). Local IT deficits (n = 236), incompatibility of import specifications and hospital information system (n = 153) and lack of technical expertise (n = 145) were cited as the most frequent reasons for not using the denominator data import feature. Conversely, reduction of workload (n = 160) was named as the main motivation to import data. Questions on data availability and accessibility in the electronic hospital information system (HIS) and options to export data from the HIS for the purpose of surveillance, yielded diverse results. Departments utilizing the import feature tended to be from larger hospitals with a higher level of care. CONCLUSIONS The degree to which digital solutions were employed for SSI surveillance differed considerably between surgical departments in Germany. Improving availability and accessibility of information in HIS and meeting interoperability standards will be prerequisites for increasing the amount of data exported directly from HIS to national databases and laying the foundation for automated SSI surveillance on a broad scale.
Collapse
Affiliation(s)
- Seven Johannes Sam Aghdassi
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Hygiene and Environmental Medicine, Hindenburgdamm 27, 12203 Berlin, Germany
- National Reference Center for Surveillance of Nosocomial Infections, Hindenburgdamm 27, 12203 Berlin, Germany
- Berlin Institute of Health at Charité – Universitätsmedizin Berlin, BIH Biomedical Innovation Academy, BIH Charité Digital Clinician Scientist Program, Anna-Louisa-Karsch-Straße 2, 10178 Berlin, Germany
| | - Hengameh Goodarzi
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Hygiene and Environmental Medicine, Hindenburgdamm 27, 12203 Berlin, Germany
- National Reference Center for Surveillance of Nosocomial Infections, Hindenburgdamm 27, 12203 Berlin, Germany
| | - Alexander Gropmann
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Hygiene and Environmental Medicine, Hindenburgdamm 27, 12203 Berlin, Germany
- National Reference Center for Surveillance of Nosocomial Infections, Hindenburgdamm 27, 12203 Berlin, Germany
| | - Jörg Clausmeyer
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Hygiene and Environmental Medicine, Hindenburgdamm 27, 12203 Berlin, Germany
- National Reference Center for Surveillance of Nosocomial Infections, Hindenburgdamm 27, 12203 Berlin, Germany
| | - Christine Geffers
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Hygiene and Environmental Medicine, Hindenburgdamm 27, 12203 Berlin, Germany
- National Reference Center for Surveillance of Nosocomial Infections, Hindenburgdamm 27, 12203 Berlin, Germany
| | - Brar Piening
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Hygiene and Environmental Medicine, Hindenburgdamm 27, 12203 Berlin, Germany
- National Reference Center for Surveillance of Nosocomial Infections, Hindenburgdamm 27, 12203 Berlin, Germany
| | - Petra Gastmeier
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Hygiene and Environmental Medicine, Hindenburgdamm 27, 12203 Berlin, Germany
- National Reference Center for Surveillance of Nosocomial Infections, Hindenburgdamm 27, 12203 Berlin, Germany
| | - Michael Behnke
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Hygiene and Environmental Medicine, Hindenburgdamm 27, 12203 Berlin, Germany
- National Reference Center for Surveillance of Nosocomial Infections, Hindenburgdamm 27, 12203 Berlin, Germany
| |
Collapse
|
22
|
ICU infection surveillance can be based on electronic routine data: results of a case study. BMC Infect Dis 2023; 23:126. [PMID: 36859254 PMCID: PMC9979400 DOI: 10.1186/s12879-023-08082-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 02/13/2023] [Indexed: 03/03/2023] Open
Abstract
BACKGROUND The surveillance of hospital-acquired infections in Germany is usually conducted via manual chart review; this, however, proves resource intensive and is prone to a certain degree of subjectivity. Documentation based on electronic routine data may present an alternative to manual methods. We compared the data derived via manual chart review to that which was derived from electronic routine data. METHODS Data used for the analyses was obtained from five of the University of Leipzig Medical Center's (ULMC) ICUs. Clinical data was collected according to the Protection against Infection Act (IfSG); documentation thereof was carried out in hospital information systems (HIS) as well as in the ICU-KISS module provided by the National Reference Center for the Surveillance of Nosocomial Infections (NRZ). Algorithmically derived data was generated via an algorithm developed in the EFFECT study; ward-movement data was linked with microbiological test results, generating a data set that allows for evaluation as to whether or not an infection was ICU-acquired. RESULTS Approximately 75% of MDRO cases and 85% of cases of sepsis/primary bacteremia were classified as ICU-acquired by both manual chart review and EFFECT. Most discrepancies between the manual and algorithmic approaches were due to differentiating definitions regarding the patients' time at risk for acquiring MDRO/bacteremia. CONCLUSIONS The concordance between manual chart review and algorithmically generated data was considerable. This study shows that hospital infection surveillance based on electronically generated routine data may be a worthwhile and sustainable alternative to manual chart review.
Collapse
|
23
|
Skender K, Machowska A, Singh V, Goel V, Marothi Y, Lundborg CS, Sharma M. Antibiotic Use, Incidence and Risk Factors for Orthopedic Surgical Site Infections in a Teaching Hospital in Madhya Pradesh, India. Antibiotics (Basel) 2022; 11:antibiotics11060748. [PMID: 35740154 PMCID: PMC9220190 DOI: 10.3390/antibiotics11060748] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 05/29/2022] [Accepted: 05/30/2022] [Indexed: 12/10/2022] Open
Abstract
Orthopedic surgeries contribute to the overall surgical site infection (SSI) events worldwide. In India, SSI rates vary considerably (1.6−38%); however, there is a lack of a national SSI surveillance system. This study aims to identify the SSI incidence, risk factors, antibiotic prescription and susceptibility patterns among operated orthopedic patients in a teaching hospital in India. Data for 1205 patients were collected from 2013 to 2016. SSIs were identified based on the European Centre for Disease Prevention and Control guidelines. The American Society for Anesthesiologists classification system was used to predict patients’ operative risk. Univariable and multivariable backward stepwise logistic regressions were performed. Overall, 7.6% of patients developed SSIs over three years. The most common SSIs causative microorganism was Staphylococcus aureus (7%), whose strains were resistant to penicillin (100%), erythromycin (80%), cotrimoxazole (80%), amikacin (60%) and cefoxitin (60%). Amikacin was the most prescribed antibiotic (36%). Male sex (OR 2.64; 95%CI 1.32−5.30), previous hospitalization (OR 2.15; 95%CI 1.25−3.69), antibiotic prescription during hospitalization before perioperative antibiotic prophylaxis (OR 4.19; 95%CI 2.51−7.00) and postoperative length of stay > 15 days (OR 3.30; 95%CI 1.83−5.95) were identified as significant risk factors. Additionally, preoperative shower significantly increased the SSI risk (OR 4.73; 95%CI 2.72−8.22), which is unconfirmed in the literature so far.
Collapse
Affiliation(s)
- Kristina Skender
- Department of Global Public Health, Health Systems and Policy, Karolinska Institutet, 17177 Stockholm, Sweden; (A.M.); (C.S.L.); (M.S.)
- Correspondence:
| | - Anna Machowska
- Department of Global Public Health, Health Systems and Policy, Karolinska Institutet, 17177 Stockholm, Sweden; (A.M.); (C.S.L.); (M.S.)
| | - Vivek Singh
- Department of Orthopedics, Ruxmaniben Deepchand Gardi Medical College, Surasa, Ujjain 456006, India; (V.S.); (V.G.)
| | - Varun Goel
- Department of Orthopedics, Ruxmaniben Deepchand Gardi Medical College, Surasa, Ujjain 456006, India; (V.S.); (V.G.)
| | - Yogyata Marothi
- Department of Microbiology, Ruxmaniben Deepchand Gardi Medical College, Surasa, Ujjain 456006, India;
| | - Cecilia Stålsby Lundborg
- Department of Global Public Health, Health Systems and Policy, Karolinska Institutet, 17177 Stockholm, Sweden; (A.M.); (C.S.L.); (M.S.)
| | - Megha Sharma
- Department of Global Public Health, Health Systems and Policy, Karolinska Institutet, 17177 Stockholm, Sweden; (A.M.); (C.S.L.); (M.S.)
- Department of Pharmacology, Ruxmaniben Deepchand Gardi Medical College, Surasa, Ujjain 456006, India
| |
Collapse
|
24
|
Estimating excess length of stay due to healthcare-associated infections by applying and comparing three time-varying approaches: multistate model, survival regression and matched case control methods. J Hosp Infect 2022; 126:44-51. [DOI: 10.1016/j.jhin.2022.04.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 02/24/2022] [Accepted: 04/03/2022] [Indexed: 11/21/2022]
|
25
|
Valik JK, Hedberg P, Holmberg F, van der Werff SD, Nauclér P. Impact of the COVID-19 pandemic on the incidence and mortality of hospital-onset bloodstream infection: a cohort study. BMJ Qual Saf 2022; 31:379-382. [DOI: 10.1136/bmjqs-2021-014243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Accepted: 01/04/2022] [Indexed: 11/04/2022]
Abstract
The COVID-19 pandemic burdens hospitals, but consequences for quality of care outcomes such as healthcare-associated infections are largely unknown. This cohort included all adult hospital episodes (n=186 945) at an academic centre between January 2018 and January 2021. Data were collected from the hospitals’ electronic health record data repository. Hospital-onset bloodstream infection (HOB) was defined as any positive blood culture obtained ≥48 hours after admission classified based on microbiological and hospital administrative data. Subgroup analyses were performed with exclusion of potential contaminant bacteria. The cohort was divided into three groups: controls (prepandemic period), non-COVID-19 (pandemic period) and COVID-19 (pandemic period) based on either PCR-confirmed SARS-CoV-2 infections from respiratory samples or International Classification of Diseases 10th Revision diagnoses U071 and U72 at discharge. Adjusted incidence rate ratios (aIRR) and risk of death in patients with HOB were compared between the prepandemic and pandemic periods using Poisson and logistic regression. The incidence of HOB was increased for the COVID-19 group compared with the prepandemic period (aIRR 3.34, 95% CI 2.97 to 3.75). In the non-COVID-19 group, the incidence was slightly increased compared with prepandemic levels (aIRR 1.20, 95% CI 1.08 to 1.32), but the difference decreased when excluding potential contaminant bacteria (aIRR 1.15, 95% CI 1.00 to 1.31, p=0.04). The risk of dying increased for both the COVID-19 group (adjusted odds ratio (aOR) 2.44, 95% CI 1.75 to 3.38) and the non-COVID-19 group (aOR 1.63, 95% CI 1.22 to 2.16) compared with the prepandemic controls. These findings were consistent also when excluding potential contaminants. In summary, we observed a higher incidence of HOB during the COVID-19 pandemic, and the mortality risk associated with HOB was greater, compared with the prepandemic period. Results call for specific attention to quality of care during the pandemic.
Collapse
|
26
|
Verberk JDM, Aghdassi SJS, Abbas M, Nauclér P, Gubbels S, Maldonado N, Palacios-Baena ZR, Johansson AF, Gastmeier P, Behnke M, van Rooden SM, van Mourik MSM. Automated surveillance systems for healthcare-associated infections: results from a European survey and experiences from real-life utilization. J Hosp Infect 2022; 122:35-43. [PMID: 35031393 DOI: 10.1016/j.jhin.2021.12.021] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 12/04/2021] [Indexed: 10/19/2022]
Abstract
BACKGROUND As most automated surveillance (AS) methods to detect healthcare-associated infections (HAIs) have been developed and implemented in research settings, information about the feasibility of large-scale implementation is scarce. AIM We aimed to describe key aspects of the design of AS systems and implementation in European institutions and hospitals. METHODS An online survey was distributed via email in February/March 2019 among 1) PRAISE (Providing a Roadmap for Automated Infection Surveillance in Europe) network members; 2) corresponding authors of peer-reviewed European publications on existing AS systems; and 3) the mailing list of national infection prevention and control focal points of the European Centre for Disease Prevention and Control. Three AS systems from the survey were selected, based on quintessential features, for in-depth review focusing on implementation in practice. FINDINGS Through the survey and the review of three selected AS systems, notable differences regarding the methods, algorithms, data sources and targeted HAIs were identified. The majority of AS systems used a classification algorithm for semi-automated surveillance and targeted HAIs were mostly surgical site infections, urinary tract infections, sepsis or other bloodstream infections. AS systems yielded a reduction of workload for hospital staff. Principal barriers of implementation were strict data security regulations as well as creating and maintaining an information technology infrastructure. CONCLUSION AS in Europe is characterized by heterogeneity in methods and surveillance targets. To allow for comparisons and encourage homogenization, future publications on AS systems should provide detailed information on source data, methods and the state of implementation.
Collapse
Affiliation(s)
- Janneke D M Verberk
- Department of Medical Microbiology and Infection Prevention, University Medical Centre Utrecht, Utrecht, the Netherlands; Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, the Netherlands; Department of Epidemiology and Surveillance, Centre for Infectious Diseases Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands.
| | - Seven J S Aghdassi
- Institute of Hygiene and Environmental Medicine, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany; Berlin Institute of Health at Charité - Universitätsmedizin Berlin, BIH Biomedical Innovation Academy, BIH Charité Digital Clinician Scientist Program, Anna-Louisa-Karsch-Straße 2, 10178 Berlin, Germany
| | - Mohamed Abbas
- Infection Control Programme, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland
| | - Pontus Nauclér
- Department of Medicine Solna, Division of Infectious Disease, Karolinska Institutet, Stockholm, Sweden; Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden
| | - Sophie Gubbels
- Department of Infectious Disease Preparedness, Statens Serum Institut, Copenhagen, Denmark
| | - Natalia Maldonado
- Unit of Infectious Diseases, Clinical Microbiology and Preventive Medicine, Hospital Universitario Virgen Macarena, Institute of Biomedicine of Seville (IBIS), Sevilla, Spain
| | - Zaira R Palacios-Baena
- Unit of Infectious Diseases, Clinical Microbiology and Preventive Medicine, Hospital Universitario Virgen Macarena, Institute of Biomedicine of Seville (IBIS), Sevilla, Spain
| | - Anders F Johansson
- Department of Clinical microbiology and the Laboratory for Molecular Infection Medicine (MIMS), Umeå University, Umeå, Sweden
| | - Petra Gastmeier
- Institute of Hygiene and Environmental Medicine, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Michael Behnke
- Institute of Hygiene and Environmental Medicine, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Stephanie M van Rooden
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, the Netherlands; Department of Epidemiology and Surveillance, Centre for Infectious Diseases Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands
| | - Maaike S M van Mourik
- Department of Medical Microbiology and Infection Prevention, University Medical Centre Utrecht, Utrecht, the Netherlands
| |
Collapse
|
27
|
Behnke M, Valik JK, Gubbels S, Teixeira D, Kristensen B, Abbas M, van Rooden SM, Gastmeier P, van Mourik MSM. Information technology aspects of large-scale implementation of automated surveillance of healthcare-associated infections. Clin Microbiol Infect 2021; 27 Suppl 1:S29-S39. [PMID: 34217465 DOI: 10.1016/j.cmi.2021.02.027] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 02/24/2021] [Accepted: 02/25/2021] [Indexed: 12/11/2022]
Abstract
INTRODUCTION Healthcare-associated infections (HAI) are a major public health concern. Monitoring of HAI rates, with feedback, is a core component of infection prevention and control programmes. Digitalization of healthcare data has created novel opportunities for automating the HAI surveillance process to varying degrees. However, methods are not standardized and vary widely between different healthcare facilities. Most current automated surveillance (AS) systems have been confined to local settings, and practical guidance on how to implement large-scale AS is needed. METHODS This document was written by a task force formed in March 2019 within the PRAISE network (Providing a Roadmap for Automated Infection Surveillance in Europe), gathering experts in HAI surveillance from ten European countries. RESULTS The document provides an overview of the key e-health aspects of implementing an AS system of HAI in a clinical environment to support both the infection prevention and control team and information technology (IT) departments. The focus is on understanding the basic principles of storage and structure of healthcare data, as well as the general organization of IT infrastructure in surveillance networks and participating healthcare facilities. The fundamentals of data standardization, interoperability and algorithms in relation to HAI surveillance are covered. Finally, technical aspects and practical examples of accessing, storing and sharing healthcare data within a HAI surveillance network, as well as maintenance and quality control of such a system, are discussed. CONCLUSIONS With the guidance given in this document, along with the PRAISE roadmap and governance documents, readers will find comprehensive support to implement large-scale AS in a surveillance network.
Collapse
Affiliation(s)
- Michael Behnke
- National Reference Center for Surveillance of Nosocomial Infections, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Hygiene and Environmental Medicine, Berlin, Germany.
| | - John Karlsson Valik
- Department of Medicine Solna, Division of Infectious Diseases, Karolinska Institutet and Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden
| | - Sophie Gubbels
- Data Integration and Analysis Secretariat, Statens Serum Institut, Copenhagen, Denmark
| | - Daniel Teixeira
- Infection Control Programme, Geneva University Hospitals, Geneva, Switzerland
| | - Brian Kristensen
- Department of Infectious Disease Epidemiology and Prevention, Statens Serum Institut, Copenhagen, Denmark
| | - Mohamed Abbas
- Infection Control Programme, Geneva University Hospitals, Geneva, Switzerland
| | - Stephanie M van Rooden
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands; Centre for Infectious Disease Epidemiology and Surveillance National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands
| | - Petra Gastmeier
- National Reference Center for Surveillance of Nosocomial Infections, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Hygiene and Environmental Medicine, Berlin, Germany
| | - Maaike S M van Mourik
- Department of Medical Microbiology and Infection Control, University Medical Center Utrecht, Utrecht, the Netherlands
| | | |
Collapse
|
28
|
van Rooden SM, Aspevall O, Carrara E, Gubbels S, Johansson A, Lucet JC, Mookerjee S, Palacios-Baena ZR, Presterl E, Tacconelli E, Abbas M, Behnke M, Gastmeier P, van Mourik MSM. Governance aspects of large-scale implementation of automated surveillance of healthcare-associated infections. Clin Microbiol Infect 2021; 27 Suppl 1:S20-S28. [PMID: 34217464 DOI: 10.1016/j.cmi.2021.02.026] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 02/24/2021] [Accepted: 02/25/2021] [Indexed: 11/29/2022]
Abstract
OBJECTIVES Surveillance of healthcare-associated infections (HAI) is increasingly automated by applying algorithms to routine-care data stored in electronic health records. Hitherto, initiatives have mainly been confined to single healthcare facilities and research settings, leading to heterogeneity in design. The PRAISE network - Providing a Roadmap for Automated Infection Surveillance in Europe - designed a roadmap to provide guidance on how to move automated surveillance (AS) from the research setting to large-scale implementation. Supplementary to this roadmap, we here discuss the governance aspects of automated HAI surveillance within networks, aiming to support both the coordinating centres and participating healthcare facilities as they set up governance structures and to enhance involvement of legal specialists. METHODS This article is based on PRAISE network discussions during two workshops. A taskforce was installed that further elaborated governance aspects for AS networks by reviewing documents and websites, consulting experts and organizing teleconferences. Finally, the article has been reviewed by an independent panel of international experts. RESULTS Strict governance is indispensable in surveillance networks, especially when manual decisions are replaced by algorithms and electronically stored routine-care data are reused for the purpose of surveillance. For endorsement of AS networks, governance aspects specifically related to AS networks need to be addressed. Key considerations include enabling participation and inclusion, trust in the collection, use and quality of data (including data protection), accountability and transparency. CONCLUSIONS This article on governance aspects can be used by coordinating centres and healthcare facilities participating in an AS network as a starting point to set up governance structures. Involvement of main stakeholders and legal specialists early in the development of an AS network is important for endorsement, inclusivity and compliance with the laws and regulations that apply.
Collapse
Affiliation(s)
- Stephanie M van Rooden
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands; Centre for Infectious Disease Epidemiology and Surveillance, National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands.
| | - Olov Aspevall
- Unit for Surveillance and Coordination, Public Health Agency of Sweden, Solna, Sweden
| | - Elena Carrara
- Infectious Diseases Section, Department of Diagnostics and Public Health, University of Verona, Verona, Italy
| | - Sophie Gubbels
- Data Integration and Analysis Secretariat, Statens Serum Institut, Copenhagen, Denmark
| | | | - Jean-Christophe Lucet
- Infection Control Unit, Hôpital Bichat-Claude Bernard Assistance Publique - Hôpitaux de Paris, Paris, France
| | - Siddharth Mookerjee
- Department of Infection Prevention and Control, Imperial College Healthcare NHS Trust, London, UK
| | - Zaira R Palacios-Baena
- Unit of Infectious Diseases, Clinical Microbiology and Preventive Medicine, Hospital Universitario Virgen Macarena, Institute of Biomedicine of Seville (IBIS), Seville, Spain
| | - Elisabeth Presterl
- Department of Infection Control and Hospital Epidemiology, Medical University of Vienna, Vienna, Austria
| | - Evelina Tacconelli
- Infectious Diseases Section, Department of Diagnostics and Public Health, University of Verona, Verona, Italy; Infectious Diseases, Research Clinical Unit, DZIF Center, University Hospital Tübingen, Tübingen, Germany
| | - Mohamed Abbas
- Infection Control Programme, Geneva University Hospitals, Geneva, Switzerland
| | - Michael Behnke
- National Reference Center for Surveillance of Nosocomial Infections, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Hygiene and Environmental Medicine, Berlin, Germany
| | - Petra Gastmeier
- National Reference Center for Surveillance of Nosocomial Infections, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Hygiene and Environmental Medicine, Berlin, Germany
| | - Maaike S M van Mourik
- Department of Medical Microbiology and Infection Control, University Medical Center Utrecht, Utrecht, the Netherlands
| | | |
Collapse
|