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Cocker D, Birgand G, Zhu N, Rodriguez-Manzano J, Ahmad R, Jambo K, Levin AS, Holmes A. Healthcare as a driver, reservoir and amplifier of antimicrobial resistance: opportunities for interventions. Nat Rev Microbiol 2024:10.1038/s41579-024-01076-4. [PMID: 39048837 DOI: 10.1038/s41579-024-01076-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/25/2024] [Indexed: 07/27/2024]
Abstract
Antimicrobial resistance (AMR) is a global health challenge that threatens humans, animals and the environment. Evidence is emerging for a role of healthcare infrastructure, environments and patient pathways in promoting and maintaining AMR via direct and indirect mechanisms. Advances in vaccination and monoclonal antibody therapies together with integrated surveillance, rapid diagnostics, targeted antimicrobial therapy and infection control measures offer opportunities to address healthcare-associated AMR risks more effectively. Additionally, innovations in artificial intelligence, data linkage and intelligent systems can be used to better predict and reduce AMR and improve healthcare resilience. In this Review, we examine the mechanisms by which healthcare functions as a driver, reservoir and amplifier of AMR, contextualized within a One Health framework. We also explore the opportunities and innovative solutions that can be used to combat AMR throughout the patient journey. We provide a perspective on the current evidence for the effectiveness of interventions designed to mitigate healthcare-associated AMR and promote healthcare resilience within high-income and resource-limited settings, as well as the challenges associated with their implementation.
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Affiliation(s)
- Derek Cocker
- David Price Evans Infectious Diseases & Global Health Group, University of Liverpool, Liverpool, UK
- Malawi-Liverpool-Wellcome Research Programme, Blantyre, Malawi
| | - Gabriel Birgand
- Centre d'appui pour la Prévention des Infections Associées aux Soins, Nantes, France
- National Institute for Health and Care Research (NIHR) Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at Imperial College London, London, UK
- Cibles et medicaments des infections et de l'immunitée, IICiMed, Nantes Universite, Nantes, France
| | - Nina Zhu
- National Institute for Health and Care Research (NIHR) Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at Imperial College London, London, UK
- Department of Infectious Disease, Imperial College London, London, UK
| | - Jesus Rodriguez-Manzano
- National Institute for Health and Care Research (NIHR) Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at Imperial College London, London, UK
- Department of Infectious Disease, Imperial College London, London, UK
| | - Raheelah Ahmad
- National Institute for Health and Care Research (NIHR) Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at Imperial College London, London, UK
- Department of Health Services Research & Management, City University of London, London, UK
- Dow University of Health Sciences, Karachi, Pakistan
| | - Kondwani Jambo
- Malawi-Liverpool-Wellcome Research Programme, Blantyre, Malawi
- Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, UK
| | - Anna S Levin
- Department of Infectious Disease, School of Medicine & Institute of Tropical Medicine, University of São Paulo, São Paulo, Brazil
| | - Alison Holmes
- David Price Evans Infectious Diseases & Global Health Group, University of Liverpool, Liverpool, UK.
- National Institute for Health and Care Research (NIHR) Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at Imperial College London, London, UK.
- Department of Infectious Disease, Imperial College London, London, UK.
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Karolcik S, Manginas V, Chanh HQ, Daniels J, Giang NT, Huyen VNT, Hoang MTV, Phan Nguyen Quoc K, Hernandez B, Ming DK, Nguyen Van H, Phan TQ, Trieu HT, Luong Thi Hue T, Holmes AH, Thwaites L, Phan Vinh T, Yacoub S, Georgiou P. Towards a machine-learning assisted non-invasive classification of dengue severity using wearable PPG data: a prospective clinical study. EBioMedicine 2024; 104:105164. [PMID: 38815363 PMCID: PMC11167237 DOI: 10.1016/j.ebiom.2024.105164] [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: 07/20/2023] [Revised: 04/28/2024] [Accepted: 05/07/2024] [Indexed: 06/01/2024] Open
Abstract
BACKGROUND Dengue epidemics impose considerable strain on healthcare resources. Real-time continuous and non-invasive monitoring of patients admitted to the hospital could lead to improved care and outcomes. We evaluated the performance of a commercially available wearable (SmartCare) utilising photoplethysmography (PPG) to stratify clinical risk for a cohort of hospitalised patients with dengue in Vietnam. METHODS We performed a prospective observational study for adult and paediatric patients with a clinical diagnosis of dengue at the Hospital for Tropical Disease, Ho Chi Minh City, Vietnam. Patients underwent PPG monitoring early during admission alongside standard clinical care. PPG waveforms were analysed using machine learning models. Adult patients were classified between 3 severity classes: i) uncomplicated (ward-based), ii) moderate-severe (emergency department-based), and iii) severe (ICU-based). Data from paediatric patients were split into 2 classes: i) severe (during ICU stay) and ii) follow-up (14-21 days after the illness onset). Model performances were evaluated using standard classification metrics and 5-fold stratified cross-validation. FINDINGS We included PPG and clinical data from 132 adults and 15 paediatric patients with a median age of 28 (IQR, 21-35) and 12 (IQR, 9-13) years respectively. 1781 h of PPG data were available for analysis. The best performing convolutional neural network models (CNN) achieved a precision of 0.785 and recall of 0.771 in classifying adult patients according to severity class and a precision of 0.891 and recall of 0.891 in classifying between disease and post-disease state in paediatric patients. INTERPRETATION We demonstrate that the use of a low-cost wearable provided clinically actionable data to differentiate between patients with dengue of varying severity. Continuous monitoring and connectivity to early warning systems could significantly benefit clinical care in dengue, particularly within an endemic setting. Work is currently underway to implement these models for dynamic risk predictions and assist in individualised patient care. FUNDING EPSRC Centre for Doctoral Training in High-Performance Embedded and Distributed Systems (HiPEDS) (Grant: EP/L016796/1) and the Wellcome Trust (Grants: 215010/Z/18/Z and 215688/Z/19/Z).
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Affiliation(s)
- Stefan Karolcik
- Centre for Bio-Inspired Technology, Imperial College London, South Kensington Campus, London, SW7 2AZ, United Kingdom.
| | - Vasileos Manginas
- Centre for Bio-Inspired Technology, Imperial College London, South Kensington Campus, London, SW7 2AZ, United Kingdom
| | - Ho Quang Chanh
- Oxford University Clinical Research Unit (OUCRU), Hospital for Tropical Diseases, Ho Chi Minh City, 700000, Viet Nam
| | - John Daniels
- Centre for Bio-Inspired Technology, Imperial College London, South Kensington Campus, London, SW7 2AZ, United Kingdom
| | - Nguyen Thi Giang
- Oxford University Clinical Research Unit (OUCRU), Hospital for Tropical Diseases, Ho Chi Minh City, 700000, Viet Nam
| | - Vu Ngo Thanh Huyen
- Oxford University Clinical Research Unit (OUCRU), Hospital for Tropical Diseases, Ho Chi Minh City, 700000, Viet Nam
| | - Minh Tu Van Hoang
- Oxford University Clinical Research Unit (OUCRU), Hospital for Tropical Diseases, Ho Chi Minh City, 700000, Viet Nam
| | - Khanh Phan Nguyen Quoc
- Oxford University Clinical Research Unit (OUCRU), Hospital for Tropical Diseases, Ho Chi Minh City, 700000, Viet Nam
| | - Bernard Hernandez
- Centre for Bio-Inspired Technology, Imperial College London, South Kensington Campus, London, SW7 2AZ, United Kingdom
| | - Damien K Ming
- Centre for Antimicrobial Optimisation, Imperial College London, Hammersmith Campus, London, W12 0NN, United Kingdom
| | - Hao Nguyen Van
- Oxford University Clinical Research Unit (OUCRU), Hospital for Tropical Diseases, Ho Chi Minh City, 700000, Viet Nam
| | - Tu Qui Phan
- Oxford University Clinical Research Unit (OUCRU), Hospital for Tropical Diseases, Ho Chi Minh City, 700000, Viet Nam
| | - Huynh Trung Trieu
- Oxford University Clinical Research Unit (OUCRU), Hospital for Tropical Diseases, Ho Chi Minh City, 700000, Viet Nam
| | - Tai Luong Thi Hue
- Oxford University Clinical Research Unit (OUCRU), Hospital for Tropical Diseases, Ho Chi Minh City, 700000, Viet Nam
| | - Alison H Holmes
- Centre for Antimicrobial Optimisation, Imperial College London, Hammersmith Campus, London, W12 0NN, United Kingdom
| | - Louise Thwaites
- Oxford University Clinical Research Unit (OUCRU), Hospital for Tropical Diseases, Ho Chi Minh City, 700000, Viet Nam
| | - Tho Phan Vinh
- Oxford University Clinical Research Unit (OUCRU), Hospital for Tropical Diseases, Ho Chi Minh City, 700000, Viet Nam
| | - Sophie Yacoub
- Oxford University Clinical Research Unit (OUCRU), Hospital for Tropical Diseases, Ho Chi Minh City, 700000, Viet Nam
| | - Pantelis Georgiou
- Centre for Bio-Inspired Technology, Imperial College London, South Kensington Campus, London, SW7 2AZ, United Kingdom
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Quality Assurance for Hepatitis C Virus Point-of-Care Diagnostics in Sub-Saharan Africa. Diagnostics (Basel) 2023; 13:diagnostics13040684. [PMID: 36832172 PMCID: PMC9955859 DOI: 10.3390/diagnostics13040684] [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: 12/01/2022] [Revised: 02/06/2023] [Accepted: 02/10/2023] [Indexed: 02/15/2023] Open
Abstract
As part of a multinational study to evaluate the Bioline Hepatitis C virus (HCV) point-of-care (POC) testing in sub-Saharan Africa (SSA), this narrative review summarises regulatory standards and quality indicators for validating and approving HCV clinical diagnostics. In addition, this review also provides a summary of their diagnostic evaluations using the REASSURED criteria as the benchmark and its implications on the WHO HCV elimination goals 2030.
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Nguyen QH, Ming DK, Luu AP, Chanh HQ, Tam DTH, Truong NT, Huy VX, Hernandez B, Van Nuil JI, Paton C, Georgiou P, Nguyen NM, Holmes A, Tho PV, Yacoub S. Mapping patient pathways and understanding clinical decision-making in dengue management to inform the development of digital health tools. BMC Med Inform Decis Mak 2023; 23:24. [PMID: 36732718 PMCID: PMC9893980 DOI: 10.1186/s12911-023-02116-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 01/19/2023] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Dengue is a common viral illness and severe disease results in life-threatening complications. Healthcare services in low- and middle-income countries treat the majority of dengue cases worldwide. However, the clinical decision-making processes which result in effective treatment are poorly characterised within this setting. In order to improve clinical care through interventions relating to digital clinical decision-support systems (CDSS), we set out to establish a framework for clinical decision-making in dengue management to inform implementation. METHODS We utilised process mapping and task analysis methods to characterise existing dengue management at the Hospital for Tropical Diseases, Ho Chi Minh City, Vietnam. This is a tertiary referral hospital which manages approximately 30,000 patients with dengue each year, accepting referrals from Ho Chi Minh city and the surrounding catchment area. Initial findings were expanded through semi-structured interviews with clinicians in order to understand clinical reasoning and cognitive factors in detail. A grounded theory was used for coding and emergent themes were developed through iterative discussions with clinician-researchers. RESULTS Key clinical decision-making points were identified: (i) at the initial patient evaluation for dengue diagnosis to decide on hospital admission and the provision of fluid/blood product therapy, (ii) in those patients who develop severe disease or other complications, (iii) at the point of recurrent shock in balancing the need for fluid therapy with complications of volume overload. From interviews the following themes were identified: prioritising clinical diagnosis and evaluation over existing diagnostics, the role of dengue guidelines published by the Ministry of Health, the impact of seasonality and caseload on decision-making strategies, and the potential role of digital decision-support and disease scoring tools. CONCLUSIONS The study highlights the contemporary priorities in delivering clinical care to patients with dengue in an endemic setting. Key decision-making processes and the sources of information that were of the greatest utility were identified. These findings serve as a foundation for future clinical interventions and improvements in healthcare. Understanding the decision-making process in greater detail also allows for development and implementation of CDSS which are suited to the local context.
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Affiliation(s)
- Quang Huy Nguyen
- grid.412433.30000 0004 0429 6814Centre for Tropical Medicine, Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
| | - Damien K. Ming
- grid.7445.20000 0001 2113 8111Centre for Antimicrobial Optimisation (CAMO), Imperial College London, London, UK
| | - An Phuoc Luu
- grid.412433.30000 0004 0429 6814Centre for Tropical Medicine, Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
| | - Ho Quang Chanh
- grid.412433.30000 0004 0429 6814Centre for Tropical Medicine, Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
| | - Dong Thi Hoai Tam
- grid.412433.30000 0004 0429 6814Centre for Tropical Medicine, Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
| | - Nguyen Thanh Truong
- grid.414273.70000 0004 0469 2382Hospital for Tropical Diseases, Ho Chi Minh City, Vietnam
| | - Vo Xuan Huy
- grid.414273.70000 0004 0469 2382Hospital for Tropical Diseases, Ho Chi Minh City, Vietnam
| | - Bernard Hernandez
- grid.7445.20000 0001 2113 8111Centre for BioInspired Technology, Imperial College London, London, UK
| | - Jennifer Ilo Van Nuil
- grid.412433.30000 0004 0429 6814Centre for Tropical Medicine, Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
| | - Chris Paton
- grid.29980.3a0000 0004 1936 7830Department of Information Science, University of Otago, Dunedin, New Zealand ,grid.4991.50000 0004 1936 8948Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Pantelis Georgiou
- grid.7445.20000 0001 2113 8111Centre for BioInspired Technology, Imperial College London, London, UK
| | - Nguyet Minh Nguyen
- grid.412433.30000 0004 0429 6814Centre for Tropical Medicine, Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
| | - Alison Holmes
- grid.7445.20000 0001 2113 8111Centre for Antimicrobial Optimisation (CAMO), Imperial College London, London, UK
| | - Phan Vinh Tho
- grid.414273.70000 0004 0469 2382Hospital for Tropical Diseases, Ho Chi Minh City, Vietnam
| | - Sophie Yacoub
- grid.412433.30000 0004 0429 6814Centre for Tropical Medicine, Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam ,grid.4991.50000 0004 1936 8948Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
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Trieu HT, Khanh LP, Ming DKY, Quang CH, Phan TQ, Van VCN, Deniz E, Mulligan J, Wills BA, Moulton S, Yacoub S. The compensatory reserve index predicts recurrent shock in patients with severe dengue. BMC Med 2022; 20:109. [PMID: 35387649 PMCID: PMC8986451 DOI: 10.1186/s12916-022-02311-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 02/21/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Dengue shock syndrome (DSS) is one of the major clinical phenotypes of severe dengue. It is defined by significant plasma leak, leading to intravascular volume depletion and eventually cardiovascular collapse. The compensatory reserve Index (CRI) is a new physiological parameter, derived from feature analysis of the pulse arterial waveform that tracks real-time changes in central volume. We investigated the utility of CRI to predict recurrent shock in severe dengue patients admitted to the ICU. METHODS We performed a prospective observational study in the pediatric and adult intensive care units at the Hospital for Tropical Diseases, Ho Chi Minh City, Vietnam. Patients were monitored with hourly clinical parameters and vital signs, in addition to continuous recording of the arterial waveform using pulse oximetry. The waveform data was wirelessly transmitted to a laptop where it was synchronized with the patient's clinical data. RESULTS One hundred three patients with suspected severe dengue were recruited to this study. Sixty-three patients had the minimum required dataset for analysis. Median age was 11 years (IQR 8-14 years). CRI had a negative correlation with heart rate and moderate negative association with blood pressure. CRI was found to predict recurrent shock within 12 h of being measured (OR 2.24, 95% CI 1.54-3.26), P < 0.001). The median duration from CRI measurement to the first recurrent shock was 5.4 h (IQR 2.9-6.8). A CRI cutoff of 0.4 provided the best combination of sensitivity and specificity for predicting recurrent shock (0.66 [95% CI 0.47-0.85] and 0.86 [95% CI 0.80-0.92] respectively). CONCLUSION CRI is a useful non-invasive method for monitoring intravascular volume status in patients with severe dengue.
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Affiliation(s)
- Huynh Trung Trieu
- Hospital for Tropical Diseases, Ho Chi Minh City, Vietnam. .,Oxford University Clinical Research Unit, 764 Vo Van Kiet, District 5, Ho Chi Minh City, Vietnam.
| | - Lam Phung Khanh
- Oxford University Clinical Research Unit, 764 Vo Van Kiet, District 5, Ho Chi Minh City, Vietnam.,University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam
| | | | - Chanh Ho Quang
- Oxford University Clinical Research Unit, 764 Vo Van Kiet, District 5, Ho Chi Minh City, Vietnam
| | - Tu Qui Phan
- Hospital for Tropical Diseases, Ho Chi Minh City, Vietnam
| | | | | | | | - Bridget Ann Wills
- Oxford University Clinical Research Unit, 764 Vo Van Kiet, District 5, Ho Chi Minh City, Vietnam.,Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, Oxford University, Oxford, UK
| | - Steven Moulton
- Context Data Analytics Ltd, Longmont, CO, USA.,Department of Surgery, University of Colorado School of Medicine, CO, Aurora, USA
| | - Sophie Yacoub
- Oxford University Clinical Research Unit, 764 Vo Van Kiet, District 5, Ho Chi Minh City, Vietnam.,Centre for Antimicrobial Optimisation, Imperial College London, London, UK
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Ming DK, Tuan NM, Hernandez B, Sangkaew S, Vuong NL, Chanh HQ, Chau NVV, Simmons CP, Wills B, Georgiou P, Holmes AH, Yacoub S. The Diagnosis of Dengue in Patients Presenting With Acute Febrile Illness Using Supervised Machine Learning and Impact of Seasonality. Front Digit Health 2022; 4:849641. [PMID: 35360365 PMCID: PMC8963938 DOI: 10.3389/fdgth.2022.849641] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 02/15/2022] [Indexed: 11/13/2022] Open
Abstract
Background Symptomatic dengue infection can result in a life-threatening shock syndrome and timely diagnosis is essential. Point-of-care tests for non-structural protein 1 and IgM are used widely but performance can be limited. We developed a supervised machine learning model to predict whether patients with acute febrile illnesses had a diagnosis of dengue or other febrile illnesses (OFI). The impact of seasonality on model performance over time was examined. Methods We analysed data from a prospective observational clinical study in Vietnam. Enrolled patients presented with an acute febrile illness of <72 h duration. A gradient boosting model (XGBoost) was used to predict final diagnosis using age, sex, haematocrit, platelet, white cell, and lymphocyte count collected on enrolment. Data was randomly split 80/20% into a training and hold-out set, respectively, with the latter not used in model development. Cross-validation and hold out set testing was used, with performance over time evaluated through a rolling window approach. Results We included 8,100 patients recruited between 16th October 2010 and 10th December 2014. In total 2,240 (27.7%) patients were diagnosed with dengue infection. The optimised model from training data had an overall median area under the receiver operator curve (AUROC) of 0.86 (interquartile range 0.84-0.86), specificity of 0.92, sensitivity of 0.56, positive predictive value of 0.73, negative predictive value (NPV) of 0.84, and Brier score of 0.13 in predicting the final diagnosis, with similar performances in hold-out set testing (AUROC of 0.86). Model performances varied significantly over time as a function of seasonality and other factors. Incorporation of a dynamic threshold which continuously learns from recent cases resulted in a more consistent performance throughout the year (NPV >90%). Conclusion Supervised machine learning models are able to discriminate between dengue and OFI diagnoses in patients presenting with an early undifferentiated febrile illness. These models could be of clinical utility in supporting healthcare decision-making and provide passive surveillance across dengue endemic regions. Effects of seasonality and changing disease prevalence must however be taken into account-this is of significant importance given unpredictable effects of human-induced climate change and the impact on health.
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Affiliation(s)
- Damien K. Ming
- Centre for Antimicrobial Optimisation, Imperial College London, London, United Kingdom
| | - Nguyen M. Tuan
- Children's Hospital 1, Ho Chi Minh City, Vietnam
- Oxford University Clinical Research Unit, Centre for Tropical Medicine, Ho Chi Minh City, Vietnam
| | - Bernard Hernandez
- Centre for BioInspired Technology, Imperial College London, London, United Kingdom
| | - Sorawat Sangkaew
- Centre for Antimicrobial Optimisation, Imperial College London, London, United Kingdom
| | - Nguyen L. Vuong
- Oxford University Clinical Research Unit, Centre for Tropical Medicine, Ho Chi Minh City, Vietnam
- University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam
| | - Ho Q. Chanh
- Oxford University Clinical Research Unit, Centre for Tropical Medicine, Ho Chi Minh City, Vietnam
- Hospital for Tropical Diseases, Ho Chi Minh City, Vietnam
| | - Nguyen V. V. Chau
- Oxford University Clinical Research Unit, Centre for Tropical Medicine, Ho Chi Minh City, Vietnam
- Hospital for Tropical Diseases, Ho Chi Minh City, Vietnam
| | - Cameron P. Simmons
- Institute of Vector Borne Disease, Monash University, Clayton, VIC, Australia
| | - Bridget Wills
- Oxford University Clinical Research Unit, Centre for Tropical Medicine, Ho Chi Minh City, Vietnam
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Pantelis Georgiou
- Centre for BioInspired Technology, Imperial College London, London, United Kingdom
| | - Alison H. Holmes
- Centre for Antimicrobial Optimisation, Imperial College London, London, United Kingdom
| | - Sophie Yacoub
- Oxford University Clinical Research Unit, Centre for Tropical Medicine, Ho Chi Minh City, Vietnam
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
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Optimizing antimicrobial use: challenges, advances and opportunities. Nat Rev Microbiol 2021; 19:747-758. [PMID: 34158654 DOI: 10.1038/s41579-021-00578-9] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/14/2021] [Indexed: 02/06/2023]
Abstract
An optimal antimicrobial dose provides enough drug to achieve a clinical response while minimizing toxicity and development of drug resistance. There can be considerable variability in pharmacokinetics, for example, owing to comorbidities or other medications, which affects antimicrobial pharmacodynamics and, thus, treatment success. Although current approaches to antimicrobial dose optimization address fixed variability, better methods to monitor and rapidly adjust antimicrobial dosing are required to understand and react to residual variability that occurs within and between individuals. We review current challenges to the wider implementation of antimicrobial dose optimization and highlight novel solutions, including biosensor-based, real-time therapeutic drug monitoring and computer-controlled, closed-loop control systems. Precision antimicrobial dosing promises to improve patient outcome and is important for antimicrobial stewardship and the prevention of antimicrobial resistance.
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Charani E, McKee M, Ahmad R, Balasegaram M, Bonaconsa C, Merrett GB, Busse R, Carter V, Castro-Sanchez E, Franklin BD, Georgiou P, Hill-Cawthorne K, Hope W, Imanaka Y, Kambugu A, Leather AJM, Mbamalu O, McLeod M, Mendelson M, Mpundu M, Rawson TM, Ricciardi W, Rodriguez-Manzano J, Singh S, Tsioutis C, Uchea C, Zhu N, Holmes AH. Optimising antimicrobial use in humans - review of current evidence and an interdisciplinary consensus on key priorities for research. THE LANCET REGIONAL HEALTH. EUROPE 2021; 7:100161. [PMID: 34557847 PMCID: PMC8454847 DOI: 10.1016/j.lanepe.2021.100161] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
Addressing the silent pandemic of antimicrobial resistance (AMR) is a focus of the 2021 G7 meeting. A major driver of AMR and poor clinical outcomes is suboptimal antimicrobial use. Current research in AMR is inequitably focused on new drug development. To achieve antimicrobial security we need to balance AMR research efforts between development of new agents and strategies to preserve the efficacy and maximise effectiveness of existing agents. Combining a review of current evidence and multistage engagement with diverse international stakeholders (including those in healthcare, public health, research, patient advocacy and policy) we identified research priorities for optimising antimicrobial use in humans across four broad themes: policy and strategic planning; medicines management and prescribing systems; technology to optimise prescribing; and context, culture and behaviours. Sustainable progress depends on: developing economic and contextually appropriate interventions; facilitating better use of data and prescribing systems across healthcare settings; supporting appropriate and scalable technological innovation. Implementing this strategy for AMR research on the optimisation of antimicrobial use in humans could contribute to equitable global health security.
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Affiliation(s)
- Esmita Charani
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, UK
- Division of Infectious Diseases & HIV Medicine, Department of Medicine, University of Cape Town, South Africa
| | - Martin McKee
- London School of Hygiene and Tropical Medicine, London, UK
| | - Raheelah Ahmad
- School of Health Sciences City, University of London, UK
| | - Manica Balasegaram
- The Global Antibiotic Research and Development Partnership, Geneva, Switzerland
| | - Candice Bonaconsa
- Division of Infectious Diseases & HIV Medicine, Department of Medicine, University of Cape Town, South Africa
| | | | | | - Vanessa Carter
- Stanford University Medicine X e-Patient Scholars Program 2017, Health Communication and Social Media South Africa, Africa CDC Civil Society Champion for AMR
| | - Enrique Castro-Sanchez
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, UK
| | - Bryony D Franklin
- University College London School of Pharmacy, London, UK
- Imperial College Healthcare NHS Trust, Centre for Medication Safety and Service Quality, Pharmacy Department, London, UK
| | - Pantelis Georgiou
- Department of Electrical and Electronic Engineering, Faculty of Engineering, Imperial College London, London, UK
| | - Kerri Hill-Cawthorne
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, UK
| | - William Hope
- Department of Molecular and Clinical Pharmacology, University of Liverpool, UK
| | - Yuichi Imanaka
- Department of Healthcare Economics and Quality Management, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Andrew Kambugu
- Infectious Diseases Institute, Makerere University College of Health Sciences, Kampala, Uganda
| | - Andrew JM Leather
- King's Centre for Global Health and Health Partnerships, School of Population Health and Environmental Sciences, King's College London, London, UK
| | - Oluchi Mbamalu
- Division of Infectious Diseases & HIV Medicine, Department of Medicine, University of Cape Town, South Africa
| | - M McLeod
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, UK
- Imperial College Healthcare NHS Trust, Centre for Medication Safety and Service Quality, Pharmacy Department, London, UK
| | - Marc Mendelson
- Division of Infectious Diseases & HIV Medicine, Department of Medicine, University of Cape Town, South Africa
| | | | - Timothy M Rawson
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, UK
- Department of Bioengineering, Imperial College London, London, UK
| | | | - Jesus Rodriguez-Manzano
- Department of Electrical and Electronic Engineering, Faculty of Engineering, Imperial College London, London, UK
- Department of Infectious Diseases, Faculty of Medicine, Imperial College London, London
| | - Sanjeev Singh
- Department of Infection Control and Epidemiology, Amrita Institute of Medical Science, Amrita Vishwa Vidyapeetham, Kochi (Kerala), India
| | - Constantinos Tsioutis
- Department of Internal Medicine and Infection Prevention and Control, School of Medicine, European University Cyprus, Nicosia, Cyprus
| | - Chibuzor Uchea
- Drug-Resistant Infections Priority Programme,Wellcome Trust, London, UK
| | - Nina Zhu
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, UK
| | - Alison H Holmes
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, UK
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Osterhaus A, Mackenzie J. Pandemic preparedness planning in peacetime: what is missing? ONE HEALTH OUTLOOK 2020; 2:19. [PMID: 32835171 PMCID: PMC7426668 DOI: 10.1186/s42522-020-00027-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Accepted: 07/31/2020] [Indexed: 05/17/2023]
Affiliation(s)
- Ab Osterhaus
- Research Center Emerging Infections and Zoonoses (RIZ) University of Veterinary Medicine Hannover, Hanover, Germany
| | - John Mackenzie
- Faculty of Health Sciences, Curtin University, Perth, Western Australia
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Queensland Australia
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Ming DK, Sangkaew S, Chanh HQ, Nhat PTH, Yacoub S, Georgiou P, Holmes AH. Continuous physiological monitoring using wearable technology to inform individual management of infectious diseases, public health and outbreak responses. Int J Infect Dis 2020; 96:648-654. [PMID: 32497806 PMCID: PMC7263257 DOI: 10.1016/j.ijid.2020.05.086] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2020] [Revised: 05/15/2020] [Accepted: 05/23/2020] [Indexed: 01/12/2023] Open
Abstract
Optimal management of infectious diseases is guided by up-to-date information at the individual and public health levels. For infections of global importance, including emerging pandemics such as COVID-19 or prevalent endemic diseases such as dengue, identifying patients at risk of severe disease and clinical deterioration can be challenging, considering that the majority present with a mild illness. In our article, we describe the use of wearable technology for continuous physiological monitoring in healthcare settings. Deployment of wearables in hospital settings for the management of infectious diseases, or in the community to support syndromic surveillance during outbreaks, could provide significant, cost-effective advantages and improve healthcare delivery. We highlight a range of promising technologies employed by wearable devices and discuss the technical and ethical issues relating to implementation in the clinic, focusing on low- and middle- income countries. Finally, we propose a set of essential criteria for the rollout of wearable technology for clinical use.
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Affiliation(s)
- Damien K Ming
- NIHR-Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, UK; Centre for Antimicrobial Optimisation (CAMO), Imperial College London, UK.
| | - Sorawat Sangkaew
- NIHR-Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, UK; Department of Family Medicine, Hat Yai Regional Hospital, Thailand
| | - Ho Q Chanh
- Oxford University Clinical Research Unit (OUCRU), Ho Chi Minh City, Viet Nam
| | - Phung T H Nhat
- Oxford University Clinical Research Unit (OUCRU), Ho Chi Minh City, Viet Nam
| | - Sophie Yacoub
- Oxford University Clinical Research Unit (OUCRU), Ho Chi Minh City, Viet Nam; Centre for Tropical Medicine and Global Health, University of Oxford, UK
| | | | - Alison H Holmes
- NIHR-Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, UK; Centre for Antimicrobial Optimisation (CAMO), Imperial College London, UK
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Rodriguez-Manzano J, Moser N, Malpartida-Cardenas K, Moniri A, Fisarova L, Pennisi I, Boonyasiri A, Jauneikaite E, Abdolrasouli A, Otter JA, Bolt F, Davies F, Didelot X, Holmes A, Georgiou P. Rapid Detection of Mobilized Colistin Resistance using a Nucleic Acid Based Lab-on-a-Chip Diagnostic System. Sci Rep 2020; 10:8448. [PMID: 32439986 PMCID: PMC7242339 DOI: 10.1038/s41598-020-64612-1] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Accepted: 04/13/2020] [Indexed: 11/30/2022] Open
Abstract
The increasing prevalence of antimicrobial resistance is a serious threat to global public health. One of the most concerning trends is the rapid spread of Carbapenemase-Producing Organisms (CPO), where colistin has become the last-resort antibiotic treatment. The emergence of colistin resistance, including the spread of mobilized colistin resistance (mcr) genes, raises the possibility of untreatable bacterial infections and motivates the development of improved diagnostics for the detection of colistin-resistant organisms. This work demonstrates a rapid response for detecting the most recently reported mcr gene, mcr−9, using a portable and affordable lab-on-a-chip (LoC) platform, offering a promising alternative to conventional laboratory-based instruments such as real-time PCR (qPCR). The platform combines semiconductor technology, for non-optical real-time DNA sensing, with a smartphone application for data acquisition, visualization and cloud connectivity. This technology is enabled by using loop-mediated isothermal amplification (LAMP) as the chemistry for targeted DNA detection, by virtue of its high sensitivity, specificity, yield, and manageable temperature requirements. Here, we have developed the first LAMP assay for mcr−9 - showing high sensitivity (down to 100 genomic copies/reaction) and high specificity (no cross-reactivity with other mcr variants). This assay is demonstrated through supporting a hospital investigation where we analyzed nucleic acids extracted from 128 carbapenemase-producing bacteria isolated from clinical and screening samples and found that 41 carried mcr−9 (validated using whole genome sequencing). Average positive detection times were 6.58 ± 0.42 min when performing the experiments on a conventional qPCR instrument (n = 41). For validating the translation of the LAMP assay onto a LoC platform, a subset of the samples were tested (n = 20), showing average detection times of 6.83 ± 0.92 min for positive isolates (n = 14). All experiments detected mcr−9 in under 10 min, and both platforms showed no statistically significant difference (p-value > 0.05). When sample preparation and throughput capabilities are integrated within this LoC platform, the adoption of this technology for the rapid detection and surveillance of antimicrobial resistance genes will decrease the turnaround time for DNA detection and resistotyping, improving diagnostic capabilities, patient outcomes, and the management of infectious diseases.
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Affiliation(s)
- Jesus Rodriguez-Manzano
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Department of Infectious Disease, Faculty of Medicine, Imperial College London, London, United Kingdom. .,Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Faculty of Engineering, Imperial College London, London, United Kingdom.
| | - Nicolas Moser
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Faculty of Engineering, Imperial College London, London, United Kingdom
| | - Kenny Malpartida-Cardenas
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Faculty of Engineering, Imperial College London, London, United Kingdom
| | - Ahmad Moniri
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Faculty of Engineering, Imperial College London, London, United Kingdom
| | - Lenka Fisarova
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Faculty of Engineering, Imperial College London, London, United Kingdom
| | - Ivana Pennisi
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Faculty of Engineering, Imperial College London, London, United Kingdom
| | - Adhiratha Boonyasiri
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Department of Infectious Disease, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Elita Jauneikaite
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Department of Infectious Disease, Faculty of Medicine, Imperial College London, London, United Kingdom.,Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
| | - Alireza Abdolrasouli
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Department of Infectious Disease, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Jonathan A Otter
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Department of Infectious Disease, Faculty of Medicine, Imperial College London, London, United Kingdom.,Imperial College Healthcare NHS Trust, St Mary's Hospital, London, United Kingdom
| | - Frances Bolt
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Department of Infectious Disease, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Frances Davies
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Department of Infectious Disease, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Xavier Didelot
- School of Life Sciences and Department of Statistics, University of Warwick, Coventry, United Kingdom
| | - Alison Holmes
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Department of Infectious Disease, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Pantelis Georgiou
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Faculty of Engineering, Imperial College London, London, United Kingdom
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