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Harrison-Brown M, Scholes C, Ebrahimi M, Bell C, Kirwan G. Applying models of care for total hip and knee arthroplasty: External validation of a published predictive model to identify extended stay risk prior to lower-limb arthroplasty. Clin Rehabil 2024; 38:700-712. [PMID: 38377957 DOI: 10.1177/02692155241233348] [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] [Indexed: 02/22/2024]
Abstract
OBJECTIVE This study aimed to externally validate a reported model for identifying patients requiring extended stay following lower limb arthroplasty in a new setting. DESIGN External validation of a previously reported prognostic model, using retrospective data. SETTING Medium-sized hospital orthopaedic department, Australia. PARTICIPANTS Electronic medical records were accessed for data collection between Sep-2019 and Feb-2020 and retrospective data extracted from 200 randomly selected total hip or knee arthroplasty patients. INTERVENTION Participants received total hip or knee replacement between 2-Feb-16 and 4-Apr-19. This study was a non-interventional retrospective study. MAIN MEASURES Model validation was assessed with discrimination, calibration on both original and adjusted forms of the candidate model. Decision curve analysis was conducted on the outputs of the adjusted model to determine net benefit at a predetermined decision threshold (0.5). RESULTS The original model performed poorly, grossly overestimating length of stay with mean calibration of -3.6 (95% confidence interval -3.9 to -3.2) and calibration slope of 0.52. Performance improved following adjustment of the model intercept and model coefficients (mean calibration 0.48, 95% confidence interval 0.16 to 0.80 and slope of 1.0), but remained poorly calibrated at low and medium risk threshold and net benefit was modest (three additional patients per hundred identified as at-risk) at the a-priori risk threshold. CONCLUSIONS External validation demonstrated poor performance when applied to a new patient population and would provide limited benefit for our institution. Implementation of predictive models for arthroplasty should include practical assessment of discrimination, calibration and net benefit at a clinically acceptable threshold.
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Affiliation(s)
| | | | | | - Christopher Bell
- Department of Orthopaedics, QEII Jubilee Hospital, Brisbane, Australia
| | - Garry Kirwan
- Department of Physiotherapy, QEII Jubilee Hospital, Brisbane, Australia
- School of Health Sciences and Social Work, Griffith University, Brisbane, Australia
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Lawrence NR, Arshad MF, Pofi R, Ashby S, Dawson J, Tomlinson JW, Newell-Price J, Ross RJ, Elder CJ, Debono M. Multivariable Model to Predict an ACTH Stimulation Test to Diagnose Adrenal Insufficiency Using Previous Test Results. J Endocr Soc 2023; 7:bvad127. [PMID: 37942292 PMCID: PMC10628819 DOI: 10.1210/jendso/bvad127] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Indexed: 11/10/2023] Open
Abstract
Context The adrenocorticotropin hormone stimulation test (AST) is used to diagnose adrenal insufficiency, and is often repeated in patients when monitoring recovery of the hypothalamo-pituitary-adrenal axis. Objective To develop and validate a prediction model that uses previous AST results with new baseline cortisol to predict the result of a new AST. Methods This was a retrospective, longitudinal cohort study in patients who had undergone at least 2 ASTs, using polynomial regression with backwards variable selection, at a Tertiary UK adult endocrinology center. Model was developed from 258 paired ASTs over 5 years in 175 adults (mean age 52.4 years, SD 16.4), then validated on data from 111 patients over 1 year (51.8, 17.5) from the same center, data collected after model development. Candidate prediction variables included previous test baseline adrenocorticotropin hormone (ACTH), previous test baseline and 30-minute cortisol, days between tests, and new baseline ACTH and cortisol used with calculated cortisol/ACTH ratios to assess 8 candidate predictors. The main outcome measure was a new test cortisol measured 30 minutes after Synacthen administration. Results Using 258 sequential ASTs from 175 patients for model development and 111 patient tests for model validation, previous baseline cortisol, previous 30-minute cortisol and new baseline cortisol were superior at predicting new 30-minute cortisol (R2 = 0.71 [0.49-0.93], area under the curve [AUC] = 0.97 [0.94-1.0]) than new baseline cortisol alone (R2 = 0.53 [0.22-0.84], AUC = 0.88 [0.81-0.95]). Conclusion Results of a previous AST can be objectively combined with new early-morning cortisol to predict the results of a new AST better than new early-morning cortisol alone. An online calculator is available at https://endocrinology.shinyapps.io/sheffield_sst_calculator/ for external validation.
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Affiliation(s)
- Neil Richard Lawrence
- Division of Clinical Medicine, School of Medicine and Population Health, University of Sheffield, Sheffield S10 2TN, UK
- Paediatric Endocrinology Department, Sheffield Children's NHS Foundation Trust, Sheffield S10 2TH, UK
| | - Muhammad Fahad Arshad
- Division of Clinical Medicine, School of Medicine and Population Health, University of Sheffield, Sheffield S10 2TN, UK
- Endocrinology Department, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield S10 2JF, UK
| | - Riccardo Pofi
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Churchill Hospital, University Hospitals NHS Trust, Oxford OX3 9DU, UK
| | - Sean Ashby
- Division of Clinical Medicine, School of Medicine and Population Health, University of Sheffield, Sheffield S10 2TN, UK
| | - Jeremy Dawson
- Division of Clinical Medicine, School of Medicine and Population Health, University of Sheffield, Sheffield S10 2TN, UK
| | - Jeremy W Tomlinson
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Churchill Hospital, University Hospitals NHS Trust, Oxford OX3 9DU, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford OX3 9DU, UK
| | - John Newell-Price
- Division of Clinical Medicine, School of Medicine and Population Health, University of Sheffield, Sheffield S10 2TN, UK
- Endocrinology Department, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield S10 2JF, UK
| | - Richard J Ross
- Division of Clinical Medicine, School of Medicine and Population Health, University of Sheffield, Sheffield S10 2TN, UK
| | - Charlotte J Elder
- Division of Clinical Medicine, School of Medicine and Population Health, University of Sheffield, Sheffield S10 2TN, UK
- Paediatric Endocrinology Department, Sheffield Children's NHS Foundation Trust, Sheffield S10 2TH, UK
| | - Miguel Debono
- Division of Clinical Medicine, School of Medicine and Population Health, University of Sheffield, Sheffield S10 2TN, UK
- Endocrinology Department, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield S10 2JF, UK
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Liu TPJ, David M, Clark JR, Low THH, Batstone MD. Prediction nomogram development and validation for postoperative radiotherapy in the management of oral squamous cell carcinoma. Head Neck 2023; 45:1503-1510. [PMID: 37019874 DOI: 10.1002/hed.27363] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Revised: 03/11/2023] [Accepted: 03/24/2023] [Indexed: 04/07/2023] Open
Abstract
BACKGROUND Predictive nomograms are useful tools to guide clinicians in estimating disease course. Oral squamous cell carcinoma (OSCC) patients would benefit from an interactive prediction calculator that defines their levels of survival-risk specific to their tumors to guide the use of postoperative radiotherapy (PORT). METHODS Patients with OSCC surgically treated with curative intent at four Head and Neck Cancer Centres were recruited retrospectively for development and validation of nomograms. Predictor variables include PORT, age, T and N classification, surgical margins, perineural invasion, and lymphovascular invasion. Outcomes were disease-free, disease-specific, and overall survivals over 5 years. RESULTS 1296 patients with OSCC were in training cohort for nomogram analysis. Algorithms were developed to show relative benefit of PORT in survivals for higher-risk patients. External validation on 1212 patients found the nomogram to be robust with favorable discrimination and calibration. CONCLUSION The proposed calculator can assist clinicians and patients in the decision-making process for PORT.
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Affiliation(s)
- Timothy P J Liu
- Department of Oral and Maxillofacial Surgery, Royal Brisbane and Women's Hospital, Bowen Bridge Road & Butterfield Street, Herston, Queensland, Australia
- Faculty of Medicine, University of Queensland, Level 2, Mayne Medical Building, 288 Herston Road, Herston, Queensland, Australia
| | - Michael David
- School of Medicine & Dentistry, Griffith University, Gold Coast, Queensland, Australia
- The Daffodil Centre, University of Sydney (A Joint Venture With Cancer Council), Kings Cross, New South Wales, Australia
| | - Jonathan R Clark
- Central Clinical School, Faculty of Medicine and Health, University of Sydney, Camperdown, New South Wales, Australia
- Sydney Head and Neck Cancer Institute, Chris O'Brien Lifehouse, 119-143 Missenden Road, Camperdown, New South Wales, Australia
- Royal Prince Alfred Institute of Academic Surgery, Sydney Local Health District, Sydney, New South Wales, Australia
| | - Tsu-Hui Hubert Low
- Central Clinical School, Faculty of Medicine and Health, University of Sydney, Camperdown, New South Wales, Australia
- Sydney Head and Neck Cancer Institute, Chris O'Brien Lifehouse, 119-143 Missenden Road, Camperdown, New South Wales, Australia
| | - Martin D Batstone
- Department of Oral and Maxillofacial Surgery, Royal Brisbane and Women's Hospital, Bowen Bridge Road & Butterfield Street, Herston, Queensland, Australia
- Faculty of Medicine, University of Queensland, Level 2, Mayne Medical Building, 288 Herston Road, Herston, Queensland, Australia
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Haider S, Adderley N, Tallouzi MO, Sadiq SN, Steel DH, Chavan R, Sheikh I, Nirantharakumar K, Snell KIE. Diabetic retinopathy progression in patients under monitoring for treatment or vision loss: external validation and update of a multivariable prediction model. BMJ Open 2023; 13:e073015. [PMID: 37012014 PMCID: PMC10083856 DOI: 10.1136/bmjopen-2023-073015] [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] [Indexed: 04/04/2023] Open
Abstract
INTRODUCTION The number of people with diabetes mellitus is increasing globally and consequently so too is diabetic retinopathy (DR). Most patients with diabetes are monitored through the diabetic eye screening programme (DESP) until they have signs of retinopathy and these changes progress, requiring referral into hospital eye services (HES). Here, they continue to be monitored until they require treatment. Due to current pressures on HES, delays can occur, leading to harm. There is a need to triage patients based on their individual risk. At present, patients are stratified according to retinopathy stage alone, yet other risk factors like glycated haemoglobin (HbA1c) may be useful. Therefore, a prediction model that combines multiple prognostic factors to predict progression will be useful for triage in this setting to improve care.We previously developed a Diabetic Retinopathy Progression model to Treatment or Vision Loss (DRPTVL-UK) using a large primary care database. The aim of the present study is to externally validate the DRPTVL-UK model in a secondary care setting, specifically in a population under care by HES. This study will also provide an opportunity to update the model by considering additional predictors not previously available. METHODS AND ANALYSIS We will use a retrospective cohort of 2400 patients with diabetes aged 12 years and over, referred from DESP to the NHS hospital trusts with referable DR between 2013 and 2016, with follow-up information recorded until December 2021.We will evaluate the external validity of the DRPTVL-UK model using measures of discrimination, calibration and net benefit. In addition, consensus meetings will be held to agree on acceptable risk thresholds for triage within the HES system. ETHICS AND DISSEMINATION This study was approved by REC (ref 22/SC/0425, 05/12/2022, Hampshire A Research Ethics Committee). The results of the study will be published in a peer-reviewed journal, presented at clinical conferences. TRIAL REGISTRATION NUMBER ISRCTN 10956293.
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Affiliation(s)
- Sajjad Haider
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - Nicola Adderley
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | | | - Salman Naveed Sadiq
- Ophthalmology, Royal Victoria Infirmary, Newcastle upon Tyne, UK
- Sunderland Eye Infirmary, Sunderland, UK
| | - David H Steel
- Sunderland Eye Infirmary, Sunderland, UK
- Newcastle University Biosciences Institute, Newcastle upon Tyne, UK
| | - Randhir Chavan
- Sandwell and West Birmingham Hospitals NHS Trust, Birmingham, UK
| | - Ijaz Sheikh
- Eye Department, Surrey and Sussex Healthcare NHS Trust, Redhill, UK
| | | | - Kym I E Snell
- Centre for Prognosis Research, School of Medicine, Keele University, Keele, UK
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Cai J, Guo L, Zhu L, Xia L, Qian L, Lure YMF, Yin X. Impact of localized fine tuning in the performance of segmentation and classification of lung nodules from computed tomography scans using deep learning. Front Oncol 2023; 13:1140635. [PMID: 37056345 PMCID: PMC10088514 DOI: 10.3389/fonc.2023.1140635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 03/16/2023] [Indexed: 03/30/2023] Open
Abstract
BackgroundAlgorithm malfunction may occur when there is a performance mismatch between the dataset with which it was developed and the dataset on which it was deployed.MethodsA baseline segmentation algorithm and a baseline classification algorithm were developed using public dataset of Lung Image Database Consortium to detect benign and malignant nodules, and two additional external datasets (i.e., HB and XZ) including 542 cases and 486 cases were involved for the independent validation of these two algorithms. To explore the impact of localized fine tuning on the individual segmentation and classification process, the baseline algorithms were fine tuned with CT scans of HB and XZ datasets, respectively, and the performance of the fine tuned algorithms was tested to compare with the baseline algorithms.ResultsThe proposed baseline algorithms of both segmentation and classification experienced a drop when directly deployed in external HB and XZ datasets. Comparing with the baseline validation results in nodule segmentation, the fine tuned segmentation algorithm obtained better performance in Dice coefficient, Intersection over Union, and Average Surface Distance in HB dataset (0.593 vs. 0.444; 0.450 vs. 0.348; 0.283 vs. 0.304) and XZ dataset (0.601 vs. 0.486; 0.482 vs. 0.378; 0.225 vs. 0.358). Similarly, comparing with the baseline validation results in benign and malignant nodule classification, the fine tuned classification algorithm had improved area under the receiver operating characteristic curve value, accuracy, and F1 score in HB dataset (0.851 vs. 0.812; 0.813 vs. 0.769; 0.852 vs. 0.822) and XZ dataset (0.724 vs. 0.668; 0.696 vs. 0.617; 0.737 vs. 0.668).ConclusionsThe external validation performance of localized fine tuned algorithms outperformed the baseline algorithms in both segmentation process and classification process, which showed that localized fine tuning may be an effective way to enable a baseline algorithm generalize to site-specific use.
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Affiliation(s)
- Jingwei Cai
- Radiology Department, Affiliated Hospital of Hebei University, Baoding, Hebei, China
- Clinical Medical College, Hebei University, Baoding, Hebei, China
| | - Lin Guo
- Shenzhen Zhiying Medical Imaging, Shenzhen, Guangdong, China
| | - Litong Zhu
- Department of Medicine, Queen Mary Hospital, University of Hong, Hong Kong, Hong Kong SAR, China
| | - Li Xia
- Shenzhen Zhiying Medical Imaging, Shenzhen, Guangdong, China
| | - Lingjun Qian
- Shenzhen Zhiying Medical Imaging, Shenzhen, Guangdong, China
| | | | - Xiaoping Yin
- Radiology Department, Affiliated Hospital of Hebei University, Baoding, Hebei, China
- *Correspondence: Xiaoping Yin,
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Luijken K, Song J, Groenwold RHH. Quantitative prediction error analysis to investigate predictive performance under predictor measurement heterogeneity at model implementation. Diagn Progn Res 2022; 6:7. [PMID: 35387683 PMCID: PMC8988417 DOI: 10.1186/s41512-022-00121-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 02/07/2022] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND When a predictor variable is measured in similar ways at the derivation and validation setting of a prognostic prediction model, yet both differ from the intended use of the model in practice (i.e., "predictor measurement heterogeneity"), performance of the model at implementation needs to be inferred. This study proposed an analysis to quantify the impact of anticipated predictor measurement heterogeneity. METHODS A simulation study was conducted to assess the impact of predictor measurement heterogeneity across validation and implementation setting in time-to-event outcome data. The use of the quantitative prediction error analysis was illustrated using an example of predicting the 6-year risk of developing type 2 diabetes with heterogeneity in measurement of the predictor body mass index. RESULTS In the simulation study, calibration-in-the-large of prediction models was poor and overall accuracy was reduced in all scenarios of predictor measurement heterogeneity. Model discrimination decreased with increasing random predictor measurement heterogeneity. CONCLUSIONS Heterogeneity of predictor measurements across settings of validation and implementation reduced predictive performance at implementation of prognostic models with a time-to-event outcome. When validating a prognostic model, the targeted clinical setting needs to be considered and analyses can be conducted to quantify the impact of anticipated predictor measurement heterogeneity on model performance at implementation.
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Affiliation(s)
- Kim Luijken
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands.
| | - Jia Song
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Rolf H H Groenwold
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
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Fernandez-Felix BM, Barca LV, Garcia-Esquinas E, Correa-Pérez A, Fernández-Hidalgo N, Muriel A, Lopez-Alcalde J, Álvarez-Diaz N, Pijoan JI, Ribera A, Elorza EN, Muñoz P, Fariñas MDC, Goenaga MÁ, Zamora J. Prognostic models for mortality after cardiac surgery in patients with infective endocarditis: a systematic review and aggregation of prediction models. Clin Microbiol Infect 2021; 27:1422-1430. [PMID: 34620380 DOI: 10.1016/j.cmi.2021.05.051] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2021] [Revised: 05/19/2021] [Accepted: 05/31/2021] [Indexed: 12/13/2022]
Abstract
BACKGROUND There are several prognostic models to estimate the risk of mortality after surgery for active infective endocarditis (IE). However, these models incorporate different predictors and their performance is uncertain. OBJECTIVE We systematically reviewed and critically appraised all available prediction models of postoperative mortality in patients undergoing surgery for IE, and aggregated them into a meta-model. DATA SOURCES We searched Medline and EMBASE databases from inception to June 2020. STUDY ELIGIBILITY CRITERIA We included studies that developed or updated a prognostic model of postoperative mortality in patient with IE. METHODS We assessed the risk of bias of the models using PROBAST (Prediction model Risk Of Bias ASsessment Tool) and we aggregated them into an aggregate meta-model based on stacked regressions and optimized it for a nationwide registry of IE patients. The meta-model performance was assessed using bootstrap validation methods and adjusted for optimism. RESULTS We identified 11 prognostic models for postoperative mortality. Eight models had a high risk of bias. The meta-model included weighted predictors from the remaining three models (EndoSCORE, specific ES-I and specific ES-II), which were not rated as high risk of bias and provided full model equations. Additionally, two variables (age and infectious agent) that had been modelled differently across studies, were estimated based on the nationwide registry. The performance of the meta-model was better than the original three models, with the corresponding performance measures: C-statistics 0.79 (95% CI 0.76-0.82), calibration slope 0.98 (95% CI 0.86-1.13) and calibration-in-the-large -0.05 (95% CI -0.20 to 0.11). CONCLUSIONS The meta-model outperformed published models and showed a robust predictive capacity for predicting the individualized risk of postoperative mortality in patients with IE. PROTOCOL REGISTRATION PROSPERO (registration number CRD42020192602).
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Affiliation(s)
- Borja M Fernandez-Felix
- Clinical Biostatistics Unit, Hospital Universitario Ramon y Cajal (IRYCIS), Madrid, Spain; CIBER Epidemiology and Public Health (CIBERESP), Madrid, Spain.
| | - Laura Varela Barca
- Department of Cardiovascular Surgery, Fundacion Jimenez Diaz University Hospital, Madrid, Spain
| | - Esther Garcia-Esquinas
- CIBER Epidemiology and Public Health (CIBERESP), Madrid, Spain; Department of Preventive Medicine and Public Health, School of Medicine, Universidad Autónoma de Madrid, Madrid, Spain; IdiPaz (Hospital Universitario La Paz-Universidad Autónoma de Madrid), Madrid, Spain
| | - Andrea Correa-Pérez
- Clinical Biostatistics Unit, Hospital Universitario Ramon y Cajal (IRYCIS), Madrid, Spain; Faculty of Medicine, Universidad Francisco de Vitoria, Madrid, Spain
| | - Nuria Fernández-Hidalgo
- Servei de Malalties Infeccioses, Hospital Universitari Vall d'Hebron, Barcelona, Spain; Red Española de Investigación en Patología Infecciosa (REIPI), Instituto de Salud Carlos III, Madrid, Spain
| | - Alfonso Muriel
- Clinical Biostatistics Unit, Hospital Universitario Ramon y Cajal (IRYCIS), Madrid, Spain; CIBER Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Jesus Lopez-Alcalde
- Clinical Biostatistics Unit, Hospital Universitario Ramon y Cajal (IRYCIS), Madrid, Spain; CIBER Epidemiology and Public Health (CIBERESP), Madrid, Spain; Faculty of Medicine, Universidad Francisco de Vitoria, Madrid, Spain; Institute for Complementary and Integrative Medicine, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Noelia Álvarez-Diaz
- Medical Library, Hospital Universitario Ramon y Cajal (IRYCIS), Madrid, Madrid, Spain
| | - Jose I Pijoan
- CIBER Epidemiology and Public Health (CIBERESP), Madrid, Spain; Hospital Universitario Cruces/OSI EEC, Barakaldo, Spain; Biocruces-Bizkaia Health Research Institute, Barakaldo, Spain
| | - Aida Ribera
- CIBER Epidemiology and Public Health (CIBERESP), Madrid, Spain; Cardiovascular Epidemiology and Research Unit, Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | - Enrique Navas Elorza
- Department of Infectology, Hospital Universitario Ramon y Cajal (IRYCIS), Madrid, Spain
| | - Patricia Muñoz
- Clinical Microbiology and Infectious Diseases Service, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón, CIBER Enfermedades Respiratorias-CIBERES, Facultad de Medicina, Universidad Complutense de Madrid, Madrid, Spain
| | - María Del Carmen Fariñas
- Infectious Diseases Service, Hospital Universitario Marqués de Valdecilla-IDIVAL, Universidad de Cantabria, Santander, Spain
| | - Miguel Ángel Goenaga
- Infectious Diseases Service, Hospital Universitario Donostia, IIS Biodonostia, OSI Donostialdea, San Sebastián, Spain
| | - Javier Zamora
- Clinical Biostatistics Unit, Hospital Universitario Ramon y Cajal (IRYCIS), Madrid, Spain; CIBER Epidemiology and Public Health (CIBERESP), Madrid, Spain; WHO Collaborating Centre for Global Women's Health, Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
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