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Daines L, Bonnett LJ, Tibble H, Boyd A, Thomas R, Price D, Turner SW, Lewis SC, Sheikh A, Pinnock H. Deriving and validating an asthma diagnosis prediction model for children and young people in primary care. Wellcome Open Res 2023; 8:195. [PMID: 37928213 PMCID: PMC10622861 DOI: 10.12688/wellcomeopenres.19078.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/05/2023] [Indexed: 11/08/2023] Open
Abstract
Introduction: Accurately diagnosing asthma can be challenging. We aimed to derive and validate a prediction model to support primary care clinicians assess the probability of an asthma diagnosis in children and young people. Methods: The derivation dataset was created from the Avon Longitudinal Study of Parents and Children (ALSPAC) linked to electronic health records. Participants with at least three inhaled corticosteroid prescriptions in 12-months and a coded asthma diagnosis were designated as having asthma. Demographics, symptoms, past medical/family history, exposures, investigations, and prescriptions were considered as candidate predictors. Potential candidate predictors were included if data were available in ≥60% of participants. Multiple imputation was used to handle remaining missing data. The prediction model was derived using logistic regression. Internal validation was completed using bootstrap re-sampling. External validation was conducted using health records from the Optimum Patient Care Research Database (OPCRD). Results: Predictors included in the final model were wheeze, cough, breathlessness, hay-fever, eczema, food allergy, social class, maternal asthma, childhood exposure to cigarette smoke, prescription of a short acting beta agonist and the past recording of lung function/reversibility testing. In the derivation dataset, which comprised 11,972 participants aged <25 years (49% female, 8% asthma), model performance as indicated by the C-statistic and calibration slope was 0.86, 95% confidence interval (CI) 0.85-0.87 and 1.00, 95% CI 0.95-1.05 respectively. In the external validation dataset, which included 2,670 participants aged <25 years (50% female, 10% asthma), the C-statistic was 0.85, 95% CI 0.83-0.88, and calibration slope 1.22, 95% CI 1.09-1.35. Conclusions: We derived and validated a prediction model for clinicians to calculate the probability of asthma diagnosis for a child or young person up to 25 years of age presenting to primary care. Following further evaluation of clinical effectiveness, the prediction model could be implemented as a decision support software.
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Affiliation(s)
- Luke Daines
- Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, EH8 9AG, UK
| | - Laura J Bonnett
- Department of Biostatistics, University of Liverpool, Liverpool, L69 3GL, UK
| | - Holly Tibble
- Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, EH8 9AG, UK
| | - Andy Boyd
- Institute of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2PS, UK
| | - Richard Thomas
- Institute of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2PS, UK
| | - David Price
- Observational and Pragmatic Research Institute, Singapore, 573969, Singapore
- Centre of Academic Primary Care, Division of Applied Health Sciences, University of Aberdeen, Aberdeen, AB25 2ZG, UK
| | - Steve W Turner
- Child Health, University of Aberdeen, Aberdeen, AB25 2ZG, UK
- Women and Children Division, NHS Grampian, Aberdeen, AB25 2ZG, UK
| | - Steff C Lewis
- Edinburgh Clinical Trials Unit, Usher Institute, University of Edinburgh, Edinburgh, EH16 4UX, UK
| | - Aziz Sheikh
- Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, EH8 9AG, UK
| | - Hilary Pinnock
- Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, EH8 9AG, UK
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Daines L, Bonnett LJ, Tibble H, Boyd A, Thomas R, Price D, Turner SW, Lewis SC, Sheikh A, Pinnock H. Deriving and validating an asthma diagnosis prediction model for children and young people in primary care. Wellcome Open Res 2023; 8:195. [PMID: 37928213 PMCID: PMC10622861 DOI: 10.12688/wellcomeopenres.19078.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/05/2023] [Indexed: 11/07/2023] Open
Abstract
Introduction: Accurately diagnosing asthma can be challenging. We aimed to derive and validate a prediction model to support primary care clinicians assess the probability of an asthma diagnosis in children and young people. Methods: The derivation dataset was created from the Avon Longitudinal Study of Parents and Children (ALSPAC) linked to electronic health records. Participants with at least three inhaled corticosteroid prescriptions in 12-months and a coded asthma diagnosis were designated as having asthma. Demographics, symptoms, past medical/family history, exposures, investigations, and prescriptions were considered as candidate predictors. Potential candidate predictors were included if data were available in ≥60% of participants. Multiple imputation was used to handle remaining missing data. The prediction model was derived using logistic regression. Internal validation was completed using bootstrap re-sampling. External validation was conducted using health records from the Optimum Patient Care Research Database (OPCRD). Results: Predictors included in the final model were wheeze, cough, breathlessness, hay-fever, eczema, food allergy, social class, maternal asthma, childhood exposure to cigarette smoke, prescription of a short acting beta agonist and the past recording of lung function/reversibility testing. In the derivation dataset, which comprised 11,972 participants aged <25 years (49% female, 8% asthma), model performance as indicated by the C-statistic and calibration slope was 0.86, 95% confidence interval (CI) 0.85-0.87 and 1.00, 95% CI 0.95-1.05 respectively. In the external validation dataset, which included 2,670 participants aged <25 years (50% female, 10% asthma), the C-statistic was 0.85, 95% CI 0.83-0.88, and calibration slope 1.22, 95% CI 1.09-1.35. Conclusions: We derived and validated a prediction model for clinicians to calculate the probability of asthma diagnosis for a child or young person up to 25 years of age presenting to primary care. Following further evaluation of clinical effectiveness, the prediction model could be implemented as a decision support software.
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Affiliation(s)
- Luke Daines
- Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, EH8 9AG, UK
| | - Laura J Bonnett
- Department of Biostatistics, University of Liverpool, Liverpool, L69 3GL, UK
| | - Holly Tibble
- Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, EH8 9AG, UK
| | - Andy Boyd
- Institute of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2PS, UK
| | - Richard Thomas
- Institute of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2PS, UK
| | - David Price
- Observational and Pragmatic Research Institute, Singapore, 573969, Singapore
- Centre of Academic Primary Care, Division of Applied Health Sciences, University of Aberdeen, Aberdeen, AB25 2ZG, UK
| | - Steve W Turner
- Child Health, University of Aberdeen, Aberdeen, AB25 2ZG, UK
- Women and Children Division, NHS Grampian, Aberdeen, AB25 2ZG, UK
| | - Steff C Lewis
- Edinburgh Clinical Trials Unit, Usher Institute, University of Edinburgh, Edinburgh, EH16 4UX, UK
| | - Aziz Sheikh
- Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, EH8 9AG, UK
| | - Hilary Pinnock
- Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, EH8 9AG, UK
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Canny A, Donaghy E, Murray V, Campbell L, Stonham C, Bush A, McKinstry B, Milne H, Pinnock H, Daines L. Patient views on asthma diagnosis and how a clinical decision support system could help: A qualitative study. Health Expect 2022; 26:307-317. [PMID: 36370457 PMCID: PMC9854294 DOI: 10.1111/hex.13657] [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: 07/06/2022] [Revised: 09/22/2022] [Accepted: 10/23/2022] [Indexed: 11/13/2022] Open
Abstract
INTRODUCTION Making a diagnosis of asthma can be challenging for clinicians and patients. A clinical decision support system (CDSS) for use in primary care including a patient-facing mode, could change how information is shared between patients and healthcare professionals and improve the diagnostic process. METHODS Participants diagnosed with asthma within the last 5 years were recruited from general practices across four UK regions. In-depth interviews were used to explore patient experiences relating to their asthma diagnosis and to understand how a CDSS could be used to improve the diagnostic process for patients. Interviews were audio recorded, transcribed verbatim and analysed using a thematic approach. RESULTS Seventeen participants (12 female) undertook interviews, including 14 individuals and 3 parents of children with asthma. Being diagnosed with asthma was generally considered an uncertain process. Participants felt a lack of consultation time and poor communication affected their understanding of asthma and what to expect. Had the nature of asthma and the steps required to make a diagnosis been explained more clearly, patients felt their understanding and engagement in asthma self-management could have been improved. Participants considered that a CDSS could provide resources to support the diagnostic process, prompt dialogue, aid understanding and support shared decision-making. CONCLUSION Undergoing an asthma diagnosis was uncertain for patients if their ideas and concerns were not addressed by clinicians and were influenced by a lack of consultation time and limitations in communication. An asthma diagnosis CDSS could provide structure and an interface to prompt dialogue, provide visuals about asthma to aid understanding and encourage patient involvement. PATIENT AND PUBLIC CONTRIBUTION Prespecified semistructured interview topic guides (young person and adult versions) were developed by the research team and piloted with members of the Asthma UK Centre for Applied Research Patient and Public Involvement (PPI) group. Findings were regularly discussed within the research group and with PPI colleagues to aid the interpretation of data.
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Affiliation(s)
- Anne Canny
- Asthma UK Centre for Applied Research, Usher InstituteUniversity of EdinburghEdinburghUK
| | - Eddie Donaghy
- Asthma UK Centre for Applied Research, Usher InstituteUniversity of EdinburghEdinburghUK
| | - Victoria Murray
- Asthma UK Centre for Applied Research, Usher InstituteUniversity of EdinburghEdinburghUK
| | - Leo Campbell
- Asthma UK Centre for Applied Research, Usher InstituteUniversity of EdinburghEdinburghUK
| | - Carol Stonham
- NHS Gloucestershire Clinical Commissioning GroupGloucesterUK,Primary Care Respiratory Society (PCRS)KnowleUK
| | - Andrew Bush
- Imperial Centre for Paediatrics and Child Health and National Heart and Lung InstituteImperial CollegeLondonUK,Department of Paediatric Respiratory MedicineRoyal Brompton HospitalLondonUK
| | - Brian McKinstry
- Centre for Population and Health Sciences, Usher InstituteUniversity of EdinburghEdinburghUK
| | - Heather Milne
- South East GP UnitNHS Education for ScotlandEdinburghUK
| | - Hilary Pinnock
- Asthma UK Centre for Applied Research, Usher InstituteUniversity of EdinburghEdinburghUK
| | - Luke Daines
- Asthma UK Centre for Applied Research, Usher InstituteUniversity of EdinburghEdinburghUK
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Gille T, Sivapalan P, Kaltsakas G, Kolekar SB, Armstrong M, Tuffnell R, Evans RA, Vagheggini G, Degani-Costa LH, Vicente C, Das N, Poberezhets V, Rolland-Debord C, Bayat S, Vogiatzis I, Franssen FM, Pinnock H, Vanfleteren LE. ERS International Congress 2021: highlights from the Respiratory Clinical Care and Physiology Assembly. ERJ Open Res 2022; 8:00710-2021. [PMID: 35615417 PMCID: PMC9125042 DOI: 10.1183/23120541.00710-2021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 04/06/2022] [Indexed: 11/05/2022] Open
Abstract
It is a challenge to keep abreast of all the clinical and scientific advances in the field of respiratory medicine. This article contains an overview of laboratory-based science, randomised controlled trials and qualitative research that were presented during the 2021 European Respiratory Society International Congress within the sessions from the five groups of the Assembly 1 - Respiratory clinical care and physiology. Selected presentations are summarised from a wide range of topics: clinical problems, rehabilitation and chronic care, general practice and primary care, electronic/mobile health (e-health/m-health), clinical respiratory physiology, exercise and functional imaging.
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Affiliation(s)
- Thomas Gille
- Service de Physiologie et Explorations Fonctionnelles, Centre Hospitalier Universitaire Avicenne, Hôpitaux Universitaires de Paris Seine-Saint-Denis, Assistance Publique-Hôpitaux de Paris, Bobigny, France
- Inserm U1272 “Hypoxia and the Lung”, UFR Santé – Médecine – Biologie Humaine Léonard de Vinci, Université Sorbonne Paris Nord, Bobigny, France
| | - Pradeesh Sivapalan
- Section of Respiratory Medicine, Herlev-Gentofte University Hospital, Hellerup, Denmark
| | - Georgios Kaltsakas
- Lane Fox Respiratory Service, Guy's and St Thomas’ NHS Foundation Trust, London, UK
- Centre of Human and Applied Physiological Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK
- 1st Respiratory Medicine Dept, “Sotiria” Hospital for Diseases of the Chest, National and Kapodistrian University of Athens, Athens, Greece
| | - Shailesh B. Kolekar
- Dept of Internal Medicine, Zealand University Hospital, Roskilde, Denmark
- Dept of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Matthew Armstrong
- Dept of Rehabilitation and Sport Sciences, Bournemouth University, Poole, UK
| | - Rachel Tuffnell
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Rachael A. Evans
- NIHR Leicester Biomedical Research Centre – Respiratory, University Hospitals of Leicester NHS Trust, Leicester, UK
- Dept of Respiratory Sciences, University of Leicester, Leicester, UK
| | - Guido Vagheggini
- Dept of Medical Specialties, Chronic Respiratory Failure Care Pathway, Azienda USL Toscana Nordovest, Volterra, Italy
- Fondazione Volterra Ricerche Onlus, Volterra, Italy
| | | | | | - Nilakash Das
- Laboratory of Respiratory Diseases and Thoracic Surgery, Chronic Diseases and Metabolism, KU Leuven, Leuven, Belgium
| | - Vitalii Poberezhets
- Dept of Propedeutics of Internal Medicine, National Pirogov Memorial Medical University, Vinnytsya, Ukraine
| | - Camille Rolland-Debord
- Service de Pneumologie, Hôpital Gabriel Montpied, CHU Clermont-Ferrand, Clermont-Ferrand, France
| | - Sam Bayat
- Service de Pneumologie et de Physiologie, CS10217, CHU Grenoble, Grenoble, France
- Univ. Grenoble Alpes, Inserm UA07 STROBE, Grenoble, France
| | - Ioannis Vogiatzis
- Dept of Sport, Exercise and Rehabilitation, Northumbria University Newcastle, Newcastle upon Tyne, UK
| | - Frits M.E. Franssen
- Dept of Research and Development, Ciro, Horn, the Netherlands
- Dept of Respiratory Medicine, Maastricht University Medical Centre (MUMC+), Maastricht, the Netherlands
- NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht, the Netherlands
| | - Hilary Pinnock
- Allergy and Respiratory Research Group, Usher Institute, The University of Edinburgh, Edinburgh, UK
| | - Lowie E.G.W. Vanfleteren
- COPD Center, Dept of Respiratory Medicine and Allergology, Sahlgrenska University Hospital, Gothenburg, Sweden
- Dept of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
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Belsti Y, Nigussie ZM, Tsegaye GW. Derivation and Validation of a Risk Score to Predict Mortality of Early Neonates at Neonatal Intensive Care Unit: The END in NICU Score. Int J Gen Med 2021; 14:8121-8134. [PMID: 34795517 PMCID: PMC8594787 DOI: 10.2147/ijgm.s336888] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 11/02/2021] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Early neonatal death is death of infants in the first week of life. And 34% to 92% of neonatal deaths happen within 7 days of postnatal period. Thus, the early neonatal period is the most critical time for an infant, requiring different strategies to prevent mortality. Among strategies, deriving and implementing early warning scores is crucial to predict early neonatal mortality earlier upon hospital admission. OBJECTIVE To derive and validate a risk score to predict mortality of early neonates at Felege Hiwot Specialized Hospital neonatal intensive care unit, Bahir Dar, 2021. METHODS The document review was conducted from February 24, to April 08, 2021, on all early neonates admitted to neonatal intensive care unit from January 1, 2018 to December 31, 2020. The total number of early neonates included in the derivation study was 1100. Data were collected by using checklists prepared on EpiCollect5 software. After exporting the data to R version 4.0.5 software, variables with (p < 0.25) from the simple binary regression were entered into a multiple logistic regression model, and significant variables (p < 0.05) were kept in the model. The discrimination and calibration were assessed. The model was internally validated using bootstrapping technique. RESULTS Admission weight, birth Apgar score, perinatal asphyxia, respiratory distress syndrome, mode of delivery, sepsis, and gestational age at birth remained in the final multiple logistic regression prediction model. The area under curve of receiver operating characteristic curve for early neonatal mortality score was 90.7%. The model retained excellent discrimination under internal validation. The sensitivity, specificity, and positive predictive value, negative predictive value of the model was 89.4%, 82.5%, 55.5%, and 96.9%, respectively. CONCLUSION The derived score has an excellent discriminative ability and good prediction performance. This is an important tool for predicting early neonatal mortality in neonatal intensive care units at admission.
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Affiliation(s)
- Yitayeh Belsti
- Department of Physiology, School of Medicine, College of Medicine and Health Science, University of Gondar, Gondar, Ethiopia
| | - Zelalem Mehari Nigussie
- Department of Epidemiology and Biostatistics, School of Public Health, College of Medicine and Health Science, Bahir Dar University, Bahir Dar, Ethiopia
| | - Gebeyaw Wudie Tsegaye
- Department of Epidemiology and Biostatistics, School of Public Health, College of Medicine and Health Science, Bahir Dar University, Bahir Dar, Ethiopia
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