Asma Alamoudi, Dominic Sandhu, Thomas D E Bithell, Nicholas M J Smith, Graham Richmond, Lorenzo S Petralia, Snapper Magor-Elliott, Haopeng Xu, Nick P Talbot, Grant A D Ritchie, Nayia Petousi, Peter A Robbins
{"title":"计算机心肺造影术:物理特征对肺参数估计的影响。","authors":"Asma Alamoudi, Dominic Sandhu, Thomas D E Bithell, Nicholas M J Smith, Graham Richmond, Lorenzo S Petralia, Snapper Magor-Elliott, Haopeng Xu, Nick P Talbot, Grant A D Ritchie, Nayia Petousi, Peter A Robbins","doi":"10.1113/EP092423","DOIUrl":null,"url":null,"abstract":"<p><p>Computed cardiopulmonography (CCP) is a technique that measures lung volumes (functional residual capacity and deadspace) together with novel parameters reflecting lung inhomogeneities (non-uniformities in lung inflation and deadspace path length). First, highly precise measurements of gas exchange are made during a nitrogen washout with a purpose-built molecular flow sensor. Second, an individual's lung physiology is then described by personalising the parameters of a bespoke cardio-respiratory model obtained by fitting the model to the data. The present study examines the effects of participants' physical characteristics on these parameter values, and from this also provides preliminary estimates for normal ranges. Data from 92 healthy individuals (27% female, age 40 ± 19 (mean ± SD) years, height 1.75 ± 0.09 m, mass 74 ± 14 kg) were used. A prediction equation for each CCP parameter was written as: y = α + βln(age) + γln(height) + δln(BMI) + ε(is_Female) + error, where BMI is body mass index. Non-significant terms (P > 0.1) were removed sequentially to identify just the significant characteristics. Physical characteristics exerted a large influence on volume-related CCP parameters. In contrast, only age had a significant influence on inhomogeneity-related CCP parameters. The prediction equations, together with their mean squared errors, were used to calculate z-scores for CCP data from three previously published studies in asthma, chronic obstructive pulmonary disease, and early cystic fibrosis. Values for these z-scores often lay beyond those commonly used to define a normal range (±1.65). In conclusion, reference values for inhomogeneity-based CCP parameters may only need correcting for age, and often appear as abnormal in airways disease.</p>","PeriodicalId":12092,"journal":{"name":"Experimental Physiology","volume":" ","pages":""},"PeriodicalIF":2.8000,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Computed cardiopulmonography: Effects of physical characteristics on lung parameter estimates.\",\"authors\":\"Asma Alamoudi, Dominic Sandhu, Thomas D E Bithell, Nicholas M J Smith, Graham Richmond, Lorenzo S Petralia, Snapper Magor-Elliott, Haopeng Xu, Nick P Talbot, Grant A D Ritchie, Nayia Petousi, Peter A Robbins\",\"doi\":\"10.1113/EP092423\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Computed cardiopulmonography (CCP) is a technique that measures lung volumes (functional residual capacity and deadspace) together with novel parameters reflecting lung inhomogeneities (non-uniformities in lung inflation and deadspace path length). First, highly precise measurements of gas exchange are made during a nitrogen washout with a purpose-built molecular flow sensor. Second, an individual's lung physiology is then described by personalising the parameters of a bespoke cardio-respiratory model obtained by fitting the model to the data. The present study examines the effects of participants' physical characteristics on these parameter values, and from this also provides preliminary estimates for normal ranges. Data from 92 healthy individuals (27% female, age 40 ± 19 (mean ± SD) years, height 1.75 ± 0.09 m, mass 74 ± 14 kg) were used. A prediction equation for each CCP parameter was written as: y = α + βln(age) + γln(height) + δln(BMI) + ε(is_Female) + error, where BMI is body mass index. Non-significant terms (P > 0.1) were removed sequentially to identify just the significant characteristics. Physical characteristics exerted a large influence on volume-related CCP parameters. In contrast, only age had a significant influence on inhomogeneity-related CCP parameters. The prediction equations, together with their mean squared errors, were used to calculate z-scores for CCP data from three previously published studies in asthma, chronic obstructive pulmonary disease, and early cystic fibrosis. Values for these z-scores often lay beyond those commonly used to define a normal range (±1.65). In conclusion, reference values for inhomogeneity-based CCP parameters may only need correcting for age, and often appear as abnormal in airways disease.</p>\",\"PeriodicalId\":12092,\"journal\":{\"name\":\"Experimental Physiology\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":2.8000,\"publicationDate\":\"2025-07-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Experimental Physiology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1113/EP092423\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"PHYSIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Experimental Physiology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1113/EP092423","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PHYSIOLOGY","Score":null,"Total":0}
Computed cardiopulmonography: Effects of physical characteristics on lung parameter estimates.
Computed cardiopulmonography (CCP) is a technique that measures lung volumes (functional residual capacity and deadspace) together with novel parameters reflecting lung inhomogeneities (non-uniformities in lung inflation and deadspace path length). First, highly precise measurements of gas exchange are made during a nitrogen washout with a purpose-built molecular flow sensor. Second, an individual's lung physiology is then described by personalising the parameters of a bespoke cardio-respiratory model obtained by fitting the model to the data. The present study examines the effects of participants' physical characteristics on these parameter values, and from this also provides preliminary estimates for normal ranges. Data from 92 healthy individuals (27% female, age 40 ± 19 (mean ± SD) years, height 1.75 ± 0.09 m, mass 74 ± 14 kg) were used. A prediction equation for each CCP parameter was written as: y = α + βln(age) + γln(height) + δln(BMI) + ε(is_Female) + error, where BMI is body mass index. Non-significant terms (P > 0.1) were removed sequentially to identify just the significant characteristics. Physical characteristics exerted a large influence on volume-related CCP parameters. In contrast, only age had a significant influence on inhomogeneity-related CCP parameters. The prediction equations, together with their mean squared errors, were used to calculate z-scores for CCP data from three previously published studies in asthma, chronic obstructive pulmonary disease, and early cystic fibrosis. Values for these z-scores often lay beyond those commonly used to define a normal range (±1.65). In conclusion, reference values for inhomogeneity-based CCP parameters may only need correcting for age, and often appear as abnormal in airways disease.
期刊介绍:
Experimental Physiology publishes research papers that report novel insights into homeostatic and adaptive responses in health, as well as those that further our understanding of pathophysiological mechanisms in disease. We encourage papers that embrace the journal’s orientation of translation and integration, including studies of the adaptive responses to exercise, acute and chronic environmental stressors, growth and aging, and diseases where integrative homeostatic mechanisms play a key role in the response to and evolution of the disease process. Examples of such diseases include hypertension, heart failure, hypoxic lung disease, endocrine and neurological disorders. We are also keen to publish research that has a translational aspect or clinical application. Comparative physiology work that can be applied to aid the understanding human physiology is also encouraged.
Manuscripts that report the use of bioinformatic, genomic, molecular, proteomic and cellular techniques to provide novel insights into integrative physiological and pathophysiological mechanisms are welcomed.