Mapping the Zone of Uncertainty in Pulmonary Function Test Interpretation

Mapping the Zone of Uncertainty in Pulmonary Function Test Interpretation | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Mapping the Zone of Uncertainty in Pulmonary Function Test Interpretation View ORCID Profile Alexander T. Moffett , View ORCID Profile Meredith C. McCormack , View ORCID Profile Gary E. Weissman doi: https://doi.org/10.1101/2025.06.03.25328441 Alexander T. Moffett 1 Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Pennsylvania , Philadelphia, PA, USA 2 Palliative and Advanced Illness Research (PAIR) Center, University of Pennsylvania , Philadelphia, PA, USA 3 Leonard Davis Institute of Health Economics, University of Pennsylvania , Philadelphia, PA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Alexander T. Moffett For correspondence: amoffett{at}upenn.edu Meredith C. McCormack 4 Division of Pulmonary and Critical Care Medicine, Department of Medicine, Johns Hopkins University , Baltimore, MD, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Meredith C. McCormack Gary E. Weissman 1 Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Pennsylvania , Philadelphia, PA, USA 2 Palliative and Advanced Illness Research (PAIR) Center, University of Pennsylvania , Philadelphia, PA, USA 3 Leonard Davis Institute of Health Economics, University of Pennsylvania , Philadelphia, PA, USA 5 Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania , Philadelphia, PA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Gary E. Weissman Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Background European Respiratory Society and American Thoracic Society (ERS/ATS) guidelines for pulmonary function test (PFT) interpretation posit the existence of a “zone of uncertainty.” However, as reference equations have been defined only for healthy lung function, the precise borders of the zone of uncertainty remain unspecified. To address this limitation, we sought to define distributions of both healthy and diseased lung function and to use these distributions to map the zone of uncertainty. Methods We used a latent class model to define distributions of healthy and diseased FEV 1 /FVC values. To represent the distribution of healthy FEV 1 /FVC values, we fit a Box-Cox Cole Green distribution to spirometry from healthy adult participants in the continuous National Health and Nutrition Examination Survey (NHANES). Using the distribution of healthy FEV 1 /FVC values, we then fit a latent class model to FEV 1 /FVC data from adult patients who underwent pulmonary function testing at the University of Pennsylvania Health System (UPHS) between 2000 and 2023. The distribution of diseased FEV 1 /FVC values and the prior probabilities that FEV 1 /FVC values had been sampled from healthy and diseased populations were selected to maximize the likelihood of the observed UPHS data. We considered the normal, Box-Cox Cole Green, and Box-Cox power exponential distributions for the distribution of diseased FEV 1 /FVC values and selected the distribution that minimized the Bayesian information criterion. We then mapped the zone of uncertainty by identifying the range of FEV 1 /FVC values in which the distributions of healthy and diseased lung function overlapped. Results Pre-bronchodilator spirometry data were collected from 14,075 NHANES participants— of whom 6,063 were without respiratory symptoms or a history of tobacco use—and 41,437 UPHS patients. Healthy lung function was represented by a Box-Cox Cole Green distribution with a median of 0.81, a coefficient of variation of 0.08, and skewness of 1.70. Diseased lung function was best represented by a Box-Cox power exponential distribution with a median of 0.56, a coefficient of variation of 0.30, skewness of 0.91, and kurtosis of 2.36. In the latent class model, the prior probability that an FEV 1 /FVC value was healthy was 76.2%, while the prior probability that it was diseased was 23.8%. The overlap of the distributions of healthy and diseased FEV 1 /FVC values defined a zone of uncertainty in the interval [0.64, 0.95]. The FEV 1 /FVC values of 33,747 (81.4%) UPHS patients fell within the zone of uncertainty, including 30,288 (99.2%) patients with a normal FEV 1 /FVC and 3,459 (31.7%) patients with an abnormal FEV 1 /FVC. Conclusion This exploratory study presents evidence that in a clinical cohort, most FEV 1 /FVC values fall within the zone of uncertainty. Further research is needed to develop optimal ways to represent and incorporate this uncertainty into PFT interpretation. Introduction European Respiratory Society and American Thoracic Society (ERS/ATS) guidelines for pulmonary function test (PFT) interpretation posit the existence of a “zone of uncertainty” defined by the overlap of distributions of healthy and diseased lung function ( Figure 1a ). 1 As PFT parameter values that fall within the zone of uncertainty may represent either health or disease, ERS/ATS guidelines recommend that these values be interpreted with caution. 1 , 2 However, as reference equations have been defined only for healthy lung function, the precise borders of the zone of uncertainty remain unspecified. To address this limitation, we sought to define distributions of both healthy and diseased lung function and to use these distributions to map the zone of uncertainty. Download figure Open in new tab Figure 1. A latent class model of the FEV 1 /FVC in health and disease. (A) The theoretical distribution of the FEV 1 /FVC in health and disease, adapted from the “ERS/ATS Technical Standard on Interpretive Strategies for Routine Lung Function Tests,” Figure 5. 1 The green and red distributions represent the FEV 1 /FVC in health and disease, while the yellow area, defined by the overlap of these distributions, represents the zone of uncertainty. (B) The empirical distribution of the FEV 1 /FVC in health and disease, from pulmonary function tests performed at three pulmonary diagnostic labs in the University of Pennsylvania Health System between 2000 and 2023 ( N = 41,437). Observed FEV 1 /FVC values are binned by 0.02. A latent class model, defining distributions of healthy and diseased lung function, was fit to these data. The green and red probability distributions once again represent the FEV 1 /FVC in health and disease, while the yellow area, defined by the overlap of these distributions, represents the zone of uncertainty. Methods We used a latent class model to define distributions of healthy and diseased lung function. In this exploratory study we considered only a single PFT parameter, the ratio of the pre-bronchodilator forced expiratory volume in 1 second to forced vital capacity (FEV 1 /FVC). To represent the distribution of healthy FEV 1 /FVC values, we fit a Box-Cox Cole Green distribution to spirometry from healthy adult participants in the continuous National Health and Nutrition Examination Survey (NHANES). 3 , 4 We defined healthy participants as adults without respiratory symptoms or a history of tobacco use. 5 , 6 Using the distribution of healthy FEV 1 /FVC values, we then fit a latent class model to FEV 1 /FVC data from adult patients who underwent pulmonary function testing at the University of Pennsylvania Health System (UPHS) between 2000 and 2023. In this model FEV 1 /FVC values were assumed to have been sampled from either a healthy or a diseased population, with the population from which a patient was sampled representing a latent class. In fitting this model, the distribution of diseased FEV 1 /FVC values and the prior probabilities that FEV 1 /FVC values had been sampled from healthy and diseased populations were selected to maximize the likelihood of the observed UPHS data. 7 We considered the normal, Box-Cox Cole Green, and Box-Cox power exponential distributions for the distribution of diseased FEV 1 /FVC values and selected the distribution that minimized the Bayesian information criterion. 8 We then mapped the zone of uncertainty by identifying the range of FEV 1 /FVC values in which the distributions of healthy and diseased lung function overlapped. All code is open source and freely available ( https://github.com/weissman-lab/zone ). The University of Pennsylvania Institutional Review Board approved this study. Results Pre-bronchodilator spirometry data were collected from 14,075 NHANES participants— of whom 6,063 were without respiratory symptoms or a history of tobacco use—and 41,437 UPHS patients. The median age of the UPHS patients was 57 (interquartile range [IQR] 21), with 23,753 (57.3%) women, and 27,058 (65.3%) non-Hispanic White patients. The median FEV 1 /FVC among the UPHS patients was 0.77 (IQR 0.14). Healthy lung function was represented by a Box-Cox Cole Green distribution with a median of 0.81, a coefficient of variation of 0.08, and skewness of 1.70. The lower limit of normal (LLN), defined by a z-score of –1.645, was 0.069 for this distribution and 10,910 (26.3%) UPHS patients had an FEV 1 /FVC less than the LLN. Diseased lung function was best represented by a Box-Cox power exponential distribution with a median of 0.56, a coefficient of variation of 0.30, skewness of 0.91, and kurtosis of 2.36. In the latent class model, the prior probability that an FEV 1 /FVC value was healthy was 76.2%, while the prior probability that it was diseased was 23.8%. The overlap of the distributions of healthy and diseased FEV 1 /FVC values defined a zone of uncertainty in the interval [0.64, 0.95] ( Figure 1b ). The FEV 1 /FVC values of 33,747 (81.4%) UPHS patients fell within the zone of uncertainty, including 30,288 (99.2%) patients with a normal FEV 1 /FVC and 3,459 (31.7%) patients with an abnormal FEV 1 /FVC. Discussion Using a latent class model to define distributions of healthy and diseased lung function, we found that the overlap of these distributions—what ERS/ATS guidelines refer to as the zone of uncertainty—is much greater than has been assumed, encompassing almost all of the FEV1/FVC values in our clinical cohort. 9 The FEV 1 /FVC values in our clinical cohort had a unimodal distribution, rather than the bimodal distribution assumed in ERS/ATS guidelines, and applying a latent class model to this unimodal distribution led to the substantial degree of overlap in our latent classes. Our exploratory results suggest that uncertainty in the interpretation of FEV 1 /FVC values is pervasive. Such broad uncertainty may demonstrate a need to move beyond the binary classification of PFT interpretations as either certain or uncertain and instead develop methods to quantify the degree of uncertainty associated with such interpretation. One way to do so is to adopt a Bayesian approach to PFT interpretation, in which, rather than report whether an FEV 1 /FVC value falls above or below a lower limit of normal, we instead report the posterior probability that the value was sampled from a healthy population. The latent class model developed in this paper allows for the estimation of the parameters necessary to use Bayes’ theorem to calculate this posterior probability. While the appeal of a Bayesian approach to PFT interpretation is often noted, it has been seen by some to represent more of a theoretical ideal than a practical reality. 10 – 12 Our results suggest the importance of adopting a Bayesian approach and provide an initial roadmap for how to do so. Future efforts can address some of the limitations of our study. First, this exploratory study was limited to the FEV 1 /FVC. The FEV 1 /FVC was chosen as this is the parameter used by ERS/ATS guidelines to introduce the concept of the zone of uncertainty, and because it can be meaningfully interpreted independently of patient age, height, and sex, allowing for the construction of simple univariate models that can be easily visualized. However, as the FEV 1 /FVC is often elevated in patients with restrictive ventilatory impairments, this parameter may be less clearly delineated in health and disease than, for instance, the FEV 1 . Further work should consider other PFT parameters, while stratifying by sex and adding age and standing height as covariates. Second, as the distributions of healthy and diseased FEV 1 /FVC values assigned non-zero probabilities to all values in the interval [0,1], an additional threshold was needed to convert some of these probabilities to 0, so that the zone of uncertainty would not a priori encompass the entire interval. In this study we used the 0.01 and 0.99 quantiles to delimit these distributions. Further work should consider how other approaches might yield different depictions of the zone of uncertainty. Third, we considered only a subset of the many possible statistical distributions and clustering algorithms that could be used for latent class analysis. Further work assessing the sensitivity of these results to different modeling strategies is needed. Fourth, this is a single-site study and it is expected that distributions of diseased lung function will differ across different sites. To address this limitation, all code used in this study is freely available and open source, allowing for the development of analogous models for other clinical sites. In conclusion, this exploratory study presents evidence that in a clinical cohort, most FEV 1 /FVC values fall within the zone of uncertainty. Further research is needed to develop optimal ways to represent and incorporate this uncertainty into PFT interpretation. Data Availability All data produced in the present study are available upon reasonable request to the authors. https://github.com/weissman-lab/zone Author Contributions ATM participated in study design and analysis, drafted and revised the manuscript. MCM GEW participated in study design and revised the manuscript. All authors have read and approved the manuscript. Funding ATM reports funding from NHLBI F32 HL167456. GEW reports funding from NHLBI R03 HL171424 and NIGMS R35 GM155262. Conflict of Interest ATM and GEW have no financial disclosures to report relevant to this manuscript. MCM has received royalties from UpToDate, and consulting income from GlaxoSmithKline, Boehringer Ingelheim, Aridis, MCG Diagnostics and NDD Medical Technologies. Acknowledgments Bibliography 1. ↵ Stanojevic S , Kaminsky DA , Miller MR , et al. ERS/ATS technical standard on interpretive strategies for routine lung function tests . Eur Respir J . 2022 ; 60 ( 1 ): 2101499 . OpenUrl Abstract / FREE Full Text 2. ↵ Crapo RO , Jensen RL . Standards and interpretive issues in lung function testing . Respir Care . 2003 ; 48 ( 8 ): 764 – 772 . OpenUrl Abstract / FREE Full Text 3. ↵ Rigby RA , Stasinopoulos DM . Generalized additive models for location, scale and shape . J Royal Statistical Soc C . 2005 ; 54 ( 3 ): 507 – 554 . OpenUrl 4. ↵ Centers for Disease Control and Prevention (CDC), National Center for Health Statistics (NCHS ). National Health and Nutrition Examination Survey Data, 2017-2012 . https://www.cdc.gov/nchs/nhanes/index.html 5. ↵ Quanjer PH , Stanojevic S , Cole TJ , et al. Multi-ethnic reference values for spirometry for the 3-95-yr age range: the Global Lung Function 2012 equations . Eur Respir J . 2012 ; 40 ( 6 ): 1324 – 1343 . OpenUrl Abstract / FREE Full Text 6. ↵ Bowerman C , Bhakta NR , Brazzale D , et al. A race-neutral approach to the interpretation of lung function measurements . Am J Respir Crit Care Med . 2023 ; 207 ( 6 ): 768 – 774 . OpenUrl CrossRef PubMed 7. ↵ Dempster AP , Laird NM , Rubin DB . Maximum likelihood from incomplete data via the EM algorithm . J R Stat Soc Series B . 1977 ; 39 : 1 – 38 . OpenUrl 8. ↵ Schwartz G. Estimating the dimension of a model . Ann Stat . 1978 ; 6 : 461 – 464 . OpenUrl CrossRef Web of Science 9. ↵ Culver BH , Graham BL , Coates AL , et al. Recommendations for a standardized pulmonary function report. An official American Thoracic Society technical statement . Am J Respir Crit Care Med . 2017 ; 196 ( 11 ): 1463 – 1472 . OpenUrl CrossRef PubMed 10. ↵ Haynes JM , Kaminsky DA , Stanojevic S , Ruppel GL . Pulmonary function reference equations: a brief history to explain all the confusion . Respir Care . 2020 ; 65 ( 7 ): 1030 – 1038 . OpenUrl Abstract / FREE Full Text 11. Neder JA , Berton DC , O’Donnell DE . The lung function laboratory to assist clinical decision-making in pulmonology: evolving challenges to an old issue . Chest . 2020 ; 158 ( 4 ): 1629 – 1643 . OpenUrl PubMed 12. ↵ Haynes JM . Is it time to abandon binary interpretation of pulmonary function data? . Am J Respir Crit Care Med . 2024 ; 209 ( 1 ): 116 – 117 . OpenUrl PubMed View the discussion thread. Back to top Previous Next Posted June 05, 2025. Download PDF Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Mapping the Zone of Uncertainty in Pulmonary Function Test Interpretation Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Mapping the Zone of Uncertainty in Pulmonary Function Test Interpretation Alexander T. Moffett , Meredith C. McCormack , Gary E. Weissman medRxiv 2025.06.03.25328441; doi: https://doi.org/10.1101/2025.06.03.25328441 Share This Article: Copy Citation Tools Mapping the Zone of Uncertainty in Pulmonary Function Test Interpretation Alexander T. Moffett , Meredith C. McCormack , Gary E. Weissman medRxiv 2025.06.03.25328441; doi: https://doi.org/10.1101/2025.06.03.25328441 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Respiratory Medicine Subject Areas All Articles Addiction Medicine (568) Allergy and Immunology (863) Anesthesia (299) Cardiovascular Medicine (4425) Dentistry and Oral Medicine (443) Dermatology (382) Emergency Medicine (607) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1507) Epidemiology (15221) Forensic Medicine (30) Gastroenterology (1123) Genetic and Genomic Medicine (6588) Geriatric Medicine (667) Health Economics (997) Health Informatics (4524) Health Policy (1368) Health Systems and Quality Improvement (1612) Hematology (540) HIV/AIDS (1264) Infectious Diseases (except HIV/AIDS) (15910) Intensive Care and Critical Care Medicine (1103) Medical Education (623) Medical Ethics (145) Nephrology (667) Neurology (6588) Nursing (346) Nutrition (998) Obstetrics and Gynecology (1143) Occupational and Environmental Health (956) Oncology (3331) Ophthalmology (970) Orthopedics (369) Otolaryngology (420) Pain Medicine (435) Palliative Medicine (129) Pathology (663) Pediatrics (1690) Pharmacology and Therapeutics (691) Primary Care Research (710) Psychiatry and Clinical Psychology (5440) Public and Global Health (9220) Radiology and Imaging (2195) Rehabilitation Medicine and Physical Therapy (1369) Respiratory Medicine (1196) Rheumatology (593) Sexual and Reproductive Health (710) Sports Medicine (529) Surgery (710) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9ffdf1841c670708',t:'MTc3OTQ3NTE2NA=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();