Diagnostic Adequacy of COPD in Primary Care: A Population-Based Analysis of Spirometry Use and Risk-Factor Documentation

Diagnostic Adequacy of COPD in Primary Care: A Population-Based Analysis of Spirometry Use and Risk-Factor Documentation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (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],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Diagnostic Adequacy of COPD in Primary Care: A Population-Based Analysis of Spirometry Use and Risk-Factor Documentation Pedro Fonte, Inês Domingues, Benvinda Barbosa, Carina Ferreira, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8610008/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background Diagnosing COPD in primary care remains challenging, with significant international evidence of underdiagnosis, misdiagnosis and miscoding. How reliably COPD diagnoses in real-world practice reflect objective diagnostic criteria is less well understood. Aim To evaluate the compatibility between recorded COPD diagnoses and key diagnostic criteria -spirometry and risk-factor documentation - and to identify patient and contextual factors associated with diagnostic adequacy. Methods A population-based, cross-sectional study including all patients with a recorded diagnosis of COPD across a large Portuguese primary care cluster (17 Family Health Units; 182,947 patients). Electronic Health Records data were extracted for demographics, comorbidities, symptoms, exposures, spirometry and clinical follow-up. Spirometry and risk-factor records were independently classified according to their compatibility with a diagnosis of COPD (compatible, uncertain or not compatible) and combined to derive diagnostic-certainty categories. Associations were analysed using non-parametric tests and multinomial logistic regression. Results Among 1817 included patients, only 3.63% had both spirometry and exposure records fully compatible with COPD at diagnosis; 24.05% had incompatible records. Using the most recent spirometry, compatibility improved only marginally (4.4%), while incompatible cases remained high (21.57%). Spirometry was absent or non-confirmatory in nearly 80% of patients, and smoking or other exposures documentation was missing or incomplete in almost 50%. Diagnostic incompatibility was strongly associated with older age, female sex, diagnosis made outside primary care, multimorbidity, and especially psychological comorbidities (OR 11.6; 95% CI 7.7–17.4). Asthma was the only respiratory comorbidity significantly associated with incompatible diagnoses. Records of symptoms, exacerbations and GOLD classification were infrequently documented. Conclusions COPD diagnoses recorded in routine primary care frequently lack spirometric confirmation and adequate documentation of risk factors, resulting in high rates of incompatible diagnoses. Diagnostic adequacy reflects demographic, social and clinical determinants, revealing important inequities. These findings highlight an urgent need to strengthen diagnostic pathways through systematic use of spirometry, structured recording of risk factors and symptoms, and enhanced Electronic Health Records documentation. Improving these core elements is essential to ensure accurate, consistent and equitable COPD diagnosis in primary care. Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Health sciences/Risk factors INTRODUCTION Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous lung condition characterized by persistent respiratory symptoms and airflow limitation caused by airway and/or alveolar abnormalities.( 1 ) It is commonly associated with multiple comorbidities that require active management and becomes increasingly prevalent with age; historically more frequent in males, although this sex gap has been narrowing.( 2 – 6 ) COPD remains a major global cause of morbidity and mortality and is responsible for substantial healthcare and socioeconomic burden.( 1 , 7 , 8 ) In Portugal, prevalence estimates vary depending on methodology, but available data consistently suggest a relevant disease burden. The BOLD study, conducted in the Lisbon region, reported a prevalence of 14.2% in adults aged ≥ 40 years, while earlier studies reported lower estimates.( 9 , 10 ) Despite methodological differences, these results indicate that COPD is an important public health issue in the country. A persistent challenge in this field is the high rate of underdiagnosis, which has been widely documented internationally.( 1 , 11 – 13 ) In Portugal, the BOLD study estimated that 86% of COPD cases in the Lisbon region were undiagnosed.( 10 ) When compared with national primary care records, which in 2025 registered 149,152 individuals aged ≥ 40 years with a COPD diagnosis (2.35% prevalence), a substantial discrepancy becomes evident, illustrating the combined effects of underdiagnosis and limitations in diagnostic recording in primary care.( 14 ) Beyond underrecognition, misdiagnosis is also frequent in primary healthcare settings. Several studies have shown that a significant proportion of COPD diagnoses are made without spirometric confirmation, relying instead on symptoms or non-specific diagnostic codes, and often complicated by the challenges of differentiating COPD from other respiratory conditions.( 11 , 13 , 15 – 17 ) In Portugal, a national report from 2017 indicated that only about one-third of COPD diagnoses coded in primary care had an associated spirometry recorded in the electronic health record (EHR).( 18 ) These findings reinforce concerns regarding diagnostic accuracy and the need to better understand factors contributing to potentially inappropriate diagnoses. In addition to underdiagnosis and misdiagnosis, several studies have shown that COPD is also frequently miscoded in EHR. A considerable proportion of diagnostic labels recorded in primary care do not meet standard spirometric criteria, nor do they consistently reflect documented exposure to risk factors. Evidence from primary care audits has shown that between 15% and 30% of patients coded with COPD lack objective confirmation when formally assessed.( 19 ) Broader analyses of administrative and clinical databases likewise demonstrate substantial misclassification, with COPD codes frequently failing validation against spirometric or clinical standards.( 20 ) In parallel, reviews of primary care EHR systems highlight pervasive problems in the completeness, structure and accuracy of diagnostic documentation, further contributing to the mismatch between recorded and clinically confirmed COPD.( 21 ) Together, these findings reinforce the need to examine how accurately COPD codes in primary care reflect objective diagnostic criteria and to identify the factors associated with unsupported diagnostic labels. In this context, rather than estimating the true prevalence of COPD or the proportion of undiagnosed cases, it becomes essential to assess how well COPD diagnoses recorded in primary care are supported by spirometry results and documented risk factors. The present study aimed to evaluate the compatibility between previous COPD diagnoses and initial spirometry results, and to identify the clinical, demographic, and contextual factors associated with diagnoses classified as compatible, incompatible, or inconclusive. METHODS Study design This was an observational, cross-sectional study conducted in primary care. Its main aim was to evaluate the compatibility between previously recorded diagnoses of COPD and the results of spirometries available in the EHR. The study focused on assessing whether the required diagnostic criteria for COPD were registered and on examining the extent to which spirometry supported or contradicted the pre-existing diagnosis. Population The study population included all patients registered in the Family Health Units (FHU) of the Health Centre Group (HCG) of Braga (Portugal) who had the code “R95 - Chronic Obstructive Pulmonary Disease” from the International Classification of Primary Care, 2nd edition (ICPC-2), recorded as an active health problem in EHR at the time of data extraction.( 22 ) The HCG of Braga comprised 17 FHU, staffed by 102 family doctors, providing care to a total of 182,947 registered patients. Family doctors and FHU that did not agree to participate were excluded without replacement. As the aim was to include all eligible patients, the study was designed as a census of the target population, eliminating the need for sampling procedures. Patient identification was carried out by each participating family doctor, who generated a list of individuals meeting the inclusion criteria. Data and collection process Data were obtained from the EHR platform SClínico ( 23 ), used by all family doctors in the participating units. Access to the records of all selected patients was granted after approvals were obtained from local and regional ethics committees, as well as from each participating FHU and their physicians. Data collection took place during 2019 and 2020 and was performed by at least one trained family doctor in each participating FHU. Extracted information included demographic characteristics (sex, age at diagnosis, age at data collection, education level, and occupation), clinical variables (body mass index, smoking status and tobacco exposure, other risk factors for COPD, comorbidities, previous exacerbations, and ongoing inhaled therapy), and health-service-related variables (place of diagnosis and follow-up in hospital respiratory clinics). Symptom assessments using the mMRC scale and the CAT score were also retrieved when available, acknowledging that these were entered as free-text fields in the EHR. All spirometry records were collected, including the one closest to the time of diagnosis and the most recent available, as well as the total number of spirometry tests registered per patient. Emphasis was placed on evaluating the compatibility of spirometry and risk-factor records with a COPD diagnosis. Each component was classified into three categories: Spirometry a) Compatible - post-bronchodilator Forced Expiratory Volume in 1 second (FEV 1 )/Forced Vital Capacity (FVC) ratio < 0.7 and/or below the lower limit of normal; b) Uncertain - description compatible with COPD without numerical values; FEV₁/FVC < 0.7 before but not after bronchodilation; or obstructive values with significant bronchodilator response; c) Not compatible — no spirometry available or spirometry inconsistent with an obstructive pattern. Risk-factor records a) Compatible - smoking history ≥ 10 pack-years and/or clear evidence of significant exposure to other risk factors; b) Uncertain - smoking mentioned but unquantified, or other exposures recorded but insufficiently quantified; c) Not compatible - no relevant exposure documented. An integrated diagnostic-certainty classification was derived from combining these two components (Table 1 ): Yes , when both spirometry and risk-factor records were compatible; Maybe when one was compatible and the other uncertain or when both were uncertain; and No , when at least one record was not compatible. To minimise variability in data collection, all physicians involved received detailed training and written guidance before the start of the process, ensuring consistent application of the predefined criteria across participating units. Table 1 Integrated matrix of spirometry and risk-factor record compatibility used to classify diagnostic certainty of COPD. “Compatible”, “Uncertain” and “Not compatible” refer to the concordance of spirometry and risk-factor records with a COPD diagnosis, as defined in Methods; “YES”, “MAYBE” and “NO” indicate the resulting diagnostic certainty. Spirometry records Risk factor records Compatible Uncertain Not compatible Compatible Uncertain Not compatible YES MAYBE NO MAYBE MAYBE NO NO NO NO Statistical analysis After data collection, datasets were checked for internal consistency and potential recording errors, and family doctors were contacted whenever necessary to clarify doubtful information. Statistical analysis was performed using IBM SPSS Statistics (IBM Corp., Armonk, NY, USA). A significance level of p < 0.05 and 95% confidence intervals (95% CI) were adopted. Descriptive statistics were used to summarise all study variables: categorical variables were presented as absolute and relative frequencies, and continuous variables as means and standard deviations (SD) or medians and interquartile ranges (IQR), according to their distribution. To explore the association between patient characteristics and diagnostic compatibility, patients were classified into three groups (compatible, uncertain, and not compatible). Continuous variables were compared across these groups using the Kruskal-Wallis test, under the null hypothesis that the distribution of each variable did not differ between diagnostic-compatibility categories. Categorical variables were compared using the chi-square test or Fisher’s exact test when expected cell counts in contingency tables were < 5; in these analyses, the null hypothesis stated that the proportion of patients in each category was equal across diagnostic-compatibility groups. Differences in spirometry-based classifications between two time points (the spirometry closest to the time of diagnosis and the most recent recorded spirometry) were evaluated by comparing the marginal proportions of patients in each category. For the specific comparison of the proportion of patients classified as “not compatible” at the two time points, McNemar’s test for paired binary outcomes was used, assuming as null hypothesis that the probability of changing from “not compatible” to “compatible/uncertain” was equal to the probability of the opposite change. To identify factors independently associated with diagnostic compatibility, a multivariable multinomial logistic regression model was fitted, using diagnostic-compatibility category (compatible, uncertain, not compatible) as the dependent variable and the “uncertain” group as the reference category. Explanatory variables included sex, age at diagnosis, time since diagnosis, number of comorbidities, presence of psychological comorbidities, presence of asthma, and place of diagnosis. Results were expressed as odds ratios (OR) with 95% CI, under the null hypothesis that OR = 1 (no association). Overall model fit and explanatory capacity were assessed using the likelihood-ratio chi-square test and Nagelkerke’s R². In secondary analyses, exploratory binary logistic regression models were fitted after dichotomising the outcome as “compatible” versus “not compatible”, excluding uncertain cases. Separate univariable models were estimated for comorbidity chapters and for individual symptom-letter variables recorded in the clinical form, with results presented as OR and 95% CI. Full model specifications, detailed regression diagnostics, and extended output tables are provided in the Supplementary Material (Tables S2 and S3). Ethics approval The study was approved by the Ethics Committee of the Northern Region Health Administration (approval number: 53/2018). Access to electronic health records was granted after the required approvals from local and regional ethics committees, as well as from each participating Family Health Unit and their physicians. RESULTS In this study, 1817 patients were included (Table 2 ), corresponding to all individuals with the ICPC-2 code R95 recorded as an active health problem in their EHR. They were distributed across 13 FHU and followed by 73 family doctors, with a mean of 20.68 (± 11.44) COPD patients per doctor’s list. The average prevalence of COPD per FHU was 2.56% (± 0.94) and per doctor’s patient list 2.72% (± 1.73), both calculated for individuals aged 40 years and older. The mean age at data collection and at diagnosis was 67.85 (± 13.7) and 62.7 (± 14) years, respectively. Most patients (n = 1743; 95.93%) were aged ≥ 40 years at diagnosis, and the majority were male (n = 1166; 64.17%). The mean time since diagnosis was 5 (± 4.55) years. At diagnosis, 41.8% were retired, with most having previous or current occupations in the secondary (manufacturing/industry) and tertiary (services) sectors. The most frequent education levels were the lowest, particularly up to the 4th grade. Higher education was recorded in only 5.6% of patients. Regarding comorbidities, most patients had at least one, with four being the most frequent number of concurrent conditions (Table 3 ). The median number of comorbidities was four (IQR: 3–5), and only 5.2% of patients had eight or more chronic conditions. Comorbidities were coded according to the ICPC-2 and grouped by system chapter (B-Z). The most common comorbidities were related to the “Endocrine, metabolic, and nutritional system”, “Circulatory system”, and “Psychological conditions”. Other chronic respiratory diseases were also recorded in some patients: asthma in 172 (9.47%), chronic bronchitis in 114 (6.27%), and emphysema in 78 (4.29%). This study focused particularly on the recording of the two main conditions required for a COPD diagnosis: spirometry results (the one closest to diagnosis, to evaluate diagnostic compatibility, and the most recent one available, to identify potential improvements in data recording) and records of risk factors (smoking and/or other exposures such as occupational, environmental, or biomass-related). Table 4 presents the findings. A substantial proportion of patients (345; 18.99%) had no spirometry record. Only 108 (5.94%) and 136 (7.48%) patients had a spirometry record fully compatible with the diagnosis at the time of diagnosis and at the most recent test, respectively. Regarding risk factors, 47.55% and 45.79% of patients had no record of prior smoking history and no record of any exposure to risk factors, respectively. Conversely, 44.91% of diagnoses were accompanied by unequivocal documentation of risk factors. In an exploratory subsample analysis (n = 198; 10.9%), limited to patients for whom hospital follow-up information was available, it was possible to determine whether follow-up consultations had occurred at the hospital level. This analysis aimed to explore whether inconsistencies in primary care diagnostic records could be explained by confirmatory data registered at the hospital level. Such follow-up was observed in 111 patients (56.1%), but confirmatory documentation of the COPD diagnosis was identified in only 49 patients (24.7%) within hospital clinical records. This association was not statistically significant (p = 0.137), suggesting that the majority of non-compatible primary care diagnoses could not be justified by hospital information. These findings should be interpreted cautiously given the exploratory nature and limited scope of this analysis. A review of follow-up data was also conducted, focusing on symptoms, exacerbations, and severity classification, as indicators of the completeness and quality of disease monitoring within primary care records. Records of CAT and/or mMRC symptom scores were found in 171 patients (9.4%). Most patients (n = 1503; 82.7%) had no records of previous exacerbations, while 216 (11.9%), 61 (3.4%), 21 (1.2%) and 16 (0.9%) patients had 1, 2, 3, and ≥ 4 recorded exacerbations, respectively. Based on the combination of symptom burden and exacerbation history, the GOLD severity class (A-D at that time) should be determined. However, 1677 patients (92.3%) had no documented GOLD classification. Among those with recorded GOLD groups, 73 (4.0%), 45 (2.5%), 6 (0.3%), and 16 (0.9%) were classified as A, B, C, and D, respectively. These findings highlight substantial gaps in the systematic recording of COPD monitoring variables in primary care EHR. One of the objectives of this study was to identify circumstances or patient characteristics associated with diagnostic adequacy. The main associations are presented in Table 6 . A higher mean age at diagnosis was associated with less accurate diagnoses, and the same pattern was observed in patients with a longer duration of diagnosis. Diagnoses made outside the FHU (hospital consultations, private practice or other settings) were more frequently classified as inadequate. This association was statistically significant when diagnostic compatibility was defined according to the initial spirometry (p < 0.001), but no longer reached statistical significance when compatibility was determined using the most recent spirometry record (p = 0.101), although the same trend persisted. Diagnoses made in women were also more frequently inadequate. Diagnostic compatibility was also associated with occupation (p < 0.001 in both analyses), with inadequate diagnoses being the predominant outcome across all occupational groups. A similar association was observed with educational level (p < 0.001 in both analyses), with inadequate diagnoses being the most frequent outcome across all categories of schooling. Regarding more clinical parameters, the recording of symptoms was more frequent in cases with adequate or uncertain diagnoses, whereas the absence of symptom records was more common in cases with inadequate diagnoses. The relationship between diagnostic compatibility and the recording of exacerbations was not statistically significant. In contrast, the GOLD severity class was more frequently recorded in patients with adequate diagnoses and much less often in those with inadequate diagnoses. In terms of comorbidities, the proportion of diagnoses classified as not compatible increased across categories of comorbidity count, although the gradient was modest. When analysing the association between diagnostic compatibility and each isolated group of comorbidities, several ICPC-2 chapters showed significant associations in at least one of the two analyses (diagnosis based on the initial spirometry versus the most recent spirometry). These included chapters L (Musculoskeletal), R (Respiratory, excluding COPD-related codes), T (Endocrine/Metabolic), and P (Psychological), as well as the aggregated ‘Others’ category (chapters with low frequency: B, H, N, S, X, Z). When analysing the association between diagnostic compatibility and the presence of asthma, chronic bronchitis or emphysema, only concomitant asthma showed a statistically significant association, being more frequent among cases classified as uncertain or not compatible. Chronic bronchitis showed no significant association, and emphysema was only associated with diagnostic inaccuracy when compatibility was assessed using the most recent spirometry. Ten participants had missing information for these conditions, but their exclusion did not affect the observed associations. The prescription of inhaled therapy showed a statistically significant association with diagnostic compatibility. However, this result should be interpreted with caution, as all therapeutic regimens were more frequently prescribed in patients with diagnoses classified as not compatible. This likely reflects treatment decisions based on symptomatic burden rather than on diagnostic confirmation, limiting the clinical interpretability of this association. A multinomial logistic regression model was fitted to explore independent predictors of diagnostic compatibility, using the “Uncertain” category as the reference group (Table 7 ). The model showed good overall performance (χ² = 534.2; df = 26; p < 0.001; Nagelkerke R² = 0.332). Full SPSS model output and detailed regression diagnostics are provided in Supplementary Material S1 (Table S1). Several variables were independently associated with a higher probability of having a diagnosis classified as “Not compatible” rather than “Uncertain”. Female sex (OR = 3.59; 95% CI: 2.72–4.74) and diagnoses made outside Family Health Units - particularly in private practice (OR = 2.58; 95% CI: 1.28–5.20), hospital consultations (OR = 1.56; 95% CI: 1.03–2.35), or other settings (OR = 2.22; 95% CI: 1.15–4.28) - were associated with significantly higher odds of diagnostic inadequacy. An increasing number of comorbidities was also significantly associated with diagnostic inadequacy, as were the presence of concomitant asthma (OR = 1.66; 95% CI: 1.05–2.64) and psychological comorbidities (OR = 11.59; 95% CI: 7.71–17.43), the latter being the strongest predictor in the model. Both older age at diagnosis (OR = 1.019 per year; 95% CI: 1.008–1.030) and longer diagnostic o (OR = 1.086 per year; 95% CI: 1.045–1.129) were also independently associated with an increased probability of diagnostic inadequacy. In contrast, the model did not identify consistent predictors of belonging to the “Compatible” group when compared with “Uncertain”, likely due to the small number of cases in this category (n = 66), limiting the stability of these estimates. Supplementary Tables S2 and S3 provide the complete ICPC-2 chapter distribution and the full set of univariable logistic regression models. DISCUSSION This study provides a population-level examination of how COPD is diagnosed in routine primary care, revealing substantial discrepancies between guideline-based diagnostic criteria and information recorded in real-world practice. By analysing all patients with an active COPD code across an entire regional health centre group, the study offers an unusually comprehensive view of diagnostic performance, illuminating where clinical pathways function effectively and where they systematically fail. These findings have direct relevance for improving diagnostic quality and shaping practice-level interventions. At the population level, only a very small proportion of COPD diagnoses were supported by spirometry and documented risk-factor exposure, while the majority fell into the incompatible or uncertain categories. Spirometry was absent or non-confirmatory in nearly four out of five patients, and documentation of smoke load or other exposures was frequently incomplete. Diagnostic incompatibility was strongly associated with older age at diagnosis, female sex, diagnosis made outside primary care, higher multimorbidity and, most distinctly, the presence of psychological comorbidities. These patterns reflect both clinical challenges and structural barriers within primary care, underscoring the complexity of achieving diagnostic certainty in chronic respiratory disease. Taken together, these patterns illustrate persistent challenges in aligning guideline-based diagnostic criteria with real-world practice in primary care. The results highlight substantial gaps in the recording of essential diagnostic elements, and these patterns remain relevant today given that recent national primary care data continue to show a recorded prevalence of COPD in Portugal much lower than expected.( 14 ) Despite variations across studies, the consistently low number of coded cases reinforces that diagnostic uncertainty and incomplete confirmation remain persistent challenges in routine care. These findings are consistent with international observations that discrepancies between epidemiological estimates and physician-recorded COPD are largely driven by limited use of confirmatory spirometry, variability in coding practices, and inconsistent documentation of risk factors.( 12 , 13 , 16 ) Studies from UK primary care similarly demonstrate that important clinical information - including lifestyle-related data, socioeconomic indicators and chronic disease markers - is frequently missing or incompletely coded, complicating diagnostic interpretation and contributing to uncertainty in disease classification.( 24 ) These challenges also reflect broader patterns observed in primary care electronic records, where large amounts of information are often captured in free-text fields rather than structured formats. This heterogeneity reduces the clarity of diagnostic records and the reliability of retrospective assessments, particularly for conditions such as COPD that require objective confirmation. Evidence from U.S. primary care HER indicates that essential clinical reasoning and contextual information are often absent from structured fields and instead buried in free-text notes, hindering diagnostic interpretation and complicating efforts to assess real-world practice through EHR data.( 25 ) This study confirmed the very low proportion of spirometry-confirmed COPD diagnoses in primary care. Only 5.94% and 7.48% of patients had spirometry fully compatible with COPD at the time of diagnosis and at the most recent test, respectively. These findings are in line with previous reports indicating that spirometry remains persistently underused or inconsistently applied in routine care. In an Australian study, more than 80% of patients receiving COPD pharmacotherapy had no spirometry recorded in the year surrounding treatment initiation, and nearly half were later found not to meet spirometric criteria for COPD when lung function was eventually assessed.( 26 ) Other global analyses link these patterns to clinician, organisational and system level barriers, including limited confidence in interpreting spirometry, workflow constraints, inadequate access and perceptions that spirometry adds limited clinical value.( 13 , 26 , 27 ) Earlier US studies similarly showed that a substantial proportion of patients labelled with COPD lacked airflow obstruction on spirometry.( 28 ) Risk-factor documentation was comparably limited. Nearly half of the patients had no recorded smoking history or lacked quantification of smoking load. Recent GOLD reports emphasise that, beyond smoking, environmental pollution and host factors also contribute to COPD pathogenesis; however these exposures were not assessed in the present analysis.( 1 ) Missing or fragmented smoking information is a well-recognised problem in primary care, with studies showing that inconsistent recording reduces the accuracy of diagnostic assessments and undermines proactive disease monitoring.( 21 ) Evidence from German primary care indicates substantial variability in how smoking behaviours and exposure histories are captured, contributing to under-recognition of COPD and lower diagnostic confidence.( 29 ) Occupational exposures, estimated to account for 15–20% of COPD cases, were also poorly documented, despite their well-established role in disease development and progression.( 30 ) When spirometry and exposure data were considered together, diagnostic adequacy was very low at baseline and improved only marginally over time, despite a statistically significant reduction in incompatible diagnoses. Although these modest changes may reflect increasing awareness of spirometry’s importance, overall diagnostic adequacy remained strikingly low. A critical insight from these findings is the distinction between genuinely inadequate diagnostic procedures and simply inadequate documentation. Many incompatible cases may reflect missing or unstructured data rather than absence of clinical evaluation. This interpretation aligns with studies showing that diagnostic steps may have been performed but remain undocumented or confined to non-extractable free text notes.( 31 ) Reviews of EHR content show that clinical reasoning, diagnostic pathways and follow-up assessments are often absent from structured fields, increasing uncertainty and risk of misclassification.( 25 ) Variability in documentation thoroughness and coding habits further contributes to inconsistent data quality and has been linked to potential patient harm.( 32 ) These findings must also be interpreted alongside persistent discrepancies between epidemiological estimates and physician-recorded prevalence. In Portugal and internationally, population-based studies such as BOLD consistently report higher prevalence than reflected in routine clinical records, due largely to underdiagnosis, limited spirometry use and diagnostic uncertainty.( 9 , 10 , 13 – 15 , 19 ) Similar gaps have been documented globally, where spirometry-confirmed COPD represents only a fraction of individuals who meet symptom or exposure-based criteria.( 33 ) Our results, therefore, mirror international evidence indicating that physician-diagnosed COPD substantially underrepresents the true burden of disease. Several patient characteristics were associated with diagnostic adequacy. Older age and longer diagnostic duration were linked to higher incompatibility rates, consistent with findings from the BOLD Australia study, which showed markedly higher underdiagnosis in older adults, particularly those ≥ 75 years, reflecting the diagnostic complexity introduced by age-related physiological changes and multimorbidity.( 11 ) Women were also more likely to have incompatible diagnoses, a pattern supported by studies showing that women are less likely to undergo spirometry, more frequently mislabelled with asthma and at higher risk of delayed or incorrect diagnosis.( 33 – 35 ) In addition to their recognised contribution to COPD risk, occupational exposures were also associated with diagnostic adequacy, with primary-sector workers showing the highest rates of incompatibility. This is consistent with long-standing evidence linking agricultural, mining, manufacturing and construction work to increased COPD risk due to exposure to dusts, gases, vapours and fumes, as well as substantial underdiagnosis in these settings.( 30 , 36 – 40 ) However, poor documentation of occupational history may attenuate observed associations, as occupational information is frequently missing or incompletely recorded in primary care records.( 41 , 42 ) Lower education levels were also associated with higher diagnostic incompatibility, consistent with evidence linking low education to poorer COPD outcomes, increased exacerbation risk and higher mortality.( 43 – 46 ) Although health literacy was not directly assessed, this finding is consistent with evidence showing that limited health literacy contributes to poorer disease understanding, higher symptom burden and increased healthcare utilisation.( 47 ) These findings underscore the role of social determinants in shaping diagnostic accuracy and clinical outcomes. Comorbidity burden further contributed to diagnostic uncertainty, with higher multimorbidity associated with greater incompatibility, consistent with evidence that competing health priorities may mask respiratory symptoms and delay diagnosis.( 48 , 49 ) Psychological comorbidities showed the strongest association, reflecting evidence that anxiety and depression alter symptom perception and complicate clinical evaluation.( 50 ) This diagnostic complexity may partly explain patterns of empirical prescribing. In this study, inhaled therapies were more frequently prescribed among incompatible diagnoses, mirroring evidence that long-acting bronchodilators and ICS are often initiated without spirometric confirmation of COPD.( 51 – 53 ) Among respiratory comorbidities, asthma was most strongly associated with incompatible diagnoses, consistent with the well-established diagnostic overlap between asthma, COPD and asthma with COPD, particularly in primary care settings where objective testing is often lacking.( 54 – 58 ) Symptoms and GOLD classification were not used in our definition of diagnostic adequacy. However, their extremely low documentation rates highlight major gaps in disease monitoring. Comparable findings have been described internationally - in one US study, none of 101 primary care patients labelled with COPD had CAT or mMRC scores recorded, and only 21% had pulmonary function testing, with nearly one-third not meeting spirometric criteria.( 59 ) In the Greek UNLOCK study, GOLD staging was inconsistently applied despite high symptom burden.( 60 ) Improving the recording of symptom burden, exacerbations and severity classification is essential for high-quality COPD management. This study has several limitations. First, the analysis relied exclusively on information recorded in EHR, which may omit relevant data that were obtained clinically but never documented, or that remain stored in inaccessible legacy paper files. This constraint may have contributed to an underestimation of diagnostic adequacy. Second, the quality and completeness of EHR entries varied across clinicians and units, particularly because key variables, such as smoking exposure, occupational history and clinical reasoning, were often documented in free text rather than structured fields, limiting extractability and introducing heterogeneity. Third, the cross-sectional design captured diagnostic information at a fixed point in time and cannot account for subsequent diagnostic refinement or changes in coding practices. Finally, data collection occurred in 2019/2020; although national figures suggest that the diagnostic challenges identified remain largely unchanged, temporal shifts in practice cannot be completely excluded. Despite these limitations, the study provides a robust and comprehensive real-world assessment of COPD diagnostic practices within a large primary care population. This study also presents several important strengths. First, by including all patients with an active COPD code across a large and diverse primary care cluster, it avoids selection bias and provides a comprehensive and population-level picture of diagnostic practices. Second, although data extraction was carried out by multiple clinicians, the use of a standardised protocol with prior training and calibration sessions ensured consistent interpretation of clinical fields and minimised inter-observer variability. Finally, access to the full breadth of routine HER enabled the assessment of diagnostic adequacy under real-world conditions, capturing the complexity, heterogeneity and pragmatic constraints of primary care practice - an essential perspective that is often absent from controlled research environments. These findings have several implications for clinical practice in primary care. The very low proportion of spirometry-confirmed diagnoses underscores the need for more systematic use of lung function testing and for structured documentation of risk factors, including smoking status and relevant environmental or occupational exposures. Diagnostic disparities observed across age, sex, education and occupational sectors highlight the importance of considering social determinants of health during diagnostic evaluation. Improvements in electronic record-keeping, particularly through the use of structured fields for key diagnostic elements, may help reduce variability and enhance diagnostic confidence. Targeted strategies such as focused clinician training and the integration of decision-support prompts within the EHR have shown potential to support more consistent diagnostic pathways, although further implementation research is needed to evaluate their impact in real-world settings.( 61 – 63 ) By clarifying the gaps between recorded and evidence-based diagnostic criteria, this study identifies a clear opportunity to reinforce COPD diagnostic pathways. Embedding objective assessment and consistent documentation into routine care is fundamental to ensuring accurate, equitable management. CONCLUSION This population-based study revealed substantial discrepancies between guideline-based diagnostic criteria and the information recorded in routine primary care for patients labelled with COPD. Spirometry confirmation and documentation of key risk factors were infrequent, contributing to high rates of incompatible or uncertain diagnoses. Diagnostic adequacy was associated with demographic, social and clinical factors, highlighting inequities and structural limitations within current diagnostic pathways. By clarifying where diagnostic processes falter, this study identifies concrete opportunities to reinforce the use of objective measures, standardise documentation and support clinicians in delivering guideline-aligned care. Strengthening these elements is essential to achieve accurate, consistent and equitable COPD diagnosis in primary care. Declarations Ethics approval and consent to participate The study was conducted in accordance with the principles of the Declaration of Helsinki. Ethical approval was obtained from the Ethics Committee of the Northern Region Health Administration (approval number: 53/2018). Additional authorisations were granted by local and regional ethics committees, as well as by each participating Family Health Unit and their physicians, allowing access to routinely collected electronic health records for research purposes. This study was a cross-sectional observational analysis based exclusively on routinely collected clinical data, with no direct involvement of patients. Informed consent was therefore waived by the Ethics Committee, as all data were analysed in a fully anonymised manner, no identifiable personal information was accessed by the research team, and the study posed no risk to patients or interference with usual clinical care. Consent for publication The authors hereby confirm that, through this submission, they consent to the publication of this manuscript in npj Primary Care Respiratory Medicine , should it be accepted. All authors have reviewed and approved the final version of the manuscript and agree to its submission and publication in accordance with the journal’s editorial policies and publishing requirements. Availability of data and materials The data that support the findings of this study are derived from routinely collected primary care electronic health records and contain sensitive personal health information. For ethical and legal reasons, the original individual-level data are not publicly available. All data presented in the article and its Supplementary Information are fully anonymised and reported in aggregated form. Derived anonymised datasets necessary to interpret and reproduce the analyses may be made available from the corresponding author upon reasonable request, subject to approval by the relevant institutional and ethical authorities. Competing Interests We declare that the authors have no competing interests (financial or non-financial) as defined by Nature Portfolio, or other interests that might be perceived to influence the results and/or discussion reported in this paper. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Authors' contributions PF developed the study protocol, collected the data, performed the data analysis, and drafted the manuscript. ID, BB, and CF contributed to data collection and critically revised the manuscript. JCS and TvM reviewed the study protocol, contributed to data analysis, and critically revised the manuscript. All authors read and approved the final version of the manuscript. Acknowledgements The authors would like to acknowledge the contribution of the following healthcare professionals for their support in data access and collaboration at the participating primary care units: Susana Fernandes (7 Fontes Family Health Unit), Rosana Dantas (Family Health Unit of Ruães), Maria Emília Faria (Pelaez Carones Family Health Unit), Sofia Melo (Bracara Augusta Family Health Unit), Maria João Barbosa (Family Health Unit of Gualtar), Joana Oliveira (Manuel Rocha Peixoto Family Health Unit), Mélina Lopes (Maxisaúde Family Health Unit), Filipa Meneses (Maxisaúde Family Health Unit), and Soraia Mendes (São Lourenço Family Health Unit), Unidade Local de Saúde de Braga, Portugal. This study received no funding. References Global Initiative for Chronic Obstructive Lung Disease (GOLD). Global Strategy for Prevention, Diagnosis and Management of COPD – 2026 Report. GOLD; 2025. Available from: https://goldcopd.org World Health Organization. World Health Organization. 2025. 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English, Portuguese. doi: 10.1016/j.rppneu.2012.11.004 . Petrie, K et al. Undiagnosed and misdiagnosed chronic obstructive pulmonary disease: Data from the bold Australia study. Int J Chron Obstruct Pulmon Dis. 2021;16:467–475. doi: 10.2147/COPD.S287172 . Ho, T; Cusack, RP; Chaudhary, N; Satia, I; Kurmi, OP. Under- and over-diagnosis of COPD: a global perspective. Breathe (Sheff). 2019;15(1):24–35. doi: 10.1183/20734735.0346-2018 . Perret, J et al. Undiagnosed and “overdiagnosed” COPD using postbronchodilator spirometry in primary healthcare settings: A systematic review and meta-analysis. BMJ Open Respir Res. 2023;10(1):e001478. doi: 10.1136/bmjresp-2022-001478 . Administração Central do Sistema de Saúde, Ministério da Saúde. BI-CSP – Problemas de saúde ativos [Internet]. Lisboa: ACSS; 2025 [cited 2025 Aug]. Available from: https://bicsp.min-saude.pt/pt/biselfservice/Paginas/problemasativos.aspx?isdlg=1&IsDlg=1 Patel, K et al. 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Murgia, N; Gambelunghe, A. Occupational COPD- The most under-recognized occupational lung disease? Respirology. 2022;27(6):399–410. doi: 10.1111/resp.14272 . Graber, ML; Byrne, C; Johnston, D. The impact of electronic health records on diagnosis. Diagnosis (Berl). 2017;4(4):211–223. doi: 10.1515/dx-2017-0012 . Cohen, GR; Friedman, CP; Ryan, AM; Richardson, CR; Adler-Milstein, J. Variation in Physicians’ Electronic Health Record Documentation and Potential Patient Harm from That Variation. J Gen Intern Med. 2019 Nov;34(11):2355–2367. doi: 10.1007/s11606-019-05025-3 . Lacasse, Y; Daigle, JM; Martin, S; Maltais, F. Validity of chronic obstructive pulmonary disease diagnoses in a large administrative database. Can Respir J. 2012 Mar-Apr;19(2):e5-9. doi: 10.1155/2012/260374 . Baldomero, AK et al. Beyond Access: Factors Associated with Spirometry Underutilization among Patients with a Diagnosis of COPD in Urban Tertiary Care Centers. Chronic Obstr Pulm Dis. 2022;9(4):538–548. doi: 10.15326/jcopdf.2022.0303 . Raherison-Semjen, C; Mezzi, K; Kostikas, K; Mackay, AJ; Singh, D. The perception of physicians on gender-specific differences in the diagnosis of COPD: Results from a questionnaire-based survey. Int J Chron Obstruct Pulmon Dis. 2021;16:901–907. doi: 10.2147/COPD.S271505 . Hnizdo, E; Sullivan, PA; Bang, KM; Wagner, G. Association between chronic obstructive pulmonary disease and employment by industry and occupation in the US population: a study of data from the Third National Health and Nutrition Examination Survey. Am J Epidemiol. 2002;156(8):738 – 46. doi: 10.1093/aje/kwf105 . PMID: 12370162. Lytras, T et al. Occupational exposures and 20-year incidence of COPD: the European Community Respiratory Health Survey. Thorax. 2018;73(11):1008–1015. doi: 10.1136/thoraxjnl-2017-211158 . Omland, Ø et al. Occupational chronic obstructive pulmonary disease: A systematic literature review. Scand J Work Environ Health. 2014;40(1):19–35. doi: 10.5271/sjweh.3400 . Silver, SR; Alarcon, WA; Li, J. Incident chronic obstructive pulmonary disease associated with occupation, industry, and workplace exposures in the Health and Retirement Study. Am J Ind Med. 2021;64(1):26–38. doi: 10.1002/ajim.23196 . Epub 2020 Oct 30. PMID: 33124723; PMCID: PMC7736507. Vinnikov, D; Rybina, T; Strizhakov, L; Babanov, S; Mukatova, I. Occupational Burden of Chronic Obstructive Pulmonary Disease in the Commonwealth of Independent States: Systematic Review and Meta-Analysis. Front Med (Lausanne). 2021;7:614827. doi: 10.3389/fmed.2020.614827 . Kuschner, WG; Hegde, S; Agrawal, M. Occupational history quality in patients with newly documented, clinician-diagnosed chronic bronchitis. Chest. 2009;135(2):378–383. doi: 10.1378/chest.08-1559 . Walters, GI; Mcgrath, EE; Ayres, JG. Audit of the recording of occupational asthma in primary care. Occup Med (Lond). 2012;62(7):570–3. doi: 10.1093/occmed/kqs114 . Song, Q et al. Clinical characteristics and risk of all-cause mortality in low education patients with chronic obstructive pulmonary disease in the Chinese population. J Glob Health. 2023;13:04163. doi: 10.7189/jogh.13.04163 . Llordés, M et al. Prevalence, risk factors and diagnostic accuracy of COPD among smokers in primary care. COPD. 2015;12(4):404–12. doi: 10.3109/15412555.2014.974736 . Granger, CL et al. Uptake of telehealth implementation for COPD patients in a high-poverty, inner-city environment: A survey. Chron Respir Dis. 2018;15(1):81–4. doi: 10.1177/1479972317707653 . Azkan Ture, D; Bhattacharya, S; Demirci, H; Yildiz, T. Health Literacy and Health Outcomes in Chronic Obstructive Pulmonary Disease Patients: An Explorative Study. Front Public Health. 2022 Mar 17;10:846768. doi: 10.3389/fpubh.2022.846768 . Puente-Maestu, L et al. Health literacy and health outcomes in chronic obstructive pulmonary disease. Respir Med. 2016;115:78–82. doi: 10.1016/j.rmed.2016.04.016 . Ansari, S; Hosseinzadeh, H; Dennis, S; Zwar, N. Patients’ perspectives on the impact of a new COPD diagnosis in the face of multimorbidity: a qualitative study. NPJ Prim Care Respir Med. 2014 Aug 14;24:14036. doi: 10.1038/npjpcrm . Jones, RCM et al. Opportunities to diagnose chronic obstructive pulmonary disease in routine care in the UK: a retrospective study of a clinical cohort. Lancet Respir Med. 2014;2(4):267–76. doi: 10.1016/S2213-2600(14)70008-6 . Mikkelsen, RL; Middelboe, T; Pisinger, C; Stage, KB. Anxiety and depression in patients with chronic obstructive pulmonary disease (COPD). A review. Nord J Psychiatry. 2004;58(1):65–70. doi: 10.1080/08039480310000824 . Koefoed, MM; Christensen, RD; Sondergaard, J; Jarbol, DE. Lack of spirometry use in Danish patients initiating medication targeting obstructive lung disease. Respir Med. 2012;106(12):1743–8. doi: 10.1016/j.rmed.2012.09.012 . Wang, C; Siff, J; Greco, PJ; Warren, E; Thornton JD; Tarabichi, Y. The Impact of an Electronic Health Record Intervention on Spirometry Completion in Patients with Chronic Obstructive Pulmonary Disease. COPD. 2022;19(1):142–8. doi: 10.1080/15412555.2022.2049736 . Gottlieb, V; Lyngsø, AM; Sæbye, D; Frølich, A; Backer, V. The use of COPD maintenance therapy following spirometry in General Practice. Eur Clin Respir J. 2016 Jun 22;3. doi: 10.3402/ecrj.v3.30232 . Miravitlles, M et al. Difficulties in differential diagnosis of COPD and asthma in primary care. Br J Gen Pract. 2012;62(595):e68-75. doi: 10.3399/bjgp12X625111 . Nissen, F; Morales, DR; Mullerova, H; Smeeth, L; Douglas, IJ; Quint, JK. Concomitant diagnosis of asthma and COPD: a quantitative study in UK primary care. Br J Gen Pract. 2018;68(676):e775-e782. doi: 10.3399/bjgp18X699389 . Pearson, M; Ayres, JG; Sarno, M; Massey, D; Price D. Diagnosis of airway obstruction in primary care in the UK: the CADRE (COPD and Asthma Diagnostic/management REassessment) programme 1997–2001. Int J Chron Obstruct Pulmon Dis. 2006;1(4):435–43. doi: 10.2147/copd.2006.1.4.435 . Yamada, J et al. Barriers and Enablers to Objective Testing for Asthma and COPD in Primary Care: A Systematic Review Using the Theoretical Domains Framework. Chest. 2022;161(4):888–905. doi: 10.1016/j.chest.2021.10.030 . Krishnan, JA et al. Prevalence and characteristics of asthma-chronic obstructive pulmonary disease overlap in routine primary care practices. Ann Am Thorac Soc. 2019;16(9):1143–50. doi: 10.1513/AnnalsATS.201809-607OC . Surani, S et al. Adoption and adherence to chronic obstructive pulmonary disease GOLD guidelines in a primary care setting. SAGE Open Med. 2019;7:2050312119842221. doi: 10.1177/2050312119842221 . Tsiligianni, I et al. COPD patients’ characteristics, usual care, and adherence to guidelines: The Greek UNLOCK study. Int J Chron Obstruct Pulmon Dis. 2019;14:547–556. doi: 10.2147/COPD.S185362 . Vijayakumar, VK et al. Role of a digital clinical decision-support system in general practitioners’ management of copd in Norway. Int J Chron Obstruct Pulmon Dis. 2021;16:2327–2336. doi: 10.2147/COPD.S319753 . Campos, M; Hagenlocker, B; Lascano, J; Riley, L. Impact of a Computerized Clinical Decision Support System to Improve Chronic Obstructive Pulmonary Disease Diagnosis and Testing for Alpha-1 Antitrypsin Deficiency. Ann Am Thorac Soc. 2023;20(8):1116–23. doi: 10.1513/AnnalsATS.202211-954OC . Cai, S et al. Effects of one-hour training course and spirometry on the ability of physicians to diagnose and treat chronic obstructive pulmonary disease. PLoS One. 2015;10(2): e0117348. doi: 10.1371/journal . Tables Tables 2 to 7 are available in the Supplementary Files section. Additional Declarations No competing interests reported. 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in males, although this sex gap has been narrowing.(\u003cspan additionalcitationids=\"CR3 CR4 CR5\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eCOPD remains a major global cause of morbidity and mortality and is responsible for substantial healthcare and socioeconomic burden.(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) In Portugal, prevalence estimates vary depending on methodology, but available data consistently suggest a relevant disease burden. The BOLD study, conducted in the Lisbon region, reported a prevalence of 14.2% in adults aged\u0026thinsp;\u0026ge;\u0026thinsp;40 years, while earlier studies reported lower estimates.(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) Despite methodological differences, these results indicate that COPD is an important public health issue in the country.\u003c/p\u003e \u003cp\u003eA persistent challenge in this field is the high rate of underdiagnosis, which has been widely documented internationally.(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) In Portugal, the BOLD study estimated that 86% of COPD cases in the Lisbon region were undiagnosed.(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) When compared with national primary care records, which in 2025 registered 149,152 individuals aged\u0026thinsp;\u0026ge;\u0026thinsp;40 years with a COPD diagnosis (2.35% prevalence), a substantial discrepancy becomes evident, illustrating the combined effects of underdiagnosis and limitations in diagnostic recording in primary care.(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eBeyond underrecognition, misdiagnosis is also frequent in primary healthcare settings. Several studies have shown that a significant proportion of COPD diagnoses are made without spirometric confirmation, relying instead on symptoms or non-specific diagnostic codes, and often complicated by the challenges of differentiating COPD from other respiratory conditions.(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) In Portugal, a national report from 2017 indicated that only about one-third of COPD diagnoses coded in primary care had an associated spirometry recorded in the electronic health record (EHR).(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) These findings reinforce concerns regarding diagnostic accuracy and the need to better understand factors contributing to potentially inappropriate diagnoses.\u003c/p\u003e \u003cp\u003eIn addition to underdiagnosis and misdiagnosis, several studies have shown that COPD is also frequently miscoded in EHR. A considerable proportion of diagnostic labels recorded in primary care do not meet standard spirometric criteria, nor do they consistently reflect documented exposure to risk factors. Evidence from primary care audits has shown that between 15% and 30% of patients coded with COPD lack objective confirmation when formally assessed.(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) Broader analyses of administrative and clinical databases likewise demonstrate substantial misclassification, with COPD codes frequently failing validation against spirometric or clinical standards.(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) In parallel, reviews of primary care EHR systems highlight pervasive problems in the completeness, structure and accuracy of diagnostic documentation, further contributing to the mismatch between recorded and clinically confirmed COPD.(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) Together, these findings reinforce the need to examine how accurately COPD codes in primary care reflect objective diagnostic criteria and to identify the factors associated with unsupported diagnostic labels.\u003c/p\u003e \u003cp\u003eIn this context, rather than estimating the true prevalence of COPD or the proportion of undiagnosed cases, it becomes essential to assess how well COPD diagnoses recorded in primary care are supported by spirometry results and documented risk factors. The present study aimed to evaluate the compatibility between previous COPD diagnoses and initial spirometry results, and to identify the clinical, demographic, and contextual factors associated with diagnoses classified as compatible, incompatible, or inconclusive.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eThis was an observational, cross-sectional study conducted in primary care. Its main aim was to evaluate the compatibility between previously recorded diagnoses of COPD and the results of spirometries available in the EHR. The study focused on assessing whether the required diagnostic criteria for COPD were registered and on examining the extent to which spirometry supported or contradicted the pre-existing diagnosis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePopulation\u003c/h3\u003e\n\u003cp\u003e The study population included all patients registered in the Family Health Units (FHU) of the Health Centre Group (HCG) of Braga (Portugal) who had the code \u0026ldquo;R95 - Chronic Obstructive Pulmonary Disease\u0026rdquo; from the International Classification of Primary Care, 2nd edition (ICPC-2), recorded as an active health problem in EHR at the time of data extraction.(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) The HCG of Braga comprised 17 FHU, staffed by 102 family doctors, providing care to a total of 182,947 registered patients.\u003c/p\u003e \u003cp\u003eFamily doctors and FHU that did not agree to participate were excluded without replacement. As the aim was to include all eligible patients, the study was designed as a census of the target population, eliminating the need for sampling procedures.\u003c/p\u003e \u003cp\u003ePatient identification was carried out by each participating family doctor, who generated a list of individuals meeting the inclusion criteria.\u003c/p\u003e\n\u003ch3\u003eData and collection process\u003c/h3\u003e\n\u003cp\u003eData were obtained from the EHR platform SCl\u0026iacute;nico (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), used by all family doctors in the participating units. Access to the records of all selected patients was granted after approvals were obtained from local and regional ethics committees, as well as from each participating FHU and their physicians. Data collection took place during 2019 and 2020 and was performed by at least one trained family doctor in each participating FHU.\u003c/p\u003e \u003cp\u003eExtracted information included demographic characteristics (sex, age at diagnosis, age at data collection, education level, and occupation), clinical variables (body mass index, smoking status and tobacco exposure, other risk factors for COPD, comorbidities, previous exacerbations, and ongoing inhaled therapy), and health-service-related variables (place of diagnosis and follow-up in hospital respiratory clinics). Symptom assessments using the mMRC scale and the CAT score were also retrieved when available, acknowledging that these were entered as free-text fields in the EHR. All spirometry records were collected, including the one closest to the time of diagnosis and the most recent available, as well as the total number of spirometry tests registered per patient.\u003c/p\u003e \u003cp\u003eEmphasis was placed on evaluating the compatibility of spirometry and risk-factor records with a COPD diagnosis. Each component was classified into three categories:\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSpirometry\u003c/span\u003e \u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ea) \u003cem\u003eCompatible\u003c/em\u003e - post-bronchodilator Forced Expiratory Volume in 1 second (FEV\u003csub\u003e1\u003c/sub\u003e)/Forced Vital Capacity (FVC) ratio\u0026thinsp;\u0026lt;\u0026thinsp;0.7 and/or below the lower limit of normal;\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eb) \u003cem\u003eUncertain\u003c/em\u003e - description compatible with COPD without numerical values; FEV₁/FVC\u0026thinsp;\u0026lt;\u0026thinsp;0.7 before but not after bronchodilation; or obstructive values with significant bronchodilator response;\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ec) \u003cem\u003eNot compatible\u003c/em\u003e \u0026mdash; no spirometry available or spirometry inconsistent with an obstructive pattern.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eRisk-factor records\u003c/span\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003ea) Compatible\u003c/em\u003e - smoking history\u0026thinsp;\u0026ge;\u0026thinsp;10 pack-years and/or clear evidence of significant exposure to other risk factors;\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eb) \u003cem\u003eUncertain\u003c/em\u003e - smoking mentioned but unquantified, or other exposures recorded but insufficiently quantified;\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ec) \u003cem\u003eNot compatible\u003c/em\u003e - no relevant exposure documented.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eAn integrated diagnostic-certainty classification was derived from combining these two components (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e): \u003cem\u003eYes\u003c/em\u003e, when both spirometry and risk-factor records were compatible; \u003cem\u003eMaybe\u003c/em\u003e when one was compatible and the other uncertain or when both were uncertain; and \u003cem\u003eNo\u003c/em\u003e, when at least one record was not compatible.\u003c/p\u003e \u003cp\u003eTo minimise variability in data collection, all physicians involved received detailed training and written guidance before the start of the process, ensuring consistent application of the predefined criteria across participating units.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIntegrated matrix of spirometry and risk-factor record compatibility used to classify diagnostic certainty of COPD. \u0026ldquo;Compatible\u0026rdquo;, \u0026ldquo;Uncertain\u0026rdquo; and \u0026ldquo;Not compatible\u0026rdquo; refer to the concordance of spirometry and risk-factor records with a COPD diagnosis, as defined in Methods; \u0026ldquo;YES\u0026rdquo;, \u0026ldquo;MAYBE\u0026rdquo; and \u0026ldquo;NO\u0026rdquo; indicate the resulting diagnostic certainty.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eSpirometry records\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRisk factor records\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCompatible\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUncertain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot compatible\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompatible\u003c/p\u003e \u003cp\u003eUncertain\u003c/p\u003e \u003cp\u003eNot compatible\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003cp\u003eMAYBE\u003c/p\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMAYBE\u003c/p\u003e \u003cp\u003eMAYBE\u003c/p\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003cp\u003eNO\u003c/p\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAfter data collection, datasets were checked for internal consistency and potential recording errors, and family doctors were contacted whenever necessary to clarify doubtful information. Statistical analysis was performed using IBM SPSS Statistics (IBM Corp., Armonk, NY, USA). A significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and 95% confidence intervals (95% CI) were adopted. Descriptive statistics were used to summarise all study variables: categorical variables were presented as absolute and relative frequencies, and continuous variables as means and standard deviations (SD) or medians and interquartile ranges (IQR), according to their distribution.\u003c/p\u003e \u003cp\u003eTo explore the association between patient characteristics and diagnostic compatibility, patients were classified into three groups (compatible, uncertain, and not compatible). Continuous variables were compared across these groups using the Kruskal-Wallis test, under the null hypothesis that the distribution of each variable did not differ between diagnostic-compatibility categories. Categorical variables were compared using the chi-square test or Fisher\u0026rsquo;s exact test when expected cell counts in contingency tables were \u0026lt;\u0026thinsp;5; in these analyses, the null hypothesis stated that the proportion of patients in each category was equal across diagnostic-compatibility groups.\u003c/p\u003e \u003cp\u003eDifferences in spirometry-based classifications between two time points (the spirometry closest to the time of diagnosis and the most recent recorded spirometry) were evaluated by comparing the marginal proportions of patients in each category. For the specific comparison of the proportion of patients classified as \u0026ldquo;not compatible\u0026rdquo; at the two time points, McNemar\u0026rsquo;s test for paired binary outcomes was used, assuming as null hypothesis that the probability of changing from \u0026ldquo;not compatible\u0026rdquo; to \u0026ldquo;compatible/uncertain\u0026rdquo; was equal to the probability of the opposite change.\u003c/p\u003e \u003cp\u003eTo identify factors independently associated with diagnostic compatibility, a multivariable multinomial logistic regression model was fitted, using diagnostic-compatibility category (compatible, uncertain, not compatible) as the dependent variable and the \u0026ldquo;uncertain\u0026rdquo; group as the reference category. Explanatory variables included sex, age at diagnosis, time since diagnosis, number of comorbidities, presence of psychological comorbidities, presence of asthma, and place of diagnosis. Results were expressed as odds ratios (OR) with 95% CI, under the null hypothesis that OR\u0026thinsp;=\u0026thinsp;1 (no association). Overall model fit and explanatory capacity were assessed using the likelihood-ratio chi-square test and Nagelkerke\u0026rsquo;s R\u0026sup2;.\u003c/p\u003e \u003cp\u003eIn secondary analyses, exploratory binary logistic regression models were fitted after dichotomising the outcome as \u0026ldquo;compatible\u0026rdquo; versus \u0026ldquo;not compatible\u0026rdquo;, excluding uncertain cases. Separate univariable models were estimated for comorbidity chapters and for individual symptom-letter variables recorded in the clinical form, with results presented as OR and 95% CI. Full model specifications, detailed regression diagnostics, and extended output tables are provided in the Supplementary Material (Tables S2 and S3).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthics approval\u003c/h3\u003e\n\u003cp\u003e The study was approved by the Ethics Committee of the Northern Region Health Administration (approval number: 53/2018). Access to electronic health records was granted after the required approvals from local and regional ethics committees, as well as from each participating Family Health Unit and their physicians.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eIn this study, 1817 patients were included (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), corresponding to all individuals with the ICPC-2 code R95 recorded as an active health problem in their EHR. They were distributed across 13 FHU and followed by 73 family doctors, with a mean of 20.68 (\u0026plusmn;\u0026thinsp;11.44) COPD patients per doctor\u0026rsquo;s list. The average prevalence of COPD per FHU was 2.56% (\u0026plusmn;\u0026thinsp;0.94) and per doctor\u0026rsquo;s patient list 2.72% (\u0026plusmn;\u0026thinsp;1.73), both calculated for individuals aged 40 years and older.\u003c/p\u003e\n\u003cp\u003eThe mean age at data collection and at diagnosis was 67.85 (\u0026plusmn;\u0026thinsp;13.7) and 62.7 (\u0026plusmn;\u0026thinsp;14) years, respectively. Most patients (n\u0026thinsp;=\u0026thinsp;1743; 95.93%) were aged\u0026thinsp;\u0026ge;\u0026thinsp;40 years at diagnosis, and the majority were male (n\u0026thinsp;=\u0026thinsp;1166; 64.17%). The mean time since diagnosis was 5 (\u0026plusmn;\u0026thinsp;4.55) years. At diagnosis, 41.8% were retired, with most having previous or current occupations in the secondary (manufacturing/industry) and tertiary (services) sectors. The most frequent education levels were the lowest, particularly up to the 4th grade. Higher education was recorded in only 5.6% of patients.\u003c/p\u003e\n\u003cp\u003eRegarding comorbidities, most patients had at least one, with four being the most frequent number of concurrent conditions (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The median number of comorbidities was four (IQR: 3\u0026ndash;5), and only 5.2% of patients had eight or more chronic conditions. Comorbidities were coded according to the ICPC-2 and grouped by system chapter (B-Z). The most common comorbidities were related to the \u0026ldquo;Endocrine, metabolic, and nutritional system\u0026rdquo;, \u0026ldquo;Circulatory system\u0026rdquo;, and \u0026ldquo;Psychological conditions\u0026rdquo;. Other chronic respiratory diseases were also recorded in some patients: asthma in 172 (9.47%), chronic bronchitis in 114 (6.27%), and emphysema in 78 (4.29%).\u003c/p\u003e\n\u003cp\u003eThis study focused particularly on the recording of the two main conditions required for a COPD diagnosis: spirometry results (the one closest to diagnosis, to evaluate diagnostic compatibility, and the most recent one available, to identify potential improvements in data recording) and records of risk factors (smoking and/or other exposures such as occupational, environmental, or biomass-related). Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e presents the findings. A substantial proportion of patients (345; 18.99%) had no spirometry record. Only 108 (5.94%) and 136 (7.48%) patients had a spirometry record fully compatible with the diagnosis at the time of diagnosis and at the most recent test, respectively. Regarding risk factors, 47.55% and 45.79% of patients had no record of prior smoking history and no record of any exposure to risk factors, respectively. Conversely, 44.91% of diagnoses were accompanied by unequivocal documentation of risk factors.\u003c/p\u003e\n\u003cp\u003eIn an exploratory subsample analysis (n\u0026thinsp;=\u0026thinsp;198; 10.9%), limited to patients for whom hospital follow-up information was available, it was possible to determine whether follow-up consultations had occurred at the hospital level. This analysis aimed to explore whether inconsistencies in primary care diagnostic records could be explained by confirmatory data registered at the hospital level. Such follow-up was observed in 111 patients (56.1%), but confirmatory documentation of the COPD diagnosis was identified in only 49 patients (24.7%) within hospital clinical records. This association was not statistically significant (p\u0026thinsp;=\u0026thinsp;0.137), suggesting that the majority of non-compatible primary care diagnoses could not be justified by hospital information. These findings should be interpreted cautiously given the exploratory nature and limited scope of this analysis.\u003c/p\u003e\n\u003cp\u003eA review of follow-up data was also conducted, focusing on symptoms, exacerbations, and severity classification, as indicators of the completeness and quality of disease monitoring within primary care records. Records of CAT and/or mMRC symptom scores were found in 171 patients (9.4%). Most patients (n\u0026thinsp;=\u0026thinsp;1503; 82.7%) had no records of previous exacerbations, while 216 (11.9%), 61 (3.4%), 21 (1.2%) and 16 (0.9%) patients had 1, 2, 3, and \u0026ge;\u0026thinsp;4 recorded exacerbations, respectively. Based on the combination of symptom burden and exacerbation history, the GOLD severity class (A-D at that time) should be determined. However, 1677 patients (92.3%) had no documented GOLD classification. Among those with recorded GOLD groups, 73 (4.0%), 45 (2.5%), 6 (0.3%), and 16 (0.9%) were classified as A, B, C, and D, respectively. These findings highlight substantial gaps in the systematic recording of COPD monitoring variables in primary care EHR.\u003c/p\u003e\n\u003cp\u003eOne of the objectives of this study was to identify circumstances or patient characteristics associated with diagnostic adequacy. The main associations are presented in Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. A higher mean age at diagnosis was associated with less accurate diagnoses, and the same pattern was observed in patients with a longer duration of diagnosis. Diagnoses made outside the FHU (hospital consultations, private practice or other settings) were more frequently classified as inadequate. This association was statistically significant when diagnostic compatibility was defined according to the initial spirometry (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but no longer reached statistical significance when compatibility was determined using the most recent spirometry record (p\u0026thinsp;=\u0026thinsp;0.101), although the same trend persisted. Diagnoses made in women were also more frequently inadequate. Diagnostic compatibility was also associated with occupation (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 in both analyses), with inadequate diagnoses being the predominant outcome across all occupational groups. A similar association was observed with educational level (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 in both analyses), with inadequate diagnoses being the most frequent outcome across all categories of schooling.\u003c/p\u003e\n\u003cp\u003eRegarding more clinical parameters, the recording of symptoms was more frequent in cases with adequate or uncertain diagnoses, whereas the absence of symptom records was more common in cases with inadequate diagnoses. The relationship between diagnostic compatibility and the recording of exacerbations was not statistically significant. In contrast, the GOLD severity class was more frequently recorded in patients with adequate diagnoses and much less often in those with inadequate diagnoses.\u003c/p\u003e\n\u003cp\u003eIn terms of comorbidities, the proportion of diagnoses classified as not compatible increased across categories of comorbidity count, although the gradient was modest. When analysing the association between diagnostic compatibility and each isolated group of comorbidities, several ICPC-2 chapters showed significant associations in at least one of the two analyses (diagnosis based on the initial spirometry versus the most recent spirometry). These included chapters L (Musculoskeletal), R (Respiratory, excluding COPD-related codes), T (Endocrine/Metabolic), and P (Psychological), as well as the aggregated \u0026lsquo;Others\u0026rsquo; category (chapters with low frequency: B, H, N, S, X, Z). When analysing the association between diagnostic compatibility and the presence of asthma, chronic bronchitis or emphysema, only concomitant asthma showed a statistically significant association, being more frequent among cases classified as uncertain or not compatible. Chronic bronchitis showed no significant association, and emphysema was only associated with diagnostic inaccuracy when compatibility was assessed using the most recent spirometry. Ten participants had missing information for these conditions, but their exclusion did not affect the observed associations.\u003c/p\u003e\n\u003cp\u003eThe prescription of inhaled therapy showed a statistically significant association with diagnostic compatibility. However, this result should be interpreted with caution, as all therapeutic regimens were more frequently prescribed in patients with diagnoses classified as not compatible. This likely reflects treatment decisions based on symptomatic burden rather than on diagnostic confirmation, limiting the clinical interpretability of this association.\u003c/p\u003e\n\u003cp\u003eA multinomial logistic regression model was fitted to explore independent predictors of diagnostic compatibility, using the \u0026ldquo;Uncertain\u0026rdquo; category as the reference group (Table \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). The model showed good overall performance (\u0026chi;\u0026sup2; = 534.2; df\u0026thinsp;=\u0026thinsp;26; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Nagelkerke R\u0026sup2; = 0.332). Full SPSS model output and detailed regression diagnostics are provided in Supplementary Material S1 (Table S1).\u003c/p\u003e\n\u003cp\u003eSeveral variables were independently associated with a higher probability of having a diagnosis classified as \u0026ldquo;Not compatible\u0026rdquo; rather than \u0026ldquo;Uncertain\u0026rdquo;. Female sex (OR\u0026thinsp;=\u0026thinsp;3.59; 95% CI: 2.72\u0026ndash;4.74) and diagnoses made outside Family Health Units - particularly in private practice (OR\u0026thinsp;=\u0026thinsp;2.58; 95% CI: 1.28\u0026ndash;5.20), hospital consultations (OR\u0026thinsp;=\u0026thinsp;1.56; 95% CI: 1.03\u0026ndash;2.35), or other settings (OR\u0026thinsp;=\u0026thinsp;2.22; 95% CI: 1.15\u0026ndash;4.28) - were associated with significantly higher odds of diagnostic inadequacy.\u003c/p\u003e\n\u003cp\u003eAn increasing number of comorbidities was also significantly associated with diagnostic inadequacy, as were the presence of concomitant asthma (OR\u0026thinsp;=\u0026thinsp;1.66; 95% CI: 1.05\u0026ndash;2.64) and psychological comorbidities (OR\u0026thinsp;=\u0026thinsp;11.59; 95% CI: 7.71\u0026ndash;17.43), the latter being the strongest predictor in the model. Both older age at diagnosis (OR\u0026thinsp;=\u0026thinsp;1.019 per year; 95% CI: 1.008\u0026ndash;1.030) and longer diagnostic o (OR\u0026thinsp;=\u0026thinsp;1.086 per year; 95% CI: 1.045\u0026ndash;1.129) were also independently associated with an increased probability of diagnostic inadequacy.\u003c/p\u003e\n\u003cp\u003eIn contrast, the model did not identify consistent predictors of belonging to the \u0026ldquo;Compatible\u0026rdquo; group when compared with \u0026ldquo;Uncertain\u0026rdquo;, likely due to the small number of cases in this category (n\u0026thinsp;=\u0026thinsp;66), limiting the stability of these estimates.\u003c/p\u003e\n\u003cp\u003eSupplementary Tables S2 and S3 provide the complete ICPC-2 chapter distribution and the full set of univariable logistic regression models.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003e This study provides a population-level examination of how COPD is diagnosed in routine primary care, revealing substantial discrepancies between guideline-based diagnostic criteria and information recorded in real-world practice. By analysing all patients with an active COPD code across an entire regional health centre group, the study offers an unusually comprehensive view of diagnostic performance, illuminating where clinical pathways function effectively and where they systematically fail. These findings have direct relevance for improving diagnostic quality and shaping practice-level interventions.\u003c/p\u003e \u003cp\u003eAt the population level, only a very small proportion of COPD diagnoses were supported by spirometry and documented risk-factor exposure, while the majority fell into the incompatible or uncertain categories. Spirometry was absent or non-confirmatory in nearly four out of five patients, and documentation of smoke load or other exposures was frequently incomplete. Diagnostic incompatibility was strongly associated with older age at diagnosis, female sex, diagnosis made outside primary care, higher multimorbidity and, most distinctly, the presence of psychological comorbidities. These patterns reflect both clinical challenges and structural barriers within primary care, underscoring the complexity of achieving diagnostic certainty in chronic respiratory disease. Taken together, these patterns illustrate persistent challenges in aligning guideline-based diagnostic criteria with real-world practice in primary care.\u003c/p\u003e \u003cp\u003eThe results highlight substantial gaps in the recording of essential diagnostic elements, and these patterns remain relevant today given that recent national primary care data continue to show a recorded prevalence of COPD in Portugal much lower than expected.(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) Despite variations across studies, the consistently low number of coded cases reinforces that diagnostic uncertainty and incomplete confirmation remain persistent challenges in routine care. These findings are consistent with international observations that discrepancies between epidemiological estimates and physician-recorded COPD are largely driven by limited use of confirmatory spirometry, variability in coding practices, and inconsistent documentation of risk factors.(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) Studies from UK primary care similarly demonstrate that important clinical information - including lifestyle-related data, socioeconomic indicators and chronic disease markers - is frequently missing or incompletely coded, complicating diagnostic interpretation and contributing to uncertainty in disease classification.(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) These challenges also reflect broader patterns observed in primary care electronic records, where large amounts of information are often captured in free-text fields rather than structured formats. This heterogeneity reduces the clarity of diagnostic records and the reliability of retrospective assessments, particularly for conditions such as COPD that require objective confirmation. Evidence from U.S. primary care HER indicates that essential clinical reasoning and contextual information are often absent from structured fields and instead buried in free-text notes, hindering diagnostic interpretation and complicating efforts to assess real-world practice through EHR data.(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThis study confirmed the very low proportion of spirometry-confirmed COPD diagnoses in primary care. Only 5.94% and 7.48% of patients had spirometry fully compatible with COPD at the time of diagnosis and at the most recent test, respectively. These findings are in line with previous reports indicating that spirometry remains persistently underused or inconsistently applied in routine care. In an Australian study, more than 80% of patients receiving COPD pharmacotherapy had no spirometry recorded in the year surrounding treatment initiation, and nearly half were later found not to meet spirometric criteria for COPD when lung function was eventually assessed.(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) Other global analyses link these patterns to clinician, organisational and system level barriers, including limited confidence in interpreting spirometry, workflow constraints, inadequate access and perceptions that spirometry adds limited clinical value.(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) Earlier US studies similarly showed that a substantial proportion of patients labelled with COPD lacked airflow obstruction on spirometry.(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eRisk-factor documentation was comparably limited. Nearly half of the patients had no recorded smoking history or lacked quantification of smoking load. Recent GOLD reports emphasise that, beyond smoking, environmental pollution and host factors also contribute to COPD pathogenesis; however these exposures were not assessed in the present analysis.(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Missing or fragmented smoking information is a well-recognised problem in primary care, with studies showing that inconsistent recording reduces the accuracy of diagnostic assessments and undermines proactive disease monitoring.(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) Evidence from German primary care indicates substantial variability in how smoking behaviours and exposure histories are captured, contributing to under-recognition of COPD and lower diagnostic confidence.(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) Occupational exposures, estimated to account for 15\u0026ndash;20% of COPD cases, were also poorly documented, despite their well-established role in disease development and progression.(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eWhen spirometry and exposure data were considered together, diagnostic adequacy was very low at baseline and improved only marginally over time, despite a statistically significant reduction in incompatible diagnoses. Although these modest changes may reflect increasing awareness of spirometry\u0026rsquo;s importance, overall diagnostic adequacy remained strikingly low. A critical insight from these findings is the distinction between genuinely inadequate diagnostic procedures and simply inadequate documentation. Many incompatible cases may reflect missing or unstructured data rather than absence of clinical evaluation. This interpretation aligns with studies showing that diagnostic steps may have been performed but remain undocumented or confined to non-extractable free text notes.(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) Reviews of EHR content show that clinical reasoning, diagnostic pathways and follow-up assessments are often absent from structured fields, increasing uncertainty and risk of misclassification.(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) Variability in documentation thoroughness and coding habits further contributes to inconsistent data quality and has been linked to potential patient harm.(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThese findings must also be interpreted alongside persistent discrepancies between epidemiological estimates and physician-recorded prevalence. In Portugal and internationally, population-based studies such as BOLD consistently report higher prevalence than reflected in routine clinical records, due largely to underdiagnosis, limited spirometry use and diagnostic uncertainty.(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) Similar gaps have been documented globally, where spirometry-confirmed COPD represents only a fraction of individuals who meet symptom or exposure-based criteria.(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) Our results, therefore, mirror international evidence indicating that physician-diagnosed COPD substantially underrepresents the true burden of disease.\u003c/p\u003e \u003cp\u003eSeveral patient characteristics were associated with diagnostic adequacy. Older age and longer diagnostic duration were linked to higher incompatibility rates, consistent with findings from the BOLD Australia study, which showed markedly higher underdiagnosis in older adults, particularly those\u0026thinsp;\u0026ge;\u0026thinsp;75 years, reflecting the diagnostic complexity introduced by age-related physiological changes and multimorbidity.(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) Women were also more likely to have incompatible diagnoses, a pattern supported by studies showing that women are less likely to undergo spirometry, more frequently mislabelled with asthma and at higher risk of delayed or incorrect diagnosis.(\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eIn addition to their recognised contribution to COPD risk, occupational exposures were also associated with diagnostic adequacy, with primary-sector workers showing the highest rates of incompatibility. This is consistent with long-standing evidence linking agricultural, mining, manufacturing and construction work to increased COPD risk due to exposure to dusts, gases, vapours and fumes, as well as substantial underdiagnosis in these settings.(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan additionalcitationids=\"CR37 CR38 CR39\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e) However, poor documentation of occupational history may attenuate observed associations, as occupational information is frequently missing or incompletely recorded in primary care records.(\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eLower education levels were also associated with higher diagnostic incompatibility, consistent with evidence linking low education to poorer COPD outcomes, increased exacerbation risk and higher mortality.(\u003cspan additionalcitationids=\"CR44 CR45\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e) Although health literacy was not directly assessed, this finding is consistent with evidence showing that limited health literacy contributes to poorer disease understanding, higher symptom burden and increased healthcare utilisation.(\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e) These findings underscore the role of social determinants in shaping diagnostic accuracy and clinical outcomes.\u003c/p\u003e \u003cp\u003eComorbidity burden further contributed to diagnostic uncertainty, with higher multimorbidity associated with greater incompatibility, consistent with evidence that competing health priorities may mask respiratory symptoms and delay diagnosis.(\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e) Psychological comorbidities showed the strongest association, reflecting evidence that anxiety and depression alter symptom perception and complicate clinical evaluation.(\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThis diagnostic complexity may partly explain patterns of empirical prescribing. In this study, inhaled therapies were more frequently prescribed among incompatible diagnoses, mirroring evidence that long-acting bronchodilators and ICS are often initiated without spirometric confirmation of COPD.(\u003cspan additionalcitationids=\"CR52\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eAmong respiratory comorbidities, asthma was most strongly associated with incompatible diagnoses, consistent with the well-established diagnostic overlap between asthma, COPD and asthma with COPD, particularly in primary care settings where objective testing is often lacking.(\u003cspan additionalcitationids=\"CR55 CR56 CR57\" citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eSymptoms and GOLD classification were not used in our definition of diagnostic adequacy. However, their extremely low documentation rates highlight major gaps in disease monitoring. Comparable findings have been described internationally - in one US study, none of 101 primary care patients labelled with COPD had CAT or mMRC scores recorded, and only 21% had pulmonary function testing, with nearly one-third not meeting spirometric criteria.(\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e) In the Greek UNLOCK study, GOLD staging was inconsistently applied despite high symptom burden.(\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e) Improving the recording of symptom burden, exacerbations and severity classification is essential for high-quality COPD management.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, the analysis relied exclusively on information recorded in EHR, which may omit relevant data that were obtained clinically but never documented, or that remain stored in inaccessible legacy paper files. This constraint may have contributed to an underestimation of diagnostic adequacy. Second, the quality and completeness of EHR entries varied across clinicians and units, particularly because key variables, such as smoking exposure, occupational history and clinical reasoning, were often documented in free text rather than structured fields, limiting extractability and introducing heterogeneity. Third, the cross-sectional design captured diagnostic information at a fixed point in time and cannot account for subsequent diagnostic refinement or changes in coding practices. Finally, data collection occurred in 2019/2020; although national figures suggest that the diagnostic challenges identified remain largely unchanged, temporal shifts in practice cannot be completely excluded. Despite these limitations, the study provides a robust and comprehensive real-world assessment of COPD diagnostic practices within a large primary care population.\u003c/p\u003e \u003cp\u003eThis study also presents several important strengths. First, by including \u003cem\u003eall\u003c/em\u003e patients with an active COPD code across a large and diverse primary care cluster, it avoids selection bias and provides a comprehensive and population-level picture of diagnostic practices. Second, although data extraction was carried out by multiple clinicians, the use of a standardised protocol with prior training and calibration sessions ensured consistent interpretation of clinical fields and minimised inter-observer variability. Finally, access to the full breadth of routine HER enabled the assessment of diagnostic adequacy under real-world conditions, capturing the complexity, heterogeneity and pragmatic constraints of primary care practice - an essential perspective that is often absent from controlled research environments.\u003c/p\u003e \u003cp\u003eThese findings have several implications for clinical practice in primary care. The very low proportion of spirometry-confirmed diagnoses underscores the need for more systematic use of lung function testing and for structured documentation of risk factors, including smoking status and relevant environmental or occupational exposures. Diagnostic disparities observed across age, sex, education and occupational sectors highlight the importance of considering social determinants of health during diagnostic evaluation. Improvements in electronic record-keeping, particularly through the use of structured fields for key diagnostic elements, may help reduce variability and enhance diagnostic confidence. Targeted strategies such as focused clinician training and the integration of decision-support prompts within the EHR have shown potential to support more consistent diagnostic pathways, although further implementation research is needed to evaluate their impact in real-world settings.(\u003cspan additionalcitationids=\"CR62\" citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eBy clarifying the gaps between recorded and evidence-based diagnostic criteria, this study identifies a clear opportunity to reinforce COPD diagnostic pathways. Embedding objective assessment and consistent documentation into routine care is fundamental to ensuring accurate, equitable management.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003e This population-based study revealed substantial discrepancies between guideline-based diagnostic criteria and the information recorded in routine primary care for patients labelled with COPD. Spirometry confirmation and documentation of key risk factors were infrequent, contributing to high rates of incompatible or uncertain diagnoses. Diagnostic adequacy was associated with demographic, social and clinical factors, highlighting inequities and structural limitations within current diagnostic pathways. By clarifying where diagnostic processes falter, this study identifies concrete opportunities to reinforce the use of objective measures, standardise documentation and support clinicians in delivering guideline-aligned care. Strengthening these elements is essential to achieve accurate, consistent and equitable COPD diagnosis in primary care.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the principles of the Declaration of Helsinki. Ethical approval was obtained from the Ethics Committee of the Northern Region Health Administration (approval number: 53/2018). Additional authorisations were granted by local and regional ethics committees, as well as by each participating Family Health Unit and their physicians, allowing access to routinely collected electronic health records for research purposes.\u003c/p\u003e\n\u003cp\u003eThis study was a cross-sectional observational analysis based exclusively on routinely collected clinical data, with no direct involvement of patients. Informed consent was therefore waived by the Ethics Committee, as all data were analysed in a fully anonymised manner, no identifiable personal information was accessed by the research team, and the study posed no risk to patients or interference with usual clinical care.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors hereby confirm that, through this submission, they consent to the publication of this manuscript in \u003cem\u003enpj Primary Care Respiratory Medicine\u003c/em\u003e, should it be accepted. All authors have reviewed and approved the final version of the manuscript and agree to its submission and publication in accordance with the journal’s editorial policies and publishing requirements.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are derived from routinely collected primary care electronic health records and contain sensitive personal health information. For ethical and legal reasons, the original individual-level data are not publicly available. All data presented in the article and its Supplementary Information are fully anonymised and reported in aggregated form. Derived anonymised datasets necessary to interpret and reproduce the analyses may be made available from the corresponding author upon reasonable request, subject to approval by the relevant institutional and ethical authorities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe declare that the authors have no competing interests (financial or non-financial) as defined by Nature Portfolio, or other interests that might be perceived to influence the results and/or discussion reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePF developed the study protocol, collected the data, performed the data analysis, and drafted the manuscript. ID, BB, and CF contributed to data collection and critically revised the manuscript. JCS and TvM reviewed the study protocol, contributed to data analysis, and critically revised the manuscript. All authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to acknowledge the contribution of the following healthcare professionals for their support in data access and collaboration at the participating primary care units: Susana Fernandes (7 Fontes Family Health Unit), Rosana Dantas (Family Health Unit of Ruães), Maria Emília Faria (Pelaez Carones Family Health Unit), Sofia Melo (Bracara Augusta Family Health Unit), Maria João Barbosa (Family Health Unit of Gualtar), Joana Oliveira (Manuel Rocha Peixoto Family Health Unit), Mélina Lopes (Maxisaúde Family Health Unit), Filipa Meneses (Maxisaúde Family Health Unit), and Soraia Mendes (São Lourenço Family Health Unit), Unidade Local de Saúde de Braga, Portugal.\u003c/p\u003e\n\u003cp\u003eThis study received no funding.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGlobal Initiative for Chronic Obstructive Lung Disease (GOLD). 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PLoS One. 2015;10(2): e0117348. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal\u003c/span\u003e\u003cspan address=\"10.1371/journal\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 2 to 7 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-primary-care-respiratory-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjpcrm","sideBox":"Learn more about [npj Primary Care Respiratory Medicine](https://www.nature.com/npjpcrm/)","snPcode":"41533","submissionUrl":"https://submission.springernature.com/new-submission/41533/3","title":"npj Primary Care Respiratory Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8610008/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8610008/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDiagnosing COPD in primary care remains challenging, with significant international evidence of underdiagnosis, misdiagnosis and miscoding. How reliably COPD diagnoses in real-world practice reflect objective diagnostic criteria is less well understood.\u003c/p\u003e\u003ch2\u003eAim\u003c/h2\u003e \u003cp\u003eTo evaluate the compatibility between recorded COPD diagnoses and key diagnostic criteria -spirometry and risk-factor documentation - and to identify patient and contextual factors associated with diagnostic adequacy.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e A population-based, cross-sectional study including all patients with a recorded diagnosis of COPD across a large Portuguese primary care cluster (17 Family Health Units; 182,947 patients). Electronic Health Records data were extracted for demographics, comorbidities, symptoms, exposures, spirometry and clinical follow-up. Spirometry and risk-factor records were independently classified according to their compatibility with a diagnosis of COPD (compatible, uncertain or not compatible) and combined to derive diagnostic-certainty categories. Associations were analysed using non-parametric tests and multinomial logistic regression.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong 1817 included patients, only 3.63% had both spirometry and exposure records fully compatible with COPD at diagnosis; 24.05% had incompatible records. Using the most recent spirometry, compatibility improved only marginally (4.4%), while incompatible cases remained high (21.57%). Spirometry was absent or non-confirmatory in nearly 80% of patients, and smoking or other exposures documentation was missing or incomplete in almost 50%. Diagnostic incompatibility was strongly associated with older age, female sex, diagnosis made outside primary care, multimorbidity, and especially psychological comorbidities (OR 11.6; 95% CI 7.7\u0026ndash;17.4). Asthma was the only respiratory comorbidity significantly associated with incompatible diagnoses. Records of symptoms, exacerbations and GOLD classification were infrequently documented.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eCOPD diagnoses recorded in routine primary care frequently lack spirometric confirmation and adequate documentation of risk factors, resulting in high rates of incompatible diagnoses. Diagnostic adequacy reflects demographic, social and clinical determinants, revealing important inequities. These findings highlight an urgent need to strengthen diagnostic pathways through systematic use of spirometry, structured recording of risk factors and symptoms, and enhanced Electronic Health Records documentation. Improving these core elements is essential to ensure accurate, consistent and equitable COPD diagnosis in primary care.\u003c/p\u003e","manuscriptTitle":"Diagnostic Adequacy of COPD in Primary Care: A Population-Based Analysis of Spirometry Use and Risk-Factor Documentation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-30 12:22:41","doi":"10.21203/rs.3.rs-8610008/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-23T11:28:52+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-19T23:52:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-08T10:00:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"322079942023731764335398542357085385508","date":"2026-01-29T21:12:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"175067689219890270342751757909913451188","date":"2026-01-28T15:24:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-28T15:03:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-18T23:50:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-18T23:48:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Primary Care Respiratory Medicine","date":"2026-01-15T11:30:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-primary-care-respiratory-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjpcrm","sideBox":"Learn more about [npj Primary Care Respiratory Medicine](https://www.nature.com/npjpcrm/)","snPcode":"41533","submissionUrl":"https://submission.springernature.com/new-submission/41533/3","title":"npj Primary Care Respiratory Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"14f63428-a6b9-4b80-9a85-9c2ac008f97d","owner":[],"postedDate":"January 30th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":61995100,"name":"Health sciences/Diseases"},{"id":61995101,"name":"Health sciences/Health care"},{"id":61995102,"name":"Health sciences/Medical research"},{"id":61995103,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-05-14T07:09:05+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-30 12:22:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8610008","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8610008","identity":"rs-8610008","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}