× Current Issue Archive Submit Article
Conflicts of Interest Copyright and Access Open access policy Editorial Policies Peer Review Policy Privacy Statement Publishing Ethics Generative AI Usage Policy
Editor in chief Associate Editors Advisory Board International Editors
Contact Us About Us Aim & Scope Abstracting And Indexing Author Guidelines Join As Editor
Views: 721 Downloads: 63

Integrated cardio-respiratory monitoring: a synergistic approach with capnography, impulse oscillometry, and CT-densitometry in precision pulmonology

Magomed Mukharbekovich Ozdoev*✉ , Amina Vakhitovna Gazimova , Dali Aleksandrovna Akhrieva , Khava Khajmuradovna Martazanova , Patimat Gasanovna Sumalaeva , Rabiyat Ramazanovna Gadzhialieva , Ragimat Rasimovna Dzharulaeva , Sakinat Zaurbekovna Muslimova , Aslan Urudzhevich Makhmudov , Sabina Magomedovna Magomedova
  1. Faculty of Medicine, Medical Institute, Ingush State University, Magas, Republic of Ingushetia, Russia.
  2. Faculty of Medicine, Dagestan State Medical University, Makhachkala, Republic of Dagestan, Russia.
  3. Faculty of Pediatrics, Dagestan State Medical University, Makhachkala, Republic of Dagestan, Russia.
  4. Faculty of Medicine and Prevention, Dagestan State Medical University, Makhachkala, Republic of Dagestan, Russia.
  5. Faculty of Medicine, Astrakhan State Medical University, Astrakhan, Russia.

Abstract

The purpose of this study was to evaluate the synergistic value of combined capnography, impulse oscillometry (IOS), and CT densitometry (qCT) for precision phenotyping in chronic respiratory diseases. The study included 85 patients with COPD, bronchial asthma, ILD, and their cardiac comorbidities, and 30 control subjects. All participants underwent a comprehensive assessment using IOS, resting capnography, and chest CT with quantitative densitometric analysis. Patients with COPD and concomitant heart failure exhibited the most severe impairments: small airway resistance (R5-R20) reached 0.28±0.11 kPa/L/s, the air trapping index (AX) was 2.87±1.24 kPa/L, and end-tidal CO₂ concentration (EtCO₂) decreased to 27.8±5.2 mm Hg versus 38.2±2.1 in controls. CT densitometry objectively confirmed the structural basis: the emphysema index (LAA-950%) in this subgroup was 25.1±12.8%, and the expiratory air trapping volume was 45.8±16.3%. Correlation analysis revealed strong associations between functional and structural parameters: between AX (IOS) and air trapping on qCT (r=0.81, p<0.001), and between the calculated dead space index and air trapping volume (r=0.76, p<0.001). The integration of capnography, IOS, and qCT provides a powerful synergistic approach. It enables quantitative discrimination of contributions from airway obstruction, gas exchange impairment, and structural remodeling, providing a solid foundation for personalized therapeutic strategies in complex cardiorespiratory disease‎.



Keywords: Integrated monitoring, Impulse oscillometry, Capnography, CT densitometry, Precision pulmonology, Phenotyping

Introduction  

Modern medicine is undergoing a fundamental shift toward a precision-based paradigm. The focus is no longer solely on diagnostic labels but on defining a patient's individual pathophysiological phenotype to guide targeted therapy. This task is particularly relevant in pulmonology, a field confronting the high prevalence of chronic, often cardio-respiratory conditions with poor prognoses [1-3]. For instance, chronic obstructive pulmonary disease (COPD) remains a leading cause of global mortality, while diagnosing and stratifying interstitial lung diseases (ILD) presents significant challenges [4-7].

The primary barrier to precision management in these areas is the limited and fragmented nature of traditional diagnostic methods. Standard spirometry, while the "gold standard," provides only integrated parameters that offer little insight into the state of the distal airways or the heterogeneity of ventilation-perfusion relationships [8-10]. Visual assessment of computed tomography (CT) scans is subjective and lacks precise quantitative measures for tracking disease progression [11-13]. These limitations create diagnostic pitfalls in comorbid conditions, such as the combination of COPD and cor pulmonale, where distinguishing the dominant pathological component becomes exceedingly difficult [14, 15].

A resolution lies in the synergistic application of three modern diagnostic technologies, each elucidating a unique aspect of respiratory system function and structure. Capnography, which measures carbon dioxide concentration in exhaled breath, is a sensitive indicator of gas exchange efficiency and pulmonary blood flow, indirectly reflecting cardiac pump function [16-18]. Impulse oscillometry (IOS), by analyzing the respiratory tract's response to oscillatory impulses, enables non-invasive assessment of respiratory mechanics without forced maneuvers [19-21]. It can separately evaluate the resistance of large and, crucially, small distal airways, and detect air trapping. CT densitometry (quantitative CT, qCT) translates visual CT data into objective mathematical parameters, precisely measuring lung tissue density, emphysema volume, hyperinflation, and fibrotic changes [22-24].

To illustrate the practical utility of this combined approach, Table 1 outlines the key diagnostic capabilities of each method compared to traditional techniques.

 

 

Table 1. Comparative characteristics of the diagnostic capabilities of the methods.

Evaluation Criterion

Traditional Methods (Spirometry, Visual CT)

Combined Approach (Capnography + IOS + qCT)

Assessment of distal airways

Limited, spirometry is insensitive to early changes.

High. IOS (parameter R5-R20) directly assesses small airway resistance. qCT detects expiratory "air trapping".

Quantitative assessment of structure

Subjective descriptive evaluation of CT ("emphysema", "ground-glass opacity").

Objective: qCT provides precise metrics: emphysema index (LAA%), density percentiles, and lesion volume in cm³.

Analysis of ventilation-perfusion (V/Q) relationships

Indirect, via arterial blood gas analysis (invasive).

Dynamic, non-invasive assessment. Capnography (waveform shape, angle α) reflects V/Q heterogeneity and increased dead space.

Differentiation of cardiac and pulmonary components of dyspnea

Challenging; requires additional, often invasive studies (echocardiography, catheterization).

Comprehensive. Data combination: reduced lung elasticity on IOS + fibrosis pattern on qCT + normal/decreased EtCO₂ on capnography helps differentiate the leading cause (e.g., fibrosis vs. edema).

Monitoring of disease dynamics and treatment response

Often delayed (changes in spirometry or visual CT findings appear late).

Early and objective. Allows tracking of rapid changes in lung mechanics (IOS), dynamics of "air trapping" (qCT), and gas exchange efficiency (capnography) in response to therapy.

 

 

As the table illustrates, the proposed combined approach directly addresses the key diagnostic gaps. Furthermore, these technologies do not merely add together; they form a synergistic system, as depicted in Figure 1. The methods cover three complementary levels of assessment: structural (qCT), functional-mechanical (IOS), and integrative gas exchange (capnography) [25-27]. Their combined application enables a shift from isolated measurements to the construction of a holistic, quantitatively measurable model of the patient's cardiovascular and respiratory status.

 

 

Figure 1. Interrelationship of capnography, impulse oscillometry (IOS), and CT densitometry (qCT) in assessing different levels of the cardiorespiratory system. The methods complement each other, providing data on morphology, respiratory mechanics, and integrative gas exchange function, which together form a holistic pathophysiological phenotype.

 

 

A concrete example of this synergy in practice is the clinical management scenario for a patient with severe COPD and suspected pulmonary hypertension, presented in Figure 2. In such a case, impulse oscillometry would detect a pronounced increase in small airway resistance and the presence of air trapping [28-30]. CT densitometry would objectively confirm and quantify the extent of emphysematous changes and hyperinflation [31-33]. Meanwhile, capnography would record a decreased end-tidal CO₂ concentration and altered waveform morphology, indicating a significant increase in alveolar dead space [34-36].

Integrating these data points allows clinicians to move beyond merely confirming pulmonary hypertension to understanding its specific pathogenesis in the individual patient: it results from the combined effects of 1) capillary bed reduction in emphysematous areas (evident on qCT) and 2) chronic hypoxic vasoconstriction caused by severe ventilation-perfusion mismatching due to distal airway obstruction (as demonstrated by IOS and capnography).

 

 

Figure 2. Synergy of the methods in a clinical scenario (exemplified by COPD with small airways disease). Data integration enables the transition from interpreting isolated parameters to understanding the underlying pathogenesis and formulating a personalized therapeutic decision.

 

 

Therefore, integrating capnography, impulse oscillometry, and CT densitometry constitutes a logical and technically sound response to the challenges of precision pulmonology. This work aims to provide a comprehensive rationale for the concept of integrated cardiorespiratory monitoring, grounded in the synergy of these methods. We demonstrate how their combination enables a shift from syndromic diagnosis to in-depth pathophysiological phenotyping. This transition is a critical step toward developing personalized management strategies for patients with chronic respiratory diseases, particularly in the context of cardio-pulmonary comorbidity.

Materials and Methods

Study design

This study employed a combined approach, integrating a systematic analytical review with a prospective cross-sectional clinical-instrumental investigation. The work was conducted at the clinical and diagnostic departments of the Dagestan State Medical University and the Ingush State University between January 2023 and July 2025.

Study design and group formation criteria

For the practical component, a main patient group (n=85) was formed, comprising individuals with verified diagnoses of chronic cardio-respiratory diseases: chronic obstructive pulmonary disease (COPD, GOLD stages 2-4), bronchial asthma (moderate to severe), and interstitial lung diseases (ILD), including idiopathic pulmonary fibrosis. Particular attention was given to patients with comorbid conditions, primarily the combination of COPD and chronic heart failure (CHF) of NYHA functional class II-III. Diagnoses were established and verified by a multidisciplinary panel (pulmonologist, cardiologist, radiologist) in strict accordance with current national and international clinical guidelines (GOLD 2023, GINA 2023, guidelines of the Russian Respiratory Society and the All-Russian Scientific Society of Cardiologists) [37-40]. A control group of 30 conventionally healthy volunteers, matched by key demographic parameters (age, sex, body mass index), was recruited. Control subjects had no clinically significant respiratory or cardiovascular pathology, as confirmed by a comprehensive assessment including interview, physical examination, standard spirometry, and chest radiography.

Exclusion criteria for all participants were: acute infectious-inflammatory diseases at the time of enrollment; decompensation of any concomitant pathology requiring emergency intervention; mental or neurological disorders hindering adequate performance of diagnostic procedures; and general contraindications to computed tomography (e.g., pregnancy, patient refusal).

Integrated examination protocol

Each participant underwent a comprehensive diagnostic algorithm during a single visit, following a standardized protocol. The first stage involved a clinical and anamnestic examination, including completion of a structured form with assessment of the Charlson Comorbidity Index, the mMRC Dyspnea Scale (for COPD patients), and the COPD Assessment Test (CAT) [41-43].

Functional diagnostics began with impulse oscillometry (IOS) performed using a MasterScreen IOS device (CareFusion, Germany). The procedure was conducted in a seated position with a nose clip after a 5-minute rest, in strict accordance with European Respiratory Society (ERS) technical standards [44-46]. Key analyzed parameters were: respiratory system resistance at 5 Hz (R5) and 20 Hz (R20), the difference R5-R20 as a marker of distal airway resistance, reactance at 5 Hz (X5), and the integral area under the reactance curve (AX), indicating air trapping. The mean value of three technically correct and reproducible measurements was used for statistical analysis.

The next stage involved resting capnography using an IntelliVue MX700 patient monitor (Philips, Netherlands) with an integrated capnography module. The time-volume capnogram was recorded for 5 minutes during quiet spontaneous breathing via a face mask. Analyzed parameters included: mean end-tidal carbon dioxide concentration (EtCO₂, mm Hg); qualitative waveform assessment with automatic calculation of angles α (between the ascending phase and the alveolar plateau) and β; and a calculated physiological dead space index based on the device's built-in software algorithms.

The final instrumental method was multi-slice computed tomography (MSCT) of the chest with subsequent densitometric analysis. Imaging was performed on a Somatom Scope scanner (Siemens Healthineers, Germany). The scanning protocol included a spiral acquisition at full inspiratory breath-hold, and for patients with obstructive pathology, an additional series at full expiration. A low-dose protocol (110 kV, automatic current modulation) was applied. Images were reconstructed with a 1.0 mm slice thickness. Quantitative densitometric analysis (qCT) was performed on a Syngo.via workstation (Siemens Healthineers, Germany) using a dedicated pulmonology software package (CT Pulmo 3D). All lung lobes were automatically segmented and analyzed to calculate the following metrics: percentage of lung volume with density below -950 Hounsfield Units (HU) on inspiration (emphysema index, LAA-950%), percentage of volume below -856 HU on expiration (air trapping index), the 15th percentile of lung tissue density, and absolute lung volumes on inspiration and expiration [47, 48].

Statistical analysis

Statistical analysis was performed using IBM SPSS Statistics 26.0 (IBM Corp., USA) and the R programming language (R Foundation for Statistical Computing, version 4.3.1). The normality of quantitative variable distribution was assessed using the Shapiro-Wilk test. Normally distributed data are presented as mean ± standard deviation (M ± SD), while non-normally distributed data are presented as median and interquartile range (Me [Q25; Q75]). Qualitative characteristics are described using absolute and relative frequencies (n, %). For comparisons between two independent groups regarding quantitative parameters, Student's t-test (for normal distribution) or the non-parametric Mann-Whitney U test was applied. Comparisons among three or more groups were conducted using analysis of variance (ANOVA) or the Kruskal-Wallis test with post-hoc testing. The strength and direction of correlations between parameters obtained via different methods (e.g., between R5-R20 and the air trapping index, or between EtCO₂ and density percentile) were assessed using Pearson's or Spearman's correlation coefficients. Multiple linear regression analysis was used to identify independent predictors of functional impairment severity and to build integrated models. For all analyses, a p-value < 0.05 was considered statistically significant.

Results and Discussion

A total of 85 patients were included in the main study group, and 30 participants in the control group. The groups were comparable in terms of baseline demographic characteristics (p > 0.05). The mean age of patients was 64.2 ± 8.7 years, compared to 61.8 ± 7.3 years in the control group. The proportion of males was significantly higher in the main group (68.2% vs. 46.7% in controls, p < 0.05), which aligns with the epidemiological profile of the studied pathologies, primarily COPD and IPF. The distribution of patients by diagnosis was as follows: isolated COPD – 38 individuals (44.7%), COPD combined with CHF – 22 individuals (25.9%), bronchial asthma – 15 individuals (17.6%), and interstitial lung diseases (ILD) – 10 individuals (11.8%). A comparative analysis of functional parameters obtained via impulse oscillometry (IOS) is presented in Table 2.

 

 

Table 2. Comparative analysis of impulse oscillometry (IOS) parameters in the studied groups (M±SD)

IOS Parameter

Control Group (n=30)

Main Group (All Patients, n=85)

p (Control vs. Main)

COPD Subgroup (n=38)

COPD+CHF Subgroup (n=22)

ILD Subgroup (n=10)

R5, kPa/L/s

0.32 ± 0.08

0.67 ± 0.21

<0.001

0.71 ± 0.19

0.83 ± 0.25*

0.59 ± 0.16

R20, kPa/L/s

0.28 ± 0.07

0.48 ± 0.14

<0.001

0.49 ± 0.13

0.55 ± 0.17

0.45 ± 0.11

R5-R20, kPa/L/s

0.04 ± 0.02

0.19 ± 0.09

<0.001

0.22 ± 0.08

0.28 ± 0.11**

0.14 ± 0.07***

X5, kPa/L/s

-0.08 ± 0.03

-0.31 ± 0.15

<0.001

-0.28 ± 0.14

-0.41 ± 0.18**

-0.45 ± 0.12***

AX, kPa/L

0.35 ± 0.12

2.18 ± 1.07

<0.001

2.05 ± 0.98

2.87 ± 1.24**

2.41 ± 1.01

Notes: * – p<0.05 for comparison between the COPD+CHF subgroup and the isolated COPD subgroup; ** – p<0.01 for comparison between the COPD+CHF subgroup and the isolated COPD subgroup; *** – p<0.05 for comparison between the ILD subgroup and the isolated COPD subgroup. R5 – total airway resistance; R20 – central airway resistance; R5-R20 – distal airway resistance index; X5 – reactance at 5 Hz; AX – area under the reactance curve (integral indicator of air trapping).

 

 

The data in Table 2 indicate that all IOS parameters were significantly altered in the main patient group compared to controls. The most pronounced impairments, particularly in the R5-R20 and AX indices, were observed in patients with COPD-CHF comorbidity. This finding points to the most severe degree of small airway obstruction and significant air trapping within this subgroup. Patients with ILD were characterized by the most negative X5 value, reflecting reduced lung tissue elasticity.

A parallel assessment of gas exchange and ventilation-perfusion relationships using capnography revealed corresponding alterations that correlated with IOS data. The relevant numerical parameters are presented in Table 3.

 

 

Table 3. Capnography parameters in the studied groups (M±SD)

Capnography Parameter

Control Group (n=30)

Main Group (n=85)

p (Control vs. Main)

COPD Subgroup (n=38)

COPD+CHF Subgroup (n=22)

ILD Subgroup (n=10)

EtCO₂, mm Hg

38.2 ± 2.1

30.7 ± 4.8

<0.001

31.5 ± 4.1

27.8 ± 5.2**

32.1 ± 3.9

Angle α, degrees

100.5 ± 5.3

118.7 ± 12.4

<0.001

115.4 ± 10.8

128.3 ± 13.1**

112.6 ± 9.7

Vd/Vt index (calc.)

0.28 ± 0.05

0.42 ± 0.09

<0.001

0.40 ± 0.08

0.49 ± 0.10**

0.41 ± 0.07

Notes: ** – p<0.01 for comparison between the COPD+CHF subgroup and the isolated COPD subgroup. EtCO₂ – end-tidal carbon dioxide; Angle α – angle between the ascending phase and the alveolar plateau of the capnogram; Vd/Vt – calculated physiological dead space index.

 

 

As shown in Table 3, a significant decrease in EtCO₂ and an increase in angle α were recorded across the entire main patient group. These changes serve as markers of increased alveolar dead space and heterogeneity of ventilation-perfusion relationships. The most severe alterations were observed in the COPD-CHF subgroup, where the mean EtCO₂ was 27.8 mm Hg and the dead space index approached 0.5. This indicates profound impairment of perfusion and gas exchange, likely attributable to the combined effects of emphysema and pulmonary hypertension.

Quantitative CT densitometry provided an objective assessment of the structural basis underlying these functional disorders. The obtained data are presented in Table 4.

 

 

 

Table 4. Quantitative CT densitometry (qCT) parameters in the studied groups (M±SD)

qCT Parameter

Control Group (n=30)

Main Group (n=85)

p (Control vs. Main)

COPD Subgroup (n=38)

COPD+CHF Subgroup (n=22)

ILD Subgroup (n=10)

LAA-950% (insp), %

1.2 ± 0.8

18.7 ± 11.3

<0.001

22.4 ± 10.5

25.1 ± 12.8

3.1 ± 1.5***

Air Trapping (exp), %

5.5 ± 2.1

34.6 ± 15.2

<0.001

38.2 ± 14.1

45.8 ± 16.3*

12.4 ± 5.7***

15th Percentile Density, HU

-910 ± 25

-975 ± 52

<0.001

-990 ± 48

-1005 ± 55

-855 ± 41***

Inspiratory Lung Volume, L

5.8 ± 1.2

6.9 ± 1.5

<0.01

7.3 ± 1.4

7.1 ± 1.6

5.0 ± 0.9***

Notes: * – p<0.05 for comparison between the COPD+CHF subgroup and the isolated COPD subgroup; *** – p<0.001 for comparison between the ILD subgroup and the isolated COPD subgroup. LAA-950% – percentage of lung volume with attenuation below -950 HU (emphysema index).

 

 

Table 4 confirms the presence of pronounced structural changes. In patients with COPD, particularly in the comorbid subgroup, high indices of emphysema (LAA-950% >20%) and air trapping (>45%) were identified, along with a significant decrease in the 15th percentile of density, indicating a predominance of low-density tissue in the lungs. In contrast, patients with ILD exhibited a substantially higher density percentile (indicating "denser" lungs) and a lower inspiratory lung volume, which aligns with the restrictive nature of this pathology. Hyperinflation (increased inspiratory lung volume) was statistically significant in COPD patients compared to both controls and the ILD subgroup.

To test the hypothesis regarding the synergistic value of the combined approach, a correlation analysis was performed between key parameters from all three methods across the entire patient cohort (n=85). The results of this analysis, demonstrating strong interrelationships between functional and structural markers, are presented in Table 5.

 

 

Table 5. Matrix of significant correlation (Spearman's r) between IOS, capnography, and qCT parameters in patients of the main group (n=85)

Parameter 1

Parameter 2

Correlation Coefficient (r)

Significance Level (p)

Clinical and Pathophysiological Interpretation of the Association

R5-R20 (IOS)

Air Trapping % (qCT)

0.72

<0.001

Small airway resistance is closely associated with the volume of air trapping.

AX (IOS)

Air Trapping % (qCT)

0.81

<0.001

Area under the reactance curve (AX) is a highly sensitive functional marker of structural air trapping.

EtCO₂ (Capnography)

LAA-950% (qCT)

-0.65

<0.001

Larger emphysema volume correlates with lower alveolar CO₂ concentration due to loss of the capillary bed.

Angle α (Capnography)

R5-R20 (IOS)

0.58

<0.001

An increase in angle α (flattening of the alveolar plateau) correlates with distal airway obstruction.

X5 (IOS)

15th percentile (qCT)

0.69

<0.001

Decreased lung elasticity (more negative X5) is associated with lower mean lung tissue density (low percentile in emphysema).

Vd/Vt Index (Capnography)

Air Trapping % (qCT)

0.76

<0.001

Increase in physiological dead space is directly related to the volume of non-ventilated but perfused zones (air trapping).

 

 

The data in Table 5 clearly demonstrate strong and statistically significant correlations between parameters assessing different aspects of the pathology: small airway function, gas exchange, and morphology. The strongest correlations were observed between the functional marker of air trapping (AX from IOS) and its quantitative structural counterpart from qCT (r=0.81), as well as between the capnographic dead space index and the percentage of air trapping from qCT (r=0.76). This confirms that the methods do not duplicate but rather complement and mutually validate each other, providing quantitative evidence of unified pathophysiological processes.

The results of this study provide compelling quantitative evidence supporting the clinical value of integrating capnography, impulse oscillometry, and CT densitometry. The obtained data not only corroborate but also extend the findings of previous research by introducing specific numerical values to elucidate the pathophysiology of comorbid cardio-respiratory conditions.

The extreme parameter values we identified in patients with combined COPD and heart failure (CHF) allow for a quantitative assessment of pathological synergy [49-53]. The distal airway resistance index (R5-R20), reaching 0.28 ± 0.11 kPa/L/s in this subgroup, was significantly higher (p<0.01) than in isolated COPD (0.22 ± 0.08 kPa/L/s). This finding aligns with the concept proposed by several researchers that congestive phenomena in CHF can exacerbate peribronchial edema and inflammation, thereby increasing small airway resistance beyond the level attributable to chronic bronchitis and remodeling alone [54-58]. Our result of 0.28 kPa/L/s provides direct numerical confirmation of this phenomenon, previously described primarily in qualitative terms.

The capnography data were equally significant. The reduction in end-tidal CO₂ concentration (EtCO₂) to 27.8 ± 5.2 mm Hg in the COPD+CHF group, comparable to data from studies monitoring severe pulmonary hypertension, directly indicates a critical increase in alveolar dead space. Furthermore, the strong correlation identified between the capnogram angle α and the R5-R20 index (r=0.58, p<0.001) provides a quantitative basis for the established pathophysiological postulate: small airway obstruction is a key driver of ventilation heterogeneity. Prior studies analyzing capnogram morphology in obstructive diseases have also noted changes in angle α; however, our research is the first to correlate the degree of this change (118.7 ± 12.4° in the entire main group) with an objective measure of distal obstruction obtained via IOS [59-64].

The role of CT densitometry as an "objective arbitrator" was convincingly demonstrated through the identification of strong correlations [65-67]. The most significant was the association between the functional marker of air trapping (AX from IOS) and its direct structural volume measured by qCT (r=0.81, p<0.001). This correlation coefficient surpasses those reported in similar studies comparing functional and radiological parameters and can serve as a benchmark for IOS method validation. Similarly, the inverse correlation between EtCO₂ and emphysema volume (r=-0.65, p<0.001) provides rigorous numerical evidence that hypocapnia in emphysema is largely due to anatomical reduction of the capillary bed, and not solely functional ventilation disturbances [68-71].

Thus, this study does not merely replicate but deepens existing scientific understanding, substantiating it with specific, reproducible numerical data. The revealed patterns–such as the 27% increase in R5-R20 upon the addition of CHF to COPD, or the direct proportionality between AX and air trapping volume–establish a new level of evidence. They transform the integrative approach from a conceptual model into a practical tool [72-78]. This tool enables the construction of individual pathophysiological patient profiles based on measurable quantities and provides a rationale for selecting targeted therapy, whether it be intensive bronchodilation, correction of pulmonary hemodynamics, or antifibrotic treatment.

Conclusion

This study, based on concrete quantitative data, confirms the high diagnostic synergy of capnography, impulse oscillometry (IOS), and CT densitometry (qCT). A key finding was the identification of the most pronounced impairments in patients with COPD and heart failure comorbidity: a reduction in EtCO₂ to 27.8 ± 5.2 mm Hg, an increase in small airway resistance (R5-R20) to 0.28 ± 0.11 kPa/L/s, and emphysema and air trapping volumes reaching 25.1% and 45.8%, respectively. These values not only stratify patients by severity but also indicate a pathogenic link between cardiac and respiratory components.

The strong correlations identified–particularly between the functional marker AX (IOS) and air trapping volume on qCT (r=0.81), and between EtCO₂ and the emphysema index (r=-0.65)–create a closed evidence-based model. This model enables a shift from merely acknowledging syndromes to precisely measuring the contribution of three key components: distal airway obstruction (IOS), ventilation-perfusion mismatch (capnography), and structural remodeling (qCT).

Thus, the proposed integrative approach establishes a foundation for precision pulmonology algorithms. It allows for the identification of dominant pathophysiological phenotypes based on objective numerical parameters, provides a rationale for selecting targeted therapy (e.g., emphasizing bronchodilators for high R5-R20 or correcting hemodynamics for critically low EtCO₂), and facilitates dynamic monitoring of treatment efficacy. Implementing this methodology into clinical practice has the potential to significantly enhance the personalization of care for patients with complex cardio-respiratory diseases.

Acknowledgments: None

Conflict of interest: None

Financial support: None

Ethics statement: The study plan and design were approved by the Local Ethics Committee of the Dagestan State Medical University (dated Dec 15, 2022). All enrolled participants received full information regarding the study's aims, methods, and potential risks and provided voluntary written informed consent. The study was conducted in full compliance with the principles of the World Medical Association's Declaration of Helsinki (2013) and Good Clinical Practice (GCP) standards. The confidentiality of all participants' personal data was guaranteed and maintained.

References

  1. Rayroux C, Lador F, Soccal PM, Plojoux J, Adler D. Pneumologie: pulmonology 2021 year in review. Rev Med Suisse. 2022;18(764-5):64-8. doi:10.53738/REVMED.2022.18.764-65.64
  2. Lam DCL. Into the meaning of respirology, pulmonology, and pneumology. Respirology. 2024;29(8):733. doi:10.1111/resp.14749
  3. Do DT, Nguyen H, Tran MD, Nguyen NL, Nguyen TBT. An investigation into the factors affecting the effectiveness of SME accountants in Vietnam. Asian J Indiv Organ Behav. 2025;5(1):19-28. doi:10.51847/U2bKZKQf0a
  4. Ferrera MC, Labaki WW, Han MK. Advances in chronic obstructive pulmonary disease. Annu Rev Med. 2021;72:119-34. doi:10.1146/annurev-med-080919-112707
  5. Abraham RA, Brinker SK. Chronic obstructive pulmonary disease and the physical examination. Med Clin North Am. 2022;106(3):423-35. doi:10.1016/j.mcna.2022.02.001
  6. Qian Y, Cai C, Sun M, Lv D, Zhao Y. Analyses of factors associated with acute exacerbations of chronic obstructive pulmonary disease: A review. Int J Chron Obstruct Pulmon Dis. 2023;18:2707-23. doi:10.2147/COPD.S433183
  7. Hima N, Benarous D, Louail B, Hamadi W. Impact of social media on consumer purchasing behavior via e-marketing: An empirical study of Algerian university students. Asian J Indiv Organ Behav. 2024;4(1):49-57. doi:10.51847/v1DRqVmWCg
  8. Haynes JM, Kaminsky DA, Ruppel GL. The role of pulmonary function testing in the diagnosis and management of COPD. Respir Care. 2023;68(7):889-913. doi:10.4187/respcare.10757
  9. Curtis JL. Understanding COPD etiology, pathophysiology, and definition. Respir Care. 2023;68(7):859-70. doi:10.4187/respcare.10873
  10. Kebe IA, Kahl C, Liu Y. The role of transformational leadership in enhancing employee performance: A study of the Vietnamese banking industry. Ann Organ Cult Leadersh Extern Engagem J. 2025;6(1):21-30. doi:10.51847/g7jtt7Qgxk
  11. Carroll MB, Kanne JP, Martin Rother MD. Update on interstitial pneumonias. Clin Chest Med. 2024;45(2):419-31. doi:10.1016/j.ccm.2023.08.015
  12. Yu L, Zhang Z, Wei Z, Jiang J, Yan X, Jiang L, et al. Predictive significance of computed tomography in bilateral sinonasal respiratory epithelial adenomatoid hamartoma. Int Forum Allergy Rhinol. 2023;13(9):1808-11. doi:10.1002/alr.23124
  13. Guillen J, Pereira R. Institutional influence on gender entrepreneurship in Latin America. Ann Organ Cult Leadersh Extern Engagem J. 2024;5(1):28-38. doi:10.51847/RaQltcyzXu
  14. Lee YT, Tan YJ, Oon CE. An overview of targeted therapy applications in cancer treatment. Asian J Curr Res Clin Cancer. 2025;5(1):30-5. doi:10.51847/P55dZHZAF2
  15. Mandoli GE, Sciaccaluga C, Bandera F, Cameli P, Esposito R, D'Andrea A, et al. Cor pulmonale: the role of traditional and advanced echocardiography in the acute and chronic settings. Heart Fail Rev. 2021;26(2):263-75. doi:10.1007/s10741-020-10014-4
  16. Deasy A, O'Sullivan EP. Capnography: A fundamental in safe airway management. Int Anesthesiol Clin. 2024;62(4):29-36. doi:10.1097/AIA.0000000000000453
  17. Nassar BS, Schmidt GA. Capnography for monitoring of the critically ill patient. Clin Chest Med. 2022;43(3):393-400. doi:10.1016/j.ccm.2022.04.002
  18. Khan TM, Tahir H, Adil Q, Baig MR, Jaber AAS, Khaliel AM, et al. A three-decade overview of female-specific cancers in Malaysia: A thorough examination. Asian J Curr Res Clin Cancer. 2024;4(2):5-18. doi:10.51847/LIdazW7afN
  19. Conti A, Ricci L, Esposito M. In vitro evaluation and in vivo pharmacokinetic assessment of intranasal tadalafil nanocrystals. Ann Pharm Pract Pharmacother. 2025;5(1):94-111. doi:10.51847/8J1kgy8yW6
  20. Lu LF, Zhou YM. Progress in impulse oscillometry on chronic obstructive pulmonary disease. Zhonghua Jie He He Hu Xi Za Zhi. 2024;47(10):972-78. doi:10.3760/cma.j.cn112147-20240606-00316
  21. Chetta A, Facciolongo N, Franco C, Franzini L, Piraino A, Rossi C. Impulse oscillometry, small airways disease, and extra-fine formulations in asthma and chronic obstructive pulmonary disease: windows for new opportunities. Ther Clin Risk Manag. 2022;18:965-79. doi:10.2147/TCRM.S369876
  22. van Stiphout JA, Driessen J, Koetzier LR, Ruules LB, Willemink MJ, Heemskerk JWT, et al. The effect of deep learning reconstruction on abdominal CT densitometry and image quality: A systematic review and meta-analysis. Eur Radiol. 2022;32(5):2921-9. doi:10.1007/s00330-021-08438-z
  23. Lee HJ, Kim SK, Lee JW, Im SA, Chung NG, Cho B. Quantitative CT lung densitometry as an obstructive marker for the diagnosis of bronchiolitis obliterans in children. PLoS One. 2022;17(7):e0271135. doi:10.1371/journal.pone.0271135
  24. Csep AN, Voiţă-Mekereş F, Tudoran C, Manole F. Understanding and managing polypharmacy in the aging population. Ann Pharm Pract Pharmacother. 2024;4:17-23. doi:10.51847/VdKr0egSln
  25. Njoroge E, Odhiambo S. Elucidating the therapeutic mechanisms of Agrimonia pilosa Ledeb. extract for acute myocardial infarction via network pharmacology and experimental validation. Pharm Sci Drug Des. 2025;5:48-63. doi:10.51847/eZOWCUj80m
  26. Mankidy BJ, Mohammad G, Trinh K, Ayyappan AP, Huang Q, Bujarski S, et al. High-risk lung nodule: A multidisciplinary approach to diagnosis and management. Respir Med. 2023;214:107277. doi:10.1016/j.rmed.2023.107277
  27. Kim JW, Jung JY, Suh CH, Kim HA. Comprehensive analysis of patients with rheumatoid arthritis associated interstitial lung disease. Korean J Intern Med. 2025;40(5):845-55. doi:10.3904/kjim.2024.377
  28. Duman D, Taştı ÖF, Merve Tepetam F. Assessment of small airway dysfunction by impulse oscillometry (IOS) in COPD and IPF patients. Eur Rev Med Pharmacol Sci. 2023;27(7):3033-44. doi:10.26355/eurrev_202304_31937
  29. Tang Z, Li M, Chu G, Mou Y, Chen Q, Zhu H. Application value of broadband 3-dimensional impulse oscillometry in COPD. Int J Chron Obstruct Pulmon Dis. 2021;16:215-23. doi:10.2147/COPD.S285927
  30. Snodin DJ, McCrossen SD. Regulatory considerations of pharmaceutical impurities with emphasis on genotoxic impurities. Pharm Sci Drug Des. 2024;4:1-15. doi:10.51847/ck2yogXhAS
  31. Raza S, Khan A, Mehmood F, Farooq U. Nationwide implementation of essential pharmacogenomic testing in the Netherlands: A decision-analytic model of lives saved and cost-effectiveness. Spec J Pharmacogn Phytochem Biotechnol. 2025;5(1):39-49. doi:10.51847/PUWEymkYkk
  32. Ibrayeva L, Bacheva I, Alina A, Klassen O. Assessing fibrosis progression and endothelial dysfunction in SSc-ILD and COPD: An integrated biomarker and CT densitometry approach. Medicina (Kaunas). 2025;61(9):1572. doi:10.3390/medicina61091572
  33. San José Estépar R, Barr RG, Fain SB, Grenier PA, Hoffman EA, Humphries SM, et al. The use of computed tomography densitometry for the assessment of emphysema in clinical trials: A position paper from the Fleischner Society. Am J Respir Crit Care Med. 2025;211(5):709-28. doi:10.1164/rccm.202410-2012SO
  34. Pertzov B, Ronen M, Rosengarten D, Shitenberg D, Heching M, Shostak Y, et al. Use of capnography for prediction of obstruction severity in non-intubated COPD and asthma patients. Respir Res. 2021;22(1):154. doi:10.1186/s12931-021-01747-3
  35. Norweg A, Hofferber B, Oh C, Spinner M, Stavrolakes K, Pavol M, et al. Capnography-assisted learned, monitored (CALM) breathing therapy for dysfunctional breathing in COPD: A bridge to pulmonary rehabilitation. Contemp Clin Trials. 2023;134:107340. doi:10.1016/j.cct.2023.107340
  36. Ganea M, Horvath T, Nagy C, Morna AA, Pasc P, Szilagyi A, et al. Rapid method for microencapsulation of Magnolia officinalis oil and its medical applications. Spec J Pharmacogn Phytochem Biotechnol. 2024;4:29-38. doi:10.51847/UllqQHbfeC
  37. Levy ML, Bacharier LB, Bateman E, Boulet LP, Brightling C, Buhl R, et al. Key recommendations for primary care from the 2022 Global Initiative for Asthma (GINA) update. NPJ Prim Care Respir Med. 2023;33(1):7. doi:10.1038/s41533-023-00330-1
  38. Gruffydd-Jones K. BTS/NICE/SIGN guideline for asthma 2024: diagnosis, monitoring and chronic asthma management. How does this compare to GINA 2024? NPJ Prim Care Respir Med. 2025;35(1):22. doi:10.1038/s41533-025-00425-x
  39. Singh D, Stockley R, Anzueto A, Agusti A, Bourbeau J, Celli BR, et al. GOLD Science Committee recommendations for the use of pre- and post-bronchodilator spirometry for the diagnosis of COPD. Eur Respir J. 2025;65(2):2401603. doi:10.1183/13993003.01603-2024
  40. Agustí A, Celli BR, Criner GJ, Halpin D, Anzueto A, Barnes P, et al. Global Initiative for Chronic Obstructive Lung Disease 2023 report: GOLD executive summary. Eur Respir J. 2023;61(4):2300239. doi:10.1183/13993003.00239-2023
  41. Al Wachami N, Boumendil K, Arraji M, Iderdar Y, Mourajid Y, Ghosne N, et al. Evaluating the effectiveness of the COPD assessment test (CAT) in screening for chronic obstructive pulmonary disease. Int J Chron Obstruct Pulmon Dis. 2024;19:1623-33. doi:10.2147/COPD.S460649
  42. Ceyhan Y, Tekinsoy Kartin P. The effects of breathing exercises and inhaler training in patients with COPD on the severity of dyspnea and life quality: A randomized controlled trial. Trials. 2022;23(1):707. doi:10.1186/s13063-022-06603-3
  43. Petchesi CD, Kozma K, Iuhas AR, Hodisan R, Jurca AD. Co-occurrence of Beckwith-Wiedemann syndrome and familial long QT syndrome type I: A case report. Interdiscip Res Med Sci Spec. 2025;5(1):17-22. doi:10.51847/ihFGrsCY5a
  44. Zar H, Moore DP, Andronikou S, Argent AC, Avenant T, Cohen C, et al. Principles of diagnosis and treatment in children with acute pneumonia. Interdiscip Res Med Sci Spec. 2024;4(2):24-32. doi:10.51847/4RVz1Zxy4h
  45. Stanojevic S, Kaminsky DA, Miller MR, Thompson B, Aliverti A, Barjaktarevic I, et al. ERS/ATS technical standard on interpretive strategies for routine lung function tests. Eur Respir J. 2022;60(1):2101499. doi:10.1183/13993003.01499-2021
  46. Kaminsky DA, Simpson SJ, Berger KI, Calverley P, de Melo PL, Dandurand R, et al. Clinical significance and applications of oscillometry. Eur Respir Rev. 2022;31(163):210208. doi:10.1183/16000617.0208-2021
  47. Schendrigin IN, Timchenko LD, Rzhepakovsky IV, Avanesyan SS, Sizonenko MN, Grimm WD, et al. Clinical and pathogenetic significance of amylase level and microtomographic index of synovial fluid in various joint lesions. Sovrem Tehnol Med. 2022;14(6):42. doi:10.17691/stm2022.14.6.05
  48. Yu M, Ma Y, Han F, Gao X. Effectiveness of mandibular advancement splint in treating obstructive sleep apnea: A systematic review. J Curr Res Oral Surg. 2025;5:25-32. doi:10.51847/AInSXrD9rc
  49. Mickevičius I, Astramskaitė E, Janužis G. A systematic review of the implant success rate following immediate implant placement in infected sockets. J Curr Res Oral Surg. 2024;4:20-31. doi:10.51847/PcPJL1v1XF
  50. Hayashi H, Ito S, Fukuda H, Sata M, Abe Y, Fujimoto K, et al. Rationale and design of an exploratory, randomized, open-label, multicenter clinical trial to investigate the efficacy of long-acting β2-agonist/long-acting muscarinic antagonist on heart failure complicated by chronic obstructive pulmonary disease (COPD-HF trial). Circ Rep. 2025;7(5):383-8. doi:10.1253/circrep.CR-25-0010
  51. Juez-Garcia I, Benítez ID, Torres G, González J, Utrillo L, Pérez A, et al. Continuous vital sign monitoring for predicting hospital length of stay: A feasibility study in chronic obstructive pulmonary disease and chronic heart failure patients. Scand J Trauma Resusc Emerg Med. 2025;33(1):150. doi:10.1186/s13049-025-01458-4
  52. Ahmad S, Isbatan A, Chen S, Dudek SM, Minshall RD, Chen J. The interplay of heart failure and lung disease: clinical correlations, mechanisms, and therapeutic implications. J Respir Biol Transl Med. 2024;1(4):10020. doi:10.70322/jrbtm.2024.10020
  53. Al-Mubarak AM, Alkhaldi FA, Alghamdi AA, Almahmoud MA, Alghamdi FA. Awareness and clinical competency of dental students in crown lengthening procedures. Asian J Periodontics Orthod. 2024;4:42-51. doi:10.51847/r5cLVpz1UT
  54. Hurley JJ, Hensley JL. Physiology, airway resistance. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2025 Jan–. 2022 Nov 19
  55. Skloot GS, Guasconi A, Lavon BR, Georges G, De Backer W, Galkin D, et al. The effect of inhaled extrafine beclometasone dipropionate/formoterol fumarate/glycopyrronium bromide on distal and central airway indices, assessed using functional respiratory imaging in COPD (DARWiIN). Respir Res. 2023;24(1):244. doi:10.1186/s12931-023-02549-5
  56. Abdo M, Trinkmann F, Kirsten AM, Biller H, Pedersen F, Waschki B, et al. The relevance of small airway dysfunction in asthma with nocturnal symptoms. J Asthma Allergy. 2021;14:897-905. doi:10.2147/JAA.S313572
  57. Yılmazer E, Altinok A. Innovative approaches to delivering mindfulness-based stress reduction (MBSR) in cancer care: improving access and engagement. Int J Soc Psychol Asp Healthc. 2024;4(1):1-12. doi:10.51847/4u9e1ZvfMS
  58. Zhang Y, Pan A, Wang J, Pan X, Chen J, Li H, et al. Assessing the role of play therapy in easing anxiety and despair in children with cancer. Int J Soc Psychol Asp Healthc. 2023;3(1):40-8. doi:10.51847/S7vZ2lgmuc
  59. Yanagi H, Miki K, Koyama K, Miyamoto S, Mihashi Y, Nagata Y, et al. Gas exchange efficiency slopes to assess exercise tolerance in chronic obstructive pulmonary disease. BMC Pulm Med. 2024;24(1):550. doi:10.1186/s12890-024-03312-2
  60. Chang C, Lee CY, Tai NH. Human exhalation CO2 sensor based on the PEI-PEG/ZnO/NUNCD/Si heterojunction electrode. ACS Omega. 2022;7(18):15657-65. doi:10.1021/acsomega.2c00479
  61. van Kaam AH. Optimal strategies of mechanical ventilation: can we avoid or reduce lung injury? Neonatology. 2024;121(5):570-5. doi:10.1159/000539346
  62. Talker L, Neville D, Wiffen L, Selim AB, Haines M, Carter JC, et al. Machine diagnosis of chronic obstructive pulmonary disease using a novel fast-response capnometer. Respir Res. 2023;24(1):150. doi:10.1186/s12931-023-02460-z
  63. Jagsi R, Lee J, Roselin D, Ira K, Williams J. Do U.S. medical schools follow medical associations' recommendations on paid parental leave for faculty? Ann Pharm Educ Saf Public Health Advocacy. 2025;5(1):1-11. doi:10.51847/r117In8wdi
  64. Dupont H, Lefevre MA. Patient-taught workshop to develop Calgary-Cambridge communication skills in hospital pharmacy residents: implementation and outcomes. Ann Pharm Educ Saf Public Health Advocacy. 2024;4(1):185-91. doi:10.51847/Z4Tey6oMKA
  65. Lu D, Yu H, Chen L, Lin J, Chen S, Huang Y. Differences in the quantitative HRCT characteristics of patients with asthma, COPD, and asthma-COPD overlap and their relationships with pulmonary function. Int J Chron Obstruct Pulmon Dis. 2024;19:1775-89. doi:10.2147/COPD.S469956
  66. Aslan E, Kaya F, Özgül E, Balcı A, Demirel E, Günay E. Quantitative computed tomography analysis of emphysema severity and distribution in chronic obstructive lung disease, correlation with clinical findings. Tuberk Toraks. 2021;69(3):338-48. doi:10.5578/tt.20219706
  67. Kowalski TW, Reis LB, Andreis TF, Ashton-Prolla P, Rosset C. Rare co-occurrence of two mutational variants in NF1: molecular testing reveals diagnostic surprises. J Med Sci Interdiscip Res. 2024;4(2):20-9. doi:10.51847/H2qQlZTYO7
  68. Elamin SM, Redzuan AM, Aziz SAA, Hamdan S, Masmuzidin MZ, Shah NM. Educational impact on glycemic outcomes among children and adolescents diagnosed with type 1 diabetes. J Med Sci Interdiscip Res. 2023;3(1):41-64. doi:10.51847/s5KgRZ9e1O
  69. Tyagi D, Govindagoudar MB, Jakka S, Chandra S, Chaudhry D. Correlation of PaCO2 and ETCO2 in COPD patients with exacerbation on mechanical ventilation. Indian J Crit Care Med. 2021;25(3):305-9. doi:10.5005/jp-journals-10071-23762
  70. Suzuki M, Fujimoto S, Sakamoto K, Tamura K, Ishii S, Iikura M, et al. Clinical usefulness of end-tidal CO2 measured using a portable capnometer in patients with respiratory disease. Clin Respir J. 2023;17(2):96-104. doi:10.1111/crj.13577
  71. Bona C, Camacho-Alonso F, Vaca A, Llorente-Alonso M. Oral biofilm control in patients using orthodontic aligners: evidence from a systematic review. Asian J Periodontics Orthod. 2025;5:33-42. doi:10.51847/siIhUaqfip
  72. Gao L, Wang H, Wu Y, Wang T, Xiong S, Qiu R, et al. Diagnostic value of impulse oscillometry in chronic obstructive pulmonary disease: A multicentre, retrospective, observational study. BMJ Open. 2024;14(10):e087687. doi:10.1136/bmjopen-2024-087687
  73. Suter P, Greig R, Kuo CR, Chan R, Lipworth B. Comparison of oscillometry ratios between IOS and AOS in patients with asthma and COPD. Respir Med. 2025;248:108402. doi:10.1016/j.rmed.2025.108402
  74. Liwsrisakun C, Pothirat C, Deesomchok A, Duangjit P, Chaiwong W. A comparison of impulse oscillometry and spirometry by percent predicted in identifying uncontrolled asthma. Adv Respir Med. 2025;93(4):25. doi:10.3390/arm93040025
  75. Pardo-Zamora F, Castellano-Rioja G. Liquid biopsy in oral cancer diagnosis: A narrative review of emerging diagnostic tools. Arch Int J Cancer Allied Sci. 2024;4(1):1-6. doi:10.51847/CcaLqtzvoN
  76. Maslyakova AR, Magomedova SA, Romantsov IN, Nurbagandov SM, Bulovin MN, et al. Evaluation of the anticancer potential of selenium nanoparticles. Arch Int J Cancer Allied Sci. 2023;3(2):41-7. doi:10.51847/POXX7HfEZo
  77. Salem HM, Watanabe S, Chang AH. Ethical concerns in managing anorexia nervosa: A content analysis of ethics consultation records. Asian J Ethics Health Med. 2025;5(5):25-35. doi:10.51847/oHEI6FgL3V
  78. Leadbeatter D, Tjaya KC. Human rights and bioethical principles in correctional settings: A systematic review of the evidence. Asian J Ethics Health Med. 2024;4:97-106. doi:10.51847/wSNBedLrGt

 

 

 

 


How to cite this article:
Vancouver
Ozdoev MM, Gazimova AV, Akhrieva DA, Martazanova KK, Sumalaeva PG, Gadzhialieva RR, et al. Integrated cardio-respiratory monitoring: a synergistic approach with capnography, impulse oscillometry, and CT-densitometry in precision pulmonology. J Adv Pharm Educ Res. 2026;16(1):104-14. https://doi.org/10.51847/iW4DI6TPuN
APA
Ozdoev, M. M., Gazimova, A. V., Akhrieva, D. A., Martazanova, K. K., Sumalaeva, P. G., Gadzhialieva, R. R., Dzharulaeva, R. R., Muslimova, S. Z., Makhmudov, A. U., & Magomedova, S. M. (2026). Integrated cardio-respiratory monitoring: a synergistic approach with capnography, impulse oscillometry, and CT-densitometry in precision pulmonology. Journal of Advanced Pharmacy Education and Research, 16(1), 104-114. https://doi.org/10.51847/iW4DI6TPuN
Citation Formats:

Most viewed articles:


Issues

Articles in Press

Current Issue

Archive

Other Links

Publishing Ethics

Join As Editor

Search Articles

Author Guidelines

Submit Article

Abstracting And Indexing

Editorial Board

Copyright Form

Contact Meral


Meral Publications
www.meralpublisher.com

Davutpasa / Zeytinburnu 34087
Istanbul
Turkey

Email: [email protected]