ABSTRACT
Objective
This study aimed to evaluate 5‐year outcomes of a structured quality improvement (QI) program implemented as part of the standard of care at our CF center, focusing on changes in body mass index (BMI) and forced expiratory volume in 1 s (FEV₁) in pwCF followed at our institution.
Methods
The medical records of pwCF registered at our CF center between January 1, 2018, and December 31, 2022, were retrospectively reviewed. Demographic and clinical data of pwCF submitted by our center to the European Cystic Fibrosis Society Patient Registry (ECFSPR) were used for this analysis. Structured QI strategies were initiated in 2018 after collaboration with the University of Michigan CF Center and focused on nutrition, respiratory care, and treatment adherence. Time‐dependent changes in FEV1 percent predicted (FEV1pp) were evaluated using a linear mixed‐effects regression model.
Results
All 440 pwCF followed at our center during the study period were included in the analysis. Over 5 years, BMI z‐scores improved significantly in children (p < 0.001), while adult BMI remained stable (p = 0.10). No significant linear change in mean FEV₁pp was observed in adjusted longitudinal models (p > 0.05), however, descriptive analyses showed an increase in median FEV₁pp among adults. Higher BMI was associated with better lung function (p < 0.001) and lower IV antibiotic burden in correlation analyses (children: p < 0.001; adults: p = 0.045). The estimated mean survival age of the analysis cohort was 46.1 years.
Conclusion
To our knowledge, this is the first report of a cohort of Turkish pwCF summarizing 5‐year longitudinal registry data with survival estimates. These findings suggest that sustained, center‐wide QI efforts may help maintain or improve clinical outcomes, particularly in settings with limited access to CFTR modulators.
Keywords: children, cystic fibrosis, lung function, nutrition, patient registry, survival analysis
1. Introduction
Cystic fibrosis (CF) is a rare monogenic disease that involves multi‐organ systems and is estimated to affect at least 100,000 individuals globally, contributing to significant health burden and early mortality [1, 2]. In recent decades, significant advancements in the treatment of CF have led to a notable increase in improving the clinical outcomes, quality of life and life expectancy among people with CF (pwCF) [3, 4].
Data regarding the epidemiology of CF is available in multiple countries in which CF registries have been established, and these registries have provided comprehensive data on pwCF for many year [2]. These registries are essential for understanding epidemiological trends, monitoring the impact of interventions, and improving patient care [4]. Registry reports annually highlight median survival [3]. Reliable survival estimates require comprehensive, longitudinal records with minimal loss to follow‐up, benefiting patients, families, and healthcare providers [4].
Global collaborations between CF centers in high‐income countries—particularly those in the United States—and CF centers in low‐ and middle‐income countries (LMICs) have been initiated and supported by the Cystic Fibrosis Foundation (CFF). Over time, these partnerships have expanded substantially, with the shared aim of identifying care gaps across CF centers and fostering improvements in CF care worldwide [5, 6, 7]. Previous QI initiatives conducted at our center, after starting collaboration with the University of Michigan CF Center, with the support of the CFF and the Middle East Cystic Fibrosis Association (MECFA), demonstrated that applying a standardized QI methodology to a sub‐group of pwCF in our center resulted in significant short‐term improvements in both pulmonary function and nutritional status over a 12‐month period [7, 8, 9]. These pilot studies, although promising, were limited to a subset of the pwCF population and focused on specific clinical outcomes within a defined timeframe. However it remains to apply QI methods to standardize care and evaluate the impact using outcomes captured in registry data for the entire patient population seen at our center.
This report will highlight how QI methods were applied to standardize care delivery at one Turkish CF program and evaluate the impact on medical outcomes reported to the European Cystic Fibrosis Society Patient Registry, in addition to assessing survival.
2. Methods
2.1. Design
Retrospective longitudinal analyses were conducted using data submitted by our center to the European Cystic Fibrosis Society Patient Registry (ECFSPR) between January 1, 2018, and December 31, 2022. This approach enabled examining the impact of QI interventions on medical outcomes captured in registry data over 5 years. Survival estimates were calculated using longitudinal registry data reported to the ECFSPR, based on mortality status and follow‐up information.
2.2. Participants
The data of pwCF followed by our CF center between January 1, 2018 and December 31, 2022 were retrospectively evaluated. Five analyses were conducted that include only participants with a valid CF diagnosis [10]. The pwCF whose follow‐up was not continued for 2 years or more and who were transferred to another center were excluded from the study. At each time point demographics, diagnosis, body mass index (BMI) z‐score, the highest lung function (by forced expiratory volume in 1 s percent predicted; FEV1pp), bacterial colonization, CF medications, CF‐related complications, and mortality were collected. Genotype was classified as p.Phe508del homozygous, p.Phe508del heterozygous, and others variants. The existing data of pwCF who died over the study period was incorporated into the analysis. In Turkey, CF newborn screening (NBS) was introduced nationally in 2015; therefore, ‘non‐NBS’ in this study refers primarily to individuals who were not screened at birth (born prior to implementation) and were diagnosed clinically later. CFTR modulators were introduced in 2021, and for patients receiving this therapy, clinical outcomes at baseline and after 1 year of treatment were compared.
2.3. Lung Function
FEV₁pp, as a marker of lung function, was defined according to the Global Lung Function Initiative (GLI) reference [11]. In pwCF aged 5 years and above, the highest FEV1pp values measured during the year were used. To compare FEV1pp results, pwCF who could perform spirometry were divided into two groups: individuals aged 5 to < 18 years and those aged ≥ 18 years. Measurements were made using a spirometry device (WinspiroPRO 2.8 MIR) every 3 months by the same device and technician in accordance with ATS/ERS technical standards [12].
2.4. Respiratory Microbiology and Infections
Respiratory microbiology data were obtained from sputum, throat swap, or bronchoscopic cultures, with the majority originating from sputum. Chronic infection in the lower airways was defined according to the ECFSPR 2023 annual report, based on the modified Leed's criteria [13, 14]. The same definition was applied for chronic infections with other gram‐negative and gram‐positive bacteria [13]. In our center, pulmonary exacerbations (Pex) were defined using a modified version of the Fuchs criteria, which is routinely implemented in clinical practice [15].
2.5. Nutrition
The nutritional status of pwCF was assessed at routine follow‐up visits by the center's dietitians. Nutritional interventions were applied according to the standardized nutritional algorithm developed in our previous pilot QI project [8]. BMI was expressed in terms of z‐scores using a reference population of healthy individuals [16].
2.6. Liver Disease: Definitions
Liver involvement in CF was categorized as advanced CF liver disease (aCFLD) and CF hepatobiliary involvement (CFHBI), as proposed in recent literature [17]. This classification allows for distinction between advanced forms of liver disease, such as cirrhosis with or without portal hypertension, and milder hepatobiliary abnormalities not meeting criteria for aCFLD.
2.7. Medications
The duration of maintenance therapy was defined as continuous use for a minimum of 3 months, as specified in the European CF Society Patient Registry (ECFSPR) 2023 annual report [13]. In Turkey, national health insurance provides universal coverage from birth and reimburses standard CF treatments, including dornase alfa, hypertonic saline, pancreatic enzymes, vitamins, and antibiotics. At our center, all pwCF perform age‐appropriate airway clearance using non‐vest techniques, including percussion and postural drainage, positive expiratory pressure (PEP) and oscillatory PEP (OPEP) devices, the active cycle of breathing technique, autogenic drainage, and exercise [18]. High‐frequency chest wall compression (vest therapy) is not reimbursed and is therefore used only by patients who independently obtain the device. CFTR modulators are not reimbursed and are accessible only through individual legal action. Routine outpatient follow‐up visits are conducted every 3 months and include physiotherapy‐based assessment of daily airway clearance as part of standard care [19].
2.8. Quality Improvement Interventions
Since 2018, our center has been actively engaged in structured QI initiatives in collaboration with the University of Michigan CF Center. Based on identified gaps in nutritional management, pulmonary care, and overall adherence, standardized algorithms and intervention protocols were developed and implemented.
The key interventions included:
-
1.
structured nutritional algorithm targeting BMI percentile improvement [8],
-
2.
standardized respiratory care protocols for patients with FEV1pp < 80% [9], and
-
3.
regular pre‐visit planning and multidisciplinary team huddles to individualize treatment plans. These interventions were systematically applied and monitored through Plan‐Do‐Study‐Act (PDSA) cycles, with continuous adaptations based on patient needs and outcomes [6].
The processes and outcomes of these QI projects have been published previously demonstrating significant clinical benefits [6, 8, 9].
2.9. Statistical Analysis
Statistical analyses were performed using SPSS version 29.0. Figures were generated using Jamovi for graphical visualization. Descriptive statistics were calculated for all variables. Continuous variables were expressed as mean ± standard deviations (±SD) or medians with interquartile ranges (IQR) depending on the normality of their distribution. Categorical variables were presented as frequencies and percentages. For variables that did not follow a normal distribution, the Kruskal‐Wallis test was utilized. Post hoc pairwise comparisons were conducted using the Dunn‐Bonferroni test to control for type I errors. The Exact test was used to analyze the association between categorical variables presented in r x c tables.
Longitudinal changes in pulmonary function and nutritional status were evaluated using linear mixed‐effects models. Although FEV₁pp, BMI z‐scores, and BMI values are presented descriptively as medians (IQR) due to non‐normal distributions, mixed‐effects modeling was used to assess longitudinal trends over time, as this approach provides valid inference on mean changes while accounting for within‐subject correlation and incomplete follow‐up. All statistical tests were two‐tailed, and a p‐value < 0.05 was considered statistically significant. Results were reported with 95% confidence intervals where applicable. Survival analysis was performed using the Kaplan‐Meier method. A detailed explanation of the two‐stage mixed‐effects regression modeling for FEV1pp change, including variable selection and statistical approach using Jamovi, is provided in the Supporting Information S1.
2.10. Ethics Approval
The study was approved by the Research Ethics Committee of Marmara University (approval number: 09.2025‐25‐0062). The need for informed consent was waived by the institutional review board due to the retrospective design and use of anonymized data.
This retrospective observational study was conducted and reported in accordance with the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines.
3. Results
In our CF center, we presented the changes identified in the treatment and follow‐up of our pwCF over the 5 years. All 440 pwCF followed at our center during the study period were included in the analysis. Of the total cohort, 52.7% (n = 232) were male. A total of 25% of individuals included in the study were diagnosed through newborn screening (NBS), and 58% of pwCF had genetic variants other than p.Phe508del. The overall median age at diagnosis was 4 [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13] months, and the oldest age at diagnosis was 41 years. When stratified by NBS status, the median age at diagnosis was 1 [1, 2, 3] month in the NBS‐positive group and 5 (3–64) months in the NBS‐negative group. Table 1 presents the basic descriptive statistics of pwCF. As shown in Table 2, the median age of the cohort increased over the study period, reflecting the longitudinal follow‐up of the population. The oldest individual was 49 years of age in 2022.
TABLE 1.
Descriptive data of pwCF registered at the center between January 1, 2018 and December 31, 2022 (N = 440).
| N | % | |
|---|---|---|
| CFTR Variations | ||
| p.Phe508del Homozygous | 80 | 18.1 |
| p.Phe508del Heterozygous | 101 | 22.9 |
| Others a | 259 | 58.8 |
| NBS | ||
| Yes (+) | 108 | 24.5 |
| No (–) | 332 | 75.5 |
| Sex | ||
| Male | 232 | 52.7 |
| Female | 208 | 47.3 |
| Eligibility of CFTR modulator therapy | ||
| Eligible | 235 | 53.4 |
| Non‐eligible | 205 | 46.6 |
| Usage of CFTR modulator therapy | ||
| Eligible and receiving | 56 | 12.7% |
| Eligible but not receiving | 179 | 40.7% |
| Age at diagnosis(months); median(IQR) | ||
| All pwCF | 4 (2;13) | |
| NBS + | 1 (1;3) | |
| NBS – | 5 (3;64) | |
Abbreviations: NBS, newborn screening; pwCF, people with cystic fibrosis.
Others: People with cystic fibrosis who do not carry the p.Phe508del variant on either allele (non‐p.Phe508del genotypes).
TABLE 2.
Demographic and anthropometric changes of pwCF by years between January 1, 2018 and December 31, 2022.
| 2018 T1 | 2019 T2 | 2020 T3 | 2021 T4 | 2022 T5 | p value | ||
|---|---|---|---|---|---|---|---|
| Age (years); median (IQR) | 9,5 (5;14) | 10,1 (5;15) | 10,7 (6;16) | 11,9 (6;17) | 11,1 (7;18) |
T1‐T2 = 0.3 T1‐T3 = 0.3 T1‐T4 = 0.01 T1‐T5 = 0.03 T2‐T3 = 0.4 |
T2‐T4 = 0.05 T2‐T5 = 0.3 T3‐T4 = 0.2 T3‐T5 = 0.6 T4‐T5 = 0.5 |
| Number of patients; n | 301 | 326 | 349 | 385 | 422 | ||
| BMI z‐score; median (IQR) | |||||||
| 0–4 years |
−1.2 (−1.9; −0.6) |
−0.6 (−2.2; 0.2) |
−0.3 (−1.3; 0.4) |
−0.5 (−1.4; 0.4) |
0.0 (−0.3; 0.1) |
T1‐T2 = 0.04 T1‐T3 < 0.01 T1‐T4 < 0.01 T1‐T5 < 0.001 T2‐T3 = 0.05 |
T2‐T4 = 0.2 T2‐T5 < 0.001 T3‐T4 = 0.4 T3‐T5 = 0.3 T4‐T5 = 0.06 |
| 5–17 years |
−0.6 (−2; 0) |
−0.7 (−2; 0) |
−0.3 (−1; 0) |
−0.1 (−1; −0.5) |
−0.2 (−1; 0.5) |
T1‐T2 = 0.6 T1‐T3 = 0.01 T1‐T4 < 0.001 T1‐T5 < 0.001 T2‐T3 = 0.006 |
T2‐T4 < 0.001 T2‐T5 < 0.001 T3‐T4 = 0.02 T3‐T5 = 0.05 T4‐T5 = 0.6 |
| BMI (kg/m 2 ); median (IQR) | |||||||
|
*n ≥ 18 years |
*46 19.9 (18.9; 22.1) |
*57 19.3 (18.1; 22.6) |
*66 20.1 (18.2; 23.2) |
*82 20.7 (18.8; 23.8) |
*102 21.1 (19.0; 23.5) |
0.1 | |
| FEV 1 pp; median (IQR) | |||||||
|
*n 5–17 years |
*133 87.6 (72; 99) |
*151 87.2 (74; 99) |
*175 89.1 (72; 98) |
*214 89.3 (76; 101) |
*225 90 (72; 101) |
0.5 | |
|
*n ≥ 18 years |
*39 55.3 (40; 72) |
*45 58.4 (42; 82) |
*59 59.5 (39; 88) |
*63 59.1 (39; 90) |
*82 70.7 (44;91) |
T1‐T2 = 0.8 T1‐T3 = 0.8 T1‐T4 = 0.5 T1‐T5 = 0.04 T2‐T3 = 0.8 |
T2‐T4 = 0.8 T2‐T5 = 0.1 T3‐T4 = 0.8 T3‐T5 = 0.4 T4‐T5 = 0.2 |
|
*n Total |
*172 85.1 (60; 97) |
*196 83.8 (65; 95) |
*234 85.5 (61; 97) |
*277 87.5 (68; 99) |
*307 87.3 (70;99) |
0,7 | |
| Duration of the number of Pulmonary Exacerbation (median (IQR)) and intravenous antibiotics (mean (±SD)) | |||||||
| Pex | 1 (0;2) | 2 (1;3) | 1 (0;3) | 2 (1;3) | 2 (1;3) |
T1‐T2 < 0.001 T2‐T3 = 0.04 T3‐T4 = 0.01 T1‐T5 < 0.001 |
|
| IV antibiotics (days); | 12 (±25) | 12 (±22) | 11 (±27) | 10 (±23) | 10 (±24) | 0.6 | |
| Medications; N (%) | |||||||
| Inhaled Hypertonic saline | 38c (13%) | 97a (30%) | 126a,b (36%) | 157b (41%) | 160a,b (38%) | < 0.001 | |
| Inhaled DNase | 263a (87%) | 303b (93%) | 320a (92%) | 348a (90%) | 365a (86%) | 0.02 | |
| İnhale Antibiotic | 91 (30%) | 79 (24%) | 96 (28%) | 110 (29%) | 93 (22%) | 0.1 | |
| Inhaled Bronchodilator | 260 86%) | 283 (87%) | 295 (85%) | 337 (88%) | 350 (83%) | 0.4 | |
| Inhaled Corticosteroid | 82 (27%) | 96 (29%) | 88 (25%) | 101 (26%) | 96 (23%) | 0.3 | |
| Azithromycin | 26b (6%) | 47a (11%) | 61b (14%) | 58a (13%) | 47a (11%) | 0.01 | |
| NIPPV | 8 (3%) | 12 (4%) | 11 (3%) | 12 (3%) | 10 (2%) | 0.8 | |
| CFTR modulatory | 0 | 0 | 0 | 28 (7.2%) | 56 (13.2%) | ||
| Microorganism; N (%) | |||||||
| P. aeruginosa | |||||||
| Chronic colonization | 93a (%31.2) | 86a (%26.7) | 90a (%26.2) | 65b (%17.4) | 63b (%15.4) | 0.04 | |
| MRSA | |||||||
| MRSA Growth | 49a (%13.8) | 84b (%23.6) | 76a,b (%21.3) | 67a,b (%18.8) | 80b (%22.5) | < 0.001 | |
| MSSA | |||||||
| MSSA Growth | 63a (%21.1) | 81a (25.1) | 70a (%20.4) | 225b (%60.3) | 243b (%59.4) | < 0.001 | |
| B. cepaciae | |||||||
| Chronic colonization | 1a (%0,3) | 1a (%0,3) | 1a (%0,3) | 1a (%0,3) | 1a (%0,2) | 0.9 | |
| CF Related disease; N (%) | |||||||
| ABPA | 7 (2%) | 7 (2%) | 7 (2%) | 5(1%) | 4 (1%) | 0.5 | |
| CFRD | 15 (5%) | 21 (6%) | 24 (7%) | 30 (8%) | 26 (6%) | 0.6 | |
| CFRLD | |||||||
| CFHBI | 56 (19%) | 66 (20%) | 62 (18%) | 64 (17%) | 67 (16%) | 0.054 | |
| aCFLD | 3 (1%) | 7 (2%) | 17 (5%) | 18 (5%) | 16 (4%) | ||
Note: *Median follow‐up duration was 5 years (IQR: 3–5).
Abbreviations: *n, Number of pwCF who performed spirometry; aCFLD, advanced cystic fibrosis–related liver disease; ABPA, allergic bronchopulmonary aspergillosis; B. cepaciae, Burkholderia cepacia complex; CFHBI, cystic fibrosis–related hepatobiliary involvement; CFRLD, cystic fibrosis–related liver disease; CFRD, cystic fibrosis–related diabetes; MRSA, methicillin‐resistant Staphylococcus aureus; MSSA, methicillin‐susceptible Staphylococcus aureus; NIPPV, noninvasive positive pressure ventilation; P. aeruginosa, Pseudomonas aeruginosa; Pex, Pulmonary Exacerbation, IV, Intravenous CFTR, cystic fibrosis transmembrane conductance regulator.
In descriptive analyses, median FEV₁pp among pwCF aged ≥ 18 years increased from 55% in 2018 to 70.7% in 2022 (p = 0.04). Among children with CF (cwCF) aged 5–17 years, median FEV₁pp rose from 88% to 90%, although this change was not statistically significant (p = 0.5). In contrast, adjusted longitudinal mixed‐effects modeling did not demonstrate a significant linear change in mean FEV₁pp over time (p > 0.05).
There was a statistically significant increase in the mean BMI z‐score. When individuals were grouped by age (0–4 years, 5–17 years), improvements were observed across all age groups: Between 2018 and 2022, the mean BMI z‐score rose from –1.2 to 0.0 (p < 0.001) in the 0–4 year group. In the 5–17 year group, it improved from –0.6 to –0.2 (p < 0.001). Among adults (≥ 18 years), BMI (kg/m 2 ) did not show a statistically significant change over time in the longitudinal analysis (p = 0.10).
Regarding microbiological results (Table 2), PA chronic colonization showed a decreasing trend (31% in 2018 vs. 15% in 2022, p = 0.04). MRSA growth increased, from 13.8% in 2018 to 22.5% in 2022 (p < 0.001), and MSSA growth also increased, from 9.2% in 2018 to 35.6% in 2022 (p < 0.001) (since chronic vs. intermittent status was not distinguished for MRSA and MSSA between 2018 and 2020, growth rates were reported instead). No statistically significant differences were found among the groups regarding chronic colonization with Burkholderia cepacia complex (p = 0.9).
The distribution of chronic therapies varied across the study years. Between 2018 and 2022, the use of inhaled hypertonic saline increased from 13% to 38% (p < 0.001). Similarly, azithromycin use rose from 6% to 11% during the same period (p = 0.01).
No statistically significant differences were observed between the groups in the use of inhaled antibiotics (p = 0.1), inhaled bronchodilators (p = 0.4), and inhaled corticosteroids (p = 0.3). No significant differences were observed in the prevalence of ABPA (p = 0.5), CFRD (p = 0.6), or in the distribution of CF‐related liver disease (aCFRLD, CFHBI, or none) over the 5‐year study period (p = 0.054). The proportion of pwCF with CFHBI remained stable between 2018 and 2022, ranging from 19% to 16%. The prevalence of aCFRLD was 1% in 2018 and 4% in 2022.
Access to CFTR modulator therapy became available for a subset of pwCF in 2021, with 28 individuals initiating treatment. One patient discontinued therapy due to severe hepatic complications. In 2022, an additional 29 pwCF gained access to CFTR modulators, increasing the total number of patients with access to modulator therapy to 56. In 2021, the baseline and 1‐year follow‐up FEV1pp values of 28 pwCF using CFTR modulators were compared. The median (IQR) FEV1pp values showed a significant increase from 57% to 68.5% (p < 0.001).
The median number of Pex increased significantly over the 5‐year period, rising from 1 (0;2) in 2018 to 2 (1;3) in 2022 (p < 0.001). However, the mean duration of IV antibiotic treatment remained stable between 2018 and 2022, with no statistically significant change observed (p = 0.6) (Table 2).
Although the adjusted longitudinal models did not show a significant linear change in mean FEV₁pp over time, the correlation analyses highlighted clear age‐specific relationships between nutritional status, lung function, and treatment burden. In participants aged < 18 years (Figure 1a), BMI z‐score was positively correlated with FEV₁pp (r = 0.194, p < 0.001), indicating better lung function with higher nutritional status. BMI z‐score was associated with fewer total IV antibiotic days (r = −0.178, p < 0.001). In addition, FEV₁pp was inversely correlated with IV antibiotic days (r = −0.211, p < 0.001). In adults aged ≥ 18 years (Figure 1b), BMI (kg/m²) showed a strong positive correlation with FEV₁pp (r = 0.472, p < 0.001), indicating better lung function with higher BMI (kg/m²). Higher BMI (kg/m²) was associated with fewer total IV antibiotic days (r = −0.108, p = 0.045). Similarly, lower FEV₁pp was associated with more IV antibiotic days (r = −0.339, p < 0.001).
FIGURE 1.
(a) Correlation between BMI z‐score and FEV₁ percent predicted (FEV₁pp) in children with cystic fibrosis aged < 18 years. (b) Correlation between BMI (kg/m²) and FEV₁ percent predicted (FEV₁pp) in people with cystic fibrosis aged ≥ 18 years. [Color figure can be viewed at wileyonlinelibrary.com]
In children aged 5–17 years, multivariable linear mixed‐effects analysis showed that BMI z‐score was independently associated with FEV₁pp, with higher BMI z‐score corresponding to better lung function (β = 3.999, p < 0.001; Table 3). In addition, the p.Phe508del genotype score was significantly associated with FEV₁pp (β = 7.244, p = 0.011). No significant associations were observed for meconium ileus, total IV antibiotic days, or PA colonization in this age group.
TABLE 3.
Results of the multivariable linear mixed‐effects model evaluating predictors of FEV₁pp in children aged 5–17 years.
| Predictor | Coef. | Std. Err. | z/t | p | 95% CI (Lower) | 95% CI (Upper) |
|---|---|---|---|---|---|---|
| Intercept | 86.860 | 10.091 | 8.607 | < 0.001 | 66.969 | 106.751 |
| Year | −0.362 | 0.318 | −1.136 | 0.257 | −0.990 | 0.267 |
| BMI z‐score | 3.999 | 0.442 | 9.043 | < 0.001 | 3.131 | 4.866 |
| Meconium ileus | −2.936 | 10.833 | −0.271 | 0.787 | −24.286 | 18.414 |
| p.Phe508del genotype | 7.244 | 2.832 | 2.558 | 0.011 | 1.666 | 12.822 |
| Total IV antibiotic days | −0.018 | 0.019 | −0.961 | 0.337 | −0.055 | 0.019 |
| PA colonization | −1.567 | 1.239 | −1.264 | 0.207 | −4.000 | 0.867 |
Note: Table 4 presents the results of a multivariable linear mixed‐effects model evaluating the association of BMI z‐score and other clinical factors with FEV₁pp. BMI z‐score showed a statistically significant positive association with FEV₁pp (β = 3.999, p < 0.001), indicating that higher BMI z‐score is associated with better lung function. In addition, a higher p.Phe508del genotype score—coded as 0 = homozygous, 1 = heterozygous, and 2 = no p.Phe508del allele—was independently associated with higher FEV₁pp (β = 7.244, p = 0.011), reflecting improved lung function with decreasing F508del mutation burden. Other variables, including meconium ileus, total IV antibiotic days, and PA colonization, were not significantly associated with FEV₁pp.
Abbreviations: CI, Confidence Interval; Coef, Coefficient; IV, Intravenous; PA, Pseudomonas aeruginosa; Std. Err, Standard Error.
In adults aged ≥ 18 years, BMI (kg/m²) remained independently associated with FEV₁pp, with higher BMI linked to better lung function (β = 1.572, p < 0.001; Table 4). A history of meconium ileus was associated with lower FEV₁pp (β = −35.253, p = 0.045). The p.Phe508del genotype score was not significantly associated with FEV₁pp in adults (β = 7.49, p = 0.191), and no significant associations were observed for total IV antibiotic days or PA colonization. The mean survival estimates and their 95% confidence intervals are presented in Table 5 (Figure 2). A log‐rank (Mantel‐Cox) test was conducted to compare survival distributions across groups, yielding a χ² = 6.095, df = 4, p = 0.192. This result suggests no statistically significant differences in survival distributions among the groups.
TABLE 4.
Results of the multivariable linear mixed‐effects model evaluating predictors of FEV₁pp in adults aged ≥ 18 years.
| Predictor | Coef. | Std. Err. | z/t | p | 95% CI (Lower) | 95% CI (Upper) |
|---|---|---|---|---|---|---|
| Intercept | 65.561 | 19.019 | 3.447 | < 0.001 | 27.887 | 103.235 |
| Year | −0.848 | 0.540 | −1.570 | 0.122 | −1.930 | 0.234 |
| BMI (kg/m 2 ) | 1.572 | 0.359 | 4.382 | < 0.001 | 0.866 | 2.279 |
| Meconium ileus | −35.253 | 17.307 | −2.037 | 0.045 | −69.662 | −0.845 |
| p.Phe508del genotype | 7.49 | 5.690 | 1.318 | 0.191 | −3.811 | 18.809 |
| Total IV antibiotic days | −0.019 | 0.026 | −0.728 | 0.467 | −0.072 | 0.033 |
| PA colonization | −0.220 | 1.483 | −0.149 | 0.882 | −3.153 | 2.712 |
Note: Table 4 presents the results of a multivariable linear mixed‐effects model evaluating predictors of FEV₁pp in adults aged ≥ 18 years. Higher BMI value (kg/m²) was independently and positively associated with FEV₁pp (β = 1.572, p < 0.001), indicating better lung function with increasing BMI. A history of meconium ileus was associated with significantly lower FEV₁pp (β = −35.253, p = 0.045). The p.Phe508del genotype score showed no significant association with FEV₁pp in adults (β = 7.49, p = 0.191). Total IV antibiotic days and PA colonization were not significantly associated with FEV₁pp.
Abbreviations: CI, Confidence Interval; Coef, Coefficient; IV, Intravenous; PA, Pseudomonas aeruginosa; Std. Err, Standard Error.
TABLE 5.
Temporal trends in mean survival time based on Kaplan–Meier estimates.
| Years | Mean Estimate (±SE) | 95% Confidence Interval (Lower ‐ Upper) |
|---|---|---|
| 2018 | 42.4 (±0.9) | 40.54–44.27 |
| 2019 | 43.7 (±0.8) | 42.14–45.35 |
| 2020 | 45.1 (±0.9) | 43.22–47.14 |
| 2021 | 43.4 (±1.6) | 40.31–46.60 |
| 2022 | 47.8 (±0.9) | 46.03–49.73 |
| Overall | 46.1 (±0.7) | 44.61–47.67 |
FIGURE 2.
Overall survival of the study cohort based on Kaplan–Meier estimates (The survival curve shows the probability of survival over time, with tick marks indicating censored observations. Shaded areas represent the 95% confidence intervals). [Color figure can be viewed at wileyonlinelibrary.com]
4. Discussion
Despite the genetic diversity, difficulties in accessing CFTR modulatory therapies, and the presence of pwCF who are not eligible for these therapies, the improvement in FEV1pp and nutrition in our pwCF over the years since QI measures were implemented supports the fact that these can lead to improving CF care. Following the implementation of the FEV1pp and BMI QI projects in 2018 [8, 9], these interventions were adopted as the standard of care for all pwCF in our center. Over the 5‐year period, we noted a significant improvement in BMI z‐scores and BMI (kg/m 2 ) across all age groups, and in FEV₁pp specifically among adults aged ≥ 18 years.
This study presents the first single‐center longitudinal survival data for pwCF in Turkey, with a mean survival estimate of 46.1 years. Although survival increased during the study period, this change was not statistically significant. Nevertheless, the stabilization and upward trends observed in lung function and nutrition suggest that structured QI interventions may contribute to longer‐term survival gains. Although CFTR modulators have transformed CF care in many settings, access in Turkey has remained limited and outside the national reimbursement system during the study period. As a result, only a small number of pwCF had access to modulators, limiting their relevance for population‐level analyses. The clinical improvements observed in this study therefore primarily reflect the impact of structured QI implemented at the center level.
Lung function is a primary indicator of health for pwCF [20]. In our center, pediatric FEV₁pp increased from 87.6% in 2018 to 90% in 2022, while adult FEV₁pp rose from 55.3% to 70.7% during the same period. Although adjusted longitudinal models did not demonstrate a statistically significant linear change over time, the descriptive improvements—particularly among adults—align with the timing of QI implementation [9]. These findings suggest that systematic monitoring, early identification of decline, and protocol‐driven intervention may help stabilize or improve pulmonary trajectories even in resource‐limited settings.
A recent study involving 96 countries found that only ≈27% of pwCF had access to any CFTR‐modulator therapy [21]. In the current study 28 pwCF, predominantly adults (n = 14/28), initiated CFTR modulator therapy, their number represented only a small proportion of the cohort—just 14 of 70 adults (17%) and 28 of 328 total pwCF (8%) in 2021. These numbers are insufficient to explain the overall improvements. This trend likely reflects the combined impact of structured QI processes and individualized care strategies [9]. Weekly multidisciplinary reviews, increased hypertonic saline access, and expanded azithromycin use contributed to stabilizing adolescent lung function and improving adult [9]. Although adult outcomes remain below those seen in the U.S. or Europe, the trajectory appears to be improving [21].
Nutritional management is a cornerstone in CF care, with early interventions improving lung function [22, 23]. While ECFSPR and CFF registries have reported steady improvements in nutrition over the years, histolically, our center faced difficulies in improving nutrition [13, 24]. However, after the implementation of a standardized nutritional QI program, we observed notable improvements in BMI z‐scores, highlighting the value of routine screening, personalized support, and consistent follow‐up [8].
The current study demonstrated an improvement in BMI z‐scores in cwCF, with the greatest gains observed in those under 4 years of age. This highlights the importance of structured nutritional support in resource‐limited settings, where QI‐driven care models can help close the outcome gap. Moreover, these improvements occurred in the context of low socioeconomic status and high food insecurity, as previously demonstrated in studies conducted at our center [25, 26].
Longitudinal registry data and new analytical tools have clarified links between FEV1pp and other clinical variables [27, 28]. BMI has been shown to correlate with better lung function, and our findings are consistent with this, as both BMI and BMI z‐score were positively associated with FEV₁pp [27, 29].
Newborn screening (NBS) has improved early CF diagnosis worldwide [30]. In Turkey, despite national implementation of IRT/IRT (ımmunoreactive trypsinogen) protocols, regional disparities remain [30, 31]. In our cohort, NBS reduced the median diagnosis age to 4 months, compared to 5 months in clinically diagnosed cases. However, 37% of infants in some regions still face delays beyond 8 weeks [31].
These findings highlight the importance of both nationwide screening programs and efforts to overcome geographical barriers to ensure timely diagnosis. While our center's median diagnosis age aligns with international data, earlier national averages suggest limited access to advanced therapies [13, 24, 32].
The overall mean survival estimate of 46.1 years remains lower than those reported in the UK, U.S., and Australia [20, 24, 32, 33]. Due to the ongoing longitudinal follow‐up and the relatively young age of many pwCF in our cohort, a definitive median survival age could not yet be established. Therefore, direct comparisons with median survival estimates from other registries should be interpreted with caution. These findings may offer insight for other resource‐limited countries, suggesting that structured national healthcare support—such as full coverage of CF‐related medications—can positively influence clinical outcomes, even in the absence of widespread access to CFTR modulators.
4.1. Limitations
This study has several limitations. It was conducted at a single center, which may limit generalizability to national outcomes. The cohort was relatively young, and follow‐up duration was limited, preventing calculation of a definitive median survival age. Access to CFTR modulators was restricted during the study period, and their contribution to outcomes could not be fully assessed.
5. Conclusion
This study reports the first mean survival estimate for pwCF in Turkey and presents 5‐year longitudinal data from a single center, reflecting changes in demographics, lung function, and nutritional status within a resource‐limited setting.
The improvements observed were closely linked to sustained and focused QI efforts, including early recognition of lung function decline, standardized treatment approaches, regular multidisciplinary review, and structured nutritional support. These measures were central to maintaining FEV₁, improving BMI, and supporting survival. Even where access to CFTR modulators remains limited, consistent and coordinated care can make a meaningful difference. Collaboration with the national CF family association (KİFDER) further strengthened support for patients and families, underscoring how partnership between care teams and families contributes to better long‐term outcomes in CF care in Turkey.
Author Contributions
S. Karabulut: conceptualization, methodology, formal analysis, data curation, software, validation, writing – original draft. M. Yuksel Kalyoncu, M. Selcuk Balcı, C. A. Yıldız, and N. Metin Cakar: methodology, investigation, data curation. B. Uzunoglu, G. Tastan, and D. Kocaman: investigation, data curation, software. C. Yılmaz Yeğit: conceptualization, methodology, investigation. A. P. Ergenekon, E. Erdem Eralp, Y. Gokdemir, F. Karakoc, and B. Karadag: conceptualization, methodology, validation, writing – review and editing. S. Z. Nasr: methodology, writing – review and editing. Approval of final manuscript: All authors.
Funding
The authors received no specific funding for this work.
Conflicts of Interest
The authors declare no conflicts of interest.
Supporting information
SUPP MAT 7 JAN 2026.
Data Availability Statement
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
SUPP MAT 7 JAN 2026.
Data Availability Statement
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.