Real-World Outcomes in Cystic Fibrosis Telemedicine Clinical Care in a Time of a Global Pandemic
2021; Elsevier BV; Volume: 161; Issue: 5 Linguagem: Inglês
10.1016/j.chest.2021.11.035
ISSN1931-3543
AutoresLindsay Somerville, R. List, Martina Compton, Heather Bruschwein, Deirdre Jennings, Marieke Jones, Rachel Murray, E. Starheim, K. Webb, Lucy Gettle, Dana Albon,
Tópico(s)Respiratory viral infections research
ResumoBackgroundDuring the COVID-19 pandemic, the University of Virginia adult cystic fibrosis (CF) center transitioned from in-person clinical encounters to a model that included interdisciplinary telemedicine. The pandemic presented an unprecedented opportunity to assess the impact of the interdisciplinary telemedicine model on clinical CF outcomes.Research QuestionWhat are the clinical outcomes of a care model that includes interdisciplinary telemedicine (IDC-TM) compared with in-person clinical care for patients with CF during the COVID-19 pandemic?Study Design and MethodsAdults with CF were included. The prepandemic year was defined as March 17, 2019, through March 16, 2020, and the pandemic year (PY) was defined as March 17, 2020, through March 16, 2021. Patients were enrolled starting in the PY. Prepandemic data were gathered retrospectively. Telemedicine visits were defined as clinical encounters via secured video communication. Hybrid visits were in-person evaluations by physician, with in-clinic video communication by other team members. In-person visits were encounters with in-person providers only. All encounters included previsit screening. Outcomes were lung function, BMI, exacerbations, and antibiotic use. FEV1 percent predicted, exacerbations, and antibiotic use were adjusted for the effect of elexacaftor/tezacaftor/ivacaftor treatment.ResultsOne hundred twenty-four patients participated. One hundred ten patients were analyzed (mean age, 35 years; range, 18-69 years). Ninety-five percent had access to telemedicine (n = 105). Telemedicine visits accounted for 64% of encounters (n = 260), hybrid visits with telemedicine support accounted for 28% of encounters (n = 114), and in-person visits accounted for 7% of encounters (n = 30). No difference in lung function or exacerbation rate during the PY was found. BMI increased from 25 to 26 kg/m2 (t100 = –4.72; P < .001). Antibiotic use decreased from 316 to 124 episodes (z = 8.81; P < .0001).InterpretationThis CF care model, which includes IDC-TM, successfully monitored lung function and BMI, identified exacerbations, and followed guidelines-based care during the pandemic. A significant decrease in antibiotic use suggests that social mitigation strategies were protective.Trial RegistryClinicalTrials.gov; No.: NCT04402801; URL: www.clinicaltrials.gov. During the COVID-19 pandemic, the University of Virginia adult cystic fibrosis (CF) center transitioned from in-person clinical encounters to a model that included interdisciplinary telemedicine. The pandemic presented an unprecedented opportunity to assess the impact of the interdisciplinary telemedicine model on clinical CF outcomes. What are the clinical outcomes of a care model that includes interdisciplinary telemedicine (IDC-TM) compared with in-person clinical care for patients with CF during the COVID-19 pandemic? Adults with CF were included. The prepandemic year was defined as March 17, 2019, through March 16, 2020, and the pandemic year (PY) was defined as March 17, 2020, through March 16, 2021. Patients were enrolled starting in the PY. Prepandemic data were gathered retrospectively. Telemedicine visits were defined as clinical encounters via secured video communication. Hybrid visits were in-person evaluations by physician, with in-clinic video communication by other team members. In-person visits were encounters with in-person providers only. All encounters included previsit screening. Outcomes were lung function, BMI, exacerbations, and antibiotic use. FEV1 percent predicted, exacerbations, and antibiotic use were adjusted for the effect of elexacaftor/tezacaftor/ivacaftor treatment. One hundred twenty-four patients participated. One hundred ten patients were analyzed (mean age, 35 years; range, 18-69 years). Ninety-five percent had access to telemedicine (n = 105). Telemedicine visits accounted for 64% of encounters (n = 260), hybrid visits with telemedicine support accounted for 28% of encounters (n = 114), and in-person visits accounted for 7% of encounters (n = 30). No difference in lung function or exacerbation rate during the PY was found. BMI increased from 25 to 26 kg/m2 (t100 = –4.72; P < .001). Antibiotic use decreased from 316 to 124 episodes (z = 8.81; P < .0001). This CF care model, which includes IDC-TM, successfully monitored lung function and BMI, identified exacerbations, and followed guidelines-based care during the pandemic. A significant decrease in antibiotic use suggests that social mitigation strategies were protective. ClinicalTrials.gov; No.: NCT04402801; URL: www.clinicaltrials.gov. FOR EDITORIAL COMMENT, SEE PAGE 1127Take-home PointsStudy Question: How does a clinical care model that includes interdisciplinary telemedicine (IDC-TM) compare with in-person clinical care for patients with cystic fibrosis (CF) during the COVID-19 pandemic, measured by primary outcomes of lung function, pulmonary exacerbations, maintenance of BMI, and use of antibiotics?Results: A clinical care model for CF that includes IDC-TM was found to have similar clinical outcomes compared with a fully in-person clinical care model in terms of maintaining lung function and BMI and identifying CF pulmonary exacerbations during the COVID-19 global pandemic. This care model also was associated with decreased overall use of antibiotics.Interpretation: Our CF care model that includes IDC-TM successfully monitored lung function, identified exacerbations, and followed guidelines-based care during the global COVID-19 pandemic. A significant decrease in antibiotic use suggests that social mitigation strategies were protective in adults with CF. FOR EDITORIAL COMMENT, SEE PAGE 1127 Study Question: How does a clinical care model that includes interdisciplinary telemedicine (IDC-TM) compare with in-person clinical care for patients with cystic fibrosis (CF) during the COVID-19 pandemic, measured by primary outcomes of lung function, pulmonary exacerbations, maintenance of BMI, and use of antibiotics? Results: A clinical care model for CF that includes IDC-TM was found to have similar clinical outcomes compared with a fully in-person clinical care model in terms of maintaining lung function and BMI and identifying CF pulmonary exacerbations during the COVID-19 global pandemic. This care model also was associated with decreased overall use of antibiotics. Interpretation: Our CF care model that includes IDC-TM successfully monitored lung function, identified exacerbations, and followed guidelines-based care during the global COVID-19 pandemic. A significant decrease in antibiotic use suggests that social mitigation strategies were protective in adults with CF. In the first 4 months of the COVID-19 pandemic, the use of telemedicine in the United States increased by 154%.1Koonin L.M. Hoots B. Tsang C.A. et al.Trends in the use of telehealth during the emergence of the COVID-19 pandemic—United States, January–March 2020.MMWR Morb Mortal Wkly Rep. 2020; 69: 1595-1599Crossref PubMed Google Scholar The global pandemic presented an unprecedented opportunity to assess the impact of telemedicine on clinical outcomes in cystic fibrosis (CF). In recent years, CF survival has improved dramatically because of advances in therapeutics and widespread adoption of guideline-based interdisciplinary clinical care focused on early identification and treatment of CF pulmonary exacerbations.2Cystic Fibrosis FoundationUnderstanding changes in life expectancy.2021https://cff.org/Research/Researcher-Resources/Patient-Registry/Understanding-Changes-in-Life-Expectancy/Date accessed: April 14, 2021Google Scholar Telemedicine increases access to care for adults with CF living in regions remote to a CF specialty center, but routine use of telemedicine did not gain widespread traction until the COVID-19 pandemic.3Wood J. Mulrennan S. Hill K. Cecins N. Morey S. Jenkins S. Telehealth clinics increase access to care for adults with cystic fibrosis living in rural and remote Western Australia.J Telemed Telecare. 2017; 23: 673-679Crossref PubMed Scopus (45) Google Scholar In March 2020, the Adult CF Clinical Care Team at the University of Virginia (UVA) rapidly transitioned from in-person clinical encounters to a CF care model that included interdisciplinary telemedicine (IDC-TM) using Health Insurance Portability and Accountability Act-compliant video communication.4Compton M. Soper M. Reilly B. et al.A feasibility study of urgent implementation of cystic fibrosis multidisciplinary telemedicine clinic in the face of COVID-19 pandemic: single-center experience.Telemed J E Health. 2020; 26: 978-984Crossref PubMed Scopus (54) Google Scholar This prospective observational study set out to answer how a clinical care model that included IDC-TM compared with the classical CF clinical care model during the COVID-19 pandemic. Outcomes evaluated were lung function, rate of pulmonary exacerbation, maintenance of BMI, and use of antibiotics, as well as qualitative observations on the potentially protective effects of social mitigation in patients with CF during the COVID-19 pandemic on antibiotic use.5Al-Hasan A. Yim D. Khuntia J. Citizens' adherence to COVID-19 mitigation recommendations by the government: a 3-country comparative evaluation using web-based cross-sectional survey data.J Med Internet Res. 2020; 22e20634Crossref Scopus (101) Google Scholar Adult patients with CF were enrolled starting in March 2020 (UVA Institutional Review Board for Health Sciences Research, Federal Wide Assurance 00006183, Identifier: 22327; ClinicalTrials.gov Identifier: NCT04402801). Patients with CF 18 years of age or older were included (Fig 1). Incarcerated patients and patients unable to provide informed consent were excluded. Patients provided informed consent to participate in a prospective, observational, cohort study monitoring real-world clinical outcomes using a CF care model that includes IDC-TM. Patients were invited by the UVA Adult CF Clinical Care Team as part of previsit planning (PVP) after the onset of the COVID-19 pandemic. The care model followed Cystic Fibrosis Foundation clinical care guidelines regarding frequency of clinical visits and spirometry once per quarter, with a comprehensive interdisciplinary evaluation at least annually.6Cystic Fibrosis FoundationAdult care clinical guidelines.2021https://www.cff.org/Care/Clinical-Care-Guidelines/Age-Specific-Clinical-Care-Guidelines/Adult-Care-Clinical-Care-Guidelines/Date accessed: June 28, 2021Google Scholar The pandemic year (PY) was defined as March 17, 2020, through March 16, 2021. The prepandemic year (PPY) was defined as March 17, 2019, through March 16, 2020. The primary outcome was stability of lung function; secondary outcomes were exacerbation rates, antibiotic use, and preservation of BMI. Data were collected by electronic medical record communications and chart review. Spirometry was measured in the laboratory or by handheld home spirometers, previously demonstrated to be valid and reproducible for spirometry analysis.7Barr R.G. Stemple K.J. Mesia-Vela S. et al.Reproducibility and validity of a handheld spirometer.Respir Care. 2008; 53: 433-441PubMed Google Scholar Data were analyzed using GraphPad Prism version 9.0.0 for Windows software (GraphPad Software) and R version 4.1.0 software (R Foundation for Statistical Computing). Power analysis was not applicable. Demographic data including sex, age, ethnicity, mutation type, lung function, exacerbations per year, use of CFTR modulator therapy, microbiological data, status of CF-related diabetes and bone disease, and baseline BMI were obtained by chart review. Adult patients with CF at UVA were contacted by secured health system e-mail communication or by phone as part of routine PVP up to 1 week before scheduled appointments. With transition to the telemedicine intervention, PVP was adjusted to include screening questions to determine appropriateness for IDC-TM clinical care. Telemedicine eligibility was based on clinical stability, anticipated needs, and patient preferences, as well as access to required technology, including a Wi-Fi connection and a computer or smartphone with internet, video, and audio access. Additional equipment that was encouraged, but not required, included home spirometry (HS), a scale for weight, a pulse oximeter, and a BP cuff. Telemedicine visits were defined as any clinical encounter conducted entirely through secured video communication via the Health Insurance Portability and Accountability Act-compliant platform WebEx (Cisco Systems). Hybrid visits were visits in which the patient was seen in person in the clinic by a subset of the team with additional in-clinic telemedicine support. During these visits, additional team members communicate with the patient via secured webcast in the clinic room. Hybrid visits were recommended for patients who did not meet the prescreening criteria for telemedicine visits. In-person visits were clinical encounters in which the patient was seen only by in-person team members, typically the CF physician and one other team member. Telephone visits were conducted completely by phone when video communication was not possible.4Compton M. Soper M. Reilly B. et al.A feasibility study of urgent implementation of cystic fibrosis multidisciplinary telemedicine clinic in the face of COVID-19 pandemic: single-center experience.Telemed J E Health. 2020; 26: 978-984Crossref PubMed Scopus (54) Google Scholar All patients who did not already own a home spirometer were provided one through the adult CF clinic at no cost to the patient. For telemedicine visits, the respiratory therapist (RT) provided education on HS by secured video communication. With each telemedicine encounter, the RT coached the patient through HS. Readings were sent by secured communication to the RT. The RT verified quality of HS and used the raw FEV1 to calculate Global Lung Initiative FEV1 percent predicted and difference compared with baseline. For hybrid and in-person visits, spirometry was performed in the clinic using the in-laboratory MedGraphics CPFS/D USB spirometer (Medical Graphics Corp.). Lung function analysis was performed for all patients who had at least one spirometry reading in both the PY and PPY and was adjusted for elexacaftor/tezacaftor/ivacaftor (ETI) use. The electronic medical record was reviewed for all episodes of antimicrobial use. Exacerbation was defined as hospital admission, use of IV antimicrobials for treatment of CF, or both. All antibiotic use additionally included all filled prescriptions for any 14-day course of oral antibiotics, excluding antibiotics intended for non-CF care. Exacerbation rates are reported in annualized exacerbations per patient. BMI was calculated from aggregate data during the PPY and PY. BMI was obtained from clinic weights and self-reported weights during telemedicine encounters using a scale at home. Patients who were pregnant at any time during the 2-year period were excluded from BMI analyses. Descriptive statistics were produced for all clinical measures. FEV1 percent predicted, exacerbations, antibiotic episodes, and BMI were collected as continuous variables. To analyze lung function, a series of linear mixed models were created that account for multiple measurements for each patient. One model was created to examine the effect of year alone, and a multivariate model was created to examine the effect of year after controlling for ETI use and other variables. Exacerbation rate was analyzed by developing a series of Poisson mixed models. Again, one model investigated year and a multivariate model was built to understand the effect of year after adjusting for ETI therapy and other variables. Two final Poisson mixed models were created to explain all antibiotic use: a univariate model for year and a multivariate model. For the multivariate models, full model statistics are reported along with estimated marginal means for any effect(s) of interest. Differences in BMI were analyzed using a paired two-tailed t test. Subgroup analyses were performed on BMI cohorts using paired two-tailed t tests that then were corrected for multiple testing using the Holm P value correction. The P value for significance was .05. One hundred twenty-four of 143 patients were enrolled and participated in the telemedicine intervention. One hundred ten patients were included in final analysis; 14 were excluded because of lack of retrospective prepandemic data. None were lost to follow-up. Patients who were ineligible for the study, unable to be reached during the enrollment window, declined to participate in the study, or joined the clinic later in the year were given the option to use telemedicine care at their request; however, these patients were not included in data collection and outcomes analyses. The mean age at the start of the PY was 35 years (range, 18-69 years), with 59 women (54%) and 51 men (46%). Ninety-five percent of the enrolled patients were White and 5% were Black. No other ethnicities were identified in this cohort. Ninety percent had at least one del-F508 genetic mutation. Fifteen percent had advanced lung disease (FEV1 < 40%) at baseline. A total of 96 exacerbations occurred during the PPY (0.13/person/y) and 38 exacerbations occurred during the PY (0.05/person/y). CFTR modulators were taken by 93% of patients during the study period (n = 102). In the PPY, 50% were prescribed tezacaftor/ivacaftor, 6% were prescribed lumacaftor/ivacaftor, and 15% were prescribed ivacaftor. During the PPY, 88 patients (80%) began ETI therapy. During the PY, 2% continued to receive tezacaftor/ivacaftor, 2% continued to receive lumacaftor/ivacaftor, 4% continued to receive ivacaftor, and an additional 6 patients began ETI treatment (total, 85%). Colonization with Pseudomonas aeruginosa was identified in 60% during the PPY and in 47% during the PY, methicillin-sensitive Staphylococcus aureus was identified in 25% during the PPY and 22% during the PY, Stenotrophomonas maltophilia was identified in 18% during the PPY and 12% during the PY, Burkholderia cepacia complex was identified in 4% during the PPY and 3% during the PY, Aspergillus species were identified in 37% during the PPY and 13% during the PY, Achromobacter species were identified in 9% during the PPY and 6% during the PY, and nontuberculous mycobacterium species were identified in 21% during the PPY and 8% during the PY. Thirty-seven percent of patients had received a confirmed diagnosis of CF-related diabetes and 33% had received a confirmed diagnosis of CF bone disease. In both the PPY and PY, 97% had a BMI of > 18 kg/m2. Demographic data are summarized in Table 1.Table 1Patient CharacteristicsCharacteristicPrepandemic Year (2019-2020)Pandemic Year (2020-2021)Sex Female59 (54)—aData only collected for the baseline year for this characteristic. Male51 (46)—aData only collected for the baseline year for this characteristic.Age, y 18-2427 (25)—aData only collected for the baseline year for this characteristic. 25-3441 (37)—aData only collected for the baseline year for this characteristic. 35-4419 (17)—aData only collected for the baseline year for this characteristic. 45-5415 (14)—aData only collected for the baseline year for this characteristic. 55+8 (7)—aData only collected for the baseline year for this characteristic.Ethnicity White105 (95)—aData only collected for the baseline year for this characteristic. Black5 (5)—aData only collected for the baseline year for this characteristic. Hispanic0—aData only collected for the baseline year for this characteristic.Genetics del F508 homozygous59 (54)—aData only collected for the baseline year for this characteristic. del F508 heterozygous40 (36)—aData only collected for the baseline year for this characteristic. Lung function, FEV1 % predicted < 40%17 (15)15 (14) 40%-69%30 (27)29 (26) 70%-89%39 (35)35 (32) > 90%24 (22)31 (28)Pulmonary exacerbations per year Total exacerbations9638 Annualized exacerbations per person0.130.05CFTR modulator use Elexacaftor/tezacaftor/ivacaftor88 (80)94 (85) Tezacaftor/ivacaftor55 (50)2 (2) Lumacaftor/ivacaftor7 (6)2 (2) Ivacaftor17 (15)4 (4)Microbiology of colonizing species P. aeruginosa66 (60)52 (47) Methicillin-resistant S. aureus28 (25)25 (22) S. maltophilia19 (18)13 (12) B. cepacia complex5 (4)4 (3) Aspergillus40 (37)14 (13) Achromobacter10 (9)7 (6) Nontuberculous mycobacterium23 (21)9 (8)CF-related diabetes Yes41 (37)45 (41) Negative screening during calendar year38 (35)28 (25) Not screened during calendar year31 (28)37 (34)CF bone disease Screened and normal43 (39)50 (45) Osteopenia33 (30)40 (36) Osteoporosis3 (3)5 (5) Unknown31 (28)15 (14)BMI, kg/m2 ≤ 173 (3)3 (3) 18-2351 (46)38 (35) ≥ 2456 (51)69 (62)Data are presented as No. (%) or No. CF = cystic fibrosis; CFTR = cystic fibrosis transmembrane conductance regulator.a Data only collected for the baseline year for this characteristic. Open table in a new tab Data are presented as No. (%) or No. CF = cystic fibrosis; CFTR = cystic fibrosis transmembrane conductance regulator. Ninety-five percent of analyzed patients had access to a telemedicine-compatible device (n = 105), whereas 5% had no telemedicine capability (n = 5). The patients with no telemedicine access were still eligible for other clinical encounter methods, including hybrid visits with telemedicine support. A total of 407 encounters were conducted during the PY, between March 17, 2020, and March 16, 2021. Telemedicine encounters accounted for 64% of all clinical visits (n = 260), whereas hybrid visits with telemedicine support accounted for 28% (n = 114) and entirely in-person visits accounted for 7% (n = 30) of all encounters. All fully in-person visits were urgent or sick appointments or were triggered as a follow-up to previous telemedicine encounters. Phone visits accounted for less than 1% of encounters (n = 3), and all occurred between March and April 2020 (Fig 2). One hundred ten patients were included in the analyses of lung function. Using a linear mixed model to explain lung function using just the year and a random effect for each patient, the mean FEV1 % predicted in the PPY was 69.27% and increased by 4.28% during the PY (t480 = 8.71; P < .01) (Fig 3A). During the study period, 85% of patients (n = 94) began taking ETI; 94% of these began therapy in the PPY (n = 88). Within the subgroup of patients who began taking ETI in the PPY, 68% (n = 60) had at least one spirometry value after initiation of therapy, but before the start of the pandemic, allowing for determination of ETI effect on FEV1 independent of the pandemic. To this end, a linear mixed model was created with the 110 analyzed patients explaining FEV1 percent predicted using ETI use and controlling for the effects of time, sex, BMI, exacerbations, age group, year, baseline lung function cohort, the interaction between age group and year, and the interaction between baseline lung function cohort and year (Table 2). The effect of ETI on lung function after controlling for the effect of the pandemic and other variables was an increase in FEV1 % predicted of 4.31% (t474 = 5.16; P < .01) (Fig 3B). Lung function adjusted for ETI use and other variables revealed no significant difference between the PPY and PY, with a difference in FEV1 % predicted of 1.30% in the PY (t319 = 0.60; P = .55) (Fig 3C).Table 2Multivariate Linear Mixed Model Explaining Lung Function in Adults With CFTermEstimateSEdftP ValueIntercept32.353.90145.288.29< .01Quarter of y0.150.27418.880.56.57Male sex–0.481.50101.28–0.32.75BMI0.000.13167.690.03.98Patient is taking ETI4.310.84474.485.16< .01Age group, y 25-34–3.561.95100.78–1.82.07 35-44–1.622.34102.22–0.70.49 45-542.322.68103.680.87.39 55+2.573.25104.780.79.43 During PY1.302.15319.330.60.55FEV1 % predicted 40-6924.672.30103.6310.71< .01 70-8949.682.32106.8421.40< .01 > 9066.312.66106.8724.92< .01All antibiotic episodes0.420.38492.931.13.26Exacerbations–1.260.62491.14–2.04.04Age during PY, y 25-34–0.961.53228.47–0.62.53 35-440.121.76233.080.07.95 45-54–2.622.07225.44–1.27.21 55+–1.772.39225.30–0.74.46FEV1 % predicted during PY 40-690.611.77226.980.34.73 70-890.651.82234.660.36.72 > 90–1.332.09235.90–0.64.52Change in lung function explained by ETI use and controlling for other variables. A multivariate linear mixed model was created with the 110 analyzed patients explaining lung function using ETI use and controlling for the effects of time, sex, BMI, exacerbations, age group, year, lung function cohort, and the interaction between age group and year and between lung function cohort and year. A random slope over time was included for each participant. The effect of ETI on lung function after controlling for the effect of the pandemic and other variables was an increase in FEV1 % predicted of 4.31% (t474 = 5.16; P < .01). Lung function adjusted for ETI use and other variables revealed no significant difference in lung function between the PPY and PY, with a difference in FEV1 % predicted of 1.30% during the PY (t319 = 0.60; P = .55). CF = cystic fibrosis; df = degrees of freedom; ETI = elexacaftor/tezacaftor/ivacaftor; PPY = prepandemic year; PY = pandemic year. Open table in a new tab Change in lung function explained by ETI use and controlling for other variables. A multivariate linear mixed model was created with the 110 analyzed patients explaining lung function using ETI use and controlling for the effects of time, sex, BMI, exacerbations, age group, year, lung function cohort, and the interaction between age group and year and between lung function cohort and year. A random slope over time was included for each participant. The effect of ETI on lung function after controlling for the effect of the pandemic and other variables was an increase in FEV1 % predicted of 4.31% (t474 = 5.16; P < .01). Lung function adjusted for ETI use and other variables revealed no significant difference in lung function between the PPY and PY, with a difference in FEV1 % predicted of 1.30% during the PY (t319 = 0.60; P = .55). CF = cystic fibrosis; df = degrees of freedom; ETI = elexacaftor/tezacaftor/ivacaftor; PPY = prepandemic year; PY = pandemic year. No patient deaths or lung transplantations occurred during the study period. Three exacerbations in the PY were the result of COVID-19. In the PPY, 14 exacerbations included treatment for confirmed or suspected influenza. No instances of confirmed or suspected influenza occurred in the PY. Based on a Poisson mixed model explaining the number of exacerbations by year and a random effect of patient, exacerbations decreased by a factor of 2.5 from a total of 96 during the PPY (0.13/person/y) to 38 during the PY (0.05/person/y; z = –4.83; P < .01). Using a model that adjusted for ETI use and other variables (Tables 3, 4), no significant difference was found in the exacerbation rate in the PPY and PY (0.065/person/y vs 0.054/person/y, respectively; z = 0.41; P = .68). Exacerbations differed by sex, with women showing 2.2 times as many exacerbations (0.088/person/y; sum = 96) as men (0.040/person/y; sum = 38; z = 2.18; P = .03) (Tables 3, 4). Overall use of antibiotics decreased by a factor of 2.5, from 316 episodes in the PPY (0.848/person/y) to 124 in the PY (0.333/person/y; z = 8.81; P < .0001). After adjusting for ETI use and other variables, this effect was preserved, although less pronounced, with 0.612/person/y in the PPY to 0.366/person/y in the PY (z = 2.31; P = .02) (Tables 5, 6). During the PY, 64% of all exacerbations were diagnosed during hybrid or in-person clinic encounters, whereas 36% were diagnosed during telemedicine clinic encounters (Fig 4).Table 3Poisson Mixed Model Explaining Exacerbations in Adults With CFTermEstimateSEz RatioP ValueIntercept0.220.910.24.81Quarter of y–0.170.11–1.56.12Male sex–0.780.36–2.18.03BMI–0.020.03–0.53.60Patient is taking ETI–0.430.33–1.29.20FEV1 % predicted 40-69–0.420.51–0.81.42 70-89–0.690.52–1.32.19 > 90–1.970.68–2.91< .01 During PY–1.120.77–1.46.14Age group, y 25-34–0.310.42–0.73.46 35-44–1.160.57–2.05.04 45-54–1.520.66–2.30.02 55+–0.940.75–1.25.21FEV1 % predicted during PY 40-690.310.640.49.63 70-890.660.631.04.30 > 901.490.791.88.06Age during PY, y 25-340.480.510.94.35 35-44–0.721.13–0.63.53 45-540.850.880.97.33 55+0.970.811.20.23Poisson mixed model explaining the number of exacerbations by year adjusted for ETI use, time, sex, BMI, year, lung function cohort, age group, and the interaction between year and lung function cohort and between age group and year. A random slope over time was included for each participant. No significant difference in exacerbation rate was found in the PPY or PY (0.065 /person/y vs 0.054/person/y, respectively; z = 0.41; P = .68). Moderate exacerbations differed by sex, with female patients experiencing 2.2 times as many exacerbations (0.088/person/y; sum = 96) as male patients (0.040/person/y; sum = 38; z = 2.18; P = .03). CF = cystic fibrosis; ETI = elexacaftor/tezacaftor/ivacaftor PPY = prepandemic year; PY = pandemic year. Open table in a new tab Table 4Effect of Sex and Year on Exacerbation RateEffect of SexEffect of YearSexRateSEz RatioP ValueYearRateSEz RatioP ValueFemale0.0880.032.18.03PPY0.0650.020.41.68Male0.0400.02......PY0.0540.02...... Open table in a new tab Table 5Poisson Mixed Model Explaining Antibiotic Episodes per Year in Adults With CFTermEstimateSEz RatioP ValueIntercept0.210.450.48.63Quarter of y–0.100.05–1.92.05Male sex–0.390.16–2.38.02BMI0.010.010.52.60Patient is taking ETI–0.090.17–0.51.61FEV1 % predicted 40-69–0.290.25–1.16.25 70-89–0.390.25–1.56.12 > 90–0.740.30–2.48.01 During PY–0.820.40–2.08.04Age group, y 25-340.070.210.33.74 35-44–0.260.26–0.99.32 45-54–0.540.30–1.78.08 55+–0.560.38–1.47.14FEV1 % predicte
Referência(s)