You Can Observe a Lot By Just Watching. Wayne J. Morgan, MD, CM

Transcription

1 You Can Observe a Lot By Just Watching Wayne J. Morgan, MD, CM

2 Disclosures Genentech Epidemiological Study of Cystic Fibrosis, Scientific Advisory Group CF Foundation Data Safety Monitoring Board Registry/Comparative Effectiveness Research Committee

3 What is the CF Foundation Patient Registry?

4 How has the CFF Patient Registry grown? Knapp et al. Ann Am Thorac Soc 13: , 2016.

5 The CFF Patient Registry Today Inclusion criteria Seen at CF care center Consent to participate Data collected at Diagnosis Clinic Visits Hospitalizations / Home IV treatments Annually Download a copy at CFF.org/InsightCF.

6 Where are CF Foundation-accredited care programs?

7 Who is followed in the Registry? Based on birth rates and CFTR allele prevalence, it is estimated that there are about 34,500 persons with CF in the United States 6% do not consent to participate in Registry ~34,500 ~31, % 90% 28,983 84% Followed in the CF Foundation Patient Registry in 2015 Followed at US CF Care Centers

8 How complete are Registry data? 90-<95% 95+% 80-<90% FTR mutation (94%) (90%) Date of Birth Sex Race Hispanic origin FEV 1 Weight Height Respiratory Cultures Knapp et al. Ann Am Thorac Soc 13: , 2016.

9 How accurate are Registry data? 90-<95% 95+% 80-<90% Demographic variables Hospitalizations Anthropometric measures Respiratory cultures Medications Knapp et al. Ann Am Thorac Soc 13: , 2016.

10 90-<95% Medical data accuracy 95+% 80-<90% Dornase alfa (94%) Azithromycin (90%) Pancreatic enzymes (99%) Hypertonic saline Inhaled tobramycin Inhaled aztreonam (89%) (83%) (83%) Knapp et al. Ann Am Thorac Soc 13: , 2016.

11 Modified from Cystic Fibrosis Data Network

12 Where are we now?

13 Survival for people with CF 12-year improvement 8-year improvement Annual Data Report 2015, CF Foundation Patient Registry

14 Annual Data Report 2015, CF Foundation Patient Registry Life expectancy by age

15 Infection prevalence by age and over time Cross-sectional Prevalence, 2015 Prevalence Change, S. aureus H. influenzae People with CF P. aeruginosa MRSA S. maltophilia A. xylosoxidans B. cepacia complex -3% 0% 3% 6% Patients Annual Data Report 2015, CF Foundation Patient Registry Salsgiver et al. Chest 149(2): , 2016.

16 What is the CF Treatment Burden? Chest Physiotherapy of people with CF take all three inhaled medications Annual Data Report 2015, CF Foundation Patient Registry

17

18 Changing demographics: Adults with CF 4,392 Adults 14,955 Adults >100 Adult CF Programs ADULTS CHILDREN Annual Data Report 2014, CF Foundation Patient Registry CDC. Behavioral Risk Factor Surveillance System Survey Data, 2015

19 What have we learned about CF and its care?

20 CFF Patient Registry data use Publications Year

21 Female vs. Male Age (5 years) Pancreatic Sufficiency Weight-for-Age z-score CF-Related Diabetes S. aureus B. cepacia Pulmonary Exacerbation What factors impact survival? FEV 1 % Predicted Equivalence Liou et al. Am J Epidemiol Feb 15;153(4):

22 How do CFTR genotypes affect phenotypes? Cl - Cl - Cl - Cl - Cl - Cl - Cl - Cl - Cl - Cl - X X X X X Unaffected Class I synthesis Class II maturation Class III regulation Class IV conductance Class V quantity severe mutations mild mutations Adapted from

23 How do CFTR genotypes affect phenotypes? Cl - Cl - Cl - Cl - Cl - Cl - Cl - Cl - Cl - Cl - X X X X X Unaffected Class I synthesis Class II maturation Class III regulation Class IV conductance Class V quantity Mortality Rate (per 1000) Age at Diagnosis (yrs) FEV 1 % Predicted Pancreatic Insufficient (%) P. aeruginosa (%) McKone et al. Lancet May 17;361(9370):

24 Cutting et al, Nat Rev Genet January; 16(1): Contributors to phenotype

25 What are the pulmonary impacts of early life nutrition? FEV 1 % Predicted at 6 Years Weight-for-Age (WFA) at 3 Years and FEV 1 at 6 Years FEV 1 % Predicted at 6 Years Age when First Reached 50 th WFA Percentile and FEV 1 at age Weight-for-Age Percentile at 3 Years Age (years) when First Reached 50 th WFA Percentile Konstan MW et al. J Pediatr 2003;142(6): Sanders et al. J Pediatr 2015;167:

26 What do we know about lung function decline? Mean FEV 1 Rate of Change (%pred/yr) Konstan et al. J Pediatr 2007;151:134-9 Dasenbrook et al. Poster 489. NACFC 2015 Rate of FEV 1 Decline by Age Age Group (Years) slower faster Predictors of Decline Pancreatic Sufficiency Wheezing F508del Heterozygote Genotype Diagnosis by Newborn Screening Female Sex Low BMI Percentile Crackles P. aeruginosa infection S. aureus Infection B. cepacia complex Infection Prior Pulmonary Exacerbations FEV 1 100% Predicted FEV 1 Variability Konstan et al. J Pediatr 2007;151:134-9 Cogen et al. Pediatr Pulmonol Aug;50(8): Waters et al. Eur Respir J Jul;40(1):61-6. Morgan et al. J Pediatr Feb;169:116-21

27 What are the long-term implications of pulmonary exacerbations for lung function decline? Best in prior 6 months Exacerbation Baseline 15% of patients failed to recover 90% of their Baseline FEV 1 75% 81% 85% 90% Recovery 90% Recovery 90% recovery Sanders DB et al. Am J Respir Crit Care Med 2010; 182:

28 What predicts pulmonary exacerbations? IV-Treated Exacerbations in the Prior Year Past IV-Treated Exacerbations are the Strongest Predictor of Future Exacerbations Mean Rate of FEV 1 Loss -1.1% predicted per year -1.8% predicted per year Median Days to Next Exacerbation (with 95% CI) VanDevanter et al. J Cyst Fibros 2016;15; Waters et al. Eur Respir J. 2012;40(1):61-6

29 IV Treatment after 10% Acute FEV 1 Decline <25% FEV 1 % Predicted Decile Odds of IV Treatment Highest vs Lowest FEV 1 Deciles: 0.15 (95% CI 0.12, 0.20) >50% Morgan et al. J Peds. 2013;163(4): It's like déjà vu all over again Patients Treated Personal communication, J Ostrenga - CFFPR 2015

30 Can we evaluate the effectiveness of treatment?

31 Efficacy (clinical trials) Effectiveness (real world) Clinical Trials Design optimized to demonstrate safety and efficacy may limit generalizability Disease stage Co-morbidities Problematic respiratory infection Poor follow up Frequent visits with careful follow-up Standardization of management and outcome measures Attention to adherence Real World All who may benefit often including those excluded from trial and/or offlabel use Less frequent visits and follow-up Less standardization of management and outcome measures Longer duration of therapy Less attention to adherence

32 Confounding by indication: sicker patients receive more treatments Inhaled tobramycin (unadjusted) a Effect of Inhaled Tobramycin on Survival 2.8 Inhaled tobramycin (adjusted) a reduced mortality increased mortality 1.2 Inhaled tobramycin (adjusted) b 0.79 a- Rothman and Wentworth. Epidemiology. 2003;14(1): 55-9 b- Sawicki et al. Ped Pulm 2012; 47:44-52.

33 Effectiveness of dornase alfa 90 FEV 1 % Predicted Comparator Patients Dornase alfa Patients Baseline 6 Months 12 Months After adjustment for cohort differences, there was a mean 4.3% increase in FEV 1 for patients treated with dornase alfa Johnson et al. J Peds. 1999; 134(6); 734-9

34 CFTR modulator effectiveness Individuals from CFTR modulator clinical trials compared to matched F508del homozygote Registry controls Sawicki et al. Am J Respir Crit Care Med. 2015;192(7): Konstan et al. NACFC 2016, Poster 180. Bessonova et al. NACFC 2016, Poster 494.

35 Was treatment with chronic macrolides increasing NTM risk? 37% of CFFPR patients with new NTM in 2011 received chronic macrolides in % of CFFPR patients without NTM in 2011 received chronic macrolides in 2010 Historical macrolide use was associated with a decreased risk of NTM isolation Binder et al. Am J Respir Crit Care Med. 2013;188(7):

36 genetic defect liver disease Putting it all together pancreatic insufficiency abnormal secretions malnutrition lung function liver transplant airway infection height, weight inflammation sex, age diabetes sinusitis exacerbations infertility transplant survival Modified from Liou, 2016

37 How does the Registry support care teams?

38 CFFPR Center-Specific Report Annual Center-specific reports

39 CFFPR Center-Specific Report Annual Center-specific reports

40

41 How Registry data can drive quality: One program s story. 105 VCU Quality Improvement Program Median FEV 1 % Predicted Schechter et al. Poster 546. NACFC 2016 Virginia Commonwealth University Pediatric Program National Average Ten Best Performing Centers Year

42 How have we partnered to create change? Quality improvement and leadership development Care center peer-review accreditation process Evidence-based clinical practice guidelines Benchmarking to learn from best practice Engaging people with CF and their families

43 What comes next?

44 More to do Continue using Registry to improve care and learn more about CF as a disease and its treatments Increase value of Registry at point of care Recognizing limitations Some important questions can only be answered by clinical trials Integrating other data sources to enhance Registry Abstract 482: Linking Transplant and CFFPR Registries Inviting CF community to participate in asking research questions

45 Patients and their families Acknowledgments CF data coordinators and CF Center care teams CF Foundation leadership Registry committee (Chairs C. Goss and E. Dasenbrook) Clinicians and researchers who use Registry and other data to improve understanding of CF and patient care Registry team at CF Foundation Bruce Marshall Alex Elbert, Kris Petren, Samar Rizvi, Shathiya Kesevan, Tom O Neil Aliza Fink, Josh Ostrenga, Deena Loeffler, Victoria Danner Ase Sewall

46

47 To learn more and submit your research questions, visit CFF.org/InsightCF.