Mukowiscydoza – Rokowania, prognozy i postęp choroby

Mukowiscydoza
Rokowania, prognozy i postęp choroby

Mukowiscydoza (CF) jest chorobą wielonarządową, w której niewydolność oddechowa odpowiada za około 80% zgonów. Mediana wieku zgonu wzrosła do 48,4 lat (dane z 2019 r.), a prognozy dla pacjentów urodzonych między 2018 a 2022 wskazują na przeżycie ≥56 lat, a nawet do 65 lat dla urodzonych po 2021 r. Kluczowym wskaźnikiem rokowniczym pozostaje FEV1 <30% wartości należnej, będący kryterium kwalifikacji do przeszczepu płuc według ISHLT. Jednakże heterogeniczność przeżywalności w tej grupie wymaga uwzględnienia dodatkowych czynników, takich jak wiek, płeć, BMI, wydolność trzustki, cukrzyca CF (CFRD), zakażenia (Burkholderia cepacia, Pseudomonas aeruginosa, Staphylococcus aureus, MRSA) oraz liczba zaostrzeń płucnych. Nowoczesne modele prognostyczne, w tym system AutoPrognosis wykorzystujący uczenie maszynowe, poprawiają precyzję przewidywań (PPV 65% vs. 48% dla FEV1) i integrują parametry spirometryczne oraz wymiany gazowej, co pozwala na lepszą selekcję pacjentów do przeszczepu płuc i planowanie leczenia.

Rokowanie i predykcja przeżycia w mukowiscydozie

Mukowiscydoza (ang. Cystic Fibrosis, CF) jest wielonarządową chorobą o znacznie skróconym okresie przeżycia, gdzie główną przyczyną śmiertelności są powikłania związane z chorobą płuc¹². Niewydolność oddechowa odpowiada za około 80% zgonów w tej chorobie³. Mimo że mukowiscydoza nadal znacząco skraca długość życia, mediana wieku zgonu i przewidywana długość przeżycia stale rosną dzięki postępom w diagnostyce i leczeniu⁴⁵.

Aktualne prognozy przeżycia

Według raportu rocznego Fundacji Mukowiscydozy z 2019 roku, średnia oczekiwana długość życia dla osób z mukowiscydozą wynosi 48,4 lat⁶. Dane z rejestru pacjentów z CF sugerują, że osoby urodzone z tą chorobą między 2018 a 2022 rokiem mogą dożyć 56 lat lub więcej, a połowa urodzonych między 2021 rokiem a dniem dzisiejszym może mieć oczekiwaną długość życia sięgającą nawet 65 lat⁷. To znaczący postęp w porównaniu z prognozami sprzed kilku lat, kiedy oczekiwana długość życia wynosiła między 30 a 40 lat⁸.

Modele prognostyczne w mukowiscydozie

Dokładne przewidywanie przeżycia pacjentów z mukowiscydozą jest kluczowe dla ustalenia optymalnego momentu skierowania pacjentów z terminalną niewydolnością oddechową do przeszczepu płuc⁹¹⁰. Zrozumienie czynników związanych z rokowaniem dotyczącym przeszczepu płuc i przeżycia może pomóc w podejmowaniu decyzji przez pacjenta, zespół lekarzy z ośrodka CF oraz zespół transplantacyjny¹¹.

Klasyczne wskaźniki prognostyczne

Zgodnie z obecnymi wytycznymi konsensusu, takimi jak zalecane przez International Society for Heart and Lung Transplantation (ISHLT), pacjenta należy kierować na ocenę do przeszczepu płuc, gdy natężona objętość wydechowa pierwszosekundowa (FEV1) spada poniżej 30% wartości należnej¹²¹³. To kryterium, które jest powszechnie stosowane w praktyce klinicznej, opiera się głównie na przełomowym badaniu Kerema i wsp., które zidentyfikowało FEV1 jako główny predyktor śmiertelności u pacjentów z CF¹⁴.

Chociaż biomarker FEV1 został wielokrotnie potwierdzony jako silny predyktor śmiertelności u pacjentów z CF, niedawne badania wykazały, że przeżywalność pacjentów z CF z FEV1 < 30% wykazuje znaczną heterogeniczność. Ponadto poprawa rokowania w mukowiscydozie w ostatnich latach zmieniła epidemiologię i demografię populacji z CF, co mogło w konsekwencji zmienić istotne czynniki ryzyka związane z CF¹⁵¹⁶.

Zaawansowane modele prognostyczne

Opracowano 5-letni model przeżycia, który zawiera wiek, FEV1 jako procent wartości należnej, płeć, wskaźnik wagi do wieku z-score, wydolność trzustki, cukrzycę, zakażenie Staphylococcus aureus, zakażenie Burkerholderia cepacia oraz roczną liczbę ostrych zaostrzeń płucnych¹⁷. Model ten dostarcza informacji o względnym wpływie każdej cechy i podkreśla znaczenie uwzględnienia wielu czynników klinicznych przy ocenie prawdopodobieństwa 5-letniego przeżycia¹⁸.

Nowatorskie podejście zastosowano w systemie AutoPrognosis, który wykorzystuje uczenie maszynowe do przewidywania rokowania u pacjentów z mukowiscydozą. System ten osiągnął dodatnią wartość predykcyjną na poziomie 65% (95% CI: 61-69%), podczas gdy wartość osiągnięta przez kryterium FEV1 zalecane przez wytyczne wynosi zaledwie 48% (95% CI: 44-52%) przy stałym poziomie czułości¹⁹²⁰.

AutoPrognosis ujawnił nowe spostrzeżenia dotyczące znaczenia zmiennych odzwierciedlających zaburzenia wymiany gazowej w płucach w poprawie precyzji i użyteczności klinicznej modeli prognostycznych²¹²². System ten był w stanie nauczyć się reguły predykcji, która dokładnie łączy zmienne spirometryczne i wymiany gazowej w celu stworzenia precyzyjnego kryterium skierowania na przeszczep płuc, które dokładnie rozróżnia pacjentów, którzy są naprawdę zagrożeni, od tych, którzy nie potrzebują płuc w najbliższej przyszłości²³.

Nomogramy prognostyczne

Opracowano również modele do przewidywania prawdopodobieństwa przeszczepu płuc lub zgonu oraz czasu do przeszczepu płuc lub zgonu u pacjentów z CF w Stanach Zjednoczonych przed wprowadzeniem wysoce skutecznej terapii modulatorami CFTR, które zostały przetłumaczone na nomogramy²⁴.

Model regresji logistycznej przewidujący prawdopodobieństwo przeszczepu płuc/zgonu zidentyfikował następujące istotne czynniki:

FEV1 (% wartości należnej)
BMI
Wiek w momencie diagnozy
Aktualny wiek
Liczba zaostrzeń płucnych
Rasa
Płeć
Cukrzyca związana z CF (CFRD)
Terapia kortykosteroidami
Zakażenia B. cepacia, P. aeruginosa, S. aureus, MRSA
Stosowanie enzymów trzustkowych
Status ubezpieczenia
Konsekwentne stosowanie ibuprofenu przez co najmniej 4 lata

²⁵²⁶

Podobne czynniki zostały zidentyfikowane w modelu regresji Coxa modelującym czas do przeszczepu płuc/zgonu, z wyjątkiem statusu ubezpieczenia, który nie był istotny w tym modelu²⁷²⁸. Modele regresji logistycznej i Coxa zostały wewnętrznie zwalidowane z dokładnością przewidywania odpowiednio 89% i 92%²⁹.

Kluczowe czynniki wpływające na rokowanie

Czynniki płucne

Najważniejszymi czynnikami związanymi z układem oddechowym, które wpływają na rokowanie w mukowiscydozie, są:

Wartość FEV1 – pozostaje najsilniejszym pojedynczym predyktorem śmiertelności, choć nie wystarcza do pełnej oceny ryzyka³⁰
Zakażenie Burkerholderia cepacia – ma największy wpływ spośród wszystkich zmiennych modelu na przewidywanie 5-letniej przeżywalności³¹
Zakażenia innymi patogenami (P. aeruginosa, S. aureus, MRSA) – istotnie wpływają na rokowanie³²
Liczba zaostrzeń płucnych – stanowi ważny czynnik predykcyjny³³
Parametry wymiany gazowej – istotnie poprawiają dokładność modeli prognostycznych³⁴

Czynniki ogólnoustrojowe

Na rokowanie w mukowiscydozie wpływają również czynniki ogólnoustrojowe:

Cukrzyca związana z mukowiscydozą (CFRD) – wykazuje znaczący negatywny wpływ prognostyczny, związany ze zwiększoną śmiertelnością, gorszym stanem odżywienia i większym nasileniem choroby płuc³⁵³⁶
Stan odżywienia (BMI, wskaźnik wagi do wieku) – stanowi istotny czynnik prognostyczny³⁷³⁸
Wydolność trzustki – niewydolność trzustki wiąże się z gorszym rokowaniem³⁹
Genotyp CFTR – może być przydatny jako wstępna miara rokowania we wczesnej diagnozie CF, kiedy często jest to jedyna dostępna informacja o chorobie⁴⁰

Czynniki demograficzne

Wśród czynników demograficznych wpływających na rokowanie w mukowiscydozie należy wymienić:

Wiek – starszy wiek wiąże się z większym ryzykiem zgonu⁴¹
Płeć – kobiety mają zazwyczaj gorsze rokowanie⁴²
Wiek w momencie diagnozy – wczesna diagnoza umożliwia wcześniejsze leczenie i lepsze rokowanie⁴³⁴⁴
Rasa – jest istotnym czynnikiem w modelach prognostycznych⁴⁵
Status ubezpieczenia – wpływa na dostęp do opieki i leczenia⁴⁶

Zastosowanie modeli prognostycznych w praktyce klinicznej

Modele prognostyczne w mukowiscydozie mają szereg zastosowań w praktyce klinicznej:

Kwalifikacja do przeszczepu płuc

Optymalizacja czasu przeszczepu zależy od dokładnego przewidywania przeżycia. Obecne kryteria, choć dają pewne wskazówki, nie opierają się na statystycznie opracowanych modelach prognostycznych⁴⁷. Dokładniejsze modele prognostyczne mogą pomóc w ustaleniu optymalnego momentu skierowania pacjenta do przeszczepu płuc⁴⁸⁴⁹.

Dzięki przyjęciu modelu opracowanego przez AutoPrognosis do kierowania na przeszczep płuc, oczekuje się, że odsetek pacjentów na liście oczekujących na przeszczep płuc, którzy są naprawdę zagrożeni, wzrośnie z 48% do 65%⁵⁰.

Planowanie leczenia i monitorowanie

Użyteczność kliniczna zaawansowanych modeli prognostycznych nie ogranicza się do kierowania na przeszczep; przewidywania generowane przez te modele służą jako szczegółowe oceny ryzyka, które mogą określić ilościowo nasilenie przyszłych wyników, a zatem mogą być wykorzystane do planowania leczenia, harmonogramu wizyt kontrolnych lub oszacowania czasu, w którym przeszczep będzie potrzebny w przyszłości⁵¹⁵².

Projektowanie badań klinicznych

Model przeżywalności w mukowiscydozie może pomóc w poprawie projektowania badań, zapewniając sposób wyboru pacjentów z równoważnymi przewidywaniami przeżycia dla grup kontrolnych i eksperymentalnych w badaniach prospektywnych⁵³.

Czynniki poprawiające rokowanie

Postęp w leczeniu mukowiscydozy przyczynił się do znacznej poprawy rokowania w ostatnich dekadach. Do najważniejszych czynników należą:

Wczesna diagnoza i leczenie

Leczenie działa najlepiej, gdy mukowiscydoza jest wcześnie zdiagnozowana, dlatego badania przesiewowe noworodków są tak ważne. Dodanie modulatorów CFTR w młodym wieku może poprawić długoterminowe zdrowie i jeszcze bardziej zwiększyć oczekiwaną długość życia w przyszłości⁵⁴.

Nowe terapie

Ostatnie dwie dekady przyniosły znaczące poszerzenie skutecznych leków i terapii w leczeniu chorób płuc związanych z mukowiscydozą⁵⁵. Szczególne nadzieje budzą modulatory CFTR, które wymagają opracowania dla każdego typu mutacji. Posiadanie dwóch nowych leków w aktywnym badaniu dla F508del, zdecydowanie najczęstszej mutacji CFTR, przybliża nas do wprowadzenia ogromnej różnicy w życiu pacjentów⁵⁶.

Opieka kompleksowa

Rozwój zaawansowanej, kompleksowej sieci opieki i stosowanie kilku nowych leków opracowanych specjalnie do leczenia chorób związanych z CF przyczyniły się do poprawy ogólnego stanu zdrowia pacjentów z CF i są wyraźnie częścią powodu, dla którego oczekiwana przeżywalność wzrosła⁵⁷.

Implikacje dla przyszłości

Najbardziej wrażliwym sposobem przewidywania rokowania pozostaje obecnie podejście wieloaspektowe, uwzględniające kilka markerów choroby. Wykorzystanie wszystkich czynników i złożonego klinicznego narzędzia predykcyjnego jest sugerowane w celu stratyfikacji ryzyka pacjenta⁵⁸.

Chociaż nie ma lekarstwa na mukowiscydozę, postępy w diagnostyce i leczeniu mogą pomóc ludziom żyć dłużej niż w poprzednich pokoleniach. Obecnie osoby z CF mogą zdobywać wykształcenie wyższe, rozpoczynać kariery zawodowe i prowadzić pełne, szczęśliwe życie, a potencjał dłuższej oczekiwanej długości życia każdego roku się wydłuża⁵⁹.

Ogólnie rzecz biorąc, osoby, które leczą objawy mukowiscydozy, prowadzą zdrowy tryb życia, unikają infekcji i powikłań oraz dbają o zdrowie psychiczne, mogą mieć dobrą jakość życia⁶⁰. Połowa osób z mukowiscydozą może żyć ponad 40 lat. To, jak długo można żyć, zależy od takich czynników, jak stadium mukowiscydozy i wszelkie inne powikłania zdrowotne⁶¹.

Kolejne rozdziały

Zapraszamy do dalszego czytania naszego leksykonu.

Wybierz kolejny rozdział z menu poniżej, aby otworzyć nową podstronę kompedium wiedzy i uzyskać szczegółowe informację o leku, substancji lub chorobie.

Materiały źródłowe

#1 Factors Affecting Prognosis and Prediction of Outcome in Cystic Fibrosis Lung Disease | IntechOpen
https://www.intechopen.com/chapters/48628
Cystic fibrosis (CF) is a multisystem disorder with a significantly shortened life expectancy with the major cause of mortality related to lung disease. […] While the life expectancy in CF is still short, the median age of death and predicted survival age are continually increasing. […] Life expectancy in patients with cystic fibrosis is in a constant state of change. […] The predicted median survival of people born with CF today continues to rise. […] Numerous factors have contributed to the changing statistics in CF prognosis. […] In this chapter, we review the evidence regarding prognosis based on key clinical and demographic parameters, and the biomarkers and prediction tools that may be used to predict outcome. […] CFTR genotype may be useful as an initial measure of prognosis in early CF diagnosis when it is often the only available information about the disease.
#2 State of progress in treating cystic fibrosis respiratory disease | BMC Medicine | Full Text
https://bmcmedicine.biomedcentral.com/articles/10.1186/1741-7015-10-88
Since the discovery of the gene associated with cystic fibrosis (CF), there has been tremendous progress in the care of patients with this disease. […] the median age of predicted survival now approaches 40 years. […] This is the result of a combination of factors including the development of a sophisticated comprehensive care network and the use of several new medications developed specifically for the treatment of CF-related disease. […] respiratory failure accounts for 80% of mortality in CF. […] These therapies have improved the overall health of patients with CF and they are clearly part of the reason that expected survival has increased. […] However, these therapies do not offer a cure and they mainly treat downstream complications of the pathophysiology of CF lung disease, meaning that patients continue to suffer the morbidity associated with chronic airways infection and predicted survival still lags well below what is normal.
#3 State of progress in treating cystic fibrosis respiratory disease | BMC Medicine | Full Text
https://bmcmedicine.biomedcentral.com/articles/10.1186/1741-7015-10-88
Since the discovery of the gene associated with cystic fibrosis (CF), there has been tremendous progress in the care of patients with this disease. […] the median age of predicted survival now approaches 40 years. […] This is the result of a combination of factors including the development of a sophisticated comprehensive care network and the use of several new medications developed specifically for the treatment of CF-related disease. […] respiratory failure accounts for 80% of mortality in CF. […] These therapies have improved the overall health of patients with CF and they are clearly part of the reason that expected survival has increased. […] However, these therapies do not offer a cure and they mainly treat downstream complications of the pathophysiology of CF lung disease, meaning that patients continue to suffer the morbidity associated with chronic airways infection and predicted survival still lags well below what is normal.
#4 Factors Affecting Prognosis and Prediction of Outcome in Cystic Fibrosis Lung Disease | IntechOpen
https://www.intechopen.com/chapters/48628
Cystic fibrosis (CF) is a multisystem disorder with a significantly shortened life expectancy with the major cause of mortality related to lung disease. […] While the life expectancy in CF is still short, the median age of death and predicted survival age are continually increasing. […] Life expectancy in patients with cystic fibrosis is in a constant state of change. […] The predicted median survival of people born with CF today continues to rise. […] Numerous factors have contributed to the changing statistics in CF prognosis. […] In this chapter, we review the evidence regarding prognosis based on key clinical and demographic parameters, and the biomarkers and prediction tools that may be used to predict outcome. […] CFTR genotype may be useful as an initial measure of prognosis in early CF diagnosis when it is often the only available information about the disease.
#5 State of progress in treating cystic fibrosis respiratory disease | BMC Medicine | Full Text
https://bmcmedicine.biomedcentral.com/articles/10.1186/1741-7015-10-88
Since the discovery of the gene associated with cystic fibrosis (CF), there has been tremendous progress in the care of patients with this disease. […] the median age of predicted survival now approaches 40 years. […] This is the result of a combination of factors including the development of a sophisticated comprehensive care network and the use of several new medications developed specifically for the treatment of CF-related disease. […] respiratory failure accounts for 80% of mortality in CF. […] These therapies have improved the overall health of patients with CF and they are clearly part of the reason that expected survival has increased. […] However, these therapies do not offer a cure and they mainly treat downstream complications of the pathophysiology of CF lung disease, meaning that patients continue to suffer the morbidity associated with chronic airways infection and predicted survival still lags well below what is normal.
#6 What Is the Prognosis for People with Cystic Fibrosis?
https://www.healthline.com/health/cystic-fibrosis/cystic-fibrosis-prognosis
According to the Cystic Fibrosis Foundations 2019 Annual Report, the mean life expectancy for people with CF is 48.4 years. […] Life expectancy has increased greatly over the years thanks to earlier treatment and improved therapies. […] CF patient registry data suggests that people born with CF between 2018 and 2022 may live to age 56 years or older. And half of those born between 2021 and today may have a life expectancy of up to 65 years. […] Overall, people who address their CF symptoms, maintain a healthy lifestyle, avoid infections and complications, and take care of their mental health may have a good quality of life. […] Half of people with CF may live past 40 years old. How long you may live depends on factors such as what stage of CF you are in and any other health complications you may have.
#7 What Is the Prognosis for People with Cystic Fibrosis?
https://www.healthline.com/health/cystic-fibrosis/cystic-fibrosis-prognosis
According to the Cystic Fibrosis Foundations 2019 Annual Report, the mean life expectancy for people with CF is 48.4 years. […] Life expectancy has increased greatly over the years thanks to earlier treatment and improved therapies. […] CF patient registry data suggests that people born with CF between 2018 and 2022 may live to age 56 years or older. And half of those born between 2021 and today may have a life expectancy of up to 65 years. […] Overall, people who address their CF symptoms, maintain a healthy lifestyle, avoid infections and complications, and take care of their mental health may have a good quality of life. […] Half of people with CF may live past 40 years old. How long you may live depends on factors such as what stage of CF you are in and any other health complications you may have.
#8 Cystic Fibrosis: Causes, Symptoms & Treatment
https://my.clevelandclinic.org/health/diseases/9358-cystic-fibrosis
Yes, cystic fibrosis can be life-threatening. Lung damage from thick mucus and frequent lung infections is the most common cause of death. […] Experts predict the life expectancy of someone born with cystic fibrosis in the past few years is around 50 years old. Improvements in treatment in recent years have increased this from a few years ago, when life expectancy was between 30 and 40 years old. […] People with atypical cystic fibrosis tend to have longer life expectancies than those with classic CF. […] Theres no cure for CF. You or your child will need lifelong treatments to manage it. This includes treating infections, maintaining nutrition and seeing a CF specialist frequently. But new treatment methods help children who have CF live well into adulthood and have a better quality of life. […] Treatments work best when CF is diagnosed early, which is why newborn screening is so important. The addition of CFTR modulators at a young age may improve long-term health and increase life expectancy even more in the future.
#9 Prognostication and Risk Factors for Cystic Fibrosis via Automated Machine Learning | Scientific Reports
https://www.nature.com/articles/s41598-018-29523-2
Accurate prediction of survival for cystic fibrosis (CF) patients is instrumental in establishing the optimal timing for referring patients with terminal respiratory failure for lung transplantation (LT). […] While FEV1 is indeed a strong predictor of CF-related mortality, we hypothesized that the survival behavior of CF patients exhibits a lot more heterogeneity. […] Current consensus guidelines, such as those recommended by the International Society for Heart and Lung Transplantation (ISHLT), consider referring a patient for LT evaluation when the forced expiratory volume (FEV1) drops below 30% of its predicted nominal value. […] This guideline, which is widely followed in clinical practice, is based mainly on the seminal study by Kerem et al., which identified FEV1 as the main predictor of mortality in CF patients using survival data from a cohort of Canadian CF patients.
#10 Prognostication and Risk Factors for Cystic Fibrosis via Automated Machine Learning
https://pmc.ncbi.nlm.nih.gov/articles/PMC6062529/
Accurate prediction of survival for cystic fibrosis (CF) patients is instrumental in establishing the optimal timing for referring patients with terminal respiratory failure for lung transplantation (LT). […] Current consensus guidelines, such as those recommended by the International Society for Heart and Lung Transplantation (ISHLT) consider referring a patient for LT evaluation when the forced expiratory volume (FEV1) drops below 30% of its predicted nominal value. […] While the FEV1 biomarker has been repeatedly confirmed to be a strong predictor of mortality in CF patients, recent studies have shown that the survival behavior of CF patients with FEV1<30% exhibits substantial heterogeneity, and that the improvements in CF prognosis over the past years have changed the epidemiology and demography of CF populations, which may have consequently altered the relevant CF risk factors.
#11 MODELING CYSTIC FIBROSIS PATIENT PROGNOSIS: NOMOGRAMS TO PREDICT LUNG TRANSPLANTATION AND SURVIVAL PRIOR TO HIGHLY EFFECTIVE MODULATOR THERAPY | medRxiv
https://www.medrxiv.org/content/10.1101/2023.09.25.23296112v1.full-text
The duration of time a person with cystic fibrosis (pwCF) spends on the lung transplant waitlist is dependent on waitlist and post-transplant survival probabilities and can extend up to 2 years. […] Understanding the characteristics involved with lung transplant and survival prognoses may help guide decision making by the patient, the referring CF Center and the transplant team. […] Predictors significant (p 0.05) in the final logistic regression model predicting probability of lung transplant/death were: FEV1 (% predicted), BMI, age of diagnosis, age, number of pulmonary exacerbations, race, sex, CF-related diabetes (CFRD), corticosteroid use, infections with B. cepacia, P. aeruginosa, S. aureus, MRSA, pancreatic enzyme use, insurance status, and consecutive ibuprofen use for at least 4 years.
#12 Prognostication and Risk Factors for Cystic Fibrosis via Automated Machine Learning | Scientific Reports
https://www.nature.com/articles/s41598-018-29523-2
Accurate prediction of survival for cystic fibrosis (CF) patients is instrumental in establishing the optimal timing for referring patients with terminal respiratory failure for lung transplantation (LT). […] While FEV1 is indeed a strong predictor of CF-related mortality, we hypothesized that the survival behavior of CF patients exhibits a lot more heterogeneity. […] Current consensus guidelines, such as those recommended by the International Society for Heart and Lung Transplantation (ISHLT), consider referring a patient for LT evaluation when the forced expiratory volume (FEV1) drops below 30% of its predicted nominal value. […] This guideline, which is widely followed in clinical practice, is based mainly on the seminal study by Kerem et al., which identified FEV1 as the main predictor of mortality in CF patients using survival data from a cohort of Canadian CF patients.
#13 Prognostication and Risk Factors for Cystic Fibrosis via Automated Machine Learning
https://pmc.ncbi.nlm.nih.gov/articles/PMC6062529/
Accurate prediction of survival for cystic fibrosis (CF) patients is instrumental in establishing the optimal timing for referring patients with terminal respiratory failure for lung transplantation (LT). […] Current consensus guidelines, such as those recommended by the International Society for Heart and Lung Transplantation (ISHLT) consider referring a patient for LT evaluation when the forced expiratory volume (FEV1) drops below 30% of its predicted nominal value. […] While the FEV1 biomarker has been repeatedly confirmed to be a strong predictor of mortality in CF patients, recent studies have shown that the survival behavior of CF patients with FEV1<30% exhibits substantial heterogeneity, and that the improvements in CF prognosis over the past years have changed the epidemiology and demography of CF populations, which may have consequently altered the relevant CF risk factors.
#14 Prognostication and Risk Factors for Cystic Fibrosis via Automated Machine Learning | Scientific Reports
https://www.nature.com/articles/s41598-018-29523-2
Accurate prediction of survival for cystic fibrosis (CF) patients is instrumental in establishing the optimal timing for referring patients with terminal respiratory failure for lung transplantation (LT). […] While FEV1 is indeed a strong predictor of CF-related mortality, we hypothesized that the survival behavior of CF patients exhibits a lot more heterogeneity. […] Current consensus guidelines, such as those recommended by the International Society for Heart and Lung Transplantation (ISHLT), consider referring a patient for LT evaluation when the forced expiratory volume (FEV1) drops below 30% of its predicted nominal value. […] This guideline, which is widely followed in clinical practice, is based mainly on the seminal study by Kerem et al., which identified FEV1 as the main predictor of mortality in CF patients using survival data from a cohort of Canadian CF patients.
#15 Prognostication and Risk Factors for Cystic Fibrosis via Automated Machine Learning | Scientific Reports
https://www.nature.com/articles/s41598-018-29523-2
While the FEV1 biomarker has been repeatedly confirmed to be a strong predictor of mortality in CF patients, recent studies have shown that the survival behavior of CF patients with FEV1<30% exhibits substantial heterogeneity, and that the improvements in CF prognosis over the past years have changed the epidemiology and demography of CF populations. [...] However, none of the existing prognostic models that combine multiple risk factors have been able to demonstrate a significant improvement in mortality prediction compared to the FEV1 criterion in terms of the positive predictive value, which is a proximal measure for the rate of premature LT referral. [...] AutoPrognosis was capable of achieving a positive predictive value of 65% (95% CI: 61-69%), whereas that achieved by the FEV1 criterion recommended by the guidelines is as low as 48% (95% CI: 44-52%), at a fixed sensitivity level.
#16 Prognostication and Risk Factors for Cystic Fibrosis via Automated Machine Learning
https://pmc.ncbi.nlm.nih.gov/articles/PMC6062529/
Accurate prediction of survival for cystic fibrosis (CF) patients is instrumental in establishing the optimal timing for referring patients with terminal respiratory failure for lung transplantation (LT). […] Current consensus guidelines, such as those recommended by the International Society for Heart and Lung Transplantation (ISHLT) consider referring a patient for LT evaluation when the forced expiratory volume (FEV1) drops below 30% of its predicted nominal value. […] While the FEV1 biomarker has been repeatedly confirmed to be a strong predictor of mortality in CF patients, recent studies have shown that the survival behavior of CF patients with FEV1<30% exhibits substantial heterogeneity, and that the improvements in CF prognosis over the past years have changed the epidemiology and demography of CF populations, which may have consequently altered the relevant CF risk factors.
#17 Predictive 5-Year Survivorship Model of Cystic Fibrosis
https://pmc.ncbi.nlm.nih.gov/articles/PMC2198936/
The objective of this study was to create a 5-year survivorship model to identify key clinical features of cystic fibrosis. Such a model could help researchers and clinicians to evaluate therapies, improve the design of prospective studies, monitor practice patterns, counsel individual patients, and determine the best candidates for lung transplantation. […] The validated 5-year survivorship model included age, forced expiratory volume in 1 second as a percentage of predicted normal, gender, weight-for-age z score, pancreatic sufficiency, diabetes mellitus, Staphylococcus aureus infection, Burkerholderia cepacia infection, and annual number of acute pulmonary exacerbations. […] The model provides insight into the relative effect of each characteristic and underscores the importance of considering multiple clinical factors when assessing the likelihood of 5-year survival.
#18 Predictive 5-Year Survivorship Model of Cystic Fibrosis
https://pmc.ncbi.nlm.nih.gov/articles/PMC2198936/
The objective of this study was to create a 5-year survivorship model to identify key clinical features of cystic fibrosis. Such a model could help researchers and clinicians to evaluate therapies, improve the design of prospective studies, monitor practice patterns, counsel individual patients, and determine the best candidates for lung transplantation. […] The validated 5-year survivorship model included age, forced expiratory volume in 1 second as a percentage of predicted normal, gender, weight-for-age z score, pancreatic sufficiency, diabetes mellitus, Staphylococcus aureus infection, Burkerholderia cepacia infection, and annual number of acute pulmonary exacerbations. […] The model provides insight into the relative effect of each characteristic and underscores the importance of considering multiple clinical factors when assessing the likelihood of 5-year survival.
#19 Prognostication and Risk Factors for Cystic Fibrosis via Automated Machine Learning | Scientific Reports
https://www.nature.com/articles/s41598-018-29523-2
While the FEV1 biomarker has been repeatedly confirmed to be a strong predictor of mortality in CF patients, recent studies have shown that the survival behavior of CF patients with FEV1<30% exhibits substantial heterogeneity, and that the improvements in CF prognosis over the past years have changed the epidemiology and demography of CF populations. [...] However, none of the existing prognostic models that combine multiple risk factors have been able to demonstrate a significant improvement in mortality prediction compared to the FEV1 criterion in terms of the positive predictive value, which is a proximal measure for the rate of premature LT referral. [...] AutoPrognosis was capable of achieving a positive predictive value of 65% (95% CI: 61-69%), whereas that achieved by the FEV1 criterion recommended by the guidelines is as low as 48% (95% CI: 44-52%), at a fixed sensitivity level.
#20 Prognostication and Risk Factors for Cystic Fibrosis via Automated Machine Learning
https://pmc.ncbi.nlm.nih.gov/articles/PMC6062529/
AutoPrognosis was capable of achieving a positive predictive value of 65% (95% CI: 61-69%), whereas that achieved by the FEV1 criterion recommended by the guidelines is as low as 48% (95% CI: 44-52%), at a fixed sensitivity level. […] AutoPrognosis revealed new insight on the importance of variables reflecting disorders in pulmonary gas exchange in improving the precision and clinical usefulness of prognostic models. […] The clinical utility of AutoPrognosis is not limited to transplant referral; the predictions prompted by AutoPrognosis serve as granular risk scores that can quantify the severity of future outcomes and hence can be used for treatment planning, follow-up scheduling, or estimating the time at which a transplant would be needed in the future. […] The results in Table 4 show that at each cutoff threshold, the model learned via AutoPrognosis outperforms both the FEV1 criterion and the best performing competing model in terms of PPV, specificity, accuracy, and F1 scores.
#21 Prognostication and Risk Factors for Cystic Fibrosis via Automated Machine Learning | Scientific Reports
https://www.nature.com/articles/s41598-018-29523-2
AutoPrognosis revealed new insight on the importance of variables reflecting disorders in pulmonary gas exchange in improving the precision and clinical usefulness of prognostic models. […] The clinical utility of AutoPrognosis is not limited to transplant referral; the predictions prompted by AutoPrognosis serve as granular risk scores that can quantify the severity of future outcomes and hence can be used for treatment planning, follow-up scheduling, or estimating the time at which a transplant would be needed in the future.
#22 Prognostication and Risk Factors for Cystic Fibrosis via Automated Machine Learning
https://pmc.ncbi.nlm.nih.gov/articles/PMC6062529/
AutoPrognosis was capable of achieving a positive predictive value of 65% (95% CI: 61-69%), whereas that achieved by the FEV1 criterion recommended by the guidelines is as low as 48% (95% CI: 44-52%), at a fixed sensitivity level. […] AutoPrognosis revealed new insight on the importance of variables reflecting disorders in pulmonary gas exchange in improving the precision and clinical usefulness of prognostic models. […] The clinical utility of AutoPrognosis is not limited to transplant referral; the predictions prompted by AutoPrognosis serve as granular risk scores that can quantify the severity of future outcomes and hence can be used for treatment planning, follow-up scheduling, or estimating the time at which a transplant would be needed in the future. […] The results in Table 4 show that at each cutoff threshold, the model learned via AutoPrognosis outperforms both the FEV1 criterion and the best performing competing model in terms of PPV, specificity, accuracy, and F1 scores.
#23 Prognostication and Risk Factors for Cystic Fibrosis via Automated Machine Learning
https://pmc.ncbi.nlm.nih.gov/articles/PMC6062529/
By adopting the model learned by AutoPrognosis for LT referral, we expect that the fraction of patients populating the lung transplant waiting list who are truly at risk would rise from 48% to 65%. […] AutoPrognosis was able to learn a prediction rule that carefully combines spirometric and gas exchange variables in order to come up with a precise lung transplant referral criterion that accurately disentangles patients who are truly at risk from those who do not need a lung in the near future.
#24 MODELING CYSTIC FIBROSIS PATIENT PROGNOSIS: NOMOGRAMS TO PREDICT LUNG TRANSPLANTATION AND SURVIVAL PRIOR TO HIGHLY EFFECTIVE MODULATOR THERAPY | medRxiv
https://www.medrxiv.org/content/10.1101/2023.09.25.23296112v1.full-text
The purpose of this study is to develop models to predict probability of lung transplant or death and time to lung transplant or death of CF patients in the United States prior to HEMT, and translate these into nomograms. […] The logistic and Cox regression models were internally validated with accuracy of prediction at 89% and 92%, respectively. […] The logistic regression model predicting probability of lung transplant/death identified FEV1pp, BMI, age of diagnosis, age, NumPulmExacerbation, race, sex, CFRD, corticosteroid therapy, B. cepacia, P. aeruginosa, S. aureus, MRSA, pancreatic enzyme usage, insurance status, and consecutive high dose ibuprofen use for at least 4 years. […] Similarly the following characteristics were identified by the Cox regression modeling time to lung transplant/death: FEV1pp, BMI, age of diagnosis, age, NumPulmExacerbation, race, sex, CFRD, corticosteroid therapy, B. cepacia, P. aeruginosa, S. aureus, MRSA, pancreatic enzyme use, and consecutive high dose ibuprofen use for at least 4 years. […] We have developed and internally validated nomograms to predict probability of lung transplant/death and probability of lung transplant-free survival in 2 year and 5 years.
#25 MODELING CYSTIC FIBROSIS PATIENT PROGNOSIS: NOMOGRAMS TO PREDICT LUNG TRANSPLANTATION AND SURVIVAL PRIOR TO HIGHLY EFFECTIVE MODULATOR THERAPY | medRxiv
https://www.medrxiv.org/content/10.1101/2023.09.25.23296112v1.full-text
The duration of time a person with cystic fibrosis (pwCF) spends on the lung transplant waitlist is dependent on waitlist and post-transplant survival probabilities and can extend up to 2 years. […] Understanding the characteristics involved with lung transplant and survival prognoses may help guide decision making by the patient, the referring CF Center and the transplant team. […] Predictors significant (p 0.05) in the final logistic regression model predicting probability of lung transplant/death were: FEV1 (% predicted), BMI, age of diagnosis, age, number of pulmonary exacerbations, race, sex, CF-related diabetes (CFRD), corticosteroid use, infections with B. cepacia, P. aeruginosa, S. aureus, MRSA, pancreatic enzyme use, insurance status, and consecutive ibuprofen use for at least 4 years.
#26 MODELING CYSTIC FIBROSIS PATIENT PROGNOSIS: NOMOGRAMS TO PREDICT LUNG TRANSPLANTATION AND SURVIVAL PRIOR TO HIGHLY EFFECTIVE MODULATOR THERAPY | medRxiv
https://www.medrxiv.org/content/10.1101/2023.09.25.23296112v1.full-text
The purpose of this study is to develop models to predict probability of lung transplant or death and time to lung transplant or death of CF patients in the United States prior to HEMT, and translate these into nomograms. […] The logistic and Cox regression models were internally validated with accuracy of prediction at 89% and 92%, respectively. […] The logistic regression model predicting probability of lung transplant/death identified FEV1pp, BMI, age of diagnosis, age, NumPulmExacerbation, race, sex, CFRD, corticosteroid therapy, B. cepacia, P. aeruginosa, S. aureus, MRSA, pancreatic enzyme usage, insurance status, and consecutive high dose ibuprofen use for at least 4 years. […] Similarly the following characteristics were identified by the Cox regression modeling time to lung transplant/death: FEV1pp, BMI, age of diagnosis, age, NumPulmExacerbation, race, sex, CFRD, corticosteroid therapy, B. cepacia, P. aeruginosa, S. aureus, MRSA, pancreatic enzyme use, and consecutive high dose ibuprofen use for at least 4 years. […] We have developed and internally validated nomograms to predict probability of lung transplant/death and probability of lung transplant-free survival in 2 year and 5 years.
#27 MODELING CYSTIC FIBROSIS PATIENT PROGNOSIS: NOMOGRAMS TO PREDICT LUNG TRANSPLANTATION AND SURVIVAL PRIOR TO HIGHLY EFFECTIVE MODULATOR THERAPY | medRxiv
https://www.medrxiv.org/content/10.1101/2023.09.25.23296112v1.full-text
The final Cox regression model predicting time to lung transplant identified these predictors as significant FEV1 (% predicted), BMI, age of diagnosis, age, number of pulmonary exacerbations, race, sex, CF-related diabetes (CFRD), corticosteroid use, infections with B. cepacia, P. aeruginosa, S. aureus, MRSA, pancreatic enzyme use, and consecutive ibuprofen use for at least 4 years. […] The models are translated into nomograms to simplify investigation of how various characteristics relate to lung transplant and survival prognosis individuals with CF not receiving highly effective CFTR modulator therapy. […] Identifying the clinical characteristics involved in predicting when an individual will require a lung transplant may lead to a more personalized approach to transplant referral and listing.
#28 MODELING CYSTIC FIBROSIS PATIENT PROGNOSIS: NOMOGRAMS TO PREDICT LUNG TRANSPLANTATION AND SURVIVAL PRIOR TO HIGHLY EFFECTIVE MODULATOR THERAPY | medRxiv
https://www.medrxiv.org/content/10.1101/2023.09.25.23296112v1.full-text
The purpose of this study is to develop models to predict probability of lung transplant or death and time to lung transplant or death of CF patients in the United States prior to HEMT, and translate these into nomograms. […] The logistic and Cox regression models were internally validated with accuracy of prediction at 89% and 92%, respectively. […] The logistic regression model predicting probability of lung transplant/death identified FEV1pp, BMI, age of diagnosis, age, NumPulmExacerbation, race, sex, CFRD, corticosteroid therapy, B. cepacia, P. aeruginosa, S. aureus, MRSA, pancreatic enzyme usage, insurance status, and consecutive high dose ibuprofen use for at least 4 years. […] Similarly the following characteristics were identified by the Cox regression modeling time to lung transplant/death: FEV1pp, BMI, age of diagnosis, age, NumPulmExacerbation, race, sex, CFRD, corticosteroid therapy, B. cepacia, P. aeruginosa, S. aureus, MRSA, pancreatic enzyme use, and consecutive high dose ibuprofen use for at least 4 years. […] We have developed and internally validated nomograms to predict probability of lung transplant/death and probability of lung transplant-free survival in 2 year and 5 years.
#29 MODELING CYSTIC FIBROSIS PATIENT PROGNOSIS: NOMOGRAMS TO PREDICT LUNG TRANSPLANTATION AND SURVIVAL PRIOR TO HIGHLY EFFECTIVE MODULATOR THERAPY | medRxiv
https://www.medrxiv.org/content/10.1101/2023.09.25.23296112v1.full-text
The purpose of this study is to develop models to predict probability of lung transplant or death and time to lung transplant or death of CF patients in the United States prior to HEMT, and translate these into nomograms. […] The logistic and Cox regression models were internally validated with accuracy of prediction at 89% and 92%, respectively. […] The logistic regression model predicting probability of lung transplant/death identified FEV1pp, BMI, age of diagnosis, age, NumPulmExacerbation, race, sex, CFRD, corticosteroid therapy, B. cepacia, P. aeruginosa, S. aureus, MRSA, pancreatic enzyme usage, insurance status, and consecutive high dose ibuprofen use for at least 4 years. […] Similarly the following characteristics were identified by the Cox regression modeling time to lung transplant/death: FEV1pp, BMI, age of diagnosis, age, NumPulmExacerbation, race, sex, CFRD, corticosteroid therapy, B. cepacia, P. aeruginosa, S. aureus, MRSA, pancreatic enzyme use, and consecutive high dose ibuprofen use for at least 4 years. […] We have developed and internally validated nomograms to predict probability of lung transplant/death and probability of lung transplant-free survival in 2 year and 5 years.
#30 Prognostication and Risk Factors for Cystic Fibrosis via Automated Machine Learning | Scientific Reports
https://www.nature.com/articles/s41598-018-29523-2
Accurate prediction of survival for cystic fibrosis (CF) patients is instrumental in establishing the optimal timing for referring patients with terminal respiratory failure for lung transplantation (LT). […] While FEV1 is indeed a strong predictor of CF-related mortality, we hypothesized that the survival behavior of CF patients exhibits a lot more heterogeneity. […] Current consensus guidelines, such as those recommended by the International Society for Heart and Lung Transplantation (ISHLT), consider referring a patient for LT evaluation when the forced expiratory volume (FEV1) drops below 30% of its predicted nominal value. […] This guideline, which is widely followed in clinical practice, is based mainly on the seminal study by Kerem et al., which identified FEV1 as the main predictor of mortality in CF patients using survival data from a cohort of Canadian CF patients.
#31 Predictive 5-Year Survivorship Model of Cystic Fibrosis
https://pmc.ncbi.nlm.nih.gov/articles/PMC2198936/
The new, validated model that we developed identified the additional covariates needed for a predictive survivorship model of cystic fibrosis. […] Infection with B. cepacia had the largest effect of any model variable for predicting 5-year survivorship. […] Our model demonstrated a large negative effect of diabetes on 5-year survival, independent of other covariates. […] Our survivorship model of cystic fibrosis may have similar widespread applications. It may help to improve study design by providing a way to select patients with equivalent survival predictions for control and experimental arms of prospective studies.
#32 MODELING CYSTIC FIBROSIS PATIENT PROGNOSIS: NOMOGRAMS TO PREDICT LUNG TRANSPLANTATION AND SURVIVAL PRIOR TO HIGHLY EFFECTIVE MODULATOR THERAPY | medRxiv
https://www.medrxiv.org/content/10.1101/2023.09.25.23296112v1.full-text
The duration of time a person with cystic fibrosis (pwCF) spends on the lung transplant waitlist is dependent on waitlist and post-transplant survival probabilities and can extend up to 2 years. […] Understanding the characteristics involved with lung transplant and survival prognoses may help guide decision making by the patient, the referring CF Center and the transplant team. […] Predictors significant (p 0.05) in the final logistic regression model predicting probability of lung transplant/death were: FEV1 (% predicted), BMI, age of diagnosis, age, number of pulmonary exacerbations, race, sex, CF-related diabetes (CFRD), corticosteroid use, infections with B. cepacia, P. aeruginosa, S. aureus, MRSA, pancreatic enzyme use, insurance status, and consecutive ibuprofen use for at least 4 years.
#33 Predictive 5-Year Survivorship Model of Cystic Fibrosis
https://pmc.ncbi.nlm.nih.gov/articles/PMC2198936/
The objective of this study was to create a 5-year survivorship model to identify key clinical features of cystic fibrosis. Such a model could help researchers and clinicians to evaluate therapies, improve the design of prospective studies, monitor practice patterns, counsel individual patients, and determine the best candidates for lung transplantation. […] The validated 5-year survivorship model included age, forced expiratory volume in 1 second as a percentage of predicted normal, gender, weight-for-age z score, pancreatic sufficiency, diabetes mellitus, Staphylococcus aureus infection, Burkerholderia cepacia infection, and annual number of acute pulmonary exacerbations. […] The model provides insight into the relative effect of each characteristic and underscores the importance of considering multiple clinical factors when assessing the likelihood of 5-year survival.
#34 Prognostication and Risk Factors for Cystic Fibrosis via Automated Machine Learning | Scientific Reports
https://www.nature.com/articles/s41598-018-29523-2
AutoPrognosis revealed new insight on the importance of variables reflecting disorders in pulmonary gas exchange in improving the precision and clinical usefulness of prognostic models. […] The clinical utility of AutoPrognosis is not limited to transplant referral; the predictions prompted by AutoPrognosis serve as granular risk scores that can quantify the severity of future outcomes and hence can be used for treatment planning, follow-up scheduling, or estimating the time at which a transplant would be needed in the future.
#35 Predictive 5-Year Survivorship Model of Cystic Fibrosis
https://pmc.ncbi.nlm.nih.gov/articles/PMC2198936/
The new, validated model that we developed identified the additional covariates needed for a predictive survivorship model of cystic fibrosis. […] Infection with B. cepacia had the largest effect of any model variable for predicting 5-year survivorship. […] Our model demonstrated a large negative effect of diabetes on 5-year survival, independent of other covariates. […] Our survivorship model of cystic fibrosis may have similar widespread applications. It may help to improve study design by providing a way to select patients with equivalent survival predictions for control and experimental arms of prospective studies.
#36 Factors Affecting Prognosis and Prediction of Outcome in Cystic Fibrosis Lung Disease | IntechOpen
https://www.intechopen.com/chapters/48628
Cystic fibrosis-related diabetes (CFRD) has a significant impact upon clinical parameters of disease and thus impacts upon prognosis. […] CFRD has been shown to have a negative prognostic effect, with increased mortality seen in association with poorer nutritional status and greater severity of lung disease. […] The most sensitive way of predicting prognosis currently remains a multifaceted approach, including several markers of disease and the use of all factors and a composite clinical prediction tool is suggested to stratify patient risk.
#37 Predictive 5-Year Survivorship Model of Cystic Fibrosis
https://pmc.ncbi.nlm.nih.gov/articles/PMC2198936/
The objective of this study was to create a 5-year survivorship model to identify key clinical features of cystic fibrosis. Such a model could help researchers and clinicians to evaluate therapies, improve the design of prospective studies, monitor practice patterns, counsel individual patients, and determine the best candidates for lung transplantation. […] The validated 5-year survivorship model included age, forced expiratory volume in 1 second as a percentage of predicted normal, gender, weight-for-age z score, pancreatic sufficiency, diabetes mellitus, Staphylococcus aureus infection, Burkerholderia cepacia infection, and annual number of acute pulmonary exacerbations. […] The model provides insight into the relative effect of each characteristic and underscores the importance of considering multiple clinical factors when assessing the likelihood of 5-year survival.
#38 MODELING CYSTIC FIBROSIS PATIENT PROGNOSIS: NOMOGRAMS TO PREDICT LUNG TRANSPLANTATION AND SURVIVAL PRIOR TO HIGHLY EFFECTIVE MODULATOR THERAPY | medRxiv
https://www.medrxiv.org/content/10.1101/2023.09.25.23296112v1.full-text
The duration of time a person with cystic fibrosis (pwCF) spends on the lung transplant waitlist is dependent on waitlist and post-transplant survival probabilities and can extend up to 2 years. […] Understanding the characteristics involved with lung transplant and survival prognoses may help guide decision making by the patient, the referring CF Center and the transplant team. […] Predictors significant (p 0.05) in the final logistic regression model predicting probability of lung transplant/death were: FEV1 (% predicted), BMI, age of diagnosis, age, number of pulmonary exacerbations, race, sex, CF-related diabetes (CFRD), corticosteroid use, infections with B. cepacia, P. aeruginosa, S. aureus, MRSA, pancreatic enzyme use, insurance status, and consecutive ibuprofen use for at least 4 years.
#39 Predictive 5-Year Survivorship Model of Cystic Fibrosis
https://pmc.ncbi.nlm.nih.gov/articles/PMC2198936/
The objective of this study was to create a 5-year survivorship model to identify key clinical features of cystic fibrosis. Such a model could help researchers and clinicians to evaluate therapies, improve the design of prospective studies, monitor practice patterns, counsel individual patients, and determine the best candidates for lung transplantation. […] The validated 5-year survivorship model included age, forced expiratory volume in 1 second as a percentage of predicted normal, gender, weight-for-age z score, pancreatic sufficiency, diabetes mellitus, Staphylococcus aureus infection, Burkerholderia cepacia infection, and annual number of acute pulmonary exacerbations. […] The model provides insight into the relative effect of each characteristic and underscores the importance of considering multiple clinical factors when assessing the likelihood of 5-year survival.
#40 Factors Affecting Prognosis and Prediction of Outcome in Cystic Fibrosis Lung Disease | IntechOpen
https://www.intechopen.com/chapters/48628
Cystic fibrosis (CF) is a multisystem disorder with a significantly shortened life expectancy with the major cause of mortality related to lung disease. […] While the life expectancy in CF is still short, the median age of death and predicted survival age are continually increasing. […] Life expectancy in patients with cystic fibrosis is in a constant state of change. […] The predicted median survival of people born with CF today continues to rise. […] Numerous factors have contributed to the changing statistics in CF prognosis. […] In this chapter, we review the evidence regarding prognosis based on key clinical and demographic parameters, and the biomarkers and prediction tools that may be used to predict outcome. […] CFTR genotype may be useful as an initial measure of prognosis in early CF diagnosis when it is often the only available information about the disease.
#41 Predictive 5-Year Survivorship Model of Cystic Fibrosis
https://pmc.ncbi.nlm.nih.gov/articles/PMC2198936/
The objective of this study was to create a 5-year survivorship model to identify key clinical features of cystic fibrosis. Such a model could help researchers and clinicians to evaluate therapies, improve the design of prospective studies, monitor practice patterns, counsel individual patients, and determine the best candidates for lung transplantation. […] The validated 5-year survivorship model included age, forced expiratory volume in 1 second as a percentage of predicted normal, gender, weight-for-age z score, pancreatic sufficiency, diabetes mellitus, Staphylococcus aureus infection, Burkerholderia cepacia infection, and annual number of acute pulmonary exacerbations. […] The model provides insight into the relative effect of each characteristic and underscores the importance of considering multiple clinical factors when assessing the likelihood of 5-year survival.
#42 Predictive 5-Year Survivorship Model of Cystic Fibrosis
https://pmc.ncbi.nlm.nih.gov/articles/PMC2198936/
The objective of this study was to create a 5-year survivorship model to identify key clinical features of cystic fibrosis. Such a model could help researchers and clinicians to evaluate therapies, improve the design of prospective studies, monitor practice patterns, counsel individual patients, and determine the best candidates for lung transplantation. […] The validated 5-year survivorship model included age, forced expiratory volume in 1 second as a percentage of predicted normal, gender, weight-for-age z score, pancreatic sufficiency, diabetes mellitus, Staphylococcus aureus infection, Burkerholderia cepacia infection, and annual number of acute pulmonary exacerbations. […] The model provides insight into the relative effect of each characteristic and underscores the importance of considering multiple clinical factors when assessing the likelihood of 5-year survival.
#43 MODELING CYSTIC FIBROSIS PATIENT PROGNOSIS: NOMOGRAMS TO PREDICT LUNG TRANSPLANTATION AND SURVIVAL PRIOR TO HIGHLY EFFECTIVE MODULATOR THERAPY | medRxiv
https://www.medrxiv.org/content/10.1101/2023.09.25.23296112v1.full-text
The duration of time a person with cystic fibrosis (pwCF) spends on the lung transplant waitlist is dependent on waitlist and post-transplant survival probabilities and can extend up to 2 years. […] Understanding the characteristics involved with lung transplant and survival prognoses may help guide decision making by the patient, the referring CF Center and the transplant team. […] Predictors significant (p 0.05) in the final logistic regression model predicting probability of lung transplant/death were: FEV1 (% predicted), BMI, age of diagnosis, age, number of pulmonary exacerbations, race, sex, CF-related diabetes (CFRD), corticosteroid use, infections with B. cepacia, P. aeruginosa, S. aureus, MRSA, pancreatic enzyme use, insurance status, and consecutive ibuprofen use for at least 4 years.
#44 Cystic Fibrosis: Causes, Symptoms & Treatment
https://my.clevelandclinic.org/health/diseases/9358-cystic-fibrosis
Yes, cystic fibrosis can be life-threatening. Lung damage from thick mucus and frequent lung infections is the most common cause of death. […] Experts predict the life expectancy of someone born with cystic fibrosis in the past few years is around 50 years old. Improvements in treatment in recent years have increased this from a few years ago, when life expectancy was between 30 and 40 years old. […] People with atypical cystic fibrosis tend to have longer life expectancies than those with classic CF. […] Theres no cure for CF. You or your child will need lifelong treatments to manage it. This includes treating infections, maintaining nutrition and seeing a CF specialist frequently. But new treatment methods help children who have CF live well into adulthood and have a better quality of life. […] Treatments work best when CF is diagnosed early, which is why newborn screening is so important. The addition of CFTR modulators at a young age may improve long-term health and increase life expectancy even more in the future.
#45 MODELING CYSTIC FIBROSIS PATIENT PROGNOSIS: NOMOGRAMS TO PREDICT LUNG TRANSPLANTATION AND SURVIVAL PRIOR TO HIGHLY EFFECTIVE MODULATOR THERAPY | medRxiv
https://www.medrxiv.org/content/10.1101/2023.09.25.23296112v1.full-text
The duration of time a person with cystic fibrosis (pwCF) spends on the lung transplant waitlist is dependent on waitlist and post-transplant survival probabilities and can extend up to 2 years. […] Understanding the characteristics involved with lung transplant and survival prognoses may help guide decision making by the patient, the referring CF Center and the transplant team. […] Predictors significant (p 0.05) in the final logistic regression model predicting probability of lung transplant/death were: FEV1 (% predicted), BMI, age of diagnosis, age, number of pulmonary exacerbations, race, sex, CF-related diabetes (CFRD), corticosteroid use, infections with B. cepacia, P. aeruginosa, S. aureus, MRSA, pancreatic enzyme use, insurance status, and consecutive ibuprofen use for at least 4 years.
#46 MODELING CYSTIC FIBROSIS PATIENT PROGNOSIS: NOMOGRAMS TO PREDICT LUNG TRANSPLANTATION AND SURVIVAL PRIOR TO HIGHLY EFFECTIVE MODULATOR THERAPY | medRxiv
https://www.medrxiv.org/content/10.1101/2023.09.25.23296112v1.full-text
The purpose of this study is to develop models to predict probability of lung transplant or death and time to lung transplant or death of CF patients in the United States prior to HEMT, and translate these into nomograms. […] The logistic and Cox regression models were internally validated with accuracy of prediction at 89% and 92%, respectively. […] The logistic regression model predicting probability of lung transplant/death identified FEV1pp, BMI, age of diagnosis, age, NumPulmExacerbation, race, sex, CFRD, corticosteroid therapy, B. cepacia, P. aeruginosa, S. aureus, MRSA, pancreatic enzyme usage, insurance status, and consecutive high dose ibuprofen use for at least 4 years. […] Similarly the following characteristics were identified by the Cox regression modeling time to lung transplant/death: FEV1pp, BMI, age of diagnosis, age, NumPulmExacerbation, race, sex, CFRD, corticosteroid therapy, B. cepacia, P. aeruginosa, S. aureus, MRSA, pancreatic enzyme use, and consecutive high dose ibuprofen use for at least 4 years. […] We have developed and internally validated nomograms to predict probability of lung transplant/death and probability of lung transplant-free survival in 2 year and 5 years.
#47 A prognostic model for the prediction of survival in cystic fibrosis. | Thorax
https://thorax.bmj.com/content/52/4/313
A prognostic model for the prediction of survival in cystic fibrosis. […] Optimising the timing of transplantation depends upon an accurate prediction of survival, but while current criteria give some guidance to this, they are not based upon statistically derived prognostic models. […] The significant variables were then subject to time dependent multivariate Cox regression analysis to generate a prognostic model. […] These variables, when combined into a prognostic index, accurately predicted one year survival in the study population and in the cohort recruited since 1988. […] This prognostic index may prove valuable in predicting prognosis in other cohorts with cystic fibrosis and thereby improve the timing of transplantation.
#48 Prognostication and Risk Factors for Cystic Fibrosis via Automated Machine Learning | Scientific Reports
https://www.nature.com/articles/s41598-018-29523-2
Accurate prediction of survival for cystic fibrosis (CF) patients is instrumental in establishing the optimal timing for referring patients with terminal respiratory failure for lung transplantation (LT). […] While FEV1 is indeed a strong predictor of CF-related mortality, we hypothesized that the survival behavior of CF patients exhibits a lot more heterogeneity. […] Current consensus guidelines, such as those recommended by the International Society for Heart and Lung Transplantation (ISHLT), consider referring a patient for LT evaluation when the forced expiratory volume (FEV1) drops below 30% of its predicted nominal value. […] This guideline, which is widely followed in clinical practice, is based mainly on the seminal study by Kerem et al., which identified FEV1 as the main predictor of mortality in CF patients using survival data from a cohort of Canadian CF patients.
#49 Prognostication and Risk Factors for Cystic Fibrosis via Automated Machine Learning
https://pmc.ncbi.nlm.nih.gov/articles/PMC6062529/
Accurate prediction of survival for cystic fibrosis (CF) patients is instrumental in establishing the optimal timing for referring patients with terminal respiratory failure for lung transplantation (LT). […] Current consensus guidelines, such as those recommended by the International Society for Heart and Lung Transplantation (ISHLT) consider referring a patient for LT evaluation when the forced expiratory volume (FEV1) drops below 30% of its predicted nominal value. […] While the FEV1 biomarker has been repeatedly confirmed to be a strong predictor of mortality in CF patients, recent studies have shown that the survival behavior of CF patients with FEV1<30% exhibits substantial heterogeneity, and that the improvements in CF prognosis over the past years have changed the epidemiology and demography of CF populations, which may have consequently altered the relevant CF risk factors.
#50 Prognostication and Risk Factors for Cystic Fibrosis via Automated Machine Learning
https://pmc.ncbi.nlm.nih.gov/articles/PMC6062529/
By adopting the model learned by AutoPrognosis for LT referral, we expect that the fraction of patients populating the lung transplant waiting list who are truly at risk would rise from 48% to 65%. […] AutoPrognosis was able to learn a prediction rule that carefully combines spirometric and gas exchange variables in order to come up with a precise lung transplant referral criterion that accurately disentangles patients who are truly at risk from those who do not need a lung in the near future.
#51 Prognostication and Risk Factors for Cystic Fibrosis via Automated Machine Learning | Scientific Reports
https://www.nature.com/articles/s41598-018-29523-2
AutoPrognosis revealed new insight on the importance of variables reflecting disorders in pulmonary gas exchange in improving the precision and clinical usefulness of prognostic models. […] The clinical utility of AutoPrognosis is not limited to transplant referral; the predictions prompted by AutoPrognosis serve as granular risk scores that can quantify the severity of future outcomes and hence can be used for treatment planning, follow-up scheduling, or estimating the time at which a transplant would be needed in the future.
#52 Prognostication and Risk Factors for Cystic Fibrosis via Automated Machine Learning
https://pmc.ncbi.nlm.nih.gov/articles/PMC6062529/
AutoPrognosis was capable of achieving a positive predictive value of 65% (95% CI: 61-69%), whereas that achieved by the FEV1 criterion recommended by the guidelines is as low as 48% (95% CI: 44-52%), at a fixed sensitivity level. […] AutoPrognosis revealed new insight on the importance of variables reflecting disorders in pulmonary gas exchange in improving the precision and clinical usefulness of prognostic models. […] The clinical utility of AutoPrognosis is not limited to transplant referral; the predictions prompted by AutoPrognosis serve as granular risk scores that can quantify the severity of future outcomes and hence can be used for treatment planning, follow-up scheduling, or estimating the time at which a transplant would be needed in the future. […] The results in Table 4 show that at each cutoff threshold, the model learned via AutoPrognosis outperforms both the FEV1 criterion and the best performing competing model in terms of PPV, specificity, accuracy, and F1 scores.
#53 Predictive 5-Year Survivorship Model of Cystic Fibrosis
https://pmc.ncbi.nlm.nih.gov/articles/PMC2198936/
The new, validated model that we developed identified the additional covariates needed for a predictive survivorship model of cystic fibrosis. […] Infection with B. cepacia had the largest effect of any model variable for predicting 5-year survivorship. […] Our model demonstrated a large negative effect of diabetes on 5-year survival, independent of other covariates. […] Our survivorship model of cystic fibrosis may have similar widespread applications. It may help to improve study design by providing a way to select patients with equivalent survival predictions for control and experimental arms of prospective studies.
#54 Cystic Fibrosis: Causes, Symptoms & Treatment
https://my.clevelandclinic.org/health/diseases/9358-cystic-fibrosis
Yes, cystic fibrosis can be life-threatening. Lung damage from thick mucus and frequent lung infections is the most common cause of death. […] Experts predict the life expectancy of someone born with cystic fibrosis in the past few years is around 50 years old. Improvements in treatment in recent years have increased this from a few years ago, when life expectancy was between 30 and 40 years old. […] People with atypical cystic fibrosis tend to have longer life expectancies than those with classic CF. […] Theres no cure for CF. You or your child will need lifelong treatments to manage it. This includes treating infections, maintaining nutrition and seeing a CF specialist frequently. But new treatment methods help children who have CF live well into adulthood and have a better quality of life. […] Treatments work best when CF is diagnosed early, which is why newborn screening is so important. The addition of CFTR modulators at a young age may improve long-term health and increase life expectancy even more in the future.
#55 State of progress in treating cystic fibrosis respiratory disease | BMC Medicine | Full Text
https://bmcmedicine.biomedcentral.com/articles/10.1186/1741-7015-10-88
The last two decades have seen a remarkable addition of effective medications and therapies to the regimen for treating CF lung disease. […] There are two main areas we need to focus on for there to be further improvement. First, we must develop new, more effective medications. […] Another approach would be to change the formulation of current medications to reduce the treatment burden, and hopefully increase adherence to the medication. […] It is the CFTR modulators that have generated the most excitement. […] New medications will have to be developed for each type of mutation and having two new medications in active investigation for F508del, by far the most common CFTR mutation, brings us that much closer to making a huge difference in the lives of our patients.
#56 State of progress in treating cystic fibrosis respiratory disease | BMC Medicine | Full Text
https://bmcmedicine.biomedcentral.com/articles/10.1186/1741-7015-10-88
The last two decades have seen a remarkable addition of effective medications and therapies to the regimen for treating CF lung disease. […] There are two main areas we need to focus on for there to be further improvement. First, we must develop new, more effective medications. […] Another approach would be to change the formulation of current medications to reduce the treatment burden, and hopefully increase adherence to the medication. […] It is the CFTR modulators that have generated the most excitement. […] New medications will have to be developed for each type of mutation and having two new medications in active investigation for F508del, by far the most common CFTR mutation, brings us that much closer to making a huge difference in the lives of our patients.
#57 State of progress in treating cystic fibrosis respiratory disease | BMC Medicine | Full Text
https://bmcmedicine.biomedcentral.com/articles/10.1186/1741-7015-10-88
Since the discovery of the gene associated with cystic fibrosis (CF), there has been tremendous progress in the care of patients with this disease. […] the median age of predicted survival now approaches 40 years. […] This is the result of a combination of factors including the development of a sophisticated comprehensive care network and the use of several new medications developed specifically for the treatment of CF-related disease. […] respiratory failure accounts for 80% of mortality in CF. […] These therapies have improved the overall health of patients with CF and they are clearly part of the reason that expected survival has increased. […] However, these therapies do not offer a cure and they mainly treat downstream complications of the pathophysiology of CF lung disease, meaning that patients continue to suffer the morbidity associated with chronic airways infection and predicted survival still lags well below what is normal.
#58 Factors Affecting Prognosis and Prediction of Outcome in Cystic Fibrosis Lung Disease | IntechOpen
https://www.intechopen.com/chapters/48628
Cystic fibrosis-related diabetes (CFRD) has a significant impact upon clinical parameters of disease and thus impacts upon prognosis. […] CFRD has been shown to have a negative prognostic effect, with increased mortality seen in association with poorer nutritional status and greater severity of lung disease. […] The most sensitive way of predicting prognosis currently remains a multifaceted approach, including several markers of disease and the use of all factors and a composite clinical prediction tool is suggested to stratify patient risk.
#59 What Is the Prognosis for People with Cystic Fibrosis?
https://www.healthline.com/health/cystic-fibrosis/cystic-fibrosis-prognosis
There is no cure for CF, but advances in diagnosis and treatment may help people live longer than they would have in previous generations. These days, people with CF may go on to earn college degrees, start careers, and live full, happy lives and the potential for longer life expectancy continues to extend each year.
#60 What Is the Prognosis for People with Cystic Fibrosis?
https://www.healthline.com/health/cystic-fibrosis/cystic-fibrosis-prognosis
According to the Cystic Fibrosis Foundations 2019 Annual Report, the mean life expectancy for people with CF is 48.4 years. […] Life expectancy has increased greatly over the years thanks to earlier treatment and improved therapies. […] CF patient registry data suggests that people born with CF between 2018 and 2022 may live to age 56 years or older. And half of those born between 2021 and today may have a life expectancy of up to 65 years. […] Overall, people who address their CF symptoms, maintain a healthy lifestyle, avoid infections and complications, and take care of their mental health may have a good quality of life. […] Half of people with CF may live past 40 years old. How long you may live depends on factors such as what stage of CF you are in and any other health complications you may have.
#61 What Is the Prognosis for People with Cystic Fibrosis?
https://www.healthline.com/health/cystic-fibrosis/cystic-fibrosis-prognosis
According to the Cystic Fibrosis Foundations 2019 Annual Report, the mean life expectancy for people with CF is 48.4 years. […] Life expectancy has increased greatly over the years thanks to earlier treatment and improved therapies. […] CF patient registry data suggests that people born with CF between 2018 and 2022 may live to age 56 years or older. And half of those born between 2021 and today may have a life expectancy of up to 65 years. […] Overall, people who address their CF symptoms, maintain a healthy lifestyle, avoid infections and complications, and take care of their mental health may have a good quality of life. […] Half of people with CF may live past 40 years old. How long you may live depends on factors such as what stage of CF you are in and any other health complications you may have.