302 A retrospective review of renal function and intravenous (IV) antibiotic use in an adult UK cystic fibrosis centre

Ewence, Alexandra; Malone, Sara; Nutbourne, A.; Higton, A.; Orchard, C.

doi:10.1016/s1569-1993(17)30641-0

Cited by 3 publications

(3 citation statements)

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“…the FEV 1 % predicted 1 year before baseline) is as predictive as the FEV 1 measurements at baseline. Variables reflecting pulmonary disorders resulting from bacterial infections (intravenous antibiotic courses in hospital 56 ) were the second most predictive in terms of the AUC-ROC performance. The most predictive complications were found to be diabetes and CF-related diabetes.…”

Section: Resultsmentioning

confidence: 99%

Prognostication and Risk Factors for Cystic Fibrosis via Automated Machine Learning

Alaa

Schaar

2018

Sci Rep

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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 practice considers referring patients for LT evaluation once the forced expiratory volume (FEV1) drops below 30% of its predicted nominal value. 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. To this end, we developed an algorithmic framework, which we call AutoPrognosis, that leverages the power of machine learning to automate the process of constructing clinical prognostic models, and used it to build a prognostic model for CF using data from a contemporary cohort that involved 99% of the CF population in the UK. AutoPrognosis uses Bayesian optimization techniques to automate the process of configuring ensembles of machine learning pipelines, which involve imputation, feature processing, classification and calibration algorithms. Because it is automated, it can be used by clinical researchers to build prognostic models without the need for in-depth knowledge of machine learning. Our experiments revealed that the accuracy of the model learned by AutoPrognosis is superior to that of existing guidelines and other competing models.

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Section: Resultsmentioning

confidence: 99%

Prognostication and Risk Factors for Cystic Fibrosis via Automated Machine Learning

Alaa

Schaar

2018

Sci Rep

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“…Likewise, not everyone who does develop CF related diabetes experiences kidney dysfunction as a result 8 . However, every person living with CF does experience elevated risk for renal impairment, especially as they grow older 9 . Several factors can contribute to kidney disease in people aging with CF.…”

Section: An Overview Of Scholarship On Cf and Kidney Diseasementioning

confidence: 99%

Original parts: Aging and reckoning with cystic fibrosis related kidney disease

Nowakowski¹

2018

Patient Experience Journal

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Kidney disease increasingly impacts people with cystic fibrosis (CF) as adult patients continue to survive longer. Yet the literature on CF related kidney disease focuses little on amplifying the voices of people aging with the condition. This article presents perspectives on CF related kidney disease from a medical sociologist who is themselves managing these issues. It (1) gives an overview of relevant literature and trends in epidemiological data on kidney disease and CF, (2) details the author's own process of adjusting to progressive changes in renal function, and (3) outlines opportunities for clinicians to make a positive impact for patients by centering their voices.

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“…These may include generalized chronic kidney disease (abnormal kidney function) and/or individual renal issues such as: mucus casts in the urine, stones in the tubules, pain in the flanks, fluid retention in the lower body, bacterial infections in the kidneys, nephritis from medication sensitivities, and wasting of electrolytes like potassium ( 4 ). Some of these issues may be specific to particular CF management strategies, such as antibiotics—and may not occur in all people treated with those approaches ( 5 ).…”

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confidence: 99%

Cystic Fibrosis Kidney Disease: 10 Tips for Clinicians

Nowakowski

2018

Front. Med.

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Increased longevity in people with cystic fibrosis (CF) means that more people are surviving long enough to develop kidney complications. Nephrologists and their colleagues now face a steep learning curve as many of them encounter patients with CF related kidney disease (CFKD) for the first time. This article presents perspectives from a medical sociologist with CF on what renal health professionals should know about people with CFKD. It outlines challenges that people with CFKD as they age, framing these struggles as opportunities for clinicians to help these unique patients achieve and maintain their best possible quality of life.

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302 A retrospective review of renal function and intravenous (IV) antibiotic use in an adult UK cystic fibrosis centre

Cited by 3 publications

References 0 publications

Prognostication and Risk Factors for Cystic Fibrosis via Automated Machine Learning

Prognostication and Risk Factors for Cystic Fibrosis via Automated Machine Learning

Original parts: Aging and reckoning with cystic fibrosis related kidney disease

Cystic Fibrosis Kidney Disease: 10 Tips for Clinicians

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