Home Monitoring of Patients with Cystic Fibrosis to Identify and Treat Acute Pulmonary Exacerbations. eICE Study Results

Lechtzin, Noah; Mayer-Hamblett, Nicole; West, Natalie E.; Allgood, Sarah; Wilhelm, Ellen; Khan, Umer; Aitken, Moira L.; Ramsey, Bonnie W.; Boyle, Michael; Mogayzel, Peter J.; Gibson, Ronald L.; Orenstein, David M.; Milla, Carlos; Clancy, John; Antony, Veena B.; Goss, Christopher H.

doi:10.1164/rccm.201610-2172oc

Cited by 102 publications

(86 citation statements)

References 27 publications

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“…These complications may be reduced both by collecting average, rather than maximum ppFEV 1 measures during baseline periods (in contradiction to published guidelines), as well as by forcing baseline measures to be temporally closer to PEx treatments. The analytical benefit of establishing a stable baseline ppFEV 1 measure “closer” to PEx diagnosis should not be confused with a potential clinical benefit of “earlier PEx intervention,” the promise of which has yet to be realized . Investigators assessing the comparative effectiveness of different PEx treatments using ppFEV 1 return to baseline as a response criterion will need to pay attention to these statistical considerations, and effect size differences for treatments will have to be substantial to be detected using this approach.…”

Section: Discussionmentioning

confidence: 99%

Lung function changes before and after pulmonary exacerbation antimicrobial treatment in cystic fibrosis

Wagener

VanDevanter

Konstan

et al. 2019

Pediatric Pulmonology

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Background In cystic fibrosis, observation of a lung function drop (as percent predicted forced expiratory volume in 1 s [FEV1]; ppFEV1) frequently precedes pulmonary exacerbation (PEx) diagnosis. Recovery of ppFEV1 to a previous “baseline” is commonly used to assess antimicrobial treatment response. However, not all diagnosed PEx are associated with a ppFEV1 drop, and it is unclear whether these are a different type of PEx from those associated with a ppFEV1 drop. Methods We analyzed pre‐ and posttreatment ppFEV1 for PEx recorded in the Epidemiologic Study of Cystic Fibrosis from 2003 through 2005. Baseline, pretreatment, and follow‐up ppFEV1 were the best recorded within 12‐months pre‐PEx, the lowest recorded −30 to +3 days of treatment, and the best recorded during 6‐month follow‐up, respectively. Logistic regression models for return of ppFEV1 to baseline during follow‐up were developed separately for PEx with ≥10%, <10%, and no ppFEV1 drop before treatment. Results Of 15 147 PEx, 10 166 (67.1%), 3479 (23.0%), and 1502 (9.9%) presented with a ≥10%, <10%, or no ppFEV1 drop at diagnosis, respectively. 19.5%, 35.2%, and 65.6% of PEx, respectively, had follow‐up ppFEV1 equal to or exceeding baseline; overall 27.7% of all PEx treatments resulted in complete recovery of baseline ppFEV1. Significant predictors of ppFEV1 recovery at follow‐up were younger patient age, absence of Aspergillus, lower baseline ppFEV1, fewer visits during the baseline, lower frequency of prior‐year PEx, shorter elapsed time from baseline measure to treatment, smaller relative ppFEV1 drop before treatment, and non intravenous (ie, oral or inhaled antibiotic) treatment. PEx with ≥10%, <10%, and no ppFEV1 drop before treatment had only modest differences in covariate odds ratios associated with complete ppFEV1 recovery. Conclusions Among the 10% of PEx presenting with no apparent ppFEV1 drop, more than one‐third resulted in a decreased ppFEV1 during follow‐up. Risk factors for this outcome were the same as those associated with lack of ppFEV1 recovery among PEx with pretreatment ppFEV1 drops. These results suggest that inherent FEV1 variability, baseline and follow‐up sampling methodologies, ppFEV1 regression to the mean, and underlying lung disease progression complicate this approach for assessing effects of PEx and treatment response.

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Section: Discussionmentioning

confidence: 99%

Lung function changes before and after pulmonary exacerbation antimicrobial treatment in cystic fibrosis

Wagener

VanDevanter

Konstan

et al. 2019

Pediatric Pulmonology

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“…Having a substantial risk of rapid decline (eg, predictive probability >0.80) could serve as a trigger to initiate more frequent clinical visits, assessments for infections or mobile reporting of cough symptoms. The approach could complement methods to monitor patients outside the clinical setting, such as the efforts made to collect at‐home spirometry . The web application could be expanded to facilitate shared decision making between the provider and patient, enabling the patient to view her accrued data and risk of rapid decline.…”

Section: Discussionmentioning

confidence: 99%

Dynamic predictive probabilities to monitor rapid cystic fibrosis disease progression

Szczesniak

Brokamp

et al. 2019

Statistics in Medicine

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Cystic fibrosis (CF) is a progressive, genetic disease characterized by frequent, prolonged drops in lung function. Accurately predicting rapid underlying lung‐function decline is essential for clinical decision support and timely intervention. Determining whether an individual is experiencing a period of rapid decline is complicated due to its heterogeneous timing and extent, and error component of the measured lung function. We construct individualized predictive probabilities for “nowcasting” rapid decline. We assume each patient's true longitudinal lung function, S(t), follows a nonlinear, nonstationary stochastic process, and accommodate between‐patient heterogeneity through random effects. Corresponding lung‐function decline at time t is defined as the rate of change, S′(t). We predict S′(t) conditional on observed covariate and measurement history by modeling a measured lung function as a noisy version of S(t). The method is applied to data on 30 879 US CF Registry patients. Results are contrasted with a currently employed decision rule using single‐center data on 212 individuals. Rapid decline is identified earlier using predictive probabilities than the center's currently employed decision rule (mean difference: 0.65 years; 95% confidence interval (CI): 0.41, 0.89). We constructed a bootstrapping algorithm to obtain CIs for predictive probabilities. We illustrate real‐time implementation with R Shiny. Predictive accuracy is investigated using empirical simulations, which suggest this approach more accurately detects peak decline, compared with a uniform threshold of rapid decline. Median area under the ROC curve estimates (Q1‐Q3) were 0.817 (0.814‐0.822) and 0.745 (0.741‐0.747), respectively, implying reasonable accuracy for both. This article demonstrates how individualized rate of change estimates can be coupled with probabilistic predictive inference and implementation for a useful medical‐monitoring approach.

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“…Reviewing PFT results appear to be an adherence motivator for many adolescents and young adults . Home PFT monitoring has enabled clinicians to obtain more frequent data and allows patients the opportunity to readily review and keep track of the results . Lechtzin et al conducted a large, randomized controlled trial evaluating the benefits of early detection of pulmonary exacerbations on PFT in adults with CF (mean age of 27 years).…”

Section: Introductionmentioning

confidence: 99%

Impact of home spirometry on medication adherence among adolescents with cystic fibrosis

Shakkottai

Kaciroti

Kasmikha

et al. 2018

Pediatric Pulmonology

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Home Monitoring of Patients with Cystic Fibrosis to Identify and Treat Acute Pulmonary Exacerbations. eICE Study Results

Abstract: An intervention of home monitoring among patients with CF was able to detect more exacerbations than usual care, but this did not result in slower decline in lung function. Clinical trial registered with www.clinicaltrials.gov (NCT01104402).

Cited by 102 publications

References 27 publications

Lung function changes before and after pulmonary exacerbation antimicrobial treatment in cystic fibrosis

Lung function changes before and after pulmonary exacerbation antimicrobial treatment in cystic fibrosis

Dynamic predictive probabilities to monitor rapid cystic fibrosis disease progression

Impact of home spirometry on medication adherence among adolescents with cystic fibrosis

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