Real-time monitoring of progression towards renal failure in primary care patients

Diggle, Peter J.; Sousa, Inês; Asar, Özgür

doi:10.1093/biostatistics/kxu053

Cited by 30 publications

(44 citation statements)

References 10 publications

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“…Covariance assumptions were examined using a variogram of the transformed residuals

{bold-italicr}_{i}^{*}

(right panel). Empirical variogram ordinates were obtained using the formulas presented by Diggle et al Random deviation about 1 implies that the model fits well. Ordinates increased up to about lag 0.1, then remained relatively steady, indicating a similar correlation through the range of lags, until a sharp drop around lag 0.5, at which point the function increased.…”

Section: Modeling Rapid Disease Progressionmentioning

confidence: 99%

“…Conditional distributions from the model were used to form predictions of each patient's true lung function, of which FEV 1 is assumed to be an unbiased proxy. These conditional distributions have been shown to be Gaussian given the previously described linear mixed effects model assumptions . Forms of these Gaussian distributions are detailed therein (equations 4.8‐4.12) …”

Section: Predicting Rapid Disease Progressionmentioning

confidence: 99%

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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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“…Covariance assumptions were examined using a variogram of the transformed residuals

{bold-italicr}_{i}^{*}

Section: Modeling Rapid Disease Progressionmentioning

confidence: 99%

Section: Predicting Rapid Disease Progressionmentioning

confidence: 99%

Dynamic predictive probabilities to monitor rapid cystic fibrosis disease progression

Szczesniak

Brokamp

et al. 2019

Statistics in Medicine

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“…A longitudinal model was developed to fit age-related FEV1 progression and account for its nonlinearity by expanding an established method that has been successfully used to monitor markers of renal disease progression [16]. The expanded model used in this paper was presented at the 40 th European Cystic Fibrosis Society Meeting (abstract to be published in Fall 2017 in the Journal of Cystic Fibrosis).…”

Section: Algorithm Developmentmentioning

confidence: 99%

Early detection of rapid cystic fibrosis disease progression tailored to point of care: A proof-of-principle study

Szczesniak

Brokamp

et al. 2017

2017 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT)

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Slowing cystic fibrosis (CF) lung disease progression is crucial to survival, but point-of-care technologies aimed at early detection—and possibly prevention—of rapid lung function decline are limited. This proof-of-principle study leverages a rich national patient registry and follow-up data on a local CF cohort to build an algorithm and prototype prognostic tool aimed at early detection of rapid lung function decline. The algorithm was developed using a novel longitudinal analysis of lung function (measured as forced expiratory volume in 1 s of % predicted, FEV1). Covariates included clinical and demographic characteristics selected from the registry based on information criterion. Preliminary assessment of algorithm performance suggested excellent predictive accuracy and earlier detection of rapid decline than standard of care being applied at a local center. Graphical displays were presented and evaluated for clinical utility. Predictions from the algorithms and chosen graphical displays were translated into a prototype web application using RShiny and underwent iterative development based on clinician feedback. This paper suggests that the algorithm and its translation could offer a means for earlier detection and treatment of rapid decline, providing clinicians with a viable point-of-care technology to intervene prior to irreversible lung damage.

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“…A widely studied class of linear mixed effects models for data with this structure is

Y_{i j} = {boldX}_{i j}^{'} α + U_{i} + W_{i} (t_{i j}) + Z_{i j} : j = 1, ... n_{i}; i = 1, ... m;

where the

U_{i} \sim N (0, ω^{2})

are mutually independent random intercepts, the

W_{i} (t)

are mutually independent copies of a zero‐mean Gaussian stochastic processes with variance σ 2 and correlation function

ρ (\cdot)

, and the

Z_{i j} \sim N (0, τ^{2})

are mutually independent measurement errors; see, for example, Diggle et al. () or (). Here,

U_{i}

W_{i} (t)

, and

Z_{i j}

are mutually independent of each other.…”

Section: Linear Mixed Effects Modelmentioning

confidence: 99%

“…Maximum‐likelihood estimation for the Gaussian model is straightforward; see, for example, Diggle et al. (; ). For maximum‐likelihood (ML) estimation in the extended model , we use an EM algorithm as in Liu and Rubin () and Pinheiro et al.…”

Section: Inferencementioning

confidence: 99%

Short‐term and long‐term effects of acute kidney injury in chronic kidney disease patients: A longitudinal analysis

et al. 2016

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We use data from an ongoing cohort study of chronic kidney patients at Salford Royal NHS Foundation Trust, Greater Manchester, United Kingdom, to investigate the influence of acute kidney injury (AKI) on the subsequent rate of change of kidney function amongst patients already diagnosed with chronic kidney disease (CKD). We use a linear mixed effects modelling framework to enable estimation of both acute and chronic effects of AKI events on kidney function. We model the fixed effects by a piece-wise linear function with three change-points to capture the acute changes in kidney function that characterise an AKI event, and the random effects by the sum of three components: a random intercept, a stationary stochastic process with Matérn correlation structure, and measurement error. We consider both multivariate Normal and multivariate t versions of the random effects. For either specification, we estimate model parameters by maximum likelihood and evaluate the plug-in predictive distributions of the random effects given the data. We find that following an AKI event the average long-term rate of decline in kidney function is almost doubled, regardless of the severity of the event. We also identify and present examples of individual patients whose kidney function trajectories diverge substantially from the population-average.

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Real-time monitoring of progression towards renal failure in primary care patients

Cited by 30 publications

References 10 publications

Dynamic predictive probabilities to monitor rapid cystic fibrosis disease progression

Dynamic predictive probabilities to monitor rapid cystic fibrosis disease progression

Early detection of rapid cystic fibrosis disease progression tailored to point of care: A proof-of-principle study

Short‐term and long‐term effects of acute kidney injury in chronic kidney disease patients: A longitudinal analysis

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