Real-time imputation of missing predictor values improved the application of prediction models in daily practice

Nijman, Steven W J; Groenhof, T. Katrien J.; Hoogland, Jeroen; Bots, Michiel L.; Brandjes, Menno; Jacobs, John J. L.; Asselbergs, Folkert W.; Moons, Karel G.M.; Debray, Thomas P A

doi:10.1016/j.jclinepi.2021.01.003

Cited by 33 publications

(20 citation statements)

References 30 publications

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“…Prior to analysis, missing values were checked and imputed using a multivariate normal imputation method. 13 Continuous data were assessed for normality using the Shapiro–Wilk test. Normally distributed variables were described as means ± standard deviation (SD), and analyzed by independent Student’s t -test, while non-normally distributed variables were expressed as median (Q1, Q3), and performed using Wilcoxon rank-sum test.…”

Section: Methodsmentioning

confidence: 99%

Admission Resting Heart Rate as an Independent Predictor of All-Cause Mortality in Elderly Patients with Hip Fracture

Wang

Chen

et al. 2021

IJGM

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Section: Methodsmentioning

confidence: 99%

Admission Resting Heart Rate as an Independent Predictor of All-Cause Mortality in Elderly Patients with Hip Fracture

Wang

Chen

et al. 2021

IJGM

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“…Missing values in the validation data were instead median imputed from the development dataset. 8 This avoids the potential bias that would be introduced if only patients with a complete set of observations, blood results and imaging were included in the validation, as the presence or absence of observations or tests may reflect clinician assessment of the severity of disease.…”

mentioning

confidence: 99%

Validation of The 4C Deterioration Model for COVID-19 in a UK Teaching Hospital During Wave 2

Cowan

Wiegand

Preller

et al. 2021

The American Journal of the Medical Sciences

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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“…A standalone internet application and paper-based nomogram will be developed for when EMR integration is not possible. Real-time imputation will be explored [ 86 ].…”

Section: Methodsmentioning

confidence: 99%

Predicting Real-world Hypoglycemia Risk in American Adults With Type 1 or 2 Diabetes Mellitus Prescribed Insulin and/or Secretagogues: Protocol for a Prospective, 12-Wave Internet-Based Panel Survey With Email Support (the iNPHORM [Investigating Novel Predictions of Hypoglycemia Occurrence Using Real-world Models] Study)

Ratzki‐Leewing¹,

Ryan²,

Zou³

et al. 2022

JMIR Res Protoc

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Background Hypoglycemia prognostic models contingent on prospective, self-reported survey data offer a powerful avenue for determining real-world event susceptibility and interventional targets. Objective This protocol describes the design and implementation of the 1-year iNPHORM (Investigating Novel Predictions of Hypoglycemia Occurrence Using Real-world Models) study, which aims to measure real-world self-reported severe and nonsevere hypoglycemia incidence (daytime and nocturnal) in American adults with type 1 or 2 diabetes mellitus prescribed insulin and/or secretagogues, and develop and internally validate prognostic models for severe, nonsevere daytime, and nonsevere nocturnal hypoglycemia. As a secondary objective, iNPHORM aims to quantify the effects of different antihyperglycemics on hypoglycemia rates. Methods iNPHORM is a prospective, 12-wave internet-based panel survey that was conducted across the United States. Americans (aged 18-90 years) with self-reported type 1 or 2 diabetes mellitus prescribed insulin and/or secretagogues were conveniently sampled via the web from a pre-existing, closed, probability-based internet panel (sample frame). A sample size of 521 baseline responders was calculated for this study. Prospective data on hypoglycemia and potential prognostic factors were self-assessed across 14 closed, fully automated questionnaires (screening, baseline, and 12 monthly follow-ups) that were piloted using semistructured interviews (n=3) before fielding; no face-to-face contact was required as part of the data collection. Participant responses will be analyzed using multivariable count regression and machine learning techniques to develop and internally validate prognostic models for 1-year severe and 30-day nonsevere daytime and nocturnal hypoglycemia. The causal effects of different antihyperglycemics on hypoglycemia rates will also be investigated. Results Recruitment and data collection occurred between February 2020 and March 2021 (ethics approval was obtained on December 17, 2019). A total of 1694 participants completed the baseline questionnaire, of whom 1206 (71.19%) were followed up for 12 months. Most follow-up waves (10,470/14,472, 72.35%) were completed, translating to a participation rate of 179% relative to our target sample size. Over 70.98% (856/1206) completed wave 12. Analyses of sample characteristics, quality metrics, and hypoglycemia incidence and prognostication are currently underway with published results anticipated by fall 2022. Conclusions iNPHORM is the first hypoglycemia prognostic study in the United States to leverage prospective, longitudinal self-reports. The results will contribute to improved real-world hypoglycemia risk estimation and potentially safer, more effective clinical diabetes management. Trial Registration ClinicalTrials.gov NCT04219514; https://clinicaltrials.gov/ct2/show/NCT04219514 International Registered Report Identifier (IRRID) DERR1-10.2196/33726

show abstract

Real-time imputation of missing predictor values improved the application of prediction models in daily practice

Cited by 33 publications

References 30 publications

Admission Resting Heart Rate as an Independent Predictor of All-Cause Mortality in Elderly Patients with Hip Fracture

Admission Resting Heart Rate as an Independent Predictor of All-Cause Mortality in Elderly Patients with Hip Fracture

Validation of The 4C Deterioration Model for COVID-19 in a UK Teaching Hospital During Wave 2

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