Non-attendance Prediction Models to Pediatric Outpatient Appointments

November 6, 2023 updated by: Mariano Esteban Ibarra, Hospital General de Niños Pedro de Elizalde

Non-attendance to Pediatric Outpatient Appointments: Prevalence, Associated Factors and Prediction Models

Non-attendance to pediatric outpatient appointments is a frequent and relevant public health problem.

Using different approaches it is possible to build non-attendance predictive models and these models can be used to guide strategies aimed at reducing no-shows. However, predictive models have limitations and it is unclear which is the best method to generate them. Regardless of the strategy used to build the predictive model, discrimination, measured as area under the curve, has a ceiling around 0.80. This implies that the models do not have a 100% discrimination capacity for no-show and therefore, in a proportion of cases they will be wrong. This classification error limits all models diagnostic performance and therefore, their application in real life situations. Despite all this, the limitations of predictive models are little explored.

Taking into account the negative effects of non-attendance, the possibility of generating predictive models and using them to guide strategies to reduce non-attendance, we propose to generate non-attendance predictive models for outpatient appointments using traditional logistic regression and machine learning techniques, evaluate their diagnostic performance and finally, identify and characterize the population misclassified by predictive models.

Study Overview

Status

Completed

Conditions

Intervention / Treatment

Study Type

Observational

Enrollment (Actual)

300000

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult

Accepts Healthy Volunteers

No

Sampling Method

Probability Sample

Study Population

All scheduled pediatric outpatient appointments between January 1 2017 and 31 december 2018 will be included. The sample will be randomized to allocate a generation cohort (two-thirds of the sample) and a validation cohort (one third of the sample).

Description

Inclusion Criteria:

  • pediatric outpatient appointments

Exclusion Criteria:

  • appointments generated for system benchmarking or appointments with missing data

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Attended appointments
An appointment scheduled by a patient that was attended
Other: No intervention
There is no intervention, observational study
Not-attended appointments
An appointment scheduled by a patient that was not-attended, regardless of the cause
Other: No intervention
There is no intervention, observational study

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Predictive Model non-attendance discrimination
Time Frame: 12 months
Area Under the ROC Curve
12 months
Predictive Model non-attendance calibration
Time Frame: 12 months
Calibration chart with predicted vs observed probability.
12 months
Predictive Model non-attendance diagnostic performance
Time Frame: 12 months
12 months

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Characterize the appointments misclassified by predictive models (FP)
Time Frame: 12 months
False positive appointments prevalence
12 months
Characterize the appointments misclassified by predictive models (FN)
Time Frame: 12 months
False negative appointments prevalence
12 months

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Hospital General de Niños Pedro de Elizalde

Investigators

  • Principal Investigator: Mariano Ibarra, MD, Mag, Hospital General de Niños Pedro de Elizalde
  • Principal Investigator: Diego H Giunta, MD, MPH, PhD, Hospital Italiano de Buenos Aires
  • Principal Investigator: Arda Yilal, Engineer, Karolinska Institutet
  • Principal Investigator: Leticia Peroni, MD, Mag, Hospital Italiano de Buenos Aires
  • Principal Investigator: Lucia Perez, MD, Hospital Italiano de Buenos Aires

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

January 1, 2017

Primary Completion (Actual)

December 31, 2018

Study Completion (Actual)

December 31, 2018

Study Registration Dates

First Submitted

October 5, 2023

First Submitted That Met QC Criteria

October 5, 2023

First Posted (Actual)

October 11, 2023

Study Record Updates

Last Update Posted (Estimated)

November 8, 2023

Last Update Submitted That Met QC Criteria

November 6, 2023

Last Verified

November 1, 2023

More Information

Terms related to this study

Other Study ID Numbers

  • 4084

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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