Clinical trial NCT06077630: Non-attendance Prediction Models to Pediatric Outpatient Appointments
Official title: Non-attendance to Pediatric Outpatient Appointments: Prevalence, Associated Factors and Prediction Models
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Non-Attendance, Patient
No-Show Patients
Lead sponsor: Hospital General de Niños Pedro de Elizalde
Sponsor class: OTHER
First posted: 2023-10-11
Enrollment: 300000
Sex: ALL
Maximum age (years): 18
Standard ages: CHILD
ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
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).

Brief summary: 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.


Eligibility criteria: Inclusion Criteria:

* pediatric outpatient appointments

Exclusion Criteria:

* appointments generated for system benchmarking or appointments with missing data

Primary outcomes:
measure: Predictive Model non-attendance discrimination
timeFrame: 12 months
measure: Predictive Model non-attendance calibration
timeFrame: 12 months
measure: Predictive Model non-attendance diagnostic performance
timeFrame: 12 months
Secondary outcomes:
measure: Characterize the appointments misclassified by predictive models (FP)
timeFrame: 12 months
measure: Characterize the appointments misclassified by predictive models (FN)
timeFrame: 12 months
Other outcomes:
Locations:
Study officials:
affiliation: Hospital General de Niños Pedro de Elizalde
name: Mariano Ibarra, MD, Mag
role: PRINCIPAL_INVESTIGATOR
affiliation: Hospital Italiano de Buenos Aires
name: Diego H Giunta, MD, MPH, PhD
role: PRINCIPAL_INVESTIGATOR
affiliation: Karolinska Institutet
name: Arda Yilal, Engineer
role: PRINCIPAL_INVESTIGATOR
affiliation: Hospital Italiano de Buenos Aires
name: Leticia Peroni, MD, Mag
role: PRINCIPAL_INVESTIGATOR
affiliation: Hospital Italiano de Buenos Aires
name: Lucia Perez, MD
role: PRINCIPAL_INVESTIGATOR
References: