Non-Attendance, PatientNCT06077630
Non-attendance Prediction Models to Pediatric Outpatient Appointments
An observational study of Non-Attendance, Patient and No-Show Patients, run by Hospital General de Niños Pedro de Elizalde.
Trial 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. Read more about this study
- Who is it for?
- Ages Up to 18 years
- Conditions the study is for
- Non-Attendance, Patient, No-Show Patients
- How many people
- 300,000 participants
- Where
- No sites listed yet
- Study dates
- 1 Jan 2017to 31 Dec 2018
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Up to 18 years
- All sexes
- No healthy volunteers
- Child, Adult
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).
Who may be able to join
- pediatric outpatient appointments
Who cannot join
- appointments generated for system benchmarking or appointments with missing data
About this study
Official title: 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.
- Conditions
- Non-Attendance, Patient, No-Show Patients
- Being tested
- No intervention
- Medical subject headings
- No-Show Patients
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
No interventionOther
There is no intervention, observational study
| Study group | Role | What they receive |
|---|---|---|
| Attended appointments | An appointment scheduled by a patient that was attended Receives: Other: No intervention | |
| Not-attended appointments | An appointment scheduled by a patient that was not-attended, regardless of the cause Receives: Other: No intervention |
What is being measured
The primary outcome is the main question the study is designed to answer.
Predictive Model non-attendance discrimination
Area Under the ROC Curve
Measured: 12 months
Predictive Model non-attendance calibration
Calibration chart with predicted vs observed probability.
Measured: 12 months
Predictive Model non-attendance diagnostic performance
Measured: 12 months
Secondary outcomes (2)
Characterize the appointments misclassified by predictive models (FP)
False positive appointments prevalence
Measured: 12 months
Characterize the appointments misclassified by predictive models (FN)
False negative appointments prevalence
Measured: 12 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted11 Oct 2023
- Study start1 Jan 2017
- Primary completion31 Dec 2018
- Study completion31 Dec 2018
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Non-Attendance, PatientStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 300,000 participants (actual)
Sponsor and investigators
- Lead sponsor
- Hospital General de Niños Pedro de Elizalde
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Mariano Esteban Ibarra, Hospital General de Niños Pedro de Elizalde
- Investigators
- Mariano Ibarra, MD, Mag, Principal investigator, Hospital General de Niños Pedro de ElizaldeDiego H Giunta, MD, MPH, PhD, Principal investigator, Hospital Italiano de Buenos AiresArda Yilal, Engineer, Principal investigator, Karolinska InstitutetLeticia Peroni, MD, Mag, Principal investigator, Hospital Italiano de Buenos AiresLucia Perez, MD, Principal investigator, Hospital Italiano de Buenos Aires
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06077630
- Sponsor's protocol ID
- 4084
- Status last verified
- Nov 2023
- First submitted
- 5 Oct 2023
- Last updated
- 8 Nov 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 8 Nov 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Non-attendance to Pediatric Outpatient Appointments: Prevalence, Associated Factors and Prediction Models
- ClinicalTrials.gov ID
- NCT06077630
- Acronym / sponsor ID
- 4084
- Conditions
- Non-Attendance, Patient, No-Show Patients
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 300,000 participants (actual)
- Lead sponsor
- Hospital General de Niños Pedro de ElizaldeUniversity, hospital or other
- Study start
- 1 Jan 2017 (actual)
- Primary completion
- 31 Dec 2018 (actual)
- Study completion
- 31 Dec 2018 (actual)
- First posted
- 11 Oct 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Attended appointments | – | Other: No interventionAn appointment scheduled by a patient that was attended |
| Not-attended appointments | – | Other: No interventionAn appointment scheduled by a patient that was not-attended, regardless of the cause |
Who may qualify
- Ages
- Up to 18 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult
- 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).
Key inclusion criteria (1 listed)
- pediatric outpatient appointments
Key exclusion criteria (1 listed)
- appointments generated for system benchmarking or appointments with missing data
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| No interventionThere is no intervention, observational study | Other | Attended appointments, Not-attended appointments |
Primary outcome
Predictive Model non-attendance discrimination
Time frame: 12 months
Area Under the ROC Curve
Predictive Model non-attendance calibration
Time frame: 12 months
Calibration chart with predicted vs observed probability.
Predictive Model non-attendance diagnostic performance
Time frame: 12 months
2 secondary outcomes
Characterize the appointments misclassified by predictive models (FP)
Time frame: 12 months
Characterize the appointments misclassified by predictive models (FN)
Time frame: 12 months
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 8 Nov 2023.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Mariano Ibarra, MD, Mag principal investigator
Hospital General de Niños Pedro de Elizalde
Diego H Giunta, MD, MPH, PhD principal investigator
Hospital Italiano de Buenos Aires
Arda Yilal, Engineer principal investigator
Karolinska Institutet
Questions about this trial
What is this clinical trial studying?
An observational study of Non-Attendance, Patient and No-Show Patients, run by Hospital General de Niños Pedro de Elizalde.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 8 Nov 2023. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Predictive Model non-attendance discrimination (time frame: 12 months). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 8 Nov 2023.