Clinical Trial Details
— Status: Completed
Administrative data
| NCT number |
NCT06070896 |
| Other study ID # |
2020111078 |
| Secondary ID |
|
| Status |
Completed |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
March 1, 2020 |
| Est. completion date |
December 31, 2023 |
Study information
| Verified date |
October 2023 |
| Source |
Hospital Galdakao-Usansolo |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial Summary
The various epidemics that health systems periodically suffers require having valid and
detailed information on its evolution and predictions in the short, medium and long term in
real time to allow the health system to organize itself in advance to be able to address the
health and sanitary problem that this entails.The objectives of this proposal are: to study
the usefulness of the health system's information and data storage system as a source for
quickly and efficiently obtaining data necessary for modeling an epidemiological outbreak;
its modeling in order to predict its evolution and the presentation of results to help in
decision making. The investigatorswill rely on the experience obtained so far during the
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, to define
semi-automatic and flexible criteria for searching, extracting, cleaning and aggregating
data. Predictions of incidence, number of hospital and ICU admissions, and number of deaths
will be made at the Basque Country level.Within the analysis of temporal data, especially in
the context of the pandemic, it is essential to have robust tools that allow accurate
predictions. In this study, the investigators employed P-splines based on the negative
binomial distribution to predict pandemic-related positive cases, hospital admissions, and
ICU admissions.
Description:
Design. Retrospective observational study. The modeling will be based on the SARS-CoV-2
pandemic that started at the beginning of 2020.
Subjects of the study. Information will be collected on daily incidence data aggregated by
age and sex for: tests performed, positive cases, hospital admissions and ICU admissions for
SARS-CoV-2, hospital discharges and ICU discharges, recovered and mortality (in ICU, in
hospital or in the community) of individuals with Coronavirus Disease of 2019 (COVID 19).
Criteria for inclusion. Of positive cases: Having a SARS-CoV-2 infection laboratory-confirmed
by a positive result on the reverse transcriptase-polymerase chain reaction assay for severe
acute respiratory syndrome coronavirus 2 (SARS-CoV-2) or a positive antigen test from March
1, 2020 to January 9, 2022.
For hospital admissions: Hospital admissions since the start of the pandemic. Considering
different episodes as a single admission when it comes to transfers from one center to
another. Consider exclusively income due to the COVID19.
Exclusion criteria: Patients admitted for other reasons who have developed the disease during
their hospital stay.
Variables. The data to be collected is aggregated data in the form of incidents. The
population will be stratified into ten age groups (0 - 9, 10 - 19, ..., 70 - 79, 80 - 89,
90+) and by sex. Variables:
- Individuals in the study population by age.
- Number of new confirmed positive cases of COVID19 by age and day.
- Number of new hospital admissions due to COVID19 by age and day. Number of ICU
admissions due to COVID19
- Number of total deaths from COVID19 by age and day.
- Number of hospital discharges (live patients) of patients who have been hospitalized for
COVID19 by age and day (excluding transfers).
- Number of deaths in hospital due to COVID19 by age and day.
- Number of deaths in the ICU due to COVID19 by age and day.
The outcome variables that will be obtained from the proposed modeling are:
- Number of estimated positive COVID19 cases by age and day.
- Number of estimated COVID19 hospital admissions by age and day.
- Number of estimated total deaths due to COVID19 estimated by age and day.
- Number of estimated ICU admissions due to COVID19 estimated by age and day.
Analysis of data. The investigators will use P-splines and Negative Binomial Distribution.
P-splines, or penalized splines, are a powerful tool for modeling nonlinear relationships in
temporal data. By combining them with the negative binomial distribution, a model is obtained
that is especially suitable for counting data with over-dispersion, as is the case with
pandemic data.
Procedure:
- Data Collection: Daily data on positive cases, hospital admissions and ICU admissions
will be obtained from the beginning of the pandemic until september 2022.
- Modeling: A P-splines model based on the negative binomial distribution will be fitted
to the data. This model will be designed to capture temporal trends and seasonal
patterns, as well as to handle the over-dispersion present in the data.
- Model with Random Effect for Day of the Week: Specifically for the prediction of
hospital admissions, a random effect for the day of the week will be incorporated. This
adjustment will be made because a systematic variability in income was identified
depending on the day of the week. Incorporating this random effect significantly will
improve the accuracy of the model for this variable.
- Prediction: Predictions will be made for two time horizons: short term (1 and 2 days)
and medium term (5 days). These predictions will allow us to anticipate the evolution of
the pandemic and make informed decisions.
Validation of Predictions: To validate the accuracy and robustness of the predictions, a
retrospective analysis will be carried out at different times (or waves) of the pandemic.
Model predictions will be compared to actual observed data, and error metrics will be
calculated to evaluate model performance.
Limitations. One of the limitations of the study is the possible loss of hospitalizations due
to the disease considered and death (or recovery) in individuals whose temporal sequence of
testing, admission and death (or recovery) has not followed the sequence used in searches
carried out.
Ethical aspects. This study uses only anonymized information to meet its objectives. There is
no data available to identify a patient.
The processing, communication and transfer of personal data of all participating persons
complies with the provisions of the European Data Protection Regulation (EU2016/679)
regarding the protection of natural persons with regard to processing. of personal data and
the free circulation of these data and Organic Law 3/2018, of December 5, on the Protection
of Personal Data and guarantee of digital rights. Virtually all of the data necessary for
this study is aggregated data that in no case can be associated with individuals. All
information will be treated absolutely confidentially.
Regarding obtaining informed consent from the patient, this research team proposes carrying
out the study without asking the patient for informed consent. The reasons why this proposal
is made are based on article 58 of Law 14/2007, of July 3, on Biomedical Research
(""..exceptionally, coded or identified samples may be treated for the purposes of biomedical
research without the consent of the source subject, when obtaining said consent is not
possible or represents an unreasonable effort. In these cases, the favorable opinion of the
corresponding Research Ethics Committee will be required. ")
