Clinical Trial Details
— Status: Recruiting
Administrative data
| NCT number |
NCT06352710 |
| Other study ID # |
2022-01510 |
| Secondary ID |
|
| Status |
Recruiting |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
June 12, 2023 |
| Est. completion date |
December 31, 2025 |
Study information
| Verified date |
April 2024 |
| Source |
Cantonal Hospital of St. Gallen |
| Contact |
Alexis PR Terrapon, MD |
| Phone |
+41 31 632 24 09 |
| Email |
alexis.terrapon[@]insel.ch |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial Summary
Analyzing the impact of surgery and adverse events (AEs) on patients' well-being is of
paramount importance as it provides essential information for benefit-risk assessment.
Current methods in outcome research are static, resource-intensive and subject to
missing-data issues. Moreover, AEs are inconsistently reported using various grading systems
that usually do not account for patients' subjective well-being. These are severe drawbacks
for outcome research as it hinders monitoring, comparison, and improvement of treatment
quality.
The increasing use of smartphones offers unprecedented opportunities for data collection. We
developed a free smartphone application to assess fluctuations of patients' well-being as a
result of surgical treatment and possible AEs. The application is installed on each patient's
smartphone and collects standardized data at defined timepoints before and after surgery
(well-being, AE description and severity).
By acquiring longitudinal patient-reported outcome before and after neurosurgical
interventions, we aim to determine the regular postoperative course for specific surgical
procedures, as well as any deviation thereof, depending on the occurrence and severity of
AEs. We will evaluate the validity of existing AE classifications and, if necessary, propose
a new patient-centered scheme. We hope that this will result in an increase in standardized
reporting of patient outcome, and ultimately allow for evidence-based patient information and
decision-making.
Description:
Understanding and analyzing the impact of surgery and adverse events (AEs) on the subjective
well-being of patients is of paramount importance as it provides objective information that
may be useful in a risk-benefit discussion. Current methods in outcome research are static,
resource-intensive and subject to missing-data issues. This results in a poor understanding
of the normal postoperative course which in turn prevents consistent reporting of AEs as they
are usually defined as a deviation thereof. As an additional challenge and because there is
no consensus and/or recommendation on this subject, AEs are graded using various
classifications that neglect the impact of AEs on the subjective well-being of patients. For
example, the most used AE grading system is the therapy-based Clavien-Dindo-Grading system
(CDG, doi:10.1097/01.sla.0000133083.54934.ae), which fails to detect the severity of AE that
are not treated by means of pharmacotherapy and/or surgery. This is an important limitation
as new neurologic deficits are frequent AEs that may imply dramatic consequences on quality
of life but are considered as low grade in therapy-based grading systems such as the CDG.
Other classifications were developed specifically for neurosurgery but they suffer the same
limitations. Recently, our group proposed the Therapy-Disability-Neurology Grade (TDN,
doi:10.1093/neuros/nyab121) to address this problem. The TDN grade takes into account the
therapy used to counteract AEs (as does the CDG), the associated neurologic deficits, and the
resulting disability, but currently lacks widespread use and validation. These are severe
drawbacks for outcome research as it hinders monitoring, comparison, and improvement of
quality of the treatment delivered.
The increasing use of smartphones across all age groups offers unprecedented opportunities
for data collection. We have created a smartphone application (app) to assess patient
well-being in a standardized and longitudinal fashion. The app named "Op-tracker app". It
collects longitudinal, self-reported data (subjective well-being rated from 0 to 10) at fixed
time points before and after surgery. Additional information such as type of disease, type of
surgery (currently four categories), AE description and severity (according to the CDG and
TDN grade) is also recorded, along with a standardized quality of life (QoL) questionnaire
(EQ-5D-5L). A simplified version recently described in a feasibility study showed good
acceptance and no major technical issues (doi:10.1007/s00701-021-04967-0). With this
innovative technique of data acquisition, we will gather a higher density of data using less
resources than traditional methods.
In a prospective observational pilot study without intervention using the "op-tracker app" to
acquire longitudinal patient reported outcome measures (the subjective well-being index, SWI)
before and after surgery, we aim to determine the regular postoperative course for certain
surgical procedures as well as the deviation thereof depending on the severity of specific
AEs. We will evaluate the validity of existing AE severity grading systems and if necessary,
propose a classification more consistent with the subjective well-being of patients. This
will greatly benefit patient information by providing essential insight about standard and
complicated postoperative course. Beyond the benefit this new data will add to the scientific
literature, we hope that the app will improve daily patient care by enabling early detection
of and reaction to AEs in case of "pathological decrease" in self-reported well-being and
QoL. Should this be confirmed, the app could be widely used and its scope could be extended
to the whole neurosurgical spectrum or even to further surgical subspecialties. We anticipate
that this will result in an increase in standardized reporting of patient outcome and
ultimately in a more evidence-based patient information and decision-making.
