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.