Key result
A query of the German Diagnosis Related Groups database was performed to calculate the prevalence of adverse events during laparoscopic cholecystectomy to estimate predictive values for ML applications.
Why the study?
It is questionable whether routine laparoscopic cholecystectomy patients would benefit from ML adverse event prediction, given the low prevalence of adverse events and consequential low diagnostic yield.
Observational
Yes
The low prevalence of adverse events in unselected laparoscopic cholecystectomy patients may limit the diagnostic yield and predictive value of machine learning algorithms in daily clinical routine.
ML adverse event prediction unlikely to alter routine LCHE practice; leaves open whether higher-complication surgeries better suit ML model development.
Laparoscopic cholecystectomy (LCHE) is a widely employed model for surgical instrument and phase recognition in the field of machine learning (ML), with the latter being assigned to identify critical events and to avoid complications. Although ML algorithms have been proven to be effective for this instance and in selected patients, it is questionable whether patients receiving LCHE in daily clinical routine would actually benefit from adverse event prediction by ML applications. We believe, that the statistical problem of low prevalence (PREV) of potential adverse events in an unselected population and consequential low diagnostic yield was not considered adequately in recent research. Therefore, we performed a query to the G-DRG (German Diagnosis Related Groups) database of the German Federal Statistical Office with the aim to calculate prevalence of surgical and postoperative adverse events coming along with LCHE. The results enable an estimation of positive (PPV) and negative (NPV) predictive values hypothetically achievable by ML applications aiming to predict an adverse surgical course.
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Berlet et al. (2022) conducted an observational in Laparoscopic cholecystectomy. Machine learning applications for adverse event prediction was evaluated on Prevalence of surgical and postoperative adverse events. A query of the German Diagnosis Related Groups database was performed to calculate the prevalence of adverse events during laparoscopic cholecystectomy to estimate predictive values for ML applications.
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