The Challenges of Gastric Cancer Surgery during the COVID-19 Pandemic.
Catalin Vladut Ionut FeierAlaviana Monique FaurCalin MunteanAndiana BlidariOana Elena ContesDiana Raluca StreinuSorin OlariuPublished in: Healthcare (Basel, Switzerland) (2023)
The aim of this study was to quantify the impact of the COVID-19 pandemic on the surgical treatment of patients with gastric cancer. Data from patients undergoing surgery for gastric cancer during the pandemic were analyzed and the results obtained were compared with the corresponding periods of 2016-2017 and 2018-2019. Various parameters were taken into consideration and their dynamics highlight significant changes in the pandemic year compared with the two pre-pandemic periods. Statistical analysis revealed a marked decrease in the number of surgeries performed during the pandemic ( p < 0.001). Severe prognostic factors for gastric cancer, including weight loss and upper gastrointestinal hemorrhage, were associated with an increased number of postoperative fistulas, while emesis was statistically correlated with a more advanced cancer stage ( p < 0.011). There was also a reduction in the total duration of hospitalization ( p = 0.044) and postoperative hospitalization ( p = 0.047); moreover, the mean duration of surgical intervention was higher during the pandemic ( p = 0.044). These findings provide evidence for the significant changes in clinical and therapeutic strategies applied to patients undergoing surgery for gastric cancer during the study period. The ongoing pandemic has exerted a substantial and complex impact, the full extent of which remains yet to be fully comprehended.
Keyphrases
- sars cov
- coronavirus disease
- patients undergoing
- minimally invasive
- coronary artery bypass
- prognostic factors
- weight loss
- advanced cancer
- palliative care
- randomized controlled trial
- surgical site infection
- bariatric surgery
- type diabetes
- early onset
- roux en y gastric bypass
- machine learning
- artificial intelligence
- electronic health record
- atrial fibrillation
- deep learning
- percutaneous coronary intervention
- drug induced