Impact of COVID-19 on the patient referral pattern and conversion rate in the university versus private facial plastic surgery centers

Abdolalizadeh, Parya; Kashkouli, Mohsen Bahmani; Jafarpour, Soheila; Rezaei, Saeid; Ghanbari, Shaghayegh; Akbarian, Shadi

doi:10.1007/s10792-022-02469-1

Cited by 4 publications

(1 citation statement)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Less obvious changes involve a negative effect on the enrolment of patients in clinical trials [ 19 ] as well as a decrease in publications and scientific output of non-infectiology disciplines [ 20 ]. With the ongoing pandemic, conditions returned to normal [ 21 ]. As the pandemic progressed, delayed elective procedures that had accumulated, the so-called surgical back-log, had to be performed nonetheless, and so the numbers of surgeries normalized again [ 22 ].…”

Section: Discussionmentioning

confidence: 99%

Impact of the Covid-19 pandemic on the performance of machine learning algorithms for predicting perioperative mortality

Andonov

Ulm

Graeßner

et al. 2023

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

Background Machine-learning models are susceptible to external influences which can result in performance deterioration. The aim of our study was to elucidate the impact of a sudden shift in covariates, like the one caused by the Covid-19 pandemic, on model performance. Methods After ethical approval and registration in Clinical Trials (NCT04092933, initial release 17/09/2019), we developed different models for the prediction of perioperative mortality based on preoperative data: one for the pre-pandemic data period until March 2020, one including data before the pandemic and from the first wave until May 2020, and one that covers the complete period before and during the pandemic until October 2021. We applied XGBoost as well as a Deep Learning neural network (DL). Performance metrics of each model during the different pandemic phases were determined, and XGBoost models were analysed for changes in feature importance. Results XGBoost and DL provided similar performance on the pre-pandemic data with respect to area under receiver operating characteristic (AUROC, 0.951 vs. 0.942) and area under precision-recall curve (AUPR, 0.144 vs. 0.187). Validation in patient cohorts of the different pandemic waves showed high fluctuations in performance from both AUROC and AUPR for DL, whereas the XGBoost models seemed more stable. Change in variable frequencies with onset of the pandemic were visible in age, ASA score, and the higher proportion of emergency operations, among others. Age consistently showed the highest information gain. Models based on pre-pandemic data performed worse during the first pandemic wave (AUROC 0.914 for XGBoost and DL) whereas models augmented with data from the first wave lacked performance after the first wave (AUROC 0.907 for XGBoost and 0.747 for DL). The deterioration was also visible in AUPR, which worsened by over 50% in both XGBoost and DL in the first phase after re-training. Conclusions A sudden shift in data impacts model performance. Re-training the model with updated data may cause degradation in predictive accuracy if the changes are only transient. Too early re-training should therefore be avoided, and close model surveillance is necessary.

show abstract