Anaesthesiologists' guideline adherence in pre-operative evaluation: a retrospective observational study.

BACKGROUND: Surveys suggest a low level of implementation of clinical guidelines, although they are intended to improve the quality of treatment and patient safety. Which guideline recommendations are not followed and why has yet to be analysed. In this study, we investigate the proportion of European and national guidelines followed in the area of pre-operative anaesthetic evaluation prior to non-cardiac surgery. METHODS: We conducted this monocentric retrospective observational study at a German university hospital with the help of software that logically links guidelines in such a way that individualised recommendations can be derived from a patient's data. We included routine logs of 2003 patients who visited our pre-anaesthesia outpatient clinic between June 2018 and June 2020 and compared the actual conducted pre-operative examinations with the recommendations issued by the software. We descriptively analysed the data for examinations not performed that would have been recommended by the guidelines and examinations that were performed even though they were not covered by a guideline recommendation. The guidelines examined in this study are the 2018 ESAIC guidelines for pre-operative evaluation of adults undergoing elective non-cardiac surgery, the 2014 ESC/ESA guidelines on non-cardiac surgery and the German recommendations on pre-operative evaluation on non-cardiothoracic surgery from the year 2017. RESULTS: Performed ECG (78.1%) and cardiac stress imaging tests (86.1%) indicated the highest guideline adherence. Greater adherence rates were associated with a higher ASA score (ASA I: 23.7%, ASA II: 41.1%, ASA III: 51.8%, ASA IV: 65.8%, P < 0.001), lower BMI and age > 65 years. Adherence rates in high-risk surgery (60.5%) were greater than in intermediate (46.5%) or low-risk (44.6%) surgery (P < 0.001). 67.2% of technical and laboratory tests performed preoperatively were not covered by a guideline recommendation. CONCLUSIONS: Guideline adherence in pre-operative evaluation leaves room for improvement. Many performed pre-operative examinations, especially laboratory tests, are not recommended by the guidelines and may cause unnecessary costs. The reasons for guidelines not being followed may be the complexity of guidelines and organisational issues. A software-based decision support tool may be helpful. TRIAL REGISTRATION: ClinicalTrials.gov ID NCT04843202.

Susceptibility of AutoML mortality prediction algorithms to model drift caused by the COVID pandemic.

BACKGROUND: Concept drift and covariate shift lead to a degradation of machine learning (ML) models. The objective of our study was to characterize sudden data drift as caused by the COVID pandemic. Furthermore, we investigated the suitability of certain methods in model training to prevent model degradation caused by data drift. METHODS: We trained different ML models with the H2O AutoML method on a dataset comprising 102,666 cases of surgical patients collected in the years 2014-2019 to predict postoperative mortality using preoperatively available data. Models applied were Generalized Linear Model with regularization, Default Random Forest, Gradient Boosting Machine, eXtreme Gradient Boosting, Deep Learning and Stacked Ensembles comprising all base models. Further, we modified the original models by applying three different methods when training on the original pre-pandemic dataset: (Rahmani K, et al, Int J Med Inform 173:104930, 2023) we weighted older data weaker, (Morger A, et al, Sci Rep 12:7244, 2022) used only the most recent data for model training and (Dilmegani C, 2023) performed a z-transformation of the numerical input parameters. Afterwards, we tested model performance on a pre-pandemic and an in-pandemic data set not used in the training process, and analysed common features. RESULTS: The models produced showed excellent areas under receiver-operating characteristic and acceptable precision-recall curves when tested on a dataset from January-March 2020, but significant degradation when tested on a dataset collected in the first wave of the COVID pandemic from April-May 2020. When comparing the probability distributions of the input parameters, significant differences between pre-pandemic and in-pandemic data were found. The endpoint of our models, in-hospital mortality after surgery, did not differ significantly between pre- and in-pandemic data and was about 1% in each case. However, the models varied considerably in the composition of their input parameters. None of our applied modifications prevented a loss of performance, although very different models emerged from it, using a large variety of parameters. CONCLUSIONS: Our results show that none of our tested easy-to-implement measures in model training can prevent deterioration in the case of sudden external events. Therefore, we conclude that, in the presence of concept drift and covariate shift, close monitoring and critical review of model predictions are necessary.

Enabling personalized perioperative risk prediction by using a machine-learning model based on preoperative data.

Preoperative risk assessment is essential for shared decision-making and adequate perioperative care. Common scores provide limited predictive quality and lack personalized information. The aim of this study was to create an interpretable machine-learning-based model to assess the patient's individual risk of postoperative mortality based on preoperative data to allow analysis of personal risk factors. After ethical approval, a model for prediction of postoperative in-hospital mortality based on preoperative data of 66,846 patients undergoing elective non-cardiac surgery between June 2014 and March 2020 was created with extreme gradient boosting. Model performance and the most relevant parameters were shown using receiver operating characteristic (ROC-) and precision-recall (PR-) curves and importance plots. Individual risks of index patients were presented in waterfall diagrams. The model included 201 features and showed good predictive abilities with an area under receiver operating characteristic (AUROC) curve of 0.95 and an area under precision-recall curve (AUPRC) of 0.109. The feature with the highest information gain was the preoperative order for red packed cell concentrates followed by age and c-reactive protein. Individual risk factors could be identified on patient level. We created a highly accurate and interpretable machine learning model to preoperatively predict the risk of postoperative in-hospital mortality. The algorithm can be used to identify factors susceptible to preoperative optimization measures and to identify risk factors influencing individual patient risk.

Neuropeptide Y - an early biomarker for cerebral vasospasm after aneurysmal subarachnoid hemorrhage.

OBJECTIVES: In the human brain, the potent vasoconstrictive neuropeptide Y (NPY) is abundantly expressed. Neuropeptide Y, which is stored in perivascular nerve fibers of the cerebral arteries, regulates the cerebral vascular diameter as well as cerebral blood flow. However, the role of NPY in the pathogenesis of cerebral vasospasm (CV) related to subarachnoid hemorrhage (SAH) is unclear. We prospectively analyzed and compared the release of endogenous NPY in the cerebrospinal fluid (CSF) of 66 patients with SAH to NPY release in a control group. Additionally, we correlated the levels of NPY with CV and consecutive ischemic stroke. METHODS: Sixty-six consecutive patients (40 women, 26 men; mean age 53·1 years) with aneurysmal SAH were included. In the SAH group, CSF was drawn daily from day 1 to day 10 after the onset of SAH. The CSF of 29 patients undergoing spinal anesthesia for orthopedic surgery served as control samples. The NPY levels were determined in duplicate CSF samples by means of a competitive enzyme immunoassay (EIA). The levels of NPY in CSF were correlated with the development of CV over the 10-day period after the onset of SAH and to the occurrence of consecutive ischemic stroke. To evaluate CSF NPY levels as a predictive biomarker for vasospasm, we calculated the sensitivity and specificity as well as the positive and negative predictive values. RESULTS: The NPY levels were significantly higher in the SAH group than in the control group (p < 0·001). The treatment modality (clip versus coil) did not influence the level of NPY in CSF (p > 0·05). Patients with CV showed significantly higher NPY levels than patients without CV during the entire observation period. The NPY levels of the non-CV group dissipated over time, whereas the CV group showed continuously increasing values. The NPY levels from day 4 to 10 were significantly higher in patients with CV-related stroke than in non-stroke patients. Using 0·3 ng/ml as a cut-off value, NPY levels on day 3 predicted the occurrence of CV with a sensitivity and specificity of 82% and 72%, respectively. High NPY levels, starting on day 4, significantly correlated with poor Glasgow Outcome Score grading at the follow-up (p < 0·05). DISCUSSION: Our data indicate that NPY is involved in the pathogenesis of SAH-related CV and ischemia. Neuropeptide Y represents an early and reliable biomarker for the prediction of CV and consecutive stroke due to aneurysmal SAH.