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.

Absence of a diurnal rhythm of oxytocin and arginine-vasopressin in human cerebrospinal fluid, blood and saliva.

PURPOSE: The aims of our study were to determine first circadian influences on central concentrations of the neuropeptides oxytocin and arginine-vasopressin and second to investigate if these central concentrations are associated with those in the peripheral compartments blood and saliva in neurocritical care patients. We therefore included patients with external ventricular drain who attended a neurosurgical intensive care unit and were not exposed to painful or stressful stimuli during the sampling period. For this purpose, blood, cerebrospinal fluid and saliva were collected in a 24-hour-interval at the timepoints 06:00, 12:00, 18:00 and 24:00. RESULTS: In none of the three body fluids examined, significant time-dependent fluctuations of oxytocin and arginine-vasopressin concentrations could be detected during the 24-hour sampling period. The only exception was the subgroup of postmenopausal women whose oxytocin concentrations in cerebrospinal fluid at 12:00 were significantly higher than at 18:00. Correlations of blood and cerebrospinal fluid and blood and saliva neuropeptide levels were very weak to weak at each timepoint. Cerebrospinal fluid and saliva oxytocin levels showed a moderate correlation at 06:00 but did correlate very weak at the other timepoints. CONCLUSIONS: Central as well as peripheral oxytocin and arginine-vasopressin concentrations in neurocritical care patients did not show significant diurnal fluctuations. No strong correlations between central and peripheral neuropeptide concentrations could be detected under basal conditions. If investigators even though decide to use saliva concentrations as surrogate parameter for central neuropeptide activity, they have to consider that correlations of cerebrospinal fluid and saliva oxytocin seem to be highest in the early morning.

Influence of perioperative stress on central and peripheral oxytocin and arginine-vasopressin concentrations.

Perioperative stress provides not only physical, but also psychic and emotional aspects, which may influence the hypothalamic neuropeptide system. Studies investigating the perioperative course of central neuropeptide activity are missing. Therefore, the present study aimed to determine perioperative fluctuations in central and concomitant peripheral concentrations of the hypothalamic neuropeptides oxytocin (OXT) and arginine-vasopressin (AVP), as well as their impact on perioperative anxiety and depression. Cerebrospinal fluid (CSF), blood and saliva were collected from 12 patients who underwent elective endovascular aortic repair with a routinely inserted spinal catheter. AVP and OXT concentrations were analysed at four timepoints: (i) the evening before the operation; (ii) the operation day immediately before anaesthesia induction; (iii) intraoperatively after the stent was placed; and (iv) on day 1 after the operation. Patients completed the Hospital Anxiety and Depression Scale (HADS) at timepoints 1 and 4. For CSF OXT, the present study showed a significant intraoperative decline, accompanied by a decrease in saliva. OXT blood concentrations before anaesthesia induction were higher than at the evening before the operation. OXT concentrations in CSF and saliva correlated well at timepoints 2-4. AVP concentrations in CSF, blood and saliva did not show any significant changes perioperatively. However, postoperative AVP blood concentrations showed a significant negative correlation with anxiety and depression scores according to the HADS. This pilot study demonstrates perioperative fluctuations in central OXT concentrations, which are better reflected by saliva than by blood. Further studies are required to determine whether OXT and AVP can predict postoperative post-traumatic stress disorder.

Influence of sex and hormonal status on initial impact and neurocognitive outcome after subarachnoid haemorrhage in rats.

The aim of this study was to detect differences in functional outcome after experimental subarachnoid haemorrhage (SAH) in rodents with different hormonal status. For this purpose, the endovascular perforation model was applied to four groups of Sprague-Dawley-Rats: male intact, male neutered, female intact and female neutered animals. Initial impact was measured by ICP, CPP and cerebral blood flow in the first hour after SAH. From day 4-14, the modified hole board test was applied to assess functional and neuro-cognitive outcome. Histological outcome was examined in the motor cortex and hippocampus of each hemisphere. Mortality was highest in the female intact group albeit not statistically significant. Physiologic parameters did not differ significantly between groups either. In the modified hole board test, male intact animals showed a greater impairment of declarative memory than the female intact and neutered groups. However, male intact animals showed greater avoidance behaviour and male animals revealed higher anxiety levels independent of hormonal status. No differences in histological damage of hippocampus and motor cortex between groups could be shown. We therefore speculate that the marginal deficits in cognitive performance that are shown by the male intact group in the modified hole board test are mostly caused by higher anxiety levels and cannot be interpreted as pure cognitive impairment.