@article{MatthesDiersSchlegeletal.2020,
  author    = {Matthes, Niels and Diers, Johannes and Schlegel, Nicolas and Hankir, Mohammed and Haubitz, Imme and Germer, Christoph-Thomas and Wiegering, Armin},
  title     = {Validation of MTL30 as a quality indicator for colorectal surgery},
  series = {PLoS One},
  volume    = {15},
  journal   = {PLoS One},
  number    = {8},
  doi       = {10.1371/journal.pone.0238473},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-230530},
  year      = {2020},
  abstract  = {Background Valid indicators are required to measure surgical quality. These ideally should be sensitive and selective while being easy to understand and adjust. We propose here the MTL30 quality indicator which takes into account 30-day mortality, transfer within 30 days, and a length of stay of 30 days as composite markers of an uneventful operative/postoperative course. Methods Patients documented in the StuDoQ|Colon and StuDoQ|Rectal carcinoma register of the German Society for General and Visceral Surgery (DGAV) were analyzed with regard to the effects of patient and tumor-related risk factors as well as postoperative complications on the MTL30. Results In univariate analysis, the MTL30 correlated significantly with patient and tumor-related risk factors such as ASA score (p<0.001), age (p<0.001), or UICC stage (p<0.001). There was a high sensitivity for the postoperative occurrence of complications such as re-operations (p<0.001) or subsequent bleeding (p<0.001), as well as a significant correlation with the CDC classification (p<0.001). In multivariate analysis, patient-related risk factors and postoperative complications significantly increased the odds ratio for a positive MTL30. A negative MTL30 showed a high specify for an uneventful operative and postoperative course. Conclusion The MTL30 is a valid indicator of colorectal surgical quality.},
  language  = {en}
}
@article{SaundersDavisKrankeetal.2018,
  author    = {Saunders, Rhodri and Davis, Jason A. and Kranke, Peter and Weissbrod, Rachel and Whitaker, David K and Lightdale, Jenifer R},
  title     = {Clinical and economic burden of procedural sedation-related adverse events and their outcomes: analysis from five countries},
  series = {Therapeutics and Clinical Risk Management},
  volume    = {14},
  journal   = {Therapeutics and Clinical Risk Management},
  doi       = {10.2147/TCRM.S154720},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-227508},
  pages     = {393-401},
  year      = {2018},
  abstract  = {Background: Studies have reported on the incidence of sedation-related adverse events (AEs), but little is known about their impact on health care costs and resource use. Methods: Health care providers and payers in five countries were recruited for an online survey by independent administrators to ensure that investigators and respondents were blinded to each other. Surveys were conducted in the local language and began with a "screener" to ensure that respondents had relevant expertise and experience. Responses were analyzed using Excel and R, with the Dixon's Q statistic used to identify and remove outliers. Global and country-specific average treatment patterns were calculated via bootstrapping; costs were mean values. The sum product of costs and intervention probability gave a cost per AE. Results: Responses were received from 101 providers and 26 payers, the majority having. 5 years of experience. At a minimum, the respondents performed a total of 3,430 procedural sedations per month. All AEs detailed occurred in clinical practice in the last year and were reported to cause procedural delays and cancellations in some patients. Standard procedural sedation costs ranged from (sic)74 (Germany) to \$2,300 (US). Respondents estimated that AEs would increase costs by between 16\% (Italy) and 179\% (US). Hypotension was reported as the most commonly observed AE with an associated global mean cost (interquartile range) of \$43 (\$27-\$68). Other frequent AEs, including mild hypotension, bradycardia, tachycardia, mild oxygen desaturation, hypertension, and brief apnea, were estimated to increase health care spending on procedural sedation by \$2.2 billion annually in the US. Conclusion: All sedation-related AEs can increase health care costs and result in substantial delays or cancellations of subsequent procedures. The prevention of even minor AEs during procedural sedation may be crucial to ensuring its value as a health care service.},
  language  = {en}
}
@article{VollmerVollmerLangetal.2022,
  author    = {Vollmer, Andreas and Vollmer, Michael and Lang, Gernot and Straub, Anton and K{\"u}bler, Alexander and Gubik, Sebastian and Brands, Roman C. and Hartmann, Stefan and Saravi, Babak},
  title     = {Performance analysis of supervised machine learning algorithms for automatized radiographical classification of maxillary third molar impaction},
  series = {Applied Sciences},
  volume    = {12},
  journal   = {Applied Sciences},
  number    = {13},
  issn      = {2076-3417},
  doi       = {10.3390/app12136740},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-281662},
  year      = {2022},
  abstract  = {Background: Oro-antral communication (OAC) is a common complication following the extraction of upper molar teeth. The Archer and the Root Sinus (RS) systems can be used to classify impacted teeth in panoramic radiographs. The Archer classes B-D and the Root Sinus classes III, IV have been associated with an increased risk of OAC following tooth extraction in the upper molar region. In our previous study, we found that panoramic radiographs are not reliable for predicting OAC. This study aimed to (1) determine the feasibility of automating the classification (Archer/RS classes) of impacted teeth from panoramic radiographs, (2) determine the distribution of OAC stratified by classification system classes for the purposes of decision tree construction, and (3) determine the feasibility of automating the prediction of OAC utilizing the mentioned classification systems. Methods: We utilized multiple supervised pre-trained machine learning models (VGG16, ResNet50, Inceptionv3, EfficientNet, MobileNetV2), one custom-made convolutional neural network (CNN) model, and a Bag of Visual Words (BoVW) technique to evaluate the performance to predict the clinical classification systems RS and Archer from panoramic radiographs (Aim 1). We then used Chi-square Automatic Interaction Detectors (CHAID) to determine the distribution of OAC stratified by the Archer/RS classes to introduce a decision tree for simple use in clinics (Aim 2). Lastly, we tested the ability of a multilayer perceptron artificial neural network (MLP) and a radial basis function neural network (RBNN) to predict OAC based on the high-risk classes RS III, IV, and Archer B-D (Aim 3). Results: We achieved accuracies of up to 0.771 for EfficientNet and MobileNetV2 when examining the Archer classification. For the AUC, we obtained values of up to 0.902 for our custom-made CNN. In comparison, the detection of the RS classification achieved accuracies of up to 0.792 for the BoVW and an AUC of up to 0.716 for our custom-made CNN. Overall, the Archer classification was detected more reliably than the RS classification when considering all algorithms. CHAID predicted 77.4\% correctness for the Archer classification and 81.4\% for the RS classification. MLP (AUC: 0.590) and RBNN (AUC: 0.590) for the Archer classification as well as MLP 0.638) and RBNN (0.630) for the RS classification did not show sufficient predictive capability for OAC. Conclusions: The results reveal that impacted teeth can be classified using panoramic radiographs (best AUC: 0.902), and the classification systems can be stratified according to their relationship to OAC (81.4\% correct for RS classification). However, the Archer and RS classes did not achieve satisfactory AUCs for predicting OAC (best AUC: 0.638). Additional research is needed to validate the results externally and to develop a reliable risk stratification tool based on the present findings.},
  language  = {en}
}