Refine
Year of publication
- 2020 (3) (remove)
Has Fulltext
- yes (3) (remove)
Is part of the Bibliography
- yes (3)
Institute
- Fakultät WiSo (3)
Background
Diabetes mellitus is a major global health issue with a growing prevalence. In this context, the number of diabetic complications is also on the rise, such as diabetic foot ulcers (DFU), which are closely linked to the risk of lower extremity amputation (LEA). Statistical prediction tools may support clinicians to initiate early tertiary LEA prevention for DFU patients. Thus, we designed Bayesian prediction models, as they produce transparent decision rules, quantify uncertainty intuitively and acknowledge prior available scientific knowledge.
Method
A logistic regression using observational collected according to the standardised PEDIS classification was utilised to compute the six-month amputation risk of DFU patients for two types of LEA: 1.) any-amputation and 2.) major-amputation. Being able to incorporate information which is available before the analysis, the Bayesian models were fitted following a twofold strategy. First, the designed prediction models waive the available information and, second, we incorporated the a priori available scientific knowledge into our models. Then, we evaluated each model with respect to the effect of the predictors and validity of the models. Next, we compared the performance of both models with respect to the incorporation of prior knowledge.
Results
This study included 237 patients. The mean age was 65.9 (SD 12.3), and 83.5% were male. Concerning the outcome, 31.6% underwent any- and 12.2% underwent a major-amputation procedure. The risk factors of perfusion, ulcer extent and depth revealed an impact on the outcomes, whereas the infection status and sensation did not. The major-amputation model using prior information outperformed the uninformed counterpart (AUC 0.765 vs AUC 0.790, Cohen’s d 2.21). In contrast, the models predicting any-amputation performed similarly (0.793 vs 0.790, Cohen’s d 0.22).
Conclusions
Both of the Bayesian amputation risk models showed acceptable prognostic values, and the major-amputation model benefitted from incorporating a priori information from a previous study. Thus, PEDIS serves as a valid foundation for a clinical decision support tool for the prediction of the amputation risk in DFU patients. Furthermore, we demonstrated the use of the available prior scientific information within a Bayesian framework to establish chains of knowledge.
Introduction: Handovers are a central process for ensuring information continuity in patient care and, therefore, possess a major influence on patient safety as errors due to poor handovers can lead to life-threatening events. Education to improve handovers and ensure safe patient care can be supported by using critical incident reporting systems (CIRS). The aim of the study is to perform a content analysis of a national CIRS-database with regard to identifying adverse events in handovers situations and to derive competencies for the development of continuing education from these findings.
Methods: A meta model served as a research framework to merge the empirical findings with the London protocol of analysing critical events and the Canadian framework of safety competencies. Relevant cases to be investigated were searched in a freely accessible German CIRS database.
Results: A total of 253 case descriptions were found and analysed. Team factors emerged as the most frequently reported influencing factors following the analysis of the London protocol. Communication errors and missing information as well as a lack of appropriate standards and processes appeared to be the main reasons for critical events to occur. Most of the events happened in units involving surgery and intensive care. A mapping of patient safety competences with the reasons for critical events was conducted in order to determine the practical, concrete and handover related competencies.
Conclusion: Data from a CIRS database and theoretical frameworks can be combined to extract meaningful information about patient safety risks in handover situations. The results are useful for developing curricula to improve handovers based on patient safety competencies.