Artificial Neural Networks Versus Multiple Logistic Regression to Predict 30-Day Mortality After Operations For Type A Ascending Aortic Dissection§

There are few comparative reports on the overall accuracy of neural networks (NN), assessed only versus multiple logistic regression (LR), to predict events in cardiovascular surgery studies and none has been performed among acute aortic dissection (AAD) Type A patients. OBJECTIVES: We aimed at investigating the predictive potential of 30-day mortality by a large series of risk factors in AAD Type A patients comparing the overall performance of NN versus LR. METHODS: We investigated 121 plus 87 AAD Type A patients consecutively operated during 7 years in two Centres. Forced and stepwise NN and LR solutions were obtained and compared, using receiver operating characteristic area under the curve (AUC) and their 95% confidence intervals (CI) and Gini's coefficients. Both NN and LR models were re-applied to data from the second Centre to adhere to a methodological imperative with NN. RESULTS: Forced LR solutions provided AUC 87.9+/-4.1% (CI: 80.7 to 93.2%) and 85.7+/-5.2% (CI: 78.5 to 91.1%) in the first and second Centre, respectively. Stepwise NN solution of the first Centre had AUC 90.5+/-3.7% (CI: 83.8 to 95.1%). The Gini's coefficients for LR and NN stepwise solutions of the first Centre were 0.712 and 0.816, respectively. When the LR and NN stepwise solutions were re-applied to the second Centre data, Gini's coefficients were, respectively, 0.761 and 0.850. Few predictors were selected in common by LR and NN models: the presence of pre-operative shock, intubation and neurological symptoms, immediate post-operative presence of dialysis in continuous and the quantity of post-operative bleeding in the first 24 h. The length of extracorporeal circulation, post-operative chronic renal failure and the year of surgery were specifically detected by NN. CONCLUSIONS: Different from the International Registry of AAD, operative and immediate post-operative factors were seen as potential predictors of short-term mortality. We report a higher overall predictive accuracy with NN than with LR. However, the list of potential risk factors to predict 30-day mortality after AAD Type A by NN model is not enlarged significantly

Impact of laboratory test use strategies in a Turkish hospital

Objectives: Eliminating unnecessary laboratory tests is a good way to reduce costs while maintain patient safety. The aim of this study was to define and process strategies to rationalize laboratory use in Ankara Numune Training and Research Hospital (ANH) and calculate potential savings in costs. Methods: A collaborative plan was defined by hospital managers; joint meetings with ANHTA and laboratory professors were set; the joint committee invited relevant staff for input, and a laboratory efficiency committee was created. Literature was reviewed systematically to identify strategies used to improve laboratory efficiency. Strategies that would be applicable in local settings were identified for implementation, processed, and the impact on clinical use and costs assessed for 12 months. Results: Laboratory use in ANH differed enormously among clinics. Major use was identified in internal medicine. The mean number of tests per patient was 15.8. Unnecessary testing for chloride, folic acid, free prostate specific antigen, hepatitis and HIV testing were observed. Test panel use was pinpointed as the main cause of overuse of the laboratory and the Hospital Information System test ordering page was reorganized. A significant decrease (between 12.6-85.0%) was observed for the tests that were taken to an alternative page on the computer screen. The one year study saving was equivalent to 371,183 US dollars. Conclusion: Hospital-based committees including laboratory professionals and clinicians can define hospital based problems and led to a standardized approach to test use that can help clinicians reduce laboratory costs through appropriate use of laboratory test

Harmonization of quality indicators in laboratory medicine. A preliminary consensus.

Quality indicators (QIs) are fundamental tools for enabling users to quantify the quality of all operational processes by comparing it against a defined criterion. QIs data should be collected over time to identify, correct, and continuously monitor defects and improve performance and patient safety by identifying and implementing effective interventions. According to the international standard for medical laboratories accreditation, the laboratory shall establish and periodically review QIs to monitor and evaluate performance throughout critical aspects of pre-, intra-, and post-analytical processes. However, while some interesting programs on indicators in the total testing process have been developed in some countries, there is no consensus for the production of joint recommendations focusing on the adoption of universal QIs and common terminology in the total testing process. A preliminary agreement has been achieved in a Consensus Conference organized in Padua in 2013, after revising the model of quality indicators (MQI) developed by the Working Group on "Laboratory Errors and Patient Safety" of the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC). The consensually accepted list of QIs, which takes into consideration both their importance and applicability, should be tested by all potentially interested clinical laboratories to identify further steps in the harmonization project