RATIONALE, AIMS AND OBJECTIVES: Scoring systems are frequently proposed in medicine to summarize a set of qualitative and quantitative items by means of a numeric score. Their design often requires modelling ability and subjective judgments. This can make it difficult to adapt a scoring system to a clinical setting different from that in which the system was developed. The objective of this study was to discuss an approach to derive scoring systems, which can be easily modified and matched to any scenario. METHODS: A naïve Bayes approach was used to develop a scoring system that is completely defined by descriptive tables obtained by frequency counts from the training set. The approach was implemented to build a locally customized scoring system for planning transfusion requirements after cardiac surgery. The performance of this system was evaluated and compared with that of a logistic regression model designed using the same predictors. The working sample was a set of 3182 consecutive patients undergoing cardiac surgery at the University Hospital of Siena, Italy. RESULTS: The area under the receiver operating characteristic curve was equal to 0.811 and 0.824 for the scoring system and for the logistic regression model, respectively. This result proves that this global index of discrimination capacity was virtually identical and very good for both models. The values of sensitivity, specificity and overall correct-classification percentage obtained by the leave-one-out method were practically the same for the two models (73.9% versus 75.3%). CONCLUSIONS: An easy-fitting and trustworthy scoring system can be directly developed using a naïve Bayes approach. The simplicity of its design allows the system to be customized to any specific institution and updated regularly. This aspect has important practical implications because it can encourage the use of scoring systems among clinicians, enabling their performance to be properly assessed in a wider clinical context.
RATIONALE, AIMS AND OBJECTIVES: Scoring systems are frequently proposed in medicine to summarize a set of qualitative and quantitative items by means of a numeric score. Their design often requires modelling ability and subjective judgments. This can make it difficult to adapt a scoring system to a clinical setting different from that in which the system was developed. The objective of this study was to discuss an approach to derive scoring systems, which can be easily modified and matched to any scenario. METHODS: A naïve Bayes approach was used to develop a scoring system that is completely defined by descriptive tables obtained by frequency counts from the training set. The approach was implemented to build a locally customized scoring system for planning transfusion requirements after cardiac surgery. The performance of this system was evaluated and compared with that of a logistic regression model designed using the same predictors. The working sample was a set of 3182 consecutive patients undergoing cardiac surgery at the University Hospital of Siena, Italy. RESULTS: The area under the receiver operating characteristic curve was equal to 0.811 and 0.824 for the scoring system and for the logistic regression model, respectively. This result proves that this global index of discrimination capacity was virtually identical and very good for both models. The values of sensitivity, specificity and overall correct-classification percentage obtained by the leave-one-out method were practically the same for the two models (73.9% versus 75.3%). CONCLUSIONS: An easy-fitting and trustworthy scoring system can be directly developed using a naïve Bayes approach. The simplicity of its design allows the system to be customized to any specific institution and updated regularly. This aspect has important practical implications because it can encourage the use of scoring systems among clinicians, enabling their performance to be properly assessed in a wider clinical context.
Authors: Ser Yee Lee; Brian K P Goh; Gholami Sepideh; John C Allen; Ryan P Merkow; Jin Yao Teo; Deepa Chandra; Ye Xin Koh; Ek Khoon Tan; Juinn Haur Kam; Peng Chung Cheow; Pierce K H Chow; London L P J Ooi; Alexander Y F Chung; Michael I D'Angelica; William R Jarnagin; T Peter Kingham; Chung Yip Chan Journal: J Gastrointest Surg Date: 2018-11-12 Impact factor: 3.452