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Developing points-based risk-scoring systems in the presence of competing risks.

ABSTRACT: Predicting the occurrence of an adverse event over time is an important issue in clinical medicine. Clinical prediction models and associated points-based risk-scoring systems are popular statistical methods for summarizing the relationship between a multivariable set of patient risk factors and the risk of the occurrence of an adverse event. Points-based risk-scoring systems are popular amongst physicians as they permit a rapid assessment of patient risk without the use of computers or other electronic devices. The use of such points-based risk-scoring systems facilitates evidence-based clinical decision making. There is a growing interest in cause-specific mortality and in non-fatal outcomes. However, when considering these types of outcomes, one must account for competing risks whose occurrence precludes the occurrence of the event of interest. We describe how points-based risk-scoring systems can be developed in the presence of competing events. We illustrate the application of these methods by developing risk-scoring systems for predicting cardiovascular mortality in patients hospitalized with acute myocardial infarction. Code in the R statistical programming language is provided for the implementation of the described methods. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

SUBMITTER: Austin PC

PROVIDER: S-EPMC5084773 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature

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Developing points-based risk-scoring systems in the presence of competing risks.

Austin Peter C PC Lee Douglas S DS D'Agostino Ralph B RB Fine Jason P JP

Statistics in medicine 20160519 22

Predicting the occurrence of an adverse event over time is an important issue in clinical medicine. Clinical prediction models and associated points-based risk-scoring systems are popular statistical methods for summarizing the relationship between a multivariable set of patient risk factors and the risk of the occurrence of an adverse event. Points-based risk-scoring systems are popular amongst physicians as they permit a rapid assessment of patient risk without the use of computers or other el ...[more]

PMID: 27197622

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Project description:BackgroundPostoperative ileus (POI) is one of the most common complications after laparoscopic radical cystectomy (LRC). Albeit its high incidence, its risk factors are obscure, and few studies have attempted to explore them. Meanwhile, risk-assessing tools for predicting its happening are lacking.MethodsClinical data of 197 patients who underwent LRC between March 2014 and October 2019 were retrospectively collected. All cases of POI were identified and double-checked. Data pertaining to the following categories were extracted as well: patients' general characteristics, preoperative laboratory tests results and preparations, intraoperative and postoperative general items, pathological results. The correlation between candidate risk factors and ileus was analyzed by multivariable binary logistic regression. Clinical and pathophysiological explanations for those results were explored. Finally, a points-based prediction model was developed and validated for predicting the happening of POI.ResultsA total of 63 out of 197 patients (31.98%) suffered from POI. Multivariate logistic regression analysis showed chronic constipation, increased dosage of laxative, elevated preoperative serum creatinine level, delayed postoperative ambulation, intestine-related urine derivations were statistically significant for developing POI (P<0.05). No significant differences were found between POI and age, gender, body mass index (BMI), antibiotics, hypertension, diabetes, smoking, hard-drinking, preoperative hemoglobin level, preoperative albumin level, history of previous abdominal surgery, surgery time, intraoperative blood loss, blood transfusion, tumor size, lymph nodes yields, TNM staging and intensive care unit hospitalization. An external cohort had been used for testing the validation of the assessment scale, and the results were promising.ConclusionsEarly recognition is of great importance in protecting vulnerable patients from developing POI, knowing the above-mentioned risk factors and using the assessment scale should help to screen them better. Cases from diverse backgrounds might contribute to a more accurate and complete scale.

Project description:Algorithmic scoring methods are widely used in the finance industry for several decades in order to prevent risk and to automate and optimize decisions. Regulatory requirements as given by the Basel Committee on Banking Supervision (BCBS) or the EU data protection regulations have led to an increasing interest and research activity on understanding black box machine learning models by means of explainable machine learning. Even though this is a step into a right direction, such methods are not able to guarantee for a fair scoring as machine learning models are not necessarily unbiased and may discriminate with respect to certain subpopulations such as a particular race, gender, or sexual orientation-even if the variable itself is not used for modeling. This is also true for white box methods like logistic regression. In this study, a framework is presented that allows analyzing and developing models with regard to fairness. The proposed methodology is based on techniques of causal inference and some of the methods can be linked to methods from explainable machine learning. A definition of counterfactual fairness is given together with an algorithm that results in a fair scoring model. The concepts are illustrated by means of a transparent simulation and a popular real-world example, the German Credit data using traditional scorecard models based on logistic regression and weight of evidence variable pre-transform. In contrast to previous studies in the field for our study, a corrected version of the data is presented and used. With the help of the simulation, the trade-off between fairness and predictive accuracy is analyzed. The results indicate that it is possible to remove unfairness without a strong performance decrease unless the correlation of the discriminative attributes on the other predictor variables in the model is not too strong. In addition, the challenge in explaining the resulting scoring model and the associated fairness implications to users is discussed.

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Developing points-based risk-scoring systems in the presence of competing risks.

Publications

Developing points-based risk-scoring systems in the presence of competing risks.

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