Automatic covariate selection in logistic models for chest pain diagnosis: A new approach

A newly established method for optimizing logistic models via a minorization-majorization procedure is applied to the problem of diagnosing acute coronary syndromes (ACS). The method provides a principled approach to the selection of covariates which would otherwise require the use of a suboptimal method owing to the size of the covariate set. A strategy for building models is proposed and two models optimized for performance and for simplicity are derived via ten-fold cross-validation. These models confirm that a relatively small set of covariates including clinical and electrocardiographic features can be used successfully in this task. The performance of the models is comparable with previously published models using less principled selection methods. The models prove to be portable when tested on data gathered from three other sites. Whilst diagnostic accuracy and calibration diminishes slightly for these new settings, it remains satisfactory overall. The prospect of building predictive models that are as simple as possible for a required level of performance is valuable if data-driven decision aids are to gain wide acceptance in the clinical situation owing to the need to minimize the time taken to gather and enter data at the bedside

TIMI, GRACE and alternative risk scores in Acute Coronary Syndromes: A meta-analysis of 40 derivation studies on 216,552 patients and of 42 validation studies on 31,625 patients.

BACKGROUND: Acute coronary syndromes (ACS) represent a difficult challenge for physicians. Risk scores have become the cornerstone in clinical and interventional decision making. METHODS AND RESULTS: PubMed was systematically searched for ACS risk score studies. They were divided into ACS studies (evaluating Unstable Angina; UA, Non ST Segment Elevation Myocardial Infarction; NSTEMI, and ST Segment Elevation Myocardial Infarction; STEMI), UA/NSTEMI studies or STEMI studies. The c-statistics of validation studies were pooled when appropriate with random-effect methods. 7 derivation studies with 25,525 ACS patients and 15 validation studies including 257,654 people were formally appraised. Pooled analysis of GRACE scores, both at short (0.82; 0.80-0.89 I.C 95%) and long term follow up (0.84; 0.82-0.87; I.C 95%) showed the best performance, with similar results to Simple Risk Index (SRI) derivation cohorts at short term. For NSTEMI/UA, 18 derivation studies with 56,560 patients and 18 validation cohorts with 56,673 patients were included. Pooled analysis of validations studies showed c-statistics of 0.54 (95% CI = 0.52-0.57) and 0.67 (95% CI = 0.62-0.71) for short and long term TIMI validation studies, and 0.83 (95% CI = 0.79-9.87) and 0.80 (95% CI = 0.74-0.89) for short and long term GRACE studies. For STEMI, 15 studies with 134,557 patients with derivation scores, and 17 validation studies with 187,619 patients showed a pooled c-statistic of 0.77 (95% CI = 0.71-0.83) and 0.77 (95% CI = 0.72-0.85) for TIMI at short and long term, and a pooled c-statistic of 0.82 (95% CI = 0.81-0.83) and 0.81 (95% CI = 0.80-0.82) for GRACE at short and long terms respectively. CONCLUSIONS: TIMI and GRACE are the risk scores that up until now have been most extensively investigated, with GRACE performing better. There are other potentially useful ACS risk scores available however these have not undergone rigorous validation. This study suggests that these other scores may be potentially useful and should be further researched