Open Access

Fabian Christopher Laqua

• Background Previous work has focused on speckle-tracking echocardiography (STE)-derived global longitudinal and circumferential peak strain as potential superior prognostic metric markers compared with left ventricular ejection fraction (LVEF). However, the value of regional distribution and the respective orientation of left ventricular wall motion (quantified as strain and derived from STE) for survival prediction have not been investigated yet. Moreover, most of the recent studies on risk stratification in primary and secondary prevention do not use neural networks for outcome prediction. Purpose To evaluate the performance of neural networks for predicting all cause-mortality with different model inputs in a moderate-sized general population cohort. Methods All participants of the second cohort of the population-based Study of Health in Pomerania (SHIP-TREND-0) without prior cardiovascular disease (CVD; acute myocardial infarction, cardiac surgery/intervention, heart failure and stroke) and with transthoracic echocardiography exams were followed for all-cause mortality from baseline examination (2008-2012) until 2019. A novel deep neural network architecture ‘nnet-Surv-rcsplines’, that extends the Royston-Parmar- cubic splines survival model to neural networks was proposed and applied to predict all-cause mortality from STE-derived global and/or regional myocardial longitudinal, circumferential, transverse, and radial strain in addition to the components of the ESC SCORE model. The models were evaluated by 8.5-year area-under-the-receiver-operating-characteristic (AUROC) and (scaled) Brier score [(S)BS]and compared to the SCORE model adjusted for mortality rates in Germany in 2010. Results In total, 3858 participants (53 % female, median age 51 years) were followed for a median time of 8.4 (95 % CI 8.3 – 8.5) years. Application of ‘nnet-Surv-rcsplines’ to the components of the ESC SCORE model alone resulted in the best discriminatory performance (AUROC 0.9 [0.86-0.91]) and lowest prediction error (SBS 21[18-23] %). The latter was significantly lower (p <0.001) than the original SCORE model (SBS 11 [9.5 - 13] %), while discrimination did not differ significantly. There was no difference in (S)BS (p= 0.66) when global circumferential and longitudinal strain were added to the model. Solely including STE-data resulted in an informative (AUROC 0.71 [0.69, 0.74]; SBS 3.6 [2.8-4.6] %) but worse (p<0.001) model performance than when considering the sociodemographic and instrumental biomarkers, too. Conclusion Regional myocardial strain distribution contains prognostic information for predicting all-cause mortality in a primary prevention sample of subjects without CVD. Still, the incremental prognostic value of STE parameters was not demonstrated. Application of neural networks on available traditional risk factors in primary prevention may improve outcome prediction compared to standard statistical approaches and lead to better treatment decisions.