Providing therapies tailored to each patient is the vision of precision medicine, enabled by the increasing ability to capture extensive data about individual patients. In this position paper, we argue that the second enabling pillar towards this vision is the increasing power of computers and algorithms to learn, reason, and build the ‘digital twin’ of a patient. Computational models are boosting the capacity to draw diagnosis and prognosis, and future treatments will be tailored not only to current health status and data, but also to an accurate projection of the pathways to restore health by model predictions. The early steps of the digital twin in the area of cardiovascular medicine are reviewed in this article, together with a discussion of the challenges and opportunities ahead. We emphasize the synergies between mechanistic and statistical models in accelerating cardiovascular research and enabling the vision of precision medicine.

We investigated the ability of time-warping-based ECG-derived markers of T-wave morphology changes in time ($$d_{w}$$ d w ) and amplitude ($$d_a$$ d a ), as well as their non-linear components ($${d_w^{{\mathrm{NL}}}}$$ d w NL and $${d_a^{\mathrm{NL}}}$$ d a NL ), and the heart rate corrected counterpart ($$d_{w,c}$$ d w , c ), to monitor potassium concentration ($$[K^{+}]$$ [ K + ] ) changes ($$\Delta [K^+]$$ Δ [ K + ] ) in end-stage renal disease (ESRD) patients undergoing hemodialysis (HD). We compared the performance of the proposed time-warping markers, together with other previously proposed $$[K^{+}]$$ [ K + ] markers, such as T-wave width ($$T_w$$ T w ) and T-wave slope-to-amplitude ratio ($$T_{S/A}$$ T S / A ), when computed from standard ECG leads as well as from principal component analysis (PCA)-based leads. 48-hour ECG recordings and a set of hourly-collected blood samples from 29 ESRD-HD patients were acquired. Values of $$d_w$$ d w , $$d_a$$ d a , $${d_w^{\mathrm{NL}}}$$ d w NL , $${d_a^{\mathrm{NL}}}$$ d a NL and $$d_{w,c}$$ d w , c were calculated by comparing the morphology of the mean warped T-waves (MWTWs) derived at each hour along the HD with that from a reference MWTW, measured at the end of the HD. From the same MWTWs $$T_w$$ T w and $$T_{S/A}$$ T S / A were also extracted. Similarly, $$\Delta [K^+]$$ Δ [ K + ] was calculated as the difference between the $$[K^{+}]$$ [ K + ] values at each hour and the $$[K^{+}]$$ [ K + ] reference level at the end of the HD session. We found that $$d_{w}$$ d w and $$d_{w,c}$$ d w , c showed higher correlation coefficients with $$\Delta [K^+]$$ Δ [ K + ] than $$T_{S/A}$$ T S / A —Spearman’s ($$\rho$$ ρ ) and Pearson’s (r)—and $$T_w$$ T w —Spearman’s ($$\rho$$ ρ )—in both SL and PCA approaches being the intra-patient median $$\rho \ge 0.82$$ ρ ≥ 0.82 and $$r \ge 0.87$$ r ≥ 0.87 in SL and $$\rho \ge 0.82$$ ρ ≥ 0.82 and $$r \ge 0.89$$ r ≥ 0.89 in PCA respectively. Our findings would point at $$d_{w}$$ d w and $$d_{w,c}$$ d w , c as the most suitable surrogate of $$\Delta [K^+]$$ Δ [ K + ] , suggesting that they could be potentially useful for non-invasive monitoring of ESRD-HD patients in hospital, as well as in ambulatory settings. Therefore, the tracking of T-wave morphology variations by means of time-warping analysis could improve continuous and remote $$[K^{+}]$$ [ K + ] monitoring of ESRD-HD patients and flagging risk of $$[K^{+}]$$ [ K + ] -related cardiovascular events.