A patient-centered multidisciplinary cardiac rehabilitation program improves glycemic control and functional outcome in coronary artery disease after percutaneous and surgical revascularization

Background: Cardiac rehabilitation (CR) is strongly associated with all-cause mortality reduction in patients with coronary artery disease (CAD). The impact of CR on pathological risk factors, such as impaired glucose tolerance (IGT), and functional recovery remains under debate. The aim of the present study is to determine whether CR has a positive effect on physical exercise improvement and on pathological risk factors in IGT and diabetic patients with CAD. Methods: One hundred and seventy-one consecutive patients participating in a 3-month CR from January 2014 to June 2015 were enrolled. The primary endpoint was defined as an improvement of peak workload and VO2-peak; glycated hemoglobin (HbA1c) reduction was considered as a secondary endpoint. Results: Euglycemic patients presented a significant improvement in peak workload compared to diabetic patients (from 5.75 ± 1.45 to 6.65 ± 1.84 METs, p = 0.018 vs. 4.8 ± 0.8 to 4.9 ± 1.4 METs). VO2-peak improved in euglycemic patients (VO2-peak from 19.3 ± 5.3 mL/min/kg to 22.5 ± 5.9, p = 0.003), while diabetic patients did not present  a  statistically significant trend (VO2-peak from 16.9 ± 4.4 mL/min/kg to 18.0 ± 3.8, p < 0.056). Diabetic patients have benefited more in terms of blood glucose control compared to IGT patients (HbA1c from 7.7 ± 1.0 to 7.4 ± 1.1 compared to 5.6 ± 0.4 to 5.9 ± 0.5, p = 0.02, respectively). Conclusions: A multidisciplinary CR program improves physical functional capacity in CAD setting, particularly in euglycemic patients. IGT patients as well as diabetic patients may benefit from a CR program, but long-term outcome needs to be clarified in larger studies

A patient-centered multidisciplinary cardiac rehabilitation program improves glycemic control and functional outcome in coronary artery disease after percutaneous and surgical revascularization

BACKGROUND Cardiac rehabilitation (CR) is strongly associated with all-cause mortality reduction in patients with coronary artery disease (CAD). The impact of CR on pathological risk factors, such as impaired glucose tolerance (IGT), and functional recovery remains under debate. The aim of the present study is to determine whether CR has a positive effect on physical exercise improvement and on pathological risk factors in IGT and diabetic patients with CAD. METHODS One hundred and seventy-one consecutive patients participating in a 3-month CR from January 2014 to June 2015 were enrolled. The primary endpoint was defined as an improvement of peak workload and VO2-peak; glycated hemoglobin (HbA1c) reduction was considered as a secondary endpoint. RESULTS Euglycemic patients presented a significant improvement in peak workload compared to diabetic patients (from 5.75 ± 1.45 to 6.65 ± 1.84 METs, p = 0.018 vs. 4.8 ± 0.8 to 4.9 ± 1.4 METs). VO2-peak improved in euglycemic patients (VO2-peak from 19.3 ± 5.3 mL/min/kg to 22.5 ± 5.9, p = 0.003), while diabetic patients did not present a statistically significant trend (VO2-peak from 16.9 ± 4.4 mL/min/kg to 18.0 ± 3.8, p < 0.056). Diabetic patients have benefited more in terms of blood glucose control compared to IGT patients (HbA1c from 7.7 ± 1.0 to 7.4 ± 1.1 compared to 5.6 ± 0.4 to 5.9 ± 0.5, p = 0.02, respectively). CONCLUSIONS A multidisciplinary CR program improves physical functional capacity in CAD setting, particularly in euglycemic patients. IGT patients as well as diabetic patients may benefit from a CR program, but long-term outcome needs to be clarified in larger studies

Accelerating Binary and Mixed-Precision NNs Inference on STMicroelectronics Embedded NPU with Digital In-Memory-Computing

The proliferation of embedded Neural Processing Units (NPUs) is enabling the adoption of Tiny Machine Learning for numerous cognitive computing applications on the edge, where maximizing energy efficiency is key. To overcome the limitations of traditional Von Neumann architectures, novel designs based on computational memories are arising. STMicroelectronics is developing an experimental low-power NPU that integrates Digital In-Memory Computing (DIMC) SRAM with a modular dataflow inference engine, capable of accelerating a wide range of DNNs. In this work, we present a 40nm preliminary version of this architecture with DIMC-SRAM tiles capable of in-memory binary computations to dramatically increase the computational efficiency of binary layers. We performed power/performance analysis to demonstrate the advantages of this paradigm, which in our experiments achieved a TOPS/W efficiency up to 40x higher than traditional NPU implementations. We have then extended the ST Neural compilation toolchain to automatically map binary and mixed-precision NNs on the NPU, applying high-level optimizations and binding the models’ binary GEMM and CONV layers to the DIMC tiles. The overall system was validated by developing three real-time applications that represent potential real-world power-constrained use-cases: Fan spinning anomaly detection, Keyword spotting and Face Presence Detection. The applications ran with a latency &lt; 3 ms, and the DIMC subsystem achieved a peak efficiency &gt; 100 TOPS/W for binary in-memory computation