Association between multimorbidity and postoperative mortality in patients undergoing major surgery: a prospective study in 29 countries across Europe

BackgroundMultimorbidity poses a global challenge to healthcare delivery. This study aimed to describe the prevalence of multimorbidity, common disease combinations and outcomes in a contemporary cohort of patients undergoing major abdominal surgery.MethodsThis was a pre-planned analysis of a prospective, multicentre, international study investigating cardiovascular complications after major abdominal surgery conducted in 446 hospitals in 29 countries across Europe. The primary outcome was 30-day postoperative mortality. The secondary outcome measure was the incidence of complications within 30 days of surgery.ResultsOf 24,227 patients, 7006 (28.9%) had one long-term condition and 10,486 (43.9%) had multimorbidity (two or more long-term health conditions). The most common conditions were primary cancer (39.6%); hypertension (37.9%); chronic kidney disease (17.4%); and diabetes (15.4%). Patients with multimorbidity had a higher incidence of frailty compared with patients <= 1 long-term health condition. Mortality was higher in patients with one long-term health condition (adjusted odds ratio 1.93 (95%CI 1.16-3.23)) and multimorbidity (adjusted odds ratio 2.22 (95%CI 1.35-3.64)). Frailty and ASA physical status 3-5 mediated an estimated 31.7% of the 30-day mortality in patients with one long-term health condition (adjusted odds ratio 1.30 (95%CI 1.12-1.51)) and an estimated 36.9% of the 30-day mortality in patients with multimorbidity (adjusted odds ratio 1.61 (95%CI 1.36-1.91)). There was no improvement in 30-day mortality in patients with multimorbidity who received pre-operative medical assessment.ConclusionsMultimorbidity is common and outcomes are poor among surgical patients across Europe. Addressing multimorbidity in elective and emergency patients requires innovative strategies to account for frailty and disease control. The development of such strategies, that integrate care targeting whole surgical pathways to strengthen current systems, is urgently needed for multimorbid patients. Interventional trials are warranted to determine the effectiveness of targeted management for surgical patients with multimorbidity

Multiphase mixed-integer nonlinear optimal control of hybrid electric vehicles

This study considers the problem of computing a non-causal minimum-fuel energy management strategy for a hybrid electric vehicle on a given driving cycle. Specifically, we address the multiphase mixed-integer nonlinear optimal control problem that arises when the optimal gear choice, torque split and engine on/off controls are sought in off-line evaluations. We propose an efficient model by introducing vanishing constraints and a phase specific right-hand side function that accounts for the different powertrain operating modes. The gearbox and driveability requirements translate into combinatorial constraints. These constraints have not been included in previous research; however, they are part of the algorithmic framework for this investigation. We devise a tailored algorithm to solve this problem by extending the combinatorial integral approximation (CIA) technique that breaks down the original mixed-integer nonlinear program into a sequence of nonlinear programs and mixed-integer linear programs, followed by a discussion of its approximation error. Finally, numerical results illustrate the proposed algorithm in terms of solution quality and run time

Minimum-fuel Engine On/Off Control for the Energy Management of a Hybrid Electric Vehicle via Iterative Linear Programming

In this paper we present models and optimization algorithms to rapidly compute the fuel-optimal energy management strategies of a hybrid electric powertrain for a given driving cycle. Specifically, we first identify a mixed-integer model of the system, including the engine on/off signal. Thereafter, by carefully relaxing the fuel-optimal control problem to a linear program, we devise an iterative algorithm to rapidly compute the minimum-fuel energy management strategies. We validate our approach by comparing its solution with the globally optimal one obtained solving the mixed-integer linear problem and demonstrate its effectiveness by assessing the impact of different battery charge targets on the achievable fuel consumption. Numerical results show that the proposed algorithm can assess fuel-optimal control strategies in a few seconds, paving the way for extensive parameter studies and real-time implementations

Minimum-Fuel Energy Management of a Hybrid Electric Vehicle via Iterative Linear Programming

This paper presents models and optimization algorithms to compute the fuel-optimal energy management strategies for a parallel hybrid electric powertrain on a given driving cycle. Specifically, we first identify a mixed-integer model of the system, including the engine on/off signal and the gear-shift commands. Thereafter, by carefully relaxing the fuel-optimal control problem to a linear program, we devise an iterative algorithm to rapidly compute the minimum-fuel energy management strategies including the optimal gear-shift trajectory. We validate our approach by comparing its solution with the globally optimal one obtained solving the mixed-integer linear program and with the one resulting from the implementation of the optimal strategies in a high-fidelity nonlinear simulator. We showcase the effectiveness of the presented algorithm by assessing the impact of different powertrain configurations and electric motor size on the achievable fuel consumption. Our numerical results show that the proposed algorithm can assess fuel-optimal control strategies with low computational burden, and that powertrain design choices significantly affect the achievable fuel consumption of the vehicle

On the dynamics of a high frequency oscillator for mechanical watches

This paper presents a new mechanical regulator for wrist watches, highlighting the methodology used to set up a comprehensive model of the device. The mechanical regulator, which is characterized by a high frequency monolithic oscillator made of monocrystalline silicon coupled to a deadbeat escapement, has been designed to provide a high quality factor, a condition necessary to guarantee enhanced chronometric performances compared to traditional mechanical regulators. The chronometric performances and the isochronism of the oscillating system have been assessed by means of a multi-body model whose parameters have been evaluated by FE and CFD simulations