Background: Health-related quality of life (HRQoL) impairment is often reported among COVID-19 ICU survivors, and little is known about their long-term outcomes. We evaluated the HRQoL trajectories between 3 months and 1 year after ICU discharge, the factors influencing these trajectories and the presence of clusters of HRQoL profiles in a population of COVID-19 patients who underwent invasive mechanical ventilation (IMV). Moreover, pathophysiological correlations of residual dyspnea were tested. Methods: We followed up 178 survivors from 16 Italian ICUs up to one year after ICU discharge. HRQoL was investigated through the 15D instrument. Available pulmonary function tests (PFTs) and chest CT scans at 1 year were also collected. A linear mixed-effects model was adopted to identify factors associated with different HRQoL trajectories and a two-step cluster analysis was performed to identify HRQoL clusters. Results: We found that HRQoL increased during the study period, especially for the significant increase of the physical dimensions, while the mental dimensions and dyspnea remained substantially unchanged. Four main 15D profiles were identified: full recovery (47.2%), bad recovery (5.1%) and two partial recovery clusters with mostly physical (9.6%) or mental (38.2%) dimensions affected. Gender, duration of IMV and number of comorbidities significantly influenced HRQoL trajectories. Persistent dyspnea was reported in 58.4% of patients, and weakly, but significantly, correlated with both DLCO and length of IMV. Conclusions: HRQoL impairment is frequent 1 year after ICU discharge, and the lowest recovery is found in the mental dimensions. Persistent dyspnea is often reported and weakly correlated with PFTs alterations. Trial registration: NCT04411459. 15D score 3 months -mean ± SD 0.857 ± 0.133 0.927 ± 0.061 0.800 ± 0.135 0.853 ± 0.114 0.637 ± 0.204 < 0.001 15D score 1 year -mean ± SD 0.880 ± 0.115 0.964 ± 0.033 0.820 ± 0.068 0.866 ± 0.088 0.572 ± 0.112 < 0.001 Mobility -mean ± SD 0.876 ± 0.207 0.963 ± 0.104 0.828 ± 0.191 0.901 ± 0.166 0.375 ± 0.298 < 0.001 Vision -mean ± SD 0.953 ± 0.119 0.992 ± 0.040 0.942 ± 0.108 0.949 ± 0.094 0.681 ± 0.280 < 0.001 Hearing -mean ± SD 0.968 ± 0.098 1.000 ± 0.000 1.000 ± 0.000 0.745 ± 0.135 0.857 ± 0.192 < 0.001 Breathing -mean ± SD 0.746 ± 0.238 0.879 ± 0.154 0.620 ± 0.227 0.753 ± 0.223 0.438 ± 0.238 < 0.001 Sleeping -mean ± SD 0.838 ± 0.238 0.940 ± 0.135 0.716 ± 0.274 0.929 ± 0.142 0.632 ± 0.312 < 0.001 Eating -mean ± SD 0.979 ± 0.102 1.000 ± 0.000 1 .000 ± 0.000 1.000 ± 0.000 0.587 ± 0.221 < 0.001 Speech -mean ± SD 0.980 ± 0.090 0.996 ± 0.032 0.996 ± 0.036 0.948 ± 0.117 0.777 ± 0.276 < 0.001 Excretion -mean ± SD 0.974 ± 0.110 1.000 ± 0.000 1.000 ± 0.000 0.872 ± 0.191 0.720 ± 0.292
The COVID-19 pandemic has increased the need for a bedside tool for lung mechanics assessment and ventilator-induced lung injury (VILI) monitoring. Mechanical power is a unifying concept including all the components which can possibly cause VILI (volume, pressures, flow, respiratory rate), but the complexity of its mathematical computation makes it not so feasible in routine practice and limits its clinical use. In this letter, we describe the development of a mobile application that allows to simply measure power associated with mechanical ventilation, identifying each component (respiratory rate, resistance, driving pressure, PEEP volume) as well. The major advantage, according to the authors who developed this mathematical description of mechanical power, is that it enables the quantification of the relative contribution of its different components (tidal volume, driving pressure, respiratory rate, resistance). Considering the potential role of medical apps to improve work efficiency, we developed an open source Progressive Web Application (PWA), named “PowerApp” (freely available at https://mechpower.goodbarber.app), in order to easily obtain a bedside measurement of mechanical power and its components. It also allows to predict how the modification of ventilatory settings or physiological conditions would affect power and each relative component. The "PowerApp" allows to measure mechanical power at a glance during mechanical ventilation, without complex mathematical computation, and making mechanical power equation useful and feasible for everyday clinical practice.
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