Model-based patient matching for in-parallel pressure-controlled ventilation.

BACKGROUND: Surges of COVID-19 infections have led to insufficient supply of mechanical ventilators (MV), resulting in rationing of MV care. In-parallel, co-mechanical ventilation (Co-MV) of multiple patients is a potential solution. However, due to lack of testing, there is currently no means to match ventilation requirements or patients, with no guidelines to date. In this research, we have developed a model-based method for patient matching for pressure control mode MV. METHODS: The model-based method uses a single-compartment lung model (SCM) to simulate the resultant tidal volume of patient pairs at a set ventilation setting. If both patients meet specified safe ventilation criteria under similar ventilation settings, the actual mechanical ventilator settings for Co-MV are determined via simulation using a double-compartment lung model (DCM). This method allows clinicians to analyse Co-MV in silico, before clinical implementation. RESULTS: The proposed method demonstrates successful patient matching and MV setting in a model-based simulation as well as good discrimination to avoid mismatched patient pairs. The pairing process is based on model-based, patient-specific respiratory mechanics identified from measured data to provide useful information for guiding care. Specifically, the matching is performed via estimation of MV delivered tidal volume (mL/kg) based on patient-specific respiratory mechanics. This information can provide insights for the clinicians to evaluate the subsequent effects of Co-MV. In addition, it was also found that Co-MV patients with highly restrictive respiratory mechanics and obese patients must be performed with extra care. CONCLUSION: This approach allows clinicians to analyse patient matching in a virtual environment without patient risk. The approach is tested in simulation, but the results justify the necessary clinical validation in human trials.

Post-intensive care syndrome after a critical COVID-19: cohort study from a Belgian follow-up clinic.

PURPOSE: Many patients with coronavirus disease 2019 (COVID-19) required critical care. Mid-term outcomes of the survivors need to be assessed. The objective of this single-center cohort study was to describe their physical, cognitive, psychological, and biological outcomes at 3 months following intensive care unit (ICU)-discharge (M3). PATIENTS AND METHODS: All COVID-19 adults who survived an ICU stay ≥ 7 days and attended the M3 consultation at our multidisciplinary follow-up clinic were involved. They benefited from a standardized assessment, addressing health-related quality of life (EQ-5D-3L), sleep disorders (PSQI), and the three principal components of post-intensive care syndrome (PICS): physical status (Barthel index, handgrip and quadriceps strength), mental health disorders (HADS and IES-R), and cognitive impairment (MoCA). Biological parameters referred to C-reactive protein and creatinine. RESULTS: Among the 92 patients admitted to our ICU for COVID-19, 42 survived a prolonged ICU stay and 32 (80%) attended the M3 follow-up visit. Their median age was 62 [49-68] years, 72% were male, and nearly half received inpatient rehabilitation following ICU discharge. At M3, 87.5% (28/32) had not regained their baseline level of daily activities. Only 6.2% (2/32) fully recovered, and had normal scores for the three MoCA, IES-R and Barthel scores. The main observed disorders were PSQI > 5 (75%, 24/32), MoCA < 26 (44%, 14/32), Barthel < 100 (31%, 10/32) and IES-R ≥ 33 (28%, 9/32). Combined disorders were observed in 13/32 (40.6%) of the patients. The EQ-5D-3L visual scale was rated at 71 [61-80]. A quarter of patients (8/32) demonstrated a persistent inflammation based on CRP blood level (9.3 [6.8-17.7] mg/L). CONCLUSION: The burden of severe COVID-19 and prolonged ICU stay was considerable in the present cohort after 3 months, affecting both functional status and biological parameters. These data are an argument on the need for closed follow-up for critically ill COVID-19 survivors.

Outcome Improvement Between the First Two Waves of the Coronavirus Disease 2019 Pandemic in a Single Tertiary-Care Hospital in Belgium.

OBJECTIVES: To compare patient management and outcome during the first and second waves of the coronavirus 2019 pandemic. DESIGN: Single-center prospective cohort study. SETTING: Tertiary-care University Hospital. PATIENTS: All adult patients admitted in either the first (from March 15 to May 15, 2020) or second (from October 1 to November 30, 2020) wave of coronavirus disease 2019. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Primary outcome was 30-day mortality. During the second wave of the coronavirus disease 2019 pandemic, 33 patients (4.8%) were transferred due to overcrowding and excluded from analysis. There were 341 (first wave of the coronavirus disease 2019 pandemic) and 695 (second wave of the coronavirus disease 2019 pandemic) coronavirus disease 2019 patients admitted to the hospital, with median age first wave of the coronavirus disease 2019 pandemic as 68 (57-80) and second wave of the coronavirus disease 2019 pandemic as 71 (60-80) (p = 0.15), and similar admission severity. For the first wave of the coronavirus disease 2019 pandemic versus second wave of the coronavirus disease 2019 pandemic, 30-day mortality was 74/341 (22%) and 98/662 (15%) (p = 0.007). In the ward, 11/341 (3.2%) and 404/662 (61%) received dexamethasone (p < 0.001); 6/341 (2%) and 79/662 (12%) received high-flow nasal oxygen (p < 0.0001); 2/341 (0.6%) and 88/662 (13.3%) received remdesivir (p < 0.0001); 249/341 (73%) and 0/662 (0%) received hydroxychloroquine (p < 0.0001); and 87/341 (26%) and 128/662 (19%) (p = 0.024) patients were transferred to ICU. On ICU admission, median Sequential Organ Failure Assessment was 6 (3-7) and 4 (3-6) (p = 0.02). High-flow nasal oxygen was given to 16/87 (18%) and 102/128 (80%) (p < 0.001); 69/87 (79%) and 56/128 (44%) received mechanical ventilation (p < 0.001) with durations 17 days (10-26 d) and 10 days (5-17 d) (p = 0.01). Median ICU length of stay was 14 days (5-27 d) and 6 days (3-11 d) (p < 0.001). Finally, 16/87 (18%) and 8/128 (6%) received renal replacement therapy (p = 0.0055); and 64/87 (74%) and 51/128 (40%) needed vasopressor support (p < 0.001). CONCLUSIONS: The main therapeutic changes between the first wave of the coronavirus disease 2019 pandemic and the second wave of the coronavirus disease 2019 pandemic were use of steroids, unrestrictive use of high-flow nasal oxygen for hypoxemic patients, and transfer of patients to other geographic areas in the case of ICU overcrowding. These changes were associated with a decrease in 30-day mortality, ICU admission, and organ support.

Virtual patients for mechanical ventilation in the intensive care unit.

BACKGROUND: Mechanical ventilation (MV) is a core intensive care unit (ICU) therapy. Significant inter- and intra- patient variability in lung mechanics and condition makes managing MV difficult. Accurate prediction of patient-specific response to changes in MV settings would enable optimised, personalised, and more productive care, improving outcomes and reducing cost. This study develops a generalised digital clone model, or in-silico virtual patient, to accurately predict lung mechanics in response to changes in MV. METHODS: An identifiable, nonlinear hysteresis loop model (HLM) captures patient-specific lung dynamics identified from measured ventilator data. Identification and creation of the virtual patient model is fully automated using the hysteresis loop analysis (HLA) method to identify lung elastances from clinical data. Performance is evaluated using clinical data from 18 volume-control (VC) and 14 pressure-control (PC) ventilated patients who underwent step-wise recruitment maneuvers. RESULTS: Patient-specific virtual patient models accurately predict lung response for changes in PEEP up to 12 cmH2O for both volume and pressure control cohorts. R2 values for predicting peak inspiration pressure (PIP) and additional retained lung volume, Vfrc in VC, are R2=0.86 and R2=0.90 for 106 predictions over 18 patients. For 14 PC patients and 84 predictions, predicting peak inspiratory volume (PIV) and Vfrc yield R2=0.86 and R2=0.83. Absolute PIP, PIV and Vfrc errors are relatively small. CONCLUSIONS: Overall results validate the accuracy and versatility of the virtual patient model for capturing and predicting nonlinear changes in patient-specific lung mechanics. Accurate response prediction enables mechanically and physiologically relevant virtual patients to guide personalised and optimised MV therapy.