Development and validation of a prognostic model for the early identification of COVID-19 patients at risk of developing common long COVID symptoms.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic demands reliable prognostic models for estimating the risk of long COVID. We developed and validated a prediction model to estimate the probability of known common long COVID symptoms at least 60 days after acute COVID-19. METHODS: The prognostic model was built based on data from a multicentre prospective Swiss cohort study. Included were adult patients diagnosed with COVID-19 between February and December 2020 and treated as outpatients, at ward or intensive/intermediate care unit. Perceived long-term health impairments, including reduced exercise tolerance/reduced resilience, shortness of breath and/or tiredness (REST), were assessed after a follow-up time between 60 and 425 days. The data set was split into a derivation and a geographical validation cohort. Predictors were selected out of twelve candidate predictors based on three methods, namely the augmented backward elimination (ABE) method, the adaptive best-subset selection (ABESS) method and model-based recursive partitioning (MBRP) approach. Model performance was assessed with the scaled Brier score, concordance c statistic and calibration plot. The final prognostic model was determined based on best model performance. RESULTS: In total, 2799 patients were included in the analysis, of which 1588 patients were in the derivation cohort and 1211 patients in the validation cohort. The REST prevalence was similar between the cohorts with 21.6% (n = 343) in the derivation cohort and 22.1% (n = 268) in the validation cohort. The same predictors were selected with the ABE and ABESS approach. The final prognostic model was based on the ABE and ABESS selected predictors. The corresponding scaled Brier score in the validation cohort was 18.74%, model discrimination was 0.78 (95% CI: 0.75 to 0.81), calibration slope was 0.92 (95% CI: 0.78 to 1.06) and calibration intercept was -0.06 (95% CI: -0.22 to 0.09). CONCLUSION: The proposed model was validated to identify COVID-19-infected patients at high risk for REST symptoms. Before implementing the prognostic model in daily clinical practice, the conduct of an impact study is recommended.

Closed-Loop Versus Conventional Mechanical Ventilation in COVID-19 ARDS.

BACKGROUND: Lung-protective ventilation is key in bridging patients suffering from COVID-19 acute respiratory distress syndrome (ARDS) to recovery. However, resource and personnel limitations during pandemics complicate the implementation of lung-protective protocols. Automated ventilation modes may prove decisive in these settings enabling higher degrees of lung-protective ventilation than conventional modes. METHOD: Prospective study at a Swiss university hospital. Critically ill, mechanically ventilated COVID-19 ARDS patients were allocated, by study-blinded coordinating staff, to either closed-loop or conventional mechanical ventilation, based on mechanical ventilator availability. Primary outcome was the overall achieved percentage of lung-protective ventilation in closed-loop versus conventional mechanical ventilation, assessed minute-by-minute, during the initial 7 days and overall mechanical ventilation time. Lung-protective ventilation was defined as the combined target of tidal volume <8 ml per kg of ideal body weight, dynamic driving pressure <15 cmH2O, peak pressure <30 cmH2O, peripheral oxygen saturation ≥88% and dynamic mechanical power <17 J/min. RESULTS: Forty COVID-19 ARDS patients, accounting for 1,048,630 minutes (728 days) of cumulative mechanical ventilation, allocated to either closed-loop (n = 23) or conventional ventilation (n = 17), presenting with a median paO2/ FiO2 ratio of 92 [72-147] mmHg and a static compliance of 18 [11-25] ml/cmH2O, were mechanically ventilated for 11 [4-25] days and had a 28-day mortality rate of 20%. During the initial 7 days of mechanical ventilation, patients in the closed-loop group were ventilated lung-protectively for 65% of the time versus 38% in the conventional group (Odds Ratio, 1.79; 95% CI, 1.76-1.82; P < 0.001) and for 45% versus 33% of overall mechanical ventilation time (Odds Ratio, 1.22; 95% CI, 1.21-1.23; P < 0.001). CONCLUSION: Among critically ill, mechanically ventilated COVID-19 ARDS patients during an early highpoint of the pandemic, mechanical ventilation using a closed-loop mode was associated with a higher degree of lung-protective ventilation than was conventional mechanical ventilation.

Capillary Leukocytes, Microaggregates, and the Response to Hypoxemia in the Microcirculation of Coronavirus Disease 2019 Patients.

OBJECTIVES: In this study, we hypothesized that coronavirus disease 2019 patients exhibit sublingual microcirculatory alterations caused by inflammation, coagulopathy, and hypoxemia. DESIGN: Multicenter case-controlled study. SETTING: Two ICUs in The Netherlands and one in Switzerland. PATIENTS: Thirty-four critically ill coronavirus disease 2019 patients were compared with 33 healthy volunteers. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The microcirculatory parameters quantified included total vessel density (mm × mm-2), functional capillary density (mm × mm-2), proportion of perfused vessels (%), capillary hematocrit (%), the ratio of capillary hematocrit to systemic hematocrit, and capillary RBC velocity (µm × s-1). The number of leukocytes in capillary-postcapillary venule units per 4-second image sequence (4 s-1) and capillary RBC microaggregates (4 s-1) was measured. In comparison with healthy volunteers, the microcirculation of coronavirus disease 2019 patients showed increases in total vessel density (22.8 ± sd 5.1 vs 19.9 ± 3.3; p < 0.0001) and functional capillary density (22.2 ± 4.8 vs 18.8 ± 3.1; p < 0.002), proportion of perfused vessel (97.6 ± 2.1 vs 94.6 ± 6.5; p < 0.01), RBC velocity (362 ± 48 vs 306 ± 53; p < 0.0001), capillary hematocrit (5.3 ± 1.3 vs 4.7 ± 0.8; p < 0.01), and capillary-hematocrit-to-systemic-hematocrit ratio (0.18 ± 0.0 vs 0.11 ± 0.0; p < 0.0001). These effects were present in coronavirus disease 2019 patients with Sequential Organ Failure Assessment scores less than 10 but not in patients with Sequential Organ Failure Assessment scores greater than or equal to 10. The numbers of leukocytes (17.6 ± 6.7 vs 5.2 ± 2.3; p < 0.0001) and RBC microaggregates (0.90 ± 1.12 vs 0.06 ± 0.24; p < 0.0001) was higher in the microcirculation of the coronavirus disease 2019 patients. Receiver-operating-characteristics analysis of the microcirculatory parameters identified the number of microcirculatory leukocytes and the capillary-hematocrit-to-systemic-hematocrit ratio as the most sensitive parameters distinguishing coronavirus disease 2019 patients from healthy volunteers. CONCLUSIONS: The response of the microcirculation to coronavirus disease 2019-induced hypoxemia seems to be to increase its oxygen-extraction capacity by increasing RBC availability. Inflammation and hypercoagulation are apparent in the microcirculation by increased numbers of leukocytes and RBC microaggregates.

Bacterial pulmonary superinfections are associated with longer duration of ventilation in critically ill COVID-19 patients.

The impact of secondary bacterial infections (superinfections) in coronavirus disease 2019 (COVID-19) is not well understood. In this prospective, monocentric cohort study, we aim to investigate the impact of superinfections in COVID-19 patients with acute respiratory distress syndrome. Patients are assessed for concomitant microbial infections by longitudinal analysis of tracheobronchial secretions, bronchoalveolar lavages, and blood cultures. In 45 critically ill patients, we identify 19 patients with superinfections (42.2%). Superinfections are detected on day 10 after intensive care admission. The proportion of participants alive and off invasive mechanical ventilation at study day 28 (ventilator-free days [VFDs] at 28 days) is substantially lower in patients with superinfection (subhazard ratio 0.37; 95% confidence interval [CI] 0.15-0.90; p = 0.028). Patients with pulmonary superinfections have a higher incidence of bacteremia, virus reactivations, yeast colonization, and required intensive care treatment for a longer time. Superinfections are frequent and associated with reduced VFDs at 28 days despite a high rate of empirical antibiotic therapy.