Residual lung damage following ARDS in COVID-19 ICU survivors.

BACKGROUND: Coronavirus disease 2019 acute respiratory distress syndrome (COVID-19 ARDS) is a disease that often requires invasive ventilation. Little is known about COVID-19 ARDS sequelae. We assessed the mid-term lung status of COVID-19 survivors and investigated factors associated with pulmonary sequelae. METHODS: All adult COVID-19 patients admitted to the intensive care unit from 25th February to 27th April 2020 were included. Lung function was evaluated through chest CT scan and pulmonary function tests (PFT). Logistic regression was used to identify predictors of persisting lung alterations. RESULTS: Forty-nine patients (75%) completed lung assessment. Chest CT scan was performed after a median (interquartile range) time of 97 (89-105) days, whilst PFT after 142 (133-160) days. The median age was 58 (52-65) years and most patients were male (90%). The median duration of mechanical ventilation was 11 (6-16) days. Median tidal volume/ideal body weight (TV/IBW) was 6.8 (5.71-7.67) ml/Kg. 59% and 63% of patients showed radiological and functional lung sequelae, respectively. The diffusion capacity of carbon monoxide (DLCO ) was reduced by 59%, with a median per cent of predicted DLCO of 72.1 (57.9-93.9) %. Mean TV/IBW during invasive ventilation emerged as an independent predictor of persistent CT scan abnormalities, whilst the duration of mechanical ventilation was an independent predictor of both CT and PFT abnormalities. The extension of lung involvement at hospital admission (evaluated through Radiographic Assessment of Lung Edema, RALE score) independently predicted the risk of persistent alterations in PFTs. CONCLUSIONS: Both the extent of lung parenchymal involvement and mechanical ventilation protocols predict morphological and functional lung abnormalities months after COVID-19.

Six-month respiratory outcomes and exercise capacity of COVID-19 acute respiratory failure patients treated with continuous positive airway pressure.

BACKGROUND: COVID-19 long-term sequelae are ill-defined since only a few studies have explored the long-term consequences of this disease so far. AIMS: To evaluate the 6-month respiratory outcome and exercise capacity of COVID-19 acute respiratory failure (ARF) patients treated with continuous positive airway pressure (CPAP) during the first wave of the ongoing COVID-19 pandemic. METHODS: A retrospective observational study included COVID-19 patients with ARF. Interventions included CPAP during hospitalisation and 6-month follow up. Frailty assessment was carried out through frailty index (FI), pO2 /FiO2 during hospitalisation and at follow up, respiratory parameters, 6-min walking test (6MWT) and the modified British Medical Research Council (mMRC) and Borg scale at follow up. RESULTS: More than half of the patients had no dyspnoea according to the mMRC scale. Lower in-hospital pO2 /FiO2 correlated with higher Borg scale levels after 6MWT (ρ 0.27; P 0.04) at the follow-up visit. FI was positively correlated with length of hospitalisation (ρ 0.3; P 0.03) and negatively with the 6MWT distance walked (ρ -0.36; P 0.004). CONCLUSIONS: Robust and frail patients with COVID-19 ARF treated with CPAP outside the intensive care unit setting had good respiratory parameters and exercise capacity at 6-month follow up, although more severe patients had slightly poorer respiratory performance compared with patients with higher PaO2 /FiO2 and lower FI.

Physical and psychological sequelae at three months after acute illness in COVID-19 survivors.

BACKGROUND: Coronavirus disease 2019 (COVID-19) may leave behind an altered health status early after recovery. We evaluated the clinical status of COVID-19 survivors at three months after hospital discharge. METHODS: In this prospective observational cohort study, hospitalized patients aged ≥18 years, evaluated at one (M1) and three (M3) months post-discharge were enrolled. 251 patients (71.3% males, median [IQR] age 61.8 [53.5-70.7] years) were included. Median (IQR) time from discharge to M3 was 89 (79.5-101) days. Primary outcome was residual respiratory dysfunction (RRD), defined by tachypnea, moderate to very severe dyspnea, or peripheral oxygen saturation ≤95% on room air at M3. RESULTS: RRD was found in 30.4% of patients, with no significant difference compared with M1. Chronic obstructive pulmonary disease and length of stay were independent predictors of RRD at multivariable logistic regression (OR [95% CI]: 4.13 [1.17-16.88], P=0.033; OR [95% CI]: 1.02 [1.00-1.04], P=0.047, respectively). Obesity and C-reactive protein levels upon admission were additional predictors at regression tree analysis. Impaired quality of life (QoL) was reported by 53.2% of patients. Anxiety and insomnia were each present in 25.5% of patients, and PTSD in 22.4%. No difference was found between M1 and M3 in QoL, anxiety or PTSD. Insomnia decreased at M3. Current major psychiatric disorder as well as anxiety, insomnia and PSTD at M1 independently predicted PTSD at M3. CONCLUSIONS: Clinical damage may persist at three months after discharge in COVID-19 survivors. Post-recovery follow-up is an essential component of patient management.

Vertebral fractures at hospitalization predict impaired respiratory function during follow-up of COVID-19 survivors.

PURPOSE: Morphometric vertebral fractures (VFs) have been recently reported as an important component of the endocrine phenotype of COVID-19 and emerging data show negative respiratory sequelae at long-term follow-up in COVID-19 survivors. The aim of this study was to evaluate the impact of VFs on respiratory function in COVID-19 survivors. METHODS: We included patients referred to our Hospital Emergency Department and re-evaluated during follow-up. VFs were detected on lateral chest X-rays on admission using a qualitative and semiquantitative assessment and pulmonary function tests were obtained by Jaeger-MasterScreen-Analyzer Unit 6 months after discharge. RESULTS: Fifty patients were included. Median age was 66 years and 66% were males. No respiratory function data were available at COVID-19 diagnosis. VFs were detected in 16 (32%) patients. No differences between fractured and non-fractured patients regarding age and sex were observed. Although no difference was observed between VF and non-VF patient groups in the severity of pneumonia as assessed by Radiological-Assessment-of-Lung-Edema score at admission, (5 vs. 6, p = 0.69), patients with VFs were characterized as compared to those without VFs by lower Forced Vital Capacity (FVC, 2.9 vs. 3.6 L, p = 0.006; 85% vs. 110% of predicted, respectively, p = 0.001), Forced Expiratory Volume 1st s (FEV1, 2.2 vs. 2.8 L, p = 0.005; 92% vs. 110% of predicted, respectively, p = 0.001) and Diffusing Capacity of the Lungs for Carbon Monoxide (DLCO 5.83 vs. 6.98 mmol/min/kPa, p = 0.036, 59% vs. 86.3% of predicted, respectively, p = 0.043) at 6-month follow up. CONCLUSIONS: VFs, expression of the endocrine phenotype of the disease, appear to influence medium-term impaired respiratory function of COVID-19 survivors which may significantly influence their recovery. Therefore, our findings suggest that a VFs assessment at baseline may help in identifying patients needing a more intensive respiratory follow-up and patients showing persistent respiratory impairment without evidence of pulmonary disease may benefit from VFs assessment to preventing the vicious circle of further fractures and respiratory deterioration.

A Nomogram-Based Model to Predict Respiratory Dysfunction at 6 Months in Non-Critical COVID-19 Survivors.

Objective: To assess the prevalence of respiratory sequelae of Coronavirus disease 2019 (COVID-19) survivors at 6 months after hospital discharge and develop a model to identify at-risk patients. Patients and Methods: In this prospective cohort study, hospitalized, non-critical COVID-19 patients evaluated at 6-month follow-up between 26 August, 2020 and 16 December, 2020 were included. Primary outcome was respiratory dysfunction at 6 months, defined as at least one among tachypnea at rest, percent predicted 6-min walking distance at 6-min walking test (6MWT) ≤ 70%, pre-post 6MWT difference in Borg score ≥ 1 or a difference between pre- and post-6MWT oxygen saturation ≥ 5%. A nomogram-based multivariable logistic regression model was built to predict primary outcome. Validation relied on 2000-resample bootstrap. The model was compared to one based uniquely on degree of hypoxemia at admission. Results: Overall, 316 patients were included, of whom 118 (37.3%) showed respiratory dysfunction at 6 months. The nomogram relied on sex, obesity, chronic obstructive pulmonary disease, degree of hypoxemia at admission, and non-invasive ventilation. It was 73.0% (95% confidence interval 67.3-78.4%) accurate in predicting primary outcome and exhibited minimal departure from ideal prediction. Compared to the model including only hypoxemia at admission, the nomogram showed higher accuracy (73.0 vs 59.1%, P < 0.001) and greater net-benefit in decision curve analyses. When the model included also respiratory data at 1 month, it yielded better accuracy (78.2 vs. 73.2%) and more favorable net-benefit than the original model. Conclusion: The newly developed nomograms accurately identify patients at risk of persistent respiratory dysfunction and may help inform clinical priorities.

Robust prediction of mortality of COVID-19 patients based on quantitative, operator-independent, lung CT densitometry.

PURPOSE: To train and validate a predictive model of mortality for hospitalized COVID-19 patients based on lung densitometry. METHODS: Two-hundred-fifty-one patients with respiratory symptoms underwent CT few days after hospitalization. "Aerated" (AV), "consolidated" (CV) and "intermediate" (IV) lung sub-volumes were quantified by an operator-independent method based on individual HU maximum gradient recognition. AV, CV, IV, CV/AV, IV/AV, and HU of the first peak position were extracted. Relevant clinical parameters were prospectively collected. The population was composed by training (n = 166) and validation (n = 85) consecutive cohorts, and backward multi-variate logistic regression was applied on the training group to build a CT_model. Similarly, models including only clinical parameters (CLIN_model) and both CT/clinical parameters (COMB_model) were developed. Model's performances were assessed by goodness-of-fit (H&L-test), calibration and discrimination. Model's performances were tested in the validation group. RESULTS: Forty-three patients died (25/18 in training/validation). CT_model included AVmax (i.e. maximum AV between lungs), CV and CV/AE, while CLIN_model included random glycemia, C-reactive protein and biological drugs (protective). Goodness-of-fit and discrimination were similar (H&L:0.70 vs 0.80; AUC:0.80 vs 0.80). COMB_model including AVmax, CV, CV/AE, random glycemia, biological drugs and active cancer, outperformed both models (H&L:0.91; AUC:0.89, 95%CI:0.82-0.93). All models showed good calibration (R2:0.77-0.97). Despite several patient's characteristics were different between training and validation cohorts, performances in the validation cohort confirmed good calibration (R2:0-70-0.81) and discrimination for CT_model/COMB_model (AUC:0.72/0.76), while CLIN_model performed worse (AUC:0.64). CONCLUSIONS: Few automatically extracted densitometry parameters with clear functional meaning predicted mortality of COVID-19 patients. Combined with clinical features, the resulting predictive model showed higher discrimination/calibration.