An adversarial learning approach to generate pressure support ventilation waveforms for asynchrony detection

Background and objective: Mechanical ventilation is a life-saving treatment for critically-ill patients. During treatment, patient-ventilator asynchrony (PVA) can occur, which can lead to pulmonary damage, complications, and higher mortality. While traditional detection methods for PVAs rely on visual inspection by clinicians, in recent years, machine learning models are being developed to detect PVAs automatically. However, training these models requires large labeled datasets, which are difficult to obtain, as labeling is a labour-intensive and time-consuming task, requiring clinical expertise. Simulating the lung-ventilator interactions has been proposed to obtain large labeled datasets to train machine learning classifiers. However, the obtained data lacks the influence of different hardware, of servo-controlled algorithms, and different sources of noise. Here, we propose VentGAN, an adversarial learning approach to improve simulated data by learning the ventilator fingerprints from unlabeled clinical data. Methods: In VentGAN, the loss functions are designed to add characteristics of clinical waveforms to the generated results, while preserving the labels of the simulated waveforms. To validate VentGAN, we compare the performance for detection and classification of PVAs when training a previously developed machine learning algorithm with the original simulated data and with the data generated by VentGAN. Testing is performed on independent clinical data labeled by experts. The McNemar test is applied to evaluate statistical differences in the obtained classification accuracy. Results: VentGAN significantly improves the classification accuracy for late cycling, early cycling and normal breaths (p &lt; 0.01); no significant difference in accuracy was observed for delayed inspirations (p = 0.2), while the accuracy decreased for ineffective efforts (p &lt; 0.01). Conclusions: Generation of realistic synthetic data with labels by the proposed framework is feasible and represents a promising avenue for improving training of machine learning models.</p

Frequency of respiratory virus infections and next-generation analysis of influenza A/H1N1pdm09 dynamics in the lower respiratory tract of patients admitted to the ICU

Recent molecular diagnostic methods have significantly improved the diagnosis of viral pneumonia in intensive care units (ICUs). It has been observed that 222G/N changes in the HA gene of H1N1pdm09 are associated with increased lower respiratory tract (LRT) replication and worse clinical outcome. In the present study, the frequency of respiratory viruses was assessed in respiratory samples from 88 patients admitted to 16 ICUs during the 2014\u20132015 winter-spring season in Lombardy. Sixty-nine out of 88 (78.4%) patients were positive for a respiratory viral infection at admission. Of these, 57/69 (82.6%) were positive for influenza A (41 A/H1N1pdm09 and 15 A/H3N2), 8/69 (11.6%) for HRV, 2/69 (2.9%) for RSV and 2/69 (2.9%) for influenza B. Phylogenetic analysis of influenza A/H1N1pdm09 strains from 28/41 ICU-patients and 21 patients with mild respiratory syndrome not requiring hospitalization, showed the clear predominance of subgroup 6B strains. The median influenza A load in LRT samples of ICU patients was higher than that observed in the upper respiratory tract (URT) (p<0.05). Overall, a greater number of H1N1pdm09 virus variants were observed using next generation sequencing on partial HA sequences (codons 180\u2013286) in clinical samples from the LRT as compared to URT. In addition, 222G/N/A mutations were observed in 30% of LRT samples from ICU patients. Finally, intra-host evolution analysis showed the presence of different dynamics of viral population in LRT of patients hospitalized in ICU with a severe influenza infection

Quality of Life in COVID-Related ARDS Patients One Year after Intensive Care Discharge (Odissea Study): A Multicenter Observational Study

Background: Investigating the health-related quality of life (HRQoL) after intensive care unit (ICU) discharge is necessary to identify possible modifiable risk factors. The primary aim of this study was to investigate the HRQoL in COVID-19 critically ill patients one year after ICU discharge. Methods: In this multicenter prospective observational study, COVID-19 patients admitted to nine ICUs from 1 March 2020 to 28 February 2021 in Italy were enrolled. One year after ICU discharge, patients were required to fill in short-form health survey 36 (SF-36) and impact of event-revised (IES-R) questionnaire. A multivariate linear or logistic regression analysis to search for factors associated with a lower HRQoL and post-traumatic stress disorded (PTSD) were carried out, respectively. Results: Among 1003 patients screened, 343 (median age 63 years [57–70]) were enrolled. Mechanical ventilation lasted for a median of 10 days [2–20]. Physical functioning (PF 85 [60–95]), physical role (PR 75 [0–100]), emotional role (RE 100 [33–100]), bodily pain (BP 77.5 [45–100]), social functioning (SF 75 [50–100]), general health (GH 55 [35–72]), vitality (VT 55 [40–70]), mental health (MH 68 [52–84]) and health change (HC 50 [25–75]) describe the SF-36 items. A median physical component summary (PCS) and mental component summary (MCS) scores were 45.9 (36.5–53.5) and 51.7 (48.8–54.3), respectively, considering 50 as the normal value of the healthy general population. In all, 109 patients (31.8%) tested positive for post-traumatic stress disorder, also reporting a significantly worse HRQoL in all SF-36 domains. The female gender, history of cardiovascular disease, liver disease and length of hospital stay negatively affected the HRQoL. Weight at follow-up was a risk factor for PTSD (OR 1.02, p = 0.03). Conclusions: The HRQoL in COVID-19 ARDS (C-ARDS) patients was reduced regarding the PCS, while the median MCS value was slightly above normal. Some risk factors for a lower HRQoL have been identified, the presence of PTSD is one of them. Further research is warranted to better identify the possible factors affecting the HRQoL in C-ARDS

Second-order grey-scale texture analysis of pleural ultrasound images to differentiate acute respiratory distress syndrome and cardiogenic pulmonary edema

Discriminating acute respiratory distress syndrome (ARDS) from acute cardiogenic pulmonary edema (CPE) may be challenging in critically ill patients. Aim of this study was to investigate if gray-level co-occurrence matrix (GLCM) analysis of lung ultrasound (LUS) images can differentiate ARDS from CPE. The study population consisted of critically ill patients admitted to intensive care unit (ICU) with acute respiratory failure and submitted to LUS and extravascular lung water monitoring, and of a healthy control group (HCG). A digital analysis of pleural line and subpleural space, based on the GLCM with second order statistical texture analysis, was tested. We prospectively evaluated 47 subjects: 16 with a clinical diagnosis of CPE, 8 of ARDS, and 23 healthy subjects. By comparing ARDS and CPE patients’ subgroups with HCG, the one-way ANOVA models found a statistical significance in 9 out of 11 GLCM textural features. Post-hoc pairwise comparisons found statistical significance within each matrix feature for ARDS vs. CPE and CPE vs. HCG (P ≤ 0.001 for all). For ARDS vs. HCG a statistical significance occurred only in two matrix features (correlation: P = 0.005; homogeneity: P = 0.048). The quantitative method proposed has shown high diagnostic accuracy in differentiating normal lung from ARDS or CPE, and good diagnostic accuracy in differentiating CPE and ARDS. Gray-level co-occurrence matrix analysis of LUS images has the potential to aid pulmonary edemas differential diagnosis

Segregation of Virulent Influenza A(H1N1) Variants in the Lower Respiratory Tract of Critically Ill Patients during the 2010–2011 Seasonal Epidemic

BACKGROUND: Since its appearance in 2009, the pandemic influenza A(H1N1) virus circulated worldwide causing several severe infections. METHODS: Respiratory samples from patients with 2009 influenza A(H1N1) and acute respiratory distress attending 24 intensive care units (ICUs) as well as from patients with lower respiratory tract infections not requiring ICU admission and community upper respiratory tract infections in the Lombardy region (10 million inhabitants) of Italy during the 2010-2011 winter-spring season, were analyzed. RESULTS: In patients with severe ILI, the viral load was higher in bronchoalveolar lavage (BAL) with respect to nasal swab (NS), (p<0.001) suggesting a higher virus replication in the lower respiratory tract. Four distinct virus clusters (referred to as cluster A to D) circulated simultaneously. Most (72.7%, n = 48) of the 66 patients infected with viruses belonging to cluster A had a severe (n = 26) or moderate ILI (n = 22). Amino acid mutations (V26I, I116M, A186T, D187Y, D222G/N, M257I, S263F, I286L/M, and N473D) were observed only in patients with severe ILI. D222G/N variants were detected exclusively in BAL samples. CONCLUSIONS: Multiple virus clusters co-circulated during the 2010-2011 winter-spring season. Severe or moderate ILI were associated with specific 2009 influenza A(H1N1) variants, which replicated preferentially in the lower respiratory tract

The PROVENT-C19 registry: A study protocol for international multicenter SIAARTI registry on the use of prone positioning in mechanically ventilated patients with COVID-19 ARDS

Background The worldwide use of prone position (PP) for invasively ventilated patients with COVID-19 is progressively increasing from the first pandemic wave in everyday clinical practice. Among the suggested treatments for the management of ARDS patients, PP was recommended in the Surviving Sepsis Campaign COVID-19 guidelines as an adjuvant therapy for improving ventilation. In patients with severe classical ARDS, some authors reported that early application of prolonged PP sessions significantly decreases 28-day and 90-day mortality. Methods and analysis Since January 2021, the COVID19 Veneto ICU Network research group has developed and implemented nationally and internationally the "PROVENT-C19 Registry", endorsed by the Italian Society of Anesthesia Analgesia Resuscitation and Intensive Care. . .'(SIAARTI). The PROVENT-C19 Registry wishes to describe 1. The real clinical practice on the use of PP in COVID-19 patients during the pandemic at a National and International level; and 2. Potential baseline and clinical characteristics that identify subpopulations of invasively ventilated patients with COVID-19 that may improve daily from PP therapy. This web-based registry will provide relevant information on how the database research tools may improve our daily clinical practice. Conclusions This multicenter, prospective registry is the first to identify and characterize the role of PP on clinical outcome in COVID-19 patients. In recent years, data emerging from large registries have been increasingly used to provide real-world evidence on the effectiveness, quality, and safety of a clinical intervention. Indeed observation-based registries could be effective tools aimed at identifying specific clusters of patients within a large study population with widely heterogeneous clinical characteristics. Copyright

Lack of SARS-CoV-2 RNA environmental contamination in a tertiary referral hospital for infectious diseases in Northern Italy

none140noNAnoneColaneri M.; Seminari E.; Piralla A.; Zuccaro V.; Di Filippo A.; Baldanti F.; Bruno R.; Mondelli M.U.; Brunetti E.; Di Matteo A.; Maiocchi L.; Pagnucco L.; Mariani B.; Ludovisi S.; Lissandrin R.; Parisi A.; Sacchi P.; Patruno S.F.A.; Michelone G.; Gulminetti R.; Zanaboni D.; Novati S.; Maserati R.; Orsolini P.; Vecchia M.; Sciarra M.; Asperges E.; Sambo M.; Biscarini S.; Lupi M.; Roda S.; Chiara Pieri T.; Gallazzi I.; Sachs M.; Valsecchi P.; Perlini S.; Alfano C.; Bonzano M.; Briganti F.; Crescenzi G.; Giulia Falchi A.; Guarnone R.; Guglielmana B.; Maggi E.; Martino I.; Pettenazza P.; Pioli di Marco S.; Quaglia F.; Sabena A.; Salinaro F.; Speciale F.; Zunino I.; De Lorenzo M.; Secco G.; Dimitry L.; Cappa G.; Maisak I.; Chiodi B.; Sciarrini M.; Barcella B.; Resta F.; Moroni L.; Vezzoni G.; Scattaglia L.; Boscolo E.; Zattera C.; Michele Fidel T.; Vincenzo C.; Vignaroli D.; Bazzini M.; Iotti G.; Mojoli F.; Belliato M.; Perotti L.; Mongodi S.; Tavazzi G.; Marseglia G.; Licari A.; Brambilla I.; Daniela B.; Antonella B.; Patrizia C.; Giulia C.; Giuditta C.; Marta C.; Rossana D.; Milena F.; Bianca M.; Roberta M.; Enza M.; Stefania P.; Maurizio P.; Elena P.; Antonio P.; Francesca R.; Antonella S.; Maurizio Z.; Guy A.; Laura B.; Ermanna C.; Giuliana C.; Luca D.; Gabriella F.; Gabriella G.; Alessia G.; Viviana L.; Claudia L.; Valentina M.; Simona P.; Marta P.; Alice B.; Giacomo C.; Irene C.; Alfonso C.; Di Martino R.; Di Napoli A.; Alessandro F.; Guglielmo F.; Loretta F.; Federica G.; Alessandra M.; Federica N.; Giacomo R.; Beatrice R.; Maria S.I.; Monica T.; Nepita Edoardo V.; Calvi M.; Tizzoni M.; Nicora C.; Triarico A.; Petronella V.; Marena C.; Muzzi A.; Lago P.; Comandatore F.; Bissignandi G.; Gaiarsa S.; Rettani M.; Bandi C.Colaneri, M.; Seminari, E.; Piralla, A.; Zuccaro, V.; Di Filippo, A.; Baldanti, F.; Bruno, R.; Mondelli, M. U.; Brunetti, E.; Di Matteo, A.; Maiocchi, L.; Pagnucco, L.; Mariani, B.; Ludovisi, S.; Lissandrin, R.; Parisi, A.; Sacchi, P.; Patruno, S. F. A.; Michelone, G.; Gulminetti, R.; Zanaboni, D.; Novati, S.; Maserati, R.; Orsolini, P.; Vecchia, M.; Sciarra, M.; Asperges, E.; Sambo, M.; Biscarini, S.; Lupi, M.; Roda, S.; Chiara Pieri, T.; Gallazzi, I.; Sachs, M.; Valsecchi, P.; Perlini, S.; Alfano, C.; Bonzano, M.; Briganti, F.; Crescenzi, G.; Giulia Falchi, A.; Guarnone, R.; Guglielmana, B.; Maggi, E.; Martino, I.; Pettenazza, P.; Pioli di Marco, S.; Quaglia, F.; Sabena, A.; Salinaro, F.; Speciale, F.; Zunino, I.; De Lorenzo, M.; Secco, G.; Dimitry, L.; Cappa, G.; Maisak, I.; Chiodi, B.; Sciarrini, M.; Barcella, B.; Resta, F.; Moroni, L.; Vezzoni, G.; Scattaglia, L.; Boscolo, E.; Zattera, C.; Michele Fidel, T.; Vincenzo, C.; Vignaroli, D.; Bazzini, M.; Iotti, G.; Mojoli, F.; Belliato, M.; Perotti, L.; Mongodi, S.; Tavazzi, G.; Marseglia, G.; Licari, A.; Brambilla, I.; Daniela, B.; Antonella, B.; Patrizia, C.; Giulia, C.; Giuditta, C.; Marta, C.; D'Alterio, Rossana; Milena, F.; Bianca, M.; Roberta, M.; Enza, M.; Stefania, P.; Maurizio, P.; Elena, P.; Antonio, P.; Francesca, R.; Antonella, S.; Maurizio, Z.; Guy, A.; Laura, B.; Ermanna, C.; Giuliana, C.; Luca, D.; Gabriella, F.; Gabriella, G.; Alessia, G.; Viviana, L.; Meisina, Claudia; Valentina, M.; Simona, P.; Marta, P.; Alice, B.; Giacomo, C.; Irene, C.; Alfonso, C.; Di Martino, R.; Di Napoli, A.; Alessandro, F.; Guglielmo, F.; Loretta, F.; Federica, G.; Albertini, Alessandra; Federica, N.; Giacomo, R.; Beatrice, R.; Maria, S. I.; Monica, T.; Nepita Edoardo, V.; Calvi, M.; Tizzoni, M.; Nicora, C.; Triarico, A.; Petronella, V.; Marena, C.; Muzzi, A.; Lago, P.; Comandatore, F.; Bissignandi, G.; Gaiarsa, S.; Rettani, M.; Bandi, C

Clinical characteristics of coronavirus disease (COVID-19) early findings from a teaching hospital in Pavia, North Italy, 21 to 28 February 2020

We describe clinical characteristics, treatments and outcomes of 44 Caucasian patients with coronavirus disease (COVID-19) at a single hospital in Pavia, Italy, from 21\u201328 February 2020, at the beginning of the outbreak in Europe. Seventeen patients developed severe disease, two died. After a median of 6 days, 14 patients were discharged from hospital. Predictors of lower odds of discharge were age&gt;65 years, antiviral treatment and for severe disease, lactate dehydrogenase &gt;300 mg/dL