The determination of pleurodesis using sonography after surgical pleurectomy for pneumothorax: a pilot study.

The aim of this study is to evaluate feasibility of monitoring the process of pleurodesis after surgical pleurectomy with thoracic ultrasound. Repetitive measurements with thoracic ultrasound after surgical pleurectomy could provide information on the extent and development speed of pleurodesis. We conducted a prospective single-center cohort study. Adult patients who required surgical pleurectomy after pneumothorax were eligible. Participants had daily thoracic ultrasound examination until discharge to determine lung sliding [present (0 point), questionable (1 point), or absent (2 points)], and pleural thickening [normal (0 point), questionable (1 point), or present (2 points)]. Thoracic ultrasound was performed in six regions, the sum of all scores was divided by the number of regions. Fourteen patients were enrolled. Thoracic ultrasound on day 1-4 was 0.25±0.26, 0.39±0.48, 0.84±0.49, 1.12±0.56 for mean lung sliding, and 1.0±0.56, 1.17±0.48, 1.44±0.44, 1.54±0.34 for mean pleural thickening. Lung sliding and pleural thickening increased significantly between day 1 and day 4 (P=0.002 and P=0.023, respectively). One (7%) and 3 (21%) patients reached the maximum achievable grade for lung sliding and pleural thickening, respectively. Thoracic ultrasound grades tended to be lower in three patients with recurrent pneumothorax, although this was not statistically significant. This study shows a significant increase in thoracic ultrasound grading for pleurodesis lung sliding and pleural thickening during the first postoperative days after surgical pleurectomy, probably attributable to progressing pleurodesis. Only a minority of patients reached complete pleurodesis before discharge despite complete surgical pleurodesis (SP). The results of this study may guide future research regarding optimal timing of chest tube removal.

Sonographic follow-up of diaphragm function in COVID-19: an exploratory study.

Introduction: Survivors of COVID-19 frequently endure chronic disabilities. We hypothesise that diaphragm function has a long recovery time after COVID-19 hospitalisation and may play a role in post-COVID-19 syndrome. The aim of this study was to assess diaphragm function during COVID-19 hospitalisation and during recovery. Methods: We conducted a prospective single-centre cohort study in 49 enrolled patients, of which 28 completed 1-year follow-up. Participants were evaluated for diaphragm function. Diaphragm function was assessed using ultrasound measuring of diaphragm thickening fraction (TF) within 24 h after admission, after 7 days of admission or at discharge, whichever came first, and 3 and 12 months after hospital admission. Results: Estimated mean TF increased from 0.56 (95% CI 0.46-0.66) on admission to 0.78 (95% CI 0.65-0.89) at discharge or 7 days after admission, to 1.05 (95% CI 0.83-1.26) 3 months after admission and to 1.54 (95% CI 1.31-1.76) 12 months after admission. The improvements from admission to discharge, 3 months and 12 months were all significant (linear mixed modelling; p=0.020, p<0.001 and p<0.001, respectively), and the improvement from discharge to 3-month follow-up was borderline significant (p<0.1). Conclusion: Diaphragm function was impaired during hospitalisation for COVID-19. During recovery in hospital and up to 1-year follow-up, diaphragm TF improved, suggesting a long recovery time of the diaphragm. Diaphragm ultrasound may be a valuable modality in the screening and follow-up of (post-)COVID-19 patients for diaphragm dysfunction.

Ambulant treatment with a digital chest tube for prolonged air leak is safe and effective.

BACKGROUND: Outpatient or ambulatory treatment for prolonged air leak (PAL) has been reported previously in various studies. Evidence regarding efficiency and safety is nevertheless poor. This report describes the experience of 10 years ambulatory care with a digital chest drain system monitored by specialized nurses in our centre. The aim of the study is to give further insights in the effectiveness and safety of this treatment. METHODS: Retrospective data of 10 years ambulatory care for PAL were examined. One hundred and forty patients with PAL after pneumothorax or pulmonary surgery were included. RESULTS: A total of 140 patients with PAL were included. Treatment was successful in 112 patients (80.0%). Hospital readmission was necessary in 33 patients (23.6%) and 28 (20.0%) of them received additional treatment. Additional treatment consisted of video-assisted thoracoscopic surgery (VATS) in 19 patients (13.6%), new chest tube placement in 8 patients (5.7%) and pleurodesis (with talc slurry) in 1 patient (0.7%). Minor complications occurred in 10 patients (7.1%), major complications requiring readmission occurred in 14 patients (10.0%). CONCLUSIONS: Ambulatory treatment of PAL with a digital monitoring device resulted in a high success rate with a limited complication rate.

Model-based Prediction of Critical Illness in Hospitalized Patients with COVID-19.

Background The prognosis of hospitalized patients with severe coronavirus disease 2019 (COVID-19) is difficult to predict, and the capacity of intensive care units was a limiting factor during the peak of the pandemic and is generally dependent on a country's clinical resources. Purpose To determine the value of chest radiographic findings together with patient history and laboratory markers at admission to predict critical illness in hospitalized patients with COVID-19. Materials and Methods In this retrospective study, which included patients from March 7, 2020, to April 24, 2020, a consecutive cohort of hospitalized patients with real-time reverse transcription polymerase chain reaction-confirmed COVID-19 from two large Dutch community hospitals was identified. After univariable analysis, a risk model to predict critical illness (ie, death and/or intensive care unit admission with invasive ventilation) was developed, using multivariable logistic regression including clinical, chest radiographic, and laboratory findings. Distribution and severity of lung involvement were visually assessed by using an eight-point scale (chest radiography score). Internal validation was performed by using bootstrapping. Performance is presented as an area under the receiver operating characteristic curve. Decision curve analysis was performed, and a risk calculator was derived. Results The cohort included 356 hospitalized patients (mean age, 69 years ± 12 [standard deviation]; 237 men) of whom 168 (47%) developed critical illness. The final risk model's variables included sex, chronic obstructive lung disease, symptom duration, neutrophil count, C-reactive protein level, lactate dehydrogenase level, distribution of lung disease, and chest radiography score at hospital presentation. The area under the receiver operating characteristic curve of the model was 0.77 (95% CI: 0.72, 0.81; P < .001). A risk calculator was derived for individual risk assessment: Dutch COVID-19 risk model. At an example threshold of 0.70, 71 of 356 patients would be predicted to develop critical illness, of which 59 (83%) would be true-positive results. Conclusion A risk model based on chest radiographic and laboratory findings obtained at admission was predictive of critical illness in hospitalized patients with coronavirus disease 2019. This risk calculator might be useful for triage of patients to the limited number of intensive care unit beds or facilities. © RSNA, 2020 Online supplemental material is available for this article.