Rehabilitation in patients with radically treated respiratory cancer: A randomised controlled trial comparing two training modalities.

INTRODUCTION: The evidence on the effectiveness of rehabilitation in lung cancer patients is limited. Whole body vibration (WBV) has been proposed as an alternative to conventional resistance training (CRT). METHODS: We investigated the effect of radical treatment (RT) and of two rehabilitation programmes in lung cancer patients. The primary endpoint was a change in 6-min walking distance (6MWD) after rehabilitation. Patients were randomised after RT to either CRT, WBVT or standard follow-up (CON). Patients were evaluated before, after RT and after 12 weeks of intervention. RESULTS: Of 121 included patients, 70 were randomised to either CON (24), CRT (24) or WBVT (22). After RT, 6MWD decreased with a mean of 38m (95% CI 22-54) and increased with a mean of 95m (95% CI 58-132) in CRT (p<0.0001), 37m (95% CI -1-76) in WBVT (p=0.06) and 1m (95% CI -34-36) in CON (p=0.95), respectively. Surgical treatment, magnitude of decrease in 6MWD by RT and allocation to either CRT or WBVT were prognostic for reaching the minimally clinically important difference of 54m increase in 6MWD after intervention. CONCLUSIONS: RT of lung cancer significantly impairs patients' exercise capacity. CRT significantly improves and restores functional exercise capacity, whereas WBVT does not fully substitute for CRT

Treatment failure and hospital readmissions in severe COPD exacerbations treated with azithromycin versus placebo - A post-hoc analysis of the BACE randomized controlled trial

Background: In the BACE trial, a 3-month (3 m) intervention with azithromycin, initiated at the onset of an infectious COPD exacerbation requiring hospitalization, decreased the rate of a first treatment failure (TF); the composite of treatment intensification (TI), step-up in hospital care (SH) and mortality. Objectives: (1) To investigate the intervention's effect on recurrent events, and (2) to identify clinical subgroups most likely to benefit, determined from the incidence rate of TF and hospital readmissions. Methods: Enrolment criteria included the diagnosis of COPD, a smoking history of ≥10 pack-years and ≥ 1 exacerbation in the previous year. Rate ratio (RR) calculations, subgroup analyses and modelling of continuous variables using splines were based on a Poisson regression model, adjusted for exposure time. Results: Azithromycin significantly reduced TF by 24% within 3 m (RR = 0.76, 95%CI:0.59;0.97, p = 0.031) through a 50% reduction in SH (RR = 0.50, 95%CI:0.30;0.81, p = 0.006), which comprised of a 53% reduction in hospital readmissions (RR = 0.47, 95%CI:0.27;0.80; p = 0.007). A significant interaction between the intervention, CRP and blood eosinophil count at hospital admission was found, with azithromycin significantly reducing hospital readmissions in patients with high CRP (> 50 mg/L, RR = 0.18, 95%CI:0.05;0.60, p = 0.005), or low blood eosinophil count (<300cells/μL, RR = 0.33, 95%CI:0.17;0.64, p = 0.001). No differences were observed in treatment response by age, FEV1, CRP or blood eosinophil count in continuous analyses. Conclusions: This post-hoc analysis of the BACE trial shows that azithromycin initiated at the onset of an infectious COPD exacerbation requiring hospitalization reduces the incidence rate of TF within 3 m by preventing hospital readmissions. In patients with high CRP or low blood eosinophil count at admission this treatment effect was more pronounced, suggesting a potential role for these biomarkers in guiding azithromycin therapy. Trial registration: ClinicalTrials.gov number. NCT02135354. © 2019 The Author(s)

Een geval van ongewone bronchitis bij een oudere patiënte

Deze casus beschrijft het relaas van een patiënte met aanhoudend respiratoire klachten met de tentatieve diagnose van amiodarongeïnduceerde pulmonale toxiciteit. Amiodaron wordt vaak gebruikt in de behandeling van supraventriculaire en ventriculaire aritmieën. Longtoxiciteit is een moeilijk te voorspellen bijwerking van amiodaron en treedt op bij tot 5% van de patiënten, onafhankelijk van de dosis en behandelduur. De tentatieve diagnose is gebaseerd op de combinatie van epidemiologische gegevens en de vaak aspecifieke klinische presentatie, radiologische bevindingen en laboratoriumresultaten (serologie en broncho-alveolaire lavage). Omdat er geen specifiek pathognomische bevindingen zijn, is dit een exclusiediagnose. Men moet bedacht zijn op amiodarongeïnduceerde longtoxiciteit bij elke patiënt die behandeld wordt met amiodaron en zich aanmeldt met respiratoire klachten zoals hoest, dyspnoe en algemene malaise. Als de diagnose van longtoxiciteit in overweging wordt genomen, bestaat de behandeling uit het onmiddellijk stopzetten van amiodaron. In ernstige gevallen dienen corticosteroïden opgestart te worden, en nadien afgebouwd op geleide van de kliniek. De prognose is meestal gunstig, maar er is een hoge mortaliteit bij gehospitaliseerde patiënten, in geval van longfibrose en bij patiënten die zich presenteren met een ‘acute respiratory distress syndrome’ (ARDS)

Een ongewone oorzaak van hemoptoë : pulmonaal-veneuze stenose na radiofrequentieablatie van voorkamerfibrillatie

Pulmonaal-veneuze stenose (PVS) is een zeldzame, maar ernstige complicatie na radiofrequentieablatie van voorkamerfibrillatie (VKF). De diagnose is vaak moeilijk door de aspecifieke presentatie en wordt dan ook gemakkelijk gemist. Een vroegtijdige behandeling met een ballonangioplastiek (BA), al dan niet in combinatie met een stentimplantatie, verbetert de klinische uitkomst en vermindert de kans op complicaties. In dit artikel wordt de ziektegeschiedenis beschreven van een 47-jarige patiënt met recidiverende hemoptoë en thoracale pijn als gevolg van PVS na radiofrequentieablatie, succesvol behandeld met een BA en de implantatie van een bare-metal stent (BMS)

Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests

The interpretation of pulmonary function tests (PFTs) to diagnose respiratory diseases is built on expert opinion which relies on the recognition of patterns and clinical context for the detection of specific diseases. In the study, we aimed to explore the accuracy and inter-rater variability of pulmonologists when interpreting PFTs and compared it against that of artificial intelligence (AI)-based software which was developed and validated in more than 1500 historical patient cases. 120 pulmonologists from 16 European hospitals evaluated 50 cases comprising with PFT and clinical information resulting in 6000 independent interpretations. AI software examined the same data. ATS/ERS guidelines were used as the gold standard for PFT pattern interpretation. The gold standard for diagnosis was derived from clinical history, PFT and all additional tests. The pattern recognition of PFTs by pulmonologists (senior 73%, junior 27%) matched the guidelines in 74.4% (±5.9) of the cases (range: 56-88%). The inter-rater variability of 0.67 (kappa) pointed to a common agreement. Pulmonologists made correct diagnoses in 44.6% (±8.7) of the cases (range: 24-62%) with a large inter-rater variability (kappa= 0.35). The AI-based software perfectly matched the PFT pattern interpretations (100%) and assigned a correct diagnosis in 82% of all cases (p<0.0001 for both measures). The interpretation of PFTs by pulmonologists leads to marked variations and errors. AI-based software provides more accurate interpretations and may serve as a powerful decision support tool to improve clinical practice

Le ruderali sensu lato a Modena: sintesi di dati carpologici da siti archeologici (III sec. a.C. – XIII sec. d.C.) e primo rilievo della flora urbica attuale del centro storico

Si presentano i risultati di un rilievo della flora ruderale della città di Modena, dal periodo romano ai giorni nostri, condotto mediante lo studio dei reperti carpologici rinvenuti in quattro siti archeologici del centro storico della città (due romani e due medievali) e un primo censimento della flora urbica cittadina attuale. Il declino della biodiversità vegetale è notevole: a fronte di una lista di 141 taxa presenti nei siti archeologici di età romana, già nel Medioevo si scende a 76, mentre il censimento della flora odierna, anche se preliminare, ne ha rilevati solo 69.The ruderal flora of the town of Modena (Italy) from Roman times to the present has been studied, taking into account the carpological remains found in four archaeological sites of the city centre (two of Roman age and two medieval) and carrying out a first survey of today’s urban flora. The decrease of vegetal biodiversity is considerable: 141 taxa are listed for the Roman period but during the Middle Ages the number dwindles to 76, whilst at the present time only 69 species have been counted, although this investigation is still in a preliminary phase

Collaboration between explainable artificial intelligence and pulmonologists improves the accuracy of pulmonary function test interpretation

Background: Few studies have investigated the collaborative potential between artificial intelligence (AI) and pulmonologists for diagnosing pulmonary disease. We hypothesised that the collaboration between a pulmonologist and AI with explanations (explainable AI (XAI)) is superior in diagnostic interpretation of pulmonary function tests (PFTs) than the pulmonologist without support. Methods: The study was conducted in two phases, a monocentre study (phase 1) and a multicentre intervention study (phase 2). Each phase utilised two different sets of 24 PFT reports of patients with a clinically validated gold standard diagnosis. Each PFT was interpreted without (control) and with XAI's suggestions (intervention). Pulmonologists provided a differential diagnosis consisting of a preferential diagnosis and optionally up to three additional diagnoses. The primary end-point compared accuracy of preferential and additional diagnoses between control and intervention. Secondary end-points were the number of diagnoses in differential diagnosis, diagnostic confidence and inter-rater agreement. We also analysed how XAI influenced pulmonologists' decisions. Results: In phase 1 (n=16 pulmonologists), mean preferential and differential diagnostic accuracy significantly increased by 10.4% and 9.4%, respectively, between control and intervention (p&lt;0.001). Improvements were somewhat lower but highly significant (p&lt;0.0001) in phase 2 (5.4% and 8.7%, respectively; n=62 pulmonologists). In both phases, the number of diagnoses in the differential diagnosis did not reduce, but diagnostic confidence and inter-rater agreement significantly increased during intervention. Pulmonologists updated their decisions with XAI's feedback and consistently improved their baseline performance if AI provided correct predictions. Conclusion: A collaboration between a pulmonologist and XAI is better at interpreting PFTs than individual pulmonologists reading without XAI support or XAI alone

Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests.

The interpretation of pulmonary function tests (PFTs) to diagnose respiratory diseases is built on expert opinion that relies on the recognition of patterns and the clinical context for detection of specific diseases. In this study, we aimed to explore the accuracy and interrater variability of pulmonologists when interpreting PFTs compared with artificial intelligence (AI)-based software that was developed and validated in more than 1500 historical patient cases.120 pulmonologists from 16 European hospitals evaluated 50 cases with PFT and clinical information, resulting in 6000 independent interpretations. The AI software examined the same data. American Thoracic Society/European Respiratory Society guidelines were used as the gold standard for PFT pattern interpretation. The gold standard for diagnosis was derived from clinical history, PFT and all additional tests.The pattern recognition of PFTs by pulmonologists (senior 73%, junior 27%) matched the guidelines in 74.4±5.9% of the cases (range 56-88%). The interrater variability of κ=0.67 pointed to a common agreement. Pulmonologists made correct diagnoses in 44.6±8.7% of the cases (range 24-62%) with a large interrater variability (κ=0.35). The AI-based software perfectly matched the PFT pattern interpretations (100%) and assigned a correct diagnosis in 82% of all cases (p<0.0001 for both measures).The interpretation of PFTs by pulmonologists leads to marked variations and errors. AI-based software provides more accurate interpretations and may serve as a powerful decision support tool to improve clinical practice