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Montellano FA, Rücker V, Ungethüm K, Penalba A, Hotter B, Giralt M, Wiedmann S, Mackenrodt D, Morbach C, Frantz S, Störk S, Whiteley WN, Kleinschnitz C, Meisel A, Montaner J, Haeusler KG, Heuschmann PU. Biomarkers to improve functional outcome prediction after ischemic stroke: Results from the SICFAIL, STRAWINSKI, and PREDICT studies. Eur Stroke J 2024:23969873241250272. [PMID: 38711254 DOI: 10.1177/23969873241250272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/08/2024] Open
Abstract
BACKGROUND AND AIMS Acute ischemic stroke (AIS) outcome prognostication remains challenging despite available prognostic models. We investigated whether a biomarker panel improves the predictive performance of established prognostic scores. METHODS We investigated the improvement in discrimination, calibration, and overall performance by adding five biomarkers (procalcitonin, copeptin, cortisol, mid-regional pro-atrial natriuretic peptide (MR-proANP), and N-terminal pro-B-type natriuretic peptide (NT-proBNP)) to the Acute Stroke Registry and Analysis of Lausanne (ASTRAL) and age/NIHSS scores using data from two prospective cohort studies (SICFAIL, PREDICT) and one clinical trial (STRAWINSKI). Poor outcome was defined as mRS > 2 at 12 (SICFAIL, derivation dataset) or 3 months (PREDICT/STRAWINSKI, pooled external validation dataset). RESULTS Among 412 SICFAIL participants (median age 70 years, quartiles 59-78; 63% male; median NIHSS score 3, quartiles 1-5), 29% had a poor outcome. Area under the curve of the ASTRAL and age/NIHSS were 0.76 (95% CI 0.71-0.81) and 0.77 (95% CI 0.73-0.82), respectively. Copeptin (0.79, 95% CI 0.74-0.84), NT-proBNP (0.80, 95% CI 0.76-0.84), and MR-proANP (0.79, 95% CI 0.75-0.84) significantly improved ASTRAL score's discrimination, calibration, and overall performance. Copeptin improved age/NIHSS model's discrimination, copeptin, MR-proANP, and NT-proBNP improved its calibration and overall performance. In the validation dataset (450 patients, median age 73 years, quartiles 66-81; 54% men; median NIHSS score 8, quartiles 3-14), copeptin was independently associated with various definitions of poor outcome and also mortality. Copeptin did not increase model's discrimination but it did improve calibration and overall model performance. DISCUSSION Copeptin, NT-proBNP, and MR-proANP improved modest but consistently the predictive performance of established prognostic scores in patients with mild AIS. Copeptin was most consistently associated with poor outcome in patients with moderate to severe AIS, although its added prognostic value was less obvious.
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Affiliation(s)
- Felipe A Montellano
- Institute of Clinical Epidemiology and Biometry, Julius-Maximilians-Universität (JMU) Würzburg, Würzburg, Germany
- Department of Neurology, University Hospital Würzburg, Würzburg, Germany
- Interdisciplinary Center for Clinical Research, University Hospital Würzburg, Würzburg, Germany
| | - Viktoria Rücker
- Institute of Clinical Epidemiology and Biometry, Julius-Maximilians-Universität (JMU) Würzburg, Würzburg, Germany
| | - Kathrin Ungethüm
- Institute of Clinical Epidemiology and Biometry, Julius-Maximilians-Universität (JMU) Würzburg, Würzburg, Germany
- Institute of Medical Data Science, University Hospital Würzburg, Würzburg, Germany
| | - Anna Penalba
- Neurovascular Research Laboratory, Vall d'Hebron Institute of Research, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Benjamin Hotter
- Department of Neurology and Experimental Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Center for Stroke Research Berlin, Charité-Universitätsmedizin Berlin, Berlin, Germany
- NeuroCure Clinical Research Center, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Marina Giralt
- Department of Biochemistry, Vall d'Hebron University Hospital, Barcelona, Spain
| | - Silke Wiedmann
- Institute of Clinical Epidemiology and Biometry, Julius-Maximilians-Universität (JMU) Würzburg, Würzburg, Germany
- Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
| | - Daniel Mackenrodt
- Institute of Clinical Epidemiology and Biometry, Julius-Maximilians-Universität (JMU) Würzburg, Würzburg, Germany
- Department of Neurology, University Hospital Würzburg, Würzburg, Germany
| | - Caroline Morbach
- Department Clinical Research & Epidemiology, Comprehensive Heart Failure Center, University Hospital Würzburg, Würzburg, Germany
- Department of Internal Medicine I, University Hospital Würzburg, Würzburg, Germany
| | - Stefan Frantz
- Department Clinical Research & Epidemiology, Comprehensive Heart Failure Center, University Hospital Würzburg, Würzburg, Germany
| | - Stefan Störk
- Department Clinical Research & Epidemiology, Comprehensive Heart Failure Center, University Hospital Würzburg, Würzburg, Germany
- Department of Internal Medicine I, University Hospital Würzburg, Würzburg, Germany
| | - William N Whiteley
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Christoph Kleinschnitz
- Department of Neurology and Center for Translational Neuroscience and Behavioural Science (C-TNBS), University Hospital Essen, Essen, Germany
| | - Andreas Meisel
- Department of Neurology and Experimental Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Center for Stroke Research Berlin, Charité-Universitätsmedizin Berlin, Berlin, Germany
- NeuroCure Clinical Research Center, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Joan Montaner
- Neurovascular Research Laboratory, Vall d'Hebron Institute of Research, Universitat Autònoma de Barcelona, Barcelona, Spain
- Stroke Research Program, Instituto de Biomedicina de Sevilla/Hospital Universitario Virgen del Rocío/Consejo Superior de Investigaciones Científicas/University of Seville, Seville, Spain
- Department of Neurology, Hospital Universitario Virgen Macarena, Seville, Spain
| | | | - Peter U Heuschmann
- Institute of Clinical Epidemiology and Biometry, Julius-Maximilians-Universität (JMU) Würzburg, Würzburg, Germany
- Institute of Medical Data Science, University Hospital Würzburg, Würzburg, Germany
- Clinical Trial Center Würzburg, University Hospital Würzburg, Würzburg, Germany
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Herskovits AZ, Newman T, Nicholas K, Colorado-Jimenez CF, Perry CE, Valentino A, Wagner I, Egan B, Gorenshteyn D, Vickers AJ, Pessin MS. Comparing Clinician Estimates versus a Statistical Tool for Predicting Risk of Death within 45 Days of Admission for Cancer Patients. Appl Clin Inform 2024; 15:489-500. [PMID: 38925539 PMCID: PMC11208110 DOI: 10.1055/s-0044-1787185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 04/29/2024] [Indexed: 06/28/2024] Open
Abstract
OBJECTIVES While clinical practice guidelines recommend that oncologists discuss goals of care with patients who have advanced cancer, it is estimated that less than 20% of individuals admitted to the hospital with high-risk cancers have end-of-life discussions with their providers. While there has been interest in developing models for mortality prediction to trigger such discussions, few studies have compared how such models compare with clinical judgment to determine a patient's mortality risk. METHODS This study is a prospective analysis of 1,069 solid tumor medical oncology hospital admissions (n = 911 unique patients) from February 7 to June 7, 2022, at Memorial Sloan Kettering Cancer Center. Electronic surveys were sent to hospitalists, advanced practice providers, and medical oncologists the first afternoon following a hospital admission and they were asked to estimate the probability that the patient would die within 45 days. Provider estimates of mortality were compared with those from a predictive model developed using a supervised machine learning methodology, and incorporated routine laboratory, demographic, biometric, and admission data. Area under the receiver operating characteristic curve (AUC), calibration and decision curves were compared between clinician estimates and the model predictions. RESULTS Within 45 days following hospital admission, 229 (25%) of 911 patients died. The model performed better than the clinician estimates (AUC 0.834 vs. 0.753, p < 0.0001). Integrating clinician predictions with the model's estimates further increased the AUC to 0.853 (p < 0.0001). Clinicians overestimated risk whereas the model was extremely well-calibrated. The model demonstrated net benefit over a wide range of threshold probabilities. CONCLUSION The inpatient prognosis at admission model is a robust tool to assist clinical providers in evaluating mortality risk, and it has recently been implemented in the electronic medical record at our institution to improve end-of-life care planning for hospitalized cancer patients.
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Affiliation(s)
- Adrianna Z. Herskovits
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, United States
| | - Tiffanny Newman
- Department of Strategy and Innovation, Memorial Sloan Kettering Cancer Center, New York, New York, United States
| | - Kevin Nicholas
- Department of Strategy and Innovation, Memorial Sloan Kettering Cancer Center, New York, New York, United States
| | - Cesar F. Colorado-Jimenez
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, United States
| | - Claire E. Perry
- Department of Strategy and Innovation, Memorial Sloan Kettering Cancer Center, New York, New York, United States
| | - Alisa Valentino
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, United States
| | - Isaac Wagner
- Department of Strategy and Innovation, Memorial Sloan Kettering Cancer Center, New York, New York, United States
| | - Barbara Egan
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, United States
| | | | - Andrew J. Vickers
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York, United States
| | - Melissa S. Pessin
- Department of Pathology, University of Chicago, Chicago, Illinois, United States
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Tsui A, Tudosiu PD, Brudfors M, Jha A, Cardoso J, Ourselin S, Ashburner J, Rees G, Davis D, Nachev P. Predicting mortality in acutely hospitalised older patients: the impact of model dimensionality. BMC Med 2023; 21:10. [PMID: 36617542 PMCID: PMC9827638 DOI: 10.1186/s12916-022-02698-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 12/07/2022] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND The prediction of long-term mortality following acute illness can be unreliable for older patients, inhibiting the delivery of targeted clinical interventions. The difficulty plausibly arises from the complex, multifactorial nature of the underlying biology in this population, which flexible, multimodal models based on machine learning may overcome. Here, we test this hypothesis by quantifying the comparative predictive fidelity of such models in a large consecutive sample of older patients acutely admitted to hospital and characterise their biological support. METHODS A set of 804 admission episodes involving 616 unique patients with a mean age of 84.5 years consecutively admitted to the Acute Geriatric service at University College Hospital were identified, in whom clinical diagnoses, blood tests, cognitive status, computed tomography of the head, and mortality within 600 days after admission were available. We trained and evaluated out-of-sample an array of extreme gradient boosted trees-based predictive models of incrementally greater numbers of investigational modalities and modelled features. Both linear and non-linear associations with investigational features were quantified. RESULTS Predictive models of mortality showed progressively increasing fidelity with greater numbers of modelled modalities and dimensions. The area under the receiver operating characteristic curve rose from 0.67 (sd = 0.078) for age and sex to 0.874 (sd = 0.046) for the most comprehensive model. Extracranial bone and soft tissue features contributed more than intracranial features towards long-term mortality prediction. The anterior cingulate and angular gyri, and serum albumin, were the greatest intracranial and biochemical model contributors respectively. CONCLUSIONS High-dimensional, multimodal predictive models of mortality based on routine clinical data offer higher predictive fidelity than simpler models, facilitating individual level prognostication and interventional targeting. The joint contributions of both extracranial and intracranial features highlight the potential importance of optimising somatic as well as neural functions in healthy ageing. Our findings suggest a promising path towards a high-fidelity, multimodal index of frailty.
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Affiliation(s)
- Alex Tsui
- MRC Unit for Lifelong Health and Ageing at UCL, London, UK.
| | | | - Mikael Brudfors
- School of Imaging and Biomedical Engineering, King's College London, London, UK
- Wellcome Centre for Human Neuroimaging, UCL, London, UK
| | - Ashwani Jha
- UCL Queen Square Institute of Neurology, UCL, London, UK
| | - Jorge Cardoso
- School of Imaging and Biomedical Engineering, King's College London, London, UK
| | - Sebastien Ourselin
- School of Imaging and Biomedical Engineering, King's College London, London, UK
| | | | - Geraint Rees
- UCL Queen Square Institute of Neurology, UCL, London, UK
| | - Daniel Davis
- MRC Unit for Lifelong Health and Ageing at UCL, London, UK
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Ju JW, Nam K, Hong H, Cheun H, Bae J, Lee S, Cho YJ, Jeon Y. Performance of the ACEF and ACEF II risk scores in predicting mortality after off-pump coronary artery bypass grafting. J Clin Anesth 2022; 79:110693. [DOI: 10.1016/j.jclinane.2022.110693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 01/11/2022] [Accepted: 02/18/2022] [Indexed: 10/19/2022]
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Li B, Zhao Y, Cai W, Ming A, Li H. Validation and update of a multivariable prediction model for the identification and management of patients at risk for hepatocellular carcinoma. Clin Proteomics 2021; 18:21. [PMID: 34412596 PMCID: PMC8374120 DOI: 10.1186/s12014-021-09326-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 08/08/2021] [Indexed: 02/07/2023] Open
Abstract
Background A hepatocellular carcinoma (HCC) prediction model (ASAP), including age, sex, and the biomarkers alpha-fetoprotein and prothrombin induced by vitamin K absence-II, showed potential clinical value in the early detection of HCC. We validated and updated the model in a real-world cohort and promoted its transferability to daily clinical practice. Methods This retrospective cohort analysis included 1012 of the 2479 eligible patients aged 35 years or older undergoing surveillance for HCC. The data were extracted from the electronic medical records. Biomarker values within the test-to-diagnosis interval were used to validate the ASAP model. Due to its unsatisfactory calibration, three logistic regression models were constructed to recalibrate and update the model. Their discrimination, calibration, and clinical utility were compared. The performance statistics of the final updated model at several risk thresholds are presented. The outcomes of 855 non-HCC patients were further assessed during a median of 10.2 months of follow-up. Statistical analyses were performed using packages in R software. Results The ASAP model had superior discriminative performance in the validation cohort [C-statistic = 0.982, (95% confidence interval 0.972–0.992)] but significantly overestimated the risk of HCC (intercept − 3.243 and slope 1.192 in the calibration plot), reducing its clinical usefulness. Recalibration-in-the-large, which exhibited performance comparable to that of the refitted model revision, led to the retention of the excellent discrimination and substantial improvements in the calibration and clinical utility, achieving a sensitivity of 100% at the median prediction probability of the absence of HCC (1.3%). The probability threshold of 1.3% and the incidence of HCC in the cohort (15.5%) were used to stratify the patients into low-, medium-, and high-risk groups. The cumulative HCC incidences in the non-HCC patients significantly differed among the risk groups (log-rank test, p-value < 0.001). The 3-month, 6-month and 18-month cumulative incidences in the low-risk group were 0.6%, 0.9% and 0.9%, respectively. Conclusions The ASAP model is an accurate tool for HCC risk estimation that requires recalibration before use in a new region because calibration varies with clinical environments. Additionally, rational risk stratification and risk-based management decision-making, e.g., 3-month follow-up recommendations for targeted individuals, helped improve HCC surveillance, which warrants assessment in larger cohorts. Supplementary Information The online version contains supplementary material available at 10.1186/s12014-021-09326-w.
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Affiliation(s)
- Bo Li
- Clinical Laboratory, Hubei Provincial Hospital of Traditional Chinese Medicine, Affiliated Hospital of Hubei University of Traditional Chinese Medicine, Hubei Province Academy of Traditional Chinese Medicine, Wuhan, China
| | - Youyun Zhao
- Clinical Laboratory, Hubei Provincial Hospital of Traditional Chinese Medicine, Affiliated Hospital of Hubei University of Traditional Chinese Medicine, Hubei Province Academy of Traditional Chinese Medicine, Wuhan, China
| | - Wangxi Cai
- Clinical Laboratory, Hubei Provincial Hospital of Traditional Chinese Medicine, Affiliated Hospital of Hubei University of Traditional Chinese Medicine, Hubei Province Academy of Traditional Chinese Medicine, Wuhan, China
| | - Anping Ming
- Clinical Laboratory, Hubei Provincial Hospital of Traditional Chinese Medicine, Affiliated Hospital of Hubei University of Traditional Chinese Medicine, Hubei Province Academy of Traditional Chinese Medicine, Wuhan, China
| | - Hanmin Li
- Institute of Hepatology, Hubei Provincial Hospital of Traditional Chinese Medicine, Affiliated Hospital of Hubei University of Traditional Chinese Medicine, Hubei Province Academy of Traditional Chinese Medicine, Wuhan, China. .,Theory and Application Research of Liver and Kidney in Traditional Chinese Medicine, Hubei Provincial Key Laboratory, Cell Molecular Biology Laboratory, Level 3 Laboratory of Traditional Chinese Medicine Research, State Administration of Traditional Chinese Medicine, 4 Huayuanshan, Yanzhi Road, Liangdao Street, Wuchang District, Hubei, 430061, Wuhan, China.
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Curtin D, Gallagher P, O’Mahony D. Deprescribing in older people approaching end-of-life: development and validation of STOPPFrail version 2. Age Ageing 2021; 50:465-471. [PMID: 32997135 DOI: 10.1093/ageing/afaa159] [Citation(s) in RCA: 54] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Screening Tool of Older Persons Prescriptions in Frail adults with limited life expectancy (STOPPFrail) criteria were developed in 2017 to assist physicians with deprescribing decisions in older people approaching end-of-life. Updating was required to make the tool more practical, patient-centred and complete. METHODS a thorough literature review was conducted to, first, devise a practical method for identifying older people who are likely to be approaching end-of-life, and second, reassess and update the existing deprescribing criteria. An eight-member panel with a wide-ranging experience in geriatric pharmacotherapy reviewed a new draft of STOPPFrail and were invited to propose new deprescribing criteria. STOPPFrail version 2 was then validated using Delphi consensus methodology. RESULTS STOPPFrail version 2 emphasises the importance of shared decision-making in the deprescribing process. A new method for identifying older people who are likely to be approaching end-of-life is included along with 25 deprescribing criteria. Guidance relating to the deprescribing of antihypertensive therapies, anti-anginal medications and vitamin D preparations comprises the new criteria. CONCLUSIONS STOPPFrail criteria have been updated to assist physicians in efforts to reduce drug-related morbidity and burden for their frailest older patients. Version 2 is based on an up-to-date literature review and consensus validation by a panel of experts.
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Affiliation(s)
- Denis Curtin
- Department of Medicine (Geriatrics), University College Cork, Cork T12 DC4A, Ireland
- Department of Geriatric Medicine, Cork University Hospital, Cork T12 DC4A, Ireland
| | - Paul Gallagher
- Department of Medicine (Geriatrics), University College Cork, Cork T12 DC4A, Ireland
- Department of Geriatric Medicine, Cork University Hospital, Cork T12 DC4A, Ireland
| | - Denis O’Mahony
- Department of Medicine (Geriatrics), University College Cork, Cork T12 DC4A, Ireland
- Department of Geriatric Medicine, Cork University Hospital, Cork T12 DC4A, Ireland
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Zaraca F, Pipitone M, Feil B, Perkmann R, Bertolaccini L, Curcio C, Crisci R. Predicting a Prolonged Air Leak After Video-Assisted Thoracic Surgery, Is It Really Possible? Semin Thorac Cardiovasc Surg 2020; 33:581-592. [PMID: 32853737 DOI: 10.1053/j.semtcvs.2020.08.012] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Accepted: 08/21/2020] [Indexed: 11/11/2022]
Abstract
Validation of predictive risk models for prolonged air leak (PAL) is essential to understand if they can help to reduce its incidence and complications. This study aimed to evaluate both the clinical and statistical performances of 4 existing models. We selected 4 predictive PAL risk models based on their scientific relevance. We referred to these models as Chicago, Bordeaux, Leeds and Pittsburgh model, respectively, according to the affiliation place of the first author. These predicting risk models were retrospectively applied to patients recorded on the second edition of the Italian Video-Assisted Thoracoscopic Surgery Group registry. Predictions for each patient were calculated based on the logistic regression coefficient values provided in the original manuscripts. All models were tested for their overall performance, discrimination, and calibration. We recalibrated the original models with the re-estimation of the model intercept and slope. We used curve decision analysis to describe and compare the clinical effects of the studied risk models. Better statistical metrics characterize the models developed on larger populations (Chicago and Bordeaux models). However, no model has a valid benefit for threshold probability greater than 0.30. The Net benefit of the most performing model (Bordeaux model) at the threshold probability of 0.11 is 23 of 1000 patients, burdened by 333 false positive cases. One of 1000 is the Net benefit at the threshold probability of 0.3. The use of PAL scores based on preoperative predictive factors cannot be currently used in a clinical setting because of a high false positive rate and low positive predictive value.
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Affiliation(s)
- Francesco Zaraca
- Department of Vascular and Thoracic Surgery, Regional Hospital, Bolzano, Italy.
| | - Marco Pipitone
- Department of Vascular and Thoracic Surgery, Regional Hospital, Bolzano, Italy
| | - Birgit Feil
- Department of Vascular and Thoracic Surgery, Regional Hospital, Bolzano, Italy
| | - Reinhold Perkmann
- Department of Vascular and Thoracic Surgery, Regional Hospital, Bolzano, Italy
| | - Luca Bertolaccini
- Division of Thoracic Surgery, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Carlo Curcio
- Department of Thoracic Surgery, Monaldi Hospital, Naples, Italy
| | - Roberto Crisci
- Division of Thoracic Surgery, Thoracic Surgery Unit, University of L'Aquila, "G. Mazzini" Hospital, Teramo, Italy
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