1. Predicting a prolonged air leak after video assisted thoracic surgery, is it really possible?
- Author
-
Francesco Londero, Luca Ampollini, Ottavio Rena, Carlo Curcio, M. Benvenuti, Giampiero Negri, F. Zaraca, A. Dell’ Amore, Francesco Carleo, Giovanna Rizzardi, Reinhold Perkmann, Andrea Droghetti, Cristiano Breda, A. Rinaldo, Marco Pipitone, Massimo Torre, R. Crisci, Nicoletta Pia Ardò, Roberto Crisci, E Pirondini, D. Gavezzoli, Angelo Morelli, R. Cherchi, Alessandro Baisi, G. Melloni, Luca Voltolini, Marco Scarci, P. Camplese, Duilio Divisi, F. Pernazza, Lorenzo Spaggiari, R. Gasparri, Felice Mucilli, Marco Ghisalberti, Andrea Imperatori, G. Cavallesco, Francesco Sollitto, S. Nicotra, Elisa Meacci, Giampiero Dolci, Alessandro Gonfiotti, Majed Refai, P.G. Solli, Lo F. Giudice, M. Giovanardi, Nicola Rotolo, Andrea Viti, C. Curcio, Stefano Margaritora, Desideria Argnani, Francesco Guerrera, L. Bortolotti, Giuseppe Cardillo, F. Mazza, Birgit Feil, Alessandro Bertani, Cristiano Benato, Davide Tosi, Luca Luzzi, Edoardo Bottoni, F. Srella, Alessandro Bandiera, D. Vinci, Pio Maniscalco, Mario Nosotti, G. Tancredi, A. De Palma, C. Surrente, G. Marulli, P. Ferrari, Camillo Lopez, Federico Raveglia, C. Risso, Alessandro Stefani, Gianluca Pariscenti, Dario Amore, Marco Alloisio, Paolo Olivo Lausi, M. Infante, A. Terzi, Luca Bertolaccini, V. Della Beffa, Francesco Puma, M. Mancuso, P Natali, Paolo Carbognani, Francesco Zaraca, and Diego Fontana
- Subjects
Pulmonary and Respiratory Medicine ,Prolonged air leak, Risk factors, VATS lobectomy, Video-assisted thoracic surgery ,Calibration (statistics) ,medicine.medical_treatment ,Video-Assisted ,Video-assisted thoracic surgery ,VATS lobectomy ,Video assisted thoracic surgery ,030204 cardiovascular system & hematology ,Logistic regression ,NO ,03 medical and health sciences ,0302 clinical medicine ,Predictive Value of Tests ,Prolonged air leak ,Statistics ,medicine ,Humans ,Risk factors ,Risk factor ,Pneumonectomy ,Retrospective Studies ,Chicago ,Thoracic Surgery, Video-Assisted ,business.industry ,Thoracic Surgery ,Retrospective cohort study ,General Medicine ,030228 respiratory system ,Predictive value of tests ,Video-assisted thoracoscopic surgery ,Surgery ,False positive rate ,Cardiology and Cardiovascular Medicine ,business ,Decision analysis - 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.
- Published
- 2020