Respiratory and immune response to maximal physical exertion following exposure to secondhand smoke in healthy adults

© 2012 The Authors. Published by PLOS. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.1371/journal.pone.0031880We assessed the cardiorespiratory and immune response to physical exertion following secondhand smoke (SHS) exposure through a randomized crossover experiment. Data were obtained from 16 (8 women) non-smoking adults during and following a maximal oxygen uptake cycling protocol administered at baseline and at 0-, 1-, and 3- hours following 1-hour of SHS set at bar/restaurant carbon monoxide levels. We found that SHS was associated with a 12% decrease in maximum power output, an 8.2% reduction in maximal oxygen consumption, a 6% increase in perceived exertion, and a 6.7% decrease in time to exhaustion (P<0.05). Moreover, at 0-hours almost all respiratory and immune variables measured were adversely affected (P<0.05). For instance, FEV 1 values at 0-hours dropped by 17.4%, while TNF-α increased by 90.1% (P<0.05). At 3-hours mean values of cotinine, perceived exertion and recovery systolic blood pressure in both sexes, IL4, TNF-α and IFN-γ in men, as well as FEV 1/FVC, percent predicted FEV 1, respiratory rate, and tidal volume in women remained different compared to baseline (P<0.05). It is concluded that a 1-hour of SHS at bar/restaurant levels adversely affects the cardiorespiratory and immune response to maximal physical exertion in healthy nonsmokers for at least three hours following SHS. © 2012 Flouris et al.Published versio

Correction: Respiratory and Immune Response to Maximal Physical Exertion following Exposure to Secondhand Smoke in Healthy Adults

We assessed the cardiorespiratory and immune response to physical exertion following secondhand smoke (SHS) exposure through a randomized crossover experiment. Data were obtained from 16 (8 women) non-smoking adults during and following a maximal oxygen uptake cycling protocol administered at baseline and at 0-, 1-, and 3- hours following 1-hour of SHS set at bar/restaurant carbon monoxide levels. We found that SHS was associated with a 12% decrease in maximum power output, an 8.2% reduction in maximal oxygen consumption, a 6% increase in perceived exertion, and a 6.7% decrease in time to exhaustion (P<0.05). Moreover, at 0-hours almost all respiratory and immune variables measured were adversely affected (P<0.05). For instance, FEV(1) values at 0-hours dropped by 17.4%, while TNF-α increased by 90.1% (P<0.05). At 3-hours mean values of cotinine, perceived exertion and recovery systolic blood pressure in both sexes, IL4, TNF-α and IFN-γ in men, as well as FEV(1)/FVC, percent predicted FEV(1), respiratory rate, and tidal volume in women remained different compared to baseline (P<0.05). It is concluded that a 1-hour of SHS at bar/restaurant levels adversely affects the cardiorespiratory and immune response to maximal physical exertion in healthy nonsmokers for at least three hours following SHS

A Machine Learning Algorithm to Identify Patients at Risk of Unplanned Subsequent Surgery After Intramedullary Nailing for Tibial Shaft Fractures

Objectives: In the SPRINT trial, 18% of patients with a tibial shaft fracture (TSF) treated with intramedullary nailing (IMN) had one or more unplanned subsequent surgical procedures. It is clinically relevant for surgeon and patient to anticipate unplanned secondary procedures, other than operations that can be readily expected such as reconstructive procedures for soft tissue defects. Therefore, the objective of this study was to develop a machine learning (ML) prediction model using the SPRINT data that can give individual patients and their care team an estimate of their particular probability of an unplanned second surgery. Methods: Patients from the SPRINT trial with unilateral TSFs were randomly divided into a training set (80%) and test set (20%). Five ML algorithms were trained in recognizing patterns associated with subsequent surgery in the training set based on a subset of variables identified by random forest algorithms. Performance of each ML algorithm was evaluated and compared based on (1) area under the ROC curve, (2) calibration slope and intercept, and (3) the Brier score. Results: Total data set comprised 1198 patients, of whom 214 patients (18%) underwent subsequent surgery. Seven variables were used to train ML algorithms: (1) Gustilo-Anderson classification, (2) Tscherne classification, (3) fracture location, (4) fracture gap, (5) polytrauma, (6) injury mechanism, and (7) OTA/AO classification. The best-performing ML algorithm had an area under the ROC curve, calibration slope, calibration intercept, and the Brier score of 0.766, 0.954, -0.002, and 0.120 in the training set and 0.773, 0.922, 0, and 0.119 in the test set, respectively. Conclusions: An ML algorithm was developed to predict the probability of subsequent surgery after IMN for TSFs. This ML algorithm may assist surgeons to inform patients about the probability of subsequent surgery and might help to identify patients who need a different perioperative plan or a more intensive approach.Orthopaedics, Trauma Surgery and Rehabilitatio