Purpose To develop and internally validate a nomogram for predicting the risk of incorrect inhalation techniques in patients with chronic airway diseases. Methods A total of 206 patients with chronic airway diseases treated with inhaled medications were recruited in this study. Patients were divided into correct (n=129) and incorrect (n=77) cohorts based on their mastery of inhalation devices, which were assessed by medical professionals. Data were collected on the basis of questionnaires and medical records. The least absolute shrinkage and selection operator method (LASSO) and multivariate logistic regression analyses were conducted to identify the risk factors of incorrect inhalation techniques. Then, calibration curve, Harrell’s C-index, area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA) and bootstrapping validation were applied to assess the apparent performance, clinical validity and internal validation of the predicting model, respectively. Results Seven risk factors including age, education level, drug cognition, self-evaluation of curative effect, inhalation device use instruction before treatment, post-instruction evaluation and evaluation at return visit were finally determined as the predictors of the nomogram prediction model. The ROC curve obtained by this model showed that the AUC was 0.814, with a sensitivity of 0.78 and specificity of 0.75. In addition, the C-index was 0.814, with a Z value of 10.31 (P<0.001). It was confirmed to be 0.783 by bootstrapping validation, indicating that the model had good discrimination and calibration. Furthermore, analysis of DCA showed that the nomogram had good clinical validity. Conclusion The application of the developed nomogram to predict the risk of incorrect inhalation techniques during follow-up visits is feasible.
Background: The Coronavirus Disease 2019 (COVID-19) already have been as a pandemic. However, knowledge about the sequelae of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection remains limited. Here we descirbe the pulmonary function test (PFT) and cardiopulmonary exercise test (CPET) of critically ill COVID-19 in four cases with sereve acute respiratory distress syndrome (ARDS) after discharge.Case presentation: We introduce four patients who complained of fever, cough, chest tightness and other symptoms, all of them were confirmed as SARS-CoV-2 infection by real-time reverse transcription polymerase chain reaction (RT-PCR). They were treated with mechanical ventilation because of severe ARDS. After respiratory support, antiviral and anti-infective treatment, they were weaned from mechanic ventilation with the improvement of hypoxemia. All patients were discharged from the hospital after completion of treatment and had no mortality. Around 1-month post-discharge, they were followed up for chest computed tomography (CT) scan, and performed PFT and CPET. Peak oxygen uptake of predicted (peakVO2% pred) decreased in all four cases, although spirometry were in the normal range, and only 2 cases had mild decline in carbon monoxide diffusion capacity of predicted (DLCO%pred).Conclusions: We found reduced exercise endurance in all four COVID-19 survivors, even parts of them with normal or slightly abnormal static lung function. We also believe that exercise endurance impairment of COVID-19 convalescents is more likely affected by extrapulmonary factors. Taken the above into consideration, our study highlights that the combination of PFT and CPET are important tests for tracking the development and recovery of COVID-19 survivors.
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