Determining the impact of an artificial intelligence tool on the management of pulmonary nodules detected incidentally on CT (DOLCE) study protocol: a prospective, non-interventional multicentre UK study.

INTRODUCTION: In a small percentage of patients, pulmonary nodules found on CT scans are early lung cancers. Lung cancer detected at an early stage has a much better prognosis. The British Thoracic Society guideline on managing pulmonary nodules recommends using multivariable malignancy risk prediction models to assist in management. While these guidelines seem to be effective in clinical practice, recent data suggest that artificial intelligence (AI)-based malignant-nodule prediction solutions might outperform existing models. METHODS AND ANALYSIS: This study is a prospective, observational multicentre study to assess the clinical utility of an AI-assisted CT-based lung cancer prediction tool (LCP) for managing incidental solid and part solid pulmonary nodule patients vs standard care. Two thousand patients will be recruited from 12 different UK hospitals. The primary outcome is the difference between standard care and LCP-guided care in terms of the rate of benign nodules and patients with cancer discharged straight after the assessment of the baseline CT scan. Secondary outcomes investigate adherence to clinical guidelines, other measures of changes to clinical management, patient outcomes and cost-effectiveness. ETHICS AND DISSEMINATION: This study has been reviewed and given a favourable opinion by the South Central-Oxford C Research Ethics Committee in UK (REC reference number: 22/SC/0142).Study results will be available publicly following peer-reviewed publication in open-access journals. A patient and public involvement group workshop is planned before the study results are available to discuss best methods to disseminate the results. Study results will also be fed back to participating organisations to inform training and procurement activities. TRIAL REGISTRATION NUMBER: NCT05389774.

A pilot study of a digital drainage system in pneumothorax.

UNLABELLED: Over recent years there has been increasing usage of digital systems within cardiothoracic surgery to quantify air leaks and aid in clinical decision-making regarding the removal of chest drains postoperatively. The literature suggests improved agreement on timing of removal of chest drains and a reduced length of stay of patients. It could be that such devices could be useful tools for the clinician managing cases of pneumothorax. METHODS: This pilot study recruited adults admitted under the medical team with a pneumothorax requiring a chest drain. Participants had the underwater seal device changed for a digital device (Thopaz) which allowed continuous monitoring of the air leak. Drains were removed when either there was no ongoing air leak and the lung had expanded, or surgery was deemed necessary. RESULTS: Thirteen patients with pneumothorax (four primary, nine secondary) used the device during their admission including one patient treated in the community (the device has internal suction). Data were used to aid the clinician in management of the pneumothorax including the timing of surgery/ removal of drain and commencement of suction. DISCUSSION: Digital devices appear to be safe and effective and may prove to be a useful tool in the management of pneumothorax.