Diagnostic accuracy of adenosine deaminase for pleural tuberculosis in a low prevalence setting: A machine learning approach within a 7-year prospective multi-center study.

OBJECTIVE: To analyze the performance of adenosine deaminase in pleural fluid combined with other parameters routinely measured in clinical practice and assisted by machine learning algorithms for the diagnosis of pleural tuberculosis in a low prevalence setting, and secondly, to identify effusions that are non-tuberculous and most likely malignant. PATIENTS AND METHODS: We prospectively analyzed 230 consecutive patients diagnosed with lymphocytic exudative pleural effusion from March 2013 to June 2020. Diagnosis according to the composite reference standard was achieved in all cases. Pre-test probability of pleural tuberculosis was 3.8% throughout the study period. Parameters included were: levels of adenosine deaminase, pH, glucose, proteins, and lactate dehydrogenase, red and white cell counts and lymphocyte percentage in pleural fluid, as well as age. We tested six different machine learning-based classifiers to categorize the patients. Two different classifications were performed: a) tuberculous/non-tuberculous and b) tuberculous/malignant/other. RESULTS: Out of a total of 230 patients with pleural effusion included in the study, 124 were diagnosed with malignant effusion and 44 with pleural tuberculosis, while 62 were given other diagnoses. In the tuberculous/non-tuberculous classification, and taking into account the validation predictions, the support vector machine yielded the best result: an AUC of 0.98, accuracy of 97%, sensitivity of 91%, and specificity of 98%, whilst in the tuberculous/malignant/other classification, this type of classifier yielded an overall accuracy of 80%. With this three-class classifier, the same sensitivity and specificity was achieved in the tuberculous/other classification, but it also allowed the correct classification of 90% of malignant cases. CONCLUSION: The level of adenosine deaminase in pleural fluid together with cell count, other routine biochemical parameters and age, combined with a machine-learning approach, is suitable for the diagnosis of pleural tuberculosis in a low prevalence scenario. Secondly, non-tuberculous effusions that are suspected to be malignant may also be identified with adequate accuracy.

Assistance continuity from primary care in an inter-level nursing plan for a case of pediatric liver transplant. / Continuidad asistencial desde atención primaria en un plan de cuidados interniveles para un caso de trasplante hepático pediátrico.

Spain is one of the countries where most solid organ transplants are performed each year, in the year 2014 a 2.7% of them were given in childhood. Given the complexity and severity of this disease it is necessary to establish a care plan that covers both pre-transplant and post-transplant, with close cooperation between different levels of care, to approach the several problems that can appear and assure continuum of care. In the following example, a Gambian teen with risk of social exclusion fostered a collaboration between the primary care nurse and transplant nurse that was the key to continuum care. Multiple strategies were used in the care plan to ensure better adherence and compliance of the treatment. However, the knowledge of the culture of origin must be deepened to establish more individualized care plans and thus improve results. The care plan included problems according to the NANDA, NOC, NIC taxonomy.