Деструктивные пневмонии у детей глазами телеконсультанта

Abstract

Aim. To study problems that pediatricians face in the management of necrotizing pneumonia, and to show ways to its optimization. Materials and methods. Necrotizing pneumonia is a serious disease that causes difficulties in the choice of antibiotics, indications for pleural interventions and termination of the therapy. Although mostly pneumococcal, pneumonias with purulent cavities and methapneumonic pleurisy (immunopathologic in its origin) are clinically resistance to antimicrobials, and it is taken by pediatricians as indications for changing antibiotics and pleural taps. Hence the importance of studying the prevalent tactics. We conducted analysis of 52 tele-requests for children with pulmonary consolidation and air-filled cavities. Its share among all “general pediatric” requests increased from 5% in 2021–2022 to 18% in 2023–2024. Of 52 patients 43 had also pleural effusions, 11 — pneumothorax. Pneumonia followed measles in 6 children, pertussis in 3, scarlet fever — in 1. All diagnostic and curative actions were compared to Clinical guidelines. Results. In all instances conditions for hospital care (including intensive) were generally adequate, diagnostic activities were often redundant (nonrelevant analyses, multiple computer tomography instead of X-ray). Only 16 patients were started on cephtriiaxone and amoxicillin/ clavulanate recommended by Clinical guides, the rest received reserve antibiotics. All patients received the 2nd course (combinations of the reserve antibiotics), 20 of them also the 3rd course. Steroids were given only to 3 patients. Of 43 children with pleurisy taps or drainage had 33, only 9 of them according to indications. Conclusion. Pediatricians are not fully conversant with Clinical guides and it causes excessive administration of 2–3 courses of reserve antibiotics, unnecessary pleural interventions, infrequent use of steroid therapy that shortens the length of hospital stay. Keywords: necrotizing pneumonia, management optimization.