title: Empirical antibiotic therapy (ABT) of lower respiratory tract infections (LRTI) in the elderly: application of artificial neural network (ANN). Preliminary results.
abstract: LRTI are among the most common diseases in developed countries, including chronic obstructive pulmonary disease (COPD), one of the most frequent conditions. Their treatment in general practice is often unsuccessful and this increases hospital admissions. We know, bacterial infections in the elderly show a higher morbidity and mortality, either for more severe symptoms, than in younger adults, or because the causing agent often remains unknown. The need for a quick initiation of ABT often requires to chose on empirical grounds. To date there are no official guidelines for empirical ABT of COPD exacerbations, but only heterogeneous and often conflicting recommendations exist. The aim of our study was to identify a tool to guide the choice of the most effective empirical ABT when symptoms are acute and bacteriological tests cannot be performed. We used an ANN to study 117 patients aged between 55 and 97 years (mean 81.5 ± 8.7 years) (± S.D.), admitted with a diagnosis of pneumonia, COPD exacerbation or pneumonia with respiratory failure. We registered symptoms at onset and some individual variables such as age, sex, risk factors, comorbidity, current drug therapies. Then the ANN was applied to choose ABT in 20 patients versus 20 subjects whose therapy was chosen by the physicians, comparing these groups for therapy's efficacy, mean durations of therapy and hospitalization (H). In the learning phase, the ANN could predict the resolution index 99.05% of the time (i.e., 104 times) with a ± S.D. = 0.23. After the training, during the test phase, the network predicted the resolution index 91.67% of the time (i.e., 11 times) with a ± S.D. = 0.54, thus proving the validity of the relations identified during the learning phase. Preliminary results of the application of our tool, show the ANN allowed us to greatly reduce the duration of the ABT and subsequently of the H. Based on preliminary results, we assume that the use of ANN can make a valuable contribution in the choice of empirical ABT in the course of acute lung diseases in elderly.
authors: Sapienza University of Rome, Dipartimento di Scienze dell'Invecchiamento, Policlinico Umberto I, Viale del Policlinico 155, I-00161 Roma, Italy.
authors: Nicolò
authors: N
authors: Gueli
authors: Andrea
authors: A
authors: Martinez
authors: Walter
authors: W
authors: Verrusio
authors: Adele
authors: A
authors: Linguanti
authors: Paola
authors: P
authors: Passador
authors: Valentina
authors: V
authors: Martinelli
authors: Giovanni
authors: G
authors: Longo
authors: Benedetta
authors: B
authors: Marigliano
authors: Flaminia
authors: F
authors: Cacciafesta
authors: Mauro
authors: M
authors: Cacciafesta
mesh_terms: D000208:Acute Disease; D000368:Aged; D000369:Aged, 80 and over; D000900:Anti-Bacterial Agents / Q000627:therapeutic use*; D018450:Disease Progression; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D011014:Pneumonia / Q000188:drug therapy*; D029424:Pulmonary Disease, Chronic Obstructive / Q000188:drug therapy*; D012131:Respiratory Insufficiency / Q000188:drug therapy*; D016896:Treatment Outcome
chemical_list: D000900:Anti-Bacterial Agents