Non-infectious diseases in infectious disease consultation: A descriptive study in a tertiary care teaching hospital.
Yoshiro HadanoTakanori MatsumotoPublished in: PloS one (2023)
In this retrospective study, we aimed to investigate the frequency, trend, and nature of non-infectious diseases (non-IDs) as the final diagnosis for patients during an infectious disease (ID) consultation in an acute care hospital in Japan. This study included adult inpatients who underwent ID consultations between October 2016 and March 2018. The demographic data, clinical manifestations, and final non-ID diagnoses of cases were explored. Among the 502 patients who underwent ID consultations, 45 (9.0%) were diagnosed with non-IDs. The most common diagnoses were tumors (22.2%, n = 10), connective tissue and collagen vascular diseases (13.3%, n = 6), other inflammatory diseases (8.9%, n = 4), and drug-induced fever (8.9%, n = 4). Multiple logistic regression analysis showed that the presence of consultations for diagnosis (odds ratio [OR], 22.0; 95% confidence interval [CI], 10.1-48.2; p<0.01), consultations from the internal medicine department (OR, 2.5; 95% CI, 1.2-5.2; p = 0.02), and non-bacteremia cases (OR, 5.2; 95% CI, 1.4-19.3; p = 0.01) were independently associated with diagnosed non-IDs. Non-IDs after ID consultations were mainly tumor-related, inflammatory diseases, and drug fever. The presence of consultations for diagnosis, consultations from the internal medicine department and non-bacteremia cases were related to non-IDs among ID consultations. Further research is needed to explore the frequency and pattern of non-IDs to improve the quality of ID consultations in daily practice.
Keyphrases
- infectious diseases
- general practice
- drug induced
- end stage renal disease
- tertiary care
- acute care
- ejection fraction
- liver injury
- newly diagnosed
- chronic kidney disease
- primary care
- healthcare
- palliative care
- peritoneal dialysis
- oxidative stress
- prognostic factors
- emergency department
- young adults
- physical activity
- big data
- electronic health record
- gram negative
- deep learning
- artificial intelligence