Precisión del diagnóstico en medicina interna e influencia de un sistema informático en el razonamiento clínico

ResumenIntroducciónEl contacto inicial entre paciente y médico es clave para plantear un diagnóstico diferencial. Este proceso está basado en la experiencia y en la recuperación de patrones almacenados en el cerebro. Un problema importante es la limitación de la memoria, por lo que un sistema electrónico de sugerencia de posibilidades podría ser de utilidad.ObjetivosConocer la precisión diagnóstica de un grupo de internistas interaccionando con un sistema informático de apoyo al diagnóstico (SIAD). Conocer el grado de acuerdo entre los clínicos y el SIAD en su capacidad para enumerar un correcto diagnóstico inicial.Pacientes y métodoestudio prospectivo realizado en un hospital general en Málaga, en el que se mide la precisión diagnóstica para clínicos y SIAD en una muestra de 50 pacientes.ResultadosLa precisión diagnóstica de los clínicos fue del 60% y la del SIAD, del 72%(diferencia no significativa). La concordancia entre ambos fue baja (kappa = 0,33).ConclusiónLos clínicos y el SIAD tienen similar precisión diagnóstica inicial, pero una baja concordancia, lo que puede evidenciar un comportamiento operativo diferente.AbstractIntroductionThe first contact between a physician and a patient is a key moment to establishing a differential diagnosis. This process is based on experience and recovery of patterns stored in the brain. One important problem is the limitation of memory, thus a computerised system that suggests possibilities could be of use in establishing a differential diagnosis.ObjectivesTo determine the diagnostic accuracy of a group of internists working with an Electronic Diagnostic Reminder Tool (EDRT). To measure the agreement between clinicians and the EDRT in the initial diagnostic work.Patients and methodA prospective study was conducted in a general hospital in Málaga, Spain. The diagnostic accuracy of clinicians and the EDRT was recorded on a sample of 50 patients.ResultsThe diagnostic accuracy for clinicians was 60%, and for the EDRT it was 72%, with a low agreement (kappa=0.33).ConclusionClinicians and the EDRT have a similar diagnostic accuracy, but a low level of concordance, showing a different operational behaviour

Characteristics and Predictors of Progression Interstitial Lung Disease in Rheumatoid Arthritis Compared with Other Autoimmune Disease: A Retrospective Cohort Study

Objectives: To describe the characteristics and progression of interstitial lung disease in patients with associated systemic autoimmune disease (ILD-SAI) and to identify factors associated with progression and mortality. Patients and methods: We performed a multicenter, retrospective, observational study of patients with ILD-SAI followed between 2015 and 2020. We collected clinical data and performed pulmonary function testing and high-resolution computed tomography at diagnosis and at the final visit. The main outcome measure at the end of follow-up was forced vital capacity (FVC) >10% or diffusing capacity of the lungs for carbon monoxide >15% and radiological progression or death. Cox regression analysis was performed to identify factors associated with worsening of ILD. Results: We included 204 patients with ILD-SAI: 123 (60.3%) had rheumatoid arthritis (RA), 58 had (28.4%) systemic sclerosis, and 23 (11.3%) had inflammatory myopathy. After a median (IQR) period of 56 (29.8–93.3) months, lung disease had stabilized in 98 patients (48%), improved in 33 (16.1%), and worsened in 44 (21.5%). A total of 29 patients (14.2%) died. Progression and hospitalization were more frequent in patients with RA (p = 0.010). The multivariate analysis showed the independent predictors for worsening of ILD-SAI to be RA (HR, 1.9 [95% CI, 1.3–2.7]), usual interstitial pneumonia pattern (HR, 1.7 [95% CI, 1.0–2.9]), FVC (%) (HR, 2.3 [95% CI, 1.4–3.9]), and smoking (HR, 2.7 [95%CI, 1.6–4.7]). Conclusion: Disease stabilizes or improves after a median of 5 years in more than half of patients with ILD-SAI, although more than one-third die. Data on subgroups and risk factors could help us to predict poorer outcomes.Ye