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Prediction of complicated lower respiratory tract infections in older patients with diabetes
Authors:Leonie M A J Venmans  Jettie Bont  Kees J Gorter  Theo J M Verheij  Guy E H M Rutten  Eelko Hak
Affiliation:Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands.
Abstract:BACKGROUND: Patients with diabetes have an elevated risk of developing complicated lower respiratory tract infections (LRTIs). However, up until now, GPs have not had the tools to assess individual risks. AIM: To assess the applicability of an existing prediction rule for complicated LRTI among patients with diabetes. DESIGN OF STUDY: Retrospective cohort study. SETTING: The Utrecht GP Research Network. METHOD: An existing rule that was used estimates the risk of 30-day hospitalisation or death following an episode of LRTI. Predictors were exacerbation of chronic obstructive pulmonary disease, or pneumonia, increasing age, heart failure, number of hospitalisations in the previous year, use of antibiotics in the previous month, diabetes medication, and prednisone use. Discriminative capacity of the rule was estimated in patients with diabetes. Other potential predictors from the original study were examined, to test for a potentially improved model. RESULTS: Of 445 episodes of LRTI in patients with diabetes, 68 had an outcome of hospitalisation or death within 30 days of diagnosis of LRTI (15.3%). Results showed good reliability of the model (goodness of fit test P=0.16) and discriminative properties (area under the receiver operating characteristic curve: 0.79, 95% confidence interval=0.73 to 0.86). No other predictors could be added. Patients with a lower-risk assignment (scoreor=7) had a probability of 36.6% for the composite endpoint of hospitalisation or death within 30 days of diagnosis of LRTI. CONCLUSION: The use of a prediction rule may help GPs to assess the risk of hospitalisation or death in patients with diabetes who have an episode of LRTI.
Keywords:diabetes mellitus   patient admission   prediction   primary health care   respiratory tract   infection
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