RESUMO
OBJECTIVES: International Classification of Diseases (ICD) codes are commonly used to identify cases of diabetic ketoacidosis (DKA) in health services research, but they have not been validated. Our aim in this study was to assess the accuracy of ICD, 10th revision (ICD-10) diagnosis codes for DKA. METHODS: We conducted a multicentre, cross-sectional study using data from 5 hospitals in Ontario, Canada. Each hospitalization event has a single most responsible diagnosis code. We identified all hospitalizations assigned diagnosis codes for DKA. A true case of DKA was defined using laboratory values (serum bicarbonate ≤18 mmol/L, arterial pH ≤7.3, anion gap ≥14 mEq/L, and presence of ketones in urine or blood). Chart review was conducted to validate DKA if laboratory values were missing or the diagnosis of DKA was unclear. Outcome measures included positive predictive value (PPV), negative predictive value (NPV), sensitivity, and specificity of ICD-10 codes in patients with laboratory-defined DKA. RESULTS: We identified 316,517 hospitalizations. Among these, 312,948 did not have an ICD-10 diagnosis code for DKA and 3,569 had an ICD-10 diagnosis code for DKA. Using a combination of laboratory and chart review, we identified that the overall PPV was 67.0%, the NPV was 99.7%, specificity was 99.6%, and sensitivity was 74.9%. When we restricted our analysis to hospitalizations in which DKA was the most responsible discharge diagnosis (n=3,374 [94.5%]), the test characteristics were PPV 69.8%, NPV 99.7%, specificity 99.7%, and sensitivity 71.9%. CONCLUSION: ICD-10 codes can identify patients with DKA among those admitted to general internal medicine.
RESUMO
Introduction: A high burden of periodontal diseases is seen in the adult population. Hence, it is important to monitor the risk and progression of periodontal diseases. Instead of using complex clinical periodontal risk assessment models, self-reported measures would be favorable for obtaining data in order to conduct research and surveillance of population over time on the progression of periodontitis. Our aim was to compare two tools for periodontal risk assessment, the originally described periodontal risk assessment (PRA) model given by Lang and Tonetti and the modified self-reported periodontal risk assessment model, in patients, depending upon can be changed to after. Materials and Methods: All the participants completed a questionnaire used for modified self-reported periodontal risk assessment model. Periodontal status of the participants was recorded using the periodontal risk assessment (PRA) model given by Lang and Tonetti. Results: Among 50 patients examined 28, 14, and eight were in low-, moderate-, high-risk groups, respectively, identified by self-reported periodontal risk assessment, whereas 34, 10, and six were in low-, moderate-, high-risk groups, respectively, when identified by the PRA model given by Lang and Tonetti. Receiver operating characteristic curve (ROC curve) showed an area under the curve (AUC) of 0.835, and it represents good predictability of self-reported periodontal risk assessment model. Conclusion: This is feasible method with self-reported measures; it is easier, of low cost, and requires less equipment for obtaining data for research and surveillance of the periodontal status of a population.
