21 resultados para Clinical validation


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There is currently no validated scoring system for quantification of airway secretions in children. A user friendly, valid scoring system of airway secretions during flexible bronchoscopy (FB) would be useful for comparative purposes in clinical medicine and research. The objective of this study was to validate our bronchoscopic secretion (BS) scoring system by examining the relationship between the amount of secretions seen at bronchoscopy with airway cellularity and microbiology. In 106 children undergoing FIB, the relationship of BS grades with bronchocalveolar lavage (BAL) cellularity and infective state (bacterial and viral infections) were examined using receptor operator curves (ROC). BAL was obtained according to European Respiratory Society guidelines; first lavage for microbiology and second lavage for cellularity Area under the ROC was significant for total cell count (TCC) and neutrophil % but not for lymphocyte %. BS grade significantly related to infection positive state (chi(2)(trend) = 5.85, P = 0,016). The area under the ROC for infection positive state versus BS grade was 0.645, 95% Cl 0.527-0.763. The BS scoring system is a valid method for quantifying airway secretions in children undergoing bronchoscopy The system related well to airway cellularity and neutrophilia, as well as to an airway infective state. However, the system is only complementary to cell counts and cultures and cannot replace these laboratory quantification techniques.

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Background Cardiac disease is the principal cause of death in patients with chronic kidney disease (CKD). Ischemia at dobutamine stress echocardiography (DSE) is associated with adverse events in these patients. We sought the efficacy of combining clinical risk evaluation with DSE. Methods We allocated 244 patients with CKD (mean age 54 years, 140 men, 169 dialysis-dependent at baseline) into low- and high-risk groups based on two disease-specific scores and the Framingham risk model. All underwent DSE and were further stratified according to DSE results. Patients were followed over 20 +/- 14 months for events (death, myocardial infarction, acute coronary syndrome). Results There were 49 deaths and 32 cardiac events. Using the different clinical scores, allocation of high risk varied from 34% to 79% of patients, and 39% to 50% of high-risk patients had an abnormal DSE. In the high-risk groups, depending on the clinical score chosen, 25% to 44% with an abnormal DSE had a cardiac event, compared with 8% to 22% with a.normal DSE. Cardiac events occurred in 2.0%, 3.1 %, and 9.7% of the low-risk patients, using the two disease-specific and Framingham scores, respectively, and DSE results did not add to risk evaluation in this subgroup. Independent DSE predictors of cardiac events were a lower resting diastolic blood pressure, angina during the test, and the combination of ischemia with resting left ventricular dysfunction. Conclusion In CKD patients, high-risk findings by DSE can predict outcome. A stepwise strategy of combining clinical risk scores with DSE for CAD screening in CKD reduces the number of tests required and identifies a high-risk subgroup among whom DSE results more effectively stratify high and low risk.

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The prognostic value of exercise (EXE) and dobutamine echocardiograms (DbE) has been well defined in large studies. However, while risk is determined by both clinical and echo features, no simple means of combining these data has been defined. We sought to combine these data into risk scores. Methods. At 3 expert centers, 7650 pts underwent standard EXE (n=5211) and DbE (w2439) for evaluation of known or suspected CAD and were followed for up to 10 years (mean 5-2) for major events (death or myocardial infarction). A subgroup of 2953 EXE and 1025 DbE pts was randomly selected to develop separate multivariate models for prediction of events. After simplication of each model for clinical use, models were validated in the remaining EXE and DbE pts. ResuI1s. The total number of events was 200 in the EXE and 225 in the DbE pts, of which 58 and 99 events occurred in the respective testing groups. The following regression equations gave equivalent results I” the testing and validation groups for both EXE and DbE; DbE = (Age’O.02) + (DM’l .O) + (Low RPP’0.6) + ([CHF+lschemia+Scar]‘O.7) EXE = ([DM+CHF]‘O.S) + O.S(lschemla #) + l.B(Scar#) - (METS0.19) (where each categorical variable scored 1 when present and 0 when absent, Ischemia# = 1 for l-2 VD. 6 for 3 VD; Scar# = 1 for 1-2 VD, 1.7 for 3 VD). The table summarizes the scores and equivalent outcomes for EXE and DbE. Conclusions. Risk scores based on clinical and EXE or DbE results may be used to quantify the risk of events during follow-up.