Oscillometric devices vs. arterial Doppler in measuring the ankle-arm index for the diagnosis of peripheral arterial disease. / Dispositivos oscilométricos frente a Doppler arterial en la medida del índice tobillo-brazo para el diagnóstico de la enfermedad arterial periférica.

INTRODUCTION: Peripheral arterial disease is a marker of vascular damage that is diagnosed by measuring the ankle-brachial index. The aim of this study was to determine the validity and agreement of the MESI ABPI-MD and Microlife WatchBP® office-ABI oscillometric devices with respect to the gold standard arterial Doppler. MATERIALS AND METHODS: Observational, cross-sectional, descriptive study of inpatients who underwent ABI measurement with the three devices. Values are considered normal between 1-1.4, indeterminate between 0.91-0.99 and pathological ≤0.9 and >1.4. RESULTS: A total of 187 patients (54.4% male) with a mean age of 66 years were included. The Doppler results were inferior to those of the oscillometric devices (median [IQR] 1.1 [0.2] vs. 1.2 [0.2], P<.05), with no significant differences between the automated devices (P=.29 for the right lower limb and P=.342 for the left lower limb). Both devices had high specificity (96.5-99.2%) and low sensitivity (29.5-45.4%). The correlation of the results was good-moderate for MESI and moderate for Microlife. The agreement between the two was acceptable-moderate. CONCLUSION: Automated oscillometric devices could be useful in asymptomatic patients as an alternative to arterial Doppler.

Preoperative clinical model to predict myocardial injury after non-cardiac surgery: a retrospective analysis from the MANAGE cohort in a Spanish hospital.

OBJECTIVES: To determine preoperative factors associated to myocardial injury after non-cardiac surgery (MINS) and to develop a prediction model of MINS. DESIGN: Retrospective analysis. SETTING: Tertiary hospital in Spain. PARTICIPANTS: Patients aged ≥45 years undergoing major non-cardiac surgery and with at least two measures of troponin levels within the first 3 days of the postoperative period. All patients were screened for the MANAGE trial. PRIMARY AND SECONDARY OUTCOME MEASURES: We used multivariable logistic regression analysis to study risk factors associated with MINS and created a score predicting the preoperative risk for MINS and a nomogram to facilitate bed-side use. We used Least Absolute Shrinkage and Selection Operator method to choose the factors included in the predictive model with MINS as dependent variable. The predictive ability of the model was evaluated. Discrimination was assessed with the area under the receiver operating characteristic curve (AUC) and calibration was visually assessed using calibration plots representing deciles of predicted probability of MINS against the observed rate in each risk group and the calibration-in-the-large (CITL) and the calibration slope. We created a nomogram to facilitate obtaining risk estimates for patients at pre-anaesthesia evaluation. RESULTS: Our cohort included 3633 patients recruited from 9 September 2014 to 17 July 2017. The incidence of MINS was 9%. Preoperative risk factors that increased the risk of MINS were age, American Status Anaesthesiology classification and vascular surgery. The predictive model showed good performance in terms of discrimination (AUC=0.720; 95% CI: 0.69 to 0.75) and calibration slope=1.043 (95% CI: 0.90 to 1.18) and CITL=0.00 (95% CI: -0.12 to 0.12). CONCLUSIONS: Our predictive model based on routinely preoperative information is highly affordable and might be a useful tool to identify moderate-high risk patients before surgery. However, external validation is needed before implementation.