Comparison of clinical scores for identification of patients with pulmonary embolism at intermediate-high risk of adverse clinical outcome: the prognostic role of plasma lactate.

To compare the prognostic accuracy of the 2014 risk model of the European Society of Cardiology (ESC) and of Bova and TELOS scores for identification of normotensive patients with pulmonary embolism (PE) at high risk for short-term adverse events (i.e., intermediate-high risk patients), we retrospectively applied these tests to a prospective cohort of 994 normotensive patients with objectively confirmed PE. Sixty-three (6.3 %) patients reached the primary outcome, a composite of hemodynamic collapse and death within 7 days from diagnosis. The Bova and TELOS scores classified the same proportion of patients in intermediate-high risk category (5.9 and 5.7 %, respectively), with a similar primary outcome rate (18.6 and 21.1 %, respectively). The 2014 ESC model classified in the intermediate-high risk category the largest proportion of patients (12.5 %, p < 0.001 vs Bova and TELOS), with the lowest primary outcome rate (13 %, p = ns vs Bova and TELOS). When lactate determination was added to the Bova score, 112 patients (11.2 %) were classified in the intermediate-high risk category (p < 0.05 vs Bova and TELOS), with a slight increase in the primary outcome rate (25.9 %, p = 0.014 vs 2014 ESC model), allowing the recognition of a twofold higher number of patients reaching the primary outcome (29 vs 15, 11 and 12 patients in the 2014 ESC model, Bova and TELOS scores, respectively, p < 0.01 for all). The 2014 ESC model, Bova and TELOS scores identify a small number of intermediate-high risk patients with PE, without differences among tests. Adding plasma lactate to the Bova score significantly improves the identification of intermediate-high risk patients.

Plasma Lactate Dehydrogenase Levels Predict Mortality in Acute Aortic Syndromes: A Diagnostic Accuracy and Observational Outcome Study.

In acute aortic syndromes (AAS), organ malperfusion represents a key event impacting both on diagnosis and outcome. Increased levels of plasma lactate dehydrogenase (LDH), a biomarker of malperfusion, have been reported in AAS, but the performance of LDH for the diagnosis of AAS and the relation of LDH with outcome in AAS have not been evaluated so far.This was a bi-centric prospective diagnostic accuracy study and a cohort outcome study. From 2008 to 2014, patients from 2 Emergency Departments suspected of having AAS underwent LDH assay at presentation. A final diagnosis was obtained by aortic imaging. Patients diagnosed with AAS were followed-up for in-hospital mortality.One thousand five hundred seventy-eight consecutive patients were clinically eligible, and 999 patients were included in the study. The final diagnosis was AAS in 201 (20.1%) patients. Median LDH was 424 U/L (interquartile range [IQR] 367-557) in patients with AAS and 383 U/L (IQR 331-460) in patients with alternative diagnoses (P < 0.001). Using a cutoff of 450 U/L, the sensitivity of LDH for AAS was 44% (95% confidence interval [CI] 37-51) and the specificity was 73% (95% CI 69-76). Overall in-hospital mortality for AAS was 23.8%. Mortality was 32.6% in patients with LDH ≥ 450 U/L and 16.8% in patients with LDH < 450 U/L (P = 0.006). Following stratification according to LDH quartiles, in-hospital mortality was 12% in the first (lowest) quartile, 18.4% in the second quartile, 23.5% in the third quartile, and 38% in the fourth (highest) quartile (P = 0.01). LDH ≥ 450 U/L was further identified as an independent predictor of death in AAS both in univariate and in stepwise logistic regression analyses (odds ratio 2.28, 95% CI 1.11-4.66; P = 0.025), in addition to well-established risk markers such as advanced age and hypotension. Subgroup analysis showed excess mortality in association with LDH ≥ 450 U/L in elderly, hemodynamically stable and in nonsurgically treated patients.Plasma LDH constitutes a biomarker of poor outcome in patients with AAS. LDH is a rapid and universally available assay that could be used to improve risk stratification and to individualize treatment in patient groups where options are controversial.