Combining Free Text and Structured Electronic Medical Record Entries to Detect Acute Respiratory Infections

The electronic medical record (EMR) contains a rich source of information that could be harnessed for epidemic surveillance. We asked if structured EMR data could be coupled with computerized processing of free-text clinical entries to enhance detection of acute respiratory infections (ARI).A manual review of EMR records related to 15,377 outpatient visits uncovered 280 reference cases of ARI. We used logistic regression with backward elimination to determine which among candidate structured EMR parameters (diagnostic codes, vital signs and orders for tests, imaging and medications) contributed to the detection of those reference cases. We also developed a computerized free-text search to identify clinical notes documenting at least two non-negated ARI symptoms. We then used heuristics to build case-detection algorithms that best combined the retained structured EMR parameters with the results of the text analysis.An adjusted grouping of diagnostic codes identified reference ARI patients with a sensitivity of 79%, a specificity of 96% and a positive predictive value (PPV) of 32%. Of the 21 additional structured clinical parameters considered, two contributed significantly to ARI detection: new prescriptions for cough remedies and elevations in body temperature to at least 38°C. Together with the diagnostic codes, these parameters increased detection sensitivity to 87%, but specificity and PPV declined to 95% and 25%, respectively. Adding text analysis increased sensitivity to 99%, but PPV dropped further to 14%. Algorithms that required satisfying both a query of structured EMR parameters as well as text analysis disclosed PPVs of 52-68% and retained sensitivities of 69-73%.Structured EMR parameters and free-text analyses can be combined into algorithms that can detect ARI cases with new levels of sensitivity or precision. These results highlight potential paths by which repurposed EMR information could facilitate the discovery of epidemics before they cause mass casualties

Inhibition of OXA-1 β-Lactamase by Penems▿ †

The partnering of a β-lactam with a β-lactamase inhibitor is a highly effective strategy that can be used to combat bacterial resistance to β-lactam antibiotics mediated by serine β-lactamases (EC 3.2.5.6). To this end, we tested two novel penem inhibitors against OXA-1, a class D β-lactamase that is resistant to inactivation by tazobactam. The Ki of each penem inhibitor for OXA-1 was in the nM range (Ki of penem 1, 45 ± 8 nM; Ki of penem 2, 12 ± 2 nM). The first-order rate constant for enzyme and inhibitor complex inactivation of penems 1 and 2 for OXA-1 β-lactamase were 0.13 ± 0.01 s−1 and 0.11 ± 0.01 s−1, respectively. By using an inhibitor-to-enzyme ratio of 1:1, 100% inactivation was achieved in ≤900 s and the recovery of OXA-1 β-lactamase activity was not detected at 24 h. Covalent adducts of penems 1 and 2 (changes in molecular masses, +306 ± 3 and +321 ± 3 Da, respectively) were identified by electrospray ionization mass spectrometry (ESI-MS). After tryptic digestion of OXA-1 inactivated by penems 1 and 2, ESI-MS and matrix-assisted laser desorption ionization-time-of-flight MS identified the adducts of 306 ± 3 and 321 ± 3 Da attached to the peptide containing the active-site Ser67. The base hydrolysis of penem 2, monitored by serial 1H nuclear magnetic resonance analysis, suggested that penem 2 formed a linear imine species that underwent 7-endo-trig cyclization to ultimately form a cyclic enamine, the 1,4-thiazepine derivative. Susceptibility testing demonstrated that the penem inhibitors at 4 mg/liter effectively restored susceptibility to piperacillin. Penem β-lactamase inhibitors which demonstrate high affinities and which form long-lived acyl intermediates may prove to be extremely useful against the broad range of inhibitor-resistant serine β-lactamases present in gram-negative bacteria