Month: September 2023

Don’t Use Lytics in Mild Stroke, Part 3

Well, PRISMS demonstrated unfavorable results.

MARISS tried to ascertain predictors of poor outcome in mild stroke, and intravenous thrombolysis was not associated with an effect on the primary outcome.

Now, again, we examine thrombolysis in “mild” stroke, in this case, NIHSS ≤3 – and fail.

Like MARISS, this is a retrospective dredge of patients selected by the treating clinicians to receive either intravenous thrombolysis or, in this case, dual-antiplatelet therapy with clopidogrel and aspirin. The population included for analysis is the Austrian Stroke Unit Registry from 2018 until 2019, an original cohort of 53,899 patients. Of these, 29,252 were NIHSS ≤3, but exclusions meant nearly 25,000 were left out – primarily those whose strokes were the result of atrial fibrillation, or whose treating clinicians chose platelet monotherapy instead of dual antiplatelet therapy.

The remaining ~4,000 were analyzed both in their unadjusted cohorts, as well as propensity scored cohorts comprised of roughly 20% of the original. In the unadjusted cohorts, efficacy and safety outcomes were universally worse in those selected for thrombolysis – but, of course, were generally more severe stroke syndromes. After propensity score matching, these differences generally disappeared – except a preponderance of sICH in the thrombolysis cohort.

The authors here conclude there’s no evidence of superiority for thrombolysis in mild stroke, and their results fit broadly with those from other cohorts. It’s observational and unreliable, but it ought to be a very reasonable stance to withhold thrombolysis for mild strokes pending trials conclusively demonstrating which, if any, mild strokes do improve with thrombolysis.

“IV Thrombolysis vs Early Dual Antiplatelet Therapy in Patients With Mild Noncardioembolic Ischemic Stroke“

Which Sepsis Alert is the Biggest Loser?

It’s a trick question – in the end, all of us have already lost.

This is a short retrospective report evaluating, primarily, the Epic Sepsis Prediction Model, and the mode in which is deployed. The Epic SPM generates a “prediction of sepsis score”, calculated at 15 minute intervals, providing a continuous risk score for the development of sepsis. Of course, in modern medicine, this is usually reduced to a trigger threshold at which point an alert is fired. Alerts, alerts, alerts – what are they good for?

In this study, the Epic SPM was evaluated at several difference SPS score thresholds ranging from ≥5 to ≥10 – and compared, as well, with SIRS, qSOFA, and SOFA. There were two goals for the evaluation: accuracy and timeliness. All prediction tools provided the same age-old tradeoff between sensitivity and specificity, with a PSS of ≥5 being 95% sensitive, but merely 53% specific. Likewise, a more specific cut-off sacrificed sensitivity. SIRS, qSOFA, and SOFA suffered from the same limitations.

The “time to detection” was a bit more interesting, but conclusions are a bit limited by the methods used to determine. The PSS is calculated at 15 minute intervals, while their calculations of SIRS, qSOFA, and SOFA all happened at hourly intervals. Then, “time zero” for their calculations was actually determined by the time of clinician action – the time at which a clinician suspected sepsis and ordered either antimicrobials or blood cultures. With respect to timeliness, only a minority of patients met threshold scores at “time zero” – except SIRS, where nearly half were at threshold.

So, it’s hard to conclude much from these data – other than, as previously alluded, we are all losers. These alerts are clearly useless, yet they, and the Surviving Sepsis bundle gestapo have trained clinicians to leap at the earliest opportunity to (over)diagnose sepsis and administer broad-spectrum antibiotics. Multiple specialty societies have asked for the SEP-1 measures to be rolled back due to these obvious harms, let alone the administrative costs, and eliminating that “quality” measure would go a long way to putting these useless alerts to bed.

Sepsis Prediction Model for “Determining Sepsis vs SIRS, qSOFA, and SOFA”