Rabinstein, Alejandro A.

Objective The Pulmonary Embolism Severity Index (PESI) and simplified PESI (sPESI) predict mortality for patients with PE. We compared PESI/sPESI to the Acute Physiology and Chronic Health Evaluation IV (APACHE-IV) in predicting mortality in patients with PE admitted to the intensive care unit (ICU). Additionally, we assessed the performance of a novel ICU-sPESI score created by adding three clinical variables associated with acuity of PE presentation (intubation, confusion [altered mental status], use of vasoactive infusions) to sPESI. Materials and Methods Using the eICU Collaborative Research Database from 2014 to 2015, we conducted a large retrospective cohort study of adult patients admitted to the ICU with a primary diagnosis of PE. We calculated APACHE-IV, PESI, sPESI, and ICU-sPESI scores and compared their performance for predicting in-hospital mortality using area under the receiver operating characteristic (AUROC) curve. Score thresholds for >99% negative predictive values (NPV) were calculated for each score. Survival was estimated using the Kaplan–Meier method. Results We included 1424 PE cases. In-hospital mortality was 6.3% [95% CI: 5.1%-7.6%]. AUROC for APACHE-IV, PESI, and sPESI were 0.870, 0.848, and 0.777, respectively. APACHE-IV and PESI outperformed sPESI (P < 0.01 for both comparisons), while APACHE-IV and PESI demonstrated similar performance (P = 0.322). The ICU-sPESI performance was similar to APACHE-IV and PESI (AUROC = 0.847; AUROC comparison: APACHE-IV vs ICU-sPESI: P = 0.396; PESI vs ICU-sPESI: P = 0.945). Hospital mortality for ICU-sPESI scores 0-2 was 1.1%, and for scores 3, 4, 5, 6, and ≥7 was 8.6%, 11.7%, 29.2%, 37.5%, and 76.9%, respectively. Score thresholds for >99% NPV were ≤48 for APACHE-IV, ≤115 for PESI, and 0 points for sPESI and ICU-sPESI. Conclusions By accounting for severity of PE presentation, our newly proposed ICU-sPESI score provided improved PE mortality prediction compared to the original sPESI score and offered excellent discrimination of mortality risk.

This Viewpoint argues, in keeping with current stroke management guidelines, that patients with mild, nondisabling strokes should not receive thrombolysis, even when presenting within the time window for treatment.

IMPORTANCE: Protein binding of valproate varies among ICU patients, altering the biologically active free valproate concentration (VPAC). Free VPAC is measured at few laboratories and is often discordant with total VPAC. Existing equations to predict free VPAC are either not validated or are inaccurate in ICU patients. OBJECTIVES: We designed this study to derive and validate a novel equation to predict free VPAC using data from ICU patients and to compare its performance to published equations. DESIGN: Retrospective cohort study. SETTING: Two academic medical centers. PARTICIPANTS: Patients older than 18 years old with concomitant free and total VPACs measured in the ICU were included in the derivation cohort if admitted from 2014 to 2018, and the validation cohort if admitted from 2019 to 2022. MAIN OUTCOMES AND MEASURES: Multivariable linear regression was used to derive an equation to predict free VPAC. Modified Bland-Altman plots and the rate of therapeutic concordance between the measured and predicted free VPAC were compared. RESULTS: Demographics, median free and total VPACs, and valproate free fractions were similar among 115 patients in the derivation cohort and 147 patients in the validation cohort. The Bland-Altman plots showed the new equation performed better (bias, 0.3 [95% limits of agreement, –13.6 to 14.2]) than the Nasreddine (–9.2 [–26.5 to 8.2]), Kodama (–9.7 [–30.0 to 10.7]), Conde Giner (–7.9 [–24.9 to 9.1]), and Parent (–9.9 [–30.7 to 11.0]) equations, and similar to Doré (–2.0 [–16.0 to 11.9]). The Doré and new equations had the highest therapeutic concordance rate (73%). CONCLUSIONS AND RELEVANCE: For patients at risk of altered protein binding such as ICU patients, existing equations to predict free VPAC are discordant with measured free VPAC. A new equation had low bias but was imprecise. External validation should be performed to improve its precision and generalizability. Until then, monitoring free valproate is recommended during critical illness.

Background Smoking represents a leading risk factor for acute ischemic stroke (AIS). Previous literature has described a “smoking paradox”, wherein smokers experience better outcomes following intravenous thrombolysis for AIS. It is unclear whether such a phenomenon exists in smokers undergoing endovascular therapy (EVT) for AIS. To assess outcomes in smokers following EVT for AIS, we conducted a systematic review and meta-analysis. Methods Following the PRISMA guidelines, a systematic literature review of the English language literature was conducted using PubMed, Embase Web of Science, and Scopus. Outcomes of interest included 90-day modified Rankin Scale (mRS) 0–2, thrombolysis in cerebral infarction (TICI) score 2b-3, symptomatic intracranial hemorrhage (sICH), and mortality. We calculated mean difference (MD), pooled odds ratios (ORs) and their corresponding 95% confidence intervals (CI). Results Eight studies with 2633 patients comprised our analysis. On average, smokers were 10.14 years (MD = 10.14, [95% CI = −14.49 to −5.79], P-value < 0.001) younger than non-smokers. Smokers achieved mRS 0–2 (OR = 1.82, [95% CI, 1.34–2.48], P-value < 0.001) and TICI 2b-3 (OR = 1.61, [95% CI, 1.19–2.19], P-value = 0.002) at a higher rate than non-smokers. sICH rates were comparable between smokers and non-smokers (OR = 1.07, [95% CI, 0.62–1.85], P-value = 0.81). Smokers had a lower rate of 90-day mortality than non-smokers (OR = 0.54, [95% CI, 0.41–0.71], P-value < 0.001). Conclusions In this meta-analysis of eight studies, we found that smokers with AIS undergoing EVT experienced better 90-day outcomes and higher rates of TICI 2b-3. This may be due to baseline differences between smokers and non-smokers, but future studies should explore alternative reasons that could explain this relationship between smoking and AIS treated with EVT.

Background and purpose Cerebral venous thrombosis (CVT) is usually treated with systemic anticoagulation, but mechanical thrombectomy (MT) and local infusion of a thrombolytic agent have been proposed as an alternative therapy. In this study, we analyze National Inpatient Sample (NIS) to determine the trends of MT including discharge other than home (DOTH) and mortality. Material and methods Healthcare Utilization Program-NIS (HCUP-NIS) was queried between 2005 and 2018 for CVT and MT. Cochran–Armitage test was conducted to assess linear trend of proportion of utilization and DOTH of MT. Multivariable logistic regression was conducted to assess odds of undergoing MT among CVT admissions, odds of in-hospital mortality, and DOTH for all admissions involving MT for CVT. Results A total of 1331 (1.56%) admissions involved MT out of 85,370 CVT cases. Utilization of MT had an upward trend of 0.13% ( p < 0.001) per year. Trend in proportion of incidence of DOTH among MT admission remained stationary (trend: 0.70%; p = 0.417). Patients with cerebral edema (odds ratio [OR]: 4.34; p < 0.001) or hematological disorders (OR: 2.28; p < 0.001) were more likely to receive MT for CVT. Additionally, patients with coma (OR: 3.17; p = 0.023) or cerebral edema (OR: 4.40; p = 0.001) had higher odds of mortality. Conclusion There was an increasing trend of utilization of MT. Proportions of DOTH among MT procedures, however, remained stable. Patients with greater risk factors, including hematological disorders and cerebral edema, were more likely to undergo MT. Among patients treated with MT, those with coma or cerebral edema were more likely to die.

Introduction: Prior AF screening trials demonstrated low yield, highlighting the need for more targeted approaches. An AI algorithm was developed to identify ECG signatures of AF risk during normal sinus rhythm, which has been validated in diverse external populations. Hypothesis: An AI-guided, targeted screening approach could improve the diagnosis of AF. Methods We conducted a pragmatic decentralized trial to prospectively recruit patients with stroke risk factors but no prior AF. The AI algorithm was applied to the ECGs performed in routine practice and divided patients into high- or low-risk groups. The primary endpoint was AF lasting ≥ 30 seconds on a subsequent 30-day continuous cardiac rhythm monitor. In a secondary analysis, trial participants were 1:1 propensity score-matched to real-world controls derived from the eligible but unenrolled population. Results A total of 1,003 patients from 40 U.S. states completed the study, with a mean age of 74 [SD 8.8] years. Over a mean of 22.3 days of continuous monitoring, AF was detected in 6 (1.6%) of low-risk patients and 48 (7.6%) of high-risk patients (OR 4.98 [2.11-11.75], p<0.001). Compared to usual care, AI-guided AF screening was associated with increased detection of AF (high-risk group: 4.2% vs. 11.1%, p<0.001; low-risk group: 0.9% vs. 2.4%, p=0.12) over a median follow-up of 10 months. Conclusions A prospective pragmatic study found that the AI a can risk-stratify a relatively uniform population (i.e., older adults at risk for stroke) to detect AF during short-term cardiac monitoring. Furthermore, when compared with usual care, AI-guided cardiac monitoring was associated with increased AF detection. As such, an AI-guided AF screening approach, leveraging existing EHR data and infrastructure, could be effective, patient-centered, and massively scalable, thereby reducing unnecessary health utilization and diagnosis-related anxiety (Clinicaltrials.gov: NCT04208971).