Riker, Richard R.

ABSTRACT Background Critically ill patients may be overexposed to valproate because altered protein binding leads to a disproportionate free valproate fraction. The Fraser equation was derived and internally validated to estimate critically ill patients' free valproate concentrations, but it requires external validation. Methods Adult intensive care unit (ICU) patients at two academic centers with concurrently measured free and total valproate concentrations were included. Free valproate concentrations were estimated using the Fraser equation which includes total valproate, albumin and BUN concentration, and whether the patient received propofol or aspirin. The primary outcome was Fraser equation performance, assessed using Bland–Altman methods. Comparative performance against estimates from the Doré equation (an alternative predictive equation), therapeutic concordance using a target free valproate range of 5–15 mg/L, and equation improvements were explored. Results Overall, 315 patients and 556 free‐total valproate concentration pairs were included. The mean (±SD) age was 58 (±17) years and 90 (29%) patients were on valproate prior to hospital admission. The Fraser equation estimated free valproate concentrations were correlated with measured concentrations ( r = 0.728) with a negative bias (mean bias −2.77 mg/L, 95% LOA –18.9, 13.4). The Fraser equation increasingly underestimated measured concentrations as measured concentrations increased. 71% of Fraser equation estimates were therapeutically concordant (e.g., estimate and measured both within reference range) compared to 61.9% of Doré estimates ( p = 0.001). Fraser equation modifications led to minor performance improvements but did not overcome worsening underestimation with higher measured free valproate concentrations. Conclusion The Fraser equation was moderately accurate and corresponded with an appropriate interpretation for 71% of free valproate concentrations. Worse underestimation with higher measured free valproate concentrations suggests direct measurement of free valproate concentrations is warranted. While the Fraser equation could complement therapeutic decision making at centers where direct free valproate measurements are unavailable or slow to return, its accuracy is currently insufficient to replace direct measurement.

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.

OBJECTIVES: Protein binding of valproate is variable in ICU patients, and the total valproate concentration does not predict the free valproate concentration, even when correcting for albumin. We sought to quantify valproate free concentration among ICU patients, identify risk factors associated with an increasing free valproate concentration, and evaluate the association between free valproate concentration with potential adverse drug effect. DESIGN: Retrospective multicenter cohort study. SETTING: Two academic medical centers. PATIENTS: Patients greater than or equal to 18 years of age with concomitant free and total valproate concentrations collected in the ICU. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Two-hundred fifty-six patients were included in the study, with a median age of 56 years (42–70) and 65% of patients were male. The median total valproate concentration was 53 µg/mL (38–70 µg/mL), the free valproate concentration was 12 µg/mL (7–20 µg/mL), and the free fraction was 23.6% (17.0–33.9%). Therapeutic discordance between the free and total valproate concentration occurred in 70% of patients. On multivariable analysis, increased free valproate concentration was associated with higher total valproate concentration (per 5 µg/mL increase, increase 1.72 µg/mL, 95% CI, 1.48–1.96) and lower serum albumin (per 1 g/dL decrease, increase 4.60 µg/mL, 95% CI, 2.71–6.49). There was no association between free valproate concentration and adverse effects. CONCLUSIONS: The valproate total and free concentration was discordant in the majority of patients (70%). Increased valproate free concentration was associated with hypoalbuminemia and total valproate concentration. Clinical decisions based on total valproate concentration may be incorrect for many ICU patients. Prospective, controlled studies are needed to confirm these findings and their clinical relevance.

Background Intensive care unit (ICU) sedation guidelines recommend targeting a light sedation level, but light sedation has no accepted definition, and inconsistent levels have been proposed. Objective To determine Sedation-Agitation Scale and Richmond Agitation-Sedation Scale scores that best describe patients’ ability to follow voice commands. Methods This prospective, observational pilot study enrolled a convenience sample of ICU patients receiving mechanical ventilation. Pairs of trained investigators evaluated scores on the Sedation-Agitation Scale and Richmond Agitation-Sedation Scale and ability to follow commands before and up to 2 hours after sedation lightening in a blind, independent, simultaneous fashion. Positive predictive values (PPVs) and likelihood ratios (LRs) of Sedation-Agitation Scale and Richmond Agitation-Sedation Scale scores associated with light sedation (ability to follow at least 3 commands) were calculated. Results Ninety-six assessments (50 before and 46 after lightening of sedation) were performed in medical ICU patients. Scores best associated with ability to follow at least 3 commands were Sedation-Agitation Scale score of 4 (PPV, 0.88; 95% CI, 0.70-0.98; LR, 14.0) and Richmond Agitation-Sedation Scale score of −1 (PPV, 0.81; 95% CI, 0.61-0.93; LR, 10.7), superior to previously recommended thresholds of Sedation-Agitation Scale score of 3 (PPV, 0.62; 95% CI, 0.48-0.75; LR, 3.1) and Richmond Agitation-Sedation Scale score of −3 (PPV, 0.52; 95% CI, 0.39-0.64; LR, 2.0). Conclusions The level of sedation most associated with the ability to follow commands appears higher than previously recommended. Further study is needed regarding the effects of sedation level on ICU patients’ ability to follow commands and assessment of delirium, pain, and patient preferences.

Objectives: In 2014, the Tele-ICU Committee of the Society of Critical Care Medicine published an article regarding the state of ICU telemedicine, one better defined today as tele-critical care. Given the rapid evolution in the field, the authors now provide an updated review. Data Sources and Study Selection: We searched PubMed and OVID for peer-reviewed literature published between 2010 and 2018 related to significant developments in tele-critical care, including its prevalence, function, activity, and technologies. Search terms included electronic ICU, tele-ICU, critical care telemedicine, and ICU telemedicine with appropriate descriptors relevant to each sub-section. Additionally, information from surveys done by the Society of Critical Care Medicine was included given the relevance to the discussion and was referenced accordingly. Data Extraction and Data Synthesis: Tele-critical care continues to evolve in multiple domains, including organizational structure, technologies, expanded-use case scenarios, and novel applications. Insights have been gained in economic impact and human and organizational factors affecting tele-critical care delivery. Legislation and credentialing continue to significantly influence the pace of tele-critical care growth and adoption. Conclusions: Tele-critical care is an established mechanism to leverage critical care expertise to ICUs and beyond, but systematic research comparing different models, approaches, and technologies is still needed.