A common data model for the standardization of intensive care unit medication features.

Objective: Common data models provide a standard means of describing data for artificial intelligence (AI) applications, but this process has never been undertaken for medications used in the intensive care unit (ICU). We sought to develop a common data model (CDM) for ICU medications to standardize the medication features needed to support future ICU AI efforts. Materials and Methods: A 9-member, multi-professional team of ICU clinicians and AI experts conducted a 5-round modified Delphi process employing conference calls, web-based communication, and electronic surveys to define the most important medication features for AI efforts. Candidate ICU medication features were generated through group discussion and then independently scored by each team member based on relevance to ICU clinical decision-making and feasibility for collection and coding. A key consideration was to ensure the final ontology both distinguished unique medications and met Findable, Accessible, Interoperable, and Reusable (FAIR) guiding principles. Results: Using a list of 889 ICU medications, the team initially generated 106 different medication features, and 71 were ranked as being core features for the CDM. Through this process, 106 medication features were assigned to 2 key feature domains: drug product-related (n = 43) and clinical practice-related (n = 63). Each feature included a standardized definition and suggested response values housed in the electronic data library. This CDM for ICU medications is available online. Conclusion: The CDM for ICU medications represents an important first step for the research community focused on exploring how AI can improve patient outcomes and will require ongoing engagement and refinement.

The use of electronic health record embedded MRC-ICU as a metric for critical care pharmacist workload.

Objectives: A lack of pharmacist-specific risk-stratification scores in the electronic health record (EHR) may limit resource optimization. The medication regimen complexity-intensive care unit (MRC-ICU) score was implemented into our center's EHR for use by clinical pharmacists. The purpose of this evaluation was to evaluate MRC-ICU as a predictor of pharmacist workload and to assess its potential as an additional dimension to traditional workload measures. Materials and methods: Data were abstracted from the EHR on adult ICU patients, including MRC-ICU scores and 2 traditional measures of pharmacist workload: numbers of medication orders verified and interventions logged. This was a single-center study of an EHR-integrated MRC-ICU tool. The primary outcome was the association of MRC-ICU with institutional metrics of pharmacist workload. Associations were assessed using the initial 24-h maximum MRC-ICU score's Pearson's correlation with overall admission workload and the day-to-day association using generalized linear mixed-effects modeling. Results: A total of 1205 patients over 5083 patient-days were evaluated. Baseline MRC-ICU was correlated with both cumulative order volume (Spearman's rho 0.41, P < .001) and cumulative interventions placed (Spearman's rho 0.27, P < .001). A 1-point increase in maximum daily MRC-ICU was associated with a 31% increase in order volume (95% CI, 24%-38%) and 4% increase in interventions (95% CI, 2%-5%). Discussion and conclusion: The MRC-ICU is a validated score that has been previously correlated with important patient-centered outcomes. Here, MRC-ICU was modestly associated with 2 traditional objective measures of pharmacist workload, including orders verified and interventions placed, which is an important step for its use as a tool for resource utilization needs.

Machine learning vs. traditional regression analysis for fluid overload prediction in the ICU.

Fluid overload, while common in the ICU and associated with serious sequelae, is hard to predict and may be influenced by ICU medication use. Machine learning (ML) approaches may offer advantages over traditional regression techniques to predict it. We compared the ability of traditional regression techniques and different ML-based modeling approaches to identify clinically meaningful fluid overload predictors. This was a retrospective, observational cohort study of adult patients admitted to an ICU ≥ 72 h between 10/1/2015 and 10/31/2020 with available fluid balance data. Models to predict fluid overload (a positive fluid balance ≥ 10% of the admission body weight) in the 48-72 h after ICU admission were created. Potential patient and medication fluid overload predictor variables (n = 28) were collected at either baseline or 24 h after ICU admission. The optimal traditional logistic regression model was created using backward selection. Supervised, classification-based ML models were trained and optimized, including a meta-modeling approach. Area under the receiver operating characteristic (AUROC), positive predictive value (PPV), and negative predictive value (NPV) were compared between the traditional and ML fluid prediction models. A total of 49 of the 391 (12.5%) patients developed fluid overload. Among the ML models, the XGBoost model had the highest performance (AUROC 0.78, PPV 0.27, NPV 0.94) for fluid overload prediction. The XGBoost model performed similarly to the final traditional logistic regression model (AUROC 0.70; PPV 0.20, NPV 0.94). Feature importance analysis revealed severity of illness scores and medication-related data were the most important predictors of fluid overload. In the context of our study, ML and traditional models appear to perform similarly to predict fluid overload in the ICU. Baseline severity of illness and ICU medication regimen complexity are important predictors of fluid overload.

Evaluation of medication regimen complexity as a predictor for mortality.

While medication regimen complexity, as measured by a novel medication regimen complexity-intensive care unit (MRC-ICU) score, correlates with baseline severity of illness and mortality, whether the MRC-ICU improves hospital mortality prediction is not known. After characterizing the association between MRC-ICU, severity of illness and hospital mortality we sought to evaluate the incremental benefit of adding MRC-ICU to illness severity-based hospital mortality prediction models. This was a single-center, observational cohort study of adult intensive care units (ICUs). A random sample of 991 adults admitted ≥ 24 h to the ICU from 10/2015 to 10/2020 were included. The logistic regression models for the primary outcome of mortality were assessed via area under the receiver operating characteristic (AUROC). Medication regimen complexity was evaluated daily using the MRC-ICU. This previously validated index is a weighted summation of medications prescribed in the first 24 h of ICU stay [e.g., a patient prescribed insulin (1 point) and vancomycin (3 points) has a MRC-ICU = 4 points]. Baseline demographic features (e.g., age, sex, ICU type) were collected and severity of illness (based on worst values within the first 24 h of ICU admission) was characterized using both the Acute Physiology and Chronic Health Evaluation (APACHE II) and the Sequential Organ Failure Assessment (SOFA) score. Univariate analysis of 991 patients revealed every one-point increase in the average 24-h MRC-ICU score was associated with a 5% increase in hospital mortality [Odds Ratio (OR) 1.05, 95% confidence interval 1.02-1.08, p = 0.002]. The model including MRC-ICU, APACHE II and SOFA had a AUROC for mortality of 0.81 whereas the model including only APACHE-II and SOFA had a AUROC for mortality of 0.76. Medication regimen complexity is associated with increased hospital mortality. A prediction model including medication regimen complexity only modestly improves hospital mortality prediction.

Utilizing eye tracking to assess electronic health record use by pharmacists in the intensive care unit.

PURPOSE: A study was conducted using high-fidelity electronic health record (EHR)-based simulations with incorporated eye tracking to understand the workflow of critical care pharmacists within the EHR, with specific attention to the data elements most frequently viewed. METHODS: Eight critical care pharmacists were given 25 minutes to review 3 simulated intensive care unit (ICU) charts deployed in the simulation instance of the EHR. Using monitor-based eye trackers, time spent reviewing screens, clinical information accessed, and screens used to access specific information were reviewed and quantified to look for trends. RESULTS: Overall, pharmacists viewed 25.5 total and 15.1 unique EHR screens per case. The majority of time was spent looking at screens focused on medications, followed by screens displaying notes, laboratory values, and vital signs. With regard to medication data, the vast majority of screen visitations were to view information on opioids/sedatives and antibiotics. With regard to laboratory values, the majority of views were focused on basic chemistry and hematology data. While there was significant variance between pharmacists, individual navigation patterns remained constant across cases. CONCLUSION: The study results suggest that in addition to medication information, laboratory data and clinical notes are key focuses of ICU pharmacist review of patient records and that navigation to multiple screens is required in order to view these data with the EHR. New pharmacy-specific EHR interfaces should consolidate these elements within a primary interface.

A descriptive report of the rapid implementation of automated MRC-ICU calculations in the EMR of an academic medical center.

PURPOSE: Numerous clinical scoring tools exist for a variety of patient populations and disease states, but few tools provide information specifically designed for use by critical care pharmacists. The medication regimen complexity-intensive care unit (MRC-ICU) score was designed to provide high-level information about the complexity of critically ill patients' medication regimens for use by critical care pharmacists. To date, implementation of this score in the electronic medical record (EMR) has not been reported. SUMMARY: Using an agile project management framework, the MRC-ICU score was rapidly implemented into an academic medical center's EMR. The score is automatically calculated for all critically ill patients and is available for critical care pharmacists to triage patient review in their individual workflow. Reporting capabilities of the score also allow for granular complexity trending over time and between units, supplementing other objective measures of pharmacist workload. CONCLUSION: The MRC-ICU score can be quickly implemented into the EMR for pharmacist use in real time. Future investigations into how pharmacists utilize this information and how to harness reporting capabilities for pharmacist workload assessment are warranted.

Achieving ventricular rate control in patients taking chronic beta-blocker therapy.

STUDY OBJECTIVE: The objective of this study is to evaluate the difference in response to ventricular rate control with intravenous (IV) metoprolol compared to IV diltiazem in patients taking chronic beta-blocker therapy who present to the emergency department (ED) in atrial fibrillation (AF) with rapid ventricular rate (RVR). METHODS: This was a single-center, retrospective study of adult patients taking chronic oral metoprolol. Chronic metoprolol therapy was defined as patients prescribed and taking oral metoprolol within 5days of study inclusion. Rate control was defined as either a decrease in ventricular rate<100bpm or <120bpm if the decrease was at least 20% from the presenting heart rate. RESULTS: A total of 332 patients were included, with 16 patients in the IV diltiazem group and 316 patients in the IV metoprolol group. In the diltiazem arm, 68.8% of patients achieved successful rate control compared to 42.4% of patients in the metoprolol group (p=0.067). Treatment with IV metoprolol resulted in more hospital admissions (58% vs. 6.25% with diltiazem, p<0.001). Treatment with diltiazem was associated with a greater incidence of bradycardia compared to IV metoprolol (13% vs. 0%, p=0.002). CONCLUSIONS: The use of IV diltiazem was associated with a higher rate of successful response to rate control compared to IV metoprolol in patients in AF with RVR on chronic beta-blocker therapy, however the difference between groups was not statistically significant.

Accuracy of temporal artery thermometry in neonatal intensive care infants.

PURPOSE: To determine the accuracy of temporal artery and axillary temperatures and the discomfort level of stable neonates during temperature measurement. SUBJECTS: Convenience sample of neonates between the ages of 32 and 40 weeks' gestation cared for in an isolette or crib. DESIGN: A method-comparison design was used to compare different methods for noninvasive temperature monitoring (infra-red temporal artery; axillary electronic) to core body temperatures (indwelling rectal probe). MAIN OUTCOME MEASURE: Bias and precision of test temperature devices (temporal artery; axillary). RESULTS: Bias and precision for the temporal artery and axillary devices were 0.30 ± 0.44 and 0.28 ± 0.33, respectively. Analysis of variance found significant differences between both temporal and axillary temperatures compared to rectal temperatures (P < .01). Statistical differences were small and did not represent a clinically important difference. No statistical difference was found between temporal artery and axillary temperatures (P = .81). Increases in neonate discomfort after temperature measurement were significantly greater with axillary than increases after temporal artery temperature measurement (P = .03). CONCLUSIONS: This study found that body temperature measured with the temporal artery thermometer was similar to temperatures obtained with an axillary thermometer in stable, afebrile neonates. The use of temporal artery thermometry appears to be an acceptable approach for noninvasive temperature measurement in neonates, which causes less discomfort in neonates.

Vasotocin treatment inhibits courtship in male zebra finches; concomitant androgen treatment inhibits this effect.

Zebra finches evolved in arid areas of Australia. Their reproduction is stimulated by water availability, which is unpredictable. Cheng (Poult. Sci. Rev. 5 (1993) 37) hypothesized that the primary mechanism controlling reproduction in species relying on unpredictable cues should be inhibitory. The onset of stimulatory environmental conditions terminates the inhibition, allowing rapid initiation of reproduction. As the primary hormone regulating water balance in birds, arginine vasotocin (AVT) appears a likely candidate to modulate reproduction in finches. Drought conditions cause sustained AVT release, which in other species inhibits androgen production. To determine whether increased AVT inhibits reproductive behavior, intact males were tested with females and divided into three groups matched for courtship behavior. Osmotic minipumps containing (a) saline, (b) 264 ng AVT, or (c) 1320 ng AVT in saline were implanted subcutaneously and males tested 48 h later. AVT-treated males socialized with females, but the high dose significantly reduced singing and courtship displays. To determine whether AVT acted by depressing androgen secretion, additional males were given subcutaneous androgen implants and divided into two groups matched for courtship behavior. Males were then implanted with minipumps containing (a) saline or (b) the high AVT dose. Males treated with AVT plus androgen showed no deficits in courtship behavior. These data suggest that AVT secretion during periods of drought may inhibit reproduction by inhibiting androgen production. Inhibition of reproductive behavior by AVT may be a more general phenomenon. Large quantities of AVT or, in mammals the closely-related peptide vasopressin (VP), are released when animals are stressed, and high levels of AVT/VP may inhibit reproductive behavior. The extremely short half-life of these peptides means that once proximal factors become more favorable, the gonads should rapidly be released from the peptides' inhibitory actions.