Clinical Pharmacist Impact on Intensive Care Unit Delirium: Intervention and Monitoring

Impact of Pharmacist Monitoring of Serum Triglycerides for Critically Ill Patients Receiving Propofol

Background: Elevated serum triglycerides due to the use of propofol for sedation in the ICU is associated with adverse effects and serum triglyceride monitoring may be improved by pharmacists. Objective: To determine if there was improvement in serum triglyceride monitoring in ICU patients receiving propofol for sedation after implementation of a pharmacist-driven triglyceride monitoring protocol. Methods: This was a single-center pre-post-intervention retrospective cohort study. The protocol was implemented on January 10 2019. Data were collected over 1 year, and patients were divided between those started on propofol before and after protocol implementation. Results: There were 412 patients included in the final analysis with no significant differences between groups. There was a significant increase in the number of patients who had a triglyceride concentration obtained after protocol implementation (31.1% pre-vs 64.0% post-protocol; P < .001). For patients on propofol greater than 24 h, there was a significant increase in baseline triglyceride concentration obtained (7.6% pre-vs 15.1% post-protocol; P = .043). More instances of elevated triglyceride concentrations were identified by pharmacists than other providers (9 vs 5; P < .001). Time between propofol being ordered and first triglyceride concentration ordered was shorter (.86 days pre-protocol vs .71 days post-protocol; P = .064), but not statistically significant. Conclusion: Implementation of a pharmacist-driven protocol in the ICU increased the number of serum triglyceride levels obtained for patients receiving propofol for sedation. Pharmacists can improve triglyceride monitoring in patients receiving propofol and future studies should investigate the impact on outcomes.

Delirium in the NICU

Delirium in the NICU is an underrecognized phenomenon in infants who are often complex and critically ill. The current understanding of NICU delirium is developing and can be informed by adult and pediatric literature. The NICU population faces many potential risk factors for delirium, including young age, developmental delay, mechanical ventilation, severe illness, and surgery. There are no diagnostic tools specific to infants. The mainstay of delirium treatment is to treat the underlying cause, address modifiable risk factors, and supportive care. This review will summarize current knowledge and areas where more research is needed.

Machine learning based prediction of prolonged duration of mechanical ventilation incorporating medication data

Rationale

Duration of mechanical ventilation is associated with adverse outcomes in critically ill patients and increased use of resources. The increasing complexity of medication regimens has been associated with increased mortality, length of stay, and fluid overload but has never been studied specifically in the setting of mechanical ventilation.

Objective

The purpose of this analysis was to develop prediction models for mechanical ventilation duration to test the hypothesis that incorporating medication data may improve model performance.

Methods

This was a retrospective cohort study of adults admitted to the ICU and undergoing mechanical ventilation for longer than 24 hours from October 2015 to October 2020. Patients were excluded if it was not their index ICU admission or if the patient was placed on comfort care in the first 24 hours of admission. Relevant patient characteristics including age, sex, body mass index, admission diagnosis, morbidities, vital signs measurements, severity of illness, medication regimen complexity as measured by the MRC-ICU, and medical treatments before intubation were collected. The primary outcome was area under the receiver operating characteristic (AUROC) of prediction models for prolonged mechanical ventilation (defined as greater than 5 days). Both logistic regression and supervised learning techniques including XGBoost, Random Forest, and Support Vector Machine were used to develop prediction models.

Results

The 318 patients [age 59.9 (SD 16.9), female 39.3%, medical 28.6%] had mean 24-hour MRC-ICU score of 21.3 (10.5), mean APACHE II score of 21.0 (5.4), mean SOFA score of 9.9 (3.3), and ICU mortality rate of 22.6% (n=72). The strongest performing logistic model was the base model with MRC-ICU added, with AUROC of 0.72, positive predictive value (PPV) of 0.83, and negative prediction value (NPV) of 0.92. The strongest overall model was Random Forest with an AUROC of 0.78, a PPV of 0.53, and NPV of 0.90. Feature importance analysis using support vector machine and Random Forest revealed severity of illness scores and medication related data were the most important predictors.

Conclusions

Medication regimen complexity is significantly associated with prolonged duration of mechanical ventilation in critically ill patients, and prediction models incorporating medication information showed modest improvement in this prediction.

Maximizing acceptance of clinical pharmacy recommendations to reduce length of hospital stay in a private hospital from Amman, Jordan

Background

Clinical pharmacy interventions (CPI) usually require prior medical authorization. Physicians approve 80% of CPI and reject 20%. If pharmacists show that physicians should authorize all 100% CPI, the profession will step closer to a fully independent prescriber status. This study used an artificial neural network (ANN) model to determine whether clinical pharmacy (CP) may improve outcomes associated with rejected CPI.

Method

This is a non-interventional, retrospective analysis of documented CPI in a 100-bed, acute-care private hospital in Amman, Jordan. Study consisted of 542 patients, 574 admissions, and 1694 CPI. Team collected demographic and clinical data using a standardized tool. Input consisted of 54 variables with some taking merely repetitive values for each CPI in each patient whereas others varying with every CPI. Therefore, CPI was consolidated to one rejected and/or one accepted per patient per admission. Groups of accepted and rejected CPI were compared in terms of matched and unmatched variables. ANN were, subsequently, trained and internally as well as cross validated for outcomes of interest. Outcomes were length of hospital and intensive care stay after the index CPI (LOSTA & LOSICUA, respectively), readmissions, mortality, and cost of hospitalization. Best models were finally used to compare the two scenarios of approving 80% versus 100% of CPI. Variable impacts (VI) automatically generated by the ANN were compared to evaluate the effect of rejecting CPI. Main outcome measure was Lengths of hospital stay after the index CPI (LOSTA).

Results

ANN configurations converged within 18 s and 300 trials. All models showed a significant reduction in LOSTA with 100% versus 80% accepted CPI of about 0.4 days (2.6 ± 3.4, median (range) of 2 (0–28) versus 3.0 ± 3.8, 2 (0–30), P-value = 0.022). Average savings with acceptance of those rejected CPI was 55 JD (~ 78 US dollars) and could help hire about 1.3 extra clinical pharmacist full-time equivalents.

Conclusions

Maximizing acceptance of CPI reduced the length of hospital stay in this model. Practicing Clinical Pharmacists may qualify for further privileges including promotion to a fully independent prescriber status.

A Narrative Review of Clinical Decision Support for Inpatient Clinical Pharmacists

OBJECTIVE

Increasingly, pharmacists provide team-based care that impacts patient care; however, the extent of recent clinical decision support (CDS), targeted to support the evolving roles of pharmacists, is unknown. Our objective was to evaluate the literature to understand the impact of clinical pharmacists using CDS.

METHODS

We searched MEDLINE, EMBASE, and Cochrane Central for randomized controlled trials, nonrandomized trials, and quasi-experimental studies which evaluated CDS tools that were developed for inpatient pharmacists as a target user. The primary outcome of our analysis was the impact of CDS on patient safety, quality use of medication, and quality of care. Outcomes were scored as positive, negative, or neutral. The secondary outcome was the proportion of CDS developed for tasks other than medication order verification. Study quality was assessed using the Newcastle-Ottawa Scale.

RESULTS

Of 4,365 potentially relevant articles, 15 were included. Five studies were randomized controlled trials. All included studies were rated as good quality. Of the studies evaluating inpatient pharmacists using a CDS tool, four showed significantly improved quality use of medications, four showed significantly improved patient safety, and three showed significantly improved quality of care. Six studies (40%) supported expanded roles of clinical pharmacists.

CONCLUSION

These results suggest that CDS can support clinical inpatient pharmacists in preventing medication errors and optimizing pharmacotherapy. Moreover, an increasing number of CDS tools have been developed for pharmacists' roles outside of order verification, whereby further supporting and establishing pharmacists as leaders in safe and effective pharmacotherapy.

Impact of pharmacists on outcomes for patients with psychiatric or neurologic disorders

INTRODUCTION

Psychiatric and neurologic illnesses are highly prevalent and are often suboptimally treated. A 2015 review highlighted the value of psychiatric pharmacists in improving medication-related outcomes. There is a need to describe areas of expansion and strengthened evidence regarding pharmacist practice and patient care impact in psychiatric and neurologic settings since 2015.

METHODS

A systematic search of literature published from January 2014 to June 2019 was conducted. Publications describing patient-level outcome results associated with pharmacist provision of care in a psychiatric/neurologic setting and/or in relation to central nervous system (CNS) medications were included.

RESULTS

A total of 64 publications were included. There was significant heterogeneity of published study methods and data, prohibiting meta-analysis. Pharmacists practicing across a wide variety of health care settings with focus on CNS medication management significantly improved patient-level outcomes, such as medication adherence, disease control, and avoidance of hospitalization. The most common practice approach associated with significant improvement in patient-level outcomes was incorporation of psychiatric pharmacist input into the interprofessional health care team.

DISCUSSION

Pharmacists who focus on psychiatric and neurologic disease improve outcomes for patients with these conditions. This is important in the current health care environment as most patients with psychiatric or neurologic conditions continue to have unmet needs. Additional studies designed to measure pharmacists' impact on patient-level outcomes are encouraged to strengthen these findings.