Comparison of fluid resuscitation weight-based dosing strategies in obese patients with severe sepsis.

OBJECTIVE: This study aims to compare the composite outcome of progression to septic shock between 30 mL/kg/ideal body weight (IBW) versus 30 mL/kg/non-IBW fluid resuscitation dosing strategies in obese patients with severe sepsis. METHODS: We retrospectively evaluated obese patients admitted to an academic tertiary care center for the management of severe sepsis. Patients were included if they had a fluid bolus order placed using the sepsis order set between Oct 2018 and Sept 2019. The primary objective was the composite of progression to septic shock, defined as either persistent hypotension within 3 h after the conclusion of the 30 mL/kg fluid bolus administration or the initiation of vasopressor(s) within 6 h of the bolus administration. RESULTS: Of 72 included patients, 49 (68%) were resuscitated using an IBW-based and 23 (32%) using a non-IBW-based dosing strategy. There were similar rates of progression to septic shock in the IBW and non-IBW groups (18% vs. 26%; p = 0.54). Median ICU and hospital LOS in the IBW group versus non-IBW group were (0 [IQR 0] vs. 0 [IQR 0 to 4] days; p = 0.13) and (6 [IQR 3 to 10] vs. 8 [IQR 5 to 12] days; p = 0.07), respectively. In-hospital mortality rates were similar between the groups. CONCLUSIONS: Our study results suggest that in obese septic patients, fluid administration using an IBW-dosing strategy did not affect the progression to septic shock.

Impact of Opioid Restrictions During a Critical Drug Shortage Period: Interrupted Time Series for Institutional Opioid Utilization.

OBJECTIVES: This study aimed to evaluate the impact of intravenous opioid product restrictions at an academic medical institution in an urban setting during the time of critical opioid shortages. We assessed the effect of ordering restrictions on inpatient opioid utilization measured by 1) changes in intermittent oral and injectable opioid product administration; 2) changes in total institutional opioid administration; and 3) changes in the utilization of individual restricted opioid agents. METHODS: This study is a single-center retrospective analysis by interrupted time series of institutional opioid utilization from 07/2017 to 06/2018. Utilization was quantified using milligrams of intravenous morphine equivalent administered or dispensed per admitted patient. Restrictions were grouped into 10 distinct phases, which informed the interruptions in linear regression models. RESULTS: Sequential restrictions during the study period led to shifts in use of individual agents but did not have a significant impact on overall total opioid utilization. "Soft" restrictions did not have a direct, statistically significant impact on medication use but did decrease utilization over time. In situations where a product was restricted with a "soft stop" followed by a "hard stop," the "hard stop" directly reduced usage. CONCLUSIONS: Targeted ordering restrictions allowed the institution to redirect drug use according to clinical need without affecting the overall utilization. Clinical decision support led providers to choose therapeutically equivalent alternatives. The demonstrated effect of restrictions will guide institutions in the selection of "hard stop" or "soft stop" restrictions in response to future shortages.

Predicting medication-associated altered mental status in hospitalized patients: Development and validation of a risk model.

PURPOSE: This study presents a medication-associated altered mental status (AMS) risk model for real-time implementation in inpatient electronic health record (EHR) systems. METHODS: We utilized a retrospective cohort of patients admitted to 2 large hospitals between January 2012 and October 2013. The study population included admitted patients aged ≥18 years with exposure to an AMS risk-inducing medication within the first 5 hospitalization days. AMS events were identified by a measurable mental status change documented in the EHR in conjunction with the administration of an atypical antipsychotic or haloperidol. AMS risk factors and AMS risk-inducing medications were identified from the literature, drug information databases, and expert opinion. We used multivariate logistic regression with a full and backward eliminated set of risk factors to predict AMS. The final model was validated with 100 bootstrap samples. RESULTS: During 194,156 at-risk days for 66,875 admissions, 262 medication-associated AMS events occurred (an event rate of 0.13%). The strongest predictors included a history of AMS (odds ratio [OR], 9.55; 95% confidence interval [CI], 5.64-16.17), alcohol withdrawal (OR, 3.34; 95% CI, 2.18-5.13), history of delirium or psychosis (OR, 3.25; 95% CI, 2.39-4.40), presence in the intensive care unit (OR, 2.53; 95% CI, 1.89-3.39), and hypernatremia (OR, 2.40; 95% CI, 1.61-3.56). With a C statistic of 0.85, among patients scoring in the 90th percentile, our model captured 159 AMS events (60.7%). CONCLUSION: The risk model was demonstrated to have good predictive ability, with all risk factors operationalized from discrete EHR fields. The real-time identification of higher-risk patients would allow pharmacists to prioritize surveillance, thus allowing early management of precipitating factors.

Development of a predictive model for drug-associated QT prolongation in the inpatient setting using electronic health record data.

PURPOSE: We aimed to construct a dynamic model for predicting severe QT interval prolongation in hospitalized patients using inpatient electronic health record (EHR) data. METHODS: A retrospective cohort consisting of all adults admitted to 2 large hospitals from January 2012 through October 2013 was established. Thirty-five risk factors for severe QT prolongation (defined as a Bazett's formula-corrected QT interval [QTc] of ≥500 msec or a QTc increase of ≥60 msec from baseline) were operationalized for automated EHR retrieval; upon univariate analyses, 26 factors were retained in models for predicting the 24-hour risk of QT events on hospital day 1 (the Day 1 model) and on hospital days 2-5 (the Days 2-5 model). RESULTS: A total of 1,672 QT prolongation events occurred over 165,847 days of risk exposure during the study period. C statistics were 0.828 for the Day 1 model and 0.813 for the Days 2-5 model. Patients in the upper 50th percentile of calculated risk scores experienced 755 of 799 QT events (94%) allocated in the Day 1 model and 804 of 873 QT events (92%) allocated in the Days 2-5 model. Among patients in the 90th percentile, the Day 1 and Days 2-5 models captured 351 of 799 (44%) and 362 of 873 (41%) QT events, respectively. CONCLUSION: The risk models derived from EHR data for all admitted patients had good predictive validity. All risk factors were operationalized from discrete EHR fields to allow full automation for real-time identification of high-risk patients. Further research to test the models in other health systems and evaluate their effectiveness on outcomes and patient care in clinical practice is recommended.

Development and validation of a dynamic inpatient risk prediction model for clinically significant hypokalemia using electronic health record data.

Purpose: The purpose of this study was to develop a dynamic risk prediction model for inpatient hypokalemia and evaluate its predictive performance. Methods: A retrospective cohort included all admissions aged 18 years and above from 2 large tertiary hospitals in Florida over a 22-month period. Hypokalemia was defined as a potassium value of less than 3 mmol/L, and subsequent initiation of potassium supplements. Twenty-five risk factors (RF) identified from literature were operationalized using discrete electronic health record (EHR) data elements. For each of the first 5 hospital days, we modeled the probability of developing hypokalemia at the subsequent hospital day using logistic regression. Predictive performance of our model was validated with 100 bootstrap datasets and evaluated by the C statistic and Hosmer-Lemeshow goodness-of-fit test. Results: A total of 4511 hypokalemia events occurred over 263 436 hospital days (1.71%). Validated C statistics of the prediction model ranged from 0.83 (Day 1 model) to 0.86 (Day 3), while p-values for the Hosmer-Lemeshow test spanned from 0.005 (Day 1) to 0.27 (Day 4 and 5). For the Day 3 prediction, 9.9% of patients with risk scores in the 90th percentile developed hypokalemia and accounted for 60.4% of all hypokalemia events. After controlling for baseline potassium values, strong predictors included diabetic ketoacidosis, increased mineralocorticoid activity, polyuria, use of kaliuretics, use of potassium supplements and watery stool. Conclusion: This is the first risk prediction model for hypokalemia. Our model achieved excellent discrimination and adequate calibration ability. Once externally validated, this risk assessment tool could use real-time EHR information to identify individuals at the highest risk for hypokalemia and support proactive interventions by pharmacists.

A dynamic risk model for inpatient falls.

PURPOSE: Construction and validation of a fall risk prediction model specific to inpatients receiving fall risk-increasing drugs (FRIDs) are described. METHODS: In a retrospective cohort study of 75,036 admissions to 2 hospitals over a designated 22-month period that involved FRID exposure during the first 5 hospital days, factors influencing fall risk were investigated via logistic regression. The resultant risk prediction model was internally validated and its performance compared with that of a model based on Morse Fall Scale (MFS) scores. RESULTS: A total of 220,904 patient-days of FRID exposure were evaluated. The three most commonly administered FRIDs were oxycodone (given on 79,697 patient-days, 36.08%), morphine (52,427, 23.73%) and hydromorphone (42,063, 19.04%). Within the 90th percentile of modeled risk scores, 144 of the 466 documented falls (30.9%) were captured by the developed risk prediction model (unbiased C statistic, 0.69), as compared with 94 falls (20.2%) captured using the MFS model (unbiased C statistic, 0.62). Strong predictors of inpatient falls included a history of falling (odds ratio [OR], 1.99; 95% confidence interval (CI), 1.42-2.80); overestimation of ability to ambulate (OR, 1.53; 95% CI, 1.12-2.09); and "comorbidity predisposition," a composite measure encompassing a history of falling and 11 past diagnoses (OR, 1.60; 95% CI, 1.30-1.97). CONCLUSION: The proposed risk model for inpatient falls achieved superior predictive performance when compared with the MFS model. All risk factors were operationalized from discrete electronic health record fields, allowing full automation of real-time identification of high-risk patients.

Implementation of Standardized Clinical Processes for TPMT Testing in a Diverse Multidisciplinary Population: Challenges and Lessons Learned.

Although thiopurine S-methyltransferase (TPMT) genotyping to guide thiopurine dosing is common in the pediatric cancer population, limited data exist on TPMT testing implementation in diverse, multidisciplinary settings. We established TPMT testing (genotype and enzyme) with clinical decision support, provider/patient education, and pharmacist consultations in a tertiary medical center and collected data over 3 years. During this time, 834 patients underwent 873 TPMT tests (147 (17%) genotype, 726 (83%) enzyme). TPMT tests were most commonly ordered for gastroenterology, rheumatology, dermatology, and hematology/oncology patients (661 of 834 patients (79.2%); 580 outpatient vs. 293 inpatient; P < 0.0001). Thirty-nine patients had both genotype and enzyme tests (n = 2 discordant results). We observed significant differences between TPMT test use and characteristics in a diverse, multispecialty environment vs. a pediatric cancer setting, which led to unique implementation needs. As pharmacogenetic implementations expand, disseminating lessons learned in diverse, real-world environments will be important to support routine adoption.

Institutional profile: University of Florida Health Personalized Medicine Program.

The University of Florida (UF) Health Personalized Medicine Program launched in 2012 with CYP2C19 genotyping for clopidogrel response at UF Health Shands Hospital. We have since expanded CYP2C19 genotyping to UF Health Jacksonville and established the infrastructure at UF Health to support clinical implementation for five additional gene-drug pairs: TPMT-thiopurines, IFNL3 (IL28B)-PEG IFN-α-based regimens, CYP2D6-opioids, CYP2D6/CYP2C19-antidepressants and CYP2C19-proton pump inhibitors. We are contributing to the evidence based on outcomes with genotype-guided therapy through pragmatic studies of our clinical implementations. In addition, we have developed a broad array of educational programs for providers, trainees and students that incorporate personal genotype evaluation to enhance participant learning.

Emerging roles for pharmacists in clinical implementation of pharmacogenomics.

Pharmacists are uniquely qualified to play essential roles in the clinical implementation of pharmacogenomics. However, specific responsibilities and resources needed for these roles have not been defined. We describe roles for pharmacists that emerged in the clinical implementation of genotype-guided clopidogrel therapy in the University of Florida Health Personalized Medicine Program, summarize preliminary program results, and discuss education, training, and resources needed to support such programs. Planning for University of Florida Health Personalized Medicine Program began in summer 2011 under leadership of a pharmacist, with clinical launch in June 2012 of a clopidogrel-CYP2C19 pilot project aimed at tailoring antiplatelet therapies for patients undergoing percutaneous coronary intervention and stent placement. More than 1000 patients were genotyped in the pilot project in year 1. Essential pharmacist roles and responsibilities that developed and/or emerged required expertise in pharmacy informatics (development of clinical decision support in the electronic medical record), medication safety, medication-use policies and processes, development of group and individual educational strategies, literature analysis, drug information, database management, patient care in targeted areas, logistical issues in genetic testing and follow-up, research and ethical issues, and clinical precepting. In the first 2 years of the program (1 year planning and 1 year postimplementation), a total of 14 different pharmacists were directly and indirectly involved, with effort levels ranging from a few hours per month, to 25-30% effort for the director and associate director, to nearly full-time for residents. Clinical pharmacists are well positioned to implement clinical pharmacogenomics programs, with expertise in pharmacokinetics, pharmacogenomics, informatics, and patient care. Education, training, and practice-based resources are needed to support these roles and to facilitate the development of financially sustainable pharmacist-led clinical pharmacogenomics practice models.

Clinical pharmacogenetics implementation: approaches, successes, and challenges.

Current challenges exist to widespread clinical implementation of genomic medicine and pharmacogenetics. The University of Florida (UF) Health Personalized Medicine Program (PMP) is a pharmacist-led, multidisciplinary initiative created in 2011 within the UF Clinical Translational Science Institute. Initial efforts focused on pharmacogenetics, with long-term goals to include expansion to disease-risk prediction and disease stratification. Herein we describe the processes for development of the program, the challenges that were encountered and the clinical acceptance by clinicians of the genomic medicine implementation. The initial clinical implementation of the UF PMP began in June 2012 and targeted clopidogrel use and the CYP2C19 genotype in patients undergoing left heart catheterization and percutaneous-coronary intervention (PCI). After 1 year, 1,097 patients undergoing left heart catheterization were genotyped preemptively, and 291 of those underwent subsequent PCI. Genotype results were reported to the medical record for 100% of genotyped patients. Eighty patients who underwent PCI had an actionable genotype, with drug therapy changes implemented in 56 individuals. Average turnaround time from blood draw to genotype result entry in the medical record was 3.5 business days. Seven different third party payors, including Medicare, reimbursed for the test during the first month of billing, with an 85% reimbursement rate for outpatient claims that were submitted in the first month. These data highlight multiple levels of success in clinical implementation of genomic medicine.

Evaluating incorporation of drug restrictions into computerized drug order entries after transition to an electronic health record.

BACKGROUND: Incorporation of drug restriction policy into electronic drug order entries (DOEs) can promote responsible medication use and resource utilization when implemented systematically. OBJECTIVE: To identify drugs that require further incorporation of formulary restriction policy into their DOEs after migration to an electronic health record with computerized prescriber order entry (CPOE). METHODS: After transition to CPOE, test orders for formulary restricted drugs were entered in the CPOE environment. Data were collected about rationale for drug restriction, type of formulary restriction, presence of incorporation of restriction policy into the DOE, and whether incorporation was consistent with a recommended method. Restricted drugs requiring revision of policy incorporation into their DOEs were analyzed to create a prioritized task list based on rationale for the restriction. RESULTS: Of all restricted drugs, 63.6% (287/451) did not have restriction policy incorporated into their DOEs consistent with the recommended method and therefore required revision. Eighteen percent (81/451) of restricted drugs had no incorporation of restriction policy in their DOEs. Safety was the rationale for restriction in 21% (17/81) of these, which received highest priority for revision. When drugs were orderable but restricted, 61.9% (78/126) lacked optimal incorporation of policy in DOEs to promote adherence. When drugs were not orderable, 64% (206/322) did not provide guidance to formulary alternatives in DOEs when they should have. CONCLUSION: After transition to CPOE, almost two-thirds of all analyzed restricted drugs lacked optimal incorporation of formulary restriction policies in their DOEs. DOEs with restrictions related to safety reasons were among those most frequently requiring revision. Some DOEs can better promote adherence and provide guidance to prescribers through revision. Predefined, systematic implementation strategies should be used during changes in computerized drug use processes.

Use of bronchoalveolar lavage to detect galactomannan for diagnosis of pulmonary aspergillosis among nonimmunocompromised hosts.

Pulmonary aspergillosis in nonimmunocompromised hosts, although rare, is being increasingly recognized. The diagnosis of pulmonary aspergillosis is difficult, since the recovery of Aspergillus from respiratory samples cannot differentiate colonization from invasion. We assessed the role of bronchoalveolar lavage (BAL) in detecting galactomannan (GM) for diagnosing pulmonary aspergillosis in 73 nonimmunocompromised patients with pulmonary infiltrates for whom the test was ordered. Six patients had pulmonary aspergillosis, two each with acute invasive pulmonary aspergillosis, chronic necrotizing pulmonary aspergillosis, and aspergilloma. All six patients had a BAL GM level of >/=1.18. The sensitivity, specificity, and negative predictive value (NPV) for a BAL GM level of >/=1.0 were 100%, 88.1%, and 100%, respectively. Notably, the positive predictive value (PPV) was only 42.9%, likely reflecting the low prevalence of pulmonary aspergillosis among nonimmunosuppressed patients. The combination of BAL microscopy and culture had a sensitivity and NPV similar to those of BAL GM detection but a higher specificity and PPV (92.5% and 54.6%, respectively). Moreover, a BAL GM test did not identify any cases that were not diagnosed by conventional methods like microscopy and culture. In conclusion, there was no conclusive benefit of determining BAL GM levels in the diagnosis of pulmonary aspergillosis among nonimmunocompromised hosts. Given the likelihood of false-positive results, a BAL GM test should not be ordered routinely in this population.

Bronchoalveolar lavage galactomannan in diagnosis of invasive pulmonary aspergillosis among solid-organ transplant recipients.

We review the experience at our institution with galactomannan (GM) testing of bronchoalveolar lavage (BAL) fluid in the diagnosis of invasive pulmonary aspergillosis (IPA) among solid-organ transplant recipients. Among 81 patients for whom BAL GM testing was ordered (heart, 24; kidney, 22; liver, 19; lung, 16), there were five cases of proven or probable IPA. All five patients had BAL GM of > or = 2.1 and survived following antifungal therapy. The sensitivity, specificity, and positive and negative predictive values for BAL GM testing at a cutoff of > or = 1.0 were 100%, 90.8%, 41.7%, and 100%, respectively. The sensitivity of BAL GM testing was better than that of conventional tests such as serum GM or BAL cytology and culture. Moreover, a positive BAL GM test diagnosed IPA several days to 4 weeks before other methods for three patients. Twelve patients had BAL GM of > or = 0.5 but no evidence of IPA. Among these, lung transplant recipients accounted for 41.7% (5/12) of the false-positive results, reflecting frequent colonization of airways in this population. Excluding lung transplants, the specificity and positive predictive value for other solid-organ transplants increased to 92.9% and 62.5%, respectively (cutoff, > or = 1.0). In conclusion, BAL GM testing facilitated more-rapid diagnoses of IPA and the institution of antifungal therapy among non-lung solid-organ transplant recipients and helped to rule out IPA.

In vitro susceptibility of breakthrough Candida bloodstream isolates correlates with daily and cumulative doses of fluconazole.

We determined MICs for 28 Candida isolates recovered from patients who developed breakthrough candidemia while receiving fluconazole. MICs correlated with daily and cumulative doses of fluconazole. Patients receiving > or =2,000 mg cumulatively or >200 mg daily were more likely to be infected with isolates that were not fluconazole susceptible.