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Real-time pharmacy surveillance and clinical decision support to
reduce adverse drug events in acute kidney injury: a
randomized, controlled trial
Allison B. McCoy, PhD
1
, Zachary L. Cox, PharmD, BCPS
2,3
, Erin B. Neal, PharmD, BCPS
2
,
Lemuel R. Waitman, PhD
4
, Neeraja B. Peterson, MD, MSc
5
, Gautam Bhave, MD, PhD
6
,
Edward D. Siew, MD
6
, Ioana Danciu, BE
1
, Julia B. Lewis, MD
6
, and Josh F. Peterson, MD,
MPH
1,5,7
1
Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN
2
Department of Pharmacy, Vanderbilt University Medical Center, Nashville, TN
3
College of Pharmacy, Lipscomb University, Nashville, TN
4
Department of Biostatistics, University of Kansas Medical Center, Kansas City, KS
5
Division of General Internal Medicine and Public Health, Department of Medicine, Vanderbilt
University Medical Center, Nashville, TN
6
Division of Nephrology, Department of Medicine, Vanderbilt University Medical Center, Nashville,
TN
7
Geriatric Research Education Clinical Center (GRECC), VA Tennessee Valley Healthcare
System, Nashville, TN
Abstract
Objectives—Clinical decision support (CDS), such as computerized alerts, improves prescribing
in the setting of acute kidney injury (AKI), but considerable opportunity remains to improve
patient safety. The authors sought to determine whether pharmacy surveillance of AKI patients
could detect and prevent medication errors that are not corrected by automated interventions.
Methods—The authors conducted a randomized clinical trial among 396 patients admitted to an
academic, tertiary care hospital between June 1, 2010 and August 31, 2010 with an acute 0.5 mg/
dl change in serum creatinine over 48 hours and a nephrotoxic or renally cleared medication order.
Patients randomly assigned to the intervention group received surveillance from a clinical
pharmacist using a web-based surveillance tool to monitor drug prescribing and kidney function
trends. CDS alerting and standard pharmacy services were active in both study arms. Outcome
measures included blinded adjudication of potential adverse drug events (pADEs), adverse drug
events (ADEs) and time to provider modification or discontinuation of targeted nephrotoxic or
renally cleared medications.
Corresponding author’s current affiliation and address: School of Biomedical Informatics, The University of Texas Health Science
Center at Houston (UTHealth), UT Houston-Memorial Hermann Center for Healthcare Quality and Safety, 6410 Fannin St, UPB
1100, Houston, TX 77030, (713) 500-6931, allison.b.mccoy@uth.tmc.edu.
Conflicts of Interest
The authors declare that they have no conflicts of interest in the research.
Protection of Human and Animal Subjects
This study was approved by the Vanderbilt Institutional Review Board.
NIH Public Access
Author Manuscript
Appl Clin Inform
. Author manuscript; available in PMC 2012 June 18.
Published in final edited form as:
Appl Clin Inform
. 2012 January 1; 3(2): 221–238. doi:10.4338/ACI-2012-03-RA-0009.
NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript
Results—Potential ADEs or ADEs occurred for 104 (8.0%) of control and 99 (7.1%) of
intervention patient-medication pairs (p=0.4). Additionally, the time to provider modification or
discontinuation of targeted nephrotoxic or renally cleared medications did not differ between
control and intervention patients (33.4 hrs vs. 30.3 hrs, p=0.3).
Conclusions—Pharmacy surveillance had no incremental benefit over previously implemented
CDS alerts
Keywords
Decision Support Systems; Clinical; Electronic Health Records; Randomized Controlled Trial;
Medication Errors/prevention & control; Adverse Drug Reaction Reporting Systems
1. Background
Clinical decision support (CDS) within electronic medical records (EMRs) and
computerized provider order entry (CPOE) systems has a high potential for reducing
medication errors [1–4]. Despite some success, substantial numbers of residual adverse drug
events (ADEs) and potential adverse drug events (pADEs) remain, suggesting that further
improvements of patient safety will require additional types of well-integrated interventions.
Furthermore, recent evaluations of CDS point out the risks of the technology, including
provider fatigue and dissatisfaction, and unintended adverse consequences [5–9].
Clinical pharmacy services have traditionally addressed medication safety for hospitalized
patients by reviewing medication orders prior to dispensing or rounding with inpatient
teams, and the support of a clinical pharmacist has been shown to improve prescribing in
multiple settings [10–17]. Pharmacy surveillance has also been shown to detect and
potentially prevent ADEs [18–21]. However, the incremental benefit of pharmacy
surveillance when added to CDS in reducing ADEs is unknown.
We developed a real-time surveillance tool for medication errors in order to integrate CDS
with clinical pharmacy surveillance. The tool recognized high-risk prescribing in patients
with acute kidney injury (AKI) and directed a clinical pharmacist to intervene on the highest
risk patients, including those where providers were not responding to alerts from an existing
CDS system [22]. AKI affects patients and medication regimens across all hospitalized units
and prescribing is not well standardized, though CDS has been shown to benefit patients
with impaired or rapidly changing renal function [21–26]. We hypothesized that real-time
surveillance by a clinical pharmacist using the web-based tool would improve the
management of renally-dosed medications. To test this, we conducted a prospective,
randomized, controlled study, comparing the effect of enhanced clinical pharmacist
surveillance of patients in the intervention group with existing CDS and standard pharmacy
services on the occurrence, preventability, and severity of ADEs.
2. Methods
2.1. Study Design and Participants
We performed a parallel group randomized, controlled trial during June 1, 2010 through
August 31, 2010 at Vanderbilt University Hospital, a large academic, tertiary care facility
with universal utilization of CPOE and extensive integrated CDS [1,25,27–30]. The study
included all admitted adult patients who experienced a 0.5 mg/dl increase or decrease in
serum creatinine over 48 hours of hospitalization following an active, recurring order for
one or more targeted nephrotoxic or renally cleared medications (Table I); these criteria
were selected by a local expert nephrology panel to identify significant renal function
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changes which should trigger reassessment of drug therapy. It included all patients who
were eligible to receive a previously implemented CDS alert for AKI [22].
Prior to randomization, we excluded patients with end-stage renal disease who received
dialysis prior to the first serum creatinine change event or were previously identified as a
chronic dialysis patient in the inpatient order entry records. In addition, we excluded patients
cared for on services with existing specialty pharmacy support including nephrology
services and renal, liver, and bone marrow transplant services. Finally, prior to analysis, a
blinded outcomes assessment pharmacist evaluated cases for additional exclusion criteria
that could not be determined electronically at the time of randomization, including chronic
dialysis patients, transplant patients, palliative care patients, false lab measurements, and no
medication administrations. This study was approved by the Vanderbilt Institutional Review
Board.
2.2. Surveillance Tool Intervention
The surveillance tool is a dynamic web application, populated by real-time clinical databases
and CDS log files, designed to complement traditional CDS and clinical pharmacy services
by facilitating monitoring and intervention for high risk patients. The tool consists of two
primary view types: the surveillance view and the patient detail view. The surveillance view
displays patient details such as demographics, providing service, and hospital location, in
addition to most recent creatinine values and alerts about declining or improving renal
function, for all currently admitted, eligible patients, allowing pharmacists to identify
patients at high risk for harm (Figure 1). The patient detail view (Figure 2) displays a graph
of events of interest and a detailed, sortable timeline of orders, order administrations,
laboratory values, and CDS logs during the patient’s admission for an individual patient,
presented in reverse chronological order to give context for the CDS logs. This view also
allows staff to enter notes directly into the EMR and to save comments for reference to other
surveillance users.
Prior to formally evaluating the surveillance tool for AKI, we performed a four month pilot
implementation. Study personnel independently reviewed select cases, discussed the
potential for intervention based on patient and drug factors, and assessed the usefulness of
the surveillance tool. Based on the feedback, we made iterative changes to the targeted
medication list and the inclusion criteria for both the surveillance tool and AKI CDS.
During the trial, the clinical pharmacist for internal medicine (EN) served as the study
pharmacist for the intervention, reviewing the surveillance tool to evaluate patients during
each workday. For patients determined to be experiencing AKI and needing an intervention,
the study pharmacist contacted the primary provider using a text page, verbal
communication, or an EMR note to recommend changes in care. All interactions, including
patient’s classification of AKI, communication with provider, recommendations provided,
and actions taken, were recorded with a timestamp in a structured form within the
surveillance tool for later analysis.
Both control and intervention patients received standard CDS and clinical pharmacy services
for all hospitalized patients. Active CDS included guided dosing advisors and CPOE alerts
for AKI, which have been described previously [22,27]. A clinical pharmacist rounded on
some medical and surgical teams, including the majority of the ICU services, and
intermittently on non-ICU services to answer provider questions, independently review
medication regimens, and offer verbal recommendations directly to providers during order
entry. Clinical pharmacists at the study institution did not typically interact with CPOE
based CDS as part of their usual workflow; in rare circumstances, a rounding pharmacist
might encounter the AKI CDS when entering medication orders on behalf of providers. Staff
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pharmacists also reviewed medication orders prior to dispensing during the trial, but these
pharmacists did not also monitor laboratory results or changes in serum creatinine.
2.3. Randomization
Patients were automatically assigned to a study group using a pseudo-random number
function within the surveillance tool at the time that he or she first met eligibility criteria and
remained in the assigned group until discharge.
2.4. Outcomes
Our primary outcome was the rate of AKI-related ADEs and pADEs within the intervention
group compared to the concurrent control group. All study events originated from non-
intercepted medication errors with a potential for injury that were active at the onset of AKI
and continued for at least 24 hours. We report only on ADEs and pADEs associated with
renal function, either through direct nephrotoxicity or with a well-described toxicity related
to drug accumulation. For example, an active order for a recurring administration of a non-
steroidal anti-inflammatory drug during the 24 hours after the initial change in serum
creatinine was rated as a pADE, and an episode of bleeding after administration of
enoxaparin unadjusted for renal function was rated as an ADE. A subcategory of ADEs,
“lab-only” ADEs, was defined as highly elevated drug levels or electrolyte abnormalities
consistent with previous ADE literature [31]. We evaluated provider behavior as our
secondary outcome, measuring the time to provider reaction to the AKI event. We calculated
the time from the first change in serum creatinine to modification or discontinuation of
targeted medications ordered prior to the change [22] and the time from the initial order to
modification or discontinuation of targeted medications ordered after the change. We
assessed all outcomes after completion of the inpatient encounter (either by death or
discharge); pADEs or ADEs occurring after patient discharge were not included in the
analysis as outpatient follow-up was not routinely available for all patients.
Outcomes assessors were blinded to patient intervention status [32]. A study pharmacist
(ZC) reviewed all enrolled cases to identify potential study events. The pharmacist recorded
comorbidities present prior to AKI and reviewed each targeted medication order for a
potential error or subsequent injury, drawing from the pre-specified lists of drugs (Table I)
and potential AKI-related adverse events. An outcomes assessment adjudication committee
composed of a nephrologist and an internal medicine physician (GB, NP) independently
reviewed cases categorized as having at least one pADE or ADE, using methods previously
applied to rate preventability and severity [33–35]. An additional nephrologist (ES)
reviewed cases when disagreement occurred. Any residual disagreements within the
committee were resolved by joint discussion.
We also measured outcomes describing the use of the surveillance tool. These included
number of patients appearing on the tool, number of data items (e.g. drugs, labs, and CDS
interactions of interest) for patients, time of day the tool was viewed, duration of views,
number of patients with comments or EMR notes submitted, and the number of patients for
which pharmacists intervened. We also evaluated comments and EMR notes recorded
through the tool.
2.5. Sample Size
We estimated the sample size based on the rate of non-adjudicated medication errors
measured from a retrospective database of medication orders and laboratory values, which
had a median of 52.5 patient-medication pair events each week and a 45.4% baseline
response rate. The planned 13 week trial produced 94% power to detect a 30% reduction in
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non-adjudicated errors, which we determined to be feasible after a previous analysis of the
existing CDS found a significant opportunity for improvement [9].
2.6. Statistical Methods
We used the Pearson chi-square test for categorical variables and the t-test for continuous
variables to perform univariate comparisons between the control and intervention groups. To
evaluate provider behavior, we applied survival analysis methods for time to provider
response, defined as a modification or discontinuation of a targeted drug order by any
provider. For this analysis, we defined patient discharge or death as a censoring event. For
medications ordered prior to the triggering event, follow-up started at the time of the
triggering serum creatinine change, and for medications ordered after the triggering event,
follow-up started at the time the medication was ordered. We used the log-rank test to
measure the difference between control and intervention groups and provide Kaplan-Meier
plots for visualization of the time to event data. Analyses were conducted with Stata 9.2.
3. Results
3.1. Study Population
Figure 3 is a diagram of the selection steps for allocating patients to control or intervention
and application of inclusion and exclusion criteria. During the three month trial period,
1,488 of 11,128 admitted adults experienced a triggering change of serum creatinine over 48
hours. We enrolled 540 cases; 278 were randomized to the control group, and 262 were
randomized to the intervention group. The blinded outcomes assessment pharmacist
identified 82 control and 62 intervention cases that met additional exclusion criteria that
could not be determined electronically at the time of randomization; we included 396 cases
in the final outcomes assessment. We compared demographic characteristics, including age,
sex, race, admitting service, and admission to an intensive care unit, and comorbidities,
which the initial outcomes assessment study pharmacist classified, between the control and
intervention groups to ensure that the study groups were similar (Table II). We found no
statistical difference between groups for any variable evaluated.
3.2. Evaluation of Adverse Drug Events
We evaluated 196 control cases with 1303 medication orders and 200 intervention cases
with 1396 medication orders. Agreement between the two outcomes adjudication physicians
was 93.97% for pADEs (kappa=0.88) and 96.55% for ADEs (kappa=0.93). Table III depicts
the detailed breakdown of pADEs and ADEs after adjudication and consensus. The
adjudication committee determined 76 (38.8%) control and 67 (33.5%) intervention cases
experienced a pADE or ADE (RR=0.86 [0.66, 1.12], p=0.3) and 104 (7.98%) control and 99
(7.09%) intervention medication orders had an associated pADE or ADE (RR=0.88 [0.68,
1.16], p=0.4). Among cases who experienced at least one pADE or ADE in the control
group, 55 (72.4%) experienced one, 15 (19.7%) experienced two, 5 (6.6%) experienced
three, and 1 (1.32%) experienced four events; in the intervention group, 41 (61.2%)
experienced one, 20 (29.9%) experienced two, and 6 (9.0%) experienced three events.
The total events included 52 (26.5%) control and 46 (23.0%) intervention cases
experiencing a pADE (RR=0.87 [0.61, 1.22], p=0.4), and 68 (5.22%) control and 63 (4.51%)
intervention medication orders having an associated pADE (RR=0.86 [0.62, 1.21], p=0.4).
Frequent responses for pADEs categorized as “other” included “dose and interval change
inappropriate for trough level” and “interacted with another prescribed medication”.
Lab-only ADEs occurred for 13 (6.6%) control and 16 (8.0%) intervention cases (RR=1.21
[0.60, 2.44], p=0.6); 14 (1.07%) control and 16 (1.15%) intervention medication orders had
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an associated lab-only ADE (RR=1.07 [0.52, 2.18], p=0.9). Actual ADEs occurred for 22
(11.2%) control and 19 (9.5%) intervention cases (RR=0.85 [0.47, 1.51], p=0.6); 22 (1.69%)
control and 20 (1.43%) intervention medication orders had an associated actual ADE
(RR=0.85 [0.47, 1.55], p=0.6). Severity and preventability of pADEs and ADEs was not
significant between control and intervention groups (Table IV).
Among drugs or drug groups with at least ten orders in the control and intervention groups,
errors most commonly occurred for vancomycin, beta lactam antibiotics, angiotensin-
converting enzyme (ACE) inhibitors, nonsteroidal anti-inflammatory drugs (NSAIDs),
quinolones, and angiotensin II receptor blockers (ARBs) comprising 22.2%, 15.3%, 12.3%,
9.4%, 8.9%, and 5.4% of orders resulting in a pADE or ADE respectively.
3.3. Evaluation of Provider Responses
For medications active at the time of patient’s triggering serum creatinine change or ordered
after the event, we compared the time to provider response, defined as drug modification or
discontinuation, using the log-rank test. While the time to response was shorter in the
intervention group compared to the control group for medications, overall response times
were highly variable, and we did not find any statistically significant differences between
the control and intervention groups. Table V shows the resulting median times to response,
hazard ratios, and p-values. Kaplan-Meier curves for these results are shown in Figure 4.
3.4. Study Pharmacist Interactions with the Surveillance Tool
During the 3-month study period, 273 intervention patients appeared on the surveillance
tool. The study pharmacist viewed the surveillance tool on 67 days (56 weekdays). Of the
displayed intervention patients, 234 (85.7%) were reviewed by the study pharmacist, with an
average of 10.75 patients reviewed each day the surveillance tool was monitored.
Monitoring occurred between 08:00 and 16:00, although the study pharmacist primarily
checked the surveillance tool in the afternoon, after providing teams had completed rounds,
updated medication orders, and entered EMR notes, and laboratory results had returned.
During a week of direct observation, the pharmacist spent 71 minutes monitoring the
surveillance tool on Monday, and a mean of 16.75 minutes on the remaining days (25 on
Tuesday, 9 on Wednesday, 15 on Thursday, and 18 on Friday).
The study pharmacist recommended an intervention for 43 (18.4%) cases, including 70
medication-specific recommendations and 8 patient-specific recommendations. Most cases
without an intervention did not require a dose change; frequencies of recommended patient
and medication interventions are described in Table VI. Medication recommendations
categorized as “other” (9 medications) included correcting the patient’s weight, holding the
medication, and monitoring for sedation. Patient recommendations categorized as “other” (2
cases) included redrawing serum creatinine, monitoring for sedation and treatment failure,
discontinuing oral potassium, and adding height and weight.
The study pharmacist most frequently indicated use of text pages and verbal communication
to contact the providing team; recorded contact included 26 text pages, 28 verbal
communications, and 1 EMR note. EMR notes were used when the providing team was
unavailable (e.g. providing team does not have an attending on campus, or provider did not
respond to text page). Providers agreed to make the recommended changes for 24
interventions (77% of recorded responses), the study pharmacist made changes to the orders
directly in the CPOE system for 5 interventions (16%), and the provider disagreed with
recommendation for 2 interventions (6%). The study pharmacist submitted 157 surveillance
tool comments for 102 cases. The comments frequently summarized patient comorbidities,
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laboratory values and trends, and indications; served as reminders for continued monitoring;
and elaborated recommendations.
4. Discussion
We performed a prospective, randomized comparative effectiveness trial to determine
whether pharmacy surveillance improved medication safety during AKI when compared to
clinical decision support alone. Despite appropriate interventions made by the study
pharmacist, we found no significant improvements in the primary outcome, potential and
actual ADEs, or the process outcome of timely medication adjustments. Overall, the number
of interventions within the CDS far exceeded the interventions by the pharmacist, reflecting
the ability for CDS to be active at all times and intervene more frequently and promptly. The
findings suggest that, when implemented in a setting with comprehensive CDS, more
intensive surveillance by a clinical pharmacist may be necessary to further improve the
safety of prescribing during AKI.
The interventions of a clinical pharmacist have been found to be valuable in a variety of
settings with manual medication dosing, significantly reducing rates of medication errors
and ADEs [10–16]. However, one prior study has suggested no incremental benefit in ADE
prevention with a rounding pharmacist when compared to order entry with CDS [36].
Similar to these findings, existing CDS during our intervention, including initial dosing of
nephrotoxic and renally cleared drugs, CDS for monitoring of these medications within
CPOE, and surveillance by other pharmacists in the event of changing laboratory values,
resulted in a large percentage of already prevented errors. While some studies have found
that surveillance successfully identifies pADEs and ADEs, they have not evaluated the
effect of systems on actual error prevention [18–20,37–41]. Some investigators have
evaluated the use of retrospective CDS surveillance and real-time aggregate CDS
surveillance [42,43], but no prior study has evaluated the effect of surveillance of CDS in
real time on patient or process outcomes. The restriction of our intervention and analysis to
ADEs only related to AKI also makes it difficult to compare our results to these studies,
which measured all types of ADEs. However, pharmacy use of the surveillance tool for
monitoring AKI patients and CDS was similar to use described for a similar tool for
aminoglycosides and anticoagulants [44]. Our study also differs from prior research in that
we evaluated the surveillance tool in a setting with extensive existing CDS and clinical
pharmacy support.
Other real-time surveillance approaches may be more successful. An alternate workflow,
such as use of a distributed surveillance tool by front-line pharmacists approving and
dispensing medication orders or by pharmacists or other providers participating in rounds
might allow earlier, more frequent prevention of medication errors and reduction of ADEs.
Because errors still occurred for patients that received an intervention, the timing of the
surveillance and the ability of a single clinical pharmacist to monitor all at-risk patients may
not have been appropriate. Greater integration of CDS and pharmacist interactions, with
communication features, such that physicians and pharmacists could act as a coordinated
team might further impact the results. Finally, implementation of surveillance may have a
larger impact in institutions without such extensive CDS and clinical pharmacy services.
Several limitations are present. We conducted the trial in an academic, tertiary care medical
center with extensive experience in CDS and clinical pharmacy services, which may have
reduced the opportunity for surveillance to be effective. Application of the technology in a
community hospital setting may yield a different result, particularly given the high number
of medication errors related to renal function that have been previously reported in
community settings [45]. Reproducing the surveillance tool requires integration of several
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advanced clinical systems, which many facilities have not implemented. However, most of
the functionality can be recreated with access to common electronic sources of data,
including patient census, laboratory, and order-entry [20]. The application of the technology
to other domains outside of medication management during AKI should also be investigated.
One methodological contribution to a negative result includes the possibility of crossover
between study arms, since contacted physicians may care for patients randomized to both
the intervention and control groups. While overlap of clinical pharmacy coverage occurred,
only 3 control (1.5%) and 6 (3.0%) intervention cases were potentially exposed to the
intervention pharmacist in the course of completing routine clinical duties. The study was
also underpowered to detect smaller differences in ADE rates. A larger study with a higher
intensity of surveillance is more likely to demonstrate a significant difference in patient
outcomes. Finally, the inability of the system to electronically identify all patients meeting
the exclusion criteria is a limitation of the CDS and affects the generalizability of the results.
Because the exclusions were designed to eliminate patients at low risk of AKI-related
ADEs, including these in the analysis would likely lower the event rates in both groups.
5. Conclusion
In conclusion, we evaluated through a randomized trial the comparative effectiveness of
real-time clinical pharmacist surveillance and existing CDS for patients with AKI. Despite
interventions made by the study pharmacist and a trend toward improved outcomes during
surveillance, we found no statistically significant improvements in occurrence of potential
ADEs or ADEs or in provider responses between control and intervention groups. The study
emphasizes that, while CDS is effective at preventing pADEs and ADEs in patients with
AKI, further research is necessary to determine whether surveillance can improve CDS
performance.
Acknowledgments
The authors were funded in part by National Library of Medicine grants T15 LM007450 and R01 LM009965.
Some data collection was supported by NCRR/NIH grant UL1 RR024975. The authors thank Aihua Bian for her
statistical review of the manuscript.
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Clinical Relevance Statement
Although a variety of informatics approaches to reducing adverse drug events exist, each
can be costly to implement and maintain, and institutions or practitioners must often
decide which is more advantageous. We sought to determine whether pharmacy
surveillance was effective when compared to traditional clinical decision support alerts.
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Figure 1.
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Figure 2.
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Figure 3.
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Figure 4.
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Table I
Targeted nephrotoxic or renally cleared medications for surveillance
Medications to Avoid Medications to Adjust Medications to Review
ACARBOSE
*
ACYCLOVIR (>400mg Q12H)
ADEFOVIR
*
ACETAZOLAMIDE
*
ALLOPURINOL (>100mg Q24H)
ALENDRONATE
+
ACETOHEXAMIDE
*
AMANTADINE
AMOXICILLIN
+
AMIKACIN AZTREONAM AMOXICILLIN-CLAVULANATE
AMPHOTERICIN B
*
BACTRIM (>1 DS tablet BID) AMPICILLIN
BENAZEPRIL
*
CARBOPLATIN
*
AZITHROMYCIN+
CANDESARTAN
*
CISPLATIN
*
BRETYLIUM
CAPREOMYCIN
*
COLCHICINE (>0.6mg Q24H)
BUMETANIDE
+
CAPTOPRIL
*
CYCLOSERINE
CEFACLOR
+
CELECOXIB
*
DAPTOMYCIN CEFAZOLIN
CHLORPROPAMIDE
*
DIDANOSINE CEFEPIME
CIDOFOVIR
*
DIGITOXIN CEFOTAXIME
CYCLOPHOSPHAMIDE
*
DIGOXIN CEFOTETAN
CYCLOSPORINE
*§
DOFETILIDE CEFOXITIN
CYTARABINE
*
DORIPENEM CEFTAZIDIME
DICLOFENAC SODIUM
*
EPTIFIBATIDE CEFUROXIME
DIFLUNISAL
*
ERTAPENEM
CEFUROXIME
+
ENALAPRIL
*
ETOPOSIDE
*
CEPHALEXIN
+
ENALAPRILAT
*
FAMCICLOVIR CHLOROQUINE
ENOXAPARIN
*
(>30mg Q24H)
FLUCYTOSINE CIPROFLOXACIN
ETODOLAC
*
FOSCARNET
CLARITHROMYCIN
+
EXENATIDE
*
GANCICLOVIR CLOFIBRATE
FENOPROFEN
*
GANCICLOVIR
Contrast Dye
+
FLURBIPROFEN
*
IMIPENEM-CILASTATIN DISOPYRAMIDE
FONDAPARINUX ITRACONAZOLE DOXACURIUM INJ
FOSINOPRIL
*
LACOSAMIDE
*
ETHACRYNATE
+
GALLAMINE
*
MEROPENEM ETHAMBUTOL
GENTAMICIN INJ
METOCLOPRAMIDE
*
FLECAINIDE
GLYBURIDE
*
MITOMYCIN
*
FLUCONAZOLE (>100mg Q24H)
IBUPROFEN
*
PENICILLIN-VK
FUROSEMIDE
+
IFOSFAMIDE
*
PENTOSTATIN
*
GEMFIBROZIL
+
IMMUNE GLOBULIN
*
PRAMIPEXOLE
*
HYDROMORPHONE
+
INDOMETHACIN
*
PREGABALIN
*
HYDROXYUREA
*
IRBESARTAN
*
PROCAINAMIDE
IBANDRONATE
+
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Medications to Avoid Medications to Adjust Medications to Review
KETOPROFEN
*
PYRIDOSTIGMINE
IDARUBICIN
*
KETOROLAC
*
SOTALOL
*
INDINAVIR
LISINOPRIL
*
STAVUDINE LAMIVUDINE
LITHIUM
TOPOTECAN
*
LEVOFLOXACIN
LOSARTAN
*
VALACYCLOVIR
MELPHALAN
*
MELOXICAM
*
VALGANCICLOVIR (>450mg Q24H) METOCURINE
MEPERIDINE
*
VANCOMYCIN MIVACURIUM
MORPHINE
*
VORICONAZOLE
MORPHINE
*
NEOSTIGMINE
*
NEOSTIGMINE
*
NORFLOXACIN NORFLOXACIN
OFLOXACIN OFLOXACIN
PAMIDRONATE
+
PAMIDRONATE
+
PENICILLIN-G PENICILLIN-G
PIPERACILLIN PIPERACILLIN
PYRAZINAMIDE PYRAZINAMIDE
QUINIDINE QUINIDINE
RIFAMPIN
+
RIFAMPIN
+
RISEDRONATE
+
RISEDRONATE
+
TEMOZOLOMIDE
*
TEMOZOLOMIDE
*
TENOFOVIR
*
TENOFOVIR
*
TICARCILLIN TICARCILLIN
TOCAINIDE TOCAINIDE
TORSEMIDE
+
TORSEMIDE
+
ZIDOVUDINE ZIDOVUDINE
ZOLEDRONIC ACID
+
ZOLEDRONIC ACID
+
*
Medication only targeted for increasing serum creatinine intervention.
+
Medication was not targeted for intervention, displayed only on surveillance tool for context.
§
Medication was not targeted for patients admitted to a renal or transplant services.
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Table II
Study population demographics for analyzed acute kidney injury surveillance cases
Control Cases n = 196 Intervention Cases n = 200 P
Age (y) 58.3 (15.7) 60.7 (16.8) 0.2
Sex (%)
Women 39.2 47.0 0.1
Men 60.7 53.0 0.1
Race (%)
White 83.2 79.0 0.3
Black 10.2 17.0 0.05
Hispanic 1.5 0.5 0.3
Other 2.0 1.0 0.4
Unknown 3.1 3.0 0.9
Admitting Service (%)
Cardiology 20.9 15.0 0.1
Critical Care 11.2 17.5 0.08
Geriatrics 2.6 2.5 0.9
Hematology/oncology 8.2 7.5 0.8
Hepatology 2.6 1.5 0.4
Infectious disease 1.5 3.0 0.3
Medicine 12.8 12.5 0.9
Orthopedics 5.1 6.0 0.7
Other 2.0 4.5 0.2
Surgery 26.0 22.5 0.4
Trauma 7.1 7.5 0.9
Intensive Care Unit (%) 54.6 58.5 0.4
Comorbidities (%)
Cancer 28.6 22.5 0.2
Cerebrovascular disease 11.7 14.5 0.4
Congestive heart failure 24.5 26.0 0.7
Coronary artery disease 32.7 34.0 0.8
Diabetes 35.7 41.5 0.2
End-stage liver disease 4.6 4.0 0.8
Hypertension 62.2 67.0 0.3
Mechanical ventilation 29.6 25.5 0.3
Peripheral vascular disease 3.6 7.5 0.09
Values shown as mean ± standard deviation or percentage.
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Table III
Evaluation of potential adverse drug events and adverse drug events
Control n=1303 Intervention n=1396 P
Potential adverse drug events 68 (5.22%) 63 (4.51%) 0.4
Contraindicated use for > 24 hours 24 14 0.1
No dose adjustment for > 24 hours 10 15 0.2
No interval adjustment for > 24 hours 31 30 0.7
Ineffective at low creatinine clearance 1 3 0.4
No drug level monitoring 5 4 0.9
No creatinine monitoring 0 3 0.09
Other 7 2 0.07
Lab-only adverse drug events 14 (1.07%) 16 (1.15%) 0.9
Hyperkalemia 1 1 0.9
Hypokalemia 0 0 -
Hypernatremia 0 0 -
Hyponatremia 0 0 -
Toxic drug levels 9 9 0.9
Subtherapeutic drug levels 5 6 0.9
Hypoglycemia (asymptomatic) 0 0 -
Adverse drug events 22 (1.69%) 20 (1.43%) 0.6
Bradyarrhythmia 0 0 -
Hypotension 7 6 0.7
QT Prolongation 0 2 0.2
Cognitive changes/somnolence 1 4 0.2
Delirium 1 0 0.3
Extrapyramidal symptoms/movement disorders 0 1 0.3
Oversedation 3 2 0.6
Seizure 0 0 --
Rash 0 1 0.3
Hypoglycemia (symptomatic) 0 0 --
Pancreatitis 0 0 --
Diarrhea 0 0 --
Anemia 0 0 --
Lactic acidosis 0 0 --
Major bleed 0 1 0.3
Minor bleed 3 2 0.6
Neutropenia 0 0 --
Thrombocytopenia 0 0 --
Neuromuscular control 0 0 --
Vision changes 0 0 --
Hearing loss 0 0 --
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Control n=1303 Intervention n=1396 P
Tinnitus 0 0 --
Acute kidney injury (AKIN Stage 2 or 3) 5 4 0.7
Crystalurea 0 1 0.3
Renal replacement therapy 0 0 --
Volume overload 0 0 --
Respiratory depression 1 1 0.9
Death 0 1 0.3
Abbreviations: AKIN = Acute Kidney Injury Network
Events represent patient-medication pairs.
More than one event may have been recorded for a patient-medication pair.
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Table IV
Evaluation of potential adverse drug events and adverse drug events
Control n=1303 Intervention n=1396 p
Potential adverse drug events
Significant 20 26 0.2
Serious 42 31
Life threatening 3 6
Fatal 1 0
Lab-only adverse drug events
Significant 0 0 0.5
Serious 10 13
Life threatening 4 3
Fatal 0 0
Not preventable 3 5 0.5
Preventable 11 11
Adverse drug events
Significant 0 2 0.1
Serious 16 8
Life threatening 6 9
Fatal 0 1
Not preventable 6 7 0.6
Preventable 16 13
Events represent patient-medication pairs.
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Table V
Evaluation of surveillance and provider response
Control Intervention
Hazard Ratio (95% CI) Pn Median Hours to Response (IQR) n Median Hours to Response (IQR)
Ordered prior to AKI 332 25.9 (6.0, 49.5) 374 18.3 (5.6, 47.2) 1.0 (0.9, 1.2) 0.2
Medications to avoid 104 26.6 (5.1, 55.1) 111 13.4 (3.3, 42.0) 1.1(0.8, 1.5) 0.8
Medications to adjust 102 14.9 (4.8, 46.8) 114 25.0 (5.5, 58.6) 1.0 (0.8, 1.4) 0.9
Medications to review 126 27.0 (7.8, 44.9) 149 24.1 (7.8, 44.3) 0.9 (0.7, 1.2) 0.6
Ordered after AKI 576 34.8 (11.6, 73.5) 625 32 (13.7, 73.0) 1.1 (1.0, 1.2) 0.2
Medications to avoid 173 25.9 (10.0, 63.9) 148 31.3 (10.4, 70.1) 1.0 (0.8, 1.3) 0.9
Medications to adjust 159 38.0 (14.4, 82.3) 217 27.2 (10.4, 69.5) 1.2 (0.9, 1.5) 0.2
Medications to review 244 43.8 (13.8, 72.0) 260 35.6 (19.9, 72.0) 1.1, (0.9, 1.3) 0.5
Abbreviations: AKI = acute kidney injury, IQR = interquartile ranges
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Table VI
Study pharmacist recommendations
Total Responses
No intervention recommended 509
Dialysis 2
Transplant 0
False lab measurement 15
Transient acute kidney injury 8
No active orders 84
Palliative care 4
No dose change required 394
Other 2
Patient intervention recommended 8
Monitor serum creatinine 3
Monitor serum potassium 0
Monitor other 3
Other 2
Medication Recommendations 70
Increase dose 13
Increase interval 8
Decrease dose 9
Decrease interval 11
Discontinue medication 14
Consider alternate medication 7
Monitor therapeutic drug levels 13
Other 6
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