Eric S. Kirkendall

Electronic Health Record Identification of Nephrotoxin Exposure and Associated Acute Kidney Injury

BACKGROUND AND OBJECTIVE: Nephrotoxic medication exposure represents a common cause of acute kidney injury (nephrotoxin-AKI) in hospitalized children. Systematic serum creatinine (SCr) screening has not been routinely performed in children receiving nephrotoxins, potentially leading to underestimating nephrotoxin-AKI rates. We aimed to accurately determine nephrotoxin exposure and nephrotoxin-AKI rates to drive appropriate interventions in non–critically ill hospitalized children. METHODS: We conducted a prospective quality improvement project implementing a systematic electronic health record (EHR) screening and decision support process (trigger) at a single quaternary pediatric hospital. Patients were all noncritically ill hospitalized children receiving an intravenous aminoglycoside for ≥3 days or ≥3 nephrotoxins simultaneously (exposure). Pharmacists recommended daily SCr monitoring in exposed patients. AKI was defined by the modified pediatric Risk, Injury, Failure, Loss and End-stage Renal Disease criteria (≥25% decrease in estimated creatinine clearance). We developed 4 novel metrics: exposure rate per 1000 patient-days, AKI rate per 1000 patient-days, AKI rate (%) per high nephrotoxin admission, and AKI days per 100 exposure days (AKI intensity). RESULTS: This study included 21 807 patients accounting for 27 711 admissions. A total of 726 (3.3%) unique exposed patients accounted for 945 hospital admissions (6713 patient-days). AKI occurred in 25% of unique exposed patients and 31% of exposure admissions (1974 patient-days). Our EHR-driven SCr nephrotoxin-AKI surveillance process was associated with a 42% reduction in AKI intensity. CONCLUSIONS: Nephrotoxin-AKI rates are high in noncritically ill children; systematic screening for nephrotoxic medication exposure and AKI detection was accomplished reliably through an EHR based trigger tool.

Acute Kidney Injury Associated with High Nephrotoxic Medication Exposure Leads to Chronic Kidney Disease after 6 Months

OBJECTIVE To assess the development of chronic kidney disease (CKD) after high nephrotoxic medication exposure-associated acute kidney injury (NTMx-AKI) in hospitalized children. STUDY DESIGN We performed a retrospective cohort study of children exposed to an aminoglycoside for ≥3 days or ≥3 nephrotoxic medications simultaneously for the development of CKD at 6 months. Follow-up data >6 months after acute kidney injury (AKI) were retrieved from electronic health records. Outcomes in children with NTMx-AKI were compared with patients of same age and primary service distribution who were exposed to nephrotoxic medications but did not develop AKI (controls). RESULTS One hundred patients with NTMx-AKI were assessed (mean age of 9.3 ± 6.9 years). Commonly involved services were bone marrow transplantation/oncology (59%), liver transplantation (13%), and pulmonary (13%). Pre-AKI estimated glomerular filtration rate (eGFR) was 119 ± 14.5 mL/min/1.73 m(2) (range 90-150 mL/min/1.73 m(2)). Mean discharge eGFR was 105.1 ± 27.1 mL/min/1.73 m(2). At 6 months after NTMx-AKI, eGFR (n = 77) was 113.8 ± 30.6 mL/min/1.73 m(2). Sixteen (20.7%) had eGFR of 60-90, 2 (2.6%) had eGFR <60, and 9 (11.6%) had eGFR >150 mL/min/1.73 m(2) (hyperfiltration). Twenty-four (68.5%) of 35 patients who were assessed for proteinuria had a urine protein-to-creatinine ratio >0.3 mg/mg, and 29 (37.6%) had hypertension. Twenty-six (33.7%) patients had CKD (proteinuria or eGFR <60 mL/min/1.73 m(2)). An additional 28 (36.3%) were considered to be at risk for CKD with hypertension, eGFR between 60 and 90 mL/min/1.73 m(2), or eGFR >150 mL/min/1.73 m(2). CKD, hypertension, and proteinuria were more common in the AKI cohort than in controls. CONCLUSIONS Six months after NTMx-AKI, 70% of patients had evidence of residual kidney damage (reduced eGFR, hyperfiltration, proteinuria, or hypertension). Few underwent a complete evaluation for CKD. With studies showing an association between AKI and CKD, we suggest systematic comprehensive follow-up in children after NTMx-AKI.

Measuring adverse events and levels of harm in pediatric inpatients with the global trigger tool

OBJECTIVES: To evaluate and characterize the Global Trigger Tool’s (GTTs) utility in a pediatric population; to measure the rate of harm at our institution and compare it with previously established trigger tools and benchmark rates; and to describe the distribution of harm of the detected events. METHODS: Per the GTT methodology, 240 random inpatient charts were retrospectively reviewed over a 12-month pilot period for the presence of 53 predefined safety triggers. When triggers were detected, the reviewers investigated the chart more thoroughly to decide whether an adverse event occurred. Agreement with a physician reviewer was then reached, and a level of harm was assigned. RESULTS: A total of 404 triggers were detected (1.7 triggers per patient), and 88 adverse events were identified. Rates of 36.7 adverse events per 100 admissions and 76.3 adverse events per 1000 patient-days were calculated. Sixty-two patients (25.8%) had at least 1 adverse event during their hospitalization, and 18 (7.5%) had >1 event identified. Three-quarters of the events were category E (temporary harm). Two events required intervention to sustain life (category H). Two of the 6 trigger modules identified 95% of the adverse events. CONCLUSIONS: The GTT demonstrated utility in the pediatric inpatient setting. With the use of the trigger tool, we identified a rate of harm 2 to 3 times higher than previously published pediatric rates. Modifications to the trigger tool to address pediatric-specific issues could increase the test characteristics of the tool.

A sustained quality improvement program reduces nephrotoxic medication-associated acute kidney injury

Exposure to nephrotoxic medication is among the most common causes of acute kidney injury (AKI) in hospitalized patients. Here we conducted a prospective quality improvement project implementing a systematic Electronic Health Record screening and decision support process (trigger) in our quaternary pediatric inpatient hospital. Eligible patients were noncritically ill hospitalized children receiving an intravenous aminoglycoside for more than 3 days or more than 3 nephrotoxins simultaneously (exposure) from September 2011 through March 2015. Pharmacists recommended daily serum creatinine monitoring in exposed patients after appearance on the trigger report and AKI was defined by the Kidney Disease Improving Global Outcomes AKI criteria. A total of 1749 patients accounted for 2358 separate hospital admissions during which a total of 3243 episodes of nephrotoxin exposure were identified with 170 patients (9.7%) experiencing 2 or more exposures. A total of 575 individual AKI episodes occurred over the 43-month study period. Overall, the exposure rate decreased by 38% (11.63-7.24 exposures/1000 patient days), and the AKI rate decreased by 64% (2.96-1.06 episodes/1000 patient days). Assuming initial baseline exposure rates would have persisted without our project implementation, we estimate 633 exposures and 398 AKI episodes were avoided. Thus, systematic surveillance for nephrotoxic medication exposure and near real-time AKI risk can lead to sustained reductions in avoidable harm. These interventions and outcomes are translatable to other pediatric and nonpediatric hospitalized settings.

Phenotyping for patient safety: algorithm development for electronic health record based automated adverse event and medical error detection in neonatal intensive care

Background Although electronic health records (EHRs) have the potential to provide a foundation for quality and safety algorithms, few studies have measured their impact on automated adverse event (AE) and medical error (ME) detection within the neonatal intensive care unit (NICU) environment. Objective This paper presents two phenotyping AE and ME detection algorithms (ie, IV infiltrations, narcotic medication oversedation and dosing errors) and describes manual annotation of airway management and medication/fluid AEs from NICU EHRs. Methods From 753 NICU patient EHRs from 2011, we developed two automatic AE/ME detection algorithms, and manually annotated 11 classes of AEs in 3263 clinical notes. Performance of the automatic AE/ME detection algorithms was compared to trigger tool and voluntary incident reporting results. AEs in clinical notes were double annotated and consensus achieved under neonatologist supervision. Sensitivity, positive predictive value (PPV), and specificity are reported. Results Twelve severe IV infiltrates were detected. The algorithm identified one more infiltrate than the trigger tool and eight more than incident reporting. One narcotic oversedation was detected demonstrating 100% agreement with the trigger tool. Additionally, 17 narcotic medication MEs were detected, an increase of 16 cases over voluntary incident reporting. Conclusions Automated AE/ME detection algorithms provide higher sensitivity and PPV than currently used trigger tools or voluntary incident-reporting systems, including identification of potential dosing and frequency errors that current methods are unequipped to detect.

A Trigger Tool to Detect Harm in Pediatric Inpatient Settings

OBJECTIVES: An efficient and reliable process for measuring harm due to medical care is needed to advance pediatric patient safety. Several pediatric studies have assessed the use of trigger tools in varying inpatient environments. Using the Institute for Healthcare Improvement’s adult-focused Global Trigger Tool as a model, we developed and pilot tested a trigger tool that would identify the most common causes of harm in pediatric inpatient environments. METHODS: After formal training, 6 academic children’s hospitals used this novel pediatric trigger tool to review 100 randomly selected inpatient records per site from patients discharged during the month of February 2012. RESULTS: From the 600 patient charts evaluated, 240 harmful events (“harms”) were identified, resulting in a rate of 40 harms per 100 patients admitted and 54.9 harms per 1000 patient days across the 6 hospitals. At least 1 harm was identified in 146 patients (24.3% of patients). Of the 240 total events, 108 (45.0%) were assessed to have been potentially or definitely preventable. The most common patient harms were intravenous catheter infiltrations/burns, respiratory distress, constipation, pain, and surgical complications. CONCLUSIONS: Consistent with earlier rates of all-cause harm in adult hospitals, harm occurs at high rates in hospitalized children. Availability and use of an all-cause harm identification tool will establish the epidemiology of harm and will provide a consistent approach to assessing the effect of interventions on harms in hospitalized children.

Evaluating the accuracy of electronic pediatric drug dosing rules

OBJECTIVE To determine the accuracy of vendor-supplied dosing eRules for pediatric medication orders. Inaccurate or absent dosing rules can lead to high numbers of false alerts or undetected prescribing errors and may potentially compromise safety in this already vulnerable population. MATERIALS AND METHODS 7 months of medication orders and alerts from a large pediatric hospital were analyzed. 30 medications were selected for study across 5 age ranges and 5 dosing parameters. The resulting 750 dosing rules from a commercial system formed the study corpus and were examined for accuracy against a gold standard created from traditional clinical resources. RESULTS Overall accuracy of the rules in the study corpus was 55.1% when the rules were transformed to fit a priori age ranges. Over a pediatric lifetime, the dosing rules were accurate an average of 57.6% of the days. Dosing rules pertaining to the newborn age range were as accurate as other age ranges on average, but exhibited more variability. Daily frequency dosing parameters showed more accuracy than total daily dose, single dose minimum, or single dose maximum. DISCUSSION The accuracy of a vendor-supplied set of dosing eRules is suboptimal when compared with traditional dosing sources, exposing a gap between dosing rules in commercial products and actual prescribing practices by pediatric care providers. More research on vendor-supplied eRules is warranted in order to understand the effects of these products on safe prescribing in children.

Transitioning from a computerized provider order entry and paper documentation system to an electronic health record: Expectations and experiences of hospital staff

OBJECTIVES To examine healthcare workers perceptions, expectations, and experiences regarding how work processes, patient-related safety, and care were affected when a quaternary care center transitioned from one computerized provider order entry (CPOE) system to a full electronic health record (EHR). METHODS The I-SEE survey was administered prior to and 1-year after transition in systems. The construct validity and reliability of the survey was assessed within the current population and also compared to previously published results. Pre- and 1-year post-implementation scale means were compared within and across time periods. RESULTS The majority of respondents were nurses and personnel working in the acute care setting. Because a confirmatory factor analysis indicated a lack of fit of our data to the I-SEE surveys 5-factor structure, we conducted an exploratory factor analysis that resulted in a 7-factor structure which showed better reliability and validity. Mean scores for each factor indicated that attitudes and expectations were mostly positive and score trends over time were positive or neutral. Nurses generally had less positive attitudes about the transition than non-nursing respondents, although the difference diminished after implementation. CONCLUSIONS Findings demonstrate that the majority of responding staff were generally positive about transitioning from CPOE system to a full electronic health record (EHR) and understood the goals of doing so, with overall improved ratings over time. In addition, the I-SEE survey, when modified based on our population, was useful for assessing patient care and safety related expectations and experiences during the transition from one CPOE system to an EHR.

Developing and evaluating an automated appendicitis risk stratification algorithm for pediatric patients in the emergency department

Objective To evaluate a proposed natural language processing (NLP) and machine-learning based automated method to risk stratify abdominal pain patients by analyzing the content of the electronic health record (EHR). Methods We analyzed the EHRs of a random sample of 2100 pediatric emergency department (ED) patients with abdominal pain, including all with a final diagnosis of appendicitis. We developed an automated system to extract relevant elements from ED physician notes and lab values and to automatically assign a risk category for acute appendicitis (high, equivocal, or low), based on the Pediatric Appendicitis Score. We evaluated the performance of the system against a manually created gold standard (chart reviews by ED physicians) for recall, specificity, and precision. Results The system achieved an average F-measure of 0.867 (0.869 recall and 0.863 precision) for risk classification, which was comparable to physician experts. Recall/precision were 0.897/0.952 in the low-risk category, 0.855/0.886 in the high-risk category, and 0.854/0.766 in the equivocal-risk category. The information that the system required as input to achieve high F-measure was available within the first 4 h of the ED visit. Conclusions Automated appendicitis risk categorization based on EHR content, including information from clinical notes, shows comparable performance to physician chart reviewers as measured by their inter-annotator agreement and represents a promising new approach for computerized decision support to promote application of evidence-based medicine at the point of care.

Automated adverse event detection collaborative: electronic adverse event identification, classification, and corrective actions across academic pediatric institutions.

Background Historically, the gold standard for detecting medical errors has been the voluntary incident reporting system. Voluntary reporting rates significantly underestimate the number of actual adverse events in any given organization. The electronic health record (EHR) contains clinical and administrative data that may indicate the occurrence of an adverse event and can be used to detect adverse events that may otherwise remain unrecognized. Automated adverse event detection has been shown to be efficient and cost effective in the hospital setting. The Automated Adverse Event Detection Collaborative (AAEDC) is a group of academic pediatric organizations working to identify optimal electronic methods of adverse event detection. The Collaborative seeks to aggregate and analyze data around adverse events as well as identify and share specific intervention strategies to reduce the rate of such events, ultimately to deliver higher quality and safer care. The objective of this study is to describe the process of automated adverse event detection, report early results from the Collaborative, identify commonalities and notable differences between 2 organizations, and suggest future directions for the Collaborative. Methods In this retrospective observational study, the implementation and use of an automated adverse event detection system was compared between 2 academic children’s hospital participants in the AAEDC, Children’s National Medical Center, and Cincinnati Children’s Hospital Medical Center. Both organizations use the EHR to identify potential adverse events as designated by specific electronic data triggers. After gathering the electronic data, a clinical investigator at each hospital manually examined the patient record to determine whether an adverse event had occurred, whether the event was preventable, and the level of harm involved. Results The Automated Adverse Event Detection Collaborative data from the 2 organizations between July 2006 and October 2010 were analyzed. Adverse event triggers associated with opioid and benzodiazepine toxicity and intravenous infiltration had the greatest positive predictive value (range, 47%– 96%). Triggers associated with hypoglycemia, coagulation disturbances, and renal dysfunction also had good positive predictive values (range, 22%–74%). In combination, the 2 organizations detected 3,264 adverse events, and 1,870 (57.3%) of these were preventable. Of these 3,264 events, clinicians submitted only 492 voluntary incident reports (15.1%). Conclusions This work demonstrates the value of EHR-derived data aggregation and analysis in the detection and understanding of adverse events. Comparison and selection of optimal electronic trigger methods and recognition of adverse event trends within and between organizations are beneficial. Automated detection of adverse events likely contributes to the discovery of opportunities, expeditious implementation of process redesign, and quality improvement.