Kevin M. Heard

Implementation of a real-time computerized sepsis alert in nonintensive care unit patients*

Objective:Early therapy of sepsis involving fluid resuscitation and antibiotic administration has been shown to improve patient outcomes. A proactive tool to identify patients at risk for developing sepsis may decrease time to interventions and improve patient outcomes. The objective of this study was to evaluate whether the implementation of an automated sepsis screening and alert system facilitated early appropriate interventions. Design:Prospective, observational, pilot study. Setting:Six medicine wards in Barnes-Jewish Hospital, a 1250-bed academic medical center. Patients:Patients identified by the sepsis screen while admitted to a medicine ward were included in the study. A total of 300 consecutive patients were identified comprising the nonintervention group (n = 200) and the intervention group (n = 100). Interventions:A real-time sepsis alert was implemented for the intervention group, which notified the charge nurse on the patients hospital ward by text page. Measurements and Main Results:Within 12 hrs of the sepsis alert, interventions by the treating physicians were assessed, including new or escalated antibiotics, intravenous fluid administration, oxygen therapy, vasopressors, and diagnostic tests. After exclusion of patients without commitment to aggressive management, 181 patients in the nonintervention group and 89 patients in the intervention group were analyzed. Within 12 hrs of the sepsis alert, 70.8% of patients in the intervention group had received ≥1 intervention vs. 55.8% in the nonintervention group (p = .018). Antibiotic escalation, intravenous fluid administration, oxygen therapy, and diagnostic tests were all increased in the intervention group. This was a single-center, institution- and patient-specific algorithm. Conclusions:The sepsis alert developed at Barnes-Jewish Hospital was shown to increase early therapeutic and diagnostic interventions among nonintensive care unit patients at risk for sepsis.

A trial of a real-time alert for clinical deterioration in patients hospitalized on general medical wards.

BACKGROUND With limited numbers of intensive care unit (ICU) beds available, increasing patient acuity is expected to contribute to episodes of inpatient deterioration on general wards. OBJECTIVE To prospectively validate a predictive algorithm for clinical deterioration in general-medical ward patients, and to conduct a trial of real-time alerts based on this algorithm. DESIGN Randomized, controlled crossover study. SETTING/PATIENTS Academic center with patients hospitalized on 8 general wards between July 2007 and December 2011. INTERVENTIONS Real-time alerts were generated by an algorithm designed to predict the need for ICU transfer using electronically available data. The alerts were sent by text page to the nurse manager on intervention wards. MEASUREMENTS Intensive care unit transfer, hospital mortality, and hospital length of stay. RESULTS Patients meeting the alert threshold were at nearly 5.3-fold greater risk of ICU transfer (95% confidence interval [CI]: 4.6-6.0) than those not satisfying the alert threshold (358 of 2353 [15.2%] vs 512 of 17678 [2.9%]). Patients with alerts were at 8.9-fold greater risk of death (95% CI: 7.4-10.7) than those without alerts (244 of 2353 [10.4%] vs 206 of 17678 [1.2%]). Among patients identified by the early warning system, there were no differences in the proportion of patients who were transferred to the ICU or who died in the intervention group as compared with the control group. CONCLUSIONS Real-time alerts were highly specific for clinical deterioration resulting in ICU transfer and death, and were associated with longer hospital length of stay. However, an intervention notifying a nurse of the risk did not result in improvement in these outcomes.

Computerized Surveillance for Adverse Drug Events in a Pediatric Hospital

There are limited data on adverse drug event rates in pediatrics. The authors describe the implementation and evaluation of an automated surveillance system modified to detect adverse drug events (ADEs) in pediatric patients. The authors constructed an automated surveillance system to screen admissions to a large pediatric hospital. Potential ADEs identified by the system were reviewed by medication safety pharmacists and a physician and scored for causality and severity. Over the 6 month study period, 6,889 study children were admitted to the hospital for a total of 40,250 patient-days. The ADE surveillance system generated 1226 alerts, which yielded 160 true ADEs. This represents a rate of 2.3 ADEs per 100 admissions or 4 per 1,000 patient-days. Medications most frequently implicated were diuretics, antibiotics, immunosuppressants, narcotics, and anticonvulsants. The composite positive predictive value of the ADE surveillance system was 13%. Automated surveillance can be an effective method for detecting ADEs in hospitalized children.

A randomized trial of real-time automated clinical deterioration alerts sent to a rapid response team

BACKGROUND Episodes of patient deterioration on hospital units are expected to increasingly contribute to morbidity and healthcare costs. OBJECTIVE To determine if real-time alerts sent to the rapid response team (RRT) improved patient care. DESIGN Randomized, controlled trial. SETTING Eight medicine units (Barnes-Jewish Hospital). PATIENTS Five hundred seventy-one patients. INTERVENTION Real-time alerts generated by a validated deterioration algorithm were sent real-time to the RRT (intervention) or hidden (control). MEASUREMENTS Intensive care unit (ICU) transfer, hospital mortality, hospital duration. RESULTS ICU transfer (17.8% vs 18.2%; odds ratio: 0.972; 95% confidence interval [CI]: 0.635-1.490) and hospital mortality (7.3% vs 7.7%; odds ratio: 0.947; 95% CI: 0.509-1.764) were similar for the intervention and control groups. The number of patients requiring transfer to a nursing home or long-term acute care hospital was similar for patients in the intervention and control groups (26.9% vs 26.3%; odds ratio: 1.032; 95% CI: 0.712-1.495). Hospital duration (8.4 ± 9.5 days vs 9.4 ± 11.1 days; P = 0.038) was statistically shorter for the intervention group. The number of RRT calls initiated by the primary care team was similar for the intervention and control groups (19.9% vs 16.5%; odds ratio: 1.260; 95% CI: 0.823-1.931). CONCLUSIONS Real-time alerts sent to the RRT did not reduce ICU transfers, hospital mortality, or the need for subsequent long term care. However, hospital length of stay was modestly reduced.

Identifying critically ill patients at risk for inappropriate antibiotic therapy: a pilot study of a point-of-care decision support alert.

Objective:To develop an automated alert aimed at reducing inappropriate antibiotic therapy of serious healthcare-associated infections. Design:Single-center cohort study from November 2011 to November 2012. Setting:Barnes-Jewish Hospital (1,250-bed academic hospital). Patients:A total of 3,616 critically ill patients receiving treatment with antibiotics targeting healthcare-associated infections due to Gram-negative bacteria. Interventions:Upon antibiotic order entry in the ICU for a Gram-negative antibiotic, the antibiotic and microbiologic history for each patient was electronically queried in real time across all 13 BJC HealthCare hospitals. Patients were assigned to the alert group if they had exposure to the same antibiotic class currently being prescribed (cefepime, meropenem, or piperacillin-tazobactam) or had a positive culture isolating a Gram-negative organism with resistance to the prescribed antibiotic in the previous 6 months. Measurements and Main Results:Nine hundred patients (24.2%) generated an alert. Alerted patients were significantly more likely to receive inappropriate antibiotic therapy (7.1% vs 2.9%; p < 0.001). Based on clinical information available in the alert, 34 of 64 of the alerted patients that received inappropriate therapy (53.1%) could have received an alternative &bgr;-lactam antibiotic with in vitro susceptibility to the identified pathogen. Independent predictors (adjusted odds ratio [95% CI]) of inappropriate therapy included alert generation (1.788 [1.167–2.740]; p = 0.008), medical ICU patients (1.528 [1.007–2.319]; p = 0.046), and a pulmonary source of infection (2.063 [1.363–3.122]; p = 0.0001). Patients in the alert group had significantly greater hospital mortality (29.9% vs 23.6%; p < 0.001) and hospital length of stay (median, 13.1 vs 10.7 d; p < 0.001) compared with nonalert patients. Conclusions:Our results suggest that a simple automated alert could identify more than 40% of critically ill patients prescribed inappropriate antibiotic therapy for healthcare-associated infections. These data suggest that an opportunity exists to employ hospital informatics systems to improve the prescription of antibiotic therapy in ICU patients with suspected healthcare-associated infections.

Prevention of inpatient hypoglycemia with a real-time informatics alert.

BACKGROUND Severe hypoglycemia (SH), defined as a blood glucose (BG) <40 mg/dL, is associated with an increased risk of adverse clinical outcomes in inpatients. OBJECTIVE To determine whether a predictive informatics hypoglycemia risk-alert supported by trained nurse responders would reduce the incidence of SH in our hospital. DESIGN A 5-month prospective cohort intervention study. SETTING Acute care medical floors in a tertiary care academic hospital in St. Louis, Missouri. PATIENTS From 655 inpatients on designated medical floors with a BG of <90 mg/dL, 390 were identified as high risk for hypoglycemia by the alert system. MEASUREMENTS The primary outcome was the incidence of SH occurring in high-risk intervention versus high-risk control patients. Secondary outcomes included: number of episodes of SH in all study patients, incidence of BG < 60 mg/dL and severe hyperglycemia with a BG >299 mg/dL, length of stay, transfer to a higher level of care, the frequency that high-risk patients orders were changed in response to the alert-intervention process, and mortality. RESULTS The alert process, when augmented by nurse-physician collaboration, resulted in a significant decrease by 68% in the rate of SH in alerted high-risk patients versus nonalerted high-risk patients (3.1% vs 9.7%, P = 0.012). Rates of hyperglycemia were similar on intervention and control floors at 28% each. There was no difference in mortality, length of stay, or patients requiring transfer to a higher level of care. CONCLUSION A real-time predictive informatics-generated alert, when supported by trained nurse responders, significantly reduced inpatient SH.

A Randomized Trial of Palliative Care Discussions Linked to an Automated Early Warning System Alert

Objective: To determine whether an Early Warning System could identify patients wishing to focus on palliative care measures. Design: Prospective, randomized, pilot study. Setting: Barnes-Jewish Hospital, Saint Louis, MO (January 15, 2015, to December 12, 2015). Patients: A total of 206 patients; 89 intervention (43.2%) and 117 controls (56.8%). Interventions: Palliative care in high-risk patients targeted by an Early Warning System. Measurements and Main Results: Advanced directive documentation was significantly greater prior to discharge in the intervention group (37.1% vs 15.4%; p < 0.001) as were first-time requests for advanced directive documentation (14.6% vs 0.0%; p < 0.001). Documentation of resuscitation status was also greater prior to discharge in the intervention group (36.0% vs 23.1%; p = 0.043). There was no difference in the number of patients requesting a change in resuscitation status between groups (11.2% vs 9.4%; p = 0.666). However, changes in resuscitation status occurred earlier and on the general medicine units for the intervention group compared to the control group. The number of patients transferred to an ICU was significantly lower for intervention patients (12.4% vs 27.4%; p = 0.009). The median (interquartile range) ICU length of stay was significantly less for the intervention group (0 [0–0] vs 0 [0–1] d; p = 0.014). Hospital mortality was similar (12.4% vs 10.3%; p = 0.635). Conclusions: This study suggests that automated Early Warning System alerts can identify patients potentially benefitting from directed palliative care discussions and reduce the number of ICU transfers.

Mortality and Length of Stay Trends Following Implementation of a Rapid Response System and Real-Time Automated Clinical Deterioration Alerts

A study was performed to determine the potential influence of a rapid response system (RRS) employing real-time clinical deterioration alerts (RTCDAs) on patient outcomes involving 8 general medicine units. Introduction of the RRS occurred in 2006 with staged addition of the RTCDAs in 2009. Statistically significant year-to-year decreases in mortality were observed through 2014 (r = −.794; P = .002). Similarly, year-to-year decreases in the number of cardiopulmonary arrests (CPAs; r = −.792; P = .006) and median lengths of stay (r = −.841; P = .001) were observed. There was a statistically significant year-to-year increase in the number of RRS activations for these units (r = .939; P < .001) that was inversely correlated with the occurrence of CPAs (r = −.784; P = .007). In this single-institution retrospective study, introduction of a RRS employing RTCDAs was associated with lower hospital mortality, CPAs, and hospital length of stay.