In children’s hospitals, most cardiopulmonary resuscitation (CPR) events occur in PICUs (1). Many children who require CPR die during or shortly after the event, and those who survive are often left with new disabilities (2–6). Identifying the children most likely to require CPR is important because evidence-based interventions currently exist to improve their outcomes. Examples include “just in time” CPR training, team-based simulation, and bundles for tracheal intubation (7–14).
Our conceptual framework for identifying and improving outcomes for the PICU patients most likely to require CPR is based on the principles of risk mitigation through situational awareness (15). Situational awareness is the ability to monitor and recognize cues that increase the awareness of what is happening around you, integrate information to develop a comprehensive picture of the current state, and extrapolate forward to determine if the knowledge obtained will adversely influence the situation immediately or in the near future (16,17). The improvement of situational awareness within the PICU and development of mitigation plans to prevent CPR events is predicated on the accurate early identification of high-risk patients.
The baseline method used to identify PICU patients most likely to require CPR at the study hospitals was based on clinician intuition alone; patients that were of concern to attending physicians were identified followed by discussion of these patients bid at situational awareness huddles. This approach had a poor positive predictive value (PPV) and a problematic number needed to screen (NNS) of 37–45 patients for each clinical deterioration event, defined as a cardiac arrest or a code bell activation with response of the unit code team. NNS is analogous to number needed to treat. This number signifies the number of patients labeled as high risk (potentially leading providers to institute mitigation plans or receive just in time training) for each patient who experienced a clinical deterioration event. As a first step to improve the identification of high-risk PICU patients, we developed a paper checklist of factors with excellent sensitivity and specificity and a NNS of six patients for each clinical deterioration event (18). Despite the accuracy of the paper checklist, we found that a major barrier to implementation was the time needed to manually screen the criteria for each patient.
Due to this implementation barrier, we shifted our focus to adapting the paper checklist into an automated electronic health record (EHR)-based clinical decision support tool, the PICU Warning Tool. Our aim was to examine the retrospective test characteristics of the PICU Warning Tool with a pragmatic approach to the tradeoffs between predictive accuracy and the effort required to implement.
MATERIALS AND METHODS
We performed a retrospective observational cohort study to evaluate the performance of a replicated version of the PICU Warning Tool to predict clinical deterioration events in PICU patients within a 24-hour window following a positive high-risk screen. The Institutional Review Boards of Cincinnati Children’s Hospital Medical Center (CCHMC) and Children’s Hospital of Philadelphia (CHOP) approved this study.
The study PICUs are tertiary care pediatric medical/surgical ICUs with 55 and 35 beds respectively and combined yearly admissions of over 6,000 patients/yr with a baseline clinical deterioration rate of 2–3% of all PICU patients. The cardiac surgical ICU and neonatal ICU were excluded.
Selection of the Cohort
The study included all patients admitted to the PICU at CHOP and CCHMC from July 1, 2014, to June 30, 2015, the year prior to the initiation of any focused situational awareness work at either institution. No patients were excluded.
The primary exposure of interest was determination of high-risk status during PICU admission via the PICU Warning Tool. The primary outcome of interest was clinical deterioration event within 24 hours of a positive screen. The date and time of the deterioration event was used as the index time point. Only the initial deterioration event was included for each patient as the goal of the PICU Warning Tool was to establish initial situational awareness for high-risk patients. This also accounted for multiple sampling.
Identification of High-Risk Patients
The primary exposure of interest was high-risk patient status as determined by the automated PICU Warning Tool. The original paper-based checklist, on which the PICU Warning Tool is based, included 15 equally weighted single parameter high-risk patient factors that predicted clinical deterioration events (18). We began the process with a review of the paper checklist to determine if the items were computable and translatable into an automated clinical decision support tool. Some criteria were excluded because the data were not reliably recorded as discrete elements, or were not documented at all, such as “provider intuition.” Although documentation fields could be added to the EHR to capture these missing elements, we chose to build a system without requiring any additional clinical documentation in order to meet the clinical decision support best practice of integration into existing workflows (19). Due to these limitations, we excluded five of the original criteria and modified others (Table 1). We added a criterion for severe cardiac dysfunction in an attempt to capture an additional high-risk population that was lost due to the excluded criteria; we have further described this process and its adherence to clinical decision support best practices in a prior manuscript (20).
We replicated the predictions of the real-time PICU Warning Tool by retrospectively querying the institutional data warehouse to identify all patients that would have flagged as high risk by the PICU Warning Tool, as well as the specific criteria that caused the patient to be identified as high risk. The EHR was screened hourly for patients meeting high-risk criteria per the established definitions. Patients who met any single criteria were considered high-risk patients.
Identification of Patients Who Experienced Clinical Deterioration
The outcome of interest was clinical deterioration event within 24 hours of a positive high-risk screen by the PICU Warning Tool. Clinical deterioration was defined as a cardiac arrest or a code bell activation with response of the unit code team. These events were identified through multiple means including the use of a quality improvement database in place at the time of the study that tracked CPR and respiratory emergency events (CHOP), an electronic log of code button presses with manual chart review (CCHMC), and a review of all code sheets documented during that time period (CHOP, CCHMC).
We evaluated the sensitivity, specificity, PPV, and negative predictive value of the overall performance of the PICU Warning Tool to retrospectively identify patients who would experience clinical deterioration within 24 hours of a positive screen. A second sensitivity analysis evaluated the test characteristics of the PICU Warning Tool to predict a clinical deterioration event at any point during a patient’s PICU admission following a positive high-risk flag. Secondary analysis included standard diagnostic test characteristics of individual components of the EHR-based PICU Warning Tool including likelihood ratios (LRs), sensitivity, specificity, PPV, and negative predictive value of each component within the combined data set from both institutions and evaluated separately. Categorical variables between institutions were compared using the chi-square test or Fisher exact test.
A total of 6,233 patients were evaluated between the two centers. There was a total of 233 clinical deterioration events experienced by 154 individual patients (2.5%). At CCHMC, 3.7% of patients (83/2,241 total patients) experienced a clinical deterioration event and 21.6% (485/2,241 total patients) met high-risk criteria during their PICU stay. At CHOP, 1.8% of patients (71/3,992 total patients) experienced clinical deterioration and 9.6% (385/3,992 total patients) met high-risk criteria during their PICU stay (Fig. 1). Both the number of clinical deterioration events and the percent of patients meeting high-risk criteria were significantly different at the two institutions (p < 0.0001 for both).
The percent of patients meeting individual high-risk criteria was also different between centers for three of the criteria—pulmonary hypertension, high mean airway pressure, and profound acidosis (Table 2). The highest frequency criteria was vasoactive shock at both centers followed by profound acidosis and high mean airway pressure.
The overall PPV of the EHR-based tool was 7.1% (95% CI, 5.9–8.6%) with a NNS of 14 patients (95% CI, 12–17) for each index clinical deterioration event. The tool performed with a PPV of 8.0% and a NNS of 13 at CCHMC as compared with CHOP with a PPV of 6.0% and a NNS of 17. All of these test characteristics were inferior to the original paper-based tool with a PPV of 17.3% and an NNS of 6 (Table 3).
The predictive value of the individual criteria varied. The most predictive of the individual criteria was elevated lactic acidosis with a positive LR of 6 (95% CI, 2.6–13.9), high mean airway pressure had a positive LR of 4.8 (95% CI, 3.3–7.0), and profound acidosis had a positive LR of 3.6 (95% CI, 2.4–5.5) (Fig. 2). The least predictive were extracorporeal membrane oxygenation and hyperkalemia which did not accurately predict any clinical deterioration events followed by intracranial hypertension with a positive LR of 1 (95% CI, 0.2–2.9). Pulmonary hypertension only predicted clinical deterioration at CHOP and renal replacement therapy only predicted clinical deterioration events at CCHMC. Complete test characteristics of the individual criteria are presented in Supplemental Table 1 (Supplemental Digital Content 1, http://links.lww.com/PCC/B100).
To predict clinical deterioration at any point following a positive screen during the patient’s PICU admission, the PICU Warning Tool had improved test characteristics with a sensitivity of 60.4% (95% CI, 52.2–68.2%), specificity of 87.2% (95% CI, 86.3–88.0%), PPV 10.7% (9.4–12.1%), negative predictive value of 98.9% (95% CI, 98.6–99.1%), and a NNS of 10 (95% CI, 9–11).
Using data from two institutions, we evaluated the performance of an automated clinical decision support tool, the PICU Warning Tool, derived from a previously published single-center paper checklist. The key findings of this study were that, with adaptation from paper to automated to ease screening, the sensitivity decreased from 100% to 40% to predict clinical deterioration within 24 hours of a positive high-risk screen, and the NNS increased from 6 to 14. As a practical example, using the PICU Warning Tool rather than the paper checklist, a 30-bed PICU with yearly admissions of 2,000 patients would have on average five high-risk patients per week requiring mitigation planning and just in time training. However, this would only train the teams of 40% of patients who would experience clinical deterioration within 24 hours and 60% of patients who would experience clinical deterioration during their PICU admission. Using the paper checklist, this same PICU would have on average one high-risk patient per week requiring just in time training and would train 100% of the teams caring for patients who experience clinical deterioration. Although this example may overestimate the performance of the paper checklist in a real-life setting, as it was only studied over a 3-month period at a single center, it does raise important concerns about the often unstudied conversion from a paper-based clinical decision support tool to an automated one.
To summarize, in adapting the tool from paper to electronic, we eliminated the workload burden of manual screening but markedly reduced the tool’s sensitivity and more than doubled the NNS. Depending on the recommended action associated with screening positive, this could increase providers’ workload burden of developing mitigation plans or delivering just in time training, limiting its feasibility and acceptability. More standard situational awareness improvements around structured communication tools (21), standardized handoffs (22), and huddles (16,23) are likely feasible for most centers with a NNS of 14 patients for each clinical deterioration event. However, more intensive training centered around individual at-risk patients (5,8,24) and team-based simulations (12–14) may not be feasible at this NNS. It is important to note that although we have focused on individual level patient identification and mitigation, prior work demonstrates that with improved situational awareness and a proactive approach to identify high-risk patients, improvements can be seen in prevention of high-risk events at a systems level (23,25–27).
This difference in test characteristics identified here demonstrates the importance of evaluating the translation from paper to automated clinical decision support tools. Optimizing the human-automation interaction may lead to improved outcomes in this context. Prior literature demonstrates that automation does not replace the human activity but changes it in planned and unplanned ways (28). To the extent to which the clinical decision support automation provides the right information at the right time, it can decrease the team workload and improve the team situational awareness (29). Although the performance of the automated tool may be inferior to that of the paper tool, our use of clinical decision support best practices (19,30,31) and our implementation plan for effective automation design (32), may lead to improved patient outcomes despite the higher NNS.
We speculate that the decrease in sensitivity and increase in NNS is likely related to the removal of components that are known to be predictive including high-risk intubations (33) and prior clinical deterioration events (34). In addition, provider intuition (i.e., identification of high-risk patients by physician judgment) has been shown to be more predictive than early warning scores alone when completed by attendings (35). As the original evaluation of the paper checklist did not include evaluation of the individual criteria, it was not possible for us to know a priori the impact of removing these elements. It is reasonable to study the addition of these criteria to the future prospective implementation of this tool to see if we are able to improve the sensitivity of the PICU Warning Tool using a combined automated screening component and a manual entry component from those criteria identified during huddle.
This work has several limitations. First, the retrospective evaluation of this automated clinical decision support tool was completed at only two centers, and the frequency of deterioration events at these centers was low. External validation in a larger number of pediatric centers is important prior to widespread adoption. Second, these data were collected retrospectively. Although designed to mimic the real-time queries of the prospective tool, there may have been missed indications due to documentation errors such as incorrect documentation of a patient’s mean airway pressure or diagnosis in the problem list. Last, we only evaluate the first clinical deterioration event of each patient. It is possible the predictability of the tool changes with subsequent events.
We will next implement and study the PICU Warning Tool as a prospective high-risk patient identifier on its impact on clinical deterioration rate. To improve the performance of the tool, we plan to add the missing criteria through manual identification to investigate if the sensitivity of the PICU Warning Tool increases using combined automated screening and manual entry of criteria identified bid during safety huddle. As we decrease the prevalence of clinical deterioration events, the PPV will fall and the NNS of the tool will rise. It will therefore be imperative that we track additional outcomes including situational awareness among team members, use of mitigation plans, and quality of resuscitation response in addition to predictive ability.
An automated clinical decision support translation of a paper checklist for PICU patients at risk for clinical deterioration demonstrates reduced accuracy in prediction with a NNS of 14 rather than 6 despite improved feasibility. We speculate that improved feasibility of identification of high-risk patients using automated EHR warning tools may allow for risk mitigation and just in time preparation and that test characteristics may improve with incorporation of more provider and procedural concerns.
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