Early intervention of patients at risk for acute respiratory failure and prolonged mechanical ventilation with a checklist aimed at the prevention of organ failure: protocol for a pragmatic stepped-wedged cluster trial of PROOFCheck.

INTRODUCTION: Acute respiratory failure (ARF) often presents and progresses outside of the intensive care unit. However, recognition and treatment of acute critical illness is often delayed with inconsistent adherence to evidence-based care known to decrease the duration of mechanical ventilation (MV) and complications of critical illness. The goal of this trial is to determine whether the implementation of an electronic medical record-based early alert for progressive respiratory failure coupled with a checklist to promote early compliance to best practice in respiratory failure can improve the outcomes of patients at risk for prolonged respiratory failure and death. METHODS AND ANALYSIS: A pragmatic stepped-wedged cluster clinical trial involving 6 hospitals is planned. The study will include adult hospitalised patients identified as high risk for MV >48 hours or death because they were mechanically ventilated outside of the operating room or they were identified as high risk for ARF on the Accurate Prediction of PROlonged VEntilation (APPROVE) score. Patients with advanced directives limiting intubation will be excluded. The intervention will consist of (1) automated identification and notification of clinician of high-risk patients by APPROVE or by invasive MV and (2) checklist of evidence-based practices in ARF (Prevention of Organ Failure Checklist-PROOFCheck). APPROVE and PROOFCheck will be developed in the pretrial period. Primary outcome is hospital mortality. Secondary outcomes include length of stay, ventilator and organ failure-free days and 6-month and 12-month mortality. Predefined subgroup analysis of patients with limitation of aggressive care after study entry is planned. Generalised estimating equations will be used to compare patients in the intervention phase with the control phase, adjusting for clustering within hospitals and time. ETHICS AND DISSEMINATION: The study was approved by the institutional review boards. Results will be published in peer-reviewed journals and presented at international meetings. TRIAL REGISTRATION NUMBER: NCT02488174.

Point-of-Care Knowledge-Based Resource Needs of Clinicians: A Survey from a Large Academic Medical Center.

OBJECTIVE: To better understand the literature searching preferences of clinical providers we conducted an institution-wide survey assessing the most preferred knowledge searching techniques. MATERIALS AND METHODS: A survey regarding literature searching preferences was sent to 1862 unique clinical providers throughout Mayo Clinic. The survey consisted of 25 items asking respondents to select which clinical scenarios most often prompt literature searches as well as identify their most preferred knowledge resources. RESULTS: A total of 450 completed surveys were returned and analyzed (24% response rate). 48% of respondents perform literature searches for more than half of their patient interactions with 91% of all searches occurring either before or within 3 hours of the patient interaction. When a search is performed 57% of respondents prefer synthesized information sources as compared to only 13% who prefer original research. 82% of knowledge searches are performed on a workstation or office computer while just 10% occur on a mobile device or at home. CONCLUSION: Providers in our survey demonstrate a need to answer clinical questions on a regular basis, especially in the diagnosis and therapy domains. Responses suggest that most of these searches occur using synthesized knowledge sources in the patient care setting within a very short time from the patient interaction.

Information needs for the OR and PACU electronic medical record.

OBJECTIVE: The amount of clinical information that anesthesia providers encounter creates an environment for information overload and medical error. In an effort to create more efficient OR and PACU EMR viewer platforms, we aimed to better understand the intraoperative and post-anesthesia clinical information needs among anesthesia providers. MATERIALS AND METHODS: A web-based survey to evaluate 75 clinical data items was created and distributed to all anesthesia providers at our institution. Participants were asked to rate the importance of each data item in helping them make routine clinical decisions in the OR and PACU settings. RESULTS: There were 107 survey responses with distribution throughout all clinical roles. 84% of the data items fell within the top 2 proportional quarters in the OR setting compared to only 65% in the PACU. Thirty of the 75 items (40%) received an absolutely necessary rating by more than half of the respondents for the OR setting as opposed to only 19 of the 75 items (25%) in the PACU. Only 1 item was rated by more than 20% of respondents as not needed in the OR compared to 20 data items (27%) in the PACU. CONCLUSION: Anesthesia providers demonstrate a larger need for EMR data to help guide clinical decision making in the OR as compared to the PACU. When creating EMR platforms for these settings it is important to understand and include data items providers deem the most clinically useful. Minimizing the less relevant data items helps prevent information overload and reduces the risk for medical error.

Towards prevention of acute syndromes: electronic identification of at-risk patients during hospital admission.

BACKGROUND: Identifying patients at risk for acute respiratory distress syndrome (ARDS) before their admission to intensive care is crucial to prevention and treatment. The objective of this study is to determine the performance of an automated algorithm for identifying selected ARDS predisposing conditions at the time of hospital admission. METHODS: This secondary analysis of a prospective cohort study included 3,005 patients admitted to hospital between January 1 and December 31, 2010. The automated algorithm for five ARDS predisposing conditions (sepsis, pneumonia, aspiration, acute pancreatitis, and shock) was developed through a series of queries applied to institutional electronic medical record databases. The automated algorithm was derived and refined in a derivation cohort of 1,562 patients and subsequently validated in an independent cohort of 1,443 patients. The sensitivity, specificity, and positive and negative predictive values of an automated algorithm to identify ARDS risk factors were compared with another two independent data extraction strategies, including manual data extraction and ICD-9 code search. The reference standard was defined as the agreement between the ICD-9 code, automated and manual data extraction. RESULTS: Compared to the reference standard, the automated algorithm had higher sensitivity than manual data extraction for identifying a case of sepsis (95% vs. 56%), aspiration (63% vs. 42%), acute pancreatitis (100% vs. 70%), pneumonia (93% vs. 62%) and shock (77% vs. 41%) with similar specificity except for sepsis and pneumonia (90% vs. 98% for sepsis and 95% vs. 99% for pneumonia). The PPV for identifying these five acute conditions using the automated algorithm ranged from 65% for pneumonia to 91 % for acute pancreatitis, whereas the NPV for the automated algorithm ranged from 99% to 100%. CONCLUSION: A rule-based electronic data extraction can reliably and accurately identify patients at risk of ARDS at the time of hospital admission.

Retrospective derivation and validation of a search algorithm to identify emergent endotracheal intubations in the intensive care unit.

BACKGROUND: The development and validation of automated electronic medical record (EMR) search strategies are important in identifying emergent endotracheal intubations in the intensive care unit (ICU). OBJECTIVE: To develop and validate an automated search algorithm (strategy) for emergent endotracheal intubation in the critically ill patient. METHODS: The EMR search algorithm was created through sequential steps with keywords applied to an institutional EMR database. The search strategy was derived retrospectively through a secondary analysis of a 450-patient subset from the 2,684 patients admitted to either a medical or surgical ICU from January 1, 2010, through December 31, 2011. This search algorithm was validated against an additional 450 randomly selected patients. Sensitivity, specificity, and negative and positive predictive values of the automated search algorithm were compared with a manual medical record review (the reference standard) for data extraction of emergent endotracheal intubations. RESULTS: In the derivation subset, the automated electronic note search strategy achieved a sensitivity of 74% (95% CI, 69%-79%) and a specificity of 98% (95% CI, 92%-100%). With refinements in the search algorithm, sensitivity increased to 95% (95% CI, 91%-97%) and specificity decreased to 96% (95% CI, 92%-98%) in this subset. After validation of the algorithm through a separate patient subset, the final reported sensitivity and specificity were 95% (95% CI, 86%-99%) and 100% (95% CI, 98%-100%). CONCLUSIONS: Use of electronic search algorithms allows for correct extraction of emergent endotracheal intubations in the ICU, with high degrees of sensitivity and specificity. Such search algorithms are a reliable alternative to manual chart review for identification of emergent endotracheal intubations.

Diagnostic performance of electronic syndromic surveillance systems in acute care: a systematic review.

CONTEXT: Healthcare Electronic Syndromic Surveillance (ESS) is the systematic collection, analysis and interpretation of ongoing clinical data with subsequent dissemination of results, which aid clinical decision-making. OBJECTIVE: To evaluate, classify and analyze the diagnostic performance, strengths and limitations of existing acute care ESS systems. DATA SOURCES: All available to us studies in Ovid MEDLINE, Ovid EMBASE, CINAHL and Scopus databases, from as early as January 1972 through the first week of September 2012. STUDY SELECTION: Prospective and retrospective trials, examining the diagnostic performance of inpatient ESS and providing objective diagnostic data including sensitivity, specificity, positive and negative predictive values. DATA EXTRACTION: Two independent reviewers extracted diagnostic performance data on ESS systems, including clinical area, number of decision points, sensitivity and specificity. Positive and negative likelihood ratios were calculated for each healthcare ESS system. A likelihood matrix summarizing the various ESS systems performance was created. RESULTS: The described search strategy yielded 1639 articles. Of these, 1497 were excluded on abstract information. After full text review, abstraction and arbitration with a third reviewer, 33 studies met inclusion criteria, reporting 102,611 ESS decision points. The yielded I2 was high (98.8%), precluding meta-analysis. Performance was variable, with sensitivities ranging from 21% -100% and specificities ranging from 5%-100%. CONCLUSIONS: There is significant heterogeneity in the diagnostic performance of the available ESS implements in acute care, stemming from the wide spectrum of different clinical entities and ESS systems. Based on the results, we introduce a conceptual framework using a likelihood ratio matrix for evaluation and meaningful application of future, frontline clinical decision support systems.

Acute lung injury prediction score: derivation and validation in a population-based sample.

Early recognition of patients at high risk of acute lung injury (ALI) is critical for successful enrollment of patients in prevention strategies for this devastating syndrome. We aimed to develop and prospectively validate an ALI prediction score in a population-based sample of patients at risk. In a retrospective derivation cohort, predisposing conditions for ALI were identified at the time of hospital admission. The score was calculated based on the results of logistic regression analysis. Prospective validation was performed in an independent cohort of patients at risk identified at the time of hospital admission. In a derivation cohort of 409 patients with ALI risk factors, the lung injury prediction score discriminated patients who developed ALI from those who did not with an area under the curve (AUC) of 0.84 (95% CI 0.80-0.89; Hosmer-Lemeshow p = 0.60). The performance was similar in a prospective validation cohort of 463 patients at risk of ALI (AUC 0.84, 95% CI 0.77-0.91; Hosmer-Lemeshow p = 0.88). ALI prediction scores identify patients at high risk for ALI before intensive care unit admission. If externally validated, this model will serve to define the population of patients at high risk for ALI in whom future mechanistic studies and ALI prevention trials will be conducted.

Novel Representation of Clinical Information in the ICU: Developing User Interfaces which Reduce Information Overload.

The introduction of electronic medical records (EMR) and computerized physician order entry (CPOE) into the intensive care unit (ICU) is transforming the way health care providers currently work. The challenge facing developers of EMR's is to create products which add value to systems of health care delivery. As EMR's become more prevalent, the potential impact they have on the quality and safety, both negative and positive, will be amplified. In this paper we outline the key barriers to effective use of EMR and describe the methodology, using a worked example of the output. AWARE (Ambient Warning and Response Evaluation), is a physician led, electronic-environment enhancement program in an academic, tertiary care institution's ICU. The development process is focused on reducing information overload, improving efficiency and eliminating medical error in the ICU.

Early identification of patients with or at risk of acute lung injury.

Acute lung injury (ALI ) and its more severe form, acute respiratory distress syndrome (ARDS ), are important critical care syndromes for which the treatment options are limited once the condition is fully established. Enormous basic and clinical research efforts have led to improvements in supportive treatment, but surprisingly little has been done on the prevention of this devastating syndrome. The development and progression of ALI /ARDS may be triggered by various intrahospital exposures including but not limited to transfusion, aspiration, mechanical ventilation, certain medications and delayed treatment of shock and infection. Early recognition of patients with or at risk of ALI /ARDS is essential for designing novel prevention and treatment strategies. Automated electronic screening tools and novel scoring systems applied at the time of hospital admission may facilitate enrollment of patients into mechanistic and outcome studies, as well as future ALI /ARDS prevention trials.

Designing and testing computer based screening engine for severe sepsis/septic shock.

This study addresses the role of a sepsis "sniffer", an automatic screening tool for the timely identification of patients with severe sepsis/septic shock, based electronic medical records. During the two months prospective implementation in a medical intensive care unit, 37 of 320 consecutive patients developed severe sepsis/septic shock. The sniffer demonstrated a sensitivity of 48% and specificity of 86%, and positive predictive value 32%. Further improvements are needed prior to the implementation of sepsis sniffer in clinical practice and research.

Stay tuned. Comparison of journal updates tracking using RSS and e-mail technologies.

Electronic subscription alerts provide new possibilities for health care providers to stay abreast with current literature and practice evidence-based medicine. During a 5 month prospective observation we compared the performance of the three common subscription methods: email and Really Simple Syndication (RSS) from the publisher and RSS from PubMed. The 3 methods were reliably updated without interruption in service but demonstrated significant variability in the contents and timing.

Rule base system for identification of patients with specific critical care syndromes: The "sniffer" for acute lung injury.

Early detection of specific critical care syndromes, such as sepsis or acute lung injury (ALI)is essential for timely implementation of evidence based therapies. Using a near-real time copy of the electronic medical records ("ICU data mart") we developed and validated custom electronic alert (ALI"sniffer") in a cohort of 485 critically ill medical patients. Compared with the gold standard of prospective screening, ALI "sniffer" demonstrated good sensitivity, 93% (95% CI 90 to 95) and specificity, 90% (95% CI 87 to 92). It is not known if the bedside implementation of ALI "sniffer" will improve the adherence to evidence-based therapies and outcome of patients with ALI.