Critical care issues following orthotopic liver transplantation.

Orthotopic liver transplantation (OLT) remains a formidable undertaking. A multidisciplinary approach to pre-operative optimization and intra- and postoperative care of patients undergoing OLT increases the chance of a successful outcome. Although there have been moves towards avoidance of Intensive Care Unit (ICU) admission for "routine" OLT recipients, critical care practitioners continue to play a key role in liver transplant programs in the MELD era. Use of protocolized care delivery and innovative ICU therapeutic interventions have streamlined the pre-operative optimization and perioperative care of OLT recipients. The postoperative course is significantly influenced by the patient's pre-operative status, the intraoperative course and the function of the liver graft. In addition to discussion of general ICU concepts such as the use of prognostic scoring systems and protocolization of care, this review will use an organ-system based approach to describe the postoperative ICU care of OLT recipients. We discuss hemodynamic management, ventilator weaning, optimization of sedation and analgesia, and the investigation and management of renal and metabolic abnormalities. In addition, we examine postoperative complications including hemorrhage, central nervous system pathology and graft dysfunction. The review concludes with a discussion of the additional challenges practitioners face when dealing with living donor liver transplantation and donation after cardiac death.

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.

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.

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.

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.