Vitaly Herasevich

Clinical Spectrum, Frequency, and Significance of Myocardial Dysfunction in Severe Sepsis and Septic Shock

OBJECTIVE To determine the frequency and spectrum of myocardial dysfunction in patients with severe sepsis and septic shock using transthoracic echocardiography and to evaluate the impact of the myocardial dysfunction types on mortality. PATIENTS AND METHODS A prospective study of 106 patients with severe sepsis or septic shock was conducted from August 1, 2007, to January 31, 2009. All patients underwent transthoracic echocardiography within 24 hours of admission to the intensive care unit. Myocardial dysfunction was classified as left ventricular (LV) diastolic, LV systolic, and right ventricular (RV) dysfunction. Frequency of myocardial dysfunction was calculated, and demographic, hemodynamic, and physiologic variables and mortality were compared between the myocardial dysfunction types and patients without cardiac dysfunction. RESULTS The frequency of myocardial dysfunction in patients with severe sepsis or septic shock was 64% (n=68). Left ventricular diastolic dysfunction was present in 39 patients (37%), LV systolic dysfunction in 29 (27%), and RV dysfunction in 33 (31%). There was significant overlap. The 30-day and 1-year mortality rates were 36% and 57%, respectively. There was no difference in mortality between patients with normal myocardial function and those with left, right, or any ventricular dysfunction. CONCLUSION Myocardial dysfunction is frequent in patients with severe sepsis or septic shock and has a wide spectrum including LV diastolic, LV systolic, and RV dysfunction types. Although evaluation for the presence and type of myocardial dysfunction is important for tailoring specific therapy, its presence in patients with severe sepsis and septic shock was not associated with increased 30-day or 1-year mortality.

The effect of two different electronic health record user interfaces on intensive care provider task load, errors of cognition, and performance*

Objectives:The care of critically ill patients generates large quantities of data. Increasingly, these data are presented to the provider within an electronic medical record. The manner in which data are organized and presented can impact on the ability of users to synthesis that data into meaningful information. The objective of this study was to test the hypothesis that novel user interfaces, which prioritize the display of high-value data to providers within system-based packages, reduce task load, and result in fewer errors of cognition compared with established user interfaces that do not. Design:Randomized crossover study. Setting:Academic tertiary referral center. Subjects:Attending, resident and fellow critical care physicians. Interventions:Novel health care record user interface. Measurement:Subjects randomly assigned to either a standard electronic medical record or a novel user interface, were asked to perform a structured task. The task required the subjects to use the assigned electronic environment to review the medical record of an intensive care unit patient said to be actively bleeding for data that formed the basis of answers to clinical questions posed in the form of a structured questionnaire. The primary outcome was task load, measured using the paper version of the NASA-task load index. Secondary outcome measures included time to task completion, number of errors of cognition measured by comparison of subject to post hoc gold standard questionnaire responses, and the quantity of information presented to subjects by each environment. Main Results:Twenty subjects completed the task on eight patients, resulting in 160 patient–provider encounters (80 in each group). The standard electronic medical record contained a much larger data volume with a median (interquartile range) number of data points per patient of 1008 (895–1183) compared with 102 (77–112) contained within the novel user interface. The median (interquartile range) NASA-task load index values were 38.8 (32–45) and 58 (45–65) for the novel user interface compared with the standard electronic medical record (p < .001). The median (interquartile range) times in seconds taken to complete the task for four consecutive patients were 93 (57–132), 60 (48–71), 68 (48–80), and 54 (42–64) for the novel user interface compared with 145 (109–201), 125 (113–162), 129 (100–145), and 112 (92–123) for the standard interface (p < .0001), respectively. The median (interquartile range) number of errors per provider was 0.5 (0–1) and two (0.25–3) for the novel user interface and standard electronic medical record interface, respectively (p = .007). Conclusions:A novel user interface was designed based on the information needs of intensive care unit providers with a specific goal of development being the reduction of task load and errors of cognition associated with filtering, extracting, and using medical data contained within a comprehensive electronic medical record. The results of this simulated clinical experiment suggest that the configuration of the intensive care unit user interface contributes significantly to the task load, time to task completion, and number of errors of cognition associated with the identification, and subsequent use, of relevant patient data. Task-specific user interfaces, developed from an understanding of provider information requirements, offer advantages over interfaces currently available within a standard electronic medical record.

Derivation and Validation of Automated Electronic Search Strategies to Extract Charlson Comorbidities From Electronic Medical Records

OBJECTIVE To develop and validate automated electronic note search strategies (automated digital algorithm) to identify Charlson comorbidities. PATIENTS AND METHODS The automated digital algorithm was built by a series of programmatic queries applied to an institutional electronic medical record database. The automated digital algorithm was derived from secondary analysis of an observational cohort study of 1447 patients admitted to the intensive care unit from January 1 through December 31, 2006, and validated in an independent cohort of 240 patients. The sensitivity, specificity, and positive and negative predictive values of the automated digital algorithm and International Classification of Diseases, Ninth Revision (ICD-9) codes were compared with comprehensive medical record review (reference standard) for the Charlson comorbidities. RESULTS In the derivation cohort, the automated digital algorithm achieved a median sensitivity of 100% (range, 99%-100%) and a median specificity of 99.7% (range, 99%-100%). In the validation cohort, the sensitivity of the automated digital algorithm ranged from 91% to 100%, and the specificity ranged from 98% to 100%. The sensitivity of the ICD-9 codes ranged from 8% for dementia to 100% for leukemia, whereas specificity ranged from 86% for congestive heart failure to 100% for leukemia, dementia, and AIDS. CONCLUSION Our results suggest that search strategies that use automated electronic search strategies to extract Charlson comorbidities from the clinical notes contained within the electronic medical record are feasible and reliable. Automated digital algorithm outperformed ICD-9 codes in all the Charlson variables except leukemia, with greater sensitivity, specificity, and positive and negative predictive values.

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 enrolment 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.

Derivation and Validation of Automated Electronic Search Strategies to Identify Pertinent Risk Factors for Postoperative Acute Lung Injury

OBJECTIVE To develop and validate time-efficient automated electronic search strategies for identifying preoperative risk factors for postoperative acute lung injury. PATIENTS AND METHODS This secondary analysis of a prospective cohort study included 249 patients undergoing high-risk surgery between November 1, 2005, and August 31, 2006. Two independent data-extraction strategies were compared. The first strategy used a manual review of medical records and the second a Web-based query-building tool. Web-based searches were derived and refined in a derivation cohort of 83 patients and subsequently validated in an independent cohort of 166 patients. Agreement between the 2 search strategies was assessed with percent agreement and Cohen κ statistics. RESULTS Cohen κ statistics ranged from 0.34 (95% confidence interval, 0.00-0.86) for amiodarone to 0.85 for cirrhosis (95% confidence interval, 0.57-1.00). Agreement between manual and automated electronic data extraction was almost complete for 3 variables (diabetes mellitus, cirrhosis, H(2)-receptor antagonists), substantial for 3 (chronic obstructive pulmonary disease, proton pump inhibitors, statins), moderate for gastroesophageal reflux disease, and fair for 2 variables (restrictive lung disease and amiodarone). Automated electronic queries outperformed manual data collection in terms of sensitivities (median, 100% [range, 77%-100%] vs median, 87% [range, 0%-100%]). The specificities were uniformly high (≥ 96%) for both search strategies. CONCLUSION Automated electronic query building is an iterative process that ultimately results in accurate, highly efficient data extraction. These strategies may be useful for both clinicians and researchers when determining the risk of time-sensitive conditions such as postoperative acute lung injury.

Limiting ventilator-induced lung injury through individual electronic medical record surveillance

Background:To improve the safety of ventilator care and decrease the risk of ventilator-induced lung injury, we designed and tested an electronic algorithm that incorporates patient characteristics and ventilator settings, allowing near-real-time notification of bedside providers about potentially injurious ventilator settings. Methods:Electronic medical records of consecutive patients who received invasive ventilation were screened in three Mayo Clinic Rochester intensive care units. The computer system alerted bedside providers via the text paging notification about potentially injurious ventilator settings. Alert criteria included a Pao2/Fio2 ratio of <300 mm Hg, free text search for the words “edema” or “bilateral + infiltrates” on the chest radiograph report, a tidal volume of >8 mL/kg predicted body weight (based on patient gender and height), a plateau pressure of >30 cm H2O, and a peak airway pressure of >35 cm H2O. Respiratory therapists answered a brief online satisfaction survey. Ventilator-induced lung injury risk was compared before and after the introduction of ventilator-induced lung injury alert. Findings:The prevalence of acute lung injury was 42% (n = 490) among 1,159 patients receiving >24 hrs of invasive ventilation. The system sent 111 alerts for 80 patients, with a positive predictive value of 59%. The exposure to potentially injurious ventilation decreased after the intervention from 40.6 ± 74.6 hrs to 26.9 ± 77.3 hrs (p = .004). Interpretations:Electronic medical record surveillance of mechanically ventilated patients accurately detects potentially injurious ventilator settings and is able to influence bedside practice at moderate costs. Its implementation is associated with decreased patient exposure to potentially injurious mechanical ventilation settings.

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 EMRs is to create products which add value to systems of health care delivery. As EMRs 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 institutions ICU. The development process is focused on reducing information overload, improving efficiency and eliminating medical error in the ICU.

Timing of the Onset of Acute Respiratory Distress Syndrome: A Population-Based Study

BACKGROUND: Many patients with acute lung injury (ALI) and acute respiratory distress syndrome (ARDS) have had recent healthcare interventions prior to developing ALI/ARDS. OBJECTIVE: To determine the timing of ALI/ARDS onset in relation to hospital admission and other healthcare interventions. METHODS: We conducted a population-based observational cohort study with a validated electronic surveillance tool, and identified patients with possible ALI/ARDS among critically ill adults at Mayo Clinic hospitals that provide critical care services for Olmsted County, Minnesota, in 2006. Trained investigators independently reviewed electronic medical records and confirmed the presence and timing of ALI/ARDS based on the American-European consensus definition. RESULTS: Of 124 episodes of ALI in 118 patients, only 5 did not fulfill the ARDS criteria. The syndrome developed a median 30 hours (IQR 10–82 h) after hospital admission in 79 patients (67%). ARDS was present on admission in 39 patients (33%), of whom 14 had recent hospitalization, 6 were transferred from nursing homes, and 3 had recent out-patient contact (1 antibiotic prescription, 1 surgical intervention, and 1 chemotherapy). Only 16 ARDS patients (14%) did not have known recent contact with a healthcare system. Compared to ARDS on admission, hospital-acquired ARDS was more likely to occur in surgery patients (54% vs 15%, P < .001), and had longer adjusted hospital stay (mean difference 8.9 d, 95% CI 0.3–17.4, P = .04). CONCLUSIONS: ARDS in the community most often develops either during hospitalization or in patients who recently had contact with a healthcare system. These findings have important implications for potential preventive strategies.

Epidemiology of critical care syndromes, organ failures, and life-support interventions in a suburban US community

BACKGROUND ICU services represent a significant and increasing proportion of medical care. Population-based epidemiologic studies are essential to inform physicians and policymakers about current and future ICU demands. We aimed to determine the incidence of critical care syndromes, organ failures, and life-support interventions in a defined US suburban community with unrestricted access to critical care services. METHODS This population-based observational cohort from January 1 to December 31, 2006, in Olmsted County, Minnesota, included all consecutive critically ill adult residents admitted to the ICU. Main outcomes were incidence of critical care syndromes, life-support interventions, and organ failures as defined by standard criteria. Incidences are reported per 100,000 population (95% CIs) and were age adjusted to the 2006 US population. RESULTS A total of 1,707 ICU admissions were identified from 1,461 patients. Incidences of critical care syndromes were respiratory failure, 430 (390-470); acute kidney injury, 290 (257-323); severe sepsis, 286 (253-319); all-cause shock, 194 (167-221); acute lung injury, 86 (68-105); all-cause coma, 43 (30-55); and overt disseminated intravascular coagulation, 18 (10-26). Incidence of mechanical ventilation was invasive, 310 (276-344); noninvasive, 180 (154-206); vasopressors and inotropes, 183(155-208). Renal replacement therapy incidence was 96 (77-116). Of the cohort, 1,330 patients (91%) survived to hospital discharge. Short- and long-term survival decreased by the number of failing organs. CONCLUSIONS In a suburban US community with high access to critical care services, cumulative incidences of critical care syndromes and life-support interventions were higher than previously reported. The results of this study have important implications for future planning of critical care delivery.

Data Utilization for Medical Decision Making at the Time of Patient Admission to Icu

Objectives:Information overload in electronic medical records can impede providers’ ability to identify important clinical data and may contribute to medical error. An understanding of the information requirements of ICU providers will facilitate the development of information systems that prioritize the presentation of high-value data and reduce information overload. Our objective was to determine the clinical information needs of ICU physicians, compared to the data available within an electronic medical record. Design:Prospective observational study and retrospective chart review. Setting:Three ICUs (surgical, medical, and mixed) at an academic referral center. Subjects:Newly admitted ICU patients and physicians (residents, fellows, and attending staff). Measurements and Main Results:The clinical information used by physicians during the initial diagnosis and treatment of admitted patients was captured using a questionnaire. Clinical information concepts were ranked according to the frequency of reported use (primary outcome) and were compared to information availability in the electronic medical record (secondary outcome). Nine hundred twenty-five of 1,277 study questionnaires (408 patients) were completed. Fifty-one clinical information concepts were identified as being useful during ICU admission. A median (interquartile range) of 11 concepts (6–16) was used by physicians per patient admission encounter with four used greater than 50% of the time. Over 25% of the clinical data available in the electronic medical record was never used, and only 33% was used greater than 50% of the time by admitting physicians. Conclusions:Physicians use a limited number of clinical information concepts at the time of patient admission to the ICU. The electronic medical record contains an abundance of unused data. Better electronic data management strategies are needed, including the priority display of frequently used clinical concepts within the electronic medical record, to improve the efficiency of ICU care.