An elevated shock index (SI) predicts worse outcomes in multiple clinical arenas. We aimed to determine whether the SI can aid in mortality risk stratification in unselected cardiac intensive care ...unit patients.
We included admissions to the Mayo Clinic from 2007 to 2015 and stratified them based on admission SI. The primary outcome was in-hospital mortality, and predictors of in-hospital mortality were analyzed using multivariable logistic regression.
We included 9,939 unique cardiac intensive care unit patients with available data for SI. Patients were grouped by SI as follows: < 0.6, 3,973 (40%); 0.6-0.99, 4,810 (48%); and ≥ 1.0, 1,156 (12%). After multivariable adjustment, both heart rate (adjusted OR 1.06 per 10 beats per minute higher; CI 1.02-1.10; p-value 0.005) and systolic blood pressure (adjusted OR 0.94 per 10 mmHg higher; CI 0.90-0.97; p-value < 0.001) remained associated with higher in-hospital mortality. As SI increased there was an incremental increase in in-hospital mortality (adjusted OR 1.07 per 0.1 beats per minute/mmHg higher, CI 1.04-1.10, p-Value < 0.001). A higher SI was associated with increased mortality across all examined admission diagnoses.
The SI is a simple and universally available bedside marker that can be used at the time of admission to predict in-hospital mortality in cardiac intensive care unit patients.
...we concur with utilizing the technology to “augment” our clinical decision making, cognitive training and education in the world of critical care medicine, instead of having “artificial” models ...that are inaccurate at best with limited clinical utility in real life. Division of Critical Care, Department of Anesthesiology and Perioperative Medicine, Multidisciplinary Epidemiology and Translational Research in Intensive Care Group, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA Vitaly Herasevich Authors 1. Rights and permissions Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
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.
BACKGROUND Pathogenic causes of acute hypoxemic respiratory failure (AHRF) can be difficult to identify at early clinical presentation. We evaluated the diagnostic utility of combined cardiac and ...thoracic critical care ultrasonography (CCUS). METHODS Adult patients in the ICU were prospectively enrolled from January through September 2010 with a Pao2 /FIO2 ratio < 300 on arterial blood gas (ABG) analysis within 6 h of a new hypoxemic event or the ICU admission. Focused cardiac and thoracic CCUS was conducted within 6 h of ABG testing. Causes of AHRF were categorized into cardiogenic pulmonary edema (CPE), ARDS, and miscellaneous causes after reviewing the hospitalization course in electronic medical records. RESULTS One hundred thirty-four patients were enrolled (median Pao2 /FIO2 ratio, 191; interquartile range, 122-253). Fifty-nine patients (44%) received a diagnosis of CPE; 42 (31%), ARDS; and 33 (25%), miscellaneous cause. Analysis of CCUS findings showed that a low B-line ratio (proportion of chest zones with positive B-lines relative to all zones examined) was predictive of miscellaneous cause vs CPE or ARDS (receiver operating characteristic area under the curve AUC, 0.82; 95% CI, 0.75-0.88). For further differentiation of CPE from ARDS, left-sided pleural effusion (> 20 mm), moderately or severely decreased left ventricular function, and a large inferior vena cava minimal diameter (> 23 mm) were predictive of CPE (AUC, 0.79; 95% CI, 0.70-0.87). CONCLUSIONS Combined cardiac and thoracic CCUS assists in early bedside differential diagnosis of ARDS, CPE, and other causes of AHRF.
There are few comparisons among the most recent versions of the major adult ICU prognostic systems (APACHE Acute Physiology and Chronic Health Evaluation IV, Simplified Acute Physiology Score SAPS 3, ...Mortality Probability Model MPM0III). Only MPM0III includes resuscitation status as a predictor.
We assessed the discrimination, calibration, and overall performance of the models in 2,596 patients in three ICUs at our tertiary referral center in 2006. For APACHE and SAPS, the analyses were repeated with and without inclusion of resuscitation status as a predictor variable.
Of the 2,596 patients studied, 283 (10.9%) died before hospital discharge. The areas under the curve (95% CI) of the models for prediction of hospital mortality were 0.868 (0.854-0.880), 0.861 (0.847-0.874), 0.801 (0.785-0.816), and 0.721 (0.704-0.738) for APACHE III, APACHE IV, SAPS 3, and MPM0III, respectively. The Hosmer-Lemeshow statistics for the models were 33.7, 31.0, 36.6, and 21.8 for APACHE III, APACHE IV, SAPS 3, and MPM0III, respectively. Each of the Hosmer-Lemeshow statistics generated P values < .05, indicating poor calibration. Brier scores for the models were 0.0771, 0.0749, 0.0890, and 0.0932, respectively. There were no significant differences between the discriminative ability or the calibration of APACHE or SAPS with and without “do not resuscitate” status.
APACHE III and IV had similar discriminatory capability and both were better than SAPS 3, which was better than MPM0III. The calibrations of the models studied were poor. Overall, models with more predictor variables performed better than those with fewer. The addition of resuscitation status did not improve APACHE III or IV or SAPS 3 prediction.
Limited critical care subspecialty training and experience is available in many low- and middle-income countries, creating barriers to the delivery of evidence-based critical care. We hypothesized ...that a structured tele-education critical care program using case-based learning and ICU management principles is an efficient method for knowledge translation and quality improvement in this setting.
Weekly 45-min case-based tele-education rounds were conducted in the recently established medical intensive care unit (MICU) in Banja Luka, Bosnia and Herzegovina. The Checklist for Early Recognition and Treatment of Acute Illness (CERTAIN) was used as a platform for structured evaluation of critically ill cases. Two practicing US intensivists fluent in the local language served as preceptors using a secure two-way video communication platform. Intensive care unit structure, processes, and outcomes were evaluated before and after the introduction of the tele-education intervention.
Patient demographics and acuity were similar before (2015) and 2 years after (2016 and 2017) the intervention. Sixteen providers (10 physicians, 4 nurses, and 2 physical therapists) evaluated changes in the ICU structure and processes after the intervention. Structural changes prompted by the intervention included standardized admission and rounding practices, incorporation of a pharmacist and physical therapist into the interprofessional ICU team, development of ICU antibiogram and hand hygiene programs, and ready access to point of care ultrasound. Process changes included daily sedation interruption, protocolized mechanical ventilation management and liberation, documentation of daily fluid balance with restrictive fluid and transfusion strategies, daily device assessment, and increased family presence and participation in care decisions. Less effective (dopamine, thiopental, aminophylline) or expensive (low molecular weight heparin, proton pump inhibitor) medications were replaced with more effective (norepinephrine, propofol) or cheaper (unfractionated heparin, H2 blocker) alternatives. The intervention was associated with reduction in ICU (43% vs 27%) and hospital (51% vs 44%) mortality, length of stay (8.3 vs 3.6 days), cost savings ($400,000 over 2 years), and a high level of staff satisfaction and engagement with the tele-education program.
Weekly, structured case-based tele-education offers an attractive option for knowledge translation and quality improvement in the emerging ICUs in low- and middle-income countries.
Despite the manpower shortage to care for the critically ill, the number of ICU beds has been rising for the last 2 decades.
The ICU intensivist physician staffing model is still in flux in this ...country. Despite a challenge by a recent single publication,
numerous studies have shown that high-intensity intensivist staffing improves patient outcome in the ICU. However, 73% of
the ICUs in this country provide low-intensity or no intensive care coverage. Although it may not be possible to implement
24 h/d intensivist coverage of all ICUs at this time, we believe it is the best model for achieving good patient outcome.
The mere presence of intensivists in the ICU is unlikely to improve patient outcome unless it is associated with the creation
of an organizational environment ideal for the implementation of evidence-based practice. In this commentary, we will discuss
the available evidence behind the current models and express our opinions about current and future ICU intensivist staffing.
Adverse outcomes for hospitalized patients with sarcopenia are well documented, and identification of patients at risk remains challenging. The sarcopenia index (SI), previously defined as (serum ...creatinine/serum cystatin C) × 100, could be an inexpensive, readily accessible, objective tool to predict muscle mass and risk for adverse clinical outcomes. The aim of this study was to assess the validity of the SI as a predictor of muscle mass.
Retrospective study of critically ill adults admitted to Mayo Clinic from 2012 to 2015 with suspected sepsis and an available creatinine and serum cystatin C. Muscle surface area was quantified at the L3/4 vertebral level in patients with an abdominal CT scan (CTMSA). Multivariable regression modeling was used to assess the relationship between SI and CTMSA, as well as short-term clinical outcomes.
The 171 included had a mean weight and body mass index (BMI) of 75.2 ± 16.4 kg and 26.0 ± 4.6 kg/m2 and abdominal CT scans were available for 81 (47%) patients. The SI correlated with CTMSA (r = 0.40). After adjustment for age, sex, severity of illness, and BMI, SI was independently associated with muscle mass (P = 0.001). A decrease in the SI (indicative of lower muscle mass) was also associated with frailty and worse short-term clinical outcomes.
The SI, a simple calculation from kidney function markers, is a significant predictor of muscle mass in this validation cohort of ICU patients. A low SI was associated with longer hospital length of stay and frailty. Future studies could explore whether the use of SI assists with identifying patients likely to benefit from pharmacotherapy-, nutrition-, or physical therapy-based interventions.
•Identification of patients with reduced muscle mass and sarcopenia is challenging.•The Sarcopenia Index is calculated as (serum creatinine/serum cystatin C) × 100.•It is an inexpensive, accessible, objective alternative to predict muscle mass.•A lower sarcopenia index was associated with lower muscle mass.•Lower sarcopenia index independently predicted worse clinical outcomes and frailty.
Background
Optimal methods of mortality risk stratification in patients in the cardiac intensive care unit (CICU) remain uncertain. We evaluated the ability of the Sequential Organ Failure Assessment ...(SOFA) score to predict mortality in a large cohort of unselected patients in the CICU.
Methods and Results
Adult patients admitted to the CICU from January 1, 2007, to December 31, 2015, at a single tertiary care hospital were retrospectively reviewed. SOFA scores were calculated daily, and Acute Physiology and Chronic Health Evaluation (APACHE)‐III and APACHE‐IV scores were calculated on CICU day 1. Discrimination of hospital mortality was assessed using area under the receiver‐operator characteristic curve values. We included 9961 patients, with a mean age of 67.5±15.2 years; all‐cause hospital mortality was 9.0%. Day 1 SOFA score predicted hospital mortality, with an area under the receiver‐operator characteristic curve value of 0.83; area under the receiver‐operator characteristic curve values were similar for the APACHE‐III score, and APACHE‐IV predicted mortality (P>0.05). Mean and maximum SOFA scores over multiple CICU days had greater discrimination for hospital mortality (P<0.01). Patients with an increasing SOFA score from day 1 and day 2 had higher mortality. Patients with day 1 SOFA score <2 were at low risk of mortality. Increasing tertiles of day 1 SOFA score predicted higher long‐term mortality (P<0.001 by log‐rank test).
Conclusions
The day 1 SOFA score has good discrimination for short‐term mortality in unselected patients in the CICU, which is comparable to APACHE‐III and APACHE‐IV. Advantages of the SOFA score over APACHE include simplicity, improved discrimination using serial scores, and prediction of long‐term mortality.
Transfusion-related acute lung injury (TRALI) is the leading cause of transfusion-related mortality. To determine TRALI incidence by prospective, active surveillance and to identify risk factors by a ...case-control study, 2 academic medical centers enrolled 89 cases and 164 transfused controls. Recipient risk factors identified by multivariate analysis were higher IL-8 levels, liver surgery, chronic alcohol abuse, shock, higher peak airway pressure while being mechanically ventilated, current smoking, and positive fluid balance. Transfusion risk factors were receipt of plasma or whole blood from female donors (odds ratio = 4.5, 95% confidence interval CI, 1.85-11.2, P = .001), volume of HLA class II antibody with normalized background ratio more than 27.5 (OR = 1.92/100 mL, 95% CI, 1.08-3.4, P = .03), and volume of anti–human neutrophil antigen positive by granulocyte immunofluoresence test (OR = 1.71/100 mL, 95% CI, 1.18-2.5, P = .004). Little or no risk was associated with older red blood cell units, noncognate or weak cognate class II antibody, or class I antibody. Reduced transfusion of plasma from female donors was concurrent with reduced TRALI incidence: 2.57 (95% CI, 1.72-3.86) in 2006 versus 0.81 (95% CI, 0.44-1.49) in 2009 per 10 000 transfused units (P = .002). The identified risk factors provide potential targets for reducing residual TRALI.
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