Troubleshooting FoCUS Image Acquisition: Patient Positioning, Transducer Manipulation, and Image Optimization.

Focused cardiac ultrasound (FoCUS) is a limited, clinician-performed application of echocardiography to add real-time information to patient care. These bedside exams are problem oriented, rapidly and repeatedly performed, and largely qualitative in nature. Competency in FoCUS includes mastery of the stereotactic and psychomotor skills required for transducer manipulation and image acquisition. Competency also requires the ability to optimize the setup, troubleshoot image acquisition, and understand the sonographic limitations because of complex clinical environments and patient pathology. This article presents concepts for successful, high-quality two-dimensional (B-mode) image acquisition in FoCUS. Concepts of high-quality image acquisition can be applied to all established sonographic windows of the FoCUS exam: the parasternal long-axis (PLAX), parasternal short-axis (PSAX), apical four chamber (A4C), subcostal fourchamber (SC4C), and the inferior vena cava (IVC). The apical five-chamber (A5C) and subcostal short-axis (SCSA) views are mentioned, but are not discussed in-depth. A pragmatic figure illustrating the movements of the phased array transducer is also provided to serve as a cognitive aid during FoCUS image acquisition.

A Comparison of Sepsis-2 (Systemic Inflammatory Response Syndrome Based) to Sepsis-3 (Sequential Organ Failure Assessment Based) Definitions-A Multicenter Retrospective Study.

OBJECTIVES: Recently, the definition of sepsis has changed from a physiologic derangement (Sepsis-1 and -2) to organ dysfunction (Sepsis-3) based. We sought to determine the concordance between the different sepsis phenotypes and how that affected mortality. DESIGN: Retrospective, multicenter study. SETTING: Three academic medical centers. PATIENTS: 29,459 patients who had suspected infection, defined as obtaining blood cultures and receiving antibiotics: 18,183 (62%) had either Sepsis-2 or Sepsis-3. MEASUREMENTS AND MAIN RESULTS: Kappa was used to show agreement between phenotypes. Conditional logistic regression was used to create models of associations between factors and phenotypes and between factors and mortality. About 12,981 patients had Sepsis-2; 12,043 had Sepsis-3; and 6,841 patients had both Sepsis-2 and Sepsis-3. Fifty-three percent of Sepsis-2 patients also had Sepsis-3, whereas 57% of Sepsis-3 patients also had Sepsis-2. Agreement between the two phenotypes was poor: kappa = 0.213 ± 0.006. Mortality was 6% in patients with only Sepsis-2, 10% with only Sepsis-3, and 18% in patients who had both phenotypes. Combining the variables in Sepsis-2 and Sepsis-3 improved the discrimination (C-statistic = 0.742 ± 0.005, p < 0.001) of mortality. CONCLUSIONS: We found that Sepsis-2 and Sepsis-3-based sepsis diagnoses represent separate phenotypes with poor agreement. Patients who have both phenotypes are at increased risk of mortality compared with having either phenotype alone. Inclusion of both systemic inflammatory response syndrome and Sequential Organ Failure Assessment criteria in the same model improves the discrimination of mortality.

Impact of the pre-illness lipid profile on sepsis mortality.

PURPOSE: To determine if baseline lipid levels contribute to the relationship between lipid levels during sepsis and outcomes. MATERIALS AND METHODS: We conducted a retrospective cohort study at a tertiary-care academic medical center. Multivariable logistic regression models were used to adjust for confounders. Both Systemic Inflammatory Response Syndrome (SIRS) and Sequential Organ Failure Assessment (SOFA) score-based definitions of sepsis were analyzed. MEASUREMENTS AND MAIN RESULTS: After adjusting for patient characteristics and severity of illness, baseline values for both low density lipoprotein (LDL) cholesterol and triglycerides were associated with mortality (LDL cholesterol odds ratio [OR] 0.44, 95% confidence interval [CI] 0.23-0.84, p = .013; triglyceride OR 0.54, 95% CI 0.37-0.78, p = .001) using a SIRS based definition of sepsis. An interaction existed between these two variables, which resulted in increased mortality with higher baseline low density lipoprotein (LDL) cholesterol values for individuals with triglycerides below 208 mg/dL and the opposite direction of association above this level (interaction OR 1.48, 95% CI 1.02-2.16, p = .039). When using a SOFA score-based definition, only triglycerides remained associated with the mortality (OR 0.55, 95% CI 0.35-0.86, p = .008). CONCLUSIONS: Baseline lipid values, particularly triglyceride concentrations, are associated with hospital mortality in septic patients.

Culture-Negative and Culture-Positive Sepsis: A Comparison of Characteristics and Outcomes.

BACKGROUND: The primary objective of this study was to compare the characteristics of culture-positive and culture-negative status in septic patients. We also determined whether culture status is associated with mortality and whether unique variables are associated with mortality in culture-positive and culture-negative patients separately. METHODS: Utilizing patient records from intensive care units, emergency department, and general care wards in a large academic medical center, we identified adult patients with suspected infection and ≥2 systemic inflammatory response syndrome criteria between January 1, 2007, and May 31, 2014. We compared the characteristics between culture-positive and culture-negative patients and used binary logistic regression to identify variables independently associated with culture status and mortality. We also did sensitivity analyses using patients with Sequential Organ Failure Assessment and quick Sequential Organ Failure Assessment criteria for sepsis. RESULTS: The study population included 9288 culture-negative patients (89%) and 1105 culture-positive patients (11%). Culture-negative patients received more antibiotics during the 48 hours preceding diagnosis but otherwise demonstrated similar characteristics as culture-positive patients. After adjusting for illness severity, a positive culture was not independently associated with mortality (odds ratio = 1.01 [95% CI, 0.81-1.26]; P = .945). The models predicting mortality separately in culture-negative and culture-positive patients demonstrated very good and excellent discrimination (C-statistic ± SD, 0.87 ± 0.01 and 0.92 ± 0.01), respectively. In the sensitivity analyses using patients with sepsis by Sequential Organ Failure Assessment and quick Sequential Organ Failure Assessment criteria, after adjustments for illness severity, positive cultures were still not associated with mortality (odds ratio = 1.13 [95% CI, 0.86-1.43]; P = .303; and odds ratio = 1.05 [95% CI, 0.83-1.33]; P = .665), respectively. In all models, physiological derangements were associated with mortality. CONCLUSIONS: While culture status is important for tailoring antibiotics, culture-negative and culture-positive patients with sepsis demonstrate similar characteristics and, after adjusting for severity of illness, similar mortality. The most important factor associated with negative cultures is receipt of antibiotics during the preceding 48 hours. The risk of death in patients suspected of having an infection is most associated with severity of illness. This is aligned with the Sepsis-3 definition using Sequential Organ Failure Assessment score to better identify those suspected of infection at highest risk of a poor outcome.

The Validity of Discharge Billing Codes Reflecting Severe Maternal Morbidity.

BACKGROUND: Discharge diagnoses are used to track national trends and patterns of maternal morbidity. There are few data regarding the validity of the International Classification of Diseases (ICD) codes used for this purpose. The goal of our study was to try to better understand the validity of administrative data being used to monitor and assess trends in morbidity. METHODS: Hospital stay billing records were queried to identify all delivery admissions at the Massachusetts General Hospital for the time period 2001 to 2011 and the University of Michigan Health System for the time period 2005 to 2011. From this, we identified patients with ICD-9-Clinical Modification (CM) diagnosis and procedure codes indicative of severe maternal morbidity. Each patient was classified with 1 of 18 different medical/obstetric categories (conditions or procedures) based on the ICD-9-CM code that was recorded. Within each category, 20 patients from each institution were selected at random, and the corresponding medical charts were reviewed to determine whether the ICD-9-CM code was assigned correctly. The percentage of correct codes for each of 18 preselected clinical categories was calculated yielding a positive predictive value (PPV) and 99% confidence interval (CI). RESULTS: The overall number of correctly assigned ICD-9-CM codes, or PPV, was 218 of 255 (86%; CI, 79%-90%) and 154 of 188 (82%; CI, 74%-88%) at Massachusetts General Hospital and University of Michigan Health System, respectively (combined PPV, 372/443 [84%; CI, 79-88%]). Codes within 4 categories (Hysterectomy, Pulmonary edema, Disorders of fluid, electrolyte and acid-base balance, and Sepsis) had a 99% lower confidence limit ≥75%. Codes within 8 additional categories demonstrated a 99% lower confidence limit between 74% and 50% (Acute respiratory distress, Ventilation, Other complications of obstetric surgery, Disorders of coagulation, Cardiomonitoring, Acute renal failure, Thromboembolism, and Shock). Codes within 6 clinical categories demonstrated a 99% lower confidence limit <50% (Puerperal cerebrovascular disorders, Conversion of cardiac rhythm, Acute heart failure [includes arrest and fibrillation], Eclampsia, Neurotrauma, and Severe anesthesia complications). CONCLUSIONS: ICD-9-CM codes capturing severe maternal morbidity during delivery hospitalization demonstrate a range of PPVs. The PPV was high when objective supportive evidence, such as laboratory values or procedure documentation supported the ICD-9-CM code. The PPV was low when greater judgment, interpretation, and synthesis of the clinical data (signs and symptoms) was required to support a code, such as with the category Severe anesthesia complications. As a result, these codes should be used for administrative research with more caution compared with codes primarily defined by objective data.

Ten Myths and Misconceptions Regarding Pain Management in the ICU.

OBJECTIVES: The aim of this article is to expose common myths and misconceptions regarding pain assessment and management in critically ill patients that interfere with effective care. We comprehensively review the literature refuting these myths and misconceptions and describe evidence-based strategies for improving pain management in the ICU. DATA SOURCES: Current peer-reviewed academic journals, as well as standards and guidelines from professional societies. STUDY SELECTION: The most current evidence was selected for review based on the highest degree of supportive evidence. DATA EXTRACTION: Data were obtained via medical search databases, including OvidSP, and the National Library of Medicine's MEDLINE database via PubMed. DATA SYNTHESIS: After a comprehensive literature review, conclusions were drawn based on the strength of evidence and the most current understanding of pain management practices in ICU. CONCLUSIONS: Myths and misconceptions regarding management of pain in the ICU are prevalent. Review of current evidence refutes these myths and misconceptions and provides insights and recommendations to ensure best practices.

Validation of a risk stratification index and risk quantification index for predicting patient outcomes: in-hospital mortality, 30-day mortality, 1-year mortality, and length-of-stay.

BACKGROUND: External validation of published risk stratification models is essential to determine their generalizability. This study evaluates the performance of the Risk Stratification Indices (RSIs) and 30-day mortality Risk Quantification Index (RQI). METHODS: 108,423 adult hospital admissions with anesthetics were identified (2006-2011). RSIs for mortality and length-of-stay endpoints were calculated using published methodology. 91,128 adult, noncardiac inpatient surgeries were identified with administrative data required for RQI calculation. RESULTS: RSI in-hospital mortality and RQI 30-day mortality Brier scores were 0.308 and 0.017, respectively. RSI discrimination, by area under the receiver operating curves, was excellent at 0.966 (95% CI, 0.963-0.970) for in-hospital mortality, 0.903 (0.896-0.909) for 30-day mortality, 0.866 (0.861-0.870) for 1-yr mortality, and 0.884 (0.882-0.886) for length-of-stay. RSI calibration, however, was poor overall (17% predicted in-hospital mortality vs. 1.5% observed after inclusion of the regression constant) as demonstrated by calibration plots. Removal of self-fulfilling diagnosis and procedure codes (20,001 of 108,423; 20%) yielded similar results. RQIs were calculated for only 62,640 of 91,128 patients (68.7%) due to unmatched procedure codes. Patients with unmatched codes were younger, had higher American Society of Anesthesiologists physical status and 30-day mortality. The area under the receiver operating curve for 30-day mortality RQI was 0.888 (0.879-0.897). The model also demonstrated good calibration. Performance of a restricted index, Procedure Severity Score + American Society of Anesthesiologists physical status, performed as well as the original RQI model (age + American Society of Anesthesiologists + Procedure Severity Score). CONCLUSION: Although the RSIs demonstrated excellent discrimination, poor calibration limits their generalizability. The 30-day mortality RQI performed well with age providing a limited contribution.