Laurent G. Glance

Association between Intraoperative Blood Transfusion and Mortality and Morbidity in Patients Undergoing Noncardiac Surgery

Background:The impact of intraoperative erythrocyte transfusion on outcomes of anemic patients undergoing noncardiac surgery has not been well characterized. The objective of this study was to examine the association between blood transfusion and mortality and morbidity in patients with severe anemia (hematocrit less than 30%) who are exposed to one or two units of erythrocytes intraoperatively. Methods:This was a retrospective analysis of the association of blood transfusion and 30-day mortality and 30-day morbidity in 10,100 patients undergoing general, vascular, or orthopedic surgery. We estimated separate multivariate logistic regression models for 30-day mortality and for 30-day complications. Results:Intraoperative blood transfusion was associated with an increased risk of death (odds ratio [OR], 1.29; 95% CI, 1.03–1.62). Patients receiving an intraoperative transfusion were more likely to have pulmonary, septic, wound, or thromboembolic complications, compared with patients not receiving an intraoperative transfusion. Compared with patients who were not transfused, patients receiving one or two units of erythrocytes were more likely to have pulmonary complications (OR, 1.76; 95% CI, 1.48–2.09), sepsis (OR, 1.43; 95% CI, 1.21–1.68), thromboembolic complications (OR, 1.77; 95% CI, 1.32–2.38), and wound complications (OR, 1.87; 95% CI, 1.47–2.37). Conclusions:Intraoperative blood transfusion is associated with a higher risk of mortality and morbidity in surgical patients with severe anemia. It is unknown whether this association is due to the adverse effects of blood transfusion or is, instead, the result of increased blood loss in the patients receiving blood.

Nurse Working Conditions and Patient Safety Outcomes

Background: System approaches, such as improving working conditions, have been advocated to improve patient safety. However, the independent effect of many working condition variables on patient outcomes is unknown. Objective: To examine effects of a comprehensive set of working conditions on elderly patient safety outcomes in intensive care units. Design: Observational study, with patient outcome data collected using the National Nosocomial Infection Surveillance system protocols and Medicare files. Several measures of health status and fixed setting characteristics were used to capture distinct dimensions of patient severity of illness and risk for disease. Working condition variables included organizational climate measured by nurse survey; objective measures of staffing, overtime, and wages (derived from payroll data); and hospital profitability and magnet accreditation. Setting and Patients: The sample comprised 15,846 patients in 51 adult intensive care units in 31 hospitals depending on the outcome analyzed; 1095 nurses were surveyed. Main Outcome Measures: Central line associated bloodstream infections (CLBSI), ventilator-associated pneumonia, catheter-associated urinary tract infections, 30-day mortality, and decubiti. Results: Units with higher staffing had lower incidence of CLBSI, ventilator-associated pneumonia, 30-day mortality, and decubiti (P ≤ 0.05). Increased overtime was associated with higher rates of catheter-associated urinary tract infections and decubiti, but slightly lower rates of CLBSI (P ≤ 0.05). The effects of organizational climate and profitability were not consistent. Conclusions: Nurse working conditions were associated with all outcomes measured. Improving working conditions will most likely promote patient safety. Future researchers and policymakers should consider a broad set of working condition variables.

Improving the Glasgow Coma Scale score: motor score alone is a better predictor

BACKGROUND The Glasgow Coma Scale (GCS) has served as an assessment tool in head trauma and as a measure of physiologic derangement in outcome models (e.g., TRISS and Acute Physiology and Chronic Health Evaluation), but it has not been rigorously examined as a predictor of outcome. METHODS Using a large trauma data set (National Trauma Data Bank, N = 204,181), we compared the predictive power (pseudo R2, receiver operating characteristic [ROC]) and calibration of the GCS to its components. RESULTS The GCS is actually a collection of 120 different combinations of its 3 predictors grouped into 12 different scores by simple addition (motor [m] + verbal [v] + eye [e] = GCS score). Problematically, different combinations summing to a single GCS score may actually have very different mortalities. For example, the GCS score of 4 can represent any of three mve combinations: 2/1/1 (survival = 0.52), 1/2/1 (survival = 0.73), or 1/1/2 (survival = 0.81). In addition, the relationship between GCS score and survival is not linear, and furthermore, a logistic model based on GCS score is poorly calibrated even after fractional polynomial transformation. The m component of the GCS, by contrast, is not only linearly related to survival, but preserves almost all the predictive power of the GCS (ROC(GCS) = 0.89, ROC(m) = 0.87; pseudo R2(GCS) = 0.42, pseudo R2(m) = 0.40) and has a better calibrated logistic model. CONCLUSION Because the motor component of the GCS contains virtually all the information of the GCS itself, can be measured in intubated patients, and is much better behaved statistically than the GCS, we believe that the motor component of the GCS should replace the GCS in outcome prediction models. Because the m component is nonlinear in the log odds of survival, however, it should be mathematically transformed before its inclusion in broader outcome prediction models.

Simplified Estimates of the Probability of Death After Burn Injuries: Extending and Updating the Baux Score

BACKGROUND : Generations of clinicians have used the Baux score, defined as the sum of age in years and percent body burn, to predict percent mortality after trauma, but advances in burn care have rendered the predictions of this score too pessimistic. Additionally, this score does not include the effects of inhalation injury. METHODS : We revised the Baux score to include inhalation injury and recalibrated its predictions using a single-term logistic regression model developed using data on 39,888 burned patients provided by the national burn repository. We compared this revised Baux score to a more complex logistic regression model derived from the same data set and predictors. RESULTS : A preliminary logistic regression model showed that age and percent burn contribute almost equally to mortality and further that the presence of inhalation injury added the equivalent of 17 years (or 17% burn). These observations suggested a revised Baux Score:Age + Percent Burn + 17 * (Inhalation Injury, 1 = yes, 0 = no)A logistic model based on the Revised Baux Score performed well, but a more complex model obtained using modern statistical model building tools had better discrimination and calibration. CONCLUSIONS : Our proposed revised Baux score is simple enough for mental calculation, and its inverse logit transformation (provided with a calculator or nomogram) can provide precise predictions of mortality. Better predictions can be obtained using our more complex statistical model. Burn surgeons and nurses accustomed to using the original Baux score may welcome an updated version.

The Surgical Mortality Probability Model: derivation and validation of a simple risk prediction rule for noncardiac surgery.

Objective:To develop a 30-day mortality risk index for noncardiac surgery that can be used to communicate risk information to patients and guide clinical management at the “point-of-care,” and that can be used by surgeons and hospitals to internally audit their quality of care. Background:Clinicians rely on the Revised Cardiac Risk Index to quantify the risk of cardiac complications in patients undergoing noncardiac surgery. Because mortality from noncardiac causes accounts for many perioperative deaths, there is also a need for a simple bedside risk index to predict 30-day all-cause mortality after noncardiac surgery. Methods:Retrospective cohort study of 298,772 patients undergoing noncardiac surgery during 2005 to 2007 using the American College of Surgeons National Surgical Quality Improvement Program database. Results:The 9-point S-MPM (Surgical Mortality Probability Model) 30-day mortality risk index was derived empirically and includes three risk factors: ASA (American Society of Anesthesiologists) physical status, emergency status, and surgery risk class. Patients with ASA physical status I, II, III, IV or V were assigned either 0, 2, 4, 5, or 6 points, respectively; intermediate- or high-risk procedures were assigned 1 or 2 points, respectively; and emergency procedures were assigned 1 point. Patients with risk scores less than 5 had a predicted risk of mortality less than 0.50%, whereas patients with a risk score of 5 to 6 had a risk of mortality between 1.5% and 4.0%. Patients with a risk score greater than 6 had risk of mortality more than 10%. S-MPM exhibited excellent discrimination (C statistic, 0.897) and acceptable calibration (Hosmer-Lemeshow statistic 13.0, P = 0.023) in the validation data set. Conclusions:Thirty-day mortality after noncardiac surgery can be accurately predicted using a simple and accurate risk score based on information readily available at the bedside. This risk index may play a useful role in facilitating shared decision making, developing and implementing risk-reduction strategies, and guiding quality improvement efforts.

Rating the quality of intensive care units: is it a function of the intensive care unit scoring system?

Objective Intensive care units (ICUs) use severity-adjusted mortality measures such as the standardized mortality ratio to benchmark their performance. Prognostic scoring systems such as Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score II, and Mortality Probability Model II0 permit performance-based comparisons of ICUs by adjusting for severity of disease and case mix. Whether different risk-adjustment methods agree on the identity of ICU quality outliers within a single database has not been previously investigated. The objective of this study was to determine whether the identity of ICU quality outliers depends on the ICU scoring system used to calculate the standardized mortality ratio. Design, Setting, Patients Retrospective cohort study of 16,604 patients from 32 hospitals based on the outcomes database (Project IMPACT) created by the Society of Critical Care Medicine. The ICUs were a mixture of medical, surgical, and mixed medical-surgical ICUs in urban and nonurban settings. Standardized mortality ratios for each ICU were calculated using APACHE II, Simplified Acute Physiology Score II, and Mortality Probability Model II0. ICU quality outliers were defined as ICUs whose standardized mortality ratio was statistically different from 1. Kappa analysis was used to determine the extent of agreement between the scoring systems on the identity of hospital quality outliers. The intraclass correlation coefficient was calculated to estimate the reliability of standardized mortality ratios obtained using the three risk-adjustment methods. Measurements and Main Results Kappa analysis showed fair to moderate agreement among the three scoring systems in identifying ICU quality outliers; the intraclass correlation coefficient suggested moderate to substantial agreement between the scoring systems. The majority of ICUs were classified as high-performance ICUs by all three scoring systems. All three scoring systems exhibited good discrimination and poor calibration in this data set. Conclusion APACHE II, Simplified Acute Physiology Score II, and Mortality Probability Model II0 exhibit fair to moderate agreement in identifying quality outliers. However, the finding that most ICUs in this database were judged to be high-performing units limits the usefulness of these models in their present form for benchmarking.

A survey of the use of ultrasound during central venous catheterization.

BACKGROUND:Complications during central venous catheterization (CVC) are not rare and can be serious. The use of ultrasound (US) during CVC has been recommended to improve patient safety. We performed a survey to evaluate the frequency of, and factors influencing, US use. METHODS:We conducted an electronic survey of all members of the Society of Cardiovascular Anesthesiologists. Univariate and multivariate logistic regressions were used to assess the association between the frequency of US use and hospital and physician factors. All tests were two-sided, and a P value <0.05 was considered statistically significant. RESULTS:Of the 4235 members, 1494 responded (response rate = 35.3%). Two-thirds of the respondents never, or almost never, use US, whereas only 15% always, or almost always, use US. Thirty-three percent of the respondents never, or almost never, have US available, whereas 41% stated that US is always, or almost always, available. Availability of US equipment was strongly associated with US use for CVC (adj OR = 18.9; P value <0.001). The most common reason cited for not using US was “no apparent need for the use of US” (46%). When US was used, rescue or screening approaches were more common (72%) than real-time use (26%). CONCLUSIONS:The use of US during CVC remains limited and is most strongly associated with the availability of equipment. Screening and rescue use of US are more common than real-time guidance. Our survey suggests that current use of US during CVC differs from existing evidence-based recommendations.

TMPM-ICD9: a trauma mortality prediction model based on ICD-9-CM codes.

Objective:To develop and validate a new ICD-9 injury model that uses regression modeling, as opposed to a simple ratio measurement, to estimate empiric injury severities for each of the injuries in the ICD-9-CM lexicon. Background:The American College of Surgeons now requires International Classification of diseases ninth Edition (ICD-9-CM) codes for injury coding in the National Trauma Databank. International Classification of diseases ninth Edition Injury Severity Score (ICISS) is the best-known risk-adjustment model when injuries are recorded using ICD-9-CM coding, and would likely be used to risk-adjust outcome measures for hospital trauma report cards. ICISS, however, has been criticized for its poor calibration. Methods:We developed and validated a new ICD-9 injury model using data on 749,374 patients admitted to 359 hospitals in the National Trauma Databank (version 7.0). Empiric measures of injury severity for each of the trauma ICD-9-CM codes were estimated using a regression-based approach, and then used as the basis for a new Trauma Mortality Prediction Model (TMPM-ICD9). ICISS and the Single-Worst Injury (SWI) model were also re-estimated. The performance of each of these models was compared using the area under the receiver operating characteristic (ROC), the Hosmer-Lemeshow statistic, and the Akaike information criterion statistic. Results:TMPM-ICD9 exhibits significantly better discrimination (ROCTMPM = 0.880 [0.876–0.883]; ROCICISS = 0.850 [0.846–0.855]; ROCSWI = 0.862 [0.858–0.867]) and calibration (HLTMPM = 29.3 [12.1–44.1]; HLICISS = 231 [176–279]; HLSWI = 462 [380–548]) compared with both ICISS and the Single Worst Injury model. All models were improved with the addition of age, gender, and mechanism of injury, but TMPM-ICD9 continued to demonstrate superior model performance. Conclusions:Because TMPM-ICD9 uniformly out-performs ICISS and the SWI model, it should be used in preference to ICISS for risk-adjusting trauma outcomes when injuries are recorded using ICD9-CM codes.

Impact of patient volume on the mortality rate of adult intensive care unit patients.

Objective:Expert task forces have proposed that adult critical care medicine services should be regionalized in order to improve outcomes. However, it is currently unknown if high intensive care unit (ICU) patient volumes are associated with reduced mortality rate. The objective was to investigate whether high-volume ICUs have better mortality outcomes than low-volume ICUs. Design:Retrospective cohort study analyzing the association between ICU volume and in-hospital mortality using Project IMPACT (a clinical outcomes database created by the Society of Critical Care Medicine). Patients:The analyses were based on 70,757 patients admitted to 92 ICUs between 2001 and 2003. Interventions:None. Measurements and Main Results:The main outcome measure was in-hospital mortality. Hierarchical logistic regression modeling was used to examine the volume-outcome association. The median (interquartile range) ICU volume was 827 (631–1,234) patient admissions per year. The overall mortality rate was 14.6%. After controlling for patient risk factors and ICU characteristics, and clustering, there was evidence that patients admitted to high-volume ICUs had improved outcomes (p = .025). However, this mortality benefit was seen only in high-risk patients treated at ICUs treating high volumes of high-risk patients. Conclusions:There is evidence that high ICU patient volumes are associated with lower mortality rates in high-risk critically ill adults.

Increases in Mortality, Length of Stay, and Cost Associated With Hospital-Acquired Infections in Trauma Patients

OBJECTIVE To explore the clinical impact and economic burden of hospital-acquired infections (HAIs) in trauma patients using a nationally representative database. DESIGN Retrospective study. SETTING The Healthcare Cost and Utilization Project Nationwide Inpatient Sample. PATIENTS Trauma patients. MAIN OUTCOME MEASURES We examined the association between HAIs (sepsis, pneumonia, Staphylococcus infections, and Clostridium difficile- associated disease) and in-hospital mortality, length of stay, and inpatient costs using logistic regression and generalized linear models. RESULTS After controlling for patient demographics, mechanism of injury, injury type, injury severity, and comorbidities, we found that mortality, cost, and length of stay were significantly higher in patients with HAIs compared with patients without HAIs. Patients with sepsis had a nearly 6-fold higher odds of death compared with patients without an HAI (odds ratio, 5.78; 95% confidence interval, 5.03-6.64; P < .001). Patients with other HAIs had a 1.5- to 1.9-fold higher odds of mortality compared with controls (P < .005). Patients with HAIs had costs that were approximately 2- to 2.5-fold higher compared with patients without HAIs (P < .001). The median length of stay was approximately 2-fold higher in patients with HAIs compared with patients without HAIs (P < .001). CONCLUSIONS Trauma patients with HAIs are at increased risk for mortality, have longer lengths of stay, and incur higher inpatient costs. In light of the preventability of many HAIs and the magnitude of the clinical and economic burden associated with HAIs, policies aiming to decrease the incidence of HAIs may have a potentially large impact on outcomes in injured patients.