Two distinct Do-Not-Resuscitate protocols leaving less to the imagination: an observational study using propensity score matching.

BACKGROUND: Do-Not-Resuscitate (DNR) patients tend to receive less medical care after the order is written. To provide a clearer approach, the Ohio Department of Health adopted the Do-Not-Resuscitate law in 1998, indicating two distinct protocols of DNR orders that allow DNR patients to choose the medical care: DNR Comfort Care (DNRCC), implying DNRCC patients receive only comfort care after the order is written; and DNR Comfort Care-Arrest (DNRCC-Arrest), implying that DNRCC-Arrest patients are eligible to receive aggressive interventions until cardiac or respiratory arrest. The aim of this study was to examine the medical care provided to patients with these two distinct protocols of DNR orders. METHODS: Data were collected from August 2002 to December 2005 at a medical intensive care unit in a university-affiliated teaching hospital. In total, 188 DNRCC-Arrest patients, 88 DNRCC patients, and 2,051 non-DNR patients were included. Propensity score matching using multivariate logistic regression was used to balance the confounding variables between the 188 DNRCC-Arrest and 2,051 non-DNR patients, and between the 88 DNRCC and 2,051 non-DNR patients. The daily cost of intensive care unit (ICU) stay, the daily cost of hospital stay, the daily discretionary cost of ICU stay, six aggressive interventions, and three comfort care measures were used to indicate the medical care patients received. The association of each continuous variable and categorical variable with having a DNR order written was analyzed using Student's t-test and the χ2 test, respectively. The six aggressive interventions and three comfort care measures performed before and after the order was initiated were compared using McNemar's test. RESULTS: DNRCC patients received significantly fewer aggressive interventions and more comfort care after the order was initiated. By contrast, for DNRCC-Arrest patients, the six aggressive interventions provided were not significantly decreased, but the three comfort care measures were significantly increased after the order was initiated. In addition, the three medical costs were not significantly different between DNRCC and non-DNR patients, or between DNRCC-Arrest and non-DNR patients. CONCLUSIONS: When medical care provided to DNR patients is clearly indicated, healthcare professionals will provide the medical care determined by patient/surrogate decision-makers and healthcare professionals, rather than blindly decreasing medical care.

Factors associated with two different protocols of do-not-resuscitate orders in a medical ICU*.

OBJECTIVE: The State of Ohio in the United States has the legislation for two different protocols of do-not-resuscitate orders. The objective of this study was to examine the clinical/demographic factors and outcomes associated with the two different do-not-resuscitate orders. DESIGN: Data were concurrently and retrospectively collected from August 2002 to December 2005. The clinical/demographic factors of do-not-resuscitate patients were compared with those of non-do-not-resuscitate patients, and the clinical/demographic factors of do-not-resuscitate comfort care-arrest patients were compared with those of do-not-resuscitate comfort care patients. SETTING: An ICU in a university-affiliated hospital located at Northeast Ohio in the United States. PATIENTS: A sample of 2,440 patients was collected: 389 patients were do-not-resuscitate; and 2,051 patients were non-do-not-resuscitate. Among the 389 do-not-resuscitate patients, 194 were do-not-resuscitate comfort care-arrest patients and 91 were do-not-resuscitate comfort care patients. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The factors associated with do-not-resuscitate were older age, race and ethnicity with white race, more severe clinical illness at admission to the ICU, and longer stay before admission to the ICU. Comparing do-not-resuscitate comfort care-arrest patients with do-not-resuscitate comfort care patients, those with more severe clinical illness, longer ICU stay before making a do-not-resuscitate decision, and being cared for by only one intensivist during ICU stay were significantly associated with do-not-resuscitate comfort care decisions. For 149 do-not-resuscitate patients who eventually survived to hospital discharge and 86 do-not-resuscitate patients who eventually did not, only eight (5.4%) and 23 (26.7%) had the order written within 48 hours before the end of ICU stay, respectively. CONCLUSIONS: Our study showed that some clinical/demographic factors predicted do-not-resuscitate comfort care orders. This study also suggested that Ohio's Do-Not-Resuscitate Law, clearly indicating two different protocols of do-not-resuscitate orders, facilitated early do-not-resuscitate decision.

Mortality burden from variation in provision of surgical care in emergency general surgery: a cohort study using the National Inpatient Sample.

Background: The decision to undertake a surgical intervention for an emergency general surgery (EGS) condition (appendicitis, diverticulitis, cholecystitis, hernia, peptic ulcer, bowel obstruction, ischemic bowel) involves a complex consideration of factors, particularly in older adults. We hypothesized that identifying variability in the application of operative management could highlight a potential pathway to improve patient survival and outcomes. Methods: We included adults aged 65+ years with an EGS condition from the 2016-2017 National Inpatient Sample. Operative management was determined from procedure codes. Each patient was assigned a propensity score (PS) for the likelihood of undergoing an operation, modeled from patient and hospital factors: EGS diagnosis, age, gender, race, presence of shock, comorbidities, and hospital EGS volumes. Low and high probability for surgery was defined using a PS cut-off of 0.5. We identified two model-concordant groups (no surgery-low probability, surgery-high probability) and two model-discordant groups (no surgery-high probability, surgery-low probability). Logistic regression estimated the adjusted OR (AOR) of in-hospital mortality for each group. Results: Of 375 546 admissions, 21.2% underwent surgery. Model-discordant care occurred in 14.6%; 5.9% had no surgery despite a high PS and 8.7% received surgery with low PS. In the adjusted regression, model-discordant care was associated with significantly increased mortality: no surgery-high probability AOR 2.06 (1.86 to 2.27), surgery-low probability AOR 1.57 (1.49 to 1.65). Model-concordant care showed a protective effect against mortality (AOR 0.83, 0.74 to 0.92). Conclusions: Nearly one in seven EGS patients received model-discordant care, which was associated with higher mortality. Our study suggests that streamlined treatment protocols can be applied in EGS patients as a means to save lives. Level of evidence: III.

Heath status, frailty, and multimorbidity in patients with emergency general surgery conditions.

BACKGROUND: Although nearly 1 million older adults are admitted for emergency general surgery conditions yearly, the extent to which baseline health influences the development and treatment of emergency general surgery conditions is unknown. We evaluated baseline health and older patients with and without emergency general surgery conditions. METHODS: We used the prospectively collected Medicare Current Beneficiary Survey with Medicare claims and 2 validated health frameworks: (1) Deficit Accumulation Frailty Score and (2) Complex Multimorbidity. Self-reported health and function items were used to derive pre-emergency general surgery conditions Deficit Accumulation Frailty Score and Complex Multimorbidity scores. Deficit Accumulation Frailty Score ranges from 0 (no frailty deficits) to 100 (all possible deficits present). Complex Multimorbidity is a 3-point categorical rank based on the presence of chronic conditions, functional limitations, and geriatric syndromes. Specific survey factors were also examined to determine association with development of emergency general surgery conditions or use of operative management. RESULTS: Of 54,417 individuals, 1,960 had emergency general surgery conditions (median age 79 [interquartile range 73-84]). Patients with emergency general surgery conditions had significantly higher Deficit Accumulation Frailty Score (19 [interquartile range 11-31] vs 14 [8-24]) and were more likely to be in the most severe Complex Multimorbidity category (38% vs 29%). Emergency general surgery conditions patients had higher proportions of nearly every health category, with the most striking differences in functional limitations. Patients who were treated nonoperatively had the poorest overall baseline health. CONCLUSION: Patients who developed emergency general surgery conditions had more severe health burden than patients who did not, particularly in functional status. Clinicians must better understand the interaction between baseline health vulnerability and emergency surgical disease to improve prognostication and ensure alignment of patient goals and treatment strategies.

Pulmonary-artery versus central venous catheter to guide treatment of acute lung injury.

BACKGROUND: The balance between the benefits and the risks of pulmonary-artery catheters (PACs) has not been established. METHODS: We evaluated the relationship of benefits and risks of PACs in 1000 patients with established acute lung injury in a randomized trial comparing hemodynamic management guided by a PAC with hemodynamic management guided by a central venous catheter (CVC) using an explicit management protocol. Mortality during the first 60 days before discharge home was the primary outcome. RESULTS: The groups had similar baseline characteristics. The rates of death during the first 60 days before discharge home were similar in the PAC and CVC groups (27.4 percent and 26.3 percent, respectively; P=0.69; absolute difference, 1.1 percent; 95 percent confidence interval, -4.4 to 6.6 percent), as were the mean (+/-SE) numbers of both ventilator-free days (13.2+/-0.5 and 13.5+/-0.5; P=0.58) and days not spent in the intensive care unit (12.0+/-0.4 and 12.5+/-0.5; P=0.40) to day 28. PAC-guided therapy did not improve these measures for patients in shock at the time of enrollment. There were no significant differences between groups in lung or kidney function, rates of hypotension, ventilator settings, or use of dialysis or vasopressors. Approximately 90 percent of protocol instructions were followed in both groups, with a 1 percent rate of crossover from CVC- to PAC-guided therapy. Fluid balance was similar in the two groups, as was the proportion of instructions given for fluid and diuretics. Dobutamine use was uncommon. The PAC group had approximately twice as many catheter-related complications (predominantly arrhythmias). CONCLUSIONS: PAC-guided therapy did not improve survival or organ function but was associated with more complications than CVC-guided therapy. These results, when considered with those of previous studies, suggest that the PAC should not be routinely used for the management of acute lung injury. (ClinicalTrials.gov number, NCT00281268.).

Comparison of two fluid-management strategies in acute lung injury.

BACKGROUND: Optimal fluid management in patients with acute lung injury is unknown. Diuresis or fluid restriction may improve lung function but could jeopardize extrapulmonary-organ perfusion. METHODS: In a randomized study, we compared a conservative and a liberal strategy of fluid management using explicit protocols applied for seven days in 1000 patients with acute lung injury. The primary end point was death at 60 days. Secondary end points included the number of ventilator-free days and organ-failure-free days and measures of lung physiology. RESULTS: The rate of death at 60 days was 25.5 percent in the conservative-strategy group and 28.4 percent in the liberal-strategy group (P=0.30; 95 percent confidence interval for the difference, -2.6 to 8.4 percent). The mean (+/-SE) cumulative fluid balance during the first seven days was -136+/-491 ml in the conservative-strategy group and 6992+/-502 ml in the liberal-strategy group (P<0.001). As compared with the liberal strategy, the conservative strategy improved the oxygenation index ([mean airway pressure x the ratio of the fraction of inspired oxygen to the partial pressure of arterial oxygen]x100) and the lung injury score and increased the number of ventilator-free days (14.6+/-0.5 vs. 12.1+/-0.5, P<0.001) and days not spent in the intensive care unit (13.4+/-0.4 vs. 11.2+/-0.4, P<0.001) during the first 28 days but did not increase the incidence or prevalence of shock during the study or the use of dialysis during the first 60 days (10 percent vs. 14 percent, P=0.06). CONCLUSIONS: Although there was no significant difference in the primary outcome of 60-day mortality, the conservative strategy of fluid management improved lung function and shortened the duration of mechanical ventilation and intensive care without increasing nonpulmonary-organ failures. These results support the use of a conservative strategy of fluid management in patients with acute lung injury. (ClinicalTrials.gov number, NCT00281268 [ClinicalTrials.gov].).

Acquired weakness, handgrip strength, and mortality in critically ill patients.

RATIONALE: ICU-acquired paresis (ICUAP) is common in survivors of critical illness. There is significant associated morbidity, including prolonged time on the ventilator and longer hospital stay. However, it is unclear whether ICUAP is independently associated with mortality, as sicker patients are more prone and existing studies have not adjusted for this. OBJECTIVES: To test the hypothesis that ICUAP is independently associated with increased mortality. Secondarily, to determine if handgrip dynamometry is a concise measure of global strength and is independently associated with mortality. METHODS: A prospective multicenter cohort study was conducted in intensive care units (ICU) of five academic medical centers. Adults requiring at least 5 days of mechanical ventilation without evidence of preexisting neuromuscular disease were followed until awakening and were then examined for strength. MEASUREMENTS AND MAIN RESULTS: We measured global strength and handgrip dynamometry. The primary outcome was in-hospital mortality and secondary outcomes were hospital and ICU-free days, ICU readmission, and recurrent respiratory failure. Subjects with ICUAP (average MRC score of < 4) had longer hospital stays and required mechanical ventilation longer. Handgrip strength was lower in subjects with ICUAP and had good test performance for diagnosing ICUAP. After adjustment for severity of illness, ICUAP was independently associated with hospital mortality (odds ratio [OR], 7.8; 95% confidence interval [CI], 2.4-25.3; P = 0.001). Separately, handgrip strength was independently associated with hospital mortality (OR, 4.5; 95% CI, 1.5-13.6; P = 0.007). CONCLUSIONS: ICUAP is independently associated with increased hospital mortality. Handgrip strength is also independently associated with poor hospital outcome and may serve as a simple test to identify ICUAP. Clinical trial registered with www.clinicaltrials.gov (NCT00106665).

Which hospitals have significantly better or worse than expected mortality rates for acute myocardial infarction patients? Improved risk adjustment with present-at-admission diagnoses.

BACKGROUND: Public reports that compare hospital mortality rates for patients with acute myocardial infarction are commonly used strategies for improving the quality of care delivered to these patients. Fair comparisons of hospital mortality rates require thorough adjustments for differences among patients in baseline mortality risk. This study examines the effect on hospital mortality rate comparisons of improved risk adjustment methods using diagnoses reported as present-at-admission. METHODS AND RESULTS: Logistic regression models and related methods originally used by California to compare hospital mortality rates for patients with acute myocardial infarction are replicated. These results are contrasted with results obtained for the same hospitals by patient-level mortality risk adjustment models using present-at-admission diagnoses, using 3 statistical methods of identifying hospitals with higher or lower than expected mortality: indirect standardization, adjusted odds ratios, and hierarchical models. Models using present-at-admission diagnoses identified substantially fewer hospitals as outliers than did California model A for each of the 3 statistical methods considered. CONCLUSIONS: Large improvements in statistical performance can be achieved with the use of present-at-admission diagnoses to characterize baseline mortality risk. These improvements are important because models with better statistical performance identify different hospitals as having better or worse than expected mortality.

Veterans Affairs intensive care unit risk adjustment model: validation, updating, recalibration.

BACKGROUND: A valid metric is critical to measure and report intensive care unit (ICU) outcomes and drive innovation in a national system. OBJECTIVES: To update and validate the Veterans Affairs (VA) ICU severity measure (VA ICU). RESEARCH DESIGN: A validated logistic regression model was applied to two VA hospital data sets: 36,240 consecutive ICU admissions to a stratified random sample of moderate and large hospitals in 1999-2000 (cohort 1) and 81,964 cases from 42 VA Medical Centers in fiscal years 2002-2004 (cohort 2). The model was updated by adding diagnostic groups and expanding the source of admission variables. MEASURES: C statistic, Hosmer-Lemeshow goodness-of-fit statistic, and Brier's score measured predictive validity. Coefficients from the 1997 model were applied to predictors (fixed) in a logistic regression model. A 10 x 10 table compared cases with both VA ICU and National Surgical Quality Improvement Performance metrics. The standardized mortality ratios divided observed deaths by the sum of predicted mortality. RESULTS: The fixed model in both cohorts had predictive validity (cohort 1: C statistic = 0.874, Hosmer-Lemeshow goodness-of-fit C statistic chi-square = 72.5; cohort 2: 0.876, 307), as did the updated model (cohort 2: C statistic = 0.887, Hosmer-Lemeshow goodness-of-fit C statistic chi-square = 39). In 7,411 cases with predictions in both systems, the standardized mortality ratio was similar (1.04 for VA ICU, 1.15 for National Surgical Quality Improvement Performance), and 92% of cases matched (+/-1 decile) when ordered by deciles of mortality. The VA ICU standardized mortality ratio correlates with the National Surgical Quality Improvement Performance standardized mortality ratio (r2 = .74). Variation in discharge and laboratory practices may affect performance measurement. CONCLUSION: The VA ICU severity model has face, construct, and predictive validity.

Glucose variability and mortality in patients with sepsis.

OBJECTIVE: Treatment and prevention of hyperglycemia has been advocated for subjects with sepsis. Glucose variability, rather than the glucose level, has also been shown to be an important factor associated with in-hospital mortality, in general, critically ill patients. Our objective was to determine the association between glucose variability and hospital mortality in septic patients and the expression of glucose variability that best reflects this risk. DESIGN: Retrospective, single-center cohort study. SETTING: Academic, tertiary care hospital. PATIENTS: Adult subjects hospitalized for >1 day, with a diagnosis of sepsis were included. INTERVENTIONS: None. MEASUREMENTS: Glucose variability was calculated for all subjects as the average and standard deviation of glucose, the mean amplitude of glycemic excursions, and the glycemic lability index. Hospital mortality was the primary outcome variable. Logistic regression was used to determine the odds of hospital death in relation to measures of glucose variability after adjustment for important covariates. MAIN RESULTS: Of the methods used to measure glucose variability, the glycemic lability index had the best discrimination for mortality (area under the curve = 0.67, p < 0.001). After adjustment for confounders, including the number of organ failures and the occurrence of hypoglycemia, there was a significant interaction between glycemic lability index and average glucose level, and the odds of hospital mortality. Higher glycemic lability index was not independently associated with mortality among subjects with average glucose levels above the median for the cohort. However, subjects with increased glycemic lability index, but lower average glucose values had almost five-fold increased odds of hospital mortality (odds ratio = 4.73, 95% confidence interval = 2.6-8.7) compared with those with lower glycemic lability index. CONCLUSIONS: Glucose variability is independently associated with hospital mortality in septic patients. Strategies to reduce glucose variability should be studied to determine whether they improve the outcomes of septic patients.

Effect of decisions to withhold life support on prolonged survival.

BACKGROUND: The effect on long-term mortality of decisions made to withhold life-supporting therapies (LST) for critically ill patients is unclear. We hypothesized that mortality 60 days after ICU admission is not influenced by a decision to withhold use of LST in the context of otherwise providing all indicated care. METHODS: We studied 2,211 consecutive, initial admissions to the adult, medical ICU of a university-affiliated teaching hospital. To achieve balanced groups for comparing outcomes, we created a multivariable regression model for the probability (propensity score [PS]) of having an order initiated in the ICU to withhold LST. Each of the 201 patients with such an order was matched to the patient without such an order having the closest PS; mortality rates were compared between the matched pairs. Cox survival analysis was performed to extend the main analysis. RESULTS: The matched pairs were well balanced with respect to all of the potentially confounding variables. Sixty days after ICU admission, 50.5% of patients who had an order initiated in the ICU to withhold life support had died, compared to 25.8% of those lacking such orders (risk ratio, 2.0; 95% confidence interval, 1.5 to 2.6). Survival analysis indicated that the difference in mortality between the two groups continued to increase for approximately 1 year. CONCLUSION: Contrary to our hypothesis, decisions made in the ICU to withhold LST were associated with increased mortality rate to at least 60 days after ICU admission.

Present-at-admission diagnoses improved mortality risk adjustment among acute myocardial infarction patients.

OBJECTIVE: Hospital mortality outcomes for acute myocardial infarction (AMI) patients are a focus of quality improvement programs conducted by government agencies. AMI mortality risk-adjustment models using administrative data typically adjust for baseline differences in mortality risk with a limited set of common and definite comorbidities. In this study, we present an AMI mortality risk-adjustment model that adjusts for comorbid disease and for AMI severity using information from secondary diagnoses reported as present at admission for California hospital patients. STUDY DESIGN AND SETTING: AMI patients were selected from California hospital administrative data for 1996 through 1999 according to criteria used by the California Hospital Outcomes Project Report on Heart Attack Outcomes, a state-mandated public report that compares hospital mortality outcomes. We compared results for the new model to two mortality risk-adjustment models used to assess hospital AMI mortality outcomes by the state of California, and to two other models used in prior research. RESULTS: The model using present-at-admission diagnoses obtained substantially better discrimination between predicted survival and inpatient death than the other models we considered. CONCLUSION: AMI mortality risk-adjustment methods can be meaningfully improved using present-at-admission diagnoses to identify comorbid disease and conditions related closely to AMI.

Physicians' influence over decisions to forego life support.

OBJECTIVE: To quantify the association between individual physicians and decisions to limit use of life supporting therapies for critically ill patients. STUDY DESIGN: Prospective, observational data collected 2002-2005 in the adult medical intensive care unit (ICU) of a publicly owned teaching hospital. Nine intensivists staff this closed-model ICU; in rotating 2 week blocks of time one intensivist is responsible for directing all care. In order to uniquely associate care with individual physicians, eligible patients were cared for by a single intensivist throughout their ICU stay. Life support decisions were identified as orders to withhold or withdraw any form of life supporting medical therapy, including cardiopulmonary resuscitation (CPR), defibrillation, invasive mechanical ventilation, vasoactive drugs, and renal replacement therapy. We used multivariable Cox modeling to identify variables associated with decisions to limit use of life support. The association with the individual physicians was assessed as the hazard ratios of indicator variables representing the individual physicians. RESULTS: A decision to limit use of life support was made in 191 (14.0%) of 1363 ICU admissions. The hazard ratios associated with individual intensivists spanned a 15-fold range (0.069-1.042; p = 0.0003). Decisions to limit life support were more strongly related to the identity of the intensivist than to comorbid conditions, acute diagnostic category, and the source of ICU admission. CONCLUSIONS: We have, for the first time, quantified the association between individual physicians and decisions made to limit life support for critically ill patients. More research is needed to understand the nature and implications of this association.

Present-at-admission diagnoses improve mortality risk adjustment and allow more accurate assessment of the relationship between volume of lung cancer operations and mortality risk.

BACKGROUND: Mortality risk adjustment is a key component of studies that examine the statistical relationship between hospital lung cancer operation volume and in-hospital mortality. Previous studies of this relationship have used different methods of adjusting for factors that influence mortality risk, but none have adjusted for differences in comorbid disease using only diagnoses identified as present-at-admission. METHODS: This study uses adjustments for conditions identified as present-at-admission to examine the statistical relationship between the volume of lung cancer operations and mortality among 14,456 California hospital patients, and compares these results to other methods of risk adjustment similar to those used in previous studies. RESULTS: Mortality risk adjustment using present-at-admission diagnoses yielded better discrimination and explained more of the variability in observed deaths. Large increases in hospital procedure volume were associated with much smaller decreases in mortality risk than those estimated using comparable risk-adjustment models. CONCLUSIONS: Present-at-admission diagnoses can be used to improve mortality risk adjustment and may allow a more accurate assessment of the relationship between procedure volume and mortality risk.

Risk adjustment effect on stroke clinical trials.

BACKGROUND AND PURPOSE: The ischemic stroke population is heterogeneous. Even in balanced randomized trials, patient heterogeneity biases estimates of the treatment effect toward no effect when dichotomous end points are used. Risk adjustment statistically addresses some of the heterogeneity and can reduce bias in the treatment effect estimate. The purpose of this study was to estimate the treatment effect of tissue plasminogen activator (tPA) in the National Institute of Neurological Disorders and Stroke (NINDS) tPA data set with and without adjustment for baseline differences. METHODS: Using a prespecified predictive model, we calculated unadjusted and risk-adjusted odds ratios (ORs) for favorable outcome for the Barthel Index, National Institutes of Health Stroke Scale, and Glasgow Outcome Scale for the patients in the NINDS tPA stroke trial. To assess the importance of the difference, a new sample size was calculated through the use of the risk-adjusted analysis. RESULTS: We analyzed 615 subjects. The ORs for the Barthel Index were 1.76 (unadjusted) and 2.04 (adjusted). The National Institutes of Health Stroke Scale and Glasgow Outcome Scale analyses also demonstrated increased ORs after adjustment. The estimated sample size required for the adjusted comparison was 13% smaller than the unadjusted sample. CONCLUSIONS: Risk adjustment in this data set suggests that the true treatment effect was larger than estimated by the unadjusted analysis. Stroke clinical trials should include prospective risk adjustment methodologies.

Combined clinical and imaging information as an early stroke outcome measure.

BACKGROUND AND PURPOSE: Imaging information has been proposed as a potential surrogate outcome in stroke clinical trials. The purpose of this study was to determine whether an early outcome measure combining clinical and imaging information is better than either alone in predicting 3-month outcome in acute ischemic stroke patients. METHODS: Clinical information (National Institutes of Health Stroke Scale) and imaging information (CT infarct volume), measured at 1 week from 201 patients from the Randomized Trial of Tirilazad Mesylate in Acute Stroke (RANTTAS) study, were used in a multivariable logistic regression analysis to predict excellent and devastating 3-month outcome. The combined models were compared with the infarct volume models and the clinical models. Discrimination, calibration, and change in global model chi-square were assessed. RESULTS: The combined models and models using clinical information alone had areas under the receiver operating characteristic curves that did not differ significantly (probability value = 0.092 to 0.4), ranging from 0.83 to 0.95. The imaging alone models performed less well (P<0.005) and had areas under the receiver operating characteristic curves that ranged from 0.70 to 0.80. CONCLUSIONS: The National Institutes of Health Stroke Scale at 1 week is highly predictive of 3-month outcome in ischemic stroke patients. The addition of 1-week infarct volume does not improve the accuracy of the predictive model.

Predicting outcome in ischemic stroke: external validation of predictive risk models.

BACKGROUND: Six multivariable models predicting 3-month outcome of acute ischemic stroke have been developed and internally validated previously. The purpose of this study was to externally validate the previous models in an independent data set. SUMMARY OF REPORT: We predicted outcomes for 299 patients with ischemic stroke who received placebo in the National Institute of Neurological Disorders and Stroke rt-PA trial. The model equations used 6 acute clinical variables and head CT infarct volume at 1 week as independent variables and 3-month National Institutes of Health Stroke Scale, Barthel Index, and Glasgow Outcome Scale as dependent variables. Previously developed model equations were used to forecast excellent and devastating outcome for subjects in the placebo tissue plasminogen activator data set. Area under the receiver operator characteristic curve was used to measure discrimination, and calibration charts were used to measure calibration. The validation data set patients were more severely ill (National Institutes of Health Stroke Scale and infarct volume) than the model development subjects. Area under the receiver operator characteristic curves demonstrated remarkably little degradation in the validation data set and ranged from 0.75 to 0.89. Calibration curves showed fair to good calibration. CONCLUSIONS: Our models have demonstrated excellent discrimination and acceptable calibration in an external data set. Development and validation of improved models using variables that are all available acutely are necessary.

The relation of autopsy rate to physicians' beliefs and recommendations regarding autopsy.

PURPOSE: Multiple factors have affected the decline in autopsy rates. Our goal was to determine the relation of physicians' recommendations regarding autopsy, as well as patient and surrogate decision-maker characteristics, to autopsy performance. METHODS: We assessed measures related to autopsy performance using data from two teaching institutions in the Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatments. We included patients who had died within 6 months of their index hospitalization and for whom information was available on autopsy performance, physicians' response to questions about autopsy, and interviews with surrogate decision makers about autopsy performance. We assessed the association between autopsy performance and the strength of a physician's recommendation for autopsy, adjusting for patient, surrogate, and physician characteristics. RESULTS: Of the 680 patients who died, 59% (n = 402) met our inclusion criteria. Based on physician and surrogate responses, the expected autopsy rate was 42% while the actual autopsy rate was 23%. The autopsy rate was higher when the physician's recommendation for autopsy was strong or very strong at the time of death compared with when autopsy was not recommended strongly or not at all (P <0.001). The strength of the physician's postmortem recommendation was independently associated with autopsy performance after adjusting for patient, surrogate, and physician characteristics (P <0.001). CONCLUSION: Autopsies are less likely to be performed when not recommended strongly or not at all. Training physicians (or others) how to recommend autopsies may increase autopsy rates.