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Turner M Osler

Publications and source records attributed to Turner M Osler.

At least 19 recordsLinked to original sources
PubMed2007

Racial differences in the use of epidural analgesia for labor.

BACKGROUND: There is strong evidence that pain is undertreated in black and Hispanic patients. The association between race and ethnicity and the use of epidural analgesia for labor is not well described. METHODS: Using the New York State Perinatal Database, the authors examined whether race and ethnicity were associated with the likelihood of receiving epidural analgesia for labor after adjusting for clinical characteristics, demographics, insurance coverage, and provider effect. This retrospective cohort study was based on 81,883 women admitted for childbirth between 1998 and 2003. RESULTS: Overall, 38.3% of the patients received epidural analgesia for labor. After adjusting for clinical risk factors, socioeconomic status, and provider fixed effects, Hispanic and black patients were less likely than non-Hispanic white patients to receive epidural analgesia: The adjusted odds ratio was 0.85 (95% CI, 0.78-0.93) for white/Hispanic and 0.78 (0.74-0.83) for blacks compared with non-Hispanic whites. Compared with patients with private insurance, patients without insurance were least likely to receive epidural analgesia (adjusted odds ratio, 0.76; 95% CI, 0.64-0.89). Black patients with private insurance had similar rates of epidural use to white/non-Hispanic patients without insurance coverage: The adjusted odds ratio was 0.66 (95% CI, 0.53-0.82) for white/non-Hispanic patients without insurance versus 0.69 (0.57-0.85) for black patients with private insurance. CONCLUSION: Black and Hispanic women in labor are less likely than non-Hispanic white women to receive epidural analgesia. These differences remain after accounting for differences in insurance coverage, provider practice, and clinical characteristics.

Adult↗
PubMed2006

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.

Critical Illness↗
PubMed2006

Impact of changing the statistical methodology on hospital and surgeon ranking: the case of the New York State cardiac surgery report card.

BACKGROUND: Risk adjustment is central to the generation of health outcome report cards. It is unclear, however, whether risk adjustment should be based on standard logistic regression, fixed-effects or random-effects modeling. OBJECTIVE: The objective of this study was to determine how robust the New York State (NYS) Coronary Artery Bypass Graft (CABG) Surgery Report Card is to changes in the underlying statistical methodology. METHODS: Retrospective cohort study based on data from the NYS Cardiac Surgery Reporting System on all patient undergoing isolated CABG surgery in NYS and who were discharged between 1997 and 1999 (51,750 patients). Using the same risk factors as in the NYS models, fixed-effects and random-effects models were fitted to the NYS data. Quality outliers were identified using 1) the ratio of observed-to-expected mortality rates (O/E ratio) and confidence intervals (CIs) calculated using both parametric (Poisson distribution) and nonparametric (bootstrapping) techniques; and 2) shrinkage estimators. RESULTS: At the surgeon level, the standard logistic regression model, the fixed-effects model, and the fixed-effects component of the random-effects model demonstrated near-perfect agreement on the identity of quality outliers using a quality indicator based on the O/E ratio and the Poisson distribution. Shrinkage estimators identified the fewest outliers, whereas the O/E ratios with bootstrap CI identified the greatest number of outliers. The results were similar for hospitals, except that the fixed-effects model identified more outliers than either the NYS model or the fixed-effects component of the random-effects model. CONCLUSION: Shrinkage estimators based on random-effects models are slightly more conservative in identifying quality outliers compared with the traditional approach based on fixed-effects modeling and standard regression. Explicitly modeling surgeon provider effect (fixed-effects and random-effects models) did not significantly alter the distribution of quality outliers when compared with standard logistic regression (which does not model provider effect). Compared with the standard parametric approach, the use of a bootstrap approach to construct 95% confidence interval around the O/E ratio resulted in more providers being identified as quality outliers.

Benchmarking↗
PubMed2006

Incorporating recent advances to make the TRISS approach universally available.

BACKGROUND: The Trauma and Injury Severity Score (TRISS), used to garner predictions of survival from the Injury Severity Score (ISS), the Revised Trauma Score (RTS, for physiologic reserve), and age is difficult for many trauma facilities to compute because it requires 8 to 10 variables and ISS depends on the specialized Abbreviated Injury Scale (AIS) scale rather than the International Classification of Diseases scale (ICD-9). It has been shown that metrics describing a patient's worst injury (WORSTSRR) are a powerful predictor of survival (regardless of coding type, AIS versus ICD-9) and that the Glasgow Coma Scale (GCS) motor component contains the majority of the information found in the full GCS score. This study hypothesized that the TRISS approach could be made more predictive and efficient with fewer variables by incorporating these advances. METHODS: A total of 310,958 patients with nonmissing TRISS variables were subset from the National Trauma Data Bank (NTDB). Logistic regression was used to model mortality as a function of anatomic, physiologic and age variables. A traditional TRISS model was computed (with NTDB-derived coefficients) that uses ISS, RTS, age index, and mechanism to predict survival. Four smaller three- or four-variable models employed the ICD-9 WORSTSRR, the GCS motor component, and age (both continuously and dichotomously). Two of the four models also use mechanism. These models were compared using the concordance index (c-index, a measure of model discrimination) and the pseudo-R statistic (estimates proportion of variance explained). RESULTS: Each experimental model (two models with 3 variables and two models with 4 variables) have superior discrimination and explain more variance than the traditional TRISS model that employs 8-10 variables. CONCLUSIONS: Recent advances in anatomic and physiologic scoring markedly simplify TRISS-type models at no cost to prediction. This approach uses routinely available data, requires up to seven fewer terms, and predicts at least as well as the original TRISS. These findings could increase the availability of accurate trauma scoring tools to smaller trauma facilities.

Abbreviated Injury Scale↗
PubMed2006

Does date stamping ICD-9-CM codes increase the value of clinical information in administrative data?

CONTEXT: Comorbidity measures are designed to exclude complications when they map International Classification of Diseases (ICD-9-CM) codes to diagnostic categories. The use of data fields that indicates whether each secondary diagnosis was present at the time of hospital admission may lead to the more accurate identification of preexisting conditions. OBJECTIVE: To examine the rate of misclassification of ICD-9-CM codes into diagnostic categories by the Dartmouth-Manitoba adaptation of the Charlson index and by the Elixhauser comorbidity algorithm. DATA SOURCE: Analysis of 178,838 patients in the California State Inpatient Database (CA SID) admitted in 2000 for one of seven major medical and surgical conditions. The CA SID includes a condition present at admission (CPAA) modifier for each ICD-9-CM code. STUDY DESIGN: The Dartmouth/Charlson index and the Elixhauser comorbidity measure were used to map the ICD-9-CM codes into diagnostic categories for patients in each study population. We calculated the misclassification rate for each mapping algorithm, using information from the CPAA as the "gold standard." PRINCIPAL FINDINGS: The Dartmouth/Charlson index underestimated the prevalence of hemiplegia/paraplegia by 70 percent, cerebrovascular disease by 70 percent, myocardial infarction by 65 percent, congestive heart failure (CHF) by 45 percent, and peptic ulcer disease by 34 percent. The Elixhauser algorithm misclassified complications as preexisting conditions for 43 percent of the coagulopathies, 25 percent of the fluid and electrolyte disorders, 18 percent of the cardiac arrhythmias, 18 percent of the cardiac arrhythmias, and 9 percent of the cases of CHF. CONCLUSION: Adding the CPAA modifier to administrative data would significantly enhance the ability of the Dartmouth/Charlson index and of the Elixhauser algorithm to map ICD-9-CM codes to diagnostic categories accurately.

Algorithms↗
PubMed2006

Accuracy of hospital report cards based on administrative data.

CONTEXT: Many of the publicly available health quality report cards are based on administrative data. ICD-9-CM codes in administrative data are not date stamped to distinguish between medical conditions present at the time of hospital admission and complications, which occur after hospital admission. Treating complications as preexisting conditions gives poor-performing hospitals "credit" for their complications and may cause some hospitals that are delivering low-quality care to be misclassified as average- or high-performing hospitals. OBJECTIVE: To determine whether hospital quality assessment based on administrative data is impacted by the inclusion of condition present at admission (CPAA) modifiers in administrative data as a date stamp indicator. DESIGN, SETTING, AND PATIENTS: Retrospective cohort study based on 648,866 inpatient admissions between 1998 and 2000 for coronary artery bypass graft (CABG) surgery, coronary angioplasty (PTCA), carotid endarterectomy (CEA), abdominal aortic aneurysm (AAA) repair, total hip replacement (THR), acute MI (AMI), and stroke using the California State Inpatient Database which includes CPAA modifiers. Hierarchical logistic regression was used to create separate condition-specific risk adjustment models. For each study population, one model was constructed using only secondary diagnoses present at admission based on the CPAA modifier: "date stamp" model. The second model was constructed using all secondary diagnoses, ignoring the information present in the CPAA modifier: the "no date stamp model." Hospital quality was assessed separately using the "date stamp" and the "no date stamp" risk-adjustment models. RESULTS: Forty percent of the CABG hospitals, 33 percent of the PTCA hospitals, 40 percent of the THR hospitals, and 33 percent of the AMI hospitals identified as low-performance hospitals by the "date stamp" models were not classified as low-performance hospitals by the "no date stamp" models. Fifty percent of the CABG hospitals, 33 percent of the PTCA hospitals, 50 percent of the CEA hospitals, and 36 percent of the AMI hospitals identified as low-performance hospitals by the "no date stamp" models were not identified as low-performance hospitals by the "date stamp" models. The inclusion of the CPAA modifier had a minor impact on hospital quality assessment for AAA repair, stroke, and CEA. CONCLUSION: This study supports the hypothesis that the use of routine administrative data without date stamp information to construct hospital quality report cards may result in the mis-identification of hospital quality outliers. However, the CPAA modifier will need to be further validated before date stamped administrative data can be used as the basis for health quality report cards.

Aged↗
PubMed2005

Evaluating trauma center quality: does the choice of the severity-adjustment model make a difference?

CONTEXT: The Major Trauma Outcome Study (MTOS) database was created by the American College of Surgeons over 20 years ago to establish national norms for trauma care. The primary trauma outcome prediction models used for evaluating the quality of trauma care, TRISS and ASCOT (A Severity Characterization of Trauma), were developed using the MTOS database. OBJECTIVE: First, to determine whether TRISS and ASCOT agree on hospital quality. Second, to determine whether TRISS and ASCOT accurately reflect contemporary outcomes in trauma care. DESIGN, SETTING AND PATIENTS: A retrospective cohort study based on 91,112 patients admitted to 69 hospitals between 2000 and 2001 in the National Trauma Databank. Using TRISS and ASCOT, the ratio of the observed to expected mortality rate (O/E ratio) was calculated for each hospital. Hospitals whose O/E ratio was statistically different from 1 were identified as quality outliers. Kappa analysis was used to assess the degree to which TRISS and ASCOT agreed on the identity of hospital quality outliers. RESULTS: TRISS and ASCOT disagreed on the outlier status of 35 of the 69 hospitals. Kappa analysis revealed only fair agreement (kappa = 0.23; p = 0.0015) between TRISS and ASCOT in identifying quality outliers. Thirty-eight hospitals were identified by the TRISS method as high-performance hospitals. CONCLUSION: First, TRISS and ASCOT exhibit substantial disagreement on the identity of quality outliers within the NTDB. Second, an unrealistically high number of hospitals were identified as high-performance outliers using either TRISS or ASCOT. These findings have important implications for the use of TRISS and ASCOT for benchmarking performance and quality improvement.

Calibration↗
PubMed2005

The relation between surgeon volume and outcome following off-pump vs on-pump coronary artery bypass graft surgery.

STUDY OBJECTIVE: Off-pump coronary artery bypass graft (CABG) surgery has been recently reintroduced into clinical practice. In light of the relatively low level of experience of most cardiac surgeons with off-pump CABG surgery, and the exceptional technical challenge of working on a "beating heart," off-pump CABG surgery presents a unique opportunity to explore the effect of surgeon case volume on surgical outcome after controlling for the effects of patient case mix and hospital volume. DESIGN: A retrospective cohort study analyzing the association between surgeon volume and in-hospital mortality rate for off-pump and on-pump CABG surgery using random-effects logistic regression modeling. SETTING AND PATIENTS: The analyses were based on the New York State clinical CABG surgery registry. The study sample consisted of 36,930 patients undergoing isolated CABG surgery between 1998 and 1999 that was performed by 181 surgeons at 33 hospitals. INTERVENTIONS: None. RESULTS: There is no association between the number of CABG procedures performed off-pump by an individual surgeon and in-hospital mortality rates (p = 0.93) after controlling for hospital CABG surgery volume and patient-level risk factors. There is also no association between the off-pump CABG surgery mortality rate and the total number of both off-pump and on-pump CABG surgery cases (p = 0.78). In the on-pump CABG surgery cohort, surgeons performing a high volume of CABG procedures had significantly lower risk-adjusted mortality rates among their patients compared to those performing a very low volume, a low-volume, and a medium volume of CABG procedures (p < 0.006). CONCLUSION: For off-pump CABG surgery, surgeons performing a high volume of procedures do not have better mortality outcomes than those performing a low volume of procedures. However, higher surgeon case volumes are associated with lower mortality rates for on-pump CABG surgery. The absence of a volume-outcome association for off-pump CABG surgery is especially surprising in light of the more technically demanding nature of off-pump CABG surgery compared to on-pump CABG surgery.

Cohort Studies↗
PubMed2004

Judging trauma center quality: does it depend on the choice of outcomes?

BACKGROUND: Trauma centers routinely benchmark their survival outcomes against a national norm using the TRISS methodology. However, the use of survival as a measure of the effectiveness of trauma care may be too limited in scope because it fails to capture information regarding functional outcomes. METHODS: The objective of this study was to develop a prediction model that allows hospitals to benchmark their functional outcomes in blunt trauma patients, and to determine whether the assessment of hospital "quality" depends on the choice of outcome measure: survival or survival combined with functional outcome. This retrospective cohort study was based on patients, aged 18 years or older, in the National Trauma Database who sustained blunt trauma in 1999 without associated head or spinal cord injury. We developed a sequential logistic model to predict the probability of a good functional outcome. The TRISS methodology was customized to this data set to obtain a survival model. Using each of these prediction models, we then obtained two standardized measures of hospital performance: one based on the number of survivors and the other based on the number of survivors with good functional outcomes. These standardized outcome measures were then used to identify low-performance and high-performance hospitals. The ranking based on these two different measures were compared. RESULTS: Fifteen of the 27 hospitals in the study cohort were categorized differently when their performance was benchmarked using survival versus functional outcome. Kappa analysis revealed minimal agreement between these two quality measures on the identity of hospital quality outliers (kappa = 0.04; p = 0.35). CONCLUSION: The evaluation of hospital quality depends on whether hospital performance is judged by looking at survival or at survival combined with functional outcome. Because functional status is an important outcome of major concern to survivors, it is important to include it in hospital performance assessment. Consideration should be given to including functional outcome in the evaluation of trauma center performance.

Benchmarking↗
PubMed2004

The relation between trauma center outcome and volume in the National Trauma Databank.

BACKGROUND: Regionalization of trauma care services aims to improve outcomes by limiting trauma care delivery to a select group of dedicated trauma centers. However, the evidence linking trauma center volume and outcome is not conclusive. The objective of this study was to examine the volume-mortality relation for patients with severe trauma in the National Trauma Databank. METHODS: This study was based on data for adult patients 18 years of age or older in the National Trauma Databank with an Injury Severity Score (ISS) of 15 or more who sustained either blunt or penetrating trauma. The main outcome measure was in-hospital survival as a function of trauma center volume. Logistic regression modeling was used to analyze the relation between survival and hospital volume for patients sustaining either severe blunt or severe penetrating trauma. RESULTS: For the blunt trauma cohort, model diagnostics showed that the single highest-volume center was an outlier. After exclusion of the patients from this center, no association could be demonstrated between trauma volume and outcome (p = 0.465) for blunt trauma. A separate multivariate analysis of patients with penetrating trauma also could not demonstrate a significant volume-mortality association (p = 0.919). Both regression models exhibited excellent discrimination and acceptable calibration. CONCLUSION: The findings of this study do not support the position that higher trauma center volumes are associated with improved survival. The implication of this study is that the hospital volume criteria established by the American College of Surgeons may need to be reexamined.

Adolescent↗
PubMed2004

A note on the disjointed nature of the injury severity score.

OBJECTIVE: The Injury Severity Score (ISS) is widely used for anatomic severity assessments. The ISS is the sum of the squares of a patient's three worst Abbreviated Injury Scale (AIS) severities (1-6) from three specified body regions. The set of three AIS severities (including 0s) is called a "triplet." ISS values of 9, 17, 18, 25, 26, 27, 29, 33, 34, 41, and 50 can originate from two unique triplets, but it is not clear whether the mortalities of the triplets are equal. A related question regards the monotonicity of the ISS, that is, whether mortality increases with successive values of ISS. This study sought to compare the mortality of equivalent ISS values from different triplets and to evaluate whether ISS is a monotonic function of mortality. METHODS: The ISS, its corresponding three-digit triplet, and the ICISS (an International Classification of Diseases, Ninth Revision-based competing score) were calculated for 361,381 National Trauma Data Bank patients. Fisher's exact tests were used to test for mortality differences between triplets that yield the same ISS. Plots of mortality by score value were produced to visually assess the monotonicity of the ICISS and the ISS. RESULTS: Six of the 11 triplet pairs had mortalities that differed by greater than 20%, with the largest difference being 32% for an ISS of 25 (triplets 0, 0, 5 and 0, 3, 4). Two other values (9 and 17) have triplet pairs whose mortality differences are less but still statistically different. The ISS is markedly nonmonotonic and is characterized by large spikes in mortality for successive ISS values. Plots of the ICISS show it to be largely monotonic. CONCLUSION: The ISS is a nonmonotonic, triplet-dependent function of mortality. Those who persist in using the ISS to describe populations or make risk adjustments should do so cautiously, being sure to account for triplet type. These suspect ISS values appear in approximately 25% of cases.

Humans↗
PubMed2003

Using hierarchical modeling to measure ICU quality.

OBJECTIVE: To determine whether hierarchical modeling agrees with conventional logistic regression modeling on the identity of ICU quality outliers within a large multi-institutional database. DESIGN: Retrospective database analysis. SETTING AND PATIENTS: Subset of the Project IMPACT database consisting of 40435 adult patients admitted to surgical, medical, and mixed surgical-medical ICUs ( n=55) between 1997 and 1999 who met inclusion criteria for SAPS II. MEASUREMENTS AND RESULTS: The SAPS II score was customized to this database using conventional logistic regression and using a hierarchical (random coefficients) model. Both models exhibited excellent discrimination ( Cstatistic) and calibration (Hosmer-Lemeshow statistic). The hierarchical and nonhierarchical models had C statistics of.870 and.865, and HL statistics of 3.71 ( p>.88, df=8) and 8.94 ( p>.35, df=8), respectively. Since the random effects component of the hierarchical model accounts for between-hospital variability, only the fixed-effects coefficients were used to calculate the expected mortality rate based on the hierarchical model. The ratio and 95% confidence intervals of the observed to expected mortality rate were calculated using both models for each ICU. ICUs whose observed/expected ratio was either less than 1 or greater than 1, and whose 95% confidence interval did not include 1 were labeled as either high-performance or low-performance outliers, respectively. Analysis using kappa statistic revealed almost perfect agreement between the two models (nonhierarchical vs. hierarchical) on the identity of ICU quality outliers. CONCLUSIONS: Models obtained by customizing SAPS II using a nonhierarchical and a hierarchical approach exhibit excellent agreement on the identity of ICU quality outliers.

APACHE↗
PubMed2003

Is the hospital volume-mortality relationship in coronary artery bypass surgery the same for low-risk versus high-risk patients?

BACKGROUND: There is evidence to support the existence of an inverse relation between mortality after coronary artery bypass graft (CABG) surgery and procedure volume. It is unclear whether all patients benefit equally from having CABG surgery performed at high-volume centers. The objective of this study was to determine whether the volume-outcome association for CABG surgery is modified by patient risk. METHODS: This retrospective cohort analysis was conducted using data from the Cardiac Surgery Reporting System database on all patients (20,078) undergoing CABG surgery in New York State who were discharged in 1996. The main outcome measure was in-hospital mortality as a function of procedure volume after adjusting for severity of disease. Logistic regression modeling was used to explore the interaction between patient risk and procedure volume. RESULTS: There is a significant interaction between procedure volume and patient risk (p = 0.01). The final model exhibits excellent discrimination (C statistic = 0.818) and goodness-of-fit (Hosmer-Lemeshow statistic = 6.02; p = 0.645). Very low (<0.5%) and low-risk (0.5%-2.0%) patients exhibit a greater reduction in CABG mortality than high (5.0%-10.0%) and very high risk (>10%) patients at high-volume centers relative to low-volume centers. Among the highest risk patients (>25% risk of mortality), higher risk patients have better outcomes at higher volume centers. CONCLUSIONS: For the vast majority of patients, low-risk patients benefit significantly more than high-risk patients from undergoing CABG surgery at high-volume centers instead of at low-volume centers. Low-risk patients benefit significantly more than high-risk patients from undergoing CABG surgery at high-volume centers instead of at low-volume centers. However, before generalizing these findings to other states, this study should be repeated using other regional population-based clinical databases.

Cohort Studies↗
PubMed2003

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.

Algorithms↗
PubMed2003

Independently derived survival risk ratios yield better estimates of survival than traditional survival risk ratios when using the ICISS.

BACKGROUND: The International Classification of Diseases, Ninth Revision Injury Severity Score (ICISS) is criticized because it relies on survival risk ratios (SRRs) that are contaminated by incidents with multiple injuries. An SRR for an International Classification of Diseases, Ninth Revision code is the number of patients who survive the injury divided by the number who display it. The ICISS is the product of SRRs that correspond to a patient's injuries. Traditional SRRs are derived from databases that include patients with multiple injuries and are biased toward mortality, making them nonindependent. Independent SRRs are derived from incidents where patients sustained only an isolated injury. The objective of this study is to compare the mortality prediction abilities of independent and traditional SRRs via the ICISS. METHODS: A 10-fold cross-validation design was used to estimate independent and traditional SRRs and their resulting ICISSs from 192,347 National Trauma Data Bank patients. Logistic regression modeled the scores as a function of mortality. The area under the receiver operating characteristic curve measured discrimination. Model fit was measured with the Akaike information criterion, a deviance statistic (lower is better). R2 values were compared to determine which score explained the most variance. RESULTS: The independent ICISS statistically outperforms the traditional ICISS. CONCLUSION: Traditional SRRs used by the ICISS produce less accurate estimates of mortality than independent SRRs. The ICISS can be calculated in 97.9% of incidents using independent SRRs.

Databases as Topic↗
PubMed2003

The worst injury predicts mortality outcome the best: rethinking the role of multiple injuries in trauma outcome scoring.

BACKGROUND: The prediction of outcome after injury must incorporate measures of injury severity, but there is no consensus on how many injuries should be used in calculating these measures. Initially, the single worst injury was used to predict outcome, but the introduction of the Injury Severity Score allowed up to three injuries to contribute to outcome prediction. Subsequently, other outcome prediction approaches used many (New Injury Severity Score [NISS]) or all (ICISS and Trauma Registry Abbreviated Injury Scale Score [TRAIS], which use International Classification of Diseases, Ninth Revision [ICD-9] and Abbreviated Injury Scale [AIS] survival risk ratios [SRRs], respectively) of a patient's injuries. The ability of only the most severe injury in predicting mortality has never been studied. Our objective was to determine the ability of a patient's worst injury to predict mortality. METHODS: A 10-fold cross-validation design was used to compute six scores for each of 160,208 patients from a large trauma database (the National Trauma Data Bank [NTDB]). The scores were ICISS, TRAIS, ICISS1 (only a patient's worst ICD-9 SRR), TRAIS1 (only a patient's worst AIS SRR), NISS (sum of squares of worst three AIS severity measures), and MAXAIS (worst AIS severity measure). Discrimination was assessed using the area under the receiver operating characteristic curve. Logistic regression R2 gauged the proportion of variance each score explained. The Akaike information criterion, a deviance statistic (lower is better), assessed model fit. RESULTS: The receiver operating characteristic curve, R2, and Akaike information criterion statistics (NC_ICISS and NC_ICDSRR1 represents scores derived from the original North Carolina Hospital Discharge Database SRRs) are summarized in tabular form in the Results section. CONCLUSION: Regardless of scoring type (ICD/AIS SRRs or AIS severity), a patient's worst injury discriminates survival better, fits better, and explains more variance than currently used multiple injury scores.

Abbreviated Injury Scale↗