Comparison of two prognostic models in trauma outcome

BACKGROUND: The Trauma Audit and Research Network (TARN) in the UK publicly reports hospital performance in the management of trauma. The TARN risk adjustment model uses a fractional polynomial transformation of the Injury Severity Score (ISS) as the measure of anatomical injury severity. The Trauma Mortality Prediction Model (TMPM) is an alternative to ISS; this study compared the anatomical injury components of the TARN model with the TMPM. METHODS: Data from the National Trauma Data Bank for 2011-2015 were analysed. Probability of death was estimated for the TARN fractional polynomial transformation of ISS and compared with the TMPM. The coefficients for each model were estimated using 80 per cent of the data set, selected randomly. The remaining 20 per cent of the data were used for model validation. TMPM and TARN were compared using calibration curves, measures of discrimination (area under receiver operating characteristic curves; AUROC), proximity to the true model (Akaike information criterion; AIC) and goodness of model fit (Hosmer-Lemeshow test). RESULTS: Some 438 058 patient records were analysed. TMPM demonstrated preferable AUROC (0·882 for TMPM versus 0·845 for TARN), AIC (18 204 versus 21 163) and better fit to the data (32·4 versus 153·0) compared with TARN. CONCLUSION: TMPM had greater discrimination, proximity to the true model and goodness-of-fit than the anatomical injury component of TARN. TMPM should be considered for the injury severity measure for the comparative assessment of trauma centres

An evaluation of POSSUM and P-POSSUM scoring in predicting post-operative mortality in a level 1 critical care setting

Background POSSUM and P-POSSUM are used in the assessment of outcomes in surgical patients. Neither scoring systems’ accuracy has been established where a level 1 critical care facility (level 1 care ward) is available for perioperative care. We compared POSSUM and P-POSSUM predicted with observed mortality on a level 1 care ward. Methods A prospective, observational study was performed between May 2000 and June 2008. POSSUM and P-POSSUM scores were calculated for all postoperative patients who were admitted to the level 1 care ward. Data for post-operative mortality were obtained from hospital records for 2552 episodes of patient care. Observed vs expected mortality was compared using receiver operating characteristic (ROC) curves and the goodness of fit assessed using the Hosmer-Lemeshow equation. Results ROC curves show good discriminative ability between survivors and non-survivors for POSSUM and P-POSSUM. Physiological score had far higher discrimination than operative score. Both models showed poor calibration and poor goodness of fit (Hosmer-Lemeshow). Observed to expected (O:E) mortality ratio for POSSUM and P-POSSUM indicated significantly fewer than expected deaths in all deciles of risk. Conclusions Our data suggest a 30-60% reduction in O:E mortality. We suggest that the use of POSSUM models to predict mortality in patients admitted to level 1 care ward is inappropriate or that a recalibration of POSSUM is required to make it useful in a level 1 care ward setting

Survival Analysis Part I: Basic concepts and first analyses

Survival analysis is a collection of statistical procedures for data analysis where the outcome variable of interest is time until an event occurs. Because of censoring - the nonobservation of the event of interest after a period of follow-up - a proportion of the survival times of interest will often be unknown. It is assumed that those patients who are censored have the same survival prospects as those who continue to be followed, that is, the censoring is uninformative. Survival data are generally described and modelled in terms of two related functions, the survivor function and the hazard function. The survivor function represents the probability that an individual survives from the time of origin to some time beyond time t. It directly describes the survival experience of a study cohort, and is usually estimated by the KM method. The logrank test may be used to test for differences between survival curves for groups, such as treatment arms. The hazard function gives the instantaneous potential of having an event at a time, given survival up to that time. It is used primarily as a diagnostic tool or for specifying a mathematical model for survival analysis. In comparing treatments or prognostic groups in terms of survival, it is often necessary to adjust for patient-related factors that could potentially affect the survival time of a patient. Failure to adjust for confounders may result in spurious effects. Multivariate survival analysis, a form of multiple regression, provides a way of doing this adjustment, and is the subject the next paper in this series

Preoperative calculation of risk for prolonged intensive care unit stay following coronary artery bypass grafting

OBJECTIVE: Patients who have prolonged stay in intensive care unit (ICU) are associated with adverse outcomes. Such patients have cost implications and can lead to shortage of ICU beds. We aimed to develop a preoperative risk prediction tool for prolonged ICU stay following coronary artery surgery (CABG). METHODS: 5,186 patients who underwent CABG between 1st April 1997 and 31st March 2002 were analysed in a development dataset. Logistic regression was used with forward stepwise technique to identify preoperative risk factors for prolonged ICU stay; defined as patients staying longer than 3 days on ICU. Variables examined included presentation history, co-morbidities, catheter and demographic details. The use of cardiopulmonary bypass (CPB) was also recorded. The prediction tool was tested on validation dataset (1197 CABG patients between 1(st )April 2003 and 31(st )March 2004). The area under the receiver operating characteristic (ROC) curve was calculated to assess the performance of the prediction tool. RESULTS: 475(9.2%) patients had a prolonged ICU stay in the development dataset. Variables identified as risk factors for a prolonged ICU stay included renal dysfunction, unstable angina, poor ejection fraction, peripheral vascular disease, obesity, increasing age, smoking, diabetes, priority, hypercholesterolaemia, hypertension, and use of CPB. In the validation dataset, 8.1% patients had a prolonged ICU stay compared to 8.7% expected. The ROC curve for the development and validation datasets was 0.72 and 0.74 respectively. CONCLUSION: A prediction tool has been developed which is reliable and valid. The tool is being piloted at our institution to aid resource management

Randomised trial of proton vs. carbon ion radiation therapy in patients with chordoma of the skull base, clinical phase III study HIT-1-Study

<p>Abstract</p> <p>Background</p> <p>Chordomas of the skull base are relative rare lesions of the bones. Surgical resection is the primary treatment standard, though complete resection is nearly impossible due to close proximity to critical and hence also dose limiting organs for radiation therapy. Level of recurrence after surgery alone is comparatively high, so adjuvant radiation therapy is very important for the improvement of local control rates. Proton therapy is the gold standard in the treatment of skull base chordomas. However, high-LET beams such as carbon ions theoretically offer biologic advantages by enhanced biologic effectiveness in slow-growing tumors.</p> <p>Methods/design</p> <p>This clinical study is a prospective randomised phase III trial. The trial will be carried out at Heidelberger Ionenstrahl-Therapie centre (HIT) and is a monocentric study.</p> <p>Patients with skull base chordoma will be randomised to either proton or carbon ion radiation therapy. As a standard, patients will undergo non-invasive, rigid immobilization and target volume delineation will be carried out based on CT and MRI data. The biologically isoeffective target dose to the PTV in carbon ion treatment (accelerated dose) will be 63 Gy E ± 5% and 72 Gy E ± 5% (standard dose) in proton therapy respectively. Local-progression free survival (LPFS) will be analysed as primary end point. Toxicity and overall survival are the secondary end points. Additional examined parameters are patterns of recurrence, prognostic factors and plan quality analysis.</p> <p>Discussion</p> <p>Up until now it was impossible to compare two different particle therapies, i.e. protons and carbon ions directly at the same facility.</p> <p>The aim of this study is to find out, whether the biological advantages of carbon ion therapy can also be clinically confirmed and translated into the better local control rates in the treatment of skull base chordomas.</p> <p>Trial registration</p> <p>ClinicalTrials.gov identifier: NCT01182779</p

The Procedural Index for Mortality Risk (PIMR): an index calculated using administrative data to quantify the independent influence of procedures on risk of hospital death

<p>Abstract</p> <p>Background</p> <p>Surgeries and other procedures can influence the risk of death in hospital. All published scales that predict post-operative death risk require clinical data and cannot be measured using administrative data alone. This study derived and internally validated an index that can be calculated using administrative data to quantify the independent risk of hospital death after a procedure.</p> <p>Methods</p> <p>For all patients admitted to a single academic centre between 2004 and 2009, we estimated the risk of all-cause death using the Kaiser Permanente Inpatient Risk Adjustment Methodology (KP-IRAM). We determined whether each patient underwent one of 503 commonly performed therapeutic procedures using Canadian Classification of Interventions codes and whether each procedure was emergent or elective. Multivariate logistic regression modeling was used to measure the association of each procedure-urgency combination with death in hospital independent of the KP-IRAM risk of death. The final model was modified into a scoring system to quantify the independent influence each procedure had on the risk of death in hospital.</p> <p>Results</p> <p>275 460 hospitalizations were included (137,730 derivation, 137,730 validation). In the derivation group, the median expected risk of death was 0.1% (IQR 0.01%-1.4%) with 4013 (2.9%) dying during the hospitalization. 56 distinct procedure-urgency combinations entered our final model resulting in a Procedural Index for Mortality Rating (PIMR) score values ranging from -7 to +11. In the validation group, the PIMR score significantly predicted the risk of death by itself (c-statistic 67.3%, 95% CI 66.6-68.0%) and when added to the KP-IRAM model (c-index improved significantly from 0.929 to 0.938).</p> <p>Conclusions</p> <p>We derived and internally validated an index that uses administrative data to quantify the independent association of a broad range of therapeutic procedures with risk of death in hospital. This scale will improve risk adjustment when administrative data are used for analyses.</p

Comparison of generalized estimating equations and quadratic inference functions using data from the National Longitudinal Survey of Children and Youth (NLSCY) database

<p>Abstract</p> <p>Background</p> <p>The generalized estimating equations (GEE) technique is often used in longitudinal data modeling, where investigators are interested in population-averaged effects of covariates on responses of interest. GEE involves specifying a model relating covariates to outcomes and a plausible correlation structure between responses at different time periods. While GEE parameter estimates are consistent irrespective of the true underlying correlation structure, the method has some limitations that include challenges with model selection due to lack of absolute goodness-of-fit tests to aid comparisons among several plausible models. The quadratic inference functions (QIF) method extends the capabilities of GEE, while also addressing some GEE limitations.</p> <p>Methods</p> <p>We conducted a comparative study between GEE and QIF via an illustrative example, using data from the "National Longitudinal Survey of Children and Youth (NLSCY)" database. The NLSCY dataset consists of long-term, population based survey data collected since 1994, and is designed to evaluate the determinants of developmental outcomes in Canadian children. We modeled the relationship between hyperactivity-inattention and gender, age, family functioning, maternal depression symptoms, household income adequacy, maternal immigration status and maternal educational level using GEE and QIF. Basis for comparison include: (1) ease of model selection; (2) sensitivity of results to different working correlation matrices; and (3) efficiency of parameter estimates.</p> <p>Results</p> <p>The sample included 795, 858 respondents (50.3% male; 12% immigrant; 6% from dysfunctional families). QIF analysis reveals that gender (male) (odds ratio [OR] = 1.73; 95% confidence interval [CI] = 1.10 to 2.71), family dysfunctional (OR = 2.84, 95% CI of 1.58 to 5.11), and maternal depression (OR = 2.49, 95% CI of 1.60 to 2.60) are significantly associated with higher odds of hyperactivity-inattention. The results remained robust under GEE modeling. Model selection was facilitated in QIF using a goodness-of-fit statistic. Overall, estimates from QIF were more efficient than those from GEE using AR (1) and Exchangeable working correlation matrices (Relative efficiency = 1.1117; 1.3082 respectively).</p> <p>Conclusion</p> <p>QIF is useful for model selection and provides more efficient parameter estimates than GEE. QIF can help investigators obtain more reliable results when used in conjunction with GEE.</p

Variants of the Matrix Metalloproteinase-2 but not the Matrix Metalloproteinase-9 genes significantly influence functional outcome after stroke

<p>Abstract</p> <p>Background</p> <p>Multiple lines of evidence suggest that genetic factors contribute to stroke recovery. The matrix metalloproteinases -2 (MMP-2) and -9 (MMP-9) are modulators of extracellular matrix components, with important regulatory functions in the Central Nervous System (CNS). Shortly after stroke, MMP-2 and MMP-9 have mainly damaging effects for brain tissue. However, MMPs also have a beneficial activity in angiogenesis and neurovascular remodelling during the delayed neuroinflammatory response phase, thus possibly contributing to stroke functional recovery.</p> <p>Methods</p> <p>In the present study, the role of <it>MMP-2 </it>and <it>MMP-9 </it>genetic variants in stroke recovery was investigated in 546 stroke patients. Functional outcome was assessed three months after a stroke episode using the modified Rankin Scale (mRS), and patients were classified in two groups: good recovery (mRS ≤ 1) or poor recovery (mRS>1). Haplotype tagging single nucleotide polymorphisms (SNPs) in the <it>MMP-2 </it>(N = 21) and <it>MMP-9 </it>(N = 4) genes were genotyped and tested for association with stroke outcome, adjusting for significant non-genetic clinical variables.</p> <p>Results</p> <p>Six SNPs in the <it>MMP-2 </it>gene were significantly associated with stroke outcome (0.0018<<it>P </it>< 0.0415), two of which survived the Bonferroni correction for multiple testing. In the subset of ischemic stroke patients, association of five of these SNPs remained positive (0.0042<<it>P </it>< 0.0306). No significant associations were found for the <it>MMP-9 </it>gene.</p> <p>Conclusions</p> <p>The results presented strongly indicate that <it>MMP-2 </it>genetic variants are an important mediator of functional outcome after stroke.</p