A comparison of prognosis calculators for geriatric trauma: A Prognostic Assessment of Life and Limitations After Trauma in the Elderly consortium study.

BACKGROUND: The nine-center Prognostic Assessment of Life and Limitations After Trauma in the Elderly consortium has validated the Geriatric Trauma Outcome Score (GTOS) as a prognosis calculator for injured elders. We compared GTOS' performance to that of the Trauma Injury Severity Score (TRISS) in a multicenter sample. METHODS: Three Prognostic Assessment of Life and Limitations After Trauma in the Elderly centers not submitting subjects to the GTOS validation study identified subjects aged 65 years to 102 years admitted from 2000 to 2013. GTOS was specified using the formula [GTOS = age + (Injury Severity Score [ISS] × 2.5) + 22 (if transfused packed red cells (PRC) at 24 hours)]. TRISS uses the Revised Trauma Score (RTS), dichotomizes age (<55 years = 0 and ≥55 years = 1), and was specified using the updated 1995 beta coefficients. TRISS Penetrating was specified as [TRISSP = -2.5355 + (0.9934 × RTS) + (-0.0651 × ISS) + (-1.1360 × Age)]. TRISS Blunt was specified as [TRISSB = -0.4499 + (0.8085 × RTS Total) + (-0.0835 × ISS) + (-1.7430 × Age)]. Each then became the sole predictor in a separate logistic regression model to estimate probability of mortality. Model performances were evaluated using misclassification rate, Brier score, and area under the curve. RESULTS: Demographics (mean + SD) of subjects with complete data (N = 10,894) were age, 78.3 years ± 8.1 years; ISS, 10.9 ± 8.4; RTS = 7.5 ± 1.1; mortality = 6.9%; blunt mechanism = 98.6%; 3.1 % of subjects received PRCs. The penetrating trauma subsample (n = 150) had a higher mortality rate of 20.0%. The misclassification rates for the models were GTOS, 0.065; TRISSB, 0.051; and TRISSP, 0.120. Brier scores were GTOS, 0.052; TRISSB, 0.041; and TRISSP, 0.084. The area under the curves were GTOS, 0.844; TRISSB, 0.889; and TRISSP, 0.897. CONCLUSION: GTOS and TRISS function similarly and accurately in predicting probability of death for injured elders. GTOS has the advantages of a single formula, fewer variables, and no reliance on data collected in the emergency room or by other observers. LEVEL OF EVIDENCE: Prognostic, level II.

Multicenter external validation of the Geriatric Trauma Outcome Score: A study by the Prognostic Assessment of Life and Limitations After Trauma in the Elderly (PALLIATE) consortium.

BACKGROUND: A prognostic tool for geriatric mortality after injury called the Geriatric Trauma Outcome Score (GTOS), where GTOS = [age] + [ISS × 2.5] + [22 if transfused any PRBCs by 24 hours after admission], was previously developed based on 13 years of data from geriatric trauma patients admitted to Parkland Hospital. We sought to validate this model. METHODS: Four Level I centers identified subjects who are 65 years or older for the period of the original study. The GTOS model was first specified using the formula [GTOS = age + (ISS × 2.5) + 22 (if given PRBC by 24 hours)] developed from the Parkland sample and then used as the sole predictor in a logistic mixed model estimating probability of mortality in the validation sample, accounting for site as a random effect. We estimated the misclassification (error) rate, Brier score, Tjur R, and the area under the curve in evaluating the predictive performance of the GTOS model. RESULTS: The original Parkland sample (n = 3,841) had a mean (SD) age of 76.6 (8.1) years, mean (SD) ISS of 12.4 (9.9), mortality of 10.8%, and 11.9% receiving PRBCs at 24 hours. The validation sample (n = 18,282) had a mean (SD) age of 77.0 (8.1) years, mean (SD) ISS of 12.3 (10.6), mortality of 11.0%, and 14.1% receiving PRBCs at 24 hours. Fitting the GTOS model to the validation sample revealed that the parameter estimates from the validation sample were similar to those of fitting it to the Parkland sample with highly overlapping 95% confidence limits. The misclassification (error) rate for the GTOS logistic model applied to the validation sample was 9.97%, similar to that of the Parkland sample (9.79%). Brier score, Tjur R, and the area under the curve for the GTOS logistic model when applied to the validation sample were 0.07, 0.25, and 0.86, respectively, compared with 0.08, 0.20, and 0.82, respectively, for the Parkland sample. CONCLUSION: With the use of the data available at 24 hours after injury, the GTOS accurately predicts in-hospital mortality for the injured elderly. LEVEL OF EVIDENCE: Prognostic study, level III.

Chemotherapy Use, Performance Status, and Quality of Life at the End of Life.

IMPORTANCE: Although many patients with end-stage cancer are offered chemotherapy to improve quality of life (QOL), the association between chemotherapy and QOL amid progressive metastatic disease has not been well-studied. American Society for Clinical Oncology guidelines recommend palliative chemotherapy only for solid tumor patients with good performance status. OBJECTIVE: To evaluate the association between chemotherapy use and QOL near death (QOD) as a function of patients' performance status. DESIGN, SETTING, AND PARTICIPANTS: A multi-institutional, longitudinal cohort study of patients with end-stage cancer recruited between September 2002 and February 2008. Chemotherapy use (n = 158 [50.6%]) and Eastern Cooperative Oncology Group (ECOG) performance status were assessed at baseline (median = 3.8 months before death) and patients with progressive metastatic cancer (N = 312) following at least 1 chemotherapy regimen were followed prospectively until death at 6 outpatient oncology clinics in the United States. MAIN OUTCOMES AND MEASURES: Patient QOD was determined using validated caregiver ratings of patients' physical and mental distress in their final week. RESULTS: Chemotherapy use was not associated with patient survival controlling for clinical setting and patients' performance status. Among patients with good (ECOG score = 1) baseline performance status, chemotherapy use compared with nonuse was associated with worse QOD (odds ratio [OR], 0.35; 95% CI, 0.17-0.75; P = .01). Baseline chemotherapy use was not associated with QOD among patients with moderate (ECOG score = 2) baseline performance status (OR, 1.06; 95% CI, 0.51-2.21; P = .87) or poor (ECOG score = 3) baseline performance status (OR, 1.34; 95% CI, 0.46-3.89; P = .59). CONCLUSIONS AND RELEVANCE: Although palliative chemotherapy is used to improve QOL for patients with end-stage cancer, its use did not improve QOD for patients with moderate or poor performance status and worsened QOD for patients with good performance status. The QOD in patients with end-stage cancer is not improved, and can be harmed, by chemotherapy use near death, even in patients with good performance status.

An examination of end-of-life care in a safety net hospital system: a decade in review.

OBJECTIVES: To examine patterns of use of end-of-life care in patients receiving treatment at a large, urban safety-net hospital from 2000 to 2010. METHODS: Data from the Parkland Hospital palliative care database, which tracked all consults for this period, were analyzed. Logistic regression was used to identify predictors of hospice use, and Cox proportional hazards modeling to examine survival. RESULTS: There were 5,083 palliative care consults over the study period. More patients were Black (41%) or White (31%), and younger than 65 years old (75%). Cancer patients or those who received palliative care services longer were more likely to receive hospice; those who had no form of health care assistance were less likely. There were no racial/ethnic differences in hospice use. CONCLUSION: In this cohort, there were no racial/ethnic disparities in hospice use. Those who had no form of health care assistance were less likely to receive hospice.

Support of cancer patients' spiritual needs and associations with medical care costs at the end of life.

BACKGROUND: Although spiritual care is associated with less aggressive medical care at the end of life (EOL), it remains infrequent. It is unclear if the omission of spiritual care impacts EOL costs. METHODS: A prospective, multisite study of 339 advanced cancer patients accrued subjects from September 2002 to August 2007 from an outpatient setting and followed them until death. Spiritual care was measured by patients' reports that the health care team supported their religious/spiritual needs. EOL costs in the last week were compared among patients reporting that their spiritual needs were inadequately supported versus those who reported that their needs were well supported. Analyses were adjusted for confounders (eg, EOL discussions). RESULTS: Patients reporting that their religious/spiritual needs were inadequately supported by clinic staff were less likely to receive a week or more of hospice (54% vs 72.8%; P = .01) and more likely to die in an intensive care unit (ICU) (5.1% vs 1.0%, P = .03). Among minorities and high religious coping patients, those reporting poorly supported religious/spiritual needs received more ICU care (11.3% vs 1.2%, P = .03 and 13.1% vs 1.6%, P = .02, respectively), received less hospice (43.% vs 75.3% ≥1 week of hospice, P = .01 and 45.3% vs 73.1%, P = .007, respectively), and had increased ICU deaths (11.2% vs 1.2%, P = .03 and 7.7% vs 0.6%, P = .009, respectively). EOL costs were higher when patients reported that their spiritual needs were inadequately supported ($4947 vs $2833, P = .03), particularly among minorities ($6533 vs $2276, P = .02) and high religious copers ($6344 vs $2431, P = .005). CONCLUSIONS: Cancer patients reporting that their spiritual needs are not well supported by the health care team have higher EOL costs, particularly among minorities and high religious coping patients.