Non-cancer mortality among people diagnosed with cancer (Australia)

Objective To investigate whether people diagnosed with cancer have an increased risk of death from non-cancer causes compared to the general population. Methods The non-cancer mortality of people diagnosed with cancer in Queensland (Australia) between 1982 and 2002 who had not died before 1 January 1993 was compared to the mortality of the total Queensland population, matching by age group and sex, and reporting by standardised mortality ratios. Results Compared to the non-cancer mortality in the general population, cancer patients (all cancers combined) were nearly 50% more likely to die of non-cancer causes (SMR = 149.9, 95% CI = [147-153]). This varied by cancer site. Overall melanoma patients had significantly lower non-cancer mortality, female breast cancer patients had similar non-cancer mortality to the general population, while increased non-cancer mortality risks were observed for people diagnosed with cervical cancer, colorectal cancer, prostate cancer, non-Hodgkin lymphoma and lung cancer. Conclusions Although cancer-specific death rates underestimate the mortality directly associated with a diagnosis of cancer, quantifying the degree of underestimation is difficult due to various competing explanations. There remains an important role for future research in understanding the causes of morbidity among cancer survivors, particularly those looking at both co-morbid illnesses and reductions in quality of life.

Comparison of measured sleeping metabolic rate and predicted basal metabolic rate during the first year of life: evidence of a bias changing with increasing metabolic rate

Objective: To compare measurements of sleeping metabolic rate (SMR) in infancy with predicted basal metabolic rate (BMR) estimated by the equations of Schofield. Methods: Some 104 serial measurements of SMR by indirect calorimetry were performed in 43 healthy infants at 1.5, 3, 6, 9 and 12 months of age. Predicted BMR was calculated using the weight only (BMR-wo) and weight and height (BMR-wh) equations of Schofield for 0-3-y-olds. Measured SMR values were compared with both predictive values by means of the Bland-Altman statistical test. Results: The mean measured SMR was 1.48 MJ/day. The mean predicted BMR values were 1.66 and 1.47 MJ/day for the weight only and weight and height equations, respectively. The Bland-Altman analysis showed that BMR-wo equation on average overestimated SMR by 0.18 MJ/day (11%) and the BMR-wh equation underestimated SMR by 0.01 MJ/day (1%). However the 95% limits of agreement were wide: - 0.64 to - 0.28MJ/day (28%) for the former equation and - 0.39 to +0.41 MJ/day (27%) for the latter equation. Moreover there was a significant correlation between the mean of the measured and predicted metabolic rate and the difference between them. Conclusions: The wide variation seen in the difference between measured and predicted metabolic rate and the bias probably with age indicates there is a need to measure actual metabolic rate for individual clinical care in this age group.

Prospective independent validation of APACHE III models in an Australian tertiary adult intensive care unit

Evaluation of the performance of the APACHE III (Acute Physiology and Chronic Health Evaluation) ICU (intensive care unit) and hospital mortality models at the Princess Alexandra Hospital, Brisbane is reported. Prospective collection of demographic, diagnostic, physiological, laboratory, admission and discharge data of 5681 consecutive eligible admissions (1 January 1995 to 1 January 2000) was conducted at the Princess Alexandra Hospital, a metropolitan Australian tertiary referral medical/surgical adult ICU. ROC (receiver operating characteristic) curve areas for the APACHE III ICU mortality and hospital mortality models demonstrated excellent discrimination. Observed ICU mortality (9.1%) was significantly overestimated by the APACHE III model adjusted for hospital characteristics (10.1%), but did not significantly differ from the prediction of the generic APACHE III model (8.6%). In contrast, observed hospital mortality (14.8%) agreed well with the prediction of the APACHE III model adjusted for hospital characteristics (14.6%), but was significantly underestimated by the unadjusted APACHE III model (13.2%). Calibration curves and goodness-of-fit analysis using Hosmer-Lemeshow statistics, demonstrated that calibration was good with the unadjusted APACHE III ICU mortality model, and the APACHE III hospital mortality model adjusted for hospital characteristics. Post hoc analysis revealed a declining annual SMR (standardized mortality rate) during the study period. This trend was present in each of the non-surgical, emergency and elective surgical diagnostic groups, and the change was temporally related to increased specialist staffing levels. This study demonstrates that the APACHE III model performs well on independent assessment in an Australian hospital. Changes observed in annual SMR using such a validated model support an hypothesis of improved survival outcomes 1995-1999.