Hospital costs and factors associated with days alive and at home after surgery (DAH30 ).

OBJECTIVE: To assess the relationships of patient and surgical factors and hospital costs with the number of days alive and at home during the 30 days following surgery (DAH30 ). DESIGN: Retrospective cohort study; analysis of Medibank Private health insurance hospital claims data, Australia, 1 January 2016 - 31 December 2017. SETTING, PARTICIPANTS: Admissions of adults (18 years or older) to hospitals for elective or emergency inpatient surgery with anaesthesia covered by private health insurance, Australia, 1 January 2016 - 31 December 2017. MAIN OUTCOME MEASURES: Associations between DAH30 and total hospital costs, and between DAH30 and surgery risk factors. RESULTS: Complete data were available for 126 788 of 181 281 eligible patients (69.9%); their median age was 62 years (IQR, 47-73 years), 72 872 were women (57%), and 115 117 had undergone elective surgery (91%). The median DAH30 was 27.1 days (IQR, 24.2-28.8 days), the median hospital cost per patient was $10 358 (IQR, $6624-20 174). The association between DAH30 and total hospital costs was moderate (Spearman ρ = -0.60; P < 0.001). Median DAH30 declined with age, comorbidity score, ASA physical status score, and surgical severity and duration, and was also lower for women. CONCLUSIONS: DAH30 is a validated, patient-centred outcome measure of post-surgical outcomes; higher values reflect shorter hospital stays and fewer serious complications, re-admissions, and deaths. DAH30 can be used to benchmark quality of surgical care and to monitor quality improvement programs for reducing the costs of surgical and other peri-operative care.

Beyond mean modelling: Bias due to misspecification of dispersion in Poisson-inverse Gaussian regression.

In clinical trials one traditionally models the effect of treatment on the mean response. The underlying assumption is that treatment affects the response distribution through a mean location shift on a suitable scale, with other aspects of the distribution (shape/dispersion/variance) remaining the same. This work is motivated by a trial in Parkinson's disease patients in which one of the endpoints is the number of falls during a 10-week period. Inspection of the data reveals that the Poisson-inverse Gaussian (PiG) distribution is appropriate, and that the experimental treatment reduces not only the mean, but also the variability, substantially. The conventional analysis assumes a treatment effect on the mean, either adjusted or unadjusted for covariates, and a constant dispersion parameter. On our data, this analysis yields a non-significant treatment effect. However, if we model a treatment effect on both mean and dispersion parameters, both effects are highly significant. A simulation study shows that if a treatment effect exists on the dispersion and is ignored in the modelling, estimation of the treatment effect on the mean can be severely biased. We show further that if we use an orthogonal parametrization of the PiG distribution, estimates of the mean model are robust to misspecification of the dispersion model. We also discuss inferential aspects that are more difficult than anticipated in this setting. These findings have implications in the planning of statistical analyses for count data in clinical trials.

White blood cell count predicts reduction in coronary heart disease mortality with pravastatin.

BACKGROUND: Elevated serum inflammatory marker levels are associated with a greater long-term risk of cardiovascular events. Because 3-hydroxy-3-methylglutaryl coenzyme-A reductase inhibitors (statins) may have an antiinflammatory action, it has been suggested that patients with elevated inflammatory marker levels may have a greater reduction in cardiovascular risk with statin treatment. METHODS AND RESULTS: We evaluated the association between the white blood cell count (WBC) and coronary heart disease mortality during a mean follow-up of 6.0 years in the Long-Term Intervention With Pravastatin in Ischemic Disease (LIPID) Study, a clinical trial comparing pravastatin (40 mg/d) with a placebo in 9014 stable patients with previous myocardial infarction or unstable angina. An increase in baseline WBC was associated with greater coronary heart disease mortality in patients randomized to placebo (hazard ratio for 1x10(9)/L increase in WBC, 1.18; 95% CI, 1.12 to 1.25; P<0.001) but not pravastatin (hazard ratio, 1.02; 95% CI, 0.96 to 1.09; P=0.56; P for interaction=0.004). The numbers of coronary heart disease deaths prevented per 1000 patients treated with pravastatin were 0, 9, 30, and 38 for baseline WBC quartiles of <5.9, 6.0 to 6.9, 7.0 to 8.1, and >8.2x10(9)/L, respectively. WBC was a stronger predictor of this treatment benefit than the ratio of total to high-density lipoprotein cholesterol and a global measure of cardiac risk. There was also a greater reduction (P=0.052) in the combined incidence of cardiovascular mortality, nonfatal myocardial infarction, and stroke with pravastatin as baseline WBC increased (by quartile: 3, 41, 61, and 60 events prevented per 1000 patients treated, respectively). CONCLUSIONS: These data support the hypothesis that individuals with evidence of inflammation may obtain a greater benefit from statin therapy.