Treatment outcome studies. pitfalls in current methods and practice

Assessment of the External Validity of Dialogue Support for Predicting Lumbar Spine Surgery Outcomes in a US Cohort

STUDY DESIGN

External validation using prospectively collected data.

OBJECTIVES

To determine the model performance of "Dialogue Support" (DS) in predicting outcomes after lumbar spine surgery.

SUMMARY OF BACKGROUND DATA

To help clinicians discuss risk versus benefit with patients considering lumbar fusion surgery, DS has been made available online. As DS was created using a Swedish sample, there is a need to study how well DS performs in alternative populations.

PATIENTS AND METHODS

Preoperative data from patients enrolled in the Quality Outcomes Database were entered into DS. The probability for each patient to report satisfaction, achieve success (leg pain improvement ≥3), or have no leg pain 12 months after surgery was extracted and compared with their actual 12-month postoperative data. The ability of DS to identify patients in the Quality Outcomes Database who report satisfaction, achieve success, or have no leg pain 12 months after surgery was determined using Receiver operating characteristic curve analysis, goodness-of-fit tests, and calibration plots.

RESULTS

There was a significant improvement in all outcomes in 23,928 cases included in the analysis from baseline to 12 months postoperative. Most (84%) reported satisfaction, 67% achieved success, and 44% were pain-free 12 months postoperative. Receiver operating characteristic analysis showed that DS had a low ability to predict satisfaction [area under the curve (AUC) = 0.606], success (AUC = 0.546), and being pain-free (AUC = 0.578) at 12 months postoperative; poor fit for satisfaction (<0.001) and being pain-free ( P = 0.004), but acceptable fit for success ( P = 0.052). Calibration plots showed underestimation for satisfaction and success, but acceptable estimates for being pain-free.

CONCLUSION

DS is not directly transferable to predict satisfaction and success after lumbar surgery in a US population. This may be due to differences in patient characteristics, weights of the variables included, or the exclusion of unknown variables associated with outcomes. Future studies to better understand and improve the transferability of these models are needed.

Heterogeneity in disease free survival between centers: lessons learned from an EORTC breast cancer trial

Background Large phase III clinical trials convey a lot of important information besides the main analysis of the treatment effect. For example, the use of multicenter clinical trial data to identify prognostic indices is now common. In addition, the study of heterogeneity in patient outcome between centers has received considerable attention in recent years. In this paper, we explain and illustrate a method used to investigate such heterogeneity with data from an early breast cancer clinical trial.

Methods The inclusion of a random effect for center in a Cox proportional hazards model allows us to study the heterogeneity in time-to-event outcomes between centers. Such a model has the major advantage that it provides a measure of the spread of outcomes over centers. This technique is illustrated using data from EORTC trial 10854, a randomized phase III trial comparing perioperative chemotherapy with no perioperative chemotherapy for early breast cancer; 2793 patients were entered by 14 centers.

Results Substantial heterogeneity between centers was detected for disease-free survival. This can be explained by the geographical area in which the center is located, with better outcomes achieved in France as compared with southern Europe and South Africa. None of the prognostic factors considered could explain this heterogeneity.

Conclusion Although clinical trials are run with the objective of removing as much heterogeneity as possible, some heterogeneity in the outcome of patients between centers may remain, as was the case in our study. The use of a random effect for center within a Cox PH model is an excellent method to investigate this heterogeneity. Such types of analyses, although exploratory, provide further insight into possible factors which may have an impact on the patient's outcome.

Early postoperative mortality after surgery for rectal cancer in Sweden, 2000-2011

AIM

Postoperative mortality has traditionally been defined as death within 30 days of surgery. Such mortality after rectal cancer resection has declined significantly during the last decades. However, it is possible that this decline can be explained merely by a shift towards an increase in 90-day mortality.

METHOD

A nationwide cohort study was based on data from the Swedish Colorectal Cancer Registry and the Swedish Patient Registry concerning patients who had undergone surgical resection for rectal cancer in 2000-2011. Unconditional logistic regression was used to calculate ORs with 95% CIs regarding mortality in different calendar periods (2000-2003, 2004-2007 and 2008-2011) in two different postoperative time periods (0-30 days and 31-90 days).

RESULTS

Some 15,437 patients were included in this surgical cohort. Mortality within 30 days of surgery decreased from 2.1% in 2000-2003 to 1.6% in 2008-2011, whilst the corresponding mortality within the 31- to 90-day time window decreased from 2.1% to 1.4%. The adjusted risk of 30-day mortality in 2008-2011 was statistically significantly decreased compared with that in 2000-2003 (OR = 0.67; 95% CI: 0.48-0.93) and mortality in the 31- to 90-day time window was also reduced for 2008-2011 compared with 2000-2003 (OR = 0.71; 95% CI: 0.51-0.99).

CONCLUSION

This population-based, nationwide Swedish study indicates that postoperative mortality, as measured within 30 days and 31-90 days after surgery, has decreased with time. However, no relevant shift from earlier to later postoperative mortality was discerned.

Cancer in women

Incidence, mortality and survival trends for the most frequent cancers affecting women are presented on a worldwide basis. Data sources are represented by several different cancer databases, as no single world cancer database covers these epidemiological measures. Monitoring cancer incidence, mortality and survival are fundamental indicators which allow estimates and predictions of geographical and temporal changes of these diseases, enabling the design and set-up of adequate cancer control activities and national health programs. The observed differences in cancer incidence, mortality and survival in more developed countries compared with less developed countries (as defined by WHO) are mainly due to different individual and social risk factors between the two geo-political areas. For some cancers, advancements in screening, diagnosis and treatment in the more developed areas were the most effective factors in reducing incidence and mortality as well as prolonging survival. These effects were not detected in the less developed areas because of the limited access to primary and specialist care.