OBJECTIVES:To compare methods to adjust for confounding by disease severity during multicenter intervention studies in ICU, when different disease severity measures are collected across centers.
...DESIGN:In silico simulation study using national registry data.
SETTING:Twenty mixed ICUs in The Netherlands.
SUBJECTS:Fifty-five–thousand six-hundred fifty-five ICU admissions between January 1, 2011, and January 1, 2016.
INTERVENTIONS:None.
MEASUREMENTS AND MAIN RESULTS:To mimic an intervention study with confounding, a fictitious treatment variable was simulated whose effect on the outcome was confounded by Acute Physiology and Chronic Health Evaluation IV predicted mortality (a common measure for disease severity). Diverse, realistic scenarios were investigated where the availability of disease severity measures (i.e., Acute Physiology and Chronic Health Evaluation IV, Acute Physiology and Chronic Health Evaluation II, and Simplified Acute Physiology Score II scores) varied across centers. For each scenario, eight different methods to adjust for confounding were used to obtain an estimate of the (fictitious) treatment effect. These were compared in terms of relative (%) and absolute (odds ratio) bias to a reference scenario where the treatment effect was estimated following correction for the Acute Physiology and Chronic Health Evaluation IV scores from all centers. Complete neglect of differences in disease severity measures across centers resulted in bias ranging from 10.2% to 173.6% across scenarios, and no commonly used methodology—such as two-stage modeling or score standardization—was able to effectively eliminate bias. In scenarios where some of the included centers had (only) Acute Physiology and Chronic Health Evaluation II or Simplified Acute Physiology Score II available (and not Acute Physiology and Chronic Health Evaluation IV), either restriction of the analysis to Acute Physiology and Chronic Health Evaluation IV centers alone or multiple imputation of Acute Physiology and Chronic Health Evaluation IV scores resulted in the least amount of relative bias (0.0% and 5.1% for Acute Physiology and Chronic Health Evaluation II, respectively, and 0.0% and 4.6% for Simplified Acute Physiology Score II, respectively). In scenarios where some centers used Acute Physiology and Chronic Health Evaluation II, regression calibration yielded low relative bias too (relative bias, 12.4%); this was not true if these same centers only had Simplified Acute Physiology Score II available (relative bias, 54.8%).
CONCLUSIONS:When different disease severity measures are available across centers, the performance of various methods to control for confounding by disease severity may show important differences. When planning multicenter studies, researchers should make contingency plans to limit the use of or properly incorporate different disease measures across centers in the statistical analysis.
The present article summarises the relevant aspects of the S3 guidelines on endometrioid carcinomas. The recommendations include the processing rules of fractional currettings as well as for ...hysterectomy specimens and lymph node resections (including sentinel lymph nodes). Besides practical aspects, the guidelines consider the needs of the clinicians for appropriate surgical and radiotherapeutic treatment of the patients. Carcinosarcomas are assigned to the endometrial carcinoma as a special variant. For the first time, an algorithmic approach for evaluation of the tumour tissue for Lynch syndrome is given. Prognostic factors based on morphologic findings are summarised.
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