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The article discusses the limitations of using standardized mortality ratios (SMRs) for evaluating ICU performance and benchmarking. The authors highlight the impact of potential organ donors (PODs) on SMRs and argue that changes in the role of ICUs and patient populations over time are not adequately reflected in the current approach to calculating and interpreting SMRs. They suggest that specific patient groups, such as PODs and moribund patients admitted for symptom management, should be excluded from SMR calculations to ensure accurate benchmarking. The authors also propose the use of machine learning and clustering techniques for analyzing ICU data and improving the methodology of SMR calculations. [Extracted from the article]

SAPS 3 included patients from 307 adult ICUs in 35 countries, but data collection took place over a mere 2 months, which resulted in a cohort of just 19,577 admissions for analysis: a mean of 539 patients per country, or 64 per ICU. Keywords: Acute Physiology and Chronic Health Evaluation; critical care outcomes; mortality probability model; predictive models; scoring systems EN Acute Physiology and Chronic Health Evaluation critical care outcomes mortality probability model predictive models scoring systems 728 740 13 04/21/21 20210501 NES 210501 When the Society of Critical Care Medicine (SCCCM) was established in 1970, it was recognized that the volume and complexity of data from admissions to acute care hospitals exceeded the human capability to process it. New outcomes included were ICU mortality, a patient's anticipated ICU length of stay, duration of mechanical ventilation, identifying low-risk monitor patients, along with daily predictions of mortality for individual patients ([14], [15]). Indeed, the transfer of extremely high-risk patients from ICUs in rural hospitals to ICUs in academic centers was accepted practice ([2]). [Extracted from the article]

In particular, the ICUs in the Phillips eICU database, given the unique technology associated with telemedicine and tele-ICU care, may not be representative of the more general American critical care population. Data for GOSSIS-1 (the first version of GOSSIS) currently comes from the merger of two extremely large multicenter datasets: the Australian and New Zealand Intensive Care Society database ([7]) (admissions in Australia and New Zealand) and the Phillips electronic ICU (eICU) database in the United States ([8]). 2006; 21:, 133-141, 8 Pollard TJ, Johnson AEW, Raffa JD,: The eICU collaborative research database, a freely available multi-center database for critical care research. [Extracted from the article]

The article discusses a study which investigated ways in which the delivery of futile care in the intensive care unit (ICU) can cause harm to patients. The authors analyzed studies of patients and family attitudes about cardiopulmonary resuscitation and other supporting modalities. It was found that ICU care was frequently used by the elderly in the U.S. and they have more serious comorbidities.

The article reviews the article "Red Cell Distribution Width and All-cause Mortality in Critically Ill Patients," by Heidi S. Bazick, Domingo Chang, and Karthik Mahadevappa, which appeared in the journal "Critical Care Medicine" 2011 vol 39 number 8.

A letter to the editor is presented in response to the article "Validity of Different Delirium Assessment Tools for Critically Ill Children: Covariates Matter" by Leutz et al.

The author expresses his views on persistent organ failure as the most common presentation of septic patients prior to death in the intensive care unit (ICU), in relation to a study published in a 2011 issue of the journal "Critical Care Medicine." He describes sepsis as a problem child of medicine. He also talks about the increase in the occurrence of sepsis-related deaths.