Simple Observational Critical Care Studies

Plain-language summary

Each year approximately 3000 patients are admitted to the intensive care unit (ICU) in the University Medical Center Groningen (UMCG). In-hospital mortality of patients with emergency admission approaches 25%. Predicting outcome in the first hours after ICU admission, however, remains a challenge. An vast amount of scoring systems has been developed for mortality prediction. Well known models, such as the LODS, MODS, CCI, SOFA, ODIN and the different generations of the APACHE, MPM and SAPS, are increasingly compared with new models, such as the SICULA, ICNARC, ANZROD and SMS-ICU. The predictive value of scoring systems deteriorates over time due to changes in patient characteristics and treatment, making it crucial to update existing models or develop new models. Other reasons given for the need of models are the complexity and lack of availability of variables in some of the existing scoring systems, the better discriminating value while using simple, standardly measured variables, and the limited generalizability of some scoring systems in different patient populations. Not only are simple systems (such as the CIS and SMS-ICU) found to be at least as predictive for mortality as complex models such as the APACHE IV, but, while using simplified systems, mortality can also be reasonably predicted within only a few hours after admission. Both simplicity and the potential to predict mortality shortly after admission increase the usability, and consequently the reliability, of those prediction models. This increases the potential of those models to be used in practice. Most studies however compare only two to four models in their patient population and lack in their description of the performance of the different models. Parameters necessary to compare the performance of models are at least calibration, discrimination, negative predicting value, positive predicting value, sensitivity and specificity. Lacking an adequate description of the performance of the model limits to what extent the study can be used to compare models in different populations. Thus, all usable models should be compared with newly build models, and the performance of the different models should be extensively described to allow comparison of the models. Not only models based on simple, readily available variables available within hours after admission are promising, but also the concept of combining measurements straight after ICU admission with information on the course of illness. It is likely that the course of a variable over time is more indicative than a static measurement. This study will provide a structure in which every patient admitted to the ICU will be investigated and included within 3 hours and after 12 hours after admission, making longitudinal measurements and various add-on studies possible. Longitudinal measurements are the first example of an add-on study; another example is the capability of nurses and physicians to predict outcome. Current evidence suggests that physicians might predict mortality more accurately than scorings systems. This finding may, however, be highly biased, since at least physicians play a major role in end-of-life decision making. More recent studies also focus on the accuracy of nurses in predicting mortality, with diverse outcomes. The role of other health care professionals, like residents and students, remain to be studied. Implementing a systematic data collection process is the first step towards making data-driven research possible, a growing need in medical disciplines such as critical care, which requires increasingly more accurate prognostic models. Therefore, the aim of this study is to systematically collect data of all selected variables, thus minimizing incompleteness, and allowing for the calculation of mortality prediction scores according to currently available mortality or severity of disease prediction models. Moreover, during investigation reliability of measurements could be checked for validity. This creates the possibility to compare the performance of all models in one population and identify models which are useful to predict severity of disease. A registry will be created with this primary objective which also provides the opportunity to start multiple ''add-on'' studies for specific research questions. Examples of add-on studies are 1) the association between time-dependent variables which are longitudinally measured, and mortality/acute and chronic co-morbidity, 2) the association between fluid status and acute kidney injury, and 3) not only the capability of the treating physician to predict mortality, but also the capability of the nurses, residents and students to do so. Purpose: The purpose of this study is to expand the infrastructure for a registry with longitudinal and repeated measurements, shortly after admittance, which is flexible to incorporate temporarily added specific research questions on the outcome of critically ill patients.

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