Acute Physiology and Chronic Health Evaluation II and Simplified Acute Physiology Score II in Predicting Hospital Mortality of Neurosurgical Intensive Care Unit Patients

We study the predictive power of Acute Physiology and Chronic Health Evaluation II (APACHE II) and Simplified Acute Physiology Score II (SAPS II) in neurosurgical intensive care unit (ICU) patients. Retrospective investigation was conducted on 672 consecutive ICU patients during the last 2 yr. Data were collected during the first 24 hours of admission and analyzed to calculate predicted mortality. Mortality predicted by two systems was compared and, multivariate analyses were then performed for subarachnoid hemorrhage (SAH) and traumatic brain injury (TBI) patients. Observed mortality was 24.8% whereas predicted mortalities were 37.7% and 38.4%, according to APACHE II and SAPS II. Calibration curve was close to the line of perfect prediction. SAPS II was not statistically significant according to a Lemeshow-Hosmer test, but slightly favored by area under the curve (AUC). In SAH patients, SAPS II was an independent predictor for mortality. In TBI patients, both systems had independent prognostic implications. Scoring systems are useful in predicting mortality and measuring performance in neurosurgical ICU setting. TBI patients are more affected by systemic insults than SAH patients, and this discrepancy of predicting mortality in each neurosurgical disease prompts us to develop a more specific scoring system targeted to cerebral dysfunction

On the dynamics of the adenylate energy system: homeorhesis vs homeostasis.

Biochemical energy is the fundamental element that maintains both the adequate turnover of the biomolecular structures and the functional metabolic viability of unicellular organisms. The levels of ATP, ADP and AMP reflect roughly the energetic status of the cell, and a precise ratio relating them was proposed by Atkinson as the adenylate energy charge (AEC). Under growth-phase conditions, cells maintain the AEC within narrow physiological values, despite extremely large fluctuations in the adenine nucleotides concentration. Intensive experimental studies have shown that these AEC values are preserved in a wide variety of organisms, both eukaryotes and prokaryotes. Here, to understand some of the functional elements involved in the cellular energy status, we present a computational model conformed by some key essential parts of the adenylate energy system. Specifically, we have considered (I) the main synthesis process of ATP from ADP, (II) the main catalyzed phosphotransfer reaction for interconversion of ATP, ADP and AMP, (III) the enzymatic hydrolysis of ATP yielding ADP, and (IV) the enzymatic hydrolysis of ATP providing AMP. This leads to a dynamic metabolic model (with the form of a delayed differential system) in which the enzymatic rate equations and all the physiological kinetic parameters have been explicitly considered and experimentally tested in vitro. Our central hypothesis is that cells are characterized by changing energy dynamics (homeorhesis). The results show that the AEC presents stable transitions between steady states and periodic oscillations and, in agreement with experimental data these oscillations range within the narrow AEC window. Furthermore, the model shows sustained oscillations in the Gibbs free energy and in the total nucleotide pool. The present study provides a step forward towards the understanding of the fundamental principles and quantitative laws governing the adenylate energy system, which is a fundamental element for unveiling the dynamics of cellular life

Comorbidity in patients with diabetes mellitus: impact on medical health care utilization

BACKGROUND: Comorbidity has been shown to intensify health care utilization and to increase medical care costs for patients with diabetes. However, most studies have been focused on one health care service, mainly hospital care, or limited their analyses to one additional comorbid disease, or the data were based on self-reported questionnaires instead of health care registration data. The purpose of this study is to estimate the effects a broad spectrum of of comorbidities on the type and volume of medical health care utilization of patients with diabetes. METHODS: By linking general practice and hospital based registrations in the Netherlands, data on comorbidity and health care utilization of patients with diabetes (n = 7,499) were obtained. Comorbidity was defined as diabetes-related comorbiiabetes-related comorbidity. Multilevel regression analyses were applied to estimate the effects of comorbidity on health care utilization. RESULTS: Our results show that both diabetes-related and non diabetes-related comorbidity increase the use of medical care substantially in patients with diabetes. Having both diabeterelated and non diabetes-related comorbidity incrases the demand for health care even more. Differences in health care utilization patterns were observed between the comorbidities. CONCLUSION: Non diabetes-related comorbidity increases the health care demand as much as diabetes-related comorbidity. Current single-disease approach of integrated diabetes care should be extended with additional care modules, which must be generic and include multiple diseases in order to meet the complex health care demands of patients with diabetes in the future