Bone Histomorphometry Revisited

Bone histomorphometry is defined as a quantitative evaluation of bone micro architecture, remodelling and metabolism. Bone metabolic assessment is based on a dynamic process, which provides data on bone matrix formation rate by incorporating a tetracycline compound. In the static evaluation, samples are stained and a semi-automatic technique is applied in order to obtain bone microarchitectural parameters such as trabecular area, perimeter and width. These parameters are in 2D, but they can be extrapolated into 3D, applying a stereological formula. Histomorphometry can be applied to different areas; however, in recent decades it has been a relevant tool in monitoring the effect of drug administration in bone. The main challenge for the future will be the development of noninvasive methods that can give similar information. In the herein review paper we will discuss the general principles and main applications of bone histomorphometry.

Routine Transthoracic Echocardiography in a General Intensive Care Unit: an 18 Month Survey in 704 Patients

The authors analyzed 704 transthoracic echocardiographic (TTE) examinations, performed routinely to all admitted patients to a general 16-bed Intensive Care Unit (ICU) during an 18-month period. Data acquisition and prevalence of abnormalities of cardiac structures and function were assessed, as well as the new, previously unknown severe diagnoses. A TTE was performed within the first 24 h of admission on 704 consecutive patients, with a mean age of 61.5+/-17.5 years, ICU stay of 10.6+/-17.1 days, APACHE II 22.6+/-8.9, and SAPS II 52.7+/-20.4. In four patients, TTE could not be performed. Left ventricular (LV) dimensions were quantified in 689 (97.8%) patients, and LV function in 670 (95.2%) patients. Cardiac output (CO) was determined in 610 (86.7%), and mitral E/A in 399 (85.9% of patients in sinus rhythm). Echocardiographic abnormalities were detected in 234 (33%) patients, the most common being left atrial (LA) enlargement (n=163), and LV dysfunction (n=132). Patients with these alterations were older (66+/-16.5 vs 58.1+/-17.4, p<0.001), presented a higher APACHE II score (24.4+/-8.7 vs 21.1+/-8.9, p<0.001), and had a higher mortality rate (40.1% vs 25.4%, p<0.001). Severe, previously unknown echocardiographic diagnoses were detected in 53 (7.5%) patients; the most frequent condition was severe LV dysfunction. Through a multivariate logistic regression analysis, it was determined that mortality was affected by tricuspid regurgitation (p=0.016, CI 1.007-1.016) and ICU stay (p<0.001, CI 1-1.019). We conclude that TTE can detect most cardiac structures in a general ICU. One-third of the patients studied presented cardiac structural or functional alterations and 7.5% severe previously unknown diagnoses.

Modeling in-Hospital Patient Survival During the First 28 Days After Intensive Care Unit Admission: a Prognostic Model for Clinical Trials in General Critically Ill Patients

OBJECTIVE: The objective of the study was to develop a model for estimating patient 28-day in-hospital mortality using 2 different statistical approaches. DESIGN: The study was designed to develop an outcome prediction model for 28-day in-hospital mortality using (a) logistic regression with random effects and (b) a multilevel Cox proportional hazards model. SETTING: The study involved 305 intensive care units (ICUs) from the basic Simplified Acute Physiology Score (SAPS) 3 cohort. PATIENTS AND PARTICIPANTS: Patients (n = 17138) were from the SAPS 3 database with follow-up data pertaining to the first 28 days in hospital after ICU admission. INTERVENTIONS: None. MEASUREMENTS AND RESULTS: The database was divided randomly into 5 roughly equal-sized parts (at the ICU level). It was thus possible to run the model-building procedure 5 times, each time taking four fifths of the sample as a development set and the remaining fifth as the validation set. At 28 days after ICU admission, 19.98% of the patients were still in the hospital. Because of the different sampling space and outcome variables, both models presented a better fit in this sample than did the SAPS 3 admission score calibrated to vital status at hospital discharge, both on the general population and in major subgroups. CONCLUSIONS: Both statistical methods can be used to model the 28-day in-hospital mortality better than the SAPS 3 admission model. However, because the logistic regression approach is specifically designed to forecast 28-day mortality, and given the high uncertainty associated with the assumption of the proportionality of risks in the Cox model, the logistic regression approach proved to be superior.