To verify four 5-year-old mathematical models to predict the outcome of ICU patients

The aim of this study is to verify calibration and discrimination after 5 years in the case mix of patients admitted to the Intensive Care Unit (ICU) during the year 2000. In this way we want to perform a quality control of our ICU in order to justify the increased amount of money spent for intensive care.A prospective study has been made on the 357 patients admitted to the ICU during the year 2000. The Apache II score was calculated within the first 24 hours and, depending on the length of stay in the ICU, on the 5(th), 10(th) and 15(th) day after ICU admission. On the basis of the 4 mathematical models death risk has been calculated for each of the 4 times. The Hosmer-Lemeshow test was performed for calibration and ROC curves for discrimination, always for each of the 4 mathematical models.The 1(st) model, at 24 hours from ICU admission, showed a bad calibration (p=0.000088), while the ROC curve was 0.744+/-0.32. Also the 2(nd) model, at the 5(th) day from admission, showed a bad calibration (p=0.000588), with ROC curve of 0.827+/-0.04. The 3(rd) model (10(th) day), was well calibrated (p=0.112247) and discriminating (ROC=0.888 +/-0.04). Finally the models at 15 days showed again a bad calibration (p=0.001422) but a very good discrimination (area=0.906+/-0.06).Developing mathematical models to predict mortality within ICUs can be useful to assess quality of care, even if these models should not be the only ICU quality controls, but must be accompanied by other indicators, looking at quality of life of the patients after ICU discharge

The relationship between group identification and satisfaction with life in a cross-cultural community sample

A variety of studies have shown that group identification (a sense of belonging to one’s social group, coupled with a sense of commonality with the group’s members) is linked to high levels of satisfaction with life (SWL). The aim of the present study was to support and extend this literature by: i) investigating the link between group identification and SWL with a large cross-cultural community sample; ii) examining whether the relationship is moderated by nationality; and iii) considering whether SWL is enhanced by possessing multiple group identifications simultaneously. Utilizing data from Wave 1 of the Health in Groups project, 3,829 participants from both Scotland and Italy completed a questionnaire assessing their identification with their family, their local community, and a group of their choice, as well as their level of SWL. Higher identification with each group predicted higher SWL. Nationality was a marginal moderator of the relationship between family identification and SWL, with the relationship being stronger for Italian participants than for Scottish participants. There was also an additive effect of group identification, with a positive relationship between the number of groups with which participants identified and their SWL. These effects were obtained even after controlling for gender, age, employment status, nationality, and extent of contact with each group. The implications for healthcare professionals and their patients are discussed

Glycaemic variability, infections and mortality in a medical-surgical intensive care unit.

In critically ill patients, glycaemic variability (GV) was reported as a better predictor of mortality than mean blood glucose level (BGL). We compared the ability of different GV indices and mean BGLs to predict mortality and intensive care unit-acquired infections in a population of ICU patients.Retrospective study on adult ICU patients with ≥ three BGL measurements. GV was assessed by SD, coefficient of variation (CV) and mean amplitude of glycaemic excursion (MAGE), and by one timeweighted index, the glycaemic lability index (GLI), and compared with mean BGL. We studied 2782 patients admitted to the 12-bed medical-surgical ICU of a teaching hospital from January 2004 until December 2010.Logistic regression analyses were performed to assess the association between GV and ICU mortality and ICU-acquired infections. The areas under receiver operating characteristic curves were calculated to compare the discriminatory ability of GV and mean BGL for infections and mortality.Mortality was 16.6%, and 30% of patients had at least one infection. Patients with infections or diabetes or who were treated with insulin had a higher mean BGL and GV than other patients. GLI, SD, CV and MAGE were significantly associated with infections and mortality; mean BGL was not. Quartiles of increasing GLI were independently associated with higher mortality and an increased infection rate. Patients in the upper quartile of mean BGL and GLI had the strongest association with infections (odds ratio, 5.044 [95% CI, 1.695-15.007]; P = 0.004).High GV is associated with higher risk of ICUCrit acquired infection and mortality

[Mathematical model for the predictive value of a test in critically ill patients studies according to APACHE II score and pathology at admission].

To find a predictive model for mortality at four different days from the admission for critically ill patients.Retrospective study on two consecutive series of critically ill patients admitted in ICU.1254 critically ill patients, subdivided into two series of 813 (561 survivors and 252 non survivors) and 441 patients (291 survivors and 150 non survivors), respectively.None.All patients had APACHE II calculated within the first 24 hours from the admission in ICU and, if the patient was still in ICU, also at the 5th, 10th and 15th day from the admission. Casistics was subdivided into two unequal series, ratio 2:1, with a random selection made on each of the 6 considered years. On the 1st series, in 1st, 5th, 10th and 15th day, for mathematical predictive models were made, using stepwise logistic regression (BMDP, Los Angeles). In the 1st day the following independent variables were utilized: APACHE II score, the specific diagnosis at admission, fitted following Knaus' diagnostic criteria, united in 6 principal categories, while for the other 3 days the variation \% of APACE II score as regards the previous day.For each of the considered day four mathematical models have been made. These models have been validated in both series in calibration from the Hosmer-Lemeshow Goodness-of-fit test and in discrimination from the ROC curves. For each day Y (Prob.\% to die) = eLogit/1 + eLogit, where Logit = beta 0 (constant) + beta 1*APACHE II + beta 2*Variat.\%APACE II (difference between actual APACHE II - APACHE II of the previous day/actual APACHE II) + beta k, (coefficient pertinent to pathology).The mathematical model, as other models do, stratifies enough the casistics according to the risk of death. Waiting for further studies to make more precise prognostic mathematical models, this one and others can help the clinical assessment in single patient evaluation

Correlation between hyperglycemia and mortality in a medical and surgical intensive care unit

Abstract AIM: The aim of this study was to assess the correlation between hyperglycemia and mortality in a group of patients admitted to a medical and surgical ICU and to evaluate if the association between hyperglycemia and reason of ICU admission significantly worsens patients' outcomes. METHODS: A retrospective clinical study was conducted in the ICU of a University Hospital. Four-hundred and twelve adult patients admitted to our ICU were enrolled. The blood glucose level was measured at the time of admission and daily at 2-4 h intervals. When the glucose level exceeded 180 mg/dL, an insulin bolus or a continuous infusion were performed to maintain the glucose level at or below 180-200 mg/dL. RESULTS: Analysing the mean blood glucose levels of patients with the receiver operating characteristic (ROC) curve, it resulted that the blood glucose level of 141.7 mg/dL had higher sensitivity (76%) and specificity (56.5%) to discriminate the probability of death. In other words, in pati..

[Mathematical model for the predictive value of a test in critically ill patients studies according to APACHE II score and pathology at admission].

To find a predictive model for mortality at four different days from the admission for critically ill patients.Retrospective study on two consecutive series of critically ill patients admitted in ICU.1254 critically ill patients, subdivided into two series of 813 (561 survivors and 252 non survivors) and 441 patients (291 survivors and 150 non survivors), respectively.None.All patients had APACHE II calculated within the first 24 hours from the admission in ICU and, if the patient was still in ICU, also at the 5th, 10th and 15th day from the admission. Casistics was subdivided into two unequal series, ratio 2:1, with a random selection made on each of the 6 considered years. On the 1st series, in 1st, 5th, 10th and 15th day, for mathematical predictive models were made, using stepwise logistic regression (BMDP, Los Angeles). In the 1st day the following independent variables were utilized: APACHE II score, the specific diagnosis at admission, fitted following Knaus' diagnostic criteria, united in 6 principal categories, while for the other 3 days the variation \% of APACE II score as regards the previous day.For each of the considered day four mathematical models have been made. These models have been validated in both series in calibration from the Hosmer-Lemeshow Goodness-of-fit test and in discrimination from the ROC curves. For each day Y (Prob.\% to die) = eLogit/1 + eLogit, where Logit = beta 0 (constant) + beta 1*APACHE II + beta 2*Variat.\%APACE II (difference between actual APACHE II - APACHE II of the previous day/actual APACHE II) + beta k, (coefficient pertinent to pathology).The mathematical model, as other models do, stratifies enough the casistics according to the risk of death. Waiting for further studies to make more precise prognostic mathematical models, this one and others can help the clinical assessment in single patient evaluation

A new and feasible model for predicting operative risk

Background. Although the POSSUM (Physiological and Operative Severity Score for the enumeration of Mortality and Morbidity) score can be used to calculate operative risk, its complexity makes its use unfeasible in the immediate clinical setting. The aim of this study was to create a new model, based on ASA status, to predict mortality. Methods. Data were collected in two hospitals. All types of surgery were included except for cardiac surgery and Caesarean delivery. Age, sex and preoperative information, including the presence of cardiocirculatory and/or lung disease, renal failure, diabetes mellitus, hepatic disease, cancer, Glasgow Coma Score, ASA grade, surgical diagnosis, severity of the procedure and type of surgery (elective, urgent or emergency), were recorded for each patient. The model was developed using a data set incorporating data from 1936 surgical patients, and validated using data from a further 1849 patients. Forward stepwise logistic regression was used to build the model. Goodness of fit was examined using the Hosmer-Lemeshow test and receiver operating characteristic (ROC) curve analyses were performed on both data sets to test calibration and discrimination. In the validation data set, the new model was compared with POSSUM and P-POSSUM for both calibration and discrimination, and with ASA alone to compare discrimination. Results. The following variables were included in the new model: ASA status, age, type of surgery (elective, urgent, emergency) and degree of surgery (minor, moderate or major). Calibration and discrimination of the new model were good in both development and validation data sets. This new model was better calibrated in the validation data set (Hosmer-Lemeshow goodness-of-fit test: chi(2)=6.8017, P=0.7440) than either P-POSSUM (chi(2)=14.4643, P=0.1528) or POSSUM, which was not calibrated (chi(2)=31.8147, P=0.0004). POSSUM and P-POSSUM had better discrimination than the new model, although this was not statistically significant. Comparing the two ROC curves, the new model had better discrimination than ASA alone (difference between areas, 0.077, se 0.034, 95% confidence interval 0.012-0.143, P=0.021). Conclusions. This new, ASA status-based model is simple to use and can be performed routinely in the operating room to predict operative risk for both elective and emergency surgery