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.

Predição de Pré-Eclâmpsia no Primeiro Trimestre em Gravidezes de Baixo Risco: Determinação do Cut-Off numa Amostra da População Portuguesa

Objective:We aimed to identify the cut-off for risk of pre-eclampsia (PE) in Portuguese population by applying the first trimester prediction model from Fetal Medicine Foundation (FMF) in a prospective enrolled cohort of low risk pregnant women. Population and methods: A prospective cohort of low risk singleton pregnancies underwent routine first-trimester scree - ning from 2011 through 2013. Maternal characteristics, blood pressure, uterine artery Doppler, levels of pregnancy-associated plasma protein-A (PAPP-A) and free b-human chorionic gonadotropin were evaluated. The prediction of PE in first trimester was calculated through software Astraia, the outcome obtained from medical records and the cutoff value was subse quently calculated. Results:Of the 273 enrolled patients, 7 (2.6%) developed PE. In first trimester women who developed PE presented higher uterine arteries resistance, represented by higher values of lowest and mean uterine pulsatility index, p <0.005. There was no statistical significance among the remaining maternal characteristics, body mass index, blood pressure and PAPP-A. Using the FMF first trimester PE algorithm, an ideal cut-off of 0.045 (1/22) would correctly detect 71% women who developed PE for a 12% false positive rate and a likelihood ratio of 12.98 (area under the curve: 0.69; confidence interval 95%: 0.39-0.99). By applying the reported cutoff to our cohort, we would obtain 71.4% true positives, 88.3% true negatives, 11.4% false positives and 28.6% false negatives. Conclusion: By applying a first trimester PE prediction model to low risk pregnancies derived from a Portuguese population, a significant proportion of patients would have been predicted as high risk. New larger studies are required to confirm the present findings.