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.

Characterizing the Risk Profiles of Intensive Care Units

OBJECTIVE: To develop a new method to evaluate the performance of individual ICUs through the calculation and visualisation of risk profiles. METHODS: The study included 102,561 patients consecutively admitted to 77 ICUs in Austria. We customized the function which predicts hospital mortality (using SAPS II) for each ICU. We then compared the risks of hospital mortality resulting from this function with the risks which would be obtained using the original function. The derived risk ratio was then plotted together with point-wise confidence intervals in order to visualise the individual risk profile of each ICU over the whole spectrum of expected hospital mortality. MAIN MEASUREMENTS AND RESULTS: We calculated risk profiles for all ICUs in the ASDI data set according to the proposed method. We show examples how the clinical performance of ICUs may depend on the severity of illness of their patients. Both the distribution of the Hosmer-Lemeshow goodness-of-fit test statistics and the histogram of the corresponding P values demonstrated a good fit of the individual risk models. CONCLUSIONS: Our risk profile model makes it possible to evaluate ICUs on the basis of the specific risk for patients to die compared to a reference sample over the whole spectrum of hospital mortality. Thus, ICUs at different levels of severity of illness can be directly compared, giving a clear advantage over the use of the conventional single point estimate of the overall observed-to-expected mortality ratio.

Methacholine Dose-Response Slopes from Maximal Bronchial Challenge Tests in Asthmatic Children: Methodological Aspects

To determine whether the slope of a maximal bronchial challenge test (in which FEV1 falls by over 50%) could be extrapolated from a standard bronchial challenge test (in which FEV1 falls up to 20%), 14 asthmatic children performed a single maximal bronchial challenge test with methacholin(dose range: 0.097–30.08 umol) by the dosimeter method. Maximal dose-response curves were included according to the following criteria: (1) at least one more dose beyond a FEV1 ù 20%; and (2) a MFEV1 ù 50%. PD20 FEV1 was calculated, and the slopes of the early part of the dose-response curve (standard dose-response slopes) and of the entire curve (maximal dose-response slopes) were calculated by two methods: the two-point slope (DRR) and the least squares method (LSS) in % FEV1 × umol−1. Maximal dose-response slopes were compared with the corresponding standard dose-response slopes by a paired Student’s t test after logarithmic transformation of the data; the goodness of fit of the LSS was also determined. Maximal dose-response slopes were significantly different (p < 0.0001) from those calculated on the early part of the curve: DRR20% (91.2 ± 2.7 FEV1% z umol−1)was 2.88 times higher than DRR50% (31.6 ± 3.4 DFEV1% z umol−1), and the LSS20% (89.1 ± 2.8% FEV1 z umol−1) was 3.10 times higher than LSS 50% (28.8 ± 1.5%FEV1 z umol−1). The goodness of fit of LSS 50% was significant in all cases, whereas LSS 20% failed to be significant in one. These results suggest that maximal dose-response slopes cannot be predicted from the data of standard bronchial challenge tests.

Falências de Orgão em Pediatria

Objectivos: 1) Caracterizar as falências mono (OF) e multiorgão (MOF) numa Unidade de Cuidados Intensivos Pediátricos em relação a; altura do internamento em que ocorrem; associação de orgãos em falência e evolução dos doentes com falência mono e multiorgão. 2) Avaliar a performance de um índice de gravidade, o Pediatric Risk of Mortality (PRISM), para a população total da Unidade e para o grupo das falências multiorgão. 3) Identificar marcadores de risco de mortalidade nos doentes com MOF. Métodos: Revisão de uma base de dados e análise retrospectiva de todos os doentes internados em relação aos critérios de OF e MOF, sugeridos por Wilkinson et al. População: Total de doentes internados na Unidade de Cuidados Intensivos Pediátricos (UCIP) de um Hospital Terciário, durante um período de dois anos (Abril de 1991 a Março de 1993). Resultados Principais / Conclusões: Foram avaliados 1120 doentes, com uma média de idades de 45.9 ± 51.1 meses, sendo 961 (85.8%) médicos e 159 (14.2%) cirúrgicos. Eram previamente saudáveis 695 (62.1%), sendo os restantes 424 (37.9%) portadores de doença crónica. A mortalidade global foi de 5% (56/1120 doentes). Cento e oitenta e sete doentes (16.7%) preencheram critérios de falência mono-orgão (OF), destes, 180 (96.3%) estavam em OF já à entrada e 7 (3.7%) tiveram falência não simultânea de mais de um orgão. A mortalidade dos doentes com falência mono-orgão foi de 3.7% (7 doentes). Cento e um doentes (9.02%) tiveram falência multiorgão (MOF), definida como falência simultânea de dois ou mais orgãos, em qualquer altura do internamento. Existia MOF já à entrada em 90 doentes (89.1%). Houve 47 doentes com falência máxima de 2 orgãos (46.6%), 42 (41.6%) com falência de 3 orgãos. 10 (9.9%) com falência de 4 orgãos e 2 (1.98%) com falência de 5 orgãos. A mortalidade por número de orgãos em falência foi respectivamente de 23.4%; 66.7%; 80% e 100%. A mortalidade global dos doentes com falência multiorgão foi de 48.5% (49/101 doentes). O PRISM revelou um bom valor predictivo quando aplicado na totalidade dos doentes: discriminação (W) (avaliada pela área sob curvas ROC) W = 0.959 SE = 0.00085 e calibração (H) (avaliada pelo Hosmer-Lemesshow goodness-of-fit test) H = 13.217 p = 0.104. Estes valores permitem considerar este índice de gravidade como estando bem aferido para a população da Unidade. Quando aplicado ao grupo das MOF a discriminação foi aceitável (W = 0.732 SE = 0.036) mas a calibração foi má (H = 29.780 p = 0.00026). A análise multivariada mostrou que um score de PRISM % 15 e um número de orgãos em falência % 3, tanto na admissão como em qualquer altura do internamento, têm uma importância significativa na probabilidade de morte.

Leber Congenital Amaurosis: Comprehensive Survey of the Genetic Heterogeneity, Refinement of the Clinical Definition, and Genotype-Phenotype Correlations as a Strategy for Molecular Diagnosis

Leber congenital amaurosis (LCA) is the earliest and most severe form of all inherited retinal dystrophies, responsible for congenital blindness. Disease-associated mutations have been hitherto reported in seven genes. These genes are all expressed preferentially in the photoreceptor cells or the retinal pigment epithelium but they are involved in strikingly different physiologic pathways resulting in an unforeseeable physiopathologic variety. This wide genetic and physiologic heterogeneity that could largely increase in the coming years, hinders the molecular diagnosis in LCA patients. The genotyping is, however, required to establish genetically defined subgroups of patients ready for therapy. Here, we report a comprehensive mutational analysis of the all known genes in 179 unrelated LCA patients, including 52 familial and 127 sporadic (27/127 consanguineous) cases. Mutations were identified in 47.5% patients. GUCY2D appeared to account for most LCA cases of our series (21.2%), followed by CRB1 (10%), RPE65 (6.1%), RPGRIP1 (4.5%), AIPL1 (3.4%), TULP1 (1.7%), and CRX (0.6%). The clinical history of all patients with mutations was carefully revisited to search for phenotype variations. Sound genotype-phenotype correlations were found that allowed us to divide patients into two main groups. The first one includes patients whose symptoms fit the traditional definition of LCA, i.e., congenital or very early cone-rod dystrophy, while the second group gathers patients affected with severe yet progressive rod-cone dystrophy. Besides, objective ophthalmologic data allowed us to subdivide each group into two subtypes. Based on these findings, we have drawn decisional flowcharts directing the molecular analysis of LCA genes in a given case. These flowcharts will hopefully lighten the heavy task of genotyping new patients but only if one has access to the most precise clinical history since birth.