Daily assessment of severity of illness and mortality prediction for individual patients
MetadataShow full item record
Objective: To refine the prognosis of critically ill patients using a statistical model that incorporates the daily probabilities of hospital mortality during the first week of stay in the intensive care unit (ICU). Design: Prospective inception cohort. Setting: Fifteen adult medical and surgical ICUs in Spain. Patients: A total of 1,441 patients aged >18 yrs who were consecutively admitted from April 1, 1995, through July 31, 1995. Interventions: Prospective data collection during the stay of the patient in the ICU. Data collected included vital status at hospital discharge as well as all variables necessary for computing the Mortality Probability Models II system at admission and during the first 7 days of stay in the ICU. Measurements and Main Results: Four logistic regression models were obtained. These models contained survival status at hospital discharge as a dependent variable and the following explanatory variables: (model 1) only the probability of dying at admission; (model 2) only the probability of dying during the current day; (model 3) the probability of dying at admission and during the current day; and (model 4) the probabilities of dying at admission and during the previous and current days. Models were evaluated using the Hosmer-Lemeshow statistic and the area under the receiver operating characteristic curve. For survivor and nonsurvivor patients, mortality probabilities obtained using the aforementioned models were compared using linear regression and the paired Student’s t-test. Although severity at admission was a statistically significant variable, models 2 and 3 produced almost the same probabilities of hospital mortality, as shown with the linear regression and paired Student’s t-test results. Conclusions: To have an accurate measurement of the prognosis, it is necessary to update the severity measure. The best estimate of hospital mortality was the probability of death on the current day. Severity at admission and at previous days did not improve the assessment of prognosis. (Crit Care Med 2001; 29:45–50) KEY WORDS: probability models; logistic regression models; hospital mortality; mortality prediction; prognosis; daily assessment; severity of illness index; Mortality Probability Models II system; intensive care; critical illness
Is part ofCritical Care Medicine, 2001, vol. 29, núm. 1, p. 45-50
European research projects
Showing items related by title, author, creator and subject.
Machine learning techniques for mortality prediction in critical traumatic patients: anatomic and physiologic variables from the RETRAUCI study Servià Goixart, Lluís; Montserrat, Neus; Badia Castello, Mariona; Llompart-Pou, Juan Antonio; Barea-Mendoza, Jesús Abelardo; Chico-Fernández, Mario; Sánchez-Casado, Marcelino; Jiménez, José Manuel; Mayor, Dolores María; Trujillano Cabello, Javier (BMC, 2020-10-20)Interest in models for calculating the risk of death in traumatic patients admitted to ICUs remains high. These models use variables derived from the deviation of physiological parameters and/or the severity of anatomical ...
Factors predicting cardiovascular events in chronic kidney disease patients. Role of subclinical atheromatosis extent assessed by vascular ultrasound Valdivielso Revilla, José Manuel; Betriu i Bars, M. Àngels; Martínez Alonso, Montserrat; Arroyo, David; Bermúdez López, Marcelino; Fernández i Giráldez, Elvira (Public Library of Science, 2017)Patients with chronic kidney disease (CKD) have an increased incidence of cardiovascular events (CVE). The contribution of subclinical atheromatosis extent, including femoral arteries, to CVE in CKD patients has not been ...
Baseline residual kidney function and its ensuing rate of decline interact to predict mortality of peritoneal dialysis patients Pérez Fontán, Miguel; Remón Rodríguez, César; Cunha Naveira, Marta da; Borràs, Mercè; Rodríguez Suárez, Carmen; Quirós Ganga, Pedro; Sánchez Alvarez, Emilio; Rodríguez Carmona, Ana (Public Library of Science, 2016)Background Baseline residual kidney function (RKF) and its rate of decline during follow-up are purported to be reliable outcome predictors of patients undergoing Peritoneal Dialysis (PD). The independent contribution ...