AIM: Although unplanned admissions to the intensive care unit (ICU) are associated with poorer prognoses, there is no published prognostic tool available for predicting this risk in an individual patient. We developed a nomogram for calculating the individualised absolute risk of unplanned ICU admission during a hospital stay. METHOD: Hospital administrative data from a large district hospital of consecutive admissions from 1 January 2000 to 31 December 2006 of aged over 14 years was used. Patient data was extracted from 94,482 hospital admissions consisted of demographic and clinical variables, including diagnostic categories, types of admission and time and day of admission. Multivariate logistic regression coefficients were used to develop a predictive nomogram of individual risk to patients admitted to the study hospital of unplanned ICU admission. RESULTS: A total of 672 incident unplanned ICU admissions were identified over this period. Independent predictors of unplanned ICU admissions included being male, older age, emergency department (ED) admissions, after-hour admissions, weekend admissions and six principal diagnosis groups: fractured femur, acute pancreatitis, liver disease, chronic airway disease, pneumonia and heart failure. The area under the receiver operating characteristic curve was 0.81. CONCLUSION: The use of a nomogram to accurately identify at-risk patients using information that is readily available to clinicians has the potential to be a useful tool in reducing unplanned ICU admissions, which in turn may contribute to the reduction of adverse events of patients in the general wards.
AIM: Although unplanned admissions to the intensive care unit (ICU) are associated with poorer prognoses, there is no published prognostic tool available for predicting this risk in an individual patient. We developed a nomogram for calculating the individualised absolute risk of unplanned ICU admission during a hospital stay. METHOD: Hospital administrative data from a large district hospital of consecutive admissions from 1 January 2000 to 31 December 2006 of aged over 14 years was used. Patient data was extracted from 94,482 hospital admissions consisted of demographic and clinical variables, including diagnostic categories, types of admission and time and day of admission. Multivariate logistic regression coefficients were used to develop a predictive nomogram of individual risk to patients admitted to the study hospital of unplanned ICU admission. RESULTS: A total of 672 incident unplanned ICU admissions were identified over this period. Independent predictors of unplanned ICU admissions included being male, older age, emergency department (ED) admissions, after-hour admissions, weekend admissions and six principal diagnosis groups: fractured femur, acute pancreatitis, liver disease, chronic airway disease, pneumonia and heart failure. The area under the receiver operating characteristic curve was 0.81. CONCLUSION: The use of a nomogram to accurately identify at-risk patients using information that is readily available to clinicians has the potential to be a useful tool in reducing unplanned ICU admissions, which in turn may contribute to the reduction of adverse events of patients in the general wards.
Authors: James Malycha; Timothy Bonnici; David A Clifton; Guy Ludbrook; J Duncan Young; Peter J Watkinson Journal: BMC Med Inform Decis Mak Date: 2019-05-15 Impact factor: 2.796
Authors: George Glass; Thomas R Hartka; Jessica Keim-Malpass; Kyle B Enfield; Matthew T Clark Journal: J Clin Monit Comput Date: 2020-03-19 Impact factor: 2.502