A Simple Scoring Tool to Predict Medical Intensive Care Unit Readmissions Based on Both Patient and Process Factors.

BACKGROUND: Although many predictive models have been developed to risk assess medical intensive care unit (MICU) readmissions, they tend to be cumbersome with complex calculations that are not efficient for a clinician planning a MICU discharge. OBJECTIVE: To develop a simple scoring tool that comprehensively takes into account not only patient factors but also system and process factors in a single model to predict MICU readmissions. DESIGN: Retrospective chart review. PARTICIPANTS: We included all patients admitted to the MICU of Robert Wood Johnson University Hospital, a tertiary care center, between June 2016 and May 2017 except those who were < 18 years of age, pregnant, or planned for hospice care at discharge. MAIN MEASURES: Logistic regression models and a scoring tool for MICU readmissions were developed on a training set of 409 patients, and validated in an independent set of 474 patients. KEY RESULTS: Readmission rate in the training and validation sets were 8.8% and 9.1% respectively. The scoring tool derived from the training dataset included the following variables: MICU admission diagnosis of sepsis, intubation during MICU stay, duration of mechanical ventilation, tracheostomy during MICU stay, non-emergency department admission source to MICU, weekend MICU discharge, and length of stay in the MICU. The area under the curve of the scoring tool on the validation dataset was 0.76 (95% CI, 0.68-0.84), and the model fit the data well (Hosmer-Lemeshow p = 0.644). Readmission rate was 3.95% among cases in the lowest scoring range and 50% in the highest scoring range. CONCLUSION: We developed a simple seven-variable scoring tool that can be used by clinicians at MICU discharge to efficiently assess a patient's risk of MICU readmission. Additionally, this is one of the first studies to show an association between MICU admission diagnosis of sepsis and MICU readmissions.

Quality improvement and cost savings after implementation of the Leapfrog intensive care unit physician staffing standard at a community teaching hospital.

BACKGROUND: Prior studies have shown that implementation of the Leapfrog intensive care unit physician staffing standard of dedicated intensivists providing 24-hr intensive care unit coverage reduces length of stay and in-hospital mortality. A theoretical model of the cost-effectiveness of intensive care unit physician staffing patterns has also been published, but no study has examined the actual cost vs. cost savings of such a program. OBJECTIVE: To determine whether improved outcomes in specific quality measures would result in an overall cost savings in patient care DESIGN: Retrospective, 1 yr before-after cohort study SETTING: A 15-bed mixed medical-surgical community intensive care unit PATIENTS: A total of 2,181 patients: 1,113 patients preimplementation and 1,068 patients postimplementation. INTERVENTION: Leapfrog intensive care unit physician staffing standard MEASUREMENTS: Intensive care unit and hospital length of stay, rates for ventilator-associated pneumonia and central venous access device infection, and cost of care. RESULTS: Following institution of the intensive care unit physician staffing, the mean intensive care unit length of stay decreased significantly from 3.5±8.9 days to 2.7±4.7 days, (p<.002). The frequency of ventilator-associated pneumonia fell from 8.1% to 1.3% (p<.0002) after intervention. Ventilator-associated pneumonia rate per 100 ventilator days decreased from 1.03 to 0.38 (p<.0002). After intervention, the frequency of the central venous access device infection events fell from 9.4% to 1.1% (p<.0002). Central venous access device infection rate per 1000 line days decreased from 8.49 to 1.69. The net savings for the hospital were $744,001. The 1-yr institutional return on investment from intensive care unit physician staffing was 105%. CONCLUSIONS: Implementation of the Leapfrog intensive care unit physician staffing standard significantly reduced intensive care unit length of stay and lowered the prevalence of ventilator-associated pneumonia and central venous access device infection. A cost analysis yielded a 1-yr institutional return on investment of 105%. Our study confirms that implementation of the Leapfrog intensive care unit physician staffing model in the community hospital setting improves quality measures and is economically feasible.

Obstetric admissions to the intensive care unit: outcomes and severity of illness.

OBJECTIVE: To determine whether mortality prediction based on a current model of outcome prediction is accurate in obstetric patients. METHODS: Consecutive obstetric admissions to a medical intensive care unit from 1991 to 1998 were reviewed to determine whether mortality prediction is feasible in obstetric patients based on a widely used model. The Simplified Acute Physiologic Score (SAPS II) was used to predict the probability of hospital mortality. RESULTS: The Simplified Acute Physiologic Score overestimated mortality in all patients (19 predicted deaths, eight observed) but accurately predicted mortality in patients admitted to the intensive care unit for medical reasons (seven predicted, five observed). The Simplified Acute Physiologic Score did not predict mortality in patients admitted for obstetric indications or postpartum hemorrhage. Median SAPS II scores were significantly higher in those patients who died, compared with survivors. For all groups, SAPS II scores were correlated with intensive care unit length of stay but not hospital length of stay. CONCLUSION: The Simplified Acute Physiologic Score accurately predicts hospital mortality in obstetric patients admitted to the intensive care unit for medical reasons but not for indications related to pregnancy and delivery. An alternate model that predicts outcomes in obstetric patients admitted for obstetric indications should be developed.