Using Shock Index as a Predictor of ICU Readmission: A Quality Iimprovement Project

Background: Adverse events will occur in one-third of patients discharged from the intensivecare unit (ICU) and evidence shows that ICU readmissions increase a patient’s length of stay,mortality, hospital costs, and nosocomial infections, as well as decrease long-term survival.Specific predictive factors that will accurately predict which patients are at risk of adverseevents requiring readmission are needed.Aim: The specific aim of this project was to identify if shock index (SI) values higher than 0.7at the time of transfer from the ICU are a useful predictor of ICU readmission.Methods: Using the Plan, Do, Study, Act (PDSA) framework, a retrospective chart review wasperformed using a matched cohort of 34 patients readmitted with 72 hours of discharge from theICU and 34 controls to obtain SI values at admission, transfer from and readmission to the ICU.A second PDSA cycle looked for SI trends within 24 hours prior to discharge from the ICU.Results: An odds ratio calculating the risk of readmission of patients with an elevated SI was2.96 (Confidence Interval (CI) 1.1 to 7.94, p-value=0.03). The odds ratio for an 80% SIelevation over 24 hours prior to discharge was 1.56 (CI 0.36 to 6.76, p-value=0.55).Conclusion and Implications for CNL Practice: Patients with elevated SIs at the time oftransfer are three times more likely to be readmitted to the ICU. Patients with elevations in atleast 80% of the 24 hour pre-discharge SIs showed no significant differences between thecontrol and readmitted cohorts. Implications of these results for the clinical nurse leader will bediscussed

Perceived Readiness for Hospital Discharge in Adult Medical-Surgical Patients

Purpose: The purpose of the study was to identify predictors and outcomes of adult medical-surgical patients\u27 perceptions of their readiness for hospital discharge. Design: A correlational, prospective, longitudinal design with path analyses was used to explore relationships among transition theory-related variables. Setting: Midwestern tertiary medical center. Sample: 147 adult medical-surgical patients. Methods: Predictor variables included patient characteristics, hospitalization factors, and nursing practices that were measured prior to hospital discharge using a study enrollment form, the Quality of Discharge Teaching Scale, and the Care Coordination Scale. Discharge readiness was measured using the Readiness for Hospital Discharge Scale administered within 4 hours prior to discharge. Outcomes were measured 3 weeks postdischarge with the Post-Discharge Coping Difficulty Scale and self-reported utilization of health services. Findings: Living alone, discharge teaching (amount of content received and nurses\u27 skill in teaching delivery), and care coordination explained 51% of readiness for discharge score variance. Patient age and discharge readiness explained 16% of variance in postdischarge coping difficulty. Greater readiness for discharge was predictive of fewer readmissions. Conclusions: Quality of the delivery of discharge teaching was the strongest predictor of discharge readiness. Study results provided support for Meleis\u27 transitions theory as a useful model for conceptualizing and investigating the discharge transition. Implications for Practice: The study results have implications for the CNS role in patient and staff education, system building for the postdischarge transition, and measurement of clinical care outcomes

Improving Postdischarge Outcomes in Acute Heart Failure

The global burden that acute heart failure (AHF) carries has remained unchanged over the past several decades (1). European registries (2–5) showed that 1-year outcome rates remain unacceptably high (Table 1) and confirm that hospitalization for AHF represents a change in the natural history of the disease process(6). As patients hospitalized for HF have a bad prognosis, it is crucial to utilize hospitalization as an opportunity to: 1) assess the individual components of the cardiac substrate; 2) identify and treat comorbidities; 3) identify early, safe endpoints of therapy to facilitate timely hospital discharge and outpatient follow-up; and 4) implement and begin optimization guideline-directed medical therapies (GDMTs). As outcomes are influenced by many factors, many of which are incompletely understood, a systematic approach is proposed that should start with admission and continues through post-discharge (7)

Validation of the Registered Nurse Assessment of Readiness for Hospital Discharge Scale

Background Statistical models for predicting readmissions have been published for high-risk patient populations but typically focus on patient characteristics; nurse judgment is rarely considered in a formalized way to supplement prediction models. Objectives The purpose of this study was to determine psychometric properties of long and short forms of the Registered Nurse Readiness for Hospital Discharge Scale (RN-RHDS), including reliability, factor structure, and predictive validity. Methods Data were aggregated from two studies conducted at four hospitals in the Midwestern United States. The RN-RHDS was completed within 4 hours before hospital discharge by the discharging nurse. Data on readmissions and emergency department visits within 30 days were extracted from electronic medical records. Results The RN-RHDS, both long and short forms, demonstrate acceptable reliability (Cronbach’s alphas of .90 and .73, respectively). Confirmatory factor analysis demonstrated less than adequate fit with the same four-factor structure observed in the patient version. Exploratory factor analysis identified three factors, explaining 60.2% of the variance. When nurses rate patients as less ready to go home (\u3c7 out of 10), patients are 6.4–9.3 times more likely to return to the hospital within 30 days, in adjusted models. Discussion The RN-RHDS, long and short forms, can be used to identify medical-surgical patients at risk for potential unplanned return to hospital within 30 days, allowing nurses to use their clinical judgment to implement interventions prior to discharge. Use of the RN-RHDS could enhance current readmission risk prediction models

Benchmarking Deep Learning Architectures for Predicting Readmission to the ICU and Describing Patients-at-Risk

Objective: To compare different deep learning architectures for predicting the risk of readmission within 30 days of discharge from the intensive care unit (ICU). The interpretability of attention-based models is leveraged to describe patients-at-risk. Methods: Several deep learning architectures making use of attention mechanisms, recurrent layers, neural ordinary differential equations (ODEs), and medical concept embeddings with time-aware attention were trained using publicly available electronic medical record data (MIMIC-III) associated with 45,298 ICU stays for 33,150 patients. Bayesian inference was used to compute the posterior over weights of an attention-based model. Odds ratios associated with an increased risk of readmission were computed for static variables. Diagnoses, procedures, medications, and vital signs were ranked according to the associated risk of readmission. Results: A recurrent neural network, with time dynamics of code embeddings computed by neural ODEs, achieved the highest average precision of 0.331 (AUROC: 0.739, F1-Score: 0.372). Predictive accuracy was comparable across neural network architectures. Groups of patients at risk included those suffering from infectious complications, with chronic or progressive conditions, and for whom standard medical care was not suitable. Conclusions: Attention-based networks may be preferable to recurrent networks if an interpretable model is required, at only marginal cost in predictive accuracy