Correlation of the Boost Risk Stratification Tool as a Predictor of Unplanned 30-Day Readmission in Elderly Patients

Carol K. Sieck Loyola University Chicago CORRELATION OF THE BOOST RISK STRATIFICATION TOOL AS A PREDICTOR OF UNPLANNED 30-DAY REAMDISSION IN ELDERLY PATIENTS Risk stratification tools can identify patients at risk for 30-day readmission but available tools lack predictive strength. While physical, functional and social determinants of health have demonstrated an association with readmission, available risk stratification tools have been inconsistent in their use of variables to predict readmission. The Better Outcomes by Optimizing Safe Transitions (BOOST) 8 P\u27s tool is a risk stratification tool developed by the Society of Hospital Medicine but has no published validation studies. The theoretical foundation used for this study was Wagner\u27s Care Model that illustrates the interconnected nature of acute and preventive care needed by chronically ill patients over a lifetime. This quantitative study using secondary data to measure the degree to which the BOOST variables predict 30-day readmission. The sample included one year of hospitalized patients 65+ (n=6849) from a tertiary hospital in the Midwest. Univariate and multivariate logistic regression demonstrated that six of the eight variables in the BOOST risk stratification tool showed significant predictive strength, including the social variables of health literacy (p=.030), depression (p=.003) and isolation (p=.011). Other significant variables included problem medications (p=.001), physical limitations (p=\u3c.001) and prior hospitalization (p=\u3c.001). The BOOST risk stratification tool had limited predictive capability with a C-statistic of .631. This study was the first attempt to validate the BOOST 8 P\u27s tool and to utilize nursing documentation within an electronic medical record to capture social determinants of health. Implications for nursing practice include the need for nurses to gain skills in using risk stratification tools to identify patients at risk for readmission to target preventive interventions including care coordination efforts. Future research should target variables, especially social factors of depression, health literacy and isolation to predict 30-day readmission, especially for the growing population of elderly patients with chronic illness

Factors associated with a prolonged hospital stay during induction chemotherapy in newly diagnosed high risk pediatric acute lymphoblastic leukemia

Background High Risk (HR) or Very High Risk (VHR) acute lymphoblastic leukemia (ALL) treated with 4 drug induction chemotherapy is often associated with adverse events. The aim of this study was to identify risk factors associated with a prolonged inpatient length of stay LOS during induction chemotherapy. Procedure Data from patients (N = 73) (age<21 years) was collected through a retrospective chart review. Univariable and multivariable logistic regression was used to test for statistical significance. The overall survival and disease (leukemia)-free survival were analyzed using the Kaplan–Meier method and log-rank test. Results Of the 73 patients, 42 (57%) patients were discharged on day 4 of induction (short LOS, group A), while 31 (43%) patients (group B) experienced a prolonged LOS or an ICU stay (16 ± 27.7 days, median hospital stay = 8 days vs 4 days (group A), p = 0.02) due to organ dysfunction, infectious or metabolic complications. Group B patients were more likely to have a lower platelet count, serum bicarbonate, and a higher blood urea nitrogen (BUN) on day 4 of treatment (OR = 4.52, 8.21, and 3.02, respectively, p < 0.05). Multivariable analysis identified low serum bicarbonate (p = 0.002) and a platelet count<20,000/μL (p = 0.02) on day 4 of induction to be predictive of a prolonged LOS. Twenty six (group A (n = 16, 36%) and B (n = 11, 35%), p = 0.8) patients experienced unplanned admissions, within 30 days of discharge. Conclusions A significant proportion of newly diagnosed HR or VHR pediatric ALL patients experience a prolonged LOS and unplanned re-admissions. Aggressive discharge planning and close follow up is indicated in this cohort of patients

Triumph of hope over experience: learning from interventions to reduce avoidable hospital admissions identified through an Academic Health and Social Care Network.

BACKGROUND: Internationally health services are facing increasing demands due to new and more expensive health technologies and treatments, coupled with the needs of an ageing population. Reducing avoidable use of expensive secondary care services, especially high cost admissions where no procedure is carried out, has become a focus for the commissioners of healthcare. METHOD: We set out to identify, evaluate and share learning about interventions to reduce avoidable hospital admission across a regional Academic Health and Social Care Network (AHSN). We conducted a service evaluation identifying initiatives that had taken place across the AHSN. This comprised a literature review, case studies, and two workshops. RESULTS: We identified three types of intervention: pre-hospital; within the emergency department (ED); and post-admission evaluation of appropriateness. Pre-hospital interventions included the use of predictive modelling tools (PARR - Patients at risk of readmission and ACG - Adjusted Clinical Groups) sometimes supported by community matrons or virtual wards. GP-advisers and outreach nurses were employed within the ED. The principal post-hoc interventions were the audit of records in primary care or the application of the Appropriateness Evaluation Protocol (AEP) within the admission ward. Overall there was a shortage of independent evaluation and limited evidence that each intervention had an impact on rates of admission. CONCLUSIONS: Despite the frequency and cost of emergency admission there has been little independent evaluation of interventions to reduce avoidable admission. Commissioners of healthcare should consider interventions at all stages of the admission pathway, including regular audit, to ensure admission thresholds don't change

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

Readmission risk prediction for patients after total hip or knee arthroplasty

Cybersecurity intelligence sharing (CIS) has the potential to help organisations improving their situational awareness. Although CIS has received more attention from organisations, participation in CIS operation is not satisfactory, and there is not too much information about the factors that are antecedent to CIS among organisations . Thus, this study aims to investigate technical and non-technical factors including organisational and environmental factors influence organisational participation in CIS practices

Utility of models to predict 28-day or 30-day unplanned hospital readmissions: an updated systematic review.

Objective: To update previous systematic review of predictive models for 28-day or 30-day unplanned hospital readmissions. Design: Systematic review. Setting/data source: CINAHL, Embase, MEDLINE from 2011 to 2015. Participants: All studies of 28-day and 30-day readmission predictive model. Outcome measures Characteristics of the included studies, performance of the identified predictive models and key predictive variables included in the models. Results: Of 7310 records, a total of 60 studies with 73 unique predictive models met the inclusion criteria. The utilisation outcome of the models included all-cause readmissions, cardiovascular disease including pneumonia, medical conditions, surgical conditions and mental health condition-related readmissions. Overall, a wide-range C-statistic was reported in 56/60 studies (0.21–0.88). 11 of 13 predictive models for medical condition-related readmissions were found to have consistent moderate discrimination ability (C-statistic ≥0.7). Only two models were designed for the potentially preventable/avoidable readmissions and had C-statistic >0.8. The variables ‘comorbidities’, ‘length of stay’ and ‘previous admissions’ were frequently cited across 73 models. The variables ‘laboratory tests’ and ‘medication’ had more weight in the models for cardiovascular disease and medical condition-related readmissions.Conclusions: The predictive models which focused on general medical condition-related unplanned hospital readmissions reported moderate discriminative ability. Two models for potentially preventable/avoidable readmissions showed high discriminative ability. This updated systematic review, however, found inconsistent performance across the included unique 73 risk predictive models. It is critical to define clearly the utilisation outcomes and the type of accessible data source before the selection of the predictive model. Rigorous validation of the predictive models with moderate-to-high discriminative ability is essential, especially for the two models for the potentially preventable/avoidable readmissions. Given the limited available evidence, the development of a predictive model specifically for paediatric 28-day all-cause, unplanned hospital readmissions is a high priority

Bringing the pieces together:Integrating cardiac and geriatric care in older patients with heart disease

Due to the increasing aging population, the number of older cardiac patients is also expected to rise in the next decades. The treatment of older cardiac patients is complex due to the simultaneously presence of comorbidities and polypharmacy, and geriatric conditions such as functional impairment, fall risk and malnutrition. However, the assessment of geriatric conditions is not part of the medical routine in cardiology and therefore these conditions are frequently unrecognized although they have a significant impact on treatment and on outcomes. In addition, treatments are mostly based on single-disease oriented guidelines and inadequately take other conditions into account. This may lead to conflicting recommendations and treatments that do not address important outcomes for older patients such as daily functioning, symptom relief and quality of life. Thus, the care of older cardiac patients is currently suboptimal which increases the risk of functional loss, readmission and mortality. The overall aim of the work described in this thesis is to explore the integration of cardiac and geriatric care for older patients with heart disease. First, by examining how hospitalized older cardiac patients at high risk for adverse events could be identified. Second, by investigating lifestyle-related secondary prevention of cardiovascular complications in older cardiac patients. And third, by developing a transitional care intervention for older cardiac patients and evaluating the effect on unplanned hospital readmission and mortality

Predictive risk models to identify people with chronic conditions at risk of hospitalisation

A disproportionately large percentage of health care costs and utilisation is spent on a small fraction of the population with complex and chronic conditions (Panattoni et al., 2011). It is widely agreed that effective and accessible primary health care (PHC) is central to reducing potentially avoidable hospitalisations (PAHs) associated with chronic disease. Predictive risk modelling is one method that is used to identify individuals who may be at risk of a hospitalisation event. The Predictive Risk Model (PRM) is a tool for identifying at-risk patients, so that appropriate preventive care can be provided, to avoid both exacerbation and complications of existing conditions, and acute events that may lead to hospitalisation. This Policy Issue Review identifies a selection of currently available PRMs, focusing on those applied in a PHC setting; and examines evidence of reliability in targeting patients with complex and chronic conditions

Risk factors associated with paediatric unplanned hospital readmissions: A systematic review

Objective To synthesise evidence on risk factors associated with paediatric unplanned hospital readmissions (UHRs). Design Systematic review. Data source CINAHL, EMBASE (Ovid) and MEDLINE from 2000 to 2017. Eligibility criteria Studies published in English with full-text access and focused on paediatric All-cause, Surgical procedure and General medical condition related UHRs were included. Data extraction and synthesis Characteristics of the included studies, examined variables and the statistically significant risk factors were extracted. Two reviewers independently assessed study quality based on six domains of potential bias. Pooling of extracted risk factors was not permitted due to heterogeneity of the included studies. Data were synthesised using content analysis and presented in narrative form. Results Thirty-six significant risk factors were extracted from the 44 included studies and presented under three health condition groupings. For All-cause UHRs, ethnicity, comorbidity and type of health insurance were the most frequently cited factors. For Surgical procedure related UHRs, specific surgical procedures, comorbidity, length of stay (LOS), age, the American Society of Anaesthesiologists class, postoperative complications, duration of procedure, type of health insurance and illness severity were cited more frequently. The four most cited risk factors associated with General medical condition related UHRs were comorbidity, age, health service usage prior to the index admission and LOS. Conclusions This systematic review acknowledges the complexity of readmission risk prediction in paediatric populations. This review identified four risk factors across all three health condition groupings, namely comorbidity; public health insurance; longer LOS and patients&lt;12 months or between 13-18 years. The identification of risk factors, however, depended on the variables examined by each of the included studies. Consideration should be taken into account when generalising reported risk factors to other institutions. This review highlights the need to develop a standardised set of measures to capture key hospital discharge variables that predict unplanned readmission among paediatric patients