Differences in the Rothman Index Score in Evolving Emergent Events in Medical-Surgical Patients

Background: The Rothman Index (RI), an early warning system using software integrated with the electronic medical record provides scores monitoring patient conditions. Minimal findings exist regarding RI scores in medical-surgical patients. Objectives: Explore differences in the RI scores in medical-surgical patients who suffered rapid response, cardiopulmonary resuscitation or death events. Methods: A retrospective comparative design of 75 subjects with a rapid response or cardiopulmonary resuscitation event on medical-surgical units over 12-months at an academic medical center using RI scores at admission, 48- and 24-hours before and at time of event. Deaths were identified immediately following the emergent events. Results: The RI scores were significantly higher on admission compared to RI scores at time of rapid response or cardiopulmonary resuscitation event (p\u3c0.001). The RI scores at 48 hours prior to event were significantly higher compared to the scores at event time (p\u3c0.001). RI scores at 24 hours before the event were significantly higher compared to the RI scores at event time (p\u3c0.001). No differences were found between the RI change scores in patients who died and those who remained alive (p=0.83). Conclusions: Differences existed in RI scores from admission, 48 and 24 hours prior to the time of emergent events. Earlier identification of patient condition changes through the nursing process, combined with an integrated early warning system in the electronic medical record, may reduce emergent events in medical-surgical patients. A collaborative dialogue between nursing and medical staff is crucial to timely recognize and treat conditions to minimize opportunities for emergent events

Organizing for Higher Performance: Case Studies of Organized Delivery Systems

Offers lessons learned from healthcare delivery systems promoting the attributes of an ideal model as defined by the Fund: information continuity, care coordination and transitions, system accountability, teamwork, continuous innovation, and easy access

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

Evaluating Risks from Antibacterial Medication Therapy

ABSTRACT EVALUATING RISKS FROM ANTIBACTERIAL MEDICATION THERAPY USING AN OBSERVATIONAL PRIMARY CARE DATABASE Sharon B. Meropol Joshua P. Metlay Virtually everyone in the U.S. is exposed to antibacterial drugs at some point in their lives. It is important to understand the benefits and risks related to these medications with nearly universal public exposure. Most information on antibacterial drug-associated adverse events comes from spontaneous reports. Without an unexposed control group, it is impossible to know the real risks for treated vs. untreated patients. We used an electronic medical record database to select a cohort of office visits for non-bacterial acute respiratory tract infections (excluding patients with pneumonia, sinusitis, or acute exacerbations of chronic bronchitis), and compared outcomes of antibacterial drug-exposed vs. -unexposed patients. By limiting our assessment to visits with acute nonspecific respiratory infections, we promoted comparability between exposed and unexposed patients. To further control for confounding by indication and practice, we explored methods to promote further comparability between exposure groups. Our rare outcome presented an additional analytic challenge. Antibacterial drug prescribing for acute nonspecific respiratory infections decreased over the study period, but, in contrast to the U.S., broad spectrum antibacterial prescribing remained low. Conditional fixed effects linear regression provided stable estimates of exposure effects on rare outcomes; results were similar to those using more traditional methods for binary outcomes. Patients with acute nonspecific respiratory infections treated with antibacterial drugs were not at increased risk of severe adverse events compared to untreated patients. Patients with acute nonspecific respiratory infections exposed to antibacterials had a small decreased risk of pneumonia hospitalizations vs. unexposed patients. This very small measurable benefit of antibacterial drug therapy for acute nonspecific respiratory infections at the patient level must be weighed against the public health risk of emerging antibacterial resistance. Our data provide valuable point estimates of risks and benefits that can be used to inform future decision analysis and guideline recommendations for patients with acute nonspecific respiratory infections. Ultimately, improved point-of-care diagnostic testing may help direct antibacterial drugs to the subset of patients most likely to derive benefit

The predictive performance and impact of pediatric early warning systems in hospitalized pediatric oncology patients-A systematic review

Pediatric early warning systems (PEWS) arewidely used to identify clinically deteriorating patients. Hospitalized pediatric oncology patients are particularly prone to clinical deterioration. We assessed the PEWS performance to predict early clinical deterioration and the effect of PEWS implementation on patient outcomes in pediatric oncology patients. PubMED, EMBASE, and CINAHL databases were systematically searched from inception up to March 2020. Quality assessment was performed using the Prediction model study Risk-Of-Bias Assessment Tool (PROBAST) and the Cochrane Risk-of-Bias Tool. Nine studies were included. Due to heterogeneity of study designs, outcome measures, and diversity of PEWS, it was not possible to conduct a meta-analysis. Although the studies reported high sensitivity, specificity, and area under the receiver operating characteristics curve (AUROC) of PEWS detecting inpatient deterioration, overall risk of bias of the studies was high. This review highlights limited evidence on the predictive performance of PEWS for clinical deterioration and the effect of PEWS implementation

Deep Risk Prediction and Embedding of Patient Data: Application to Acute Gastrointestinal Bleeding

Acute gastrointestinal bleeding is a common and costly condition, accounting for over 2.2 million hospital days and 19.2 billion dollars of medical charges annually. Risk stratification is a critical part of initial assessment of patients with acute gastrointestinal bleeding. Although all national and international guidelines recommend the use of risk-assessment scoring systems, they are not commonly used in practice, have sub-optimal performance, may be applied incorrectly, and are not easily updated. With the advent of widespread electronic health record adoption, longitudinal clinical data captured during the clinical encounter is now available. However, this data is often noisy, sparse, and heterogeneous. Unsupervised machine learning algorithms may be able to identify structure within electronic health record data while accounting for key issues with the data generation process: measurements missing-not-at-random and information captured in unstructured clinical note text. Deep learning tools can create electronic health record-based models that perform better than clinical risk scores for gastrointestinal bleeding and are well-suited for learning from new data. Furthermore, these models can be used to predict risk trajectories over time, leveraging the longitudinal nature of the electronic health record. The foundation of creating relevant tools is the definition of a relevant outcome measure; in acute gastrointestinal bleeding, a composite outcome of red blood cell transfusion, hemostatic intervention, and all-cause 30-day mortality is a relevant, actionable outcome that reflects the need for hospital-based intervention. However, epidemiological trends may affect the relevance and effectiveness of the outcome measure when applied across multiple settings and patient populations. Understanding the trends in practice, potential areas of disparities, and value proposition for using risk stratification in patients presenting to the Emergency Department with acute gastrointestinal bleeding is important in understanding how to best implement a robust, generalizable risk stratification tool. Key findings include a decrease in the rate of red blood cell transfusion since 2014 and disparities in access to upper endoscopy for patients with upper gastrointestinal bleeding by race/ethnicity across urban and rural hospitals. Projected accumulated savings of consistent implementation of risk stratification tools for upper gastrointestinal bleeding total approximately $1 billion 5 years after implementation. Most current risk scores were designed for use based on the location of the bleeding source: upper or lower gastrointestinal tract. However, the location of the bleeding source is not always clear at presentation. I develop and validate electronic health record based deep learning and machine learning tools for patients presenting with symptoms of acute gastrointestinal bleeding (e.g., hematemesis, melena, hematochezia), which is more relevant and useful in clinical practice. I show that they outperform leading clinical risk scores for upper and lower gastrointestinal bleeding, the Glasgow Blatchford Score and the Oakland score. While the best performing gradient boosted decision tree model has equivalent overall performance to the fully connected feedforward neural network model, at the very low risk threshold of 99% sensitivity the deep learning model identifies more very low risk patients. Using another deep learning model that can model longitudinal risk, the long-short-term memory recurrent neural network, need for transfusion of red blood cells can be predicted at every 4-hour interval in the first 24 hours of intensive care unit stay for high risk patients with acute gastrointestinal bleeding. Finally, for implementation it is important to find patients with symptoms of acute gastrointestinal bleeding in real time and characterize patients by risk using available data in the electronic health record. A decision rule-based electronic health record phenotype has equivalent performance as measured by positive predictive value compared to deep learning and natural language processing-based models, and after live implementation appears to have increased the use of the Acute Gastrointestinal Bleeding Clinical Care pathway. Patients with acute gastrointestinal bleeding but with other groups of disease concepts can be differentiated by directly mapping unstructured clinical text to a common ontology and treating the vector of concepts as signals on a knowledge graph; these patients can be differentiated using unbalanced diffusion earth mover’s distances on the graph. For electronic health record data with data missing not at random, MURAL, an unsupervised random forest-based method, handles data with missing values and generates visualizations that characterize patients with gastrointestinal bleeding. This thesis forms a basis for understanding the potential for machine learning and deep learning tools to characterize risk for patients with acute gastrointestinal bleeding. In the future, these tools may be critical in implementing integrated risk assessment to keep low risk patients out of the hospital and guide resuscitation and timely endoscopic procedures for patients at higher risk for clinical decompensation