Display and perception of risk: Analysis of decision support system display and its impact on perceived clinical risk of sepsis-induced health deterioration.

Despite acknowledging the value of clinical decision support systems (CDSS) in identifying risk for sepsis-induced health deterioration in-hospitalized patients, the relationship between display features, decision maker characteristics, and recognition of risk by the clinical decision maker remains an understudied, yet promising, area. The objective of this study is to explore the relationship between CDSS display design and perceived clinical risk of in-hospital mortality associated with sepsis. The study utilized data collected through in-person experimental sessions with 91 physicians from the general medical and surgical floors who were recruited across 12 teaching hospitals within the United States. Results of descriptive and statistical analyses provided evidence supporting the impact of display configuration and clinical case severity on perceived risk associated with in-hospital mortality. Specifically, findings showed that a high level of information (represented by the Predisposition, Infection, Response and Organ dysfunction (PIRO) score) and Figure display (as opposed to Text or baseline) increased awareness to recognizing the risk for in-hospital mortality of hospitalized sepsis patients. A CDSS display that synthesizes the optimal features associated with information level and design elements has the potential to enhance the quantification and communication of clinical risk in complex health conditions beyond sepsis.

Proximity of Cellular and Physiological Response Failures in Sepsis.

Sepsis is a devastating multi-stage health condition with a high mortality rate. Its complexity, prevalence, and dependency of its outcomes on early detection have attracted substantial attention from data science and machine learning communities. Previous studies rely on individual cellular and physiological responses representing organ system failures to predict health outcomes or the onset of different sepsis stages. However, it is known that organ systems' failures and dynamics are not independent events. In this study, we identify the dependency patterns of significant proximate sepsis-related failures of cellular and physiological responses using data from 12,223 adult patients hospitalized between July 2013 and December 2015. The results show that proximate failures of cellular and physiological responses create better feature sets for outcome prediction than individual responses. Our findings reveal the few significant proximate failures that play the major roles in predicting patients' outcomes. This study's results can be simply translated into clinical practices and inform the prediction and improvement of patients' conditions and outcomes.

The Available Criteria for Different Sepsis Scoring Systems in the Emergency Department-A Retrospective Assessment.

OBJECTIVE: The goal of the study was to assess the criteria availability of eight sepsis scoring methods within 6 hours of triage in the emergency department (ED). DESIGN: Retrospective data analysis study. SETTING: ED of MedStar Washington Hospital Center (MWHC), a 912-bed urban, tertiary hospital. PATIENTS: Adult (age ≥ 18 years) patients presenting to the MWHC ED between June 1, 2017 and May 31, 2018 and admitted with a diagnosis of severe sepsis with or without shock. MAIN OUTCOMES MEASURED: Availability of sepsis scoring criteria of eight different sepsis scoring methods at three time points-0 Hours (T0), 3 Hours (T1) and 6 Hours (T2) after arrival to the ED. RESULTS: A total of 50 charts were reviewed, which included 23 (46%) males and 27 (54%) females. Forty-eight patients (96%) were Black or African American. Glasgow Coma Scale was available for all 50 patients at T0. Vital signs, except for temperature, were readily available (>90%) at T0. The majority of laboratory values relevant for sepsis scoring criteria were available (>90%) at T1, with exception to bilirubin (66%) and creatinine (80%). NEWS, PRESEP and qSOFA had greater than 90% criteria availability at triage. SOFA and SIRS consistently had the least percent of available criteria at all time points in the ED. CONCLUSION: The availability of patient data at different time points in a patient's ED visit suggests that different scoring methods could be utilized to assess for sepsis as more patient information becomes available.

Parkes Weber syndrome associated with two somatic pathogenic variants in RASA1.

Parkes Weber syndrome is associated with autosomal dominant inheritance, caused by germline heterozygous inactivating changes in the RASA1 gene, characterized by multiple micro arteriovenous fistulas and segmental overgrowth of soft tissue and skeletal components. The focal nature and variable expressivity associated with this disease has led to the hypothesis that somatic "second hit" inactivating changes in RASA1 are necessary for disease development. We report a 2-yr-old male with extensive capillary malformation and segmental overgrowth of his lower left extremity. Ultrasound showed subcutaneous phlebectasia draining the capillary malformation; magnetic resonance imaging showed overgrowth of the extremity with prominence of fatty tissues, fatty infiltration, and enlargement of all the major muscle groups. Germline RASA1 testing was normal. Later somatic testing from affected tissue showed two pathogenic variants in RASA1 consistent with the c.934_938del, p.(Glu312Argfs*14) and the c.2925del, p.(Asn976Metfs*20) with variant allele fractions of 3.6% and 4.2%, respectively. The intrafamilial variability of Parkes Weber syndrome involving segmental overgrowth of soft tissue, endothelium, and bone is strongly suggestive of a somatic second-hit model. There are at least two reports of confirmed second somatic hits in RASA1 To our knowledge, this is the first report of an individual with two somatic pathogenic variants in the RASA1 gene in DNA from a vascular lesion.

The Cost-Effectiveness of Platelet-Rich Plasma Compared With Hyaluronic Acid Injections for the Treatment of Knee Osteoarthritis.

PURPOSE: To examine the cost-effectiveness of a series (total of 3 injections) of intra-articular platelet-rich plasma (PRP) injections in comparison to that of hyaluronic acid (HA) viscosupplementation for the treatment of symptomatic knee osteoarthritis. METHODS: Outcome data regarding the use of PRP or HA injections for the treatment of symptomatic knee osteoarthritis were determined from the highest-quality data (Level I) available in the literature until 2015. Health utility values were then derived from these high-quality data. Costs were determined by examining typical charges for patients undergoing a series of either PRP or HA injections for the treatment of this condition at a large private orthopaedic practice. These health utility values and costs were used to create an expected-value decision analysis model. RESULTS: The results of the model revealed that the cost per quality-adjusted life-year (QALY) of a series of PRP injections was $8,635.23/QALY and that of a series of HA injections was $5,331.75/QALY. A series of PRP injections was associated with a higher initial cost than a series of HA injections (difference, $1,433.67); however, PRP was also more effective (higher utility value) than HA by 0.11 QALYs (0.69 vs 0.58, P = .0062) at 1 year. The incremental cost-effectiveness ratio of the use of PRP injections as opposed to HA was $12,628.15/QALY. CONCLUSIONS: Although a series of either PRP ($8,635.23/QALY) or HA ($5,331.75/QALY) injections for the treatment of symptomatic knee osteoarthritis would be considered cost-effective (cost per QALY < $50,000), PRP injections were not more cost-effective than HA injections. However, PRP was significantly more effective at 1 year, and being associated with an incremental cost-effectiveness ratio of $12,628.15/QALY when compared with HA, a series of PRP injections should be considered a reasonable and acceptable alternative to HA injections for the treatment of symptomatic knee osteoarthritis. LEVEL OF EVIDENCE: Level II, economic and decision analysis of Level I studies.

Heroin Adulterated with the Novel Synthetic Cannabinoid, 5F-MDMB-PINACA: A Case Series.

INTRODUCTION: Heroin can be adulterated with various substances that may or may not have pharmacological effects. Here we report a case series of 8 patients who presented to the emergency department after overdose with intravenous heroin preparation adulterated with the synthetic cannabinoid methyl 2-(1-(5-fluoropentyl)-1H-indazole-3-carboxamido)-3,3-dimethylbutanoate (5F-MDMB-PINACA). CASE SERIES: Except for one patient, all of them presented with a typical initial opioid toxidrome consisting of central nervous system and respiratory depression along with pinpoint pupils. Naloxone was given to them, triggering severe agitation and combative behavior along with overlapping features of anticholinergic and sympathomimetic toxidrome. All patients required multiple doses of benzodiazepines. Three were successfully treated with physostigmine. DISCUSSION: 5F-MDMB-PINACA is a synthetic cannabinoid that was added to heroin in samples obtained from patients reported in this case series. Patients demonstrated significant agitation after receiving naloxone for opioid toxidrome, presumably because of the removal of the depressant effect of opioids, which unmasked the excitatory effects of the synthetic cannabinoids. Three patients required physostigmine along with the benzodiazepines for control of their agitation, urine retention and abnormal vitals, suggesting the possibility of an anticholinergic toxidrome to have developed in these patients. CONCLUSION: Heroin contaminated with 5F-MDMB-PINACA exhibits variable severities of anticholinergic effects, some on presentation and others only after opiate antagonism.

Notice to comply: A systematic review of clinician compliance with guidelines surrounding acute hospital-based infection management.

PURPOSE: To identify and characterize studies evaluating clinician compliance with infection-related guidelines, and to explore trends in guideline design and implementation strategies. DATA SOURCES: PubMed database, April 2017. Followed the PRISMA Statement for systematic reviews. STUDY SELECTION: Scope was limited to studies reporting compliance with guidelines pertaining to the prevention, detection, and/or treatment of acute hospital-based infections. Initial search (1,499 titles) was reduced to 49 selected articles. DATA EXTRACTION: Extracted publication and guideline characteristics, outcome measures reported, and any results related to clinician compliance. Primary summary measures were frequencies and distributions of characteristics. Interventions that led to improved compliance results were analyzed to identify trends in guideline design and implementation. RESULTS OF DATA SYNTHESIS: Of the 49 selected studies, 18 (37%), 13 (27%), and 10 (20%) focused on sepsis, pneumonia, and general infection, respectively. Six (12%), 17 (35%), and 26 (53%) studies assessed local, national, and international guidelines, respectively. Twenty studies (41%) reported 1-instance compliance results, 28 studies (57%) reported 2-instance compliance results (either before-and-after studies or control group studies), and 1 study (2%) described compliance qualitatively. Average absolute change in compliance for minimal, decision support, and multimodal interventions was 10%, 14%, and 25%, respectively. Twelve studies (24%) reported no patient outcome alongside compliance. CONCLUSIONS: Multimodal interventions and quality improvement initiatives seem to produce the greatest improvement in compliance, but trends in other factors were inconsistent. Additional research is required to investigate these relationships and understand the implications behind various approaches to guideline design, communication, and implementation, in addition to effectiveness of protocol impact on relevant patient outcomes.

Informatics and interaction: Applying human factors principles to optimize the design of clinical decision support for sepsis.

In caring for patients with sepsis, the current structure of electronic health record systems allows clinical providers access to raw patient data without imputation of its significance. There are a wide range of sepsis alerts in clinical care that act as clinical decision support tools to assist in early recognition of sepsis; however, there are serious shortcomings in existing health information technology for alerting providers in a meaningful way. Little work has been done to evaluate and assess existing alerts using implementation and process outcomes associated with health information technology displays, specifically evaluating clinician preference and performance. We developed graphical model displays of two popular sepsis scoring systems, quick Sepsis Related Organ Failure Assessment and Predisposition, Infection, Response, Organ Failure, using human factors principles grounded in user-centered and interaction design. Models will be evaluated in a larger research effort to optimize alert design to improve the collective awareness of high-risk populations and develop a relevant point-of-care clinical decision support system for sepsis.

The value of missing information in severity of illness score development.

OBJECTIVE: We aim to investigate the hypothesis that using information about which variables are missing along with appropriate imputation improves the performance of severity of illness scoring systems used to predict critical patient outcomes. STUDY DESIGN AND SETTING: We quantify the impact of missing and imputed variables on the performance of prediction models used in the development of a sepsis-related severity of illness scoring system. Electronic health records (EHR) data were compiled from Christiana Care Health System (CCHS) on 119,968 adult patients hospitalized between July 2013 and December 2015. Two outcomes of interest were considered for prediction: (1) first transfer to intensive care unit (ICU) and (2) in-hospital mortality. Five different prediction models were employed. Indicators were utilized in these prediction models to identify when variables were missing and imputed. RESULTS: We observed statistically significant gains in prediction performance when moving from models that did not indicate missing information to those that did. Moreover, this increase was higher in models that use summary variables as predictors compared to those that use all variables. CONCLUSION: When developing prediction models using longitudinal EHR data, researchers should explore the incorporation of indicators for missing variables along with appropriate imputation.

Risky Business: Predicting patient mortality at a glance.

Clinicians are constantly forecasting patient trajectories to make critical point of care decisions intended to influence clinical outcomes. Little is known, however, about how providers interpret mortality risk against validated scoring systems. This research aims to understand how providers forecast mortality specifically for that of patients with sepsis. Defined as life-threatening organ dysfunction caused by a dysregulated host response to infection, sepsis is commonly hard to diagnose, progresses rapidly, and lacks a "gold standard" test. Participants were nurses and doctors from the general medical and surgical floors of six different hospitals. Each was presented with ten different patient cases, categorized into low and high severity sepsis, and were asked about care decisions, along with estimations of mortality risk. The resulting data provides a unique look into the differences of risk forecasting between profession and patient severity.

FUTURES: Forecasting the Unexpected Transfer to Upgraded REsources in Sepsis.

Sepsis is one of the most deadly and costly diseases. The Emergency Department (ED) is the initial point of care for most patients who become hospitalized due to sepsis. Quantifying the accuracy of ED clinician forecasting regarding patients' clinical trajectories and outcomes can provide insight into clinical decision making and inform sepsis management.

Microsimulation Model Using Christiana Care Early Warning System (CEWS) to Evaluate Physiological Deterioration.

While physiological warning signs prior to deterioration events during hospitalization have been widely studied, evaluating clinical interventions, such as rapid response team (RRT) activations, based on scoring systems remains an understudied area. Simulation of physiological deterioration patterns represented by scoring systems can facilitate testing different RRT policies without disturbing care processes. Christiana Care Early Warning System (CEWS) is a scoring system developed at the study hospital to detect the physiological warning signs and inform RRT activations. The objective of this study is to evaluate CEWS-triggered RRT policies based on patient demographics and policy structures. Using retrospective data derived from a subset of electronic health records between December 2015 and December 2016 (6000 patients), we developed a microsimulation model with integrated regression analysis to compare RRT policies on subpopulations defined by age, gender, and comorbidities to find score thresholds that result in the lowest percent of time spent above critical CEWS values. Policies that rely on average scores were more sensitive to threshold changes compared to policies that rely on current value and change in the CEWS. Policy using score threshold 10 provided the lowest percentage of time under the critical condition for majority of subpopulations. The proposed model is a novel framework to simulate individual deterioration patterns and systematically evaluate RRT policies based on their impact on health conditions. Our work highlights the importance of integration of data-driven models into personalized care and represents a significant opportunity to inform biomedical and health informatics research on designing and evaluating EWS-based clinical interventions.

Not all organ dysfunctions are created equal - Prevalence and mortality in sepsis.

PURPOSE: While organ dysfunctions within sepsis have been widely studied, interaction between measures of organ dysfunction remains an understudied area. The objective of this study is to quantify the impact of organ dysfunction on in-hospital mortality in infected population. MATERIALS AND METHODS: Descriptive and multivariate analyses of retrospective data including patients (age ≥ 18 years) hospitalized at the study hospital from July 2013 to April 2016 who met the criteria for an infection visit (62,057 unique visits). RESULTS: The multivariate logistic regression model had an area under the curve of 0.9. Highest odds ratio (OR) associated with increased mortality risk was identified as fraction of inspired oxygen (FiO2) > 21% (OR = 5.8 and 95% Confidence Interval (CI) 1.8-35.6), and elevated lactate >2.0 mmol/L (OR = 2.45 (95% CI = 2.1-2.8)). Most commonly observed measures of organ dysfunction within mortality visits included elevated lactate (> 2.0 mmol/L), mechanical ventilation, and oxygen saturation (SpO2)/FiO2 ratio (< 421) at least once within 48 h prior to or 24 h after anti-infective administration. CONCLUSION: There exist differences in measures of organ dysfunction occurrence and their association with mortality. These findings support increased clinical efforts to identify sepsis patients to inform diagnostic decisions.

Data-driven approach to Early Warning Score-based alert management.

BACKGROUND: Increasing adoption of electronic health records (EHRs) with integrated alerting systems is a key initiative for improving patient safety. Considering the variety of dynamically changing clinical information, it remains a challenge to design EHR-driven alerting systems that notify the right providers for the right patient at the right time while managing alert burden. The objective of this study is to proactively develop and evaluate a systematic alert-generating approach as part of the implementation of an Early Warning Score (EWS) at the study hospitals. METHODS: We quantified the impact of an EWS-based clinical alert system on quantity and frequency of alerts using three different alert algorithms consisting of a set of criteria for triggering and muting alerts when certain criteria are satisfied. We used retrospectively collected EHRs data from December 2015 to July 2016 in three units at the study hospitals including general medical, acute care for the elderly and patients with heart failure. RESULTS: We compared the alert-generating algorithms by opportunity of early recognition of clinical deterioration while proactively estimating alert burden at a unit and patient level. Results highlighted the dependency of the number and frequency of alerts generated on the care location severity and patient characteristics. CONCLUSION: EWS-based alert algorithms have the potential to facilitate appropriate alert management prior to integration into clinical practice. By comparing different algorithms with regard to the alert frequency and potential early detection of physiological deterioration as key patient safety opportunities, findings from this study highlight the need for alert systems tailored to patient and care location needs, and inform alternative EWS-based alert deployment strategies to enhance patient safety.

Evaluation of User-Interface Alert Displays for Clinical Decision Support Systems for Sepsis.

BACKGROUND: Hospitals are increasingly turning to clinical decision support systems for sepsis, a life-threatening illness, to provide patient-specific assessments and recommendations to aid in evidence-based clinical decision-making. Lack of guidelines on how to present alerts has impeded optimization of alerts, specifically, effective ways to differentiate alerts while highlighting important pieces of information to create a universal standard for health care providers. OBJECTIVE: To gain insight into clinical decision support systems-based alerts, specifically targeting nursing interventions for sepsis, with a focus on behaviors associated with and perceptions of alerts, as well as visual preferences. METHODS: An interactive survey to display a novel user interface for clinical decision support systems for sepsis was developed and then administered to members of the nursing staff. RESULTS: A total of 43 nurses participated in 2 interactive survey sessions. Participants preferred alerts that were based on an established treatment protocol, were presented in a pop-up format, and addressed the patient's clinical condition rather than regulatory guidelines. CONCLUSIONS: The results can be used in future research to optimize electronic medical record alerting and clinical practice workflow to support the efficient, effective, and timely delivery of high-quality care to patients with sepsis. The research also may advance the knowledge base of what information health care providers want and need to improve the health and safety of their patients.

The design of decisions: Matching clinical decision support recommendations to Nielsen's design heuristics.

OBJECTIVE: While general design heuristics exist for graphic user interfaces, it remains a challenge to facilitate the implementation of these heuristics for the design of clinical decision support. Our goals were to map a set of recommendations for clinical decision support design found in current literature to Jakob Nielsen's traditional usability heuristics and to suggest usability areas that need more investigation. MATERIALS AND METHODS: Using a modified nominal group process, the research team discussed, classified, and mapped recommendations, organized as interface, information, and interaction, to design heuristics. A previous narrative review identified 42 recommendations from the literature to define the design and functional characteristics that impact the performance of CDS in terms of provider preference, process of care, and patient outcomes. MAIN FINDINGS: We matched 20 out of 42 recommendations to heuristics. The mapping reveals gaps in both heuristics and recommendations, identifying a set of Nielsen's heuristics that are underrepresented in the literature and subsets of recommendations important to design not covered in Nielsen's heuristics. We attributed this, in part, to the evolution of technology since the inception of Nielsen's heuristics. The team created a new interaction heuristic: Integration into real-time workflow to consider the needs of the end-user in the clinical space. DISCUSSION: Clinical decision support has enabled clinicians to better address arising information needs; however there remains a lack of evidence-based guidelines in terms of functional and design requirements. CONCLUSION: Results from this review suggest that interaction design principles were not fully satisfied by the current literature of clinical decision support.

A usability and safety analysis of electronic health records: a multi-center study.

To characterize the variability in usability and safety of EHRs from two vendors across four healthcare systems (2 Epic and 2 Cerner). Twelve to 15 emergency medicine physicians participated from each site and completed six clinical scenarios. Keystroke, mouse click, and video data were collected. From the six scenarios, two diagnostic imaging, laboratory, and medication tasks were analyzed. There was wide variability in task completion time, clicks, and error rates. For certain tasks, there were an average of a nine-fold difference in time and eight-fold difference in clicks. Error rates varied by task (X-ray 16.7% to 25%, MRI: 0 to 10%, Lactate: 0% to 14.3%, Tylenol: 0 to 30%; Taper: 16.7% to 50%). The variability in time, clicks, and error rates highlights the need for improved implementation optimization. EHR implementation, in addition to vendor design and development, is critical to usable and safe products.