Early inpatient calculation of laboratory-based 30-day readmission risk scores empowers clinical risk modification during index hospitalization.

Improving 30-day readmission continues to be problematic for most hospitals. This study reports the creation and validation of sex-specific inpatient (i) heart failure (HF) risk scores using electronic data from the beginning of inpatient care for effective and efficient prediction of 30-day readmission risk. METHODS: HF patients hospitalized at Intermountain Healthcare from 2005 to 2012 (derivation: n=6079; validation: n=2663) and Baylor Scott & White Health (North Region) from 2005 to 2013 (validation: n=5162) were studied. Sex-specific iHF scores were derived to predict post-hospitalization 30-day readmission using common HF laboratory measures and age. Risk scores adding social, morbidity, and treatment factors were also evaluated. RESULTS: The iHF model for females utilized potassium, bicarbonate, blood urea nitrogen, red blood cell count, white blood cell count, and mean corpuscular hemoglobin concentration; for males, components were B-type natriuretic peptide, sodium, creatinine, hematocrit, red cell distribution width, and mean platelet volume. Among females, odds ratios (OR) were OR=1.99 for iHF tertile 3 vs. 1 (95% confidence interval [CI]=1.28, 3.08) for Intermountain validation (P-trend across tertiles=0.002) and OR=1.29 (CI=1.01, 1.66) for Baylor patients (P-trend=0.049). Among males, iHF had OR=1.95 (CI=1.33, 2.85) for tertile 3 vs. 1 in Intermountain (P-trend <0.001) and OR=2.03 (CI=1.52, 2.71) in Baylor (P-trend < 0.001). Expanded models using 182-183 variables had predictive abilities similar to iHF. CONCLUSIONS: Sex-specific laboratory-based electronic health record-delivered iHF risk scores effectively predicted 30-day readmission among HF patients. Efficient to calculate and deliver to clinicians, recent clinical implementation of iHF scores suggest they are useful and useable for more precise clinical HF treatment.

Multiple poisonings with sodium azide at a local restaurant.

BACKGROUND: Sodium azide is a chemical with a mechanism similar to cyanide. There is concern that it could be used as a chemical warfare agent. OBJECTIVES: We report a cluster of poisonings that occurred at a public restaurant and the subsequent investigation that identified iced tea contaminated with sodium azide (NaN3) and hydrazoic acid, as the foodborne vehicle and agents, respectively. CASE REPORT: Five patients became ill within minutes of drinking iced tea at a restaurant. They all presented to the same Emergency Department with similar symptoms, and improved with fluids, antiemetics, and supportive care. A joint investigation by the Dallas County Department of Health and Human Services, the Texas State Health Department, the Dallas County Southwestern Institute of Forensic Sciences, and the medical toxicologists at the University of Texas Southwestern School of Medicine identified iced tea, contaminated with sodium azide (NaN3) and hydrazoic acid, as the foodborne vehicle and agents, respectively. CONCLUSION: The recurrence, and seriousness, of these events suggests a need for continued education of emergency providers. Emergency physicians should consider exposures to toxic chemicals in their differential when a cluster of patients presents with similar symptoms over a short period of time.

Developing electronic data methods infrastructure to participate in collaborative research networks.

CONTEXT: Collaborative networks support the goals of a learning health system by sharing, aggregating, and analyzing data to facilitate identification of best practices care across delivery organizations. This case study describes the infrastructure and process developed by an integrated health delivery system to successfully prepare and submit a complex data set to a large national collaborative network. CASE DESCRIPTION: We submitted four years of data for a diverse population of patients in specific clinical areas: diabetes, chronic heart failure, sepsis, and hip, knee, and spine. The most recent submission included 19 tables, more than 376,000 unique patients, and almost 5 million patient encounters. Data was extracted from multiple clinical and administrative systems. LESSONS LEARNED: We found that a structured process with documentation was key to maintaining communication, timelines, and quality in a large-scale data submission to a national collaborative network. The three key components of this process were the experienced project team, documentation, and communication. We used a formal QA and feedback process to track and review data. Overall, the data submission was resource intensive and required an incremental approach to data quality. CONCLUSION: Participation in collaborative networks can be time and resource intense, however it can serve as a catalyst to increase the technical data available to the learning health system.

Postdeployment health care for returning OEF/OIF military personnel and their social networks: a qualitative approach.

Little is known regarding the health care needs of Operation Enduring Freedom/Operation Iraqi Freedom (OEF/OIF) deployed veterans seeking care outside of Veterans Affairs facilities. Responding to this gap, we conducted a mixed methods study of postdeployment health among veterans and members of their social networks in six community-based primary care clinics. A total of 347 patients completed a survey dealing with deployment to Iraq/Afghanistan (whether their own or that of a family member, friend, or coworker), and subsequent psychosocial and health problems. A subset of 52 participants responded to an open-ended question requesting additional information about experiences during and postdeployment. Content analysis of these responses revealed five overarching themes: connectedness, perceptions of conflict, consequences of deployment, health and treatment concerns, and respect and concern for service members/veterans. These data point to significant deployment-related disruptions in the well-being of service members and those in their broader social networks, with implications for defining service needs in community health settings.