Development and Validation of an Automated, Real-Time Predictive Model for Postpartum Hemorrhage.

OBJECTIVE: To develop and validate a predictive model for postpartum hemorrhage that can be deployed in clinical care using automated, real-time electronic health record (EHR) data and to compare performance of the model with a nationally published risk prediction tool. METHODS: A multivariable logistic regression model was developed from retrospective EHR data from 21,108 patients delivering at a quaternary medical center between January 1, 2018, and April 30, 2022. Deliveries were divided into derivation and validation sets based on an 80/20 split by date of delivery. Postpartum hemorrhage was defined as blood loss of 1,000 mL or more in addition to postpartum transfusion of 1 or more units of packed red blood cells. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC) and was compared with a postpartum hemorrhage risk assessment tool published by the CMQCC (California Maternal Quality Care Collaborative). The model was then programmed into the EHR and again validated with prospectively collected data from 928 patients between November 7, 2023, and January 31, 2024. RESULTS: Postpartum hemorrhage occurred in 235 of 16,862 patients (1.4%) in the derivation cohort. The predictive model included 21 risk factors and demonstrated an AUC of 0.81 (95% CI, 0.79-0.84) and calibration slope of 1.0 (Brier score 0.013). During external temporal validation, the model maintained discrimination (AUC 0.80, 95% CI, 0.72-0.84) and calibration (calibration slope 0.95, Brier score 0.014). This was superior to the CMQCC tool (AUC 0.69 [95% CI, 0.67-0.70], P<.001). The model maintained performance in prospective, automated data collected with the predictive model in real time (AUC 0.82 [95% CI, 0.73-0.91]). CONCLUSION: We created and temporally validated a postpartum hemorrhage prediction model, demonstrated its superior performance over a commonly used risk prediction tool, successfully coded the model into the EHR, and prospectively validated the model using risk factor data collected in real time. Future work should evaluate external generalizability and effects on patient outcomes; to facilitate this work, we have included the model coefficients and examples of EHR integration in the article.

Implementable Prediction of Pressure Injuries in Hospitalized Adults: Model Development and Validation.

Background: Numerous pressure injury prediction models have been developed using electronic health record data, yet hospital-acquired pressure injuries (HAPIs) are increasing, which demonstrates the critical challenge of implementing these models in routine care. Objective: To help bridge the gap between development and implementation, we sought to create a model that was feasible, broadly applicable, dynamic, actionable, and rigorously validated and then compare its performance to usual care (ie, the Braden scale). Methods: We extracted electronic health record data from 197,991 adult hospital admissions with 51 candidate features. For risk prediction and feature selection, we used logistic regression with a least absolute shrinkage and selection operator (LASSO) approach. To compare the model with usual care, we used the area under the receiver operating curve (AUC), Brier score, slope, intercept, and integrated calibration index. The model was validated using a temporally staggered cohort. Results: A total of 5458 HAPIs were identified between January 2018 and July 2022. We determined 22 features were necessary to achieve a parsimonious and highly accurate model. The top 5 features included tracheostomy, edema, central line, first albumin measure, and age. Our model achieved higher discrimination than the Braden scale (AUC 0.897, 95% CI 0.893-0.901 vs AUC 0.798, 95% CI 0.791-0.803). Conclusions: We developed and validated an accurate prediction model for HAPIs that surpassed the standard-of-care risk assessment and fulfilled necessary elements for implementation. Future work includes a pragmatic randomized trial to assess whether our model improves patient outcomes.

Identifying antinuclear antibody positive individuals at risk for developing systemic autoimmune disease: development and validation of a real-time risk model.

Objective: Positive antinuclear antibodies (ANAs) cause diagnostic dilemmas for clinicians. Currently, no tools exist to help clinicians interpret the significance of a positive ANA in individuals without diagnosed autoimmune diseases. We developed and validated a risk model to predict risk of developing autoimmune disease in positive ANA individuals. Methods: Using a de-identified electronic health record (EHR), we randomly chart reviewed 2,000 positive ANA individuals to determine if a systemic autoimmune disease was diagnosed by a rheumatologist. A priori, we considered demographics, billing codes for autoimmune disease-related symptoms, and laboratory values as variables for the risk model. We performed logistic regression and machine learning models using training and validation samples. Results: We assembled training (n = 1030) and validation (n = 449) sets. Positive ANA individuals who were younger, female, had a higher titer ANA, higher platelet count, disease-specific autoantibodies, and more billing codes related to symptoms of autoimmune diseases were all more likely to develop autoimmune diseases. The most important variables included having a disease-specific autoantibody, number of billing codes for autoimmune disease-related symptoms, and platelet count. In the logistic regression model, AUC was 0.83 (95% CI 0.79-0.86) in the training set and 0.75 (95% CI 0.68-0.81) in the validation set. Conclusion: We developed and validated a risk model that predicts risk for developing systemic autoimmune diseases and can be deployed easily within the EHR. The model can risk stratify positive ANA individuals to ensure high-risk individuals receive urgent rheumatology referrals while reassuring low-risk individuals and reducing unnecessary referrals.

Assessment of the CLOT (children's likelihood of thrombosis) real-time risk prediction model of hospital-associated venous thromboembolism in children with congenital heart disease.

BACKGROUND: Children with congenital heart disease (CHD) are at high risk for hospital-associated venous thromboembolism (HA-VTE). The children's likelihood of thrombosis (CLOT) trial validated a real-time predictive model for HA-VTE using data extracted from the EHR for pediatric inpatients. We tested the hypothesis that addition of CHD specific data would improve model prediction in the CHD population. METHODS: Model performance in CHD patients from 2010 to 2022, was assessed using 3 iterations of the CLOT model: 1) the original CLOT model, 2) the original model refit using only data from the CHD cohort, and 3) the model updated with the addition of cardiopulmonary bypass time, STAT Mortality Category, height, and weight as covariates. The discrimination of the three models was quantified and compared using AUROC. RESULTS: Our CHD cohort included 1457 patient encounters (median 2.0 IQR [0.5-5.2] years-old). HA-VTE was present in 5% of our CHD cohort versus 1% in the general pediatric population. Several features from the original model were associated with thrombosis in the CHD cohort including younger age, thrombosis history, infectious disease consultation, and EHR coding of a central venous line. Lower height and weight were associated with thrombosis. HA-VTE rate was 12% (18/149) amongst those with STAT Category 4-5 operation versus 4% (49/1256) with STAT Category 1-3 operation (P < .001). Longer cardiopulmonary bypass time (124 [92-205] vs. 94 [65-136] minutes, P < .001) was associated with thrombosis. The AUROC for the original (0.80 95% CI [0.75-0.85]), refit (0.85 [0.81-0.89]), and updated (0.86 [0.81-0.90]) models demonstrated excellent discriminatory ability within the CHD cohort. CONCLUSION: The automated approach with EHR data extraction makes the applicability of such models appealing for ease of clinical use. The addition of cardiac specific features improved model discrimination; however, this benefit was marginal compared to refitting the original model to the CHD cohort. This suggests strong predictive generalized models, such as CLOT, can be optimized for cohort subsets without additional data extraction, thus reducing cost of model development and deployment.

External validation of a predictive model for reintubation after cardiac surgery: A retrospective, observational study.

STUDY OBJECTIVE: Explore validation of a model to predict patients' risk of failing extubation, to help providers make informed, data-driven decisions regarding the optimal timing of extubation. DESIGN: We performed temporal, geographic, and domain validations of a model for the risk of reintubation after cardiac surgery by assessing its performance on data sets from three academic medical centers, with temporal validation using data from the institution where the model was developed. SETTING: Three academic medical centers in the United States. PATIENTS: Adult patients arriving in the cardiac intensive care unit with an endotracheal tube in place after cardiac surgery. INTERVENTIONS: Receiver operating characteristic (ROC) curves and concordance statistics were used as measures of discriminative ability, and calibration curves and Brier scores were used to assess the model's predictive ability. MEASUREMENTS: Temporal validation was performed in 1642 patients with a reintubation rate of 4.8%, with the model demonstrating strong discrimination (optimism-corrected c-statistic 0.77) and low predictive error (Brier score 0.044) but poor model precision and recall (Optimal F1 score 0.29). Combined domain and geographic validation were performed in 2041 patients with a reintubation rate of 1.5%. The model displayed solid discriminative ability (optimism-corrected c-statistic = 0.73) and low predictive error (Brier score = 0.0149) but low precision and recall (Optimal F1 score = 0.13). Geographic validation was performed in 2489 patients with a reintubation rate of 1.6%, with the model displaying good discrimination (optimism-corrected c-statistic = 0.71) and predictive error (Brier score = 0.0152) but poor precision and recall (Optimal F1 score = 0.13). MAIN RESULTS: The reintubation model displayed strong discriminative ability and low predictive error within each validation cohort. CONCLUSIONS: Future work is needed to explore how to optimize models before local implementation.

Molecular signature incorporating the immune microenvironment enhances thyroid cancer outcome prediction.

Genomic and transcriptomic analysis has furthered our understanding of many tumors. Yet, thyroid cancer management is largely guided by staging and histology, with few molecular prognostic and treatment biomarkers. Here, we utilize a large cohort of 251 patients with 312 samples from two tertiary medical centers and perform DNA/RNA sequencing, spatial transcriptomics, and multiplex immunofluorescence to identify biomarkers of aggressive thyroid malignancy. We identify high-risk mutations and discover a unique molecular signature of aggressive disease, the Molecular Aggression and Prediction (MAP) score, which provides improved prognostication over high-risk mutations alone. The MAP score is enriched for genes involved in epithelial de-differentiation, cellular division, and the tumor microenvironment. The MAP score also identifies aggressive tumors with lymphocyte-rich stroma that may benefit from immunotherapy. Future clinical profiling of the stromal microenvironment of thyroid cancer could improve prognostication, inform immunotherapy, and support development of novel therapeutics for thyroid cancer and other stroma-rich tumors.

Model-Guided Decision-Making for Thromboprophylaxis and Hospital-Acquired Thromboembolic Events Among Hospitalized Children and Adolescents: The CLOT Randomized Clinical Trial.

Importance: Rates of hospital-acquired venous thromboembolism (HA-VTE) are increasing among pediatric patients. Identifying at-risk patients for whom prophylactic interventions should be considered remains challenging. Objective: To determine whether use of a previously validated HA-VTE prognostic model, together with pediatric hematologist review, could reduce pediatric inpatient rates of HA-VTE. Design, Setting, and Participants: This pragmatic randomized clinical trial was performed from November 2, 2020, through January 31, 2022, at a single-center academic children's hospital (Monroe Carell Jr Children's Hospital at Vanderbilt). All pediatric hospital admissions (aged <22 years) under inpatient status were included and randomized. Intervention: All patients had an HA-VTE probability automatically calculated daily, which was visible to the hematology research team for patients in the intervention group. Patients with an elevated risk (predicted probability ≥2.5%) underwent additional medical record review by the research team to determine eligibility for thromboprophylaxis. Main Outcomes and Measures: The primary outcome was rate of HA-VTE. Secondary outcomes included rates of prophylactic anticoagulation and anticoagulation-associated bleeding events. Results: A total of 17 427 hospitalizations met eligibility criteria, were randomized, and were included in the primary analysis: patients had a median (IQR) age of 1.7 (0 to 11.1) years; there were 9143 (52.5%) female patients and 8284 (47.5%) male patients, and there were 445 (2.6%) Asian patients, 2739 (15.9%) Black patients, and 11 752 (67.4%) White patients. The 2 groups were evenly balanced in number (8717 in the intervention group and 8710 in the control group) and patient characteristics. A total of 58 patients (0.7%) in the control group and 77 (0.9%) in the intervention group developed HA-VTE (risk difference: 2.2 per 1000 patients; 95% CI, -0.4 to 4.8 per 1000 patients; P = .10). Recommendations to initiate thromboprophylaxis were accepted by primary clinical teams 25.8% of the time (74 of 287 hospitalizations). Minor bleeding events were rare among patients who received anticoagulation (3 of 74 [4.1%]), and no major bleeding events were observed during the study period. Among patients randomized to the control group, the model exhibited high discrimination accuracy (C statistic, 0.799, 95% CI, 0.725 to 0.856). Conclusions and Relevance: In this randomized clinical trial of the use of a HA-VTE prognostic model to reduce pediatric inpatient rates of HA-VTE, despite the use of an accurate and validated prognostic model for HA-VTE, there was substantial reluctance by primary clinical teams to initiate thromboprophylaxis as recommended. In this context, rates of HA-VTE between the control and intervention groups were not different. Future research is needed to identify improved strategies for prevention of HA-VTE and to overcome clinician concerns regarding thromboprophylaxis. Trial Registration: ClinicalTrials.gov Identifier: NCT04574895.

Protocol for a randomised controlled trial: reducing reintubation among high-risk cardiac surgery patients with high-flow nasal cannula (I-CAN).

INTRODUCTION: Heated, humidified, high-flow nasal cannula oxygen therapy has been used as a therapy for hypoxic respiratory failure in numerous clinical settings. To date, limited data exist to guide appropriate use following cardiac surgery, particularly among patients at risk for experiencing reintubation. We hypothesised that postextubation treatment with high-flow nasal cannula would decrease the all-cause reintubation rate within the 48 hours following initial extubation, compared with usual care. METHODS AND ANALYSIS: Adult patients undergoing cardiac surgery (open surgery on the heart or thoracic aorta) will be automatically enrolled, randomised and allocated to one of two treatment arms in a pragmatic randomised controlled trial at the time of initial extubation. The two treatment arms are administration of heated, humidified, high-flow nasal cannula oxygen postextubation and usual care (treatment at the discretion of the treating provider). The primary outcome will be all-cause reintubation within 48 hours of initial extubation. Secondary outcomes include all-cause 30-day mortality, hospital length of stay, intensive care unit length of stay and ventilator-free days. Interaction analyses will be conducted to assess the differential impact of the intervention within strata of predicted risk of reintubation, calculated according to our previously published and validated prognostic model. ETHICS AND DISSEMINATION: Vanderbilt University Medical Center IRB approval, 15 March 2021 with waiver of written informed consent. Plan for publication of study protocol prior to study completion, as well as publication of results. TRIAL REGISTRATION NUMBER: clinicaltrials.gov, NCT04782817 submitted 25 February 2021. DATE OF PROTOCOL: 29 August 2022. Version 2.0.

Use of a real-time risk-prediction model to identify pediatric patients at risk for thromboembolic events: study protocol for the Children's Likelihood Of Thrombosis (CLOT) trial.

BACKGROUND: Pediatric patients have increasing rates of hospital-associated venous thromboembolism (HA-VTE), and while several risk-prediction models have been developed, few are designed to assess all general pediatric patients, and none has been shown to improve patient outcomes when implemented in routine clinical care. METHODS: The Children's Likelihood Of Thrombosis (CLOT) trial is an ongoing pragmatic randomized trial being conducted starting November 2, 2020, in the inpatient units at Monroe Carell Jr. Children's Hospital at Vanderbilt in Nashville, TN, USA. All admitted patients who are 21 years of age and younger are automatically enrolled in the trial and randomly assigned to receive either the current standard-of-care anticoagulation practice or the study intervention. Patients randomized to the intervention arm are assigned an HA-VTE risk probability that is calculated from a validated VTE risk-prediction model; the model is updated daily with the most recent clinical information. Patients in the intervention arm with elevated risk (predicted probability of HA-VTE ≥ 0.025) have an additional review of their clinical course by a team of dedicated hematologists, who make recommendations including pharmacologic prophylaxis with anticoagulation, if appropriate. The anticipated enrollment is approximately 15,000 patients. The primary outcome is the occurrence of HA-VTE. Secondary outcomes include initiation of anticoagulation, reasons for not initiating anticoagulation among patients for whom it was recommended, and adverse bleeding events. Subgroup analyses will be conducted among patients with elevated HA-VTE risk. DISCUSSION: This ongoing pragmatic randomized trial will provide a prospective assessment of a pediatric risk-prediction tool used to identify hospitalized patients at elevated risk of developing HA-VTE.  TRIAL REGISTRATION: ClinicalTrials.gov NCT04574895. Registered on September 28, 2020. Date of first patient enrollment: November 2, 2020.

Palliative Care Exposure Relative to Predicted Risk of Six-Month Mortality in Hospitalized Adults.

CONTEXT: The optimal strategy for implementing mortality-predicting algorithms to facilitate clinical care, prognostic discussions, and palliative care interventions remains unknown. OBJECTIVES: To develop and validate a real-time predictive model for 180 day mortality using routinely available clinical and laboratory admission data and determine if palliative care exposure varies with predicted mortality risk. METHODS: Adult admissions between October 1, 2013 and October.1, 2017 were included for the model derivation. A separate cohort was collected between January 1, 2018 and July 31, 2020 for validation. Patients were followed for 180 days from discharge, and logistic regression with selected variables was used to estimate patients' risk for mortality. RESULTS: In the model derivation cohort, 7963 events of 180 day mortality (4.5% event rate) were observed. Median age was 53.0 (IQR 24.0-66.0) with 92,734 females (52.5%). Variables with strongest association with 180 day mortality included: Braden Score (OR 0.83; 95% CI 0.82-0.84); admission Do Not Resuscitate orders (OR 2.61; 95% CI 2.43-2.79); admission service and admission status. The model yielded excellent discriminatory ability in both the derivation (c-statistic 0.873; 95% CI 0.870-0.877; Brier score 0.04) and validation cohorts (c-statistic 0.844; 95% CI 0.840-0.847; Brier score 0.072). Inpatient palliative care consultations increased from 3% of minimal-risk encounters to 41% of high-risk encounters (P < 0.01). CONCLUSION: We developed and temporally validated a predictive mortality model for adults from a large retrospective cohort, which helps quantify the potential need for palliative care referrals based on risk strata. Machine learning algorithms for mortality require clinical interpretation, and additional studies are needed to design patient-centered and risk-specific interventions.

A Predictive Model of Reintubation After Cardiac Surgery Using the Electronic Health Record.

BACKGROUND: Reintubation and prolonged intubation after cardiac surgery are associated with significant complications. Despite these competing risks, providers frequently extubate patients with limited insight into the risk of reintubation at the time of extubation. Achieving timely, successful extubation remains a significant clinical challenge. METHODS: Based on an analysis of 2835 patients undergoing cardiac surgery at our institution between November 2017 and July 2020, we developed a model for an individual's risk of reintubation at the time of extubation. Predictors were screened for inclusion in the model based on clinical plausibility and availability at the time of extubation. Rigorous data reduction methods were used to create a model that could be easily integrated into clinical workflow at the time of extubation. RESULTS: In total, 90 patients (3.2%) were reintubated within 48 hours of initial extubation. Number of inotropes (1 [adjusted odds ratio (OR), 15.4; 95% confidence interval (CI) 6.5-47.6; P < .001], ≥2 [OR, 62.7; 95% CI 14.3-279.5; P < .001]); dexmedetomidine dose (OR, 3.0 [per µg/kg/h]; 95% CI 1.9-4.7; P < .001), time to extubation (OR, 1.04 [per 6-hour increase]; 95% CI 1.02-1.05; P < .001), and respiratory rate (OR, 1.04 [per breath/min]; 95% CI 1.01-1.07; P < .001) were the best predictors for the model, which displayed excellent discriminative capacity (area under the receiver operating characteristic, 0.86; 95% CI 0.84-0.89). CONCLUSIONS: An improved understanding of reintubation risk may lead to improved decision-making at extubation and targeted interventions to decrease reintubation in high-risk patients. Future studies are needed to optimize timing of extubation.

Effects of financial incentives on volunteering for clinical trials: A randomized vignette experiment.

BACKGROUND: Financial incentives may aid recruitment to clinical trials, but evidence regarding risk/burden-driven variability in participant preferences for incentives is limited. We developed and tested a framework to support real-world decisions on recruitment budget. METHODS: We included two phases: an Anchoring Survey, to ensure we could capture perceived unpleasantness on a range of life events, and a Vignette Experiment, to explore relationships between financial incentives and participants' perceived risk/burden and willingness to participate in high- and low-risk/burden versions of five vignettes drawn from common research activities. We compared vignette ratings to identify similarly rated life events from the Anchoring Survey to contextualize ratings of study risk. RESULTS: In our Anchoring Survey (n = 643), mean ratings (scale 1 = lowest risk/burden to 5 = highest risk/burden) indicated that the questions made sense to participants, with highest risk assigned to losing house in a fire (4.72), and lowest risk assigned to having blood pressure taken (1.13). In the Vignette Experiment (n = 534), logistic regression indicated that amount of offered financial incentive and perceived risk/burden level were the top two drivers of willingness to participate in four of the five vignettes. Comparison of event ratings in the Anchoring Survey with the Vignette Experiment ratings suggested reasonable concordance on severity of risk/burden. CONCLUSIONS: We demonstrated feasibility of a framework for assessing participant perceptions of risk for study activities and discerned directionality of relationship between financial incentives and willingness to participate. Future work will explore use of this framework as an evidence-gathering approach for gauging appropriate incentives in real-world study contexts.

The first 1000 symptomatic pediatric SARS-CoV-2 infections in an integrated health care system: a prospective cohort study.

BACKGROUND: The spectrum of illness and predictors of severity among children with SARS-CoV-2 infection are incompletely understood. METHODS: Active surveillance was performed for SARS-CoV-2 by polymerase chain reaction among symptomatic pediatric patients in a quaternary care academic hospital laboratory beginning March 12, 2020. We obtained sociodemographic and clinical data 5 (+/-3) and 30 days after diagnosis via phone follow-up and medical record review. Logistic regression was used to assess predictors of hospitalization. RESULTS: The first 1000 symptomatic pediatric patients were diagnosed in our institution between March 13, 2020 and September 28, 2020. Cough (52 %), headache (43 %), and sore throat (36 %) were the most common symptoms. Forty-one (4 %) were hospitalized; 8 required ICU admission, and 2 required mechanical ventilation (< 1 %). One patient developed multisystem inflammatory syndrome in children; one death was possibly associated with SARS-CoV-2 infection. Symptom resolution occurred by follow-up day 5 in 398/892 (45 %) patients and by day 30 in 443/471 (94 %) patients. Pre-existing medical condition (OR 7.7; 95 % CI 3.9-16.0), dyspnea (OR 6.8; 95 % CI 3.2-14.1), Black race or Hispanic ethnicity (OR 2.7; 95 % CI 1.3-5.5), and vomiting (OR 5.4; 95 % CI 1.2-20.6) were the strongest predictors of hospitalization. The model displayed excellent discriminative ability (AUC = 0.82, 95 % CI 0.76-0.88, Brier score = 0.03). CONCLUSIONS: In 1000 pediatric patients with systematic follow-up, most SARS-CoV-2 infections were mild, brief, and rarely required hospitalization. Pediatric predictors of hospitalization included comorbid conditions, Black race, Hispanic ethnicity, dyspnea and vomiting and were distinct from those reported among adults.

A Real-time Risk-Prediction Model for Pediatric Venous Thromboembolic Events.

BACKGROUND: Hospital-associated venous thromboembolism (HA-VTE) is an increasing cause of morbidity in pediatric populations, yet identification of high-risk patients remains challenging. General pediatric models have been derived from case-control studies, but few have been validated. We developed and validated a predictive model for pediatric HA-VTE using a large, retrospective cohort. METHODS: The derivation cohort included 111 352 admissions to Monroe Carell Jr. Children's Hospital at Vanderbilt. Potential variables were identified a priori, and corresponding data were extracted. Logistic regression was used to estimate the association of potential risk factors with development of HA-VTE. Variable inclusion in the model was based on univariate analysis, availability in routine medical records, and clinician expertise. The model was validated by using a separate cohort with 44 138 admissions. RESULTS: A total of 815 encounters were identified with HA-VTE in the derivation cohort. Variables strongly associated with HA-VTE include history of thrombosis (odds ratio [OR] 8.7; 95% confidence interval [CI] 6.6-11.3; P < .01), presence of a central line (OR 4.9; 95% CI 4.0-5.8; P < .01), and patients with cardiology conditions (OR 4.0; 95% CI 3.3-4.8; P < .01). Eleven variables were included, which yielded excellent discriminatory ability in both the derivation cohort (concordance statistic = 0.908) and the validation cohort (concordance statistic = 0.904). CONCLUSIONS: We created and validated a risk-prediction model that identifies pediatric patients at risk for HA-VTE development. We anticipate early identification of high-risk patients will increase prophylactic interventions and decrease the incidence of pediatric HA-VTE.

Learning From What We Do, and Doing What We Learn: A Learning Health Care System in Action.

Different models of learning health systems are emerging. At Vanderbilt University Medical Center, the Learning Health Care System (LHS) Platform was established with the goal of creating generalizable knowledge. This differentiates the LHS Platform from other efforts that have adopted a quality improvement paradigm. By supporting pragmatic trials at the intersection of research, operations, and clinical care, the LHS Platform was designed to yield evidence for advancing content and processes of care through carefully designed, rigorous study. The LHS Platform provides the necessary infrastructure and governance to leverage translational, transdisciplinary team science to inform clinical and operational decision making across the health system. The process transforms a clinical or operational question into a research question amenable to a pragmatic trial. Scientific, technical, procedural, and human infrastructure is maintained for the design and execution of individual LHS projects. This includes experienced pragmatic trialists, project management, data science inclusive of biostatistics and clinical informatics, and regulatory support. Careful attention is paid to stakeholder engagement, including health care providers and the community. Capturing lessons from each new study, the LHS Platform continues to mature with plans to integrate implementation science and to complement clinical and process outcomes with cost and value considerations. The Vanderbilt University Medical Center LHS Platform is now a pillar of the health care system and leads the evolving culture of learning from what we do and doing what we learn.

Impact of a Follow-up Telephone Call Program on 30-Day Readmissions (FUTR-30): A Pragmatic Randomized Controlled Real-world Effectiveness Trial.

BACKGROUND: Telephone call programs are a common intervention used to improve patients' transition to outpatient care after hospital discharge. OBJECTIVE: To examine the impact of a follow-up telephone call program as a readmission reduction initiative. RESEARCH DESIGN: Pragmatic randomized controlled real-world effectiveness trial. SUBJECTS: We enrolled and randomized all patients discharged home from a hospital general medicine service to a follow-up telephone call program or usual care discharge. Patients discharged against medical advice were excluded. The intervention was a hospital program, delivering a semistructured follow-up telephone call from a nurse within 3-7 days of discharge, designed to assess understanding and provide education, and assistance to support discharge plan implementation. MEASURES: Our primary endpoint was hospital inpatient readmission within 30 days identified by the electronic health record. Secondary endpoints included observation readmission, emergency department revisit, and mortality within 30 days, and patient experience ratings. RESULTS: All 3054 patients discharged home were enrolled and randomized to the telephone call program (n=1534) or usual care discharge (n=1520). Using a prespecified intention-to-treat analysis, we found no evidence supporting differences in 30-day inpatient readmissions [14.9% vs. 15.3%; difference -0.4 (95% confidence interval, 95% CI), -2.9 to 2.1; P=0.76], observation readmissions [3.8% vs. 3.6%; difference 0.2 (95% CI, -1.1 to 1.6); P=0.74], emergency department revisits [6.1% vs. 5.4%; difference 0.7 (95% CI, -1.0 to 2.3); P=0.43], or mortality [4.4% vs. 4.9%; difference -0.5 (95% CI, -2.0 to 1.0); P=0.51] between telephone call and usual care groups. CONCLUSIONS: We found no evidence of an impact on 30-day readmissions or mortality due to the postdischarge telephone call program.

Cutaneous-hemolytic loxoscelism following brown recluse spider envenomation: new understandings.

Background: Brown recluse spider (BRS) (Loxosceles reclusa) envenomation can cause local dermonecrotic lesions, constitutional symptoms, and potentially fatal hemolysis (i.e., cutaneous-hemolytic loxoscelism). As the incidence of hemolysis is low and the spider habitat is limited, little is known regarding the clinical course of cutaneous-hemolytic loxoscelism.Methods: We performed a retrospective observational study of patients following BRS envenomation over an eight-year period. Demographics, clinical course, laboratories, and interventions were assessed. Wilcoxon rank-sum tests and Pearson chi-square tests were used in the univariate analyses. Logistic regression assessed the independent contribution of symptoms in a multivariate analysis.Results: Of the 97 patients, 40.2% (n = 39) developed hemolysis; the majority (66.7%) were 18 years old or younger. Univariate analysis revealed that constitutional symptoms were associated with hemolysis, but multivariate analysis showed only myalgia (aOR: 7.1; 95% CI: 2.2-22.7; p < .001) and malaise (aOR: 12.76; 95% CI: 1.4-119.9; p = .026) were independently associated with hemolysis. The median time to hemolysis onset was 1.0 days (IQR: 1.0-2.5) and all occurred within a week of envenomation. Hemolysis durations were longer in patients DAT positive for IGG antibodies (7.5 vs. 4.0 days; p = .042). Most (76.9%) of hemolyzing patients received blood. In patients with cutaneous-hemolytic loxoscelism, hematuria occurred in 32.4%, rhabdomyolysis occurred in 60.9%, and elevated transaminases with normal hepatic synthetic function occurred in 29.4% but all of these patients developed rhabdomyolysis. Hemolysis was both intravascular and extravascular. Complications (hyperkalemia, INR ≥2.0, metabolic acidosis requiring bicarbonate, hypotension requiring vasopressors, and hypoxia requiring intubation) occurred only in patients with profound hemolytic anemia (hemoglobin <4 g/dL); one patient died.Conclusions: Constitutional symptoms occur in both cutaneous and cutaneous-hemolytic loxoscelism, although they occur more frequently in patients who develop hemolysis. Children may be at a higher risk of hemolysis after envenomation. Renal involvement (as evidenced by hematuria) and rhabdomyolysis may occur more frequently than has been previously reported. Hemolysis was both intravascular and extravascular.

Use of airborne infection isolation in potential cases of pulmonary tuberculosis.

OBJECTIVE: To identify risk factors of patients placed in airborne infection isolation (AII) for possible pulmonary tuberculosis (TB) to better predict TB diagnosis and allow more judicious use of AII. METHODS: Case-control, retrospective study at a single tertiary-care academic medical center. The study included all adult patients admitted from October 1, 2014, through October 31, 2017, who were placed in AII for possible pulmonary TB. Cases were defined as those ultimately diagnosed with pulmonary TB. Controls were defined as those not diagnosed with pulmonary TB. Those with TB diagnosed prior to admission were excluded. In total, 662 admissions (558 patients) were included. RESULTS: Overall, 15 cases of pulmonary TB were identified (2.7%); of these, 2 were people living with human immunodeficiency virus (HIV; PLWH). Statistical analysis was limited by low case number. Those diagnosed with pulmonary TB were more likely to have been born outside the United States (53% vs 13%; P < .001) and to have had prior positive TB testing, regardless of prior treatment (50% vs 19%; P = .015). A multivariate analysis using non-US birth and prior positive TB testing predicted an 18.2% probability of pulmonary TB diagnosis when present, compared with 1.0% if both factors were not present. CONCLUSIONS: The low number of pulmonary TB cases indicated AII overuse, especially in PLWH, and more judicious use of AII is warranted. High-risk groups, including those born outside the United States and those with prior positive TB testing, should be considered for AII in the appropriate clinical setting.

Acute kidney injury risk-based screening in pediatric inpatients: a pragmatic randomized trial.

BACKGROUND: Pediatric acute kidney injury (AKI) is common and associated with increased morbidity, mortality, and length of stay. We performed a pragmatic randomized trial testing the hypothesis that AKI risk alerts increase AKI screening. METHODS: All intensive care and ward admissions of children aged 28 days through 21 years without chronic kidney disease from 12/6/2016 to 11/1/2017 were included. The intervention alert displayed if calculated AKI risk was > 50% and no serum creatinine (SCr) was ordered within 24 h. The primary outcome was SCr testing within 48 h of AKI risk > 50%. RESULTS: Among intensive care admissions, 973/1909 (51%) were randomized to the intervention. Among those at risk, more SCr tests were ordered for the intervention group than for controls (418/606, 69% vs. 361/597, 60%, p = 0.002). AKI incidence and severity were the same in intervention and control groups. Among ward admissions, 5492/10997 (50%) were randomized to the intervention, and there were no differences between groups in SCr testing, AKI incidence, or severity of AKI. CONCLUSIONS: Alerts based on real-time prediction of AKI risk increased screening rates in intensive care but not pediatric ward settings. Pragmatic clinical trials provide the opportunity to assess clinical decision support and potentially eliminate ineffective alerts.