Leveraging Bluetooth low-energy technology to improve contact tracing among healthcare personnel in hospital setting during the coronavirus disease 2019 (COVID-19) pandemic

To improve contact tracing for healthcare workers, we built and configured a Bluetooth low-energy system. We predicted close contacts with great accuracy and provided an additional contact yield of 14.8%. This system would decrease the effective reproduction number by 56% and would unnecessarily quarantine 0.74% of employees weekly.

Genomic Analyses of Longitudinal Mycobacterium abscessus Isolates in a Multicenter Cohort Reveal Parallel Signatures of In-Host Adaptation

BACKGROUND

Nontuberculous mycobacteria (NTM) are ubiquitous in the environment and an increasingly frequent cause of opportunistic infections. Mycobacterium abscessus complex (MABC) is one of the major NTM lung pathogens that disproportionately colonize and infect the lungs of individuals with cystic fibrosis (CF). MABC infection can persist for years, and antimicrobial treatment is frequently ineffective.

METHODS

We sequenced the genomes of 175 isolates longitudinally collected from 30 patients with MABC lung infection. We contextualized our cohort amidst the broader MABC phylogeny and investigated genes undergoing parallel adaptation across patients. Finally, we tested the phenotypic consequences of parallel mutations by conducting antimicrobial resistance and mercury-resistance assays.

RESULTS

We identified highly related isolate pairs across hospital centers with low likelihood of transmission. We further annotated nonrandom parallel mutations in 22 genes and demonstrated altered macrolide susceptibility co-occurring with a nonsynonymous whiB1 mutation. Finally, we highlighted a 23-kb mercury-resistance plasmid whose loss during chronic infection conferred phenotypic susceptibility to organic and nonorganic mercury compounds.

CONCLUSIONS

We characterized parallel genomic processes through which MABC is adapting to promote survival within the host. The within-lineage polymorphisms we observed have phenotypic effects, potentially benefiting fitness in the host at the putative detriment of environmental survival.

Spatial Epidemiologic Analysis and Risk Factors for Nontuberculous Mycobacteria Infections, Missouri, USA, 2008-2019

Nontuberculous mycobacteria (NTM) infections are caused by environmental exposure. We describe spatial distribution of NTM infections and associations with sociodemographic factors and flooding in Missouri, USA. Our retrospective analysis of mycobacterial cultures reported to the Missouri Department of Health and Social Services surveillance system during January 1, 2008-December 31, 2019, detected geographic clusters of infection. Multilevel Poisson regression quantified small-area geographic variations and identified characteristics associated with risk for infection. Median county-level NTM infection rate was 66.33 (interquartile range 51-91)/100,000 persons. Risk of clustering was significantly higher in rural areas (rate ratio 2.82, 95% CI 1.90-4.19) and in counties with >5 floodings per year versus no flooding (rate ratio 1.38, 95% CI 1.26-1.52). Higher risk for NTM infection was associated with older age, rurality, and more flooding. Clinicians and public health professionals should be aware of increased risk for NTM infections, especially in similar environments.

Body Imaging of Bacterial and Parasitic Zoonoses: Keys to Diagnosis

Zoonotic infections, which are transmitted from animals to humans, have been a substantial source of human disease since antiquity. As the human population continues to grow and human influence on the planet expands, humans frequently encounter both domestic and wild animals. This has only increased as deforestation, urbanization, agriculture, habitat fragmentation, outdoor recreation, and international travel evolve in modern society, all of which have resulted in the emergence and reemergence of zoonotic infections. Zoonotic infections pose a diagnostic challenge because of their nonspecific clinical manifestations and the need for specialized testing procedures to confirm these diagnoses. Affected patients often undergo imaging during their evaluation, and a radiologist familiar with the specific and often subtle imaging patterns of these infections can add important clinical value. The authors review the multimodality thoracic, abdominal, and musculoskeletal imaging findings of zoonotic bacterial (eg, Bartonella henselae, Pasteurella multocida, Francisella tularensis, Coxiella burnetii, and Brucella species), spirochetal (eg, Leptospira species), and parasitic (eg, Echinococcus, Paragonimus, Toxocara, and Dirofilaria species) infections that are among the more commonly encountered zoonoses in the United States. Relevant clinical, epidemiologic, and pathophysiologic clues such as exposure history, occupational risk factors, and organism life cycles are also reviewed. Although many of the imaging findings of zoonotic infections overlap with those of nonzoonotic infections, granulomatous diseases, and malignancies, radiologists' familiarity with the imaging patterns can aid in the differential diagnosis in a patient with a suspected or unsuspected zoonotic infection. ^© RSNA, 2023 Quiz questions for this article are available through the Online Learning Center.

Missed Opportunities in the Diagnosis of Tuberculosis Meningitis

Background

Tuberculosis meningitis (TBM) has high mortality and morbidity. Diagnostic delays can impact TBM outcomes. We aimed to estimate the number of potentially missed opportunities (MOs) to diagnose TBM and determine its impact on 90-day mortality.

Methods

This is a retrospective cohort of adult patients with a central nervous system (CNS) TB International Classification of Diseases, Ninth/Tenth Revision (ICD-9/10) diagnosis code (013*, A17*) identified in the Healthcare Cost and Utilization Project, State Inpatient and State Emergency Department (ED) Databases from 8 states. Missed opportunity was defined as composite of ICD-9/10 diagnosis/procedure codes that included CNS signs/symptoms, systemic illness, or non-CNS TB diagnosis during a hospital/ED visit 180 days before the index TBM admission. Demographics, comorbidities, admission characteristics, mortality, and admission costs were compared between those with and without a MO, and 90-day in-hospital mortality, using univariate and multivariable analyses.

Results

Of 893 patients with TBM, median age at diagnosis was 50 years (interquartile range, 37-64), 61.3% were male, and 35.2% had Medicaid as primary payer. Overall, 407 (45.6%) had a prior hospital or ED visit with an MO code. In-hospital 90-day mortality was not different between those with and without an MO, regardless of the MO coded during an ED visit (13.7% vs 15.2%, P = .73) or a hospitalization (28.2% vs 30.9%, P = .74). Independent risk of 90-day in-hospital mortality was associated with older age, hyponatremia (relative risk [RR], 1.62; 95% confidence interval [CI], 1.1-2.4; P = .01), septicemia (RR, 1.6; 95% CI, 1.03-2.45; P = .03), and mechanical ventilation (RR, 3.4; 95% CI, 2.25-5.3; P < .001) during the index admission.

Conclusions

Approximately half the patients coded for TBM had a hospital or ED visit in the previous 6 months meeting the MO definition. We found no association between having an MO for TBM and 90-day in-hospital mortality.

Impact of risk-based sexually transmitted infection screening in the emergency department

OBJECTIVES

Sexually transmitted infections (STIs), including chlamydia, gonorrhea, and human immunodeficiency virus (HIV) pose a significant health burden in adolescents. Many adolescents receiving care in the emergency department (ED) are in need of testing, regardless of their chief complaint. Our objective was to determine whether an electronic, risk-based STI screening program in our ED was associated with an increase in STI testing among at-risk adolescents.

METHODS

We conducted a retrospective cohort analysis of patient outcomes in our pediatric ED after integrating an Audio-enhanced Computer-Assisted Self-Interview (ACASI) as standard of care. It obtained a focused sexual history and generated STI testing recommendations. Patient answers and testing recommendations were integrated in real-time into the electronic health record. Patients who tested positive received treatment according to our standard-of-care practices. All patients 15-21 years of age were asked to complete this on an opt-out basis, regardless of the reason for their ED visit. Exclusions included those unable to independently use a tablet, severe illness, sexual assault, or non-English speaking. Our primary outcome was to describe STI-testing recommendations and test results among ACASI participants. We also compared STI testing between ACASI participants and those who were eligible but did not use it.

RESULTS

In the first 13 months, 28.9% (1788/6194) of eligible adolescents completed the ACASI and 44.2% (321/790) accepted recommended STI testing. The mean age of participants was 16.6 ± 1.3 years, with 65.4% (1169) being female. Gonorrhea/chlamydia testing was significantly higher among participants vs. non-participants (20.1% [359/1788] vs 4.8% [212/4406]; p < 0.0001). The proportion of positive STI tests was similar between the two groups: 24.8% (89/359) vs. 24.5% (52/212; p = 0.94) were positive for chlamydia and/or gonorrhea, while 0.6% (2/354) participants vs. 0% non-participants (p > 0.99) were positive for HIV. Among participants whose chief complaints were unlikely to be related to STIs but accepted recommended testing, 20.9% (37/177) were positive for gonorrhea or chlamydia.

CONCLUSIONS

Our program facilitated STI testing in the ED and identified many adolescents with STIs, even when their ED complaint was for unrelated reasons. More rigorous implementation is needed to determine the impact of deployment of ACASI to all eligible adolescents and addressing barriers to accepting STI testing recommendations.

Risk factors for mortality and multidrug resistance in pulmonary tuberculosis in Guatemala: A retrospective analysis of mandatory reporting

•

National TB cohort analyzing risk factors associated with MDR-TB and mortality in Guatemala.

•

Indigenous ethnicity and prior TB treatment were associated with increased risk of mortality and MDR-TB.

•

HIV/Unknown HIV status were associated with increased mortality and diabetes with risk for MDR-TB.

Background

Risk factors for mortality and MDR-TB in Guatemala are poorly understood. We aimed to identify risk factors to assist in targeting public health interventions.

Methods

We performed a retrospective study of adults with pulmonary TB reported to the Guatemalan TB Program between January 1, 2016 and December 31, 2017. The primary objective was to determine risk factors for mortality in pulmonary TB. The secondary objective was to determine risk factors associated with MDR-TB.

Results

Among 3,945 patients with pulmonary TB, median age was 39 years (IQR 25–54), 59% were male, 25% of indigenous ethnicity, 1.1% had MDR-TB and 3.9% died. On multivariable analysis, previous TB treatment (odds ratio [OR] 3.57, CI 2.24–5.68 [p < 0.001]), living with HIV (OR 3.98, CI 2.4–6.17 [p < 0.001]), unknown HIV diagnosis (OR 2.65, CI 1.68–4.18 [p < 0.001]), indigenous ethnicity (OR 1.79, CI 1.18–2.7 [p = 0.005]), malnutrition (OR 7.33, CI 3.24–16.59 [p < 0.001]), and lower educational attainment (OR 2.86, CI 1.43–5.88 [p = 0.003]) were associated with mortality. Prior treatment (OR 53.76, CI 25.04–115.43 [p < 0.001]), diabetes (OR 4.13, CI 2.04–8.35 [p < 0.001]), and indigenous ethnicity (OR 11.83, CI 1.46–95.73 [p = 0.02]) were associated with MDR-TB.

Conclusions

In Guatemala, both previous TB treatment and indigenous ethnicity were associated with higher TB mortality and MDR-TB risk among patients with pulmonary TB.

Diagnostic Accuracy of Health Care Administrative Diagnosis Codes to Identify Nontuberculous Mycobacteria Disease: A Systematic Review

Background

Health care administrative database research frequently uses standard medical codes to identify diagnoses or procedures. The aim of this review was to establish the diagnostic accuracy of codes used in administrative data research to identify nontuberculous mycobacterial (NTM) disease, including lung disease (NTMLD).

Methods

We searched Ovid Medline, Embase, Scopus, Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, and ClinicalTrials.gov from inception to April 2019. We included studies assessing the diagnostic accuracy of International Classification of Diseases, 9th edition, Clinical Modification (ICD-9-CM) diagnosis codes to identify NTM disease and NTMLD. Studies were independently assessed by 2 researchers, and the Quality Assessment of Diagnostic Accuracy Studies 2 tool was used to assess bias and quality.

Results

We identified 5549 unique citations. Of the 96 full-text articles reviewed, 7 eligible studies of moderate quality (3730 participants) were included in our review. The diagnostic accuracy of ICD-9-CM diagnosis codes to identify NTM disease varied widely across studies, with positive predictive values ranging from 38.2% to 100% and sensitivity ranging from 21% to 93%. For NTMLD, 4 studies reported diagnostic accuracy, with positive predictive values ranging from 57% to 64.6% and sensitivity ranging from 21% to 26.9%.

Conclusions

Diagnostic accuracy measures of codes used in health care administrative data to identify patients with NTM varied across studies. Overall the positive predictive value of ICD-9-CM diagnosis codes alone is good, but the sensitivity is low; this method is likely to underestimate case numbers, reflecting the current limitations of coding systems to capture NTM diagnoses.

A Cluster of Cefepime-induced Neutropenia During Outpatient Parenteral Antimicrobial Therapy

A cluster of cefepime-induced neutropenia (CIN) was identified from June 2017 to May 2018 in a regional outpatient parenteral antimicrobial therapy population. Our data suggest prolonged courses of cefepime (≥2 weeks), administered by rapid intravenous push, were associated with a higher risk of CIN.

Outcomes of Outpatient Parenteral Antimicrobial Therapy With Ceftriaxone for Methicillin-Susceptible Staphylococcus aureus Bloodstream Infections-A Single-Center Observational Study

Background

Staphylococcus aureus bloodstream infections (BSIs) are associated with significant morbidity and mortality. Ceftriaxone is convenient for outpatient parenteral antimicrobial therapy (OPAT), but data for this indication are limited.

Methods

Adult patients with methicillin-susceptible Staphylococcus aureus (MSSA) BSI discharged on OPAT with cefazolin, oxacillin, or ceftriaxone for at least 7 days were included. We compared outcomes of ceftriaxone vs either oxacillin or cefazolin. Ninety-day all-cause mortality, readmission due to MSSA infection, and microbiological failure were examined as a composite outcome and compared among groups. Rates of antibiotic switches due to intolerance were assessed.

Results

Of 243 patients included, 148 (61%) were discharged on ceftriaxone and 95 (39%) were discharged on either oxacillin or cefazolin. The ceftriaxone group had lower rates of intensive care unit care, endocarditis, and shorter duration of bacteremia, but higher rates of cancer diagnoses. There was no significant difference in the composite adverse outcome in the oxacillin or cefazolin group vs the ceftriaxone group (18 [19%] vs 31 [21%]; P = .70), comprising microbiological failure (6 [6.3%] vs 9 [6.1%]; P = .94), 90-day all-cause mortality (7 [7.4%] vs 15 [10.1%]; P = .46), and readmission due to MSSA infection (10 [10.5%] vs 13 [8.8%]; P = .65). Antibiotic intolerance necessitating a change was similar between the 2 groups (4 [4.2%] vs 6 [4.1%]; P = .95).

Conclusions

For patients with MSSA BSI discharged on OPAT, within the limitations of the small numbers and retrospective design we did not find a significant difference in outcomes for ceftriaxone therapy when compared with oxacillin or cefazolin therapy.

Using wearable technology to predict health outcomes: a literature review

Objective

To review and analyze the literature to determine whether wearable technologies can predict health outcomes.

Materials and methods

We queried Ovid Medline 1946 -, Embase 1947 -, Scopus 1823 -, the Cochrane Library, clinicaltrials.gov 1997 – April 17, 2018, and IEEE Xplore Digital Library and Engineering Village through April 18, 2018, for studies utilizing wearable technology in clinical outcome prediction. Studies were deemed relevant to the research question if they involved human subjects, used wearable technology that tracked a health-related parameter, and incorporated data from wearable technology into a predictive model of mortality, readmission, and/or emergency department (ED) visits.

Results

Eight unique studies were directly related to the research question, and all were of at least moderate quality. Six studies developed models for readmission and two for mortality. In each of the eight studies, data obtained from wearable technology were predictive of or significantly associated with the tracked outcome.

Discussion

Only eight unique studies incorporated wearable technology data into predictive models. The eight studies were of moderate quality or higher and thereby provide proof of concept for the use of wearable technology in developing models that predict clinical outcomes.

Conclusion

Wearable technology has significant potential to assist in predicting clinical outcomes, but needs further study. Well-designed clinical trials that incorporate data from wearable technology into clinical outcome prediction models are required to realize the opportunities of this advancing technology.

Tobacco Use Prevalence and Smoking Cessation Pharmacotherapy Prescription Patterns Among Hospitalized Patients by Medical Specialty

INTRODUCTION

Effective smoking cessation medications are readily available but may be underutilized in hospital settings. In our large, tertiary care hospital, we aimed to (1) characterize patient tobacco use prevalence across medical specialties, (2) determine smoking cessation pharmacotherapy prescription variation across specialties, and (3) identify opportunities for improvement in practice.

METHODS

Using electronic health records at Barnes Jewish Hospital, we gathered demographic data, admitting service, admission route, length of stay, self-reported tobacco use, and smoking cessation prescriptions over a 6-year period, from 2010 to 2016. We then compared tobacco use prevalence and smoking cessation prescriptions across medical specialties using a cross-sectional, retrospective design.

RESULTS

Past 12-month tobacco use was reported by patients in 27.9% of inpatient admissions; prescriptions for smoking cessation pharmacotherapy were provided during 21.5% of these hospitalizations. The proportion of patients reporting tobacco use was highest in psychiatry (55.3%) and lowest in orthopedic surgery (17.1%). Psychiatric patients who reported tobacco use were most likely to receive pharmacotherapy (71.8% of admissions), and plastic surgery patients were least likely (4.7% of admissions). Compared with Caucasian tobacco users, African American patients who used tobacco products were less likely to receive smoking cessation medications (adjusted odds ratio [aOR] = 0.65; 95% confidence interval [CI] = 0.62 to 0.68).

CONCLUSIONS

Among hospitalized tobacco users, safe and cost-effective pharmacotherapies are under-prescribed. We identified substantial variation in prescribing practices across different medical specialties and demographic groups, suggesting the need for an electronic medical record protocol that facilitates consistent tobacco use cessation pharmacotherapy treatment.

IMPLICATIONS

Tobacco use cessation pharmacotherapy is underutilized during hospitalization, and prescription rates vary greatly across medical specialties and patient characteristics. Hospitals may benefit from implementing policies and practices that standardize and automate the offer of smoking pharmacotherapy for all hospitalized patients who use tobacco.

Mortality Prediction in ICUs Using A Novel Time-Slicing Cox Regression Method

Over the last few decades, machine learning and data mining have been increasingly used for clinical prediction in ICUs. However, there is still a huge gap in making full use of the time-series data generated from ICUs. Aiming at filling this gap, we propose a novel approach entitled Time Slicing Cox regression (TS-Cox), which extends the classical Cox regression into a classification method on multi-dimensional time-series. Unlike traditional classifiers such as logistic regression and support vector machines, our model not only incorporates the discriminative features derived from the time-series, but also naturally exploits the temporal orders of these features based on a Cox-like function. Empirical evaluation on MIMIC-II database demonstrates the efficacy of the TS-Cox model. Our TS-Cox model outperforms all other baseline models by a good margin in terms of AUC_PR, sensitivity and PPV, which indicates that TS-Cox may be a promising tool for mortality prediction in ICUs.

The number of discharge medications predicts thirty-day hospital readmission: a cohort study

BACKGROUND

Hospital readmission occurs often and is difficult to predict. Polypharmacy has been identified as a potential risk factor for hospital readmission. However, the overall impact of the number of discharge medications on hospital readmission is still undefined.

METHODS

To determine whether the number of discharge medications is predictive of thirty-day readmission using a retrospective cohort study design performed at Barnes-Jewish Hospital from January 15, 2013 to May 9, 2013. The primary outcome assessed was thirty-day hospital readmission. We also assessed potential predictors of thirty-day readmission to include the number of discharge medications.

RESULTS

The final cohort had 5507 patients of which 1147 (20.8 %) were readmitted within thirty days of their hospital discharge date. The number of discharge medications was significantly greater for patients having a thirty-day readmission compared to those without a thirty-day readmission (7.2 ± 4.1 medications [7.0 medications (4.0 medications, 10.0 medications)] versus 6.0 ± 3.9 medications [6.0 medications (3.0 medications, 9.0 medications)]; P < 0.001). There was a statistically significant association between increasing numbers of discharge medications and the prevalence of thirty-day hospital readmission (P < 0.001). Multiple logistic regression identified more than six discharge medications to be independently associated with thirty-day readmission (OR, 1.26; 95 % CI, 1.17-1.36; P = 0.003). Other independent predictors of thirty-day readmission were: more than one emergency department visit in the previous six months, a minimum hemoglobin value less than or equal to 9 g/dL, presence of congestive heart failure, peripheral vascular disease, cirrhosis, and metastatic cancer. A risk score for thirty-day readmission derived from the logistic regression model had good predictive accuracy (AUROC = 0.661 [95 % CI, 0.643-0.679]).

CONCLUSIONS

The number of discharge medications is associated with the prevalence of thirty-day hospital readmission. A risk score, that includes the number of discharge medications, accurately predicts patients at risk for thirty-day readmission. Our findings suggest that relatively simple and accessible parameters can identify patients at high risk for hospital readmission potentially distinguishing such individuals for interventions to minimize readmissions.

Mycobacterium ulcerans infection imported from Australia to Missouri, USA, 2012

Buruli ulcer, the third most common mycobacterial disease worldwide, rarely affects travelers and is uncommon in the United States. We report a travel-associated case imported from Australia and review 3 previous cases diagnosed and treated in the United States. The differential diagnoses for unusual chronic cutaneous ulcers and those nonresponsive to conventional therapy should include Mycobacterium ulcerans infection.

Cost-effectiveness of using Quantiferon Gold (QFT-G)® versus tuberculin skin test (TST) among U.S. and foreign born populations at a public health department clinic with a low prevalence of tuberculosis

OBJECTIVE

The purpose of this study was to determine the cost benefit to routinely using QFT-G versus the standard TST for screening U.S. and foreign born populations at a public health department clinic with a low prevalence of tuberculosis.

DESIGN AND SAMPLE

A comparative cost analysis of the monetization between QFT-G and TST was conducted: Data from the health department's Chest Clinic patients seen in 2007 were used to model cost predictions.

MEASURES

The net costs of screening, x-rays, the standard 9 months of latent tuberculosis infection treatment, laboratory, and administration for U.S. born patients and foreign born patients were investigated.

RESULTS

There are no apparent cost savings for U.S. born individuals, but due to the higher specificity of QFT-G for foreign born BCG-vaccinated individuals, there are unnecessary expenditures associated with the higher number of false positives incurred when using TST compared with QFT-G on 1,000 foreign born individuals (69%, 18%).

CONCLUSION

QFT-G is cost-effective and should be used at local health department clinics that want to achieve savings in screening and treating those suspected of having TB infection, especially for high-risk populations such as foreign born individuals.

Body temperature patterns as a predictor of hospital-acquired sepsis in afebrile adult intensive care unit patients: a case-control study

Introduction

Early treatment of sepsis improves survival, but early diagnosis of hospital-acquired sepsis, especially in critically ill patients, is challenging. Evidence suggests that subtle changes in body temperature patterns may be an early indicator of sepsis, but data is limited. The aim of this study was to examine whether abnormal body temperature patterns, as identified by visual examination, could predict the subsequent diagnosis of sepsis in afebrile critically ill patients.

Methods

Retrospective case-control study of 32 septic and 29 non-septic patients in an adult medical and surgical ICU. Temperature curves for the period starting 72 hours and ending 8 hours prior to the clinical suspicion of sepsis (for septic patients) and for the 72-hour period prior to discharge from the ICU (for non-septic patients) were rated as normal or abnormal by seven blinded physicians. Multivariable logistic regression was used to compare groups in regard to maximum temperature, minimum temperature, greatest change in temperature in any 24-hour period, and whether the majority of evaluators rated the curve to be abnormal.

Results

Baseline characteristics of the groups were similar except the septic group had more trauma patients (31.3% vs. 6.9%, p = .02) and more patients requiring mechanical ventilation (75.0% vs. 41.4%, p = .008). Multivariable logistic regression to control for baseline differences demonstrated that septic patients had significantly larger temperature deviations in any 24-hour period compared to control patients (1.5°C vs. 1.1°C, p = .02). An abnormal temperature pattern was noted by a majority of the evaluators in 22 (68.8%) septic patients and 7 (24.1%) control patients (adjusted OR 4.43, p = .017). This resulted in a sensitivity of 0.69 (95% CI [confidence interval] 0.50, 0.83) and specificity of 0.76 (95% CI 0.56, 0.89) of abnormal temperature curves to predict sepsis. The median time from the temperature plot to the first culture was 9.40 hours (IQR [inter-quartile range] 8.00, 18.20) and to the first dose of antibiotics was 16.90 hours (IQR 8.35, 34.20).

Conclusions

Abnormal body temperature curves were predictive of the diagnosis of sepsis in afebrile critically ill patients. Analysis of temperature patterns, rather than absolute values, may facilitate decreased time to antimicrobial therapy.

A trial of a real-time alert for clinical deterioration in patients hospitalized on general medical wards

BACKGROUND

With limited numbers of intensive care unit (ICU) beds available, increasing patient acuity is expected to contribute to episodes of inpatient deterioration on general wards.

OBJECTIVE

To prospectively validate a predictive algorithm for clinical deterioration in general-medical ward patients, and to conduct a trial of real-time alerts based on this algorithm.

DESIGN

Randomized, controlled crossover study.

SETTING/PATIENTS

Academic center with patients hospitalized on 8 general wards between July 2007 and December 2011.

INTERVENTIONS

Real-time alerts were generated by an algorithm designed to predict the need for ICU transfer using electronically available data. The alerts were sent by text page to the nurse manager on intervention wards.

MEASUREMENTS

Intensive care unit transfer, hospital mortality, and hospital length of stay.

RESULTS

Patients meeting the alert threshold were at nearly 5.3-fold greater risk of ICU transfer (95% confidence interval [CI]: 4.6-6.0) than those not satisfying the alert threshold (358 of 2353 [15.2%] vs 512 of 17678 [2.9%]). Patients with alerts were at 8.9-fold greater risk of death (95% CI: 7.4-10.7) than those without alerts (244 of 2353 [10.4%] vs 206 of 17678 [1.2%]). Among patients identified by the early warning system, there were no differences in the proportion of patients who were transferred to the ICU or who died in the intervention group as compared with the control group.

CONCLUSIONS

Real-time alerts were highly specific for clinical deterioration resulting in ICU transfer and death, and were associated with longer hospital length of stay. However, an intervention notifying a nurse of the risk did not result in improvement in these outcomes.

Paragonimus kellicotti flukes in Missouri, USA

Paragonimiasis is an infection caused by lung flukes of the genus Paragonimus. In Asia, P. westermani infections are relatively common because of dietary practices. However, in North America, cases of paragonimiasis, which are caused by P. kellicotti flukes, are rare. Only 7 autochthonous cases of paragonimiasis were reported during 1968-2008. In 2009, we reported 3 new case-patients with paragonimiasis who had been seen at our medical center over an 18-month period. Six additional case-patients were identified in St. Louis, Missouri, USA, and treated at Washington University-affiliated health centers in 2009-2010. We report detailed descriptions of these case-patients, which includes unusual clinical manifestations. We also describe public health interventions that were undertaken to inform the general public and physicians about the disease and its mode of transmission.

Toward a two-tier clinical warning system for hospitalized patients

Clinical study has found early detection and intervention to be essential for preventing clinical deterioration in patients at general hospital units. In this paper, we envision a two-tiered early warning system designed to identify the signs of clinical deterioration and provide early warning of serious clinical events. The first tier of the system automatically identifies patients at risk of clinical deterioration from existing electronic medical record databases. The second tier performs real-time clinical event detection based on real-time vital sign data collected from on-body wireless sensors attached to those high-risk patients. We employ machine-learning techniques to analyze data from both tiers, assigning scores to patients in real time. The assigned scores can then be used to trigger early-intervention alerts. Preliminary study of an early warning system component and a wireless clinical monitoring system component demonstrate the feasibility of this two-tiered approach.

Unrecognized fatalities related to colchicine in hospitalized patients

BACKGROUND

Colchicine is commonly used for the treatment of gout and occasionally for other inflammatory diseases. It has a narrow therapeutic index and the potential for severe or fatal toxicity.

OBJECTIVES

We sought to determine (1) the frequency of colchicine toxicity among hospitalized patients taking colchicine who died during an admission, (2) the likelihood that colchicine contributed to death, (3) whether patients were taking interacting medications that could have contributed to toxicity, and (4) whether colchicine dosing among these patients adhered to established guidelines.

METHODS

We conducted an IRB-approved, retrospective chart review at an urban, tertiary care, 1228-bed, university hospital. Subjects included hospitalized patients who received colchicine and died in hospital between 1 January 2000 and 28 February 2007. We reviewed charts for signs and symptoms of colchicine toxicity. An expert panel reviewed each case and classified the likelihood of colchicine toxicity, the likelihood of a causal role of colchicine in the death using the WHO classification system, and the appropriateness of colchicine dosing.

RESULTS

Thirty-seven hospitalized patients who died during the 86-month study period received colchicine. Toxicity was unlikely in 20/37, possible in 8/37, likely in 5/37, and certain in 4/37. A contributing role for colchicine in causing death was unlikely in 24/37, possible in 7/37, likely in 3/37, and certain in 3/37. Colchicine doses (based on creatinine clearance) exceeded the accepted range for 12 patients, including 10 of 17 cases of toxicity and 8 of 13 cases of death classified as possible or higher. Seventeen patients received interacting medications, including 8 of 17 cases of toxicity and 8 of 13 cases of death classified as possible or higher.

CONCLUSION

Colchicine toxicity was frequent in this cohort and may have contributed to about one-third of the deaths. Inappropriate dosing of colchicine occurred frequently and was related to toxicity and death.

Modelling multivariate disease rates with a latent structure mixture model

There has been considerable recent interest in multivariate modelling of the geographical distribution of morbidity or mortality rates for potentially related diseases. The motivations for this include investigation of similarities or dissimilarities in the risk distribution for the different diseases, as well as ‘borrowing strength’ across disease rates to shrink the uncertainty in geographical risk assessment for any particular disease. A number of approaches to such multivariate modelling have been suggested and this paper proposes an extension to these which may provide a richer range of dependency structures than those encompassed so far. We develop a model which incorporates a discrete mixture of latent structures and argue that this provides potential to represent an enhanced range of correlation structures between diseases at the same time as implicitly allowing for less restrictive spatial correlation structures between geographical units. We compare and contrast our approach to other commonly used multivariate disease models and demonstrate comparative results using data taken from cancer registries on four carcinomas in some 300 geographical units in England, Scotland and Wales.

Importance of routine public health influenza surveillance: detection of an unusual W-shaped influenza morbidity curve

Seasonal influenza causes excess morbidity and mortality at the extremes of age: It disproportionately affects the very young and the very old, typically resulting in "U"-shaped age-distributed curves. By means of a well-established public health department surveillance system using positive influenza tests submitted from sentinel sites, the authors generated annual influenza-specific morbidity curves over a 10-year period (1998-2008) for St. Louis County, Missouri. The authors detected an unusually high incidence of cases of medically attended test-positive influenza, particularly in young adults, during the 2007-2008 season, resulting in an unexpected "W"-shaped age-distributed morbidity curve that was distinctly unique in comparison with the prior 9 influenza seasons. Public health influenza surveillance programs are useful tools for detecting emerging epidemiologic trends that may have clinical importance.

Reliable real-time clinical monitoring using sensor network technology

We propose wireless sensor networks composed of nodes using low-power 802.15.4 radios as an enabling technology for patient monitoring in general hospital wards. A key challenge for such applications is to reliably deliver sensor data from mobile patients. We propose a monitoring system with two types of nodes: patient nodes equipped with wireless pulse oximeters and relays nodes used to route data to a base station. A reliability analysis of data collection from mobile users shows that mobility leads to packet losses exceeding 30%. The majority of packet losses occur between the mobile subjects and the first-hop relays. Based on this insight we developed the Dynamic Relay Association Protocol (DRAP), an effective mechanism for discovering the right relays for patient nodes. DRAP enables highly reliable data collection from mobile subjects. Empirical evaluation showed that DRAP delivered at least 96% of data from multiple users. Our results demonstrate the feasibility of wireless sensor networks for real-time clinical monitoring.

Clinical validation of the AHRQ postoperative venous thromboembolism patient safety indicator

BACKGROUND

The Agency for Healthcare Research and Quality (AHRQ) patient safety indicators (PSIs) screen for potentially preventable complications in hospitalized patients using hospital administrative data. The PSI for postoperative venous thromboembolism (VTE) relies on International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes for deep vein thrombosis (DVT) or pulmonary embolism (PE) in secondary diagnoses fields. In a clinical validation study of the PSI for postoperative VTE, natural language processing (NLP), supplemented by pharmacy and billing data, was used to identify VTE events missed by medical records coders.

METHODS

In a retrospective review of postsurgical discharges, charts were processed using the AHRQ PSI software. Cases were identified as possible false negatives by flagging charts for possible VTEs using pharmacy and billing data to identify all patients who were therapeutically anticoagulated or had placement of an inferior vena caval filter. All charts were reviewed by a physician blinded to screening results. Physician interpretation was considered the gold standard for VTE classification.

RESULTS

The AHRQ PSI had a positive predictive value (PPV) of .545 (95% confidence interval [CI], .453-.634) and a negative predictive value (NPV) of .997 (95% CI, .995-.999). Sensitivity was .87 and specificity was .98. Secondary coding review suggested that all 9 false-negative results were miscoded; if they had been properly coded, the sensitivity would increase to 1.00. Most false-positive cases resulted from superficial venous clots identified by the PSI due to coding ambiguity.

DISCUSSION

The VTE PSI performed well as a screening tool but generated a significant number of false-positive cases, a problem that could be substantially reduced with improved coding methods.

Computerized surveillance for adverse drug events in a pediatric hospital

There are limited data on adverse drug event rates in pediatrics. The authors describe the implementation and evaluation of an automated surveillance system modified to detect adverse drug events (ADEs) in pediatric patients. The authors constructed an automated surveillance system to screen admissions to a large pediatric hospital. Potential ADEs identified by the system were reviewed by medication safety pharmacists and a physician and scored for causality and severity. Over the 6 month study period, 6,889 study children were admitted to the hospital for a total of 40,250 patient-days. The ADE surveillance system generated 1226 alerts, which yielded 160 true ADEs. This represents a rate of 2.3 ADEs per 100 admissions or 4 per 1,000 patient-days. Medications most frequently implicated were diuretics, antibiotics, immunosuppressants, narcotics, and anticonvulsants. The composite positive predictive value of the ADE surveillance system was 13%. Automated surveillance can be an effective method for detecting ADEs in hospitalized children.

Automated surveillance for central line-associated bloodstream infection in intensive care units

OBJECTIVE

To develop and evaluate computer algorithms with high negative predictive values that augment traditional surveillance for central line-associated bloodstream infection (CLABSI).

SETTING

Barnes-Jewish Hospital, a 1,250-bed tertiary care academic hospital in Saint Louis, Missouri.

METHODS

We evaluated all adult patients in intensive care units who had blood samples collected during the period from July 1, 2005, to June 30, 2006, that were positive for a recognized pathogen on culture. Each isolate recovered from culture was evaluated using the definitions for nosocomial CLABSI provided by the National Healthcare Safety Network of the Centers for Disease Control and Prevention. Using manual surveillance by infection prevention specialists as the gold standard, we assessed the ability of various combinations of dichotomous rules to determine whether an isolate was associated with a CLABSI. Sensitivity, specificity, and predictive values were calculated.

RESULTS

Infection prevention specialists identified 67 cases of CLABSI associated with 771 isolates recovered from blood samples. The algorithms excluded approximately 40%-62% of the isolates from consideration as possible causes of CLABSI. The simplest algorithm, with 2 dichotomous rules (ie, the collection of blood samples more than 48 hours after admission and the presence of a central venous catheter within 48 hours before collection of blood samples), had the highest negative predictive value (99.4%) and the lowest specificity (44.2%) for CLABSI. Augmentation of this algorithm with rules for common skin contaminants confirmed by another positive blood culture result yielded in a negative predictive value of 99.2% and a specificity of 68.0%.

CONCLUSIONS

An automated approach to surveillance for CLABSI that is characterized by a high negative predictive value can accurately identify and exclude positive culture results not representing CLABSI from further manual surveillance.

Improving adherence to dyslipidemia medication guidelines in hospitalized diabetic patients using a technology-assisted pharmacist intervention

We tested whether a technology-assisted pharmacist intervention improved physician adherence to guidelines for lipid-lowering therapy in diabetic patients. Computerized alerts identified diabetic patients above LDL-Cholesterol (LDL-C) goal. During Period 1 prescribing behavior was observed in both control and intervention physician groups without intervening. In Period 2, pharmacists conducted academic detailing with intervention group physicians. Control group physicians were observed. The intervention significantly improved the proportion of diabetic patients discharged on statin therapy.

Natural language processing to identify adverse drug events

We tested and adapted Cancer Text Information Extraction System (caTIES), a publicly available natural language processing tool (NLP), as a method for identifying terms suggestive of adverse drug events (ADEs). Although caTIES was intended to extract concepts from surgical pathology reports, we report that it can successfully be used to search for ADEs on a much broader range of documents.

Computerized surveillance for adverse drug events in a pediatric hospital

Adverse drug event (ADE) surveillance is needed to inform processes and methods for prevention. Voluntary reporting and manual chart review have limitations. Automated surveillance systems are gaining recognition and provide useful information to supplement the other methods. Preliminary evaluation of a pediatric automated adverse drug event application shows a positive predictive value of 13%, discovering events with harm in 1.3% of inpatient admissions.

Improving healthcare-associated infection surveillance at a multi-hospital institution using an existing data repository

The accurate and timely reporting of healthcare-associated infections is an essential infection control practice. Rates provide benchmarks for detecting trends and can help facilities identify intervention opportunities. Standardizing how hospitals within an organization calculate these rates is critical if the rates are to be compared among hospitals and to national standards. We describe a new web-based tool to improve how infection control practitioners identify and report healthcare-associated infections at BJC HealthCare.

Migrating toward a next-generation clinical decision support application: the BJC HealthCare experience

The next-generation model outlined in the AMIA Roadmap for National Action on Clinical Decision Support (CDS) is aimed to optimize the effectiveness of CDS interventions, and to achieve widespread adoption. BJC HealthCare re-engineered its existing CDS system in alignment with the AMIA roadmap and plans to use it for guidance on further enhancements. We present our experience and discuss an incremental approach to migrate towards the next generation of CDS applications from the viewpoint of a healthcare institution. Specifically, a CDS rule engine service with a standards-based rule representation format was built to simplify maintenance and deployment. Rules were separated from execution code and made customizable for multi-facility deployment. Those changes resulted in system improvement in the short term while aligning with long-term strategic objectives.

Automated dose checking and intervention for bariatric patients

According to a recent Center for Disease Control survey, 33% of the US population is obese. Because labeled dosing guidelines are based upon non-obese individuals, under dosing of antibiotics may be problematic in this population. We developed an automated dose checking tool to efficiently detect potentially inappropriate dosing of antibiotics in bariatric (morbidly obese patients).

Alerts to improve chart documentation for National Quality Measures

Clinical decision support (CDS) rules monitoring adherence to guidelines for secondary prevention of acute myocardial infarction (AMI) have been in use at BJC HealthCare's academic facility for five years. The alert web response form for these rules was enhanced to facilitate documentation of contraindications for ACE/ARB, beta blocker, aspirin, and lipid-lowering medications. An analysis of the impact of these enhancements and the changes to pharmacy workflow are presented here.

Physicians' knowledge and attitudes about coronary heart disease secondary prevention

We administered a knowledge and attitudes questionnaire regarding a technology assisted pharmacist mediated academic detailing intervention designed to improve physician adherence to coronary heart disease (CHD) secondary-prevention guidelines. Questionnaires were administered in two settings: an academic hospital and a community hospital. More knowledgeable physicians reported being more likely to prescribe a pharmacists' recommended medication and to agree that lipid profiles should be automatically performed for inpatients with elevated troponin.

Using business intelligence to monitor clinical quality metrics

BJC HealthCare (BJC) uses a number of industry standard indicators to monitor the quality of services provided by each of its hospitals. By establishing an enterprise data warehouse as a central repository of clinical quality information, BJC is able to monitor clinical quality performance in a timely manner and improve clinical outcomes.

Natural language processing to identify venous thromboembolic events

The Agency for Healthcare Research and Quality (AHRQ) has promulgated patient safety indicators to identify potentially preventable adverse safety events, including venous thromboembolism (VTE). Identification of these events for quality reporting is commonly done with AHRQ-defined ICD9-CM codes. We tested a natural language processing service (NLP) as an alternative method of identification.

Long-term effectiveness of an automated guideline adherence monitor for secondary prevention of acute myocardial infarction

An automated guideline monitor for secondary prevention of acute myocardial infarction (AMI) has been favorably tested at an academic medical center using a randomized controlled trial. Subsequently, the monitor was implemented in a production mode and has been in production use for five years. Statistical process control analysis shows a generally sustainable effect of the intervention.

Automated clinical data collection for national quality measurement reporting

Collecting data for the Joint Commission on Accreditation of Healthcare Organizations (JCAHO) ORYX Core Measurement Reporting can be automated using an object-oriented, client-developed program that extracts data from a clinical data repository and utilizes an MHA vendor upload process. The process eliminated 39% of the manual data collection efforts.

A technology-assisted approach for discontinuing contact isolation

While contact isolation can be an effective tool in reducing the transmission of nosocomial methicillin-resistant Staphylococcus aureus (MRSA), it can increase costs and may decrease the quality of patient care. Therefore, it is important to insure that only patients who need contact precautions are isolated. We describe a simple automated report to help infection control practitioners pro-actively identify patients that may no longer need isolation.

An intervention to improve secondary prevention of coronary heart disease

BACKGROUND

Translating guidelines into clinical practice has proved to be quite difficult, even when the guidelines are well accepted and noncontroversial. Both computerized reminders and academic detailing have been effective in changing physician prescribing behavior. In this study, we sought to use these methods, mediated by clinical pharmacists, to improve adherence to the secondary prevention guidelines in hospitalized patients with myocardial infarction.

METHODS

A randomized, prospective study was performed in which computerized alerts identifying hospitalized patients with elevated troponin I levels were routed to clinical pharmacists. The pharmacists then conducted academic detailing for physicians caring for patients with acute myocardial infarction who were randomized to the intervention group. Patients in the control group received standard care. The main outcome measure was the proportion of patients discharged on a regimen of aspirin, beta-blockers, angiotensin-converting enzyme inhibitors, and statins.

RESULTS

The intervention had a significant impact on the proportion of patients discharged on a regimen of angiotensin-converting enzyme inhibitors (328/365 [89.9%] vs 409/488 [83.8%], intervention vs control, respectively, P = .02), and statins (344/365 [94.2%] vs 436/488 [89.3%], P = .02). There was no statistical impact on beta-blocker (350/365 [95.9%] vs 448/488 [91.8%], P = .10) or aspirin use (352/365 [96.4%] vs 471/488 [96.5%], P = .87). When all 4 classes were considered together, 305 (83.6%) of 365 patients vs 343 (70.3%) of 488 patients were discharged on a regimen of all secondary prevention medications to which they did not have a contraindication (P<.001).

CONCLUSION

A computerized alert with pharmacist-mediated academic detailing is an effective means to increase adherence to secondary prevention guidelines for coronary heart disease.