Predictive Models for Very Preterm Birth: Developing a Point-of-Care Tool.

OBJECTIVE: The objective of this study was to create three point-of-care predictive models for very preterm birth using variables available at three different time points: prior to pregnancy, at the end of the first trimester, and mid-pregnancy. STUDY DESIGN: This is a retrospective cohort study of 359,396 Ohio Medicaid mothers from 2008 to 2015. The last baby for each mother was included in the final dataset. Births prior to 22 weeks were excluded. Multivariable logistic regression was used to create three models. These models were validated on a cohort that was set aside and not part of the model development. The main outcome measure was birth prior to 32 weeks. RESULTS: The final dataset contained 359,396 live births with 6,516 (1.81%) very preterm births. All models had excellent calibration. Goodness-of-fit tests suggested strong agreement between the probabilities estimated by the model and the actual outcome experience in the data. The mid-pregnancy model had acceptable discrimination with an area under the receiver operator characteristic curve of approximately 0.75 in both the developmental and validation datasets. CONCLUSION: Using data from a large Ohio Medicaid cohort we developed point-of-care predictive models that could be used before pregnancy, after the first trimester, and in mid-pregnancy to estimate the probability of very preterm birth. Future work is needed to determine how the calculator could be used to target interventions to prevent very preterm birth. KEY POINTS: · We developed predictive models for very preterm birth.. · All models showed excellent calibration.. · The models were integrated into a risk calculator..

Implementing a Novel Interprofessional Clinical Informatics Curriculum.

A novel interprofessional clinical informatics curriculum was developed, piloted, and implemented, using an academic medical record. Targeted learners included undergraduate, graduate, and professional students across five health science colleges. A team of educators and practitioners representing those five health science colleges was formed in 2016, to design, develop, and refine educational modules covering the essentials of clinical informatics. This innovative curriculum consists of 10 online learning modules and 18 unique imbedded exercises that use standardized patient charts and tailored user views. The exercises allow learners to adopt the role of various providers who document in EMRs. Students are exposed to the unique perspectives of an attending physician, nurse, radiological technician, and health information manager, with the goal of developing knowledge and skills necessary for efficient and effective interprofessional communication within the EMR. The campus-wide clinical informatics curriculum is online, flexible, asynchronous, and well-established within each college, allowing faculty to select and schedule content based on discipline-specific learner and course needs. Program modifications over the past 4 years have correlated with a positive impact on the students' experience.

Prediction of Antibiotic Susceptibility for Urinary Tract Infection in a Hospital Setting.

Empiric antibiotic prescribing can be supported by guidelines and/or local antibiograms, but these have limitations. We sought to use data from a comprehensive electronic health record to use statistical learning to develop predictive models for individual antibiotics that incorporate patient- and hospital-specific factors. This paper reports on the development and validation of these models with a large retrospective cohort. This was a retrospective cohort study including hospitalized patients with positive urine cultures in the first 48 h of hospitalization at a 1,500-bed tertiary-care hospital over a 4.5-year period. All first urine cultures with susceptibilities were included. Statistical learning techniques, including penalized logistic regression, were used to create predictive models for cefazolin, ceftriaxone, ciprofloxacin, cefepime, and piperacillin-tazobactam. These were validated on a held-out cohort. The final data set used for analysis included 6,366 patients. Final model covariates included demographics, comorbidity score, recent antibiotic use, recent antimicrobial resistance, and antibiotic allergies. Models had acceptable to good discrimination in the training data set and acceptable performance in the validation data set, with a point estimate for area under the receiver operating characteristic curve (AUC) that ranged from 0.65 for ceftriaxone to 0.69 for cefazolin. All models had excellent calibration. We used electronic health record data to create predictive models to estimate antibiotic susceptibilities for urinary tract infections in hospitalized patients. Our models had acceptable performance in a held-out validation cohort.

Network analysis of intra-hospital transfers and hospital onset clostridium difficile infection.

OBJECTIVES: To explore how social network analysis (SNA) can be used to analyse intra-hospital patient networks of individuals with a hospital acquired infection (HAI) for further analysis in a geographical information systems (GIS) environment. METHODS: A case and control study design was used to select 2008 patients. We retrieved locational data for the patients, which was then translated into a network with the SNA software and then GIS software. Overall metrics were calculated for the SNA based on three datasets and further analysed with a GIS. RESULTS: The SNA analysis compared cases to control indicating significant differences in the overall structure of the networks. A GIS visual representation of these metrics was developed, showing spatial variation across the example hospital floor. DISCUSSION: This study confirmed the importance that intra-hospital patient networks play in the transmission of HAIs, highlighting opportunities for interventions utilising these data. Due to spatial variation differences, further research is necessary to confirm this is not a localised phenomenon, but instead a common situation occurring within many hospitals. CONCLUSION: Utilising SNA and GIS analysis in conjunction with one another provided a data-rich environment in which the risk inherent in intra-hospital transfer networks was quantified, visualised and interpreted for potential interventions.

Life after endometrial cancer: A systematic review of patient-reported outcomes.

BACKGROUND: Women with endometrial cancer (EC) are the second largest population of female cancer survivors in the United States. However, the outcomes of EC survivors, from the patient perspective, are not well-understood. Therefore, we conducted a systematic review of patient-reported outcomes (PROs) following an EC diagnosis. METHODS: We searched MEDLINE, EMBASE, Scopus, CINAHL, and reference lists to identify published observational studies that examined PROs among women with EC. Reviewers independently reviewed eligible full-text study articles and conducted data extraction. We qualitatively summarized included articles according to exposures [e.g. body mass index (BMI), treatment, etc.] or specific PROs (e.g. sexual function). RESULTS: Of 1722 unique studies, 102 full-text articles were reviewed, of which a total of 27 studies fulfilled the inclusion criteria. The most commonly used PRO questionnaires were the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-Core 30 (EORTC QLQ-C30) (n=9), Short Form 36 Questionnaire (SF-36, n=8), the Functional Assessment of Cancer Therapy-General (FACT-G, n=5), and the Female Sexual Function Index (FSFI, n=4). Obesity was associated with lower quality of life (QOL) and physical functioning. Treatment type affected several outcomes. Laparoscopy generally resulted in better QOL outcomes than laparotomy. Likewise, vaginal brachytherapy was associated with better outcomes compared to external beam radiation. Sexual function outcomes were dependent on age, time since diagnosis, and having consulted a physician before engaging in sexual activities. In addition, a physical activity intervention was associated with improved sexual interest but not sexual function. CONCLUSIONS: Our review provides insight into the experience of EC survivors from the patient perspective. Factors that contribute to QOL, such as pain, fatigue, emotional and social functioning, should be monitored following an EC diagnosis.

Staphylococcus-related glomerulonephritis and poststreptococcal glomerulonephritis: why defining "post" is important in understanding and treating infection-related glomerulonephritis.

A spate of recent publications describes a newly recognized form of glomerulonephritis associated with active staphylococcal infection. The key kidney biopsy findings, glomerular immunoglobulin A (IgA) deposits dominant or codominant with IgG deposits, resemble those of IgA nephritis. Many authors describe this condition as "postinfectious" and have termed it "poststaphylococcal glomerulonephritis." However, viewed through the prism of poststreptococcal glomerulonephritis, the prefix "post" in poststaphylococcal glomerulonephritis is historically incorrect, illogical, and misleading with regard to choosing therapy. There are numerous reports describing the use of high-dose steroids to treat poststaphylococcal glomerulonephritis. The decision to use steroid therapy suggests that the treating physician believed that the dominant problem was a postinfectious glomerulonephritis, not the infection itself. Unfortunately, steroid therapy in staphylococcus-related glomerulonephritis can precipitate severe staphylococcal sepsis and even death and provides no observable benefits. Poststreptococcal glomerulonephritis is an authentic postinfectious glomerulonephritis; poststaphylococcal glomerulonephritis is not. Making this distinction is important from the perspective of history, pathogenesis, and clinical management.

Textual inference for eligibility criteria resolution in clinical trials.

Clinical trials are essential for determining whether new interventions are effective. In order to determine the eligibility of patients to enroll into these trials, clinical trial coordinators often perform a manual review of clinical notes in the electronic health record of patients. This is a very time-consuming and exhausting task. Efforts in this process can be expedited if these coordinators are directed toward specific parts of the text that are relevant for eligibility determination. In this study, we describe the creation of a dataset that can be used to evaluate automated methods capable of identifying sentences in a note that are relevant for screening a patient's eligibility in clinical trials. Using this dataset, we also present results for four simple methods in natural language processing that can be used to automate this task. We found that this is a challenging task (maximum F-score=26.25), but it is a promising direction for further research.

Diagnosis-specific readmission risk prediction using electronic health data: a retrospective cohort study.

BACKGROUND: Readmissions after hospital discharge are a common occurrence and are costly for both hospitals and patients. Previous attempts to create universal risk prediction models for readmission have not met with success. In this study we leveraged a comprehensive electronic health record to create readmission-risk models that were institution- and patient- specific in an attempt to improve our ability to predict readmission. METHODS: This is a retrospective cohort study performed at a large midwestern tertiary care medical center. All patients with a primary discharge diagnosis of congestive heart failure, acute myocardial infarction or pneumonia over a two-year time period were included in the analysis.The main outcome was 30-day readmission. Demographic, comorbidity, laboratory, and medication data were collected on all patients from a comprehensive information warehouse. Using multivariable analysis with stepwise removal we created three risk disease-specific risk prediction models and a combined model. These models were then validated on separate cohorts. RESULTS: 3572 patients were included in the derivation cohort. Overall there was a 16.2% readmission rate. The acute myocardial infarction and pneumonia readmission-risk models performed well on a random sample validation cohort (AUC range 0.73 to 0.76) but less well on a historical validation cohort (AUC 0.66 for both). The congestive heart failure model performed poorly on both validation cohorts (AUC 0.63 and 0.64). CONCLUSIONS: The readmission-risk models for acute myocardial infarction and pneumonia validated well on a contemporary cohort, but not as well on a historical cohort, suggesting that models such as these need to be continuously trained and adjusted to respond to local trends. The poor performance of the congestive heart failure model may suggest that for chronic disease conditions social and behavioral variables are of greater importance and improved documentation of these variables within the electronic health record should be encouraged.

Describing the monthly variability of hospital-onset Clostridioides difficile during early coronavirus disease 2019 (COVID-19) using electronic health record data.

OBJECTIVE: To assess the relative risk of hospital-onset Clostridioides difficile (HO-CDI) during each month of the early coronavirus disease 2019 (COVID-19) pandemic and to compare it with historical expectation based on patient characteristics. DESIGN: This study used a retrospective cohort design. We collected secondary data from the institution's electronic health record (EHR). SETTING: The Ohio State University Wexner Medical Center, Ohio, a large tertiary healthcare system in the Midwest. PATIENTS OR PARTICIPANTS: All adult patients admitted to the inpatient setting between January 2018 and May 2021 were eligible for the study. Prisoners, children, individuals presenting with Clostridioides difficile on admission, and patients with <4 days of inpatient stay were excluded from the study. RESULTS: After controlling for patient characteristics, the observed numbers of HO-CDI cases were not significantly different than expected. However, during 3 months of the pandemic period, the observed numbers of cases were significantly different from what would be expected based on patient characteristics. Of these 3 months, 2 months had more cases than expected and 1 month had fewer. CONCLUSIONS: Variations in HO-CDI incidence seemed to trend with COVID-19 incidence but were not fully explained by our case mix. Other factors contributing to the variability in HO-CDI incidence beyond listed patient characteristics need to be explored.

When Infections Are Found: A Qualitative Study Characterizing Best Management Practices for Central Line-Associated Bloodstream Infection and Catheter-Associated Urinary Tract Infection Performance Monitoring and Feedback.

Healthcare-associated infections (HAIs) remain a significant patient safety problem that can lead to illness and death, despite the implementation of clinical bundles to prevent HAIs. Management practices can support HAI prevention, but their role in HAI performance monitoring and feedback is not well understood. To address this knowledge gap, we previously conducted semi-structured interviews with staff at 18 hospitals to examine the role of management practices around the prevention of central line-associated bloodstream infections (CLABSIs) and catheter-associated urinary tract infections (CAUTIs). Interview transcripts were analyzed to identify themes related to HAI performance monitoring and feedback. The current analysis focuses on 10 higher-performing hospitals that were successful in preventing CLABSIs and CAUTIs. These institutions had robust practices including timely event analysis, leadership engagement, and multidisciplinary participation in HAI reviews. Across these sites, we found common goals including investigating HAIs without blame and identifying opportunities for improvement. Management practices such as timely analysis of HAIs, collaboration between facility leadership and multidisciplinary team members, and a focus on identifying the failure of a procedure or protocol, rather than the failure of staff members, are all approaches that can support infection prevention efforts. These management practices may be especially important as hospitals attempt to address increases in CLABSI and CAUTI rates that may have occurred during the coronavirus pandemic.

Changes in prospectively collected patient-reported outcomes among women with incident endometrial cancer.

PURPOSE: We examined associations between patient and treatment characteristics with longitudinally collected patient-reported outcome (PRO) measures to provide a data-informed description of the experiences of women undergoing treatment for endometrial cancer. METHODS: We administered National Institutes of Health Patient Reported Outcomes Measurement Information System (PROMIS) questionnaires at the preoperative visit and at 6 and 12 months after surgery. Anxiety, depression, fatigue, sleep disturbance, pain, physical function, and ability to participate in social roles were assessed. Analysis of variance (ANOVA) and linear mixed models were used to examine associations between patient characteristics and PRO measures at baseline and through time. RESULTS: Of 187 women enrolled, 174 (93%) and 103 (69%) completed the 6- and 12-month questionnaires, respectively. Anxiety was substantially elevated at baseline (half of one population-level standard deviation) and returned to general population mean levels at 6 and 12 months. Younger age, Medicaid/None/Self-pay insurance, prevalent diabetes, and current smoking were associated with higher symptom burden on multiple PRO measures across the three time points. Women with aggressive histology, higher disease stage, or those with adjuvant treatment had worse fatigue at 6 months, which normalized by 12 months. CONCLUSIONS: We observed a high symptom burden at endometrial cancer diagnosis, with most PRO measures returning to general population means by 1 year. Information on risk factor-PRO associations can be used during the clinical visit to inform supportive service referral. IMPLICATIONS FOR CANCER SURVIVORS: These findings can inform clinicians' discussions with endometrial cancer survivors regarding expected symptom trajectory following diagnosis and treatment.

Knowledge management and informatics considerations for comparative effectiveness research: a case-driven exploration.

BACKGROUND: As clinical data are increasingly collected and stored electronically, their potential use for comparative effectiveness research (CER) grows. Despite this promise, challenges face those wishing to leverage such data. In this paper we aim to enumerate some of the knowledge management and informatics issues common to such data reuse. DESIGN: After reviewing the current state of knowledge regarding biomedical informatics challenges and best practices related to CER, we then present 2 research projects at our institution. We analyze these and highlight several common themes and challenges related to the conduct of CER studies. Finally, we represent these emergent themes. RESULTS: The informatics challenges commonly encountered by those conducting CER studies include issues related to data information and knowledge management (eg, data reuse, data preparation) as well as those related to people and organizational issues (eg, sociotechnical factors and organizational factors). Examples of these are described in further detail and a formal framework for describing these findings is presented. CONCLUSIONS: Significant challenges face researchers attempting to use often diverse and heterogeneous datasets for CER. These challenges must be understood in order to be dealt with successfully and can often be overcome with the appropriate use of informatics best practices. Many research and policy questions remain to be answered in order to realize the full potential of the increasingly electronic clinical data available for such research.

The influence of context on antimicrobial prescribing for febrile respiratory illness: a cohort study.

BACKGROUND: Little is known about the influence of contextual factors on a physician's likelihood to prescribe antimicrobials for febrile respiratory illness (FRI). Context includes epidemiologic context (for example, a pandemic period) and personal context (for example, recent exposure to other patients with FRI). OBJECTIVE: To examine the association between contextual factors and antimicrobial prescribing for FRI. DESIGN: 5.5-year retrospective cohort study. SETTING: A network of Midwestern primary care providers. PATIENTS: All patients presenting with FRI during influenza seasons between 2006 and 2011. MEASUREMENTS: Antimicrobial prescribing for FRI during pandemic and seasonal influenza periods. RESULTS: 28 301 unique patient encounters for FRI with 69 physicians in 26 practices were included. An antibiotic was prescribed in 12 795 (45.2%) cases. The range of prescribing among physicians was 17.9% to 83.7%. Antibiotics were prescribed in 47.5% of encounters during the seasonal period and 39.2% during the pandemic period (P < 0.001). After multivariable adjustment for patient and physician characteristics, antibiotic prescribing was lower in the pandemic period (odds ratio [OR], 0.72 [95% CI, 0.68 to 0.77]) than in the seasonal period. The likelihood of prescribing an antibiotic decreased as the number of FRI cases that a physician had seen in the previous week increased (OR, 0.93 [CI, 0.86 to 1.01] for 2 to 3 patients with FRI seen in the previous week; OR, 0.84 [CI, 0.77 to 0.91] for 4 to 6 patients; OR, 0.71 [CI, 0.64 to 0.78] for 7 to 11 patients; and OR, 0.57 [CI, 0.51 to 0.63] for ≥12 patients compared with the reference range of 0 to 1 patients). Pandemic season and recent personal context were also associated with antiviral prescribing. LIMITATION: Retrospective study in a single geographic area. CONCLUSION: Epidemiologic context and the number of cases of FRI that a physician had recently seen were associated with his or her likelihood to prescribe antimicrobials for FRI. Interventions that enhance a physician's contextual awareness may improve antimicrobial use. PRIMARY FUNDING SOURCE: NorthShore University HealthSystem.

Understanding comorbidities and health disparities related to COVID-19: a comprehensive study of 776 936 cases and 1 362 545 controls in the state of Indiana, USA.

Objective: To characterize COVID-19 patients in Indiana, United States, and to evaluate their demographics and comorbidities as risk factors to COVID-19 severity. Materials and Methods: EHR data of 776 936 COVID-19 cases and 1 362 545 controls were collected from the COVID-19 Research Data Commons (CoRDaCo) in Indiana. Data regarding county population and per capita income were obtained from the US Census Bureau. Statistical analysis was conducted to determine the association of demographic and clinical variables with COVID-19 severity. Predictive analysis was conducted to evaluate the predictive power of CoRDaCo EHR data in determining COVID-19 severity. Results: Chronic obstructive pulmonary disease, cardiovascular disease, and type 2 diabetes were found in 3.49%, 2.59%, and 4.76% of the COVID-19 patients, respectively. Such COVID-19 patients have significantly higher ICU admission rates of 10.23%, 14.33%, and 11.11%, respectively, compared to the entire COVID-19 patient population (1.94%). Furthermore, patients with these comorbidities have significantly higher mortality rates compared to the entire COVID-19 patient population. Health disparity analysis suggests potential health disparities among counties in Indiana. Predictive analysis achieved F1-scores of 0.8011 and 0.7072 for classifying COVID-19 cases versus controls and ICU versus non-ICU cases, respectively. Discussion: Black population in Indiana was more adversely affected by COVID-19 than the White population. This is consistent to findings from existing studies. Our findings also indicate other health disparities in terms of demographic and economic factors. Conclusion: This study characterizes the relationship between comorbidities and COVID-19 outcomes with respect to ICU admission across a large COVID-19 patient population in Indiana.

Concerns and frustrations about the public reporting of device-related healthcare-associated infections: Perspectives of hospital leaders and staff.

BACKGROUND: Public reporting of healthcare-associated infections (HAIs) aims to incentivize improvement in infection prevention. The motivation and mechanisms of public reporting have raised concerns about the reliability of this data, but little is known about the specific concerns of hospital leaders and staff. This study sought to better understand perspectives of individuals in these roles regarding the identification and public reporting of HAIs. METHODS: We conducted interviews with 471 participants including hospitals leaders (eg, administrative and clinical leaders) and hospital staff (eg, physicians and nurses) between 2017 and 2019 across 18 US hospitals. A semistructured interview guide was used to explore perspectives about the use of HAI data within the context of management strategies used to support infection prevention. RESULTS: Interviewees described concerns about public reporting of HAI data, including a lack of trust in the data and inadvertent consequences of its public reporting, as well as specific frustrations related to the identification and accountability for publicly-reported HAIs. CONCLUSION: Concerns and frustrations related to public reporting of HAI data highlight the need for improved guidelines, transparency, and incentives. Efforts to build trust in publicly-reported HAI data can help ensure this information is used effectively to improve infection prevention practices.

Inventory of Patient-Reported Outcome Measures Used in the Non-Operative Care of Scoliosis: A Scoping Review.

It is unclear which patient-reported outcome measures (PROMs) can assess non-operative care for scoliosis. Most existing tools aim to assess the effects of surgery. This scoping review aimed to inventory the PROMs used to assess non-operative scoliosis treatment by population and languages. We searched Medline (OVID) as per COSMIN guidelines. Studies were included if patients were diagnosed with idiopathic scoliosis or adult degenerative scoliosis and used PROMs. Studies without quantitative data or reporting on fewer than 10 participants were excluded. Nine reviewers extracted the PROMs used, the population(s), language(s), and study setting(s). We screened 3724 titles and abstracts. Of these, the full texts of 900 articles were assessed. Data were extracted from 488 studies, in which 145 PROMs were identified across 22 languages and 5 populations (Adolescent Idiopathic Scoliosis, Adult Degenerative Scoliosis, Adult Idiopathic Scoliosis, Adult Spine Deformity, and an Unclear category). Overall, the most used PROMs were the Oswestry Disability Index (ODI, 37.3%), Scoliosis Research Society-22 (SRS-22, 34.8%), and the Short Form-36 (SF-36, 20.1%), but the frequency varied by population. It is now necessary to determine the PROMs that demonstrate the best measurement properties in the non-operative treatment of scoliosis to include in a core set of outcomes.

Exploring the Incorporation of a Novel Cardiotoxicity Mobile Health App Into Care of Patients With Cancer: Qualitative Study of Patient and Provider Perspectives.

BACKGROUND: Cardiotoxicity is a limitation of several cancer therapies and early recognition improves outcomes. Symptom-tracking mobile health (mHealth) apps are feasible and beneficial, but key elements for mHealth symptom-tracking to indicate early signs of cardiotoxicity are unknown. OBJECTIVE: We explored considerations for the design of, and implementation into a large academic medical center, an mHealth symptom-tracking tool for early recognition of cardiotoxicity in patients with cancer after cancer therapy initiation. METHODS: We conducted semistructured interviews of >50% of the providers (oncologists, cardio-oncologists, and radiation oncologists) who manage cancer treatment-related cardiotoxicity in the participating institution (n=11), and either interviews or co-design or both with 6 patients. Data were coded and analyzed using thematic analysis. RESULTS: Providers indicated that there was no existing process to enable early recognition of cardiotoxicity and felt the app could reduce delays in diagnosis and lead to better patient outcomes. Signs and symptoms providers recommended for tracking included chest pain or tightness, shortness of breath, heart racing or palpitations, syncope, lightheadedness, edema, and excessive fatigue. Implementation barriers included determining who would receive symptom reports, ensuring all members of the patient's care team (eg, oncologist, cardiologist, and primary care) were informed of the symptom reports and could collaborate on care plans, and how to best integrate the app data into the electronic health record. Patients (n=6, 100%) agreed that the app would be useful for enhanced symptom capture and education and indicated willingness to use it. CONCLUSIONS: Providers and patients agree that a patient-facing, cancer treatment-related cardiotoxicity symptom-tracking mHealth app would be beneficial. Additional studies evaluating the role of mHealth as a potential strategy for targeted early cardioprotective therapy initiation are needed.

Emergency department patient and physician survey accuracy compared to chart abstraction in patients with acute respiratory illness.

BACKGROUND: High-quality research studies in older adults are needed. Unfortunately, the accuracy of chart review data in older adult patients has been called into question by previous studies. Little is known on this topic in patients with suspected pneumonia, a disease with 500,000 annual older adult U.S. emergency department (ED) visits that presents a diagnostic challenge to ED physicians. The study objective was to compare direct interview and chart abstraction as data sources. METHODS: We present a preplanned secondary analysis of a prospective, observational cohort of ED patients ≥65 years of age with suspected pneumonia in two Midwest EDs. We describe the agreement between chart review and a criterion standard of prospective direct patient survey (symptoms) or direct physician survey (examination findings). Data were collected by chart review and from the patient and treating physician by survey. RESULTS: The larger study enrolled 135 older adults; 134 with complete symptom data and 129 with complete examination data were included in this analysis. Pneumonia symptoms (confusion, malaise, rapid breathing, any cough, new/worse cough, any sputum production, change to sputum) had agreement between patient/legally authorized representative survey and chart review ranging from 47.8% (malaise) to 80.6% (confusion). All examination findings (rales, rhonchi, wheeze) had percent agreement between physician survey and chart review of ≥80%. However, all kappas except wheezing were less than 0.60, indicating weak agreement. CONCLUSIONS: Both patient symptoms and examination findings demonstrated discrepancies between chart review and direct survey with larger discrepancies in symptoms reported. Researchers should consider these potential discrepancies during study design and data interpretation.

Risk of Cytomegalovirus Infection with Post-Transplantation Cyclophosphamide in Haploidentical and HLA-Matched Unrelated Donor Transplantation.

Post-transplantation cyclophosphamide (PTCy) is effective for graft-versus-host disease (GVHD) prevention but is associated with an increased risk of cytomegalovirus (CMV) infection. The effect of PTCy on CMV infection in matched unrelated donor (MUD) hematopoietic stem cell transplantation (HSCT) is unclear, and whether there is any difference in CMV risk with 1 or 2 doses of PTCy is not well established. This study aimed to compare the incidence of CMV infection and the association between T cell recovery in patients who received PTCy-based GVHD prophylaxis and those who received non-PTCy-based GVHD prophylaxis. We conducted a retrospective study to compare the risk of CMV infection in 3 cohorts: cohort A (n = 94), matched related donor (MRD)/MUD HSCT with calcineurin inhibitor-based GVHD prophylaxis; cohort B (n = 103), MRD/MUD HSCT with 1 dose of PTCy, tacrolimus, and mycophenolate mofetil (MMF); and cohort C (n = 28), haploidentical donor HSCT with 2 doses of PTCy, tacrolimus, and MMF. The day +100 cumulative incidence of CMV infection was 29% for cohort A, 39% for cohort B, and 61% for cohort C (P = .009), with no difference among the cohorts in the duration of viremia (P = .46). CD3+ and CD4+ T cell counts were significantly higher in cohort A at day +30 but not at days +60 and +90. Nonrelapse mortality (NRM) trended higher and relapse was significantly lower with PTCy. There was no difference in survival among the 3 cohorts. There is an increased risk of early CMV infection in patients receiving PTCy irrespective of donor type and number of PTCy doses compared with those not receiving PTCy. Strategies focusing on CMV prevention in PTCy recipients to mitigate the risk of NRM may lead to improved long-term outcomes.

Review of applied health informatics courses in a multidisciplinary biomedical informatics department.

Introduction: Applied health informatics infrastructure is a requirement for learning health systems and it is imperative that we train a workforce that can support this infrastructure. Our department offers courses in several interdisciplinary programs with topics ranging from bioinformatics to population health informatics. Due to changes in the field and our faculty members, we sought to assess our courses relevant to applied health informatics. Methods: In this paper, we discuss the three-phase evaluation of our program and include the survey we developed to identify the skills and knowledge base of our faculty. Results: We show how this assessment allowed us to identify gaps and develop strategies for program expansion. Conclusions: A focus on workforce development can help to guide and focus curricular review in an interdisciplinary graduate program.