Phenotypic distinctions in depression and anxiety: a comparative analysis of comorbid and isolated cases

BACKGROUND

Anxiety and depression are frequently comorbid yet phenotypically distinct. This study identifies differences in the clinically observable phenome across a wide variety of physical and mental disorders comparing patients with diagnoses of depression without anxiety, anxiety without depression, or both depression and anxiety.

METHODS

Using electronic health records for 14 994 participants with depression and/or anxiety in the Mayo Clinic Biobank, a phenotype-based phenome-wide association study (Phe2WAS) was performed to test for differences between these groups across a broad range of clinical diagnoses observed in the electronic health record. Additional analyses were performed to determine the temporal sequencing of diagnoses.

RESULTS

Compared to patients diagnosed only with anxiety, those diagnosed only with depression were more likely to have diagnoses of obesity (OR 1.75; p = 1 × 10-27), sleep apnea (OR 1.71; p = 1 × 10-22), and type II diabetes (OR 1.74; p = 9 × 10-18). Compared to those diagnosed only with depression, those diagnosed only with anxiety were more likely to have diagnoses of palpitations (OR 1.91; p = 2 × 10-25), benign skin neoplasms (OR 1.61; p = 2 × 10-17), and cardiac dysrhythmias (OR 1.45; p = 2 × 10-12). Patients with comorbid depression and anxiety were more likely to have diagnoses of other mental health disorders, substance use disorders, sleep problems, and gastroesophageal reflux relative to isolated depression.

CONCLUSIONS

While depression and anxiety are closely related, this study suggests that phenotypic distinctions exist between depression and anxiety. Improving phenotypic characterization within the broad categories of depression and anxiety could improve the clinical assessment of depression and anxiety.

The genetic contribution to the comorbidity of depression and anxiety: a multi-site electronic health records study of almost 178 000 people

BACKGROUND

Depression and anxiety are common and highly comorbid, and their comorbidity is associated with poorer outcomes posing clinical and public health concerns. We evaluated the polygenic contribution to comorbid depression and anxiety, and to each in isolation.

METHODS

Diagnostic codes were extracted from electronic health records for four biobanks [N = 177 865 including 138 632 European (77.9%), 25 612 African (14.4%), and 13 621 Hispanic (7.7%) ancestry participants]. The outcome was a four-level variable representing the depression/anxiety diagnosis group: neither, depression-only, anxiety-only, and comorbid. Multinomial regression was used to test for association of depression and anxiety polygenic risk scores (PRSs) with the outcome while adjusting for principal components of ancestry.

RESULTS

In total, 132 960 patients had neither diagnosis (74.8%), 16 092 depression-only (9.0%), 13 098 anxiety-only (7.4%), and 16 584 comorbid (9.3%). In the European meta-analysis across biobanks, both PRSs were higher in each diagnosis group compared to controls. Notably, depression-PRS (OR 1.20 per s.d. increase in PRS; 95% CI 1.18-1.23) and anxiety-PRS (OR 1.07; 95% CI 1.05-1.09) had the largest effect when the comorbid group was compared with controls. Furthermore, the depression-PRS was significantly higher in the comorbid group than the depression-only group (OR 1.09; 95% CI 1.06-1.12) and the anxiety-only group (OR 1.15; 95% CI 1.11-1.19) and was significantly higher in the depression-only group than the anxiety-only group (OR 1.06; 95% CI 1.02-1.09), showing a genetic risk gradient across the conditions and the comorbidity.

CONCLUSIONS

This study suggests that depression and anxiety have partially independent genetic liabilities and the genetic vulnerabilities to depression and anxiety make distinct contributions to comorbid depression and anxiety.

The Role of Individual-Level Socioeconomic Status on Nursing Home Placement Accounting for Neighborhood Characteristics

OBJECTIVE

Independent living is desirable for many older adults. Although several factors such as physical and cognitive functions are important predictors for nursing home placement (NHP), it is also reported that socioeconomic status (SES) affects the risk of NHP. In this study, we aimed to examine whether an individual-level measure of SES is associated with the risk of NHP after accounting for neighborhood characteristics.

DESIGN

A population-based study (Olmsted County, Minnesota, USA).

SETTING AND PARTICIPANTS

Older adults (age 65+ years) with no prior history of NHP.

METHODS

Electronic health records (EHR) were used to identify individuals with any NHP between April 1, 2012 (baseline date) and April 30, 2019. Association between the (HOUsing-based index of SocioEconomic Status (HOUSES) index, an individual-level SES measure based on housing characteristics of current residence, and risk of NHP was tested using random effects Cox proportional hazard model adjusting for area deprivation index (ADI), an aggregated SES measure that captures neighborhood characteristics, and other pertinent confounders such as age and chronic disease burden.

RESULTS

Among 15,031 older adults, 3341 (22.2%) experienced NHP during follow-up period (median: 7.1 years). At baseline date, median age was 73 years old with 55% female persons, 91% non-Hispanic Whites, and median number of chronic conditions of 4. Accounting for pertinent confounders, the HOUSES index was strongly associated with risk of NHP (hazard ratio 1.89; 95% confidence interval 1.66‒2.15 for comparing the lowest vs highest quartiles), which was not influenced by further accounting for ADI.

CONCLUSIONS AND IMPLICATIONS

This study demonstrates that an individual-level SES measure capturing current individual-specific socioeconomic circumstances plays a significant role for predicting NHP independent of neighborhood characteristics where they reside. This study suggests that older adults who are at higher risk of NHP can be identified by utilizing the HOUSES index and potential individual-level intervention strategies can be applied to reduce the risk for those with higher risk.

Predictors of Metformin Failure: Repurposing Electronic Health Record Data to Identify High-Risk Patients

CONTEXT

Metformin is the first-line drug for treating diabetes but has a high failure rate.

OBJECTIVE

To identify demographic and clinical factors available in the electronic health record (EHR) that predict metformin failure.

METHODS

A cohort of patients with at least 1 abnormal diabetes screening test that initiated metformin was identified at 3 sites (Arizona, Mississippi, and Minnesota). We identified 22 047 metformin initiators (48% female, mean age of 57 ± 14 years) including 2141 African Americans, 440 Asians, 962 Other/Multiracial, 1539 Hispanics, and 16 764 non-Hispanic White people. We defined metformin failure as either the lack of a target glycated hemoglobin (HbA1c) (<7%) within 18 months of index or the start of dual therapy. We used tree-based extreme gradient boosting (XGBoost) models to assess overall risk prediction performance and relative contribution of individual factors when using EHR data for risk of metformin failure.

RESULTS

In this large diverse population, we observed a high rate of metformin failure (43%). The XGBoost model that included baseline HbA1c, age, sex, and race/ethnicity corresponded to high discrimination performance (C-index of 0.731; 95% CI 0.722, 0.740) for risk of metformin failure. Baseline HbA1c corresponded to the largest feature performance with higher levels associated with metformin failure. The addition of other clinical factors improved model performance (0.745; 95% CI 0.737, 0.754, P < .0001).

CONCLUSION

Baseline HbA1c was the strongest predictor of metformin failure and additional factors substantially improved performance suggesting that routinely available clinical data could be used to identify patients at high risk of metformin failure who might benefit from closer monitoring and earlier treatment intensification.

The importance of social activity to risk of major depression in older adults

BACKGROUND

Several social determinants of health (SDoH) have been associated with the onset of major depressive disorder (MDD). However, prior studies largely focused on individual SDoH and thus less is known about the relative importance (RI) of SDoH variables, especially in older adults. Given that risk factors for MDD may differ across the lifespan, we aimed to identify the SDoH that was most strongly related to newly diagnosed MDD in a cohort of older adults.

METHODS

We used self-reported health-related survey data from 41 174 older adults (50-89 years, median age = 67 years) who participated in the Mayo Clinic Biobank, and linked ICD codes for MDD in the participants' electronic health records. Participants with a history of clinically documented or self-reported MDD prior to survey completion were excluded from analysis (N = 10 938, 27%). We used Cox proportional hazards models with a gradient boosting machine approach to quantify the RI of 30 pre-selected SDoH variables on the risk of future MDD diagnosis.

RESULTS

Following biobank enrollment, 2073 older participants were diagnosed with MDD during the follow-up period (median duration = 6.7 years). The most influential SDoH was perceived level of social activity (RI = 0.17). Lower level of social activity was associated with a higher risk of MDD [hazard ratio = 2.27 (95% CI 2.00-2.50) for highest v. lowest level].

CONCLUSION

Across a range of SDoH variables, perceived level of social activity is most strongly related to MDD in older adults. Monitoring changes in the level of social activity may help identify older adults at an increased risk of MDD.

Comorbidity and healthcare utilization in patients with treatment resistant depression: A large-scale retrospective cohort analysis using electronic health records

BACKGROUND

Medical comorbidity and healthcare utilization in patients with treatment resistant depression (TRD) is usually reported in convenience samples, making estimates unreliable. There is only limited large-scale clinical research on comorbidities and healthcare utilization in TRD patients.

METHODS

Electronic Health Record data from over 3.3 million patients from the INSIGHT Clinical Research Network in New York City was used to define TRD as initiation of a third antidepressant regimen in a 12-month period among patients diagnosed with major depressive disorder (MDD). Age and sex matched TRD and non-TRD MDD patients were compared for anxiety disorder, 27 comorbid medical conditions, and healthcare utilization.

RESULTS

Out of 30,218 individuals diagnosed with MDD, 15.2 % of patients met the criteria for TRD (n = 4605). Compared to MDD patients without TRD, the TRD patients had higher rates of anxiety disorder and physical comorbidities. They also had higher odds of ischemic heart disease (OR = 1.38), stroke/transient ischemic attack (OR = 1.57), chronic kidney diseases (OR = 1.53), arthritis (OR = 1.52), hip/pelvic fractures (OR = 2.14), and cancers (OR = 1.41). As compared to non-TRD MDD, TRD patients had higher rates of emergency room visits, and inpatient stays. In relation to patients without MDD, both TRD and non-TRD MDD patients had significantly higher levels of anxiety disorder and physical comorbidities.

LIMITATIONS

The INSIGHT-CRN data lack information on depression severity and medication adherence.

CONCLUSIONS

TRD patients compared to non-TRD MDD patients have a substantially higher prevalence of various psychiatric and medical comorbidities and higher health care utilization. These findings highlight the challenges of developing interventions and care coordination strategies to meet the complex clinical needs of TRD patients.

Incidence of Respiratory Syncytial Virus Infection in Older Adults Before and During the COVID-19 Pandemic

Importance

Little is known about the burden and outcomes of respiratory syncytial virus (RSV)-positive acute respiratory infection (ARI) in community-dwelling older adults.

Objective

To assess the incidence of RSV-positive ARI before and during the COVID-19 pandemic, and to assess outcomes for RSV-positive ARI in older adults.

Design, Setting, and Participants

This was a community-based cohort study of adults residing in southeast Minnesota that followed up with 2325 adults aged 50 years or older for 2 RSV seasons (2019-2021) to assess the incidence of RSV-positive ARI. The study assessed outcomes at 2 to 4 weeks, 6 to 7 months, and 12 to 13 months after RSV-positive ARI.

Exposure

RSV-positive and -negative ARI.

Main Outcomes and Measures

RSV status was the main study outcome. Incidence and attack rates of RSV-positive ARI were calculated during each RSV season, including before (October 2019 to April 2020) and during (October 2020 to April 2021) COVID-19 pandemic, and further calculated during non-RSV season (May to September 2021) for assessing impact of COVID-19. The self-reported quality of life (QOL) by Short-Form Health Survey-36 (SF-36) and physical functional measures (eg, 6-minute walk and spirometry) at each time point was assessed.

Results

In this study of 2325 participants, the median (range) age of study participants was 67 (50-98) years, 1380 (59%) were female, and 2240 (96%) were non-Hispanic White individuals. The prepandemic incidence rate of RSV-positive ARI was 48.6 (95% CI, 36.9-62.9) per 1000 person-years with a 2.50% (95% CI, 1.90%-3.21%) attack rate. No RSV-positive ARI case was identified during the COVID-19 pandemic RSV season. Incidence of 10.2 (95% CI, 4.1-21.1) per 1000 person-years and attack rate of 0.42%; (95% CI, 0.17%-0.86%) were observed during the summer of 2021. Based on prepandemic RSV season results, participants with RSV-positive ARI (vs matched RSV-negative ARI) reported significantly lower QOL adjusted mean difference (limitations due to physical health, -16.7 [95% CI, -31.8 to -1.8]; fatigue, -8.4 [95% CI, -14.3 to -2.4]; and difficulty in social functioning, -11.9 [95% CI, -19.8 to -4.0] within 2 to 4 weeks after RSV-positive ARI [ie, short-term outcome]). Compared with participants with RSV-negative ARI, those with RSV-positive ARI also had lower QOL (fatigue: -4.0 [95% CI, -8.5 to -1.3]; difficulty in social functioning, -5.8 [95% CI, -10.3 to -1.3]; and limitation due to emotional problem, -7.0 [95% CI, -12.7 to -1.3] at 6 to 7 months after RSV-positive ARI [intermediate-term outcome]; fatigue, -4.4 [95% CI, -7.3 to -1.5]; difficulty in social functioning, -5.2 [95% CI, -8.7 to -1.7] and limitation due to emotional problem, -5.7 [95% CI, -10.7 to -0.6] at 12-13 months after RSV-positive ARI [ie, long-term outcomes]) independent of age, sex, race and/or ethnicity, socioeconomic status, and high-risk comorbidities.

Conclusions and Relevance

In this cohort study, the burden of RSV-positive ARI in older adults during the pre-COVID-19 period was substantial. After a reduction of RSV-positive ARI incidence from October 2020 to April 2021, RSV-positive ARI re-emerged during the summer of 2021. RSV-positive ARI was associated with significant long-term lower QOL beyond the short-term lower QOL in older adults.

Paired Indoor and Outdoor Nitrogen Dioxide Associated With Childhood Asthma Outcomes in a Mixed Rural-Urban Setting: A Feasibility Study

INTRODUCTION

Nitrogen dioxide (NO₂) is known to be a trigger for asthma exacerbation. However, little is known about the role of seasonal variation in indoor and outdoor NO₂ levels in childhood asthma in a mixed rural-urban setting of North America.

METHODS

This prospective cohort study, as a feasibility study, included 62 families with children (5-17 years) that had diagnosed persistent asthma residing in Olmsted County, Minnesota. Indoor and outdoor NO₂ concentrations were measured using passive air samples over 2 weeks in winter and 2 weeks in summer. We assessed seasonal variation in NO₂ levels in urban and rural residential areas and the association with asthma control status collected from participants' asthma diaries during the study period.

RESULTS

Outdoor NO₂ levels were lower (median: 2.4 parts per billion (ppb) in summer, 3.9 ppb in winter) than the Environmental Protection Agency (EPA) annual standard (53 ppb). In winter, a higher level of outdoor NO₂ was significantly associated with urban residential living area (P = .014) and lower socioeconomic status (SES) (P = .027). For both seasons, indoor NO₂ was significantly higher (P < .05) in rural versus urban areas and in homes with gas versus electric stoves (P < .05). Asthma control status was not associated with level of indoor or outdoor NO₂ in this cohort.

CONCLUSIONS

NO₂ levels were low in this mixed rural-urban community and not associated with asthma control status in this small feasibility study. Further research with a larger sample size is warranted for defining a lower threshold of NO₂ concentration with health effect on asthma in mixed rural-urban settings.

Application of Innovative Subject Recruitment System for Batch Enrollment: A Pilot Study

INTRODUCTION

Using a digital process that leverages electronic health records (EHRs) can ease many of the challenges presented by the traditional enrollment process for clinical trials. We tested if automated batch enrollment using a technology-enabled subject recruitment system (TESRS) enhances recruitment while preserving representation of research subjects for the study population in our study setting.

METHODS

An ongoing community-based prospective adult cohort study was used to randomize 600 subjects who were eligible by age and residential address to TESRS (n = 300) and standard mailing method (n = 300), respectively, for 3 months. Then, TESRS was initiated and included automatic identification of patients' preference for being contacted (online patient portal vs postal mail) from EHRs and automatic sending out of invitation letters followed by completion of a short online survey for checking eligibility and the digital consent process if eligible. We compared (1) median time to consent from invitation sent out per subject and total subjects recruited after a 3-month recruitment period, (2) the estimated study staff's time, and (3) representation of sociodemographic characteristics (e.g., age, sex, race, SES measured by HOUSES index, and rural residence) between subjects recruited via TESRS and those via traditional mailing methods.

RESULTS

Median age of randomized subjects (n = 600) was 63 years with 52.0% female and 89.2% non-Hispanic White. Over a 3-month period, results showed consent rate via TESRS was 13% (39/297) similar to 11% (31/295) via standard mailing. However, recruitment was significantly faster with the TESRS approach (median 7 vs 26 days) given the study staff's effort. Study staff's time saved by using TESRS compared to standard mailing approach was estimated at 40 min per subject (equivalent to 200 h for 300 subjects). No significant differences in characteristics of research subjects from the study population were found.

CONCLUSION

Our study demonstrated the utility of TESRS as a subject recruitment digital technology which significantly enhanced the recruitment effort while reducing the study staff burden of recruitment while maintaining the consistency of characteristics of recruited subjects. The strategy and support for implementing and testing TESRS in other study settings should be considered.

Social connectedness as a determinant of mental health: A scoping review

Public health and epidemiologic research have established that social connectedness promotes overall health. Yet there have been no recent reviews of findings from research examining social connectedness as a determinant of mental health. The goal of this review was to evaluate recent longitudinal research probing the effects of social connectedness on depression and anxiety symptoms and diagnoses in the general population. A scoping review was performed of PubMed and PsychInfo databases from January 2015 to December 2021 following PRISMA-ScR guidelines using a defined search strategy. The search yielded 66 unique studies. In research with other than pregnant women, 83% (19 of 23) studies reported that social support benefited symptoms of depression with the remaining 17% (5 of 23) reporting minimal or no evidence that lower levels of social support predict depression at follow-up. In research with pregnant women, 83% (24 of 29 studies) found that low social support increased postpartum depressive symptoms. Among 8 of 9 studies that focused on loneliness, feeling lonely at baseline was related to adverse outcomes at follow-up including higher risks of major depressive disorder, depressive symptom severity, generalized anxiety disorder, and lower levels of physical activity. In 5 of 8 reports, smaller social network size predicted depressive symptoms or disorder at follow-up. In summary, most recent relevant longitudinal studies have demonstrated that social connectedness protects adults in the general population from depressive symptoms and disorders. The results, which were largely consistent across settings, exposure measures, and populations, support efforts to improve clinical detection of high-risk patients, including adults with low social support and elevated loneliness.

Atopic asthma as a potentially significant but unrecognized risk factor for Kawasaki disease in children

OBJECTIVES

Childhood asthma is known to be associated with risks of both respiratory and non-respiratory infections. Little is known about the relationship between asthma and the risk of Kawasaki disease (KD). We assessed associations of asthma status and asthma phenotype (e.g. atopic asthma) with KD.

METHODS

We performed a population-based retrospective case-control study, using KD cases between January 1, 1979, and December 31, 2016, and two matched controls per case. KD cases were defined by the American Heart Association diagnostic criteria. Asthma status prior to KD (or control) index dates was ascertained by the two asthma criteria, Predetermined Asthma Criteria (PAC) and Asthma Predictive Index (API, a surrogate phenotype of atopic asthma). We assessed whether 4 phenotypes (both PAC + and API+; PAC + only; API + only, and non-asthmatics) were associated with KD.

RESULTS

There were 124 KD cases during the study period. The group having both PAC + and API + was significantly associated with the increased odds of KD, compared to non-asthmatics (odds ratio [OR] 4.3; 95% CI: 1.3 - 14.3). While asthma defined by PAC was not associated with KD, asthma defined by PAC positive with eosinophilia (≥4%) was significantly associated with the increased odds of KD (OR: 6.7; 95% CI: 1.6 - 28.6) compared to non-asthmatics. Asthma status defined by API was associated with KD (OR = 4.7; 95% CI: 1.4-15.1).

CONCLUSIONS

Atopic asthma may be associated with increased odds of KD. Further prospective studies are needed to determine biological mechanisms underlying the association between atopic asthma and increased odds of KD.

Influenza infection is not associated with phenotypical frailty in older patients, a prospective cohort study

Background and Aims

Influenza is a challenging infectious illness for older adults. It is not completely clear whether influenza is associated with frailty or functional decline. We sought to determine the association between incident influenza infection and frailty and prefrailty in community patients over 50 years of age. We also investigated the association between influenza vaccination and frailty and prefrailty as a secondary aim.

Methods

This was a prospective community cohort study from October 2019 to November 2020 in participants over 50 years. The primary outcome was the development of frailty as defined by three of five frailty criteria (slow gait speed, low grip strength, 5% weight loss, low energy, and low physical functioning). The primary predictor was a positive polymerase chain reaction (PCR) for influenza infection. Influenza vaccination was based on electronic health record reviewing 1 year before enrollment. We reported the relationship between influenza and frailty by calculating odds ratios (OR) with 95% confidence intervals (CI) after adjustment for age, sex, socioeconomic status, Charlson Comorbidity Index (CCI), influenza vaccine, and previous self‐rated frailty from multinomial logistic regression model comparing frail and prefrail to nonfrail subjects.

Results

In 1135 participants, the median age was 67 years (interquartile range  60−74), with 41% men. Eighty‐one participants had PCR‐confirmed influenza (7.1%). Frailty was not associated with influenza, with an OR of 0.50 (95% CI 0.17−1.43) for frail participants compared to nonfrail participants. Influenza vaccination is associated with frailty, with an OR of 1.69 (95% CI 1.09−2.63) for frail compared to nonfrail. Frailty was associated with a higher CCI with an OR of 1.52 (95% CI 1.31−1.76).

Conclusion

We did not find a relationship between influenza infection and frailty. We found higher vaccination rates in participants with frailty compared to nonfrail participants While influenza was not associated with frailty, future work may involve longer follow‐up.

Assessing socioeconomic bias in machine learning algorithms in health care: a case study of the HOUSES index

OBJECTIVE

Artificial intelligence (AI) models may propagate harmful biases in performance and hence negatively affect the underserved. We aimed to assess the degree to which data quality of electronic health records (EHRs) affected by inequities related to low socioeconomic status (SES), results in differential performance of AI models across SES.

MATERIALS AND METHODS

This study utilized existing machine learning models for predicting asthma exacerbation in children with asthma. We compared balanced error rate (BER) against different SES levels measured by HOUsing-based SocioEconomic Status measure (HOUSES) index. As a possible mechanism for differential performance, we also compared incompleteness of EHR information relevant to asthma care by SES.

RESULTS

Asthmatic children with lower SES had larger BER than those with higher SES (eg, ratio = 1.35 for HOUSES Q1 vs Q2-Q4) and had a higher proportion of missing information relevant to asthma care (eg, 41% vs 24% for missing asthma severity and 12% vs 9.8% for undiagnosed asthma despite meeting asthma criteria).

DISCUSSION

Our study suggests that lower SES is associated with worse predictive model performance. It also highlights the potential role of incomplete EHR data in this differential performance and suggests a way to mitigate this bias.

CONCLUSION

The HOUSES index allows AI researchers to assess bias in predictive model performance by SES. Although our case study was based on a small sample size and a single-site study, the study results highlight a potential strategy for identifying bias by using an innovative SES measure.

Artificial Intelligence Assesses Clinicians' Adherence to Asthma Guidelines Using Electronic Health Records

BACKGROUND

Clinicians' asthma guideline adherence in asthma care is suboptimal. The effort to improve adherence can be enhanced by assessing and monitoring clinicians' adherence to guidelines reflected in electronic health records (EHRs), which require costly manual chart review because many care elements cannot be identified by structured data.

OBJECTIVE

This study was designed to demonstrate the feasibility of an artificial intelligence tool using natural language processing (NLP) leveraging the free text EHRs of pediatric patients to extract key components of the 2007 National Asthma Education and Prevention Program guidelines.

METHODS

This is a retrospective cross-sectional study using a birth cohort with a diagnosis of asthma at Mayo Clinic between 2003 and 2016. We used 1,039 clinical notes with an asthma diagnosis from a random sample of 300 patients. Rule-based NLP algorithms were developed to identify asthma guideline-congruent elements by examining care description in EHR free text.

RESULTS

Natural language processing algorithms demonstrated a sensitivity (0.82-1.0), specificity (0.95-1.0), positive predictive value (0.86-1.0), and negative predictive value (0.92-1.0) against manual chart review for asthma guideline-congruent elements. Assessing medication compliance and inhaler technique assessment were the most challenging elements to assess because of the complexity and wide variety of descriptions.

CONCLUSIONS

Natural language processing technologies may enable the automated assessment of clinicians' documentation in EHRs regarding adherence to asthma guidelines and can be a useful population management and research tool to assess and monitor asthma care quality. Multisite studies with a larger sample size are needed to assess the generalizability of these NLP algorithms.

Risk of pneumonia in asthmatic children using inhaled corticosteroids: a nested case-control study in a birth cohort

BACKGROUND

Inhaled corticosteroids (ICSs) are important in asthma management, but there are concerns regarding associated risk of pneumonia. While studies in asthmatic adults have shown inconsistent results, this risk in asthmatic children is unclear.

OBJECTIVE

Our aim was to determine the association of ICS use with pneumonia risk in asthmatic children.

METHODS

A nested case-control study was performed in the Mayo Clinic Birth Cohort. Asthmatic children (<18 years) with a physician diagnosis of asthma were identified from electronic medical records of children born at Mayo Clinic from 1997 to 2016 and followed until 31 December 2017. Pneumonia cases defined by Infectious Disease Society of America were 1:1 matched with controls without pneumonia by age, sex and asthma index date. Exposure was defined as ICS prescription at least 90 days prior to pneumonia. Associations of ICS use, type and dose (low, medium and high) with pneumonia risk were analysed using conditional logistic regression.

RESULTS

Of the 2108 asthmatic children eligible for the study (70% mild intermittent and 30% persistent asthma), 312 children developed pneumonia during the study period. ICS use overall was not associated with risk of pneumonia (adjusted OR: 0.94, 95% CI: 0.62 to 1.41). Poorly controlled asthma was significantly associated with the risk of pneumonia (OR: 2.03, 95% CI: 1.35 to 3.05; p<0.001). No ICS type or dose was associated with risk of pneumonia.

CONCLUSION

ICS use in asthmatic children was not associated with risk of pneumonia but poorly controlled asthma was. Future asthma studies may need to include pneumonia as a potential outcome of asthma management.

Association of First Employment Characteristics and Hospitalization in the Mayo Clinic Biobank

Work history is associated with long term health outcomes We hypothesize that characteristics of the first work experience, such as age at first job and length of work (hereafter job) are associated with future risk of hospitalization. We further hypothesize that the length of work will be associated with hospitalization. We conducted a survey of adults >60 years using a nested case-control approach within the Mayo Clinic Biobank. We collected job related variables including age at job start, reason for ending, and length of time. To test associations between each variable and hospitalization, we used age and gender adjusted logistic regression models. Our study included 4,024 subjects: 1,801 cases and 2223 controls. The mean age at time of match was 77.3 years (SD 7.2 years) with 49.2% males. Older age at the first full-time job was associated with lower chance of hospitalization later in life (OR=0.81 [0.67, 0.97] for those who started the job over 22 compared to those started at 18 or less). Cases were more likely to have stopped working because of illness (OR=2.04 [95% CI 1.29,3.27]). Cases were less likely to have stopped working because of retirement (OR=0.82 [95% CI: 0.72, 0.93]). We found cases were employed with a slightly shorter time (20.5 yrs. (SD 16.6)) compared to controls (21.8 yrs. (SD 16.3)) (p=0.005). Cases started work earlier and stopped work more frequently because of illness/disability compared to controls. This could reflect educational attainment in controls. This study highlights work history as potential predictor of future hospitalization.

Extracting social determinants of health from electronic health records using natural language processing: a systematic review

OBJECTIVE

Social determinants of health (SDoH) are nonclinical dispositions that impact patient health risks and clinical outcomes. Leveraging SDoH in clinical decision-making can potentially improve diagnosis, treatment planning, and patient outcomes. Despite increased interest in capturing SDoH in electronic health records (EHRs), such information is typically locked in unstructured clinical notes. Natural language processing (NLP) is the key technology to extract SDoH information from clinical text and expand its utility in patient care and research. This article presents a systematic review of the state-of-the-art NLP approaches and tools that focus on identifying and extracting SDoH data from unstructured clinical text in EHRs.

MATERIALS AND METHODS

A broad literature search was conducted in February 2021 using 3 scholarly databases (ACL Anthology, PubMed, and Scopus) following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 6402 publications were initially identified, and after applying the study inclusion criteria, 82 publications were selected for the final review.

RESULTS

Smoking status (n = 27), substance use (n = 21), homelessness (n = 20), and alcohol use (n = 15) are the most frequently studied SDoH categories. Homelessness (n = 7) and other less-studied SDoH (eg, education, financial problems, social isolation and support, family problems) are mostly identified using rule-based approaches. In contrast, machine learning approaches are popular for identifying smoking status (n = 13), substance use (n = 9), and alcohol use (n = 9).

CONCLUSION

NLP offers significant potential to extract SDoH data from narrative clinical notes, which in turn can aid in the development of screening tools, risk prediction models, and clinical decision support systems.

Establishing an expert consensus for the operational definitions of asthma-associated infectious and inflammatory multimorbidities for computational algorithms through a modified Delphi technique

BACKGROUND

A subgroup of patients with asthma has been reported to have an increased risk for asthma-associated infectious and inflammatory multimorbidities (AIMs). To systematically investigate the association of asthma with AIMs using a large patient cohort, it is desired to leverage a broad range of electronic health record (EHR) data sources to automatically identify AIMs accurately and efficiently.

METHODS

We established an expert consensus for an operational definition for each AIM from EHR through a modified Delphi technique. A series of questions about the operational definition of 19 AIMS (11 infectious diseases and 8 inflammatory diseases) was generated by a core team of experts who considered feasibility, balance between sensitivity and specificity, and generalizability. Eight internal and 5 external expert panelists were invited to individually complete a series of online questionnaires and provide judgement and feedback throughout three sequential internal rounds and two external rounds. Panelists' responses were collected, descriptive statistics tabulated, and results reported back to the entire group. Following each round the core team of experts made iterative edits to the operational definitions until a moderate (≥ 60%) or strong (≥ 80%) level of consensus among the panel was achieved.

RESULTS

Response rates for each Delphi round were 100% in all 5 rounds with the achievement of the following consensus levels: (1) Internal panel consensus: 100% for 8 definitions, 88% for 10 definitions, and 75% for 1 definition, (2) External panel consensus: 100% for 12 definitions and 80% for 7 definitions.

CONCLUSIONS

The final operational definitions of AIMs established through a modified Delphi technique can serve as a foundation for developing computational algorithms to automatically identify AIMs from EHRs to enable large scale research studies on patient's multimorbidities associated with asthma.

Risk, Mechanisms and Implications of Asthma-Associated Infectious and Inflammatory Multimorbidities (AIMs) among Individuals With Asthma: a Systematic Review and a Case Study

Our prior work and the work of others have demonstrated that asthma increases the risk of a broad range of both respiratory (e.g., pneumonia and pertussis) and non-respiratory (e.g., zoster and appendicitis) infectious diseases as well as inflammatory diseases (e.g., celiac disease and myocardial infarction [MI]), suggesting the systemic disease nature of asthma and its impact beyond the airways. We call these conditions asthma-associated infectious and inflammatory multimorbidities (AIMs). At present, little is known about why some people with asthma are at high-risk of AIMs, and others are not, to the extent to which controlling asthma reduces the risk of AIMs and which specific therapies mitigate the risk of AIMs. These questions represent a significant knowledge gap in asthma research and unmet needs in asthma care, because there are no guidelines addressing the identification and management of AIMs. This is a systematic review on the association of asthma with the risk of AIMs and a case study to highlight that 1) AIMs are relatively under-recognized conditions, but pose major health threats to people with asthma; 2) AIMs provide insights into immunological and clinical features of asthma as a systemic inflammatory disease beyond a solely chronic airway disease; and 3) it is time to recognize AIMs as a distinctive asthma phenotype in order to advance asthma research and improve asthma care. An improved understanding of AIMs and their underlying mechanisms will bring valuable and new perspectives improving the practice, research, and public health related to asthma.

Artificial intelligence-assisted clinical decision support for childhood asthma management: A randomized clinical trial

RATIONALE

Clinical decision support (CDS) tools leveraging electronic health records (EHRs) have been an approach for addressing challenges in asthma care but remain under-studied through clinical trials.

OBJECTIVES

To assess the effectiveness and efficiency of Asthma-Guidance and Prediction System (A-GPS), an Artificial Intelligence (AI)-assisted CDS tool, in optimizing asthma management through a randomized clinical trial (RCT).

METHODS

This was a single-center pragmatic RCT with a stratified randomization design conducted for one year in the primary care pediatric practice of the Mayo Clinic, MN. Children (<18 years) diagnosed with asthma receiving care at the study site were enrolled along with their 42 primary care providers. Study subjects were stratified into three strata (based on asthma severity, asthma care status, and asthma diagnosis) and were blinded to the assigned groups.

MEASUREMENTS

Intervention was a quarterly A-GPS report to clinicians including relevant clinical information for asthma management from EHRs and machine learning-based prediction for risk of asthma exacerbation (AE). Primary endpoint was the occurrence of AE within 1 year and secondary outcomes included time required for clinicians to review EHRs for asthma management.

MAIN RESULTS

Out of 555 participants invited to the study, 184 consented for the study and were randomized (90 in intervention and 94 in control group). Median age of 184 participants was 8.5 years. While the proportion of children with AE in both groups decreased from the baseline (P = 0.042), there was no difference in AE frequency between the two groups (12% for the intervention group vs. 15% for the control group, Odds Ratio: 0.82; 95%CI 0.374-1.96; P = 0.626) during the study period. For the secondary end points, A-GPS intervention, however, significantly reduced time for reviewing EHRs for asthma management of each participant (median: 3.5 min, IQR: 2-5), compared to usual care without A-GPS (median: 11.3 min, IQR: 6.3-15); p<0.001). Mean health care costs with 95%CI of children during the trial (compared to before the trial) in the intervention group were lower than those in the control group (-$1,036 [-$2177, $44] for the intervention group vs. +$80 [-$841, $1000] for the control group), though there was no significant difference (p = 0.12). Among those who experienced the first AE during the study period (n = 25), those in the intervention group had timelier follow up by the clinical care team compared to those in the control group but no significant difference was found (HR = 1.93; 95% CI: 0.82-1.45, P = 0.10). There was no difference in the proportion of duration when patients had well-controlled asthma during the study period between the intervention and the control groups.

CONCLUSIONS

While A-GPS-based intervention showed similar reduction in AE events to usual care, it might reduce clinicians' burden for EHRs review resulting in efficient asthma management. A larger RCT is needed for further studying the findings.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT02865967.

Role of Geographic Risk Factors in COVID-19 Epidemiology: Longitudinal Geospatial Analysis

Objective

To perform a geospatial and temporal trend analysis for coronavirus disease 2019 (COVID-19) in a Midwest community to identify and characterize hot spots for COVID-19.

Participants and Methods

We conducted a population-based longitudinal surveillance assessing the semimonthly geospatial trends of the prevalence of test confirmed COVID-19 cases in Olmsted County, Minnesota, from March 11, 2020, through October 31, 2020. As urban areas accounted for 84% of the population and 86% of all COVID-19 cases in Olmsted County, MN, we determined hot spots for COVID-19 in urban areas (Rochester and other small cities) of Olmsted County, MN, during the study period by using kernel density analysis with a half-mile bandwidth.

Results

As of October 31, 2020, a total of 37,141 individuals (30%) were tested at least once, of whom 2433 (7%) tested positive. Testing rates among race groups were similar: 29% (black), 30% (Hispanic), 25% (Asian), and 31% (white). Ten urban hot spots accounted for 590 cases at 220 addresses (2.68 cases per address) as compared with 1843 cases at 1292 addresses in areas outside hot spots (1.43 cases per address). Overall, 12% of the population residing in hot spots accounted for 24% of all COVID-19 cases. Hot spots were concentrated in neighborhoods with low-income apartments and mobile home communities. People living in hot spots tended to be minorities and from a lower socioeconomic background.

Conclusion

Geographic and residential risk factors might considerably account for the overall burden of COVID-19 and its associated racial/ethnic and socioeconomic disparities. Results could geospatially guide community outreach efforts (eg, testing/tracing and vaccine rollout) for populations at risk for COVID-19.

Pneumococcal serotype-specific cut-offs based on antibody responses to pneumococcal polysaccharide vaccination in healthy adults

Antibody responses to pneumococcal polysaccharide vaccination are frequently used as a diagnostic tool for humoral immunodeficiencies, part of the larger collection of inborn errors of immunity. Currently, arbitrary criteria, such as a serotype specific titer of >/= 1.3 µg/mL is most often used as a cut-off for interpretation of pneumococcal antibody responses. The magnitude of the antibody response to each of the 23 serotypes in Pneumovax®, and serotype-specific cut-offs in healthy pneumococcal vaccine-naïve adults has not been previously characterized. IgG antibody concentrations were measured prospectively for 23 pneumococcal serotypes pre and 4-6 weeks post-Pneumovax® vaccination in 100 healthy adults, using a multiplex bead-based assay. Antibodies to 19 of 23 serotypes were informative for distinguishing subjects who responded to vaccination, and the serotype threshold was determined to be 9 of 19 serotypes, which characterized an antibody response to pneumococcal vaccination. While this study may facilitate classification of IgG serotype-specific antibody responses post-pneumococcal vaccination in adult patients undergoing diagnostic immunological evaluation for antibody immunodeficiencies or other relevant contexts, additional studies in healthy children and S. pneumoniae protein-conjugate-vaccinated healthy adults will need to be undertaken in the future.

Adherence to Public Health Measures Mitigates the Risk of COVID-19 Infection in Older Adults: A Community-Based Study

OBJECTIVE

To assess the prevalence and characteristics of coronavirus disease 2019 (COVID-19) cases during the reopening period in older adults, given that little is known about the prevalence of COVID-19 after the stay-at-home order was lifted in the United States, nor the actual effects of adherence to recommended public health measures (RPHM) on the risk of COVID-19.

PATIENTS AND METHODS

This was a cross-sectional study nested in a parent prospective cohort study, which followed a population-based sample of 2325 adults 50 years and older residing in southeast Minnesota to assess the incidence of viral infections. Participants were instructed to self-collect both nasal and oropharyngeal swabs, which were tested by reverse transcription polymerase chain reaction-based severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) assay between May 8, 2020, and June, 30, 2020. We assessed the prevalence of COVID-19 cases and characteristics of study subjects.

RESULTS

A total of 1505 eligible subjects participated in the study whose mean age was 68 years, with 885 (59%) women, 32 (2%) racial/ethnic minorities, and 906 (60%) with high-risk conditions for influenza. The prevalence of other Coronaviridae (human coronavirus [HCoV]-229E, HCoV-NL63, and HCoV-OC43) during the 2019 to 2020 flu season was 109 (7%), and none tested positive for SARS-CoV-2. Almost all participants reported adhering to the RPHM (1,488 [99%] for social distancing, 1,438 [96%] for wearing mask in a public space, 1,476 [98%] for hand hygiene, and 1,441 (96%) for staying home mostly). Eighty-six percent of participants resided in a single-family home.

CONCLUSION

We did not identify SARS-COV-2 infection in our study cohort. The combination of participants' behavior in following the RPHM and their living environment may considerably mitigate the risk of COVID-19.

No Association Between Pharmacogenomics Variants and Hospital and Emergency Department Utilization: A Mayo Clinic Biobank Retrospective Study

Background

The use of pharmacogenomics data is increasing in clinical practice. However, it is unknown if pharmacogenomics data can be used more broadly to predict outcomes like hospitalization or emergency department (ED) visit. We aim to determine the association between selected pharmacogenomics phenotypes and hospital utilization outcomes (hospitalization and ED visits).

Methods

This cohort study utilized 10,078 patients from the Mayo Clinic Biobank in the RIGHT protocol with sequence and interpreted phenotypes for 10 selected pharmacogenes including CYP2D6, CYP2C9, CYP2C19, CYP3A5, HLA B 5701, HLA B 5702, HLA B 5801, TPMT, SLCO1B1, and DPYD. The primary outcome was hospitalization with ED visits as a secondary outcome. We used Cox proportional hazards model to test the association between each pharmacogenomics phenotype and the risk of the outcomes.

Results

During the follow-up period (median [in years] = 7.3), 13% (n=1354) and 8% (n=813) of the subjects experienced hospitalization and ED visits, respectively. Compared to subjects who did not experience hospitalization, hospitalized patients were older (median age [in years]: 67 vs 65), poorer self-rated health (15% vs 4.7% for fair/poor), and higher disease burden (median number of chronic conditions: 7 vs 4) at baseline. There was no association of hospitalization with any of the pharmacogenomics phenotypes. The pharmacogenomics phenotypes were not associated with disease burden, a well-established risk factor for hospital utilization outcomes. Similar findings were observed for patients with ED visits during the follow-up period.

Conclusion

We found no association of 10 well-established pharmacogenomics phenotypes with either hospitalization or ED visits in this relatively large biobank population and outside the context of specific drug use related to these genes. Traditional risk factors for hospitalization like age and self-rated health were much more likely to predict hospitalization and/or ED visits than this pharmacogenomics information.

Bi-directional association between depression and HF: An electronic health records-based cohort study

Objective:

To determine whether a bi-directional relationship exists between depression and HF within a single population of individuals receiving primary care services, using longitudinal electronic health records (EHRs).

Methods:

This retrospective cohort study utilized EHRs for adults who received primary care services within a large healthcare system in 2006. Validated EHR-based algorithms identified 10,649 people with depression (depression cohort) and 5,911 people with HF (HF cohort) between January 1, 2006 and December 31, 2018. Each person with depression or HF was matched 1:1 with an unaffected referent on age, sex, and outpatient service use. Each cohort (with their matched referents) was followed up electronically to identify newly diagnosed HF (in the depression cohort) and depression (in the HF cohort) that occurred after the index diagnosis of depression or HF, respectively. The risks of these outcomes were compared (vs. referents) using marginal Cox proportional hazard models adjusted for 16 comorbid chronic conditions.

Results:

2,024 occurrences of newly diagnosed HF were observed in the depression cohort and 944 occurrences of newly diagnosed depression were observed in the HF cohort over approximately 4–6 years of follow-up. People with depression had significantly increased risk for developing newly diagnosed HF (HR 2.08, 95% CI 1.89–2.28) and people with HF had a significantly increased risk of newly diagnosed depression (HR 1.34, 95% CI 1.17–1.54) after adjusting for all 16 comorbid chronic conditions.

Conclusion:

These results provide evidence of a bi-directional relationship between depression and HF independently of age, sex, and multimorbidity from chronic illnesses.

GGCX mutations in a patient with overlapping pseudoxanthoma elasticum/cutis laxa-like phenotype

Pseudoxanthoma elasticum (PXE) is a multisystem disorder characterized by ectopic mineralization of connective tissues with primary manifestations in the skin, eyes and the cardiovascular system. The classic forms of PXE are caused by mutations in the ABCC6 gene encoding the ABCC6 protein, expressed primarily in the liver. Cutis laxa (CL) manifests with loose and sagging skin with loss of recoil. In 2009 we investigated a 19-year-old patient with overlapping cutaneous features of PXE and CL, together with alpha thalassaemia. Genetic analysis failed to identify pathogenic mutations in ABCC6. More recently we developed a gene-targeted panel of next-generation sequencing technology. This panel has 29 genes, 22 of which, including ABCC6 and GGCX, are associated with ectopic mineralization phenotypes. Mutation analysis in the patient identified two heterozygous GGCX mutations: c.200_201delTT in exon 2 and c.763G>A, p.V255M in exon 7. The GGCX gene encodes a γ-glutamyl carboxylase necessary for activation of blood coagulation factors in the liver. The p.V255M mutation was previously reported to result in reduced γ-glutamyl carboxylase activity in vitro, while the c.200_201delTT mutation is novel. Previous studies reported that mutations in GGCX cause overlapping PXE/CL skin phenotypes in association with or without multiple vitamin K-dependent coagulation factor deficiency. Our patient had loose redundant skin, moderate-to-severe angioid streaks and characteristic calcification of elastic structures in the mid dermis, consistent with PXE/CL overlap, but no coagulation abnormalities. Our studies expand the GGCX mutation landscape in patients with PXE-like phenotypes.

Inferring multimodal latent topics from electronic health records

Electronic health records (EHR) are rich heterogeneous collections of patient health information, whose broad adoption provides clinicians and researchers unprecedented opportunities for health informatics, disease-risk prediction, actionable clinical recommendations, and precision medicine. However, EHRs present several modeling challenges, including highly sparse data matrices, noisy irregular clinical notes, arbitrary biases in billing code assignment, diagnosis-driven lab tests, and heterogeneous data types. To address these challenges, we present MixEHR, a multi-view Bayesian topic model. We demonstrate MixEHR on MIMIC-III, Mayo Clinic Bipolar Disorder, and Quebec Congenital Heart Disease EHR datasets. Qualitatively, MixEHR disease topics reveal meaningful combinations of clinical features across heterogeneous data types. Quantitatively, we observe superior prediction accuracy of diagnostic codes and lab test imputations compared to the state-of-art methods. We leverage the inferred patient topic mixtures to classify target diseases and predict mortality of patients in critical conditions. In all comparison, MixEHR confers competitive performance and reveals meaningful disease-related topics.

A diagnostic codes-based algorithm improves accuracy for identification of childhood asthma in archival data sets

OBJECTIVE

While a single but truncated ICD code (493) had been widely used for identifying asthma in asthma care and research, it significantly under-identifies asthma. We aimed to develop and validate a diagnostic codes-based algorithm for identifying asthmatics using Predetermined Asthma Criteria (PAC) as the reference.

METHODS

This is a retrospective cross-sectional study which utilized two different coding systems, the Hospital Adaptation of the International Classification of Diseases, Eighth Revision (H-ICDA) and the International Classification of Diseases, Ninth Revision (ICD-9). The algorithm was developed using two population-based asthma study cohorts, and validated in a validation cohort, a random sample of the 1976-2007 Olmsted County Birth Cohort. Performance of the diagnostic codes-based algorithm for ascertaining asthma status against manual chart review for PAC (gold standard) was assessed by determining both criterion and construct validity.

RESULTS

Among eligible 267 subjects of the validation cohort, 50% were male, 70% white, and the median age at last follow-up was 17 (interquartile range, 8.7-24.4) years. Asthma prevalence by PAC through manual chart review was 34%. Sensitivity and specificity of the codes-based algorithm for identifying asthma were 82% and 98% respectively. Associations of asthma-related risk factors with asthma status ascertained by the code-based algorithm were similar to those by the manual review.

CONCLUSIONS

The diagnostic codes-based algorithm for identifying asthmatics improves accuracy of identification of asthma and can be a useful tool for large scale studies in a setting without automated chart review capabilities.

Rural-urban health disparities for mood disorders and obesity in a midwestern community

INTRODUCTION

Prior studies indicate greater disease burden for obesity among rural compared with urban residents but no differences for mood disorder based on geographic location. Recent attention has focused on the need to examine regional rural-urban disparities in disease burden. We focused on mood disorders and obesity prevalence within three southeastern Minnesota counties served by the Mayo Clinic Center for Translational Science Award, in Rochester, Minnesota, as these were top priorities identified in community health needs assessments.

METHODS

Cross-sectional study to assess the association of rural-urban locality on 5-year (2009-2014) prevalence of mood disorder and obesity obtained using the Rochester Epidemiological Project medical records linkage system, among subjects residing in three mixed rural-urban counties on April 1, 2014. Multivariable analyses adjusted for demographics, socioeconomic status using an individual housing-based measure, and counties.

RESULTS

The study cohort (percent rural location) included 91,202 (15%) for Olmsted, 10,197 (51%) in Dodge, and 10,184 (57%) in Wabasha counties. On multivariate analysis, 5-year prevalence of mood disorders and obesity was significantly greater for urban compared with rural residents, after adjusting for confounders; odds ratios (95% confidence intervals): 1.21 (1.17-1.26), P < 0.001, and 1.05 (1.01-1.10), P = 0.016, respectively. Observed effects were not modified in additional models adjusted for health care utilization (HCU; ≥1 general medical examination visit and flu vaccination).

CONCLUSIONS

Rural-urban health disparities for burden of mood disorders and obesity are independent of socioeconomic status and HCU in a Midwestern community. It is important to assess potential regional heterogeneity of rural-urban disparities on health outcomes.

The impact of sample processing on inflammatory markers in serum: Lessons learned

Objectives: To investigate the effect of sample handling on inflammatory cytokines in serum and highlight challenges with using samples pre-collected from biobanks for biomarker research.Methods: Cytokine concentrations (IL-1β, IL-2, IL-6, IL-8, IL-10, TNFα, and IFNγ) were measured in serum samples of 205 patients with bipoldar disorder (BD) from the Mayo Clinic Bipolar Disorder Biobank and 205 non-psychiatric controls from the Mayo Clinic Biobank. As cytokine concentrations varied by recruitment site, post-hoc models were used to test the effect of clinical variables and pre-processing time on cytokines. To evaluate the effect of pre-processing time experimentally, cytokines were assayed in serum and plasma from 6 healthy volunteers processed at different time points.Results: Cytokine levels were significantly higher in the BD group. However, both cytokine levels and pre-processing times differed by recruitment site, and post-hoc analyses revealed that pre-processing time was significantly associated with several cytokines. An experiment using samples from healthy volunteers confirmed that concentrations for most cytokines increased with longer pre-processing times.Conclusions: Delays in processing influence cytokine concentrations in blood samples. Given the increasing use of biobanks in research, this study highlights the need to carefully evaluate sample collection and handling methods when designing biomarker studies.

Expert artificial intelligence-based natural language processing characterises childhood asthma

INTRODUCTION

The lack of effective, consistent, reproducible and efficient asthma ascertainment methods results in inconsistent asthma cohorts and study results for clinical trials or other studies. We aimed to assess whether application of expert artificial intelligence (AI)-based natural language processing (NLP) algorithms for two existing asthma criteria to electronic health records of a paediatric population systematically identifies childhood asthma and its subgroups with distinctive characteristics.

METHODS

Using the 1997-2007 Olmsted County Birth Cohort, we applied validated NLP algorithms for Predetermined Asthma Criteria (NLP-PAC) as well as Asthma Predictive Index (NLP-API). We categorised subjects into four groups (both criteria positive (NLP-PAC⁺/NLP-API⁺); PAC positive only (NLP-PAC⁺ only); API positive only (NLP-API⁺ only); and both criteria negative (NLP-PAC^-/NLP-API^-)) and characterised them. Results were replicated in unsupervised cluster analysis for asthmatics and a random sample of 300 children using laboratory and pulmonary function tests (PFTs).

RESULTS

Of the 8196 subjects (51% male, 80% white), we identified 1614 (20%), NLP-PAC⁺/NLP-API⁺; 954 (12%), NLP-PAC⁺ only; 105 (1%), NLP-API⁺ only; and 5523 (67%), NLP-PAC^-/NLP-API^-. Asthmatic children classified as NLP-PAC⁺/NLP-API⁺ showed earlier onset asthma, more Th2-high profile, poorer lung function, higher asthma exacerbation and higher risk of asthma-associated comorbidities compared with other groups. These results were consistent with those based on unsupervised cluster analysis and lab and PFT data of a random sample of study subjects.

CONCLUSION

Expert AI-based NLP algorithms for two asthma criteria systematically identify childhood asthma with distinctive characteristics. This approach may improve precision, reproducibility, consistency and efficiency of large-scale clinical studies for asthma and enable population management.

Characteristics Associated With Recruitment and Re-contact in Mayo Clinic Biobank

Objective: To better understand the characteristics associated with a participant's willingness to consent to the Mayo Clinic Biobank (MCB) and examine factors associated with willingness to participate in follow-up studies embedded within MCB that require re-contact and participant approval. Participants and Methods: Consent rates were compared across patient demographics to the MCB. Rates of participation to follow-up studies were also compared across demographics and request types. Results: Among 272,102 Mayo Clinic patients invited to the MCB, 48,314 (19%) consented across the three recruitment sites within 90 days of initial invitation. A significant age by gender interaction was identified, showing young males consent at a lower rate than young females and older males consent at a higher rate than older females. Over the recruitment time frame of 2009-2015, there was a significant decrease in consent rates (decline of 2.5%/year). Of the 57,041 consented MCB participants, 33,487 participants (59%) have been invited to participate in follow-up studies via re-contact. Follow-up studies of the MCB may require participants to provide additional samples, complete questionnaires, and/or release their identity to a research team. MCB participants have been invited to enroll in a median of two studies (IQR: 1-3). Seventy-one percent of participants consented to at least one follow-up study, with individual follow-up study consent rates ranging from 14 to 87% depending on study type, with a median consent rate of 61% (IQR: 47-70%). Studies requesting return of a questionnaire had the highest participation rates. White participants, older participants, and participants with some college or a degree were significantly more likely to participate to follow-up studies, while there was no association with gender. Conclusion: Consent rates among younger and non-white patients were lower than in older, white patients. However, we also found that participation rates among those already enrolled in the biobank were much higher than those seen in new recruitment efforts, external to an existing biobank. We thus demonstrate an important way that biobanks can advance precision medicine goals: through provision of populations from which studies can draw participants for future studies.

Long-term incidence of glioma in Olmsted County, Minnesota, and disparities in postglioma survival rate: a population-based study

Background

We assessed glioma incidence and disparities in postglioma survival rate in the Olmsted County, Minnesota, population.

Methods

This population-based study assessed the incidence of pathologically confirmed primary gliomas between January 1, 1995, and December 31, 2014. Age- and sex-adjusted incidence rates per 100 000 person-years were calculated and standardized to the US white 2010 population. We compared incidence trends of glioma during our study period with previously published Olmsted County data from 1950 to 1990. We assessed postglioma survival rates among individuals with different socioeconomic status (SES), which was measured by a validated individual HOUsing-based SES index (HOUSES).

Results

We identified 135 incident glioma cases (93% white) with 20 pediatric (50% female) and 115 adult cases (44% female). Overall incidence rate during our study period, 5.51 per 100 000 person-years (95% CI: 4.56-6.46), showed no significant changes and was similar to that seen in 1950 to 1990, 5.5 per 100 000 person-years. The incidence of pediatric (age < 20 years) glioma was 2.49 (95% CI: 1.40-3.58), whereas adult glioma incidence was 6.47 (95% CI: 5.26-7.67). Among those with grade II to IV gliomas, individuals with lower SES (< median HOUSES) had significantly lower 5-year survival rates compared to those with higher SES, adjusted hazard ratio 1.61 (95% CI: 1.01-2.85).

Conclusion

In a well-defined North American population, long-term glioma incidence appears stable since 1950. Significant socioeconomic disparities exist for postglioma survival.

Characteristics and utilisation of the Mayo Clinic Biobank, a clinic-based prospective collection in the USA: cohort profile

PURPOSE

The Mayo Clinic Biobank was established to provide a large group of patients from which comparison groups (ie, controls) could be selected for case-control studies, to create a prospective cohort with sufficient power for common outcomes and to support electronic health record (EHR) studies.

PARTICIPANTS

A total of 56 862 participants enrolled (21% response rate) into the Mayo Clinic Biobank from Rochester, Minnesota (77%, n=43 836), Jacksonville, Florida (18%, n=10 368) and La Crosse, Wisconsin (5%, n=2658). Participants were all Mayo Clinic patients, 18 years of age or older and US residents.

FINDINGS TO DATE

Overall, 43% of participants were 65 years of age or older and female participants were more frequent (59%) than males at all sites. Most participants resided in the Upper Midwest regions of the USA (Minnesota, Iowa, Illinois or Wisconsin), Florida or Georgia. Self-reported race among Biobank participants was 90% white. Here we provide examples of the types of studies that have successfully utilised the resource, including (1) investigations of the population itself, (2) provision of controls for case-control studies, (3) genotype-driven research, (4) EHR-based research and (5) prospective recruitment to other studies. Over 270 projects have been approved to date to access Biobank data and/or samples; over 200 000 sample aliquots have been approved for distribution.

FUTURE PLANS

The data and samples in the Mayo Clinic Biobank can be used for various types of epidemiological and clinical studies, especially in the setting of case-control studies for which the Biobank samples serve as control samples. We are planning cohort studies with additional follow-up and acquisition of genetic information on a large scale.

Asthma and risk of glioma: a population-based case-control study

OBJECTIVES

Literature suggests an inconsistent, but largely inverse, association between asthma and risk of glioma, which is primarily due to methodological inconsistency in sampling frame and ascertainment of asthma. The objective of the study was to clarify the association between asthma and risk of glioma by minimising methodological biases (eg, recall and detection bias).

DESIGN

A population-based case-control study.

SETTING

General population in Olmsted County, Minnesota, USA.

PARTICIPANTS

All eligible biopsy-proven incident glioma cases (1995-2014) and two sets of controls among residents matched to age and sex (first set: community controls without glioma; second set: MRI-negative controls from the same community).

METHODS

The predetermined asthma criteria via medical record review were applied to ascertain asthma status of cases and controls. History of asthma prior to index date was compared between glioma cases and their matched controls using conditional logistic regression models. Propensity score for asthma status was adjusted for multivariate analysis.

RESULTS

We enrolled 135 glioma cases (median age at index date: 53 years) and 270 controls. Of the cases, 21 had a history of asthma (16%), compared with 36 of MRI controls (27%) (OR (95% CI) 0.48 (0.26 to 0.91), p=0.03). With MRI controls, an inverse association between asthma and risk of glioma persisted after adjusting for the propensity score for asthma status, but did not reach statistical significance probably due to the lack of statistical power (OR (95% CI) 0.48 (0.21 to 1.09); p=0.08). Based on comparison of characteristics of controls and cases, community controls seem to be more susceptible to a detection bias.

CONCLUSIONS

While differential detection might account for the association between asthma and risk of glioma, asthma may potentially pose a protective effect on risk of glioma. Our study results need to be replicated by a larger study.

Spatio-temporal comparison of pertussis outbreaks in Olmsted County, Minnesota, 2004-2005 and 2012: a population-based study

OBJECTIVE

Two pertussis outbreaks occurred in Olmsted County, Minnesota, during 2004-2005 and 2012 (5-10 times higher than other years), with significantly higher incidence than for the State. We aimed to assess whether there were similar spatio-temporal patterns between the two outbreaks.

SETTING

Olmsted County, Minnesota, USA PARTICIPANTS: We conducted a population-based retrospective cohort study of all Olmsted County residents during the 2004-2005 and 2012 outbreaks, including laboratory-positive pertussis cases.

PRIMARY OUTCOME MEASURE

For each outbreak, we estimated (1) age-specific incidence rate using laboratory-positive pertussis cases (numerator) and the Rochester Epidemiology Project Census (denominator), a medical record-linkage system for virtually all Olmsted County residents, and (2) pertussis case density using kernel density estimation to identify areas with high case density. To account for population size, we calculated relative difference of observed density and expected density based on age-specific incidence.

RESULTS

We identified 157 and 195 geocoded cases in 2004-2005 and 2012, respectively. Incidence was the highest among adolescents (ages 11 to <14 years) for both outbreaks (9.6 and 7.9 per 1000). The 2004-2005 pertussis outbreak had higher incidence in winter (52% of cases) versus summer in 2012 (53%). We identified a consistent area with higher incidence at the beginning (ie, first quartile) of two outbreaks, but it was inconsistent for later quartiles. The relative difference maps for the two outbreaks suggest a greater role of neighbourhood population size in 2012 compared with 2004-2005.

CONCLUSIONS

Comparing spatio-temporal patterns between two pertussis outbreaks identified a consistent geographical area with higher incidence of pertussis at the beginning of outbreaks in this community. This finding can be tested in future outbreaks, and, if confirmed, can be used for identifying epidemiological risk factors clustered in such areas for geographically targeted intervention.

Genome-wide association study identifies 30 loci associated with bipolar disorder

Bipolar disorder is a highly heritable psychiatric disorder. We performed a genome-wide association study (GWAS) including 20,352 cases and 31,358 controls of European descent, with follow-up analysis of 822 variants with P < 1 × 10-4 in an additional 9,412 cases and 137,760 controls. Eight of the 19 variants that were genome-wide significant (P < 5 × 10-8) in the discovery GWAS were not genome-wide significant in the combined analysis, consistent with small effect sizes and limited power but also with genetic heterogeneity. In the combined analysis, 30 loci were genome-wide significant, including 20 newly identified loci. The significant loci contain genes encoding ion channels, neurotransmitter transporters and synaptic components. Pathway analysis revealed nine significantly enriched gene sets, including regulation of insulin secretion and endocannabinoid signaling. Bipolar I disorder is strongly genetically correlated with schizophrenia, driven by psychosis, whereas bipolar II disorder is more strongly correlated with major depressive disorder. These findings address key clinical questions and provide potential biological mechanisms for bipolar disorder.

Epidemiology of Children With Multiple Complex Chronic Conditions in a Mixed Urban-Rural US Community

OBJECTIVES

Children with multiple complex chronic conditions (MCCs) represent a small fraction of our communities but a disproportionate amount of health care cost and mortality. Because the temporal trends of children with MCCs within a geographically well-defined US pediatric population has not been previously assessed, health care planning and policy for this vulnerable population is limited.

METHODS

In this population-based, repeated cross-sectional study, we identified and enrolled all eligible children residing in Olmsted County, Minnesota, through the Rochester Epidemiology Project, a medical record linkage system of Olmsted County residents. The pediatric complex chronic conditions classification system version 2 was used to identify children with MCCs. Five-year period prevalence and incidence rates were calculated during the study period (1999-2014) and characterized by age, sex, ethnicity, and socioeconomic status (SES) by using the housing-based index of socioeconomic status, a validated individual housing-based SES index. Age-, sex-, and ethnicity-adjusted prevalence and incidence rates were calculated, adjusting to the 2010 US total pediatric population.

RESULTS

Five-year prevalence and incidence rates of children with MCCs in Olmsted County increased from 1200 to 1938 per 100 000 persons and from 256 to 335 per 100 000 person-years, respectively, during the study period. MCCs tend to be slightly more prevalent among children with a lower SES and with a racial minority background.

CONCLUSIONS

Both 5-year prevalence and incidence rates of children with MCCs have significantly increased over time, and health disparities are present among these children. The clinical and financial outcomes of children with MCCs need to be assessed for formulating suitable health care planning given limited resources.

Relationship between asthma status and antibody response pattern to 23-valent pneumococcal vaccination

Objective: Asthma poses an increased risk for serious pneumococcal disease, but little is known about the influence of asthma status on the 23-valent serotype-specific pneumococcal antibody response. We examined differences in antibody titers between pre- and post-vaccination with 23-valent pneumococcal polysaccharide vaccine (PPSV-23) in relation to asthma status. Methods: Asthma status was retrospectively ascertained by the Predetermined Asthma Criteria in an existing vaccine cohort through comprehensive medical record review. Twenty-three serotype-specific pneumococcal antibody titers measured at baseline and 4-6 weeks post-vaccination were analyzed. Vaccine responses to PPSV-23 were calculated from pre- to post-vaccine titers for each of the serotypes. Results: Of the 64 eligible and enrolled subjects, 18 (28%) had asthma. Controls (i.e., subjects without asthma) demonstrated a statistically significant fold change response compared to their baseline for all serotypes, while those with asthma did not mount a significant response to serotypes 7F, 22F, and 23F. The overall vaccine response as measured by fold change over baseline was lower in subjects with asthma than controls. Conclusions: Poorer humoral immune responses to PPSV-23 as measured by fold change were more likely to be observed in subjects with asthma compared to controls. We recommend the consideration of asthma status when interpreting vaccine response for immune competence workup through larger studies. Further studies are warranted to replicate these findings.

Novel Complex Interactions between Mitochondrial and Nuclear DNA in Schizophrenia and Bipolar Disorder

Mitochondrial dysfunction has been associated with schizophrenia (SZ) and bipolar disorder (BD). This review examines recent publications and novel associations between mitochondrial genes and SZ and BD. Associations of nuclear-encoded mitochondrial variants with SZ were found using gene- and pathway-based approaches. Two control region mitochondrial DNA (mtDNA) SNPs, T16519C and T195C, both showed an association with SZ and BD. A review of 4 studies of A15218G located in the cytochrome B oxidase gene (CYTB, SZ = 11,311, control = 35,735) shows a moderate association with SZ (p = 2.15E-03). Another mtDNA allele A12308G was nominally associated with psychosis in BD type I subjects and SZ. The first published study testing the epistatic interaction between nuclear-encoded and mitochondria-encoded genes demonstrated evidence for potential interactions between mtDNA and the nuclear genome for BD. A similar analysis for the risk of SZ revealed significant joint effects (34 nuclear-mitochondria SNP pairs with joint effect p ≤ 5E-07) and significant enrichment of projection neurons. The mitochondria-encoded gene CYTB was found in both the epistatic interactions for SZ and BD and the single SNP association of SZ. Future efforts considering population stratification and polygenic risk scores will test the role of mitochondrial variants in psychiatric disorders.