Validity of ICD-10-CM codes for determination of diabetes type for persons with youth-onset type 1 and type 2 diabetes

IVF and risk of Type 1 diabetes mellitus: a population-based nested case-control study

STUDY QUESTION

Is the mode of conception (natural, subfertility and non-IVF, and IVF) associated with the risk of Type 1 diabetes mellitus among offspring?

SUMMARY ANSWER

The risk of Type 1 diabetes in offspring does not differ among natural, subfertility and non-IVF, and IVF conceptions.

WHAT IS KNOWN ALREADY

Evidence has shown that children born through IVF have an increased risk of impaired metabolic function.

STUDY DESIGN, SIZE, DURATION

A population-based, nested case-control study was carried out, including 769 children with and 3110 children without Type 1 diabetes mellitus within the prospective cohort of 2 228 073 eligible parent-child triads between 1 January 2004 and 31 December 2017.

PARTICIPANTS/MATERIALS, SETTING, METHODS

Using registry data from Taiwan, the mode of conception was divided into three categories: natural conception, subfertility, and non-IVF (indicating infertility diagnosis but no IVF-facilitated conception), and IVF conception. The diagnosis of Type 1 diabetes mellitus was determined according to the International Classification of Diseases, 9th or 10th Revision, Clinical Modification. Each case was matched to four controls randomly selected after matching for child age and sex, residential township, and calendar date of Type 1 diabetes mellitus occurrence.

MAIN RESULTS AND THE ROLE OF CHANCE

Based on 14.3 million person-years of follow-up (median, 10 years), the incidence rates of Type 1 diabetes were 5.33, 5.61, and 4.74 per 100 000 person-years for natural, subfertility and non-IVF, and IVF conceptions, respectively. Compared with natural conception, no significant differences in the risk of Type 1 diabetes were observed for subfertility and non-IVF conception (adjusted odds ratio, 1.04 [95% CI, 0.85-1.27]) and IVF conception (adjusted odds ratio, 1.00 [95% CI, 0.50-2.03]). In addition, there were no significant differences in the risk of Type 1 diabetes according to infertility source (male/female/both) and embryo type (fresh/frozen).

LIMITATIONS, REASONS FOR CAUTION

Although the population-level data from Taiwanese registries was used, a limited number of exposed cases was included. We showed risk of Type 1 diabetes was not associated with infertility source or embryo type; however, caution with interpretation is required owing to the limited number of exposed events after the stratification. The exclusion criterion regarding parents' history of diabetes mellitus was only applicable after 1997, and this might have caused residual confounding.

WIDER IMPLICATIONS OF THE FINDINGS

It has been reported that children born to parents who conceived through IVF had worse metabolic profiles than those who conceived naturally. Considering the findings of the present and previous studies, poor metabolic profiles may not be sufficient to develop Type 1 diabetes mellitus during childhood.

STUDY FUNDING/COMPETING INTEREST(S)

This study was supported by grants from Shin Kong Wu Ho-Su Memorial Hospital (No. 109GB006-1). The funders had no role in considering the study design or in the collection, analysis, interpretation of data, writing of the report, or decision to submit the article for publication. The authors have no competing interests to disclose.

TRIAL REGISTRATION NUMBER

N/A.

Association Between the COI and Excess Health Care Utilization and Costs for ACSC

BACKGROUND AND OBJECTIVES

The authors of previous work have associated the Childhood Opportunity Index (COI) with increased hospitalizations for ambulatory care sensitive conditions (ACSC). The burden of this inequity on the health care system is unknown. We sought to understand health care resource expenditure in terms of excess hospitalizations, hospital days, and cost.

METHODS

We performed a retrospective cross-sectional study of the Pediatric Health Information Systems database, including inpatient hospitalizations between January 1, 2016 and December 31, 2022 for children <18 years of age. We compared ACSC hospitalizations, mortality, and cost across COI strata.

RESULTS

We identified 2 870 121 hospitalizations among 1 969 934 children, of which 44.5% (1 277 568/2 870 121) were for ACSCs. A total of 49.1% (331 083/674 548) of hospitalizations in the very low stratum were potentially preventable, compared with 39.7% (222 037/559 003) in the very high stratum (P < .001). After adjustment, lower COI was associated with higher odds of potentially preventable hospitalization (odds ratio 1.18, 95% confidence interval [CI] 1.17-1.19). Compared with the very high COI stratum, there were a total of 137 550 (95% CI 134 582-140 517) excess hospitalizations across all other strata, resulting in an excess cost of $1.3 billion (95% CI $1.28-1.35 billion). Compared with the very high COI stratum, there were 813 (95% CI 758-871) excess deaths, with >95% from the very low and low COI strata.

CONCLUSIONS

Children with lower neighborhood opportunity have increased risk of ACSC hospitalizations. The COI may identify communities in which targeted intervention could reduce health care utilization and costs.

Sodium-Glucose Cotransporter-2 Inhibitors and Nephritis Among Patients With Systemic Lupus Erythematosus

Importance

Lupus nephritis is a major complication of systemic lupus erythematosus (SLE). Randomized clinical trials have shown nephroprotective and cardioprotective effects of sodium-glucose cotransporter-2 inhibitors (SGLT2is).

Objective

To investigate whether the use of SGLT2is is associated with the onset and progression of lupus nephritis and other kidney and cardiac outcomes in patients with SLE and type 2 diabetes.

Design, Setting, and Participants

This multicenter cohort study used the US Collaborative Network of the TriNetX clinical data platform to identify patients with SLE and type 2 diabetes from January 1, 2015, to December 31, 2022. Data collection and analysis were conducted in September 2023.

Exposures

Individuals were categorized into 2 groups by SGLT2i use or nonuse with 1:1 propensity score matching.

Main Outcomes and Measures

The Kaplan-Meier method and Cox proportional hazards regression models were used to calculate the 5-year adjusted hazard ratios (AHRs) of lupus nephritis, dialysis, kidney transplant, heart failure, and mortality for the 2 groups.

Results

From 31 790 eligible participants, 1775 matched pairs of SGLT2i users and nonusers (N = 3550) were selected based on propensity scores. The mean (SD) age of matched participants was 56.8 (11.6) years, and 3012 (84.8%) were women. SGLT2i users had a significantly lower risk of lupus nephritis (AHR, 0.55; 95% CI, 0.40-0.77), dialysis (AHR, 0.29; 95% CI, 0.17-0.48), kidney transplant (AHR, 0.14; 95% CI, 0.03-0.62), heart failure (AHR, 0.65; 95% CI, 0.53-0.78), and all-cause mortality (AHR, 0.35; 95% CI, 0.26-0.47) than SGLT2i nonusers.

Conclusions and Relevance

In this cohort study of patients with SLE and type 2 diabetes, SGLT2i users had a significantly lower risk of lupus nephritis, dialysis, kidney transplant, heart failure, and all-cause mortality than nonusers. The findings suggest that SGLT2is may provide some nephroprotective and cardioprotective benefits.

Neighborhood Walkability Is Associated With Lower Burden of Cardiovascular Risk Factors Among Cancer Patients

Background

Modifiable cardiovascular risk factors constitute a significant cause of cardiovascular disease and mortality among patients with cancer. Recent studies suggest a potential link between neighborhood walkability and favorable cardiovascular risk factor profiles in the general population.

Objectives

This study aimed to investigate whether neighborhood walkability is correlated with favorable cardiovascular risk factor profiles among patients with a history of cancer.

Methods

We conducted a cross-sectional study using data from the Houston Methodist Learning Health System Outpatient Registry (2016-2022) comprising 1,171,768 adults aged 18 years and older. Neighborhood walkability was determined using the 2019 Walk Score and divided into 4 categories. Patients with a history of cancer were identified through International Classification of Diseases-10th Revision-Clinical Modification codes (C00-C96). We examined the prevalence and association between modifiable cardiovascular risk factors (hypertension, diabetes, smoking, dyslipidemia, and obesity) and neighborhood walkability categories in cancer patients.

Results

The study included 121,109 patients with a history of cancer; 56.7% were female patients, and 68.8% were non-Hispanic Whites, with a mean age of 67.3 years. The prevalence of modifiable cardiovascular risk factors was lower among participants residing in the most walkable neighborhoods compared with those in the least walkable neighborhoods (76.7% and 86.0%, respectively). Patients with a history of cancer living in very walkable neighborhoods were 16% less likely to have any risk factor compared with car-dependent-all errands neighborhoods (adjusted OR: 0.84, 95% CI: 0.78-0.92). Sensitivity analyses considering the timing of events yielded similar results.

Conclusions

Our findings demonstrate an association between neighborhood walkability and the burden of modifiable cardiovascular risk factors among patients with a medical history of cancer. Investments in walkable neighborhoods may present a viable opportunity for mitigating the growing burden of modifiable cardiovascular risk factors among patients with a history of cancer.

Using electronic health records to enhance surveillance of diabetes in children, adolescents and young adults: a study protocol for the DiCAYA Network

INTRODUCTION

Traditional survey-based surveillance is costly, limited in its ability to distinguish diabetes types and time-consuming, resulting in reporting delays. The Diabetes in Children, Adolescents and Young Adults (DiCAYA) Network seeks to advance diabetes surveillance efforts in youth and young adults through the use of large-volume electronic health record (EHR) data. The network has two primary aims, namely: (1) to refine and validate EHR-based computable phenotype algorithms for accurate identification of type 1 and type 2 diabetes among youth and young adults and (2) to estimate the incidence and prevalence of type 1 and type 2 diabetes among youth and young adults and trends therein. The network aims to augment diabetes surveillance capacity in the USA and assess performance of EHR-based surveillance. This paper describes the DiCAYA Network and how these aims will be achieved.

METHODS AND ANALYSIS

The DiCAYA Network is spread across eight geographically diverse US-based centres and a coordinating centre. Three centres conduct diabetes surveillance in youth aged 0-17 years only (component A), three centres conduct surveillance in young adults aged 18-44 years only (component B) and two centres conduct surveillance in components A and B. The network will assess the validity of computable phenotype definitions to determine diabetes status and type based on sensitivity, specificity, positive predictive value and negative predictive value of the phenotypes against the gold standard of manually abstracted medical charts. Prevalence and incidence rates will be presented as unadjusted estimates and as race/ethnicity, sex and age-adjusted estimates using Poisson regression.

ETHICS AND DISSEMINATION

The DiCAYA Network is well positioned to advance diabetes surveillance methods. The network will disseminate EHR-based surveillance methodology that can be broadly adopted and will report diabetes prevalence and incidence for key demographic subgroups of youth and young adults in a large set of regions across the USA.

Incidence of Diabetes Among Youth Before and During the COVID-19 Pandemic

Importance

Prior research found increases in diabetes among youth during the COVID-19 pandemic, but few studies examined variation across sociodemographics.

Objective

To examine diabetes incidence rates among a diverse population of youth in the US before and during the COVID-19 pandemic.

Design, Setting, and Participants

This cohort study included data from Kaiser Permanente Southern California (KPSC) between January 1, 2016, and December 31, 2021. KPSC members aged from birth to 19 years with no history of diabetes were included. Individuals were followed up using electronic health records for diabetes incidence defined using diagnoses, laboratory values, and medications. Analyses were conducted between November 2022 and January 2023.

Main Outcome and Measures

Age- and sex-standardized annual and quarterly incidence rates per 100 000 person-years (PYs) were calculated for type 1 diabetes and type 2 diabetes between 2016 and 2021. Rates were calculated within strata of age (<10 and 10-19 years), sex, and race and ethnicity (Asian/Pacific Islander, Hispanic, non-Hispanic Black, non-Hispanic White, and other/multiple/unknown). Using Poisson regression with robust error variances, incidence rate ratios (IRR) comparing 2020 to 2021 with 2016 to 2019 were calculated by diabetes type and within age, sex, and race and ethnicity strata and adjusting for health care utilization.

Results

Between 2016 to 2021, there were 1200, 1100, and 63 patients with type 1 diabetes (mean [SD] age, 11.0 [4.5] years; 687 [57.3%] male), type 2 diabetes (mean [SD] age, 15.7 [2.7] years; 516 [46.9%] male), and other diabetes, respectively. Incidence of type 1 diabetes increased from 18.5 per 100 000 PYs in 2016 to 2019 to 22.4 per 100 000 PYs from 2020 to 2021 with increased IRRs among individuals aged 10 to 19 years, male individuals, and Hispanic individuals. Incidence of type 2 diabetes increased from 14.8 per 100 000 PYs from 2016 to 2019 to 24.7 per 100 000 PYs from 2020 to 2021 with increased IRRs among individuals aged 10 to 19 years, male and female individuals, and those with Black, Hispanic, and other/unknown race and ethnicity.

Conclusions and Relevance

In this cohort study of youth in KPSC, incidence of diabetes increased during the COVID-19 pandemic and was more pronounced in specific racial and ethnic groups. Future research to understand differential impacts of physiologic and behavioral risk factors is warranted.

Algorithms to define diabetes type using data from administrative databases: A systematic review of the evidence

AIMS

To find the best-performing algorithms to distinguish type 1 and type 2 diabetes in administrative data.

METHODS

Embase and MEDLINE databases were searched from January 2000 until January 2023. Papers evaluating the performance of algorithms to define type 1 and type 2 diabetes by reporting diagnostic metrics against a range of reference standards were selected. Study quality was evaluated using the Quality Assessment of Diagnostic Accuracy Studies.

RESULTS

Of the 24 studies meeting the eligibility criteria, 19 demonstrated a low risk of bias and low concerns about the applicability of the study population across all domains. Algorithms considering multiple diabetes diagnostic codes alone were sensitive and specific approaches to classify diabetes type (both metrics >92.1% for type 1 diabetes; >86.9% for type 2 diabetes). Among the top 10-performing algorithms to detect type 1 and type 2 diabetes, 70% and 100% featured multiple criteria, respectively. Information on insulin use was more sensitive and specific for detecting diabetes type than were criteria based on use of oral hypoglycaemic agents.

CONCLUSIONS

Algorithms based on multiple diabetes diagnostic codes and insulin use are the most accurate approaches to distinguish type 1 from type 2 diabetes using administrative data. Approaches with more than one criterion may also increase sensitivity in distinguishing diabetes type.

Are fewer cases of diabetes mellitus diagnosed in the months after SARS-CoV-2 infection? A population-level view in the EHR-based RECOVER program

Long-term sequelae of severe acute respiratory coronavirus-2 (SARS-CoV-2) infection may include increased incidence of diabetes. Here we describe the temporal relationship between new type 2 diabetes and SARS-CoV-2 infection in a nationwide database. We found that while the proportion of newly diagnosed type 2 diabetes increased during the acute period of SARS-CoV-2 infection, the mean proportion of new diabetes cases in the 6 months post-infection was about 83% lower than the 6 months preinfection. These results underscore the need for further investigation to understand the timing of new diabetes after COVID-19, etiology, screening, and treatment strategies.

Are fewer cases of diabetes mellitus diagnosed in the months after SARS-CoV-2 infection?

Long-term sequelae of severe acute respiratory coronavirus-2 (SARS-CoV-2) infection may include an increased incidence of diabetes. Our objective was to describe the temporal relationship between new diagnoses of diabetes mellitus and SARS-CoV-2 infection in a nationally representative database. There appears to be a sharp increase in diabetes diagnoses in the 30 days surrounding SARS-CoV-2 infection, followed by a decrease in new diagnoses in the post-acute period, up to 360 days after infection. These results underscore the need for further investigation, as understanding the timing of new diabetes onset after COVID-19 has implications regarding potential etiology and screening and treatment strategies.

Comparative Safety of Sodium-Glucose Cotransporter 2 Inhibitors Versus Dipeptidyl Peptidase 4 Inhibitors and Sulfonylureas on the Risk of Diabetic Ketoacidosis

OBJECTIVE

To assess the association of sodium-glucose cotransporter 2 (SGLT2) inhibitors with diabetic ketoacidosis compared with dipeptidyl peptidase 4 (DPP-4) inhibitors and sulfonylureas in patients with type 2 diabetes.

RESEARCH DESIGN AND METHODS

We conducted a new-user active comparator cohort study to examine two pairwise comparisons: 1) SGLT2 inhibitors versus DPP-4 inhibitors and 2) SGLT2 inhibitors versus sulfonylureas. The main outcome was diabetic ketoacidosis present on hospital admission. We adjusted for confounders through propensity score matching. We used Cox proportional hazards regression with a robust variance estimator to estimate hazard ratios (HRs) and corresponding 95% CIs while adjusting for calendar time.

RESULTS

In cohort 1 (n = 85,125 for SGLT2 inhibitors and n = 85,125 for DPP-4 inhibitors), the incidence rates of diabetic ketoacidosis per 1,000 person-years were 6.0 and 4.3 for SGLT2 inhibitors and DPP4 inhibitors, respectively. In cohort 2 (n = 72,436 for SGLT2 inhibitors and n = 72,436 for sulfonylureas), the incidence rates of diabetic ketoacidosis per 1,000 person-years were 6.3 and 4.5 for SGLT2 inhibitors and sulfonylureas, respectively. In Cox proportional hazards regression models, the use of SGLT2 inhibitors was associated with a higher rate of diabetic ketoacidosis compared with DPP-4 inhibitors (adjusted HR [aHR] 1.63; 95% CI 1.36, 1.96) and sulfonylureas (aHR 1.56; 95% CI 1.30, 1.87).

CONCLUSIONS

In this comparative safety study using real-world data, patients with type 2 diabetes who were newly prescribed SGLT2 inhibitors had a higher rate of diabetic ketoacidosis compared with DPP-4 inhibitors and sulfonylureas. Clinicians should be vigilant about this association.

Prediction of diabetic kidney disease with machine learning algorithms, upon the initial diagnosis of type 2 diabetes mellitus

INTRODUCTION

Diabetic kidney disease (DKD) accounts for the majority of increased risk of mortality for patients with diabetes, and eventually manifests in approximately half of those patients diagnosed with type 2 diabetes mellitus (T2DM). Although increased screening frequency can avoid delayed diagnoses, this is not uniformly implemented. The purpose of this study was to develop and retrospectively validate a machine learning algorithm (MLA) that predicts stages of DKD within 5 years upon diagnosis of T2DM.

RESEARCH DESIGN AND METHODS

Two MLAs were trained to predict stages of DKD severity, and compared with the Centers for Disease Control and Prevention (CDC) risk score to evaluate performance. The models were validated on a hold-out test set as well as an external dataset sourced from separate facilities.

RESULTS

The MLAs outperformed the CDC risk score in both the hold-out test and external datasets. Our algorithms achieved an area under the receiver operating characteristic curve (AUROC) of 0.75 on the hold-out set for prediction of any-stage DKD and an AUROC of over 0.82 for more severe endpoints, compared with the CDC risk score with an AUROC <0.70 on all test sets and endpoints.

CONCLUSION

This retrospective study shows that an MLA can provide timely predictions of DKD among patients with recently diagnosed T2DM.

Prevalence and Predictors of Stroke in Patients with Crohn's Disease: A Nationwide Study

OBJECTIVES

Mounting evidence points to the microbiome as a susceptibility factor for neurological disorders. Patients with Crohn's disease (CD) are at higher ischemic stroke (IS) risk, but no large scale epidemiologic studies have identified risk factors for stroke in this population.

MATERIALS AND METHODS

We analyzed the 2017 Nationwide Inpatient Sample (NIS) dataset to identify patients with a discharge diagnosis of Crohn's disease using the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) code K50.X. We identified patients with a primary/secondary discharge diagnosis of IS using ICD-10-CM code I63.X. We compared sociodemographic and clinical variables between stroke and non-stroke patients with CD. Logistic regression analysis was applied to identify factors associated with IS.

RESULTS

Of 30,212 patients with CD, 369 (1.2 %) had a discharge diagnosis of IS. Older age (odds ratio [OR], 1.03 [95% CI, 1.02-1.04], top quartile income (OR, 1.58 [95% CI, 1.10-2.30]), and hospitalization in a South Atlantic (OR, 1.82 [95% CI, 1.11-3.14]), East South Central (OR, 2.30 [95% CI, 1.28-4.25]), or West South Central hospital (OR, 2.40 [95% CI, 1.39-4.28]) were independently associated with IS. Clinical variables independently associated with IS in patients with CD included: atrial fibrillation (OR, 1.66 [95% CI, 1.15-2.33]), atherosclerosis (OR, 2.41 [95% CI, 1.32-4.10]), hyperlipidemia (OR, 1.69 [95% CI, 1.33-2.15]), hypertension (OR, 1.53 [95% CI, 1.18-1.98]) and valvular disease (OR, 1.62 [95% CI, 1.01-2.48).

CONCLUSION

A subset of traditional stroke risk factors are associated with IS in patients with CD. CD patients with these conditions could be targeted for vascular risk reduction and surveillance.

Clinical and biochemical profile of 786 sequential episodes of diabetic ketoacidosis in adults with type 1 and type 2 diabetes mellitus

INTRODUCTION

We explored the clinical and biochemical differences in demographics, presentation and management of diabetic ketoacidosis (DKA) in adults with type 1 and type 2 diabetes.

RESEARCH DESIGN AND METHODS

This observational study included all episodes of DKA from April 2014 to September 2020 in a UK tertiary care hospital. Data were collected on diabetes type, demographics, biochemical and clinical features at presentation, and DKA management.

RESULTS

From 786 consecutive DKA, 583 (75.9%) type 1 diabetes and 185 (24.1%) type 2 diabetes episodes were included in the final analysis. Those with type 2 diabetes were older and had more ethnic minority representation than those with type 1 diabetes. Intercurrent illness (39.8%) and suboptimal compliance (26.8%) were the two most common precipitating causes of DKA in both cohorts. Severity of DKA as assessed by pH, glucose and lactate at presentation was similar in both groups. Total insulin requirements and total DKA duration were the same (type 1 diabetes 13.9 units (9.1-21.9); type 2 diabetes 13.9 units (7.7-21.1); p=0.4638). However, people with type 2 diabetes had significantly longer hospital stay (type 1 diabetes: 3.0 days (1.7-6.1); type 2 diabetes: 11.0 days (5.0-23.1); p<0.0001).

CONCLUSIONS

In this population, a quarter of DKA episodes occurred in people with type 2 diabetes. DKA in type 2 diabetes presents at an older age and with greater representation from ethnic minorities. However, severity of presentation and DKA duration are similar in both type 1 and type 2 diabetes, suggesting that the same clinical management protocol is equally effective. People with type 2 diabetes have longer hospital admission.

Determining diagnosis date of diabetes using structured electronic health record (EHR) data: the SEARCH for diabetes in youth study

BACKGROUND

Disease surveillance of diabetes among youth has relied mainly upon manual chart review. However, increasingly available structured electronic health record (EHR) data have been shown to yield accurate determinations of diabetes status and type. Validated algorithms to determine date of diabetes diagnosis are lacking. The objective of this work is to validate two EHR-based algorithms to determine date of diagnosis of diabetes.

METHODS

A rule-based ICD-10 algorithm identified youth with diabetes from structured EHR data over the period of 2009 through 2017 within three children's hospitals that participate in the SEARCH for Diabetes in Youth Study: Cincinnati Children's Hospital, Cincinnati, OH, Seattle Children's Hospital, Seattle, WA, and Children's Hospital Colorado, Denver, CO. Previous research and a multidisciplinary team informed the creation of two algorithms based upon structured EHR data to determine date of diagnosis among diabetes cases. An ICD-code algorithm was defined by the year of occurrence of a second ICD-9 or ICD-10 diabetes code. A multiple-criteria algorithm consisted of the year of first occurrence of any of the following: diabetes-related ICD code, elevated glucose, elevated HbA1c, or diabetes medication. We assessed algorithm performance by percent agreement with a gold standard date of diagnosis determined by chart review.

RESULTS

Among 3777 cases, both algorithms demonstrated high agreement with true diagnosis year and differed in classification (p = 0.006): 86.5% agreement for the ICD code algorithm and 85.9% agreement for the multiple-criteria algorithm. Agreement was high for both type 1 and type 2 cases for the ICD code algorithm. Performance improved over time.

CONCLUSIONS

Year of occurrence of the second ICD diabetes-related code in the EHR yields an accurate diagnosis date within these pediatric hospital systems. This may lead to increased efficiency and sustainability of surveillance methods for incidence of diabetes among youth.

Prevalence of Comorbidities in Active and Reserve Service Members Pre and Post Traumatic Brain Injury, 2017-2019

OBJECTIVE

To understand the prevalence of comorbidities associated with traumatic brain injury (TBI) patients among active and reserve service members in the U.S. Military.

METHODS

Active and reserve SMs diagnosed with an incident TBI from January 2017 to October 2019 were selected. Nineteen comorbidities associated with TBI as identified in the literature and by clinical subject matter experts were described in this article. Each patient's medical encounters were evaluated from 6 months before to 2 years following the initial TBI diagnoses date in the Military Data Repository, if data were available. Time-to-event analyses were conducted to assess the cumulative prevalence over time of each comorbidity to the incident TBI diagnosis.

RESULTS

We identified 47,299 TBI patients, of which most were mild (88.8%), followed by moderate (10.5%), severe (0.5%), and of penetrating (0.2%) TBI severity. Two years from the initial TBI diagnoses, the top five comorbidities within our cohort were cognitive disorders (51.9%), sleep disorders (45.0%), post-traumatic stress disorder (PTSD; 36.0%), emotional disorders (22.7%), and anxiety disorders (22.6%) across severity groups. Cognitive, sleep, PTSD, and emotional disorders were the top comorbidities seen within each TBI severity group. Comorbidities increased pre-TBI to post-TBI; the more severe the TBI, the greater the prevalence of associated comorbidities.

CONCLUSION

A large proportion of our TBI patients are afflicted with comorbidities, particularly post-TBI, indicating many have a complex profile. The military health system should continue tracking comorbidities associated with TBI within the U.S. Military and devise clinical practices that acknowledge the complexity of the TBI patient.

Intensive Care Unit Admission, Mechanical Ventilation, and Mortality Among Patients With Type 1 Diabetes Hospitalized for COVID-19 in the U.S

OBJECTIVE

To assess whether risk of severe outcomes among patients with type 1 diabetes mellitus (T1DM) hospitalized for coronavirus disease 2019 (COVID-19) differs from that of patients without diabetes or with type 2 diabetes mellitus (T2DM).

RESEARCH DESIGN AND METHODS

Using the Premier Healthcare Database Special COVID-19 Release records of patients discharged after COVID-19 hospitalization from U.S. hospitals from March to November 2020 (N = 269,674 after exclusion), we estimated risk differences (RD) and risk ratios (RR) of intensive care unit admission or invasive mechanical ventilation (ICU/MV) and of death among patients with T1DM compared with patients without diabetes or with T2DM. Logistic models were adjusted for age, sex, and race or ethnicity. Models adjusted for additional demographic and clinical characteristics were used to examine whether other factors account for the associations between T1DM and severe COVID-19 outcomes.

RESULTS

Compared with patients without diabetes, T1DM was associated with a 21% higher absolute risk of ICU/MV (RD 0.21, 95% CI 0.19-0.24; RR 1.49, 95% CI 1.43-1.56) and a 5% higher absolute risk of mortality (RD 0.05, 95% CI 0.03-0.07; RR 1.40, 95% CI 1.24-1.57), with adjustment for age, sex, and race or ethnicity. Compared with T2DM, T1DM was associated with a 9% higher absolute risk of ICU/MV (RD 0.09, 95% CI 0.07-0.12; RR 1.17, 95% CI 1.12-1.22), but no difference in mortality (RD 0.00, 95% CI -0.02 to 0.02; RR 1.00, 95% CI 0.89-1.13). After adjustment for diabetic ketoacidosis (DKA) occurring before or at COVID-19 diagnosis, patients with T1DM no longer had increased risk of ICU/MV (RD 0.01, 95% CI -0.01 to 0.03) and had lower mortality (RD -0.03, 95% CI -0.05 to -0.01) in comparisons with patients with T2DM.

CONCLUSIONS

Patients with T1DM hospitalized for COVID-19 are at higher risk for severe outcomes than those without diabetes. Higher risk of ICU/MV in patients with T1DM than in patients with T2DM was largely accounted for by the presence of DKA. These findings might further guide recommendations related to diabetes management and the prevention of COVID-19.

Twenty years of pediatric diabetes surveillance: what do we know and why it matters

SEARCH for Diabetes in Youth (SEARCH) was initiated in 2000 as a multicenter study to address major gaps in the understanding of childhood diabetes in the United States. An active registry of youth diagnosed with diabetes at age <20 years since 2002 assessed prevalence, annual incidence, and trends by age, race/ethnicity, sex, and diabetes type. An observational cohort nested within the population-based registry was established to assess the natural history and risk factors for acute and chronic diabetes-related complications, as well as the quality of care and quality of life of children and adolescents with diabetes from diagnosis into young adulthood. SEARCH findings have contributed to a better understanding of the complex and heterogeneous nature of youth-onset diabetes. Continued surveillance of the burden and risk of type 1 and type 2 diabetes is important to track and monitor incidence and prevalence within the population. SEARCH reported evidence of early diabetes complications highlighting that continuing the long-term follow-up of youth with diabetes is necessary to further our understanding of its natural history and to develop the most appropriate approaches to primary, secondary, and tertiary prevention of diabetes and its complications. This review summarizes two decades of research and suggests avenues for further work.

A clinical diagnostic model based on an eXtreme Gradient Boosting algorithm to distinguish type 1 diabetes

Background

Accurate classification of type 1 diabetes (T1DM) and type 2 diabetes (T2DM) in the early phase is crucial for individual precision treatment. This study aimed to develop a classification model having fewer and easier to access clinical variables to distinguish T1DM in newly diagnosed diabetes in adults.

Methods

Clinical and laboratory data were collected from 15,206 adults with newly diagnosed diabetes in this cross-sectional study. This cohort represented 20 provinces and 4 municipalities in China. Types of diabetes were determined based on postprandial C-peptide (PCP) level and glutamic acid decarboxylase autoantibody (GADA) titer. We developed multivariable clinical diagnostic models using the eXtreme Gradient Boosting (XGBoost) algorithm. Classification variables included in the final model were based on their scores of importance. Model performance was evaluated by area under the receiver operating characteristic curve (ROC AUC), sensitivity, and specificity. The performance of models with different variable combinations was compared. Calibration intercept and slope were evaluated for the final model.

Results

Among the newly diagnosed diabetes cohort, 1,465 (9.63%) persons had T1DM and 13,741 (90.37%) had T2DM. Body mass index (BMI) contributed the most to the model, followed by age of onset and hemoglobin A1c (HbA1c). Compared with models with other clinical variable combinations, a final model that integrated age of onset, BMI and HbA1c had relatively higher performance. The ROC AUC, sensitivity, and specificity for this model were 0.83 (95% CI, 0.80 to 0.85), 0.77, and 0.76, respectively. The calibration intercept and slope were 0.02 (95% CI, –0.03 to 0.06) and 0.90 (95% CI, 0.79 to 1.02), respectively, which suggested a good calibration performance.

Conclusions

Our classification model that integrated age of onset, BMI, and HbA1c could distinguish T1DM from T2DM, which provides a useful tool in assisting physicians in subtyping and precising treatment in diabetes.

Electronic Medical Record-Based Case Phenotyping for the Charlson Conditions: Scoping Review

Background

Electronic medical records (EMRs) contain large amounts of rich clinical information. Developing EMR-based case definitions, also known as EMR phenotyping, is an active area of research that has implications for epidemiology, clinical care, and health services research.

Objective

This review aims to describe and assess the present landscape of EMR-based case phenotyping for the Charlson conditions.

Methods

A scoping review of EMR-based algorithms for defining the Charlson comorbidity index conditions was completed. This study covered articles published between January 2000 and April 2020, both inclusive. Embase (Excerpta Medica database) and MEDLINE (Medical Literature Analysis and Retrieval System Online) were searched using keywords developed in the following 3 domains: terms related to EMR, terms related to case finding, and disease-specific terms. The manuscript follows the Preferred Reporting Items for Systematic reviews and Meta-analyses extension for Scoping Reviews (PRISMA) guidelines.

Results

A total of 274 articles representing 299 algorithms were assessed and summarized. Most studies were undertaken in the United States (181/299, 60.5%), followed by the United Kingdom (42/299, 14.0%) and Canada (15/299, 5.0%). These algorithms were mostly developed either in primary care (103/299, 34.4%) or inpatient (168/299, 56.2%) settings. Diabetes, congestive heart failure, myocardial infarction, and rheumatology had the highest number of developed algorithms. Data-driven and clinical rule–based approaches have been identified. EMR-based phenotype and algorithm development reflect the data access allowed by respective health systems, and algorithms vary in their performance.

Conclusions

Recognizing similarities and differences in health systems, data collection strategies, extraction, data release protocols, and existing clinical pathways is critical to algorithm development strategies. Several strategies to assist with phenotype-based case definitions have been proposed.

Incidence and predictors of type 1 diabetes among younger adults aged 20-45 years: The diabetes in young adults (DiYA) study

AIMS

To estimate incidence of type 1 diabetes (T1D) and to develop a T1D prediction model among young adults.

METHODS

Adults 20-45 years newly-diagnosed with diabetes in 2017 were identified within Kaiser Permanente's healthcare systems in California and invited for diabetes autoantibody (DAA) testing. Multiple imputation was conducted to assign missing DAA status. The primary outcome for incidence rates (IR) and the prediction model was T1D defined by ≥1 positive DAA.

RESULTS

Among 2,347,989 persons at risk, 7862 developed diabetes, 2063 had DAA measured, and 166 (8.0%) had ≥1 positive DAA. T1D IR (95% CI) per 100,000 person-years was 15.2 (10.2-20.1) for ages 20-29 and 38.2 (28.6-47.8) for ages 30-44 years. The age-standardized IRs were 32.5 (22.2-42.8) for men and 27.2 (21.0-34.5) for women. The age/sex-standardized IRs were 30.1 (23.5-36.8) overall; 41.4 (25.3-57.5) for Hispanics, 37.0 (11.6-62.4) for Blacks, 21.4 (14.3-28.6) for non-Hispanic Whites, and 19.4 (8.5-30.2) for Asians. Predictors of T1D among cases included female sex, younger age, lower BMI, insulin use and having T1D based on diagnostic codes.

CONCLUSIONS

T1D may account for up to 8% of incident diabetes cases among young adults. Follow-up is needed to establish the clinical course of patients with one DAA at diagnosis.

The accuracy of provider diagnosed diabetes type in youth compared to an etiologic criteria in the SEARCH for Diabetes in Youth Study

BACKGROUND

Although surveillance for diabetes in youth relies on provider-assigned diabetes type from medical records, its accuracy compared to an etiologic definition is unknown.

METHODS

Using the SEARCH for Diabetes in Youth Registry, we evaluated the validity and accuracy of provider-assigned diabetes type abstracted from medical records against etiologic criteria that included the presence of diabetes autoantibodies (DAA) and insulin sensitivity. Youth who were incident for diabetes in 2002-2006, 2008, or 2012 and had complete data on key analysis variables were included (n = 4001, 85% provider diagnosed type 1). The etiologic definition for type 1 diabetes was ≥1 positive DAA titer(s) or negative DAA titers in the presence of insulin sensitivity and for type 2 diabetes was negative DAA titers in the presence of insulin resistance.

RESULTS

Provider diagnosed diabetes type correctly agreed with the etiologic definition of type for 89.9% of cases. Provider diagnosed type 1 diabetes was 96.9% sensitive, 82.8% specific, had a positive predictive value (PPV) of 97.0% and a negative predictive value (NPV) of 82.7%. Provider diagnosed type 2 diabetes was 82.8% sensitive, 96.9% specific, had a PPV and NPV of 82.7% and 97.0%, respectively.

CONCLUSION

Provider diagnosis of diabetes type agreed with etiologic criteria for 90% of the cases. While the sensitivity and PPV were high for youth with type 1 diabetes, the lower sensitivity and PPV for type 2 diabetes highlights the value of DAA testing and assessment of insulin sensitivity status to ensure estimates are not biased by misclassification.

Detection of Diabetes Status and Type in Youth Using Electronic Health Records: The SEARCH for Diabetes in Youth Study

OBJECTIVE

Diabetes surveillance often requires manual medical chart reviews to confirm status and type. This project aimed to create an electronic health record (EHR)-based procedure for improving surveillance efficiency through automation of case identification.

RESEARCH DESIGN AND METHODS

Youth (<20 years old) with potential evidence of diabetes (N = 8,682) were identified from EHRs at three children's hospitals participating in the SEARCH for Diabetes in Youth Study. True diabetes status/type was determined by manual chart reviews. Multinomial regression was compared with an ICD-10 rule-based algorithm in the ability to correctly identify diabetes status and type. Subsequently, the investigators evaluated a scenario of combining the rule-based algorithm with targeted chart reviews where the algorithm performed poorly.

RESULTS

The sample included 5,308 true cases (89.2% type 1 diabetes). The rule-based algorithm outperformed regression for overall accuracy (0.955 vs. 0.936). Type 1 diabetes was classified well by both methods: sensitivity (Se) (>0.95), specificity (Sp) (>0.96), and positive predictive value (PPV) (>0.97). In contrast, the PPVs for type 2 diabetes were 0.642 and 0.778 for the rule-based algorithm and the multinomial regression, respectively. Combination of the rule-based method with chart reviews (n = 695, 7.9%) of persons predicted to have non-type 1 diabetes resulted in perfect PPV for the cases reviewed while increasing overall accuracy (0.983). The Se, Sp, and PPV for type 2 diabetes using the combined method were ≥0.91.

CONCLUSIONS

An ICD-10 algorithm combined with targeted chart reviews accurately identified diabetes status/type and could be an attractive option for diabetes surveillance in youth.

An Iterative Process for Identifying Pediatric Patients With Type 1 Diabetes: Retrospective Observational Study

Background

The incidence of both type 1 diabetes (T1DM) and type 2 diabetes (T2DM) in children and youth is increasing. However, the current approach for identifying pediatric diabetes and separating by type is costly, because it requires substantial manual efforts.

Objective

The purpose of this study was to develop a computable phenotype for accurately and efficiently identifying diabetes and separating T1DM from T2DM in pediatric patients.

Methods

This retrospective study utilized a data set from the University of Florida Health Integrated Data Repository to identify 300 patients aged 18 or younger with T1DM, T2DM, or that were healthy based on a developed computable phenotype. Three endocrinology residents/fellows manually reviewed medical records of all probable cases to validate diabetes status and type. This refined computable phenotype was then used to identify all cases of T1DM and T2DM in the OneFlorida Clinical Research Consortium.

Results

A total of 295 electronic health records were manually reviewed; of these, 128 cases were found to have T1DM, 35 T2DM, and 132 no diagnosis. The positive predictive value was 94.7%, the sensitivity was 96.9%, specificity was 95.8%, and the negative predictive value was 97.6%. Overall, the computable phenotype was found to be an accurate and sensitive method to pinpoint pediatric patients with T1DM.

Conclusions

We developed a computable phenotype for identifying T1DM correctly and efficiently. The computable phenotype that was developed will enable researchers to identify a population accurately and cost-effectively. As such, this will vastly improve the ease of identifying patients for future intervention studies.

Twelve-year trends in pharmacologic treatment of type 2 diabetes among patients with heart failure in the United States

We conducted a cross-sectional analysis using a database from commercial health plans in the United States to describe trends in the use of antidiabetic medications among patients with type 2 diabetes and heart failure (HF) from 2006 through 2017. We used loop diuretic dose as a surrogate for HF severity (mild HF 0-40 mg/day, moderate-severe HF >40 mg/day). We assessed antidiabetic medication dispensing in the 90 days following HF diagnosis. Over the 12-year period, we identified an increase in the use of metformin (39.2% vs. 62.6%), dipeptidyl peptidase-4 inhibitors (DPP-4i) (0.5% vs. 17.1%) and sodium-glucose co-transporter-2 inhibitors (SGLT-2i) (0.0% vs. 9.0%), but a decrease in the use of sulphonylureas (47.8% vs. 27.8%) and thiazolidinediones (TZDs) (31.7% vs. 5.3%). In 2017, patients with moderate-severe HF more commonly used insulin (43.1%); a majority of mild HF patients used metformin (62.8%). A proportion of patients with moderate-severe HF used TZDs (4.4%). Among patients with diabetes and HF, the use of metformin and DPP-4i rapidly increased, but a proportion of patients with moderate-severe HF continued to use TZDs. Despite their promising cardiovascular safety profile, SGLT-2i use remains limited.

Use of Sodium-Glucose Cotransporter 2 Inhibitors in Patients With Type 1 Diabetes and Rates of Diabetic Ketoacidosis

OBJECTIVE

To estimate real-world off-label use of sodium-glucose cotransporter 2 (SGLT2) inhibitors in patients with type 1 diabetes, estimate rates of diabetic ketoacidosis (DKA), and compare them with DKA rates observed in sotagliflozin clinical trials.

RESEARCH DESIGN AND METHODS

We identified initiators of SGLT2 inhibitors in the Sentinel System from March 2013 to June 2018, determined the prevalence of type 1 diabetes using a narrow and a broad definition, and measured rates of DKA using administrative claims data. Standardized incidence ratios (SIRs) were calculated using age- and sex-specific follow-up time in Sentinel and age- and sex-specific DKA rates from sotagliflozin trials 309, 310, and 312.

RESULTS

Among 475,527 initiators of SGLT2 inhibitors, 0.50% and 0.92% met narrow and broad criteria for type 1 diabetes, respectively. Rates of DKA in the narrow and broad groups were 7.3/100 person-years and 4.5/100 person-years, respectively. Among patients who met narrow criteria for type 1 diabetes, rates of DKA were highest for patients aged 25-44 years, especially females aged 25-44 years (19.7/100 person-years). More DKA events were observed during off-label use of SGLT2 inhibitors in Sentinel than would be expected based on sotagliflozin clinical trials (SIR = 1.83; 95% CI 1.45-2.28).

CONCLUSIONS

Real-world off-label use of SGLT2 inhibitors among patients with type 1 diabetes accounted for a small proportion of overall SGLT2 inhibitor use. However, the risk for DKA during off-label use was notable, especially among young, female patients. Although real-word rates of DKA exceeded the expectation based on clinical trials, results should be interpreted with caution due to differences in study methods, patient samples, and study drugs.

Medical Costs Among Youth Younger Than 20 Years of Age With and Without Diabetic Ketoacidosis at the Time of Diabetes Diagnosis

OBJECTIVE

While diabetic ketoacidosis (DKA) is common in youth at the onset of the diabetes, the excess costs associated with DKA are unknown. We aimed to quantify the health care services use and medical care costs related to the presence of DKA at diagnosis of diabetes.

RESEARCH DESIGN AND METHODS

We analyzed data from the U.S. MarketScan claims database for 4,988 enrollees aged 3-19 years insured in private fee-for-service plans and newly diagnosed with diabetes during 2010-2016. Youth with and without DKA at diabetes diagnosis were compared for mean health care service use (outpatient, office, emergency room, and inpatient visits) and medical costs (outpatient, inpatient, prescription drugs, and total) for 60 days prior to and 60 days after diabetes diagnosis. A two-part model using generalized linear regression and logistic regression was used to estimate medical costs, controlling for age, sex, rurality, health plan, year, presence of hypoglycemia, and chronic pulmonary condition. All costs were adjusted to 2016 dollars.

RESULTS

At diabetes diagnosis, 42% of youth had DKA. In the 60 days prior to diabetes diagnosis, youth with DKA at diagnosis had less health services usage (e.g., number of outpatient visits: -1.17; P < 0.001) and lower total medical costs (-$635; P < 0.001) compared with youth without DKA at diagnosis. In the 60 days after diagnosis, youth with DKA had significantly greater health care services use and health care costs ($6,522) compared with those without DKA.

CONCLUSIONS

Among youth with newly diagnosed diabetes, DKA at diagnosis is associated with significantly higher use of health care services and medical costs.