Development and validation of algorithms to identify newly diagnosed type 1 and type 2 diabetes in pediatric population using electronic medical records and claims data

Examination of Prediabetes and Diabetes Testing Among US Pediatric Patients With Overweight or Obesity Using an Electronic Health Record

Background: Youth with excess weight are at risk of developing type 2 diabetes (T2DM). Guidelines recommend screening for prediabetes and/or T2DM after 10 years of age or after puberty in youth with excess weight who have ≥1 risk factor(s) for T2DM. Electronic health records (EHRs) offer an opportunity to study the use of tests to detect diabetes in youth. Methods: We examined the frequency of (1) diabetes testing and (2) elevated test results among youth aged 10-19 years with at least one BMI measurement in an EHR from 2019 to 2021. We examined the presence of hemoglobin A1C (A1C), fasting plasma glucose (FPG), or oral glucose tolerance test (2-hour plasma glucose [2-hrPG]) results and, among those tested, the frequency of elevated values (A1C ≥6.5%, FPG ≥126 mg/dL, or 2-hrPG ≥200 mg/dL). Patients with pre-existing diabetes (n = 6793) were excluded. Results: Among 1,024,743 patients, 17% had overweight, 21% had obesity, including 8% with severe obesity. Among patients with excess weight, 10% had ≥1 glucose test result. Among those tested, elevated values were more common in patients with severe obesity (27%) and obesity (22%) than in those with healthy weight (8%), and among Black youth (30%) than White youth (13%). Among patients with excess weight, >80% of elevated values fell in the prediabetes range. Conclusions: In youth with excess weight, the use of laboratory tests for prediabetes and T2DM was infrequent. Among youth with test results, elevated FPG, 2hrPG, or A1C levels were most common in those with severe obesity and Black youth.

Real-World Evaluation of an Automated Algorithm to Detect Patients With Potentially Undiagnosed Hypertension Among Patients With Routine Care in Hawai'i

BACKGROUND

This real-world evaluation considers an algorithm designed to detect patients with potentially undiagnosed hypertension, receiving routine care, in a large health system in Hawai'i. It quantifies patients identified as potentially undiagnosed with hypertension; summarizes the individual, clinical, and health system factors associated with undiagnosed hypertension; and examines if the COVID-19 pandemic affected detection.

METHODS AND RESULTS

We analyzed the electronic health records of patients treated across 6 clinics from 2018 to 2021. We calculated total patients with potentially undiagnosed hypertension and compared patients flagged for undiagnosed hypertension to those with diagnosed hypertension and to the full patient panel across individual characteristics, clinical and health system factors (eg, clinic of care), and timing. Modified Poisson regression was used to calculate crude and adjusted risk ratios. Among the eligible patients (N=13 364), 52.6% had been diagnosed with hypertension, 2.7% were flagged as potentially undiagnosed, and 44.6% had no evidence of hypertension. Factors associated with a higher risk of potentially undiagnosed hypertension included individual characteristics (ages 40-84 compared with 18-39 years), clinical (lack of diabetes diagnosis) and health system factors (clinic site and being a Medicaid versus a Medicare beneficiary), and timing (readings obtained after the COVID-19 Stay-At-Home Order in Hawai'i).

CONCLUSIONS

This evaluation provided evidence that a clinical algorithm implemented within a large health system's electronic health records could detect patients in need of follow-up to determine hypertension status, and it identified key individual characteristics, clinical and health system factors, and timing considerations that may contribute to undiagnosed hypertension among patients receiving routine care.

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.

A Project ECHO and Community Health Worker Intervention for Patients with Diabetes

BACKGROUND

Both community health workers and the Project ECHO model of specialist telementoring are innovative approaches to support primary care providers in the care of complex patients with diabetes. We studied the effect of an intervention that combined these 2 approaches on glycemic control.

METHODS

Patients with diabetes were recruited from 10 federally qualified health centers in New Mexico. We used electronic health record (EHR) data to compare HbA1c levels prior to intervention enrollment with HbA1c levels after 3 months (early follow-up) and 12 months (late follow-up) following enrollment. We propensity matched intervention patients to comparison patients from other sites within the same electronic health records databases to estimate the average treatment effect.

RESULTS

Among 557 intervention patients with HbA1c data, mean HbA1c decreased from 10.5% to 9.3% in the pre- versus postintervention periods (P < .001). As compared to the comparison group, the intervention was associated with a change in HbA1c of -0.2% (95% confidence interval [CI] -0.4%-0.5%) and -0.3 (95% CI -0.5-0.0) in the early and late follow-up cohorts, respectively. The intervention was associated with a significant increase in percentage of patients with HbA1c <8% in the late follow-up cohort (8.1%, 95% CI 2.2%-13.9%) but not the early follow-up cohort (3.6%, 95% CI -1.5% to 8.7%) DISCUSSION: The intervention was associated with a substantial decrease in HbA1c in intervention patients, although this improvement was not different from matched comparison patients in early follow-up. Although combining community health workers with Project ECHO may hold promise for improving glycemic control, particularly in the longer term, further evaluations are needed.

Use of Linked Databases for Improved Confounding Control: Considerations for Potential Selection Bias

Pharmacoepidemiologic studies are increasingly conducted within linked databases, often to obtain richer confounder data. However, the potential for selection bias is frequently overlooked when linked data is available only for a subset of patients. We highlight the importance of accounting for potential selection bias by evaluating the association between antipsychotics and type 2 diabetes in youths within a claims database linked to a smaller laboratory database. We used inverse probability of treatment weights (IPTW) to control for confounding. In analyses restricted to the linked cohorts, we applied inverse probability of selection weights (IPSW) to create a population representative of the full cohort. We used pooled logistic regression weighted by IPTW only or IPTW and IPSW to estimate treatment effects. Metabolic conditions were more prevalent in linked cohorts compared with the full cohort. Within the full cohort, the confounding-adjusted hazard ratio was 2.26 (95% CI: 2.07, 2.49) comparing initiation of antipsychotics with initiation of control medications. Within the linked cohorts, a different magnitude of association was obtained without adjustment for selection, whereas applying IPSW resulted in point estimates similar to the full cohort's (e.g., an adjusted hazard ratio of 1.63 became 2.12). Linked database studies may generate biased estimates without proper adjustment for potential selection bias.

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.

Association of Selective Serotonin Reuptake Inhibitors With the Risk of Type 2 Diabetes in Children and Adolescents

IMPORTANCE

Concerns exist that use of selective serotonin reuptake inhibitors (SSRIs) increases the risk of developing type 2 diabetes (T2D) in adults, but evidence in children and adolescents is limited. In the absence of a randomized clinical trial, evidence must be generated using real-world data.

OBJECTIVE

To evaluate the safety of SSRI use in children and adolescents with respect to the associated risk of T2D.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study of patients aged 10 to 19 years with a diagnosis for an SSRI treatment indication was conducted within the nationwide Medicaid Analytic eXtract (MAX; January 1, 2000, to December 31, 2014) and the IBM MarketScan (January 1, 2003, to September 30, 2015) databases. Data were analyzed from November 1, 2018, to December 6, 2019.

EXPOSURES

New users of an SSRI medication and comparator groups with no known metabolic adverse effects (no antidepressant exposure, bupropion hydrochloride exposure, or psychotherapy exposure). Within-class individual SSRI medications were compared with fluoxetine hydrochloride.

MAIN OUTCOMES AND MEASURES

Incident T2D during follow-up. Intention-to-treat effects were estimated using Cox proportional hazards regression models, adjusting for confounding through propensity score stratification. As-treated effects to account for continuous treatment were estimated using inverse probability weighting and marginal structural models.

RESULTS

A total of 1 582 914 patients were included in the analysis (58.3% female; mean [SD] age, 15.1 [2.3] years). The SSRI-treated group included 316 178 patients in the MAX database (publicly insured; mean [SD] age, 14.7 [2.1] years; 62.2% female) and 211 460 in the MarketScan database (privately insured; mean [SD] age, 15.8 [2.3] years; 63.9% female) with at least 2 SSRI prescriptions filled, followed up for a mean (SD) of 2.3 (2.0) and 2.2 (1.9) years, respectively. In publicly insured patients, initiation of SSRI treatment was associated with a 13% increased hazard of T2DM (intention-to-treat adjusted hazard ratio [aHR], 1.13; 95% CI, 1.04-1.22) compared with untreated patients. The association strengthened for continuous SSRI treatment (as-treated aHR, 1.33; 95% CI, 1.21-1.47), corresponding to 6.6 (95% CI, 4.2-10.4) additional cases of T2D per 10 000 patients treated for at least 2 years. Adjusted HRs were lower in privately insured patients (intention-to-treat aHR, 1.01 [95% CI, 0.84-1.23]; as-treated aHR, 1.10 [95% CI, 0.88-1.36]). Findings were similar when comparing SSRI treatment with psychotherapy (publicly insured as-treated aHR, 1.44 [95% CI, 1.25-1.65]; privately insured as-treated aHR, 1.21 [95% CI, 0.93-1.57]), whereas no increased risk was observed compared with bupropion treatment publicly insured as-treated aHR, 1.01 [95% CI, 0.79-1.29]; privately insured as-treated aHR, 0.87 [95% CI, 0.44-1.70]). For the within-class analysis, no medication had an increased hazard of T2D compared with fluoxetine.

CONCLUSIONS AND RELEVANCE

These findings suggest that children and adolescents initiating SSRI treatment may be at a small increased risk of developing T2D, particularly publicly insured patients. The magnitude of association was more modest than previously reported, and the absolute risk was small. The potential small risk should be viewed in relation to the efficacy of SSRIs for its major indications in young patients.

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.