Development and Validation of a Claims-Based Model to Predict Categories of Obesity.

We developed and validated a claims-based algorithm that classifies patients into obesity categories. Using Medicare (2007-2017) and Medicaid (2000-2014) claims data linked to 2 electronic health record (EHR) systems in Boston, Massachusetts, we identified a cohort of patients with an EHR-based body mass index (BMI) measurement (calculated as weight (kg)/height (m)2). We used regularized regression to select from 137 variables and built generalized linear models to classify patients with BMIs of ≥25, ≥30, and ≥40. We developed the prediction model using EHR system 1 (training set) and validated it in EHR system 2 (validation set). The cohort contained 123,432 patients in the Medicare population and 40,736 patients in the Medicaid population. The model comprised 97 variables in the Medicare set and 95 in the Medicaid set, including BMI-related diagnosis codes, cardiovascular and antidiabetic drugs, and obesity-related comorbidities. The areas under the receiver-operating-characteristic curve in the validation set were 0.72, 0.75, and 0.83 (Medicare) and 0.66, 0.66, and 0.70 (Medicaid) for BMIs of ≥25, ≥30, and ≥40, respectively. The positive predictive values were 81.5%, 80.6%, and 64.7% (Medicare) and 81.6%, 77.5%, and 62.5% (Medicaid), for BMIs of ≥25, ≥30, and ≥40, respectively. The proposed model can identify obesity categories in claims databases when BMI measurements are missing and can be used for confounding adjustment, defining subgroups, or probabilistic bias analysis.

Identifying Functional Status Impairment in People Living With Dementia Through Natural Language Processing of Clinical Documents: Cross-Sectional Study.

BACKGROUND: Assessment of activities of daily living (ADLs) and instrumental ADLs (iADLs) is key to determining the severity of dementia and care needs among older adults. However, such information is often only documented in free-text clinical notes within the electronic health record and can be challenging to find. OBJECTIVE: This study aims to develop and validate machine learning models to determine the status of ADL and iADL impairments based on clinical notes. METHODS: This cross-sectional study leveraged electronic health record clinical notes from Mass General Brigham's Research Patient Data Repository linked with Medicare fee-for-service claims data from 2007 to 2017 to identify individuals aged 65 years or older with at least 1 diagnosis of dementia. Notes for encounters both 180 days before and after the first date of dementia diagnosis were randomly sampled. Models were trained and validated using note sentences filtered by expert-curated keywords (filtered cohort) and further evaluated using unfiltered sentences (unfiltered cohort). The model's performance was compared using area under the receiver operating characteristic curve and area under the precision-recall curve (AUPRC). RESULTS: The study included 10,000 key-term-filtered sentences representing 441 people (n=283, 64.2% women; mean age 82.7, SD 7.9 years) and 1000 unfiltered sentences representing 80 people (n=56, 70% women; mean age 82.8, SD 7.5 years). Area under the receiver operating characteristic curve was high for the best-performing ADL and iADL models on both cohorts (>0.97). For ADL impairment identification, the random forest model achieved the best AUPRC (0.89, 95% CI 0.86-0.91) on the filtered cohort; the support vector machine model achieved the highest AUPRC (0.82, 95% CI 0.75-0.89) for the unfiltered cohort. For iADL impairment, the Bio+Clinical bidirectional encoder representations from transformers (BERT) model had the highest AUPRC (filtered: 0.76, 95% CI 0.68-0.82; unfiltered: 0.58, 95% CI 0.001-1.0). Compared with a keyword-search approach on the unfiltered cohort, machine learning reduced false-positive rates from 4.5% to 0.2% for ADL and 1.8% to 0.1% for iADL. CONCLUSIONS: In this study, we demonstrated the ability of machine learning models to accurately identify ADL and iADL impairment based on free-text clinical notes, which could be useful in determining the severity of dementia.

Occurrence of inflammatory bowel disease in patients with chronic inflammatory skin diseases: a cohort study: Classification: Epidemiology.

BACKGROUND: Several studies have linked various chronic inflammatory skin diseases (CISDs) with inflammatory bowel disease (IBD) in a range of data sources with mixed conclusions. OBJECTIVES: We compared the incidence of IBD - ulcerative colitis (UC) and Crohn disease (CD) - in patients with a CISD vs. similar persons without a CISD. METHODS: In this cohort study using nationwide, longitudinal, commercial insurance claims data from the USA, we identified adults and children who were seen by a dermatologist between 2004 and 2020, and diagnosed with either psoriasis, atopic dermatitis, alopecia areata, vitiligo or hidradenitis suppurativa. Comparator patients were identified through risk-set sampling; they were eligible if they were seen by a dermatologist at least twice and not diagnosed with a CISD. Patient follow-up lasted until either IBD diagnosis, death, disenrolment or end of data stream, whichever came first. IBD events, UC or CD, were identified via validated algorithms: hospitalization or diagnosis with endoscopic confirmation. Incidence rates were computed before and after adjustment via propensity-score decile stratification to account for IBD risk factors. Hazard ratios (HR) and 95% confidence intervals (CIs) were estimated to compare the incidence of IBD in CISD vs. non-CISD. RESULTS: We identified patients with atopic dermatitis (n = 123 614), psoriasis (n = 83 049), alopecia areata (n = 18 135), vitiligo (n = 9003) or hidradenitis suppurativa (n = 6806), and comparator patients without a CISD (n = 2 376 120). During a median follow-up time of 718 days, and after applying propensity-score adjustment for IBD risk factors, we observed increased risk of both UC (HRUC 2·30, 95% CI 1·61-3·28) and CD (HRCD 2·70, 1·69-4·32) in patients with hidradenitis suppurativa, an increased risk of CD (HRCD 1·23, 1·03-1·46) but not UC (HRUC 1·01, 0·89-1·14) in psoriasis, and no increased risk of IBD in atopic dermatitis (HRUC 1·02, 0·92-1·12; HRCD 1·08, 0·94-1·23), alopecia areata (HRUC 1·18, 0·89-1·56; HRCD 1·26, 0·86-1·86) or vitiligo (HRUC 1·14, 0·77-1·68; HRCD 1·45, 0·87-2·41). CONCLUSIONS: IBD was increased in patients with hidradenitis suppurativa. CD alone was increased in patients with psoriasis. Neither UC nor CD was increased in patients with atopic dermatitis, alopecia areata or vitiligo. What is already known about this topic? Several studies have linked various chronic inflammatory skin diseases (CISDs) with inflammatory bowel disease (IBD) utilizing a range of data sources, with mixed conclusions. What does this study add? This large-scale, claims-based cohort study expands current knowledge by providing background rates for IBD across multiple CISDs using consistent methods and within a single, nationally representative patient population. We observed a relative increased risk of IBD in patients with hidradenitis suppurativa, but the overall incidence rate difference of IBD was generally low. Crohn disease alone was significantly increased in patients with psoriasis, and neither ulcerative colitis nor Crohn disease was increased in patients with atopic dermatitis, vitiligo or alopecia areata.

Accuracy of identifying diagnosis of moderate to severe chronic kidney disease in administrative claims data.

BACKGROUND: Prior validation studies of claims-based definitions of chronic kidney disease (CKD) using ICD-9 codes reported overall low sensitivity, high specificity, and variable but reasonable PPV. No studies to date have evaluated the accuracy of ICD-10 codes to identify a US patient population with CKD. METHODS: We assessed the accuracy of claims-based algorithms to identify adults with CKD Stages 3-5 compared with laboratory values in a subset (~40%) of a US commercial insurance claims database (Optum's de-identified Clinformatics® Data Mart Database). We calculated the positive predictive value (PPV) of one or two ICD-9 (2012-2014) or ICD-10 (2016-2018) codes for CKD compared with a lab-based estimated glomerular filtration rate (eGFR) occurring within prespecified windows (±90 days, ±180 days, ±365 days) of the ICD-based CKD code(s). RESULTS: The study population ranged between 104 774 and 161 305 patients (ICD-9 cohorts) and between 285 520 and 373 220 patients (ICD-10 cohorts). The mean age was 74.4 years (ICD-9) and 75.6 years (ICD-10) and the median eGFR was 48 ml/min/1.73 m2 . The algorithm of two CKD codes compared with a lab value ±90 days of the first code achieved the highest PPV (PPV 86.36% [ICD-9] and 86.07% [ICD-10]). Overall, ICD-10 based codes had comparable PPVs to ICD-9 based codes and all ICD-10 based algorithms had PPVs >80%. The algorithm of one CKD code compared with laboratory value ±180 days maintained the PPV above 80% but still retained a large number of patients (PPV 80.32% [ICD-9] and 81.56% [ICD-10]). CONCLUSION: An ICD-10-based definition of CKD identified with sufficient accuracy a patient population with CKD Stages 3-5. Our findings suggest that claims databases could be used for future real-world research studies in patients with CKD Stages 3-5.

Medication Burden and Prescribing Patterns in Patients on Hemodialysis in the USA, 2013-2017.

INTRODUCTION: The medication burden of patients with end-stage renal disease (ESRD) on hemodialysis, a patient population with a high comorbidity burden and complex care requirements, is among the highest of any of the chronic diseases. The goal of this study was to describe the medication burden and prescribing patterns in a contemporary cohort of patients with ESRD on hemodialysis in the USA. METHODS: We used the United States Renal Data System database from January 1, 2013, and December 31, 2017, to quantify the medication burden of patients with ESRD on hemodialysis aged ≥18 years. We calculated the average number of prescription medications per patient during each respective year (January-December), number of medications within classes, including potentially harmful medications, and trends in the number of medications and classes over the 5-year study period. RESULTS: We included a total of 163,228 to 176,133 patients from 2013 to 2017. The overall medication burden decreased slightly, from a mean of 7.4 (SD 3.8) medications in 2013 to 6.8 (SD 3.6) medications in 2017. Prescribing of potentially harmful medications decreased over time (74.0% with at least one harmful medication class in 2013-68.5% in 2017). In particular, the prescribing of non-benzodiazepine hypnotics, benzodiazepines, and opioids decreased from 2013 to 2017 (12.2%-6.3%, 23.4%-19.3%, and 60.0%-53.4%, respectively). This trend was consistent across subgroups of age, sex, race, and low-income subsidy status. CONCLUSIONS: Patients with ESRD on hemodialysis continued to have a high overall medication burden, with a slight reduction over time accompanied by a decrease in prescribing of several classes of harmful medications. Continued emphasis on assessment of appropriateness of high medication burden in patients with ESRD is needed to avoid exposure to potentially harmful or futile medications in this patient population.

Validity of claims-based algorithms to identify neurodevelopmental disorders in children.

PURPOSE: To validate healthcare claim-based algorithms for neurodevelopmental disorders (NDD) in children using medical records as the reference. METHODS: Using a clinical data warehouse of patients receiving outpatient or inpatient care at two hospitals in Boston, we identified children (≤14 years between 2010 and 2014) with at least one of the following NDDs according to claims-based algorithms: autism spectrum disorder/pervasive developmental disorder (ASD), attention deficit disorder/other hyperkinetic syndromes of childhood (ADHD), learning disability, speech/language disorder, developmental coordination disorder (DCD), intellectual disability, and behavioral disorder. Fifty cases per outcome were randomly sampled and their medical records were independently reviewed by two physicians to adjudicate the outcome presence. Positive predictive values (PPVs) and 95% confidence intervals (CIs) were calculated. RESULTS: PPVs were 94% (95% CI, 83%-99%) for ASD, 88% (76%-95%) for ADHD, 98% (89%-100%) for learning disability, 98% (89%-100%) for speech/language disorder, 82% (69%-91%) for intellectual disability, and 92% (81%-98%) for behavioral disorder. A total of 19 of the 50 algorithm-based cases of DCD were confirmed as severe coordination disorders with functional impairment, with a PPV of 38% (25%-53%). Among the 31 false-positive cases of DCD were 7 children with coordination deficits that did not persist throughout childhood, 7 with visual-motor integration deficits, 12 with coordination issues due to an underlying medical condition and 5 with ADHD and at least one other severe NDD. CONCLUSIONS: PPVs were generally high (range: 82%-98%), suggesting that claims-based algorithms can be used to study NDDs. For DCD, additional criteria are needed to improve the classification of true cases.

Antipsychotic Medication Use Among Older Adults Following Infection-Related Hospitalization.

Importance: There are limited data on discontinuation rates of antipsychotic medications (APMs) used to treat delirium due to acute hospitalization in the routine care of older adults. Objective: To investigate discontinuation rates and patient characteristics of APMs used to treat delirium following infection-related hospitalization among older US adults. Design, Setting, and Participants: This retrospective cohort study was conducted using US claims data (Optum's deidentified Clinformatics Data Mart database) for January 1, 2004, to May 31, 2022. Patients were aged 65 years or older without prior psychiatric disorders and had newly initiated an APM prescription within 30 days of an infection-related hospitalization. Statistical analysis was performed on December 15, 2022. Exposures: New use (no prior use any time before cohort entry) of oral haloperidol and atypical APMs (aripiprazole, olanzapine, quetiapine, risperidone, etc). Main Outcomes and Measures: The primary outcome was APM discontinuation, defined as a gap of more than 15 days following the end of an APM dispensing. Survival analyses and Kaplan-Meier analyses were used. Results: Our study population included 5835 patients. Of these individuals, 790 (13.5%) were new haloperidol users (mean [SD] age, 81.5 [6.7] years; 422 women [53.4%]) and 5045 (86.5%) were new atypical APM users (mean [SD] age, 79.8 [7.0] years; 2636 women [52.2%]). The cumulative incidence of discontinuation by 30 days after initiation was 11.4% (95% CI, 10.4%-12.3%) among atypical APM users and 52.1% (95% CI, 48.2%-55.7%) among haloperidol users (P < .001 for difference between haloperidol vs atypical APMs). We observed an increasing trend in discontinuation rates from 2004 to 2022 (5% increase [95% CI, 3%-7%] per year) for haloperidol users (adjusted hazard ratio, 1.05 [1.03-1.07]; P < .001) but not for atypical APM users (1.00 [0.99-1.01]; P = .67). Prolonged hospitalization and dementia were inversely associated with the discontinuation of haloperidol and atypical APMs. Conclusions and Relevance: The findings of this cohort study suggest that the discontinuation rate of newly initiated APMs for delirium following infection-related hospitalization was lower in atypical APM users than in haloperidol users, with prolonged hospitalization and dementia as major associated variables. The discontinuation rate was substantially higher in recent years for haloperidol but not for atypical APMs.

Development and Validation of a Novel Tool to Predict Model for End-Stage Liver Disease (MELD) Scores in Cirrhosis, Using Administrative Datasets.

Background: The Model for End-Stage Liver Disease (MELD) score predicts disease severity and mortality in cirrhosis. To improve cirrhosis phenotyping in administrative databases lacking laboratory data, we aimed to develop and externally validate claims-based MELD prediction models, using claims data linked to electronic health records (EHR). Methods: We included adults with established cirrhosis in two Medicare-linked EHR networks (training and internal validation; 2007-2017), and a Medicaid-linked EHR network (external validation; 2000-2014). Using least absolute shrinkage and selection operator (LASSO) with 5-fold cross-validation, we selected among 146 investigator-specified variables to develop models for predicting continuous MELD and relevant MELD categories (MELD<10, MELD≥15 and MELD≥20), with observed MELD calculated from laboratory data. Regression coefficients for each model were applied to the validation sets to predict patient-level MELD and assess model performance. Results: We identified 4501 patients in the Medicare training set (mean age 75.1 years, 18.5% female, mean MELD=13.0), and 2435 patients in the Medicare validation set (mean age: 74.3 years, 31.7% female, mean MELD=12.3). Our final model for predicting continuous MELD included 112 variables, explaining 58% of observed MELD variability; in the Medicare validation set, the area-under-the-receiver operating characteristic curves (AUC) for MELD<10 and MELD≥15 were 0.84 and 0.90, respectively; the AUC for the model predicting MELD≥20 (using 27 variables) was 0.93. Overall, these models correctly classified 77% of patients with MELD<10 (95% CI=0.75-0.78), 85% of patients with MELD≥15 (95% CI=0.84-0.87), and 87% of patients with MELD≥20 (95% CI=0.86-0.88). Results were consistent in the external validation set (n=2240). Conclusion: Our MELD prediction tools can be used to improve cirrhosis phenotyping in administrative datasets lacking laboratory data.

Neurodevelopmental Disorders Among Publicly or Privately Insured Children in the United States.

IMPORTANCE: Neurodevelopmental disorders are associated with poor health and social outcomes. Population-based data on incidence, age at diagnosis, and demographic variations are essential to identify modifiable risk factors and inform the planning of services and interventions. OBJECTIVES: To assess the incidence and timing of diagnosis of neurodevelopmental disorders during childhood in the US and to evaluate differences by population characteristics. DESIGN, SETTING, AND PARTICIPANTS: This population-based cohort study used nationwide data on birth cohorts nested in the 2000-2014 Medicaid Analytic eXtract and the 2003-2015 IBM MarketScan Research Database on 2 070 541 publicly and 1 309 900 privately insured children enrolled at birth. Data were analyzed between May 1, 2020, and June 30, 2021. MAIN OUTCOMES AND MEASURES: Neurodevelopmental disorders, autism spectrum disorders, attention-deficit/hyperactivity disorder, learning disabilities, speech or language disorders, developmental coordination disorders, intellectual disabilities, and behavioral disorders were identified based on validated algorithms. Kaplan-Meier analyses were used to estimate the incidence and timing of diagnosis, stratified by child's sex, birth year, maternal age at delivery, and race and ethnicity. RESULTS: The cohorts comprised 2 070 541 publicly insured children (1 045 426 boys [50.5%]) and 1 309 900 privately insured children (667 607 boys [51.0%]) enrolled at birth. By 8 years of age, 23.9% of publicly insured children and 11.0% of privately insured children received a diagnosis of 1 or more neurodevelopmental disorders (autism spectrum disorder, 1.6% and 1.3%; attention-deficit/hyperactivity disorder, 14.5% and 5.8%; learning disability, 1.2% and 0.6%; speech or language disorder, 8.4% and 4.5%; developmental coordination disorder, 0.9% and 0.7%; intellectual disability, 0.7% and 0.1%; and behavioral disorder, 8.4% and 1.5%). Risks were substantially higher among boys (incidence of ≥1 neurodevelopmental disorder by age 8 years for boys vs girls: 30.7% vs 16.7% among publicly insured children and 15.0% vs 6.7% among privately insured children) and White children (30.2% vs 9.1% among Asian children, 23.0% among Black children, 15.4% among Hispanic children, and 22.7% among children of unknown race or ethnicity; information on race and ethnicity was available only for publicly insured children). The association of maternal age and birth year with incidence of neurodevelopmental disorders varied by outcome. Except for attention-deficit/hyperactivity disorder, the diagnosis tended to be established somewhat earlier for privately insured children. The association of race and ethnicity with age at diagnosis varied by outcome. Co-occurring neurodevelopmental disorders were common, especially among children with autism spectrum disorder and intellectual disability (>70% had ≥1 other disorder). CONCLUSIONS AND RELEVANCE: In this population-based cohort study, a relatively high incidence of and co-occurrence of neurodevelopmental disorders as well as the disparity in incidence and timing of diagnosis by insurance type and race and ethnicity were found. These findings represent important public health concerns and underscore the need for timely and accessible developmental assessments and educational services to help reduce the burden of these disorders.