Validation of International Classification of Diseases, Tenth Revision, Clinical Modification Codes for Identifying Cases of Chlamydia and Gonorrhea.

BACKGROUND: While researchers seek to use administrative health data to examine outcomes for individuals with sexually transmitted infections (STIs), the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes used to identify persons with chlamydia and gonorrhea have not been validated. Objectives were to determine the validity of using ICD-10-CM codes to identify individuals with chlamydia and gonorrhea. METHODS: We used data from electronic health records gathered from public and private health systems from October 1, 2015, to December 31, 2016. Patients were included if they were aged 13 to 44 years and received either (1) laboratory testing for chlamydia or gonorrhea or (2) an ICD-10-CM diagnosis of chlamydia, gonorrhea, or an unspecified STI. To validate ICD-10-CM codes, we calculated positive and negative predictive values, sensitivity, and specificity based on the presence of a laboratory test result. We further examined the timing of clinical diagnosis relative to laboratory testing. RESULTS: The positive predictive values for chlamydia, gonorrhea, and unspecified STI ICD-10-CM codes were 87.6%, 85.0%, and 32.0%, respectively. Negative predictive values were high (>92%). Sensitivity for chlamydia diagnostic codes was 10.6%, and gonorrhea was 9.7%. Specificity was 99.9% for both chlamydia and gonorrhea. The date of diagnosis occurred on or after the date of the laboratory result for 84.8% of persons with chlamydia, 91.9% for gonorrhea, and 23.5% for unspecified STI. CONCLUSIONS: Disease-specific ICD-10-CM codes accurately identify persons with chlamydia and gonorrhea. However, low sensitivities suggest that most individuals could not be identified in administrative data alone without laboratory test results.

Provider Adherence to Syphilis Testing Guidelines Among Stillbirth Cases.

BACKGROUND: The Centers for Disease Control and Prevention (CDC) recommends that all women with a stillbirth have a syphilis test after delivery. Our study seeks to evaluate adherence to CDC guidelines for syphilis screening among women with a stillbirth delivery. METHODS: We used data recorded in electronic health records for women who gave birth between January 1, 2014, and December 31, 2016. Patients were included if they were 18 to 44 years old and possessed an International Classification of Diseases, Ninth Revision or Tenth Revision, Clinical Modification diagnosis of stillbirth. Stillbirth diagnoses were confirmed through a random sample of medical chart reviews. To evaluate syphilis screening, we estimated the proportion of women who received syphilis testing within 300 days before stillbirth, women who received syphilis testing within 30 days after a stillbirth delivery, and women who received syphilis testing both before and after stillbirth delivery. RESULTS: We identified 1111 stillbirths among a population of 865,429 unique women with encounter data available from electronic health records. Among a sample of 127 chart-reviewed cases, only 35 (27.6%) were confirmed stillbirth cases, 45 (35.4%) possible stillbirth cases, 39 (30.7%) cases of miscarriage, and 8 (6.3%) cases of live births. Among confirmed stillbirth cases, 51.4% had any syphilis testing conducted, 31.4% had testing before their stillbirth delivery, 42.9% had testing after the delivery, and only 22.9% had testing before and after delivery. CONCLUSIONS: A majority of women with a stillbirth delivery do not receive syphilis screening adherent to CDC guidelines. Stillbirth International Classification of Diseases codes do not accurately identify cases of stillbirth.

Sarcopenia, frailty and cachexia patients detected in a multisystem electronic health record database.

BACKGROUND: Sarcopenia, cachexia and frailty have overlapping features and clinical consequences, but often go unrecognized. The objective was to detect patients described by clinicians as having sarcopenia, cachexia or frailty within electronic health records (EHR) and compare clinical variables between cases and matched controls. METHODS: We conducted a case-control study using retrospective data from the Indiana Network for Patient Care multi-health system database from 2016 to 2017. The computable phenotype combined ICD codes for sarcopenia, cachexia and frailty, with clinical note text terms for sarcopenia, cachexia and frailty detected using natural language processing. Cases with these codes or text terms were matched to controls without these codes or text terms matched on birth year, sex and race. Two physicians reviewed EHR for all cases and a subset of controls. Comorbidity codes, laboratory values, and other coded clinical variables were compared between groups using Wilcoxon matched-pair sign-rank test for continuous variables and conditional logistic regression for binary variables. RESULTS: Cohorts of 9594 cases and 9594 matched controls were generated. Cases were 59% female, 69% white, and a median (1st, 3rd quartiles) age 74.9 (62.2, 84.8) years. Most cases were detected by text terms without ICD codes n = 8285 (86.4%). All cases detected by ICD codes (total n = 1309) also had supportive text terms. Overall 1496 (15.6%) had concurrent terms or codes for two or more of the three conditions (sarcopenia, cachexia or frailty). Of text term occurrence, 97% were used positively for sarcopenia, 90% for cachexia, and 95% for frailty. The remaining occurrences were negative uses of the terms or applied to someone other than the patient. Cases had lower body mass index, albumin and prealbumin, and significantly higher odds ratios for diabetes, hypertension, cardiovascular and peripheral vascular diseases, chronic kidney disease, liver disease, malignancy, osteoporosis and fractures (all p < 0.05). Cases were more likely to be prescribed appetite stimulants and caloric supplements. CONCLUSIONS: Patients detected with a computable phenotype for sarcopenia, cachexia and frailty differed from controls in several important clinical variables. Potential uses include detection among clinical cohorts for targeting recruitment for research and interventions.

Leveraging data visualization and a statewide health information exchange to support COVID-19 surveillance and response: Application of public health informatics.

OBJECTIVE: We sought to support public health surveillance and response to coronavirus disease 2019 (COVID-19) through rapid development and implementation of novel visualization applications for data amalgamated across sectors. MATERIALS AND METHODS: We developed and implemented population-level dashboards that collate information on individuals tested for and infected with COVID-19, in partnership with state and local public health agencies as well as health systems. The dashboards are deployed on top of a statewide health information exchange. One dashboard enables authorized users working in public health agencies to surveil populations in detail, and a public version provides higher-level situational awareness to inform ongoing pandemic response efforts in communities. RESULTS: Both dashboards have proved useful informatics resources. For example, the private dashboard enabled detection of a local community outbreak associated with a meat packing plant. The public dashboard provides recent trend analysis to track disease spread and community-level hospitalizations. Combined, the tools were utilized 133 637 times by 74 317 distinct users between June 21 and August 22, 2020. The tools are frequently cited by journalists and featured on social media. DISCUSSION: Capitalizing on a statewide health information exchange, in partnership with health system and public health leaders, Regenstrief biomedical informatics experts rapidly developed and deployed informatics tools to support surveillance and response to COVID-19. CONCLUSIONS: The application of public health informatics methods and tools in Indiana holds promise for other states and nations. Yet, development of infrastructure and partnerships will require effort and investment after the current pandemic in preparation for the next public health emergency.