Development and validation of algorithms to build an electronic health record based cohort of patients with systemic sclerosis

Objectives To evaluate methods of identifying patients with systemic sclerosis (SSc) using International Classification of Diseases, Tenth Revision (ICD-10) codes (M34*), electronic health record (EHR) databases and organ involvement keywords, that result in a validated cohort comprised of true cases with high disease burden. Methods We retrospectively studied patients in a healthcare system likely to have SSc. Using structured EHR data from January 2016 to June 2021, we identified 955 adult patients with M34* documented 2 or more times during the study period. A random subset of 100 patients was selected to validate the ICD-10 code for its positive predictive value (PPV). The dataset was then divided into a training and validation sets for unstructured text processing (UTP) search algorithms, two of which were created using keywords for Raynaud’s syndrome, and esophageal involvement/symptoms. Results Among 955 patients, the average age was 60. Most patients (84%) were female; 75% of patients were White, and 5.2% were Black. There were approximately 175 patients per year with the code newly documented, overall 24% had an ICD-10 code for esophageal disease, and 13.4% for pulmonary hypertension. The baseline PPV was 78%, which improved to 84% with UTP, identifying 788 patients likely to have SSc. After the ICD-10 code was placed, 63% of patients had a rheumatology office visit. Patients identified by the UTP search algorithm were more likely to have increased healthcare utilization (ICD-10 codes 4 or more times 84.1% vs 61.7%, p Conclusion EHRs can be used to identify patients with SSc. Using unstructured text processing keyword searches for SSc clinical manifestations improved the PPV of ICD-10 codes alone and identified a group of patients most likely to have SSc and increased healthcare needs

Performance characteristics of ICD-10 code and algorithms.

Performance characteristics of ICD-10 code and algorithms.</p

Select comparisons of patients identified by Algorithm 2 compared to patients with ICD-10 code use at least two times.

Select comparisons of patients identified by Algorithm 2 compared to patients with ICD-10 code use at least two times.</p

Cohort assembly.

Overview of multi-stage process for SSc ICD-10 code validation and algorithm testing and validation. Patients were selected from healthsystem databases if the ICD-10 code was present at least twice in encounters, billing codes or the problem list. Of the 2138 potential patients 1183 were excluded and a random selection of 100 patients underwent code validation. The 955 patient cohort was divided in half for testing of two algorithms using disease manifestation keywords and internal validation of highest performing algorithm. When applied to the entire cohort, 788 patients were identified as likely cases. ICD-10 = International Classification of Diseases, Tenth Revision.</p

Characteristics of entire cohort by randomization for baseline code validation subset n = 955.

Characteristics of entire cohort by randomization for baseline code validation subset n = 955.</p

Characteristics of patients based on case classification N = 100 (baseline validation cohort).

Characteristics of patients based on case classification N = 100 (baseline validation cohort).</p

Tolerogenic nanoparticles inhibit T cell-mediated autoimmunity through SOCS2.

Type 1 diabetes (T1D) is a T cell-dependent autoimmune disease that is characterized by the destruction of insulin-producing β cells in the pancreas. The administration to patients of ex vivo-differentiated FoxP3(+) regulatory T (Treg) cells or tolerogenic dendritic cells (DCs) that promote Treg cell differentiation is considered a potential therapy for T1D; however, cell-based therapies cannot be easily translated into clinical practice. We engineered nanoparticles (NPs) to deliver both a tolerogenic molecule, the aryl hydrocarbon receptor (AhR) ligand 2-(1'H-indole-3'-carbonyl)-thiazole-4-carboxylic acid methyl ester (ITE), and the β cell antigen proinsulin (NPITE+Ins) to induce a tolerogenic phenotype in DCs and promote Treg cell generation in vivo. NPITE+Ins administration to 8-week-old nonobese diabetic mice suppressed autoimmune diabetes. NPITE+Ins induced a tolerogenic phenotype in DCs, which was characterized by a decreased ability to activate inflammatory effector T cells and was concomitant with the increased differentiation of FoxP3(+) Treg cells. The induction of a tolerogenic phenotype in DCs by NPs was mediated by the AhR-dependent induction of Socs2, which resulted in inhibition of nuclear factor κB activation and proinflammatory cytokine production (properties of tolerogenic DCs). Together, these data suggest that NPs constitute a potential tool to reestablish tolerance in T1D and potentially other autoimmune disorders