Murugadoss, Karthik

ABSTRACT Clinical worsening events are increasingly recognized as a meaningful outcome in pulmonary arterial hypertension (PAH). We applied machine‐learning models to real‐world data to identify clinical features that may predict clinical worsening events in PAH. Data were obtained retrospectively from the electronic health records of adults diagnosed with PAH at Mayo Clinic locations (January 2015–December 2019). Machine‐learning LASSO regularized logistic regression models were developed to analyze the association between 100 clinical features and occurrence of clinical worsening events. In total, 455 patients were included (mean age 62.1 years; 59.3% female). Of these, 232 (51.0%) experienced a clinical worsening event after a median (quartile 1, quartile 3) of 10.9 (4.1, 21.4) months. The best‐performing model had an area under the curve of 0.78, sensitivity of 78%, and specificity of 59%. The model identified 11 clinical features associated with clinical worsening events in PAH with a non‐zero coefficient: baseline age, body mass index (BMI), creatinine level, red cell (erythrocyte) distribution width (RDW), mean pulmonary arterial pressure, pulmonary vascular resistance, sodium concentration, and QT interval, along with dyspnea, number of clinic visits, and other forms of heart disease within a 1‐month window before a clinical worsening event. Baseline RDW and BMI, and number of clinic visits within the 1‐month window were statistically significantly associated with clinical worsening events. Our study used machine‐learning models to identify clinical features associated with risk of clinical worsening events in PAH. Automation of risk prediction could lead to earlier therapeutic intervention to optimize patient outcomes.

ABSTRACT Pulmonary arterial hypertension (PAH) is a progressive disease with significant morbidity and mortality. Due to nonspecific symptoms, diagnosis can be challenging and subject to substantial delays. Using data from Mayo Clinic′s electronic health records, we looked at causes of delayed diagnosis and whether earlier diagnosis means better outcomes. This retrospective cohort study included adults with PAH confirmed by right heart catheterization (RHC) between 2015 and 2019. Univariate and multivariate analyses evaluated the association of 229 clinical and laboratory features with time to PAH diagnosis and survival outcomes. Early diagnosis was defined as < 6 months and a delayed diagnosis as ≥ 22.2 months from an eligible event. Survival probability was determined using Kaplan‐Meier analysis . The study enrolled 160 patients. The most common initial PAH symptoms were dyspnea (42.5%) and fatigue (16.9%). It took a median of 7, 13, 26, and 123 days from eligible event to the first X‐ray, electrocardiogram, echocardiogram, and RHC, respectively. Factors most closely associated with delayed diagnosis were chronic obstructive pulmonary disease, normal creatinine, high systolic blood pressure, acute respiratory infection, normal iron, normal diastolic blood pressure, and being male aged 18.5 to < 25 years. Compared with delayed diagnosis patients (22.6%), more early diagnosis patients saw a pulmonologist (35.3%) before another provider. After 5 years, 73% of patients in the early diagnosis and 31% in the delayed diagnosis group were still alive. This study identified factors associated with a delayed diagnosis of PAH and found that earlier diagnosis and treatment initiation were associated with significantly improved survival.

PURPOSE Teclistamab is initiated with a step-up dosing (SUD) schedule to mitigate the risk of cytokine release syndrome (CRS) and immune effector cell–associated neurotoxicity syndrome (ICANS). Early teclistamab users commonly received SUD in a hospital setting. This study aimed to evaluate safety and health care resource utilization (HRU) in real-world patients with multiple myeloma who initiated teclistamab SUD in an outpatient setting. METHODS This was a retrospective study using Mayo Clinic's electronic medical records from October 26, 2022, to October 31, 2023. Patient characteristics were summarized for all patients treated with teclistamab and separately for patients who started SUD outpatient. SUD pattern, safety, HRU, and post-SUD dosing schedule were described in patients with complete SUD. RESULTS At data cutoff, 65 patients received ≥1 teclistamab dose, including 58 patients who initiated SUD outpatient (median age, 69.2 years; male, 63.8%; White, 89.7%). Among 57 patients who completed SUD in an outpatient setting, all received premedications on the days of teclistamab administrations per label recommendation; 18 (31.6%) developed CRS (13 grade 1, four grade 2, and one grade 4) and two developed ICANS (one each with grade 2 and 4). All CRS and ICANS resolved with supportive care and all patients continued treatment. Eighteen patients were admitted to the hospital for CRS treatment, with a median CRS-related hospital stay of 2 days per admission. Most (60%) doses during SUD required <1 hour clinic time between administration and checkout. Post-SUD, clinic time for treatment doses decreased to <30 minutes for most doses (82%). CONCLUSION Outcomes of this study support outpatient administration as a safe and feasible option for teclistamab SUD to potentially reduce HRU and improve patient experiences.

Abstract Case reports of patients infected with COVID-19 and influenza virus (“flurona”) have raised questions around the prevalence and severity of coinfection. Using data from HHS Protect Public Data Hub, NCBI Virus, and CDC FluView, we analyzed trends in SARS-CoV-2 and influenza hospitalized coinfection cases and strain prevalences. We also characterized coinfection cases across the Mayo Clinic Enterprise from January 2020 to April 2022. We compared expected and observed coinfection case counts across different waves of the pandemic and assessed symptoms and outcomes of coinfection and COVID-19 monoinfection cases after propensity score matching on clinically relevant baseline characteristics. From both the Mayo Clinic and nationwide datasets, the observed coinfection rate for SARS-CoV-2 and influenza has been higher during the Omicron era (2021 December 14 to 2022 April 2) compared to previous waves, but no higher than expected assuming infection rates are independent. At the Mayo Clinic, only 120 coinfection cases were observed among 197,364 SARS-CoV-2 cases. Coinfected patients were relatively young (mean age: 26.7 years) and had fewer serious comorbidities compared to monoinfected patients. While there were no significant differences in 30-day hospitalization, ICU admission, or mortality rates between coinfected and matched COVID-19 monoinfection cases, coinfection cases reported higher rates of symptoms including congestion, cough, fever/chills, headache, myalgia/arthralgia, pharyngitis, and rhinitis. While most coinfection cases observed at the Mayo Clinic occurred among relatively healthy individuals, further observation is needed to assess outcomes among subpopulations with risk factors for severe COVID-19 such as older age, obesity, and immunocompromised status.

Temporal inference from laboratory testing results and triangulation with clinical outcomes extracted from unstructured electronic health record (EHR) provider notes is integral to advancing precision medicine. Here, we studied 246 SARS-CoV-2 PCR-positive (COVID pos ) patients and propensity-matched 2460 SARS-CoV-2 PCR-negative (COVID neg ) patients subjected to around 700,000 lab tests cumulatively across 194 assays. Compared to COVID neg patients at the time of diagnostic testing, COVID pos patients tended to have higher plasma fibrinogen levels and lower platelet counts. However, as the infection evolves, COVID pos patients distinctively show declining fibrinogen, increasing platelet counts, and lower white blood cell counts. Augmented curation of EHRs suggests that only a minority of COVID pos patients develop thromboembolism, and rarely, disseminated intravascular coagulopathy (DIC), with patients generally not displaying platelet reductions typical of consumptive coagulopathies. These temporal trends provide fine-grained resolution into COVID-19 associated coagulopathy (CAC) and set the stage for personalizing thromboprophylaxis.

Understanding temporal dynamics of COVID-19 symptoms could provide fine-grained resolution to guide clinical decision-making. Here, we use deep neural networks over an institution-wide platform for the augmented curation of clinical notes from 77,167 patients subjected to COVID-19 PCR testing. By contrasting Electronic Health Record (EHR)-derived symptoms of COVID-19-positive (COVID pos ; n = 2,317) versus COVID-19-negative (COVID neg ; n = 74,850) patients for the week preceding the PCR testing date, we identify anosmia/dysgeusia (27.1-fold), fever/chills (2.6-fold), respiratory difficulty (2.2-fold), cough (2.2-fold), myalgia/arthralgia (2-fold), and diarrhea (1.4-fold) as significantly amplified in COVID pos over COVID neg patients. The combination of cough and fever/chills has 4.2-fold amplification in COVID pos patients during the week prior to PCR testing, in addition to anosmia/dysgeusia, constitutes the earliest EHR-derived signature of COVID-19. This study introduces an Augmented Intelligence platform for the real-time synthesis of institutional biomedical knowledge. The platform holds tremendous potential for scaling up curation throughput, thus enabling EHR-powered early disease diagnosis.

Abstract Current unbiased approaches to mine the large amounts of patient-level data on mutations, structural variations and gene expression result in an unwieldy amount of interactions and correlations, which cannot be parsed to identify disease drivers. Here we present an approach to encode mutational and structural variant data at a patient level in a semantic association space. This approach transforms the presence of a mutation (or other feature) in each patient into the quantitative semantic association score of the corresponding gene and the phenotype of interest, which we have trained on all publicly available literature using word-embedding neural networks. Using data from The Cancer Genome Atlas (TCGA), we encoded the mutation or structural variant status (incl. copy number, fusion and chromothripsis) of all patients in the Lung Adenocarcinoma and Mesothelioma cohorts into our semantic space. For each cancer, we first defined the set of genes that are most associated to it according to the literature. To project each patient into this semantic space, we next determined if each patient had a mutation in the genes representing the disease semantic vector (e.g. NSCLC). For TCGA data we only counted non-Silent mutations and represented them as a binary number for each gene, i.e. 0 if the patient had no mutations in that gene and 1 if the patient had a non-Silent mutation in the gene. Each patient was then encoded in a binary vector with each member corresponding to a gene from the disease semantic vector. For example, lung adenocarcinoma was associated to 1,367 genes in our semantic space. A lung adenocarcinoma patient’s vector would them be composed of 1,367 binary numbers dictating if the gene is mutated or not in that patient. We then multiply these binary vectors with the semantic disease vector to obtain the patient’s projection in the disease space, which in effect replaces the binary number with the Semantic Association Score between the gene and the disease. Contrary to clustering patient samples by their mutation or structural variant data alone, our projected patient vectors clustered patients together into 22 groups with high patient-to-patient similarity. These clusters recapitulate canonical knowledge about the disease, e.g. Lung Adenocarcinoma patients form clusters that include EGFR-driven and KRAS-driven cohorts. We also see novel groups of patients driven by genes such as MET, STK11 and MALAT1. These clusters can be further stratified by their survival status and other clinical features. We validated our approach with a non-TCGA Mesothelioma cohort, revealing similarities in patient stratification regardless of the data source. This approach represents a dramatic shift in patient segmentation, delivering real-time grouping of patients and biomarker identification, which can accelerate clinical trial design and therapeutic development strategy. Citation Format: Enrique Garcia-Rivera, Aaron S. Mansfield, Karthik Murugadoss, Murali Aravamudan. Patient segmentation using machine-learning based literature and genomic data synthesis uncovers novel cohorts of NSCLC and mesothelioma patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2452.