Towards a practical use of text mining approaches in electrodiagnostic data.

Healthcare professionals produce abounding textual data in their daily clinical practice. Text mining can yield valuable insights from unstructured data. Extracting insights from multiple information sources is a major challenge in computational medicine. In this study, our objective was to illustrate how combining text mining techniques with statistical methodologies can yield new insights and contribute to the development of neurological and neuromuscular-related health information. We demonstrate how to utilize and derive knowledge from medical text, identify patient groups with similar diagnostic attributes, and examine differences between groups using demographical data and past medical history (PMH). We conducted a retrospective study for all patients who underwent electrodiagnostic (EDX) evaluation in Israel's Sheba Medical Center between May 2016 and February 2022. The data extracted for each patient included demographic data, test results, and unstructured summary reports. We conducted several analyses, including topic modeling that targeted clinical impressions and topic analysis to reveal age- and sex-related differences. The use of suspected clinical condition text enriched the data and generated additional attributes used to find associations between patients' PMH and the emerging diagnosis topics. We identified 6096 abnormal EMG results, of which 58% (n = 3512) were males. Based on the latent Dirichlet allocation algorithm we identified 25 topics that represent different diagnoses. Sex-related differences emerged in 7 topics, 3 male-associated and 4 female-associated. Brachial plexopathy, myasthenia gravis, and NMJ Disorders showed statistically significant age and sex differences. We extracted keywords related to past medical history (n = 37) and tested them for association with the different topics. Several topics revealed a close association with past medical history, for example, length-dependent symmetric axonal polyneuropathy with diabetes mellitus (DM), length-dependent sensory polyneuropathy with chemotherapy treatments and DM, brachial plexopathy with motor vehicle accidents, myasthenia gravis and NMJ disorders with botulin treatments, and amyotrophic lateral sclerosis with swallowing difficulty. Summarizing visualizations were created to easily grasp the results and facilitate focusing on the main insights. In this study, we demonstrate the efficacy of utilizing advanced computational methods in a corpus of textual data to accelerate clinical research. Additionally, using these methods allows for generating clinical insights, which may aid in the development of a decision-making process in real-life clinical practice.

Incidence and Outcome of Neurologic Immune-Related Adverse Events Associated With Immune Checkpoint Inhibitors in Patients With Melanoma.

BACKGROUND AND OBJECTIVES: Neurologic immune-related adverse events (n-irAEs) reportedly occur in up to 8% of patients treated with immune checkpoint inhibitors (ICIs) of all age groups. We investigated the association between age and n-irAEs in patients treated with ICIs and examined the effect of n-irAEs on survival outcomes in a large cohort of patients with melanoma. METHODS: We conducted a retrospective analysis of patients with advanced melanoma treated with ICIs at Ella Institute for Immuno-oncology and Melanoma between January 1, 2015, and April 20, 2022. The outcomes of interest were defined as the investigation of age-related frequency and severity of n-irAEs, the need for ICI interruption, the treatment required for n-irAE management, the safety of ICI reintroduction, and n-irAE's effect on survival. RESULTS: ICI was administered to 937 patients. At least one irAE occurred in 73.5% (n = 689) of them. Among the study population, 8% (n = 76) developed a n-irAE, with a median age of 66 years in female and 68 years in male patients at onset. The median follow-up after n-irAE was 1,147 days (IQR: 1,091.5 range: 3,938). Fewer irAEs occurred in patients older than 70 years (median: 3 events, p = 0.04, CI 2.5-4.7) while specifically colitis and pneumonitis were more common in the 18-60 age group (p = 0.03, 95% CI 0.8-0.38, p = 0.009, 95% CI 0.06-0.2). Grade ≥ 3 toxicity was seen in 35.5% of patients across age groups. The median time from ICI administration to n-irAE development was 48 days across age groups. Common n-irAE phenotypes were myositis (44.7%), encephalitis (10.5%), and neuropathy (10.5%). N-irAE required hospitalization in 40% of patients and steroids treatment in 46% with a median of 4 days from n-irAE diagnosis to steroids treatment initiation. Nine patients needed second-line immunosuppressive treatment. Rechallenge did not cause additional n-irAE in 71% of patients. Developing n-irAE (HR = 0.4, 95% CI 0.32-0.77) or any irAE (HR = 0.7195% CI 0.56-0.88) was associated with longer survival. DISCUSSION: N-irAEs are a relatively common complication of ICIs (8% of our cohort). Older age was not associated with its development or severity, in contrast with non-n-irAEs which occurred less frequently in the elderly population. Rechallenge did not result in life-threatening AEs. Development of any irAEs was associated with longer survival; this association was stronger with n-irAEs.

Nerve Conduction Differences in a Large Clinical Population: The Role of Age and Sex.

BACKGROUND: The normal limits of nerve conduction studies are commonly determined by testing healthy subjects. However, in comprehensive real-life nerve conduction electrodiagnostic (EDX) evaluations, multiple nerves are tested, including normal nerves, for purposes of comparison with abnormal ones. OBJECTIVE: This study aims to evaluate the average values of normal nerve conduction studies in a large population and examined the influence of age and sex. METHODS: EDX parameters were extracted from an electronic database of studies performed from May 2016 to February 2022. Established normal values were used to determine the classification of a nerve study as normal. RESULTS: We identified 10,648 EDX reports with 5077 normally interpreted nerve conduction studies (47.6%) of which 57% (n = 2890) were for females. The median age of studies with no abnormalities was 45.1 years (range < 1 to 92) overall and 42.5 years (range: 0.16 -89.5 years) for males and 47.5 years (range:<1 -91.7) for females. Correlations between age and amplitude, latency, and velocity (p < 0.001) were observed in most nerves. Amplitude correlated negatively with age in adults in all nerves with a mean of -0.44 (range: -0.24 to -0.62). However, in the pediatric population (age < 18 years), amplitude as well as velocity increased significantly with age. In the adult cohort, sex differences were noted, where females had higher mean sensory nerve action potentials in ulnar, median, and radial evaluations (p < 0.001). In older patients (aged > 70 years) with normally interpreted EDX studies (845 records of 528 patients), sural responses were present in 97%. CONCLUSIONS: This real-life study confirms that advanced aging is associated with decreased nerve conduction amplitudes, increased latency, and the slowing of conduction velocity. The findings also indicate higher sensory amplitudes and conduction velocities in females. Sural nerve responses were identified in most adults over age 70.

Utilizing shared frailty with the Cox proportional hazards regression: Post discharge survival analysis of CHF patients.

Understanding patients' survival probability as well as the factors affecting it constitute a significant concern for researchers and practitioners, in particular for patients with severe chronic illnesses such as congestive heart failure (CHF). CHF is a clinical syndrome characterized by comorbidities and adverse medical events. Risk stratification to identify patients most likely to die shortly after hospital discharge can improve the quality of care by better allocating organizational resources and personalized interventions. Probability assessment improves clinical decision-making, contributes to personalized care, and saves costs. Although one of the most informative indices is the time to an adverse event for each patient, commonly analyzed using survival analysis methods, these are often challenging to implement due to the complexity of the medical data. Numerous studies have used the Cox proportional hazards (PH) regression method to generate the survival distribution pattern and factors affecting survival. This model, although advantageous for survival analysis, assumes the homogeneity of the hazard ratio across patients and independence of the observations in terms of survival time. These assumptions are often violated in real-world data, especially when the dataset is composed of readmission data for chronically ill patients, since these recurring observations are inherently dependent. This study ran the Cox PH regression on a feature set selected by machine learning algorithms from a rich hospital dataset. The event modeled here was patient mortality within 90 days post-hospital discharge. The sample was composed of medical records of patients hospitalized in the Israeli Sheba Medical Center more than once, with CHF as the primary diagnosis. We modeled the survival of CHF patients using the Cox PH regression with and without the shared frailty correction that addresses the shortcomings of the Cox Model. The results of the two models of the Cox PH regression - with and without the shared frailty correction were compared. The results demonstrate that the shared frailty correction, which was statistically significant in our analysis, improved the performance of the basic Cox PH model. While this is the main contribution, we also show that this model outperforms two commonly used measures (ADHERE and EFFECT) for predicting early mortality of CHF patients. Thus, the results illustrate how applying advanced analytics can outperform traditional methods. An additional contribution is the feature set selected using machine-learning methods that is different from those used in the extant literature.