Consistency of Linguistic and Cognitive Processing Measures to Discriminate Children with and without Developmental Language Disorder (DLD): Comparing Likelihood Ratios (LHs) and Elastic Net Regression Computational Models.

Because both monothetic and polythetic diagnostic classification approaches focus on the presence of individual symptom(s) to identify individuals in a clinical population, they may be diagnostically sensitive clinical markers of multidimensional disorders such as developmental language disorder (DLD). DLD researchers have also used likelihood ratios (LHs) to identify possible diagnostic clinical markers of DLD, however the diagnostic sensitivity of LHs varies markedly across studies. A recent multidimensional computational elastic-net regression examined a total of 71 measures of spoken language and cognitive processing from a cohort of 223 children ages 7;0 to 11;0 with and without DLD (DLD = 110; typically developing (TD) controls = 113). All 200 iterations of the model had high discriminative power (87% - 88%) in positively identifying and distinguishing the DLD participants across all thresholds. Notably, the models identified a sparse DLD-specific deficit profile which only included nine of the 71 measures. In this study, we ask if the individual LHs for each of these nine measures are equally sensitive in identifying and discriminating the children with DLD from TD controls or if diagnostic markers of multidimensional disorders such as DLD can only be identified based on computational modeling approaches. The LHs for each of the nine measures were in the moderately high ranged (3.25 - 10). However, at the the highest LH cut points for each measure, there was little to no overlap in the children each measure identified as having DLD. Follow up analysis revealed that the elastic net model-derived predictive scores for each participant were significantly correlated with the participants language ability. The model also identified a subgroup of TD participants as having the same DLD-deficit profile as the DLD participants. This subgroup were younger, predominantly male participants whose standardized language assessment scores were lower as compared to the larger TD cohort. Taken together, the results from this study show that, because multidimensional modeling approaches such as elastic net regression leverage the variability in the deficit profiles across individual members of a diagnostic group and the unique contributions of each of the behavioral features of the phenotype, they may be an effective tool in deriving diagnostically specific deficit profiles for phenotypically complex, multicausal, multidimensional, neurodevelopmental disorders such as DLD. The results also demonstrate the robustness of the derived DLD-specific deficit profile in identifying individuals with "mild" or subclinical DLD, demonstrating the potential utility of this approach in both clinical and research arenas. What this paper adds.O_ST_ABSWhat is already known on this subject.C_ST_ABSThe identification of diagnostic markers for DLD has been a challenge for both clinicians and researchers across multiple decades. Monothetic classification markers such as non-word repetition, optional infinitive, or syntax dependencies have been explored, as well as polythetic classification approaches where a list of diagnostic symptoms is used together. However, each assumes different criteria and symptoms that should be included as diagnostic markers of DLD. What this study adds.Our study assessed the feasibility and effectiveness of monothetic vs. polythetic classification approaches for identifying DLD. Since our prior work, which used elastic net logistic regression computational modeling with strong discriminatory power, consistently selected nine key features as the DLD-deficit profile, in this effort, we calculated each of the nine features likelihood ratios to examine each measures ability to identify children with DLD. The monothetic approach failed to identify a consistent set of children with DLD, and the polythetic classification approach also did not identify participants who were shown to have mild DLD by the elastic net modeling approach. Instead, our analysis showed that a computational modeling approach, such as elastic net regression, that included small but important input from multiple cognitive and linguistic aspects of children, could better capture multifaceted information about the disorder, better account for individual variability, and consistently identify most participants with DLD. Clinical implications of this study.Elastic net logistic regression identifies a small subset of important features for distinguishing DLD and can assign a probability of DLD presence for each participant. Instead of the polythetic and monothetic approaches commonly used in the field, our study shows that integrating advanced computational modeling, such as elastic net regression, with clinician judgment can better refine assessment processes and address prior and ongoing inconsistencies in the DLD literature and diagnostic practices.