Development and validation of a nomogram based on lymphocyte subsets to distinguish bipolar depression from major depressive disorder

Abstract

ObjectiveBipolar depression (BD) and major depressive disorder (MDD) are both common affective disorders. The common depression episodes make it difficult to distinguish between them, even for experienced clinicians. Failure to properly diagnose them in a timely manner leads to inappropriate treatment strategies. Therefore, it is important to distinguish between BD and MDD. The aim of this study was to develop and validate a nomogram model that distinguishes BD from MDD based on the characteristics of lymphocyte subsets.Materials and methodsA prospective cross-sectional study was performed. Blood samples were obtained from participants who met the inclusion criteria. The least absolute shrinkage and selection operator (LASSO) regression model was used for factor selection. A differential diagnosis nomogram for BD and MDD was developed using multivariable logistic regression and the area under the curve (AUC) with 95% confidence interval (CI) was calculated, as well as the internal validation using a bootstrap algorithm with 1,000 repetitions. Calibration curve and decision curve analysis (DCA) were used to evaluate the calibration and clinical utility of the nomogram, respectively.ResultsA total of 166 participants who were diagnosed with BD (83 cases) or MDD (83 cases), as well as 101 healthy controls (HCs) between June 2018 and January 2022 were enrolled in this study. CD19+ B cells, CD3+ T cells, CD3–CD16/56+ NK cells, and total lymphocyte counts were strong predictors of the diagnosis of BD and MDD and were included in the differential diagnosis nomogram. The AUC of the nomogram and internal validation were 0.922 (95%; CI, 0.879–0.965), and 0.911 (95% CI, 0.838–0.844), respectively. The calibration curve used to discriminate BD from MDD showed optimal agreement between the nomogram and the actual diagnosis. The results of DCA showed that the net clinical benefit was significant.ConclusionThis is an easy-to-use, repeatable, and economical nomogram for differential diagnosis that can help clinicians in the individual diagnosis of BD and MDD patients, reduce the risk of misdiagnosis, facilitate the formulation of appropriate treatment strategies and intervention plans.

Development and validation of a nomogram for predicting depressive symptoms in dentistry patients: A cross-sectional study

“…However, the majority of research attention remains focused on the predictive role of physiological indicators on depressive symptoms, and these factors were also screened for influences that could be included in the Nomogram by a combination of multivariate logistic regression and univariate logistic regression. [48][49][50] We wanted to build on this base as, on the one hand, we wanted to screen variables by combining machine learning methods (e.g., LASSO regression) and multivariate logistic regression to better identify independent predictors. On the other hand, we combined demographic variables from previous studies that were much this included in the Nomogram and on this basis selected some psychological variables and behavioral factors that may have an impact on depressive symptoms to be included in the model.…”

Clinical Application of Machine Learning Methods in Psychiatric Disorders

“…The study leverages lymphocyte subpopulation-based features to predict the maladies with an accuracy exceeding 90% [11]. The samples for the clinical trials are obtained from peripheral blood, which is less invasive, reinforcing the validity of the proposed method.…”

“…ML also provides a feasible platform to differentiate psychiatric disorders, a crucial role considering there are over 200 types of known mental health disorders. Su et al [11] illustrate the use of ML to distinguish major depressive disorder from bipolar depression.…”

T cells: an emerging cast of roles in bipolar disorder

“… Maes M et al, 2021 [ 15 ] 25 21 CD3, CD4, CD8, CD71, CD69, CD25, CD152, CD154, FOXP3 Activated T effector, Tregs BD and anti-HCMV IgG levels significantly interact to decrease the expression of CD4 + CD25 + FOXP3 + T phenotypes. Su L et al, 2022 [ 136 ] 83 101 CD3, CD4, CD8, CD45 Th, Tc BD patients had greater CD3 + T cell levels than HC. The inclusion and exclusion criteria for the studies are shown in Supplementary Fig.…”