Machine Learning Methods to Estimate Individualized Treatment Effects for Use in Health Technology Assessment.

BACKGROUND: Recent developments in causal inference and machine learning (ML) allow for the estimation of individualized treatment effects (ITEs), which reveal whether treatment effectiveness varies according to patients' observed covariates. ITEs can be used to stratify health policy decisions according to individual characteristics and potentially achieve greater population health. Little is known about the appropriateness of available ML methods for use in health technology assessment. METHODS: In this scoping review, we evaluate ML methods available for estimating ITEs, aiming to help practitioners assess their suitability in health technology assessment. We present a taxonomy of ML approaches, categorized by key challenges in health technology assessment using observational data, including handling time-varying confounding and time-to event data and quantifying uncertainty. RESULTS: We found a wide range of algorithms for simpler settings with baseline confounding and continuous or binary outcomes. Not many ML algorithms can handle time-varying or unobserved confounding, and at the time of writing, no ML algorithm was capable of estimating ITEs for time-to-event outcomes while accounting for time-varying confounding. Many of the ML algorithms that estimate ITEs in longitudinal settings do not formally quantify uncertainty around the point estimates. LIMITATIONS: This scoping review may not cover all relevant ML methods and algorithms as they are continuously evolving. CONCLUSIONS: Existing ML methods available for ITE estimation are limited in handling important challenges posed by observational data when used for cost-effectiveness analysis, such as time-to-event outcomes, time-varying and hidden confounding, or the need to estimate sampling uncertainty around the estimates. IMPLICATIONS: ML methods are promising but need further development before they can be used to estimate ITEs for health technology assessments. HIGHLIGHTS: Estimating individualized treatment effects (ITEs) using observational data and machine learning (ML) can support personalized treatment advice and help deliver more customized information on the effectiveness and cost-effectiveness of health technologies.ML methods for ITE estimation are mostly designed for handling confounding at baseline but not time-varying or unobserved confounding. The few models that account for time-varying confounding are designed for continuous or binary outcomes, not time-to-event outcomes.Not all ML methods for estimating ITEs can quantify the uncertainty of their predictions.Future work on developing ML that addresses the concerns summarized in this review is needed before these methods can be widely used in clinical and health technology assessment-like decision making.

Modern approaches for evaluating treatment effect heterogeneity from clinical trials and observational data.

In this paper, we review recent advances in statistical methods for the evaluation of the heterogeneity of treatment effects (HTE), including subgroup identification and estimation of individualized treatment regimens, from randomized clinical trials and observational studies. We identify several types of approaches using the features introduced in Lipkovich et al (Stat Med 2017;36: 136-196) that distinguish the recommended principled methods from basic methods for HTE evaluation that typically rely on rules of thumb and general guidelines (the methods are often referred to as common practices). We discuss the advantages and disadvantages of various principled methods as well as common measures for evaluating their performance. We use simulated data and a case study based on a historical clinical trial to illustrate several new approaches to HTE evaluation.

Estimating individualized treatment rules by optimizing the adjusted probability of a longer survival.

Individualized treatment rules inform tailored treatment decisions based on the patient's information, where the goal is to optimize clinical benefit for the population. When the clinical outcome of interest is survival time, most of current approaches typically aim to maximize the expected time of survival. We propose a new criterion for constructing Individualized treatment rules that optimize the clinical benefit with survival outcomes, termed as the adjusted probability of a longer survival. This objective captures the likelihood of living longer with being on treatment, compared to the alternative, which provides an alternative and often straightforward interpretation to communicate with clinicians and patients. We view it as an alternative to the survival analysis standard of the hazard ratio and the increasingly used restricted mean survival time. We develop a new method to construct the optimal Individualized treatment rule by maximizing a nonparametric estimator of the adjusted probability of a longer survival for a decision rule. Simulation studies demonstrate the reliability of the proposed method across a range of different scenarios. We further perform data analysis using data collected from a randomized Phase III clinical trial (SWOG S0819).

High viral loads combined with inflammatory markers predict disease severity in hospitalized COVID-19 patients: Results from the NOR-Solidarity trial.

OBJECTIVES: To investigate temporal changes in the association between SARS-CoV2 viral load (VL) and markers of inflammation during hospitalization, as well as the ability of these markers alone or in combination to predict severe outcomes. METHODS: Serial oropharyngeal and blood samples were obtained from hospitalized COVID-19 patients (n = 160). Levels of inflammatory markers and oropharyngeal VL were measured during hospitalization (admission, days 3-5, and days 7-10) and related to severe outcomes (respiratory failure/intensive care unit admission). RESULTS: Elevated admission levels of IL (interleukin)-6, IL-33, IL-8, monocyte chemoattractant protein-1 (MCP-1), interferon-γ-induced protein 10 (IP-10), IL-1ß, and IL-1Ra were associated with severe outcomes during hospitalization. Although no inflammatory markers correlated with VL at baseline, there was a significant correlation between VL and levels of IP-10 and MCP-1 at days 3-5, accompanied by IL-8 and IL-6 at days 7-10. Finally, there was a seemingly additive effect of IP-10, MCP-1, and IL-6 in predicting severe outcomes when combined with high VL at baseline. CONCLUSIONS: An increasing number of inflammatory markers were associated with VL during the first 10 days of hospitalization, and several of these markers were associated with severe outcomes, in particular when combined with elevated VL. Future studies should assess the potential for combining antiviral and immunomodulatory treatment, preferably guided by viral and inflammatory biomarkers, for the selection of high-risk patients.

Individualized treatment effects of a digital alcohol intervention and their associations with participant characteristics and engagement.

AIMS: Conditional average treatment effects are often reported in intervention studies, in which assumptions are made regarding how effects are similar across a heterogeneous sample. Nonetheless, differing factors, such as genetics, age, and sex, can impact an intervention's effect on outcomes. The study aimed to estimate the individualized effects of a digital alcohol intervention among individuals looking online to reduce their drinking. METHODS: We used data from a randomized controlled trial (RCT), including 2129 adults from the Swedish general population. The RCT concerned a text message-based alcohol intervention that sought to engender change through increasing knowledge on how to change and instilling confidence in changing behaviour. Outcomes were total weekly alcohol consumption and monthly heavy episodic drinking. Individualized treatment effects were modelled using baseline characteristics (age, gender, alcohol consumption, and psychosocial variables) and engagement with the intervention content. RESULTS: We found evidence that the effects of the digital alcohol intervention were heterogeneous concerning participants' age, baseline alcohol consumption, confidence, and importance. For heavy episodic drinking, there was evidence that effects were heterogeneous concerning age, sex, and baseline alcohol consumption. Overall, women, older individuals, and heavier drinkers benefitted more from the intervention in terms of effect size. In addition, participants who engaged more with the goal-setting and screening content reported better outcomes. CONCLUSIONS: The results highlight how different individuals respond differently to a digital alcohol intervention. This allows insight into who benefits the most and least from the intervention and highlights the potential merit of designing interventions adapted to different individuals' needs.

Clinical and Demographic Profiling of Periodontal Diseases: A Retrospective Analysis Using the 2018 Periodontal Classification Algorithm.

The research presented in this retrospective study aimed to investigate the prevalence of periodontal diseases within a population and explore potential demographic and clinical variations among patients diagnosed with periodontitis. The study involved the analysis of 104 patient records from the Periodontology Clinic at the University of Medicine and Pharmacy in Craiova, collected between 2018 and 2019. Utilizing the 2018 classification algorithm for periodontal diseases, patients were categorized into three groups: a control group without periodontal issues (Group H), a gingivitis group (Group G), and a periodontitis group (Group P). The collected data encompassed demographic information, oral hygiene indicators, and clinical parameters. Statistical analysis and graphical representation revealed notable trends, such as the higher prevalence of periodontitis (51%) compared to gingivitis (14%), with 35% of participants showing no signs of periodontal involvement. Detailed group analyses highlighted specific patterns, including a substantial male predominance among patients without periodontal issues and varying distributions of periodontal stages based on gender. Additionally, the study explored age-related variations, revealing an increasing average age from the control group (33.04 years) to the gingivitis group (34.86 years) and a significant rise in the periodontitis group (45.49 years). The findings underscore the importance of early detection and intervention in periodontal diseases and provide valuable insights for clinicians in tailoring individualized diagnostic and treatment approaches.

Identification of bladder cancer subtypes and predictive signature for prognosis, immune features, and immunotherapy based on immune checkpoint genes.

Immunotherapy based on immune checkpoint genes (ICGs) has recently made significant progress in the treatment of bladder cancer patients, but many patients still cannot benefit from it. In the present study, we aimed to perform a comprehensive analysis of ICGs in bladder cancer tissues with the aim of evaluating patient responsiveness to immunotherapy and prognosis. We scored ICGs in each BLCA patient from TCGA and GEO databases by using ssGSEA and selected genes that were significantly associated with ICGs scores by using the WCGNA algorithm. NMF clustering analysis was performed to identify different bladder cancer molecular subtypes based on the expression of ICGs-related genes. Based on the immune related genes differentially expressed among subgroups, we further constructed a novel stratified model containing nine genes by uni-COX regression, LASSO regression, SVM algorithm and multi-COX regression. The model and the nomogram constructed based on the model can accurately predict the prognosis of bladder cancer patients. Besides, the patients classified based on this model have large differences in sensitivity to immunotherapy and chemotherapy, which can provide a reference for individualized treatment of bladder cancer.

Real-World Case Series of Efgartigimod for Japanese Generalized Myasthenia Gravis: Well-Tailored Treatment Cycle Intervals Contribute to Sustained Symptom Control.

INTRODUCTION: Myasthenia gravis (MG), an immune disorder affecting nerve-muscle transmission, often necessitates tailored therapies to alleviate longitudinal symptom fluctuations. Here, we aimed to examine and compare the treatment cycle intervals and efficacy of efgartigimod in four patients. This case series mainly offers insights into personalized treatment cycle intervals and the efficacy of efgartigimod for patients with MG in our facility in Japan. METHODS: We retrospectively analyzed four patients with MG (2 patients with early-onset, 1 with late-onset, and 1 with seronegative MG, mainly managed with oral immunosuppressants as prior treatments) who completed four or more cycles of efgartigimod treatment from January 2022 to September 2023. We focused on changes in serum immunoglobulin (IgG) level, acetylcholine receptor antibody (AChR-Ab) titer, and quantitative MG (QMG) score. RESULTS: Efgartigimod, administered at a median of 5.0 [IQR 5.0, 7.5] weeks between cycles, led to decreased serum IgG levels in all patients and reduced AChR-Ab titers in seropositive patients. All patients showed sustained MG symptom improvement, with considerably reduced QMG scores before efgartigimod treatment. None of the patients required rescue medications or developed treatment-related adverse events. CONCLUSIONS: Customized efgartigimod administration intervals effectively enhanced clinical outcomes in patients with MG without notable symptom fluctuations, demonstrating the benefits of individualized treatment approaches and validating the safety of efgartigimod during the study period.

Causal inference for time-to-event data with a cured subpopulation.

When studying the treatment effect on time-to-event outcomes, it is common that some individuals never experience failure events, which suggests that they have been cured. However, the cure status may not be observed due to censoring which makes it challenging to define treatment effects. Current methods mainly focus on estimating model parameters in various cure models, ultimately leading to a lack of causal interpretations. To address this issue, we propose 2 causal estimands, the timewise risk difference and mean survival time difference, in the always-uncured based on principal stratification as a complement to the treatment effect on cure rates. These estimands allow us to study the treatment effects on failure times in the always-uncured subpopulation. We show the identifiability using a substitutional variable for the potential cure status under ignorable treatment assignment mechanism, these 2 estimands are identifiable. We also provide estimation methods using mixture cure models. We applied our approach to an observational study that compared the leukemia-free survival rates of different transplantation types to cure acute lymphoblastic leukemia. Our proposed approach yielded insightful results that can be used to inform future treatment decisions.

Effectiveness and safety of regimen containing bedaquiline and delamanid in patients with drug-resistant tuberculosis.

Background: Bedaquiline and delamanid have been included in the individualized treatment regimen (ITR) to treat patients with drug-resistant tuberculosis (DR-TB). Objective: The objective of this study is to compare the effectiveness of sputum culture conversion and the safety of ITR containing bedaquiline and delamanid. Methods: Data were collected retrospectively from medical records of DR-TB patients who received ITR between January 2020 and December 2021. Patients were divided into bedaquiline and bedaquiline-delamanid groups. Sputum culture was evaluated until 6 months of treatment. Measurement of QTc interval, renal and liver function test, and serum potassium were evaluated to assess safety during the study period. We used Chi-square to analyze a difference in cumulative culture conversion; meanwhile, Wilcoxon and Mann-Whitney tests were used to analyze differences in laboratory data for each and between the two groups, respectively. Results: Fifty-one eligible DR-TB patients met the inclusion criteria, 41 in the bedaquiline and 10 in bedaquiline-delamanid group. 43/51 patients had a positive culture at baseline. After 6 months of treatment, 42/43 DR-TB patients (97.6%) had sputum culture conversion and no difference between the two groups (P ≥ 0.05). QTc interval within normal limit and no patient had a QTc >500 ms during the study period. Creatinine levels significantly differed between the two groups 6 months after treatment (P < 0.05). Conclusion: DR-TB patients who received all oral ITR containing bedaquiline and or delamanid demonstrated favorable sputum conversion with a tolerable safety profile.

Value of Molecular Typing Combined with Integrated Positron Emission Tomography/Magnetic Resonance Imaging in Risk Stratification of Endometrial Cancer.

Objective: In this study, we investigated the value of molecular typing combined with integrated positron emission tomography (PET)/magnetic resonance imaging (MRI) semi-quantitative indices in endometrial cancer risk stratification. Methods: A retrospective study was conducted on 86 patients who were pathologically diagnosed with endometrial cancer and underwent surgical treatment after curettage at the Department of Obstetrics and Gynecology, Xuanwu Hospital, Capital Medical University between January 2017 and March 2023. Prior to surgery, each patient underwent integrated PET/MRI examination. The postoperative samples were subjected to pathological diagnosis, immunohistochemistry, and POLE gene sequencing. The differences in clinicopathological features between the four molecular subtypes and the differences in integrated PET/MRI semi-quantitative indexes (SUV max, ADC min) between the four molecular subtypes were analyzed. The cutoff value of molecular typing combined with integrated PET/MRI semi-quantitative indices for endometrial cancer risk stratification was determined. Results: There were statistically significant differences in pathological types and tumor grades among the four molecular subtypes of endometrial cancer. The values of the four integrated PET/MRI semi-quantitative indices (SUV max and ADC min) of the molecular subtypes were statistically different. The SUV max was greater in the p53abn mutation group than in the POLE mutation group (P < 0.05). The ADC minimum of the POLE mutation group and the MMR-d group was lower than the NSMP group (P < 0.05). Molecular typing combined with the integrated PET/MRI semi-quantitative SUV max index can predict the low/medium risk group of endometrial cancer and the medium-high/high risk group, and the cut-off value of SUV max for predicting the risk of early endometrial cancer was 14.72 (sensitivity 66.7%, specificity 68.7%). Conclusion: Molecular typing combined with integrated PET/MRI semi-quantitative indicators is useful to achieve risk stratification in patients diagnosed with endometrial cancer and guide individualized treatment.

Treatment Effects of Introducing the Neurosequential Model of Therapeutics in a Norwegian Residential Treatment Facility for Children Aged 7-13.

This study investigates the impact of the Neurosequential Model of Therapeutics (NMT) in child and adolescent psychiatric care, addressing a gap in current clinical methodologies that tend to focus on single problems rather than the interconnected nature of many real-life mental health issues. The study was conducted in a residential setting over an extended period, including children aged 7-13, to observe the effects of implementing NMT. The children presented with complex symptoms and multiple diagnoses. The methods incorporated the NMT approach, emphasizing individualized treatment plans based on each child's unique brain development, and aimed at addressing multiple, interconnected problems simultaneously. Results from multilevel model analyses of behavioral difficulties, measured using the Child Behavior Checklist (CBCL), revealed substantial improvements in treatment effectiveness post-NMT implementation. Despite the limitations, such as a non-randomized participant selection and limited sample size, the findings strongly suggest that NMT enhances care effectiveness in real-world clinical settings, particularly for children with complex mental health issues. The study concludes that relationally oriented milieu therapy, and specifically the NMT approach, holds great promise for advancing pediatric psychiatric care, advocating for its broader application and further research to refine and substantiate its efficacy.

A pseudo-response approach to constructing confidence intervals for the subset of patients expected to benefit from a new treatment.

In precision medicine, there is much interest in estimating the expected-to-benefit (EB) subset, i.e. the subset of patients who are expected to benefit from a new treatment based on a collection of baseline characteristics. There are many statistical methods for estimating the EB subset, most of which produce a 'point estimate' without a confidence statement to address uncertainty. Confidence intervals for the EB subset have been defined only recently, and their construction is a new area for methodological research. This article proposes a pseudo-response approach to EB subset estimation and confidence interval construction. Compared to existing methods, the pseudo-response approach allows us to focus on modelling a conditional treatment effect function (as opposed to the conditional mean outcome given treatment and baseline covariates) and is able to incorporate information from baseline covariates that are not involved in defining the EB subset. Simulation results show that incorporating such covariates can improve estimation efficiency and reduce the size of the confidence interval for the EB subset. The methodology is applied to a randomized clinical trial comparing two drugs for treating HIV infection.

Establishment of Prognosis Nomogram for Primary Splenic Diffuse Large B Cell Lymphoma: A Study Based on SEER Database.

Despite being the most common primary tumor of the spleen, in the past, few studies have predicted the prognosis of primary spleen diffuse large B cell lymphoma. This study aimed to establish a nomogram prediction model of overall survival in primary DLBCL of the spleen. We screened out 347 patients with primary splenic DLBCL from surveillance, epidemiology, and end results database. According to the Cox regression results (age, Ann Arbor Stage, splenectomy and chemotherapy was the independent risk factor for primary splenic DLBCL), the nomogram was constructed. We evaluated the predictive ability of nomogram with C-Index (training cohort: 0.719 [0.669-0.769]; validation cohort: 0.711 [0.641-0.781]) and 3-year/5-year receiver operating characteristic area under curve (3-year/5-year ROCAUC, training cohort: 0.731/0.742; validation cohort: 0.721/0.742). Calibratioin plot shows that our predicted values fluctuate around the actual value, indicating good agreement with nomogram. The decision curve analysis (DCA) results showed that our nomogram could benefit more than Ann Arbor Stage for predicts the prognosis of the primary splenic DLBCL. The Kaplan-Meier and landmark analysis showed that a great discrimination between high-risk group and low-risk group (P < 0.05) and indicating that our nomogram has the good ability to identify high-risk patients. In this study, a nomogram prediction model for primary spleen DLBCL was established, which has good ability of prediction and generalization. It can help clinicians carry out individualized treatment measures.

Individualized strategies for depression: narrative review of clinical profiles responsive to vortioxetine.

BACKGROUND: Depression is a highly heterogeneous disorder, often resulting in suboptimal response and remission rates. This underscores the need for more nuanced clinical characterization of patients to tailor individualized treatment plans. Emerging evidence highlights the critical role of cognitive and emotional dysfunction in major depression, prompting the exploration of novel therapeutic interventions that target these specific symptom domains. MAIN TEXT: Vortioxetine, a multimodal antidepressant, enhances serotonergic activity while also modulating several other neurotransmitter systems involved in depressive symptoms such as emotional blunting, anhedonia, and cognitive dysfunction. Numerous randomized, placebo-controlled trials have demonstrated vortioxetine's efficacy and safety in treating depression, particularly in specific subgroups of depressed patients, including those with cognitive deficits and comorbid anxiety symptoms or disorders. Although not randomized or placebo-controlled, studies have also shown vortioxetine's efficacy in depressed patients with emotional blunting or anhedonia. Vortioxetine's ability to effectively treat a range of depressive symptoms, including anhedonia, emotional blunting, anxiety, and cognitive dysfunction, provides an individualized treatment solution for depressed individuals suffering from these symptoms. The purpose of this paper is to identify clinical profiles of patients who may benefit from vortioxetine, with the goal of optimizing therapeutic outcomes. CONCLUSION: Vortioxetine has been shown to be effective for patients with depression and symptoms such as anhedonia, emotional blunting, anxiety, and cognitive dysfunction. Tailoring treatment plans to individual needs and personalizing treatment choices based on the specific symptoms presented by depressed patients improve treatment outcomes.

Clinical implications of a mathematical-geometrical model to predict changes in the anterior arch dimension.

PURPOSE: Anterior arch length (AL) and the alterations in its dimension following incisor movements were shown to be predictable for an individual patient using a mathematical-geometrical model based on a third-degree parabola. Although the model has been validated previously, it is hard to apply in daily orthodontic routine. Thus, the aim of this study was to modify the model using different approaches to allow its establishment in daily routine. METHODS: This retrospective study was based on a study collective, which was described previously and consisted of 50 randomly chosen dental casts and lateral cephalograms taken before (T0) and after (T1) orthodontic treatment with fixed appliances. A JAVA computer program (Oracle, Austin, TX, USA) was developed to predict AL changes following therapeutic changes of arch width, depth or incisor inclination/position, taking the type of tooth movement into account. Performing exemplary AL calculations with the computer program, general rules and nomograms were set up, followed by multiple linear regression analyses to establish easy-to-use regression equations. RESULTS: The JAVA computer program is available for download. Sagittal changes showed more effect on AL than transverse modifications. Protruding incisors increased AL, but also reduced overbite. The extent of alteration in AL depended on the initial depth, width, incisor inclination, tooth movement type and distance between the incisal edge and the centre of rotation. CONCLUSIONS: The computer program precisely predicts individual changes in AL but is time-consuming. The presented regression equations and nomograms, considering metric variables, are easier to apply clinically and the differences compared to the AL calculated by the computer program are negligible.

Influence of design parameters on sustained drug release properties of 3D-printed theophylline tablets.

The application of three-dimensional printing (3DP) in the pharmaceutical industry brings a broad spectrum of benefits to patients by addressing individual needs and improve treatment success. This study investigates the sustained release properties of 3DP tablets containing Theophylline (TPH), which is commonly used to treat respiratory diseases and recently having a comeback due to its potential in the treatment of conditions like Covid-19. Since TPH is a narrow therapeutic window (NTW) drug with serious side effects in the event of overdose, the release properties must be observed particularly closely. We employed a state-of-the-art single screw extrusion 3D printer, which is fed with granules containing the drug. By employing a Taguchi orthogonal array design of experiments (DOE), tablet design parameters and factor related process stability were sought to be evaluated fundamentally. Following this, examinations regarding tailored TPH dosages were undertaken and a relationship between the real printed dose of selected tablet designs and their sustained drug release was established. The release profiles were analyzed using different mathematical model fits and compared in terms of mean dissolution times (MDT). Finally, in-vivo/in-vitro correlation (IVIVC) and physiologically based pharmacokinetic (PBPK) modeling showed that a paradigm patient group could be covered with the dosage forms produced.

Individualized Treatment Approach for Rectal Adenocarcinoma in the Setting of Congenital Neutropenia.

Congenial neutropenia is a rare genetic disorder that puts individuals at risk of life-threatening bacterial infections early in life, and the current standard of care includes the use of colony-stimulating factors or curative intent bone marrow transplant. Cancer treatment strategies that include surgery, chemotherapy, radiation, and immunotherapy present significant challenges to an individual with a baseline immunodeficiency as seen in this condition. Evidence-based national guidelines aid physicians and patients in moving through complex cancer care regimens. However, these are altered when the intensity of the patient's comorbidities puts them at increased risk of developing a potentially life-threatening infection. Here, we present a patient treated for rectal carcinoma in the setting of severe congenital neutropenia.

Penalized robust learning for optimal treatment regimes with heterogeneous individualized treatment effects.

The growing popularity of personalized medicine motivates people to explore individualized treatment regimes according to heterogeneous characteristics of the patients. For the large-scale data analysis, however, the data are collected at different times and different locations, i.e. subjects are usually from a heterogeneous population, which causes that the optimal treatment regimes also vary for patients across different subgroups. In this paper, we mainly focus on the estimation of optimal treatment regimes for subjects come from a heterogeneous population with high-dimensional data. We first remove the main effects of the covariates for each subgroup to eliminate non-ignorable residual confounding. Based on the centralized outcome, we propose a penalized robust learning that estimates the coefficient matrix of the interactions between covariates and treatment by penalizing pairwise differences of the coefficients of any two subgroups for the same covariate, which can automatically identify the latent complex structure of the coefficient matrix with heterogeneous and homogeneous columns. At the same time, the penalized robust learning can also select the important variables that truly contribute to the individualized treatment decisions with commonly used sparsity structure penalty. Extensive simulation studies show that our proposed method outperforms current popular methods, and it is further illustrated in the real analysis of the Tamoxifen breast cancer data.

Longitudinal studies of bipolar patients and their families: translating findings to advance individualized risk prediction, treatment and research.

BACKGROUND: Bipolar disorder is a broad diagnostic construct associated with significant phenotypic and genetic heterogeneity challenging progress in clinical practice and discovery research. Prospective studies of well-characterized patients and their family members have identified lithium responsive (LiR) and lithium non-responsive (LiNR) subtypes that hold promise for advancement. METHOD: In this narrative review, relevant observations from published longitudinal studies of well-characterized bipolar patients and their families spanning six decades are highlighted. DSM diagnoses based on SADS-L interviews were decided in blind consensus reviews by expert clinicians. Genetic, neurobiological, and psychosocial factors were investigated in subsets of well-characterized probands and adult relatives. Systematic maintenance trials of lithium, antipsychotics, and lamotrigine were carried out. Clinical profiles that included detailed histories of the clinical course, symptom sets and disorders segregating in families were documented. Offspring of LiR and LiNR families were repeatedly assessed up to 20 years using KSADS-PL format interviews and DSM diagnoses and sub-threshold symptoms were decided by expert clinicians in blind consensus reviews using all available clinical and research data. RESULTS: A characteristic clinical profile differentiated bipolar patients who responded to lithium stabilization from those who did not. The LiR subtype was characterized by a recurrent fully remitting course predominated by depressive episodes and a positive family history of episodic remitting mood disorders, and not schizophrenia. Response to lithium clustered in families and the characteristic clinical profile predicted lithium response, with the episodic remitting course being a strong correlate. There is accumulating evidence that genetic and neurobiological markers differ between LiR and LiNR subtypes. Further, offspring of bipolar parents subdivided by lithium response differed in developmental history, clinical antecedents and early course of mood disorders. Moreover, the nature of the emergent course bred true from parent to offspring, independent of the nature of emergent psychopathology. CONCLUSIONS: Bipolar disorders are heterogeneous and response to long-term lithium is associated with a familial subtype with characteristic course, treatment response, family history and likely pathogenesis. Incorporating distinctive clinical profiles that index valid bipolar subtypes into routine practice and research will improve patient outcomes and advance the development and translation of novel treatment targets to improve prevention and early intervention.