Personalizing Medical Treatment Decisions: Integrating Meta-analytic Treatment Comparisons with Patient-Specific Risks and Preferences

Association of HTR1A Gene Polymorphisms with Efficacy and Plasma Concentrations of Atypical Antipsychotics in the Treatment of Male Patients with Schizophrenia

Purpose

We investigate the association of HTR1A rs10042486 and rs6295 with efficacy and plasma concentrations of atypical antipsychotics in the treatment of male patients with schizophrenia.

Patients and Methods

A total of 140 male patients diagnosed with schizophrenia who were treated with any single atypical antipsychotic between May 2020 and May 2022 were retrospectively included. Clinical symptoms were assessed using Positive and Negative Syndrome Scale (PANSS). All SNPs were typed using Agena Bioscience MassARRAY DNA mass spectrometry. Plasma concentrations of antipsychotics at week 3, 6 and 12 after treatment commence were analyzed using mass spectrometry.

Results

For efficacy of atypical antipsychotics, we observed no significant difference between HTR1A rs10042486, rs6295 and positive symptom improvement, where the patients with heterozygous mutant at the rs10042486 and rs6295 locus were superior to those with wild-type or homozygous mutant genotypes on negative symptom improvement, especially at 12 weeks of follow-up when the difference between genotypes at the rs6295 locus have statistical significance (P = 0.037). For plasma concentration, we found that quetiapine plasma concentrations were significantly lower in patients with mutation-heterozygous types than in wild-type and homozygous mutation genotypes at week 6. In contrast, higher plasma concentrations were found for mutant heterozygous than wild genotypes in the risperidone monotherapy analysis, and the difference among genotypes at the rs6295 locus was statistically significant at 6 weeks of follow-up.

Conclusion

The assessment of the correlation of genetic polymorphisms of HTR1A rs6295 and rs10042486 in male patients with schizophrenia with the monitoring of therapeutic drug concentrations and therapeutic efficacy provides a constructive foundation for the clinical individualization of antipsychotics, such as quetiapine and risperidone, which is important in selecting the dose of the medication and improving the improvement of negative symptoms.

Personalization of Medical Treatment Decisions: Simplifying Complex Models while Maintaining Patient Health Outcomes

BACKGROUND

Personalizing medical treatments based on patient-specific risks and preferences can improve patient health. However, models to support personalized treatment decisions are often complex and difficult to interpret, limiting their clinical application.

METHODS

We present a new method, using machine learning to create meta-models, for simplifying complex models for personalizing medical treatment decisions. We consider simple interpretable models, interpretable ensemble models, and noninterpretable ensemble models. We use variable selection with a penalty for patient-specific risks and/or preferences that are difficult, risky, or costly to obtain. We interpret the meta-models to the extent permitted by their model architectures. We illustrate our method by applying it to simplify a previously developed model for personalized selection of antipsychotic drugs for patients with schizophrenia.

RESULTS

The best simplified interpretable, interpretable ensemble, and noninterpretable ensemble models contained at most half the number of patient-specific risks and preferences compared with the original model. The simplified models achieved 60.5% (95% credible interval [crI]: 55.2-65.4), 60.8% (95% crI: 55.5-65.7), and 83.8% (95% crI: 80.8-86.6), respectively, of the net health benefit of the original model (quality-adjusted life-years gained). Important variables in all models were similar and made intuitive sense. Computation time for the meta-models was orders of magnitude less than for the original model.

LIMITATIONS

The simplified models share the limitations of the original model (e.g., potential biases).

CONCLUSIONS

Our meta-modeling method is disease- and model- agnostic and can be used to simplify complex models for personalization, allowing for variable selection in addition to improved model interpretability and computational performance. Simplified models may be more likely to be adopted in clinical settings and can help improve equity in patient outcomes.

Partial Personalization of Medical Treatment Decisions: Adverse Effects and Possible Solutions

BACKGROUND

Personalizing medical treatment decisions based on patient-specific risks and/or preferences can improve health outcomes. Decision makers frequently select treatments based on partial personalization (e.g., personalization based on risks but not preferences or vice versa) due to a lack of data about patient-specific risks and preferences. However, partially personalizing treatment decisions based on a subset of patient risks and/or preferences can result in worse population-level health outcomes than no personalization and can increase the variance of population-level health outcomes.

METHODS

We develop a new method for partially personalizing treatment decisions that avoids these problems. Using a case study of antipsychotic treatment for schizophrenia, as well as 4 additional illustrative examples, we demonstrate the adverse effects and our method for avoiding them.

RESULTS

For the schizophrenia treatment case study, using a previously proposed modeling approach for personalizing treatment decisions and using only a subset of patient preferences regarding treatment efficacy and side effects, mean population-level health outcomes decreased by 0.04 quality-adjusted life-years (QALYs; 95% credible interval [crI]: 0.02-0.06) per patient compared with no personalization. Using our new method and considering the same subset of patient preferences, mean population-level health outcomes increased by 0.01 QALYs (95% crI: 0.00-0.03) per patient as compared with no personalization, and the variance decreased.

LIMITATIONS

We assumed a linear and additive utility function.

CONCLUSIONS

Selecting personalized treatments for patients should be done in a way that does not decrease expected population-level health outcomes and does not increase their variance, thereby resulting in worse risk-adjusted, population-level health outcomes compared with treatment selection with no personalization. Our method can be used to ensure this, thereby helping patients realize the benefits of treatment personalization without the potential harms.

Acknowledging Patient Heterogeneity in Economic Evaluations in Schizophrenia: A Systematic Review

OBJECTIVES

Schizophrenia is a severe mental illness with heterogeneous etiology, range of symptoms, and course of illness. Cost-effectiveness analysis often applies averages from populations, which disregards patient heterogeneity even though there are a range of methods available to acknowledge patient heterogeneity. This review evaluates existing economic evaluations of interventions in schizophrenia to understand how patient heterogeneity is currently reflected in economic evaluation.

METHODS

Electronic searches of MEDLINE, Embase, and PsycINFO via Ovid and the Health Technology Assessment database were run to identify full economic evaluations of interventions aiming to reduce the symptoms associated with schizophrenia. Two levels of screening were used, and explicit inclusion criteria were applied. Prespecified data extraction and critical appraisal were performed.

RESULTS

Seventy-six relevant studies were identified. More than half (41 of 76) of the articles acknowledged patient heterogeneity in some way through discussion or methods. There was a range of patient characteristics considered, including demographics and socioeconomic factors (eg, age, educational level, ethnicity), clinical characteristics (eg, symptom severity, comorbidities), and preferences (eg, preferences related to outcomes or symptoms). Subgroup analyses were rarely reported (8 of 76).

CONCLUSIONS

Patient heterogeneity was frequently mentioned in studies but was rarely thoroughly investigated in the identified economic evaluations. When investigated, included patient characteristics and methods were found to be heterogeneous. Understanding and acknowledging patient heterogeneity may alter the conclusions of cost-effectiveness evaluations; subsequently, we would encourage further research in this area.

Focus on disability-free life expectancy: implications for health-related quality of life

BACKGROUND

Since the end of the industrial revolution, advances in public health and clinical medicine have contributed to dramatic decreases in infant and childhood mortality, improvements in health-related quality of life (HRQoL), increases in overall life expectancy (LE), and rectangularization of survival curves.

OBJECTIVES

In this article, we focus on disability that has occurred with the overall lengthening of LE in many populations and the implications this has for decreased HRQoL.

METHODS

We utilize the concept of rectangularization of population survival to depict the rising prevalence of disability associated with increased LE, especially among racial and ethnic minorities and people of low socioeconomic status (SES) and relate this to HRQoL.

RESULTS

Disability-free life expectancy (DFLE) and healthy life expectancy (HLE) are defined in terms of HRQoL. Specific attention is focused on disability experienced by disparate populations around the globe. By focusing on disparities in DFLE, and the need to expand LE to include HLE as a central component of HRQoL, this work provides an important counterpoint to the attention that has been paid to LE disparities according to race, gender, ethnicity, education, and SES.

DISCUSSION

By calling attention to those factors that appear to be the most important drivers of the differences in quality and length of DFLE between different groups (i.e., the components of the social gradient, exposure to chronic stress, systemic inflammation, and the psychological and biological mechanisms associated with the gut-brain axis) and, by logical extension, HRQoL, we hope to promote research in this arena with the ultimate goal of improving DFLE, HLE, and overall HRQoL, especially in disparate populations around the globe.