Effect of ramelteon on insomnia severity: evaluation of patient characteristics affecting treatment response

Prognostic models for predicting insomnia treatment outcomes: A systematic review

OBJECTIVE

To identify and critically evaluate models predicting insomnia treatment response in adult populations.

METHODS

Pubmed, EMBASE, and PsychInfo databases were searched from January 2000 to January 2023 to identify studies reporting the development or validation of multivariable models predicting insomnia treatment outcomes in adults. Data were extracted according to CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) guidelines and study quality was assessed using the Prediction model study Risk Of Bias Assessment Tool (PROBAST).

RESULTS

Eleven studies describing 53 prediction models were included and appraised. Treatment response was most frequently assessed using wake after sleep onset (n = 10; 18.9%), insomnia severity index (n = 10; 18.9%), and sleep onset latency (n = 9, 17%). Dysfunctional Beliefs About Sleep (DBAS) score was the most common predictor in final models (n = 33). R2 values ranged from 0.06 to 0.80 for models predicting continuous response and area under the curve (AUC) ranged from 0.73 to 0.87 for classification models. Only two models were internally validated, and none were externally validated. All models were rated as having a high risk of bias according to PROBAST, which was largely driven by the analysis domain.

CONCLUSION

Prediction models may be a useful tool to assist clinicians in selecting the optimal treatment strategy for patients with insomnia. However, no externally validated models currently exist. These results highlight an important gap in the literature and underscore the need for the development and validation of modern, methodologically rigorous models.

Acupuncture for patients with insomnia and predictors of treatment response: a chart review

BACKGROUND

Acupuncture is a potentially effective non-pharmacological treatment for insomnia.

OBJECTIVE

We observed the responses of patients with insomnia to acupuncture in routine clinical practice. In addition, we explored patient characteristics that might affect the treatment response to acupuncture for insomnia.

METHODS

Medical records of patients with insomnia in a Korean medicine clinic with baseline Insomnia Severity Index (ISI) scores ⩾8 and Pittsburgh Sleep Quality Index (PSQI) scores ⩾5 were reviewed. Acupuncture was applied at ST43, GB41, ST41, SI5, HT3, KI10, HT7 and ST3, for 1-2 months. The ISI and PSQI were measured monthly to assess insomnia severity. The effect of acupuncture over time was analyzed using a multilevel linear model for repeated measures. In addition, logistic regression was used to explore predictors of treatment response.

RESULTS

A total of 91 patients with insomnia aged 59.2 ± 12.5 years (mean ± standard deviation (SD)) (90.1% female) were included in the analysis. After the acupuncture treatment, ISI scores were significantly reduced by -3.75 (95% confidence interval (CI) = -4.99, -2.50) and -4.69 (95% CI = -6.22, -3.16) after the first and second month, respectively. The PSQI global scores also improved, and sleep duration showed a tendency to increase by 0.35 h (95% CI = -0.17, 0.86) after acupuncture treatment. Three cases of mild fatigue were reported. In addition, higher baseline pain/discomfort predicted a greater likelihood of response after acupuncture treatment (odds ratio (OR) = 1.66, 95% CI = 1.10, 2.60).

CONCLUSION

In a real-world setting, the insomnia of outpatients in a clinic was slightly alleviated after acupuncture treatment. These findings require validation by randomized controlled trials.

Pharmacological management of co-morbid obstructive sleep apnoea and insomnia

INTRODUCTION

Clinical presentation of both insomnia and obstructive sleep apnea (COMISA) is common. Approximately 30% of clinical cohorts with OSA have insomnia symptoms and vice versa. The underlying pathophysiology of COMISA is multifactorial. This poses a complex clinical challenge. Currently, there are no clinical guidelines or recommendations outside of continuous positive airway pressure (CPAP) therapy and cognitive behavioral therapy for insomnia (CBTi). Clinically translatable precision medicine approaches to characterize individual causes or endotypes may help optimize future pharmacological management of COMISA.

AREAS COVERED

This review article provides an up-to-date account of COMISA and its consequences, the underlying pathophysiology of sleep apnea, insomnia and COMISA, current treatment approaches and limitations, pharmacotherapy targets and future priorities.

EXPERT OPINION

There are multiple promising emerging therapies, but clinical trial data specifically in COMISA populations are lacking. This is a priority for future investigation to inform development of evidence-based guidelines. Pharmacotherapies, particularly for insomnia, do not target the underlying causes of the disorder thus, are indicated for short-term use only and should remain second line. Future multidisciplinary research should be directed toward the multifactorial nature of COMISA and the challenges of adapting COMISA treatment in clinical practice and overcoming the practical barriers that health-care providers and consumers encounter.

Toward personalized care for insomnia in the US Army: development of a machine-learning model to predict response to pharmacotherapy

STUDY OBJECTIVES

Although many military personnel with insomnia are treated with prescription medication, little reliable guidance exists to identify patients most likely to respond. As a first step toward personalized care for insomnia, we present results of a machine-learning model to predict response to insomnia medication.

METHODS

The sample comprised n = 4,738 nondeployed US Army soldiers treated with insomnia medication and followed 6-12 weeks after initiating treatment. All patients had moderate-severe baseline scores on the Insomnia Severity Index (ISI) and completed 1 or more follow-up ISIs 6-12 weeks after baseline. An ensemble machine-learning model was developed in a 70% training sample to predict clinically significant ISI improvement, defined as reduction of at least 2 standard deviations on the baseline ISI distribution. Predictors included a wide range of military administrative and baseline clinical variables. Model accuracy was evaluated in the remaining 30% test sample.

RESULTS

21.3% of patients had clinically significant ISI improvement. Model test sample area under the receiver operating characteristic curve (standard error) was 0.63 (0.02). Among the 30% of patients with the highest predicted probabilities of improvement, 32.5.% had clinically significant symptom improvement vs 16.6% in the 70% sample predicted to be least likely to improve (χ21 = 37.1, P < .001). More than 75% of prediction accuracy was due to 10 variables, the most important of which was baseline insomnia severity.

CONCLUSIONS

Pending replication, the model could be used as part of a patient-centered decision-making process for insomnia treatment, but parallel models will be needed for alternative treatments before such a system is of optimal value.

CITATION

Gabbay FH, Wynn GH, Georg MW, et al. Toward personalized care for insomnia in the US Army: development of a machine-learning model to predict response to pharmacotherapy. J Clin Sleep Med. 2023;19(8):1399-1410.

Use of Melatonin and/on Ramelteon for the Treatment of Insomnia in Older Adults: A Systematic Review and Meta-Analysis

To investigate the efficacy of melatonin and/or ramelteon reporting sleep outcomes for older adults with chronic insomnia, a systematic review and a meta-analysis of PubMed, EMBASE, Cochrane library, International Pharmaceutical Abstracts, PsycINFO, science citation index, center for reviews and dissemination, CINAHL, grey literature and relevant sleep journal searches were conducted from 1 January 1990 to 20 June 2021. Randomized controlled trials and other comparative studies with melatonin and/or ramelteon use among older patients with chronic insomnia were included. Funnel plot and Egger’s test was used to determine publication bias. A forest plot was constructed to obtain a pooled standardized mean difference using either a fixed or random effects model for each of the two broad categories of sleep outcomes: objective and subjective. Of 5247 studies identified, 17 studies met the inclusion criteria for MA. Study sample size ranged from 10 to 829 with the mean age ≥55 years. There were significant improvements in total sleep time (objective), sleep latency and sleep quality (objective and subjective) for melatonin and/or ramelteon users compared with placebo. Sleep efficiency was not significantly different. The effects of these agents are modest but with limited safe treatment options for insomnia in older adults, these could be the drugs of choice.