Common postpartum mental health (PMH) disorders such as depression and anxiety are preventable, but determining individual-level risk is difficult.

To create and internally validate a clinical risk index for common PMH disorders.

Using population-based health administrative data in Ontario, Canada, comprising sociodemographic, clinical and health service variables easily collectible from hospital birth records, we developed and internally validated a predictive model for common PMH disorders and converted the final model into a risk index. We developed the model in 75% of the cohort (n = 152 362), validating it in the remaining 25% (n = 75 772).

The 1-year prevalence of common PMH disorders was 6.0%. Independently associated variables (forming the mnemonic PMH CAREPLAN) that made up the risk index were: (P) prenatal care provider; (M) mental health diagnosis history and medications during pregnancy; (H) psychiatric hospital admissions or emergency department visits; (C) conception type and complications; (A) apprehension of newborn by child services (newborn taken into care); (R) region of maternal origin; (E) extremes of gestational age at birth; (P) primary maternal language; (L) lactation intention; (A) maternal age; (N) number of prenatal visits. In the index (scored 0–39), 1-year common PMH disorder risk ranged from 1.5 to 40.5%. Discrimination (C-statistic) was 0.69 in development and validation samples; the 95% confidence interval of expected risk encompassed observed risk for all scores in development and validation samples, indicating adequate risk index calibration.

Individual-level risk of developing a common postpartum mental health disorder can be estimated with data feasibly collectable from birth records. Next steps are external validation and evaluation of various cut-off scores for their utility in guiding postpartum individuals to interventions that reduce their risk of illness.

Depression is a major cause of disability worldwide. Recent data suggest that, in industrialised countries, the prevalence of depression peaks in middle age. Identifying factors predictive of future depressive episodes is crucial for developing prevention strategies for this age group.

We aimed to identify future depression in middle-aged adults with no previous psychiatric history.

To predict a diagnosis of depression 1 year or more following a comprehensive baseline assessment, we used a data-driven, machine-learning methodology. Our data-set was the UK Biobank of middle-aged participants (N = 245 036) with no psychiatric history.

Overall, 2.18% of the study population developed a depressive episode at least 1 year following baseline. Basing predictions on a single mental health questionnaire led to an area under the curve of the receiver operating characteristic of 0.66, and a predictive model leveraging the combined results of 100 UK Biobank questionnaires and measurements improved this to 0.79. Our findings were robust to demographic variations (place of birth, gender) and variations in methods of depression assessment. Thus, machine-learning-based models best predict diagnoses of depression when allowing the inclusion of multiple features.

Machine-learning approaches show potential for being beneficial for the identification of clinically relevant predictors of depression. Specifically, we can identify, with moderate success, people with no recorded psychiatric history as at risk for depression by using a relatively small number of features. More work is required to improve these models and evaluate their cost-effectiveness before integrating them into the clinical workflow.

Multiple Sclerosis (MS) is a demyelinating, neurodegenerative, and immune-mediated disease that affects the central nervous system. Usually co-occurs with difficulties in emotional regulation and psychopathology. Anxiety is one of the most common psychiatric manifestations in patients with MS. Nonetheless, empirical evidences on the joint predictive effect of MS clinical conditions and emotion regulation processes on the development of anxiety in MS patients are scarce.

This preliminary study aimed to explore whether fatigue, physical disability (MS clinical conditions) and a low compassionate attitude (maladaptive emotion regulation process based on self-judgment, over-identification, and isolation) predict anxiety symptoms in MS patients.

A convenience sample of 107 patients with MS diagnosis and without other neurological disorders was used in this cross-sectional study. Participants completed the Anxiety Subscale of the Depression, Anxiety and Stress Scales-21, the Analogic Fatigue Scale, the World Health Organization Disability Assessment Schedule, and the Self-judgment, Isolation and Over-identification Subscales of the Self-Compassion Scale.

All potential predictors showed significant correlations with anxiety symptoms and predicted this symptomatology through simple linear regressions. Therefore, they were selected as covariates of the multiple linear regression model, which explained 32% of the variance of anxiety symptoms. This model revealed that fatigue, physical disability, and low compassionate attitude are significant predictors.

The results support the relevance of psychological interventions for MS patients to implement effective strategies to regulate anxiety associated with fatigue and physical disability. Helping patients to adopt a more compassionate attitude toward the self can reduce their anxiety.

No significant relationships.

Multiple Sclerosis (MS) is a chronic inflammatory, immune-mediated, demyelinating disease of the central nervous system, with a progressive course. It is potentially disabling and affects mainly young adults. Depression is the mental disorder with the greatest comorbidity with MS and tends to worsen its symptomatology and course. However, knowledge about the predictors of depression in patients with MS is scarce.

This preliminary study aimed to verify whether neuropathic pain (NP), internal (IS) and external (ES) shame and mindfulness predict depressive symptoms in patients with MS.

This cross-sectional study included a convenience sample of 95 patients diagnosed with MS and without other identified neurological diseases. Participants completed the Depression Subscale of the Depression, Anxiety and Stress Scales-21, the Analogue Pain Scale of the Pain Detect Questionnaire, the External and Internal Shame Scale, and the Mindfulness Subscale of the Self-Compassion Scale.

All potential predictors exhibited significant correlations with depressive symptoms and significantly predicted this symptomatology in simple linear regression models. Thus, they were included as covariates in the multiple linear regression model. This model explained a high percentage of the variance of depressive symptoms (40.5%) and identified NP, IS and mindfulness as significant predictors.

Interventions aimed at preventing/reducing depression in patients with MS should minimize IS and develop mindfulness and NP coping skills, in order to promote mental health in this target population and possibly prevent the exacerbation and progression of MS symptomatology.

No significant relationships.

Multiple sclerosis (MS) is a chronic inflammatory, demyelinating, and neurodegenerative disease of the central nervous system. This condition is enhanced by stress. In turn, stress symptoms are a risk factor for the onset and progression of MS. However, knowledge about predictors of stress in patients with MS is scarce.

This preliminary study aimed to verify whether the number of relapses, fatigue, physical disability (MS characteristics), experiential avoidance and self-judgment (emotion regulation processes) predict stress symptoms in patients diagnosed with MS.

A convenience sample of 101 patients diagnosed with MS and without other neurological diseases participated in this study. Participants completed the Depression Scale of the Depression, Anxiety and Stress Scales-21, Analog Fatigue Scale, World Health Organization Disability Assessment Schedule-12, Acceptance and Action Questionnaire-II, and Self-Judgment Subscale of the Self-Compassion Scale.

All predictors initially hypothesized and years of education have significant correlations with stress symptoms. Simple linear regression analyses showed that the variables significantly predicted stress symptoms and were, therefore, included in the multiple linear regression model. This model explained 51.8% of the variance of the stress symptoms and showed that years of education, the number of relapses, fatigue, and experiential avoidance significantly predicted those symptoms.

The promotion of mental health mental in patients with MS must develop functional skills to deal with stress induced by years of education (possibly responsible for the degree of awareness about MS and its consequences), recurrence of relapses and fatigue, and should minimize emotion regulation strategies focused on experiential avoidance.

Early identification of patients with novel corona virus disease 2019 (COVID-19) who may be at high mortality risk is of great importance.

In this retrospective study, we included all patients with COVID-19 at Huanggang Central Hospital from January 23 to March 5, 2020. Data on clinical characteristics and outcomes were compared between survivors and nonsurvivors. Univariable and multivariable logistic regression were used to explore risk factors associated with in-hospital death. A nomogram was established based on the risk factors selected by multivariable analysis.

A total of 150 patients were enrolled, including 31 nonsurvivors and 119 survivors. The multivariable logistic analysis indicated that increasing the odds of in-hospital death associated with higher Sequential Organ Failure Assessment score (odds ratio [OR], 3.077; 95% confidence interval [CI]: 1.848-5.122; P < 0.001), diabetes (OR, 10.474; 95% CI: 1.554-70.617; P = 0.016), and lactate dehydrogenase greater than 245 U/L (OR, 13.169; 95% CI: 2.934-59.105; P = 0.001) on admission. A nomogram was established based on the results of the multivariable analysis. The AUC of the nomogram was 0.970 (95% CI: 0.947-0.992), showing good accuracy in predicting the risk of in-hospital death.

This finding would facilitate the early identification of patients with COVID-19 who have a high-risk for fatal outcome.

More than 80% of coronavirus disease 2019 (COVID-19) cases are mild or moderate. In this study, a risk model was developed for predicting rehabilitation duration (the time from hospital admission to discharge) of the mild-moderate COVID-19 cases and was used to conduct refined risk management for different risk populations.

A total of 90 consecutive patients with mild-moderate COVID-19 were enrolled. Large-scale datasets were extracted from clinical practices. Through the multivariable linear regression analysis, the model was based on significant risk factors and was developed for predicting the rehabilitation duration of mild-moderate cases of COVID-19. To assess the local epidemic situation, risk management was conducted by weighing the risk of populations at different risk.

Ten risk factors from 44 high-dimensional clinical datasets were significantly correlated to rehabilitation duration (P < 0.05). Among these factors, 5 risk predictors were incorporated into a risk model. Individual rehabilitation durations were effectively calculated. Weighing the local epidemic situation, threshold probability was classified for low risk, intermediate risk, and high risk. Using this classification, risk management was based on a treatment flowchart tailored for clinical decision-making.

The proposed novel model is a useful tool for individualized risk management of mild-moderate COVID-19 cases, and it may readily facilitate dynamic clinical decision-making for different risk populations.

The objective of this paper is to prepare the government and citizens of India to take or implement the control measures proactively to reduce the impact of coronavirus disease 2019 (COVID-19).

In this work, the COVID-19 outbreak in India has been predicted based on the pattern of China using a machine learning approach. The model is built to predict the number of confirmed cases, recovered cases, and death cases based on the data available between January 22, 2020, and April 3, 2020. The time series forecasting method is used for prediction models.

The COVID-19 effects are predicted to be at peak between the third and fourth weeks of April 2020 in India. This outbreak is predicted to be controlled around the end of May 2020. The total number of predicted confirmed cases of COVID-19 might reach around 68 978, and the number of deaths due to COVID-19 are predicted to be 1557 around April 25, 2020, in India. If this outbreak is not controlled by the end of May 2020, then India will face a severe shortage of hospitals, and it will make this outbreak even worse.

The COVID-19 pandemic may be controlled if the Government of India takes proactive steps to aggressively implement a lockdown in the country and extend it further. This presented epidemiological model is an effort to predict the future forecast of COVID-19 spread, based on the present scenario, so that the government can frame policy decisions, and necessary actions can be initiated.

Major depressive disorder (MDD) is associated with high risk of suicide. Conventional neuroimaging works showed abnormalities of static brain activity and connectivity in MDD with suicidal ideation (SI). However, little is known regarding alterations of brain dynamics. More broadly, it remains unclear whether temporal dynamics of the brain activity could predict the prognosis of SI.

We included MDD patients (n = 48) with and without SI and age-, gender-, and education-matched healthy controls (n = 30) who underwent resting-state functional magnetic resonance imaging. We first assessed dynamic amplitude of low-frequency fluctuation (dALFF) – a proxy for intrinsic brain activity (iBA) – using sliding-window analysis. Furthermore, the temporal variability (dynamics) of iBA was quantified as the variance of dALFF over time. In addition, the prediction of the severity of SI from temporal variability was conducted using a general linear model.

Compared with MDD without SI, the SI group showed decreased brain dynamics (less temporal variability) in the dorsal anterior cingulate cortex, the left orbital frontal cortex, the left inferior temporal gyrus, and the left hippocampus. Importantly, these temporal variabilities could be used to predict the severity of SI (r = 0.43, p = 0.03), whereas static ALFF could not in the current data set.

These findings suggest that alterations of temporal variability in regions involved in executive and emotional processing are associated with SI in MDD patients. This novel predictive model using the dynamics of iBA could be useful in developing neuromarkers for clinical applications.

It is important for doctors and patients to know what factors help recovery from depression. Our objectives were to predict the probability of sustained recovery for patients presenting with mild to moderate depression in primary care and to devise a means of estimating this probability on an individual basis.

Participants in a randomized controlled trial were identified through general practitioners (GPs) around three academic centres in England. Participants were aged >18 years, with Hamilton Depression Rating Scale (HAMD) scores 12–19 inclusive, and at least one physical symptom on the Bradford Somatic Inventory (BSI). Baseline assessments included demographics, treatment preference, life events and difficulties and health and social care use. The outcome was sustained recovery, defined as HAMD score <8 at both 12 and 26 week follow-up. We produced a predictive model of outcome using logistic regression clustered by GP and created a probability tree to demonstrate estimated probability of recovery at the individual level.

Of 220 participants, 74% provided HAMD scores at 12 and 26 weeks. A total of 39 (24%) achieved sustained recovery, associated with being female, married/cohabiting, having a low BSI score and receiving preferred treatment. A linear predictor gives individual probabilities for sustained recovery given specific characteristics and probability trees illustrate the range of probabilities and their uncertainties for some important combinations of factors.

Sustained recovery from mild to moderate depression in primary care appears more likely for women, people who are married or cohabiting, have few somatic symptoms and receive their preferred treatment.