Predicting Treatment Resistance in Severe Psychiatric Disorder: A Cross-Diagnostic Analysis of Biopsychosocial Risk Factors in a Pakistani Clinical Cohort: Predicting Treatment Resistance in Severe Psychiatric Disorder: Biopsychosocial Risk Factors

Ayyat Nadeem; Maha Ikram

doi:10.54393/pbmj.v9i7.1380

Authors

Ayyat Nadeem Department of Psychology, Government Degree College for Women, Wahdat Colony, Lahore, Pakistan
Maha Ikram Faculty of Health Sciences, Charles Darwin University, Darwin , Australia

DOI:

https://doi.org/10.54393/pbmj.v9i7.1380

Keywords:

Mental Disorders, Depressive Disorder, Treatment-Resistant, Risk Factors, Machine Learning

Abstract

Severe psychiatric disorders impose a high burden in Pakistan, yet local data on predictors of poor treatment response remain scarce. Objectives: To identify key biopsychosocial correlates of poor treatment response and develop a pragmatic clinical risk-prediction model suitable for resource-limited Pakistani psychiatric settings. Methods: This secondary, retrospective, cross-sectional analysis used a de-identified Pakistani psychiatric dataset (n=764; 409 good, 355 poor responders). Group differences were assessed via Mann-Whitney U and chi-square tests. Multivariable logistic regression identified independent predictors, with performance benchmarked against LASSO regression, Random Forest, and a simplified six-item clinical decision rule. Multicollinearity was checked using variance inflation factors. Results: Poor responders had significantly longer illness duration and higher rates of poor insight, hallucinations, and aggression. Suicidal ideation (adjusted OR=3.84) and speech pressure (OR=3.44) emerged as the strongest independent correlates, while preserved insight was protective (OR=0.33). The logistic model showed acceptable calibration (Hosmer-Lemeshow p=0.401) but explained only 32.4% of the variance. Random Forest achieved the best discrimination (AUC=0.854), modestly outperforming the clinical decision rule (AUC=0.823). Suicidal ideation combined with substance use showed marked synergy (79.1% poor response; OR=5.67). Conclusions: A cross-diagnostic cluster of easily assessable markers—poor insight, suicidality, substance use, and chronicity can inform risk stratification. The simplified decision rule offers a feasible alternative to complex algorithms in overburdened Pakistani psychiatric services, though prospective external validation is essential before clinical implementation.

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