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Surono Sugiyarto
Fakultas Sains dan Teknologi Terapan (FAST), Universitas Ahmad Dahlan, Yogyakarta
Chemistry Medicine & Pharmacology Public Health

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CLINICAL FACTORS ASSOCIATED WITH PAIN INTENSITY AND ADJUVANT ANALGESIC USE IN OSTEOARTHRITIS PATIENTS Nuh Muhammad; Darmawan Endang; Surono Sugiyarto
Pharmacoscript Vol. 9 No. 1 (2026): Pharmacoscript
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat, Universitas Perjuangan Tasikmalaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36423/pharmacoscript.v9i1.2660

Abstract

Osteoarthritis (OA) is a degenerative joint disease that commonly causes chronic pain and functional limitations. In routine practice, adjuvant analgesics are frequently used as part of multimodal pain management; however, clinical factors associated with pain intensity and adjuvant analgesic use are not well characterized. This retrospective observational study aimed to identify clinical factors associated with final pain intensity and their relationship with adjuvant analgesic use in patients with OA. Medical records of OA patients treated at Sultan Agung Islamic Hospital, Indonesia, between 2020 and 2025 were reviewed. Data included patient characteristics, OA severity based on the Kellgren–Lawrence (KL) grading system, pain intensity assessed using the Visual Analog Scale (VAS), and types of adjuvant analgesics prescribed, Multivariate logistic regression analysis showed that severe OA (KL grade ≥3) was the strongest factor associated with higher final pain intensity (OR = 3.56; 95% CI: 2.9–4.3). Knee OA (OR = 1.70; 95% CI: 1.1–2.4) and hyperlipidemia (OR = 2.33; 95% CI: 1.3–4.1) were also independently associated with higher final pain intensity. The use of adjuvant analgesics, particularly joint supplements and neuropathic pain agents, was associated with higher final pain intensity, reflecting greater disease severity and clinical complexity rather than direct analgesic effectiveness. Muscle relaxants were not significantly associated with pain intensity. In conclusion, final pain intensity in OA is primarily associated with disease severity and clinical complexity, and causal relationships cannot be established due to the observational design.
EXPLORING MULTIMORBIDITY PATTERNS IN PATIENTS WITH OSTEOARTHRITIS USING MACHINE LEARNING Sari Dewi Nirmala; Darmawan Endang; Surono Sugiyarto
Pharmacoscript Vol. 9 No. 1 (2026): Pharmacoscript
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat, Universitas Perjuangan Tasikmalaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36423/pharmacoscript.v9i1.2755

Abstract

Osteoarthritis (OA) is a degenerative joint disease often accompanied by multimorbidity, particularly cardiometabolic diseases. OA is also associated with comorbidities, thus requiring an analytical approach capable of identifying patterns of relationships between diseases and rational therapies. This study aims to explore patterns of multimorbidity and treatment patterns in hospitalized patients with osteoarthritis using a machine learning (ML) approach. This study employed a retrospective design using medical records of hospitalized OA patients from January 2020 to January 2025 at Sultan Agung Islamic Hospital in Semarang. Analysis was performed using the Frequent Pattern Growth (FP-Growth) algorithm with support, confidence, and lift parameters. The minimum support value was set at 1% to identify a wider variety of patterns. A total of 25 patients were analyzed, with the majority being female (14 patients; 56%) and aged ≥59 years (14 patients;96%), with comorbidities predominantly obesity and hypertension. Association Rule Mining (ARM) results showed cardiometabolic multimorbidity patterns, with the strongest association in the combination OA+HTàDM (lift 1.93). Therapy pattern analysis indicated that disease combinations were associated with the use of therapies such as NSAIDs for OA and metformin for diabetes, as well as the addition of adjuvant therapies. Multimorbidity patterns in hospitalized OA patients are dominated by the cardiometabolic group, with complex therapeutic regimens. ML approaches are effective in identifying patterns of disease and therapy relationships, therapy supporting more rational clinical decision-making.
Co-Authors Darmawan Endang Nuh Muhammad Sari Dewi Nirmala