AI-Based Predictive Modeling for Clinical Outcomes in Tropical Febrile Illnesses requiring CRRT: A Retrospective Observational Study
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 发起方
- 入组人数
- 300
- 试验地点
- 1
- 主要终点
- To assess the effectiveness of CRRT in reducing mortality and improving renal recovery in patients with tropical fever presenting with thrombocytopenia.
研究概览
简要总结
Tropical febrile illnesses such as Dengue and Leptospirosis are common in endemic regions such as Wayanad, Kerala and often present with thrombocytopenia and acute kidney injury (AKI). ContinUous renal replacement therapy (CRRT) has been life-saving in many such cases, but predictors of response and outcomes remain unclear. AI can help identify patterns, predict disease progression, and support early intervention strategies.
研究设计
- 研究类型
- Observational
入排标准
- 年龄范围
- 18.00 Year(s) 至 65.00 Year(s)（—）
- 性别
- All
入选标准
- •Patients aged 18 to 65 years Diagnosis of tropical fever (Leptospirosis & Dengue) Thrombocytopenia (platelets less than 1,00,000/mm³) Received ICU care.
排除标准
- •Chronic kidney disease prior to admission Prior Thrombocytopenia secondary to Hematological causes Pregnancy.
结局指标
主要结局
To assess the effectiveness of CRRT in reducing mortality and improving renal recovery in patients with tropical fever presenting with thrombocytopenia.
时间窗: Baseline values will be recorded on admission to the ICU as time zero, followed by 24 hours, 48 hours and 72 hours and 4 weeks after dscharge from the ICU.
次要结局
- 1. Predict the need for CRRT.
研究者
Dr ABHISHEK R
Dr Moopens Medical College