Clinical Decision Support for Total Parenteral Nutrition Constituents in Neonatal Intensive Care Unit (NICU) Patients: A Pilot Study
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 260
- 试验地点
- 1
- 主要终点
- System Acceptance Rate
研究概览
简要总结
This study tests whether an artificial intelligence (AI) tool can help doctors order total parenteral nutrition (TPN) for babies in the neonatal intensive care unit (NICU). Premature babies often cannot eat by mouth and need nutrition delivered through an IV. Ordering TPN is complex, time-consuming, and mistakes can happen. This study will test an AI tool that suggests TPN formulas to doctors based on each baby's lab values and health information. Doctors can accept, change, or reject the suggestions at any time. The main goal is to measure how often doctors accept the AI suggestions. The study will also track time to complete TPN orders, weight changes, days on TPN, whether lab values stay in normal ranges, provider satisfaction, and baby health outcomes including complications such as lung disease, brain bleeding, infections, and other conditions common in premature babies. Babies admitted to the NICU who need TPN may participate if their doctors agree to use the tool. Each baby will be in the study while they need TPN, typically about 14 days. The AI tool only makes suggestions and does not replace doctor decision-making. All other care remains the same as standard practice.
详细描述
Our AI-driven TPN (TPN2.0) platform is a combination of AI and a premade set of TPN units. The AI is used to formulate and assign the optimal TPN unit to each infant, given their daily profile and lab test values. It is driven by decades of data, including our published morbidity risks, basic demographics, and routinely collected lab test values. The approach will save staff time and eliminate high errors in the current TPN ordering process.
Our pilot will be deployed as a clinical decision support tool that only makes recommendations, and doctors can always override it. As such, this minimally affects the current practice. We aim to enroll 260 neonates in this pilot study. The primary outcome is physician acceptance rate of TPN2.0 recommendations. Secondary outcomes include time to complete TPN orders, change in weight z-score, days on TPN, lab value abnormalities (values outside normal range), provider satisfaction, and a composite morbidity index comprising rates of bronchopulmonary dysplasia, necrotizing enterocolitis, retinopathy of prematurity, respiratory distress syndrome, congenital heart disease, sepsis, anemia, intraventricular hemorrhage, cholestasis, jaundice, pulmonary hemorrhage, pulmonary hypertension, readmission during the study, and mortality.
研究设计
- 研究类型
- Interventional
- 分配方式
- Non Randomized
- 干预模型
- Sequential
- 主要目的
- Health Services Research
- 盲法
- None
入排标准
- 年龄范围
- — 至 6 Months（Child）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Any newborns or infants requiring total parenteral nutrition in a neonatal ICU that performs daily laboratory tests
- •6-month old at the time of admission
- •Any gestational age or birthweight
- •Any race or sex
排除标准
- •- Infants deem unfit for the suggested TPN due to safety concerns by physicians
研究组 & 干预措施
Standard TPN Ordering (Control)
Patients admitted during Period 1. Providers use current standard TPN ordering practice without AI assistance. Serves as baseline comparison.
AI-driven total parenteral nutrition (TPN)
Patients admitted during Period 2. Providers use the AI-assisted TPN decision support tool integrated with Epic to order TPN. Providers may opt out and use traditional ordering if needed.
干预措施: AI-driven total parenteral nutrition (TPN) (Device)
结局指标
主要结局
System Acceptance Rate
时间窗: 10 months
Percentage of AI-generated recommendations that are accepted or modified by providers for each ingredient of TPN. Measured by retrospective comparison between AI suggestion and actual TPN order submitted.
次要结局
- Change in Weight Z-Score(10 months)
- Days on TPN(10 months)
- Composite Clinical Outcome(13 months)
- Rate of Laboratory Value Abnormalities(10 months)