治療用抗生物質の中止のための説明可能な機械学習
Contact us if these videos are not relevant.
Contact us if these videos are not relevant.
PubMedで要約を見る
まとめ
この要約は機械生成です。集中治療室 (ICU) での抗生物質再投与を予測するのは困難です. 短い抗生物質の投与期間は,抗生物質の再開の最も強い予測因子であり,ICUでの抗生物質の使用を最適化するためのデータ主導の決定の可能性を強調しています.
科学分野
- 集中治療 医療
- 臨床薬理学
- 医療情報学
背景
- ICUでの最適な抗生物質投与期間を決定することは,耐性と感染リスクのバランスを取ることで,困難です.
- 抗生物質の長期使用は耐性や副作用を増加させ,早期に中止すると感染が再発する危険性があります.
- 説明可能な機械学習は,ICU患者の抗生物質再投与を予測する可能性を秘めています.
研究 の 目的
- ICU患者の抗生物質再導入を予測するための機械学習モデルを開発し,評価する.
- 72時間以内に抗生物質の再投与の重要な予測要因を特定する.
主な方法
- オランダの2つの大学院のICUの成人のデータ収集.
- モニターデータ,検査結果,浄化戦略,薬剤,培養物を予測要素として含める.
- 抗生物質の再開を予測するためのロジスティック回帰,軽量GBM,および自動予測モデルのトレーニング.
主要な成果
- 2, 486人の患者と3, 645人の抗生物質の投与を分析し,19%が抗生物質の再投与を経験した.
- 最新の抗生物質投与期間が短かったことが再開の最も重要な予測因子でした.
- ロジスティック・リグレーションで最高の結果 (AUROC 0. 675) が得られ,90日間の死亡率は再開群で高かった (39. 8% 対 25. 0%).
結論
- 集中治療室での抗生物質再投与を予測することは困難ですが,実現可能です.
- 抗生物質の投与期間が短くなることは 治療中止の最適化の可能性を示唆する 重要な予測因子です
- 同じ抗生物質の頻繁な再投与と高い死亡率は,データに基づいた意思決定の必要性を強調しています.
関連する概念動画
Overview
Researchers use antibiotic resistance genes to identify bacteria that possess a plasmid containing their gene of interest. Antibiotic resistance naturally occurs when a spontaneous DNA mutation creates changes in bacterial genes that eliminate antibiotic activity. Bacteria can share these new resistance genes with their offspring and other bacteria. The overuse and misuse of antibiotics have created a public health crisis, as resistant and multi-resistant bacteria continue to develop.
Clearance measures drug elimination from the central compartment, including plasma and highly perfused organs like kidneys and liver. Its calculation varies depending on pharmacokinetic models and administration routes. The one-compartment model, for instance, portrays the pharmacokinetics of polar drugs such as aminoglycoside antibiotics administered intravenously and readily excreted in urine. In this case, clearance is influenced by the terminal rate constant (λz) and the total volume...
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
Predicting Outbreaks
Predictive analytics, a branch of statistics, uses historical data, algorithmic models, and...
Medical management of tuberculosis (TB) patients involves a comprehensive approach that includes diagnosis, treatment, and monitoring. The specific strategies can vary depending on the type of tuberculosis (latent or active), the patient's overall health status, and other considerations.
Latent tuberculosis infection occurs when TB bacteria are present in a person's body, but are not causing illness or symptoms. It is not contagious, and preventive treatment is crucial to avoid the...
Healthcare-associated infections (HAIs) occur in a healthcare facility while a person receives care for another ailment. This category also includes work-related infections among healthcare staff.
HAIs significantly increase the cost of health care. Extended stays in healthcare institutions, increased disability, increased costs of medications, including specialized antibiotics, and prolonged recovery times add to the patient's expenses and the healthcare institution and funding bodies.