PDF(Pubmed)
Abstract：
UNASSIGNED: Pain, sedation, delirium, and iatrogenic withdrawal syndrome are conditions that often coexist, algorithms can be used to assist healthcare professionals in decision making. However, a comprehensive review is lacking. This systematic review aimed to assess the effectiveness, quality, and implementation of algorithms for the management of pain, sedation, delirium, and iatrogenic withdrawal syndrome in all pediatric intensive care settings.
UNASSIGNED: A literature search was conducted on November 29, 2022, in PubMed, Embase, CINAHL and Cochrane Library, ProQuest Dissertations & Theses, and Google Scholar to identify algorithms implemented in pediatric intensive care and published since 2005. Three reviewers independently screened the records for inclusion, verified and extracted data. Included studies were assessed for risk of bias using the JBI checklists, and algorithm quality was assessed using the PROFILE tool (higher % = higher quality). Meta-analyses were performed to compare algorithms to usual care on various outcomes (length of stay, duration and cumulative dose of analgesics and sedatives, length of mechanical ventilation, and incidence of withdrawal).
UNASSIGNED: From 6,779 records, 32 studies, including 28 algorithms, were included. The majority of algorithms (68%) focused on sedation in combination with other conditions. Risk of bias was low in 28 studies. The average overall quality score of the algorithm was 54%, with 11 (39%) scoring as high quality. Four algorithms used clinical practice guidelines during development. The use of algorithms was found to be effective in reducing length of stay (intensive care and hospital), length of mechanical ventilation, duration of analgesic and sedative medications, cumulative dose of analgesics and sedatives, and incidence of withdrawal. Implementation strategies included education and distribution of materials (95%). Supportive determinants of algorithm implementation included leadership support and buy-in, staff training, and integration into electronic health records. The fidelity to algorithm varied from 8.2% to 100%.
UNASSIGNED: The review suggests that algorithm-based management of pain, sedation and withdrawal is more effective than usual care in pediatric intensive care settings. There is a need for more rigorous use of evidence in the development of algorithms and the provision of details on the implementation process.
UNASSIGNED: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021276053, PROSPERO [CRD42021276053].
UNASSIGNED: A literature search was conducted on November 29, 2022, in PubMed, Embase, CINAHL and Cochrane Library, ProQuest Dissertations & Theses, and Google Scholar to identify algorithms implemented in pediatric intensive care and published since 2005. Three reviewers independently screened the records for inclusion, verified and extracted data. Included studies were assessed for risk of bias using the JBI checklists, and algorithm quality was assessed using the PROFILE tool (higher % = higher quality). Meta-analyses were performed to compare algorithms to usual care on various outcomes (length of stay, duration and cumulative dose of analgesics and sedatives, length of mechanical ventilation, and incidence of withdrawal).
UNASSIGNED: From 6,779 records, 32 studies, including 28 algorithms, were included. The majority of algorithms (68%) focused on sedation in combination with other conditions. Risk of bias was low in 28 studies. The average overall quality score of the algorithm was 54%, with 11 (39%) scoring as high quality. Four algorithms used clinical practice guidelines during development. The use of algorithms was found to be effective in reducing length of stay (intensive care and hospital), length of mechanical ventilation, duration of analgesic and sedative medications, cumulative dose of analgesics and sedatives, and incidence of withdrawal. Implementation strategies included education and distribution of materials (95%). Supportive determinants of algorithm implementation included leadership support and buy-in, staff training, and integration into electronic health records. The fidelity to algorithm varied from 8.2% to 100%.
UNASSIGNED: The review suggests that algorithm-based management of pain, sedation and withdrawal is more effective than usual care in pediatric intensive care settings. There is a need for more rigorous use of evidence in the development of algorithms and the provision of details on the implementation process.
UNASSIGNED: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021276053, PROSPERO [CRD42021276053].
摘要：
■疼痛，镇静,谵妄,医源性戒断综合征是经常共存的情况，算法可用于帮助医疗保健专业人员做出决策。然而,缺乏全面的审查。这项系统的审查旨在评估有效性，质量,以及疼痛管理算法的实施，镇静,谵妄,以及所有儿科重症监护机构中的医源性戒断综合征。
■于2022年11月29日在PubMed进行了文献检索，Embase,CINAHL和Cochrane图书馆，ProQuest论文&论文,和谷歌学者确定在儿科重症监护中实施的算法，并自2005年以来发布。三名审稿人独立筛选了要收录的记录，验证和提取数据。纳入的研究使用JBI检查表评估偏倚风险，使用PROFILE工具评估算法质量(较高%=较高质量)。进行了荟萃分析，以比较算法与常规护理对各种结果的影响(住院时间，止痛药和镇静剂的持续时间和累积剂量，机械通气的长度，和戒断发生率)。
■来自6,779条记录，32项研究,包括28种算法，包括在内。大多数算法（68％）专注于与其他条件结合的镇静。在28项研究中，偏倚风险较低。该算法的平均总体质量分数为54%，11分（39%）为高质量。四种算法在开发过程中使用了临床实践指南。发现算法的使用在减少住院时间(重症监护和住院)方面是有效的，机械通气的长度，镇痛和镇静药物的持续时间，止痛药和镇静剂的累积剂量，和戒断的发生率。实施战略包括教育和分发材料(95%)。算法实现的支持决定因素包括领导支持和买入，员工培训,并集成到电子健康记录中。算法的保真度从8.2%到100%不等。
■该评论表明，基于算法的疼痛管理，在儿科重症监护中,镇静和停药比常规治疗更有效.在开发算法和提供实施过程的细节时，需要更严格地使用证据。
■https://www.crd.约克。AC.uk/prospro/display_record.php?ID=CRD42021276053,PROSPERO[CRD42021276053]。
■于2022年11月29日在PubMed进行了文献检索，Embase,CINAHL和Cochrane图书馆，ProQuest论文&论文,和谷歌学者确定在儿科重症监护中实施的算法，并自2005年以来发布。三名审稿人独立筛选了要收录的记录，验证和提取数据。纳入的研究使用JBI检查表评估偏倚风险，使用PROFILE工具评估算法质量(较高%=较高质量)。进行了荟萃分析，以比较算法与常规护理对各种结果的影响(住院时间，止痛药和镇静剂的持续时间和累积剂量，机械通气的长度，和戒断发生率)。
■来自6,779条记录，32项研究,包括28种算法，包括在内。大多数算法（68％）专注于与其他条件结合的镇静。在28项研究中，偏倚风险较低。该算法的平均总体质量分数为54%，11分（39%）为高质量。四种算法在开发过程中使用了临床实践指南。发现算法的使用在减少住院时间(重症监护和住院)方面是有效的，机械通气的长度，镇痛和镇静药物的持续时间，止痛药和镇静剂的累积剂量，和戒断的发生率。实施战略包括教育和分发材料(95%)。算法实现的支持决定因素包括领导支持和买入，员工培训,并集成到电子健康记录中。算法的保真度从8.2%到100%不等。
■该评论表明，基于算法的疼痛管理，在儿科重症监护中,镇静和停药比常规治疗更有效.在开发算法和提供实施过程的细节时，需要更严格地使用证据。
■https://www.crd.约克。AC.uk/prospro/display_record.php?ID=CRD42021276053,PROSPERO[CRD42021276053]。
