Pub Date : 2024-06-01DOI: 10.1016/j.ccrj.2024.01.001
Louis W. Kirton MBChB , Raulle Sol Cruz BSN , Leanlove Navarra BSN , Allie Eathorne BSc , Julie Cook MBChB , Richard Beasley DSc , Paul J. Young MBChB, PhD
Objective
The objective of this study was to determine whether automated titration of the fraction of inspired oxygen (FiO2) increases the time spent with oxygen saturation (SpO2) within a predetermined target SpO2 range compared with manually adjusted high-flow oxygen therapy in postoperative cardiac surgical patients managed in the intensive care unit (ICU).
Recently extubated adults following elective cardiac surgery who required supplemental oxygen.
Interventions
Automatically adjusted FiO2 (using an automated oxygen control system) compared with manual FiO2 titration, until cessation of oxygen therapy, ICU discharge, or 24 h (whichever was sooner).
Main outcome measures
The primary outcome was the proportion of time receiving oxygen therapy with the SpO2 in a SpO2 target range of 92–96 %.
Results
Among 65 participants, the percentage of time per patient spent in the target SpO2 range was a median of 97.7 % (interquartile range: 87.9–99.2 %) and 91.3 % (interquartile range: 77.1–96.1 %) in the automated (n = 28) and manual (n = 28) titration groups, respectively. The estimated effect of automated FiO2, compared to manual FiO2 titration, was to increase the percentage of time spent in the target range by a median of 4.8 percentage points (95 % confidence interval: 1.6 to 10.3 percentage points, p = 0.01).
Conclusion
In patients recently extubated after cardiac surgery, automated FiO2 titration significantly increased time spent in a target SpO2 range of 92–96 % compared to manual FiO2 titration.
{"title":"Effect of automated titration of oxygen on time spent in a prescribed oxygen saturation range in adults in the ICU after cardiac surgery","authors":"Louis W. Kirton MBChB , Raulle Sol Cruz BSN , Leanlove Navarra BSN , Allie Eathorne BSc , Julie Cook MBChB , Richard Beasley DSc , Paul J. Young MBChB, PhD","doi":"10.1016/j.ccrj.2024.01.001","DOIUrl":"10.1016/j.ccrj.2024.01.001","url":null,"abstract":"<div><h3>Objective</h3><p>The objective of this study was to determine whether automated titration of the fraction of inspired oxygen (FiO<sub>2</sub>) increases the time spent with oxygen saturation (SpO<sub>2</sub>) within a predetermined target SpO<sub>2</sub> range compared with manually adjusted high-flow oxygen therapy in postoperative cardiac surgical patients managed in the intensive care unit (ICU).</p></div><div><h3>Design</h3><p>Single-centre, open-label, randomised clinical trial.</p></div><div><h3>Setting</h3><p>Tertiary centre ICU.</p></div><div><h3>Participants</h3><p>Recently extubated adults following elective cardiac surgery who required supplemental oxygen.</p></div><div><h3>Interventions</h3><p>Automatically adjusted FiO<sub>2</sub> (using an automated oxygen control system) compared with manual FiO<sub>2</sub> titration, until cessation of oxygen therapy, ICU discharge, or 24 h (whichever was sooner).</p></div><div><h3>Main outcome measures</h3><p>The primary outcome was the proportion of time receiving oxygen therapy with the SpO<sub>2</sub> in a SpO<sub>2</sub> target range of 92–96 %.</p></div><div><h3>Results</h3><p>Among 65 participants, the percentage of time per patient spent in the target SpO<sub>2</sub> range was a median of 97.7 % (interquartile range: 87.9–99.2 %) and 91.3 % (interquartile range: 77.1–96.1 %) in the automated (n = 28) and manual (n = 28) titration groups, respectively. The estimated effect of automated FiO<sub>2</sub>, compared to manual FiO<sub>2</sub> titration, was to increase the percentage of time spent in the target range by a median of 4.8 percentage points (95 % confidence interval: 1.6 to 10.3 percentage points, p = 0.01).</p></div><div><h3>Conclusion</h3><p>In patients recently extubated after cardiac surgery, automated FiO<sub>2</sub> titration significantly increased time spent in a target SpO<sub>2</sub> range of 92–96 % compared to manual FiO<sub>2</sub> titration.</p></div>","PeriodicalId":49215,"journal":{"name":"Critical Care and Resuscitation","volume":"26 2","pages":"Pages 64-70"},"PeriodicalIF":1.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1441277224000012/pdfft?md5=d919093c4ea4da4a465fbf8f92c3ea67&pid=1-s2.0-S1441277224000012-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140401657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.1016/j.ccrj.2024.02.001
Kate Wagner MBBS, M Bioeth , Neil Orford MBBS, FCICM, FANZCA, PGDipEcho, PhD , Sharyn Milnes RN, PGCertCCN, PGDipEd, MBioeth, PhD , Paul Secombe BMBS(Hons), MClinSc, FCICM , Steve Philpot MBBS (Hons), FANZCA, FCICM, PGDipEcho, MHealth&MedLaw, GChPOM , David Pilcher MBBS, FCICM, FRACP
Objective
Determine the prevalence and outcomes of patients with life-limiting illness (LLI) admitted to Australian and New Zealand Intensive Care Units (ICUs).
Design, setting, participants
Retrospective registry-linked observational cohort study of all adults admitted to Australian and New Zealand ICUs from 1st January 2018 until 31st December 2020 (New Zealand) and 31st March 2022 (Australia), recorded in the Australian and New Zealand Intensive Care Society Adult Patient Database.
Main outcome measures
The primary outcome was 1-year mortality. Secondary outcomes included ICU and hospital mortality, ICU and hospital length of stay, and 4-year survival.
Results
A total of 566,260 patients were included, of whom 129,613 (22.9%) had one or more LLI. Mortality at one year was 28.1% in those with LLI and 10.4% in those without LLI (p < 0.001). Mortality in intensive care (6.8% v 3.4%, p < 0.001), hospital (11.8% v 5.0%, p < 0.001), and at two (36.6% v 14.1%, p < 0.001), three (43.7% v 17.7%, p < 0.001) and four (55.6% v 24.5%, p < 0.001) years were all higher in the cohort of patients with LLI. Patients with LLI had a longer ICU (1.9 [0.9, 3.7] v 1.6 [0.9, 2.9] days, p < 0.001) and hospital length of stay (8.8 [49,16.0] v 7.2 [3.9, 12.9] days, p < 0.001), and were more commonly readmitted to ICU during the same hospitalisation than patients without LLI (5.2% v 3.7%, p < 0.001). After multivariate analysis the LLI with the strongest adverse effect on survival was frailty (HR 2.08, 95% CI 2.03 to 2.12, p < 0.001), followed by the presence of metastatic cancer (HR 1.97, 95% CI 1.92 to 2.02, p < 0.001), and chronic liver disease (HR 1.65, 95% CI 1.65 to 1.71, p < 0.001).
Conclusion
Patients with LLI account for almost a quarter of ICU admissions in Australia and New Zealand, require prolonged ICU and hospital care, and have high mortality in subsequent years. This knowledge should be used to identify this vulnerable cohort of patients, and to ensure that treatment is aligned to each patient's values and realistic goals.
{"title":"Prevalence and long-term outcomes of patients with life-limiting illness admitted to intensive care units in Australia and New Zealand","authors":"Kate Wagner MBBS, M Bioeth , Neil Orford MBBS, FCICM, FANZCA, PGDipEcho, PhD , Sharyn Milnes RN, PGCertCCN, PGDipEd, MBioeth, PhD , Paul Secombe BMBS(Hons), MClinSc, FCICM , Steve Philpot MBBS (Hons), FANZCA, FCICM, PGDipEcho, MHealth&MedLaw, GChPOM , David Pilcher MBBS, FCICM, FRACP","doi":"10.1016/j.ccrj.2024.02.001","DOIUrl":"https://doi.org/10.1016/j.ccrj.2024.02.001","url":null,"abstract":"<div><h3>Objective</h3><p>Determine the prevalence and outcomes of patients with life-limiting illness (LLI) admitted to Australian and New Zealand Intensive Care Units (ICUs).</p></div><div><h3>Design, setting, participants</h3><p>Retrospective registry-linked observational cohort study of all adults admitted to Australian and New Zealand ICUs from 1st January 2018 until 31st December 2020 (New Zealand) and 31st March 2022 (Australia), recorded in the Australian and New Zealand Intensive Care Society Adult Patient Database.</p></div><div><h3>Main outcome measures</h3><p>The primary outcome was 1-year mortality. Secondary outcomes included ICU and hospital mortality, ICU and hospital length of stay, and 4-year survival.</p></div><div><h3>Results</h3><p>A total of 566,260 patients were included, of whom 129,613 (22.9%) had one or more LLI. Mortality at one year was 28.1% in those with LLI and 10.4% in those without LLI (p < 0.001). Mortality in intensive care (6.8% v 3.4%, p < 0.001), hospital (11.8% v 5.0%, p < 0.001), and at two (36.6% v 14.1%, p < 0.001), three (43.7% v 17.7%, p < 0.001) and four (55.6% v 24.5%, p < 0.001) years were all higher in the cohort of patients with LLI. Patients with LLI had a longer ICU (1.9 [0.9, 3.7] v 1.6 [0.9, 2.9] days, p < 0.001) and hospital length of stay (8.8 [49,16.0] v 7.2 [3.9, 12.9] days, p < 0.001), and were more commonly readmitted to ICU during the same hospitalisation than patients without LLI (5.2% v 3.7%, p < 0.001). After multivariate analysis the LLI with the strongest adverse effect on survival was frailty (HR 2.08, 95% CI 2.03 to 2.12, p < 0.001), followed by the presence of metastatic cancer (HR 1.97, 95% CI 1.92 to 2.02, p < 0.001), and chronic liver disease (HR 1.65, 95% CI 1.65 to 1.71, p < 0.001).</p></div><div><h3>Conclusion</h3><p>Patients with LLI account for almost a quarter of ICU admissions in Australia and New Zealand, require prolonged ICU and hospital care, and have high mortality in subsequent years. This knowledge should be used to identify this vulnerable cohort of patients, and to ensure that treatment is aligned to each patient's values and realistic goals.</p></div>","PeriodicalId":49215,"journal":{"name":"Critical Care and Resuscitation","volume":"26 2","pages":"Pages 116-122"},"PeriodicalIF":1.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1441277224000048/pdfft?md5=cf5cf03517b366ba43e008fcb7f1313f&pid=1-s2.0-S1441277224000048-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141486191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.1016/j.ccrj.2024.03.001
Thilo von Groote MD , Moritz Fabian Danzer MSc , Melanie Meersch MD , Alexander Zarbock MD , Joachim Gerß PhD
Objective
This article describes the statistical analysis plan for the Biomarker-guided intervention to prevent AKI after major surgery (BigpAK-2) trial.
Design
Adaptive trial design with an interim analysis after enrolment of 618 evaluable patients.
Setting
The BigpAK.-2 trial is an international, prospective, randomised controlled multicentre study.
Participants
The BigpAK-2 study enrols patients after major surgery who are admitted to the intensive care or high dependency unit and are at high-risk for postoperative AKI as identified by urinary biomarkers (tissue inhibitor of metalloproteinases-2 and insulin-like growth factor binding protein 7 ([TIMP-2]∗[IGFBP7]) will be enrolled.
Intervention
Patients are randomly and evenly allocated to standard of care (control) group or the implementation of a nephroprotective care bundle (intervention group), as recommended by the Kidney Disease: Improving Global Outcomes (KDIGO) guidelines. The KDIGO care bundle recommends discontinuation of nephrotoxic agents if possible, ensuring adequate volume status and perfusion pressure, considering functional haemodynamic monitoring, regular monitoring of serum creatinine and urine output, avoiding hyperglycemia, and considering alternatives to radiocontrast procedures when possible.
Results
The BigpAK-2 study investigates whether the biomarker-gudied implementation of the KDIGO care bundle reduces the incidence of moderate or severe AKI (stage 2 or 3), according to the KDIGO 2012 criteria, within 72 h after surgery.
Conclusion
AKI is a common and often severe complication after major surgery. As no specific treatments exist, prevention of AKI is of high importance. The BigpAK-2 study investigates a promising approach to prevent AKI after major surgery.
Trial registration
The trial was registered prior to start at clinicaltrials.gov; NCT04647396.
本文介绍了生物标志物指导下预防大手术后 AKI 的干预试验（BigpAK-2）的统计分析计划。BigpAK.-2 试验是一项国际性、前瞻性、随机对照多中心研究。参与者BigpAK.-2 试验将招募大手术后入住重症监护室或高依赖性病房的患者，这些患者通过尿液生物标记物（金属蛋白酶组织抑制剂-2 和胰岛素样生长因子结合蛋白 7 ([TIMP-2]∗[IGFBP7]) 确定为术后 AKI 的高危人群。干预患者被随机平均分配到标准护理组（对照组）或实施肾脏病：改善全球疗效》（KDIGO）指南的建议。KDIGO 护理包建议尽可能停用肾毒性药物，确保足够的血容量状态和灌注压，考虑进行功能性血流动力学监测，定期监测血清肌酐和尿量，避免高血糖，并在可能的情况下考虑放射对比剂的替代疗法。结果BigpAK-2研究调查了根据KDIGO 2012标准，生物标记物监测KDIGO护理包的实施是否能降低术后72小时内中度或重度AKI（2期或3期）的发生率。由于目前尚无特效治疗方法，因此预防 AKI 至关重要。BigpAK-2研究调查了一种预防大手术后AKI的有效方法。试验注册试验开始前已在clinicaltrials.gov; NCT04647396注册。
{"title":"Statistical analysis plan for the biomarker-guided intervention to prevent acute kidney injury after major surgery (BigpAK-2) study: An international randomised controlled multicentre trial","authors":"Thilo von Groote MD , Moritz Fabian Danzer MSc , Melanie Meersch MD , Alexander Zarbock MD , Joachim Gerß PhD","doi":"10.1016/j.ccrj.2024.03.001","DOIUrl":"https://doi.org/10.1016/j.ccrj.2024.03.001","url":null,"abstract":"<div><h3>Objective</h3><p>This article describes the statistical analysis plan for the Biomarker-guided intervention to prevent AKI after major surgery (BigpAK-2) trial.</p></div><div><h3>Design</h3><p>Adaptive trial design with an interim analysis after enrolment of 618 evaluable patients.</p></div><div><h3>Setting</h3><p>The BigpAK.-2 trial is an international, prospective, randomised controlled multicentre study.</p></div><div><h3>Participants</h3><p>The BigpAK-2 study enrols patients after major surgery who are admitted to the intensive care or high dependency unit and are at high-risk for postoperative AKI as identified by urinary biomarkers (tissue inhibitor of metalloproteinases-2 and insulin-like growth factor binding protein 7 ([TIMP-2]∗[IGFBP7]) will be enrolled.</p></div><div><h3>Intervention</h3><p>Patients are randomly and evenly allocated to standard of care (control) group or the implementation of a nephroprotective care bundle (intervention group), as recommended by the Kidney Disease: Improving Global Outcomes (KDIGO) guidelines. The KDIGO care bundle recommends discontinuation of nephrotoxic agents if possible, ensuring adequate volume status and perfusion pressure, considering functional haemodynamic monitoring, regular monitoring of serum creatinine and urine output, avoiding hyperglycemia, and considering alternatives to radiocontrast procedures when possible.</p></div><div><h3>Results</h3><p>The BigpAK-2 study investigates whether the biomarker-gudied implementation of the KDIGO care bundle reduces the incidence of moderate or severe AKI (stage 2 or 3), according to the KDIGO 2012 criteria, within 72 h after surgery.</p></div><div><h3>Conclusion</h3><p>AKI is a common and often severe complication after major surgery. As no specific treatments exist, prevention of AKI is of high importance. The BigpAK-2 study investigates a promising approach to prevent AKI after major surgery.</p></div><div><h3>Trial registration</h3><p>The trial was registered prior to start at <span>clinicaltrials.gov</span><svg><path></path></svg>; NCT04647396.</p></div>","PeriodicalId":49215,"journal":{"name":"Critical Care and Resuscitation","volume":"26 2","pages":"Pages 80-86"},"PeriodicalIF":1.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1441277224000061/pdfft?md5=7603b8051ebdcce17d01c61033c5963a&pid=1-s2.0-S1441277224000061-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141486284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.1016/j.ccrj.2024.02.002
Melissa J. Parker MD, MSc , Gary Foster PhD , Alison Fox-Robichaud MD, MSc , Karen Choong MB BCh, MSc , Lawrence Mbuagbaw MD, PhD , Lehana Thabane PhD , With the SQUEEZE Trial Steering Committee and on behalf of the SQUEEZE Trial Investigators, the Canadian Critical Care Trials Group, Pediatric Emergency Research Canada, and the Canadian Critical Care Translational Biology Group
Background
The SQUEEZE trial is a multicentred randomized controlled trial which seeks to determine the optimal approach to fluid resuscitation in paediatric septic shock. SQUEEZE also includes a nested translational study, SQUEEZE-D, investigating the value of plasma cell-free DNA for prediction of clinical outcomes.
Objective
To present a pre-specified statistical analysis plan (SAP) for the SQUEEZE trial prior to finalizing the trial data set and prior to commencing data analysis.
Design
SQUEEZE is a pragmatic, two-arm, open-label, prospective multicentre randomized controlled trial.
Setting
Canadian paediatric tertiary care centres.
Participants
Paediatric patients with suspected sepsis and persistent signs of shock in need of ongoing resuscitation. Sample size target: 400 participants.
Interventions
The trial is designed to compare a fluid-sparing resuscitation strategy to usual care.
Main outcome measures
The primary outcome for the SQUEEZE trial is the time to shock reversal (in hours). The primary outcome analysis will assess the difference in time to shock reversal between the intervention and control groups, reported as point estimate with 95% confidence intervals. The statistical test for the primary analysis will be a two-sided t-test. Secondary outcome measures include clinical outcomes and adverse events including measures of organ dysfunction and mortality outcomes.
Results
The SAP presented here is reflective of and where necessary clarifies in detail the analysis plan as presented in the trial protocol. The SAP includes a mock CONSORT diagram, figures and tables. Data collection methods are summarized, primary and secondary outcomes are defined, and outcome analyses are described.
Conclusions
We have developed a statistical analysis plan for the SQUEEZE Trial for transparency and to align with best practices. Analysis of SQUEEZE Trial data will adhere to the SAP to reduce the risk of bias.
Registration
ClinicalTrials.gov identifiers: Definitive trial NCT03080038; Registered Feb 28, 2017. Pilot Trial NCT 01973907; Registered Oct 27, 2013.
{"title":"Statistical analysis plan for the SQUEEZE trial: A trial to determine whether septic shock reversal is quicker in pediatric patients randomized to an early goal-directed fluid-sparing strategy vs. usual care (SQUEEZE)","authors":"Melissa J. Parker MD, MSc , Gary Foster PhD , Alison Fox-Robichaud MD, MSc , Karen Choong MB BCh, MSc , Lawrence Mbuagbaw MD, PhD , Lehana Thabane PhD , With the SQUEEZE Trial Steering Committee and on behalf of the SQUEEZE Trial Investigators, the Canadian Critical Care Trials Group, Pediatric Emergency Research Canada, and the Canadian Critical Care Translational Biology Group","doi":"10.1016/j.ccrj.2024.02.002","DOIUrl":"https://doi.org/10.1016/j.ccrj.2024.02.002","url":null,"abstract":"<div><h3>Background</h3><p>The SQUEEZE trial is a multicentred randomized controlled trial which seeks to determine the optimal approach to fluid resuscitation in paediatric septic shock. SQUEEZE also includes a nested translational study, SQUEEZE-D, investigating the value of plasma cell-free DNA for prediction of clinical outcomes.</p></div><div><h3>Objective</h3><p>To present a pre-specified statistical analysis plan (SAP) for the SQUEEZE trial prior to finalizing the trial data set and prior to commencing data analysis.</p></div><div><h3>Design</h3><p>SQUEEZE is a pragmatic, two-arm, open-label, prospective multicentre randomized controlled trial.</p></div><div><h3>Setting</h3><p>Canadian paediatric tertiary care centres.</p></div><div><h3>Participants</h3><p>Paediatric patients with suspected sepsis and persistent signs of shock in need of ongoing resuscitation. Sample size target: 400 participants.</p></div><div><h3>Interventions</h3><p>The trial is designed to compare a fluid-sparing resuscitation strategy to usual care.</p></div><div><h3>Main outcome measures</h3><p>The primary outcome for the SQUEEZE trial is the time to shock reversal (in hours). The primary outcome analysis will assess the difference in time to shock reversal between the intervention and control groups, reported as point estimate with 95% confidence intervals. The statistical test for the primary analysis will be a two-sided t-test. Secondary outcome measures include clinical outcomes and adverse events including measures of organ dysfunction and mortality outcomes.</p></div><div><h3>Results</h3><p>The SAP presented here is reflective of and where necessary clarifies in detail the analysis plan as presented in the trial protocol. The SAP includes a mock CONSORT diagram, figures and tables. Data collection methods are summarized, primary and secondary outcomes are defined, and outcome analyses are described.</p></div><div><h3>Conclusions</h3><p>We have developed a statistical analysis plan for the SQUEEZE Trial for transparency and to align with best practices. Analysis of SQUEEZE Trial data will adhere to the SAP to reduce the risk of bias.</p></div><div><h3>Registration</h3><p>ClinicalTrials.gov identifiers: Definitive trial NCT03080038; Registered Feb 28, 2017. Pilot Trial NCT 01973907; Registered Oct 27, 2013.</p></div>","PeriodicalId":49215,"journal":{"name":"Critical Care and Resuscitation","volume":"26 2","pages":"Pages 123-134"},"PeriodicalIF":1.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S144127722400005X/pdfft?md5=53a156cc434d1a572db1b8d0726b35e8&pid=1-s2.0-S144127722400005X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141486287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.1016/j.ccrj.2024.04.002
Paul J. Young BSc (Hons), MBChB, PhD , Michael Bailey PhD , the ANZICS CORE Management Committee
Objective
To describe the characteristics and outcomes of Pacific and European patients admitted to New Zealand (NZ) intensive care units (ICUs) 2009–2018.
Design
Retrospective cohort study.
Setting and participants
The NZ Ministry of Health National Minimum Dataset and the Australia NZ Intensive Care Society Adult Patient Database were matched. Data were for ICU admissions in NZ hospitals from July 2009 until June 2018; long-term mortality outcomes were obtained from the NZ death registry until June 2020.
Main outcome measures
The primary outcome was day 180 mortality. Secondary outcomes were ICU mortality, hospital mortality, discharge to home, ICU and hospital length of stay, and survival. We evaluated the associations between Pacific ethnicity and outcomes with European as the reference using regression analyses. We adjusted sequentially for site, deprivation status, sex, year of admission, Charlson Comorbidity Index, age, admission source and type, ICU admission diagnosis, ventilation status, and illness severity.
Results
Pacific people had a median age of 14 years younger than Europeans. 644/4603 (14.0%) Pacific, and 6407/42,871 (14.9%) European patients died within 180 days of ICU admission; odds ratio (OR) 0.93; 95% CI, 0.85–1.01. When adjusting for age, the OR for day 180 mortality for Pacific vs. European patients increased. The OR decreased after adjustment for admission source and type, and after accounting for Pacific patients having a higher comorbidity index and more severe illness. In the final model, incorporating adjustments for all specified variables, Pacific ethnicity was not significantly associated with day 180 mortality (adjusted OR 0.91; 95% CI, 0.80–1.05). Findings were similar for secondary outcomes except for the proportion of patients discharged home; Pacific ethnicity was associated with significantly increased odds of being discharged home compared to European ethnicity.
Conclusions
Pacific ethnicity was not associated with increased day 180 mortality compared to European ethnicity; Pacific patients admitted to the ICU were more likely to be discharged home than European patients.
目的描述2009-2018年入住新西兰（NZ）重症监护病房（ICU）的太平洋地区和欧洲地区患者的特征和预后.设计回顾性队列研究.设置和参与者新西兰卫生部国家最低数据集与澳大利亚新西兰重症监护协会成人患者数据库进行了匹配。数据来自2009年7月至2018年6月期间新西兰医院的重症监护病房入院情况；长期死亡率结果来自2020年6月之前的新西兰死亡登记。次要结果为重症监护室死亡率、住院死亡率、出院回家率、重症监护室和住院时间以及存活率。我们使用回归分析评估了太平洋岛屿族裔与以欧洲裔为参照的结果之间的关系。我们依次对地点、贫困状况、性别、入院年份、夏尔森综合症指数、年龄、入院来源和类型、ICU入院诊断、通气状况和疾病严重程度进行了调整。644/4603（14.0%）名太平洋裔患者和 6407/42,871 （14.9%）名欧洲裔患者在入住 ICU 后 180 天内死亡；几率比 (OR) 为 0.93；95% CI 为 0.85-1.01。在对年龄进行调整后，太平洋裔患者与欧裔患者的 180 天死亡率比值增加。在对入院来源和类型进行调整，并考虑到太平洋裔患者的合并症指数更高、病情更严重的因素后，OR 有所下降。在对所有特定变量进行调整后的最终模型中，太平洋岛屿族裔与第 180 天死亡率无显著相关性（调整后 OR 为 0.91；95% CI 为 0.80-1.05）。除了出院回家的患者比例外，其他次要结果的研究结果相似；与欧洲裔患者相比，太平洋岛屿族裔患者出院回家的几率明显增加。结论与欧洲裔患者相比，太平洋岛屿族裔患者与第180天死亡率增加无关；与欧洲裔患者相比，入住重症监护室的太平洋岛屿族裔患者更有可能出院回家。
{"title":"Outcomes for Pacific and European patients admitted to New Zealand intensive care units from 2009 to 2018","authors":"Paul J. Young BSc (Hons), MBChB, PhD , Michael Bailey PhD , the ANZICS CORE Management Committee","doi":"10.1016/j.ccrj.2024.04.002","DOIUrl":"https://doi.org/10.1016/j.ccrj.2024.04.002","url":null,"abstract":"<div><h3>Objective</h3><p>To describe the characteristics and outcomes of Pacific and European patients admitted to New Zealand (NZ) intensive care units (ICUs) 2009–2018.</p></div><div><h3>Design</h3><p>Retrospective cohort study.</p></div><div><h3>Setting and participants</h3><p>The NZ Ministry of Health National Minimum Dataset and the Australia NZ Intensive Care Society Adult Patient Database were matched. Data were for ICU admissions in NZ hospitals from July 2009 until June 2018; long-term mortality outcomes were obtained from the NZ death registry until June 2020.</p></div><div><h3>Main outcome measures</h3><p>The primary outcome was day 180 mortality. Secondary outcomes were ICU mortality, hospital mortality, discharge to home, ICU and hospital length of stay, and survival. We evaluated the associations between Pacific ethnicity and outcomes with European as the reference using regression analyses. We adjusted sequentially for site, deprivation status, sex, year of admission, Charlson Comorbidity Index, age, admission source and type, ICU admission diagnosis, ventilation status, and illness severity.</p></div><div><h3>Results</h3><p>Pacific people had a median age of 14 years younger than Europeans. 644/4603 (14.0%) Pacific, and 6407/42,871 (14.9%) European patients died within 180 days of ICU admission; odds ratio (OR) 0.93; 95% CI, 0.85–1.01. When adjusting for age, the OR for day 180 mortality for Pacific vs. European patients increased. The OR decreased after adjustment for admission source and type, and after accounting for Pacific patients having a higher comorbidity index and more severe illness. In the final model, incorporating adjustments for all specified variables, Pacific ethnicity was not significantly associated with day 180 mortality (adjusted OR 0.91; 95% CI, 0.80–1.05). Findings were similar for secondary outcomes except for the proportion of patients discharged home; Pacific ethnicity was associated with significantly increased odds of being discharged home compared to European ethnicity.</p></div><div><h3>Conclusions</h3><p>Pacific ethnicity was not associated with increased day 180 mortality compared to European ethnicity; Pacific patients admitted to the ICU were more likely to be discharged home than European patients.</p></div>","PeriodicalId":49215,"journal":{"name":"Critical Care and Resuscitation","volume":"26 2","pages":"Pages 100-107"},"PeriodicalIF":1.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1441277224000115/pdfft?md5=880b7a9f126f0c2ff2413131fdc633b9&pid=1-s2.0-S1441277224000115-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141486189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.1016/j.ccrj.2024.04.003
Laurent Bitker MD, PhD , Inès Noirot MD , Louis Chauvelot MD , Mehdi Mezidi MD, MSc , François Dhelft MD, MSc , Maxime Gaillet MD , Hodane Yonis MD , Guillaume Deniel MD, MSc , Jean-Christophe Richard MD, PhD
Objective
To evaluate the accuracy of non-calibrated multi-beat analysis continuous cardiac output (CCOMBA), against calibrated pulse-contour analysis continuous cardiac output (CCOPCA) during a passive leg raise (PLR) and/or a fluid challenge (FC).
Design
Observational, single-centre, prospective study.
Setting
Tertiary academic medical intensive care unit, Lyon, France.
Participants
Adult patients receiving norepinephrine, monitored by CCOPCA, and in which a PLR and/or a FC was indicated.
Main outcome measures
CCOMBA and CCOPCA were recorded prior to and during the PLR/FC to evaluate bias and evaluate changes in CCOMBA and CCOPCA (∆%CCOMBA and ∆%CCOPCA). Fluid responsiveness was identified by an increase >15% in calibrated cardiac output after FC, to identify the optimal ∆%CCOMBA threshold during PLR to predict fluid responsiveness.
Results
29 patients (median age 68 [IQR: 57–74]) performed 28 PLR and 16 FC. The bias between methods increased with higher CCOPCA values, with a percentage error of 64% (95%confidence interval: 52%–77%). ∆%CCOMBA adequately tracked changes in ∆%CCOPCA with an angular bias of 2 ± 29°. ∆%CCOMBA during PLR had an AUROC of 0.92 (P < 0.05), with an optimal threshold >14% to predict fluid responsiveness (sensitivity: 0.99, specificity: 0.87).
Conclusions
CCOMBA showed a non-constant bias and a percentage error >30% against calibrated CCOPCA, but an adequate ability to track changes in CCOPCA and to predict fluid responsiveness.
{"title":"Bias, trending ability and diagnostic performance of a non-calibrated multi-beat analysis continuous cardiac output monitor to identify fluid responsiveness in critically ill patients","authors":"Laurent Bitker MD, PhD , Inès Noirot MD , Louis Chauvelot MD , Mehdi Mezidi MD, MSc , François Dhelft MD, MSc , Maxime Gaillet MD , Hodane Yonis MD , Guillaume Deniel MD, MSc , Jean-Christophe Richard MD, PhD","doi":"10.1016/j.ccrj.2024.04.003","DOIUrl":"https://doi.org/10.1016/j.ccrj.2024.04.003","url":null,"abstract":"<div><h3>Objective</h3><p>To evaluate the accuracy of non-calibrated multi-beat analysis continuous cardiac output (CCO<sub>MBA</sub>), against calibrated pulse-contour analysis continuous cardiac output (CCO<sub>PCA</sub>) during a passive leg raise (PLR) and/or a fluid challenge (FC).</p></div><div><h3>Design</h3><p>Observational, single-centre, prospective study.</p></div><div><h3>Setting</h3><p>Tertiary academic medical intensive care unit, Lyon, France.</p></div><div><h3>Participants</h3><p>Adult patients receiving norepinephrine, monitored by CCO<sub>PCA</sub>, and in which a PLR and/or a FC was indicated.</p></div><div><h3>Main outcome measures</h3><p>CCO<sub>MBA</sub> and CCO<sub>PCA</sub> were recorded prior to and during the PLR/FC to evaluate bias and evaluate changes in CCO<sub>MBA</sub> and CCO<sub>PCA</sub> (∆%CCO<sub>MBA</sub> and ∆%CCO<sub>PCA</sub>). Fluid responsiveness was identified by an increase >15% in calibrated cardiac output after FC, to identify the optimal ∆%CCO<sub>MBA</sub> threshold during PLR to predict fluid responsiveness.</p></div><div><h3>Results</h3><p>29 patients (median age 68 [IQR: 57–74]) performed 28 PLR and 16 FC. The bias between methods increased with higher CCO<sub>PCA</sub> values, with a percentage error of 64% (<sub>95%</sub>confidence interval: 52%–77%). ∆%CCO<sub>MBA</sub> adequately tracked changes in ∆%CCO<sub>PCA</sub> with an angular bias of 2 ± 29°. ∆%CCO<sub>MBA</sub> during PLR had an AUROC of 0.92 (<em>P</em> < 0.05), with an optimal threshold >14% to predict fluid responsiveness (sensitivity: 0.99, specificity: 0.87).</p></div><div><h3>Conclusions</h3><p>CCO<sub>MBA</sub> showed a non-constant bias and a percentage error >30% against calibrated CCO<sub>PCA</sub>, but an adequate ability to track changes in CCO<sub>PCA</sub> and to predict fluid responsiveness.</p></div>","PeriodicalId":49215,"journal":{"name":"Critical Care and Resuscitation","volume":"26 2","pages":"Pages 108-115"},"PeriodicalIF":1.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1441277224000127/pdfft?md5=b5598a5c4abd944b51bf2371a07c3fa7&pid=1-s2.0-S1441277224000127-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141486190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.1016/j.ccrj.2024.03.002
Paul Ross RN, BHSc Nur, PGCert ICU, MN Research, Med Adult, PhD Candidate , Rose Jaspers RN, BN(Hons), MAdvClinNur , Jason Watterson RN, BHSc Nur, PGDipAdvNur CritCare, Med Adult, PhD , Michelle Topple RN, BHSc Nur, PGDipSci, PGCert ICU , Tania Birthisel RN, BN (Distinction), PGDip Nursing ICU, CertIV TAE, MProfEd&Trng , Melissa Rosenow , Jason McClure MB ChB, MRCP, FRCA, FCICM, Dip Engineering , Ged Williams AO, RN, PGCert ICU, BHSc. Adv. Nursing, LLM, MHA, FACN, FACHSM, FAAN , Wendy Pollock RN, RM, Grad Cert Adv Learning & Leadership, Grad Dip Ed, Grad Dip Crit Car Nsg, PhD , David Pilcher MBBS MRCP(UK) FCICM FRACP
Objective
This article aims to examine the impact of nursing workforce skill-mix (percentage of critical care registered nurses [CCRN]) in the intensive care unit (ICU) during a patient's stay.
Design
Registry linked cohort study of the Australian and New Zealand Intensive Care Society Adult Patient Database and the Critical Health Resources Information System using real-time nursing workforce data.
Settings
Fifteen public and 5 private hospital ICUs in Victoria, Australia.
Participants
There were 16,618 adult patients admitted between 1 December 2021 and 30 September 2022.
Main outcome measures
Primary outcome: in-hospital mortality. Secondary outcomes: in-ICU mortality, development of delirium, pressure injury, duration of stay in-ICU and hospital, after-hours discharge from ICU and readmission to ICU.
Results
In total, 6563 (39.5%) patients were cared for in ICUs with >75% CCRN, 7695 (46.3%) in ICUs with 50–75% CCRN, and 2360 (14.2%) in ICUs with <50% CCRN. In-hospital mortality was 534 (8.1%) vs. 859 (11.2%) vs. 252 (10.7%) respectively. After adjusting for confounders, patients cared for in ICUs with 50–75% CCRN (adjusted OR 1.21 [95% CI 1.02–1.45]) were more likely to die compared to patients in ICUs with >75% CCRN. A similar but non-significant trend was seen in ICUs with <50% CCRN (adjusted OR 1.21 [95% CI 0.94–1.55]), when compared to patients in ICUs with >75% CCRN. In-ICU mortality, delirium, pressure injuries, after-hours discharge and ICU length of stay were lower in ICUs with CCRN>75%.
Conclusion
The nursing skill-mix in ICU impacts outcomes and should be routinely monitored. Health system regulators, hospital administrators and ICU leaders should ensure nursing workforce planning and education align with these findings to maximise patient outcomes.
本文旨在研究重症监护病房（ICU）护理人员技能组合（重症监护注册护士[CCRN]的比例）对患者住院期间的影响。参与者2021年12月1日至2022年9月30日期间收治的16618名成人患者。主要结果测量主要结果：院内死亡率。次要结果：重症监护室内死亡率、谵妄发生率、压伤、重症监护室和住院时间、重症监护室下班后出院情况以及重症监护室再入院情况。结果共有 6563 名（39.5%）患者在 CCRN 为 75% 的重症监护室接受治疗，7695 名（46.3%）患者在 CCRN 为 50%-75% 的重症监护室接受治疗，2360 名（14.2%）患者在 CCRN 为 50% 的重症监护室接受治疗。院内死亡率分别为 534 (8.1%) vs. 859 (11.2%) vs. 252 (10.7%)。在对混杂因素进行调整后，与 CCRN 为 50%-75% 的重症监护病房的患者相比，CCRN 为 50%-75% 的重症监护病房的患者更容易死亡（调整后 OR 为 1.21 [95% CI 为 1.02-1.45]）。与CCRN为75%的重症监护病房相比，CCRN为50%的重症监护病房（调整后OR值为1.21 [95% CI 0.94-1.55]）的患者也有类似的趋势，但并不显著。结论 ICU 中的护理技能组合对治疗效果有影响，应进行常规监测。卫生系统监管者、医院管理者和重症监护室领导应确保护理人员的规划和教育与这些研究结果相一致，以最大限度地提高患者的治疗效果。
{"title":"The impact of nursing workforce skill-mix on patient outcomes in intensive care units in Victoria, Australia","authors":"Paul Ross RN, BHSc Nur, PGCert ICU, MN Research, Med Adult, PhD Candidate , Rose Jaspers RN, BN(Hons), MAdvClinNur , Jason Watterson RN, BHSc Nur, PGDipAdvNur CritCare, Med Adult, PhD , Michelle Topple RN, BHSc Nur, PGDipSci, PGCert ICU , Tania Birthisel RN, BN (Distinction), PGDip Nursing ICU, CertIV TAE, MProfEd&Trng , Melissa Rosenow , Jason McClure MB ChB, MRCP, FRCA, FCICM, Dip Engineering , Ged Williams AO, RN, PGCert ICU, BHSc. Adv. Nursing, LLM, MHA, FACN, FACHSM, FAAN , Wendy Pollock RN, RM, Grad Cert Adv Learning & Leadership, Grad Dip Ed, Grad Dip Crit Car Nsg, PhD , David Pilcher MBBS MRCP(UK) FCICM FRACP","doi":"10.1016/j.ccrj.2024.03.002","DOIUrl":"https://doi.org/10.1016/j.ccrj.2024.03.002","url":null,"abstract":"<div><h3>Objective</h3><p>This article aims to examine the impact of nursing workforce skill-mix (percentage of critical care registered nurses [CCRN]) in the intensive care unit (ICU) during a patient's stay.</p></div><div><h3>Design</h3><p>Registry linked cohort study of the Australian and New Zealand Intensive Care Society Adult Patient Database and the Critical Health Resources Information System using real-time nursing workforce data.</p></div><div><h3>Settings</h3><p>Fifteen public and 5 private hospital ICUs in Victoria, Australia.</p></div><div><h3>Participants</h3><p>There were 16,618 adult patients admitted between 1 December 2021 and 30 September 2022.</p></div><div><h3>Main outcome measures</h3><p>Primary outcome: in-hospital mortality. Secondary outcomes: in-ICU mortality, development of delirium, pressure injury, duration of stay in-ICU and hospital, after-hours discharge from ICU and readmission to ICU.</p></div><div><h3>Results</h3><p>In total, 6563 (39.5%) patients were cared for in ICUs with >75% CCRN, 7695 (46.3%) in ICUs with 50–75% CCRN, and 2360 (14.2%) in ICUs with <50% CCRN. In-hospital mortality was 534 (8.1%) vs. 859 (11.2%) vs. 252 (10.7%) respectively. After adjusting for confounders, patients cared for in ICUs with 50–75% CCRN (adjusted OR 1.21 [95% CI 1.02–1.45]) were more likely to die compared to patients in ICUs with >75% CCRN. A similar but non-significant trend was seen in ICUs with <50% CCRN (adjusted OR 1.21 [95% CI 0.94–1.55]), when compared to patients in ICUs with >75% CCRN. In-ICU mortality, delirium, pressure injuries, after-hours discharge and ICU length of stay were lower in ICUs with CCRN>75%.</p></div><div><h3>Conclusion</h3><p>The nursing skill-mix in ICU impacts outcomes and should be routinely monitored. Health system regulators, hospital administrators and ICU leaders should ensure nursing workforce planning and education align with these findings to maximise patient outcomes.</p></div>","PeriodicalId":49215,"journal":{"name":"Critical Care and Resuscitation","volume":"26 2","pages":"Pages 135-152"},"PeriodicalIF":1.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1441277224000073/pdfft?md5=ad56c53e3b427ddb83eca17ebc80ad04&pid=1-s2.0-S1441277224000073-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141486286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.1016/j.ccrj.2024.01.002
Jessica A. Schults RN, PhD , Karina R. Charles RN, MNurs PICU , Johnny Millar MBChB, PhD, MRCP, FRACP, FCICM , Claire M. Rickard RN, PhD , Vineet Chopra MD, MSc , Anna Lake RN, GradCertClinNurs , Kristen Gibbons PhD , Debbie Long RN, PhD , Sarfaraz Rahiman MD, FCICM , Katrina Hutching RN, MHlthLd , Jacinta Winderlich BNutDietet, MClinRes , Naomi E. Spotswood BMedSc, MBBS, MIPH, FRACP , Amy Johansen RN, MANP Research , Paul Secombe BA, DipAud, BMBS (Hons), MClinSc, FCICM , Georgina A. Pizimolas BPhty , Quyen Tu BPharm , Michaela Waak MBBS, MD , Meredith Allen MBBS, FRACP, FCICM, PhD, MSafSc , Brendan McMullan BMed (Hons), PhD , Lisa Hall BTech BiomedSci (Hons), PhD
Introduction
Monitoring healthcare quality is challenging in paediatric critical care due to measure variability, data collection burden, and uncertainty regarding consumer and clinician priorities.
Objective
We sought to establish a core quality measure set that (i) is meaningful to consumers and clinicians and (ii) promotes alignment of measure use and collection across paediatric critical care.
Design
We conducted a multi-stakeholder Delphi study with embedded consumer prioritisation survey. The Delphi involved two surveys, followed by a consensus meeting. Triangulation methods were used to integrate survey findings prior tobefore the consensus meeting. In the consensus panel, broad agreement was reached on a core measure set, and recommendations were made for future measurement directions in paediatric critical care.
Setting and participants
Australian and New Zealand paediatric critical care survivors (aged >18 years) and families were invited to rank measure priorities in an online survey distributed via social media and consumer groups. A concurrent Delphi study was undertaken with paediatric critical care clinicians, policy makers, and a consumer representative.
Interventions
None.
Main outcome measures
Priorities for quality measures.
Results
Respondents to the consumer survey (n = 117) identified (i) nurse-patient ratios; (ii) visible patient goals; and (iii) long-term follow-up as their quality measure priorities. In the Delphi process, clinicians (Round 1 n = 191; Round 2 n = 117 [61% retention]; Round 3 n = 14) and a consumer representative reached broad agreement on a 51-item (61% of 83 initial measures) core measure set. Clinician priorities were (i) nurse-patient ratio; (ii) staff turnover; and (iii) long term-follow up. Measure feasibility was rated low due to a perceived lack of standardised case definitions or data collection burden. Five recommendations were generated.
Conclusion(s)
We defined a 51-item core measurement set for paediatric critical care, aligned with clinician and consumer priorities. Next steps are implementation and methodological evaluation in quality programs, and where appropriate, retirement of redundant measures.
引言在儿科危重症护理中，由于衡量标准的多变性、数据收集的负担以及消费者和临床医生优先考虑事项的不确定性，医疗质量监控具有挑战性。德尔菲研究包括两项调查，然后召开共识会议。在召开共识会议之前，我们使用三角测量法对调查结果进行了整合。会议邀请澳大利亚和新西兰的儿科危重症幸存者（18 岁）及其家属通过社交媒体和消费者团体发布的在线调查，对衡量标准的优先级进行排序。与此同时，还与儿科危重症护理临床医生、政策制定者和一名消费者代表进行了德尔菲研究。结果消费者调查的受访者（n = 117）将(i) 护患比例；(ii) 患者可视目标；(iii) 长期随访确定为其优先考虑的质量措施。在德尔菲过程中，临床医生（第一轮 n = 191；第二轮 n = 117 [61% 保留]；第三轮 n = 14）和一名消费者代表就 51 个项目（占 83 个初始衡量标准的 61%）的核心衡量标准集达成了广泛一致。临床医生优先考虑的是：(i) 护患比例；(ii) 人员流动；(iii) 长期随访。由于缺乏标准化病例定义或数据收集负担，衡量标准的可行性较低。结论：我们为儿科危重症护理定义了一套 51 项的核心测量指标，符合临床医生和消费者的优先考虑。接下来的步骤是在质量计划中实施和进行方法评估，并在适当的情况下取消多余的测量项目。
{"title":"Establishing a paediatric critical care core quality measure set using a multistakeholder, consensus-driven process","authors":"Jessica A. Schults RN, PhD , Karina R. Charles RN, MNurs PICU , Johnny Millar MBChB, PhD, MRCP, FRACP, FCICM , Claire M. Rickard RN, PhD , Vineet Chopra MD, MSc , Anna Lake RN, GradCertClinNurs , Kristen Gibbons PhD , Debbie Long RN, PhD , Sarfaraz Rahiman MD, FCICM , Katrina Hutching RN, MHlthLd , Jacinta Winderlich BNutDietet, MClinRes , Naomi E. Spotswood BMedSc, MBBS, MIPH, FRACP , Amy Johansen RN, MANP Research , Paul Secombe BA, DipAud, BMBS (Hons), MClinSc, FCICM , Georgina A. Pizimolas BPhty , Quyen Tu BPharm , Michaela Waak MBBS, MD , Meredith Allen MBBS, FRACP, FCICM, PhD, MSafSc , Brendan McMullan BMed (Hons), PhD , Lisa Hall BTech BiomedSci (Hons), PhD","doi":"10.1016/j.ccrj.2024.01.002","DOIUrl":"10.1016/j.ccrj.2024.01.002","url":null,"abstract":"<div><h3>Introduction</h3><p>Monitoring healthcare quality is challenging in paediatric critical care due to measure variability, data collection burden, and uncertainty regarding consumer and clinician priorities.</p></div><div><h3>Objective</h3><p>We sought to establish a core quality measure set that (i) is meaningful to consumers and clinicians and (ii) promotes alignment of measure use and collection across paediatric critical care.</p></div><div><h3>Design</h3><p>We conducted a multi-stakeholder Delphi study with embedded consumer prioritisation survey. The Delphi involved two surveys, followed by a consensus meeting. Triangulation methods were used to integrate survey findings prior tobefore the consensus meeting. In the consensus panel, broad agreement was reached on a core measure set, and recommendations were made for future measurement directions in paediatric critical care.</p></div><div><h3>Setting and participants</h3><p>Australian and New Zealand paediatric critical care survivors (aged >18 years) and families were invited to rank measure priorities in an online survey distributed via social media and consumer groups. A concurrent Delphi study was undertaken with paediatric critical care clinicians, policy makers, and a consumer representative.</p></div><div><h3>Interventions</h3><p>None.</p></div><div><h3>Main outcome measures</h3><p>Priorities for quality measures.</p></div><div><h3>Results</h3><p>Respondents to the consumer survey (n = 117) identified (i) nurse-patient ratios; (ii) visible patient goals; and (iii) long-term follow-up as their quality measure priorities. In the Delphi process, clinicians (Round 1 n = 191; Round 2 n = 117 [61% retention]; Round 3 n = 14) and a consumer representative reached broad agreement on a 51-item (61% of 83 initial measures) core measure set. Clinician priorities were (i) nurse-patient ratio; (ii) staff turnover; and (iii) long term-follow up. Measure feasibility was rated low due to a perceived lack of standardised case definitions or data collection burden. Five recommendations were generated.</p></div><div><h3>Conclusion(s)</h3><p>We defined a 51-item core measurement set for paediatric critical care, aligned with clinician and consumer priorities. Next steps are implementation and methodological evaluation in quality programs, and where appropriate, retirement of redundant measures.</p></div>","PeriodicalId":49215,"journal":{"name":"Critical Care and Resuscitation","volume":"26 2","pages":"Pages 71-79"},"PeriodicalIF":1.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1441277224000024/pdfft?md5=1c8a257336d16ba76cf05b0274d701de&pid=1-s2.0-S1441277224000024-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140407310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.1016/j.ccrj.2024.03.003
Alayna Carrandi MPH , Cheelim Liew DNP , Matthew J. Maiden PhD , Edward Litton PhD , Colman Taylor PhD , Kelly Thompson PhD , Alisa Higgins PhD
Objective
Intensive care unit (ICU) cost estimates are critical to achieving healthcare system efficiency and sustainability. We aimed to review the published literature describing ICU costs in Australia.
Design
A systematic review was conducted to identify studies that estimated the cost of ICU care in Australia. Studies conducted in specific patient cohorts or on specific treatments were excluded.
Data sources
Relevant studies were sourced from a previously published review (1970–2016), a systematic search of MEDLINE and EMBASE (2016–5 May 2023), and reference checking.
Review methods
A tool was developed to assess study quality and risk of bias (maximum score 57/57). Total and component costs were tabulated and indexed to 2022 Australian Dollars. Costing methodologies and study quality assessments were summarised.
Results
Six costing studies met the inclusion criteria. Study quality scores were low (15/41 to 35/47). Most studies were conducted only in tertiary metropolitan public ICUs; sample sizes ranged from 100 to 10,204 patients. One study used data collected within the past 10 years. Mean daily ICU costs ranged from $966 to $5381 and mean total ICU admission costs $4888 to $14,606. Three studies used a top-down costing approach, deriving cost estimates from budget reports. The other three studies used both bottom-up and top-down costing approaches. Bottom-up approaches collected individual patient resource use.
Conclusions
Available ICU cost estimates are largely outdated and lack granular data. Future research is needed to estimate ICU costs that better reflect current practice and patient complexity and to determine the best methods for generating these estimates.
{"title":"Costs of Australian intensive care: A systematic review","authors":"Alayna Carrandi MPH , Cheelim Liew DNP , Matthew J. Maiden PhD , Edward Litton PhD , Colman Taylor PhD , Kelly Thompson PhD , Alisa Higgins PhD","doi":"10.1016/j.ccrj.2024.03.003","DOIUrl":"https://doi.org/10.1016/j.ccrj.2024.03.003","url":null,"abstract":"<div><h3>Objective</h3><p>Intensive care unit (ICU) cost estimates are critical to achieving healthcare system efficiency and sustainability. We aimed to review the published literature describing ICU costs in Australia.</p></div><div><h3>Design</h3><p>A systematic review was conducted to identify studies that estimated the cost of ICU care in Australia. Studies conducted in specific patient cohorts or on specific treatments were excluded.</p></div><div><h3>Data sources</h3><p>Relevant studies were sourced from a previously published review (1970–2016), a systematic search of MEDLINE and EMBASE (2016–5 May 2023), and reference checking.</p></div><div><h3>Review methods</h3><p>A tool was developed to assess study quality and risk of bias (maximum score 57/57). Total and component costs were tabulated and indexed to 2022 Australian Dollars. Costing methodologies and study quality assessments were summarised.</p></div><div><h3>Results</h3><p>Six costing studies met the inclusion criteria. Study quality scores were low (15/41 to 35/47). Most studies were conducted only in tertiary metropolitan public ICUs; sample sizes ranged from 100 to 10,204 patients. One study used data collected within the past 10 years. Mean daily ICU costs ranged from $966 to $5381 and mean total ICU admission costs $4888 to $14,606. Three studies used a top-down costing approach, deriving cost estimates from budget reports. The other three studies used both bottom-up and top-down costing approaches. Bottom-up approaches collected individual patient resource use.</p></div><div><h3>Conclusions</h3><p>Available ICU cost estimates are largely outdated and lack granular data. Future research is needed to estimate ICU costs that better reflect current practice and patient complexity and to determine the best methods for generating these estimates.</p></div>","PeriodicalId":49215,"journal":{"name":"Critical Care and Resuscitation","volume":"26 2","pages":"Pages 153-158"},"PeriodicalIF":1.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1441277224000085/pdfft?md5=c935f67b062c4593cc05e60fc4ca039c&pid=1-s2.0-S1441277224000085-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141486193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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