Pub Date : 2024-03-09DOI: 10.1101/2024.03.07.24303951
Alexander Wilson, Kylie McDonald, Matthew Cooper, Paul Stevenson, Jonathan Davis, Sanjay Patole
Objectives To assess whether undertaking retrieval was associated with fatigue independent of sleep and circadian disruption. Background Fatigue is associated impaired clinician performance and safety. The association between shift work, sleep deprivation and circadian disruption is well established. No studies have specifi-cally assessed the independent effect of the retrieval environment on fatigue. Method Medical and nursing staff of the neonatal retrieval team were prospectively recruited over a 12-month period. Simple reaction times (RT) were recorded at the start and end of a day shift using a validated 3-min Psychometric Vigilance Test (PVT). Results End of shift RT increased by 6.38ms (95% CI: -2.17 to 14.92ms, p = 0.149) when retrieval was undertaken. A 1ms increase in RT increased the odds of being in a subjective sleepy cate-gory by 0.57% (log odds: 0.0057, 95% CI: 0.0036 to 0.0078). Consuming caffeine during the shift increased mean RT by 16.26 ms (95% CI: 4.43 to 28.1 ms, p <0.01). Conclusion The 3-min PVT was found to be an easy method of objectively assessing fatigue in the re-trieval setting. The effects of caffeine consumption on RT warrants further investigation.
{"title":"Assessment of Psychometric Vigilance on Neonatal Transport: A Western Australian Experience","authors":"Alexander Wilson, Kylie McDonald, Matthew Cooper, Paul Stevenson, Jonathan Davis, Sanjay Patole","doi":"10.1101/2024.03.07.24303951","DOIUrl":"https://doi.org/10.1101/2024.03.07.24303951","url":null,"abstract":"Objectives\u0000To assess whether undertaking retrieval was associated with fatigue independent of sleep and circadian disruption. Background\u0000Fatigue is associated impaired clinician performance and safety. The association between shift work, sleep deprivation and circadian disruption is well established. No studies have specifi-cally assessed the independent effect of the retrieval environment on fatigue. Method\u0000Medical and nursing staff of the neonatal retrieval team were prospectively recruited over a 12-month period. Simple reaction times (RT) were recorded at the start and end of a day shift using a validated 3-min Psychometric Vigilance Test (PVT). Results\u0000End of shift RT increased by 6.38ms (95% CI: -2.17 to 14.92ms, p = 0.149) when retrieval was undertaken. A 1ms increase in RT increased the odds of being in a subjective sleepy cate-gory by 0.57% (log odds: 0.0057, 95% CI: 0.0036 to 0.0078). Consuming caffeine during the shift increased mean RT by 16.26 ms (95% CI: 4.43 to 28.1 ms, p <0.01).\u0000Conclusion\u0000The 3-min PVT was found to be an easy method of objectively assessing fatigue in the re-trieval setting. The effects of caffeine consumption on RT warrants further investigation.","PeriodicalId":501249,"journal":{"name":"medRxiv - Intensive Care and Critical Care Medicine","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140075222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-08DOI: 10.1101/2024.03.07.24303926
Suraj Rajendran, Zhenxing Xu, Weishen Pan, Chengxi Zang, Ilias Siempos, Lisa K Torres, Jie Xu, Jiang Bian, Edward J Schenck, Fei Wang
Corticosteroids decrease the duration of organ dysfunction in a range of infectious critical illnesses, but their risk and benefit are not fully defined using this construct. This retrospective multicenter study aimed to evaluate the association between usage of corticosteroids and mortality of patients with infectious critical illness by emulating a target trial framework. The study employed a novel stratification method with predictive machine learning (ML) subphenotyping based on organ dysfunction trajectory. Our analysis revealed that corticosteroids' effectiveness varied depending on the stratification method. The ML-based approach identified four distinct subphenotypes, two of which had a large enough sample size in our patient cohorts for further evaluation: "Rapidly Improving" (RI) and "Rapidly Worsening," (RW) which showed divergent responses to corticosteroid treatment. Specifically, the RW group either benefited or were not harmed from corticosteroids, whereas the RI group appeared to derive harm. In the development cohort, which comprised of a combination of patients from the eICU and MIMIC-IV datasets, hazard ratio estimates for the primary outcome, 28-day mortality, in the RW group was 1.05 (95% CI: 0.96 - 1.04) whereas for the RW group, it was 1.40 (95% CI: 1.28 - 1.54). For the validation cohort, which comprised of patients from the Critical carE Database for Advanced Research, estimates for 28-day mortality for the RW and RI groups were 1.24 (95% CI: 1.05 - 1.46) and 1.34 (95% CI: 1.14 - 1.59), respectively. For secondary outcomes, the RW group had a shorter time to ICU discharge and time to cessation of mechanical ventilation with corticosteroid treatment, where the RI group again demonstrated harm. The findings support matching treatment strategies to empirically observed pathobiology and offer a more nuanced understanding of corticosteroid utility. Our results have implications for the design and interpretation of both observational studies and randomized controlled trials (RCTs), suggesting the need for stratification methods that account for the differential response to standard of care.
{"title":"Corticosteroids for infectious critical illness: A multicenter target trial emulation stratified by predicted organ dysfunction trajectory","authors":"Suraj Rajendran, Zhenxing Xu, Weishen Pan, Chengxi Zang, Ilias Siempos, Lisa K Torres, Jie Xu, Jiang Bian, Edward J Schenck, Fei Wang","doi":"10.1101/2024.03.07.24303926","DOIUrl":"https://doi.org/10.1101/2024.03.07.24303926","url":null,"abstract":"Corticosteroids decrease the duration of organ dysfunction in a range of infectious critical illnesses, but their risk and benefit are not fully defined using this construct. This retrospective multicenter study aimed to evaluate the association between usage of corticosteroids and mortality of patients with infectious critical illness by emulating a target trial framework. The study employed a novel stratification method with predictive machine learning (ML) subphenotyping based on organ dysfunction trajectory. Our analysis revealed that corticosteroids' effectiveness varied depending on the stratification method. The ML-based approach identified four distinct subphenotypes, two of which had a large enough sample size in our patient cohorts for further evaluation: \"Rapidly Improving\" (RI) and \"Rapidly Worsening,\" (RW) which showed divergent responses to corticosteroid treatment. Specifically, the RW group either benefited or were not harmed from corticosteroids, whereas the RI group appeared to derive harm. In the development cohort, which comprised of a combination of patients from the eICU and MIMIC-IV datasets, hazard ratio estimates for the primary outcome, 28-day mortality, in the RW group was 1.05 (95% CI: 0.96 - 1.04) whereas for the RW group, it was 1.40 (95% CI: 1.28 - 1.54). For the validation cohort, which comprised of patients from the Critical carE Database for Advanced Research, estimates for 28-day mortality for the RW and RI groups were 1.24 (95% CI: 1.05 - 1.46) and 1.34 (95% CI: 1.14 - 1.59), respectively. For secondary outcomes, the RW group had a shorter time to ICU discharge and time to cessation of mechanical ventilation with corticosteroid treatment, where the RI group again demonstrated harm. The findings support matching treatment strategies to empirically observed pathobiology and offer a more nuanced understanding of corticosteroid utility. Our results have implications for the design and interpretation of both observational studies and randomized controlled trials (RCTs), suggesting the need for stratification methods that account for the differential response to standard of care.","PeriodicalId":501249,"journal":{"name":"medRxiv - Intensive Care and Critical Care Medicine","volume":"249 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140075218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-27DOI: 10.1101/2024.02.25.24303342
Mina Yuan, Isabella M Tincher, Bhanvi Sachdeva, Sabine Abukhadra, Danielle A Rojas, Christine DeForge, Sachin Agarwal
Background: Poor perceived social support has been associated with worse psychological distress in close family members after their loved one hospitalization with prolonged mechanical ventilation, but never been tested after cardiac arrest. Methods: Close family members of consecutive cardiac arrest patients hospitalized at an academic tertiary care center were recruited before hospital discharge, and perceived social support was assessed using the Multidimensional Scale of Perceived Social Support (MSPSS). Indicators of psychological distress were administered via telephone at 1 month after cardiac arrest. Multivariate linear regressions were used to estimate the associations between MSPSS total score and total Patient Health Questionnaire 8 (PHQ 8) score (primary outcome) and total PTSD (PCL 5) and generalized anxiety (GAD 2) scores, after adjusting for previously known covariates. Results: Of 102 close family members (mean age 52 Standard deviation 15 years, 70% female, 40% Non Hispanic white, 21% Black, 33% Hispanic/Latinx, 22% with preexisting psychiatric illness) with complete data, the mean PHQ 8 total score at a median duration of 28.5 days (interquartile range 10 to 63 days) from cardiac arrest was 7 with standard deviation of 6, and the mean MSPSS score was 69 with standard deviation of 15. Lower perceived social support was significantly associated with elevated levels of depressive symptoms in univariate (beta= negative 0.11; p<0.01) and after adjusting for age, sex, race/ethnicity, and previous psychiatric history (beta= negative 0.11; p<0.01). Similar inverse associations were seen with 1 month PTSD and generalized anxiety symptoms as secondary outcomes. Conclusions: Close family members of cardiac arrest survivors perception of poor social support during hospitalization is associated with increased levels of depressive symptoms at 1 month. Longitudinal studies understanding the temporal associations between social support and psychological distress are warranted.
{"title":"Lower perceived social support is significantly associated with elevated levels of psychological distress in racially and ethnically diverse close family members of cardiac arrest survivors","authors":"Mina Yuan, Isabella M Tincher, Bhanvi Sachdeva, Sabine Abukhadra, Danielle A Rojas, Christine DeForge, Sachin Agarwal","doi":"10.1101/2024.02.25.24303342","DOIUrl":"https://doi.org/10.1101/2024.02.25.24303342","url":null,"abstract":"Background: Poor perceived social support has been associated with worse psychological distress in close family members after their loved one hospitalization with prolonged mechanical ventilation, but never been tested after cardiac arrest. Methods: Close family members of consecutive cardiac arrest patients hospitalized at an academic tertiary care center were recruited before hospital discharge, and perceived social support was assessed using the Multidimensional Scale of Perceived Social Support (MSPSS). Indicators of psychological distress were administered via telephone at 1 month after cardiac arrest. Multivariate linear regressions were used to estimate the associations between MSPSS total score and total Patient Health Questionnaire 8 (PHQ 8) score (primary outcome) and total PTSD (PCL 5) and generalized anxiety (GAD 2) scores, after adjusting for previously known covariates. Results: Of 102 close family members (mean age 52 Standard deviation 15 years, 70% female, 40% Non Hispanic white, 21% Black, 33% Hispanic/Latinx, 22% with preexisting psychiatric illness) with complete data, the mean PHQ 8 total score at a median duration of 28.5 days (interquartile range 10 to 63 days) from cardiac arrest was 7 with standard deviation of 6, and the mean MSPSS score was 69 with standard deviation of 15. Lower perceived social support was significantly associated with elevated levels of depressive symptoms in univariate (beta= negative 0.11; p<0.01) and after adjusting for age, sex, race/ethnicity, and previous psychiatric history (beta= negative 0.11; p<0.01). Similar inverse associations were seen with 1 month PTSD and generalized anxiety symptoms as secondary outcomes. Conclusions: Close family members of cardiac arrest survivors perception of poor social support during hospitalization is associated with increased levels of depressive symptoms at 1 month. Longitudinal studies understanding the temporal associations between social support and psychological distress are warranted.","PeriodicalId":501249,"journal":{"name":"medRxiv - Intensive Care and Critical Care Medicine","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139980434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-23DOI: 10.1101/2024.02.22.24303211
Sotirios G. Liliopoulos, Alexander Dejaco, Lucas Paseiro-Garcia, Vasileios S. Dimakopoulos, Ioannis A. Gkouzionis
Background: Sepsis is the third leading cause of death worldwide and the main cause of in-hospital mortality. Despite decades of research, sepsis remains a major challenge faced by patients, clinicians, and medical systems worldwide. Early identification and prediction of patients at risk of sepsis and adverse outcomes associated with sepsis are critical. In this work, we aimed to develop an artificial intelligence algorithm that can predict sepsis early. Materials and Methods: We developed a predictive model for sepsis using data from the Physionet Cardiology Challenge 2019 ICU database. Our cohort consisted of adult patients who were admitted to the ICU. Sepsis diagnoses were determined using the Sepsis-3 criteria. The model, built with the XGBoost algorithm, was designed to anticipate sepsis prior to the appearance of clinical symptoms. An internal validation was conducted using a hold-off test dataset to evaluate the AI model's predictive performance. Results: We have developed the VIOSync Sepsis Prediction Index (SPI), an AI-based predictive model designed to forecast sepsis up to six hours before its clinical onset, as defined by Sepsis-3 criteria. The AI model, trained on a dataset comprising approximately 40,000 adult patients, integrates variables such as vital signs, laboratory data, and demographic information. The model demonstrated a high prediction accuracy rate of 97%, with a sensitivity of 87% and a specificity of 98% in predicting sepsis up to 6 hours before the onset. When compared to the established qSOFA score, which has a specificity of 89% for sepsis prediction, our VIOSync SPI algorithm significantly enhances predictive reliability, potentially reducing false positive rates by a factor of 5.5. Conclusions: The VIOSync SPI demonstrated superior prediction performance over current sepsis early warning scores and predictive algorithms for sepsis onset. To validate the generalizability of our method across populations and treatment protocols, external validation studies are essential.
{"title":"Development and Validation of the VIOSync Sepsis Prediction Index: A Novel Machine Learning Model for Sepsis Prediction in ICU Patients","authors":"Sotirios G. Liliopoulos, Alexander Dejaco, Lucas Paseiro-Garcia, Vasileios S. Dimakopoulos, Ioannis A. Gkouzionis","doi":"10.1101/2024.02.22.24303211","DOIUrl":"https://doi.org/10.1101/2024.02.22.24303211","url":null,"abstract":"Background: Sepsis is the third leading cause of death worldwide and the main cause of in-hospital mortality. Despite decades of research, sepsis remains a major challenge faced by patients, clinicians, and medical systems worldwide. Early identification and prediction of patients at risk of sepsis and adverse outcomes associated with sepsis are critical. In this work, we aimed to develop an artificial intelligence algorithm that can predict sepsis early. Materials and Methods: We developed a predictive model for sepsis using data from the Physionet Cardiology Challenge 2019 ICU database. Our cohort consisted of adult patients who were admitted to the ICU. Sepsis diagnoses were determined using the Sepsis-3 criteria. The model, built with the XGBoost algorithm, was designed to anticipate sepsis prior to the appearance of clinical symptoms. An internal validation was conducted using a hold-off test dataset to evaluate the AI model's predictive performance. Results: We have developed the VIOSync Sepsis Prediction Index (SPI), an AI-based predictive model designed to forecast sepsis up to six hours before its clinical onset, as defined by Sepsis-3 criteria. The AI model, trained on a dataset comprising approximately 40,000 adult patients, integrates variables such as vital signs, laboratory data, and demographic information. The model demonstrated a high prediction accuracy rate of 97%, with a sensitivity of 87% and a specificity of 98% in predicting sepsis up to 6 hours before the onset. When compared to the established qSOFA score, which has a specificity of 89% for sepsis prediction, our VIOSync SPI algorithm significantly enhances predictive reliability, potentially reducing false positive rates by a factor of 5.5.\u0000Conclusions: The VIOSync SPI demonstrated superior prediction performance over current sepsis early warning scores and predictive algorithms for sepsis onset. To validate the generalizability of our method across populations and treatment protocols, external validation studies are essential.","PeriodicalId":501249,"journal":{"name":"medRxiv - Intensive Care and Critical Care Medicine","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139951681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-14DOI: 10.1101/2024.02.13.24301089
Jonathan E Millar, Sara Clohisey-Hendry, Megan McManus, Marie Zechner, Bo Wang, Nicholas Parkinson, Melissa Jungnickel, Nureen Mohamad Zaki, Erola E Pairo-Castineira, Konrad Rawlik, Joshua Rogers, Clark D Russell, Lieuwe DJ Bos, Nuala J Meyer, Carolyn Calfee, Daniel F McAuley, Manu Shankar-Hari, J Kenneth Baillie
Acute respiratory distress syndrome (ARDS) is a clinically defined syndrome of acute hypoxaemic respiratory failure secondary to non-cardiogenic pulmonary oedema. It arises from a diverse set of triggers and encompasses marked biological heterogeneity, complicating efforts to develop effective therapies. An extensive body of recent work (including transcriptomics, proteomics, and genome-wide association studies) has sought to identify proteins/genes implicated in ARDS pathogenesis. These diverse studies have not been systematically collated and interpreted. To solve this, we performed a systematic review and computational integration of existing omics data implicating host response pathways in ARDS pathogenesis. We identified 40 unbiased studies reporting associations, correlations, and other links with genes and single nucleotide polymorphisms (SNPs), from 6,856 ARDS patients. We used meta-analysis by information content (MAIC) to integrate and evaluate these data, ranking over 7,000 genes and SNPs and weighting cumulative evidence for association. Functional enrichment of strongly-supported genes revealed cholesterol metabolism, endothelial dysfunction, innate immune activation and neutrophil degranulation as key processes. We identify 51 hub genes, most of which are potential therapeutic targets. To explore biological heterogeneity, we conducted a separate analysis of ARDS severity/outcomes, revealing distinct gene associations and tissue specificity. Our large-scale integration of existing omics data in ARDS enhances understanding of the genomic landscape by synthesising decades of data from diverse sources. The findings will help researchers refine hypotheses, select candidate genes for functional validation, and identify potential therapeutic targets and repurposing opportunities. Our study and the publicly available computational framework represent an open, evolving platform for interpretation of ARDS genomic data.
{"title":"The genomic landscape of Acute Respiratory Distress Syndrome: a meta-analysis by information content of genome-wide studies of the host response.","authors":"Jonathan E Millar, Sara Clohisey-Hendry, Megan McManus, Marie Zechner, Bo Wang, Nicholas Parkinson, Melissa Jungnickel, Nureen Mohamad Zaki, Erola E Pairo-Castineira, Konrad Rawlik, Joshua Rogers, Clark D Russell, Lieuwe DJ Bos, Nuala J Meyer, Carolyn Calfee, Daniel F McAuley, Manu Shankar-Hari, J Kenneth Baillie","doi":"10.1101/2024.02.13.24301089","DOIUrl":"https://doi.org/10.1101/2024.02.13.24301089","url":null,"abstract":"Acute respiratory distress syndrome (ARDS) is a clinically defined syndrome of acute hypoxaemic respiratory failure secondary to non-cardiogenic pulmonary oedema. It arises from a diverse set of triggers and encompasses marked biological heterogeneity, complicating efforts to develop effective therapies. An extensive body of recent work (including transcriptomics, proteomics, and genome-wide association studies) has sought to identify proteins/genes implicated in ARDS pathogenesis. These diverse studies have not been systematically collated and interpreted. To solve this, we performed a systematic review and computational integration of existing omics data implicating host response pathways in ARDS pathogenesis. We identified 40 unbiased studies reporting associations, correlations, and other links with genes and single nucleotide polymorphisms (SNPs), from 6,856 ARDS patients. We used meta-analysis by information content (MAIC) to integrate and evaluate these data, ranking over 7,000 genes and SNPs and weighting cumulative evidence for association. Functional enrichment of strongly-supported genes revealed cholesterol metabolism, endothelial dysfunction, innate immune activation and neutrophil degranulation as key processes. We identify 51 hub genes, most of which are potential therapeutic targets. To explore biological heterogeneity, we conducted a separate analysis of ARDS severity/outcomes, revealing distinct gene associations and tissue specificity. Our large-scale integration of existing omics data in ARDS enhances understanding of the genomic landscape by synthesising decades of data from diverse sources. The findings will help researchers refine hypotheses, select candidate genes for functional validation, and identify potential therapeutic targets and repurposing opportunities. Our study and the publicly available computational framework represent an open, evolving platform for interpretation of ARDS genomic data.","PeriodicalId":501249,"journal":{"name":"medRxiv - Intensive Care and Critical Care Medicine","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139761880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-13DOI: 10.1101/2024.02.09.24302454
Simon Tavabie, Stephen Pearson, Janet Balabanovic, Anna Batho, Manoj Juj, Priscilla Katsande, Joanne Bennetts, Emily Collis, Timothy Bonnici
Objectives: Critical care is a place of frequent death, up to a quarter of those admitted die during admission. Caring for dying people provides many challenges, practically, professionally and personally. The aim of this study was to better understand the perspectives of staff caring for dying people in critical care and identify their priorities for improvement. Method: Three multidisciplinary focus groups of critical care staff at a large central London hospitals trust were facilitated with a semi structured format and digitally transcribed. Inductive thematic analysis was conducted to extract themes. Results: N=34 (18 nursing, 7 allied health professionals, 6 medical, 3 clerical/administrative) The five themes were structured as priority statements: 'We need to recognise' included the subthemes of being 'sick enough to die' and potential rapid deteriorations in this setting; 'We need to understand' with subthemes of perspectives on dying and prioritising time for conversations; 'We need to connect' with subthemes of therapeutic relationship and physical presence; 'We need to collaborate' with subthemes of critical care working and empowerment, and cross teams working; 'We need support' with themes of experiencing support and making time to support others. Conclusion: We present an approach to identifying critical care departmental priorities for an end-of-life care improvement programme. The themes extracted will be used to evaluate systems for dying in critical care, aiming to empower staff to provide excellent care every time they look after a dying person.
{"title":"Understanding staff needs for Improving End-of-life Care in Critical Care Units","authors":"Simon Tavabie, Stephen Pearson, Janet Balabanovic, Anna Batho, Manoj Juj, Priscilla Katsande, Joanne Bennetts, Emily Collis, Timothy Bonnici","doi":"10.1101/2024.02.09.24302454","DOIUrl":"https://doi.org/10.1101/2024.02.09.24302454","url":null,"abstract":"Objectives: Critical care is a place of frequent death, up to a quarter of those admitted die during admission. Caring for dying people provides many challenges, practically, professionally and personally. The aim of this study was to better understand the perspectives of staff caring for dying people in critical care and identify their priorities for improvement. Method: Three multidisciplinary focus groups of critical care staff at a large central London hospitals trust were facilitated with a semi structured format and digitally transcribed. Inductive thematic analysis was conducted to extract themes. Results: N=34 (18 nursing, 7 allied health professionals, 6 medical, 3 clerical/administrative) The five themes were structured as priority statements: 'We need to recognise' included the subthemes of being 'sick enough to die' and potential rapid deteriorations in this setting; 'We need to understand' with subthemes of perspectives on dying and prioritising time for conversations; 'We need to connect' with subthemes of therapeutic relationship and physical presence; 'We need to collaborate' with subthemes of critical care working and empowerment, and cross teams working; 'We need support' with themes of experiencing support and making time to support others. Conclusion: We present an approach to identifying critical care departmental priorities for an end-of-life care improvement programme. The themes extracted will be used to evaluate systems for dying in critical care, aiming to empower staff to provide excellent care every time they look after a dying person.","PeriodicalId":501249,"journal":{"name":"medRxiv - Intensive Care and Critical Care Medicine","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139761696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-11DOI: 10.1101/2024.02.09.24302599
Sooin Lee, Bryce Benson, Ashwin Belle, Richard P. Medlin, David Jerkins, Foster Goss, Ashish K. Khanna, Michael A. DeVita, Kevin Ralph Ward
Identifying the onset of patient deterioration is challenging despite the potential to respond to patients earlier with better vital sign monitoring and rapid response team (RRT) activation. In this study an ECG based software as a medical device, the Analytic for Hemodynamic Instability Predictive Index (AHI-PI), was compared to the vital signs of heart rate, blood pressure, and respiratory rate, evaluating how early it indicated risk before an RRT activation. A higher proportion of the events had risk indication by AHI-PI (92.71%) than by vital signs (41.67%). AHI-PI indicated risk early, with an average of over a day before RRT events. In events whose risks were indicated by both AHI-PI and vital signs, AHI-PI demonstrated earlier recognition of deterioration compared to vital signs. A case-control study showed that situations requiring RRTs were more likely to have AHI-PI risk indication than those that did not. The study derived several insights in support of AHI-PI’s efficacy as a clinical decision support system. The findings demonstrated AHI-PI’s potential to serve as a reliable predictor of future RRT events. It could potentially help clinicians recognize early clinical deterioration and respond to those unnoticed by vital signs, thereby helping clinicians improve clinical outcomes.
{"title":"Use of a Continuous Single Lead Electrocardiogram Analytic to Predict Patient Deterioration Requiring Rapid Response Team Activation","authors":"Sooin Lee, Bryce Benson, Ashwin Belle, Richard P. Medlin, David Jerkins, Foster Goss, Ashish K. Khanna, Michael A. DeVita, Kevin Ralph Ward","doi":"10.1101/2024.02.09.24302599","DOIUrl":"https://doi.org/10.1101/2024.02.09.24302599","url":null,"abstract":"Identifying the onset of patient deterioration is challenging despite the potential to respond to patients earlier with better vital sign monitoring and rapid response team (RRT) activation. In this study an ECG based software as a medical device, the Analytic for Hemodynamic Instability Predictive Index (AHI-PI), was compared to the vital signs of heart rate, blood pressure, and respiratory rate, evaluating how early it indicated risk before an RRT activation. A higher proportion of the events had risk indication by AHI-PI (92.71%) than by vital signs (41.67%). AHI-PI indicated risk early, with an average of over a day before RRT events. In events whose risks were indicated by both AHI-PI and vital signs, AHI-PI demonstrated earlier recognition of deterioration compared to vital signs. A case-control study showed that situations requiring RRTs were more likely to have AHI-PI risk indication than those that did not. The study derived several insights in support of AHI-PI’s efficacy as a clinical decision support system. The findings demonstrated AHI-PI’s potential to serve as a reliable predictor of future RRT events. It could potentially help clinicians recognize early clinical deterioration and respond to those unnoticed by vital signs, thereby helping clinicians improve clinical outcomes.","PeriodicalId":501249,"journal":{"name":"medRxiv - Intensive Care and Critical Care Medicine","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139761850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Background This study aimed to investigate the linear and nonlinear relationships between ionized calcium levels and 28-day mortality in patients with sepsis in the intensive care unit (ICU) and to provide clinicians with a direction for laboratory index testing and a basis for a calcium supplementation program.
{"title":"Nonlinear relationship between ionized calcium and 28-day mortality in patients with sepsis: A retrospective cohort study from MIMIC-IV database","authors":"Zhanyao Liang, Yunting Chen, Yuanshen Zhou, Congqi Hu, Lu Chen, Fangfang Zhu","doi":"10.1101/2024.02.08.24302495","DOIUrl":"https://doi.org/10.1101/2024.02.08.24302495","url":null,"abstract":"<strong>Background</strong> This study aimed to investigate the linear and nonlinear relationships between ionized calcium levels and 28-day mortality in patients with sepsis in the intensive care unit (ICU) and to provide clinicians with a direction for laboratory index testing and a basis for a calcium supplementation program.","PeriodicalId":501249,"journal":{"name":"medRxiv - Intensive Care and Critical Care Medicine","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139761798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-07DOI: 10.1101/2024.02.07.24302392
Catherine A. Gao, Nikolay S. Markov, Chiagozie Pickens, Anna Pawlowski, Mengjia Kang, James M. Walter, Benjamin D. Singer, Richard G. Wunderink, NU SCRIPT Study Investigators
Rationale Critically ill patients who develop invasive pulmonary aspergillosis (IPA) have high mortality rates despite antifungal therapy. Diagnosis is difficult in these patients. Bronchoalveolar lavage (BAL) fluid galactomannan (GM) is a helpful marker of infection, although the optimal cutoff for IPA is unclear. We aimed to evaluate the BAL fluid GM and fungal culture results, demographics, and outcomes among a large cohort of mechanically ventilated patients with suspected pneumonia.
理由 患有侵袭性肺曲霉菌病（IPA）的重症患者尽管接受了抗真菌治疗，但死亡率仍然很高。这些患者很难确诊。支气管肺泡灌洗液（BAL）中的半乳甘露聚糖（GM）是一种有用的感染标志物，但 IPA 的最佳临界值尚不明确。我们的目的是评估一大批疑似肺炎的机械通气患者的 BAL 液半乳甘露聚糖和真菌培养结果、人口统计学特征和预后。
{"title":"An observational cohort study of bronchoalveolar lavage fluid galactomannan and Aspergillus culture positivity in patients requiring mechanical ventilation","authors":"Catherine A. Gao, Nikolay S. Markov, Chiagozie Pickens, Anna Pawlowski, Mengjia Kang, James M. Walter, Benjamin D. Singer, Richard G. Wunderink, NU SCRIPT Study Investigators","doi":"10.1101/2024.02.07.24302392","DOIUrl":"https://doi.org/10.1101/2024.02.07.24302392","url":null,"abstract":"<strong>Rationale</strong> Critically ill patients who develop invasive pulmonary aspergillosis (IPA) have high mortality rates despite antifungal therapy. Diagnosis is difficult in these patients. Bronchoalveolar lavage (BAL) fluid galactomannan (GM) is a helpful marker of infection, although the optimal cutoff for IPA is unclear. We aimed to evaluate the BAL fluid GM and fungal culture results, demographics, and outcomes among a large cohort of mechanically ventilated patients with suspected pneumonia.","PeriodicalId":501249,"journal":{"name":"medRxiv - Intensive Care and Critical Care Medicine","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139762098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-06DOI: 10.1101/2024.02.05.24301960
Matthew M. Churpek, Ryan Ingebritsen, Kyle A. Carey, Saieesh A Rao, Emily Murnin, Tonela Qyli, Madeline K. Oguss, Jamila Picart, Leena Penumalee, Benjamin D. Follman, Lily K Nezirova, Sean T. Tully, Charis Benjamin, Christopher Nye, Emily R. Gilbert, Nirav S. Shah, Christopher J. Winslow, Majid Afshar, Dana P. Edelson
OBJECTIVE Timely intervention for clinically deteriorating ward patients requires that care teams accurately diagnose and treat their underlying medical conditions. However, the most common diagnoses leading to deterioration and the relevant therapies provided are poorly characterized. Therefore, we aimed to determine the diagnoses responsible for clinical deterioration, the relevant diagnostic tests ordered, and the treatments administered among high-risk ward patients using manual chart review.
{"title":"Causes, Diagnostic Testing, and Treatments Related to Clinical Deterioration Events among High-Risk Ward Patients","authors":"Matthew M. Churpek, Ryan Ingebritsen, Kyle A. Carey, Saieesh A Rao, Emily Murnin, Tonela Qyli, Madeline K. Oguss, Jamila Picart, Leena Penumalee, Benjamin D. Follman, Lily K Nezirova, Sean T. Tully, Charis Benjamin, Christopher Nye, Emily R. Gilbert, Nirav S. Shah, Christopher J. Winslow, Majid Afshar, Dana P. Edelson","doi":"10.1101/2024.02.05.24301960","DOIUrl":"https://doi.org/10.1101/2024.02.05.24301960","url":null,"abstract":"<strong>OBJECTIVE</strong> Timely intervention for clinically deteriorating ward patients requires that care teams accurately diagnose and treat their underlying medical conditions. However, the most common diagnoses leading to deterioration and the relevant therapies provided are poorly characterized. Therefore, we aimed to determine the diagnoses responsible for clinical deterioration, the relevant diagnostic tests ordered, and the treatments administered among high-risk ward patients using manual chart review.","PeriodicalId":501249,"journal":{"name":"medRxiv - Intensive Care and Critical Care Medicine","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139761974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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