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Hryciw BN, Ghossein J, Rochwerg B, Meggison H, Fernando SM, Kyeremanteng K, Tran A, Seely AJE. Glycemic Variability As a Prognostic Factor for Mortality in Patients With Critical Illness: A Systematic Review and Meta-Analysis. Crit Care Explor 2024; 6:e1025. [PMID: 38222872 PMCID: PMC10786590 DOI: 10.1097/cce.0000000000001025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2024] Open
Abstract
OBJECTIVES To perform a systematic review and meta-analysis to evaluate the association of various measures of glycemic variability, including time-domain and complexity-domain, with short-term mortality in patients with critical illness. DATA SOURCES We searched Embase Classic +, MEDLINE, and the Cochrane Database of Systematic Reviews from inception to November 3, 2023. STUDY SELECTION We included English language studies that assessed metrics of glycemic variation or complexity and short-term mortality in patients admitted to the ICU. DATA EXTRACTION Two authors performed independent data abstraction and risk-of-bias assessments. We used a random-effects model to pool binary and continuous data and summarized estimates of effect using odds ratios and mean difference. We used the Quality in Prognosis Studies tool to assess risk of bias and the Grading of Recommendations, Assessment, Development and Evaluations to assess certainty of pooled estimates. DATA SYNTHESIS We included 41 studies (n = 162,259). We demonstrate that increased sd, coefficient of variance, glycemic lability index, and decreased time in range are probably associated with increased mortality in critically ill patients (moderate certainty) and that increased mean absolute glucose, mean amplitude of glycemic excursion, and detrended fluctuation analysis may be associated with increased mortality (low certainty). CONCLUSIONS We found a consistent association between increased measures of glycemic variability and higher short-term mortality in patient with critical illness. Further research should focus on standardized measurements of glycemic variation and complexity, along with their utility as therapeutic targets and prognostic markers.
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Affiliation(s)
- Brett N Hryciw
- Division of Critical Care, Department of Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Jamie Ghossein
- Department of Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Bram Rochwerg
- Department of Medicine, Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, ON, Canada
| | - Hilary Meggison
- Division of Critical Care, Department of Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Shannon M Fernando
- Department of Critical Care, Lakeridge Health Corporation, Oshawa, ON, Canada
| | - Kwadwo Kyeremanteng
- Division of Critical Care, Department of Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Alexandre Tran
- Division of Critical Care, Department of Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Andrew J E Seely
- Division of Critical Care, Department of Medicine, University of Ottawa, Ottawa, ON, Canada
- Division of Thoracic Surgery, Department of Surgery, The Ottawa Hospital, Ottawa, ON, Canada
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, University of Ottawa, Ottawa, ON, Canada
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Fernández-González O, González-Quevedo D, Zúñiga G, Arrabal-Sánchez R, Tamimi I. Predictive Factors for Length of Hospital Stay and Intensive Care Admission in Patients With Rib Fractures. Arch Bronconeumol 2023; 59:836-838. [PMID: 37777379 DOI: 10.1016/j.arbres.2023.09.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 08/28/2023] [Accepted: 09/05/2023] [Indexed: 10/02/2023]
Affiliation(s)
| | - David González-Quevedo
- Department of Orthopedic Surgery and Traumatology, Regional University Hospital of Málaga, Spain; School of Medicine, University of Málaga, Spain.
| | - Gerardo Zúñiga
- Department of Thoracic Surgery, Regional University Hospital of Málaga, Spain
| | | | - Iskandar Tamimi
- Department of Orthopedic Surgery and Traumatology, Regional University Hospital of Málaga, Spain; School of Medicine, University of Málaga, Spain
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Dong M, Liu W, Luo Y, Li J, Huang B, Zou Y, Liu F, Zhang G, Chen J, Jiang J, Duan L, Xiong D, Fu H, Yu K. Glycemic Variability Is Independently Associated With Poor Prognosis in Five Pediatric ICU Centers in Southwest China. Front Nutr 2022; 9:757982. [PMID: 35284444 PMCID: PMC8905539 DOI: 10.3389/fnut.2022.757982] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 01/17/2022] [Indexed: 12/15/2022] Open
Abstract
Background Glucose variability (GV) is a common complication of dysglycemia in critically ill patients. However, there are few studies on the role of GV in the prognosis of pediatric patients, and there is no consensus on the appropriate method for GV measurement. The objective of this study was to determine the “optimal” index of GV in non-diabetic critically ill children in a prospective multicenter cohort observational study. Also, we aimed to confirm the potential association between GV and unfavorable outcomes and whether this association persists after controlling for hypoglycemia or hyperglycemia. Materials and Methods Blood glucose values were recorded for the first 72 h and were used to calculate the GV for each participant. Four different metrics [SD, glycemic lability index (GLI), mean absolute glucose (MAG), and absolute change of percentage (ACACP)] were considered and compared to identify the “best” GV index associated with poor prognosis in non-diabetic critically ill children. Among the four metrics, the SD was most commonly used in previous studies, while GLI- and MAG-integrated temporal information, that is the rate and magnitude of change and the time interval between glucose measurements. The fourth metric, the average consecutive ACACP, was introduced in our study, which can be used in real-time clinical decisions. The primary outcome of this study was the 28-day mortality. The receiver operating characteristic (ROC) curve analysis was conducted to compare the predictive power of different metrics of GV for the primary outcome. The GV index with the largest area under ROC curve (AUC) was chosen for subsequent multivariate analyses. Multivariate Cox regression analysis was performed to identify the potential predictors of the outcome. To compare the contribution in 28-day mortality prognosis between glycemic variability and hyper- or hypoglycemia, performance metrics were calculated, which included AUC, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Results Among 780 participants, 12.4% (n = 97) died within 28 days after admission to the pediatric intensive care unit (PICU). Statistically significant differences were found between survivors and non-survivors in terms of four GV metrics (SD, GLI, MAG, and ACACP), in which MAG (AUC: 0.762, 95% CI: 0.705–0.819, p < 0.001) achieved the largest AUC and showed a strong independent association with ICU mortality. Subsequent addition of MAG to the multivariate Cox model for hyperglycemia resulted in further quantitative evolution of the model statistics (AUC = 0.651–0.681, p = 0.001; IDI: 0.017, p = 0.044; NRI: 0.224, p = 0.186). The impact of hyperglycemia (adjusted hazard ratio [aHR]: 1.419, 95% CI: 0.815–2.471, p = 0.216) on outcome was attenuated and no longer statistically relevant after adjustment for MAG (aHR: 2.455, 95% CI: 1.411–4.270, p = 0.001). Conclusions GV is strongly associated with poor prognosis independent of mean glucose level, demonstrating more predictive power compared with hypoglycemia and hyperglycemia after adjusting for confounding factors. GV metrics that contain information, such as time and rate of change, are the focus of future research; thus, the MAG may be a good choice. The findings of this study emphasize the crucial role of GVs in children in the PICU. Clinicians should pay more attention to GV for clinical glucose management.
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Affiliation(s)
- Milan Dong
- Department of Critical Care Medicine, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China
- Department of Pediatrics, The People's Hospital of Yubei District of Chongqing City, Chongqing, China
| | - Wenjun Liu
- Department of Critical Care Medicine, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China
| | - Yetao Luo
- Department of Clinical Epidemiology and Biostatistics, Children's Institute of Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Jing Li
- Department of Critical Care Medicine, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China
- *Correspondence: Jing Li
| | - Bo Huang
- Department of Pediatric Critical Care, The First People's Hospital of Zunyi, Zunyi, China
| | - Yingbo Zou
- Department of Pediatric Critical Care, The First People's Hospital of Zunyi, Zunyi, China
| | - Fuyan Liu
- Department of Pediatric Critical Care, The First People's Hospital of Zunyi, Zunyi, China
| | - Guoying Zhang
- Department of Pediatric Critical Care, Chengdu Women's and Children's Central Hospital, Chengdu, China
| | - Ju Chen
- Department of Pediatric Critical Care, Chengdu Women's and Children's Central Hospital, Chengdu, China
| | - Jianyu Jiang
- Department of Pediatrics, Chongqing Three Gorges Women and Children's Hospital, Chongqing, China
| | - Ling Duan
- Department of Pediatrics, Chongqing Three Gorges Women and Children's Hospital, Chongqing, China
| | - Daoxue Xiong
- Department of Pediatrics, Chongqing Three Gorges Women and Children's Hospital, Chongqing, China
| | - Hongmin Fu
- Department of Pediatric Critical Care, Kunming Children's Hospital, Kunming, China
| | - Kai Yu
- Department of Pediatric Critical Care, Kunming Children's Hospital, Kunming, China
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Blood glucose-related indicators are associated with in-hospital mortality in critically ill patients with acute pancreatitis. Sci Rep 2021; 11:15351. [PMID: 34321549 PMCID: PMC8319392 DOI: 10.1038/s41598-021-94697-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Accepted: 07/15/2021] [Indexed: 11/08/2022] Open
Abstract
Acute pancreatitis (AP) results in potentially harmful blood glucose fluctuations, affecting patient prognosis. This study aimed to explore the relationship between blood glucose-related indicators and in-hospital mortality in critically ill patients with AP. We extracted data on AP patients from the Multiparameter Intelligent Monitoring in Intensive Care III database. Initial glucose (Glucose_initial), maximum glucose (Glucose_max), minimum glucose (Glucose_min), mean glucose (Glucose_mean), and glucose variability (glucose standard deviation [Glucose_SD] and glucose coefficient of variation [Glucose_CV]) were selected as blood glucose-related indicators. Logistic regression models and the Lowess smoothing curves were used to display the association between significant blood glucose-related indicators and in-hospital mortality. Survivors and non-survivors showed significant differences in Glucose_max, Glucose_mean, Glucose_SD, and Glucose_CV (P < 0.05). Glucose_max, Glucose_mean, Glucose_SD, and Glucose_CV were risk factors for in-hospital mortality in AP patients (OR > 1; P < 0.05). According to the Lowess smoothing curve, the overall trends of blood glucose-related indicators showed a non-linear correlation with in-hospital mortality. Glucose_max, Glucose_mean, Glucose_SD, and Glucose_CV were associated with in-hospital mortality in critically ill patients with AP.
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Wang D, Zhang W, Luo J, Fang H, Jing S, Mei Z. Prediction models for acute kidney injury in critically ill patients: a protocol for systematic review and critical appraisal. BMJ Open 2021; 11:e046274. [PMID: 34011595 PMCID: PMC8137185 DOI: 10.1136/bmjopen-2020-046274] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Revised: 04/07/2021] [Accepted: 04/26/2021] [Indexed: 12/23/2022] Open
Abstract
INTRODUCTION Acute kidney injury (AKI) has high morbidity and mortality in intensive care units, which can lead to chronic kidney disease, more costs and longer hospital stay. Early identification of AKI is crucial for clinical intervention. Although various risk prediction models have been developed to identify AKI, the overall predictive performance varies widely across studies. Owing to the different disease scenarios and the small number of externally validated cohorts in different prediction models, the stability and applicability of these models for AKI in critically ill patients are controversial. Moreover, there are no current risk-classification tools that are standardised for prediction of AKI in critically ill patients. The purpose of this systematic review is to map and assess prediction models for AKI in critically ill patients based on a comprehensive literature review. METHODS AND ANALYSIS A systematic review with meta-analysis is designed and will be conducted according to the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS). Three databases including PubMed, Cochrane Library and EMBASE from inception through October 2020 will be searched to identify all studies describing development and/or external validation of original multivariable models for predicting AKI in critically ill patients. Random-effects meta-analyses for external validation studies will be performed to estimate the performance of each model. The restricted maximum likelihood estimation and the Hartung-Knapp-Sidik-Jonkman method under a random-effects model will be applied to estimate the summary C statistic and 95% CI. 95% prediction interval integrating the heterogeneity will also be calculated to pool C-statistics to predict a possible range of C-statistics of future validation studies. Two investigators will extract data independently using the CHARMS checklist. Study quality or risk of bias will be assessed using the Prediction Model Risk of Bias Assessment Tool. ETHICS AND DISSEMINATION Ethical approval and patient informed consent are not required because all information will be abstracted from published literatures. We plan to share our results with clinicians and publish them in a general or critical care medicine peer-reviewed journal. We also plan to present our results at critical care international conferences. OSF REGISTRATION NUMBER 10.17605/OSF.IO/X25AT.
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Affiliation(s)
- Danqiong Wang
- Department of Critical Care Medicine, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, China
| | - Weiwen Zhang
- Department of Critical Care Medicine, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, China
| | - Jian Luo
- Department of Critical Care Medicine, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, China
| | - Honglong Fang
- Department of Critical Care Medicine, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, China
| | - Shanshan Jing
- Department of Critical Care Medicine, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, China
| | - Zubing Mei
- Department of Anorectal Surgery, Anorectal Disease Institute of Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
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Affinati AH, Wallia A, Gianchandani RY. Severe hyperglycemia and insulin resistance in patients with SARS-CoV-2 infection: a report of two cases. Clin Diabetes Endocrinol 2021; 7:8. [PMID: 33992101 PMCID: PMC8123093 DOI: 10.1186/s40842-021-00121-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 04/27/2021] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Severe insulin resistance is an uncommon finding in patients with type 2 diabetes but is often associated with difficult to managing blood glucose. While severe insulin resistance is most frequently seen in the setting of medication side effects or rare genetic conditions, this report of two cases highlights the presence of severe insulin resistance in the setting of severe COVID-19 and explores how this may contribute to the poor prognosis of patients with diabetes who become infected with SARS-CoV-2. CASE PRESENTATION Here we present the cases of two African-American women with pre-existing type 2 diabetes who developed severe COVID-19 requiring mechanical ventilation and concurrent severe insulin resistance with total daily insulin dose requirements of greater than 5 unit/kg. Both patients received aggressive insulin infusion and subcutaneous insulin therapy to obtain adequate glucose management. As their COVID-19 clinical course improved, their severe insulin resistance improved as well. CONCLUSIONS The association between critical illness and hyperglycemia is well documented in the literature, however severe insulin resistance is not commonly identified and may represent a unique clinical feature of the interaction between SARS-CoV-2 infection and type 2 diabetes.
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Affiliation(s)
- Alison H Affinati
- Department of Internal Medicine, Division of Metabolism, Endocrinology and Diabetes, University of Michigan, Domino's Farms (Lobby G, Suite 1500), 24 Frank Lloyd Wright Drive, MI, 48106, Ann Arbor, USA
| | - Amisha Wallia
- Division of Endocrinology, Metabolism and Molecular Medicine, Northwestern University Feinberg School of Medicine, IL, Chicago, USA
| | - Roma Y Gianchandani
- Department of Internal Medicine, Division of Metabolism, Endocrinology and Diabetes, University of Michigan, Domino's Farms (Lobby G, Suite 1500), 24 Frank Lloyd Wright Drive, MI, 48106, Ann Arbor, USA.
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Lazzeri C, Bonizzoli M, Batacchi S, Di Valvasone S, Chiostri M, Peris A. The prognostic role of hyperglycemia and glucose variability in covid-related acute respiratory distress Syndrome. Diabetes Res Clin Pract 2021; 175:108789. [PMID: 33812902 PMCID: PMC8015370 DOI: 10.1016/j.diabres.2021.108789] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 03/07/2021] [Accepted: 03/27/2021] [Indexed: 01/08/2023]
Abstract
AIMS Due to heterogeneity on the prognostic role of glucose values and glucose variability in Novel Coronavirus (COVID) disease, we aimed at assessing the prognostic role for Intensive Care Unit (ICU) death of admission hyperglycaemia, peak glycemia and glucose variability in critically ill COVID patients: METHODS: 83 patients consecutively admitted for COVID-related Acute Respiratory Distress Syndrome (ARDS) from from 1st March to 1st October 2020. RESULTS Non survivors were older, with more comorbidities and a more severe disease. Corticosteroids were used in the majority of patients (54/83, 65%) with no difference between survivors and non survivors. Mean blood glucose values, (during the first 24 and 48 h, respectively), were comparable between the two subgroups, as well as SD 24 and CV 24. During the first 48 h, survivors showed significantly lower values of SD 48 (p < 0.001) and CV 48, respectively (p < 0.001) than non survivors. CONCLUSIONS in consecutive COVID-related ARDS patients admitted to ICU hyperglycemia (>180 mg/dl) is more common in non survivors who also showed a significantly higher glucose variability in the first 48 h since ICU admission. Our findings point to the clinical significance of in-ICU glucose control in severe COVID patients.
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Affiliation(s)
- Chiara Lazzeri
- Intensive Care Unit and Regional ECMO Referral Centre, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.
| | - Manuela Bonizzoli
- Intensive Care Unit and Regional ECMO Referral Centre, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | - Stafano Batacchi
- Intensive Care Unit and Regional ECMO Referral Centre, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | - Simona Di Valvasone
- Intensive Care Unit and Regional ECMO Referral Centre, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | - Marco Chiostri
- Intensive Care Unit and Regional ECMO Referral Centre, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | - Adriano Peris
- Intensive Care Unit and Regional ECMO Referral Centre, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
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