1
|
Luo JC, Luo MH, Zhang YJ, Liu WJ, Ma GG, Hou JY, Su Y, Hao GW, Tu GW, Luo Z. Skin mottling score assesses peripheral tissue hypoperfusion in critically ill patients following cardiac surgery. BMC Anesthesiol 2024; 24:130. [PMID: 38580909 PMCID: PMC10996133 DOI: 10.1186/s12871-024-02474-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 02/27/2024] [Indexed: 04/07/2024] Open
Abstract
BACKGROUND Skin mottling is a common manifestation of peripheral tissue hypoperfusion, and its severity can be described using the skin mottling score (SMS). This study aims to evaluate the value of the SMS in detecting peripheral tissue hypoperfusion in critically ill patients following cardiac surgery. METHODS Critically ill patients following cardiac surgery with risk factors for tissue hypoperfusion were enrolled (n = 373). Among these overall patients, we further defined a hypotension population (n = 178) and a shock population (n = 51). Hemodynamic and perfusion parameters were recorded. The primary outcome was peripheral hypoperfusion, defined as significant prolonged capillary refill time (CRT, > 3.0 s). The characteristics and hospital mortality of patients with and without skin mottling were compared. The area under receiver operating characteristic curves (AUROC) were used to assess the accuracy of SMS in detecting peripheral hypoperfusion. Besides, the relationships between SMS and conventional hemodynamic and perfusion parameters were investigated, and the factors most associated with the presence of skin mottling were identified. RESULTS Of the 373-case overall population, 13 (3.5%) patients exhibited skin mottling, with SMS ranging from 1 to 5 (5, 1, 2, 2, and 3 cases, respectively). Patients with mottling had lower mean arterial pressure, higher vasopressor dose, less urine output (UO), higher CRT, lactate levels and hospital mortality (84.6% vs. 12.2%, p < 0.001). The occurrences of skin mottling were higher in hypotension population and shock population, reaching 5.6% and 15.7%, respectively. The AUROC for SMS to identify peripheral hypoperfusion was 0.64, 0.68, and 0.81 in the overall, hypotension, and shock populations, respectively. The optimal SMS threshold was 1, which corresponded to specificities of 98, 97 and 91 and sensitivities of 29, 38 and 67 in the three populations (overall, hypotension and shock). The correlation of UO, lactate, CRT and vasopressor dose with SMS was significant, among them, UO and CRT were identified as two major factors associated with the presence of skin mottling. CONCLUSION In critically ill patients following cardiac surgery, SMS is a very specific yet less sensitive parameter for detecting peripheral tissue hypoperfusion.
Collapse
Affiliation(s)
- Jing-Chao Luo
- Cardiac Intensive Care Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
- Shanghai Geriatric Medical Center, Shanghai, 200032, China
| | - Ming-Hao Luo
- Cardiac Intensive Care Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
- Shanghai Medical College, Fudan University, Shanghai, 200032, China
| | - Yi-Jie Zhang
- Cardiac Intensive Care Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Wen-Jun Liu
- Cardiac Intensive Care Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Guo-Guang Ma
- Cardiac Intensive Care Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Jun-Yi Hou
- Cardiac Intensive Care Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Ying Su
- Cardiac Intensive Care Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Guang-Wei Hao
- Cardiac Intensive Care Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Guo-Wei Tu
- Cardiac Intensive Care Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
| | - Zhe Luo
- Cardiac Intensive Care Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
| |
Collapse
|
2
|
Li L, Tu B, Xiong Y, Hu Z, Zhang Z, Liu S, Yao Y. Machine Learning-Based Model for Predicting Prolonged Mechanical Ventilation in Patients with Congestive Heart Failure. Cardiovasc Drugs Ther 2024; 38:359-369. [PMID: 36383267 DOI: 10.1007/s10557-022-07399-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/24/2022] [Indexed: 11/17/2022]
Abstract
BACKGROUND Mechanical ventilation (MV) is widely used to relieve respiratory failure in patients with congestive heart failure (CHF). Prolonged MV (PMV) is associated with a poor prognosis. We aimed to establish a prediction model based on machine learning (ML) algorithms for the early identification of patients with CHF requiring PMV. METHODS Twelve commonly used ML algorithms were used to build the prediction model. The least absolute shrinkage and selection operator (LASSO) regression was employed to select the key features. We examined the area under the curve (AUC) statistics to evaluate the prediction performance. Data from another database were used to conduct external validation. RESULTS We screened out 10 key features from the initial 65 variables via LASSO regression to improve the practicability of the model. The CatBoost model showed the best performance for predicting PMV among the 12 commonly used ML algorithms, with favorable discrimination (AUC = 0.790) and calibration (Brier score = 0.154). Moreover, hospital mortality could be accurately predicted using the CatBoost model as well (AUC = 0.844). In the external validation, the CatBoost model also showed satisfactory prediction performance (AUC = 0.780), suggesting certain generalizability of the model. Finally, a nomogram with risk classification of PMV was shown in this study. CONCLUSION The present study developed and validated a CatBoost model, which could accurately predict PMV in mechanically ventilated patients with CHF. Moreover, this model has a favorable performance in predicting hospital mortality in these patients.
Collapse
Affiliation(s)
- Le Li
- Chinese Academy of Medical Sciences, Peking Union Medical College, National Center for Cardiovascular Diseases, Fu Wai Hospital, Beijing, 100037, China
| | - Bin Tu
- Chinese Academy of Medical Sciences, Peking Union Medical College, National Center for Cardiovascular Diseases, Fu Wai Hospital, Beijing, 100037, China
| | - Yulong Xiong
- Chinese Academy of Medical Sciences, Peking Union Medical College, National Center for Cardiovascular Diseases, Fu Wai Hospital, Beijing, 100037, China
| | - Zhao Hu
- Chinese Academy of Medical Sciences, Peking Union Medical College, National Center for Cardiovascular Diseases, Fu Wai Hospital, Beijing, 100037, China
| | - Zhenghao Zhang
- Chinese Academy of Medical Sciences, Peking Union Medical College, National Center for Cardiovascular Diseases, Fu Wai Hospital, Beijing, 100037, China
| | - Shangyu Liu
- Chinese Academy of Medical Sciences, Peking Union Medical College, National Center for Cardiovascular Diseases, Fu Wai Hospital, Beijing, 100037, China
| | - Yan Yao
- Chinese Academy of Medical Sciences, Peking Union Medical College, National Center for Cardiovascular Diseases, Fu Wai Hospital, Beijing, 100037, China.
| |
Collapse
|
3
|
Luo X, Wang R, Zhang X, Wen X, Deng S, Xie W. Identification CCL2,CXCR2,S100A9 of the immune-related gene markers and immune infiltration characteristics of inflammatory bowel disease and heart failure via bioinformatics analysis and machine learning. Front Cardiovasc Med 2023; 10:1268675. [PMID: 38034382 PMCID: PMC10687362 DOI: 10.3389/fcvm.2023.1268675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 11/02/2023] [Indexed: 12/02/2023] Open
Abstract
Background Recently, heart failure (HF) and inflammatory bowel disease (IBD) have been considered to be related diseases with increasing incidence rates; both diseases are related to immunity. This study aims to analyze and identify immune-related gene (IRG) markers of HF and IBD through bioinformatics and machine learning (ML) methods and to explore their immune infiltration characteristics. Methods This study used gene expressiondata (GSE120895, GSE21610, GSE4183) from the Gene Expression Omnibus (GEO) database to screen differentially expressed genes (DEGs) and compare them with IRGs from the ImmPort database to obtain differentially expressed immune-related genes (DIRGs). Functional enrichment analysis of IRGs was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Subsequently, three machine models and protein-protein interactions (PPIs) were established to identify diagnostic biomarkers. The receiver operating characteristic (ROC) curves were applied to evaluate the diagnostic value of the candidate biomarkersin the validation set (GSE1145, GSE36807) and obtain their correlations with immune cells through the Spearman algorithm. Finally, the CIBERSORT algorithm was used to evaluate the immune cell infiltration of the two diseases. Results Thirty-four DIRGs were screened and GO and KEGG analysis results showed that these genes are mainly related to inflammatory and immune responses. CCL2, CXCR2 and S100A9 were identified as biomarkers.The immune correlation results indicated in both diseases that CCL2 is positively correlated with mast cell activation, CXCR2 is positively correlated with neutrophils and S100A9 is positively correlated with neutrophils and mast cell activation. Analysis of immune characteristics showed that macrophages M2, macrophages M0 and neutrophils were present in both diseases. Conclusions CCL2, CXCR2 and S100A9 are promising biomarkers that will become potential immunogenetic biomarkers for diagnosing comorbidities of HF and IBD. macrophages M2, macrophages M0, neutrophil-mediated inflammation and immune regulation play important roles in the development of HF and IBD and may become diagnostic and therapeutic targets.
Collapse
Affiliation(s)
- Xu Luo
- College of Clinical Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Rui Wang
- College of Clinical Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Xin Zhang
- College of Clinical Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Xin Wen
- College of Clinical Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Siwei Deng
- College of Clinical Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Wen Xie
- College of Clinical Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Department of Cardiology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, China
| |
Collapse
|
4
|
Luo JC, Zhang JD, Zhao QY, Wang H, Tu GW, Luo MH, Huang DL, Zhang JY, Lu W, Gao F, Luo Z. INFRARED THERMOGRAPHY-BASED BODY-SURFACE THERMAL INHOMOGENEITY MONITORING TO ASSESS THE SEVERITY OF HYPOPERFUSION IN CRITICALLY ILL PATIENTS. Shock 2022; 58:366-373. [PMID: 36155398 DOI: 10.1097/shk.0000000000001998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
ABSTRACT Background: Uneven body-surface thermal distribution is a manifestation of hypoperfusion and can be quantified by infrared thermography. Our aim was to investigate whether body-surface thermal inhomogeneity could accurately evaluate the severity of patients at risk of hypoperfusion. Methods: This was a prospective cohort study in which infrared thermography images were taken from unilateral legs of critically ill patients at high risk of hypoperfusion in a cardiac surgical intensive care unit. For each patient, five body-surface thermal inhomogeneity parameters, including standard deviation (SD), kurtosis, skewness, entropy, and low-temperature area rate (LTAR), were calculated. Demographic, clinical, and thermal characteristics of deceased and living patients were compared. The risk of mortality and capillary refill time (CRT) were chosen as the primary outcome and benchmarking parameter for hypoperfusion, respectively. The area under the receiver operating characteristic curve (AUROC) was used to evaluate predictive accuracy. Results: Three hundred seventy-three patients were included, and 55 (14.7%) died during hospital stay. Of inhomogeneity parameters, SD (0.738) and LTAR (0.768) had similar AUROC to CRT (0.757) for assessing mortality risk. Besides, there was a tendency for LTAR (1%-3%-7%) and SD (0.81°C-0.88°C-0.94°C) to increase in normotensive, hypotensive, and shock patients. These thermal parameters are associated with CRT, lactate, and blood pressure. The AUROC of a combined prediction incorporating three thermal inhomogeneity parameters (SD, kurtosis, and entropy) was considerably higher at 0.866. Conclusions: Body-surface thermal inhomogeneity provided a noninvasive and accurate assessment of the severity of critically ill patients at high risk of hypoperfusion.
Collapse
Affiliation(s)
- Jing-Chao Luo
- Department of Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Jia-Dong Zhang
- Hybrid Imaging System Laboratory, Shanghai Engineering Research Center of Intelligent Vision and Imaging, School of Information Science and Technology, ShanghaiTech University, Shanghai, China
| | - Qin-Yu Zhao
- College of Engineering and Computer Science, Australian National University, Canberra, ACT, Australia
| | - Huan Wang
- Department of Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Guo-Wei Tu
- Department of Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Ming-Hao Luo
- Shanghai Medical College, Fudan University, Shanghai, China
| | - Dan-Lei Huang
- Shanghai Medical College, Fudan University, Shanghai, China
| | - Ji-Yang Zhang
- Department of Information and Intelligence Development, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Wei Lu
- School of Physical Science and Technology, ShanghaiTech University, Shanghai, China
| | - Fei Gao
- Hybrid Imaging System Laboratory, Shanghai Engineering Research Center of Intelligent Vision and Imaging, School of Information Science and Technology, ShanghaiTech University, Shanghai, China
| | | |
Collapse
|
5
|
Delano M, Ganapati V, Kamal R, Le B, Le J, Mendoza R. Evaluating Research Grade Bioimpedance Hardware Using Textile Electrodes for Long-Term Fluid Status Monitoring. FRONTIERS IN ELECTRONICS 2022. [DOI: 10.3389/felec.2021.762442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Fluid overload is a chronic medical condition that affects over six million Americans with conditions such as congestive heart failure, end-stage renal disease, and lymphedema. Remote management of fluid overload continues to be a leading clinical challenge. Bioimpedance is one technique that can be used to estimate the hydration of tissue and track it over time. However, commercially available bioimpedance measurement systems are bulky, expensive, and rely on Ag/AgCl electrodes that dry out and can irritate the skin. The use of bioimpedance today is therefore limited to clinical and research settings, with measurements performed at daily intervals or over short periods of time rather than continuously and long-term. This paper proposes using wearable calf bioimpedance measurements integrated into a compression sock for long-term fluid overload management. A PCB was developed using standard measurement techniques that measures the calf bioimpedance using a custom analog front-end built around an AD8302 gain-phase detection chip. Data is transmitted wirelessly via Bluetooth Low Energy to an iOS device using a custom iOS app. Bioimpedance data were collected both from the wearable system and a commercial measurement system (ImpediMed SFB7) using RRC networks, Ag/AgCl electrodes, and the textile compression sock. Bioimpedance data collected from the wearable system showed close agreement with data from the SFB7 when using RRC networks and in five healthy human subjects with Ag/AgCl electrodes. However, when using the textile compression sock the wearable system had worse precision than the SFB7 (4% run to run compared to <1% run to run) and there were larger differences between the two systems than when using the RRC networks and the Ag/AgCl electrodes. Wearable system precision and agreement with the SFB7 was improved by pressure or light wetting of the current electrodes on the sock. Future research should focus on reliable elimination of low-frequency artifacts in research grade hardware to enable long-term calf bioimpedance measurements for fluid overload management.
Collapse
|