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Su QY, Chen WJ, Zheng YJ, Shi W, Gong FC, Huang SW, Yang ZT, Qu HP, Mao EQ, Wang RL, Zhu DM, Zhao G, Chen W, Wang S, Wang Q, Zhu CQ, Yuan G, Chen EZ, Chen Y. Development and external validation of a nomogram for the early prediction of acute kidney injury in septic patients: a multicenter retrospective clinical study. Ren Fail 2024; 46:2310081. [PMID: 38321925 PMCID: PMC10851832 DOI: 10.1080/0886022x.2024.2310081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 01/21/2024] [Indexed: 02/08/2024] Open
Abstract
Background and purpose: Acute kidney injury (AKI) is a common serious complication in sepsis patients with a high mortality rate. This study aimed to develop and validate a predictive model for sepsis associated acute kidney injury (SA-AKI). Methods: In our study, we retrospectively constructed a development cohort comprising 733 septic patients admitted to eight Grade-A tertiary hospitals in Shanghai from January 2021 to October 2022. Additionally, we established an external validation cohort consisting of 336 septic patients admitted to our hospital from January 2017 to December 2019. Risk predictors were selected by LASSO regression, and a corresponding nomogram was constructed. We evaluated the model's discrimination, precision and clinical benefit through receiver operating characteristic (ROC) curves, calibration plots, decision curve analysis (DCA) and clinical impact curves (CIC) in both internal and external validation. Results: AKI incidence was 53.2% in the development cohort and 48.2% in the external validation cohort. The model included five independent indicators: chronic kidney disease stages 1 to 3, blood urea nitrogen, procalcitonin, D-dimer and creatine kinase isoenzyme. The AUC of the model in the development and validation cohorts was 0.914 (95% CI, 0.894-0.934) and 0.923 (95% CI, 0.895-0.952), respectively. The calibration plot, DCA, and CIC demonstrated the model's favorable clinical applicability. Conclusion: We developed and validated a robust nomogram model, which might identify patients at risk of SA-AKI and promising for clinical applications.
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Affiliation(s)
- Qin-Yue Su
- Department of Emergency Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wen-Jie Chen
- Department of Emergency Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yan-Jun Zheng
- Department of Emergency Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wen Shi
- Department of Emergency Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Fang-Chen Gong
- Department of Emergency Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shun-Wei Huang
- Department of Emergency Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhi-tao Yang
- Department of Emergency Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hong-Ping Qu
- Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - En-Qiang Mao
- Department of Emergency Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Rui-Lan Wang
- Department of Emergency Medicine, Shanghai First People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Du-Ming Zhu
- Department of Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Gang Zhao
- Department of Emergency Medicine, Shanghai Sixth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wei Chen
- Department of Critical Care Medicine, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Sheng Wang
- Department of Critical Care Medicine, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China
| | - Qian Wang
- Department of Emergency Medicine, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Chang-Qing Zhu
- Department of Emergency Medicine, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Gao Yuan
- Department of Critical Care Medicine, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Er-Zhen Chen
- Department of Emergency Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ying Chen
- Department of Emergency Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Donaldson LH, Vlok R, Sakurai K, Burrows M, McDonald G, Venkatesh K, Bagshaw SM, Bellomo R, Delaney A, Myburgh J, Hammond NE, Venkatesh B. Quantifying the Impact of Alternative Definitions of Sepsis-Associated Acute Kidney Injury on its Incidence and Outcomes: A Systematic Review and Meta-Analysis. Crit Care Med 2024; 52:1264-1274. [PMID: 38557802 DOI: 10.1097/ccm.0000000000006284] [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: 04/04/2024]
Abstract
OBJECTIVES To derive a pooled estimate of the incidence and outcomes of sepsis-associated acute kidney injury (SA-AKI) in ICU patients and to explore the impact of differing definitions of SA-AKI on these estimates. DATA SOURCES Medline, Medline Epub, EMBASE, and Cochrane CENTRAL between 1990 and 2023. STUDY SELECTION Randomized clinical trials and prospective cohort studies of adults admitted to the ICU with either sepsis and/or SA-AKI. DATA EXTRACTION Data were extracted in duplicate. Risk of bias was assessed using adapted standard tools. Data were pooled using a random-effects model. Heterogeneity was assessed by using a single covariate logistic regression model. The primary outcome was the proportion of participants in ICU with sepsis who developed AKI. DATA SYNTHESIS A total of 189 studies met inclusion criteria. One hundred fifty-four reported an incidence of SA-AKI, including 150,978 participants. The pooled proportion of patients who developed SA-AKI across all definitions was 0.40 (95% CI, 0.37-0.42) and 0.52 (95% CI, 0.48-0.56) when only the Risk Injury Failure Loss End-Stage, Acute Kidney Injury Network, and Improving Global Outcomes definitions were used to define SA-AKI. There was significant variation in the incidence of SA-AKI depending on the definition of AKI used and whether AKI defined by urine output criteria was included; the incidence was lowest when receipt of renal replacement therapy was used to define AKI (0.26; 95% CI, 0.24-0.28), and highest when the Acute Kidney Injury Network score was used (0.57; 95% CI, 0.45-0.69; p < 0.01). Sixty-seven studies including 29,455 participants reported at least one SA-AKI outcome. At final follow-up, the proportion of patients with SA-AKI who had died was 0.48 (95% CI, 0.43-0.53), and the proportion of surviving patients who remained on dialysis was 0.10 (95% CI, 0.04-0.17). CONCLUSIONS SA-AKI is common in ICU patients with sepsis and carries a high risk of death and persisting kidney impairment. The incidence and outcomes of SA-AKI vary significantly depending on the definition of AKI used.
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Affiliation(s)
- Lachlan H Donaldson
- Critical Care Program, The George Institute for Global Health, Faculty of Medicine, UNSW Sydney, Kensington, NSW, Australia
- Malcolm Fisher Department of Intensive Care Medicine, Royal North Shore Hospital, St Leonards, NSW, Australia
| | - Ruan Vlok
- Malcolm Fisher Department of Intensive Care Medicine, Royal North Shore Hospital, St Leonards, NSW, Australia
| | - Ken Sakurai
- Malcolm Fisher Department of Intensive Care Medicine, Royal North Shore Hospital, St Leonards, NSW, Australia
| | - Morgan Burrows
- Intensive Care Unit, North Shore Private Hospital, St Leonards, NSW, Australia
| | - Gabrielle McDonald
- Malcolm Fisher Department of Intensive Care Medicine, Royal North Shore Hospital, St Leonards, NSW, Australia
| | - Karthik Venkatesh
- Critical Care Program, The George Institute for Global Health, Faculty of Medicine, UNSW Sydney, Kensington, NSW, Australia
- Malcolm Fisher Department of Intensive Care Medicine, Royal North Shore Hospital, St Leonards, NSW, Australia
| | - Sean M Bagshaw
- Department of Critical Care Medicine, Faculty of Medicine and Dentistry, University of Alberta and Alberta Health Services, Edmonton, AB, Canada
| | - Rinaldo Bellomo
- Department of Intensive Care, Royal Melbourne Hospital, Melbourne, VIC, Australia
- Department of Epidemiology and Preventive Medicine, Australian and New Zealand Intensive Care Research Centre, Monash University, Melbourne, VIC, Australia
| | - Anthony Delaney
- Critical Care Program, The George Institute for Global Health, Faculty of Medicine, UNSW Sydney, Kensington, NSW, Australia
- Malcolm Fisher Department of Intensive Care Medicine, Royal North Shore Hospital, St Leonards, NSW, Australia
| | - John Myburgh
- Critical Care Program, The George Institute for Global Health, Faculty of Medicine, UNSW Sydney, Kensington, NSW, Australia
- Department of Intensive Care Medicine, St George Hospital, Sydney, NSW, Australia
| | - Naomi E Hammond
- Critical Care Program, The George Institute for Global Health, Faculty of Medicine, UNSW Sydney, Kensington, NSW, Australia
- Malcolm Fisher Department of Intensive Care Medicine, Royal North Shore Hospital, St Leonards, NSW, Australia
| | - Balasubramanian Venkatesh
- Critical Care Program, The George Institute for Global Health, Faculty of Medicine, UNSW Sydney, Kensington, NSW, Australia
- Intensive Care Unit, Princess Alexandra and Wesley Hospitals, Brisbane, QLD, Australia
- Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
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Zhao F, Zhang L, Chen X, Lei M, Sun L, Ma L, Wang C. Construction and Verification of Urinary Tract Infection Prediction Model for Hospitalized Rehabilitation Patients with Spinal Cord Injury. World Neurosurg 2024; 188:e396-e404. [PMID: 38810877 DOI: 10.1016/j.wneu.2024.05.122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 05/20/2024] [Indexed: 05/31/2024]
Abstract
OBJECTIVE To explore the influencing factors of urinary tract infection (UTI) in hospitalized patients with spinal cord injury and to construct and verify the nomogram prediction model. METHODS This study is a retrospective cohort study. From January 2017 to March 2022, 558 patients with spinal cord injury admitted to the Department of Rehabilitation Medicine of a tertiary hospital in Anhui Province, China, were selected as the research objects, and they were randomly divided into training group (n = 390) and verification group (n = 168) according to the ratio of 7:3, and clinical data including socio-demographic characteristics, disease-related data, and laboratory examination data were collected. Univariate analysis and multivariate logistic regression were used to analyze the influencing factors of UTI in hospitalized patients with spinal cord injuries. Based on this, a nomogram prediction model was constructed with the use of R software, and the risk prediction efficiency of the nomogram model was verified by the receiver operating characteristic curve and calibration curve. RESULTS Logistic regression analysis showed that the American Spinal Cord Injury Association (ASIA)-E grade (compared with ASIA-A grade) was an independent protective factor for UTI in hospitalized patients with spinal cord injury (odds ratio < 1, P < 0.05), while white blood cell count and indwelling catheter were independent risk factors for UTI in hospitalized patients with spinal cord injury (odds ratio > 1, P < 0.05). Based on this, a nomogram risk predictive model for predicting UTI in hospitalized rehabilitation patients with spinal cord injury was constructed, which proved to have good predictive efficiency. In the training group and the verification group, the area under the receiver operating characteristic curve of the nomogram model is 0.808 and 0.767, and the 95% confidence interval of the area under the receiver operating characteristic curve of the nomogram in the training group and the verification group is 0.760∼0.856 and 0.688∼0.845, respectively, indicating the nomogram model has good discrimination. According to the calibration curve, the prediction probability of the nomogram model and the actual frequency of UTI in the training group and the verification group are in good consistency, and the results of the Hosmer-Lemeshow bias test also suggest that the nomogram model has a good calibration degree in both the training group and the verification group (P = 0.329, 0.067). CONCLUSIONS ASIA classification level, white blood cell count, and indwelling catheter are independent influencing factors of UTI in hospitalized patients with spinal cord injury. The nomogram prediction model based on the above factors can simply and effectively predict the risk of UTI in hospitalized patients with spinal cord injury, which is helpful for clinical medical staff to identify high-risk groups early and implement prevention, treatment, and nursing strategies in time.
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Affiliation(s)
- Fangfang Zhao
- Department of Rehabilitation Medicine, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Lixiang Zhang
- Department of Cardiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Xia Chen
- Department of Nursing, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Mengling Lei
- Department of Rehabilitation Medicine, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China; The Graduate School, Bengbu Medical University, Bengbu, Anhui, China
| | - Liai Sun
- Department of Rehabilitation Medicine, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Lina Ma
- Department of Rehabilitation Medicine, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Cheng Wang
- Department of Rehabilitation Medicine, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China.
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Tan X, Deng M, Fang Z, Yang Q, Zhang M, Wu J, Chen W. A nomogram to predict cryptococcal meningitis in patients with pulmonary cryptococcosis. Heliyon 2024; 10:e30281. [PMID: 38726150 PMCID: PMC11079104 DOI: 10.1016/j.heliyon.2024.e30281] [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] [Received: 08/30/2023] [Revised: 04/22/2024] [Accepted: 04/23/2024] [Indexed: 05/12/2024] Open
Abstract
Background The most serious manifestation of pulmonary cryptococcosis is complicated with cryptococcal meningitis, while its clinical manifestations lack specificity with delayed diagnosis and high mortality. The early prediction of this complication can assist doctors to carry out clinical interventions in time, thus improving the cure rate. This study aimed to construct a nomogram to predict the risk of cryptococcal meningitis in patients with pulmonary cryptococcosis through a scoring system. Methods The clinical data of 525 patients with pulmonary cryptococcosis were retrospectively analyzed, including 317 cases (60.38 %) with cryptococcal meningitis and 208 cases (39.62 %) without cryptococcal meningitis. The risk factors of cryptococcal meningitis were screened by univariate analysis, LASSO regression analysis and multivariate logistic regression analysis. Then the risk factors were incorporated into the nomogram scoring system to establish a prediction model. The model was validated by receiver operating characteristic (ROC) curve, decision curve analysis (DCA) and clinical impact curve. Results Fourteen risk factors for cryptococcal meningitis in patients with pulmonary cryptococcosis were screened out by statistical method, including 6 clinical manifestations (fever, headache, nausea, psychiatric symptoms, tuberculosis, hematologic malignancy) and 8 clinical indicators (neutrophils, lymphocytes, glutamic oxaloacetic transaminase, T cells, helper T cells, killer T cells, NK cells and B cells). The AUC value was 0.978 (CI 96.2 %∼98.9 %), indicating the nomogram was well verified. Conclusion The nomogram scoring system constructed in this study can accurately predict the risk of cryptococcal meningitis in patients with pulmonary cryptococcosis, which may provide a reference for clinical diagnosis and treatment of patients with cryptococcal meningitis.
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Affiliation(s)
- Xiaoli Tan
- Department of Respiration, The Affiliated Hospital of Jiaxing University, Jiaxing, China
| | - Min Deng
- Department of Infectious Diseases, The Affiliated Hospital of Jiaxing University, Jiaxing, China
| | - Zhixian Fang
- Department of Respiration, The Affiliated Hospital of Jiaxing University, Jiaxing, China
| | - Qi Yang
- Department of Respiration, The Affiliated Hospital of Jiaxing University, Jiaxing, China
| | - Ming Zhang
- Department of Respiration, The Affiliated Hospital of Jiaxing University, Jiaxing, China
| | - Jiasheng Wu
- Department of Respiratory and Critical Care Medicine, Jiaxing Second Hospital, Jiaxing, China
- Department of Respiratory Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Wenyu Chen
- Department of Respiration, The Affiliated Hospital of Jiaxing University, Jiaxing, China
- Department of Respiratory Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
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Lai K, Lin G, Chen C, Xu Y. Development and Validation of a Predictive Model for Acute Kidney Injury in Sepsis Patients Based on Recursive Partition Analysis. J Intensive Care Med 2024; 39:465-476. [PMID: 37964547 DOI: 10.1177/08850666231214243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2023]
Abstract
BACKGROUND Sepsis-associated acute kidney injury (SA-AKI) is a critical condition with significant clinical implications, yet there is a need for a predictive model that can reliably assess the risk of its development. This study is undertaken to bridge a gap in healthcare by creating a predictive model for SA-AKI with the goal of empowering healthcare providers with a tool that can revolutionize patient care and ultimately lead to improved outcomes. METHODS A cohort of 615 patients afflicted with sepsis, who were admitted to the intensive care unit, underwent random stratification into 2 groups: a training set (n = 435) and a validation set (n = 180). Subsequently, a multivariate logistic regression model, imbued with nonzero coefficients via LASSO regression, was meticulously devised for the prognostication of SA-AKI. This model was thoughtfully rendered in the form of a nomogram. The salience of individual risk factors was assessed and ranked employing Shapley Additive Interpretation (SHAP). Recursive partition analysis was performed to stratify the risk of patients with sepsis. RESULTS Among the panoply of clinical variables examined, hypertension, diabetes mellitus, C-reactive protein, procalcitonin (PCT), activated partial thromboplastin time, and platelet count emerged as robust and independent determinants of SA-AKI. The receiver operating characteristic curve analysis for SA-AKI risk discrimination in both the training set and validation set yielded an area under the curve estimates of 0.843 (95% CI: 0.805 to 0.882) and 0.834 (95% CI: 0.775 to 0.893), respectively. Notably, PCT exhibited the most conspicuous influence on the model's predictive capacity. Furthermore, statistically significant disparities were observed in the incidence of SA-AKI and the 28-day survival rate across high-risk, medium-risk, and low-risk cohorts (P < .05). CONCLUSION The composite predictive model, amalgamating the quintet of SA-AKI predictors, holds significant promise in facilitating the identification of high-risk patient subsets.
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Affiliation(s)
- Kunmei Lai
- Department of Nephrology, Blood Purification Research Center, the First Affiliated Hospital, Fujian Medical University, Fuzhou, China
| | - Guo Lin
- Department of Intensive Care Unit, The First Affifiliated Hospital, Fujian Medical University, Fuzhou, China
| | - Caiming Chen
- Department of Nephrology, Blood Purification Research Center, the First Affiliated Hospital, Fujian Medical University, Fuzhou, China
- Research Center for Metabolic Chronic Kidney Disease, the First Affiliated Hospital, Fujian Medical University, Fuzhou, China
- Department of Nephrology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, China
| | - Yanfang Xu
- Department of Nephrology, Blood Purification Research Center, the First Affiliated Hospital, Fujian Medical University, Fuzhou, China
- Research Center for Metabolic Chronic Kidney Disease, the First Affiliated Hospital, Fujian Medical University, Fuzhou, China
- Department of Nephrology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, China
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An S, Yao Y, Hu H, Wu J, Li J, Li L, Wu J, Sun M, Deng Z, Zhang Y, Gong S, Huang Q, Chen Z, Zeng Z. PDHA1 hyperacetylation-mediated lactate overproduction promotes sepsis-induced acute kidney injury via Fis1 lactylation. Cell Death Dis 2023; 14:457. [PMID: 37479690 PMCID: PMC10362039 DOI: 10.1038/s41419-023-05952-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 06/24/2023] [Accepted: 07/05/2023] [Indexed: 07/23/2023]
Abstract
The increase of lactate is an independent risk factor for patients with sepsis-induced acute kidney injury (SAKI). However, whether elevated lactate directly promotes SAKI and its mechanism remain unclear. Here we revealed that downregulation of the deacetylase Sirtuin 3 (SIRT3) mediated the hyperacetylation and inactivation of pyruvate dehydrogenase E1 component subunit alpha (PDHA1), resulting in lactate overproduction in renal tubular epithelial cells. We then found that the incidence of SAKI and renal replacement therapy (RRT) in septic patients with blood lactate ≥ 4 mmol/L was increased significantly, compared with those in septic patients with blood lactate < 2 mmol/L. Further in vitro and in vivo experiments showed that additional lactate administration could directly promote SAKI. Mechanistically, lactate mediated the lactylation of mitochondrial fission 1 protein (Fis1) lysine 20 (Fis1 K20la). The increase in Fis1 K20la promoted excessive mitochondrial fission and subsequently induced ATP depletion, mitochondrial reactive oxygen species (mtROS) overproduction, and mitochondrial apoptosis. In contrast, PDHA1 activation with sodium dichloroacetate (DCA) or SIRT3 overexpression decreased lactate levels and Fis1 K20la, thereby alleviating SAKI. In conclusion, our results show that PDHA1 hyperacetylation and inactivation enhance lactate overproduction, which mediates Fis1 lactylation and exacerbates SAKI. Reducing lactate levels and Fis1 lactylation attenuate SAKI.
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Affiliation(s)
- Sheng An
- Department of Critical Care Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
- Guangdong Provincial Key Laboratory of Cardiac Function and Microcirculation, Department of Pathophysiology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, 510515, China
| | - Yi Yao
- Department of Critical Care Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
- Guangdong Provincial Key Laboratory of Cardiac Function and Microcirculation, Department of Pathophysiology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, 510515, China
| | - Hongbin Hu
- Department of Critical Care Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Junjie Wu
- Department of Critical Care Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
- Guangdong Provincial Key Laboratory of Cardiac Function and Microcirculation, Department of Pathophysiology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, 510515, China
| | - Jiaxin Li
- Department of Critical Care Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Lulan Li
- Department of Critical Care Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Jie Wu
- Department of Critical Care Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Maomao Sun
- Department of Critical Care Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
- Guangdong Provincial Key Laboratory of Cardiac Function and Microcirculation, Department of Pathophysiology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, 510515, China
| | - Zhiya Deng
- Department of Critical Care Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
- Guangdong Provincial Key Laboratory of Cardiac Function and Microcirculation, Department of Pathophysiology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, 510515, China
| | - Yaoyuan Zhang
- Department of Critical Care Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Shenhai Gong
- School of Traditional Chinese Medicine, Southern Medical University, Guangzhou, 510515, China
| | - Qiaobing Huang
- Department of Critical Care Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
- Guangdong Provincial Key Laboratory of Cardiac Function and Microcirculation, Department of Pathophysiology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, 510515, China
| | - Zhongqing Chen
- Department of Critical Care Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
- Guangdong Provincial Key Laboratory of Cardiac Function and Microcirculation, Department of Pathophysiology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, 510515, China.
| | - Zhenhua Zeng
- Department of Critical Care Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
- Guangdong Provincial Key Laboratory of Cardiac Function and Microcirculation, Department of Pathophysiology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, 510515, China.
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7
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Feng Y, Wang AY, Jun M, Pu L, Weisbord SD, Bellomo R, Hong D, Gallagher M. Characterization of Risk Prediction Models for Acute Kidney Injury: A Systematic Review and Meta-analysis. JAMA Netw Open 2023; 6:e2313359. [PMID: 37184837 DOI: 10.1001/jamanetworkopen.2023.13359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/16/2023] Open
Abstract
Importance Despite the expansion of published prediction models for acute kidney injury (AKI), there is little evidence of uptake of these models beyond their local derivation nor data on their association with patient outcomes. Objective To systematically review published AKI prediction models across all clinical subsettings. Data Sources MEDLINE via PubMed (January 1946 to April 2021) and Embase (January 1947 to April 2021) were searched using medical subject headings and text words related to AKI and prediction models. Study Selection All studies that developed a prediction model for AKI, defined as a statistical model with at least 2 predictive variables to estimate future occurrence of AKI, were eligible for inclusion. There was no limitation on study populations or methodological designs. Data Extraction and Synthesis Two authors independently searched the literature, screened the studies, and extracted and analyzed the data following the Preferred Reporting Items for Systematic Review and Meta-analyses guideline. The data were pooled using a random-effects model, with subgroups defined by 4 clinical settings. Between-study heterogeneity was explored using multiple methods, and funnel plot analysis was used to identify publication bias. Main Outcomes and Measures C statistic was used to measure the discrimination of prediction models. Results Of the 6955 studies initially identified through literature searching, 150 studies, with 14.4 million participants, met the inclusion criteria. The study characteristics differed widely in design, population, AKI definition, and model performance assessments. The overall pooled C statistic was 0.80 (95% CI, 0.79-0.81), with pooled C statistics in different clinical subsettings ranging from 0.78 (95% CI, 0.75-0.80) to 0.82 (95% CI, 0.78-0.86). Between-study heterogeneity was high overall and in the different clinical settings (eg, contrast medium-associated AKI: I2 = 99.9%; P < .001), and multiple methods did not identify any clear sources. A high proportion of models had a high risk of bias (126 [84.4%]) according to the Prediction Model Risk Of Bias Assessment Tool. Conclusions and Relevance In this study, the discrimination of the published AKI prediction models was good, reflected by high C statistics; however, the wide variation in the clinical settings, populations, and predictive variables likely drives the highly heterogenous findings that limit clinical utility. Standardized procedures for development and validation of prediction models are urgently needed.
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Affiliation(s)
- Yunlin Feng
- Department of Nephrology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
- The George Institute for Global Health, University of New South Wales, Sydney, Australia
| | - Amanda Y Wang
- The George Institute for Global Health, University of New South Wales, Sydney, Australia
- Concord Clinical School, University of Sydney, Sydney, Australia
- The Faculty of Medicine and Health Sciences, Macquarie University, Sydney, Australia
| | - Min Jun
- The George Institute for Global Health, University of New South Wales, Sydney, Australia
| | - Lei Pu
- Department of Nephrology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Steven D Weisbord
- Renal Section, Medicine Service, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, Pennsylvania
- Renal-Electrolyte Division, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Rinaldo Bellomo
- Department of Critical Care, University of Melbourne, Melbourne, Australia
| | - Daqing Hong
- Department of Nephrology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Martin Gallagher
- The George Institute for Global Health, University of New South Wales, Sydney, Australia
- South Western Sydney Clinical School, University of New South Wales, Sydney, Australia
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Development of a nomogram for predicting 90-day mortality in patients with sepsis-associated liver injury. Sci Rep 2023; 13:3662. [PMID: 36871054 PMCID: PMC9985651 DOI: 10.1038/s41598-023-30235-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Accepted: 02/20/2023] [Indexed: 03/06/2023] Open
Abstract
The high mortality rate in sepsis patients is related to sepsis-associated liver injury (SALI). We sought to develop an accurate forecasting nomogram to estimate individual 90-day mortality in SALI patients. Data from 34,329 patients were extracted from the public Medical Information Mart for Intensive Care (MIMIC-IV) database. SALI was defined by total bilirubin (TBIL) > 2 mg/dL and the occurrence of an international normalized ratio (INR) > 1.5 in the presence of sepsis. Logistic regression analysis was performed to establish a prediction model called the nomogram based on the training set (n = 727), which was subsequently subjected to internal validation. Multivariate logistic regression analysis showed that SALI was an independent risk factor for mortality in patients with sepsis. The Kaplan‒Meier curves for 90-day survival were different between the SALI and non-SALI groups after propensity score matching (PSM) (log rank: P < 0.001 versus P = 0.038), regardless of PSM balance. The nomogram demonstrated better discrimination than the sequential organ failure assessment (SOFA) score, logistic organ dysfunction system (LODS) score, simplified acute physiology II (SAPS II) score, and Albumin-Bilirubin (ALBI) score in the training and validation sets, with areas under the receiver operating characteristic curve (AUROC) of 0.778 (95% CI 0.730-0.799, P < 0.001) and 0.804 (95% CI 0.713-0.820, P < 0.001), respectively. The calibration plot showed that the nomogram was sufficiently successful to predict the probability of 90-day mortality in both groups. The DCA of the nomogram demonstrated a higher net benefit regarding clinical usefulness than SOFA, LODS, SAPSII, and ALBI scores in the two groups. The nomogram performs exceptionally well in predicting the 90-day mortality rate in SALI patients, which can be used to assess the prognosis of patients with SALI and may assist in guiding clinical practice to enhance patient outcomes.
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Wang H, Meng R, Wang X, Si Z, Zhao Z, Lu H, Wang H, Hu J, Zheng Y, Chen J, Zheng Z, Chen Y, Yang Y, Li X, Xue L, Sun J, Wu J. Development and Internal Validation of Risk Assessment Models for Chronic Obstructive Pulmonary Disease in Coal Workers. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:3655. [PMID: 36834351 PMCID: PMC9960526 DOI: 10.3390/ijerph20043655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 02/06/2023] [Accepted: 02/16/2023] [Indexed: 06/18/2023]
Abstract
Coal workers are more likely to develop chronic obstructive pulmonary disease due to exposure to occupational hazards such as dust. In this study, a risk scoring system is constructed according to the optimal model to provide feasible suggestions for the prevention of chronic obstructive pulmonary disease in coal workers. Using 3955 coal workers who participated in occupational health check-ups at Gequan mine and Dongpang mine of Hebei Jizhong Energy from July 2018 to August 2018 as the study subjects, random forest, logistic regression, and convolutional neural network models are established, and model performance is evaluated to select the optimal model, and finally a risk scoring system is constructed according to the optimal model to achieve model visualization. The training set results show that the logistic, random forest, and CNN models have sensitivities of 78.55%, 86.89%, and 77.18%; specificities of 85.23%, 92.32%, and 87.61%; accuracies of 81.21%, 85.40%, and 83.02%; Brier scores of 0.14, 0.10, and 0.14; and AUCs of 0.76, 0.88, and 0.78, respectively, and similar results are obtained for the test set and validation set, with the random forest model outperforming the other two models. The risk scoring system constructed according to the importance ranking of random forest predictor variables has an AUC of 0.842; the evaluation results of the risk scoring system shows that its accuracy rate is 83.7% and the AUC is 0.827, and the established risk scoring system has good discriminatory ability. The random forest model outperforms the CNN and logistic regression models. The chronic obstructive pulmonary disease risk scoring system constructed based on the random forest model has good discriminatory power.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Jian Sun
- School of Public Health, North China University of Science and Technology, No. 21 Bohai Avenue, Caofeidian New Town, Tangshan 063210, China
| | - Jianhui Wu
- School of Public Health, North China University of Science and Technology, No. 21 Bohai Avenue, Caofeidian New Town, Tangshan 063210, China
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Predicting Risk Factors of Acute Kidney Injury in the First 7 Days after Admission: Analysis of a Group of Critically Ill Patients. Cardiovasc Ther 2022; 2022:1407563. [PMID: 36628120 PMCID: PMC9797299 DOI: 10.1155/2022/1407563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 11/22/2022] [Accepted: 12/03/2022] [Indexed: 12/24/2022] Open
Abstract
Background Acute kidney injury (AKI) is a common complication in critically ill patients. Some predictive models have been reported, but the conclusions are controversial. The aim of this study was the formation of nomograms to predict risk factors for AKI in critically ill patients within the first 7 days after admission to the intensive care unit (ICU). Methods Data were extracted from the Medical Information Mart for Intensive Care- (MIMIC-) III database. The random forest method was used to fill in the missing values, and least absolute shrinkage and selection operator (Lasso) regression analysis was performed to screen for possible risk factors. Results A total of 561 patients were enrolled. Complication with AKI is significantly associated with a longer length of stay (LOS). For all patients, the predictors contained in the prediction nomogram included hypertension, coronary artery disease (CAD), cardiopulmonary bypass (CPB), coronary artery bypass grafting (CABG), Simplified Acute Physiology Score II (SAPS II), central venous pressure (CVP) measured for the first time after admission, and maximum and minimum mean artery pressure (MAP). The model showed good discrimination (C - index = 0.818, 95% CI: 0.779-0.857). In the subgroup of patients with well-controlled blood glucose levels, the significant predictors included hypertension, CABG, CPB, SAPS II, and maximum and minimum MAP. Good discrimination was also present before (C - index = 0.785, 95% CI: 0.736-0.834) and after adjustment (adjusted C - index = 0.770). Conclusion Hypertension, CAD, CPB, CABG, SAPS II, CVP measured for the first time after admission, and maximum and minimum MAP were independent risk factors for AKI in critically ill patients.
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Liu Y, Zhang Y, Lu Y, Li HT, Yu C. Development and Validation of a Prognostic Nomogram to Predict 30-Day Mortality Risk in Patients with Sepsis-Induced Cardiorenal Syndrome. KIDNEY DISEASES (BASEL, SWITZERLAND) 2022; 8:334-346. [PMID: 36157260 PMCID: PMC9386441 DOI: 10.1159/000524483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 03/30/2022] [Indexed: 06/16/2023]
Abstract
INTRODUCTION Sepsis-induced cardiorenal syndrome (sepsis-induced CRS) is a devastating medical condition that is frequently associated with a high fatality rate. In this study, we aimed to develop an individualized nomogram that may help clinicians assess 30-day mortality risk in patients diagnosed with sepsis-induced CRS. METHODS A total of 340 patients with sepsis-induced CRS admitted from January 2015 to May 2019 in Shanghai Tongji Hospital were used as a training cohort to develop a nomogram prognostic model. The model was constructed using multivariable logistic analyses and was then externally validated by an independent cohort of 103 patients diagnosed with sepsis-induced CRS from June 2019 to December 2020. The prognostic ability of the nomogram was assessed through discrimination, calibration, and accuracy. RESULTS Five prognostic factors were determined and included in the nomogram: age, Sequential (sepsis-related) Organ Failure Assessment (SOFA) score, vasopressors, baseline serum creatinine, and the rate of change in myoglobin. Our prognostic nomogram showed well-fitted calibration curves and yielded strong discrimination power with the area under the curve of 0.879 and 0.912 in model development and validation, respectively. In addition, the nomogram prognostic model exhibited an evidently higher predictive accuracy than the SOFA score. CONCLUSIONS We developed a prognostic nomogram model for patients with sepsis-induced CRS and externally validated the model in another independent cohort. The nomogram exhibited greater strength in predicting 30-day mortality risk than the SOFA score, which may help clinicians estimate short-term prognosis and modulate therapeutic strategies.
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Affiliation(s)
- Yiguo Liu
- Department of Nephrology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Yingying Zhang
- Department of Nephrology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Yuqiu Lu
- Department of Nephrology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Hao Tian Li
- Faculty of Science, University of Western Ontario, London, Ontario, Canada
| | - Chen Yu
- Department of Nephrology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
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Liu K, Zhang X, Chen W, Yu ASL, Kellum JA, Matheny ME, Simpson SQ, Hu Y, Liu M. Development and Validation of a Personalized Model With Transfer Learning for Acute Kidney Injury Risk Estimation Using Electronic Health Records. JAMA Netw Open 2022; 5:e2219776. [PMID: 35796212 PMCID: PMC9250052 DOI: 10.1001/jamanetworkopen.2022.19776] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
IMPORTANCE Acute kidney injury (AKI) is a heterogeneous syndrome prevalent among hospitalized patients. Personalized risk estimation and risk factor identification may allow effective intervention and improved outcomes. OBJECTIVE To develop and validate personalized AKI risk estimation models using electronic health records (EHRs), examine whether personalized models were beneficial in comparison with global and subgroup models, and assess the heterogeneity of risk factors and their outcomes in different subpopulations. DESIGN, SETTING, AND PARTICIPANTS This diagnostic study analyzed EHR data from 1 tertiary care hospital and used machine learning and logistic regression to develop and validate global, subgroup, and personalized risk estimation models. Transfer learning was implemented to enhance the personalized model. Predictor outcomes across subpopulations were analyzed, and metaregression was used to explore predictor interactions. Adults who were hospitalized for 2 or more days from November 1, 2007, to December 31, 2016, were included in the analysis. Patients with moderate or severe kidney dysfunction at admission were excluded. Data were analyzed between August 28, 2019, and May 8, 2022. EXPOSURES Clinical and laboratory variables in the EHR. MAIN OUTCOMES AND MEASURES The main outcome was AKI of any severity, and AKI was defined using the Kidney Disease: Improving Global Outcomes serum creatinine criteria. Performance of the models was measured with area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve, and calibration. RESULTS The study cohort comprised 76 957 inpatient encounters. Patients had a mean (SD) age of 55.5 (17.4) years and included 42 159 men (54.8%). The personalized model with transfer learning outperformed the global model for AKI estimation in terms of AUROC among general inpatients (0.78 [95% CI, 0.77-0.79] vs 0.76 [95% CI, 0.75-0.76]; P < .001) and across the high-risk subgroups (0.79 [95% CI, 0.78-0.80] vs 0.75 [95% CI, 0.74-0.77]; P < .001) and low-risk subgroups (0.74 [95% CI, 0.73-0.75] vs 0.71 [95% CI, 0.70-0.72]; P < .001). The AUROC improvement reached 0.13 for the high-risk subgroups, such as those undergoing liver transplant and cardiac surgery. Moreover, the personalized model with transfer learning performed better than or comparably with the best published models in well-studied AKI subgroups. Predictor outcomes varied significantly between patients, and interaction analysis uncovered modifiers of the predictor outcomes. CONCLUSIONS AND RELEVANCE Results of this study demonstrated that a personalized modeling with transfer learning is an improved AKI risk estimation approach that can be used across diverse patient subgroups. Risk factor heterogeneity and interactions at the individual level highlighted the need for agile, personalized care.
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Affiliation(s)
- Kang Liu
- Big Data Decision Institute, Jinan University, Guangzhou, Guangdong, China
| | - Xiangzhou Zhang
- Big Data Decision Institute, Jinan University, Guangzhou, Guangdong, China
| | - Weiqi Chen
- Big Data Decision Institute, Jinan University, Guangzhou, Guangdong, China
| | - Alan S. L. Yu
- Division of Nephrology and Hypertension and the Jared Grantham Kidney Institute, School of Medicine, University of Kansas Medical Center, Kansas City
| | - John A. Kellum
- Center for Critical Care Nephrology, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Michael E. Matheny
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee
- Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee
- Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, Tennessee
- Geriatrics Research Education and Clinical Care Center, Veterans Affairs Tennessee Valley Healthcare System, Nashville
| | - Steven Q. Simpson
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Internal Medicine, University of Kansas Medical Center, Kansas City
| | - Yong Hu
- Big Data Decision Institute, Jinan University, Guangzhou, Guangdong, China
| | - Mei Liu
- Division of Medical Informatics, Department of Internal Medicine, University of Kansas Medical Center, Kansas City
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Liu K, Yuan B, Zhang X, Chen W, Patel LP, Hu Y, Liu M. Characterizing the temporal changes in association between modifiable risk factors and acute kidney injury with multi-view analysis. Int J Med Inform 2022; 163:104785. [DOI: 10.1016/j.ijmedinf.2022.104785] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 04/15/2022] [Accepted: 04/24/2022] [Indexed: 12/15/2022]
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Clinical Value of Prognostic Nutritional Index and Neutrophil-to-Lymphocyte Ratio in Prediction of the Development of Sepsis-Induced Kidney Injury. DISEASE MARKERS 2022; 2022:1449758. [PMID: 35711566 PMCID: PMC9197608 DOI: 10.1155/2022/1449758] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 05/17/2022] [Accepted: 05/25/2022] [Indexed: 11/27/2022]
Abstract
Background Sepsis-related acute kidney injury (S-AKI) is a frequent complication of hospitalized patients and is linked to increased morbidity and mortality. Early prediction and detection remain conducive to optimizing treatment strategies and limiting further insults. This study was aimed at evaluating the potential predictive value of the combined prognostic nutrition index (PNI) and neutrophil-to-lymphocyte ratio (NLR) to predict the risk of AKI in septic patients. Methods In this retrospective study, 1238 adult patients with sepsis who were admitted to the First Affiliated Hospital of Xi'an Jiaotong University from January 2015 to June 2021 were enrolled. Patients were divided into two groups: the non-AKI group (n = 731) and the S-AKI group (n = 507). Univariate and multivariate logistic regression analyses were performed to screen the independent predictive factors of S-AKI. A receiver operating characteristic (ROC) curve was used to evaluate the predictive value of PNI and NLR. Results Multivariate logistic regression analysis indicated that age, chronic liver disease, cardiovascular disease, respiratory rate (RR), white blood cells (WBC), blood urea nitrogen (BUN), creatinine (CRE), international normalized ratio (INR), neutrophil-to-lymphocyte ratio (NLR), and prognostic nutrition index (PNI) were independent prognostic factors of S-AKI. In the three models, the adjusted OR of PNI for S-AKI was 0.802 (0.776-0.829), 0.801 (0.775-0.829), and 0.717 (0.666-0.772), while that of NLR was 1.094 (1.078-1.111), 1.097 (1.080-1.114), and 1.044 (1.016-1.072), respectively. In addition, the area under the ROC curve of the PNI plus NLR group was significantly greater than that of the CRE plus BUN group (0.801, 95% CI: 0.775-0.827 vs. 0.750, 95% CI: 0.722-0.778, respectively; P < 0.001). Conclusions PNI and NLR have been identified as readily available and independent predictors in septic patients with S-AKI. PNI, in combination with NLR, is of vital significance for early warning and efficient intervention of S-AKI and is superior to combined BUN and CRE.
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Wang Z, Shao H, Xu Q, Wang Y, Ma Y, Diaty DM, Zhang J, Ye Z. Establishment and Verification of Prognostic Nomograms for Young Women With Breast Cancer Bone Metastasis. Front Med (Lausanne) 2022; 9:840024. [PMID: 35492327 PMCID: PMC9039285 DOI: 10.3389/fmed.2022.840024] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 02/17/2022] [Indexed: 12/29/2022] Open
Abstract
Purpose The prognosis of patients with metastatic breast cancer usually varies greatly among individuals. At present, the application of nomogram is very popular in metastatic tumors. The present study was conducted to identify independent survival predictors and construct nomograms among young women with breast cancer bone metastasis (BCBM). Patients and Methods We searched the Surveillance, Epidemiology, and End Results (SEER) database to identify young women diagnosed with BCBM between 2010 and 2016. We first analyzed the potential risk factors of overall survival (OS) and cancer-specific survival (CSS) by applying univariate Cox regression analysis. Then we conducted multivariate Cox analysis to identify independent survival predictors. Based on significant independent predictors, we developed and validated novel prognostic nomograms by using the R version 4.1.0 software. Results We finally identified 715 eligible young women with BCBM for survival analysis, of which 358 patients were in the training set, and 357 patients in the validation set. Approximately four-fifths of patients are between 31 and 40 years old. The 5-year OS and CSS rates of this research population were 41.9 and 43.3%, respectively. Multivariate analysis revealed seven independent predictors of both OS and CSS, including race, tumor subtype, tumor size, surgical treatment, brain metastasis, liver metastasis, and lung metastasis. Based on these predictors, we developed and validated OS and CSS nomograms. The C-index of the OS nomogram reached 0.728 and 0.73 in the training and validation sets, respectively. The C-index of the CSS nomogram reached 0.743 and 0.695 in the training and validation sets, respectively. Meanwhile, high quality calibration plots were revealed in both OS and CSS nomograms. Conclusion The current novel nomograms can provide an individualized survival evaluation of young women with BCBM and instruct clinicians to treat them appropriately.
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Affiliation(s)
- Zhan Wang
- Department of Orthopedic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Orthopedics Research Institute of Zhejiang University, Hangzhou, China.,Key Laboratory of Motor System Disease Research and Precision Therapy of Zhejiang Province, Hangzhou, China
| | - Haiyu Shao
- Department of Orthopedic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Orthopedics Research Institute of Zhejiang University, Hangzhou, China.,Key Laboratory of Motor System Disease Research and Precision Therapy of Zhejiang Province, Hangzhou, China.,Department of Orthopedics, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, China
| | - Qiang Xu
- Department of Orthopaedics, Xuzhou Central Hospital, Xuzhou, China
| | - Yongguang Wang
- Department of Orthopedic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yaojing Ma
- Department of Orthopedic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Orthopedics Research Institute of Zhejiang University, Hangzhou, China.,Key Laboratory of Motor System Disease Research and Precision Therapy of Zhejiang Province, Hangzhou, China
| | - Diarra Mohamed Diaty
- Department of Orthopedic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Orthopedics Research Institute of Zhejiang University, Hangzhou, China.,Key Laboratory of Motor System Disease Research and Precision Therapy of Zhejiang Province, Hangzhou, China
| | - Jiahao Zhang
- Department of Orthopedic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Orthopedics Research Institute of Zhejiang University, Hangzhou, China.,Key Laboratory of Motor System Disease Research and Precision Therapy of Zhejiang Province, Hangzhou, China
| | - Zhaoming Ye
- Department of Orthopedic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Orthopedics Research Institute of Zhejiang University, Hangzhou, China.,Key Laboratory of Motor System Disease Research and Precision Therapy of Zhejiang Province, Hangzhou, China
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Construction and Validation of a Risk Prediction Model for Acute Kidney Injury in Patients Suffering from Septic Shock. DISEASE MARKERS 2022; 2022:9367873. [PMID: 35035614 PMCID: PMC8758295 DOI: 10.1155/2022/9367873] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 12/22/2021] [Indexed: 12/17/2022]
Abstract
Background. Acute kidney injury (AKI) is an important complication in critically ill patients, especially in sepsis and septic shock patients. Early prediction of AKI in septic shock can provide clinicians with sufficient information for timely intervention so that improve the patients’ survival rate and quality of life. The aim of this study was to establish a nomogram that predicts the risk of AKI in patients with septic shock in the intensive care unit (ICU). Methods. The data were collected from the Medical Information Mart for Intensive Care III (MIMIC-III) database between 2001 and 2012. The primary outcome was AKI in the 48 h following ICU admission. Univariate and multivariate logistic regression analyses were used to screen the independent risk factors of AKI. The performance of the nomogram was evaluated according to the calibration curve, receiver operating characteristic (ROC) curve, decision curve analysis, and clinical impact curve. Results. A total of 2415 patients with septic shock were included in this study. In the training and validation cohort, 1091 (64.48%) of 1690 patients and 475 (65.52%) of 725 patients developed AKI, respectively. The predictive factors for nomogram construction were gender, ethnicity, congestive heart failure, diabetes, obesity, Simplified Acute Physiology Score II (SAPS II), angiotensin-converting enzyme inhibitors (ACEI) or angiotensin receptor blockers (ARBs), bilirubin, creatinine, blood urea nitrogen (BUN), and mechanical ventilation. The model had a good discrimination with the area under the ROC curve of 0.756 and 0.760 in the training and validation cohorts, respectively. The calibration curve for probability of AKI in septic shock showed optimal agreement between prediction by nomogram and actual observation. Decision curve and clinical impact curve analysis indicated that the nomogram conferred high clinical net benefit. Conclusion. The proposed nomogram can quickly and effectively predict the risk of AKI at an early stage in patients with septic shock in ICU, which can provide information for timely and efficient intervention in patients with septic shock in the ICU setting.
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Uchino E, Sato N, Okuno Y. Artificial Intelligence in Predicting Kidney Function and Acute Kidney Injury. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Ozrazgat-Baslanti T, Loftus TJ, Ren Y, Ruppert MM, Bihorac A. Advances in artificial intelligence and deep learning systems in ICU-related acute kidney injury. Curr Opin Crit Care 2021; 27:560-572. [PMID: 34757993 PMCID: PMC8783984 DOI: 10.1097/mcc.0000000000000887] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
PURPOSE OF REVIEW Acute kidney injury (AKI) affects nearly 60% of all patients admitted to ICUs. Large volumes of clinical, monitoring and laboratory data produced in ICUs allow the application of artificial intelligence analytics. The purpose of this article is to assimilate and critically evaluate recently published literature regarding artificial intelligence applications for predicting, diagnosing and subphenotyping AKI among critically ill patients. RECENT FINDINGS Among recent studies regarding artificial intelligence implementations for predicting, diagnosing and subphenotyping AKI among critically ill patients, there are many promising models, but few had external validation, clinical interpretability and high predictive performance. Deep learning techniques leveraging multimodal clinical data show great potential to provide continuous, accurate, early predictions of AKI risk, which could be implemented clinically to optimize preventive and early therapeutic management strategies. SUMMARY Use of consensus criteria, standard definitions and common data models could facilitate access to machine learning-ready data sets for external validation. The lack of interpretability, explainability, fairness and transparency of artificial intelligence models hinder their entrustment and clinical implementation; compliance with standardized reporting guidelines can mitigate these challenges.
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Affiliation(s)
- Tezcan Ozrazgat-Baslanti
- Department of Medicine, College of Medicine, University of Florida, Gainesville, FL, USA
- Precision and Intelligent Systems in Medicine (PrismaP), University of Florida, Gainesville, FL, USA
| | - Tyler J. Loftus
- Department of Surgery, College of Medicine, University of Florida, Gainesville, FL, USA
- Precision and Intelligent Systems in Medicine (PrismaP), University of Florida, Gainesville, FL, USA
| | - Yuanfang Ren
- Department of Medicine, College of Medicine, University of Florida, Gainesville, FL, USA
- Precision and Intelligent Systems in Medicine (PrismaP), University of Florida, Gainesville, FL, USA
| | - Matthew M. Ruppert
- Department of Medicine, College of Medicine, University of Florida, Gainesville, FL, USA
- Precision and Intelligent Systems in Medicine (PrismaP), University of Florida, Gainesville, FL, USA
| | - Azra Bihorac
- Department of Medicine, College of Medicine, University of Florida, Gainesville, FL, USA
- Precision and Intelligent Systems in Medicine (PrismaP), University of Florida, Gainesville, FL, USA
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Sabaz MS, Aşar S, Sertçakacılar G, Sabaz N, Çukurova Z, Hergünsel GO. The effect of body mass index on the development of acute kidney injury and mortality in intensive care unit: is obesity paradox valid? Ren Fail 2021; 43:543-555. [PMID: 33745415 PMCID: PMC7993374 DOI: 10.1080/0886022x.2021.1901738] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 03/03/2021] [Accepted: 03/03/2021] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND The conflicting results of studies on intensive care unit (ICU) mortality of obese patients and obese patients with acute kidney injury (AKI) reveal a paradox within a paradox. The aim of this study was to determine the effects of body mass index and obesity on AKI development and ICU mortality. METHODS The 4,459 patients treated between January 2015 and December 2019 in the ICU at a Tertiary Care Center in Turkey were analyzed retrospectively. RESULTS AKI developed more in obese patients with 69.8% (620). AKI development rates were similar in normal-weight (65.1%; 1172) and overweight patients (64.9%; 1149). The development of AKI in patients who presented with cerebrovascular diseases was higher in obese patients (81; 76.4%) than in normal-weight (158; 62.7%) and overweight (174; 60.8%) patients (p < 0.05). The risk of developing AKI was approximately 1.4 times (CI 95% = 1.177-1.662) higher in obese patients than in normal-weight patients. Dialysis was used more frequently in obese patients (24.3%, p < 0.001), who stayed longer in the ICU (p < 0.05). It was determined that the development of AKI in normal-weight and overweight patients increased mortality (p < 0.001) and that there was not a difference in mortality rates between obese patients with and without AKI. CONCLUSION The risk of AKI development was higher in obese patients but not in those who were in serious conditions. Another paradox was that the development of AKI was associated with a higher mortality rate in normal-weight and overweight patients, but not in obese patients. Cerebrovascular diseases as a cause of admission pose additional risks for AKI.
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Affiliation(s)
- Mehmet Süleyman Sabaz
- Department of Anesthesiology and Reanimation, Marmara University Pendik Training and Research Hospital, Istanbul, Turkey
| | - Sinan Aşar
- Department of Anesthesiology and Reanimation, Health Sciences University, Bakırköy Dr Sadi Konuk Training and Research Hospital, Istanbul, Turkey
| | - Gökhan Sertçakacılar
- Department of Anesthesiology and Reanimation, Health Sciences University, Bakırköy Dr Sadi Konuk Training and Research Hospital, Istanbul, Turkey
| | - Nagihan Sabaz
- Division of Nursing, Department of Pediatric Nursing, Faculty of Health Sciences, Marmara University, Istanbul, Turkey
| | - Zafer Çukurova
- Department of Anesthesiology and Reanimation, Health Sciences University, Bakırköy Dr Sadi Konuk Training and Research Hospital, Istanbul, Turkey
| | - Gülsüm Oya Hergünsel
- Department of Anesthesiology and Reanimation, Health Sciences University, Bakırköy Dr Sadi Konuk Training and Research Hospital, Istanbul, Turkey
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Yao X, Zhang L, Huang L, Chen X, Geng L, Xu X. Development of a Nomogram Model for Predicting the Risk of In-Hospital Death in Patients with Acute Kidney Injury. Risk Manag Healthc Policy 2021; 14:4457-4468. [PMID: 34754252 PMCID: PMC8572105 DOI: 10.2147/rmhp.s321399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 10/08/2021] [Indexed: 11/23/2022] Open
Abstract
OBJECTIVE To analyze the risk factors of in-hospital death in patients with acute kidney injury (AKI) in the intensive care unit (ICU), and to develop a personalized risk prediction model. METHODS The clinical data of 137 AKI patients hospitalized in the ICU of Anhui provincial hospital from January 2018 to December 2020 were retrospectively analyzed. Patients were divided into two groups: those that survived to discharge ("survival" group, 100 cases) and those that died while in hospital ("death" group, 37 cases), and risk factors for in-hospital death analyzed. RESULTS The in-hospital mortality of AKI patients in the ICU was 27.01% (37/137). A multivariate logistic regression analysis indicated age, mechanical ventilation and vasoactive drugs were significant risk factors for in-hospital death in AKI patients, and a nomogram risk prediction model was developed. The Harrell's C-index of the nomogram model was 0.891 (95% CI: 0.837-0.945), and the area under the receiver operating characteristic (ROC) curve was 0.886 (95% CI: 0.823-0.936) after internal validation, indicating that the nomogram model had good discrimination. The Hosmer-Lemeshow goodness of fit test and calibration curve indicated the predicted probability of the nomogram model was consistent with the actual frequency of death in ICU patients with AKI. The decision curve analysis (DCA) showed that the clinical net benefit level of the nomogram model is highest when the probability threshold of AKI is between 0.01 and 0.75. CONCLUSION Patients in the ICU with AKI had high in-hospital mortality and were affected by a variety of risk factors. The nomogram prediction model based on the risk factors of AKI showed good prediction efficiency and clinical applicability, which could help medical staff in the ICU to identify AKI patients with high-risk, allowing early prevention, detection and intervention, and reducing the risk of in-hospital deaths in ICU patients with AKI.
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Affiliation(s)
- Xiuying Yao
- Department of Intensive Care Unit, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230036, People’s Republic of China
| | - Lixiang Zhang
- Department of Nursing DepartmeThe First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230036, People’s Republic of China
| | - Lei Huang
- Department of Intensive Care Unit, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230036, People’s Republic of China
| | - Xia Chen
- Department of Nursing DepartmeThe First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230036, People’s Republic of China
| | - Li Geng
- Department of Intensive Care Unit, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230036, People’s Republic of China
| | - Xu Xu
- Department of Intensive Care Unit, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230036, People’s Republic of China
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Tang Y, Chen Q, Zha L, Feng Y, Zeng X, Liu Z, Li F, Yu Z. Development and Validation of Nomogram to Predict Long-Term Prognosis of Critically Ill Patients with Acute Myocardial Infarction. Int J Gen Med 2021; 14:4247-4257. [PMID: 34393504 PMCID: PMC8357623 DOI: 10.2147/ijgm.s310740] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Accepted: 07/23/2021] [Indexed: 12/22/2022] Open
Abstract
Purpose Acute myocardial infarction (AMI) is a common cardiovascular disease with a poor prognosis. The aim of this study was to construct a nomogram for predicting the long-term survival of critically ill patients with AMI. This nomogram will help in assessing disease severity, guiding treatment, and improving prognosis. Patients and Methods The clinical data of patients with AMI were extracted from the MIMIC-III v1.4 database. Cox proportional hazards models were adopted to identify independent prognostic factors. A nomogram for predicting the long-term survival of these patients was developed on the basis of the results of multifactor analysis. The discriminative ability and accuracy of the multifactor analysis were evaluated according to concordance index (C-index) and calibration curves. Results A total of 1202 patients were included in the analysis. The patients were randomly divided into a training set (n = 841) and a validation set (n = 361). Multivariate analysis revealed that age, blood urea nitrogen, respiratory rate, hemoglobin, pneumonia, cardiogenic shock, dialysis, and mechanical ventilation, all of which were incorporated into the nomogram, were independent predictive factors of AMI. Moreover, the nomogram exhibited favorable performance in predicting the 4-year survival of patients with AMI. The training set and the validation set had a C-index of 0.789 (95% confidence interval [CI]: 0.765–0.813) and 0.762 (95% CI: 0.725–0.799), respectively. Conclusion The nomogram constructed herein can accurately predict the long-term survival of critically ill patients with AMI.
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Affiliation(s)
- Yiyang Tang
- Department of Cardiology, Xiangya Hospital, Central South University, Changsha, Hunan, People's Republic of China
| | - Qin Chen
- Department of Cardiology, Xiangya Hospital, Central South University, Changsha, Hunan, People's Republic of China
| | - Lihuang Zha
- Department of Cardiology, Xiangya Hospital, Central South University, Changsha, Hunan, People's Republic of China
| | - Yilu Feng
- Department of Cardiology, Xiangya Hospital, Central South University, Changsha, Hunan, People's Republic of China
| | - Xiaofang Zeng
- Department of Cardiology, Xiangya Hospital, Central South University, Changsha, Hunan, People's Republic of China
| | - Zhenghui Liu
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan, People's Republic of China
| | - Famei Li
- Department of Cardiology, Xiangya Hospital, Central South University, Changsha, Hunan, People's Republic of China
| | - Zaixin Yu
- Department of Cardiology, Xiangya Hospital, Central South University, Changsha, Hunan, People's Republic of China
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Ma J, Deng Y, Lao H, Ouyang X, Liang S, Wang Y, Yao F, Deng Y, Chen C. A nomogram incorporating functional and tubular damage biomarkers to predict the risk of acute kidney injury for septic patients. BMC Nephrol 2021; 22:176. [PMID: 33985459 PMCID: PMC8120900 DOI: 10.1186/s12882-021-02388-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Accepted: 04/26/2021] [Indexed: 12/11/2022] Open
Abstract
Background Combining tubular damage and functional biomarkers may improve prediction precision of acute kidney injury (AKI). Serum cystatin C (sCysC) represents functional damage of kidney, while urinary N-acetyl-β-D-glucosaminidase (uNAG) is considered as a tubular damage biomarker. So far, there is no nomogram containing this combination to predict AKI in septic cohort. We aimed to compare the performance of AKI prediction models with or without incorporating these two biomarkers and develop an effective nomogram for septic patients in intensive care unit (ICU). Methods This was a prospective study conducted in the mixed medical-surgical ICU of a tertiary care hospital. Adults with sepsis were enrolled. The patients were divided into development and validation cohorts in chronological order of ICU admission. A logistic regression model for AKI prediction was first constructed in the development cohort. The contribution of the biomarkers (sCysC, uNAG) to this model for AKI prediction was assessed with the area under the receiver operator characteristic curve (AUC), continuous net reclassification index (cNRI), and incremental discrimination improvement (IDI). Then nomogram was established based on the model with the best performance. This nomogram was validated in the validation cohort in terms of discrimination and calibration. The decision curve analysis (DCA) was performed to evaluate the nomogram’s clinical utility. Results Of 358 enrolled patients, 232 were in the development cohort (69 AKI), while 126 in the validation cohort (52 AKI). The first clinical model included the APACHE II score, serum creatinine, and vasopressor used at ICU admission. Adding sCysC and uNAG to this model improved the AUC to 0.831. Furthermore, incorporating them significantly improved risk reclassification over the predictive model alone, with cNRI (0.575) and IDI (0.085). A nomogram was then established based on the new model including sCysC and uNAG. Application of this nomogram in the validation cohort yielded fair discrimination with an AUC of 0.784 and good calibration. The DCA revealed good clinical utility of this nomogram. Conclusions A nomogram that incorporates functional marker (sCysC) and tubular damage marker (uNAG), together with routine clinical factors may be a useful prognostic tool for individualized prediction of AKI in septic patients. Supplementary Information The online version contains supplementary material available at 10.1186/s12882-021-02388-w.
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Affiliation(s)
- Jianchao Ma
- Department of Nephrology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 510080, Guangzhou, Guangdong, PR China
| | - Yujun Deng
- Department of Critical Care Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 510080, Guangzhou, Guangdong Province, PR China
| | - Haiyan Lao
- Department of Pharmacy, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, 510080, Guangzhou, Guangdong, PR China
| | - Xin Ouyang
- Department of Intensive Care Unit of Cardiovascular Surgery, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, 510080, Guangzhou, PR China
| | - Silin Liang
- Department of Intensive Care Unit of Cardiovascular Surgery, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, 510080, Guangzhou, PR China
| | - Yifan Wang
- Department of Intensive Care Unit of Cardiovascular Surgery, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, 510080, Guangzhou, PR China
| | - Fen Yao
- Department of Intensive Care Unit of Cardiovascular Surgery, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, 510080, Guangzhou, PR China
| | - Yiyu Deng
- Department of Critical Care Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 510080, Guangzhou, Guangdong Province, PR China.
| | - Chunbo Chen
- Department of Intensive Care Unit of Cardiovascular Surgery, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, 510080, Guangzhou, PR China. .,The Second School of Clinical Medicine, Southern Medical University, 510280, Guangzhou, Guangdong, PR China.
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A Simple Nomogram to Predict Contrast-Induced Acute Kidney Injury in Patients with Congestive Heart Failure Undergoing Coronary Angiography. Cardiol Res Pract 2021; 2021:9614953. [PMID: 33859840 PMCID: PMC8009707 DOI: 10.1155/2021/9614953] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 02/12/2021] [Accepted: 03/10/2021] [Indexed: 11/30/2022] Open
Abstract
Background Patients with congestive heart failure (CHF) are vulnerable to contrast-induced kidney injury (CI-AKI), but few prediction models are currently available. Therefore, we aimed to establish a simple nomogram for CI-AKI risk assessment for patients with CHF undergoing coronary angiography. Methods A total of 1876 consecutive patients with CHF (defined as New York Heart Association functional class II-IV or Killip class II-IV) were enrolled and randomly (2:1) assigned to a development cohort and a validation cohort. The endpoint was CI-AKI defined as serum creatinine elevation of ≥0.3 mg/dL or 50% from baseline within the first 48–72 hours following the procedure. Predictors for the simple nomogram were selected by multivariable logistic regression with a stepwise approach. The discriminative power was assessed using the area under the receiver operating characteristic (ROC) curve and was compared with the classic Mehran score in the validation cohort. Calibration was assessed using the Hosmer–Lemeshow test and 1000 bootstrap samples. Results The incidence of CI-AKI was 9.06% (170) in the total sample, 8.64% (n = 109) in the development cohort, and 9.92% (n = 61) in the validation cohort (P=0.367). The simple nomogram including four predictors (age, intra-aortic balloon pump, acute myocardial infarction, and chronic kidney disease) demonstrated a similar predictive power as the Mehran score (area under the curve: 0.80 vs. 0.75, P=0.061), as well as a well-fitted calibration curve. Conclusions The present simple nomogram including four predictors is a simple and reliable tool to identify CHF patients at risk of CI-AKI, whereas further external validations are needed.
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Fan T, Wang H, Wang J, Wang W, Guan H, Zhang C. Nomogram to predict the risk of acute kidney injury in patients with diabetic ketoacidosis: an analysis of the MIMIC-III database. BMC Endocr Disord 2021; 21:37. [PMID: 33663489 PMCID: PMC7931351 DOI: 10.1186/s12902-021-00696-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Accepted: 02/10/2021] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND This study aimed to develop and validate a nomogram for predicting acute kidney injury (AKI) during the Intensive Care Unit (ICU) stay of patients with diabetic ketoacidosis (DKA). METHODS A total of 760 patients diagnosed with DKA from the Medical Information Mart for Intensive Care III (MIMIC-III) database were included and randomly divided into a training set (70%, n = 532) and a validation set (30%, n = 228). Clinical characteristics of the data set were utilized to establish a nomogram for the prediction of AKI during ICU stay. The least absolute shrinkage and selection operator (LASSO) regression was utilized to identified candidate predictors. Meanwhile, a multivariate logistic regression analysis was performed based on variables derived from LASSO regression, in which variables with P < 0.1 were included in the final model. Then, a nomogram was constructed applying these significant risk predictors based on a multivariate logistic regression model. The discriminatory ability of the model was determined by illustrating a receiver operating curve (ROC) and calculating the area under the curve (AUC). Moreover, the calibration plot and Hosmer-Lemeshow goodness-of-fit test (HL test) were conducted to evaluate the performance of our newly bullied nomogram. Decision curve analysis (DCA) was performed to evaluate the clinical net benefit. RESULTS A multivariable model that included type 2 diabetes mellitus (T2DM), microangiopathy, history of congestive heart failure (CHF), history of hypertension, diastolic blood pressure (DBP), urine output, Glasgow coma scale (GCS), and respiratory rate (RR) was represented as the nomogram. The predictive model demonstrated satisfied discrimination with an AUC of 0.747 (95% CI, 0.706-0.789) in the training dataset, and 0.712 (95% CI, 0.642-0.782) in the validation set. The nomogram showed well-calibrated according to the calibration plot and HL test (P > 0.05). DCA showed that our model was clinically useful. CONCLUSION The nomogram predicted model for predicting AKI in patients with DKA was constructed. This predicted model can help clinical physicians to identify the patients with high risk earlier and prevent the occurrence of AKI and intervene timely to improve prognosis.
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Affiliation(s)
- Tingting Fan
- Department of Endocrinology, Second Affiliated Hospital of Jilin University, Ziqiang Street 218, Changchun, 130041, Jilin, China
| | - Haosheng Wang
- Department of Orthopedics, Second Affiliated Hospital of Jilin University, Changchun, China
| | - Jiaxin Wang
- Department of Endocrinology, Second Affiliated Hospital of Jilin University, Ziqiang Street 218, Changchun, 130041, Jilin, China
| | - Wenrui Wang
- Department of Endocrinology, Second Affiliated Hospital of Jilin University, Ziqiang Street 218, Changchun, 130041, Jilin, China
| | - Haifei Guan
- Department of Endocrinology, Second Affiliated Hospital of Jilin University, Ziqiang Street 218, Changchun, 130041, Jilin, China
| | - Chuan Zhang
- Department of Endocrinology, Second Affiliated Hospital of Jilin University, Ziqiang Street 218, Changchun, 130041, Jilin, China.
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Abosamak MF, Alkholy AF. Urinary kidney injury molecule-1 and neutrophil gelatinase-associated lipocalin are early predictors for acute kidney injury among patients admitted to the surgical ICU. EGYPTIAN JOURNAL OF ANAESTHESIA 2021. [DOI: 10.1080/11101849.2020.1866883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022] Open
Affiliation(s)
- Mohammed F Abosamak
- Department of Anesthesia & ICU, Faculty of Medicine, Tanta University, Tanta, Egypt
| | - Adel F Alkholy
- Department of Medical Biochemistry, Faculty of Medicine, Benha University, Benha, Egypt
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Artificial Intelligence in Predicting Kidney Function and Acute Kidney Injury. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_270-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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