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The Diagnostic Value of High-Resolution Computed Tomography Features Combined with Mycoplasma Pneumoniae Ribonucleic Acid Load Detection for Refractory Mycoplasma Pneumonia. CONTRAST MEDIA & MOLECULAR IMAGING 2022; 2022:6460865. [PMID: 35601566 PMCID: PMC9095367 DOI: 10.1155/2022/6460865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 03/31/2022] [Accepted: 04/02/2022] [Indexed: 11/24/2022]
Abstract
The aim of this study was to investigate the value of high-resolution computed tomography (CT) images and mycoplasma pneumoniae (MP) ribonucleic acid (RNA) load detection in the early diagnosis of refractory mycoplasma pneumoniae (RMP) and provide more methods for the diagnosis and treatment of RMP. Seventy children with MP were divided into the RMP group (H1 group, 31 cases) and the MP group (H2 group, 39 cases) according to pathological findings, and all of them underwent CT scanning. MP-RNA load and genotype distribution were analyzed in both groups, and the diagnostic efficacy of CT combined with MP-RNA load for RMP was calculated. The sensitivity of children in the H1 group to erythromycin (59.17% vs 71.56%) and clarithromycin (53.21% vs 67.03%) was lower than that in the H2 group, and the resistance rate of children in the H1 group to erythromycin (71.43% vs 67.53%) and clarithromycin (64.24% vs 50.37%) was higher than that in the H2 group (P < 0.05); the regression coefficients between lactate dehydrogenase (LDH) and the MPLI value of RMP were −0.064 and −0.413, respectively, which were significantly negatively correlated (P < 0.05); the accuracy (96.5%), sensitivity (92.5%), and specificity (88%) of CT + MP-RNA in the diagnosis of RMP were significantly higher than those of CT alone (91%, 88%, and 82%) and MP-RNA alone (88%, 84.5%, and 74%), which were significantly different (P < 0.05). The results of high MP-RNA load detection can be used as an indicator to predict RMP, and the diagnostic efficacy is significantly improved after combination with high-resolution CT, with high clinical application value.
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Iwanaga Y, Yamasaki K, Nemoto K, Akata K, Ikegami H, Uchimura K, Noguchi S, Nishida C, Kawanami T, Fukuda K, Mukae H, Yatera K. Combined Radiographic Features and Age Can Distinguish Mycoplasma pneumoniae Pneumonia from Other Bacterial Pneumonias: Analysis Using the 16S rRNA Gene Sequencing Data. J Clin Med 2022; 11:jcm11082201. [PMID: 35456296 PMCID: PMC9032927 DOI: 10.3390/jcm11082201] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 04/08/2022] [Accepted: 04/11/2022] [Indexed: 02/04/2023] Open
Abstract
The study objective was to evaluate chest radiographic features that distinguish Mycoplasma pneumoniae pneumonia (MPP) from other bacterial pneumonias diagnosed based on the bacterial floral analysis with 16S rRNA gene sequencing, using bronchoalveolar lavage fluid samples directly obtained from pneumonia lesions. Patients were grouped according to the dominant bacterial phenotype; among 120 enrolled patients with CAP, chest CT findings were evaluated in 55 patients diagnosed with a mono-bacterial infection (one bacterial phylotype occupies more than 80% of all phylotypes in a sample) by three authorized respiratory physicians. Among this relatively small sample size of 55 patients with CAP, 10 had MPP, and 45 had other bacterial pneumonia and were categorized into four groups according to their predominant bacterial phylotypes. We created a new scoring system to discriminate MPP from other pneumonias, using a combination of significant CT findings that were observed in the M. pneumoniae group, and age (<60 years) (MPP−CTA scoring system). When the cutoff value was set to 1, this scoring system had a sensitivity of 80%, a specificity of 93%, a positive predictive value of 73%, and a negative predictive value of 95%. Among the CT findings, centrilobular nodules were characteristic findings in patients with MPP, and a combination of chest CT findings and age might distinguish MPP from other bacterial pneumonias.
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Affiliation(s)
- Yuto Iwanaga
- Department of Respiratory Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu 807-8555, Japan; (Y.I.); (K.N.); (K.A.); (H.I.); (K.U.); (S.N.); (C.N.); (T.K.); (K.Y.)
| | - Kei Yamasaki
- Department of Respiratory Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu 807-8555, Japan; (Y.I.); (K.N.); (K.A.); (H.I.); (K.U.); (S.N.); (C.N.); (T.K.); (K.Y.)
- Correspondence: ; Tel.: +81-93-691-7453; Fax: +81-93-602-9373
| | - Kazuki Nemoto
- Department of Respiratory Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu 807-8555, Japan; (Y.I.); (K.N.); (K.A.); (H.I.); (K.U.); (S.N.); (C.N.); (T.K.); (K.Y.)
| | - Kentaro Akata
- Department of Respiratory Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu 807-8555, Japan; (Y.I.); (K.N.); (K.A.); (H.I.); (K.U.); (S.N.); (C.N.); (T.K.); (K.Y.)
| | - Hiroaki Ikegami
- Department of Respiratory Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu 807-8555, Japan; (Y.I.); (K.N.); (K.A.); (H.I.); (K.U.); (S.N.); (C.N.); (T.K.); (K.Y.)
| | - Keigo Uchimura
- Department of Respiratory Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu 807-8555, Japan; (Y.I.); (K.N.); (K.A.); (H.I.); (K.U.); (S.N.); (C.N.); (T.K.); (K.Y.)
| | - Shingo Noguchi
- Department of Respiratory Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu 807-8555, Japan; (Y.I.); (K.N.); (K.A.); (H.I.); (K.U.); (S.N.); (C.N.); (T.K.); (K.Y.)
| | - Chinatsu Nishida
- Department of Respiratory Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu 807-8555, Japan; (Y.I.); (K.N.); (K.A.); (H.I.); (K.U.); (S.N.); (C.N.); (T.K.); (K.Y.)
| | - Toshinori Kawanami
- Department of Respiratory Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu 807-8555, Japan; (Y.I.); (K.N.); (K.A.); (H.I.); (K.U.); (S.N.); (C.N.); (T.K.); (K.Y.)
| | - Kazumasa Fukuda
- Department of Microbiology, University of Occupational and Environmental Health, Japan, Kitakyushu 807-8555, Japan;
| | - Hiroshi Mukae
- Department of Respiratory Medicine, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki 852-8523, Japan;
| | - Kazuhiro Yatera
- Department of Respiratory Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu 807-8555, Japan; (Y.I.); (K.N.); (K.A.); (H.I.); (K.U.); (S.N.); (C.N.); (T.K.); (K.Y.)
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Chen W, Han X, Wang J, Cao Y, Jia X, Zheng Y, Zhou J, Zeng W, Wang L, Shi H, Feng J. Deep diagnostic agent forest (DDAF): A deep learning pathogen recognition system for pneumonia based on CT. Comput Biol Med 2021; 141:105143. [PMID: 34953357 DOI: 10.1016/j.compbiomed.2021.105143] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 12/05/2021] [Accepted: 12/12/2021] [Indexed: 11/03/2022]
Abstract
BACKGROUND Even though antibiotics agents are widely used, pneumonia is still one of the most common causes of death around the world. Some severe, fast-spreading pneumonia can even cause huge influence on global economy and life security. In order to give optimal medication regimens and prevent infectious pneumonia's spreading, recognition of pathogens is important. METHOD In this single-institution retrospective study, 2,353 patients with their CT volumes are included, each of whom was infected by one of 12 known kinds of pathogens. We propose Deep Diagnostic Agent Forest (DDAF) to recognize the pathogen of a patient based on ones' CT volume, which is a challenging multiclass classification problem, with large intraclass variations and small interclass variations and very imbalanced data. RESULTS The model achieves 0.899 ± 0.004 multi-way area under curves of receiver (AUC) for level-I pathogen recognition, which are five rough groups of pathogens, and 0.851 ± 0.003 AUC for level-II recognition, which are 12 fine-level pathogens. The model also outperforms the average result of seven human readers in level-I recognition and outperforms all readers in level-II recognition, who can only reach an average result of 7.71 ± 4.10% accuracy. CONCLUSION Deep learning model can help in recognition pathogens using CTs only, which might help accelerate the process of etiological diagnosis.
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Affiliation(s)
- Weixiang Chen
- Department of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China
| | - Xiaoyu Han
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Department of Laboratory Medicine, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jian Wang
- Department of Clinical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Research Center for Tissue Engineering and Regenerative Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yukun Cao
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Department of Laboratory Medicine, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xi Jia
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Department of Laboratory Medicine, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yuting Zheng
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Department of Laboratory Medicine, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jie Zhou
- Department of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China
| | - Wenjuan Zeng
- Department of Clinical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Research Center for Tissue Engineering and Regenerative Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Lin Wang
- Department of Clinical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Research Center for Tissue Engineering and Regenerative Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Heshui Shi
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Department of Laboratory Medicine, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Jianjiang Feng
- Department of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China.
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Fu X, Yang N, Ji J. Application of CT images based on the optimal atlas segmentation algorithm in the clinical diagnosis of Mycoplasma Pneumoniae Pneumonia in Children. Pak J Med Sci 2021; 37:1647-1651. [PMID: 34712299 PMCID: PMC8520366 DOI: 10.12669/pjms.37.6-wit.4860] [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: 04/09/2021] [Revised: 06/12/2021] [Accepted: 07/08/2021] [Indexed: 11/15/2022] Open
Abstract
Objective Use of optimal Atlas segmentation algorithm to study the imaging signs of mycoplasma pneumonia with multi-slice spiral CT (HRCT), and to explore the value of HRCT in the diagnosis and efficacy in evaluation of mycoplasma pneumonia in children. Methods The study retrospectively analyzed 72 patients diagnosed with mycoplasma pneumonia in our hospital from January 2017 to January 2019. The imaging data and clinical data of 72 patients were collected. The optimal Atlas segmentation algorithm was used to analyze the characteristics of CT examination, and the value of CT in the diagnosis of mycoplasma pneumonia and the evaluation of curative effect was summarized. Results Among all patients, 37 cases were unilateral lesions, 35 cases were bilateral lesions, 19 cases were in the left upper lobe, 24 cases were in the left lower lobe, 21 cases were in the right upper lobe, 13 cases were in the right middle lobe, 25 The lesion was located in the right lower lobe. The main CT findings of the lesions before treatment were large patchy, spot-shaped shadows, and strip-shaped or ground-glass shadows. After treatment, the main CT findings of the lesions were reduced lesion density and reduced lesion range. Conclusion CT can clearly show the pulmonary lesions of mycoplasma pneumonia, and its unique imaging signs can improve the clinical diagnosis accuracy. In addition, CT scans can evaluate the treatment effect according to the changes in the characteristics of the lesion, which has important value for the evaluation of the effect for clinical diagnosis and efficacy evaluation of mycoplasma pneumonia.
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Affiliation(s)
- Xilin Fu
- Xilin Fu, Attending Physician, Department of Pediatrics, Yiwu Central Hospital, Yiwu, 322000, China
| | - Ningfei Yang
- Ningfei Yang, Attending Physician, Department of Pediatrics, Yiwu Central Hospital, Yiwu, 322000, China
| | - Jianwei Ji
- Jianwei Ji, Attending Physician, Department of Pediatrics, Yiwu Central Hospital, Yiwu, 322000, China
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Chen XY, Chen JY, Chen Y, Chen JF, Lin N, Ding CY, Kang DZ, Wang DL, Fang WH. Preoperative serum lactate dehydrogenase level predicts progression and prognosis in patients with glioma. Clin Neurol Neurosurg 2021; 209:106912. [PMID: 34509141 DOI: 10.1016/j.clineuro.2021.106912] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 08/10/2021] [Accepted: 08/24/2021] [Indexed: 11/17/2022]
Abstract
BACKGROUND To evaluate the value of serum Lactate Dehydrogenase (LDH) level in predicting recurrence and the overall survival (OS) of glioma patients. MATERIALS AND METHODS A total number of 216 patients with glioma in our institution were retrospectively recruited to analyze the relationship between preoperative serum LDH level and prognosis. RESULTS Overall, the median age of patients was 46.0 (31.0-57.0) years old; 53.7% (116 of 216) of the enrolled patients were male. Multivariate analysis revealed that serum LDH level (odds ratio [OR] = 0.97, 95% confidence interval [CI] = 0.96-0.98, P < 0.001) and World Health Organization (WHO) grade (grade II: OR = 19.64, 95%CI = 5.56-69.35, P < 0.001; grade III: OR =1 9.50, 95%CI = 7.08-53.73, P < 0.001; grade IV: OR = 15.23, 95%CI = 4.94-46.97, P < 0.001) were significant and independent of 1-year Progression-free survival (PFS) after adjusting for confounders. The predictive performance of serum LDH level was represented with area under curve (AUC) = 0.741, 95%CI = 0.677-0.798. Multivariate Cox analysis revealed that LDH level (hazard ratio [HR] = 2.56, 95%CI = 1.59-4.15, P < 0.001) and WHO grade (grade II: HR = 4.58, 95%CI = 0.56-37.23, P = 0.155; grade III: HR = 16.35, 95%CI = 2.16-123.80, P = 0.007; grade IV: HR = 42.13, 95%CI = 5.83-304.47, P < 0.001) remained associated with survival at 2-year follow-up. At 3-year follow-up, lymphocyte count (HR = 0.68, 95%CI = 0.51-0.91, P = 0.008), LDH level (HR = 2.21, 95%CI = 1.40-3.49, P = 0.001), and WHO grade (grade II: HR = 1.44, 95%CI = 0.44-4.68, P = 0.543; grade III: HR = 4.99, 95%CI = 1.68-14.87, P = 0.004; grade IV: HR = 16.96, 95%CI = 6.13-46.93, P < 0.001) remained associated with survival in multivariate Cox analysis. CONCLUSION Our study demonstrated that preoperative serum LDH level could serve as a reliable indicator for predicting prognosis of glioma patients. Further multicenter studies are still required to verify our findings.
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Affiliation(s)
- Xiao-Yong Chen
- Department of Neurosurgery, Neurosurgical Research Institute, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Jin-Yuan Chen
- Department of Ophthalmology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Yue Chen
- Department of Neurosurgery, Neurosurgical Research Institute, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Jia-Fang Chen
- Department of Pain Treatment, The First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China
| | - Ni Lin
- The School of Medical Technology and Engineering, Fujian Medical University, Fuzhou, Fujian, China
| | - Chen-Yu Ding
- Department of Neurosurgery, Neurosurgical Research Institute, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China; Fujian Key Laboratory of Precision Medicine for Cancer, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China
| | - De-Zhi Kang
- Department of Neurosurgery, Neurosurgical Research Institute, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China; Fujian Key Laboratory of Precision Medicine for Cancer, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China; Key Laboratory of Radiation Biology of Fujian higher education institutions, The First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China.
| | - Deng-Liang Wang
- Department of Neurosurgery, Neurosurgical Research Institute, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China.
| | - Wen-Hua Fang
- Department of Neurosurgery, Neurosurgical Research Institute, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China.
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Liu G, Zhang B, Zhang S, Hu H, Liu T. LDH, CRP and ALB predict nucleic acid turn negative within 14 days in symptomatic patients with COVID-19. Scott Med J 2021; 66:108-114. [PMID: 33663273 PMCID: PMC8326898 DOI: 10.1177/0036933021994243] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
AIMS To search for biochemical indicators that can identify symptomatic patients with COVID-19 whose nucleic acid could turn negative within 14 days, and assess the prognostic value of these biochemical indicators in patients with COVID-19. PATIENTS AND METHODS We collected the clinical data of patients with COVID-19 admitted to our hospital, by using logistic regression analysis and AUC curves, explored the relationship between biochemical indicators and nucleic acid positive duration, the severity of COVID-19, and hospital stay respectively. RESULTS A total of two hundred and thirty-three patients with COVID-19 were enrolled in the study. We found patients whose nucleic acid turned negative within 14 days had lower LDH, CRP and higher ALB (P < 0.05). ROC curve results indicated that lower LDH, TP, CRP and higher ALB predicted the nucleic acid of patients turned negative within 14 days with statistical significance(P < 0.05), AST, LDH, CRP and PCT predicted the severe COVID-19 with statistical significance, and CRP predicted hospital stay >31days with statistical significance (P < 0.05). After verification, the probability of nucleic acid turning negative within 14 days in patients with low LDH (<256 U/L), CRP (<44.5 mg/L) and high ALB (>35.8 g/L) was about 4 times higher than that in patients with high LDH, CRP and low ALB (P < 0.05). CONCLUSIONS LDH, CRP and ALB are useful prognostic marker for predicting nucleic acid turn negative within 14 days in symptomatic patients with COVID-19.
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Affiliation(s)
- Gaoli Liu
- Attending Physician, Department of Thoracic Surgery, Renmin Hospital of Wuhan University, PR China
| | - Bicheng Zhang
- Associate Chief Physician, Department of Oncology, Renmin Hospital of Wuhan University, PR China
| | - Shaowen Zhang
- Attending Physician, Department of Thoracic Surgery, Renmin Hospital of Wuhan University, PR China
| | - Haifeng Hu
- Attending Physician, Department of Thoracic Surgery, Renmin Hospital of Wuhan University, PR China
| | - TingTing Liu
- Attending Physician, Department of Cardiac Function, Renmin Hospital of Wuhan University, PR China
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Lv XT, Zhu YP, Cheng AG, Jin YX, Ding HB, Wang CY, Zhang SY, Chen GP, Chen QQ, Liu QC. High serum lactate dehydrogenase and dyspnea: Positive predictors of adverse outcome in critical COVID-19 patients in Yichang. World J Clin Cases 2020; 8:5535-5546. [PMID: 33344544 PMCID: PMC7716337 DOI: 10.12998/wjcc.v8.i22.5535] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Revised: 09/04/2020] [Accepted: 09/16/2020] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak in China, constitutes a Public Health Emergency of International Concern. It is well known that COVID-19 patients may have increased serum lactate dehydrogenase (LDH) levels in the early stage. The clinical changes in LDH may have predictive value in disease evolution and prognosis in critically ill COVID-19 patients.
AIM To examine serum LDH and clinical characteristics in patients with COVID-19 and their predictive value for prognosis.
METHODS This retrospective study analyzed the clinical data of forty-seven critical COVID-19 patients in the intensive care unit of the Third People's Hospital of Yichang City from January 27 to March 25, 2020 and divided them into survivors and non-survivors. The patients were diagnosed according to the World Health Organization interim guidance and critical cases met any one of the following criteria: Respiratory failure and required mechanical ventilation, the occurrence of shock, and the combined failure of other organs that required intensive care unit monitoring and treatments, according to the diagnostic criteria of critical COVID-19. Clinical data including symptoms, detection of SARS-CoV-2, chest computed tomography (CT) images, changes in serum LDH in different clinical phases, and prognosis were collected. Statistical analysis of the data was performed. Continuous variables were expressed as median (interquartile range) and compared with the Mann-Whitney U test. Categorical variables were compared with the Chi-square test. Survival data were analyzed using Kaplan-Meier survival curves and log-rank tests.
RESULTS According to chest CT images, we observed the alveolitis and fibrosis stages in all critical patients in this study. Most non-survivors died in the fibrosis stage. Non-survivors had fewer days of hospitalization, shorter disease duration, shorter duration of alveolitis and fibrosis, and had dyspnea symptoms at disease onset (P = 0.05). Both first and lowest LDH values in the alveolitis stage were more pronounced in non-survivors than in survivors (449.0 U/L vs 288.0 U/L, P = 0.0243; 445.0 U/L vs 288.0 U/L, P = 0.0199, respectively), while the first, lowest and highest values of serum LDH in non-survivors were all significantly increased compared to survivors in the fibrosis phase (449.0 U/L vs 225.5 U/L, P = 0.0028; 432.0 U/L vs 191.0 U/L, P = 0.0007; 1303.0 U/L vs 263.5 U/L, P = 0.0001, respectively). The cut-off points of first LDH values in the alveolitis and fibrosis phase for distinction of non-survivors from survivors were 397.0 U/L and 263.0 U/L, respectively. In the fibrosis stage, non-survivors had more days with high LDH than survivors (7.0 d vs 0.0 d, P = 0.0002). Importantly, patients with high LDH had a significantly shorter median survival time than patients with low LDH in the alveolitis phase (22.0 d vs 36.5 d, P = 0.0002), while patients with high LDH also had a significantly shorter median survival time than patients with low LDH in the fibrosis phase (27.5 d vs 40.0 d, P = 0.0008). The proportion of non-survivors with detectable SARS-CoV-2 until death in the alveolitis stage was significantly increased compared with that in the fibrosis stage (100% vs 35.7%, P = 0.0220).
CONCLUSION High LDH and dyspnea symptoms were positive predictors of an adverse outcome in critical COVID-19. The rapid progressive fibrosis stage was more perilous than the alveolitis stage, even if SARS-CoV-2 is undetectable.
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Affiliation(s)
- Xiao-Ting Lv
- Department of Respiratory and Critical Care Medicine, the First Affiliated Hospital of Fujian Medical University, Fuzhou 350005, Fujian Province, China
- Institute of Respiratory Disease, Fujian Medical University, Fuzhou 350005, Fujian Province, China
| | - Yong-Ping Zhu
- Department of Cardiovascular Surgery, Fujian Medical University Attached Union Hospital, Fuzhou 350001, Fujian Province, China
| | - Ai-Guo Cheng
- Department of Critical Medicine, the Third People's Hospital of Yichang, Yichang 443000, Hubei Province, China
| | - Yong-Xu Jin
- Department of Respiratory and Critical Care Medicine, the First Affiliated Hospital of Fujian Medical University, Fuzhou 350005, Fujian Province, China
| | - Hai-Bo Ding
- Department of Respiratory and Critical Care Medicine, the First Affiliated Hospital of Fujian Medical University, Fuzhou 350005, Fujian Province, China
| | - Cai-Yun Wang
- Department of Respiratory and Critical Care Medicine, the First Affiliated Hospital of Fujian Medical University, Fuzhou 350005, Fujian Province, China
| | - Shu-Yu Zhang
- Department of Laboratory Medicine, Fujian Medical University, Fuzhou 350004, Fujian Province, China
| | - Gong-Ping Chen
- Department of Respiratory and Critical Care Medicine, the First Affiliated Hospital of Fujian Medical University, Fuzhou 350005, Fujian Province, China
| | - Qing-Quan Chen
- Department of Laboratory Medicine, Fujian Medical University, Fuzhou 350004, Fujian Province, China
| | - Qi-Cai Liu
- Department of Reproductive Medicine Centre, First Affiliated Hospital of Fujian Medical University, Fuzhou 350005, Fujian Province, China
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Elevated Lactate Dehydrogenase Level Predicts Postoperative Pneumonia in Patients with Aneurysmal Subarachnoid Hemorrhage. World Neurosurg 2019; 129:e821-e830. [DOI: 10.1016/j.wneu.2019.06.041] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2019] [Revised: 06/02/2019] [Accepted: 06/03/2019] [Indexed: 12/11/2022]
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