Risk Factor Analysis and Nomogram Construction for Non-Survivors among Critical Patients with COVID-19

Wang, Binchen; Zhong, Feiyang; Zhang, Hanfei; An, Wenting; Liao, Meiyan; Cao, Ye

doi:10.7883/yoken.jjid.2020.227

Cited by 11 publications

(17 citation statements)

References 26 publications

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“…Two studies were conducted in Turkey [ 34 , 58 ], 1 in the USA [ 57 ], 1 in Korea [ 52 ], and the remaining 51 in China [ 14 , [35] , [36] , [37] , [38] , [39] , [40] , [41] , [42] , [43] , [44] , [45] , [46] , [47] , [48] , [49] , [50] , [51] , [53] , [54] , [55] , [56] , [59] , [60] , [61] , [62] , [63] , [64] , [65] , [66] , [67] , [68] , [69] , [70] , [71] , [72] , [73] , [74] , [75] , [76] , [77] , [78] , [79] , [80] , [81] , [82] , [83] , [84] , [85] , [86] , [87] ]. Three studies were prospective [ 42 , 53 , 68 ], 47 retrospective [ [34] , [35] , [36] , [37] , [38] , [39] , [40] , [41] ...…”

Section: Resultsmentioning

confidence: 99%

“…Three studies were prospective [ 42 , 53 , 68 ], 47 retrospective [ [34] , [35] , [36] , [37] , [38] , [39] , [40] , [41] , [43] , [44] , [45] , [46] , [47] , [48] , [50] , [51] , [52] , [54] , [55] , [56] , [57] , [58] , [59] , [60] , [61] , [62] , [63] , [64] , [65] , 67 , [69] , [70] , [71] , [72] , [73] , [74] , [75] , [76] , [77] , [78] , [79] , [80] , [81] , [82] , [83] , [84] , [85] , 87 ], whereas the remaining 5 did not report information regarding the study design [ 14 , 49 , 66 , 71 , 86 ]. Clinical endpoints included disease severity based on current clinical guidelines in 21 studies [ 38 , 43 , 45 , [48] , [49] , [50] , [51] , [52] , 54 ...…”

Section: Resultsmentioning

confidence: 99%

“…In 5 studies, patients with high severity or non-survivor status had lower CK-MB concentrations when compared to those with low severity or survivor status (mean difference range, -0.07 to -0.97) [ 37 , 51 , 60 , 66 , 86 ], although only 1 study reported a statistically significant difference [ 66 ]. In 1 study, no difference was observed (mean difference 0.00) [ 38 ], whereas in the remaining 49 studies the CK-MB concentrations were lower in patients with low severity or survivor status (mean difference range, 0.11 to 2.91), with a non-significant difference in 17 [ 34 , 38 , 41 , 42 , 46 , 57 , [62] , [63] , [64] , [65] , [70] , [71] , [72] , 74 , 77 , 78 , 85 ]. The pooled results confirmed that the CK-MB concentrations were statistically significantly higher in patients with high disease severity or non-survivor status during follow up (SMD 0.81, 95% CI 0.61 to 1.01, p<0.001) ( Figure 2 ).…”

Section: Resultsmentioning

confidence: 99%

“…In particular, a sub-group of 13 retrospective studies conducted in China [ 40 , 44 , 47 , 55 , 62 , 69 , 73 , [75] , [76] , [77] , 81 , 83 , 87 ] assessed survival and measured CK-MB using an assay based on activity evaluation. Funnel plot analysis showed that 3 of these studies were located outside of the plot ( Figure 8 A) [ 62 , 77 , 83 ]. After removing these studies, the effect size of the remaining 10 studies confirmed that the CK-MB concentrations were statistically significantly higher in patients with high disease severity or non-survivor status during follow up (SMD 0.97, 95% CI 0.84 to 1.09, p<0.001) ( Figure 8 B).…”

Section: Resultsmentioning

confidence: 99%

See 3 more Smart Citations

Serum CK-MB, COVID-19 severity and mortality: An updated systematic review and meta-analysis with meta-regression

Zinellu

Sotgia

Fois

et al. 2021

Advances in Medical Sciences

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Objectives We conducted a systematic review and meta-analysis with meta-regression of creatine kinase-MB (CK-MB), a biomarker of myocardial injury, in COVID-19 patients. Methods We searched PubMed, Web of Science and Scopus, for studies published between January 2020 and January 2021 that reported CK-MB, COVID-19 severity and mortality (PROSPERO registration number: CRD42021239657). Results Fifty-five studies in 11,791 COVID-19 patients were included in the meta-analysis. The pooled results showed that CK-MB concentrations were significantly higher in patients with high disease severity or non-survivor status than patients with low severity or survivor status (standardized mean difference, SMD, 0.81, 95% CI 0.61 to 1.01, p<0.001). The rate of patients with CK-MB values above the normal range was also significantly higher in the former than the latter (60/350 vs 98/1,780; RR=2.84, 95%CI 1.89 to 4.27, p<0.001; I 2 =19.9, p=0.254). Extreme between-study heterogeneity was observed (I 2 =93.4%, p<0.001). Sensitivity analysis, performed by sequentially removing each study and re-assessing the pooled estimates, showed that the magnitude and direction of the effect size was not modified (effect size range, 0.77 to 0.84). Begg's (p=0.50) and Egger's (p=0.86) t-tests did not show publication bias. In meta-regression analysis, the SMD was significantly and positively associated with the white blood count, aspartate aminotransferase, myoglobin, troponin, brain natriuretic peptide, lactate dehydrogenase, and D-dimer. Conclusions Higher CK-MB concentrations were significantly associated with severe disease and mortality in COVID-19 patients. This biomarker of myocardial injury might be useful for risk stratification in this group.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Serum CK-MB, COVID-19 severity and mortality: An updated systematic review and meta-analysis with meta-regression

Zinellu

Sotgia

Fois

et al. 2021

Advances in Medical Sciences

View full text Add to dashboard Cite

show abstract

“…A nomogram for predicting 14-day and 21day probability of in-hospital survival based on the three factors was developed, with high c-index and AUC in both training and validation cohorts. Moreover, Wang et al constructed a nomogram based on age, chest tightness, AST and BUN on admission to predict the risk of death in critical COVID-19 patients 22 . The nomogram had a sensitivity of 96.0%, a speci city of 74.1% and an AUC of 0.893.…”

Section: Discussionmentioning

confidence: 99%

A Machine Learning Model to Predict Death Outcome in Severe COVID-19 Patients

Qiao

Zhang

et al. 2020

Preprint

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BackgroundThe novel coronavirus disease 2019 (COVID-19) spreads rapidly among people and causes a global pandemic. It is of great clinical significance to identify COVID-19 patients with high risk of death.MethodsA total of 2,169 adult COVID-19 patients were enrolled from Wuhan, China between February 10th and April 15th, 2020. Difference analyses of medical records were performed between severe and non-severe groups as well as between survivors and non-survivors. In addition, we developed a decision tree classifier to identify risk factors for death outcome.ResultsOf the 2,169 COVID-19 patients, the median age was 61 years and male patients accounted for 48%. A total of 646 patients were diagnosed with severe illness, and 75 patients died. The most common system symptoms were respiratory, systemic and digestive symptoms. Obvious differences in demographics, clinical characteristics and laboratory examinations were found between severe and non-severe groups, as well as between survivors and non-survivors. A machine learning model was developed to predict death outcome in severe patients. The decision tree classifier included three biomarkers, neutrophil-to-lymphocyte ratio, C-reactive protein and lactic dehydrogenase. The area under the curve of the receiver operating characteristic of this model was 0.96. This model performed well both in train dataset and test dataset. The accuracy of this model was 0.98 and 0.98, respectively.ConclusionThe machine learning model was robust and effective in predicting the death outcome in severe COVID-19 patients.

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External Validation of Prognostic Models in Critical Care: A Cautionary Tale From COVID-19 Pneumonitis

Bate,

Stokes,

Greenlee

et al. 2024

Critical Care Explorations

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OBJECTIVES (BACKGROUND): To externally validate clinical prediction models that aim to predict progression to invasive ventilation or death on the ICU in patients admitted with confirmed COVID-19 pneumonitis. DESIGN: Single-center retrospective external validation study. DATA SOURCES: Routinely collected healthcare data in the ICU electronic patient record. Curated data recorded for each ICU admission for the purposes of the U.K. Intensive Care National Audit and Research Centre (ICNARC). SETTING: The ICU at Manchester Royal Infirmary, Manchester, United Kingdom. PATIENTS: Three hundred forty-nine patients admitted to ICU with confirmed COVID-19 Pneumonitis, older than 18 years, from March 1, 2020, to February 28, 2022. Three hundred two met the inclusion criteria for at least one model. Fifty-five of the 349 patients were admitted before the widespread adoption of dexamethasone for the treatment of severe COVID-19 (pre-dexamethasone patients). OUTCOMES: Ability to be externally validated, discriminate, and calibrate. METHODS: Articles meeting the inclusion criteria were identified, and those that gave sufficient details on predictors used and methods to generate predictions were tested in our cohort of patients, which matched the original publications’ inclusion/exclusion criteria and endpoint. RESULTS: Thirteen clinical prediction articles were identified. There was insufficient information available to validate models in five of the articles; a further three contained predictors that were not routinely measured in our ICU cohort and were not validated; three had performance that was substantially lower than previously published (range C-statistic = 0.483–0.605 in pre-dexamethasone patients and C = 0.494–0.564 among all patients). One model retained its discriminative ability in our cohort compared with previously published results (C = 0.672 and 0.686), and one retained performance among pre-dexamethasone patients but was poor in all patients (C = 0.793 and 0.596). One model could be calibrated but with poor performance. CONCLUSIONS: Our findings, albeit from a single center, suggest that the published performance of COVID-19 prediction models may not be replicated when translated to other institutions. In light of this, we would encourage bedside intensivists to reflect on the role of clinical prediction models in their own clinical decision-making.

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Risk Factor Analysis and Nomogram Construction for Non-Survivors among Critical Patients with COVID-19

Cited by 11 publications

References 26 publications

Serum CK-MB, COVID-19 severity and mortality: An updated systematic review and meta-analysis with meta-regression

Serum CK-MB, COVID-19 severity and mortality: An updated systematic review and meta-analysis with meta-regression

A Machine Learning Model to Predict Death Outcome in Severe COVID-19 Patients

External Validation of Prognostic Models in Critical Care: A Cautionary Tale From COVID-19 Pneumonitis

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