Incidence and risk factors of kidney impairment on patients with COVID-19: A meta-analysis of 10180 patients

Yang, Qixin; Yang, Xiyao

doi:10.1371/journal.pone.0241953

Cited by 23 publications

(19 citation statements)

References 39 publications

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“…These findings were congruent with previous studies. Several studies have suggested that increased BUN can be attributed to acute kidney injury during the course of the disease, which can be a cause of adverse effects [ [53] , [54] , [55] ]. Colombi et al [ 56 ] reported lung involvement area and hypoxia as reliable predictors of ICU admission and mortality among COVID-19 patients.…”

Section: Discussionmentioning

confidence: 99%

Machine learning-based prognostic modeling using clinical data and quantitative radiomic features from chest CT images in COVID-19 patients

Shiri

Sorouri

Geramifar

et al. 2021

Computers in Biology and Medicine

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Objective To develop prognostic models for survival (alive or deceased status) prediction of COVID-19 patients using clinical data (demographics and history, laboratory tests, visual scoring by radiologists) and lung/lesion radiomic features extracted from chest CT images. Methods Overall, 152 patients were enrolled in this study protocol. These were divided into 106 training/validation and 46 test datasets (untouched during training), respectively. Radiomic features were extracted from the segmented lungs and infectious lesions separately from chest CT images. Clinical data, including patients’ history and demographics, laboratory tests and radiological scores were also collected. Univariate analysis was first performed (q-value reported after false discovery rate (FDR) correction) to determine the most predictive features among all imaging and clinical data. Prognostic modeling of survival was performed using radiomic features and clinical data, separately or in combination. Maximum relevance minimum redundancy (MRMR) and XGBoost were used for feature selection and classification. The receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC), sensitivity, specificity, and accuracy were used to assess the prognostic performance of the models on the test datasets. Results For clinical data, cancer comorbidity (q-value < 0.01), consciousness level (q-value < 0.05) and radiological score involved zone (q-value < 0.02) were found to have high correlated features with outcome. Oxygen saturation (AUC = 0.73, q-value < 0.01) and Blood Urea Nitrogen (AUC = 0.72, q-value = 0.72) were identified as high clinical features. For lung radiomic features, SAHGLE (AUC = 0.70) and HGLZE (AUC = 0.67) from GLSZM were identified as most prognostic features. Amongst lesion radiomic features, RLNU from GLRLM (AUC = 0.73), HGLZE from GLSZM (AUC = 0.73) had the highest performance. In multivariate analysis, combining lung, lesion and clinical features was determined to provide the most accurate prognostic model (AUC = 0.95 ± 0.029 (95%CI: 0.95-0.96), accuracy = 0.88 ± 0.046 (95% CI: 0.88-0.89), sensitivity = 0.88 ± 0.066 (95% CI = 0.87-0.9) and specificity = 0.89 ± 0.07 (95% CI = 0.87-0.9)). Conclusion Combination of radiomic features and clinical data can effectively predict outcome in COVID-19 patients. The developed model has significant potential for improved management of COVID-19 patients.

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Section: Discussionmentioning

confidence: 99%

Machine learning-based prognostic modeling using clinical data and quantitative radiomic features from chest CT images in COVID-19 patients

Shiri

Sorouri

Geramifar

et al. 2021

Computers in Biology and Medicine

View full text Add to dashboard Cite

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“…A lot of authors emphasized the interplay between renal impairment and severe Covid-19 infection, and the effect of the baseline kidney impairment on the prognosis and intensive care unit admission rate of Covid-19 patients. [ 12 ]…”

Section: Discussionmentioning

confidence: 99%

Risk of Covid-19 infection after resection of high grade transitional cell carcinoma with renal impairment

Murshidi

Ihmeidan

Murshidi

et al. 2021

International Journal of Surgery Case Reports

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Introduction and importance Covid-19 pandemic has had huge impact on health care system and put the health care system under strain, so efforts made to minimize the elective surgeries however some surgeries especially those for high risk malignant tumors cannot be postponed. The aim of this case report is to highlight the importance of screening cancer patients and those with co-morbidities such as renal impairment for Covid-19 and encouraging them to get vaccinated before undergoing elective surgeries. Case presentation We report a case of an 80 year old male patient with renal impairment who developed Covid-19 infection after transurethral resection of high grade transitional cell carcinoma of urinary bladder. Clinical Discussion Although intra-hospital contagion of Covid-19 is not rare, increased risk of acquiring Covid-19 among cancer patient particularly if they have co-morbidities like renal impairment should be kept in mind and strict protective measures for Covid-19 for those patients should be done before, during and after the procedure. Conclusion We theorized that patients with high grade transitional cell carcinoma of urinary bladder should be screened for Covid-19 and get vaccinated before the procedure.

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“…The differences in pathological types suggest that the mechanism of kidney injury is complex. Furthermore, since the prevalence of AKI is significantly higher within COVID-19 patients from intensive care unit (ICU), indicating different mechanisms of AKI between ICU and non-ICU patients [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

A validation study of UCSD-Mayo risk score in predicting hospital-acquired acute kidney injury in COVID-19 patients

et al. 2021

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Introduction: Acute kidney injury (AKI) in coronavirus disease 2019 patients is associated with poor prognosis. Early prediction and intervention of AKI are vital for improving clinical outcome of COVID-19 patients. As lack of tools for early AKI detection in COVID-19 patients, this study aimed to validate the USCD-Mayo risk score in predicting hospital-acquired AKI in an extended multi-center COVID-19 cohort. Methods: Five hundred seventy-two COVID-19 patients from Wuhan Tongji Hospital Guanggu Branch, Wuhan Leishenshan Hospital, and Wuhan No. Ninth Hospital was enrolled for this study. Patients who developed AKI or reached an outcome of recovery or death during the study period were included. Predictors were evaluated according to data extracted from medical records. Results: Of all patients, a total of 44 (8%) developed AKI. The UCSD-Mayo risk score achieved excellent discrimination in predicting AKI with the C-statistic of 0.88 (95%CI: 0.84-0.91). Next, we determined the UCSD-Mayo risk score had good overall performance (Nagelkerke R 2 ¼ 0.32) and calibration in our cohort. Further analysis showed that the UCSD-Mayo risk score performed well in subgroups defined by gender, age, and several chronic comorbidities. However, the discrimination of the UCSD-Mayo risk score in ICU patients and patients with mechanical ventilation was not good which might be resulted from different risk factors of these patients. Conclusions: We validated the performance of UCSD-Mayo risk score in predicting hospitalacquired AKI in COVID-19 patients was excellent except for patients from ICU or patients with mechanical ventilation.

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Incidence and risk factors of kidney impairment on patients with COVID-19: A meta-analysis of 10180 patients

Cited by 23 publications

References 39 publications

Machine learning-based prognostic modeling using clinical data and quantitative radiomic features from chest CT images in COVID-19 patients

Machine learning-based prognostic modeling using clinical data and quantitative radiomic features from chest CT images in COVID-19 patients

Risk of Covid-19 infection after resection of high grade transitional cell carcinoma with renal impairment

A validation study of UCSD-Mayo risk score in predicting hospital-acquired acute kidney injury in COVID-19 patients

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