A nomogram for predicting mortality in patients with COVID-19 and solid tumors: a multicenter retrospective cohort study

Liu, Chao; Li, Li; Song, Kunpeng; Zhan, Zhi Ying; Yao, Yi; Gong, Hongyun; Chen, Yuan; Wang, Qun; Dong, Xiaorong; Xie, Zhibin; Ou, Chun Quan; Hu, Qinyong; Song, Qibin

doi:10.1136/jitc-2020-001314

Cited by 30 publications

(26 citation statements)

References 29 publications

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“… 3 Nevertheless, several underlying diseases, such as cancer, hypertension, and cardiovascular diseases, act as risk factors for severe COVID-19 or COVID-19-related mortality. 2 12 32 In COVID-19 patients with cancer, systemic immunosuppression associated with the cancer itself or anticancer therapy might affect the vulnerability to severe COVID-19 or COVID-19-related mortality, which is consistent with our findings. Diabetes, known to be a hazardous comorbidity in fatal COVID-19 cases, was also associated with COVID-19-related mortality in the present study.…”

Section: Discussionsupporting

confidence: 91%

Prediction of COVID-19-related Mortality and 30-Day and 60-Day Survival Probabilities Using a Nomogram

et al. 2021

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Background Prediction of mortality in patients with coronavirus disease 2019 (COVID-19) is a key to improving the clinical outcomes, considering that the COVID-19 pandemic has led to the collapse of healthcare systems in many regions worldwide. This study aimed to identify the factors associated with COVID-19 mortality and to develop a nomogram for predicting mortality using clinical parameters and underlying diseases. Methods This study was performed in 5,626 patients with confirmed COVID-19 between February 1 and April 30, 2020 in South Korea. A Cox proportional hazards model and logistic regression model were used to construct a nomogram for predicting 30-day and 60-day survival probabilities and overall mortality, respectively in the train set. Calibration and discrimination were performed to validate the nomograms in the test set. Results Age ≥ 70 years, male, presence of fever and dyspnea at the time of COVID-19 diagnosis, and diabetes mellitus, cancer, or dementia as underling diseases were significantly related to 30-day and 60-day survival and mortality in COVID-19 patients. The nomogram showed good calibration for survival probabilities and mortality. In the train set, the areas under the curve (AUCs) for 30-day and 60-day survival was 0.914 and 0.954, respectively; the AUC for mortality of 0.959. In the test set, AUCs for 30-day and 60-day survival was 0.876 and 0.660, respectively, and that for mortality was 0.926. The online calculators can be found at https://koreastat.shinyapps.io/RiskofCOVID19/ . Conclusion The prediction model could accurately predict COVID-19-related mortality; thus, it would be helpful for identifying the risk of mortality and establishing medical policies during the pandemic to improve the clinical outcomes.

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Section: Discussionsupporting

confidence: 91%

Prediction of COVID-19-related Mortality and 30-Day and 60-Day Survival Probabilities Using a Nomogram

et al. 2021

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“…Lymphocyte infiltration in the tumor is also associated with a better response to immunotherapy and prognosis in solid tumor patients ( 29 ). LDH and dNLR are independent risk factors for mortality among patients, and are correlated with poor outcomes in several solid cancer types according to the current evidence ( 30 , 31 ).…”

Section: Discussionmentioning

confidence: 86%

The Prediction Potential of the Pretreatment Lung Immune Prognostic Index for the Therapeutic Outcomes of Immune Checkpoint Inhibitors in Patients With Solid Cancer: A Systematic Review and Meta-Analysis

Liu

Yang

et al. 2021

Front. Oncol.

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BackgroundThe lung immune prognostic index (LIPI) is recently developed to predict immune checkpoint inhibitors (ICIs) treatment outcomes for non-small cell lung cancer. However, its predictive value for other types of cancer remained unclear. This meta-analysis aimed to evaluate the association between pretreatment LIPI score and therapeutic outcomes in cancer patients treated with ICIs.MethodsWe searched PubMed, Cochrane Library literature databases and EMBASE for abstracts and full-text articles published from the inception of the database until 16th, Nov 2020. Meta-analyses were performed separately for progression-free survival (PFS) and overall survival (OS) by using the random-effects model.ResultsA total of 12 studies involving 4883 patients receiving ICIs treatment were identified for the primary analysis. The pooled results implied that compared with good LIPI score groups, patients with poor or intermediate LIPI score were significantly associated with worse OS (HR=3.33, 95%CI 2.64-4.21, P < 0.001, I2 = 64.2%; HR=1.71, 95%CI 1.43-2.04, P < 0.001, I2 = 43.6%, respectively) and PFS (HR=2.73,95%CI 2.00-3.73, P < 0.001, I2 = 78.2%; HR=1.43, 95%CI 1.28-1.61, P < 0.001, I2 = 16.3%, respectively). Also, for 1873 patients receiving chemotherapy, a poor LIPI score was significantly associated with worse OS (HR=2.30, 95%CI 1.73-3.07, P < 0.001; I2 = 56.2%) and PFS (HR=1.92,95%CI 1.69-2.17; P < 0.001; I2 = 0.0%) compared with good LIPI score groups.ConclusionsA good LIPI score was significantly correlated with improved OS and PFS in cancer patients receiving ICIs or chemotherapy, regardless of the types of cancer.

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“…Solid tumor mortality of 12% and hematological malignancy of 14% were found in a study done by the COVID-19 and Cancer Consortium (CCC19). One study from the Memorial Sloan Kettering Cancer Center (MSKCC) reported the CFR as 12% [9][10][11][12][13].…”

Section: Discussionmentioning

confidence: 99%

Clinical Presentations and Outcomes of Coronavirus Disease 2019 in Patients With Solid Tumors

Farooque¹,

Farooque²,

Karimi³

et al. 2021

Cureus

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BackgroundCoronavirus disease 2019 (COVID-19) is a global health crisis. The literature suggests that cancer patients are more prone to be affected by COVID-19 because cancer suppresses the immune system and such patients usually present poor results. The objective of this study is to present all clinical, laboratory, and demographic characteristics of COVID-19 patients with solid tumors. MethodologyThis study was conducted at the Dow University of Health Sciences for a period of six months from April 2020 to September 2020. In this study, we included a total of 1,519 confirmed patients diagnosed with solid tumors via polymerase chain reaction. The mortality timeline within 30 days of contracting the virus was considered, and the median age of the included individuals was 61 years, with a range of 20-95 years. Of the patients included in the study, 49.4% (750) were men; moreover, 3.15% of our study population had prostate cancer, 10.20% had colorectal cancer, 2.76% had breast cancer, and 10.46% had lung cancer. Of the patients, 25.93% presented with at least one comorbidity. For 73% of the patients, at least one direct therapy for COVID-19 was included in the treatment; 56.6% of the patients were hospitalized, and 11.32% were admitted to the intensive care unit. ResultsThe mortality rate was 4.74% in the first 30 days after diagnosis, where 72 patients died. The findings of the first multi-variation model showed that males at older ages who were diabetic and going through cytotoxic therapy were prone to die within the first 30 days. However, the 30-day mortality rate was lower in patients diagnosed with prostate and breast cancer. The second set incorporated laboratory factors, where we found that higher values of leukocytosis, thrombocytopenia, and lymphocytopenia were correlated with higher rates of mortality within 30 days. ConclusionsWe conclude that there is a higher mortality rate of COVID-19 in patients with solid tumors than in the general population. However, it was found to be lower in the Pakistani population compared with the Chinese and Western populations. Intensive care can decrease mortality rates in COVID-19 and cancer patients.

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A nomogram for predicting mortality in patients with COVID-19 and solid tumors: a multicenter retrospective cohort study

Cited by 30 publications

References 29 publications

Prediction of COVID-19-related Mortality and 30-Day and 60-Day Survival Probabilities Using a Nomogram

Prediction of COVID-19-related Mortality and 30-Day and 60-Day Survival Probabilities Using a Nomogram

The Prediction Potential of the Pretreatment Lung Immune Prognostic Index for the Therapeutic Outcomes of Immune Checkpoint Inhibitors in Patients With Solid Cancer: A Systematic Review and Meta-Analysis

Clinical Presentations and Outcomes of Coronavirus Disease 2019 in Patients With Solid Tumors

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