key: cord-0867199-w39h7ra8 authors: Xu, Bin; Song, Ke‐Han; Yao, Yi; Dong, Xiao‐Rong; Li, Lin‐Jun; Wang, Qun; Yang, Ji‐Yuan; Hu, Wei‐Dong; Xie, Zhi‐Bin; Luo, Zhi‐Guo; Luo, Xiu‐Li; Liu, Jing; Rao, Zhi‐Guo; Zhang, Hui‐Bo; Wu, Jie; Li, Lan; Gong, Hong‐Yun; Chu, Qian; Song, Qi‐Bin; Wang, Jie title: Individualized model for predicting COVID‐19 deterioration in patients with cancer: A multicenter retrospective study date: 2021-05-01 journal: Cancer Sci DOI: 10.1111/cas.14882 sha: a8aab0f763e5f082ab96d9dc73fbf7605c7998c5 doc_id: 867199 cord_uid: w39h7ra8 The 2019 novel coronavirus has spread rapidly around the world. Cancer patients seem to be more susceptible to infection and disease deterioration, but the factors affecting the deterioration remain unclear. We aimed to develop an individualized model for prediction of coronavirus disease (COVID‐19) deterioration in cancer patients. The clinical data of 276 cancer patients diagnosed with COVID‐19 in 33 designated hospitals of Hubei, China from December 21, 2019 to March 18, 2020, were collected and randomly divided into a training and a validation cohort by a ratio of 2:1. Cox stepwise regression analysis was carried out to select prognostic factors. The prediction model was developed in the training cohort. The predictive accuracy of the model was quantified by C‐index and time‐dependent area under the receiver operating characteristic curve (t‐AUC). Internal validation was assessed by the validation cohort. Risk stratification based on the model was carried out. Decision curve analysis (DCA) were used to evaluate the clinical usefulness of the model. We found age, cancer type, computed tomography baseline image features (ground glass opacity and consolidation), laboratory findings (lymphocyte count, serum levels of C‐reactive protein, aspartate aminotransferase, direct bilirubin, urea, and d‐dimer) were significantly associated with symptomatic deterioration. The C‐index of the model was 0.755 in the training cohort and 0.779 in the validation cohort. The t‐AUC values were above 0.7 within 8 weeks both in the training and validation cohorts. Patients were divided into two risk groups based on the nomogram: low‐risk (total points ≤ 9.98) and high‐risk (total points > 9.98) group. The Kaplan‐Meier deterioration‐free survival of COVID‐19 curves presented significant discrimination between the two risk groups in both training and validation cohorts. The model indicated good clinical applicability by DCA curves. This study presents an individualized nomogram model to individually predict the possibility of symptomatic deterioration of COVID‐19 in patients with cancer. The 2019 novel coronavirus, named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), 1,2 has spread rapidly around the world since it was initially isolated and identified in Wuhan, China, causing the pandemic of corona virus disease . By April 30, 2020, besides China, SARS-CoV-2 had been reported in 211 countries, with 3 131 014 confirmed cases of infection and more than 200 000 deaths. Increasing evidence indicates that all populations are generally susceptible to the SARS-CoV-2, 3-5 but elderly men with comorbidities are more likely to be affected, and develop severe respiratory diseases. [6] [7] [8] Risk of worse prognosis of COVID-19 increases in patients with various preexisting conditions, especially those with cancer. Anticancer treatments or malignancy itself cause general immunosuppression in cancer patients, thus increase susceptibility to infection and disease deterioration. 9, 10 As a result, COVID-19 patients with cancer suffer poorer outcomes, thus more attention should be paid to this cohort. In the course of clinical treatment, the condition of some patients could deteriorate, but the factors affecting the deterioration remain unclear. In the present study, we tried to analyze the risk factors for symptomatic deterioration of COVID-19 patients with cancer by establishing a nomogram prognostic model in a large cohort of cancer patients, providing a better understanding of COVID-19 and management of infected cancer patients. We retrospectively analyzed the medical records of cancer patients (359 cases) who were primarily diagnosed with COVID-19 in The follow-up cut-off date was April 2, 2020. All the patients' survival data were available. The primary endpoint was deteriorationfree survival of COVID-19 (C-DFS). The event was defined as the first symptomatic deterioration that occurred after the initial symptom assessment on admission. The C-DFS was defined as the period from the date of initial symptom assessment to the date of the first symptomatic deterioration or the date of death without deterioration. The symptomatic deterioration was defined as the deterioration of the disease severity, and the severity level assessment of COVID-19 symptoms included five levels: asymptomatic, mild, moderate, severe, and critical type, which were defined according to the 7th edition of the COVID-19 Diagnosis and Treatment Plan, released by the National Health Commission and National Administration of Traditional Chinese Medicine. The cases with unrecorded deterioration dates (55 cases), incomplete CT results (26 cases), missing symptom information (1 case), and elusive comorbidity information (1 case) were excluded, leaving 276 eligible cases that were formally enrolled in the present study ( Figure S1 ). analysis (DCA) were used to evaluate the clinical usefulness of the model. We found age, cancer type, computed tomography baseline image features (ground glass opacity and consolidation), laboratory findings (lymphocyte count, serum levels of C-reactive protein, aspartate aminotransferase, direct bilirubin, urea, and d-dimer) were significantly associated with symptomatic deterioration. The C-index of the model was 0.755 in the training cohort and 0.779 in the validation cohort. The t-AUC values were above 0.7 within 8 weeks both in the training and validation cohorts. Patients were divided into two risk groups based on the nomogram: low-risk (total points ≤ 9.98) and high-risk (total points > 9.98) group. The Kaplan-Meier deterioration-free survival of COVID-19 curves presented significant discrimination between the two risk groups in both training and validation cohorts. Patient data were randomly assigned to the training cohort and the validation cohort by a ratio of 2:1. The Kaplan-Meier method was applied to estimate C-DFS. In the training cohort, we used the least absolute shrinkage and selection operator (LASSO) regression analysis 11 to select the characteristic variables of CT scans and clinical symptoms that might have potential clinical significance to influence C-DFS, then the symptom LASSO score and CT LASSO score were built according to the following formula. The cut-off point of symptom and CT LASSO score was selected based on log-rank statistics: Finally, log-rank statistics were applied to create a risk stratification according to the total risk scores based on the nomogram to illustrate the independent discrimination ability of the nomogram model. All analyses were undertaken in R software (version 3.6.1). A value of P < .05 was statistically significant for all analyses. A total of 276 patients were enrolled in the present study and as- Specific treatment approaches and laboratory findings are listed in Table 1 . Among all the 12 analyzed symptom features, two features (dyspnea and fatigue) were considered to be potentially predictive (referred to hereafter as predictors) on the basis of patients in the training cohort ( Figure 1A ,B) using the LASSO Cox regression analysis, which were further used to build a symptom LASSO score (coefficient: dyspnea 0.231, fatigue 0.043). The same analyzing process was applied for CT performance, where two of 10 CT image features (ground glass opacity and consolidation) were considered as predictors ( Figure 1C,D) , and CT LASSO score was calculated (co- A nomogram based on the model that incorporated the selected variables was established (Figure 2A ). The total points were determined based on the score of each variable that was calculated. The predictive ability of the nomogram for individual deterioration risk possibility was then evaluated in the training cohort and independently validated in the validation cohort. The C-index of the nomogram was 0.755 in the training cohort and 0.779 in the validation cohort. Figure 2B shows that t-AUC values were above 0.7 for the prediction of deterioration risk within 8 weeks both in the training and validation cohort, indicating that a stable and continuous prediction model was successfully constructed. Furthermore, the calibration curves of the nomogram showed high consistencies between the predicted and observed 2-week, 4- week, and 8-week C-DFS probability both in the training and validation cohorts ( Figure 2C-H) . In summary, the nomogram showed considerable discriminative and calibrating abilities. deterioration in patients with cancer. A, LASSO coefficient profiles of the 12 symptoms. A coefficient profile plot was produced against the λ sequence. Vertical line was drawn at the value selected using 10-fold cross-validation, where optimal λ (0.070) resulted in two nonzero coefficients. B, λ selection in the LASSO analysis used 10-fold cross-validation by minimum criteria for 12 symptoms. The partial likelihood deviance was plotted against λ. Dotted vertical lines were drawn at the optimal values (0.070) by using the minimum criteria. C, LASSO coefficient profiles of the 10 computed tomography (CT) image features. A coefficient profile plot was produced against the λ sequence. Vertical line was drawn at the value selected using 10-fold cross-validation, where optimal λ (0.051) resulted in two nonzero coefficients. D, λ selection in the LASSO analysis used 10-fold cross-validation by minimum criteria for 10 CT image features. The partial likelihood deviance was plotted against λ. Dotted vertical lines were drawn at the optimal values (0.051) by using the minimum criteria. E, LASSO coefficient profiles of 18 laboratory findings. A coefficient profile plot was produced against the λ sequence. Dotted vertical lines were drawn at the optimal values (0.088) by using the minimum criteria. F, λ selection in the LASSO analysis used 10-fold cross-validation by minimum criteria for 18 laboratory findings. The partial likelihood deviance was plotted against λ. Dotted vertical lines were drawn at the optimal values (0.088) by using the minimum criteria Abbreviations: -, not included in analysis; CI, confidence interval; COPD, chronic obstructive pulmonary disease; CT, computed tomography; HR, hazard ratio; LASSO, least absolute shrinkage and selection operator; Ref, reference. a Antitumor therapies used more than 2 months prior to COVID-19 infection were not included. Decision curve analysis was carried out to evaluate the clinical usefulness of the nomogram by quantifying the net benefits at different threshold probabilities (Figure 3 ). It showed more net benefits across a wider range of threshold probabilities than either the treatall-patients scheme or the treat-none scheme both in the training cohort and validation cohort. Finally, a risk stratification was made based on the nomogram. Coronavirus disease patients with cancer were assigned into two risk groups according to their total points: low-risk (total points of 9.98 or less) group and high-risk (total points more than 9.98) group based on standardized log-rank statistics ( Figure 4A ). The risk plot showed that the progressed events occurred more frequently in high-risk group in both the training and validation cohort ( Figure 4B ,C). The Kaplan-Meier C-DFS curves ( Figure 4D ,E) presented the significant discrimination among the two risk groups both in the training (log-rank P < .001) and the validation cohort (log-rank P = .016). Hazard ratio (HR) values of the high-risk group vs low-risk group The establishment of clinical prognostic models is of great importance for predicting the outcome of disease and guiding clinical decisions. A nomogram uses clinical, pathological, or other features to establish a statistical predictive model that can provide the possibility of a certain clinical event. [13] [14] [15] In this pioneering study, we incorporated age, clinical symptoms, More importantly, the model indicated good clinical applicability by DCA curves. In the course of clinical treatment, the symptoms of some patients could worsen. This is the first study to undertake risk stratifi- Figure 2 . Patients with COVID-19 and clinical symptoms such as dyspnea and fatigue had higher risks for symptomatic deterioration. In addition, we found that consolidation on CT was associated with a higher risk of deterioration, which was consistent with the findings in the study from Zhang et al. 16 Age was also an important risk factor for symptom progression in COVID-19 patients with cancer, which was consistent with other studies. [17] [18] [19] To conclude, in the COVID-19 pandemic, our nomogram provides personalized prediction of the probability of symptomatic deterioration in COVID-19 patients with cancer. We strongly recommend its use in COVID-19 patients with cancer, and hopefully this powerful tool/method will help clinicians all over the world take more comprehensive and timely measures to prevent symptomatic deterioration and reduce mortality. The authors have no conflict of interest. https://orcid.org/0000-0002-0499-0430 Wei-Dong Hu https://orcid.org/0000-0003-3646-8572 Jie Wang https://orcid.org/0000-0002-5602-0487 Mass gathering events and reducing further global spread of COVID-19: a political and public health dilemma Therapeutic options for the 2019 novel coronavirus (2019-nCoV) The novel coronavirus: A bird's eye view The epidemic of 2019-novel-coronavirus (2019-nCoV) pneumonia and insights for emerging infectious diseases in the future The epidemiological and clinical features of COVID-19 and lessons from this global infectious public health event Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study Clinical features of patients infected with 2019 novel coronavirus in Wuhan Clinical findings in a group of patients infected with the 2019 novel coronavirus (SARS-Cov-2) outside of Wuhan, China: retrospective case series Cancer patients in SARS-CoV-2 infection: a nationwide analysis in China SARS-CoV-2 Transmission in patients with cancer at a tertiary care hospital in Wuhan, China Selection of important variables and determination of functional form for continuous predictors in multivariable model building Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer How to build and interpret a nomogram for cancer prognosis Factors affecting sentinel node metastasis in Thin (T1) cutaneous melanomas: development and external validation of a predictive nomogram Clinical characteristics of COVID-19-infected cancer patients: a retrospective case study in three hospitals within Wuhan Risk of COVID-19 for patients with cancer Taking care of older patients with cancer in the context of COVID-19 pandemic Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan