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

Association of lung immune prognostic index with overall survival in pancreatic ductal adenocarcinoma patients treated using chemotherapy

Background: The lung immune prognostic index (LIPI) has attracted considerable interest for its prognostic value in several malignancies. However, its prognostic value in pancreatic ductal adenocarcinoma (PDAC) has not yet been clarified. Objective: This study aimed to assess the role of LIPI with regard to overall survival (OS) in locally advanced or metastatic PDAC patients undergoing chemotherapy. Methods: Data from 256 patients with PDAC treated via chemotherapy at the Chinese PLA General Hospital between January 1, 2011 and July 1, 2018 were retrospectively reviewed. Their neutrophil-to-lymphocyte ratio (dNLR) with lactate dehydrogenase (LDH) values were used to calculate each one's LIPI. The Cox proportional hazard model was used to identify the association between LIPI and OS. Results: Of the included patients, 154 were in the good LIPI group and 102 were in the intermediate/poor LIPI group. The OS in the two groups were 9.0 months (95% CI: 7.351-10.649) and 6.0 months (95% CI: 4.812-7.188), respectively. Patients in the good LIPI group had better OS compared to those in the intermediate/poor LIPI group (HR, 0.720; 95% CI: 0.554-0.935; P = 0.014). Conclusion: This study revealed LIPI is significantly associated with OS in PDAC and could play a significant role in helping clinicians make appropriate decisions for PDAC patients undergoing chemotherapy.

Development and Validation of Nomograms for Predicting Pneumonia in Patients with COVID-19 and Lung Cancer

Background

COVID-19 has spread worldwide, becoming a global threat to public health and can lead to complications, especially pneumonia, which can be life-threatening. However, in lung cancer patients, the prediction of pneumonia and severe pneumonia has not been studied. We aimed to develop effective models to assess pneumonia after SARS-CoV-2 infection in lung cancer patients to guide COVID-19 management.

Methods

We retrospectively recruited 621 lung cancer patients diagnosed with COVID-19 via SARS-CoV-2 RT-PCR analysis in two medical centers and divided into training and validation group, respectively. Univariate and multivariate logistic regression analysis were used to identify independent risk factors of all-grade pneumonia and ≥ grade 2 pneumonia in the training group. Nomograms were established based on independent predictors and verified in the validation group. C-index, ROC curves, calibration curve, and DCA were used to evaluate the nomograms. Subgroup analyses in immunotherapy or thoracic radiotherapy patients were then conducted.

Results

Among 621 lung cancer patients infected with SARS-CoV-2, 203 (32.7%) developed pneumonia, and 66 (10.6%) were ≥ grade 2. Multivariate logistic regression analysis showed that diabetes, thoracic radiotherapy, low platelet and low albumin at diagnosis of COVID-19 were significantly associated with all-grade pneumonia. The C-indices of the prediction nomograms in the training group and validation group were 0.702 and 0.673, respectively. Independent predictors of ≥ grade 2 pneumonia were age, KPS, thoracic radiotherapy, platelet and albumin at COVID 19 diagnosis, with C-indices of 0.811 and 0.799 in the training and validation groups. In the thoracic radiotherapy subgroup, 40.8% and 11% patients developed all-grade and ≥grade 2 pneumonia, respectively. The rates in the immunotherapy subgroup were 31.3% and 6.6%, respectively.

Conclusion

We developed nomograms predicting the probability of pneumonia in lung cancer patients infected with SARS-CoV-2. The models showed good performance and can be used in the clinical management of COVID-19 in lung cancer patients. Higher-risk patients should be managed with enhanced protective measures and appropriate intervention.

A nano-platform combats the "attack" and "defense" of cytoskeleton to block cascading tumor metastasis

The cytoskeleton facilitates tumor cells invasion into the bloodstream via vasculogenic mimicry (VM) for "attack", and protects cells against external threats through cytoskeletal remodeling and tunneling nanotubes (TNTs) for "defense". However, the existing strategies involving cytoskeleton are not sufficient to eliminate tumor metastasis due to mitochondrial energy supply, both within tumor cells and from outside microenvironment. Here, considering the close relationship between cytoskeleton and mitochondria both in location and function, we construct a nano-platform that combats the "attack" and "defense" of cytoskeleton in the cascading metastasis. The nano-platform is composed of KFCsk@LIP and KTMito@LIP for the cytoskeletal collapse and mitochondrial dysfunction. KFCsk@LIP prevents the initiation and circulation of cascading tumor metastasis, but arouses limited suppression in tumor cell proliferation. KTMito@LIP impairs mitochondria to trigger apoptosis and impede energy supply both from inside and outside, leading to an amplified effect for metastasis suppression. Further mechanisms studies reveal that the formation of VM and TNTs are seriously obstructed. Both in situ and circulating tumor cells are disabled. Subsequently, the broken metastasis cascade results in a remarkable anti-metastasis effect. Collectively, based on the nano-platform, the cytoskeletal collapse with synchronous mitochondrial dysfunction provides a potential therapeutic strategy for cascading tumor metastasis suppression.

Combined systemic inflammatory indexes as reflectors of outcome in patients with COVID‑19 infection admitted to ICU

INTRODUCTION

The principal etiology of mortality in COVID-19 patients is the systemic pro-inflammatory processes which may lead to acute respiratory distress syndrome. Hematologic indices are reachable representatives of inflammation in patients with COVID-19 infection. The purpose of the current study was to evaluate the potential predictive value of these inflammatory indices in the in-hospital mortality of ICU-admitted COVID-19 patients. The studied indexes included AISI, dNLR, NLPR, NLR, SII, and SIRI.

METHOD

315 COVID-19 patients admitted to ICU managed in Imam Khomeini Hospital of Urmia, Iran, during the last 6 months of 2020 were retrospectively enrolled in the study and divided into two subgroups based on their final outcome, discharge or death.

RESULTS

Total leucocyte count (TLC), absolute neutrophil count (NLC), urea, Cr, RDW, AISI, dNLR, NLPR, NLR, SII, and SIRI were drastically elevated in the dead patients (P < 0.05). The optimal cut-off points for AISI (378.81), dNLR (5.66), NLPR (0.03), NLR (5.97), SII (1589.25), and SIRI (2.31) were obtained using ROC curves. NLR and SII had the highest sensitivity (71.4%) and specificity (73.6%), respectively. Patients with above-cut-off levels of ISI, dNLR, NLPR, NLR, and SII had lower average survival time. Age (OR = 1.057, CI95%: 1.030-1.085, p < 0.001) and dNLR (OR = 1.131, CI95%: 1.061-1.206, p < 0.001) were the independent predictors for mortality in the studied COVID-19 patients based on multivariate logistic regression.

CONCLUSION

Age and dNLR are valuable predictive factors for in-hospital death of ICU-admitted COVID-19 patients. Besides, other indices, AISI, NLPR, NLR, SII, and SIRI, may have an additional role that requires further investigation.

The Inflammatory Profile Correlates with COVID-19 Severity and Mortality in Cancer Patients

BACKGROUND

The correlation of the inflammatory profile with the severity of the disease in neoplastic patients with SARS-CoV-2 infection was addressed.

METHODS

A database of 1537 patients hospitalized in the pneumology department was analyzed. After applying the inclusion and exclusion criteria, 83 patients (67% males, 33% females) were included.

RESULTS

Most of the analyzed patients were hospitalized with a moderate form of disease, explaining the significant percentage of 25% mortality. The frequency of the type of neoplasm was higher for lung cancer, followed by malignant colon tumor. We identified a significant association between the increased value of ferritin (p < 0.0001, OR = 22.31), fibrinogen (p = 0.009, OR = 13.41), and C-reactive protein (p = 0.01, OR = 7.65), respectively, and the level of severity of COVID-19. The results of the univariate logistic regression analysis for predicting the severity of the disease revealed that the increased values of ferritin (p = 0.001, OR = 22.31) and fibrinogen (p = 0.02, OR = 13.41) represent a risk for a serious negative prognosis of COVID-19.

CONCLUSIONS

Our study demonstrated that the value of the analyzed inflammatory parameters increased in direct proportion to the severity of the disease and that higher values were associated with increased mortality in the study group.

A novel tool for improving the accuracy of major depressive disorder screening: A prospective study on andrology with external validation

Patient Health Questionnaire-9 (PHQ-9) is the most widely used tool for screening for major depressive disorder (MDD). Although its reliability and validity have been proven, missed or misjudged cases during MDD screening are often encountered. A nomogram that considers the weights of depressive symptoms was developed using data from premature ejaculation patients to improve screening accuracy. During a 33-month prospective study, a training cohort comprising 605 participants from Xijing Hospital was used to develop and internally validate the nomogram. A validation cohort comprising 461 patients from Xi'an Daxing Hospital was also used to externally test the nomogram. The nomogram was established by integrating the LASSO regression-based optimal predictors of MDD according to their coefficients in a multivariate logistic regression model. The nomogram was well-calibrated during internal and external validations. Moreover, it showed a better discriminatory capacity and yielded more net benefits in both validations than PHQ-9. With better performance, the nomogram may help reduce the number of missed or misjudged cases during MDD screening. This study is the first to weigh the direct indicators of MDD under the DSM-5 criteria, presenting a fresh concept that can be applied to other populations to enhance screening accuracy.

A Nomogram for Early Diagnosis of Community-Acquired Pneumonia Based on Bronchoalveolar Lavage Fluid Metabolomics

Purpose

There is a high disease burden associated with community-acquired pneumonia (CAP) around the world. A timely and correct diagnosis of CAP can facilitate early treatment and prevent illness progression. The present study aimed to find some novel biomarkers of CAP by metabolic analysis and construct a nomogram model for precise diagnosis and individualized treatment of CAP patients.

Patients and Methods

42 CAP patients and 20 controls were enrolled in this study. The metabolic profiles of bronchoalveolar lavage fluid (BALF) samples were identified by untargeted LC-MS/MS analysis. With a VIP score ≥ 1 in OPLS-DA analysis and P < 0.05, the significantly dysregulated metabolites were estimated as potential biomarkers of CAP, which were further included in the construction of the diagnostic prediction model along with laboratory inflammatory indexes via stepwise backward regression analysis. Discrimination, calibration, and clinical applicability of the nomogram were evaluated by the C-index, the calibration curve, and the decision curve analysis (DCA) estimated by bootstrap resampling.

Results

The metabolic profiles differed obviously between CAP patients and healthy controls, as shown by PCA and OPLS-DA plots. Seven metabolites significantly dysregulated in CAP were established: dimethyl disulfide, oleic acid (d5), N-acetyl-a-neuraminic acid, pyrimidine, choline, LPC (12:0/0:0) and PA (20:4/2:0). Multivariate logistic regression revealed that the expression levels of PA (20:4/2:0), N-acetyl-a-neuraminic acid, and CRP were associated with CAP. After being validated by bootstrap resampling, this model showed satisfactory diagnostic performance.

Conclusion

A novel nomogram prediction model containing metabolic potential biomarkers in BALF that was developed for the early diagnosis of CAP offers insights into the pathogenesis and host response in CAP.

Impact of coronavirus disease 2019 on lung cancer patients: A meta-analysis

Background

The coronavirus disease 2019 (COVID-19) pandemic poses a great challenge to the treatment of lung cancer patients.

Materials and methods

The PubMed, Embase, and Web of Science databases were searched for studies published before March 15, 2022, and Stata 14.0 software was used to perform a meta-analysis with a random-effects model. The odds ratio (OR) along with the corresponding 95% confidence interval (CI) was reported.

Results

Our meta-analysis included 80 articles with 318,352 patients involved. The proportion of lung cancer patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was 2.4% (95% CI: 0.02-0.03) prior to the Omicron variant outbreak. Among COVID-19 patients, those with lung cancer showed a higher mortality rate than those with other types of malignant solid tumors (OR = 1.82, 95% CI: 1.61-2.06) and non-cancer patients (OR = 4.67, 95% CI: 3.61-6.05); however, no significant difference was observed in the mortality rate between patients with lung cancer and those with hematologic malignancies (OR = 1.07, 95% CI: 0.85-1.33). SARS-CoV-2 infection significantly increased the mortality rate in lung cancer patients (OR = 8.94, 95% CI: 6.50-12.31). By contrast, the all-cause mortality rate in lung cancer patients (OR = 1.04, 95% CI: 0.69-1.57) and the proportion of patients diagnosed with advanced lung cancer (OR = 1.04, 95% CI: 0.85-1.27) did not significantly change before and after the pandemic.

Conclusions

More attention should be paid on improving the health of lung cancer patients during the COVID-19 pandemic.

Colonization of Fusobacterium nucleatum is an independent predictor of poor prognosis in gastric cancer patients with venous thromboembolism: a retrospective cohort study

BACKGROUND

Fusobacterium nucleatum (F. nucleatum) often colonizes cancerous gastric tissues and is characterized by the promotion of platelet aggregation and the development of visceral thrombosis. Venous thromboembolism (VTE) leads to a significant increase in the mortality of gastric cancer (GC) patients. However, the relationship between the colonization of F. nucleatum and the prognosis of GC patients is still unknown.

AIM

The aim of this study was to explore whether the colonization of F. nucleatum is related to the prognosis of GC patients complicated with VTE and to explore other potential risk factors.

METHODS

From 2017-2021, the data of 304 patients with new VTEs during the treatment of GC at the Affiliated Cancer Hospital of Zhengzhou University were collected. Fluorescence in situ hybridization of F. nucleatum was performed on pathological sections of cancer tissues from the patients. Survival analysis methods, including the Kaplan‒Meier method and Cox proportional hazard model, were performed.

RESULTS

F. nucleatum colonization was significantly associated with splanchnic vein thrombosis, higher platelet-lymphocyte ratio (PLR), and lower absolute lymphocyte count. In the multivariable Cox model, F. nucleatum colonization was found to be an independent risk factor for the prognosis of GC, with an adjusted HR of 1.77 (95% CI, 1.17 to 2.69 [P = 0.007]). In addition, patients with high PLR (HR: 2.65, P = 0.004) or VTE occurring during four cycles of chemotherapy (HR: 2.32, P = 0.012) exhibited shorter survival. Conversely, those experiencing VTE later (HR per month from diagnosis of GC: 0.95, P = 0.006) or using IVC filters (HR: 0.27, P = 0.011) had longer survival.

CONCLUSION

Colonization of F. nucleatum in GC tissues was associated with lower absolute lymphocyte count and higher PLR in GC patients with VTE. F. nucleatum colonization also appeared to be associated with the development of VTE in specific sites, in particular the splanchnic vein. Colonization of F. nucleatum may potentially represent an independent predictor of poor prognosis in GC patients. Additional research is necessary to validate these findings.

Evaluation of clinical outcomes, laboratory and imaging data of patients with solid tumor infected with COVID-19 infection

BACKGROUND

COVID-19 is associated with higher mortality rates in patients with cancer. In this study, we aimed to evaluate the clinical outcomes, and laboratory and imaging data of patients with solid tumor infected with COVID-19 infection.

METHODS

This is a cross-sectional retrospective study performed in 2020-2022 on 85 patients with a previous diagnosis of solid tumors infected with COVID-19. We included all patients with tumors of solid organs that were diagnosed with COVID-19 infection and required hospitalization those patients previously hospitalized for treatments and were infected with COVID-19 during hospitalization. Demographic data of patients were collected using a checklist. We collected data regarding clinical outcome (discharge, hospitalization or death), duration of hospitalization, requiring ICU admission, duration of hospitalization divided by received drugs and type of tumor and mean survival time. Furthermore, we collected laboratory data from all patients. The radiologic characteristics of patients were also extracted from their data.

RESULTS

Breast cancer was the most common solid tumor (34.9%), followed by lung cancer (19.3%). The mortality rate was 24.1% (20 patients). The highest mortality rate in this study was for metastatic intestinal cancer to the lung (100%, one patient), followed by metastatic prostatic cancer to lung (50%, three patients). The highest hospitalization duration was for patients with glioblastoma multiform (GBM) (30 days). The mean survival time among patients with mortality was 19.15±1.80 days. The mean CT severity score of all patients was 27.53±22.90. Patient's most common radiologic sign was air space consolidation (89.1%). The highest CT severity score was found in patients with stomach cancer (46.67±5.77).

CONCLUSION

The mortality rate in this study was 24.1%. Based on the results of our study and previous research, special care should be provided to patients with solid tumors during the COVID-19 pandemic and in infected cases.

The Risk Factors for Cervical Cytological Abnormalities Among Women Infected With Non-16/18 High-Risk Human Papillomavirus: Cross-sectional Study

BACKGROUND

High-risk human papillomavirus (hrHPV) infection is a necessary cause of almost all cervical cancers. Relative to hrHPV 16/18 infection, non-16/18 hrHPV infection is of less concern. However, the increasing prevalence of non-16/18 hrHPV infections has become an important public health issue. The early identification and treatment of cervical cytological abnormalities in women infected with non-16/18 hrHPV reduces the incidence of cervical cancer. To date, no study has examined the risk factors for cytological abnormalities in this high-risk population.

OBJECTIVE

This population-based, cross-sectional study aimed to identify the risk factors for cervical cytological abnormalities in women infected with non-16/18 hrHPV.

METHODS

A total of 314,587 women from the general population were recruited for cervical cancer screening at 136 primary care hospitals in Xiangyang, China. Of these, 311,604 women underwent HPV genotyping, and 17,523 non-16/18 hrHPV-positive women were referred for cytological screening according to the screening program. A logistic regression model was used to assess the risk factors for cytological abnormalities among these non-16/18 hrHPV-positive women. A separate analysis was performed to determine the factors influencing high-grade cytological abnormalities.

RESULTS

The non-16/18 hrHPV infection rate was 5.88% (18,323/311,604), which was 3-fold higher than that of hrHPV 16/18 (6068/311,604, 1.95%). Among the non-16/18 hrHPV-positive women who underwent ThinPrep cytologic test, the overall prevalence rates of cervical cytological abnormalities and high-grade cytological abnormalities were 13.46% (2359/17,523) and 1.18% (206/17,523), respectively. Multivariate logistic regression analysis revealed that women with middle or high school educational attainment were at a higher risk of having cytological abnormalities than those who received primary education (odds ratio [OR] 1.31, 95% CI 1.17-1.45; P<.001, and OR 1.32, 95% CI 1.14-1.53; P<.001, respectively). Living in rural areas (OR 2.58, 95% CI 2.29-2.90; P<.001), gravidity ≥3 (OR 2.77, 95% CI 1.19-6.45; P=.02), cervix abnormalities detected in pelvic examination (OR 1.22, 95% CI 1.11-1.34; P<.001), and having a cervical cancer screening 3 years ago (OR 0.79, 95% CI 0.62-1.00; P=.048) were associated with cytological abnormalities. The risk factors for high-grade cytological abnormalities included middle school education (OR 1.45, 95% CI 1.07-1.98; P=.02), living in rural regions (OR 1.52, 95% CI 1.10-2.10; P=.01), and cervix abnormality (OR 1.72, 95% CI 1.30-2.26; P<.001).

CONCLUSIONS

The dominant epidemic of non-16/18 hrHPV infection is revealed in Chinese women. Multiple risk factors for cervical cytological abnormalities have been identified in women infected with non-16/18 hrHPV. These findings can provide important information for clinically actionable decisions for the screening, early diagnosis, intervention, and prevention of cervical cancer in non-16/18 hrHPV-positive women.

The derived neutrophil to lymphocyte ratio can be the predictor of prognosis for COVID-19 Omicron BA.2 infected patients

BACKGROUND

Several systemic inflammatory biomarkers have been associated with poor overall survival (OS) and disease severity in patients with coronavirus disease 2019 (COVID-19). However, it remains unclear which markers are better for predicting prognosis, especially for COVID-19 Omicron BA.2 infected patients. The present study aimed to identify reliable predictors of prognosis of COVID-19 Omicron BA.2 from inflammatory indicators.

METHODS

A cohort of 2645 COVID-19 Omicron BA.2 infected patients were retrospectively analyzed during the Omicron BA.2 surge in Shanghai between April 12, 2022, and June 17, 2022. The patients were admitted to the Shanghai Fourth People's Hospital, School of Medicine, Tongji University. Six systemic inflammatory indicators were included, and their cut-off points were calculated using maximally selected rank statistics. The analysis involved Kaplan-Meier curves, univariate and multivariate Cox proportional hazard models, and time-dependent receiver operating characteristic curves (time-ROC) for OS-associated inflammatory indicators.

RESULTS

A total of 2347 COVID-19 Omicron BA.2 infected patients were included. All selected indicators proved to be independent predictors of OS in the multivariate analysis (all P < 0.01). A high derived neutrophil to lymphocyte ratio (dNLR) was associated with a higher mortality risk of COVID-19 [hazard ratio, 4.272; 95% confidence interval (CI), 2.417-7.552]. The analyses of time-AUC and C-index showed that the dNLR (C-index: 0.844, 0.824, and 0.718 for the 5^th, 10^th, and 15^th day, respectively) had the best predictive power for OS in COVID-19 Omicron BA.2 infected patients. Among different sub-groups, the dNLR was the best predictor for OS regardless of age (0.811 for patients aged ≥70 years), gender (C-index, 0.880 for men and 0.793 for women) and disease severity (C-index, 0.932 for non-severe patients and 0.658 for severe patients). However, the platelet to lymphocyte ratio was superior to the other indicators in patients aged <70 years.

CONCLUSIONS

The prognostic ability of the dNLR was higher than the other evaluated inflammatory indicators for all COVID-19 Omicron BA.2 infected patients.

Predicting progression to severe COVID-19 using the PAINT score

Objectives

One of the major challenges in treating patients with coronavirus disease 2019 (COVID-19) is predicting the severity of disease. We aimed to develop a new score for predicting progression from mild/moderate to severe COVID-19.

Methods

A total of 239 hospitalized patients with COVID-19 from two medical centers in China between February 6 and April 6, 2020 were retrospectively included. The prognostic abilities of variables, including clinical data and laboratory findings from the electronic medical records of each hospital, were analysed using the Cox proportional hazards model and Kaplan–Meier methods. A prognostic score was developed to predict progression from mild/moderate to severe COVID-19.

Results

Among the 239 patients, 216 (90.38%) patients had mild/moderate disease, and 23 (9.62%) progressed to severe disease. After adjusting for multiple confounding factors, pulmonary disease, age > 75, IgM, CD16⁺/CD56⁺ NK cells and aspartate aminotransferase were independent predictors of progression to severe COVID-19. Based on these five factors, a new predictive score (the ‘PAINT score’) was established and showed a high predictive value (C-index = 0.91, 0.902 ± 0.021, p < 0.001). The PAINT score was validated using a nomogram, bootstrap analysis, calibration curves, decision curves and clinical impact curves, all of which confirmed its high predictive value.

Conclusions

The PAINT score for progression from mild/moderate to severe COVID-19 may be helpful in identifying patients at high risk of progression.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07466-4.

Novel biomarker-based score (SAD-60) for predicting mortality in patients with COVID-19 pneumonia: a multicenter retrospective cohort of 1013 patients

Background: The aim was to explore a novel risk score to predict mortality in hospitalized patients with COVID-19 pneumonia. Methods: This was a retrospective, multicenter study. Results: A total of 1013 patients with COVID-19 were included. The mean age was 60.5 ± 14.4 years, and 581 (57.4%) patients were male. In-hospital death occurred in 124 (12.2%) patients. Multivariate analysis revealed peripheral capillary oxygen saturation (SpO2), albumin, D-dimer and age as independent predictors. The mortality score model was given the acronym SAD-60, representing SpO2, Albumin, D-dimer, age ≥60 years. The SAD-60 score (0.776) had the highest area under the curve compared with CURB-65 (0.753), NEWS2 (0.686) and qSOFA (0.628) scores. Conclusion: The SAD-60 score has a promising predictive capacity for mortality in hospitalized patients with COVID-19.

The impact of anti-tumor approaches on the outcomes of cancer patients with COVID-19: a meta-analysis based on 52 cohorts incorporating 9231 participants

BACKGROUND

This study was designed to investigate the impact of anti-tumor approaches (including chemotherapy, targeted therapy, endocrine therapy, immunotherapy, surgery and radiotherapy) on the outcomes of cancer patients with COVID-19.

METHODS

Electronic databases were searched to identify relevant trials. The primary endpoints were severe disease and death of cancer patients treated with anti-tumor therapy before COVID-19 diagnosis. In addition, stratified analyses were implemented towards various types of anti-tumor therapy and other prognostic factors. Furthermore, odds ratios (ORs) were hereby adopted to measure the outcomes with the corresponding 95% confidence intervals (CIs).

RESULTS

As indicated in the study consisting of 9231 individuals from 52 cohorts in total, anti-tumor therapy before COVID-19 diagnosis could elevate the risk of death in cancer patients (OR: 1.21, 95%CI: 1.07-1.36, P = 0.0026) and the incidence of severe COVID-19 (OR: 1.19, 95%CI: 1.01-1.40, P = 0.0412). Among various anti-tumor approaches, chemotherapy distinguished to increase the incidence of death (OR = 1.22, 95%CI: 1.08-1.38, P = 0.0013) and severe COVID-19 (OR = 1.10, 95%CI: 1.02-1.18, P = 0.0165) as to cancer patients with COVID-19. Moreover, for cancer patients with COVID-19, surgery and targeted therapy could add to the risk of death (OR = 1.27, 95%CI: 1.00-1.61, P = 0.0472), and the incidence of severe COVID-19 (OR = 1.14, 95%CI: 1.01-1.30, P = 0.0357) respectively. In the subgroup analysis, the incidence of death (OR = 1.17, 95%CI: 1.03-1.34, P = 0.0158) raised in case of chemotherapy adopted for solid tumor with COVID-19. Besides, age, gender, hypertension, COPD, smoking and lung cancer all served as potential prognostic factors for both death and severe disease of cancer patients with COVID-19.

CONCLUSIONS

Anti-tumor therapy, especially chemotherapy, augmented the risk of severe disease and death for cancer patients with COVID-19, so did surgery for the risk of death and targeted therapy for the incidence of severe COVID-19.

OUP accepted manuscript

Purpose

Provide real-world data regarding the risk for SARS-CoV-2 infection and mortality in breast cancer (BC) patients on active cancer treatment.

Methods

Clinical data were abstracted from the 3778 BC patients seen at a multisite cancer center in New York between February 1, 2020 and May 1, 2020, including patient demographics, tumor histology, cancer treatment, and SARS-CoV-2 testing results. Incidence of SARS-CoV-2 infection by treatment type (chemotherapy [CT] vs endocrine and/or HER2 directed therapy [E/H]) was compared by Inverse Probability of Treatment Weighting. In those diagnosed with SARS-CoV-2 infection, Mann–Whitney test was used to a assess risk factors for severe disease and mortality.

Results

Three thousand sixty-two patients met study inclusion criteria with 641 patients tested for SARS-COV-2 by RT-PCR or serology. Overall, 64 patients (2.1%) were diagnosed with SARS-CoV-2 infection by either serology, RT-PCR, or documented clinical diagnosis. Comparing matched patients who received chemotherapy (n = 379) with those who received non-cytotoxic therapies (n = 2343) the incidence of SARS-CoV-2 did not differ between treatment groups (weighted risk; 3.5% CT vs 2.7% E/H, P = .523). Twenty-seven patients (0.9%) expired over follow-up, with 10 deaths attributed to SARS-CoV-2 infection. Chemotherapy was not associated with increased risk for death following SARS-CoV-2 infection (weighted risk; 0.7% CT vs 0.1% E/H, P = .246). Advanced disease (stage IV), age, BMI, and Charlson’s Comorbidity Index score were associated with increased mortality following SARS-CoV-2 infection (P ≤ .05).

Conclusion

BC treatment, including chemotherapy, can be safely administered in the context of enhanced infectious precautions, and should not be withheld particularly when given for curative intent.

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

Background

The 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.

Methods

We 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.

Results

A 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.

Conclusions

A good LIPI score was significantly correlated with improved OS and PFS in cancer patients receiving ICIs or chemotherapy, regardless of the types of cancer.

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

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.

Applications of laboratory findings in the prevention, diagnosis, treatment, and monitoring of COVID-19

The worldwide pandemic of coronavirus disease 2019 (COVID-19) presents us with a serious public health crisis. To combat the virus and slow its spread, wider testing is essential. There is a need for more sensitive, specific, and convenient detection methods of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Advanced detection can greatly improve the ability and accuracy of the clinical diagnosis of COVID-19, which is conducive to the early suitable treatment and supports precise prophylaxis. In this article, we combine and present the latest laboratory diagnostic technologies and methods for SARS-CoV-2 to identify the technical characteristics, considerations, biosafety requirements, common problems with testing and interpretation of results, and coping strategies of commonly used testing methods. We highlight the gaps in current diagnostic capacity and propose potential solutions to provide cutting-edge technical support to achieve a more precise diagnosis, treatment, and prevention of COVID-19 and to overcome the difficulties with the normalization of epidemic prevention and control.

Mortality in adult patients with solid or hematological malignancies and SARS-CoV-2 infection with a specific focus on lung and breast cancers: A systematic review and meta-analysis

BACKGROUND

A systematic review and meta-analysis was performed to estimate mortality in adult patients with solid or hematological malignancies and SARS-CoV-2 infection.

METHODS

A systematic search of PubMed, up to 31 January 2021, identified publications reporting the case-fatality rate (CFR) among adult patients with solid or hematological malignancies and SARS-CoV-2 infection. The CFR, defined as the rate of death in this population, was assessed with a random effect model; 95% confidence intervals (CI) were calculated.

RESULTS

Among 135 selected studies (N = 33,879 patients), the CFR was 25.4% (95% CI 22.9%-28.2%). At a sensitivity analysis including studies with at least 100 patients, the CFR was 21.9% (95% CI 19.1%-25.1%). Among COVID-19 patients with lung (N = 1,135) and breast (N = 1,296) cancers, CFR were 32.4% (95% CI 26.5%-39.6%) and 14.2% (95% CI 9.3%-21.8%), respectively.

CONCLUSIONS

Patients with solid or hematological malignancies and SARS-CoV-2 infection have a high probability of mortality, with comparatively higher and lower CFRs in patients with lung and breast cancers, respectively.

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

Background Coronavirus 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. Methodology This 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. Results The 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. Conclusions We 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.

The effect of anticancer treatment on cancer patients with COVID-19: A systematic review and meta-analysis

BACKGROUND

The relationship between cancer and COVID-19 has been revealed during the pandemic. Some anticancer treatments have been reported to have negative influences on COVID-19-infected patients while other studies did not support this hypothesis.

METHODS

A literature search was conducted in WOS, PubMed, Embase, Cochrane Library, CNKI and VIP between Dec 1, 2019 and Sept 23, 2020 for studies on anticancer treatments in patients with COVID-19. Cohort studies involving over 20 patients with cancer were included. The characteristics of the patients and studies, treatment types, mortality, and other additional outcomes were extracted and pooled for synthesis. RRs and forest plots were adopted to present the results. The literature quality and publication bias were assessed using NOS and Egger's test, respectively.

RESULTS

We analyzed the data from 29 studies, with 5121 cancer patients with COVID-19 meeting the inclusion criteria. There were no significant differences in mortality between patients receiving anticancer treatment and those not (RR 1.17, 95%CI: 0.96-1.43, I2 =66%, p = 0.12). Importantly, in patients with hematological malignancies, chemotherapy could markedly increase the mortality (RR 2.68, 95% CI: 1.90-3.78, I2 =0%, p < 0.00001). In patients with solid tumors, no significant differences in mortality were observed (RR 1.16, 95% CI: 0.57-2.36, I2 =72%, p = 0.67). In addition, our analysis revealed that anticancer therapies had no effects on the ICU admission rate (RR 0.87, 95% CI: 0.70-1.09, I2 =25%, p = 0.23), the severe rate (RR 1.04, 95% CI: 0.95-1.13, I2 =31%, p = 0.42), or respiratory support rate (RR 0.92, 95% CI: 0.70-1.21, I2 =32%, p = 0.55) in COVID-19-infected patients with cancer. Notably, patients receiving surgery had a higher rate of respiratory support than those without any antitumor treatment (RR 1.87, 95%CI: 1.02-3.46, I2 =0%, p = 0.04).

CONCLUSIONS

No significant difference was seen in any anticancer treatments in the solid tumor subgroup. Chemotherapy, however, will lead to higher mortality in patients with hematological malignancies. Multicenter, prospective studies are needed to re-evaluate the results.

Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal

OBJECTIVE

To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital with the disease.

DESIGN

Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group.

DATA SOURCES

PubMed and Embase through Ovid, up to 1 July 2020, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020.

STUDY SELECTION

Studies that developed or validated a multivariable covid-19 related prediction model.

DATA EXTRACTION

At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool).

RESULTS

37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models.

CONCLUSION

Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. Methodological guidance as provided in this paper should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline.

SYSTEMATIC REVIEW REGISTRATION

Protocol https://osf.io/ehc47/, registration https://osf.io/wy245.

READERS' NOTE

This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity.