Low Hematocrit Is a Strong Predictor of Poor Prognosis in Lung Cancer Patients

Relationship between preoperative body mass index and overall mortality in patients who have undergone lobectomy for lung cancer

AIMS

Preoperative body mass index (BMI) has been shown to be an important prognostic factor after lobectomy in patients with lung cancer. However, few studies have investigated the relationship between preoperative BMI and overall mortality in these patients. In this study, we aimed to identify the range of BMI that heralds a favorable prognosis in patients who have undergone lobectomy for lung cancer.

METHODS

The association between BMI and overall survival was examined using primary data from an affiliated hospital database and fitted adjusted Cox regression models. The restricted cubic spline (RCS) method was used to report the relationship between preoperative BMI and overall mortality. Fully adjusted models were stratified by and adjusted for sex, age, disease stage, respiratory function, and adjuvant chemotherapy.

RESULTS

Of 3307 patients identified to have undergone radical resection of lung cancer between November 2009 and July 2019, 2365 underwent lobectomy and 558 died. BMI had a J-shaped association with overall mortality; we estimated that the overall mortality risk reached a nadir at BMI values of 23.2-29.4, with an inverse association below 23.2 (hazard ratio 0.104 per 5-unit decrease; 95% confidence interval 0.089-0.119), a positive association above 29.4 (hazard ratio 0.022 per 5-unit increase; 95% confidence interval 0.004-0.040), and the lowest mortality at 25.7.

CONCLUSION

Preoperative BMI is an important prognostic factor after lobectomy in patients with lung cancer. A BMI of 23.2-29.4 has a prognostic benefit.

Hemoglobin and hematocrit levels are positively correlated with phase angle in mixed cancer patients: an exploratory cross-sectional study

The aim of this study is to verify the correlation between hemoglobin (Hb) and hematocrit (Ht) levels with phase angle (PhA) values in patients with cancer of the gastrointestinal tract and accessory digestive organs. A cross-sectional study was conducted with 82 patients (38 females/44 males) diagnosed with cancer of gastrointestinal tract and accessory organs of digestion. Hb (g/dL) and Ht (%) levels were assessed by the cyanomethemoglobin and microhematocrit methods, respectively. Body composition and PhA were evaluated by the bioelectrical impedance analysis (BIA). The cut-off point used to classify patients with low PhA was set < 4°. Fifty-five (67%) of patients had PhA ≥ 4° values. We observed that body weight, BMI, fat mass, % weight loss, Ht, and Hb were higher in the PhA ≥ 4° group than in the PhA < 4° group. In the regression analysis, we found a positive association between PhA, Ht, and Hb without and after adjustment for sex, age, BMI, and type of treatment. In mixed cancer patients, there was a positive correlation between blood-red cell markers and PhA. This may be evidence that we can use of hematological parameters to suggest changes in PhA values during oncological treatment.

Construction and validation of a nomogram model for lymph node metastasis of stage II-III gastric cancer based on machine learning algorithms

Background

Gastric cancer, a pervasive malignancy globally, often presents with regional lymph node metastasis (LNM), profoundly impacting prognosis and treatment options. Existing clinical methods for determining the presence of LNM are not precise enough, necessitating the development of an accurate risk prediction model.

Objective

Our primary objective was to employ machine learning algorithms to identify risk factors for LNM and establish a precise prediction model for stage II-III gastric cancer.

Methods

A study was conducted at Renji Hospital Affiliated to Shanghai Jiao Tong University School of Medicine between May 2010 and December 2022. This retrospective study analyzed 1147 surgeries for gastric cancer and explored the clinicopathological differences between LNM and non-LNM cohorts. Utilizing univariate logistic regression and two machine learning methodologies-Least absolute shrinkage and selection operator (LASSO) and random forest (RF)-we identified vascular invasion, maximum tumor diameter, percentage of monocytes, hematocrit (HCT), and lymphocyte-monocyte ratio (LMR) as salient factors and consolidated them into a nomogram model. The area under the receiver operating characteristic (ROC) curve (AUC), calibration curves, and decision curves were used to evaluate the test efficacy of the nomogram. Shapley Additive Explanation (SHAP) values were utilized to illustrate the predictive impact of each feature on the model's output.

Results

Significant differences in tumor characteristics were discerned between LNM and non-LNM cohorts through appropriate statistical methods. A nomogram, incorporating vascular invasion, maximum tumor diameter, percentage of monocytes, HCT, and LMR, was developed and exhibited satisfactory predictive capabilities with an AUC of 0.787 (95% CI: 0.749-0.824) in the training set and 0.753 (95% CI: 0.694-0.812) in the validation set. Calibration curves and decision curves affirmed the nomogram's predictive accuracy.

Conclusion

In conclusion, leveraging machine learning algorithms, we devised a nomogram for precise LNM risk prognostication in stage II-III gastric cancer, offering a valuable tool for tailored risk assessment in clinical decision-making.

Development and external validation of a novel score for predicting postoperative 30‑day mortality in tumor craniotomy patients: A cross‑sectional diagnostic study

The identification of patients with craniotomy at high risk for postoperative 30-day mortality may contribute to achieving targeted delivery of interventions. The present study aimed to develop a personalized nomogram and scoring system for predicting the risk of postoperative 30-day mortality in such patients. In this retrospective cross-sectional study, 18,642 patients with craniotomy were stratified into a training cohort (n=7,800; year of surgery, 2012-2013) and an external validation cohort (n=10,842; year of surgery, 2014-2015). The least absolute shrinkage and selection operator (LASSO) model was used to select the most important variables among the candidate variables. Furthermore, a stepwise logistic regression model was established to screen out the risk factors based on the predictors chosen by the LASSO model. The model and a nomogram were constructed. The area under the receiver operating characteristic (ROC) curve (AUC) and calibration plot analysis were used to assess the model's discrimination ability and accuracy. The associated risk factors were categorized according to clinical cutoff points to create a scoring model for postoperative 30-day mortality. The total score was divided into four risk categories: Extremely high, high, intermediate and low risk. The postoperative 30-day mortality rates were 2.43 and 2.58% in the training and validation cohort, respectively. A simple nomogram and scoring system were developed for predicting the risk of postoperative 30-day mortality according to the white blood cell count; hematocrit and blood urea nitrogen levels; age range; functional health status; and incidence of disseminated cancer cells. The ROC AUC of the nomogram was 0.795 (95% CI: 0.764 to 0.826) in the training cohort and it was 0.738 (95% CI: 0.7091 to 0.7674) in the validation cohort. The calibration demonstrated a perfect fit between the predicted 30-day mortality risk and the observed 30-day mortality risk. Low, intermediate, high and extremely high risk statuses for 30-day mortality were associated with total scores of (-1.5 to -1), (-0.5 to 0.5), (1 to 2) and (2.5 to 9), respectively. A personalized nomogram and scoring system for predicting postoperative 30-day mortality in adult patients who underwent craniotomy were developed and validated, and individuals at high risk of 30-day mortality were able to be identified.

Association between preoperative hematocrit and postoperative 30-day mortality in adult patients with tumor craniotomy

Background

The purpose of this research was to synthesize the American College of Surgeons National Surgical Quality Improvement Program database to investigate the link between preoperative hematocrit and postoperative 30-day mortality in patients with tumor craniotomy.

Methods

A secondary retrospective analysis of electronic medical records of 18,642 patients with tumor craniotomy between 2012 and 2015 was performed. The principal exposure was preoperative hematocrit. The outcome measure was postoperative 30-day mortality. We used the binary logistic regression model to explore the link between them and conducted a generalized additive model and smooth curve fitting to investigate the link and its explicit curve shape. We conducted sensitivity analyses by converting a continuous HCT into a categorical variable and calculated an E-value.

Results

A total of 18,202 patients (47.37% male participants) were included in our analysis. The postoperative 30-day mortality was 2.5% (455/18,202). After adjusting for covariates, we found that preoperative hematocrit was positively associated with postoperative 30-day mortality (OR = 0.945, 95% CI: 0.928, 0.963). A non-linear relationship was also discovered between them, with an inflection point at a hematocrit of 41.6. The effect sizes (OR) on the left and right sides of the inflection point were 0.918 (0.897, 0.939) and 1.045 (0.993, 1.099), respectively. The sensitivity analysis proved that our findings were robust. The subgroup analysis demonstrated that a weaker association between preoperative hematocrit and postoperative 30-day mortality was found for patients who did not use steroids for chronic conditions (OR = 0.963, 95% CI: 0.941-0.986), and a stronger association was discovered in participants who used steroids (OR = 0.914, 95% CI: 0.883-0.946). In addition, there were 3,841 (21.1%) cases in the anemic group (anemia is defined as a hematocrit (HCT) <36% in female participants and <39% in male participants). In the fully adjusted model, compared with the non-anemic group, patients in the anemic group had a 57.6% increased risk of postoperative 30-day mortality (OR = 1.576; 95% CI: 1.266, 1.961).

Conclusion

This study confirms that a positive and nonlinear association exists between preoperative hematocrit and postoperative 30-day mortality in adult patients undergoing tumor craniotomy. Preoperative hematocrit was significantly associated with postoperative 30-day mortality when the preoperative hematocrit was <41.6.

Efficacy of circulating microRNA-130b and blood routine parameters in the early diagnosis of gastric cancer

Patients with gastric cancer (GC) have a poor prognosis, which is mainly due to the low rate of early diagnosis. The present study aimed to evaluate whether circulating microRNA-130b (miR-130b) and blood routine parameters [neutrophil count (N#), lymphocyte count (L#), monocyte count (M#), neutrophil percentage (N%), lymphocyte percentage (L%), monocyte percentage (M%), hemoglobin (Hb) level, hematocrit (Hct), red blood cell distribution width (RDW), platelet count, platelet distribution width (PDW), mean platelet volume (MPV), MPV to platelet count ratio (MPV/PC), monocyte to lymphocyte ratio (MLR), neutrophil to lymphocyte ratio (NLR) and platelet to lymphocyte ratio (PLR)] are useful biomarkers for GC, early stage GC (EGC) and precancerous lesion (Pre) detection, and to identify more effective diagnostic models by combining circulating blood markers. Circulating levels of M#, M%, RDW-coefficient of variation (RDW-CV), MPV, PDW, MLR and NLR were significantly higher, and the levels of Hb and L% were significantly lower in patients with GC and Pre compared with those in healthy controls (NCs) (all P<0.05). The N#, N% and PLR in patients with GC were significantly higher and the Hct was significantly lower than those in the NCs (all P<0.05). The values of MPV/PC were significantly higher in the Pre cohort compared with those in the NCs. The area under the curve (AUC) of the receiver operating characteristic curve of potential biomarkers for GC was 0.634-0.887 individually, and this increased to 0.978 in the combination model of miR-130b-PDW-MLR-Hb. Additionally, the values for RDW-CV, PLR, NLR, N# and N% were positively correlated with cancer stage, while the values for MPV, L#, L%, Hb and Hct were negatively correlated with cancer stage. Furthermore, the circulating levels of miRNA-130b, and the values for NLR, RDW-CV, PDW, M%, red blood cell count, Hct, Hb and MLR differed between the EGC and NC groups. The AUC values of these biomarkers were 0.6491-0.911 individually in the diagnosis of EGC, and these increased to 0.960 in combination. In addition, the AUC values for miR-130b, RDW-CV, MPV/PC ratio, MLR, NLR, PDW, L%, M%, M# and Hb in the diagnosis of Pre were 0.638-0.811 individually. The dual-model of miR-130b-PDW manifested the largest AUC of 0.896 in the diagnosis of Pre, and the sensitivity and accuracy were increased when miR-130b and PDW were combined. All these results suggested that circulating miR-130b and blood routine parameters might be potential biomarkers, and combinations of measurements of these biomarkers may improve the GC, EGC and Pre diagnostic accuracy.

Comparison of Prognostic Value of Red Cell-Related Parameters of Biliary Tract Cancer After Surgical Resection and Integration of a Prognostic Nomogram: A Retrospective Study

INTRODUCTION

Biliary tract cancer (BTC) comprises infrequently occurring neoplasms with poor prognoses. Red blood cell-related parameters are commonly reported prognostic factors. We aimed to compare and evaluate the clinical value of red blood cell-related parameters and develop a prognostic nomogram.

METHODS

The analysis involved 418 patients with BTC who underwent surgery from December 2003 to April 2017. Patients were divided into training and validation cohorts. Red blood cell-related parameters were compared using Kaplan-Meier analysis, the area under receiver-operating characteristic curve (AUC), and C-index. Predictive abilities were evaluated using Cox regression. We developed a nomogram incorporating superior parameters verified using calibration curves, internal validation, and subgroup analysis. The nomogram was compared with the tumour-node-metastasis staging system through ROC, C-index, and Kaplan-Meier analysis.

RESULTS

A combined parameter comprising haemoglobin, albumin, lymphocytes, and platelets (HALP), which was superior to other red blood cell-related parameters, indicated a high risk of worse overall survival when low. Univariate analysis revealed that HALP together with other clinical characteristics was associated with overall survival. Multivariate analysis revealed that HALP, tumour-node-metastasis staging, and operative outcome were independent predictors of poor overall survival. Internal validation proved the predictive value of the nomogram. Additional statistical analyses established the advantages of the nomogram vs. tumour-node-metastasis staging.

CONCLUSION

HALP was a superior red blood cell-related parameter and an independent predictor of prognosis. Our nomogram based on HALP, tumour-node-metastasis staging, and operative outcome is a promising model for predicting overall survival.

Preoperative low hematocrit is an adverse prognostic biomarker in ovarian cancer

OBJECTIVE

The study aimed to investigate the prognostic value of preoperative hematocrit (HCT) on the survival of epithelial ovarian cancer (EOC) patients.

METHODS

Patients who underwent primary debulking surgery (PDS) in our institution, from January 2010 to December 2015, were enrolled. The preoperative HCT, hemoglobin (Hb), tumor stage, ascites volume, age, albumin, BMI, ASA score, diabetes and other factors were collected and analyzed to find the risk factors for poor prognosis of EOC patients using Cox regression. Survival analysis was conducted with Kaplan-Meier method and log-rank test.

RESULTS

192 patients met the inclusion criteria. HCT < 35% (P = 0.031, HR: 1.715, 95% CI 1.050-2.802) was an independent risk factor for poor overall survival in patients. The mean survival time was 83.7 months in patients with preoperative HCT ≥ 35% and 61.7 months in patients with HCT < 35% (P = 0.002). Patients with low HCT (< 35%) had a poor prognosis compared with patients with normal HCT, specifically in the patients of stage III/IV, age ≥ 65 years, BMI ≥ 25.0 kg/m², ascites volume ≤ 500 mL, ASA score < 3, albumin ≥ 35 g/L and nondiabetic. Low HCT was more likely to occur in patients with advanced stage (III/IV), anemia (Hb < 110 g/mL), low albumin (< 35 g/L), high ASA score (≥ 3) and platelet > 400 × 10⁹/L.

CONCLUSIONS

Preoperative low HCT was a valuable predictor for EOC patients' poor prognosis, specifically in obese, nondiabetic, elder, advanced stage but having relatively good performance status patients.

Impact of calculated plasma volume status on all-cause and cardiovascular mortality: 4-year nationwide community-based prospective cohort study

Background

Plasma volume status (PVS), a marker of plasma volume expansion and contraction, is gaining attention in the field of cardiovascular disease because of its role in the prevention and of the management of heart failure. However, it remains undetermined whether an abnormal PVS is a risk for all-cause and cardiovascular mortality in the general population.

Methods and results

We used a nationwide database of 230,882 subjects (age 40–75 years) who participated in the annual “Specific Health Check and Guidance in Japan” check-up between 2008 and 2011. There were 586 cardiovascular deaths, 2,552 non-cardiovascular deaths, and 3,138 all-cause deaths during the follow-up period of four years. Abnormally high and low PVS were identified from the results of 80% of all subjects (high and low PVS ≥ 7 and < -13.3, respectively). Multivariate Cox proportional hazard regression analysis demonstrated that high PVS was an independent risk factor for all-cause, cardiovascular and non-cardiovascular deaths. Although low PVS was a positive risk factor for cardiovascular deaths as well, it was a negative risk factor for non-cardiovascular deaths. The addition of PVS to cardiovascular risk factors significantly improved the C-statistic, net reclassification, and integrated discrimination indexes.

Conclusions

This is the first prospective report to reveal the impact of PVS on all-cause and cardiovascular mortality. PVS could be an additional risk factor for all-cause and cardiovascular mortality in the general population.

Hospitalization and definitive radiotherapy in lung cancer: incidence, risk factors and survival impact

BACKGROUND

Unplanned hospitalization during cancer treatment is costly, can disrupt treatment, and affect patient quality of life. However, incidence and risks factors for hospitalization during lung cancer radiotherapy are not well characterized.

METHODS

Patients treated with definitive intent radiation (≥45 Gy) for lung cancer between 2008 and 2018 at a tertiary academic institution were identified. In addition to patient, tumor, and treatment related characteristics, specific baseline frailty markers (Charlson comorbidity index, ECOG, patient reported weight loss, BMI, hemoglobin, creatinine, albumin) were recorded. All cancer-related hospitalizations during or within 30 days of completing radiation were identified. Associations between baseline variables and any hospitalization, number of hospitalizations, and overall survival were identified using multivariable linear regression and multivariable Cox proportional-hazards models, respectively.

RESULTS

Of 270 patients included: median age was 66.6 years (31-88), 50.4% of patients were male (n = 136), 62% were Caucasian (n = 168). Cancer-related hospitalization incidence was 17% (n = 47), of which 21% of patients hospitalized (n = 10/47) had > 1 hospitalization. On multivariable analysis, each 1 g/dL baseline drop in albumin was associated with a 2.4 times higher risk of any hospitalization (95% confidence interval (CI) 1.2-5.0, P = 0.01), and baseline hemoglobin ≤10 was associated with, on average, 2.7 more hospitalizations than having pre-treatment hemoglobin > 10 (95% CI 1.3-5.4, P = 0.01). After controlling for baseline variables, cancer-related hospitalization was associated with 1.8 times increased risk of all-cause death (95% CI: 1.02-3.1, P = 0.04).

CONCLUSIONS

Our data show baseline factors can predict those who may be at increased risk for hospitalization, which was independently associated with increased mortality. Taken together, these data support the need for developing further studies aimed at early and aggressive interventions to decrease hospitalizations during treatment.