Category: Statistics

Clin Res Cardiol. 2024 Nov 18. doi: 10.1007/s00392-024-02575-3. Online ahead of print.

ABSTRACT

AIM: Delirium in patients treated in the intensive care unit (ICU) is linked to adverse outcome, according to previous observations. However, data on patients recovering after cardiac arrest are sparse. The aim of this study was to assess incidence, risk factors, and outcome of patients with delirium after cardiac arrest in the Freiburg Delirium Registry (FDR).

METHODS: In this retrospective registry study, all patients after cardiac arrest treated in the Freiburg University Medical Center medical ICU between 08/2016 and 03/2021 were included. Delirium was diagnosed using the Nursing Delirium screening scale (NuDesc), assessed three times daily. Favorable neurological outcome was defined as cerebral performance category (CPC) score at ICU discharge ≤ 2.

RESULTS: Two hundred seventeen patients were included and among them, delirium was detected in one hundred ninety-nine (91.7%) patients. Age was independently associated with the incidence of delirium (p = 0.003), and inversely associated with the number of delirium-free days (p < 0.001). Favorable neurological outcome was present in 145/199 (72.9%) with, and 17/18 (94.4%) patients without delirium (p = 0.048). While the incidence of delirium was not independently associated with a favorable neurologic outcome, the number of delirium-free days strongly predicted the primary endpoint [OR 2.14 (1.73-2.64), p > 0.001].

CONCLUSION: Delirium complicated the ICU course in almost all patients after cardiac arrest. The number of delirium-free days was associated with favorable outcome while incidence of delirium itself was not.

PMID:39556214 | DOI:10.1007/s00392-024-02575-3

Biochem Genet. 2024 Nov 18. doi: 10.1007/s10528-024-10972-6. Online ahead of print.

ABSTRACT

The study aimed to estimate the genetic parameters and predict the genotypic values of postharvest physiological deterioration and root characteristics in cassava (Manihot esculentaCrantz) using restricted maximum likelihood (REML) and the best linear unbiased prediction (BLUP). A total of 76 cassava accessions were evaluated over two growing seasons. The evaluated traits included postharvest physiological deterioration response (PPD), root length (RL), root diameter (RD), root weight (RW), dry matter content (DMC), total starch content (TS) and total sugar content (TSU). All the traits had a higher phenotypic variance component than genetic or environmental variance, with genotypic variance making up a larger portion of the total phenotypic variance. Heritability estimates ranged from low to high, with high heritability values being recorded for dry matter content, PPD, and root diameter. The study discovered high genotypic coefficients of variation (CVg) for PPD, root weight and diameter, indicating strong genotypic variability beneficial for selection. As larger genetic effects than non-genetic effects lead to increased selection gains, the highest CVr values for dry matter content and PPD suggest the biggest probability of selection gain. Postharvest Physiological deterioration (PPD) had the highest genetic advance, indicating significant gain in the following generation. Thirty eight genotypes were selected as the most promising based on BLUP index, promoting improvement and genetic gain in several traits. The genotypes selected can be included in cassava breeding programs for PPD tolerance and other tuber traits.

PMID:39556191 | DOI:10.1007/s10528-024-10972-6

J Cancer Surviv. 2024 Nov 18. doi: 10.1007/s11764-024-01709-w. Online ahead of print.

ABSTRACT

PURPOSE: To characterize the rate of human papillomavirus (HPV) vaccine uptake among adolescents after hematopoietic stem cell transplant (HSCT).

METHODS: This retrospective study evaluated the vaccine history of adolescent patients ≥ 11 years of age who underwent either auto- or allo-HSCT between 2015 and 2022 at a tertiary care medical center. Logistic regression was used to examine bivariate associations between HPV vaccine uptake and covariates including demographic factors, indication for and type of HSCT, receipt of HPV vaccine prior to transplant, and receipt of other vaccines after transplant.

RESULTS: Among 119 (n = 53 female; n = 66 male) eligible patients, 66 (55.5%) received at least one dose of the HPV vaccine after HSCT. Among those who initiated vaccination, 45/66 (68.2%) completed two or more doses. Of the 69 patients who were eligible to receive the vaccine prior to HSCT, 19/36 (52%) were vaccinated both before and after HSCT, compared to 14/33 (42%) who did not receive the vaccine before HSCT but chose to be vaccinated after HSCT. No statistically significant difference was identified between those who did and did not initiate HPV vaccination after HSCT among covariates examined.

CONCLUSIONS: Rate of HPV vaccine uptake after HSCT was lower than the rate of other recommended vaccines. Receiving HPV vaccine prior to HSCT was not associated with HPV re-uptake after HSCT.

IMPLICATIONS FOR CANCER SURVIVORS: HPV vaccination continues to be suboptimal in HSCT survivorship and should be a targeted goal for improvement in preventing secondary malignancy in this high-risk population.

PMID:39556189 | DOI:10.1007/s11764-024-01709-w

Breast Cancer (Dove Med Press). 2024 Nov 11;16:745-760. doi: 10.2147/BCTT.S456422. eCollection 2024.

ABSTRACT

PURPOSE: To analyze the impacts of distinct chemotherapy methods on blood lipid levels in breast cancer patients.

METHODS: Three hundred breast cancer patients were selected as the research subjects. The inclusion time limit was from January 2021 to January 2023, and all received treatment in our hospital. Based on the therapy plan, the patients were divided into group A (epirubicin + cyclophosphamide followed by paclitaxel regimen, 103 premenopausal cases + 61 postmenopausal cases), group B (docetaxel + cyclophosphamide regimen, 41 premenopausal instances + 37 postmenopausal instances), group C (docetaxel + carboplatin regimen, 61 premenopausal instances + 24 postmenopausal instances), comparing the changes in blood lipid levels of patients in each group at pre-therapy and post-therapy, and the abnormality frequency of blood lipids in every group of patients after therapy.

RESULTS: After treatment, the triglyceride (TG) levels of the three groups of patients were clearly greater than those at pre-therapy, and the high-density lipoprotein cholesterol (HDL-C) levels were clearly less than before therapy. The levels of low-density lipoprotein cholesterol (LDL-C) in group B and C patients were clearly greater than those before therapy in the same one, while the LDL-C levels in group A were clearly less than those before therapy in the same one; after therapy, the TG levels of patients in group A were clearly less than those in group B, and LDL-C, total Cholesterol (TC) levels were clearly less than that in group B and C (P < 0.05). The proportion of dyslipidemia in patients in the group A after therapy was clearly less than in group B (P < 0.05). After treatment, the HDL-C levels of premenopausal patients in the three groups were clearly less than those at pre-therapy. The TG, TC, and LDL-C levels of premenopausal patients in groups B and C were clearly greater than those at pre-therapy. The TG levels of premenopausal patients in group A were clearly less than those before therapy. After treatment, the TG and TC levels of premenopausal patients in group A were clearly less than those in group C, and the LDL-C levels were clearly less than those in group B and C (P < 0.05). The proportion of dyslipidemia in premenopausal patients in the group A and C after therapy was clearly less than the group B (P < 0.05). After therapy, the TG levels of postmenopausal patients in the three groups were clearly greater than those at pre-therapy, and the HDL-C levels were clearly less than those at pre-therapy. The LDL-C levels of postmenopausal patients in group B and C were clearly greater than those at pre-therapy. The TC and LDL-C levels of postmenopausal patients in group A were clearly less than those at pre-therapy; after therapy, the LDL-C and TC levels of postmenopausal patients in group A were clearly less than those in group B and C (P < 0.05). It had no statistically clear distinction in dyslipidemia among the three groups of postmenopausal patients after therapy (P > 0.05).

CONCLUSION: Chemotherapy has adverse effects on the blood lipid levels of premenopausal and postmenopausal breast carcinoma patients and increases the incidence of dyslipidemia. Compared with other regimens, epirubicin+cyclophosphamide sequential paclitaxel regimen has little impact on blood lipid level of breast carcinoma.

PMID:39553240 | PMC:PMC11566600 | DOI:10.2147/BCTT.S456422

Am J Cancer Res. 2024 Oct 15;14(10):4909-4921. doi: 10.62347/XADT5737. eCollection 2024.

ABSTRACT

The objective of this study was to investigate the utility of conventional imaging combined with diffusion-weighted magnetic resonance imaging (MRI) in identifying high-risk tumor characteristics in patients with localized prostate cancer. A retrospective cohort study was conducted on 194 patients who underwent surgery for localized prostate cancer. Patients were categorized into low-risk and high-risk groups based on clinical criteria. Imaging data were obtained using a MRI system, and various imaging parameters were analyzed, including T1-weighted imaging (T1WI), T2-weighted imaging (T2WI) signal intensities, diffusion-weighted MRI parameters, and their correlations with clinical characteristics. Statistical methods such as logistic regression, and receiver operating characteristic (ROC) analysis were employed to assess the diagnostic performance of the imaging parameters and to construct joint prediction models. A verification set prediction model was established and compared. The comparison of demographic and clinical characteristics between the low and high-risk groups revealed significant differences in the prostate-specific antigen (PSA) level, Gleason score, Tumor Size and prostate volume (PV). Standard imaging parameters, T1WI and T2WI signal intensities, exhibited significant differences between the low and high-risk groups. Additionally, diffusion-weighted MRI parameters, including signal intensities at different b values, apparent diffusion coefficient (ADC), K^trans, and K_ep, were notably associated with high-risk tumor characteristics in localized prostate cancer. Logistic regression analysis identified both standard imaging and diffusion-weighted MRI parameters as independent predictors of high-risk tumor characteristics. Furthermore, the ROC analysis demonstrated the diagnostic potential of T2WI signal intensity, signal intensity at 800 s/mm², and ADC in identifying high-risk tumors. Joint prediction models combining standard imaging and diffusion-weighted MRI parameters showed high predictive accuracy for high-risk tumor characteristics in localized prostate cancer, with Area Under the Curve (AUC) values of 0.777 for standard imaging, 0.826 for diffusion-weighted MRI, and 0.892 for the combined model. The AUC value for the prediction model in validation set was 0.860. In conclusion, this study underscores the diagnostic potential of conventional imaging combined with diffusion-weighted MRI in identifying high-risk tumor characteristics in patients with localized prostate cancer. Both standard imaging and diffusion-weighted MRI parameters were identified as non-invasive biomarkers for risk assessment and prognosis. These findings have implications for precision treatment of localized prostate cancer, highlighting the potential integration of imaging-based risk assessment tools into clinical practice for tailored treatment strategies and improved patient outcomes.

PMID:39553232 | PMC:PMC11560813 | DOI:10.62347/XADT5737

Am J Cancer Res. 2024 Oct 15;14(10):4760-4771. doi: 10.62347/RIWX7204. eCollection 2024.

ABSTRACT

Nasopharyngeal carcinoma (NPC) is a malignant tumor that targets the nasopharyngeal mucosal epithelium. Concurrent chemoradiotherapy (CCRT) is a pivotal treatment modality for NPC, yet it poses a risk for osteoradionecrosis (ORN), a complication that can impede further treatment. This study sought to explore the risk factors for ORN in NPC patients post-CCRT and to construct predictive models. We performed a retrospective analysis of clinical data from 417 NPC patients treated with CCRT at the Affiliated Hospital of Jiangnan University, with 204 patients from Longyan First Hospital as a validation cohort for the models. Our findings indicated that a high radiation dose, tooth extraction after radiotherapy, inadequate oral hygiene, smoking, anemia, and advanced T staging were associated with an elevated risk of ORN in NPC patients following CCRT. We formulated risk prediction models for ORN utilizing a nomogram, gradient boosting machine (GBM), and random forest (RF) algorithms. The area under the curve (AUC) was 0.813 (95% CI: 0.724-0.902) for the nomogram model in the validation cohort, 0.821 (95% CI: 0.732-0.910) for the GBM, and 0.735 (95% CI: 0.614-0.855) for the RF. Delong’s test indicated no statistically significant differences in the AUC values among the three models. The nomogram has strong performance across both the training and validation cohorts, featuring a straightforward structure that is both intuitive and comprehensible. Taking into account the model’s discriminative power, generalizability, and clinical practicability, the nomogram was proven to be highly applicable in the current study.

PMID:39553231 | PMC:PMC11560816 | DOI:10.62347/RIWX7204

Am J Cancer Res. 2024 Oct 15;14(10):4880-4895. doi: 10.62347/YAWK6271. eCollection 2024.

ABSTRACT

The objective of this study was to identify characteristic factors for pathological complete response (pCR) in patients with locally advanced breast cancer (LABC) undergoing surgery and neoadjuvant chemotherapy (NACT). We retrospectively collected pathological data from 237 LABC patients treated in Affiliated Fuzhou First Hospital of Fujian Medical University from January 2010 to June 2021 and divided them into a training group (n = 166) and a validation group (n = 71) in a 7:3 ratio. A predictive model for pCR was established through logistic regression analysis and evaluated using the receiver operating characteristic (ROC) curve and the area under the curve (AUC). Significant differences between the pCR and non-pCR groups were observed in tumor size (P = 0.001), T stage (P = 0.003), estrogen receptor (ER) (P = 0.031), progesterone receptor (PR) (P = 0.013), human epidermal growth factor receptor 2 (HER-2) (P = 0.001), and molecular type (P = 0.001). The pCR group also had lower levels of carbohydrate antigen 19-9 (P = 0.013), cancer antigen 125 (P = 0.011), carcinoembryonic antigen (CEA) (P = 0.001), and systemic inflammatory index (SII) (P = 0.006), but a higher prognostic nutritional index (PNI) (P = 0.001) compared to the non-pCR group. There were no statistical differences in baseline data between the training and validation groups (P>0.05). Multivariate logistic regression analysis identified tumor size (P = 0.001), HER-2 (P = 0.010), CA125 (P = 0.005), CEA (P = 0.001), SII (P = 0.010), and PNI (P = 0.001) as independent risk factors for pCR. We constructed and visualized a nomogram model that included these 6 factors and developed a dynamic prediction model using the Dynamic Nomogram (DynNom) package. In a random sample of 6 patients, the probability of non-pCR reached 98.8%. The model’s AUC was 0.881 in the training group, with a clinical benefit rate of 71.68% and a concordance index (C-index) of 0.881, indicating a good fit. In the validation group, the AUC was 0.722, with a clinical benefit rate of 70.2% and a C-index of 0.722, also indicating a good fit. The Delong test showed a significant difference in AUC between the two groups (P = 0.027). In conclusion, this study constructed and validated a Nomogram model based on clinical pathological features and hematological indicators, finding that higher pCR rates were associated with smaller tumor size, HER-2 positivity, lower levels of CA125 and CEA, lower SII, and higher PNI, significantly enhancing breast cancer management and offering important clinical implications.

PMID:39553222 | PMC:PMC11560830 | DOI:10.62347/YAWK6271

Am J Cancer Res. 2024 Oct 15;14(10):4788-4802. doi: 10.62347/ZSWV1767. eCollection 2024.

ABSTRACT

Ovarian cancer is usually detected in the advanced stages. Existing treatments for high grade serous ovarian cancer (HGSOC) are not adequate and approximately fifty percent of patients succumb to this disease and die within five years after diagnosis. We conducted pre-clinical studies in a mouse model of ovarian cancer to evaluate disease outcome in response to treatment with the multi-kinase inhibitor cabozantinib. Cabozantinib is a receptor tyrosine kinase inhibitor with multiple targets including vascular endothelial growth factor receptor-2 (VEGFR-2), associated with immune suppression in ovarian cancer. Mice (C57BL/6) were injected with ID8-RFP ovarian tumor cells and treated with cabozantinib. Studies investigated ascites development, tumor burden and regulation of anti-tumor immunity with treatment. Mice treated with cabozantinib had significantly decreased solid tumor burden and decreased malignant ascites as compared to untreated controls. Improved outcome in cabozantinib treated mice was associated with a significantly higher percentage of CD69 early activated T cells, a higher percentage of granzyme B secreting CD8 T cells, the enhanced release of cytokines and chemokines known to recruit CD8 T cells and amplify T cell function, as well as reduced VEGFR-2. Findings suggest that cabozantinib is an important clinical agent capable of improving ovarian cancer in mice potentially in part by priming the autologous immune system to promote anti-tumor immunity.

PMID:39553221 | PMC:PMC11560812 | DOI:10.62347/ZSWV1767

Am J Cancer Res. 2024 Oct 15;14(10):4946-4955. doi: 10.62347/MHXS8480. eCollection 2024.

ABSTRACT

OBJECTIVE: To explore the value of preoperative prognostic nutritional index (PNI) and systemic immune inflammation index (SII) for predicting the efficacy and prognosis of patients with osteosarcoma undergoing neoadjuvant chemotherapy (NACT) combined with surgery.

METHODS: A retrospective study was conducted on patients with osteosarcoma undergoing NACT combined with surgery in Sun Yat-sen University Cancer Center from January 2017 to May 2019. The patients were grouped into a remission group (pCR group, 85 patients) and a non-remission group (non-pCR, 79 patients), according to the treatment efficacy. The pathological data as well as clinical data were collected from patients, which were subsequently employed for statistical analysis to determine the factors affecting the efficacy of the treatment. The diagnostic value of PNI and SII for predicting the efficacy were assessed through following up the patients for 5 years to observe their overall survival rate. COX regression analysis was leveraged to identify risk factors affecting the survival time. The impact of different PNI and SII levels on the survival time was observed.

RESULTS: Multivariate regression analysis showed that factors including Enneking stage, PNI level and SII level were in association with poor efficacy after NATC combined with surgery. The mortality within 5 years was higher and the 5-year overall survival rate was lower in the non-pCR group than those in the pCR group (both P < 0.05). The COX regression analysis indicated that PNI and SII levels were risk factors for poor prognosis in patients with osteosarcoma following NACT combined with surgery. Further analysis showed that patients with low PNI and high SII levels had a lower 5-year survival rate (P < 0.05).

CONCLUSION: Enneking stage, PNI, and SII levels were risk factors for poor efficacy in patients with osteosarcoma after NACT combined with surgery. Patients whose PNI level was low and SII level was high presented poor prognosis following the treatment.

PMID:39553218 | PMC:PMC11560818 | DOI:10.62347/MHXS8480

Am J Cancer Res. 2024 Oct 15;14(10):4803-4816. doi: 10.62347/MWLI5585. eCollection 2024.

ABSTRACT

OBJECTIVE: To evaluate the potential of leukocyte-specific protein 1 (LCP1) and adenosine diphosphate-dependent glucokinase (ADPGK) as predictive biomarkers for immunotherapy-related adverse events in late-stage non-small cell lung cancer (NSCLC) patients with the KARS G12C mutation undergoing treatment with programmed cell death protein-1 (PD-1) monoclonal antibodies.

METHODS: A total of 160 late-stage NSCLC patients with the KARS G12C mutation receiving PD-1 monoclonal antibody treatment were retrospectively analyzed. LCP1 and ADPGK expression levels were assessed at both mRNA and protein levels using validated methods. Statistical analyses, including correlation analysis, logistic regression, and receiver operating characteristic (ROC) curve analysis, were conducted to explore the association between LCP1 and ADPGK expression levels and the occurrence of immunotherapy-related adverse events.

RESULTS: The mRNA levels of LCP1 (2.43 ± 0.72 vs. 2.14 ± 0.67, t=2.311, P=0.023) and ADPGK (2.31 ± 0.61 vs. 1.98 ± 0.59, t=3.145, P=0.002) were significantly elevated in patients with adverse reactions. Similarly, protein levels of LCP1 (1.22 ± 0.28 vs. 1.07 ± 0.25, t=3.179, P=0.002) and ADPGK (1.01 ± 0.18 vs. 0.93 ± 0.19, t=2.488, P=0.015) were higher in this group. Correlation and logistic regression analyses revealed positive correlations between LCP1 and ADPGK mRNA levels and adverse event occurrence (LCP1: rho=0.186, P=0.019, OR=1.842; ADPGK: rho=0.246, P=0.002, OR=2.549). Protein levels of LCP1 and ADPGK also correlated with immunotherapy-related adverse events (LCP1: rho=0.254, P=0.001, OR=9.554; ADPGK: rho=0.19, P=0.016, OR=10.058). The combined assessment of LCP1 and ADPGK expression showed strong predictive power for identifying patients at increased risk of adverse events during PD-1 treatment (AUC=0.808), with the validation group achieving an AUC of 0.751.

CONCLUSION: LCP1 and ADPGK are potential independent predictive biomarkers for immunotherapy-related adverse events in late-stage NSCLC patients with the KARS G12C mutation. Their combined assessment may offer a valuable tool for risk stratification during PD-1 monoclonal antibody treatment.

PMID:39553202 | PMC:PMC11560835 | DOI:10.62347/MWLI5585