External validation and updating of prediction models for estimating the 1-year risk of low health-related quality of life in colorectal cancer survivors

The association between Glycated Hemoglobin to High Density Lipoprotein Cholesterol Ratio and risk of cardiovascular diseases caused death among adult cancer survivors: evidence from NHANES 1999-2018

BACKGROUND

The population of cancer survivors is growing markedly, facing an elevated risk of overall mortality as well as death from cardiovascular diseases (CVDs). Uncovering biomarkers that associated with CVDs among cancer survivors appears to be vital.

METHODS

We collected data from NHANES (1999-2018), focusing on cancer survivors with comprehensive Glycated Hemoglobin (GH), High Density Lipoprotein Cholesterol (HDL-C), CVDs history and survival follow-up information. We first executed test for Proportional Hazards assumptions among the variables, paving the way for constructing the COX proportional hazards model. By stratifying participants by age, we explored the association between GH/HDL-C levels and the CVDs-caused mortality risk across various age segments. Restricted cubic spline (RCS) curves were employed to detect any potential non-linear associations. When non-linear associations were identified, we proceeded with segmented analyses based on reference values to better understand the association between GH/HDL-C and the risk of CVDs-related mortality among cancer survivors. To further affirm the robustness of our findings, subgroup and sensitivity analyses were conducted.

RESULTS

A total of 3,244 eligible participants were included in this study. The GH/HDL-C levels in cancer survivors died from CVDs were markedly higher than those who survived the follow-up period. According to the results from the Proportional Hazards assumptions test, the endpoint for CVDs mortality was established at 168 months, and the subjects were classified into three age groups: <60 years, between 60 and 74 years, and ≥ 75 years. For the young cohort (< 60 years), there was no significant association between GH/HDL-C levels and CVDs mortality. However, in the 60 ~ 74 age group, a linear association was noted, with higher GH/HDL-C levels indicating a greater CVDs-related mortality risk. For cancer survivors aged 75 and older, the association appeared nonlinear, resembling a U-shaped curve, where high GH/HDL-C levels were associated with higher mortality risk above the certain reference point (4.25mmol/L^-1), while lower levels were associated with reduced risk or no significant impact.

CONCLUSION

The study highlighted that in cancer survivors, the GH/HDL-C is significantly associated with the risk of CVDs mortality. Those between 60 and 74 years old showed a straightforward increase in CVD death risk with higher GH/HDL-C levels. In individuals aged 75 and older, the association was more complex, exhibiting a non-linear U-shaped trend.

Exploring the role of health-related quality of life measures in predictive modelling for oncology: a systematic review

Health related quality of life (HRQoL) is increasingly assessed in oncology research and routine care, which has led to the inclusion of HRQoL in prediction models. This review aims to describe the current state of oncological prediction models incorporating HRQoL. A systematic literature search for the inclusion of HRQoL in prediction models in oncology was conducted. Selection criteria were a longitudinal study design and inclusion of HRQoL data in prediction models as predictor, outcome, or both. Risk of bias was assessed using the PROBAST tool and quality of reporting was scored with an adapted TRIPOD reporting guideline. From 4747 abstracts, 98 records were included in this review. High risk of bias was found in 71% of the publications. HRQoL was mainly incorporated as predictor (78% (55% predictor only, 23% both predictor and outcome)), with physical functioning and symptom domains selected most frequently as predictor. Few models (23%) predicted HRQoL domains by other or baseline HRQoL domains. HRQoL was used as outcome in 21% of the publications, with a focus on predicting symptoms. There were no difference between AI-based (16%) and classical methods (84%) in model type selection or model performance when using HRQoL data. This review highlights the role of HRQoL as a tool in predicting disease outcomes. Prediction of and with HRQoL is still in its infancy as most of the models are not fully developed. Current models focus mostly on the physical aspects of HRQoL to predict clinical outcomes, and few utilize AI-based methods.

Development and validation of machine learning models for predicting cancer-related fatigue in lymphoma survivors

BACKGROUND

New cases of lymphoma are rising, and the symptom burden, like cancer-related fatigue (CRF), severely impacts the quality of life of lymphoma survivors. However, clinical diagnosis and treatment of CRF are inadequate and require enhancement.

OBJECTIVE

The main objective of this study is to construct machine learning-based CRF prediction models for lymphoma survivors to help healthcare professionals accurately identify the CRF population and better personalize treatment and care for patients.

METHODS

A cross-sectional study in China recruited lymphoma patients from June 2023 to March 2024, dividing them into two datasets for model construction and external validation. Six machine learning algorithms were used in this study: Logistic Regression (LR), Random Forest, Single Hidden Layer Neural Network, Support Vector Machine, eXtreme Gradient Boosting, and Light Gradient Boosting Machine (LightGBM). Performance metrics like the area under the receiver operating characteristic (AUROC) and calibration curves were compared. The clinical applicability was assessed by decision curve, and Shapley additive explanations was employed to explain variable significance.

RESULTS

CRF incidence was 40.7 % (dataset I) and 44.8 % (dataset II). LightGBM showed strong performance in training and internal validation. LR excelled in external validation with the highest AUROC and best calibration. Pain, total protein, physical function, and sleep disturbance were important predictors of CRF.

CONCLUSION

The study presents a machine learning-based CRF prediction model for lymphoma patients, offering dynamic, data-driven assessments that could enhance the development of automated CRF screening tools for personalized management in clinical practice.

Use of Patient-Reported Outcomes in Risk Prediction Model Development to Support Cancer Care Delivery: A Scoping Review

PURPOSE

The integration of patient-reported outcomes (PROs) into electronic health records (EHRs) has enabled systematic collection of symptom data to manage post-treatment symptoms. The use and integration of PRO data into routine care are associated with overall treatment success, adherence, and satisfaction. Clinical trials have demonstrated the prognostic value of PROs including physical function and global health status in predicting survival. It is unknown to what extent routinely collected PRO data are used in the development of risk prediction models (RPMs) in oncology care. The objective of the scoping review is to assess how PROs are used to train risk RPMs to predict patient outcomes in oncology care.

METHODS

Using the scoping review methodology outlined in the Joanna Briggs Institute Manual for Evidence Synthesis, we searched four databases (MEDLINE, CINAHL, Embase, and Web of Science) to locate peer-reviewed oncology articles that used PROs as predictors to train models. Study characteristics including settings, clinical outcomes, and model training, testing, validation, and performance data were extracted for analyses.

RESULTS

Of the 1,254 studies identified, 18 met inclusion criteria. Most studies performed retrospective analyses of prospectively collected PRO data to build prediction models. Post-treatment survival was the most common outcome predicted. Discriminative performance of models trained using PROs was better than models trained without PROs. Most studies did not report model calibration.

CONCLUSION

Systematic collection of PROs in routine practice provides an opportunity to use patient-reported data to develop RPMs. Model performance improves when PROs are used in combination with other comprehensive data sources.

A bio-behavioral model of systemic inflammation at breast cancer diagnosis and fatigue of clinical importance 2 years later

BACKGROUND

We aimed to generate a model of cancer-related fatigue (CRF) of clinical importance 2 years after diagnosis of breast cancer building on clinical and behavioral factors and integrating pre-treatment markers of systemic inflammation.

PATIENTS AND METHODS

Women with stage I-III hormone receptor-positive/human epidermal growth factor receptor 2-negative breast cancer were included from the multimodal, prospective CANTO cohort (NCT01993498). The primary outcome was global CRF of clinical importance [European Organisation for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire (QLQ)-C30 ≥40/100] 2 years after diagnosis (year 2). Secondary outcomes included physical, emotional, and cognitive CRF (EORTC QLQ-FA12). All pre-treatment candidate variables were assessed at diagnosis, including inflammatory markers [interleukin (IL)-1α, IL-1β, IL-2, IL-4, IL-6, IL-8, IL-10, interferon γ, IL-1 receptor antagonist, tumor necrosis factor-α, and C-reactive protein], and were tested in multivariable logistic regression models implementing multiple imputation and validation by 100-fold bootstrap resampling.

RESULTS

Among 1208 patients, 415 (34.4%) reported global CRF of clinical importance at year 2. High pre-treatment levels of IL-6 (quartile 4 versus 1) were associated with global CRF at year 2 [adjusted odds ratio (aOR): 2.06 (95% confidence interval [CI] 1.40-3.03); P = 0.0002; area under the receiver operating characteristic curve = 0.74]. Patients with high pre-treatment IL-6 had unhealthier behaviors, including being frequently either overweight or obese [62.4%; mean body mass index 28.0 (standard deviation 6.3 kg/m2)] and physically inactive (53.5% did not meet World Health Organization recommendations). Clinical and behavioral associations with CRF at year 2 included pre-treatment CRF [aOR versus no pre-treatment CRF: 3.99 (95% CI 2.81-5.66)], younger age [aOR per 1-year decrement: 1.02 (95% CI 1.01-1.03)], current tobacco smoking [aOR versus never: 1.81 (95% CI 1.26-2.58)], and worse insomnia or pain [aOR per 10-unit increment: 1.08 (95% CI 1.04-1.13), and 1.12 (95% CI 1.04-1.21), respectively]. Secondary analyses indicated additional associations of IL-2 [aOR per log-unit increment: 1.32 (95% CI 1.03-1.70)] and IL-10 [0.73 (95% CI 0.57-0.93)] with global CRF and of C-reactive protein [1.42 (95% CI 1.13-1.78)] with cognitive CRF at year 2. Emotional distress was consistently associated with physical, emotional, and cognitive CRF.

CONCLUSIONS

This study proposes a bio-behavioral framework linking pre-treatment systemic inflammation with CRF of clinical importance 2 years later among a large prospective sample of survivors of breast cancer.

Quality of Life of Colorectal Cancer Survivors: Mapping the Key Indicators by Expert Consensus and Measures for Their Assessment

Quality of life (QoL) assessments are integral to cancer care, yet their effectiveness in providing essential information for supporting survivors varies. This study aimed to elucidate key indicators of QoL among colorectal cancer survivors from the perspective of healthcare professionals, and to evaluate existing QoL questionnaires in relation to these indicators. Two studies were conducted: a Delphi study to identify key QoL indicators and a scoping review of questionnaires suitable for colorectal cancer survivors. Fifty-four healthcare professionals participated in the Delphi study's first round, with 25 in the second. The study identified two primary QoL domains (physical and psychological) and 17 subdomains deemed most critical. Additionally, a review of 12 questionnaires revealed two instruments assessing the most important general domains. The findings underscored a misalignment between existing assessment tools and healthcare professionals' clinical priorities in working with colorectal cancer survivors. To enhance support for survivors' QoL, efforts are needed to develop instruments that better align with the demands of routine QoL assessment in clinical practice.

Nomogram for soiling prediction in postsurgery hirschsprung children: a retrospective study

PURPOSE

The aim of this study was to develop a nomogram for predicting the probability of postoperative soiling in patients aged greater than 1 year operated for Hirschsprung disease (HSCR).

MATERIALS AND METHODS

The authors retrospectively analyzed HSCR patients with surgical therapy over 1 year of age from January 2000 and December 2019 at our department. Eligible patients were randomly categorized into the training and validation set at a ratio of 7:3. By integrating the least absolute shrinkage and selection operator [LASSO] and multivariable logistic regression analysis, crucial variables were determined for establishment of the nomogram. And, the performance of nomogram was evaluated by C-index, area under the receiver operating characteristic curve, calibration curves, and decision curve analysis. Meanwhile, a validation set was used to further assess the model.

RESULTS

This study enrolled 601 cases, and 97 patients suffered from soiling. Three risk factors, including surgical history, length of removed bowel, and surgical procedures were identified as predictive factors for soiling occurrence. The C-index was 0.871 (95% CI: 0.821-0.921) in the training set and 0.878 (95% CI: 0.811-0.945) in the validation set, respectively. And, the AUC was found to be 0.896 (95% CI: 0.855-0.929) in the training set and 0.866 (95% CI: 0.767-0.920) in the validation set. Additionally, the calibration curves displayed a favorable agreement between the nomogram model and actual observations. The decision curve analysis revealed that employing the nomogram to predict the risk of soiling occurrence would be advantageous if the threshold was between 1 and 73% in the training set and 3-69% in the validation set.

CONCLUSION

This study represents the first efforts to develop and validate a model capable of predicting the postoperative risk of soiling in patients aged greater than 1 year operated for HSCR. This model may assist clinicians in determining the individual risk of soiling subsequent to HSCR surgery, aiding in personalized patient care and management.

Prediction of postoperative health-related quality of life among patients with metastatic spinal cord compression secondary to lung cancer

Background

Health-related quality of life (HRQoL) is a critical aspect of overall well-being for patients with lung cancer, particularly those with metastatic spinal cord compression (MSCC). However, there is currently a lack of universal evaluation of HRQoL in this specific patient population. The aim of this study was to develop a nomogram that can accurately predict HRQoL outcomes in patients with lung cancer-related MSCC.

Methods

A total of 119 patients diagnosed with MSCC secondary to lung cancer were prospectively collected for analysis in the study. The least absolute shrinkage and selection operator (LASSO) regression analysis, along with 10-fold cross-validation, was employed to select the most significant variables for inclusion in the nomogram. Discriminative and calibration abilities were assessed using the concordance index (C-index), discrimination slope, calibration plots, and goodness-of-fit tests. Net reclassification index (NRI) and integrated discrimination improvement (IDI) analyses were conducted to compare the nomogram's performance with and without the consideration of comorbidities.

Results

Four variables were selected to construct the final nomogram, including the Eastern Cooperative Oncology Group (ECOG) score, targeted therapy, anxiety scale, and number of comorbidities. The C-index was 0.87, with a discrimination slope of 0.47, indicating a favorable discriminative ability. Calibration plots and goodness-of-fit tests revealed a high level of consistency between the predicted and observed probabilities of poor HRQoL. The NRI (0.404, 95% CI: 0.074-0.734, p = 0.016) and the IDI (0.035, 95% CI: 0.004-0.066, p = 0.027) confirmed the superior performance of the nomogram with the consideration of comorbidities.

Conclusions

This study develops a prediction nomogram that can assist clinicians in evaluating postoperative HRQoL in patients with lung cancer-related MSCC. This nomogram provides a valuable tool for risk stratification and personalized treatment planning in this specific patient population.