Development of a Clinical Prediction Model for 1-Year Mortality in Patients With Advanced Cancer

Development and external validation of a risk prediction model for depression in patients with coronary heart disease

BACKGROUND

Depression is an independent risk factor for adverse outcomes of coronary heart disease (CHD). This study aimed to develop a depression risk prediction model for CHD patients.

METHODS

This study utilized data from the National Health and Nutrition Examination Survey (NHANES). In the training set, reference literature, logistic regression, LASSO regression, optimal subset algorithm, and machine learning random forest algorithm were employed to screen prediction variables, respectively. The optimal prediction model was selected based on the C-index, Net Reclassification Improvement (NRI), and Integrated Discrimination Improvement (IDI). A nomogram for the optimal prediction model was constructed. 3 external validations were performed.

RESULTS

The training set comprised 1375 participants, with a depressive symptoms prevalence of 15.2 %. The optimal prediction model was constructed using predictors obtained from optimal subsets algorithm (C-index = 0.774, sensitivity = 0.751, specificity = 0.685). The model includes age, gender, education, marriage, diabetes, tobacco use, antihypertensive drugs, high-density lipoprotein cholesterol (HDLC), and aspartate aminotransferase (AST). The model demonstrated consistent discrimination ability, accuracy, and clinical utility across the 3 external validations.

LIMITATIONS

The applicable population of the model is CHD patients. And the clinical benefits of interventions based on the prediction results are still unknown.

CONCLUSION

We developed a depression risk prediction model for CHD patients, which was presented in the form of a nomogram for clinical application.

Extracting lung cancer staging descriptors from pathology reports: A generative language model approach

BACKGROUND

In oncology, electronic health records contain textual key information for the diagnosis, staging, and treatment planning of patients with cancer. However, text data processing requires a lot of time and effort, which limits the utilization of these data. Recent advances in natural language processing (NLP) technology, including large language models, can be applied to cancer research. Particularly, extracting the information required for the pathological stage from surgical pathology reports can be utilized to update cancer staging according to the latest cancer staging guidelines.

OBJECTIVES

This study has two main objectives. The first objective is to evaluate the performance of extracting information from text-based surgical pathology reports and determining pathological stages based on the extracted information using fine-tuned generative language models (GLMs) for patients with lung cancer. The second objective is to determine the feasibility of utilizing relatively small GLMs for information extraction in a resource-constrained computing environment.

METHODS

Lung cancer surgical pathology reports were collected from the Common Data Model database of Seoul National University Bundang Hospital (SNUBH), a tertiary hospital in Korea. We selected 42 descriptors necessary for tumor-node (TN) classification based on these reports and created a gold standard with validation by two clinical experts. The pathology reports and gold standard were used to generate prompt-response pairs for training and evaluating GLMs which then were used to extract information required for staging from pathology reports.

RESULTS

We evaluated the information extraction performance of six trained models as well as their performance in TN classification using the extracted information. The Deductive Mistral-7B model, which was pre-trained with the deductive dataset, showed the best performance overall, with an exact match ratio of 92.24% in the information extraction problem and an accuracy of 0.9876 (predicting T and N classification concurrently) in classification.

CONCLUSION

This study demonstrated that training GLMs with deductive datasets can improve information extraction performance, and GLMs with a relatively small number of parameters at approximately seven billion can achieve high performance in this problem. The proposed GLM-based information extraction method is expected to be useful in clinical decision-making support, lung cancer staging and research.

Machine Learning to Allocate Palliative Care Consultations During Cancer Treatment

PURPOSE

For patients with advanced cancer, early consultations with palliative care (PC) specialists reduce costs, improve quality of life, and prolong survival. However, capacity limitations prevent all patients from receiving PC shortly after diagnosis. We evaluated whether a prognostic machine learning system could promote early PC, given existing capacity.

METHODS

Using population-level administrative data in Ontario, Canada, we assembled a cohort of patients with incurable cancer who received palliative-intent systemic therapy between July 1, 2014, and December 30, 2019. We developed a machine learning system that predicted death within 1 year of each treatment using demographics, cancer characteristics, treatments, symptoms, laboratory values, and history of acute care admissions. We trained the system in patients who started treatment before July 1, 2017, and evaluated the potential impact of the system on PC in subsequent patients.

RESULTS

Among 560,210 treatments received by 54,628 patients, death occurred within 1 year of 45.2% of treatments. The machine learning system recommended the same number of PC consultations observed with usual care at the 60.0% 1-year risk of death, with a first-alarm positive predictive value of 69.7% and an outcome-level sensitivity of 74.9%. Compared with usual care, system-guided care could increase early PC by 8.5% overall (95% CI, 7.5 to 9.5; P < .001) and by 15.3% (95% CI, 13.9 to 16.6; P < .001) among patients who live 6 months beyond their first treatment, without requiring more PC consultations in total or substantially increasing PC among patients with a prognosis exceeding 2 years.

CONCLUSION

Prognostic machine learning systems could increase early PC despite existing resource constraints. These results demonstrate an urgent need to deploy and evaluate prognostic systems in real-time clinical practice to increase access to early PC.

Comparing Clinician Estimates versus a Statistical Tool for Predicting Risk of Death within 45 Days of Admission for Cancer Patients

OBJECTIVES

 While clinical practice guidelines recommend that oncologists discuss goals of care with patients who have advanced cancer, it is estimated that less than 20% of individuals admitted to the hospital with high-risk cancers have end-of-life discussions with their providers. While there has been interest in developing models for mortality prediction to trigger such discussions, few studies have compared how such models compare with clinical judgment to determine a patient's mortality risk.

METHODS

 This study is a prospective analysis of 1,069 solid tumor medical oncology hospital admissions (n = 911 unique patients) from February 7 to June 7, 2022, at Memorial Sloan Kettering Cancer Center. Electronic surveys were sent to hospitalists, advanced practice providers, and medical oncologists the first afternoon following a hospital admission and they were asked to estimate the probability that the patient would die within 45 days. Provider estimates of mortality were compared with those from a predictive model developed using a supervised machine learning methodology, and incorporated routine laboratory, demographic, biometric, and admission data. Area under the receiver operating characteristic curve (AUC), calibration and decision curves were compared between clinician estimates and the model predictions.

RESULTS

 Within 45 days following hospital admission, 229 (25%) of 911 patients died. The model performed better than the clinician estimates (AUC 0.834 vs. 0.753, p < 0.0001). Integrating clinician predictions with the model's estimates further increased the AUC to 0.853 (p < 0.0001). Clinicians overestimated risk whereas the model was extremely well-calibrated. The model demonstrated net benefit over a wide range of threshold probabilities.

CONCLUSION

 The inpatient prognosis at admission model is a robust tool to assist clinical providers in evaluating mortality risk, and it has recently been implemented in the electronic medical record at our institution to improve end-of-life care planning for hospitalized cancer patients.

Risk Prediction of Emergency Department Visits in Patients With Lung Cancer Using Machine Learning: Retrospective Observational Study

BACKGROUND

Patients with lung cancer are among the most frequent visitors to emergency departments due to cancer-related problems, and the prognosis for those who seek emergency care is dismal. Given that patients with lung cancer frequently visit health care facilities for treatment or follow-up, the ability to predict emergency department visits based on clinical information gleaned from their routine visits would enhance hospital resource utilization and patient outcomes.

OBJECTIVE

This study proposed a machine learning-based prediction model to identify risk factors for emergency department visits by patients with lung cancer.

METHODS

This was a retrospective observational study of patients with lung cancer diagnosed at Seoul National University Bundang Hospital, a tertiary general hospital in South Korea, between January 2010 and December 2017. The primary outcome was an emergency department visit within 30 days of an outpatient visit. This study developed a machine learning-based prediction model using a common data model. In addition, the importance of features that influenced the decision-making of the model output was analyzed to identify significant clinical factors.

RESULTS

The model with the best performance demonstrated an area under the receiver operating characteristic curve of 0.73 in its ability to predict the attendance of patients with lung cancer in emergency departments. The frequency of recent visits to the emergency department and several laboratory test results that are typically collected during cancer treatment follow-up visits were revealed as influencing factors for the model output.

CONCLUSIONS

This study developed a machine learning-based risk prediction model using a common data model and identified influencing factors for emergency department visits by patients with lung cancer. The predictive model contributes to the efficiency of resource utilization and health care service quality by facilitating the identification and early intervention of high-risk patients. This study demonstrated the possibility of collaborative research among different institutions using the common data model for precision medicine in lung cancer.

Biceps brachii muscle cross-sectional area measured by ultrasonography is independently associated with one-month mortality: A prospective observational study

PURPOSE

Studies examining mortality in palliative care units are limited. We aimed to investigate the mortality and associated factors including ultrasonographic muscle parameters in hospitalized palliative patients with a subgroup analysis of older patients.

METHODS

A prospective-observational study. We recorded the demographics, number of diseases, diagnoses, and the Charlson comorbidity index (CCI), palliative performance scale (PPS), and nutritional risk screening-2002 (NRS-2002) scores. We noted the nutritional parameters and mortality. We measured the subcutaneous fat thickness (SFT), muscle thickness (MT), and cross-sectional area (CSA) of the rectus femoris and biceps brachii using ultrasonography.

RESULTS

We enrolled 100 patients (mean age: 73.2 ± 16.4 years, 53%: female). One-month mortality was 42%. The non-survivors had significantly higher malignancy, increased CCI and NRS-2002 scores, lower required energy intake, calorie sufficiency rate, and biceps brachii SFT, MT, and CSA than the survivors. The independent mortality predictors were malignancy and biceps brachii CSA while the PPS score and malignancy were significantly associated with mortality in the older subgroup.

CONCLUSION

The malignancy and biceps brachii CSA might have prognostic value in predicting mortality in palliative patients. This was the first study investigating the mortality-associated factors including ultrasonographic muscle measurements of both the lower and upper limbs in a palliative care center.