Reducing the Time From Diagnosis to Treatment of Patients With Stage II/III Rectal Cancer at a Large Public Hospital

Surgical Cancer Care in Safety-Net Hospitals: a Systematic Review

BACKGROUND

Tertiary medical centers in the USA provide specialized, high-volume surgical cancer care, contributing standards for quality and outcomes. For the most vulnerable populations, safety-net hospitals (SNHs) remain the predominant provider of both complex and routine healthcare needs. The objective of this study was to evaluate access to and quality of surgical oncology care within SNHs.

METHODS

A comprehensive and systematic review of the literature was conducted using PubMed, EMBASE, and Cochrane Library databases to identify all studies (January 2000-October 2021) reporting the delivery of surgical cancer care at SNHs in the USA (PROSPERO #CRD42021290092). These studies describe the process and/or outcomes of surgical care for gastrointestinal, hepatopancreatobiliary, or breast cancer patients seeking treatment at SNHs.

RESULTS

Of 3753 records, 37 studies met the inclusion criteria. Surgical care for breast cancer (43%) was the most represented, followed by colorectal (30%) and hepatopancreatobiliary (16%) cancers. Financial constraints, cultural and language barriers, and limitations to insurance coverage were cited as common reasons for disparities in care within SNHs. Advanced disease at presentation was common among cancer patients seeking care at SNHs (range, 24-61% of patients). Though reports comparing cancer survival between SNHs and non-SNHs were few, results were mixed, underscoring the variability in care seen across SNHs.

CONCLUSIONS

These findings highlight barriers in care facing many cancer patients. Continued efforts should address improving both access and quality of care for SNH patients. Future models include a transition away from a two-tiered system of resourced and under-resourced hospitals toward an integrated cancer system.

Development of a Multilevel Model to Identify Patients at Risk for Delay in Starting Cancer Treatment

Importance

Delays in starting cancer treatment disproportionately affect vulnerable populations and can influence patients' experience and outcomes. Machine learning algorithms incorporating electronic health record (EHR) data and neighborhood-level social determinants of health (SDOH) measures may identify at-risk patients.

Objective

To develop and validate a machine learning model for estimating the probability of a treatment delay using multilevel data sources.

Design, Setting, and Participants

This cohort study evaluated 4 different machine learning approaches for estimating the likelihood of a treatment delay greater than 60 days (group least absolute shrinkage and selection operator [LASSO], bayesian additive regression tree, gradient boosting, and random forest). Criteria for selecting between approaches were discrimination, calibration, and interpretability/simplicity. The multilevel data set included clinical, demographic, and neighborhood-level census data derived from the EHR, cancer registry, and American Community Survey. Patients with invasive breast, lung, colorectal, bladder, or kidney cancer diagnosed from 2013 to 2019 and treated at a comprehensive cancer center were included. Data analysis was performed from January 2022 to June 2023.

Exposures

Variables included demographics, cancer characteristics, comorbidities, laboratory values, imaging orders, and neighborhood variables.

Main Outcomes and Measures

The outcome estimated by machine learning models was likelihood of a delay greater than 60 days between cancer diagnosis and treatment initiation. The primary metric used to evaluate model performance was area under the receiver operating characteristic curve (AUC-ROC).

Results

A total of 6409 patients were included (mean [SD] age, 62.8 [12.5] years; 4321 [67.4%] female; 2576 [40.2%] with breast cancer, 1738 [27.1%] with lung cancer, and 1059 [16.5%] with kidney cancer). A total of 1621 (25.3%) experienced a delay greater than 60 days. The selected group LASSO model had an AUC-ROC of 0.713 (95% CI, 0.679-0.745). Lower likelihood of delay was seen with diagnosis at the treating institution; first malignant neoplasm; Asian or Pacific Islander or White race; private insurance; and lacking comorbidities. Greater likelihood of delay was seen at the extremes of neighborhood deprivation. Model performance (AUC-ROC) was lower in Black patients, patients with race and ethnicity other than non-Hispanic White, and those living in the most disadvantaged neighborhoods. Though the model selected neighborhood SDOH variables as contributing variables, performance was similar when fit with and without these variables.

Conclusions and Relevance

In this cohort study, a machine learning model incorporating EHR and SDOH data was able to estimate the likelihood of delays in starting cancer therapy. Future work should focus on additional ways to incorporate SDOH data to improve model performance, particularly in vulnerable populations.

Time to initial cancer treatment in the United States and association with survival over time: An observational study

Background

Delays in time to treatment initiation (TTI) for new cancer diagnoses cause patient distress and may adversely affect outcomes. We investigated trends in TTI for common solid tumors treated with curative intent, determinants of increased TTI and association with overall survival.

Methods and findings

We utilized prospective data from the National Cancer Database for newly diagnosed United States patients with early-stage breast, prostate, lung, colorectal, renal and pancreas cancers from 2004–13. TTI was defined as days from diagnosis to first treatment (surgery, systemic or radiation therapy). Negative binomial regression and Cox proportional hazard models were used for analysis. The study population of 3,672,561 patients included breast (N = 1,368,024), prostate (N = 944,246), colorectal (N = 662,094), non-small cell lung (N = 363,863), renal (N = 262,915) and pancreas (N = 71,419) cancers. Median TTI increased from 21 to 29 days (P<0.001). Aside from year of diagnosis, determinants of increased TTI included care at academic center, race, education, prior history of cancer, transfer of facility, comorbidities and age. Increased TTI was associated with worsened survival for stages I and II breast, lung, renal and pancreas cancers, and stage I colorectal cancers, with hazard ratios ranging from 1.005 (95% confidence intervals [CI] 1.002–1.008) to 1.030 (95% CI 1.025–1.035) per week of increased TTI.

Conclusions

TTI has lengthened significantly and is associated with absolute increased risk of mortality ranging from 1.2–3.2% per week in curative settings such as early-stage breast, lung, renal and pancreas cancers. Studies of interventions to ease navigation and reduce barriers are warranted to diminish potential harm to patients.