The impact of the COVID-19 pandemic on the diagnosis, stage, and treatment of esophagogastric cancer.

BACKGROUND: The COVID-19 pandemic has affected the entire global healthcare system, including oncological care. This study investigated the effects of the COVID-19 pandemic on the diagnosis, stage, and treatment of esophagogastric cancer in the Netherlands. METHODS: Patients diagnosed in 2020 were divided into 5 periods, based on the severity of the COVID-19 pandemic in the Netherlands, and compared to patients diagnosed in the same period in the years 2017-2019. Patient characteristics and treatments were evaluated for esophageal cancer (EC) and gastric cancer (GC) separately. RESULTS: The number of esophagogastric cancer diagnoses decreased prominently during the first 2 months of the COVID-19 pandemic. During this period, a significantly higher percentage of GC patients was diagnosed with incurable disease (52.5% in 2017-2019 and 67.7% in 2020, p = 0.011). We observed a significant reduction in the percentage of patients with potentially curable EC treated with resection and neoadjuvant chemoradiotherapy (from 35.0% in 2017-2019 to 27.3% in 2020, p < 0.001). Also, patients diagnosed with incurable GC were treated less frequently with a resection (from 4.6% in 2017-2019 to 1.5% in 2020, p = 0.009) in the second half of 2020. CONCLUSIONS: Compared to previous years, the number of esophagogastric cancer diagnoses decreased in the first 2 months of the COVID-19 pandemic, while an increased percentage of patients was diagnosed with incurable disease. Both in the curative and palliative setting, patients were less likely to be treated with a surgical resection.

Predicting Clinically Relevant Patient-Reported Symptom Improvement After Carpal Tunnel Release: A Machine Learning Approach.

BACKGROUND: Symptom improvement is an important goal when considering surgery for carpal tunnel syndrome. There is currently no prediction model available to predict symptom improvement for patients considering a carpal tunnel release (CTR). OBJECTIVE: To predict using a model the probability of clinically relevant symptom improvement at 6 mo after CTR. METHODS: We split a cohort of 2119 patients who underwent a mini-open CTR and completed the Boston Carpal Tunnel Questionnaire preoperatively and 6 mo postoperatively into training (75%) and validation (25%) data sets. Patients who improved more than the minimal clinically important difference of 0.8 at the Boston Carpal Tunnel Questionnaire-symptom severity scale were classified as "improved." Logistic regression, random forests, and gradient boosting machines were considered to train prediction models. The best model was selected based on discriminative ability (area under the curve) and calibration in the validation data set. This model was further assessed in a holdout data set (N = 397). RESULTS: A gradient boosting machine with 5 predictors was chosen as optimal trade-off between discriminative ability and the number of predictors. In the holdout data set, this model had an area under the curve of 0.723, good calibration, sensitivity of 0.77, and specificity of 0.55. The positive predictive value was 0.50, and the negative predictive value was 0.81. CONCLUSION: We developed a prediction model for clinically relevant symptom improvement 6 mo after a CTR, which required 5 patient-reported predictors (18 questions) and has reasonable discriminative ability and good calibration. The model is available online and might help shared decision making when patients are considering a CTR.