A supervised machine learning model for identifying predictive factors for recommending head and neck cancer surgery.

BACKGROUND: New patient referrals are often processed by practice coordinators with little-to-no medical background. Treatment delays due to incorrect referral processing, however, have detrimental consequences. Identifying variables that are associated with a higher likelihood of surgical oncological resection may improve patient referral processing and expedite the time to treatment. The study objective is to develop a supervised machine learning (ML) platform that identifies relevant variables associated with head and neck surgical resection. METHODS: A retrospective cohort study was conducted on 64 222 patient datapoints from the SEER database. RESULTS: The random forest ML model correctly classified patients who were offered head and neck surgery with an 81% accuracy rate. The sensitivity and specificity rates were 86% and 71%. The positive and negative predictive values were 85% and 73%. CONCLUSIONS: ML modeling accurately predicts head and neck cancer surgery recommendations based on patient and cancer information from a large population-based dataset. ML adjuncts for referral processing may decrease the time to treatment for patients with cancer.

Selective serotonin reuptake inhibitors may lead to improved cataract surgery outcomes in patients with amblyopia.

PURPOSE: To evaluate the effects of selective serotonin reuptake inhibitor (SSRI)/serotonin norepinephrine reuptake inhibitor (SNRI) medications in combination with cataract surgery in treating amblyopia in adult patients. PATIENTS AND METHODS: A retrospective chart review study was conducted on patients who had undergone cataract surgery at the Johns Hopkins Hospital Wilmer Eye Institute. Six inclusion criteria were used to assess patient eligibility: 1) >18 years of age, 2) diagnosis of amblyopia, 3) diagnosis of cataract and treatment with surgery, 4) electronic medical record contains pre-surgery and post-surgery visual acuity (VA) measurements, 5) electronic medical record contains information on whether the patient was ever prescribed a SSRI/SNRI and the treatment duration, and 6) interocular VA difference of two lines or more on Snellen chart prior to cataract surgery. From each record, preoperative VA, postoperative VA, date of surgery, date at which postoperative VA was measured, and age at surgery were collected. RESULTS: A total of 237 patients were included, with 38 of them being on SSRI/SNRI. The mean improvement in VA after surgery was not significantly greater in patients on SSRI/SNRI (SSRI/SNRI: -0.276 logMAR, control: -0.192 logMAR, p=0.15). Multivariable regression was subsequently performed and while holding all other variables constant, demonstrated a statistically significant improvement in VA in patients on SSRI/SNRI (95% CI: -0.194, -0.0116, p=0.03). The regression analysis further demonstrated that advanced age has an adverse effect on the change in post-op VA (CI: 3.34×10-3 logMAR, 9.77×10-3 logMAR, p<0.005). Worse baseline VA is associated with a greater improvement in post-op VA (95% CI: -0.659 logMAR, -0.463 logMAR, p<0.005) but adverse effect on the absolute post-op VA (95% CI: 0.341 logMAR, 0.544 logMAR, p<0.005). CONCLUSION: This study suggests that patients with amblyopia undergoing cataract surgery may potentially have a greater visual improvement when treated with SSRI/SNRIs.