Identifying barriers individuals face in accessing fertility care after a gynecologic cancer diagnosis.

Objective: To (1) identify the major barriers premenopausal individuals face in accessing fertility care at the time of gynecologic cancer diagnosis and (2) to assess patient experiences pertaining to fertility. Methods: We distributed an online survey about cancer diagnosis and fertility goals to patients ages 18-40 who had been treated for ovarian, endometrial, or cervical cancer at a single, large academic hospital. Descriptive statistics were used to analyze survey results. Patients who completed the survey were given the option to participate in a follow-up virtual interview. We conducted semi-structured interviews to discuss their fertility goals and barriers to these. Grounded theory was used to qualitatively analyze the interviews. Results: Fifty-five patients completed the survey, and 20 patients participated in the interview. The median age at diagnosis was 32 years old. Seventy-three percent of patients recalled that at the time of their diagnosis they were considering future childbearing, and 32% underwent fertility preservation. Patients reported the emotional response to their diagnosis as a barrier to receiving fertility care, with patients reporting lack of control (80%), shock (55%), and confusion (45%). Patients also identified inadequate counseling (60.0%), lack of time (60.0%), economic constraints (55.0%) and prioritization of cancer treatment (55.0%) as barriers. Nearly all patients had a positive interview experience and expressed desire to help patients in similar situations. Conclusion: Many premenopausal patients diagnosed with gynecologic malignancies are considering future childbearing at the time of diagnosis. Both logistical and emotional barriers prevent them from undergoing fertility preservation before initiating oncologic treatment.

Calculating surgical readmission rates in gynecologic oncology: The impact of patient factors.

OBJECTIVE: To determine the 30-day surgical readmission rate after major gynecologic oncology surgeries at a high-volume academic institution and correlated risk factors. METHODS: Retrospective cohort study was conducted of surgical admissions from January 2016 - December 2019 at a single institution. Data were extracted from patient charts, including reason for readmission and length of stay. A readmission rate was calculated. Nested case control design was used to identify correlations between readmission and patient specific risk-factors. Multivariable logistic regression models were used to determine risk factors with readmission. RESULTS: A total of 2152 patients were included. The readmission rate was 3.5%, most commonly due to GI disturbance and surgical site infection. Average readmission length was 5 days. Prior to adjusting for covariates, insurance status, primary diagnosis, index admission length, and disposition at discharge differed between patients who were and were not readmitted. After adjusting for co-variates, younger patients, index admission >2 days, and higher Charlson co-morbidity index were associated with readmission. CONCLUSIONS: Our surgical readmission rate was lower than previously reported rates in gynecologic oncology patients. Patient factors associated with readmission included younger age, longer index hospital admission, and higher medical co-morbidity index scores. Provider factors and institutional practice patterns could contribute to the decreased readmission rate. These findings underscore the importance of standardizing how we calculate readmission rate and interpret these data. Varying readmission rates and institutional practice patterns deserve closer scrutiny to inform best practice and future policies.

HE4 and CA125 serum biomarker monitoring in women with epithelial ovarian cancer.

BACKGROUND: CA125 is the gold standard serum biomarker for monitoring patients with epithelial ovarian cancer (EOC). Human epididymal protein 4 (HE4) is a novel serum biomarker for EOC patients. OBJECTIVE: The objective of this trial was to examine the utility of measuring serum HE4 levels for monitoring EOC patients and to compare HE4 performance parameters to serum CA125. METHODS: A retrospective trial using residual longitudinal serum samples drawn during treatment and monitoring from EOC patients. Serum CA125 and HE4 levels were analyzed at each time point, and a velocity of change was calculated and correlated with clinical status. The null hypothesis was that HE4 is inferior to CA125, and this was tested using concordance and two-sided Fisher's exact testing. McNemar's test was used to assess the overall agreement of the two assays with the clinical status. RESULTS: A total of 129 patients with 272 separate clinical periods and 1739 events (serum samples) were evaluated. Using a 25% change in serum biomarker levels to indicate change in disease status, the accuracy and NPV determined for HE4 versus CA125 were 81.8% versus 82.6% (p = 0.846) and 87.4% versus 89.7% (p = 0.082), respectively. Concordance comparison of HE4 accuracy / CA125 accuracy was 0.990, indicating HE4 was not inferior to CA125 (McNemar's test p-value = 0.522). Performing a velocity of change analysis, the accuracy and NPV determined for HE4 versus CA125 were 78.3% versus 78.6% (p = 0.995) and 74.9% versus 76.3% (p = 0.815), respectively. Concordance comparison of HE4 velocity accuracy / CA125 velocity accuracy was 0.996, again indicating HE4 was not inferior to CA125 (McNemar's test p-value = 0.884). The combination of HE4 and CA125 velocity changes showed a similar accuracy of 81.3% (p = 0.797 compared to HE4 and CA125 alone) and NPV of 81.1% (p≥0.172 compared to HE4 and CA125 alone), and an increased sensitivity of 70.5% (p≤0.070 compared to HE4 and CA125 alone). CONCLUSION: HE4 is equivalent to CA125 for monitoring of EOC patients. The combination of CA125 and HE4 velocities is superior to either marker alone.