For the treatment of Covid‐19 patients with remdesivir, poor renal and liver function were both exclusion criteria in randomized clinical trials and contraindication for treatment. Also, nephrotoxicity and hepatotoxicity are reported as adverse events. We retrospectively reviewed renal and liver functions of Covid‐19 103 patients who received remdesivir in the 15 days after treatment initiation. Approximately 20% of the patient population met randomized clinical trial exclusion criteria. In total, 11% of the patients had a decrease in estimated glomerular filtration rate >10 mL/min/1.73m2. Also, 25 and 35% had increased alanine transaminase and aspartate transaminase levels, respectively. However, serious adverse events were limited. Therefore, based on these preliminary results, contraindications based on kidney and liver function should not be absolute for remdesivir treatment in patients with Covid‐19 if these functions are monitored regularly. A larger patient cohort is warranted to confirm our results.

Real‐world evidence can close the inferential gap between marketing authorization studies and clinical practice. However, the current standard for real‐world data extraction from electronic health records (EHRs) for treatment evaluation is manual review (MR), which is time‐consuming and laborious. Clinical Data Collector (CDC) is a novel natural language processing and text mining software tool for both structured and unstructured EHR data and only shows relevant EHR sections improving efficiency. We investigated CDC as a real‐world data (RWD) collection method, through application of CDC queries for patient inclusion and information extraction on a cohort of patients with metastatic renal cell carcinoma (RCC) receiving systemic drug treatment. Baseline patient characteristics, disease characteristics, and treatment outcomes were extracted and these were compared with MR for validation. One hundred patients receiving 175 treatments were included using CDC, which corresponded to 99% with MR. Calculated median overall survival was 21.7 months (95% confidence interval (CI) 18.7–24.8) vs. 21.7 months (95% CI 18.6–24.8) and progression‐free survival 8.9 months (95% CI 5.4–12.4) vs. 7.6 months (95% CI 5.7–9.4) for CDC vs. MR, respectively. Highest F1‐score was found for cancer‐related variables (88.1–100), followed by comorbidities (71.5–90.4) and adverse drug events (53.3–74.5), with most diverse scores on international metastatic RCC database criteria (51.4–100). Mean data collection time was 12 minutes (CDC) vs. 86 minutes (MR). In conclusion, CDC is a promising tool for retrieving RWD from EHRs because the correct patient population can be identified as well as relevant outcome data, such as overall survival and progression‐free survival.

The number of treatment options for patients with metastatic renal cell carcinoma (mRCC) has significantly grown in the last 15 years. Although randomized controlled trials are fundamental in investigating mRCC treatment efficacy, their external validity can be limited. Therefore, the efficacy of the different treatment options should also be evaluated in clinical practice. We performed a chart review of electronic health records using text mining software to study the current treatment patterns and outcomes. mRCC patients from two large hospitals in the Netherlands, starting treatment between January 2015 and May 2020, were included. Data were collected from electronic health records using a validated text mining tool. Primary endpoints were progression-free survival (PFS) and overall survival (OS). Statistical analyses were performed using the Kaplan–Meier method. Most frequent first-line treatments were pazopanib (n = 70), sunitinib (n = 34), and nivolumab with ipilimumab (n = 28). The overall median PFS values for first-line treatment were 15.7 months (95% confidence interval [95%CI], 8.8–20.7), 16.3 months (95%CI, 9.3–not estimable [NE]) for pazopanib, and 6.9 months (95% CI, 4.4–NE) for sunitinib. The overall median OS values were 33.4 months (95%CI, 28.1–50.9 months), 39.3 months (95%CI, 29.5–NE) for pazopanib, and 28.1 months (95%CI, 7.0–NE) for sunitinib. For nivolumab with ipilimumab, median PFS and median OS were not reached. Of the patients who finished first- and second-line treatments, 64 and 62% received follow-up treatments, respectively. With most patients starting on pazopanib and sunitinib, these real-world treatment outcomes were most likely better than in pivotal trials, which may be due to extensive follow-up treatments.

Purpose Chemotherapy-induced febrile neutropenia (FN) is a life-threatening and chemotherapy dose-limiting adverse event. FN can be prevented with granulocyte-colony stimulating factors (G-CSFs). Guidelines recommend primary G-CSF use for patients receiving either high (> 20%) FN risk (HR) chemotherapy, or intermediate (10–20%) FN risk (IR) chemotherapy if the overall risk with additional patient-related risk factors exceeds 20%. In this study, we applied an EHR text-mining tool for real-world G-CSF treatment evaluation in breast cancer patients. Methods Breast cancer patients receiving IR or HR chemotherapy treatments between January 2015 and February 2021 at LUMC, the Netherlands, were included. We retrospectively collected data from EHR with a text-mining tool and assessed G-CSF use, risk factors, and the FN and neutropenia (grades 3–4) and incidence. Results A total of 190 female patients were included, who received 77 HR and 113 IR treatments. In 88.3% of the HR regimens, G-CSF was administered; 7.3% of these patients developed FN vs. 33.3% without G-CSF. Although most IR regimen patients had ≥ 2 risk factors, only 4% received G-CSF, of which none developed neutropenia. However, without G-CSF, 11.9% developed FN and 31.2% severe neutropenia. Conclusions Our text-mining study shows high G-CSF use among HR regimen patients, and low use among IR regimen patients, although most had ≥ 2 risk factors. Therefore, current practice is not completely in accordance with the guidelines. This shows the need for increased awareness and clarity regarding risk factors. Also, text-mining can effectively be implemented for the evaluation of patient care.

Of 368 included patients, 167 (45.4%) had at least 1 MEA. ROC analysis revealed significant differences in the area under the curve of 0.535 ( P = 0.26; validation cohort) versus 0.752 ( P < 0.0001; derivation cohort). The sensitivity in this validating cohort was 66%, with a specificity of 40%. Conclusion and Relevance: The risk prediction model developed in a general hospital population is not suitable to identify patients at risk for MEA in a university hospital population. However, number of medications is a common risk factor in both patient populations and should, thus, form the basis of an adapted risk prediction model.