Application of Electronic Health Record Text Mining: Real-World Tolerability, Safety, and Efficacy of Adjuvant Melanoma Treatments.

Introduction: Nivolumab (N), pembrolizumab (P), and dabrafenib plus trametinib (D + T) have been registered as adjuvant treatments for resected stage III and IV melanoma since 2018. Electronic health records (EHRs) are a real-world data source that can be used to review treatments in clinical practice. In this study, we applied EHR text-mining software to evaluate the real-world tolerability, safety, and efficacy of adjuvant melanoma treatments. Methods: Adult melanoma patients receiving adjuvant treatment between January 2019 and October 2021 at the Leiden University Medical Center, the Netherlands, were included. CTcue text-mining software (v3.1.0, CTcue B.V., Amsterdam, The Netherlands) was used to construct rule-based queries and perform context analysis for patient inclusion and data collection from structured and unstructured EHR data. Results: In total, 122 patients were included: 54 patients treated with nivolumab, 48 with pembrolizumab, and 20 with D + T. Significantly more patients discontinued treatment due to toxicity on D + T (N: 16%, P: 6%, D + T: 40%), and X2 (6, n = 122) = 14.6 and p = 0.024. Immune checkpoint inhibitors (ICIs) mainly showed immune-related treatment-limiting adverse events (AEs), and chronic thyroid-related AE occurred frequently (hyperthyroidism: N: 15%, P: 13%, hypothyroidism: N: 20%, P: 19%). Treatment-limiting toxicity from D + T was primarily a combination of reversible AEs, including pyrexia and fatigue. The 1-year recurrence-free survival was 70.3% after nivolumab, 72.4% after pembrolizumab, and 83.0% after D + T. Conclusions: Text-mining EHR is a valuable method to collect real-world data to evaluate adjuvant melanoma treatments. ICIs were better tolerated than D + T, in line with RCT results. For BRAF+ patients, physicians must weigh the higher risk of reversible treatment-limiting AEs of D + T against the risk of long-term immune-related AEs.

Real-world evaluation of supportive care using an electronic health record text-mining tool: G-CSF use in breast cancer patients.

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.

The Effect of Medication Reconciliation via a Patient Portal on Medication Discrepancies: A Randomized Noninferiority Study.

BACKGROUND: Medication reconciliation has become standard care to prevent medication transfer errors. However, this process is time-consuming but could be more efficient when patients are engaged in medication reconciliation via a patient portal. OBJECTIVES: To explore whether medication reconciliation by the patient via a patient portal is noninferior to medication reconciliation by a pharmacy technician. DESIGN (INCLUDING INTERVENTION): Open randomized controlled noninferiority trial. Patients were randomized between medication reconciliation via a patient portal (intervention) or medication reconciliation by a pharmacy technician at the preoperative screening (usual care). SETTING AND PARTICIPANTS: Patients scheduled for elective surgery using at least 1 chronic medication were included. MEASURES: The primary endpoint was the number of medication discrepancies compared to the electronic nationwide medication record system (NMRS). For the secondary endpoint, time investment of the pharmacy technician for the medication reconciliation interview and patient satisfaction were studied. Noninferiority was analyzed with an independent t test, and the margin was set at 20%. RESULTS: A total of 499 patients were included. The patient portal group contained 241 patients; the usual care group contained 258 patients. The number of medication discrepancies was 2.6 ± 2.5 in the patient portal group and 2.8 ± 2.7 in the usual care group. This was not statistically different and within the predefined noninferiority margin. Patients were satisfied with the use of the patient portal tool. Also, the use of the portal can save on average 6.8 minutes per patient compared with usual care. CONCLUSIONS AND IMPLICATIONS: Medication reconciliation using a patient portal is noninferior to medication reconciliation by a pharmacy technician with respect to medication discrepancies, and saves time in the medication reconciliation process. Future studies should focus on identifying patient characteristics for successful implementation of patient portal medication reconciliation.

Liver and kidney function in patients with Covid-19 treated with remdesivir.

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.

An Electronic Health Record Text Mining Tool to Collect Real-World Drug Treatment Outcomes: A Validation Study in Patients With Metastatic Renal Cell Carcinoma.

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.

Prospective Validation of a Risk Prediction Model to Identify High-Risk Patients for Medication Errors at Hospital Admission.

BACKGROUND: Pharmacy-led medication reconciliation in elective surgery patients is often performed at the preoperative screening (POS). Because of the time lag between POS and admission, changes in medication may lead to medication errors at admission (MEAs). In a previous study, a risk prediction model for MEA was developed. OBJECTIVE: To validate this risk prediction model to identify patients at risk for MEAs in a university hospital setting. METHODS: The risk prediction model was derived from a cohort of a Dutch general hospital and validated within a comparable cohort from a Dutch University Medical Centre. MEAs were assessed by comparing the POS medication list with the reconciled medication list at hospital admission. This was considered the gold standard. For every patient, a risk score using the risk prediction model was calculated and compared with the gold standard. The risk prediction model was assessed with receiver operating characteristic (ROC) analysis. RESULTS: 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.

Risk factors for medication errors at admission in preoperatively screened patients.

BACKGROUND: Preoperative screening (POS) may help to reduce medication errors at admission (MEA). However, due to the time window between POS and hospital admission, unintentional medication discrepancies may still occur and thus a second medication reconciliation at hospital admission can be necessary. Insight into potential risk factors associated with these discrepancies would be helpful to focus the second medication reconciliation on high-risk patients. OBJECTIVE: To determine the proportion of POS patients with MEA and to identify risk factors for MEA. METHODS: This single-centre observational cross-sectional study included elective surgical patients between October 26 and December 18, 2015. Main exclusion criteria were age younger than 18 years and daycare admissions. Medication reconciliation took place at the POS and was repeated within 30 hours of admission. Unintended discrepancies between the first and second medication reconciliation were defined as MEA. The primary outcome was the proportion of patients with one or more MEA. The association of this outcome with potential risk factors was analysed using multivariate logistic regression analysis. RESULTS: Of the 183 included patients, 60 (32.8%) patients had at least one MEA. In a multivariate model, the number of medications at POS (adjusted odds ratio 1.16 [95% confidence interval, 1.04-1.30]) and respiratory disease (4.25 [1.52-11.83]) were significantly associated with MEA. CONCLUSION: In one-third of preoperatively screened patients, an MEA was found. The number of medications and respiratory comorbidities are risk factors for MEA in preoperatively screened patients.

Cost-effectiveness of ward-based pharmacy care in surgical patients: protocol of the SUREPILL (Surgery & Pharmacy In Liaison) study.

BACKGROUND: Preventable adverse drug events (pADEs) are widely known to be a health care issue for hospitalized patients. Surgical patients are especially at risk, but prevention of pADEs in this population is not demonstrated before. Ward-based pharmacy interventions seem effective in reducing pADEs in medical patients. The cost-effectiveness of these preventive efforts still needs to be assessed in a comparative study of high methodological standard and also in the surgical population. For these aims the SUREPILL (Surgery & Pharmacy in Liaison) study is initiated. METHODS/DESIGN: A multi-centre controlled trial, with randomisation at ward-level and preceding baseline assessments is designed. Patients admitted to the surgical study wards for elective surgery with an expected length of stay of more than 48 hours will be included. Patients admitted to the intervention ward, will receive ward-based pharmacy care from the clinical pharmacy team, i.e. pharmacy practitioners and hospital pharmacists. This ward-based pharmacy intervention includes medication reconciliation in consultation with the patient at admission, daily medication review with face-to-face contact with the ward doctor, and patient counselling at discharge. Patients admitted in the control ward, will receive standard pharmaceutical care.The primary clinical outcome measure is the number of pADEs per 100 elective admissions. These pADEs will be measured by systematic patient record evaluation using a trigger tool. Patient records positive for a trigger will be evaluated on causality, severity and preventability by an independent expert panel. In addition, an economic evaluation will be performed from a societal perspective with the costs per preventable ADE as the primary economic outcome. Other outcomes of this study are: severity of pADEs, number of patients with pADEs per total number of admissions, direct (non-)medical costs and indirect non-medical costs, extra costs per prevented ADE, number and type of pharmacy interventions, length of hospital stay, complications registered in a national complication registration system for surgery, number of readmissions within three months after initial admission (follow-up), quality of life and number of non-institutionalized days during follow-up. DISCUSSION: This study will assess the cost-effectiveness of ward-based pharmacy care on preventable adverse drug events in surgical patients from a societal perspective, using a comparative study design. TRIAL REGISTRATION: Netherlands Trial Register (NTR): NTR2258.