Development of prediction model for identification of drug-related problems in ovarian cancer patients

Ovarian cancer (OC) is often detected at an advanced stage of disease and needs complex anticancer treatment regimens to manage it. Anticancer drugs are highly toxic and act on normal and malignant cells, which may increase the occurrence of drug-related problems (DRPs). OC patients undergoing anticancer therapy are anticipated to produce serious adverse health outcomes. DRPs are the events linked to drug therapy that possibly affect healthcare outcomes. Management of such events is challenging. DRPs must be promptly identified and resolved on time to optimize patient care and prevent extra health-related burdens. The Prediction model will help the healthcare team prevent the occurrence of DRPs in patients and reduce adverse effects due to DRPs. The current study was a prospective observational study conducted among OC patients. Enrolled patients were screened for possible DRPs throughout their hospital admission. A prediction model for DRPs was developed and validated using stepwise linear regression. The cumulative medication entries were 1836 among 143 patients. Most patients were in an extreme polypharmacy condition (80.42%). 724 DRPs were observed among 142 patients, consisting of actual DRPs (44.61%, n = 323) and potential DRPs (55.39%, n = 401). 56.34% of the patients had ≥ 5 DRPs. Age, length of stay, stage of cancer, line of anticancer agents, anticancer agent cycle, comorbidities, and number of drugs prescribed were the substantial predictors for DRPs. This study reveals a higher prevalence of DRPs in ovarian cancer patients and triggers the implementation of therapeutic vigilance for early identification and timely management of DRPs. Timely management of DRPs can help reduce the treatment burden.