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PURPOSE Early-phase clinical trials (EP-CTs) are designed to determine optimal dosing, tolerability, and preliminary activity of novel cancer therapeutics. Little is known about the time that patients spend interacting with the health care system (eg, time toxicity) while participating in these studies. METHODS We retrospectively reviewed the electronic health records of consecutive patients enrolled in EP-CTs from 2017 to 2019 to obtain baseline characteristics and number of health care–associated days, defined as all inpatient and outpatient visits while on trial. We used univariable and multivariable analyses to identify predictors of increased time toxicity, defined as the proportion of health care–associated days among total days on trial. For ease of interpretation, we created a dichotomous variable, with high time toxicity defined as ≥20% health care–associated days during time on trial and used regression models to evaluate relationships between time toxicity and clinical outcomes. RESULTS Among 408 EP-CT participants (mean age, 60.5 years standard deviation, SD, 12.6; 56.5% female; 88.2% White; 96.0% non-Hispanic), patients had an average of 22.5% health care–associated days while on trial (SD, 13.8%). Those with GI ( B = 0.07; P = .002), head/neck ( B = 0.09; P = .004), and breast ( B = 0.06; P = .015) cancers and those with worse performance status ( B = 0.04; P = .017) and those receiving targeted therapies ( B = 0.04; P = .014) experienced higher time toxicity. High time toxicity was associated with decreased disease response rates (odds ratio, 0.07; P < .001), progression-free survival (hazard ratio HR, 2.10; P < .001), and overall survival (HR, 2.16; P < .001). CONCLUSION In this cohort of EP-CT participants, patients spent more than one-fifth of days on trial with health care contact. We identified characteristics associated with higher time toxicity and found that high toxicity correlated with worse clinical outcomes. These data could help inform patient-clinician discussions about EP-CTs, guide future trial design, and identify at-risk patients.
Durbin et al. (Mon,) studied this question.