br The following clinical data
The following clinical data were obtained: demographics (age, sex, and race), metastasis or relapse, ECOG PS, site of metastasis, previous treatment, MAX2 index [24,25], unplanned hospitaliza-tion, and adverse events (AEs), including abnormal laboratory
Characteristics All patients (N = 153)
a First line chemotherapy for metastatic disease. May or may not include monoclonal Calpain Inhibitor I ALLN as part of the regimen.
findings, graded with the National Cancer Institute Common Termi-nology Criteria for AEs (CTCAE) ver.4.0. The MAX2 index is a vali-dated tool that allows a comparison of diverse chemotherapy regimens for their risk of grade 4 hematologic or grade 3–4 non-hematologic toxicity (“severe toxicity”). Adverse events and unplanned hospitalizations were computed as the first occurrence after initiation of their frontline chemotherapy. Overall survival (OS) was also collected. All data were collected from electronic medical records of Moffitt Cancer Center and Total Cancer Care Database. We analyzed the association of comorbidities with OS, adverse events, and unplanned hospitalization by the Cox propor-tional hazard regression model. Of note, as various molecular tests were progressively introduced during this period, many patients did not have this information available, and therefore this data was not included in this analysis.
Heat maps visualized the comorbidity distribution in the following way. Each patient was attributed a line. Organ systems were attributed columns, and the heat color was based on each organ's CIRS-G severity rating, from blue (0) to red (4). The comorbidity types and levels of ex-pression were divided by their association with adverse events, overall survival and unplanned hospitalization.
This study was approved by the Institutional Review Board of the University of South Florida.
Fig. 1. Comorbidity distribution in the cohort according to CIRS-G grade.
Adverse events of all patients.
Patients' clinical and demographical characteristics were summarized using descriptive statistics: frequency and proportion for categorical measures and mean, standard deviation, median, and range for continu-ous measures. OS was measured from the date of diagnosis of metastatic disease to date of death or last follow-up date. The survival function was estimated by the Kaplan-Meier method, and the difference between the functions was assessed by the log-rank test. The Cox proportional haz-ards regression model was used to assess the association with OS. In ad-dition to the CIRS-G scores mentioned above, we tested the performance of a TRS we had developed in a previous project . The score was then constructed as follows: the impact of comorbidity on OS was evaluated, and the risk score was developed based on the hazard ratio and signifi-cance; risk score 1 was given to those osmoregulators had CIRS-G categories with
Hematologic toxicity of all grades, n (%) 17 (11.1) Grade 4 hematologic toxicity
p-value of b0.1 and hazard ratio of 1 to 2, and score 2 was assigned to those with p-value of b0.1 and hazard ratio of N2. The TRS was defined as the sum of risk scores. Based on TRS, patients were divided into two risk groups. The high risk patients were defined as those who had a TRS of 2 or more, while the low risk patients were those who had a TRS of 0 or 1. The association with binary endpoints such as unplanned
Total CIRS-G score
Fig. 2. Distribution of patients according to CIRS-G total scores.
Fig. 3. Comorbidity heat map for hematologic toxicity (A) and non-hematologic toxicity (B).
hospitalization, non-hematologic, and hematologic toxicity was assessed by the logistic regression model. The multivariable models for the un-planned hospitalization and OS were built by the backward elimination method, when adjusting for potential confounding variables. A variable
with two-sided p-value of N0.05 was eliminated at each step. No multiple comparisons were considered. All p-values were two-sided and p-value of b0.05 was considered statistically significant. All data analysis was conducted by SAS version 13.1, and heat maps were created by R.
3.1. Patient Characteristics
Comorbidity distribution for all patients according to CIRS-G scores is shown in Fig. 1. All patients had at least one comorbidity. The most common comorbidities were vascular (79.8%), eye/ear/nose/throat (68%), and respiratory disease (52.4%). The median total CIRS-G score was 8 (1−20), and the distribution is shown in Fig. 2. The median sever-ity index (total score/number of categories) was 0.57 (0.07–1.43). Pa-tients with eye/ear/nose/throat and endocrine/metabolic/breast disease had relatively lower CIRS-G score comorbidity than those with vascular and respiratory disease. Categories with the highest proportion of CIRS-G score 3 or 4 were vascular (29.4%), respiratory (13.1%) and cardiac disease (11.1%). Fourty-four patients (28.8%) had one level 3 comorbidity, 27(17.7%) had two, and 6(3.9%) had three. Eleven patients(7.2%) had one level 4 comorbidity, and 1(0.7%) had two.