Incidence and Economic Burden of Infections in Cancer Patients Receiving Immune Checkpoint Inhibitors: A Retrospective Cohort Study
Bibliographic record
Abstract
Abstract BACKGROUNDAlong with antitumor effects, Immune Checkpoint Inhibitors (ICPI) have shown great potential in treating chronic infections such as HIV, Hepatitis B and malaria, in ex-vivo studies. However, several case reports and case series have suggested an increased infection risk in cancer patients. The purpose of our study was to assess the risk of infections in cancer patients receiving ICPI. We also attempted to evaluate the role of a multidisciplinary approach (Oncology and Infectious disease specialists) and the cost associated with treatment. METHODS:Records on all cancer patients over age ≥18 years old who had received at least one dose of ICPI between 2015 to 2018 at a major community teaching hospital in the central Massachusetts region were reviewed. Several risk factors associated with infection were identified. A two-tailed, unpaired t-test was used to analyze the association between risk factors and infection. We calculated the cumulative length of stay (LOS) and cost per admission with a multidisciplinary vs. non-multidisciplinary approach. The calculated total average cost per admission was compared to a matched population (without an oncologic diagnosis) admitted with infections similar to that in our study, to compare the economic burden. RESULTSRetrospective chart review of 169 cancer patients receiving ICPI showed sixty-two episodes of infection in thirty-seven (21.8%) patients and a mortality rate of 3.5% due to associated complications. Risk factors like COPD, prior chemotherapy and steroid use were significantly associated (P<0.05) with infections. Further sub-group analysis showed increase in cumulative LOS from 5.9 to 8.1 days but approximately similar average cost per admission ($52,047 vs. $54,510) with non-multidisciplinary vs. multidisciplinary approach. The calculated total cost per admission during an episode of infection in this cohort of patients was $35,484; three-fold higher when matched to similar infections in a general non-oncologic population ($11,527). CONCLUSIONSA significant incidence of infections and associated health care resource utilization continues to prevail in cancer patients despite the utility of ICPI. A multidisciplinary approach to manage the infections and associated complications in cancer patients receiving ICPI increased the cumulative LOS but not the average cost per admission.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".