Should age be a criterion for intensive care unit admission in cancer patients?—Still an issue of uncertainty

Daganou, Maria; Kyriakoudi, Anna; Koutsoukou, Antonia

doi:10.21037/jtd.2017.08.161

Cited by 1 publication

(1 citation statement)

References 19 publications

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“…Comparison of the results for the cohort to those for the underlying treatment subgroups suggests that PF research could benefit from differentiating between specific (surgical) treatments . To illustrate, there has been debate on whether age should always be considered when determining the risk of ICU admission 16 . We found age to be a PF for ICU admission risk in some treatments, but not all.…”

Section: Discussionmentioning

confidence: 99%

Identifying prognostic factors for clinical outcomes and costs in four high-volume surgical treatments using routinely collected hospital data

Salet

Stangenberger

Eijkenaar

et al. 2022

Sci Rep

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Identifying prognostic factors (PFs) is often costly and labor-intensive. Routinely collected hospital data provide opportunities to identify clinically relevant PFs and construct accurate prognostic models without additional data-collection costs. This multicenter (66 hospitals) study reports on associations various patient-level variables have with outcomes and costs. Outcomes were in-hospital mortality, intensive care unit (ICU) admission, length of stay, 30-day readmission, 30-day reintervention and in-hospital costs. Candidate PFs were age, sex, Elixhauser Comorbidity Score, prior hospitalizations, prior days spent in hospital, and socio-economic status. Included patients dealt with either colorectal carcinoma (CRC, n = 10,254), urinary bladder carcinoma (UBC, n = 17,385), acute percutaneous coronary intervention (aPCI, n = 25,818), or total knee arthroplasty (TKA, n = 39,214). Prior hospitalization significantly increased readmission risk in all treatments (OR between 2.15 and 25.50), whereas prior days spent in hospital decreased this risk (OR between 0.55 and 0.95). In CRC patients, women had lower risk of in-hospital mortality (OR 0.64), ICU admittance (OR 0.68) and 30-day reintervention (OR 0.70). Prior hospitalization was the strongest PF for higher costs across all treatments (31–64% costs increase/hospitalization). Prognostic model performance (c-statistic) ranged 0.67–0.92, with Brier scores below 0.08. R-squared ranged from 0.06–0.19 for LoS and 0.19–0.38 for costs. Identified PFs should be considered as building blocks for treatment-specific prognostic models and information for monitoring patients after surgery. Researchers and clinicians might benefit from gaining a better insight into the drivers behind (costs) prognosis.

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Section: Discussionmentioning

confidence: 99%