2020
DOI: 10.1038/s41598-020-74910-3
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Sex-specific outcome disparities in very old patients admitted to intensive care medicine: a propensity matched analysis

Abstract: Female and male very elderly intensive patients (VIPs) might differ in characteristics and outcomes. We aimed to compare female versus male VIPs in a large, multinational collective of VIPs with regards to outcome and predictors of mortality. In total, 7555 patients were included in this analysis, 3973 (53%) male and 3582 (47%) female patients. The primary endpoint was 30-day-mortality. Baseline characteristics, data on management and geriatric scores including frailty assessed by Clinical Frailty Scale (CFS) … Show more

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Cited by 12 publications
(15 citation statements)
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References 32 publications
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“…Three studies recorded limitation of life-sustaining treatment (LLST) at ICU admission, there was no statistically significant difference in the prevalence of LLST among women and men in these studies (20,23,31). No study recorded decisions to withhold or withdraw specific treatments such as MV or RRT.…”
Section: Characteristics Of Study Participantsmentioning
confidence: 99%
“…Three studies recorded limitation of life-sustaining treatment (LLST) at ICU admission, there was no statistically significant difference in the prevalence of LLST among women and men in these studies (20,23,31). No study recorded decisions to withhold or withdraw specific treatments such as MV or RRT.…”
Section: Characteristics Of Study Participantsmentioning
confidence: 99%
“…In very old patients, mortality was higher in female patients and in those who did not receive mechanical ventilation or vasopressors. We are aware of the limitations of subgroup analyses (44), and we demonstrated recently that there were no clinically relevant differences between the sexes in septic patients (45,46). However, the trend toward a higher mortality in patients that did not receive intubation or vasopressors could reflect a more restrictive use of this therapy in very old patients.…”
Section: Discussionmentioning
confidence: 97%
“…We conduct the disproportionality estimation [67, 76] on each adverse event to examine the association between the adverse event and pandemic (March 11–September 30, 2020) in contrast to before the pandemic (March 11–September 30, 2019). While disproportionality analysis is an established approach for pharmacovigilance to generate hypotheses on possible causal relations between drugs and adverse effects [72, 77], we here use it in a novel way that quantifying the association between adverse events and their submission periods.…”
Section: Methodsmentioning
confidence: 99%
“…Step 1) Disproportionality estimation. We conduct the disproportionality estimation [67,76] on each adverse event to examine the association between the adverse event and pandemic (March 11-September 30, 2020) in contrast to before the pandemic (March 11-September 30, 2019).…”
Section: Population-scale Adverse Event Model Of Patient Safetymentioning
confidence: 99%