Survival of Frail Elderly with Delirium

Abstract

This study aims to determine when frailty increases the risks of delirium mortality. Hospital patients falling into the elderly frail or pre-frail category were recruited, some without delirium, some with delirium at admission, and some who developed delirium during admission. We screened for frailty, cognitive status, and co-morbidities whenever possible and extracted drug information and mortality data from electronic health records. Kaplan-Meier estimates of survival probability functions were computed at four times, comparing delirium versus non delirium patients. Differences in survival were assessed by a log-rank test. Independent Cox's regression was carried out to identify significant hazard risks (HR) at 1 month, 6 months, 1 year, and 2 years. Delirium predicted mortality (log-rank test, p < 0.0001) at all four censoring points. Variables with significant HRs were frailty indicators, comorbidities, polypharmacy, and the use of specific drugs. For the delirium cohort, variables with the most significant 2-year hazard risks (HR(95%CI)) were: male gender (0.43 20 (0.26,0.69)), weight loss (0.45 (0.26,0.74)), sit and stand up test (0.67 (0.49,0.92)), readmission within 30 days of discharge (0.50 (0.30,0.80)), cerebrovascular disease (0.45 (0.27,0.76)), head trauma (0.54 22 (0.29,0.98)), number of prescribed drugs (1.10 (1.03,1.18)), and the use of diuretics (0.57 (0.34,0.96)). These results suggest that polypharmacy and the use of diuretics increase mortality in frail elderly patients with delirium.

Keywords: ageing population; delirium; frailty; hospital admission; polypharmacy; survival.

Figure 1

Flow diagram of the recruitment process.

Figure 2

Kaplan–Meier survival curves for patients diagnosed with delirium at admittance (prevalent cohort) versus patients without delirium diagnosis. Data were censored after 1 month in plot (A), 6 months in plot (B), 1 year in plot (C), and 2 years in plot (D). Note: In the plots, delirium = 1 for patients with no delirium diagnosis (blue curve in the plots). Time was expressed in days. The p-value shown in the plots corresponds to the log-rank test comparison of no delirium versus delirium survival probability curves.

Figure 3

Kaplan–Meier survival curves for patients diagnosed with delirium at admittance or during the stay (incident cohort) versus patients without delirium diagnosis. Data were censored after 1 month in plot (A), 6 months in plot (B), 1 year in plot (C), and 2 years in plot (D). Note: In the plots, ConfusionalSyndrome = 1 for patients with no delirium diagnosis (blue curve in the plots). Time was expressed in days. The p-value shown in the plots corresponds to the log-rank test comparison of no delirium versus delirium survival probability curves.

Figure 4

Hazard ratios of demographic variables and frailty scores for the prevalent cohort, data censored at 2 years. Note: left to right columns are the variable name, population size, HR (95% confidence interval), graphical representation of HR (95%CI), and-p-value $Pr\left(>\left|z\right|\right)$. AIC—Akaike Information Criterion; signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1; concordance is the probability of agreement between two random observations; global score log-rank test was significant at 0.05.

Figure 5

Hazard ratios of demographic variables and frailty scores for the incident cohort, data censored at 2 years. Note: left to right columns are the variable name, population size, HR (95% confidence interval), graphical representation of HR (95%CI), and p-value $Pr\left(>\left|z\right|\right)$. AIC—Akaike Information Criterion; signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1; concordance is the probability of agreement between two random observations; global score log-rank test was significant at 0.05.

Figure 6

Hazard ratios of comorbidities for the prevalent cohort, data censored at 2 years. Note: left to right columns are the variable name, population size, HR (95% confidence interval), graphical representation of HR (95%CI), and p-value $Pr\left(>\left|z\right|\right)$. AIC—Akaike Information Criterion; signif. codes: 0 ‘***’ 0.001‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1; concordance is the probability of agreement between two random observations; global score log-rank test was significant at 0.05.

Figure 7

Hazard ratios of comorbidities for the incident cohort, data censored at 2 years. Note: left to right columns are the variable name, population size, HR (95% confidence interval), graphical representation of HR (95%CI), and p-value $Pr\left(>\left|z\right|\right)$. AIC—Akaike Information Criterion; signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1; concordance is the probability of agreement between two random observations; global score log-rank test was significant at 0.05.

Figure 8

Hazard ratios of pharmacological variables for the prevalent cohort, data censored at 2 years. Note: left to right columns are the variable name, population size, HR (95% confidence interval), graphical representation of HR (95%CI), and p-value $Pr\left(>\left|z\right|\right)$. AIC—Akaike Information Criterion; signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1; concordance is the probability of agreement between two random observations; global score log-rank test was significant at 0.05.

Figure 9

Hazard ratios of pharmacological variables for the incident cohort, data censored at 2 years. Note: left to right columns are the variable name, population size, HR (95% confidence interval), graphical representation of HR (95%CI), and p-value $Pr\left(>\left|z\right|\right)$. AIC—Akaike Information Criterion; signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1; concordance is the probability of agreement between two random observations; global score log-rank test was significant at 0.05.