The cohort included a diverse array of residents. Demographically, the majority were female (62%), 29.7% were married, 40.1% widowed, 12.3% divorced, 1.5% separated, and 16.4% never married. The average age at admission was 76.3 years, 14.2% were under 60 and 17.1% were 90 years of age or older. Most residents came into the facility from a hospital (76.5%) while 15.2% came directly from home and 5.5% from another nursing home.
Review of all MDS cognitive items revealed about three-quarters had a memory problem, but only about 4% could not be understood and 3% could not understand others. The Cognitive Performance Scale (CPS) indicated that 10% were free of cognitive deficits, while 19.8% had moderate or more impaired cognitive status.14 The functional performance of the residents can best be described by their status on two summary scales—the ADL Long Form (which summarizes seven ADLs) 15 and the Nursing Home Frailty Scale.16. On the 28-point ADL scale, where a higher score indicates a more impaired resident, the mean value was 16.7, or about 60% along the continuum, whereas 13.1% of residents had a score from 0 to 10. At the frail end of the scale, 11.6% of persons had a score of 23 or higher. A similar result emerged on the 15-point Frailty Scale.
Death Rate and Construction of the Logistic Prediction Model
Table 1 displays selected parameters for the variables that entered the final logistic model. Of the thirty-five independent variables that entered the multivariate model, 5 suggested an imminent risk of death. The prevalence rates of the 5 were relatively low, with a range of 0.3–8.4%. The remaining 30 variables fall into the following categories: treatments, diagnoses, clinical conditions, cognition and communication, function, and age. No one subset of these measures overwhelms the model. Generally, if one exhibits one or more of these baseline conditions, one’s risk of death was somewhat higher.
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The four treatment measures in this set, oxygen therapy, parenteral/IV feeding, chemotherapy, and respite care, depict residents who were seriously compromised. For the last three of these measures, very few of the residents received the treatment but the associated risk of death ranged from 34.8% to 43.8%. Oxygen therapy, however, was given to several residents (18.4%), and the associated risk of death was 38.1%.
Three diagnoses entered the logistic model: cirrhosis, heart failure, and malnutrition. Of these measures, the aspect of nutritional status was present in 7 of the 12 clinical measures. Included were measures of unintended weight loss and low BMI, as well as a series of more symptomatic indicators—dehydrated, poor appetite, and three swallowing related indicators. Other clinical risk factors included incontinence, unhealed pressure ulcers, and shortness of breath.
Four indicators suggested more advanced cognitive loss: having a moderate or more severe Cognitive Performance Scale score (the equivalent of a Mini Status Scale Score of less than 10); only able to understand at best simple, direct communication with others; lethargic or difficult to keep aroused during the interview; and difficulty focusing attention during the interview.
While all functional activities of daily living measures had significant positive univariate odds ratio with death by day 365, only 4 entered the final model. Two measures—locomotion off the unit and bathing—referenced persons who were totally dependent in ADLs.
The 2 remaining ADLs in the logistic model referenced persons who received any physical help from others. These ADLs are eating and toilet use. Eating is the last of the ADLs in which one typically requires support, here only 31.8% received any help, and when defined in this way, 34.2% died by day 365 (a univariate odds ratio of 2.2). Toilet use, unlike eating, is an area in which large numbers of residents received assistance. Here, death rate by day 365 was 25.6%, compared to 13% among those who were independent. The final measures in the initial multivariate logistic model were three dichotomous age variables.
The next step in the analysis was to identify variables in the first logistic model that should be selected to create a system for assigning persons into discrete risk of death categories. Two subscales were created—in each case adding together the dichotomous scores for the selected items and thus, depending on the person’s scores on these two subscales, a death-risk assignment was made. The first subscale was based on a count of the 5, very high risk variables, and had a possible score range of 0 to 5.
The second subscale was based on a subset of the variables in the remaining set that first entered a second logistic model, excluding the 5 very high risk measures. The first 18 variables to enter this second logistic equation were selected to form the second subscale. These variables are noted by a “**” in Table 1, and this subscale has a range of 0 to 18. Variables represent 6 domains: two treatments, two diagnoses, six clinical, two cognitive, three functional, and three demographics. Table 2 presents the assignment rules using the 2 subscales. For example, the 4 lower risk categories all have a score of zero on the very high risk subscale and scores of 0 to 5 on the remaining variable subscale.
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Figure 1 displays both the distribution of cases across the categories of the Risk of Death Scale (those for whom a day 365 alive or dead determination was possible) and the percent who died in each of these scale categories by day 365. The majority of persons in the cohort are in the 4 lowest scores on the scale (53.1%), while 19.6% are in the 6 higher categories of the 12-point scale. The death rate by day 365 rose linearly from category “0” (3%) to category “11” (90.5%). The death rate reached one-third by category “6” and exceeded 50% by category”8.” In line with the linear progression of rates, the eta measure of association was reasonably high at.449.
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Figure 2 presents the death rate profiles when the time period extends from 30 days to 3 years after admission. At 30 days post admission, only persons with risk of death scale scores of 9 through 11 had a death rate of 10% or higher—12%, 22%, and 36% respectively [note—this is the follow-up with the highest case count where it was possible to say whether the members of the cohort were alive or dead at the end of the period—90%, see materials below for sample discussion]. All 3 of the groups (scale scores of 9,10 and 11) experienced a distinct increase in death rates by three and six months post admission, with the death rates at six months being 52%, 69%, and 84% respectively. No other group reached the point where 50% or more of the residents died until group 8 at one year—56% died by this point in time.
At the other death extreme, risk group “0” had death rates of 3% or lower up to the end of the first year, a death rate level corresponding to the general US population.17 These rates rose to only 5% and 7% at years 2 and 3 post admission. Groups “1” thru “3” had rates that were only slightly higher than group “0”. All were less than 1% at 30 days, less than 7% at 6 months, and 6% to 16% at one year. By the end of year 3, the rates had taken a step up to 25%, 36% and 47% respectively. At these later dates, it is clear that group “3” was distinct from groups “1” and “2”. The more mid-range Death-Risk groups, categories “4” thru “8”, displayed stepped risks over time. At 6 months, the death rates ranged from 10% to 38%, at one year, the rates ranged from 22% to 56%, and finally at 3 years, the rates ranged from 55% to 84%. When viewing the full range of Risk of Death categories, there was a steady increase in death rates at all time periods, with higher rates of death by days 30 and 90 post admission. The very lowest rates were found for categories “0” and “1”. Rates at about the study population average occurred for categories “4” and “5,” and categories with rates at about double the average death rate or higher were “7” thru “11”.
For all persons, we have a baseline and discharge assessment. If the person remained in the facility up to a given date or died at that date or earlier, we can track their death status. But some persons were discharged alive prior to the end of a given follow-up period. When that occurred the resident could not be entered into the equation that determined the time-specific death rate.
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To provide insight on these cases, we looked at both the rate at which persons left the nursing home for other sites and a comparison of the Death-Risk Scale profile of categories for persons who left and did not leave the facility by day 365. Figure 3 displays the rate at which persons left the nursing home (and did not return), by the destination site. Here, case loss is steady over time with the exception of those residents who returned home. For these residents, there is a higher rate of discharge in the months closer to the baseline assessment. Supplemental file presents a summary table comparing demographic and study variables and vulnerability items not used in the scale for those with follow-up data at year 1 and those lost to follow-up.
Among the 30 items compared, only one, age in years, had an eta value of 0.15 or higher. There were no major differences between the study sample and the group lost to follow-up for the other items.
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Figure 4 displays the baseline Risk of Death Scale scores for persons without follow-up data at day 365, persons with follow-up data at day 365, those discharged home, those discharged to an acute hospital, and those discharged to another nursing home. Of note, those without follow-up data at day 365 had a Risk of Death Scale score profile that differed little from those for whom we could calculate whether they had died prior to day 365 or were alive at that time. In total, the two distributions of Death-Risk Scale scores were about the same.
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