Baseline characteristics of the study population (Table 1)
One thousand one hundred and six patients were randomised from the intake for potential enrolment into the study, of which 121 did not meet the inclusion criteria (73 were readmissions, 6 were under the age of 18, 8 were elective admissions and 34 were not admitted to the general medical wards). Another 163 were excluded (77 were not included in the data collection of the original main study due to its exclusion criteria of “coma” or “aphasia”, 22 had other incomplete data, 28 refused consent, 32 were not on the ward on the day of study and 4 died before testing) (Additional File 2. STARD Flow diagram). Of the 822 patients included in the analysis the median age was 52 (37-67) and 46% were male. The baseline demographics are shown in Table1. Patients were admitted with a wide variety of diagnoses, of which the most common primary diagnoses were infection other than tuberculosis (19%), tuberculosis (12%), acute coronary syndrome (12%), stroke (8%), heart failure (8%), exacerbation of chronic obstructive airways disease (5%) and cancer (4%). Multi-morbidity was very common – 72% of patients had a previous chronic disease diagnosis requiring chronic medication before admission; 40% had 2 or more pre-existing chronic diseases. Patients were generally independent before admission; 76% had a Barthel Index of 100 and only 9% of patients had a pre-admission Barthel Index score of 50 or less.
One hundred and seventy eight (22%) of the patients were HIV-infected and 440 (54%) confirmed HIV-uninfected. In 204 (25%) patients HIV status was not known. HIV-infected patients were more likely than HIV-uninfected patients to be female and were younger (median age 35; 30-44). Amongst HIV-infected patients, the most common admission diagnoses were communicable diseases (tuberculosis and infection), with non-communicable diseases being far less common. Patients not tested for HIV were much older (median age 71; 60-78) and the disease profile was similar to HIV-uninfected patients, with a larger proportion being admitted for non-communicable diseases, particularly stroke.
Overall inpatient mortality was 5.1%, 3-month mortality 14.8% and total 12-month mortality 20.0%. 12-month mortality in HIV-infected patients was 21.3% and 17.3% in HIV-uninfected patients with a non-significant OR for 12-month mortality in HIV-infected patients of 1.30 (0.87-2.0).
Table 1. Baseline, diagnosis and outcomes overall and according to HIV status
|
|
All
|
HIV-uninfected
|
HIV-unknown
|
HIV-infected
|
|
N (822)
|
(%)
|
N (440)
|
(53.50%)
|
N (204)
|
(24.80%)
|
N (178)
|
(21.70%)
|
Gender, n (%)
|
|
|
|
|
|
|
|
|
Male,
|
378
|
(46.0)
|
234
|
(53.2)
|
70
|
(38.9)*
|
66
|
(37.1)*
|
Female
|
444
|
(54.0)
|
206
|
(46.8)
|
110
|
(61.1)
|
112
|
(62.9)
|
Age
|
|
|
|
|
|
|
|
|
Median (IQR)
|
52
|
(37-67)
|
50
|
(39-62)
|
71
|
(60-78)*
|
35
|
(30-44)*
|
Primary diagnosis
|
|
|
|
|
|
|
|
|
Infection (other than TB)
|
145
|
(19)
|
55
|
(13.3)
|
26
|
(16.0)
|
54
|
((31.6)*
|
Tuberculosis
|
92
|
(12)
|
32
|
(7.7)
|
4
|
(2.5)*
|
56
|
(32.7)*
|
Acute coronary syndrome
|
93
|
(12)
|
50
|
(12.1)
|
40
|
(24.7)*
|
2
|
(1.2)*
|
Stroke
|
66
|
(8)
|
28
|
(9.2)
|
28
|
(17.3)*
|
6
|
(3.5)*
|
CCF
|
60
|
(8)
|
38
|
(6.8)
|
8
|
(4.9)
|
6
|
(3.5)*
|
COPD
|
38
|
(5)
|
28
|
(6.8)
|
8
|
(4.9)
|
2
|
(1.2)*
|
Cancer
|
28
|
(4)
|
18
|
(4.3)
|
4
|
(2.5)
|
6
|
(3.5)
|
Barthell pre-admission function, n (%)
|
|
|
|
|
|
|
|
|
< 50
|
72
|
(8.8)
|
26
|
(5.9)
|
24
|
(11.8)*
|
14
|
(7.9)
|
Mortality
|
|
|
|
|
|
|
|
|
In-Patient
|
42
|
(5.1)
|
18
|
(4.1)
|
12
|
(6)
|
12
|
(6.7)
|
3mo
|
122
|
(14.8)
|
54
|
(12.3)
|
38
|
(19)
|
22
|
(12.4)
|
12mo
|
164
|
(20.0)
|
76
|
(17.3)
|
38
|
(19)
|
38
|
(21.3)
|
*p < 0.01 vs the HIV-uninfected group
TB = tuberculosis; CCF = congestive cardiac failure; COPD = chronic obstructive pulmonary disease;
Performance of the Identification Tool
The performance of the identification tool in predicting mortality at 12 months is shown in Table 2 for the population as a whole and divided by HIV-infected and -uninfected populations. In 144 patients identified by the SQ only (in whom no specific indicator box was ticked) the test had a good sensitivity of 89% (68 – 100%), but a very poor specificity of 8% (3 -11%) and we did not analyse data using the SQ alone any further, but only the combination (of the SQ and the clinical indicators, as opposed to using the GSF-PIG Guidance of “or”) and an assessment based on only the specific indicators without including those only identified by the Surprise Question.
Table 2. Performance of the tests in the overall cohort (N 822)
|
No IDed (%)
|
Sensitivity
|
Specificity
|
PPV
|
NPV
|
LR +
|
LR-
|
Overall
|
|
|
|
|
|
|
|
SQ + criteria
|
366 (45%)
|
0.77 (0.71-0.83)
|
0.64 (0.60-0.68)
|
0.35 (0.30-0.40)
|
0.92 (0.89-0.94)
|
2.13 (1.87-2.43)
|
0.36 (0.27-0.48)
|
Criteria only
|
218 (27%)
|
0.74 (0.68-0.81)
|
0.85 (|0.83-0.88)*
|
0.56 (0.49-0.63)*
|
0.93 (0.91-0.95)
|
5.10 (4.15-6.26)*
|
0.30 (0.23-0.39)
|
HIV-uninfected
|
|
|
|
|
|
|
|
SQ + criteria
|
166 (38%)
|
0.68 (0.58-0.79)
|
0.69 (0.64-0.73)
|
0.31 (0.24-0.38)
|
0.31 (0.24-0.38)
|
2.18 (1.76-2.71)
|
0.46 (0.33-0.64)
|
Criteria only
|
104 (24%)
|
0.71 (0.61-0.81)
|
0.86 (0.86-0.83)*
|
0.52 (0.42-0.62)*
|
0.52 (0.42-0.62)*
|
5.17 (3.85-6.95)*
|
0.34 (0.24-0.48)
|
HIV-infected
|
|
|
|
|
|
|
|
SQ + criteria
|
76 (43%)
|
0.74 (0.60-0.88)
|
0.66 (0.58-0.74)
|
0.37 (0.26-0.48)
|
0.90()0.84-0.96)
|
2.15 (1.60-2.89)
|
0.40 (0.23-0.69)
|
Criteria only
|
34 (19%)
|
0.58 (0.42-0.74)
|
0.91 (0.87-0.96)**
|
0.65 (0.49-0.81)*
|
0.89 (0.84-0.94)
|
6.75 (3.69-12.37)*
|
0.46 (0.32-0.67)
|
PPV – positive predictive value; NPV -= negative predictive value; LR + = positive likelihood ratio; LR - = negative likelihood ratio. SQ = surprise question.
* p< 0.001 for criteria only vs SQ + criteria
The combination of the SQ AND Clinical Indicators performed well; however when the indicators ALONE were used to predict outcome, without including the SQ, the best test performance was obtained with a similar sensitivity of 74% (71-83%) to the SQ and indicators combined, but an improved specificity of 85% (83-88%), a PPV of 56% (49-63%) and a negative predictive value (NPV) of 93% (91-95%). The indicator-only method of predicting outcome performed equally well in the HIV-infected cohort of patients as compared with the overall or the HIV-uninfected cohort. In HIV-infected patients sensitivity, PPV and Positive Likelihood ratio were better using the indicator alone vs indicator and SQ.
Characteristics and outcomes of patients “identified” vs “non-identified”
Using the indicator-only component of the identification tool, 218 of the 822 patients were “identified” (IDed) as being in the last year of their life (Table 3). There were no gender differences between “identified” and “non-identified patients” (non-IDed), though identified patients were older (median age 61 vs 49). A greater majority of patients “identified” presented with stroke, heart failure, COPD and cancer; less with an infection or tuberculosis. Less of the identified patients were HIV-infected, but more were HIV-unknown. The in-patient, 3-month and 12-month mortality for the “identified” vs the “non-identified” patients were 16% vs 1.3%, 48% vs 3% and 56% vs 7% respectively.
Table 3. Demographics and Outcome of “Non-identified and “identified” patients (Criteria only)
|
|
"Non-identified"
|
“Identified”
|
|
N (604)
|
N (218)
|
Gender
|
|
|
Male
|
280 (46%)
|
98 (45%)
|
Female
|
324 (54%)
|
120 (55%)
|
Age
|
|
|
Median (IQR)
|
49 (35-61)
|
61 (48-75)
|
lowest - 30
|
100
|
14
|
31-40
|
80
|
14
|
41-50
|
104
|
20
|
51-60
|
112
|
24
|
61-70
|
56
|
44
|
71-80
|
60
|
48
|
81 and older
|
22
|
28
|
Primary diagnosis
|
|
|
Infection (other than Tb)
|
123 (20.4%)
|
22 (10.1%)
|
Tuberculosis
|
78 (12.9%)
|
14 (6.4%)
|
Acute coronary syndrome
|
85 (14.1%)
|
8 (3.7%)
|
Stroke
|
26 (4.3%)
|
40 (18.3%)
|
CCF
|
22 (3.6%)
|
38 (17.4%)
|
COPD
|
18 (3.0%)
|
20 (9.2%)
|
Cancer
|
10 (1.7%)
|
18 (8.3%)
|
HIV status
|
|
|
Uninfected
|
336 (55.6%)
|
104 (47.7%)
|
Infected
|
144 (23.8%)
|
34 (15.6%)
|
Unknown
|
114 (18.9%)
|
66 (30.3%)
|
Refused
|
10 (1.7%)
|
14 (6.4%)
|
Barthell pre-admission function
|
|
|
< 50
|
52 (8.6%)
|
20 (9.2%)
|
Mortality
|
|
|
In-Patient
|
8 (1.3%)
|
34 (16%)
|
3mo
|
18 (3.0%)
|
104 (48%)
|
12mo
|
42 (7.0%)
|
122 (56%)
|
Figure 1 shows the Kaplan Meier survival curve for the IDed vs non-ID patients. Survival was significantly worse for the “IDed” patients (p < 0.0001) with a hazard ratio for 12-month mortality for the IDed versus the non-IDed of 11.52 (7.87 – 16.9; p < 0.001). The tool strongly predicted mortality particularly in the next 3 months: 104 of 122 IDed patients (85%) vs 18 of 42 (43%) non-IDed patients died by 3 months after admission.
Figure 2 shows the total number of patients identified according to the specific indicator met, and the number and percentage that actually had died at 12 months. Patients were identified from all the indicators, but heart failure and respiratory disease were the most common, with neurological disease (predominantly stroke) with the second highest frequency. The main indicator that was not specified and where the assessor completed the “other” box was liver failure. 12-month mortality was best predicted for patients with renal failure (100% correct), cancer (90% correct) and worst for patients with respiratory/COPD (33%), dementia/frailty (40%) and liver failure (33%). Fifty four percent of patients that fulfilled the heart failure criteria and 58% of patients that fulfilled the AIDS criteria had died by 12 months.