In this cohort of patients admitted to acute medical wards in a country with a high burden of communicable and non-communicable disease and a high prevalence of HIV, a simple checklist of specific indicators (based on the GSF-PIG) was able to identify patients with nearly 12-fold increase in hazard of death within 12-month of admission. Diagnostic accuracy measures showed good sensitivity (74% (71–83%)) and specificity (85% (83–88%)), a low to moderate positive predictive value (56% (49–63%)) and high negative predictive value (93% (91–95%)). There was no significant advantage to adding the “Surprise Question” to the specific indicators to identify such patients in this study.
The high burden of non-communicable diseases reflect the mortality statistics of South Africa,20 and the young age of patients dying are typical of developing low and middle income economies. The prediction tool’s indicators performed equally well in a younger cohort of HIV-infected patients as in an older cohort of HIV-uninfected patients. Using this tool clinicians were better able to identify patients in the last year of life with renal failure (100% IDed) and cancer (90% IDed) than with heart failure (54% IDed) or respiratory disease (33% IDed). Other studies have previously shown that predicting mortality in organ specific disease such as heart failure or respiratory failure is more difficult for clinicians than for cancer.21 The accurate prediction of death of patients with renal failure reflects the access to management in this hospital context—where acute dialysis is available, but chronic dialysis access is restricted,22 such that patients who died were likely largely excluded from chronic dialysis programmes. These patients may be very young and thus often provide a unique challenge to palliative care services.
Strengths and limitations of this study
This study is the first study assessing the utility of an identification tool for assessing patients in their last year of life in a low to middle income country with a high HIV burden. The use of record linkage ensured that outcome could be assessed objectively. The prospective enrolment of a large number of patients from the acute medical services across two hospitals, a tertiary and a large general hospital are typical of the types of patients admitted nationally and these findings are likely generalisable across South and Southern Africa and may be very similar in other low or middle income countries with a similar burden of disease. Further generalisability was ensured by patients being assessed by generalists, not palliative care specialists.
Limitations of the study were that it was only performed in acute medical admissions where patients are acutely unwell and may not be relevant to the primary care outpatient setting. Patients were also assessed relatively early in their admission, when all information may not have been available to make an accurate assessment. Mortality as reported should be considered a minimum: vital status was not obtained for all patients; patients who have moved to another Province would not have been captured as deceased. Futhermore it should be considered that this tool does not necessarily predict overall palliative care need.
Comparison with the other studies
O’Callahan et al23 performed a similar assessment of the (full) GSF-PIG tool in 501 patients admitted to a New Zealand teaching hospital, where the average age of patients admitted was 70 years, the major diagnosis was cancer and the 12-month mortality was 67.7%. In that setting the tool performed well and very similar to in our setting with a sensitivity of 62.6%, a specificity of 91.9 a PPV 67.6% of and a NPV of 90.0%. Another widely used prediction tool, The Supportive and Palliative Indicators Tool (SPICT) was validated on a cohort of 130 patients admitted acutely with organ failure to specialist beds. 48% of identified patients had died at 12 months, reasonably similar to our data here.16 De Bock et al24 found a sensitivity of 84.1% and a specificity of 57.9% for SPICT in a retrospective cohort study in a geriatric population general and clinical indicators performed equally in that study. The predictive value of the NECPAL CCOMS_ICO tool was evaluated in a prospective, longitudinal study in primary care centres and a hospital in Spain in 1057 patients, with a mean age of 81years and a 12-month mortality of 27.0%. The sensitivity was 91.3%, specificity 32.9%, PPV 33.5 and NPV 91.0.17 The high number of false positives in comparison to our study may reflect the different nature of the patients or the tool itself (which includes the SQ, disease indicators but also general indictors of severity, disease progression, co-morbidity and resource usage).
Implications of this research
There is a need to improve the quality of care for patients near the end of their life admitted to hospitals, and the recognition that a large number of patients admitted are in the final year of their life, and that clinicians have difficulty identifying such patients, have led to the development of simple “identification” tools to assist. Ideally such tools, if used in busy acute admission wards, need to be quick, simple to use and accurate, so that resources are allocated only as appropriate. This is even more relevant in low and middle income countries where the patient burden far exceeds the availability of clinicians. The simple one page tool is an example of how that may be possible. Importantly, inclusion of the Surprise Question offered no additional benefit above criteria specific indicators.