Overall Survival and Survival Time by Palliative Performance Scale: A Retrospective Cohort Study in Thailand

INTRODUCTION

Palliative care is an approach that focuses on improving patients’ and their families’ quality of life when facing life-threatening illnesses. This has been prioritised for patients close to the end-of-life stage, where complicated treatments are considered unnecessary.^[1] In general, palliative care is delivered by a multiprofessional specialist team that needs at least basic training. A well-designed referral system is also essential to be integrated into this service to refer severe cases to other levels of care.^[2]

In 2021, Thailand become an ageing society; this means that the proportion of its older adults has exceeded 20% of the population; this has several effects for the country, including an increase in healthcare needs.^[3] Palliative care has been identified as a high demand service for older people, and it is expected that palliative care will be available to 80% of endof-life patients in the country. In 2018, the Ministry of Public Health included palliative care as one of the key performance indicators for all public hospitals,^[4] which generally involves the following steps: First, when no other curative treatment is applicable, a patient is diagnosed as requiring palliative care. Once a doctor has officially confirmed this diagnosis, the palliative patient is then registered at the palliative care centre of the hospital. Second, the patient’s functional status is assessed using the palliative performance scale (PPS). At the early palliative stage, patients may remain admitted to the hospital where they are receiving palliative care. Otherwise, the patients or their families may choose to go home and receive palliative care from a primary care team.

PPS was developed by Anderson et al. (1996). It assesses patients’ function from five dimensions: Ambulation, activity level and evidence of disease, self-care, oral intake and status of consciousness.^[5] The PPS yields results, ranging from 0% (death) to 100% (fully ambulatory), in 10% increments. Moreover, this scale is also useful for predicting the survival time of palliative patients, and such data can assist multidisciplinary teams in designing an appropriate care plan for patients and their families. The previous studies have confirmed that PPS is a good predictor of survival time in palliative patients; however, the studies showed median survival times that were slightly different.^[6-8] A meta-analysis conducted by Downing et al.^[6] which pooled data from hospitalised patients in developed countries, demonstrated the combined survival time of PPS 10–70% from 2 to 78 days.^[6] Meanwhile, a retrospective cohort study in Canada reported a survival time of PPS 10–70% from 1 to 63 days.^[8] Another prospective Canadian study reported that the 5-month survival rate of cancer patients was higher among those with greater initial PPS result.^[7]

Based on these results, since PPS can help predict survival time, we may hypothesise that the values it yields for survival time differ by country. Moreover, the available evidence on this variable comes predominantly from developed countries, especially Canada, where palliative care is well established. In Thailand, such evidence is more limited, leading healthcare professionals to frequently utilise PPS-predicted survival time based on the study of Downing et al.^[6] Another study that compared PPS survival time between cancer and non-cancer patients at a hospital in Thailand showed no differences between the groups regarding overall survival time.^[9] Therefore, this study aimed to examine survival time among Thai palliative care patients. The results would be valuable for multidisciplinary specialist teams in the country to estimate survival time and thus deliver better healthcare services.

MATERIALS AND METHODS

RESULTS

There were 2792 registered patients at the palliative care centre across the five hospitals. Of these, 1163 were excluded due to unavailability of PPS score (n = 154), loss of patient records (n = 940) and unknown current status (n = 69). Thus, 1629 participants were included in the analysis. Thirty-three patients were censored as they survived after the last observation date. The most of the sample was male (55.62%), the mean age was 64.59 (±15.38) and most held universal coverage (77.72%). Approximately half (56.23%) of the participants had cancer and about a quarter (27.13%) had an initial PPS result of 30. About one-third (36.53%) were first diagnosed as requiring palliative care by hospital A [Table 1].

Survival patterns

[Figure 1] shows the Kaplan–Meier survival estimates of palliative care patients and the number at risk. The median survival time was 14 days (mean = 64.08, 95% CI: 12–16); the total time at risk for all patients was 104,391 person-days, of which 1596 deaths were observed. The mortality rate after receiving palliative care was 1.53 times in 100 person-days (0.015%).

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Figure 1:

Kaplan–Meier survival estimate of palliative care patients and the number at risk.

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Univariate analysis

From the univariate Cox regression, five covariates were found as potential predictors of survival time in palliative patients: Age, sex, hospital, disease and initial PPS result [Table 2]. Although not associated with the univariate analysis, the health insurance scheme was also included in the multivariate analysis because of its impact on health outcomes.^[10]

Table 2: Results of the univariate analysis by participants’ characteristics.

Covariates	Median survival time, days (95% CI)	Crude HR (95% CI)	P-value
Agea		0.99 (0.99–0.99)	0.017
Sex
Male	12 (10–14)	1
Female	16 (13–21)	0.83 (0.76–0.93)	<0.001
Hospital
A (provincial 1000 beds)	6 (4–7)	1
B (district 264 beds)	12 (8–22)	0.66 (0.52–0.84)	0.001
C (district 172 beds)	35 (29–42)	0.45 (0.39–0.51)	<0.001
D (district 69 beds)	20 (13–24)	0.58 (0.49–0.68)	<0.001
E (district 60 beds)	9 (5–13)	0.79 (0.67–0.93)	0.004
Insurance
Universal coverage	14 (12–16)	1
Civil servant scheme	9 (6–16)	1.04 (0.89–1.21)	0.587
Social security scheme	16 (7–32)	1.07 (0.81–1.39)	0.647
Out-of-pocket or uninsured	15 (10–28)	1.11 (0.91–1.35)	0.311
Disease
Non-cancer	12 (9–14)	1
Cancer	16 (13–18)	1.12 (1.01–1.24)	0.028
Initial PPS (%)
10	1 (1–2)	1	0.007
20	3 (2–4)	0.77 (0.63–0.93)	<0.001
30	9 (7–11)	0.58 (0.49–0.67)	<0.001
40	22 (15–30)	0.39 (0.32–0.46)	<0.001
50	35 (24–55)	0.34 (0.27–0.41)	<0.001
60	43 (37–52)	0.29 (0.25–0.35)	<0.001
≥70	86 (62–117)	0.21 (0.16–0.28)	<0.001

aAge was a continuous variable, denoting that the median (95% confidence interval) was unavailable. PPS: Palliative performance scale

Multivariate analysis

The initial model included six covariates. Age, health insurance scheme and disease did not significantly contribute to survival time [Table 3]; thus, these factors were removed using the backward elimination approach. Then, three covariates (i.e., sex, hospital and initial PPS result) were found to be significantly associated with survival time. However, the proportional hazard assumption of the three-covariate model was violated because of the time-varying PPS variable. Therefore, we performed the extended Cox regression by including the three covariates, but while treating PPS as a time-varying variable.

Table 3: Results of the multivariate analysis by participants’ characteristics.

Covariates	HR (95% CI)
	Initial model	P-value	Final model	P-value
Age	1.00 (0.99–1.00)	0.879
Sex
Male	1.00		1.00
Female	0.84 (0.76–0.93)	0.001	0.84 (0.76–0.93)	0.001
Hospital
A (provincial 1000 beds)	1.00		1.00
B (district 264 beds)	0.72 (0.56–0.92)	0.009	0.70 (0.55–0.89)	0.004
C (district 172 beds)	0.59 (0.51–0.69)	<0.001	0.59 (0.52–0.68)	<0.001
D (district 69 beds)	0.44 (0.38–0.53)	<0.001	0.54 (0.46–0.63)	<0.001
E (district 60 beds)	0.71 (0.51–0.69)	<0.001	0.70 (0.59–0.83)	<0.001
Insurance
Universal coverage	1.00
Civil servant scheme	0.90 (0.77–1.05)	0.189
Social security scheme	1.02 (0.77–1.34)	0.911
Out-of-pocket or uninsured	0.91 (0.74–1.13)	0.406
Disease
Non-cancer	1.00
Cancer	1.03 (0.92–1.15)	0.592
PPS (%)
10	1.00		1.00
20	0.69 (0.57–0.86)	0.001	0.63 (0.52–0.77)	<0.001
30	0.52 (0.44–0.61)	<0.001	0.38 (0.32–0.45)	<0.001
40	0.34 (0.28–0.41)	<0.001	0.18 (0.15–0.23)	<0.001
50	0.32 (0.26–0.39)	<0.001	0.11 (0.86–0.14)	<0.001
60	0.29 (0.23–0.36)	<0.001	0.06 (0.05–0.09)	<0.001
≥70	0.19 (0.15–0.24)	<0.001	0.02 (0.01–0.03)	<0.001

PPS: Palliative performance scale, HR: Hazard ratio

The results of the final model illustrate that sex, hospital and PPS were significantly associated with survival time [Table 3]. The hazard ratio (HR) of female palliative care patients was lower than that of male patients (HR = 0.84, 95% CI: 0.76–0.93), indicating that women have a longer survival time. Thereafter, we used the provincial hospital as a reference to compare the HR of the hospitals. It was found that the HR was lower among district hospitals, but not in order by hospital size. Moreover, the PPS variable was deemed as time varying, in that the higher the PPS, the lower the HR (using PPS of 10 as a reference); thus, patients with high PPS had longer survival times [Table 3].

DISCUSSION

This study examined the overall survival time and factors related to survival time among Thai palliative patients. It was found that the overall median survival time was 14 days (95% CI: 12.01–15.99), which was in the middle range (9–28 days) based on the previous meta-analysis.^[6]

However, our median survival time was longer than that in a Canadian study, which was of 8 days.^[8] Thus, we hypothesise that these variations in overall survival time among different studies might owe to factors such as race, the healthcare system or details regarding the palliative care service in the countries.

Three factors were found to be significant predictors of survival time in palliative patients: Sex, hospital and initial PPS result. These predictors find consistency in the previous reports.^[6-8,11] Regarding sex, the present study iteratively confirmed that female survival time (16 days) was longer than that of males (12 days), and that the HR of the female group was 0.84 (95% CI: 0.76–0.93), indicating that the female mortality risk was about 14% less than that of males. This is consistent with the previous studies.^[6,7]

Regarding hospital, we defined this predictor differently from the prior research, which used the term ‘location’ instead of ‘hospital.’^[8] In Thailand, palliative care is currently operated by palliative care centres at public hospitals. Tertiary care and home-based care are inclusive but there is no hospice centre. This denotes that the services provided by tertiary care and home-based care in Thailand are not like those in developed countries. Hence, in Thailand, we compared all palliative care services delivered by each hospital, and our results showed that HR was lower among district hospitals, but not in order by hospital size [Table 3]. We conducted this examination not to critique the service by hospital, but to demonstrate that survival time differences might be related to the care processes used by each hospital. These results suggest that palliative care teams from different hospitals should share and learn how they can improve their palliative care service delivery. Moreover, we observed that the provincial hospital was likely to have a higher mortality risk and shorter survival time, a reality that may have been related to the nature of tertiary care, denoting that these hospitals receive patients in severer conditions.

In our study, the results for survival time by initial PPS concur with those in the previous studies; they showed that the higher the PPS, the longer the survival time.^[6-8,11] However, we also discovered that the survival estimates proposed by the meta-analysis might not be applicable to Thai patients.^[6] The survival time proposed by Downing et al. could only correctly predict 5–15% for a 10–50% PPS or 40% for a 60–70% PPS [Table 4].^[6] However, on the other side, the median survival time (95% CI) by PPS of both studies (Downing et al.^[6] vs. our study) were similar or overlapped. These contradict findings may suggest that further research is needed to compare survival time between different countries. Baik et al. previously commented that additional research is needed from countries such as South America, Africa, Europe and Asia to obtain more reliable survival measures from various ethnicities.^[11] Nevertheless, the use of PPS by multidisciplinary palliative care teams can allow them to grasp an idea of how long a patient may survive; this may lead to more appropriate care plan development and assessment. Therefore, our results reiterate the need for survival time assessments with improved accuracy in the Thai population.

CONCLUSION

The survival time of palliative patients in Thailand was shown to be relatively short, and the variables of sex, hospital and initial PPS result were shown to be significant predictors of survival time in these patients. The median survival time by PPS was similar to the values reported by the previous study but proportion of correct prediction was low. Hence, the future research is warranted to investigate survival time in palliative patients by country.