Prognostic Value of the Objective Prognostic Score and Palliative Prognostic Index for Short-Term Mortality in Terminal Cancer Patients Receiving Best Supportive Care: A Prospective Study

Prognostic Value of the Objective Prognostic Score and Palliative Prognostic Index for Short-Term Mortality in Terminal Cancer Patients Receiving Best Supportive Care: A Prospective Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prognostic Value of the Objective Prognostic Score and Palliative Prognostic Index for Short-Term Mortality in Terminal Cancer Patients Receiving Best Supportive Care: A Prospective Study Alperen Akansel Çağlar, Zekeriya Hannarici, Mehmet Emin Buyukbayram, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9419641/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Accurate prognostication in terminal cancer patients receiving best supportive care (BSC) is essential for guiding end-of-life decision-making and avoiding non-beneficial interventions. Several prognostic models have been developed for advanced cancer, including the Palliative Prognostic Index (PPI) and the Objective Prognostic Score (OPS). However, prospective data evaluating their performance specifically in patients managed with best supportive care are limited. This study aimed to evaluate the prognostic performance of PPI and OPS and to assess whether their combined use improves short-term mortality prediction in terminal cancer patients receiving BSC. Methods This prospective observational cohort study included hospitalized adult patients with terminal-stage cancer and a documented best supportive care decision. Eligible patients had poor performance status (ECOG 3–4) and had not received oncologic treatment within the preceding month. PPI and OPS were calculated at baseline using predefined criteria. Patients were followed until death, and survival time was defined as the interval between baseline assessment and death. The ability of the scores to predict 3-, 4-, and 6-week mortality was evaluated, and survival outcomes were analyzed using standard survival analysis methods. Results A total of 112 patients were included in the final analysis. Both PPI > 6 and OPS ≥ 3 were associated with significantly higher short-term mortality, although their individual predictive performance was modest (AUC < 0.70). Patients with concurrent high PPI and OPS scores had markedly higher early mortality rates. The combined OPS–PPI model improved risk stratification and identified patients at the highest risk of imminent death. In multivariable analysis, PPI > 6 (HR 1.97, 95% CI 1.25–3.10; p = 0.003) and OPS ≥ 3 (HR 1.65, 95% CI 1.01–2.69; p = 0.047) remained independent predictors of poorer overall survival. Conclusion Although the individual prognostic performance of PPI and OPS was modest, their combined application provided clearer risk stratification for short-term mortality in terminal cancer patients receiving best supportive care. Palliative Prognostic Index Objective Prognostic Score Terminal cancer Best supportive care Prognostication Palliative care Figures Figure 1 Introduction Advanced cancer patients in the terminal phase frequently experience rapid clinical deterioration, making accurate prognostication a cornerstone of high-quality palliative care. Reliable survival prediction facilitates timely best supportive care (BSC) decisions, prevents futile interventions, optimizes referral to palliative services, and supports realistic communication with patients and families. Nevertheless, multiple studies have demonstrated that clinicians tend to overestimate survival in terminal cancer patients, leading to delayed end-of-life discussions and potentially non-beneficial treatments ( 1 – 3 ). To address this challenge, several prognostic models have been developed specifically for patients with advanced or terminal cancer. Among these, the Palliative Prognostic Index (PPI) is one of the most widely used and validated tools in palliative care settings ( 4 ). PPI is a purely clinical index composed of five parameters: Palliative Performance Scale (PPS), oral intake, presence of edema, dyspnea at rest, and delirium. Each component is assigned a weighted score, with higher total scores indicating poorer prognosis. Previous studies have demonstrated that PPI is particularly effective for predicting short-term survival, especially 3- to 6-week mortality, and that a PPI score ≥ 6 is strongly associated with survival of less than three weeks ( 4 – 6 ). Despite its robust validation, PPI incorporates clinical assessments such as performance status, oral intake, and delirium, which may be subject to interobserver variability, especially across different care settings and levels of clinician experience ( 7 ). This limitation has stimulated interest in prognostic tools based exclusively on objectively measurable variables. The OPS was developed as a prognostic model primarily based on objective clinical and laboratory parameters, minimizing reliance on subjective clinical judgment ( 8 ). OPS includes eight variables: Eastern Cooperative Oncology Group performance status (ECOG PS = 4), anorexia (defined as intake of fewer than five spoonfuls per meal or less than one-third of a normal meal), dyspnea at rest, elevated white blood cell count (> 11,000/µL), hyperbilirubinemia (total bilirubin > 2.0 mg/dL), renal dysfunction (serum creatinine ≥ 1.5 mg/dL), and elevated lactate dehydrogenase (LDH ≥ 502 IU/L). Renal dysfunction (serum creatinine ≥ 1.5 mg/dL) is assigned 2 points, whereas each of the remaining parameters contributes 1 point to the total score. Higher OPS values are associated with poorer prognosis and shorter expected survival ( 8 – 10 ). Comparative studies evaluating PPI, OPS, and other prognostic models suggest that these tools demonstrate broadly comparable prognostic performance, while differing in complexity, objectivity, and clinical applicability ( 11 – 13 ). However, most existing studies have been retrospective in design, conducted in heterogeneous palliative care populations, or have included patients receiving mixed treatment intents. Importantly, evidence derived from prospective cohorts specifically limited to terminal cancer patients for whom a best supportive care decision has been made remains scarce ( 14 ). Moreover, accurate prediction of short-term mortality in patients receiving best supportive care remains a critical yet challenging aspect of palliative oncology. Reliable prognostic estimation is essential for guiding clinical decision-making, facilitating timely end-of-life discussions, optimizing resource allocation, and aligning care with patient and family expectations. However, prognostic uncertainty in this setting remains substantial, and available data specifically focusing on patients managed exclusively with supportive care are relatively limited. Improving risk stratification in this population may therefore contribute to more individualized and clinically meaningful care planning. Study Aim Prognostic uncertainty in terminal cancer patients receiving best supportive care continues to complicate end-of-life decision-making and care planning. In this context, more precise risk stratification may support clinically meaningful and individualized management. Therefore, this prospective study aimed to evaluate the prognostic performance of the PPI and the OPS separately in terminal cancer patients receiving best supportive care, and additionally to analyze the contribution of their combined use to short-term mortality prediction. Materials and Methods Study Design and Patient Population : This prospective observational study was conducted in a tertiary-level comprehensive cancer center. Hospitalized adult patients with advanced cancer were screened for eligibility. Patients were included if they met all of the following criteria: ( 1 ) a documented decision for best supportive care determined by a multidisciplinary oncology team, ( 2 ) no receipt of any oncological treatment (chemotherapy, targeted therapy, immunotherapy, or radiotherapy) within the preceding one month, ( 3 ) poor performance status defined as ECOG PS 3 or 4, and ( 4 ) a clinical diagnosis of terminal-stage cancer. Data Collection and Prognostic Score Assessment All included patients underwent a comprehensive clinical evaluation on the first day of hospital admission. Physical examination findings and laboratory parameters were recorded at baseline by the responsible investigator. On the same day, the PPI and the OPS were calculated for each patient according to their original definitions. PPI was calculated based on five clinical variables: Palliative Performance Scale, oral intake, presence of edema, dyspnea at rest, and delirium. OPS was calculated using objective clinical and laboratory parameters, including ECOG performance status, anorexia, dyspnea at rest, white blood cell count, total bilirubin, serum creatinine, and lactate dehydrogenase levels. Follow-up and Outcome Measures A total of 114 consecutive patients were prospectively enrolled starting from 12 April 2024. Patients were followed until death, with data collection continuing until the date of death of the last patient, 05 May 2025. Survival time was defined as the interval between the date of hospital admission (baseline assessment) and the date of death. Clinical outcome data were unavailable for two patients during follow-up. Therefore, the final analysis included 112 patients. Statistical Analysis Statistical analyses were performed using IBM SPSS Statistics for Windows, Version 25.0 (IBM Corp. Armonk, NY, USA). Descriptive statistics were expressed as number and percentage for categorical variables, and as mean ± standard deviation or median (minimum–maximum) for continuous variables, as appropriate. The discriminatory performance of clinical variables for short-term mortality was evaluated using receiver operating characteristic (ROC) curve analysis. Survival outcomes were estimated using the Kaplan–Meier method and compared between groups with the log-rank test. A two-sided p value of < 0.05 was considered statistically significant. Results A total of 114 consecutive patients were prospectively enrolled in the study. Survival outcome data were unavailable for two patients; therefore, the final analysis included 112 patients. The mean age of the cohort was 62.3 ± 12.3 years (median, 61.5; range, 29–86), and 66 patients (58.9%) were male. For risk stratification, patients were categorized using their established cut-off values for the PPI >6 and the OPS ≥3, and an additional combined OPS–PPI risk grouping was constructed. Based on this combined classification, 61 of 112 patients (54.5%) were assigned to the highest-risk category (OPS ≥3 and PPI >6). The remaining baseline sociodemographic and clinical characteristics, as well as short-term mortality outcomes, are summarized in Table 1. The discriminatory performance of OPS and PPI for predicting short-term mortality at 3, 4, and 6 weeks is presented in Table 2. For 3-week mortality, the AUC was 0.622 (95% CI, 0.426–0.880) for OPS ≥3 and 0.679 (95% CI, 0.400–0.785) for PPI >6, both were statistically significant (p=0.020 and p=0.001, respectively). At 4 weeks, neither OPS nor PPI showed statistically significant discrimination (AUC 0.579 [p=0.132] and 0.598 [p=0.072], respectively). For 6-week mortality, both scores again showed statistically significant discrimination, with AUC values of 0.647 for OPS and 0.640 for PPI (p=0.007 and p=0.013, respectively). The combined OPS–PPI model yielded AUC values of 0.671, 0.611, and 0.646 for predicting 3-, 4-, and 6-week mortality, respectively, all of which reached statistical significance (p<0.001, p=0.018, and p=0.002, respectively). Table 2. Discriminatory performance of OPS and PPI for predicting short-term mortality using ROC curve analysis Variable AUC (95% CI) Sens (%) Spec (%) PPV NPV LR+ LR− p value 3-week mortality OPS ≥3 0.622 (0.426–0.880) 89.0 76.0 0.54 0.78 1.35 0.33 0.020 PPI >6 0.679 (0.400–0.785) 75.0 62.0 0.56 0.69 1.44 0.52 0.001 4-week mortality OPS ≥3 0.579 (0.323–0.775) 85.0 76.0 0.60 0.67 1.29 0.43 0.132 PPI >6 0.598 (0.350–0.800) 67.0 58.0 0.57 0.52 1.15 0.79 0.072 6-week mortality OPS ≥3 0.647 (0.365–0.825) 86.0 79.0 0.72 0.63 1.47 0.33 0.007 PPI >6 0.640 (0.290–0.890) 68.0 64.0 0.69 0.45 1.28 0.68 0.013 Combined model (OPS ≥3 & PPI >6) 3-week mortality 0.671 (0.200–0.885) 73.6 60.7 0.62 0.73 1.87 0.44 <0.001 4-week mortality 0.611 (0.341–0.860) 65.6 56.6 0.63 0.59 1.51 0.61 0.018 6-week mortality 0.646 (0.269–0.800) 65.8 63.4 0.76 0.51 1.80 0.54 0.002 AUC , area under the curve; CI , confidence interval; LR+ , positive likelihood ratio; LR− , negative likelihood ratio; NPV , negative predictive value; OPS , Objective Prognostic Score; PPI , Palliative Prognostic Index; PPV , positive predictive value; Sens , sensitivity; Spec , specificity. Discriminatory performance was assessed using receiver operating characteristic (ROC) curve analysis . Table 3 presents the comparison of 3-, 4-, and 6-week mortality rates according to the combined OPS–PPI risk groups. A statistically significant difference in mortality was observed among the groups at all evaluated time points (3-week p=0.002, 4-week p=0.021, and 6-week p=0.003). The highest mortality consistently occurred in the OPS≥3 & PPI>6 group, with rates of 73.6% at 3 weeks, 65.6% at 4 weeks, and 65.8% at 6 weeks. Detailed mortality distributions for the remaining groups are shown in Table 3. Table 3. Comparison of short-term mortality rates according to combined OPS–PPI risk groups Combined risk group 3-week mortality (%) p value 4-week mortality (%) p value 6-week mortality (%) p value OPS <3 & PPI ≤6 9.4 0.002 13.1 0.021 11.0 0.003 OPS ≥3 & PPI ≤6 15.1 19.7 20.5 OPS 6 1.9 1.6 2.7 OPS ≥3 & PPI >6 73.6 65.6 65.8 Pearson chi-square test; p 6) had significantly shorter median overall survival than those with PPI ≤6 (11 vs 52 days, p < 0.001). Similarly, patients with OPS ≥3 demonstrated significantly poorer survival compared with those with OPS <3 (15 vs 63 days, p < 0.001). When patients were stratified according to the combined OPS–PPI model, a clear gradient in survival was observed across the four risk categories. The worst survival was seen in patients with both high OPS and high PPI (OPS ≥3 and PPI >6; median OS 11 days), whereas the best survival occurred in patients with both low OPS and low PPI (OPS <3 and PPI ≤6; median OS 65 days) (Figure 1). Multivariable Cox regression analysis was performed to evaluate the independent prognostic impact of OPS and PPI on overall survival after adjustment for age, sex, and primary tumor site (Table 4). PPI >6 remained a significant independent predictor of poorer overall survival (HR 1.97, 95% CI 1.25–3.10, p = 0.003). Similarly, OPS ≥3 was independently associated with worse survival (HR 1.65, 95% CI 1.01–2.69, p = 0.047). In contrast, primary tumor site (p = 0.959), age (p = 0.293), and sex (p = 0.311) were not independently associated with overall survival. Table 4. Multivariable Cox regression analysis for overall survival Variable HR (Exp[B]) 95% CI p value PPI >6 1.97 1.25–3.10 0.003 OPS ≥3 1.65 1.01–2.69 0.047 Primary tumor site — — 0.959 Male sex 0.80 0.51–1.24 0.311 Age (per year) 0.99 0.97–1.01 0.293 HR , hazard ratio; CI , confidence interval; OPS , Objective Prognostic Score; PPI , Palliative Prognostic Index. Multivariable model adjusted for age, sex, and primary tumor site. Discussion Accurate prognostication in terminal cancer patients is essential for guiding end-of-life decision-making. The PPI is a well-validated clinical tool for predicting short-term survival, particularly within the last weeks of life ( 4 – 6 ). In contrast, the OPS was developed to reduce subjectivity by incorporating measurable laboratory and physiological parameters ( 8 , 9 ). Recent literature suggests that integrating clinical and objective prognostic indicators may improve risk stratification in advanced cancer patients ( 11 , 12 ). However, prospective data evaluating the complementary performance of PPI and OPS within a homogeneous best supportive care population remain limited. When analyzed according to predefined OPS and PPI cut-off values, both scores demonstrated statistically significant discriminatory ability for short-term mortality ( 4 – 6 , 8 , 9 ). However, AUC values remained below 0.70, indicating modest stand-alone predictive strength despite statistical significance. Similar moderate discrimination has been reported in previous validation studies of both PPI and OPS in advanced cancer populations ( 4 – 6 , 9 , 15 , 16 ). These findings are consistent with the understanding that prognostication in terminal cancer is multifactorial and unlikely to be fully captured by a single index ( 13 , 17 ). The distribution of short-term mortality across combined OPS–PPI risk groups further supports this interpretation. Patients with concurrent high OPS and high PPI (OPS ≥ 3 & PPI > 6) represented the vast majority of early deaths, with markedly higher mortality within 3, 4, and 6 weeks compared with lower-risk groups. In contrast, patients with low values on both indices exhibited substantially lower early mortality. This graded pattern suggests that PPI and OPS reflect complementary dimensions of terminal decline—clinical deterioration and objective physiologic burden. Although PPI and OPS have been evaluated within the same patient cohorts in comparative studies ( 11 , 12 ), to our knowledge, no prior prospective study has formally integrated both scores into a single combined prognostic model within a homogeneous best supportive care population. Kaplan–Meier analyses were fully concordant with both ROC and categorical mortality findings. Higher PPI and OPS categories were associated with significantly shorter median overall survival, and the combined OPS–PPI framework demonstrated clear stepwise separation of survival curves. Similar survival gradients have been reported individually for PPI ( 4 – 6 , 15 ) and OPS ( 8 , 9 ); however, formal combined modeling of these two indices has not previously been described in the literature. Taken together, the concordance across ROC discrimination, early mortality distribution, and overall survival curves strengthens the internal consistency of our findings. While the predictive power of each score alone was modest, their combined application provided clinically meaningful stratification of patients at very high risk of imminent death. In multivariable Cox regression analysis adjusted for age, sex, and primary tumor site, both PPI > 6 and OPS ≥ 3 remained independent predictors of poorer overall survival. Importantly, their prognostic significance persisted irrespective of tumor type and age, indicating that these indices reflect global clinical and physiologic deterioration rather than tumor-specific characteristics. In contrast, neither primary tumor site nor age independently influenced survival in this terminal cohort. These findings further support the concept that in the end-of-life setting, systemic functional decline outweighs tumor histology in determining short-term survival ( 13 , 17 , 18 ). From a clinical perspective, both the PPI and the OPS combine readily available clinical findings with routine laboratory parameters, allowing a practical assessment of functional and physiologic decline. Their simple structure makes them suitable for bedside use in palliative care settings. Identifying patients with a high probability of death within a few weeks may help clinicians prioritize timely palliative interventions, avoid non-beneficial procedures, and facilitate earlier communication with patients and families. Conclusion These findings highlight the clinical relevance of structured prognostic assessment in terminal cancer patients receiving best supportive care. Although the individual predictive performance of PPI and OPS was modest, their combined use improved risk stratification and helped identify patients at high risk of imminent mortality. The persistence of their prognostic value independent of tumor type and age emphasizes the importance of functional and physiologic decline in the end-of-life setting. Further prospective studies with larger cohorts are needed to validate and refine combined prognostic models. Limitations Several limitations should be acknowledged. First, this was a single-center study with a relatively limited sample size, which may restrict generalizability. Second, although all patients were managed with a best supportive care approach, the cohort included heterogeneous tumor types, potentially introducing biological variability. Third, predefined literature-based cut-off values were used for OPS and PPI without recalibration for this specific population. Additionally, other potentially relevant laboratory or inflammatory markers were not incorporated into the multivariable model. Finally, the moderate discriminatory performance observed underscores that prognostication in terminal cancer remains inherently complex and cannot be fully captured by structured scoring systems alone. Abbreviations • AUC Area under the curve • BSC Best supportive care • CI Confidence interval • ECOG Eastern Cooperative Oncology Group • HR Hazard ratio • LDH Lactate dehydrogenase • LR+ Positive likelihood ratio • LR− Negative likelihood ratio • NSCLC Non-small cell lung cancer • NPV Negative predictive value • OPS Objective Prognostic Score • OS Overall survival • PPI Palliative Prognostic Index • PPS Palliative Performance Scale • PPV Positive predictive value • ROC Receiver operating characteristic • SCLC Small cell lung cancer • SD Standard deviation • Sens Sensitivity • Spec Specificity • WBC White blood cell count Declarations Ethics approval This study was conducted in accordance with the principles of the Declaration of Helsinki. Ethical approval was obtained from the Atatürk University Non-Interventional Clinical Research Ethics Committee (decision no: 07, meeting no: 6, 27 September 2024). Given the observational nature of the study, informed consent procedures were conducted in accordance with institutional regulations. Clinical trial registration was not required because the study was observational and did not involve any interventional procedures. Consent for publication Not applicable. Data availability The data underlying this article will be shared on reasonable request to the corresponding author. Conflict of interest Authors report no Conflict of Interest in any product mentioned or concept discussed in this article. Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Authors' contributions The corresponding author contributed to all stages of the study, including the conception and design, administrative support, provision of study materials and patients, collection and assembly of data, as well as data analysis and interpretation. The other authors made substantial contributions to the conception and design of the work, provided study materials and patients, and participated in the collection, assembly, analysis, and interpretation of the data. The manuscript was written by all authors, and all authors reviewed and approved the final version of the manuscript and agree to be accountable for all aspects of the work. References Christakis NA, Lamont EB. Extent and determinants of error in physicians' prognoses in terminally ill patients: prospective cohort study. West J Med. 2000;172(5):310–3. 10.1136/ewjm.172.5.310 . PMID: 18751282; PMCID: PMC1070876. Glare P, Sinclair C, Downing M, Stone P, Maltoni M, Vigano A. Predicting survival in patients with advanced disease. Eur J Cancer. 2008;44(8):1146-56. 10.1016/j.ejca.2008.02.030 . Epub 2008 Apr 3. PMID: 18394880. Hui D. Prognostication of Survival in Patients With Advanced Cancer: Predicting the Unpredictable? 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Survival prediction for advanced cancer patients in the real world: A comparison of the Palliative Prognostic Score, Delirium-Palliative Prognostic Score, Palliative Prognostic Index and modified Prognosis in Palliative Care Study predictor model. Eur J Cancer. 2015;51(12):1618–29. 10.1016/j.ejca.2015.04.025 . Epub 2015 Jun 11. PMID: 26074396. Yoon SJ, Suh SY, Lee YJ, Park J, Hwang S, Lee SS, Ahn HY, Koh SJ, Park KU. Prospective Validation of Objective Prognostic Score for Advanced Cancer Inpatients in South Korea: A Multicenter Study. J Palliat Med. 2017;20(1):65–8. 10.1089/jpm.2016.0044 . Epub 2016 Nov 29. PMID: 27898288. Table 1 Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files ObjectivePrognosticScoreOPS.docx PalliativePrognosticIndexPPI.docx PalliativePerformanceScalePPS.docx Table1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9419641","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":625584455,"identity":"d6c11d26-9cd8-4f56-a472-94f23ec5325f","order_by":0,"name":"Alperen Akansel Çağlar","email":"data:image/png;base64,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","orcid":"","institution":"Başakşehir Çam and Sakura City Hospital","correspondingAuthor":true,"prefix":"","firstName":"Alperen","middleName":"Akansel","lastName":"Çağlar","suffix":""},{"id":625584460,"identity":"02e377f1-d08a-4319-96f4-c708127a9c76","order_by":1,"name":"Zekeriya Hannarici","email":"","orcid":"","institution":"Bursa Yuksek Ihtisas Training and Research Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zekeriya","middleName":"","lastName":"Hannarici","suffix":""},{"id":625584463,"identity":"71a9a5ba-ea6b-4a99-a16c-1faafa2fb533","order_by":2,"name":"Mehmet Emin Buyukbayram","email":"","orcid":"","institution":"Yalova State Hospital","correspondingAuthor":false,"prefix":"","firstName":"Mehmet","middleName":"Emin","lastName":"Buyukbayram","suffix":""},{"id":625584464,"identity":"dea52e09-70f9-4332-8bee-6227bd3221bc","order_by":3,"name":"Aykut Turhan","email":"","orcid":"","institution":"Ordu University Training and Research Hospital","correspondingAuthor":false,"prefix":"","firstName":"Aykut","middleName":"","lastName":"Turhan","suffix":""},{"id":625584465,"identity":"a1312028-2f95-4290-bbae-e4092635a0ee","order_by":4,"name":"Yasin Emrah Soylu","email":"","orcid":"","institution":"Atatürk University Faculty of Medicine Training and Research Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yasin","middleName":"Emrah","lastName":"Soylu","suffix":""},{"id":625584466,"identity":"65bdb0c3-578a-4b32-b247-a9510a5c047f","order_by":5,"name":"Mehmet Bilici","email":"","orcid":"","institution":"Atatürk University Faculty of Medicine Training and Research Hospital","correspondingAuthor":false,"prefix":"","firstName":"Mehmet","middleName":"","lastName":"Bilici","suffix":""},{"id":625584467,"identity":"f9f8f460-57f1-4ef3-9035-5c22be35c03d","order_by":6,"name":"Salim Başol Tekin","email":"","orcid":"","institution":"Acibadem Bursa Hospital","correspondingAuthor":false,"prefix":"","firstName":"Salim","middleName":"Başol","lastName":"Tekin","suffix":""}],"badges":[],"createdAt":"2026-04-14 21:23:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9419641/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9419641/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107675933,"identity":"e5bd9bf1-300c-4f2c-9dfa-3000c7e6987d","added_by":"auto","created_at":"2026-04-24 00:48:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":128182,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier overall survival curves according to PPI, OPS, and combined OPS–PPI risk groups\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eOverall survival according to PPI group (≤6 vs \u0026gt;6).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(B)\u003c/strong\u003e Overall survival according to OPS group (\u0026lt;3 vs ≥3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(C)\u003c/strong\u003e Overall survival according to the combined OPS-PPI risk groups.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSurvival curves were compared using the log-rank test\u003c/em\u003e. \u003cem\u003e\u003cstrong\u003eOPS\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e, Objective Prognostic Score; \u003c/em\u003e\u003cem\u003e\u003cstrong\u003ePPI\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e, Palliative Prognostic Index; \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eOS\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e, overall survival.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9419641/v1/adc2135bd2c07539c5d3120a.png"},{"id":109164176,"identity":"b3512159-b5da-48f7-a9e0-7227cf57274b","added_by":"auto","created_at":"2026-05-13 08:02:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":444888,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9419641/v1/d8c5553c-8cb7-4a1f-a3ae-bcfc4ce8307d.pdf"},{"id":107707879,"identity":"f3330142-f5e1-4c20-a2e6-270211e74421","added_by":"auto","created_at":"2026-04-24 09:21:19","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17340,"visible":true,"origin":"","legend":"","description":"","filename":"ObjectivePrognosticScoreOPS.docx","url":"https://assets-eu.researchsquare.com/files/rs-9419641/v1/6aa02d5583fbe3f133858f24.docx"},{"id":107706344,"identity":"7d8a83b3-50ea-4386-8c5c-d74b66805604","added_by":"auto","created_at":"2026-04-24 09:17:54","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":16688,"visible":true,"origin":"","legend":"","description":"","filename":"PalliativePrognosticIndexPPI.docx","url":"https://assets-eu.researchsquare.com/files/rs-9419641/v1/e4c1a24798515fe894dd6fc5.docx"},{"id":107675935,"identity":"12aca7d9-e1f3-4fdd-bdf4-d24fdb3c721d","added_by":"auto","created_at":"2026-04-24 00:48:22","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":17998,"visible":true,"origin":"","legend":"","description":"","filename":"PalliativePerformanceScalePPS.docx","url":"https://assets-eu.researchsquare.com/files/rs-9419641/v1/b78c9f2b532a36ee8317c006.docx"},{"id":107707951,"identity":"e9110ba2-69b2-4c55-8668-0277e0041282","added_by":"auto","created_at":"2026-04-24 09:21:29","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":16631,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9419641/v1/4a82da740a6c2216b4383794.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognostic Value of the Objective Prognostic Score and Palliative Prognostic Index for Short-Term Mortality in Terminal Cancer Patients Receiving Best Supportive Care: A Prospective Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAdvanced cancer patients in the terminal phase frequently experience rapid clinical deterioration, making accurate prognostication a cornerstone of high-quality palliative care. Reliable survival prediction facilitates timely best supportive care (BSC) decisions, prevents futile interventions, optimizes referral to palliative services, and supports realistic communication with patients and families. Nevertheless, multiple studies have demonstrated that clinicians tend to overestimate survival in terminal cancer patients, leading to delayed end-of-life discussions and potentially non-beneficial treatments (\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo address this challenge, several prognostic models have been developed specifically for patients with advanced or terminal cancer. Among these, the Palliative Prognostic Index (PPI) is one of the most widely used and validated tools in palliative care settings (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). PPI is a purely clinical index composed of five parameters: Palliative Performance Scale (PPS), oral intake, presence of edema, dyspnea at rest, and delirium. Each component is assigned a weighted score, with higher total scores indicating poorer prognosis. Previous studies have demonstrated that PPI is particularly effective for predicting short-term survival, especially 3- to 6-week mortality, and that a PPI score\u0026thinsp;\u0026ge;\u0026thinsp;6 is strongly associated with survival of less than three weeks (\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite its robust validation, PPI incorporates clinical assessments such as performance status, oral intake, and delirium, which may be subject to interobserver variability, especially across different care settings and levels of clinician experience (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). This limitation has stimulated interest in prognostic tools based exclusively on objectively measurable variables.\u003c/p\u003e \u003cp\u003eThe OPS was developed as a prognostic model primarily based on objective clinical and laboratory parameters, minimizing reliance on subjective clinical judgment (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). OPS includes eight variables: Eastern Cooperative Oncology Group performance status (ECOG PS\u0026thinsp;=\u0026thinsp;4), anorexia (defined as intake of fewer than five spoonfuls per meal or less than one-third of a normal meal), dyspnea at rest, elevated white blood cell count (\u0026gt;\u0026thinsp;11,000/\u0026micro;L), hyperbilirubinemia (total bilirubin\u0026thinsp;\u0026gt;\u0026thinsp;2.0 mg/dL), renal dysfunction (serum creatinine\u0026thinsp;\u0026ge;\u0026thinsp;1.5 mg/dL), and elevated lactate dehydrogenase (LDH\u0026thinsp;\u0026ge;\u0026thinsp;502 IU/L). Renal dysfunction (serum creatinine\u0026thinsp;\u0026ge;\u0026thinsp;1.5 mg/dL) is assigned 2 points, whereas each of the remaining parameters contributes 1 point to the total score. Higher OPS values are associated with poorer prognosis and shorter expected survival (\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eComparative studies evaluating PPI, OPS, and other prognostic models suggest that these tools demonstrate broadly comparable prognostic performance, while differing in complexity, objectivity, and clinical applicability (\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). However, most existing studies have been retrospective in design, conducted in heterogeneous palliative care populations, or have included patients receiving mixed treatment intents. Importantly, evidence derived from prospective cohorts specifically limited to terminal cancer patients for whom a best supportive care decision has been made remains scarce (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMoreover, accurate prediction of short-term mortality in patients receiving best supportive care remains a critical yet challenging aspect of palliative oncology. Reliable prognostic estimation is essential for guiding clinical decision-making, facilitating timely end-of-life discussions, optimizing resource allocation, and aligning care with patient and family expectations. However, prognostic uncertainty in this setting remains substantial, and available data specifically focusing on patients managed exclusively with supportive care are relatively limited. Improving risk stratification in this population may therefore contribute to more individualized and clinically meaningful care planning.\u003c/p\u003e\n\u003ch3\u003eStudy Aim\u003c/h3\u003e\n\u003cp\u003ePrognostic uncertainty in terminal cancer patients receiving best supportive care continues to complicate end-of-life decision-making and care planning. In this context, more precise risk stratification may support clinically meaningful and individualized management. Therefore, this prospective study aimed to evaluate the prognostic performance of the PPI and the OPS separately in terminal cancer patients receiving best supportive care, and additionally to analyze the contribution of their combined use to short-term mortality prediction.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e \u003cb\u003eStudy Design and Patient Population\u003c/b\u003e: This prospective observational study was conducted in a tertiary-level comprehensive cancer center. Hospitalized adult patients with advanced cancer were screened for eligibility. Patients were included if they met all of the following criteria: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) a documented decision for best supportive care determined by a multidisciplinary oncology team, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) no receipt of any oncological treatment (chemotherapy, targeted therapy, immunotherapy, or radiotherapy) within the preceding one month, (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) poor performance status defined as ECOG PS 3 or 4, and (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) a clinical diagnosis of terminal-stage cancer.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eData Collection and Prognostic Score Assessment\u003c/strong\u003e \u003cp\u003eAll included patients underwent a comprehensive clinical evaluation on the first day of hospital admission. Physical examination findings and laboratory parameters were recorded at baseline by the responsible investigator. On the same day, the PPI and the OPS were calculated for each patient according to their original definitions.\u003c/p\u003e \u003c/p\u003e \u003cp\u003ePPI was calculated based on five clinical variables: Palliative Performance Scale, oral intake, presence of edema, dyspnea at rest, and delirium. OPS was calculated using objective clinical and laboratory parameters, including ECOG performance status, anorexia, dyspnea at rest, white blood cell count, total bilirubin, serum creatinine, and lactate dehydrogenase levels.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eFollow-up and Outcome Measures\u003c/strong\u003e \u003cp\u003eA total of 114 consecutive patients were prospectively enrolled starting from 12 April 2024. Patients were followed until death, with data collection continuing until the date of death of the last patient, 05 May 2025. Survival time was defined as the interval between the date of hospital admission (baseline assessment) and the date of death.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eClinical outcome data were unavailable for two patients during follow-up. Therefore, the final analysis included 112 patients.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eStatistical Analysis\u003c/strong\u003e \u003cp\u003eStatistical analyses were performed using IBM SPSS Statistics for Windows, Version 25.0 (IBM Corp. Armonk, NY, USA). Descriptive statistics were expressed as number and percentage for categorical variables, and as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or median (minimum\u0026ndash;maximum) for continuous variables, as appropriate.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe discriminatory performance of clinical variables for short-term mortality was evaluated using receiver operating characteristic (ROC) curve analysis. Survival outcomes were estimated using the Kaplan\u0026ndash;Meier method and compared between groups with the log-rank test. A two-sided p value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 114 consecutive patients were prospectively enrolled in the study. Survival outcome data were unavailable for two patients; therefore, the final analysis included 112 patients.\u003c/p\u003e\n\u003cp\u003eThe mean age of the cohort was 62.3 \u0026plusmn; 12.3 years (median, 61.5; range, 29\u0026ndash;86), and 66 patients (58.9%) were male.\u003c/p\u003e\n\u003cp\u003eFor risk stratification, patients were categorized using their established cut-off values for the PPI \u0026gt;6 and the OPS \u0026ge;3, and an additional combined OPS\u0026ndash;PPI risk grouping was constructed. Based on this combined classification, 61 of 112 patients (54.5%) were assigned to the highest-risk category (OPS \u0026ge;3 and PPI \u0026gt;6). The remaining baseline sociodemographic and clinical characteristics, as well as short-term mortality outcomes, are summarized in Table 1.\u003c/p\u003e\n\u003cp\u003eThe discriminatory performance of OPS and PPI for predicting short-term mortality at 3, 4, and 6 weeks is presented in Table 2.\u003c/p\u003e\n\u003cp\u003eFor 3-week mortality, the AUC was 0.622 (95% CI, 0.426\u0026ndash;0.880) for OPS \u0026ge;3 and 0.679 (95% CI, 0.400\u0026ndash;0.785) for PPI \u0026gt;6, both were statistically significant (p=0.020 and p=0.001, respectively).\u003c/p\u003e\n\u003cp\u003eAt 4 weeks, neither OPS nor PPI showed statistically significant discrimination (AUC 0.579 [p=0.132] and 0.598 [p=0.072], respectively).\u003c/p\u003e\n\u003cp\u003eFor 6-week mortality, both scores again showed statistically significant discrimination, with AUC values of 0.647 for OPS and 0.640 for PPI (p=0.007 and p=0.013, respectively).\u003c/p\u003e\n\u003cp\u003eThe combined OPS\u0026ndash;PPI model yielded AUC values of 0.671, 0.611, and 0.646 for predicting 3-, 4-, and 6-week mortality, respectively, all of which reached statistical significance (p\u0026lt;0.001, p=0.018, and p=0.002, respectively).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eDiscriminatory performance of OPS and PPI for predicting short-term mortality using ROC curve analysis\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"596\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAUC (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSens\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpec (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLR+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLR\u0026minus;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3-week\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003emortality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOPS \u0026ge;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.622 (0.426\u0026ndash;0.880)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e89.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e76.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePPI \u0026gt;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.679 (0.400\u0026ndash;0.785)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e75.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e62.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e4-week\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003emortality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOPS \u0026ge;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.579 (0.323\u0026ndash;0.775)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e85.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e76.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePPI \u0026gt;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.598 (0.350\u0026ndash;0.800)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e67.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e58.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" valign=\"top\" style=\"width: 596px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6-week\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003emortality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOPS \u0026ge;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.647 (0.365\u0026ndash;0.825)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e86.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e79.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePPI \u0026gt;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.640 (0.290\u0026ndash;0.890)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e68.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e64.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" valign=\"top\" style=\"width: 596px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCombined model\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(OPS \u0026ge;3\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026amp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;PPI \u0026gt;6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3-week mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.671 (0.200\u0026ndash;0.885)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e73.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4-week mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.611 (0.341\u0026ndash;0.860)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e65.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e56.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6-week mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.646 (0.269\u0026ndash;0.800)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e65.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e63.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAUC\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e, area under the curve;\u003cstrong\u003e\u0026nbsp;CI\u003c/strong\u003e, confidence interval; \u003cstrong\u003eLR+\u003c/strong\u003e, positive likelihood ratio;\u003cstrong\u003e\u0026nbsp;LR\u0026minus;\u003c/strong\u003e, negative likelihood ratio; \u003cstrong\u003eNPV\u003c/strong\u003e, negative predictive value; \u003cstrong\u003eOPS\u003c/strong\u003e, Objective Prognostic Score; \u003cstrong\u003ePPI\u003c/strong\u003e, Palliative Prognostic Index; \u003cstrong\u003ePPV\u003c/strong\u003e, positive predictive value; \u003cstrong\u003eSens\u003c/strong\u003e, sensitivity; \u003cstrong\u003eSpec\u003c/strong\u003e, specificity. Discriminatory performance was assessed using receiver operating characteristic (ROC) curve analysis\u003c/em\u003e. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3 presents the comparison of 3-, 4-, and 6-week mortality rates according to the combined OPS\u0026ndash;PPI risk groups.\u003c/p\u003e\n\u003cp\u003eA statistically significant difference in mortality was observed among the groups at all evaluated time points (3-week p=0.002, 4-week p=0.021, and 6-week p=0.003).\u003c/p\u003e\n\u003cp\u003eThe highest mortality consistently occurred in the OPS\u0026ge;3 \u0026amp; PPI\u0026gt;6 group, with rates of 73.6% at 3 weeks, 65.6% at 4 weeks, and 65.8% at 6 weeks. Detailed mortality distributions for the remaining groups are shown in Table 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003eComparison of short-term mortality rates according to combined OPS\u0026ndash;PPI risk groups\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"610\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCombined risk group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3-week mortality (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e4-week mortality\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e6-week mortality\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOPS \u0026lt;3 \u0026amp; PPI \u0026le;6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOPS \u0026ge;3 \u0026amp; PPI \u0026le;6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOPS \u0026lt;3 \u0026amp; PPI \u0026gt;6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOPS \u0026ge;3 \u0026amp; PPI \u0026gt;6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e73.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e65.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e65.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003ePearson chi-square test; p \u0026lt; 0.05 considered statistically significant.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOverall survival was compared between risk groups using Kaplan\u0026ndash;Meier analysis (Figure 1).\u003c/p\u003e\n\u003cp\u003ePatients with higher PPI scores (\u0026gt;6) had significantly shorter median overall survival than those with PPI \u0026le;6 (11 vs 52 days, p \u0026lt; 0.001). Similarly, patients with OPS \u0026ge;3 demonstrated significantly poorer survival compared with those with OPS \u0026lt;3 (15 vs 63 days, p \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003eWhen patients were stratified according to the combined OPS\u0026ndash;PPI model, a clear gradient in survival was observed across the four risk categories. The worst survival was seen in patients with both high OPS and high PPI (OPS \u0026ge;3 and PPI \u0026gt;6; median OS 11 days), whereas the best survival occurred in patients with both low OPS and low PPI (OPS \u0026lt;3 and PPI \u0026le;6; median OS 65 days) (Figure 1).\u003c/p\u003e\n\u003cp\u003eMultivariable Cox regression analysis was performed to evaluate the independent prognostic impact of OPS and PPI on overall survival after adjustment for age, sex, and primary tumor site (Table 4). PPI \u0026gt;6 remained a significant independent predictor of poorer overall survival (HR 1.97, 95% CI 1.25\u0026ndash;3.10, p = 0.003). Similarly, OPS \u0026ge;3 was independently associated with worse survival (HR 1.65, 95% CI 1.01\u0026ndash;2.69, p = 0.047). In contrast, primary tumor site (p = 0.959), age (p = 0.293), and sex (p = 0.311) were not independently associated with overall survival.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u0026nbsp;\u003c/strong\u003eMultivariable Cox regression analysis for overall survival\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"3\" cellpadding=\"0\" width=\"502\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHR (Exp[B])\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePPI \u0026gt;6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.97\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.25\u0026ndash;3.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eOPS \u0026ge;3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1.65\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.01\u0026ndash;2.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.047\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePrimary tumor site\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.959\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMale sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.51\u0026ndash;1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.311\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge (per year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.97\u0026ndash;1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.293\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHR\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e, hazard ratio; \u003cstrong\u003eCI\u003c/strong\u003e, confidence interval; \u003cstrong\u003eOPS\u003c/strong\u003e, Objective Prognostic Score; \u003cstrong\u003ePPI\u003c/strong\u003e, Palliative Prognostic Index. Multivariable model adjusted for age, sex, and primary tumor site.\u003c/em\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAccurate prognostication in terminal cancer patients is essential for guiding end-of-life decision-making. The PPI is a well-validated clinical tool for predicting short-term survival, particularly within the last weeks of life (\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). In contrast, the OPS was developed to reduce subjectivity by incorporating measurable laboratory and physiological parameters (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Recent literature suggests that integrating clinical and objective prognostic indicators may improve risk stratification in advanced cancer patients (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). However, prospective data evaluating the complementary performance of PPI and OPS within a homogeneous best supportive care population remain limited. When analyzed according to predefined OPS and PPI cut-off values, both scores demonstrated statistically significant discriminatory ability for short-term mortality (\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). However, AUC values remained below 0.70, indicating modest stand-alone predictive strength despite statistical significance. Similar moderate discrimination has been reported in previous validation studies of both PPI and OPS in advanced cancer populations (\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). These findings are consistent with the understanding that prognostication in terminal cancer is multifactorial and unlikely to be fully captured by a single index (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe distribution of short-term mortality across combined OPS\u0026ndash;PPI risk groups further supports this interpretation. Patients with concurrent high OPS and high PPI (OPS\u0026thinsp;\u0026ge;\u0026thinsp;3 \u0026amp; PPI\u0026thinsp;\u0026gt;\u0026thinsp;6) represented the vast majority of early deaths, with markedly higher mortality within 3, 4, and 6 weeks compared with lower-risk groups. In contrast, patients with low values on both indices exhibited substantially lower early mortality. This graded pattern suggests that PPI and OPS reflect complementary dimensions of terminal decline\u0026mdash;clinical deterioration and objective physiologic burden. Although PPI and OPS have been evaluated within the same patient cohorts in comparative studies (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), to our knowledge, no prior prospective study has formally integrated both scores into a single combined prognostic model within a homogeneous best supportive care population.\u003c/p\u003e \u003cp\u003eKaplan\u0026ndash;Meier analyses were fully concordant with both ROC and categorical mortality findings. Higher PPI and OPS categories were associated with significantly shorter median overall survival, and the combined OPS\u0026ndash;PPI framework demonstrated clear stepwise separation of survival curves. Similar survival gradients have been reported individually for PPI (\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) and OPS (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e); however, formal combined modeling of these two indices has not previously been described in the literature.\u003c/p\u003e \u003cp\u003eTaken together, the concordance across ROC discrimination, early mortality distribution, and overall survival curves strengthens the internal consistency of our findings. While the predictive power of each score alone was modest, their combined application provided clinically meaningful stratification of patients at very high risk of imminent death.\u003c/p\u003e \u003cp\u003eIn multivariable Cox regression analysis adjusted for age, sex, and primary tumor site, both PPI\u0026thinsp;\u0026gt;\u0026thinsp;6 and OPS\u0026thinsp;\u0026ge;\u0026thinsp;3 remained independent predictors of poorer overall survival. Importantly, their prognostic significance persisted irrespective of tumor type and age, indicating that these indices reflect global clinical and physiologic deterioration rather than tumor-specific characteristics. In contrast, neither primary tumor site nor age independently influenced survival in this terminal cohort. These findings further support the concept that in the end-of-life setting, systemic functional decline outweighs tumor histology in determining short-term survival (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFrom a clinical perspective, both the PPI and the OPS combine readily available clinical findings with routine laboratory parameters, allowing a practical assessment of functional and physiologic decline. Their simple structure makes them suitable for bedside use in palliative care settings. Identifying patients with a high probability of death within a few weeks may help clinicians prioritize timely palliative interventions, avoid non-beneficial procedures, and facilitate earlier communication with patients and families.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThese findings highlight the clinical relevance of structured prognostic assessment in terminal cancer patients receiving best supportive care. Although the individual predictive performance of PPI and OPS was modest, their combined use improved risk stratification and helped identify patients at high risk of imminent mortality. The persistence of their prognostic value independent of tumor type and age emphasizes the importance of functional and physiologic decline in the end-of-life setting. Further prospective studies with larger cohorts are needed to validate and refine combined prognostic models.\u003c/p\u003e"},{"header":"Limitations","content":"\u003cp\u003eSeveral limitations should be acknowledged. First, this was a single-center study with a relatively limited sample size, which may restrict generalizability. Second, although all patients were managed with a best supportive care approach, the cohort included heterogeneous tumor types, potentially introducing biological variability. Third, predefined literature-based cut-off values were used for OPS and PPI without recalibration for this specific population. Additionally, other potentially relevant laboratory or inflammatory markers were not incorporated into the multivariable model. Finally, the moderate discriminatory performance observed underscores that prognostication in terminal cancer remains inherently complex and cannot be fully captured by structured scoring systems alone.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; AUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArea under the curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; BSC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBest supportive care\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; CI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; ECOG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEastern Cooperative Oncology Group\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; HR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHazard ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; LDH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLactate dehydrogenase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; LR+\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePositive likelihood ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; LR\u0026minus;\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNegative likelihood ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; NSCLC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNon-small cell lung cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; NPV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNegative predictive value\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; OPS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eObjective Prognostic Score\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; OS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOverall survival\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; PPI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePalliative Prognostic Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; PPS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePalliative Performance Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; PPV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePositive predictive value\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; ROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceiver operating characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; SCLC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSmall cell lung cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; SD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; Sens\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; Spec\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; WBC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWhite blood cell count\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the principles of the Declaration of Helsinki. Ethical approval was obtained from the Atat\u0026uuml;rk University Non-Interventional Clinical Research Ethics Committee (decision no: 07, meeting no: 6, 27 September 2024). Given the observational nature of the study, informed consent procedures were conducted in accordance with institutional regulations. Clinical trial registration was not required because the study was observational and did not involve any interventional procedures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data underlying this article will be shared on reasonable request to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors report no Conflict of Interest in any product mentioned or concept discussed in this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe corresponding author contributed to all stages of the study, including the conception and design, administrative support, provision of study materials and patients, collection and assembly of data, as well as data analysis and interpretation. The other authors made substantial contributions to the conception and design of the work, provided study materials and patients, and participated in the collection, assembly, analysis, and interpretation of the data. The manuscript was written by all authors, and all authors reviewed and approved the final version of the manuscript and agree to be accountable for all aspects of the work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChristakis NA, Lamont EB. Extent and determinants of error in physicians' prognoses in terminally ill patients: prospective cohort study. West J Med. 2000;172(5):310\u0026ndash;3. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/ewjm.172.5.310\u003c/span\u003e\u003cspan address=\"10.1136/ewjm.172.5.310\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 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J Palliat Med. 2017;20(1):65\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1089/jpm.2016.0044\u003c/span\u003e\u003cspan address=\"10.1089/jpm.2016.0044\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2016 Nov 29. PMID: 27898288.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Palliative Prognostic Index, Objective Prognostic Score, Terminal cancer, Best supportive care, Prognostication, Palliative care","lastPublishedDoi":"10.21203/rs.3.rs-9419641/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9419641/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAccurate prognostication in terminal cancer patients receiving best supportive care (BSC) is essential for guiding end-of-life decision-making and avoiding non-beneficial interventions. Several prognostic models have been developed for advanced cancer, including the Palliative Prognostic Index (PPI) and the Objective Prognostic Score (OPS). However, prospective data evaluating their performance specifically in patients managed with best supportive care are limited. This study aimed to evaluate the prognostic performance of PPI and OPS and to assess whether their combined use improves short-term mortality prediction in terminal cancer patients receiving BSC.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis prospective observational cohort study included hospitalized adult patients with terminal-stage cancer and a documented best supportive care decision. Eligible patients had poor performance status (ECOG 3\u0026ndash;4) and had not received oncologic treatment within the preceding month. PPI and OPS were calculated at baseline using predefined criteria. Patients were followed until death, and survival time was defined as the interval between baseline assessment and death. The ability of the scores to predict 3-, 4-, and 6-week mortality was evaluated, and survival outcomes were analyzed using standard survival analysis methods.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 112 patients were included in the final analysis. Both PPI\u0026thinsp;\u0026gt;\u0026thinsp;6 and OPS\u0026thinsp;\u0026ge;\u0026thinsp;3 were associated with significantly higher short-term mortality, although their individual predictive performance was modest (AUC\u0026thinsp;\u0026lt;\u0026thinsp;0.70). Patients with concurrent high PPI and OPS scores had markedly higher early mortality rates. The combined OPS\u0026ndash;PPI model improved risk stratification and identified patients at the highest risk of imminent death. In multivariable analysis, PPI\u0026thinsp;\u0026gt;\u0026thinsp;6 (HR 1.97, 95% CI 1.25\u0026ndash;3.10; p\u0026thinsp;=\u0026thinsp;0.003) and OPS\u0026thinsp;\u0026ge;\u0026thinsp;3 (HR 1.65, 95% CI 1.01\u0026ndash;2.69; p\u0026thinsp;=\u0026thinsp;0.047) remained independent predictors of poorer overall survival.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eAlthough the individual prognostic performance of PPI and OPS was modest, their combined application provided clearer risk stratification for short-term mortality in terminal cancer patients receiving best supportive care.\u003c/p\u003e","manuscriptTitle":"Prognostic Value of the Objective Prognostic Score and Palliative Prognostic Index for Short-Term Mortality in Terminal Cancer Patients Receiving Best Supportive Care: A Prospective Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-24 00:48:17","doi":"10.21203/rs.3.rs-9419641/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d1ac4737-733b-4a63-8a32-d7bcea1292fd","owner":[],"postedDate":"April 24th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Withdrawn","date":"2026-05-13T07:38:24+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-13T07:59:15+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-24 00:48:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9419641","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9419641","identity":"rs-9419641","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}