A Predictive Tool for Ability to Remain at Home After Cancer Surgery in Older Adults

Julie Hallet; Alyson L Mahar; Wing C Chan; Daniel I McIsaac; Natalie Coburn; Anna Gombay; Matthew Guttman; Barbara Haas; Amy Hsu

doi:10.1001/jamasurg.2025.1888

. 2025 Jun 25;160(8):903–911. doi: 10.1001/jamasurg.2025.1888

A Predictive Tool for Ability to Remain at Home After Cancer Surgery in Older Adults

Julie Hallet ^1,^2,^3,^4,^✉, Alyson L Mahar ^3,⁵, Wing C Chan ³, Daniel I McIsaac ^6,^7,⁸, Natalie Coburn ^1,^2,^3,⁴, Anna Gombay ⁴, Matthew Guttman ^1,^3,⁴, Barbara Haas ^1,^3,^4,⁹, Amy Hsu ^7,¹⁰, for the Recovery After Surgical Therapy for Older Adults Research—Cancer (RESTORE-Cancer) Group

¹Department of Surgery, University of Toronto, Toronto, Ontario, Canada

²Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada

³ICES, Toronto, Ontario, Canada

⁴Clinical Evaluative Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada

⁵Health Quality Program, School of Nursing, Queen’s University, Kingston, Ontario, Canada

⁶Department of Anesthesiology, University of Ottawa and The Ottawa Hospital, Ottawa, Ontario, Canada

⁷Ottawa Hospital Research Institute, Ottawa, Ontario, Canada

⁸Department of Pain Medicine, University of Ottawa and The Ottawa Hospital, Ottawa, Ontario, Canada

⁹Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, Ontario, Canada

¹⁰Bruyère Research Institute, Ottawa, Ontario, Canada

Group Information: The members of the Recovery After Surgical Therapy for Older Adults Research—Cancer (RESTORE-Cancer) Group appear in Supplement 2.

Accepted for Publication: May 2, 2025.

Published Online: June 25, 2025. doi:10.1001/jamasurg.2025.1888

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Corresponding Author: Julie Hallet, MD, MSc, Odette Cancer Centre, Sunnybrook Health Sciences Centre, 2075 Bayview Ave, T2-063, Toronto, ON M4N 3M5, Canada (julie.hallet@sunnybrook.ca).

Author Contributions: Dr Hallet had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Hallet, Mahar, McIsaac, Coburn, Gombay, Haas.

Acquisition, analysis, or interpretation of data: Hallet, Chan, McIsaac, Coburn, Guttman.

Drafting of the manuscript: Hallet, Haas.

Critical review of the manuscript for important intellectual content: Hallet, Mahar, Chan, McIsaac, Coburn, Gombay, Guttman.

Statistical analysis: Hallet, Chan, McIsaac, Guttman, Haas.

Obtained funding: Hallet, Mahar, McIsaac.

Administrative, technical, or material support: Hallet, Gombay.

Supervision: Hallet, Coburn.

Conflict of Interest Disclosures: Dr Hallet reported receiving speaker fees from Ipsen outside the submitted work. No other disclosures were reported.

Funding/Support: This study was funded by operating grants from the Canadian Institutes for Health Research and the Ontario Cancer Research Institute and ICES, which is funded by an annual grant from the Ontario Ministry of Health and the Ministry of Long-Term Care.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Group Information: The members of the Recovery After Surgical Therapy for Older Adults Research—Cancer (RESTORE-Cancer) Group are listed in Supplement 2.

Disclaimer: The opinions, results, view, and conclusions reported in this paper are those of the authors and do not necessarily reflect those of Ontario Health, Canadian Institute for Health Information, or Ontario Ministry of Health, and no endorsement is intended or should be inferred.

Meeting Presentation: Part of this work was presented at the 43rd Congress of the European Society of Surgical Oncology; October 3, 2024; Antwerp, Belgium.

Data Sharing Statement: See Supplement 3.

Additional Contributions: We thank the Toronto Community Health Profiles Partnership for providing access to the Ontario Marginalization Index. No one received financial compensation for this contribution.

Additional Information: This document used data adapted from the Statistics Canada Postal Code^OM Conversion File, which is based on data licensed from Canada Post Corporation, and/or data adapted from the Ontario Ministry of Health Postal Code Conversion File, which contains data copied under license from Canada Post Corporation and Statistics Canada. Parts of this material are based on data and information provided by Ontario Health, Canadian Institute for Health Information, and the Ontario Ministry of Health.

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Corresponding author.

PMCID: PMC12199182 PMID: 40560594

This prognostic study investigates if a predictive model can estimate the ability of older adults to remain alive and at home after cancer surgery.

Key Points

Question

Can older adults’ ability to remain alive and at home after cancer surgery be estimated with a predictive model?

Findings

In this prognostic study including 97 353 patients, the STAYHOME predictive model estimating the probability of not staying home at 6 and 12 months after cancer surgery was developed.

Meaning

Results reveal that STAYHOME used information available preoperatively to predict the risk of not remaining home after cancer surgery for older adults; individualized risk estimates from the STAYHOME tool may support counseling, shared decision-making, and setting of expectations before surgery.

Abstract

Importance

Shared decision-making with older adults regarding cancer surgery is critical. Prognostication tools that report individualized risk estimates of patient-centered outcomes can facilitate discussions.

Objective

To develop and internally validate a risk prediction model, STAYHOME, to estimate the risk of losing the ability to live at home for older adults after cancer surgery.

Design, Setting, and Participants

This was a retrospective population-based prognostic study conducted in Ontario, Canada. Included were adults 70 years and older undergoing cancer surgery over the period 2007 to 2019. Data were analyzed from June 2023 to January 2024.

Exposures

Predictor variables selected among information available preoperatively. The predictive model included age, sex, rural residence, previous cancer diagnosis, type of surgery, frailty, preoperative home care use, neoadjuvant therapy, cancer site, and cancer stage.

Main Outcomes and Measures

Inability to stay at home, defined as admission to nursing home. Fine-Gray models accounting for the competing risk of death were used. Discrimination and calibration were assessed. Bootstrap validation using 1000 samples with replacement was performed.

Results

Among 97 353 patients (median [IQR] age, 76 [73-81] years; 61 370 female [63.0%]), there were 2658 events (2.7%) at 6 months and 3746 events (3.8%) events at 12 months. The mean predicted risk of not staying home was 2.4% at 6 months and 3.4% at 12 months. Areas under the curve were 0.76 and 0.75 for 6-and 12-month predictions, respectively. Deviation from the observed risk of not staying home was 0.33% (95% CI, 0.31%-0.34%) for 6-month predictions and 0.46% (95% CI, 0.44%-0.48%) for 12-month predictions. Calibration was maintained across risk deciles.

Conclusions and Relevance

Results of this prognostic study reveal that STAYHOME used information available preoperatively to predict the risk of not remaining home after cancer surgery for older adults. It presented good discrimination and was well calibrated. Individualized risk estimates from STAYHOME may support counseling, shared decision-making, and setting of expectations before surgery.

Introduction

Surgery remains a cornerstone of cancer therapy. For older adults, aged 70 years or older, who represent the fastest-growing group of individuals requiring surgery for cancer, it presents unique complexities.^1,2,3,4 This population faces distinct challenges in surgical decision-making and management due to their unique risk-benefit profiles. Older adults place a higher priority on long-term functional independence and quality of life over surgical risks measured in the short-term after surgery, such as 30- or 90-day morbidity and mortality that are often presented to illustrate the risk of surgery.^5,6,7,8 A key concern for many older adults is the ability to return and remain living at home after surgery. Existing data focus mostly on whether one goes home directly from the hospital (home discharge disposition) and fail to capture the complexities of long-term recovery.^{9,10,11,12,13,14,15,16,17,18,19,20,21,22}

Individualized, patient-centered data on long-term outcomes for older adults after cancer surgery are limited. In the absence of robust, individualized data pertinent to older adults, preoperative conversations often fail to address factors of importance to senior-friendly care, such as anticipated postoperative disposition and long-term functional independence.^23,24,25 This gap in information can lead to decisional conflict and decision regret—an issue commonly reported in cancer surgical care, with up to 27% of patients undergoing surgery and 45% of care partners experiencing regret.^{26,27,28,29,30,31} Decision regret not only affects patient satisfaction but can also increase the use of health care resources and costs.³² Improving the availability and quality of data on outcomes that matter most to older adults, especially those centered on long-term independence, could enhance patient experience, improve outcomes, and reduce health care expenditures.

To meaningfully engage in decision-making, patients need to understand their prognosis as well as the risks and benefits associated with treatment.^7,33,34 Individualized risk data are also crucial for family members, care partners, and health care professionals involved in the patient’s care journey. Risk prediction tools can offer individualized prognostic information regarding outcomes.³⁵ Most existing prediction tools regarding disposition and living environment of older adults after surgery are methodologically limited.

We developed and internally validated a risk prediction model, named STAYHOME, to estimate the risk of losing the ability to live at home after cancer surgery for community-dwelling older adults. Such a prediction model can help offer answers to questions such as, “What will my life look like after surgery?” or “Will I be able to live independently after surgery?”

Methods

Study Design and Setting

We derived and internally validated the STAYHOME tool using population-based data in Ontario, Canada, where 15 million residents receive publicly funded health services through a government-administered health insurance plan. The use of these data is authorized under section 45 and approved by ICES’ Privacy and Legal Office. Reporting adhered to the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) reporting guidelines.³⁶

Study Population

The study population was adults 70 years and older with a new diagnosis of solid malignancy between January 1, 2007, and March 31, 2019. This included individuals with a new diagnosis of oropharyngeal, breast, esophageal, gastrointestinal, colorectal, hepatobiliary, pancreatic, genitourinary, gynecologic, bronchopulmonary cancer or melanoma, using International Classification of Diseases (ICD-O.3) codes in the Ontario Cancer Registry (OCR).^37,38,39,40 Older adults were defined as those 70 years or older based on recommendations for designing clinical research in geriatrics oncology as well as the age at which mortality from major surgery increased.^41,42 Patients were excluded if their date of death preceded their date of diagnosis, their date of death was missing, or they had 2 or more cancer diagnoses recorded on the same index diagnosis date. We also excluded individuals living in nursing homes before surgery, as these patients were not at risk for the outcome of interest. Patient race and ethnicity data are not available in the datasets.

Data Sources

Administrative databases stored at ICES in Ontario, Canada, were deterministically linked using unique patient identifiers.⁴³ The databases are detailed in eTable 1 in Supplement 1.

Outcome

The primary outcome of our prediction model was inability to stay at home after cancer surgery, defined as time to nursing home admission, and measured within 2 windows defined as clinically relevant in prior work: 6 and 12 months. Admission to a nursing home in Ontario is funded by provincial health insurance for those who need 24-hour nursing and personal care, frequent assistance with activities of daily living, constant supervision, or the need for such care can no longer be safely met through publicly funded community-based services. Thus, admission to a nursing home is a measure of clinically significant functional decline. Patients were followed up from the date of surgery to date of death, date of admission to nursing home, or end of study date of March 31, 2020, to ensure an opportunity for a minimum of 12 months of follow-up for all patients.

Predictors

To avoid bias associated with data driven predictor selection, candidate predictors were selected a priori.⁴⁴ Candidate predictors were selected based on clinical experience, review of the literature, and prior work on the outcome of losing the ability to remain home.^38,40,45 Predictors were measured at the anchor time for the model, which was in the month before surgery. Age and sex were obtained, rural living was determined using the Rurality Index of Ontario, and socioeconomic status was captured using quintiles of the material deprivation index, a composite index of the inability for individuals or households to afford consumption goods and activities typical in a society at a given point in time, categorized into quintiles.^46,47,48 The comorbidity burden was measured using the Elixhauser score, and preoperative frailty was captured using a validated algorithm for administrative data.^49,50,51 Preoperative receipt of home care support was captured via the home care database using a strategy previously reported.^37,39 Cancer site was defined using ICD-O.3 codes and cancer stage was obtained from the OCR, which captures best available stage at diagnosis with the TNM (ie, tumor-node-metastasis) staging system by the American Joint Committee on Cancer, 8th edition.^52,53 The surgical procedure intensity was dichotomized into high and low intensity using a standardized classification.⁵⁴ Finally, neoadjuvant therapy was defined as receipt of chemo and/or radiation therapy in the 6 months before surgery.

Missing data were explored. Data were missing in 0.1% for rural living and 0.7% for socioeconomic statistics. Because these data were missing in less than 1%, a complete case analysis was used for those variables. Data were missing in 18.3% for stage at diagnosis. Due to higher frequency of missing data, a missing category was created for that variable.

Statistical Analysis

We described the cohort using descriptive statistics including median with IQR for continuous variables and absolute number with percentage for categorical variables. We built Fine-Gray regression models to predict the outcome (admission to nursing home) at 6 months and 12 months after surgery, accounting for the competing risk of death from any cause. Age was specified as a continuous variable. All other predictors were specified as categorical variables. We grouped cancer sites and types of surgery based on clinical relevance and occurrence of the outcome to allow for enough events within strata. The final model included 10 predictors with 26 degrees of freedom.

For internal validation, bootstrapping with 1000 samples with replacement was used. Simulation studies have demonstrated that performance metrics derived from bootstrap methods with 500 repetitions are stable, and reflect multiple sources of model uncertainty.^36,44,55 The predicted risk of event at 6 and 12 months after surgery was estimated in the bootstrap samples by applying the coefficient from the development cohort. Model performance was assessed using the area under the curve (AUC) for discrimination. Calibration was determined by examining differences between predicted and observed risks. Calibration was evaluated for prediction at 6 and at 12 months after surgery, as well as across all predictors included in the STAYHOME tool.

Statistical significance was set at a 2-sided P value ≤.05. All analyses were conducted using SAS Enterprise Guide, version 7.1 (SAS Institute).

Results

The development cohort comprised 97 353 community-dwelling older adults (median [IQR] age, 76 [73-81] years; 61 370 female [63.0%]; 35 983 male [37.0%]), who underwent surgery for cancer over the study period. The characteristics of included patients are presented in Table 1. A total of 11 920 patients (12.2%) had preoperative frailty. At 6 months after surgery, 2658 patients (2.7%) had experienced the outcome of interest of being admitted to a nursing home, and 6458 (6.6%) had died. At 12 months after surgery, those numbers were 3742 patients (3.8%) and 10 262 patients (10.5%), respectively. The mean predicted risk of not staying home was 2.4% at 6 months and 3.4% at 12 months. The event rates at 6 and 12 months for each level of the model predictors are presented in Table 2.

Table 1. Characteristics of the Derivation Cohort, Stratified by Occurrence of the Event and Competing Event in the 12 Months After Surgery.

Preoperative characteristics	No. (%)				P value^a
Preoperative characteristics	All patients (n = 97 353)	Event (not staying home) [n = 3742]	At home (n = 83 349)	Death (competing risk) [n = 10 262]	P value^a
Age, mean (IQR), y	76 (73-81)	81 (76-86)	76 (72-81)	79 (74-84)	<.001
Sex
Female	61 370 (63.0)	2308 (61.7)	53 920 (64.7)	5142 (50.1)	<.001
Male	35 983 (37.0)	1434 (38.3)	29 429 (35.3)	5120 (49.9)	<.001
Rural living	13 588 (14.0)	461 (12.3)	11 641 (14.0)	1486 (14.5)	.005
SES
1st Quintile (highest SES)	19 002 (19.5)	554 (14.8)	16 661 (20.0)	1787 (17.4)	<.001
2nd Quintile	19 234 (19.8)	673 (18.0)	16 627 (19.9)	1934 (18.8)
3rd Quintile	19 331 (19.9)	699 (18.7)	16 534 (19.8)	2098 (20.4)
4th Quintile	20 610 (21.2)	865 (23.1)	17 547 (21.1)	2198 (21.4)
5th Quintile (lowest SES)	18 620 (19.1)	916 (24.5)	15 529 (18.6)	2175 (21.2)
High comorbidity burden (Elixhauser ≥4)	9840 (10.1)	676 (18.1)	7269 (8.7)	1895 (18.5)	<.001
Preoperative frailty (pFI >0.21)	11 920 (12.2)	1111 (29.7)	7964 (9.6)	2845 (27.7)	<.001
Previous cancer diagnosis (<5 y prior)	903 (0.9)	49 (1.3)	719 (0.9)	135 (1.3)	<.001
Preoperative home care (6 mo before surgery)	12 712 (13.1)	1264 (33.8)	8893 (10.7)	2555 (24.9)	<.001
Cancer site group
Bone, CNS, or other	1627 (1.7)	94 (2.5)	1090 (1.3)	443 (4.3)	<.001
Breast	26 572 (27.3)	588 (15.7)	25 244 (30.3)	740 (7.2)
Bronchopulmonary	8857 (9.1)	277 (7.4)	7328 (8.8)	1252 (12.2)
Endocrine or skin	4575 (4.7)	120 (3.2)	4152 (5.0)	303 (3.0)
Gastrointestinal	35 974 (37.0)	2019 (54.0)	28 419 (34.1)	5536 (53.9)
Genitourinary	10 796 (11.1)	344 (9.2)	9352 (11.2)	1100 (10.7)
Gynecologic	7919 (8.1)	252 (6.7)	6934 (8.3)	733 (7.1)
Head and neck	1033 (1.1)	48 (1.3)	830 (1.0)	155 (1.5)
Stage at diagnosis
Missing	17 815 (18.3)	747 (20.0)	14 283 (17.1)	2785 (27.1)	<.001
I	30 004 (30.8)	668 (17.9)	28 256 (33.9)	1080 (10.5)
II	26 779 (27.5)	1090 (29.1)	23 743 (28.5)	1946 (19.0)
III	17 407 (17.9)	899 (24.0)	14 102 (16.9)	2406 (23.4)
IV	5348 (5.5)	338 (9.0)	2965 (3.6)	2045 (19.9)
Receipt of neoadjuvant therapy	4459 (4.6)	186 (5.0)	3599 (4.3)	674 (6.6)	<.001
Intensity of surgery
High	57 237 (58.8)	2730 (73.0)	46 219 (55.5)	8288 (80.8)	<.001
Low	40 116 (41.2)	1012 (27.0)	37 130 (44.5)	1974 (19.2)	<.001
Type of surgery
Colectomy and enterectomy	25 255 (30.3)	1876 (50.1)	4588 (44.7)	31 719 (32.6)	<.001
Esophagectomy and gastrectomy	1958 (2.3)	146 (3.9)	724 (7.1)	2828 (2.9)
HNC resection	837 (1.0)	47 (1.3)	145 (1.4)	1029 (1.1)
Hepatectomy, pancreatectomy, and adrenalectomy	1835 (2.2)	91 (2.4)	693 (6.8)	2619 (2.7)
Hysterectomy and oophorectomy	7012 (8.4)	239 (6.4)	696 (6.8)	7947 (8.2)
Lung resection	7293 (8.7)	248 (6.6)	1090 (10.6)	8631 (8.9)
Nephroureterectomy, cystectomy, and prostatectomy	9059 (10.9)	324 (8.7)	1053 (10.3)	10 436 (10.7)
Partial and total mastectomy	25 288 (30.3)	590 (15.8)	742 (7.2)	26 620 (27.3)
Skin resection	4812 (5.8)	181 (4.8)	531 (5.2)	5524 (5.7)

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Abbreviations: CNS, central nervous system; HNC, head and neck cancer; pFI, Preoperative Frailty Index; SES, socioeconomic status.

^{^a}

χ² Test.

Table 2. Even Rate at 6 and 12 Months After Surgery for Each Level of Predictors Included in the Predictive Model.

Candidate predictors	Event rate, %
Candidate predictors	6 mo	12 mo
Sex
Female	2.58	3.76
Male	2.99	3.99
Rural living	2.37	3.39
Preoperative frailty (pFI >0.21)	7.19	9.32
Previous cancer diagnosis (<5 y prior)	3.99	5.43
Preoperative home care (6 mo before surgery)	7.09	9.94
Cancer site group
Bone, CNS, or other	4.12	5.78
Breast	1.24	2.21
Bronchopulmonary	2.03	3.13
Endocrine or skin	1.27	2.62
Gastrointestinal	4.36	5.61
Genitourinary	2.32	3.19
Gynecologic	2.18	3.18
Head and neck	3.10	4.65
Stage at diagnosis
Missing	4.12	4.19
I	1.24	2.23
II	2.03	4.07
III	1.27	5.16
IV	4.36	6.32
Receipt of neoadjuvant therapy	3.01	4.17
Intensity of surgery
High	3.60	4.77
Low	1.49	2.52

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Abbreviations: CNS, central nervous system; pFI, Preoperative Frailty Index.

Fine-Gray Regression

Subdistribution hazard ratios (HRs) for the Fine-Gray regression model (accounting for the competing risk of death) for all candidate predictors are presented in eTable 2 in Supplement 1. Age (continuous), sex, rural living, previous cancer diagnosis (<5 years from index diagnosis), preoperative frailty, receipt of preoperative home care, receipt of neoadjuvant therapy, intensity of surgery, cancer site, and cancer stage were included in the final model. Subdistribution HRs for the final model are presented in Figure 1. The final model specification is available in eTable 3 in Supplement 1.

Figure 1. — CNS indicates central nervous system.

Model Performance

The discrimination was assessed by computing AUC. The AUC for the final model was 0.76 for prediction at 6 months and 0.75 for prediction at 12 months. AUCs at both time points across predictors are detailed in eTable 4 in Supplement 1. They ranged from 0.66 to 0.78 for 6-month prediction, and 0.65 to 0.77 for 12-month prediction.

We evaluated calibration by computing the deviation of predicted risk (estimated with cumulative incidence function accounting for the competing risk of death) from the observed risk of not staying home after surgery. The deviation for the 6-month prediction was 0.33 percentage points on average (95% CI, 0.31-0.34) and for the 12-month prediction, it was 0.46 percentage points on average (95% CI, 0.44-0.48). The calibration slope was 1.27 for the 6-month prediction (intercept, −0.003) and 1.17 for the 12-month prediction (intercept, −0.0009). This does not represent a major concern regarding overfitting and good generalizability.

Calibration was also examined across categories of the predictors included in the model (eTable 5 in Supplement 1). The model was well calibrated among most predictors, with less than 0.8–percentage point deviation from the observed probability of not staying home in all categories, for both 6- and 12-month predictions. The only predictor categories with over 1–percentage point deviation from the observed were age over 85 years (1.13%; 95% CI, 1.03-1.24), preoperative frailty (1.16%; 95% CI, 1.05-1.27), and receipt of preoperative home care (1.25%; 95% CI, 1.15-1.36), for 12-month prediction. Even across those predictor categories, the deviation was minimal, and the model considered well calibrated.

Finally, calibration was evaluated across risk deciles (Figure 2). The deciles corresponded to 10 risk groups representing decreasing risk of continuing to live at home (increasing risk of losing the ability to live at home). The deviation between predicted and observed probabilities ranged from 0.1% to 1.5% across risk deciles for 6-month prediction and from 0.1% to 1.9% for 12-month prediction. This indicated good calibration across risk deciles, although with mild overestimation in higher-risk categories starting at the 7th decile (deviation remaining <2% for both 6- and 12-month prediction).

Discussion

Using population-based data in over 95 000 older adults undergoing surgery for cancer, we developed a predictive model of 6- and 12-month ability to remain living in one’s own home after surgery. We observed that a small proportion of the cohort was not able to remain at home in the year after cancer surgery. The STAYHOME tool demonstrated good discrimination and was well calibrated. Thus, it may be a useful tool to identify a specific group of individuals at risk of not remaining home. STAYHOME can provide individualized risk assessments of the ability to continue living at home after cancer surgery, which can aid in the counseling and management of older adults selected for cancer surgery. Indeed, for older adults, the ability to stay in their home is a critical, long-term, patient-centered outcome.^5,56,57,58

Loss of time at home postoperatively is often the result of poor functional outcomes, such as mobility impairments, depression, and challenges with self-care.⁵⁹ Most existing studies are limited to outcomes, such as mortality and complications, and short-term follow-up, and most do not focus on older adults or on cancer surgery.^{58,60,61,62,63} Our group has previously reported on the ability to stay alive and at home and its association with preoperative frailty, as well as time spent at home and factors associated with higher time at home.^38,40,64 However, those studies do not provide individualized estimates for those important outcomes. Predictive models for maintaining the ability to live at home after surgery are scarce and often methodologically limited. They focus on binary discharge disposition without capturing the complexity or dynamic nature of long-term post-operative recovery.^{9,10,11,12,13,14,15,16,17,18,19,20,21,22,65} Methodological limitations include small sample size, selection bias, variable selection based on statistical significance, lack of inclusion of frailty (a critical variable in older adults), overfitting, and lack of external validation.^36,66,67 Therefore, our study fills a gap in risk prediction and clinical management of older adults selected for cancer surgery, by developing a robust tool targeting a patient-centered and patient-prioritized outcome.^36,66,68 We used a population-based design, ensured a large sample size, selected variable based on clinical reasoning and availability at the anchor time for utilization of the tool (preoperative), included a validated measure of frailty, and reported rate and handling of missing data. External validation is being done with population-based data in Manitoba, Canada, and discussions are ongoing for testing in other international jurisdictions pending data availability.

The STAYHOME tool demonstrated good model performance in both discrimination and calibration, which remained consistent across different levels of key predictors. The model performance observed herein are comparable with that observed for the GIST (gastrointestinal stromal tumor) calculator (Memorial Sloan Kettering Cancer Center), the Sarculator (Istituto Nazionale dei Tumori), and the ACS-NSQIP (American College of Surgeons National Surgical Quality Improvement Program) mortality calculator (ACS).^69,70,71 We acknowledge that most predictive tools never reach clinical practice due to issues like focusing on irrelevant outcomes, lack of pragmatic use, poor methodological rigor, or absence of external validation and clinical usability/impact assessment.⁷² The STAYHOME tool was developed for preoperative use with variables that are both available preoperatively and that can be populated by health care professionals or self-reported by patients and their care partners. While awaiting external validation, it is important to note the tool’s context. We provided detailed information on our health care setting (publicly funded universally accessible health system), the cohort creation, and the characteristics of our population, so that others can appreciate how it applies to their own populations. In public health care, nursing home admission was fully covered. Thus, STAYHOME risk estimates reflect health-based need for nursing home care, not financial or insurance access. It assesses the risk of losing independence, making the predictions meaningful across systems. However, how the risk is communicated to patients may vary by health system; for instance, in private systems, additional counseling on available resources if the event occurs would be needed. Of note, all patients in the derivation cohort had been selected for surgery and survived the index postoperative hospitalization. STAYHOME is intended to guide counseling and preparation for postoperative recovery, not to exclude patients from surgery. Because other strong validated tools exist to predict mortality after surgery for older adults, such as the ACS-NSQIP calculator, it is intended to complement those other tools by estimating the likelihood of remaining living at home after surgery for the majority that survives the index hospitalization.⁷³ For instance, an older adult candidate for surgery may be informed of their risk of mortality after surgery and, in the likelihood they survive the postoperative stay, of their probabilities of remaining home at 6 and 12 months after surgery.

Given finite health care resources, information from STAYHOME may help prioritize older adults for additional intervention and support. Moreover, for patients identified as having a lower risk of not remaining home, individualized risk information may be reassuring and support their decision to proceed with cancer surgery. For example, using STAYHOME, an 85-year-old woman with no previous cancer history, now diagnosed with stage III pancreatic cancer, no frailty, living in a rural area, not receiving home care, with a plan for distal pancreatectomy without neoadjuvant therapy has a predicted risk of not being home 6 months after surgery of 4.4% and 12 months after surgery of 6.2%. For a 70-year-old man living with frailty but otherwise with the same characteristics, disease, and surgical plan, the risk of not being home 6 months after surgery of 2.3% and 12 months after surgery of 3.2%. This would lead to different conversations and expectation setting before surgery. With regard to implementation of STAYHOME, we are currently developing a web-based risk calculator and communication tool, complete with an application programming interface to facilitate its use by other organizations. The calculator will be hosted on a knowledge translation platform.⁷⁴ Detailed user guides and documentation will support its clinical usability and integration.

Limitations

This study has limitations. Like many predictive models, STAYHOME was less well calibrated at the extremes of the risk distribution. It somewhat overestimated the probability of not staying at home in the 7th to 10th risk deciles in individuals at higher risk who may need this information the most. It also showed slightly reduced discrimination for predictor levels of preoperative frailty, preoperative home care use, neoadjuvant therapy, and stage IV disease—typically markers of more vulnerable older adults.^{37,38,39,40,45} This may be due to the influence of additional predictors or interaction effects that we were unable to capture. Nonetheless, even in those areas, the tool still performed well in terms of both discrimination and calibration, with minimal overfitting and good generalizability. We tested the addition of other variables and interactions based on available data, but these adjustments did not lead to better model performance. Considerations of selection bias as well as external validation and clinical impact assessment have been discussed above.

Conclusions

In this prognostic study, STAYHOME was developed from routinely collected population-based data. It provided useful information about the probability of remaining at home at 6 and 12 months after cancer surgery to inform counseling, preparation, and expectation setting for older adults considered candidates for cancer surgery. It used information readily available to patients, care partners, and health care professionals and may be implemented to provide them with individualized risk estimates and improve surgical oncology care delivery and experience for older adults.

Supplement 1.

eTable 1. Data Sources

eTable 2. Unadjusted Subhazard Ratios of Candidate Predictors (Both Included and Excluded From the Final Model)

eTable 3. Final Model Specification (Fine-Gray Model With Competing Risk of Death)

eTable 4. Discrimination—Area Under the Curve for Prediction Model at 6 Months and at 12 Months After Surgery, for the Overall Model and by Predictors Groups

eTable 5. Calibration—Deviation of Predicted Risk From Observed Risk at 6 Months (A) and at 12 Months (B) After Surgery, for the Entire Model

jamasurg-e251888-s001.pdf^{(247.7KB, pdf)}

Supplement 2.

Nonauthor Collaborators. Recovery After Surgical Therapy for Older Adults Research—Cancer (RESTORE-Cancer) Group

jamasurg-e251888-s002.pdf^{(115.2KB, pdf)}

Supplement 3.

Data Sharing Statement.

jamasurg-e251888-s003.pdf^{(13.9KB, pdf)}

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplement 1.

eTable 1. Data Sources

eTable 2. Unadjusted Subhazard Ratios of Candidate Predictors (Both Included and Excluded From the Final Model)

eTable 3. Final Model Specification (Fine-Gray Model With Competing Risk of Death)

eTable 4. Discrimination—Area Under the Curve for Prediction Model at 6 Months and at 12 Months After Surgery, for the Overall Model and by Predictors Groups

eTable 5. Calibration—Deviation of Predicted Risk From Observed Risk at 6 Months (A) and at 12 Months (B) After Surgery, for the Entire Model

jamasurg-e251888-s001.pdf^{(247.7KB, pdf)}

Supplement 2.

Nonauthor Collaborators. Recovery After Surgical Therapy for Older Adults Research—Cancer (RESTORE-Cancer) Group

jamasurg-e251888-s002.pdf^{(115.2KB, pdf)}

Supplement 3.

Data Sharing Statement.

jamasurg-e251888-s003.pdf^{(13.9KB, pdf)}

[soi250031r1] 1.Lundebjerg NE, Trucil DE, Hammond EC, Applegate WB. When it comes to older adults, language matters: Journal of the American Geriatrics Society adopts modified American Medical Association style. J Am Geriatr Soc. 2017;65(7):1386-1388. doi: 10.1111/jgs.14941 [DOI] [PubMed] [Google Scholar]

[soi250031r2] 2.Balducci L, Ershler WB. Cancer and ageing: a nexus at several levels. Nat Rev Cancer. 2005;5(8):655-662. doi: 10.1038/nrc1675 [DOI] [PubMed] [Google Scholar]

[soi250031r3] 3.Etzioni DA, Liu JH, Maggard MA, Ko CY. The aging population and its impact on the surgery workforce. Ann Surg. 2003;238(2):170-177. doi: 10.1097/01.SLA.0000081085.98792.3d [DOI] [PMC free article] [PubMed] [Google Scholar]

[soi250031r4] 4.Smith BD, Smith GL, Hurria A, Hortobagyi GN, Buchholz TA. Future of cancer incidence in the US: burdens upon an aging, changing nation. J Clin Oncol. 2009;27(17):2758-2765. doi: 10.1200/JCO.2008.20.8983 [DOI] [PubMed] [Google Scholar]

[soi250031r5] 5.Abdellatif S, Hladkowicz E, Lalu MM, Boet S, Gagne S, McIsaac DI. Patient prioritization of routine and patient-reported postoperative outcome measures: a prospective, nested cross-sectional study. Can J Anaesth. 2022;69(6):693-703. doi: 10.1007/s12630-022-02191-7 [DOI] [PubMed] [Google Scholar]

[soi250031r6] 6.Fried TR, Bradley EH, Towle VR, Allore H. Understanding the treatment preferences of seriously ill patients. N Engl J Med. 2002;346(14):1061-1066. doi: 10.1056/NEJMsa012528 [DOI] [PubMed] [Google Scholar]

[soi250031r7] 7.Ghignone F, van Leeuwen BL, Montroni I, et al. ; International Society of Geriatric Oncology (SIOG) Surgical Task Force . The assessment and management of older cancer patients: a SIOG surgical task force survey on surgeons’ attitudes. Eur J Surg Oncol. 2016;42(2):297-302. doi: 10.1016/j.ejso.2015.12.004 [DOI] [PubMed] [Google Scholar]

[soi250031r8] 8.Berian JR, Mohanty S, Ko CY, Rosenthal RA, Robinson TN. Association of loss of independence with readmission and death after discharge in older patients after surgical procedures. JAMA Surg. 2016;151(9):e161689-e161689. doi: 10.1001/jamasurg.2016.1689 [DOI] [PubMed] [Google Scholar]

[soi250031r9] 9.Kobewka DM, McIsaac D, Chassé M, et al. Risk assessment tools to predict location of discharge and need for supportive services for medical patients after discharge from hospital: a systematic review protocol. Syst Rev. 2017;6(1):8. doi: 10.1186/s13643-016-0401-7 [DOI] [PMC free article] [PubMed] [Google Scholar]

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PERMALINK

A Predictive Tool for Ability to Remain at Home After Cancer Surgery in Older Adults

Julie Hallet, MD, MSc

Alyson L Mahar, PhD

Wing C Chan, MPH

Daniel I McIsaac, MD, MSc

Natalie Coburn, MD, MPH

Anna Gombay, BA(Hons)

Matthew Guttman, MD, PhD

Barbara Haas, MD, PhD

Amy Hsu, PhD

Key Points

Question

Findings

Meaning

Abstract

Importance

Objective

Design, Setting, and Participants

Exposures

Main Outcomes and Measures

Results

Conclusions and Relevance

Introduction

Methods

Study Design and Setting

Study Population

Data Sources

Outcome

Predictors

Statistical Analysis

Results

Table 1. Characteristics of the Derivation Cohort, Stratified by Occurrence of the Event and Competing Event in the 12 Months After Surgery.

Table 2. Even Rate at 6 and 12 Months After Surgery for Each Level of Predictors Included in the Predictive Model.

Fine-Gray Regression

Figure 1. Subdistribution Hazard Ratios (sHRs) in the Prediction Model (Multivariable Fine-Gray Model).

Model Performance

Figure 2. Calibration-Decile Plots Representing Deviation of Predicted Risk From Observed Risk of Not Staying Home at 6 Months and at 12 Months After Surgery, for the Entire Cohort.

Discussion

Limitations

Conclusions

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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