Beyond Gatekeeping: SIPAT as a Guide to Psychosocial Prehabilitation in a Single-Center Cohort of Lung Transplant Candidates

Aleksandra Stańska; Wojciech Karolak; Sławomir Żegleń; Jacek Wojarski

doi:10.20944/preprints202604.2122.v1

Submitted:

28 April 2026

Posted:

30 April 2026

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Abstract

Background: Psychosocial assessment is central to lung transplant evaluation. Structured tools such as the Stanford Integrated Psychosocial Assessment for Transplantation (SIPAT) can be used either to support exclusionary decisions or to guide psychosocial prehabilitation by identifying modifiable targets for intervention. We examined how SIPAT functions in a program that explicitly prioritizes remediation of modifiable psychosocial risks. Methods: We conducted a retrospective observational cohort study of consecutive adult lung transplant candidates evaluated at a single center in Poland between December 2021 and November 2025. Psychosocial risk was assessed using SIPAT (locally translated), including total and domain scores, candidate categories, and binary indicators of clinically relevant alcohol, illicit substance and nicotine related risk. The primary endpoint was a pragmatic program outcome, defined as ever being listed (including transplanted) versus not listed. Analyses focused on describing psychosocial risk profiles and their relationship to the program pathway rather than on building a predictive model of listing decisions. Results: In 491 candidates (mean age 57.2 years; 40.5% women), psychosocial burden was generally low (mean total SIPAT 12.4, SD 6.8) and most patients were rated as excellent or good candidates. SIPAT total, domain scores and candidate categories were not meaningfully associated with ever being listed. Nicotine related risk was more frequent among listed candidates, consistent with a clinical strategy in which smoking histories in predominantly COPD and emphysema patients trigger intensive cessation support rather than automatic exclusion. Cluster analyses identified a smaller high-risk subgroup, and ROC analyses showed modest discrimination for alcohol and nicotine related risk, supporting SIPAT as a structured needs assessment. Conclusions: In this prehabilitation oriented program, SIPAT did not operate as a binary gatekeeping instrument for listing. Instead, it primarily served to identify modifiable psychosocial targets that trigger tailored support. These findings support using SIPAT as a structured roadmap for psychosocial prehabilitation rather than a stand-alone exclusion tool.

Keywords:

lung transplantation

;

SIPAT

;

psychosocial assessment

;

prehabilitation

;

candidate selection

;

substance use

Subject:

Medicine and Pharmacology - Transplantation

Background

Lung transplantation is a life-saving treatment option for selected patients with end-stage respiratory disease, but it remains constrained by donor organ scarcity and a high burden of post-transplant complications [1,2]. International guidelines therefore emphasize careful candidate selection that integrates medical, surgical and psychosocial factors in order to maximize benefit while using organs responsibly [2]. Within this framework, psychosocial assessment has become a core component of transplant evaluation, particularly because potentially modifiable factors such as adherence, substance use and social support can strongly influence outcomes [3,4,5].

A large body of research in solid organ transplantation shows that nonadherence to medical recommendations and relapse to harmful substance use are common and have been linked to poorer clinical and functional outcomes, including higher morbidity and, in some studies, increased mortality [3,4,6,7,8]. However, psychosocial assessment practices have historically been heterogeneous, ranging from unstructured clinical impressions to locally developed checklists. This variability raises concerns about the consistency, transparency and fairness of psychosocial decision making, including the risk that similar patients may be judged differently across centers or even across clinicians within the same centers [9,10,11].

The Stanford Integrated Psychosocial Assessment for Transplantation (SIPAT) was developed to address these limitations by providing a structured, theory-driven rating system for key psychosocial domains relevant to transplant candidacy and outcomes [12]. SIPAT covers four domains: readiness and illness management, social support system, psychological stability and psychopathology, and lifestyle and substance use. In contrast to purely categorical decisions, SIPAT yields both a continuous total score and risk categories that are intended to support multidisciplinary decision making, highlight modifiable risk factors and guide pre-transplant interventions rather than serve as a stand-alone pass or fail criterion [12].

Empirical work has begun to evaluate the predictive utility of SIPAT across different organs [13,14,15,16]. In liver transplantation, higher SIPAT scores have been linked to poorer adherence and more complicated post-transplant courses in several studies [13,14], whereas other work has reported no clear association with key outcomes such as immunosuppressant adherence or rejection [16]. A recent single center study in lung transplantation reported that higher baseline SIPAT scores were associated with a greater burden of medical and psychosocial complications during the first post-transplant year [17]. A narrative review of psychosocial assessment tools concluded that SIPAT is among the best studied instruments and may help identify patients at elevated risk of adverse psychosocial and behavioral outcomes, although effect sizes are often modest and findings heterogeneous [9]. Together, these studies suggest that SIPAT captures clinically meaningful risk, but also highlight that its performance and clinical use may vary substantially across settings [9,12,13,14,15,16,17].

At the same time, international listing guidelines increasingly recommend that psychosocial risk factors should not automatically lead to exclusion from transplantation, especially when they are potentially modifiable through targeted intervention [2,18]. Instead, high risk profiles are often viewed as indications for additional support, such as structured addiction treatment, intensified education about the transplant regimen or efforts to stabilize the patient’s living situation [18,19,20,21]. This is essentially the logic of psychosocial prehabilitation: identifying risk early, then deliberately intervening before transplant to improve readiness rather than simply declining candidates [19,20,21].

This raises an important conceptual tension. SIPAT can be used either as a gatekeeping tool that contributes to denying access to transplantation, or as a support and flag tool that helps allocate psychosocial prehabilitation resources to vulnerable candidates while maintaining equitable access. Data on how SIPAT scores actually relate to listing decisions in real-world clinical practice are still limited. Existing studies have typically focused on post-transplant outcomes among patients who were already listed and transplanted, or have pooled different solid organ populations, making it difficult to draw conclusions specific to lung transplantation [9,12,13,14,15,16,17]. Moreover, most reports come from centers where high psychosocial risk frequently contributes to deferral or denial of listing, which may reinforce a more restrictive interpretation of the tool. There is a paucity of evidence from programs that systematically work with high-risk patients to remediate psychosocial vulnerabilities instead of using them primarily as exclusion criteria.

Our center has previously described the demographic and psychosocial characteristics of lung transplant candidates, including the distribution of SIPAT scores and domains in a single center cohort [22]. That analysis showed a wide spectrum of psychosocial risk, with a sizeable subgroup presenting with relevant substance use histories, psychiatric comorbidity or fragile social support, but did not examine whether these factors translated into different listing outcomes. Building on that work, the present study focuses specifically on the relationship between SIPAT scores, substance use risk indicators derived from SIPAT and subsequent listing status in a program that explicitly treats SIPAT as an entry point into psychosocial prehabilitation rather than a definitive barrier to transplantation.

In a lung transplant program that aims not to “disqualify for life” on psychosocial grounds but to identify and address modifiable risks, it is not obvious whether higher SIPAT scores should still predict a lower probability of being listed. One possibility is that structured psychosocial prehabilitation attenuates the impact of baseline risk, so that even candidates with substantial psychosocial burden ultimately reach listing after targeted interventions. Alternatively, SIPAT might still function as a gatekeeping tool, with high-risk profiles less likely to be listed despite the availability of support. By examining SIPAT domains, total scores, substance use risk indicators and high-risk clusters in relation to listing decisions in this supportive, prehabilitation-oriented setting, we aim to clarify how psychosocial assessment operates in practice and to inform a more nuanced, equity-oriented use of SIPAT in lung transplantation.

Methods

Setting and Participants

This retrospective observational cohort study study included consecutive adult candidates for lung transplantation who were admitted for the first time to either the Lung Transplantation Unit or the Clinic of Pulmonology at the University Clinical Center in Gdańsk, Poland, between December 2021 and November 2025. During this initial hospital stay all patients underwent a standard pre-transplant work-up and were discussed at the center’s multidisciplinary lung transplant board.

In our program, psychosocial assessment is an obligatory component of the first in-patient transplant evaluation. All patients admitted for lung transplant work-up are routinely referred for consultation with a clinical psychologist. For the present analyses we included all individuals who had a complete psychosocial evaluation and a fully scored SIPAT form. Repeated evaluations, readmissions and incomplete SIPAT protocols were excluded. Basic sociodemographic variables (age, sex) and primary pulmonary diagnosis were retrieved from medical records.

Psychosocial eligibility was evaluated with SIPAT as part of routine clinical care. For this study, SIPAT ratings and clinical data were analyzed retrospectively in anonymized form only. No procedures beyond standard care were introduced and no additional written informed consent was obtained.

Psychosocial Assessment and Prehabilitation Approach

Psychosocial evaluations were carried out by clinical psychologists with experience in transplantation and chronic lung disease. Each assessment combined a semi-structured clinical interview, review of the medical chart and, when clinically useful, discussion with the treating team or family.

The consultation focused on illness understanding, history of treatment adherence, health-related behaviors, psychiatric symptoms, coping strategies, social support and substance use. Based on all available information the psychologist completed the SIPAT rating and prepared a concise psychosocial report for presentation at the lung transplant board.

Importantly, in this program SIPAT is embedded in a psychosocial prehabilitation framework rather than a purely exclusionary one. Candidates who present with elevated psychosocial risk in specific domains are routinely offered targeted interventions before a final listing decision, including structured smoking cessation support, addiction treatment, psychiatric care, and social work input aimed at stabilizing housing or caregiving arrangements [19,20,21]. In other words, high SIPAT scores trigger psychosocial prehabilitation efforts rather than an automatic decision not to list. No research-specific questionnaires or experimental procedures were added.

Stanford Integrated Psychosocial Assessment for Transplantation (SIPAT)

Psychosocial risk was assessed using the Stanford Integrated Psychosocial Assessment for Transplantation (SIPAT), developed by Maldonado and colleagues as a structured clinician rated tool to capture psychosocial factors relevant for transplant candidacy and outcomes [12,13].

The instrument groups items into four domains:

Readiness and illness management (for example illness insight, treatment adherence, lifestyle factors, motivation for transplantation),

Social support system (living arrangements, availability, reliability and stability of caregivers),

Psychological stability and psychopathology (current and past psychiatric symptoms, coping style, personality factors),

Lifestyle and substance use (alcohol, nicotine and other psychoactive substances).

Items are scored on ordered categorical scales, with higher values indicating higher psychosocial risk. For each patient we calculated:

the total SIPAT score (sum of all items),
four domain scores (sums of items in domains A-D),
and the original SIPAT global risk category (excellent, good, minimally acceptable, poor or high-risk candidate) [12].

At the time of the study no officially validated Polish version of SIPAT was available. In our center we use a locally prepared Polish translation, developed by a team of psychologists and physicians fluent in English. The wording was minimally adapted to the lung transplant context and reviewed by bilingual clinicians. This version is used in everyday practice but has not yet undergone full cultural adaptation and validation.

Substance Use Indicators

From the Lifestyle and effect of substance use SIPAT domain we derived three binary indicators reflecting clinically relevant substance use risk:

alcohol-related risk (yes or no),
illicit-substances-related risk (yes or no),
nicotine-related risk (yes or no).

Patients were coded as having risk present if the SIPAT rating reflected current or past alcohol, drug or nicotine use at a level considered clinically significant in the original scoring guidelines, for example harmful use, dependence or a high estimated risk of relapse [12,13]. All remaining patients were coded as having no risk in that domain. These indicators were used both in descriptive analyses and as predictors in regression models.

Transplant Listing Status

For each candidate we obtained the final decision of the lung transplant board from the multidisciplinary meeting records. Clinical status was coded into four mutually exclusive categories:

transplanted,
currently on the active lung transplant waiting list,
definitively not listed (psychosocial or medical disqualification),
temporarily suspended or deferred (for example ongoing work-up, need for additional treatment, weight reduction or further stabilization).

In a subset of patients for whom the board had already reached a definite decision regarding eligibility we also distinguished a binary variable “qualified for listing” versus “not qualified”, regardless of whether transplantation had already been performed.

For the main analyses we created a pragmatic binary outcome variable reflecting whether a patient had ever been listed for lung transplantation. This pragmatic outcome reflects progression within the evaluation pathway in a real-world program and should not be interpreted as a pure psychosocial decision endpoint, as listing decisions integrate dynamic medical, logistical and multidisciplinary factors. Patients who were transplanted or currently on the waiting list were classified as listed, while those disqualified, temporarily suspended or still undergoing extended evaluation without being placed on the list were classified as non-listed. This variable was used as the primary dependent variable between-group comparisons and logistic regression.

Statistical Analysis

All analyses were conducted using IBM SPSS Statistics, version 23 (IBM Corp., Armonk, NY, USA). Two-sided p values below 0.05 were considered statistically significant. Given the exploratory and hypothesis-generating nature of the study, no formal correction for multiple testing was applied.

Continuous variables were inspected for outliers and distributional properties and are reported as mean and standard deviation (M ± SD) or, where appropriate, median and interquartile range. Categorical variables are presented as counts and percentages. Normality was assessed using the Shapiro-Wilk and Kolmogorov-Smirnov tests together with visual inspection of histograms. Because SIPAT total and domain scores deviated from a normal distribution, non-parametric tests were used for group comparisons involving these variables.

To describe the sample, we summarized age, sex and primary pulmonary diagnoses, as well as SIPAT total scores, domain scores, global candidate categories and the prevalence of clinically relevant alcohol-, drug- and nicotine-related risk indicators derived from SIPAT domain D.

The primary outcome was transplant listing status at the time of data extraction, dichotomized as listed (ever on the active waiting list or already transplanted) versus non-listed. Continuous variables, including SIPAT total and domain scores and age, were compared between listed and non-listed patients using Mann-Whitney U tests. In exploratory analyses, the ordinal SIPAT items reflecting alcohol use, illicit substance use and nicotine dependence were also compared between listed and non-listed candidates using Mann-Whitney U tests. Categorical variables such as SIPAT risk categories were analyzed with chi-square (χ²) tests.

To examine categorical aspects of psychosocial risk, we analyzed the distribution of SIPAT global risk categories (excellent, good, minimally acceptable, poor and high-risk candidate). For the main analyses we contrasted candidates rated as excellent or good (categories 1-2) with those rated as minimally acceptable, poor or high-risk (categories 3-5), and tested associations with listing status using χ² tests.

To explore whether more complex psychosocial profiles could be identified, we performed a two-step cluster analysis using SIPAT domain scores (readiness and illness management, social support, psychological stability/psychopathology, lifestyle/substance use), the SIPAT total score and the three substance-related items (alcohol, illicit substances, nicotine) as input variables. The log-likelihood distance measure and Schwarz’s Bayesian information criterion were used to determine the optimal number of clusters. The resulting clusters were interpreted clinically as lower versus higher psychosocial risk. Associations between cluster membership and listing status were examined using χ² tests and logistic regression.

Receiver operating characteristic (ROC) analyses were conducted to evaluate how well the SIPAT total score discriminated between patients with and without clinically relevant alcohol-, drug- or nicotine-related risk. For each binary indicator (risk present versus absent) we calculated the area under the ROC curve (AUC) with 95% confidence intervals (CI).

To investigate predictors of being listed, we estimated a series of binary logistic regression models with listing status (listed vs. non-listed) as the dependent variable. Across these models we included combinations of SIPAT total score, SIPAT domain scores, binary indicators of clinically relevant alcohol-, drug- and nicotine-related risk, age, sex and psychosocial risk cluster (low vs. high) as predictors. In an additional model we added an interaction term between centered age and centered SIPAT total score to test whether the association between psychosocial risk and listing was moderated by age. Odds ratios (OR) with 95% CI were reported for all models, alongside measures of model fit (Cox and Snell and Nagelkerke R²) and overall classification accuracy.

Secondary analyses focused on the subset of patients with a definitive qualitative decision about transplant eligibility. In this subgroup we created a binary variable reflecting “qualified” versus “not qualified” status (ever accepted or listed versus disqualified or definitively not listed). We then compared SIPAT domain and total scores between qualified and not qualified candidates using Mann-Whitney U tests and examined the distribution of SIPAT candidate categories across qualification status using χ² tests.

Results

Sample Characteristics

A total of 491 lung transplant candidates were included in the analyses. The mean age was 57.20 years (SD = 10.73, range 19-78). Women constituted 40.5% (n = 199) and men 59.5% (n = 292). At the time of data extraction, 151 patients (30.8%) were listed (either actively on the waiting list or already transplanted), whereas 340 (69.2%) were not listed.

Detailed sociodemographic and clinical characteristics, including underlying diagnoses, are presented in Table 1.

SIPAT Scores and Listing Status (Listed vs. Non-Listed)

Psychosocial risk was assessed using SIPAT. The following components were analyzed: readiness and illness management, social support system, psychological stability and psychopathology, lifestyle and substance use, and the total SIPAT score. In the full cohort, the mean total SIPAT score was 12.38 (SD = 6.84, range 0-45). Most candidates were classified as excellent or good according to the original SIPAT categories.

Overall SIPAT scores and global candidate categories in the full sample are presented in Table 2.

All SIPAT indices showed significant deviations from normality (Shapiro-Wilk, p < 0.001), therefore non-parametric tests were applied. Comparisons between listed and non-listed patients using the Mann-Whitney U test showed no significant differences for any SIPAT domain or for the total score. Descriptively, mean values were almost identical in both groups, for example, the total SIPAT score was approximately 12.4 points in both listed and non-listed candidates. In other words, overall psychosocial burden as quantified by SIPAT was not associated with being listed versus remaining non-listed.

Separate SIPAT items reflecting alcohol use, drug use and nicotine dependence were also analyzed as continuous scores. Alcohol-related risk showed only a trend toward higher scores among non-listed patients (p ≈ 0.059), which did not reach conventional significance. Drug-related risk showed no significant difference between listed and non-listed patients (p ≈ 0.79). Nicotine-related risk, in contrast, was significantly higher in listed candidates (Mann-Whitney U = 22 161.0, z = -2.73, p = 0.006), indicating that candidates who were eventually listed more often had relevant smoking histories at baseline.

Clinically relevant alcohol-related risk was present in 39.5%, nicotine-related in 43.8%, and illicit substance-related risk in 13.4% of candidates.

Recommended SIPAT Categories and Listing

SIPAT global risk was also examined categorically. For descriptive purposes, we first compared the distribution of original SIPAT categories between listed and non-listed candidates. These distributions are shown in Table 3.

For the main categorical analyses, SIPAT categories were collapsed into lower risk (excellent or good candidates, categories 1-2) versus higher risk (minimally acceptable, poor candidates and high-risk candidates categories 3-5). This dichotomized classification was then cross-tabulated with listing status (listed vs. non-listed). The association was non-significant (Pearson chi-square(1) = 0.10, p = 0.753; odds ratio for higher vs. lower risk ≈ 0.90, 95% CI [0.48, 1.70]), indicating that being categorized as a higher risk SIPAT candidate did not reduce the odds of being listed. In the full cohort, only four patients were rated in the “poor candidate” category, and none of them were listed for transplantation. Given this very small number, these cases were not analyzed separately and are reported descriptively only.

Cluster Analysis of Psychosocial Risk Profiles

To explore whether more complex psychosocial profiles could be identified, a two-step cluster analysis was conducted using SIPAT domain scores (readiness and illness management, social support, psychological stability and psychopathology, lifestyle and substance use), the total SIPAT score and the three substance-related items (alcohol, illicit substances, nicotine) as input variables. The Bayesian Information Criterion supported a two-cluster solution:

Cluster 1: low psychosocial risk (82.1%), with lower scores across all domains and lower alcohol-, drug- and nicotine-related risk.

Cluster 2: high psychosocial risk (17.9%), with clearly higher total SIPAT scores, higher psychological instability and elevated lifestyle and substance use scores.

Despite clearly differentiated psychosocial profiles, cluster membership was not associated with listing status (Pearson chi-square(1) = 0.06, p = 0.811; odds ratio low vs. high risk ≈ 1.06, 95% CI [0.65, 1.75]). High-risk patients identified by the cluster solution did not simply vanish from the transplant pathway; many remained under active follow-up, received psychosocial prehabilitation and were eventually listed.

ROC Analyses: SIPAT Total as a Screener for Substance-Related Risk

Receiver operating characteristic (ROC) analyses were conducted to evaluate how well the total SIPAT score discriminated between patients with and without clinically relevant risk in specific substance-related domains:

Nicotine-related risk: N = 215 positive, 276 negative, AUC = 0.630

Alcohol-related risk: N = 194 positive, 297 negative, AUC = 0.680

Drug-related risk: N = 66 positive, 425 negative, AUC = 0.540

Overall, the SIPAT total showed modest ability to detect alcohol and nicotine related risk (AUC in the 0.63-0.68 range) and virtually no discriminative value for drug-related risk (AUC ≈ 0.54).

Logistic Regression: Predictors of Being Listed

Several logistic regression models were tested with listing status (listed vs. non-listed) as the dependent variable. Predictors included different combinations of SIPAT total score, SIPAT domain scores, substance-related indicators (alcohol, illicit substances, nicotine), age, sex, psychosocial risk cluster and an interaction term between age and total SIPAT.

Across models, the overall pattern was consistent:

Age was a robust predictor. Higher age was associated with lower odds of being listed (for example B ≈ -0.03 to -0.04, p < 0.01; odds ratio per year ≈ 0.96-0.97).
Nicotine-related risk sometimes emerged as a positive predictor of listing. In models including a binary nicotine risk indicator, higher nicotine-related risk increased the odds of being listed (odds ratios around 1.4-2.5, with p ≤ 0.01 in some specifications).
SIPAT total and the psychosocial domains themselves did not show stable, independent predictive value once age and substance-related variables were included. Odds ratios were close to 1.00 and confidence intervals consistently crossed 1.00. In models including both SIPAT total and individual domains, coefficients were unstable and not interpretable due to multicollinearity.
Model fit and explained variance were low. Cox and Snell R² ranged around 0.03-0.06, Nagelkerke R² around 0.05-0.09. Classification accuracy barely exceeded the base rate (about 69-70%), and the models performed particularly poorly in correctly identifying listed patients (sensitivity below 15% in most models, despite high specificity).
The age × SIPAT total interaction term was not significant (p ≈ 0.74), indicating that the association between SIPAT and listing did not meaningfully vary across age.
Including psychosocial cluster membership (low vs. high-risk) did not improve prediction. Cluster membership was non-significant and model fit remained weak (Nagelkerke R² ≈ 0.05).

Taken together, the logistic regression analyses suggest that chronological age and, to some extent, nicotine-related risk were the only consistent predictors of being listed, whereas global psychosocial risk as measured by SIPAT did not meaningfully predict listing decisions in this cohort.

Secondary Analysis: Qualified vs. not Qualified Among Patients with Definitive Status

In the subgroup of 217 patients with a fully determined qualification outcome (171 qualified, 46 not qualified), Mann-Whitney tests were again used to compare SIPAT domain and total scores. Results mirrored the main analyses:

No significant differences between qualified and not qualified patients in readiness and illness management, social support system, psychological stability and psychopathology, lifestyle and substance use, or SIPAT total (all p > 0.24).
Substance-related scores (including nicotine, alcohol and illicit substances) also did not significantly distinguish qualified versus not qualified candidates.
Age remained descriptively lower in qualified patients, consistent with the main logistic models.

The detailed cross-tabulation of SIPAT categories in this subgroup showed that minimally acceptable candidates were not at higher risk of non-qualification. On the contrary, 83.3% of candidates in this higher risk category were accepted after the complete evaluation process, while no patients in the poor category were present in this subgroup. The association between SIPAT candidate category (excellent or good vs. minimally acceptable) and qualification status was non-significant in chi square tests. This pattern is congruent with the center’s philosophy that minimally acceptable does not mean “too risky to touch” but “acceptable after psychosocial prehabilitation”.

Discussion

In this large single-center cohort of 491 lung transplant candidates, psychosocial risk as captured by the Stanford Integrated Psychosocial Assessment for Transplantation (SIPAT) was generally low and showed no meaningful association with listing decisions. Mean total SIPAT scores were almost identical in candidates who were ultimately listed (including transplanted and actively waitlisted patients) and those who remained non-listed (approximately 12 points in both groups), with no significant differences in any of the four SIPAT domains (readiness and illness management, social support, psychological stability/psychopathology, and lifestyle/substance use). Non-parametric comparisons and logistic regression models consistently indicated that age, rather than psychosocial risk, was the main predictor of being listed, with younger patients more likely to progress to listing (odds ratios per year around 0.96-0.97, p < 0.01).

At the same time, SIPAT behaved as a clinically sensible risk-profiling tool. Two-step cluster analysis identified a smaller “high-risk” subgroup (approximately 18% of candidates) with clearly higher total SIPAT scores and elevated lifestyle/substance-use and psychological risk compared with the majority “low-risk” cluster. ROC analyses showed that total SIPAT scores displayed acceptable discrimination for clinically relevant alcohol risk (AUC ≈ 0.68) and modest discrimination for nicotine-related risk (AUC ≈ 0.63), with very limited ability to distinguish candidates with clinically relevant illicit substances risk (AUC ≈ 0.54). This pattern suggests that SIPAT does pick up clinically relevant psychosocial burden, particularly in the substance use domain, but that these risks are not translated into straightforward “yes/no” listing decisions in this program.

Perhaps the most counterintuitive finding from a traditional gatekeeping perspective is that nicotine-related risk scores were higher in listed than in non-listed candidates, both in univariate analyses and in multivariable models where nicotine risk increased the odds of being listed. At face value this appears paradoxical, but it is consistent with the clinical reality of this center: patients with a history of tobacco use are actively identified, intensively supported in cessation, and then moved toward listing once they demonstrate sustained change. In other words, a positive nicotine history flags patients for more work and more support, not for automatic exclusion. The elevated nicotine scores in listed candidates likely reflect that many of them started from a high-risk baseline but received successful intervention rather than being screened out. This pattern is also coherent with the underlying disease profile in this cohort: a substantial proportion of candidates had COPD or emphysema, conditions for which long-term cigarette smoking is typically a primary etiological factor. In such a population it would be both clinically and ethically questionable if one of the most common indications for lung transplantation were systematically disfavored solely because many candidates have a recent smoking history that is intrinsically linked to their disease. Instead, our findings suggest that nicotine use is understood as part of the natural pathway to COPD rather than as an automatic disqualifier, provided that sustained abstinence can be achieved within a structured prehabilitation framework.

Overall, the findings are consistent with a model in which SIPAT functions as a descriptive, intervention-triggering tool rather than a binary gatekeeper. Psychosocial vulnerabilities are identified and addressed through what is essentially psychosocial prehabilitation, including targeted education, addiction treatment, social work and psychological support, instead of being treated as permanent reasons for denial [19,20,21]. As a result, cross-sectional SIPAT scores do not “predict” being listed, because high-risk candidates are not abandoned; they are worked with until they become clinically and psychosocially acceptable candidates.

Positioning our Findings within the SIPAT Literature

Most existing SIPAT studies have addressed a different question from ours. They typically examine whether higher pre-transplant SIPAT scores predict post-transplant outcomes such as immunosuppression non-adherence, rejection, hospital utilization or mortality, rather than whether SIPAT predicts listing itself [9,12,13,14,15,16,17]. Maldonado and colleagues, who developed SIPAT, originally demonstrated that higher scores were associated with poorer adherence and worse psychosocial outcomes after transplantation, although associations with graft failure and mortality were more modest and variable [12,13]. Subsequent work in liver transplantation showed that higher SIPAT scores were related to greater risk of immunosuppression non-adherence and more complicated post-transplant courses [14], with partially similar patterns reported in kidney and kidney/pancreas recipients [15], although other studies have found no clear associations with key outcomes such as adherence or rejection [16], resulting in moderate effect sizes and heterogeneous findings overall.

In lung transplantation specifically, characterization studies have reported that SIPAT scores are often clustered in the “good candidate” range, with a smaller subgroup of clearly higher-risk patients, very similar to the two clusters we observed [22,23]. Hinton-Froese et al. found that higher SIPAT scores in lung recipients were associated with poorer 1-year psychosocial outcomes and aspects of medical complexity, but not with strong mortality signals [17]. A recent narrative review concluded that psychosocial tools such as SIPAT can modestly stratify risk but should not be interpreted as a standalone gatekeeping instrument [9].

Our findings extend this literature in several ways. First, we show in a large, unselected cohort of lung transplant candidates that SIPAT category, total score and domain scores do not meaningfully differentiate candidates who are ultimately listed from those who remain non-listed. Chi-square tests for SIPAT categories and listing status were non-significant, and logistic regression models that included SIPAT total, domains, substance-use indicators, age and sex improved model fit only minimally (Nagelkerke R² around 0.05-0.09). Classification accuracy was almost entirely driven by the base rate of non-listing and did not meaningfully improve once psychosocial variables were added.

Second, our data show that this lack of predictive power is not due to SIPAT being “blind” to psychosocial risk. The cluster analysis and ROC results confirm that higher SIPAT scores are concentrated in a high-risk subgroup and are reasonably sensitive to alcohol and nicotine risk. Instead, what appears to happen in this program is that psychosocial risk triggers help, not exclusion. Patients with problematic substance use, fragile support or unstable mental health are offered treatment, social work and rehabilitation. High-risk patients are therefore not necessarily “high-risk candidates forever”; many improve and eventually meet listing criteria despite starting with elevated SIPAT scores.

This is a crucial conceptual shift compared with settings in which SIPAT is used explicitly as a gatekeeping cut-off, for example, excluding candidates above thresholds such as 21 points [14]. In such systems, one would expect a clearer gradient between SIPAT scores and listing decisions [9,14,18,19,20]. In our data, the absence of such a gradient is not a failure of the instrument but rather a reflection of a different ethical and clinical philosophy: psychosocial risk is treated as a target for psychosocial prehabilitation rather than as a static contraindication.

SIPAT as a Roadmap for Psychosocial Prehabilitation Rather than a Binary Filter

The core implication of these findings is that SIPAT should not be reduced to a single cut-off that mechanically decides who gets listed. In a program where psychosocial services are explicitly integrated into the transplant pathway, SIPAT functions more as a roadmap for psychosocial prehabilitation [19,20,21]:

The readiness and illness management domain identifies patients who need additional education, adherence support or cognitive scaffolding.
The social support domain highlights those who require structured involvement of caregivers, social work or case management to ensure safe discharge and follow-up.
The psychological stability domain marks individuals who need targeted psychiatric or psychotherapeutic input before and after transplantation.
The lifestyle/substance-use domain signals those who should be prioritized for smoking cessation, alcohol treatment or addiction services.

Our finding that nicotine risk is more common in listed candidates is actually very consistent with such a model. Rather than being disqualified for smoking, these patients are identified early, referred to intensive cessation support, monitored, and then listed once they can realistically maintain abstinence. In this sense, SIPAT “predicts intervention” rather than “predicts denial”.

This perspective aligns with evolving transplant ethics and guideline recommendations, which increasingly emphasize that psychosocial risk factors should not automatically lead to exclusion from transplantation, particularly when they are potentially modifiable [2,9,10,11,18,19,20,21]. Our data show that such an approach is not only theoretically attractive but also empirically visible in routine practice: psychosocially complex patients do not disappear from the pathway, they simply move through it with more intensive psychosocial prehabilitation.

Why is SIPAT not a Stronger Predictor of Listing in this Cohort?

Several features of our setting help explain why SIPAT is only weakly linked to listing probability:

Restriction of range. Average SIPAT scores were low and most candidates fell into the “excellent” or “good” categories. As in other lung cohorts, this compression of scores inevitably reduces correlations with downstream decisions [17,22,23].
Dynamic clinical decision making. SIPAT is administered relatively early, whereas listing is a dynamic decision that incorporates changes in clinical status, psychosocial functioning and response to interventions over time. A static baseline score can only imperfectly capture these moving targets.
Deliberate decoupling of risk and exclusion. The transplant team explicitly treats psychosocial problems as modifiable. Structural factors such as housing instability are addressed via social work and family support; psychiatric and addiction issues are treated and followed rather than treated as absolute contraindications [9,10,11,18,19,20,21]. By design, this weakens any cross-sectional association between initial SIPAT scores and ultimate listing.
Medical factors dominate the final decision. As in other studies, age and medical status remain the primary determinants of transplant suitability. In our analyses, age was by far the strongest predictor of being listed, which is consistent with current lung transplant guidelines [1,2].

Strengths and Limitations

Key strengths of this study include the large sample size, the specific focus on lung transplant candidates (a population still underrepresented in the SIPAT literature), and the comprehensive use of SIPAT total scores, domain scores, dichotomized categories, binary risk indicators, ROC curves and cluster analysis to characterize psychosocial risk. The fact that SIPAT was administered systematically to virtually all candidates enhances ecological validity; we are not dealing with a highly selected sub-cohort. The analyses directly reflect how SIPAT operates in a real-world, multidisciplinary program that explicitly invests in psychosocial prehabilitation [19,20,21].

Several limitations should be acknowledged. First, this was a single-center retrospective study, and SIPAT scoring was conducted by a specialized team within a particular institutional culture that is explicitly oriented toward rehabilitation rather than exclusion; generalizability to more restrictive programs is uncertain [9,10,11,18,19,20,21]. Second, we did not have standardized, coded reasons for non-listing and could not reliably disentangle “pure” psychosocial disqualifications from medical or logistical factors. Third, we examined only a single baseline SIPAT; longitudinal changes in psychosocial risk and the impact of specific interventions were not captured and may be much more informative than a single assessment in a program that actively modifies risk over time.

Importantly, we also considered whether baseline SIPAT scores might be associated with post-transplant mortality among transplanted recipients. However, only nine deaths occurred in this cohort during the observation period, providing too few events for any meaningful or stable predictive modelling. Any regression estimates in this context would be highly unreliable and at high risk of overfitting. For this reason, we deliberately chose not to present these exploratory checks in detail and do not interpret them as evidence for or against the prognostic value of SIPAT with respect to survival. Larger, prospective studies with sufficient numbers of events are needed to address the relationship between psychosocial risk and mortality in a robust way [12,13,14,15,16,17].

Finally, although our models included key psychosocial and demographic covariates, pseudo-R² values were small and classification performance modest, indicating that many unmeasured clinical and contextual variables contribute to listing decisions. Our results should therefore be interpreted as describing how SIPAT is used within a specific, support-oriented program rather than as establishing universal thresholds or rules for candidate selection.

Clinical and Research Implications

Despite these limitations, our findings have several important implications. They provide empirical support for using SIPAT as a structured needs-assessment and psychosocial prehabilitation tool rather than as a rigid exclusion criterion [9,12,13,14,15,16,17,19,20,21]. Programs that adopt a similar philosophy may reasonably expect that higher SIPAT scores will flag patients who require more intensive psychosocial work, without necessarily condemning them to permanent non-listing. At the same time, our data underscore the ethical and clinical value of investing in psychosocial rehabilitation: high-risk patients in this cohort did not simply disappear from consideration; many progressed to listing despite elevated initial risk scores, particularly when substance-use and social problems were actively addressed.

For future research, our results suggest that work on SIPAT should move beyond a simple reliance on fixed cut-offs and toward dynamic, multistage models. Combining baseline SIPAT with repeated follow-up assessments, adherence trajectories, clearly documented psychosocial interventions and detailed clinical endpoints (including survival) may yield richer predictive frameworks for both listing and post-transplant outcomes. Finally, our findings support ongoing calls within transplantation to view psychosocial risk as modifiable and relational, rather than as a fixed property of the patient [9,10,11,18,19,20,21]. Tools like SIPAT can be powerful in this context, but only if they are used to open doors to support, not to close doors to care.

Abbreviations

A1AT—alpha-1 antitrypsin deficiency
AUC—area under the curve (receiver operating characteristic)
CF—cystic fibrosis
CI—confidence interval
COPD—chronic obstructive pulmonary disease
COVID-19—coronavirus disease 2019
CTD-ILD—connective tissue disease-related interstitial lung disease
HP—hypersensitivity pneumonitis
ILD—interstitial lung disease
IQR—interquartile range
LAM—lymphangioleiomyomatosis
LIP—lymphocytic interstitial pneumonia
M—mean
OR—odds ratio
ROC—receiver operating characteristic
SD—standard deviation
SIPAT—Stanford Integrated Psychosocial Assessment for Transplant

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Table 1. Sociodemographic and clinical characteristics (N = 491).

Characteristic	Category / value	n	%
Age (years)	Mean ± SD (range)	57.20 ± 10.73 (19-78)	-
Sex	Female	199	40.5
	Male	292	59.5
Underlying diagnosis	COPD / emphysema (incl. A1AT, asthma-COPD)	165	33.6
	Idiopathic / nonspecific ILD (incl. IPF)	121	24.6
	Pulmonary arterial / chronic pulmonary hypertension	40	8.1
	ILD in connective tissue disease (CTD-ILD)	36	7.3
	Hypersensitivity pneumonitis (HP)	30	6.1
	Post-COVID chronic lung disease (ILD / mixed)	21	4.3
	Sarcoidosis (± other)	17	3.5
	Other / mixed (cardiac, oncologic, etc.)	17	3.5
	Bronchiectasis (non-CF)	10	2.0
	Pneumoconiosis	8	1.6
	LAM	5	1.0
	Asthma (without clear COPD / ILD)	5	1.0
	Cystic fibrosis / CF / bronchiectasis	3	0.6
	Other rare ILD (e.g., LIP)	1	0.2
	Missing / not specified	12	2.4

Abbreviations: COPD, chronic obstructive pulmonary disease; ILD, interstitial lung disease; IPF, idiopathic pulmonary fibrosis; CTD-ILD, connective tissue disease related interstitial lung disease; HP, hypersensitivity pneumonitis; A1AT, alpha 1 antitrypsin deficiency; CF, cystic fibrosis; LAM, lymphangioleiomyomatosis; LIP, lymphocytic interstitial pneumonia; COVID, coronavirus disease 2019.

Table 2. Overall SIPAT scores and candidate categories (N = 491).

SIPAT measure	Value
Total SIPAT score—mean ± SD (range)	12.38 ± 6.84 (0-45)
SIPAT category—Excellent candidate	90 (18.3%)
SIPAT category—Good candidate	349 (71.1%)
SIPAT category—Minimally acceptable candidate	48 (9.8%)
SIPAT category—Poor candidate	4 (0.8%)

Table 3. SIPAT candidate categories by listing status.

SIPAT category	Non-listed (n = 340) n (%)	Listed (n = 151) n (%)	Total (N = 491) n (%)
Excellent candidate	59 (17.4%)	31 (20.5%)	90 (18.3%)
Good candidate	244 (71.8%)	105 (69.5%)	349 (71.1%)
Minimally acceptable candidate	33 (9.7%)	15 (9.9%)	48 (9.8%)
Poor candidate	4 (1.2%)	0 (0.0%)	4 (0.8%)
High-risk candidate	0 (0.0%)	0 (0.0%)	0 (0.0%)

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