Clinical judgment versus lung allocation score in predicting lung transplant waitlist mortality

Pre-transplant Anemia as a Marker of Short-term Outcomes in Lung Transplant Recipients

BACKGROUND

Anemia is a risk factor for increased morbidity and mortality in multiple medical conditions, yet the impact of pretransplant anemia in patients with advanced lung disease on post-transplant outcomes remains under-explored. We sought to determine whether pretransplant anemia serves as a marker of altered inflammation in the host and associates with short-term outcomes following lung transplantation.

STUDY DESIGN AND METHODS

We performed a single-center, retrospective analysis of 238 lung transplant recipients. We assessed for risk factors of pretransplant anemia and identified associations with short-term post-transplant outcomes.

RESULTS

Pretransplant anemia was associated with increased intraoperative transfusion of packed red blood cells and a trend towards increased index hospital length of stay and 1-year mortality. Conversely, pretransplant anemia was associated with a decreased incidence of acute cellular rejection.

CONCLUSION

These preliminary data suggest that anemia may be a biomarker of altered inflammation in the host recipient and influences post-transplant outcomes.

Disparities in lung transplantation in children

Lung transplantation is a recognized therapy for end-stage respiratory failure in children and young people. It is only available in selected countries and is limited by access to suitable organs. Data on disparities in access and outcomes for children undergoing lung transplantation are limited. It is clear from data from studies in adults, and from studies in other solid organ transplants in children, that systemic inequities exist in this field. While data relating specifically to pediatric lung transplantation are relatively sparse, professionals should be aware of the risk that healthcare systems may result in disparities in access and outcomes following lung transplantation in children.

Outcomes after flow cytometry crossmatch-positive lung transplants managed with perioperative desensitization

Our program previously reported successful outcomes following virtual crossmatch (VXM)-positive lung transplants managed with perioperative desensitization, but our ability to stratify their immunologic risk was limited without flow cytometry crossmatch (FCXM) data before 2014. The aim of this study was to determine allograft and chronic lung allograft dysfunction (CLAD)-free survival following VXM-positive/FCXM-positive lung transplants, which are performed at a minority of programs due to the high immunologic risk and lack of data on outcomes. All first-time lung transplant recipients between January 2014 and December 2019 were divided into 3 cohorts: VXM-negative (n = 764), VXM-positive/FCXM-negative (n = 64), and VXM-positive/FCXM-positive (n = 74). Allograft and CLAD-free survival were compared using Kaplan-Meier and multivariable Cox proportional hazards models. Five-year allograft survival was 53% in the VXM-negative cohort, 64% in the VXM-positive/FCXM-negative cohort, and 57% in the VXM-positive/FCXM-positive cohort (P = .7171). Five-year CLAD-free survival was 53% in the VXM-negative cohort, 60% in the VXM-positive/FCXM-negative cohort, and 63% in the VXM-positive/FCXM-positive cohort (P = .8509). This study confirms that allograft and CLAD-free survival of patients who undergo VXM-positive/FCXM-positive lung transplants with the use of our protocol does not differ from those of other lung transplant recipients. Our protocol for VXM-positive lung transplants improves access to transplant for sensitized candidates and mitigates even high immunologic risk.

Clinical judgment shows similar and sometimes superior discrimination compared to prognostic clinical prediction models. A systematic review

OBJECTIVES

To systematically review the comparative statistical performance (discrimination and /or calibration) of prognostic clinical prediction models (CPMs) and clinician judgment (CJ).

STUDY DESIGN AND SETTING

We conducted a systematic review of observational studies in PubMed, Medline, Embase, and CINAHL. Eligible studies reported direct statistical comparison between prognostic CPMs and CJ. Risk of bias was assessed using the PROBAST tool.

RESULTS

We identified 41 studies, most with high risk of bias (39 studies). Of these, 41 studies, 39 examined discrimination and 12 studies assessed calibration. Prognostic CPMs had a median AUC of 0.73 (IQR, 0.62 - 0.81), while CJ had a median AUC of 0.71 (IQR, 0.62 - 0.81). 29 studies provided 124 discrimination metrics useful for comparative analysis. Among these, 58 (46.7%) found no significant difference between prognostic CPMs and CJ (p > 0.05); 31 (25%) favored prognostic CPMs, and 35 (28.2%) favored CJ. Four studies compared calibration, showing better performance on prognostic CPMs.

CONCLUSIONS

In many instances CJ frequently demonstrates comparable or superior discrimination compared to prognostic CPMs, although models outperform CJ on calibration. Studies comparing performance of prognostic CPMs and CJ require large improvements in reporting.

A machine-learning approach to human ex vivo lung perfusion predicts transplantation outcomes and promotes organ utilization

Ex vivo lung perfusion (EVLP) is a data-intensive platform used for the assessment of isolated lungs outside the body for transplantation; however, the integration of artificial intelligence to rapidly interpret the large constellation of clinical data generated during ex vivo assessment remains an unmet need. We developed a machine-learning model, termed InsighTx, to predict post-transplant outcomes using n = 725 EVLP cases. InsighTx model AUROC (area under the receiver operating characteristic curve) was 79 ± 3%, 75 ± 4%, and 85 ± 3% in training and independent test datasets, respectively. Excellent performance was observed in predicting unsuitable lungs for transplantation (AUROC: 90 ± 4%) and transplants with good outcomes (AUROC: 80 ± 4%). In a retrospective and blinded implementation study by EVLP specialists at our institution, InsighTx increased the likelihood of transplanting suitable donor lungs [odds ratio=13; 95% CI:4-45] and decreased the likelihood of transplanting unsuitable donor lungs [odds ratio=0.4; 95%CI:0.16-0.98]. Herein, we provide strong rationale for the adoption of machine-learning algorithms to optimize EVLP assessments and show that InsighTx could potentially lead to a safe increase in transplantation rates.

External validation and longitudinal application of the DO-GAP index to individualise survival prediction in idiopathic pulmonary fibrosis

Background

The Distance-Oxygen-Gender-Age-Physiology (DO-GAP) index has been shown to improve prognostication in idiopathic pulmonary fibrosis (IPF) compared to the Gender-Age-Physiology (GAP) score. We sought to externally validate the DO-GAP index compared to the GAP index for baseline risk assessment in patients with IPF. Additionally, we evaluated the utility of serial change in the DO-GAP index in predicting survival.

Methods

We performed an analysis of patients with IPF from the Pulmonary Fibrosis Foundation-Patient Registry (PFF-PR). Discrimination and calibration of the two models were assessed to predict transplant-free survival and IPF-related mortality. Joint longitudinal time-to-event modelling was utilised to individualise survival prediction based on DO-GAP index trajectory.

Results

There were 516 patients with IPF from the PFF-PR with available demographics, pulmonary function tests, 6-min walk test data and outcomes included in this analysis. The DO-GAP index (C-statistic: 0.73) demonstrated improved discrimination in discerning transplant-free survival compared to the GAP index (C-statistic: 0.67). DO-GAP index calibration was adequate, and the model retained predictive accuracy to identify IPF-related mortality (C-statistic: 0.74). The DO-GAP index was similarly accurate in the subset of patients taking antifibrotic medications. Serial change in the DO-GAP index improved model discrimination (cross-validated area under the curve: 0.83) enabling the personalised prediction of disease trajectory in individual patients.

Conclusion

The DO-GAP index is a simple, validated, multidimensional score that accurately predicts transplant-free survival in patients with IPF and can be adapted longitudinally in individual patients. The DO-GAP may also find use in studies of IPF to risk stratify patients and possibly as a clinical trial end-point.

The Past, Present, and Near Future of Lung Allocation in the United States

The first official donor lung allocation system in the United States was initiated by the United Network of Organ Sharing in 1990. The initial policy for lung allocation was simple with donor lungs allocated based on ABO match and the amount of time the candidates accrued on the waiting list. Donor offers were first given to candidates' donor service area. In March 2005, the implementation of the lung allocation score (LAS) was the major change in organ allocation. International adoption of the LAS-based allocation system can be seen worldwide.

Outcomes following heart or bilateral-lung transplantation from donors who died of drug toxicity in British Columbia, Canada

INTRODUCTION

The illicit drug toxicity (overdose) crisis has worsened across Canada; between 2016 and 2021, more than 28,000 individuals have died of drug toxicity. Organ donation from persons who experience drug toxicity death (DTD) has increased in recent years. This study examines whether survival after heart or bilateral-lung transplantation differed by donor cause of death.

METHODS

We studied transplant recipients in British Columbia who received heart (N = 110) or bilateral-lung (N = 223) transplantation from deceased donors aged 12-70 years between 2013 and 2019. Transplant recipient survival was compared by donor cause of death from drug toxicity or other. Five-year Kaplan-Meier estimates of survival and 3-year inverse probability treatment weighted Cox proportional hazards models were conducted.

RESULTS

DTD donors made up 36% (40/110) of heart and 24% (54/223) of bilateral-lung transplantations. DTD donors were more likely to be young, white, and male. Unadjusted 5-year recipient survival was similar by donor cause of death (heart: 87% for DTD and 86% for non-DTD, p = .75; bilateral- lung: 80% for DTD and 76% for non-DTD, p = .65). Adjusted risk of mortality at 3-years post-transplant was similar between recipients of DTD and non-DTD donor heart (hazard ratio [HR]: .94, 95% confidence interval (CI): .22-4.07, p = .938) and bilateral-lung (HR: 1.06, 95% CI: .41-2.70, p = .908).

CONCLUSION

Recipient survival after heart or bilateral-lung transplantation from DTD donors and non-DTD donors was similar. Donation from DTD donors is safe and should be considered more broadly to increase organ donation.

External validity of machine learning-based prognostic scores for cystic fibrosis: A retrospective study using the UK and Canadian registries

Precise and timely referral for lung transplantation is critical for the survival of cystic fibrosis patients with terminal illness. While machine learning (ML) models have been shown to achieve significant improvement in prognostic accuracy over current referral guidelines, the external validity of these models and their resulting referral policies has not been fully investigated. Here, we studied the external validity of machine learning-based prognostic models using annual follow-up data from the UK and Canadian Cystic Fibrosis Registries. Using a state-of-the-art automated ML framework, we derived a model for predicting poor clinical outcomes in patients enrolled in the UK registry, and conducted external validation of the derived model using the Canadian Cystic Fibrosis Registry. In particular, we studied the effect of (1) natural variations in patient characteristics across populations and (2) differences in clinical practice on the external validity of ML-based prognostic scores. Overall, decrease in prognostic accuracy on the external validation set (AUCROC: 0.88, 95% CI 0.88-0.88) was observed compared to the internal validation accuracy (AUCROC: 0.91, 95% CI 0.90-0.92). Based on our ML model, analysis on feature contributions and risk strata revealed that, while external validation of ML models exhibited high precision on average, both factors (1) and (2) can undermine the external validity of ML models in patient subgroups with moderate risk for poor outcomes. A significant boost in prognostic power (F1 score) from 0.33 (95% CI 0.31-0.35) to 0.45 (95% CI 0.45-0.45) was observed in external validation when variations in these subgroups were accounted in our model. Our study highlighted the significance of external validation of ML models for cystic fibrosis prognostication. The uncovered insights on key risk factors and patient subgroups can be used to guide the cross-population adaptation of ML-based models and inspire new research on applying transfer learning methods for fine-tuning ML models to cope with regional variations in clinical care.

Mortality risk factors in patients on waiting list for lung transplantation between 2005 and 2018: A single institutional experience

BACKGROUND

Lung transplantation is a therapeutic option for patients with end-stage lung disease. However, the increase in organ demand has surpassed the number of donors, with many patients unable to outlive the long waiting period. This study aimed to assess mortality and its risk factors in patients on the waiting list for lung transplantation in a single medical centre.

METHODS

All evaluated clinical and laboratory data of the patients with end-stage lung disease assessed for lung transplantation between February 2005 and November 2018 in National Taiwan University Hospital were recorded in the waiting list database. The patients in this study were divided into two groups: survival and death groups.

RESULTS

Between February 2005 and November 2018, 169 patients were enrolled in the waiting list. Thirty-one patients were alive and waiting for the chance of lung transplantation, 56 underwent lung transplantation, and 82 died while waiting. The mean age of all patients was 43.7 years, and 91 were women. The mean body mass index (BMI) was 20.3. The most common blood type was type O. All patients were in New York Heart Association (NYHA) class III or IV. After analysis of the two groups, lower BMI presented as a mortality factor.

CONCLUSION

This is the first Taiwanese study to describe the mortality factors in patients waiting for lung transplantation. The main factors influencing the survival of these patients were lower BMI, NYHA class IV, and diseases which cause end-stage lung diseases (infection and pulmonary fibrosis).

Association between the Predicted Value of APACHE IV Scores and Intensive Care Unit Mortality: A Secondary Analysis Based on EICU Dataset

Objective

The evidence regarding the relationship between Acute Physiological and Chronic Health Assessment (APACHE) IV scores and emergency intensive care unit (EICU) mortality in patients following organ transplantation remains controversial. The purpose of this study was to investigate the relationship between APACHE IV score and EICU mortality.

Methods

Data from 391 American men and women admitted to the EICU after undergoing organ transplants including heart, bone marrow, liver, kidney, lung, and pancreas in the United States. We used this data to analyze the relationship between APACHE IV scores and in-hospital mortality in the postoperative EICU. The primary endpoint was ICU hospitalization mortality after organ transplantation. The entire study data was extracted from the EICU database and uploaded to the DataDryad website.

Results

Interaction tests indicate age, respiratory failure, and hormone use can modify the association between APACHE IV and EICU mortality. A stronger association of APACHE and mortality can be observed at <60 years old, no respiratory failure, and no use of hormones. In contrast, there was no association between respiratory failure, hormone use, APACHE, and ICU mortality in patients over 60 years of age.

Conclusion

When using the APACHE score for risk stratification of critically ill patients after transplantation, the patient's age, respiratory failure, and use of hormones should be taken into account.

Recipient selection, timing of referral, and listing for lung transplantation

Recipient selection for lung transplantation is a balance between providing access to transplantation to maximum patients, while utilizing this limited resource in the most optimal way. This review summarizes the current literature and recommendations about referral, listing, and evaluation of lung transplant candidates, with a focus on patients considered to have high risk characteristics.