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Using donor, transplant, and recipient characteristics known at the decision time of a transplant, high accuracy in matching donors and recipients can be achieved, potentially providing assistance with clinical decision making.

We compared the outcome of COVID‐19 in immunosuppressed solid organ transplant (SOT) patients to a transplant naïve population. In total, 10 356 adult hospital admissions for COVID‐19 from March 1, 2020 to April 27, 2020 were analyzed. Data were collected on demographics, baseline clinical conditions, medications, immunosuppression, and COVID‐19 course. Primary outcome was combined death or mechanical ventilation. We assessed the association between primary outcome and prognostic variables using bivariate and multivariate regression models. We also compared the primary endpoint in SOT patients to an age, gender, and comorbidity‐matched control group. Bivariate analysis found transplant status, age, gender, race/ethnicity, body mass index, diabetes, hypertension, cardiovascular disease, COPD, and GFR <60 mL/min/1.73 m2 to be significant predictors of combined death or mechanical ventilation. After multivariate logistic regression analysis, SOT status had a trend toward significance (odds ratio [OR] 1.29; 95% CI 0.99–1.69, p = .06). Compared to an age, gender, and comorbidity‐matched control group, SOT patients had a higher combined risk of death or mechanical ventilation (OR 1.34; 95% CI 1.03–1.74, p = .027).

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Aim:  To determine if hospital treatment in residential care facilities, led by a geriatric team, might be a viable alternative to inpatient admission for selected patients. Methods:  Case series with a new intervention were compared with historical controls receiving the conventional treatment. Treatment in residential care facilities (TRC) by the Residential Care Intervention Program in The Elderly (RECIPE) service was compared against the conventional treatment group, aged care unit (ACU) inpatients. Results:  A total of 95 patients in TRC and 167 patients in ACU were included. The mean Charlson Comorbidity Index score was 7 in both groups and demographics were similar, except more patients in the TRC group had dementia. Palliative care support was provided to 35.8% in the TRC group, compared with 7.8% in ACU, P < 0.001. Six‐month mortality rates were similar at 30% for both groups. Rehospitalization rates at 6 months were similar at 41% for both groups. Length of care was significantly shorter for TRC (mean 2 days) compared with ACU (mean 11 days), P < 0.001. Conclusions:  Hospital treatment in residential care is viable for most patients, including those with dementia and those who need palliative care support. This model of care offers a valuable geriatric service to residents who would prefer to avoid hospital transfers, with no difference in mortality or rehospitalization rates for those treated in residential care, but a significant reduction in length of care. Geriatr Gerontol Int 2013; 13: 378–383.

Modern multidisciplinary therapy for colorectal liver metastases (CRLM) is associated with significant morbidity and must be adapted to the patient's relative risk. The tools currently available to risk-stratify patients are limited. This study assessed the prognostic utility of metabolic measurements derived from 18 F-FDG PET compared with previously proposed prognostic scoring systems. Methods: Preoperative 18 F-FDG PET/CT studies from a series of 30 patients who underwent liver resection for CRLM after neoadjuvant chemotherapy were evaluated. Quantitative 18 F-FDG PET analysis calculated the maximum and mean standardized uptake value, metabolic tumor volume (MTV), and tumor glycolytic volume (TGV) as measures of the metabolic activity of tumors. The predictive value of these parameters was compared with that of 4 prognostic scores developed by Fong, Iwatsuki, Nordlinger, and Rees. Results: High MTV and TGV in patients before metastasectomy were significantly associated with poorer overall survival (MTV: P 5 0.001; TGV: P 5 0.004) and recurrence-free survival (MTV: P 5 0.001, TGV; P 5 0.002). Maximum and mean standardized uptake value did not show any significant predictive ability. Of the prognostic scores, prediction of outcome was most accurate using the Basingstoke index (area under the curve, 0.898). Conclusion: Assessment of metabolic tumor burden with volumetric 18 F-FDG PET parameters appears to be a valuable adjunct in determining the biology of CRLM before surgical resection and may enable better risk stratification of patients.

Liver transplantation (LT) listing criteria for hepatocellular carcinoma (HCC) remain controversial. To optimize the utility of limited donor organs, this study aims to leverage machine learning to develop an accurate posttransplantation HCC recurrence prediction calculator. Patients with HCC listed for LT from 2000 to 2016 were identified, with 739 patients who underwent LT used for modeling. Data included serial imaging, alpha‐fetoprotein (AFP), locoregional therapies, treatment response, and posttransplantation outcomes. We compared the CoxNet (regularized Cox regression), survival random forest, survival support vector machine, and DeepSurv machine learning algorithms via the mean cross‐validated concordance index. We validated the selected CoxNet model by comparing it with other currently available recurrence risk algorithms on a held‐out test set (AFP, Model of Recurrence After Liver Transplant [MORAL], and Hazard Associated with liver Transplantation for Hepatocellular Carcinoma [HALT‐HCC score]). The developed CoxNet‐based recurrence prediction model showed a satisfying overall concordance score of 0.75 (95% confidence interval [CI], 0.64‐0.84). In comparison, the recalibrated risk algorithms’ concordance scores were as follows: AFP score 0.64 (outperformed by the CoxNet model, 1‐sided 95% CI, >0.01; P = 0.04) and MORAL score 0.64 (outperformed by the CoxNet model 1‐sided 95% CI, >0.02; P = 0.03). The recalibrated HALT‐HCC score performed well with a concordance of 0.72 (95% CI, 0.63‐0.81) and was not significantly outperformed (1‐sided 95% CI, ≥0.05; P = 0.29). Developing a comprehensive posttransplantation HCC recurrence risk calculator using machine learning is feasible and can yield higher accuracy than other available risk scores. Further research is needed to confirm the utility of machine learning in this setting.

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