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Objective
The admission interview in oncological inpatient rehabilitation might be a good opportunity to identify cancer patients' needs present after acute treatment. However, a relevant number of patients may not express their needs. In this study, we examined (a) the proportion of cancer patients with unexpressed needs, (b) topics of unexpressed needs and reasons for not expressing needs, (c) correlations of not expressing needs with several patient characteristics, and (d) predictors of not expressing needs.
Methods
We enrolled 449 patients with breast, prostate, and colon cancer at beginning and end of inpatient rehabilitation. We obtained self‐reports about unexpressed needs and health‐related variables (quality of life, depression, anxiety, adjustment disorder, and health literacy). We estimated frequencies and conducted correlation and ordinal logistic regression analyses.
Results
A quarter of patients stated they had “rather not” or “not at all” expressed all relevant needs. Patients mostly omitted fear of cancer recurrence. Most frequent reasons for not expressing needs were being focused on physical consequences of cancer, concerns emerging only later, and not knowing about the possibility of talking about distress. Not expressing needs was associated with several health‐related outcomes, for example, emotional functioning, adjustment disorder, fear of progression, and health literacy. Depression measured at the beginning of rehabilitation showed only small correlations and is therefore not sufficient to identify patients with unexpressed needs.
Conclusions
A relevant proportion of cancer patients reported unexpressed needs in the admission interview. This was associated with decreased mental health. Therefore, it seems necessary to support patients in expressing needs.
Background
Radioligand therapy (RLT) with \(^{177}\)Lu-labeled prostate-specific membrane antigen (PSMA) ligands is associated with prolonged overall survival (OS) in patients with advanced, metastatic castration-resistant prostate cancer (mCRPC). A substantial number of patients, however, are prone to treatment failure. We aimed to determine clinical baseline characteristics to predict OS in patients receiving [\(^{177}\)Lu]Lu-PSMA I&T RLT in a long-term follow-up.
Materials and methods
Ninety-two mCRPC patients treated with [\(^{177}\)Lu]Lu-PSMA I&T with a follow-up of at least 18 months were retrospectively identified. Multivariable Cox regression analyses were performed for various baseline characteristics, including laboratory values, Gleason score, age, prior therapies, and time interval between initial diagnosis and first treatment cycle (interval\(_{Diagnosis-RLT}\), per 12 months). Cutoff values for significant predictors were determined using receiver operating characteristic (ROC) analysis. ROC-derived thresholds were then applied to Kaplan–Meier analyses.
Results
Baseline C-reactive protein (CRP; hazard ratio [HR], 1.10, 95% CI 1.02–1.18; P = 0.01), lactate dehydrogenase (LDH; HR, 1.07, 95% CI 1.01–1.11; P = 0.01), aspartate aminotransferase (AST; HR, 1.16, 95% CI 1.06–1.26; P = 0.001), and interval\(_{Diagnosis-RLT}\) (HR, 0.95, 95% CI 0.91–0.99; P = 0.02) were identified as independent prognostic factors for OS. The following respective ROC-based thresholds were determined: CRP, 0.98 mg/dl (area under the curve [AUC], 0.80); LDH, 276.5 U/l (AUC, 0.83); AST, 26.95 U/l (AUC, 0.73); and interval\(_{Diagnosis-RLT}\), 43.5 months (AUC, 0.68; P < 0.01, respectively). Respective Kaplan–Meier analyses demonstrated a significantly longer median OS of patients with lower CRP, lower LDH, and lower AST, as well as prolonged interval\(_{Diagnosis-RLT}\) (P ≤ 0.01, respectively).
Conclusion
In mCRPC patients treated with [\(^{177}\)Lu]Lu-PSMA I&T, baseline CRP, LDH, AST, and time interval until RLT initiation (thereby reflecting a possible indicator for tumor aggressiveness) are independently associated with survival. Our findings are in line with previous findings on [\(^{177}\)Lu]Lu-PSMA-617, and we believe that these clinical baseline characteristics may support the nuclear medicine specialist to identify long-term survivors.
The rating of perceived exertion (RPE) is a subjective load marker and may assist in individualizing training prescription, particularly by adjusting running intensity. Unfortunately, RPE has shortcomings (e.g., underreporting) and cannot be monitored continuously and automatically throughout a training sessions. In this pilot study, we aimed to predict two classes of RPE (≤15 “Somewhat hard to hard” on Borg’s 6–20 scale vs. RPE >15 in runners by analyzing data recorded by a commercially-available smartwatch with machine learning algorithms. Twelve trained and untrained runners performed long-continuous runs at a constant self-selected pace to volitional exhaustion. Untrained runners reported their RPE each kilometer, whereas trained runners reported every five kilometers. The kinetics of heart rate, step cadence, and running velocity were recorded continuously ( 1 Hz ) with a commercially-available smartwatch (Polar V800). We trained different machine learning algorithms to estimate the two classes of RPE based on the time series sensor data derived from the smartwatch. Predictions were analyzed in different settings: accuracy overall and per runner type; i.e., accuracy for trained and untrained runners independently. We achieved top accuracies of 84.8 % for the whole dataset, 81.8 % for the trained runners, and 86.1 % for the untrained runners. We predict two classes of RPE with high accuracy using machine learning and smartwatch data. This approach might aid in individualizing training prescriptions.
Acute graft-versus-host disease (aGvHD) is a severe and often life-threatening complication of allogeneic hematopoietic cell transplantation (allo-HCT). AGvHD is mediated by alloreactive donor T-cells targeting predominantly the gastrointestinal tract, liver, and skin. Recent work in mice and patients undergoing allo-HCT showed that alloreactive T-cells can be identified by the expression of α4β7 integrin on T-cells even before manifestation of an aGvHD. Here, we investigated whether the detection of a combination of the expression of T-cell surface markers on peripheral blood (PB) CD8\(^+\) T-cells would improve the ability to predict aGvHD. To this end, we employed two independent preclinical models of minor histocompatibility antigen mismatched allo-HCT following myeloablative conditioning. Expression profiles of integrins, selectins, chemokine receptors, and activation markers of PB donor T-cells were measured with multiparameter flow cytometry at multiple time points before the onset of clinical aGvHD symptoms. In both allo-HCT models, we demonstrated a significant upregulation of α4β7 integrin, CD162E, CD162P, and conversely, a downregulation of CD62L on donor T-cells, which could be correlated with the development of aGvHD. Other surface markers, such as CD25, CD69, and CC-chemokine receptors were not found to be predictive markers. Based on these preclinical data from mouse models, we propose a surface marker panel on peripheral blood T-cells after allo-HCT combining α4β7 integrin with CD62L, CD162E, and CD162P (cutaneous lymphocyte antigens, CLA, in humans) to identify patients at risk for developing aGvHD early after allo-HCT.