Acute primary testicular failure due to radiotherapy increases risk of severe postoperative adverse events in rectal cancer patients

Abstract Background We reported previously that rectal cancer patients given curative-intent chemotherapy, radiation, and surgery for non-metastatic disease had enhanced risk of metastatic progression and death if circulating levels of 25-hydroxyvitamin D [25(OH) D] were low. Here we investigated whether the association between the vitamin D status and prognosis pertains to the general, unselected population of rectal cancer patients. Methods Serum 25(OH) D at the time of diagnosis was assessed in 129 patients, enrolled 2013–2017 and representing the entire range of rectal cancer stages, and analyzed with respect to season, sex, systemic inflammation, and survival. Results In the population-based cohort residing at latitude 60°N, 25(OH) D varied according to season in men only, who were overrepresented among the vitamin D-deficient (< 50 nmol/L) patients. Consistent with our previous findings, the individuals presenting with T4 disease had significantly reduced 25(OH) D levels. Low vitamin D was associated with systemic inflammation, albeit with distinct modes of presentation. While men with low vitamin D showed circulating markers typical for the systemic inflammatory response (e.g., elevated erythrocyte sedimentation rate), the corresponding female patients had elevated serum levels of interleukin-6 and the chemokine (C-X-C motif) ligand 7. Despite disparities in vitamin D status and the potential effects on disease attributes, significantly shortened cancer-specific survival was observed in vitamin D-deficient patients irrespective of sex. Conclusion This unselected rectal cancer cohort confirmed the interconnection of low vitamin D, more advanced disease presentation, and poor survival, and further suggested it may be conditional on disparate modes of adverse systemic inflammation in men and women. Trial registration ClinicalTrials.govNCT01816607; registration date: 22 March 2013.

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Electronic patient-reported outcomes and machine learning in predicting immune-related adverse events of immune checkpoint inhibitor therapies

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-021-01564-0 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Sanna Iivanainen ◽

Jussi Ekstrom ◽

Henri Virtanen ◽

Vesa V. Kataja ◽

Jussi P. Koivunen

Keyword(s):

Machine Learning ◽

Early Detection ◽

Adverse Events ◽

Cancer Patients ◽

Immune Checkpoint ◽

Patient Reported Outcomes ◽

Prediction Models ◽

Accuracy Score ◽

Patient Reported ◽

Auc Value

Abstract Background Immune-checkpoint inhibitors (ICIs) have introduced novel immune-related adverse events (irAEs), arising from various organ systems without strong timely dependency on therapy dosing. Early detection of irAEs could result in improved toxicity profile and quality of life. Symptom data collected by electronic (e) patient-reported outcomes (PRO) could be used as an input for machine learning (ML) based prediction models for the early detection of irAEs. Methods The utilized dataset consisted of two data sources. The first dataset consisted of 820 completed symptom questionnaires from 34 ICI treated advanced cancer patients, including 18 monitored symptoms collected using the Kaiku Health digital platform. The second dataset included prospectively collected irAE data, Common Terminology Criteria for Adverse Events (CTCAE) class, and the severity of 26 irAEs. The ML models were built using extreme gradient boosting algorithms. The first model was trained to detect the presence and the second the onset of irAEs. Results The model trained to predict the presence of irAEs had an excellent performance based on four metrics: accuracy score 0.97, Area Under the Curve (AUC) value 0.99, F1-score 0.94 and Matthew’s correlation coefficient (MCC) 0.92. The prediction of the irAE onset was more difficult with accuracy score 0.96, AUC value 0.93, F1-score 0.66 and MCC 0.64 but the model performance was still at a good level. Conclusion The current study suggests that ML based prediction models, using ePRO data as an input, can predict the presence and onset of irAEs with a high accuracy, indicating that ePRO follow-up with ML algorithms could facilitate the detection of irAEs in ICI-treated cancer patients. The results should be validated with a larger dataset. Trial registration Clinical Trials Register (NCT3928938), registration date the 26th of April, 2019

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