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Nosrati JD, Ma D, Bloom B, Kapur A, Sidiqi BU, Thakur R, Tchelebi LT, Herman JM, Adair N, Potters L, Chen WC. Treatment Terminations During Radiation Therapy: A 10-Year Experience. Pract Radiat Oncol 2024:S1879-8500(24)00142-5. [PMID: 38972541 DOI: 10.1016/j.prro.2024.06.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 04/01/2024] [Accepted: 06/05/2024] [Indexed: 07/09/2024]
Abstract
PURPOSE Patients undergoing radiation therapy may terminate treatment for any number of reasons. The incidence of treatment termination (TT) during radiation therapy has not been studied. Herein, we present a cohort of TT at a large multicenter radiation oncology department over 10 years. METHODS AND MATERIALS TTs between January 2013 and January 2023 were prospectively analyzed as part of an ongoing departmental quality and safety program. TT was defined as any premature discontinuation of therapy after initiating radiation planning. The rate of TT was calculated as a percentage of all patients starting radiation planning. All cases were presented at monthly morbidity and mortality conferences with a root cause reviewed. RESULTS A total of 1448 TTs were identified out of 31,199 planned courses of care (4.6%). Six hundred eighty-six (47.4%) involved patients treated with curative intent, whereas 753 (52.0%) were treated with palliative intent, and 9 (0.6%) were treated for benign disease. The rate of TT decreased from 8.49% in 2013 to 3.02% in 2022, with rates decreasing yearly. The most common disease sites for TT were central nervous system (21.7%), head and neck (19.3%), thorax (17.5%), and bone (14.2%). The most common causes of TT were hospice and/or patient expiration (35.9%), patient choice unrelated to toxicity (35.2%), and clinician choice unrelated to toxicity (11.5%). CONCLUSIONS This 10-year prospective review of TTs identified a year-over-year decrease in TTs as a percentage of planned patients. This decrease may be associated with the addition of root cause reviews for TTs and discussions monthly at morbidity and mortality rounds, coupled with departmental upstream quality initiatives implemented over time. Understanding the reasons behind TTs may help decrease preventable TTs. Although some TTs may be unavoidable, open discourse and quality improvement changes effectively reduce TT incidents over time.
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Affiliation(s)
- Jason D Nosrati
- Northwell, New Hyde Park, New York; Department of Radiation Medicine, Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York
| | - Daniel Ma
- Northwell, New Hyde Park, New York; Department of Radiation Medicine, Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York
| | - Beatrice Bloom
- Northwell, New Hyde Park, New York; Department of Radiation Medicine, Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York
| | - Ajay Kapur
- Northwell, New Hyde Park, New York; Department of Radiation Medicine, Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York
| | - Baho U Sidiqi
- Northwell, New Hyde Park, New York; Department of Radiation Medicine, Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York
| | - Richa Thakur
- Northwell, New Hyde Park, New York; Department of Hematology and Medical Oncology, Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York
| | - Leila T Tchelebi
- Northwell, New Hyde Park, New York; Department of Radiation Medicine, Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York
| | - Joseph M Herman
- Northwell, New Hyde Park, New York; Department of Radiation Medicine, Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York
| | - Nilda Adair
- Northwell, New Hyde Park, New York; Department of Radiation Medicine, Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York
| | - Louis Potters
- Northwell, New Hyde Park, New York; Department of Radiation Medicine, Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York
| | - William C Chen
- Northwell, New Hyde Park, New York; Department of Radiation Medicine, Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York.
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2
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Yoong SQ, Bhowmik P, Kapparath S, Porock D. Palliative prognostic scores for survival prediction of cancer patients: a systematic review and meta-analysis. J Natl Cancer Inst 2024; 116:829-857. [PMID: 38366659 DOI: 10.1093/jnci/djae036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Revised: 02/05/2024] [Accepted: 02/13/2024] [Indexed: 02/18/2024] Open
Abstract
BACKGROUND The palliative prognostic score is the most widely validated prognostic tool for cancer survival prediction, with modified versions available. A systematic evaluation of palliative prognostic score tools is lacking. This systematic review and meta-analysis aimed to evaluate the performance and prognostic utility of palliative prognostic score, delirium-palliative prognostic score, and palliative prognostic score without clinician prediction in predicting 30-day survival of cancer patients and to compare their performance. METHODS Six databases were searched for peer-reviewed studies and grey literature published from inception to June 2, 2023. English studies must assess palliative prognostic score, delirium-palliative prognostic score, or palliative prognostic score without clinician-predicted survival for 30-day survival in adults aged 18 years and older with any stage or type of cancer. Outcomes were pooled using the random effects model or summarized narratively when meta-analysis was not possible. RESULTS A total of 39 studies (n = 10 617 patients) were included. Palliative prognostic score is an accurate prognostic tool (pooled area under the curve [AUC] = 0.82, 95% confidence interval [CI] = 0.79 to 0.84) and outperforms palliative prognostic score without clinician-predicted survival (pooled AUC = 0.74, 95% CI = 0.71 to 0.78), suggesting that the original palliative prognostic score should be preferred. The meta-analysis found palliative prognostic score and delirium-palliative prognostic score performance to be comparable. Most studies reported survival probabilities corresponding to the palliative prognostic score risk groups, and higher risk groups were statistically significantly associated with shorter survival. CONCLUSIONS Palliative prognostic score is a validated prognostic tool for cancer patients that can enhance clinicians' confidence and accuracy in predicting survival. Future studies should investigate if accuracy differs depending on clinician characteristics. Reporting of validation studies must be improved, as most studies were at high risk of bias, primarily because calibration was not assessed.
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Affiliation(s)
- Si Qi Yoong
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Priyanka Bhowmik
- Maharaja Jitendra Narayan Medical College and Hospital, Coochbehar, West Bengal, India
| | | | - Davina Porock
- Centre for Research in Aged Care, Edith Cowan University, Australia
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Treleaven L, Komesaroff P, La Brooy C, Olver I, Kerridge I, Philip J. A review of the utility of prognostic tools in predicting 6-month mortality in cancer patients, conducted in the context of voluntary assisted dying. Intern Med J 2023; 53:2180-2197. [PMID: 37029711 DOI: 10.1111/imj.16081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 03/07/2023] [Indexed: 04/09/2023]
Abstract
BACKGROUND Eligibility to access the Victorian voluntary assisted dying (VAD) legislation requires that people have a prognosis of 6 months or less (or 12 months or less in the setting of a neurodegenerative diagnosis). Yet prognostic determination is frequently inaccurate and prompts clinician discomfort. Based on functional capacity and clinical and biochemical markers, prognostic tools have been developed to increase the accuracy of life expectancy predictions. AIMS This review of prognostic tools explores their accuracy to determine 6-month mortality in adults when treated under palliative care with a primary diagnosis of cancer (the diagnosis of a large proportion of people who are requesting VAD). METHODS A systematic search of the literature was performed on electronic databases Medline, Embase and Cinahl. RESULTS Limitations of prognostication identified include the following: (i) prognostic tools still provide uncertain prognoses; (ii) prognostic tools have greater accuracy predicting shorter prognoses, such as weeks to months, rather than 6 months; and (iii) functionality was often weighted significantly when calculating prognoses. Challenges of prognostication identified include the following: (i) the area under the curve (a value that represents how well a model can distinguish between two outcomes) cannot be directly interpreted clinically and (ii) difficulties exist related to determining appropriate thresholds of accuracy in this context. CONCLUSIONS Prognostication is a significant aspect of VAD, and the utility of the currently available prognostic tools appears limited but may prompt discussions about prognosis and alternative means (other than prognostic estimates) to identify those eligible for VAD.
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Affiliation(s)
- Lydia Treleaven
- Department of Medicine, The University of Melbourne, Melbourne, Victoria, Australia
| | - Paul Komesaroff
- School of Public Health and Preventive Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia
- Department of Medicine, Alfred Hospital, Melbourne, Victoria, Australia
| | - Camille La Brooy
- School of Public Health and Preventive Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia
| | - Ian Olver
- School of Psychology, Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, South Australia, Australia
| | - Ian Kerridge
- Department of Medicine, Royal North Shore Hospital, St Leonards, New South Wales, Australia
- Sydney Health Ethics, The University of Sydney, Camperdown, New South Wales, Australia
| | - Jennifer Philip
- Department of Medicine, The University of Melbourne, Melbourne, Victoria, Australia
- Palliative Care Service, St Vincent's Hospital, Melbourne, Victoria, Australia
- Palliative Care Service, Peter MacCallum Cancer Centre, Royal Melbourne Hospital, Melbourne, Victoria, Australia
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Wieland MWM, Pilz W, Winkens B, Hoeben A, Willemsen ACH, Kremer B, Baijens LWJ. Multi-Domain Screening: Identification of Patient's Risk Profile Prior to Head-and-Neck Cancer Treatment. Cancers (Basel) 2023; 15:5254. [PMID: 37958427 PMCID: PMC10648822 DOI: 10.3390/cancers15215254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Accepted: 10/27/2023] [Indexed: 11/15/2023] Open
Abstract
BACKGROUND Head-and-neck cancer (HNC) can give rise to oropharyngeal dysphagia (OD), malnutrition, sarcopenia, and frailty. Early identification of these phenomena in newly diagnosed HNC patients is important to reduce the risk of complications and to improve treatment outcomes. The aim of this study was (1) to determine the prevalence of the risk of OD, malnutrition, sarcopenia, and frailty; and (2) to investigate the relation between these phenomena and patients' age, performance status, and cancer group staging. METHODS Patients (N = 128) underwent multi-domain screening consisting of the Eating Assessment Tool-10 for OD, Short Nutritional Assessment Questionnaire and BMI for malnutrition, Short Physical Performance Battery and Hand Grip Strength for sarcopenia, and Distress Thermometer and Maastricht Frailty Screening Tool for frailty. RESULTS 26.2%, 31.0%, 73.0%, and 46.4% of the patients were at risk for OD, malnutrition, sarcopenia, or frailty, respectively. Patients with an advanced cancer stage had a significantly higher risk of OD and high levels of distress prior to cancer treatment. CONCLUSIONS This study identified the risk profile of newly diagnosed HNC patients using a standardized 'quick and easy' multi-domain screening prior to cancer treatment.
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Affiliation(s)
- Monse W. M. Wieland
- Department of Otorhinolaryngology, Head and Neck Surgery, Maastricht University Medical Center, 6202 AZ Maastricht, The Netherlands
- GROW-School for Oncology and Reproduction, Maastricht University, 6229 ER Maastricht, The Netherlands
| | - Walmari Pilz
- Department of Otorhinolaryngology, Head and Neck Surgery, Maastricht University Medical Center, 6202 AZ Maastricht, The Netherlands
- GROW-School for Oncology and Reproduction, Maastricht University, 6229 ER Maastricht, The Netherlands
| | - Bjorn Winkens
- Department of Methodology and Statistics, Maastricht University, 6200 MD Maastricht, The Netherlands
- Care and Public Health Research Institute—CAPHRI, Maastricht University, 6200 MD Maastricht, The Netherlands
| | - Ann Hoeben
- GROW-School for Oncology and Reproduction, Maastricht University, 6229 ER Maastricht, The Netherlands
- Division of Medical Oncology, Department of Internal Medicine, Maastricht University Medical Center, 6202 AZ, The Netherlands
| | - Anna C. H. Willemsen
- Department of Internal Medicine, Diakonessenhuis, 3508 TG Utrecht, The Netherlands
| | - Bernd Kremer
- Department of Otorhinolaryngology and Head and Neck Surgery, Erasmus MC Cancer Institute, University Medical Center Rotterdam, 3000 CA Rotterdam, The Netherlands
| | - Laura W. J. Baijens
- Department of Otorhinolaryngology, Head and Neck Surgery, Maastricht University Medical Center, 6202 AZ Maastricht, The Netherlands
- GROW-School for Oncology and Reproduction, Maastricht University, 6229 ER Maastricht, The Netherlands
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Harris D, Kalir D, Chevalier C, Dobbie K, Fielding F, Lagman R, Makhoul A, McInnes S, Najafi S, Neale K, Rybicki L, Robbins-Ong M, Neuendorf K. Response Rates to Methylnaltrexone in Hospitalized Cancer Patients. Am J Hosp Palliat Care 2023; 40:1093-1097. [PMID: 36565253 DOI: 10.1177/10499091221147903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Context: Methylnaltrexone is a peripherally-acting mu-opioid receptor antagonist studied in both cancer and non-cancer patients with opioid-induced constipation (OIC), but mostly in the outpatient setting. For adult hospitalized cancer patients with OIC, its effectiveness is unknown. Objectives: Describe the efficacy of methylnaltrexone for OIC in the inpatient setting, defined as bowel movement (BM) within 24 hours of methylnaltrexone administration. Methods: We performed a single-center, retrospective chart review of all hospitalized, adult patients with a cancer diagnosis who received methylnaltrexone from the palliative care team between January 1st, 2012 and July 1st, 2019. Results: We identified 194 patients. The mean age was 59, 50.5% were male and 88% were white. 192 patients (98%) received the 8 mg dose subcutaneously. The median oral morphine equivalent (OME) was 135 mg (IQR 70-354 mg). 45% (95% confidence interval, 38-53%) had a BM within 24 hours. Higher OME was correlated with successful BM, with a response in 93% (86/92) of patients receiving ≥150 OME and 2% (2/102) of patients receiving <150 OME (P < .0001). Prior laxative use did not predict response at 24 hours whether these were osmotic laxatives (40.7% vs 47.1%, P = .52), stimulant laxatives (45.7% vs 45.2%, P > .99), or stool softeners (44.7% vs 46.1%, P = .89). Conclusion: Methylnaltrexone has a high response rate when used as treatment for OIC in hospitalized adult cancer patients, especially for patients taking ≥150 OME.
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Affiliation(s)
- David Harris
- Department of Palliative and Supportive Care, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA
| | - David Kalir
- Department of Palliative and Supportive Care, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Cory Chevalier
- Department of Palliative and Supportive Care, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Krista Dobbie
- Department of Palliative and Supportive Care, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Flannery Fielding
- Department of Palliative and Supportive Care, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Ruth Lagman
- Department of Palliative and Supportive Care, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Ahed Makhoul
- Department of Palliative and Supportive Care, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Susan McInnes
- Department of Palliative and Supportive Care, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Sina Najafi
- Department of Supportive and Palliative Care, Baylor Scott and White Health, Dallas, TX, USA
| | - Kyle Neale
- Department of Palliative and Supportive Care, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Lisa Rybicki
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Melanie Robbins-Ong
- Department of Palliative and Supportive Care, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Kathleen Neuendorf
- Department of Palliative and Supportive Care, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA
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Yoong SQ, Porock D, Whitty D, Tam WWS, Zhang H. Performance of the Palliative Prognostic Index for cancer patients: A systematic review and meta-analysis. Palliat Med 2023; 37:1144-1167. [PMID: 37310019 DOI: 10.1177/02692163231180657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
BACKGROUND Clinician predicted survival for cancer patients is often inaccurate, and prognostic tools may be helpful, such as the Palliative Prognostic Index (PPI). The PPI development study reported that when PPI score is greater than 6, it predicted survival of less than 3 weeks with a sensitivity of 83% and specificity of 85%. When PPI score is greater than 4, it predicts survival of less than 6 weeks with a sensitivity of 79% and specificity of 77%. However, subsequent PPI validation studies have evaluated various thresholds and survival durations, and it is unclear which is most appropriate for use in clinical practice. With the development of numerous prognostic tools, it is also unclear which is most accurate and feasible for use in multiple care settings. AIM We evaluated PPI model performance in predicting survival of adult cancer patients based on different thresholds and survival durations and compared it to other prognostic tools. DESIGN This systematic review and meta-analysis was registered in PROSPERO (CRD42022302679). We calculated the pooled sensitivity and specificity of each threshold using bivariate random-effects meta-analysis and pooled diagnostic odds ratio of each survival duration using hierarchical summary receiver operating characteristic model. Meta-regression and subgroup analysis were used to compare PPI performance with clinician predicted survival and other prognostic tools. Findings which could not be included in meta-analyses were summarised narratively. DATA SOURCES PubMed, ScienceDirect, Web of Science, CINAHL, ProQuest and Google Scholar were searched for articles published from inception till 7 January 2022. Both retrospective and prospective observational studies evaluating PPI performance in predicting survival of adult cancer patients in any setting were included. The Prediction Model Risk of Bias Assessment Tool was used for quality appraisal. RESULTS Thirty-nine studies evaluating PPI performance in predicting survival of adult cancer patients were included (n = 19,714 patients). Across meta-analyses of 12 PPI score thresholds and survival durations, we found that PPI was most accurate for predicting survival of <3 weeks and <6 weeks. Survival prediction of <3 weeks was most accurate when PPI score>6 (pooled sensitivity = 0.68, 95% CI 0.60-0.75, specificity = 0.80, 95% CI 0.75-0.85). Survival prediction of <6 weeks was most accurate when PPI score>4 (pooled sensitivity = 0.72, 95% CI 0.65-0.78, specificity = 0.74, 95% CI 0.66-0.80). Comparative meta-analyses found that PPI performed similarly to Delirium-Palliative Prognostic Score and Palliative Prognostic Score in predicting <3-week survival, but less accurately in <30-day survival prediction. However, Delirium-Palliative Prognostic Score and Palliative Prognostic Score only provide <30-day survival probabilities, and it is uncertain how this would be helpful for patients and clinicians. PPI also performed similarly to clinician predicted survival in predicting <30-day survival. However, these findings should be interpreted with caution as limited studies were available for comparative meta-analyses. Risk of bias was high for all studies, mainly due to poor reporting of statistical analyses. while there were low applicability concerns for most (38/39) studies. CONCLUSIONS PPI score>6 should be used for <3-week survival prediction, and PPI score>4 for <6-week survival. PPI is easily scored and does not require invasive tests, and thus would be easily implemented in multiple care settings. Given the acceptable accuracy of PPI in predicting <3- and <6-week survival and its objective nature, it could be used to cross-check clinician predicted survival especially when clinicians have doubts about their own judgement, or when clinician estimates seem to be less reliable. Future studies should adhere to the reporting guidelines and provide comprehensive analyses of PPI model performance.
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Affiliation(s)
- Si Qi Yoong
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Davina Porock
- School of Nursing and Midwifery, Edith Cowan University, Perth, WA, Australia
| | - Dee Whitty
- School of Nursing and Midwifery, Edith Cowan University, Perth, WA, Australia
| | - Wilson Wai San Tam
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Hui Zhang
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- St. Andrew's Community Hospital, Singapore
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Porcu L, Recchia A, Bosetti C, Chiaruttini MV, Uggeri S, Lonati G, Ubezio P, Rizzi B, Corli O. Development and external validation of a predictive multivariable model for last-weeks survival of advanced cancer patients in the palliative home care setting (PACS). Support Care Cancer 2023; 31:536. [PMID: 37624424 DOI: 10.1007/s00520-023-07990-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 08/09/2023] [Indexed: 08/26/2023]
Abstract
PURPOSE Various prognostic indexes have been proposed to improve physicians' ability to predict survival time in advanced cancer patients, admitted to palliative care (PC) with a survival probably to a few weeks of life, but no optimal score has been identified. The study aims therefore to develop and externally validate a new multivariable predictive model in this setting. METHODS We developed a model to predict short-term overall survival in cancer patients on the basis of clinical factors collected at PC admission. The model was developed on 1020 cancer patients prospectively enrolled to home palliative care at VIDAS Milan, Italy, between May 2018 and February 2020 and followed-up to June 2020, and validated in two separate samples of 544 home care and 247 hospice patients. RESULTS Among 68 clinical factors considered, five predictors were included in the predictive model, i.e., rattle, heart rate, anorexia, liver failure, and the Karnofsky performance status. Patient's survival probability at 5, 15, 30 and 45 days was estimated. The predictive model showed a good calibration and moderate discrimination (area under the receiver operating characteristic curve between 0.72 and 0.79) in the home care validation set, but model calibration was suboptimal in hospice patients. CONCLUSIONS The new multivariable predictive model for palliative cancer patients' survival (PACS model) includes clinical parameters routinely at patient's admission to PC and can be easily used to facilitate immediate and appropriate short-term clinical decisions for PC cancer patients in the home setting.
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Affiliation(s)
- Luca Porcu
- Methodological Research Unit, Department of Oncology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Angela Recchia
- Fondazione VIDAS, Via U. Ojetti, 66, 20151, Milan, Italy.
| | - Cristina Bosetti
- Unit of Cancer Epidemiology, Department of Oncology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Maria Vittoria Chiaruttini
- Unit of Cancer Epidemiology, Department of Oncology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Sara Uggeri
- Unit of Pain and Palliative Care Research, Department of Oncology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | | | - Paolo Ubezio
- Unit of Biophysics, Department of Oncology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Barbara Rizzi
- Fondazione VIDAS, Via U. Ojetti, 66, 20151, Milan, Italy
| | - Oscar Corli
- Unit of Pain and Palliative Care Research, Department of Oncology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
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Silva J, Tavares V, Afonso A, Garcia J, Cerqueira F, Medeiros R. Plasmatic MicroRNAs and Treatment Outcomes of Patients with Metastatic Castration-Resistant Prostate Cancer: A Hospital-Based Cohort Study and In Silico Analysis. Int J Mol Sci 2023; 24:ijms24109101. [PMID: 37240449 DOI: 10.3390/ijms24109101] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Revised: 05/15/2023] [Accepted: 05/18/2023] [Indexed: 05/28/2023] Open
Abstract
Prostate cancer (PCa) is one of the most common malignancies among men worldwide. Inevitably, all advanced PCa patients develop metastatic castration-resistant prostate cancer (mCRPC), an aggressive phase of the disease. Treating mCRPC is challenging, and prognostic tools are needed for disease management. MicroRNA (miRNA) deregulation has been reported in PCa, constituting potential non-invasive prognostic biomarkers. As such, this study aimed to evaluate the prognostic potential of nine miRNAs in the liquid biopsies (plasma) of mCRPC patients treated with second-generation androgen receptor axis-targeted (ARAT) agents, abiraterone acetate (AbA) and enzalutamide (ENZ). Low expression levels of miR-16-5p and miR-145-5p in mCRPC patients treated with AbA were significantly associated with lower progression-free survival (PFS). The two miRNAs were the only predictors of the risk of disease progression in AbA-stratified analyses. Low miR-20a-5p levels in mCRPC patients with Gleason scores of <8 were associated with worse overall survival (OS). The transcript seems to predict the risk of death regardless of the ARAT agent. According to the in silico analyses, miR-16-5p, miR-145-5p, and miR-20a-5p seem to be implicated in several processes, namely, cell cycle, proliferation, migration, survival, metabolism, and angiogenesis, suggesting an epigenetic mechanism related to treatment outcome. These miRNAs may represent attractive prognostic tools to be used in mCRPC management, as well as a step further in the identification of new potential therapeutic targets, to use in combination with ARAT for an improved treatment outcome. Despite the promising results, real-world validation is necessary.
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Affiliation(s)
- Jani Silva
- Molecular Oncology and Viral Pathology Group, Research Center of IPO Porto (CI-IPOP)/Pathology and Laboratory Medicine Department, Clinical Pathology SV/RISE@CI-IPOP (Health Research Network), Portuguese Oncology Institute of Porto (IPO Porto)/Porto Comprehensive Cancer Center (Porto.CCC), 4200-072 Porto, Portugal
- AquaValor-Centro de Valorização e Transferência de Tecnologia da Água, Rua Dr. Júlio Martins, nº1, 5400-342 Chaves, Portugal
| | - Valéria Tavares
- Molecular Oncology and Viral Pathology Group, Research Center of IPO Porto (CI-IPOP)/Pathology and Laboratory Medicine Department, Clinical Pathology SV/RISE@CI-IPOP (Health Research Network), Portuguese Oncology Institute of Porto (IPO Porto)/Porto Comprehensive Cancer Center (Porto.CCC), 4200-072 Porto, Portugal
- Faculty of Medicine, University of Porto (FMUP), Alameda Prof. Hernâni Monteiro, 4200-319 Porto, Portugal
- Abel Salazar Institute for the Biomedical Sciences (ICBAS), Universidade do Porto, Rua de Jorge Viterbo Ferreira, 228, 4050-313 Porto, Portugal
| | - Ana Afonso
- Department of Oncology, Portuguese Institute of Oncology, Rua Dr. António Bernardino Almeida, 4200-072 Porto, Portugal
| | - Juliana Garcia
- AquaValor-Centro de Valorização e Transferência de Tecnologia da Água, Rua Dr. Júlio Martins, nº1, 5400-342 Chaves, Portugal
- Centre for the Research and Technology of Agro-Environment and Biological Sciences (CITAB)/Institute for Innovation, Capacity Building and Sustainability of Agri-Food Production (Inov4Agro), University of Trás-os-Montes e Alto Douro, 5001-801 Vila Real, Portugal
| | - Fátima Cerqueira
- Molecular Oncology and Viral Pathology Group, Research Center of IPO Porto (CI-IPOP)/Pathology and Laboratory Medicine Department, Clinical Pathology SV/RISE@CI-IPOP (Health Research Network), Portuguese Oncology Institute of Porto (IPO Porto)/Porto Comprehensive Cancer Center (Porto.CCC), 4200-072 Porto, Portugal
- Instituto de Investigação, Inovação e Desenvolvimento Fernando Pessoa (FP-I3ID), Biomedical and Health Sciences (FP-BHS), Universidade Fernando Pessoa, Praça 9 de Abril, 349, 4249-004 Porto, Portugal
- Faculty of Health Sciences, University Fernando Pessoa, Rua Carlos da Maia, 296, 4200-150 Porto, Portugal
| | - Rui Medeiros
- Molecular Oncology and Viral Pathology Group, Research Center of IPO Porto (CI-IPOP)/Pathology and Laboratory Medicine Department, Clinical Pathology SV/RISE@CI-IPOP (Health Research Network), Portuguese Oncology Institute of Porto (IPO Porto)/Porto Comprehensive Cancer Center (Porto.CCC), 4200-072 Porto, Portugal
- Faculty of Medicine, University of Porto (FMUP), Alameda Prof. Hernâni Monteiro, 4200-319 Porto, Portugal
- Abel Salazar Institute for the Biomedical Sciences (ICBAS), Universidade do Porto, Rua de Jorge Viterbo Ferreira, 228, 4050-313 Porto, Portugal
- Instituto de Investigação, Inovação e Desenvolvimento Fernando Pessoa (FP-I3ID), Biomedical and Health Sciences (FP-BHS), Universidade Fernando Pessoa, Praça 9 de Abril, 349, 4249-004 Porto, Portugal
- Faculty of Health Sciences, University Fernando Pessoa, Rua Carlos da Maia, 296, 4200-150 Porto, Portugal
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Pan X, Cong H, Wang X, Zhang H, Ge Y, Hu S. Deep learning-extracted CT imaging phenotypes predict response to total resection in colorectal cancer. Acta Radiol 2023; 64:1783-1791. [PMID: 36762417 DOI: 10.1177/02841851231152685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Abstract
BACKGROUND Deep learning surpasses many traditional methods for many vision tasks, allowing the transformation of hierarchical features into more abstract, high-level features. PURPOSE To evaluate the prognostic value of preoperative computed tomography (CT) image texture features and deep learning self-learning high-throughput features (SHF) on postoperative overall survival in the treatment of patients with colorectal cancer (CRC). MATERIAL AND METHODS The dataset consisted of 810 enrolled patients with CRC confirmed from 10 November 2011 to 10 February 2018. In contrast, SHF extracted by deep learning with multi-task training mechanism and texture features were extracted from the CT with tumor volume region of interest, respectively, and combined with the Cox proportional hazard (CoxPH) model for initial validation to obtain a RAD score to classify patients into high- and low-risk groups. The SHF stability was further validated in combination with Neural Multi-Task Logistic Regression (N-MTLR) model. The overall recognition ability and accuracy of CoxPH and N-MTLR model were evaluated by C-index and Integrated Brier Score (IBS). RESULTS SHF had a more significant degree of differentiation than texture features. The result is (SHF vs. texture features: C-index: 0.884 vs. 0.611; IBS: 0.025 vs. 0.073) in the CoxPH model, and (SHF vs. texture features: C-index: 0.861 vs. 0.630; IBS: 0.024 vs. 0.065) in N-MTLR. CONCLUSION SHF is superior to texture features and has potential application for the preoperative prediction of the individualized treatment of CRC.
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Affiliation(s)
- Xiang Pan
- The School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, PR China
- Faculty of Health Sciences, University of Macau, Macau, PR China
| | - He Cong
- The School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, PR China
| | - Xiaolei Wang
- The School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, PR China
| | - Heng Zhang
- Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, PR China
| | - Yuxi Ge
- Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, PR China
| | - Shudong Hu
- Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, PR China
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10
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Xie SH, Santoni G, Bottai M, Gottlieb-Vedi E, Lagergren P, Lagergren J. Prediction of conditional survival in esophageal cancer in a population-based cohort study. Int J Surg 2023; 109:1141-1148. [PMID: 36999825 PMCID: PMC10389626 DOI: 10.1097/js9.0000000000000347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 03/06/2023] [Indexed: 04/01/2023]
Abstract
BACKGROUND The authors aimed to produce a prediction model for survival at any given date after surgery for esophageal cancer (conditional survival), which has not been done previously. MATERIALS AND METHODS Using joint density functions, the authors developed and validated a prediction model for all-cause and disease-specific mortality after surgery with esophagectomy, for esophageal cancer, conditional on postsurgery survival time. The model performance was assessed by the area under the receiver operating characteristic curve (AUC) and risk calibration, with internal cross-validation. The derivation cohort was a nationwide Swedish population-based cohort of 1027 patients treated in 1987-2010, with follow-up throughout 2016. This validation cohort was another Swedish population-based cohort of 558 patients treated in 2011-2013, with follow-up throughout 2018. RESULTS The model predictors were age, sex, education, tumor histology, chemo(radio)therapy, tumor stage, resection margin status, and reoperation. The medians of AUC after internal cross-validation in the derivation cohort were 0.74 (95% CI: 0.69-0.78) for 3-year all-cause mortality, 0.76 (95% CI: 0.72-0.79) for 5-year all-cause mortality, 0.74 (95% CI: 0.70-0.78) for 3-year disease-specific mortality, and 0.75 (95% CI: 0.72-0.79) for 5-year disease-specific mortality. The corresponding AUC values in the validation cohort ranged from 0.71 to 0.73. The model showed good agreement between observed and predicted risks. Complete results for conditional survival any given date between 1 and 5 years of surgery are available from an interactive web-tool: https://sites.google.com/view/pcsec/home . CONCLUSION This novel prediction model provided accurate estimates of conditional survival any time after esophageal cancer surgery. The web-tool may help guide postoperative treatment and follow-up.
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Affiliation(s)
- Shao-Hua Xie
- School of Public Health and Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Karolinska University Hospital
| | - Giola Santoni
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Karolinska University Hospital
| | - Matteo Bottai
- Division of Biostatistics, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Eivind Gottlieb-Vedi
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Karolinska University Hospital
| | - Pernilla Lagergren
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Karolinska University Hospital
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London
| | - Jesper Lagergren
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Karolinska University Hospital
- School of Cancer and Pharmaceutical Sciences, Guy’s Hospital Campus, King’s College London, London, UK
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11
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Afrash MR, Mirbagheri E, Mashoufi M, Kazemi-Arpanahi H. Optimizing prognostic factors of five-year survival in gastric cancer patients using feature selection techniques with machine learning algorithms: a comparative study. BMC Med Inform Decis Mak 2023; 23:54. [PMID: 37024885 PMCID: PMC10080884 DOI: 10.1186/s12911-023-02154-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 03/15/2023] [Indexed: 04/08/2023] Open
Abstract
BACKGROUND Gastric cancer is the most common malignant tumor worldwide and a leading cause of cancer deaths. This neoplasm has a poor prognosis and heterogeneous outcomes. Survivability prediction may help select the best treatment plan based on an individual's prognosis. Numerous clinical and pathological features are generally used in predicting gastric cancer survival, and their influence on the survival of this cancer has not been fully elucidated. Moreover, the five-year survivability prognosis performances of feature selection methods with machine learning (ML) classifiers for gastric cancer have not been fully benchmarked. Therefore, we adopted several well-known feature selection methods and ML classifiers together to determine the best-paired feature selection-classifier for this purpose. METHODS This was a retrospective study on a dataset of 974 patients diagnosed with gastric cancer in the Ayatollah Talleghani Hospital, Abadan, Iran. First, four feature selection algorithms, including Relief, Boruta, least absolute shrinkage and selection operator (LASSO), and minimum redundancy maximum relevance (mRMR) were used to select a set of relevant features that are very informative for five-year survival prediction in gastric cancer patients. Then, each feature set was fed to three classifiers: XG Boost (XGB), hist gradient boosting (HGB), and support vector machine (SVM) to develop predictive models. Finally, paired feature selection-classifier methods were evaluated to select the best-paired method using the area under the curve (AUC), accuracy, sensitivity, specificity, and f1-score metrics. RESULTS The LASSO feature selection algorithm combined with the XG Boost classifier achieved an accuracy of 89.10%, a specificity of 87.15%, a sensitivity of 89.42%, an AUC of 89.37%, and an f1-score of 90.8%. Tumor stage, history of other cancers, lymphatic invasion, tumor site, type of treatment, body weight, histological type, and addiction were identified as the most significant factors affecting gastric cancer survival. CONCLUSIONS This study proved the worth of the paired feature selection-classifier to identify the best path that could augment the five-year survival prediction in gastric cancer patients. Our results were better than those of previous studies, both in terms of the time required to form the models and the performance measurement criteria of the algorithms. These findings may be very promising and can, therefore, inform clinical decision-making and shed light on future studies.
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Affiliation(s)
- Mohammad Reza Afrash
- Department of Artificial Intelligence, Smart University of Medical Sciences, Tehran, Iran
| | - Esmat Mirbagheri
- Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Mehrnaz Mashoufi
- Department of Health Information Management, Ardabil University of Medical Sciences, Ardabil, Iran
| | - Hadi Kazemi-Arpanahi
- Department of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran.
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12
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Chen PY, Huang CH, Peng JK, Yeh SY, Hung SH. Prediction Accuracy Between Terminally Ill Patients' Survival Length and the Estimations Made From Different Medical Staff, a Prospective Cohort Study. Am J Hosp Palliat Care 2023; 40:440-446. [PMID: 35701084 DOI: 10.1177/10499091221108507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Background: Previous reports suggested the clinical predictions of survival (CPS) and prognostic scores had similar accuracy in patients with days to weeks of life. Objective: We aimed to evaluate and compare the accuracy of CPS by attending physicians, residents, and nurses in an acute palliative care unit at a medical center. Methods: This was a 1-year prospective cohort study. Survival prediction was made within 3 days after patients' admission and re-evaluated every week until patients' discharge or death. Associated factors of accurate survival predictions were also explored by multivariate logistic regression. Results: A total of 179 inpatients were recruited and 115 of them were included in this analysis. The mean age of participants was 72.9 years and the average length of actual survival was 11.5 ± 12.0 days. For patients with survival within 30 days, the medical staff tended to overestimate their life span. The predictions made by physicians and nurses showed much closer to actual survival length through repeated estimations. Patients with metastatic cancer (odds ratio: OR 2.77, 95% CI 1.23-6.22) or cognitive impairment (OR 2.39, 95% CI 1.12-5.11) had higher associations with accurate CPS. Poor performance status of ECOG (OR 1.82, 95% CI 1.09-3.02) and dysphagia (OR 2.01, 95% CI 1.07-3.77) were significant predictors for accurate CPS in patients with the survival of less than 2 weeks. Conclusions: The accuracy of CPS between different medical staff did not reveal significant differences in the study. The importance of re-evaluation for patients' survival length in clinical practice is worthy of attention.
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Affiliation(s)
- Pei-Yun Chen
- Department of Family Medicine, 210835National Taiwan University Hospital Bei-Hu Branch, Taipei, Taiwan.,Department of Family Medicine, 38006National Taiwan University Hospital, Taipei, Taiwan
| | - Chien-Hsun Huang
- Department of Family Medicine, 38006National Taiwan University Hospital, Taipei, Taiwan.,Department of Community and Family Medicine, 37999National Taiwan University Hospital Yun-Lin Branch, Yunlin, Taiwan.,Department of Family Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Jen-Kuei Peng
- Department of Family Medicine, 38006National Taiwan University Hospital, Taipei, Taiwan.,Department of Family Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Shin-Yu Yeh
- Department of Family Medicine, 38006National Taiwan University Hospital, Taipei, Taiwan.,Department of Community and Family Medicine, 37999National Taiwan University Hospital Yun-Lin Branch, Yunlin, Taiwan
| | - Shou-Hung Hung
- Department of Family Medicine, 38006National Taiwan University Hospital, Taipei, Taiwan.,Department of Community and Family Medicine, 37999National Taiwan University Hospital Yun-Lin Branch, Yunlin, Taiwan.,Department of Family Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
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13
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Yerramilli D, Johnstone CA. Radiation Therapy at the End of-Life: Quality of Life and Financial Toxicity Considerations. Semin Radiat Oncol 2023; 33:203-210. [PMID: 36990637 DOI: 10.1016/j.semradonc.2022.11.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
Abstract
In patients with advanced cancer, radiation therapy is considered at various time points in the patient's clinical course from diagnosis to death. As some patients are living longer with metastatic cancer on novel therapeutics, radiation oncologists are increasingly using radiation therapy as an ablative therapy in appropriately selected patients. However, most patients with metastatic cancer still eventually die of their disease. For those without effective targeted therapy options or those who are not candidates for immunotherapy, the time frame from diagnosis to death is still relatively short. Given this evolving landscape, prognostication has become increasingly challenging. Thus, radiation oncologists must be diligent about defining the goals of therapy and considering all treatment options from ablative radiation to medical management and hospice care. The risks and benefits of radiation therapy vary based on an individual patient's prognosis, goals of care, and the ability of radiation to help with their cancer symptoms without undue toxicity over the course of their expected lifetime. When considering recommending a course of radiation, physicians must broaden their understanding of risks and benefits to include not only physical symptoms, but also various psychosocial burdens. These include financial burdens to the patient, to their caregiver and to the healthcare system. The burden of time spent at the end-of-life receiving radiation therapy must also be considered. Thus, the consideration of radiation therapy at the end-of-life can be complex and requires careful attention to the whole patient and their goals of care.
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14
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Iyer K, Beeche CA, Gezer NS, Leader JK, Ren S, Dhupar R, Pu J. CT-Derived Body Composition Is a Predictor of Survival after Esophagectomy. J Clin Med 2023; 12:2106. [PMID: 36983109 PMCID: PMC10058526 DOI: 10.3390/jcm12062106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 02/27/2023] [Accepted: 03/04/2023] [Indexed: 03/11/2023] Open
Abstract
BACKGROUND Body composition can be accurately quantified based on computed tomography (CT) and typically reflects an individual's overall health status. However, there is a dearth of research examining the relationship between body composition and survival following esophagectomy. METHODS We created a cohort consisting of 183 patients who underwent esophagectomy for esophageal cancer without neoadjuvant therapy. The cohort included preoperative PET-CT scans, along with pathologic and clinical data, which were collected prospectively. Radiomic, tumor, PET, and body composition features were automatically extracted from the images. Cox regression models were utilized to identify variables associated with survival. Logistic regression and machine learning models were developed to predict one-, three-, and five-year survival rates. Model performance was evaluated based on the area under the receiver operating characteristics curve (ROC/AUC). To test for the statistical significance of the impact of body composition on survival, body composition features were excluded for the best-performing models, and the DeLong test was used. RESULTS The one-year survival model contained 10 variables, including three body composition variables (bone mass, bone density, and visceral adipose tissue (VAT) density), and demonstrated an AUC of 0.817 (95% CI: 0.738-0.897). The three-year survival model incorporated 14 variables, including three body composition variables (intermuscular adipose tissue (IMAT) volume, IMAT mass, and bone mass), with an AUC of 0.693 (95% CI: 0.594-0.792). For the five-year survival model, 10 variables were included, of which two were body composition variables (intramuscular adipose tissue (IMAT) volume and visceral adipose tissue (VAT) mass), with an AUC of 0.861 (95% CI: 0.783-0.938). The one- and five-year survival models exhibited significantly inferior performance when body composition features were not incorporated. CONCLUSIONS Body composition features derived from preoperative CT scans should be considered when predicting survival following esophagectomy.
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Affiliation(s)
- Kartik Iyer
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Cameron A. Beeche
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Naciye S. Gezer
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Joseph K. Leader
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Shangsi Ren
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Rajeev Dhupar
- Department of Cardiothoracic Surgery, Division of Thoracic and Foregut Surgery, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
- Surgical Services Division, Thoracic Surgery, VA Pittsburgh Healthcare System, Pittsburgh, PA 15213, USA
| | - Jiantao Pu
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, USA
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15
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Chen W, Chung JOK, Lam KKW, Molassiotis A. End-of-life communication strategies for healthcare professionals: A scoping review. Palliat Med 2023; 37:61-74. [PMID: 36349371 DOI: 10.1177/02692163221133670] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
BACKGROUND Timely and effective communication about end-of-life issues, including conversations about prognosis and goals of care, are extremely beneficial to terminally ill patients and their families. However, given the context, healthcare professionals may find it challenging to initiate and facilitate such conversations. Hence, it is critical to improving the available communication strategies to enhance end-of-life communication practices. AIM To summarise the end-of-life communication strategies recommended for healthcare professionals, identify research gaps and inform future research. DESIGN A scoping review performed in accordance with the Arksey and O'Malley framework. DATA SOURCES A literature search was conducted between January 1990 and January 2022 using PubMed, CINAHL, Embase, PsycINFO, Web of Science, Scopus, Cochrane Library and China National Knowledge Infrastructure databases and Google, Google Scholar and ProQuest Dissertations & Theses Global. Studies that described recommended end-of-life communication strategies for healthcare professionals were included. RESULTS Fifty-nine documents were included. Seven themes of communication strategies were found: (a) preparation; (b) exploration and assessment; (c) family involvement; (d) provision and tailoring of information; (e) empathic emotional responses; (f) reframing and revisiting the goals of care; and (g) conversation closure. CONCLUSIONS The themes of communication strategies found in this review provide a framework to integrally promote end-of-life communication. Our results will help inform healthcare professionals, thereby promoting the development of specialised training and education on end-of-life communication.
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Affiliation(s)
- Weilin Chen
- School of Nursing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China
| | - Joyce Oi Kwan Chung
- School of Nursing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China
| | - Katherine Ka Wai Lam
- School of Nursing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China
| | - Alex Molassiotis
- School of Nursing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China.,Health and Social Care Research Centre, University of Derby, Derby, UK
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16
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Pandiyan S, Wang L. A comprehensive review on recent approaches for cancer drug discovery associated with artificial intelligence. Comput Biol Med 2022; 150:106140. [PMID: 36179510 DOI: 10.1016/j.compbiomed.2022.106140] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Revised: 07/20/2022] [Accepted: 09/18/2022] [Indexed: 11/03/2022]
Abstract
Through the revolutionization of artificial intelligence (AI) technologies in clinical research, significant improvement is observed in diagnosis of cancer. Utilization of these AI technologies, such as machine and deep learning, is imperative for the discovery of novel anticancer drugs and improves existing/ongoing cancer therapeutics. However, building a model for complicated cancers and their types remains a challenge due to lack of effective therapeutics that hinder the establishment of effective computational tools. In this review, we exploit recent approaches and state-of-the-art in implementing AI methods for anticancer drug discovery, and discussed how advances in these applications need to be considered in the current cancer therapeutics. Considering the immense potential of AI, we explore molecular docking and their interactions to recognize metabolic activities that support drug design. Finally, we highlight corresponding strategies in applying machine and deep learning methods to various types of cancer with their pros and cons.
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Affiliation(s)
- Sanjeevi Pandiyan
- Research Center for Intelligent Information Technology, Nantong University, Nantong, China; School of Information Science and Technology, Nantong University, Nantong, China; Nantong Research Institute for Advanced Communication Technologies, Nantong, China
| | - Li Wang
- Research Center for Intelligent Information Technology, Nantong University, Nantong, China; School of Information Science and Technology, Nantong University, Nantong, China; Nantong Research Institute for Advanced Communication Technologies, Nantong, China.
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17
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Comparison of Objective Prognostic Score and Palliative Prognostic Score performance in inpatients with advanced cancer in Japan and Korea. Palliat Support Care 2022; 20:662-670. [PMID: 36111731 DOI: 10.1017/s1478951521001589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
OBJECTIVE Accurate prognostication is important for patients and their families to prepare for the end of life. Objective Prognostic Score (OPS) is an easy-to-use tool that does not require the clinicians' prediction of survival (CPS), whereas Palliative Prognostic Score (PaP) needs CPS. Thus, inexperienced clinicians may hesitate to use PaP. We aimed to evaluate the accuracy of OPS compared with PaP in inpatients in palliative care units (PCUs) in three East Asian countries. METHOD This study was a secondary analysis of a cross-cultural, multicenter cohort study. We enrolled inpatients with far-advanced cancer in PCUs in Japan, Korea, and Taiwan from 2017 to 2018. We calculated the area under the receiver operating characteristics (AUROC) curve to compare the accuracy of OPS and PaP. RESULTS A total of 1,628 inpatients in 33 PCUs in Japan and Korea were analyzed. OPS and PaP were calculated in 71.7% of the Japanese patients and 80.0% of the Korean patients. In Taiwan, PaP was calculated for 81.6% of the patients. The AUROC for 3-week survival was 0.74 for OPS in Japan, 0.68 for OPS in Korea, 0.80 for PaP in Japan, and 0.73 for PaP in Korea. The AUROC for 30-day survival was 0.70 for OPS in Japan, 0.71 for OPS in Korea, 0.79 for PaP in Japan, and 0.74 for PaP in Korea. SIGNIFICANCE OF RESULTS Both OPS and PaP showed good performance in Japan and Korea. Compared with PaP, OPS could be more useful for inexperienced physicians who hesitate to estimate CPS.
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18
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Park EM, Deal AM, Heiling HM, Jung A, Yopp JM, Bowers SM, Hanson LC, Song MK, Valle CG, Yi B, Cassidy A, Won H, Rosenstein DL. Families Addressing Cancer Together (FACT): feasibility and acceptability of a web-based psychosocial intervention for parents with cancer. Support Care Cancer 2022; 30:8301-8311. [PMID: 35831719 PMCID: PMC9530016 DOI: 10.1007/s00520-022-07278-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 07/04/2022] [Indexed: 10/17/2022]
Abstract
PURPOSE Although parents with cancer report that talking with their children about cancer and dying is distressing, accessible support is rare. We assessed the feasibility, acceptability, and preliminary effects of Families Addressing Cancer Together (FACT), a web-based, tailored psychosocial intervention to help parents talk about their cancer with their children. METHODS This pilot study used a pre-posttest design. Eligible participants were parents with new or metastatic solid tumors who had minor (ages 3-18) children. Participants who completed baseline assessments received online access to FACT. We assessed feasibility through enrollment and retention rates and reasons for study refusal. Acceptability was evaluated by satisfaction ratings. We examined participants' selection of intervention content and preliminary effects on communication self-efficacy and other psychosocial outcomes (depression and anxiety symptoms, health-related quality of life, family functioning) at 2- and 12-week post-intervention. RESULTS Of 68 parents we approached, 53 (78%) agreed to participate. Forty-six parents completed baseline assessments and received the FACT intervention. Of the 46 participants, 35 (76%) completed 2-week assessments, and 25 (54%) completed 12-week assessments. Parents reported that FACT was helpful (90%), relevant (95%), and easy to understand (100%). Parents' psychosocial outcomes did not significantly improve post-intervention, but parents endorsed less worry about talking with their child (46% vs. 37%) and reductions in the number of communication concerns (3.4 to 1.8). CONCLUSION The FACT intervention was feasible, acceptable, and has potential to address communication concerns of parents with cancer. A randomized trial is needed to test its efficacy in improving psychological and parenting outcomes. TRIAL REGISTRATION This study was IRB-approved and registered with clinicaltrials.gov (NCT04342871).
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Affiliation(s)
- Eliza M Park
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, USA.
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, USA.
- Department of Medicine, Division of Oncology, University of North Carolina at Chapel Hill, Chapel Hill, USA.
| | - Allison M Deal
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - Hillary M Heiling
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - Ahrang Jung
- School of Nursing, University of North Carolina at Chapel Hill, Chapel Hill, USA
- School of Nursing, University of North Carolina at Greensboro, Greensboro, USA
| | - Justin M Yopp
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - Savannah M Bowers
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - Laura C Hanson
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, USA
- Department of Medicine, Division of Geriatrics, University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - Mi-Kyung Song
- Center for Nursing Excellence in Palliative Care, Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, USA
| | - Carmina G Valle
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, USA
- Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - Brian Yi
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - Anna Cassidy
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - Hannah Won
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - Donald L Rosenstein
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, USA
- Department of Medicine, Division of Oncology, University of North Carolina at Chapel Hill, Chapel Hill, USA
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19
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Shanbehzadeh M, Afrash MR, Mirani N, Kazemi-Arpanahi H. Comparing machine learning algorithms to predict 5-year survival in patients with chronic myeloid leukemia. BMC Med Inform Decis Mak 2022; 22:236. [PMID: 36068539 PMCID: PMC9450320 DOI: 10.1186/s12911-022-01980-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 08/30/2022] [Indexed: 12/03/2022] Open
Abstract
INTRODUCTION Chronic myeloid leukemia (CML) is a myeloproliferative disorder resulting from the translocation of chromosomes 19 and 22. CML includes 15-20% of all cases of leukemia. Although bone marrow transplant and, more recently, tyrosine kinase inhibitors (TKIs) as a first-line treatment have significantly prolonged survival in CML patients, accurate prediction using available patient-level factors can be challenging. We intended to predict 5-year survival among CML patients via eight machine learning (ML) algorithms and compare their performance. METHODS The data of 837 CML patients were retrospectively extracted and randomly split into training and test segments (70:30 ratio). The outcome variable was 5-year survival with potential values of alive or deceased. The dataset for the full features and important features selected by minimal redundancy maximal relevance (mRMR) feature selection were fed into eight ML techniques, including eXtreme gradient boosting (XGBoost), multilayer perceptron (MLP), pattern recognition network, k-nearest neighborhood (KNN), probabilistic neural network, support vector machine (SVM) (kernel = linear), SVM (kernel = RBF), and J-48. The scikit-learn library in Python was used to implement the models. Finally, the performance of the developed models was measured using some evaluation criteria with 95% confidence intervals (CI). RESULTS Spleen palpable, age, and unexplained hemorrhage were identified as the top three effective features affecting CML 5-year survival. The performance of ML models using the selected-features was superior to that of the full-features dataset. Among the eight ML algorithms, SVM (kernel = RBF) had the best performance in tenfold cross-validation with an accuracy of 85.7%, specificity of 85%, sensitivity of 86%, F-measure of 87%, kappa statistic of 86.1%, and area under the curve (AUC) of 85% for the selected-features. Using the full-features dataset yielded an accuracy of 69.7%, specificity of 69.1%, sensitivity of 71.3%, F-measure of 72%, kappa statistic of 75.2%, and AUC of 70.1%. CONCLUSIONS Accurate prediction of the survival likelihood of CML patients can inform caregivers to promote patient prognostication and choose the best possible treatment path. While external validation is required, our developed models will offer customized treatment and may guide the prescription of personalized medicine for CML patients.
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Affiliation(s)
- Mostafa Shanbehzadeh
- Department of Health Information Technology, Faculty of Paramedical, Ilam University of Medical Sciences, Ilam, Iran
| | - Mohammad Reza Afrash
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Nader Mirani
- Department of Treatment, Head of the Medical Truism, Zanjan University of Medical Sciences, Zanjan, Iran
| | - Hadi Kazemi-Arpanahi
- Department of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran
- Department of Student Research Committee, Abadan University of Medical Sciences, Abadan, Iran
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Afrash MR, Shanbehzadeh M, Kazemi-Arpanahi H. Design and Development of an Intelligent System for Predicting 5-Year Survival in Gastric Cancer. Clin Med Insights Oncol 2022; 16:11795549221116833. [PMID: 36035639 PMCID: PMC9403452 DOI: 10.1177/11795549221116833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 07/13/2022] [Indexed: 11/17/2022] Open
Abstract
Background Gastric cancer remains one of the leading causes of worldwide cancer-specific deaths. Accurately predicting the survival likelihood of gastric cancer patients can inform caregivers to boost patient prognostication and choose the best possible treatment path. This study intends to develop an intelligent system based on machine learning (ML) algorithms for predicting the 5-year survival status in gastric cancer patients. Methods A data set that includes the records of 974 gastric cancer patients retrospectively was used. First, the most important predictors were recognized using the Boruta feature selection algorithm. Five classifiers, including J48 decision tree (DT), support vector machine (SVM) with radial basic function (RBF) kernel, bootstrap aggregating (Bagging), hist gradient boosting (HGB), and adaptive boosting (AdaBoost), were trained for predicting gastric cancer survival. The performance of the used techniques was evaluated with specificity, sensitivity, likelihood ratio, and total accuracy. Finally, the system was developed according to the best model. Results The stage, position, and size of tumor were selected as the 3 top predictors for gastric cancer survival. Among the 6 selected ML algorithms, the HGB classifier with the mean accuracy, mean specificity, mean sensitivity, mean area under the curve, and mean F1-score of 88.37%, 86.24%, 89.72%, 88.11%, and 89.91%, respectively, gained the best performance. Conclusions The ML models can accurately predict the 5-year survival and potentially act as a customized recommender for decision-making in gastric cancer patients. The developed system in our study can improve the quality of treatment, patient safety, and survival rates; it may guide prescribing more personalized medicine.
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Affiliation(s)
- Mohammad Reza Afrash
- Department of Health Information
Technology and Management, School of Allied Medical Sciences, Shahid Beheshti
University of Medical Sciences, Tehran, Iran
| | - Mostafa Shanbehzadeh
- Department of Health Information
Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam,
Iran
| | - Hadi Kazemi-Arpanahi
- Department of Health Information
Technology, Abadan University of Medical Sciences, Abadan, Iran
- Student Research Committee, Abadan
University of Medical Sciences, Abadan, Iran
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Development and Validation of the PaP Score Nomogram for Terminally Ill Cancer Patients. Cancers (Basel) 2022; 14:cancers14102510. [PMID: 35626114 PMCID: PMC9139266 DOI: 10.3390/cancers14102510] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Revised: 05/12/2022] [Accepted: 05/16/2022] [Indexed: 02/01/2023] Open
Abstract
The validated Palliative Prognostic (PaP) score predicts survival in terminally ill cancer patients, assigning patients to three different risk groups according to a 30-day survival probability: group A, >70%; group B, 30−70%; and group C, <30%. We aimed to develop and validate a PaP nomogram to provide individualized prediction of survival at 15, 30 and 60 days. Three cohorts of consecutive terminally ill cancer patients were used: one (n = 519) for nomogram development and internal validation, and a second (n = 451) and third (n = 549) for external validation. Multivariate analyses included dyspnea, anorexia, Karnofsky performance status, clinical prediction of survival, total white blood count and lymphocyte percentage. The predictive accuracy of the nomogram was determined by Harrell’s concordance index (95% CI), and calibration plots were generated. The nomogram had a concordance index of 0.74 (0.72−0.75) and showed good calibration. The internal validation showed no departures from ideal prediction. The accuracy of the nomogram at 15, 30 and 60 days was 74% (70−77), 89% (85−92) and 72% (68−76) in the external validation cohorts, respectively. The PaP nomogram predicts the individualized estimate of survival and could greatly facilitate clinical care decision-making at the end of life.
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22
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Lee W, Chang S, DiGiacomo M, Draper B, Agar MR, Currow DC. Caring for depression in the dying is complex and challenging - survey of palliative physicians. BMC Palliat Care 2022; 21:11. [PMID: 35034640 PMCID: PMC8761382 DOI: 10.1186/s12904-022-00901-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 12/28/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Depression is prevalent in people with very poor prognoses (days to weeks). Clinical practices and perceptions of palliative physicians towards depression care have not been characterised in this setting. The objective of this study was to characterise current palliative clinicians' reported practices and perceptions in depression screening, assessment and management in the very poor prognosis setting. METHODS In this cross-sectional cohort study, 72 palliative physicians and 32 psychiatrists were recruited from Australian and New Zealand Society of Palliative Medicine and Royal Australian and New Zealand College of Psychiatrists between February and July 2020 using a 23-item anonymous online survey. RESULTS Only palliative physicians results were reported due to poor psychiatry representation. Palliative physicians perceived depression care in this setting to be complex and challenging. 40.0% reported screening for depression. All experienced uncertainty when assessing depression aetiology. Approaches to somatic symptom assessment varied. Physicians were generally less likely to intervene for depression than in the better prognosis setting. Most reported barriers to care included the perceived lack of rapidly effective therapeutic options (77.3%), concerns of patient burden and intolerance (71.2%), and the complexity in diagnostic differentiation (53.0%). 66.7% desired better collaboration between palliative care and psychiatry. CONCLUSIONS Palliative physicians perceived depression care in patients with very poor prognoses to be complex and challenging. The lack of screening, variations in assessment approaches, and the reduced likelihood of intervening in comparison to the better prognosis setting necessitate better collaboration between palliative care and psychiatry in service delivery, training and research.
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Affiliation(s)
- Wei Lee
- University of Technology Sydney, Improving Palliative, Aged and Chronic Care through Clinical Research and Translation (IMPACCT), Ultimo, NSW, 2007, Australia.
- St Vincent's Clinical School, University of New South Wales, 390 Victoria St, Darlinghurst, NSW, 2010, Australia.
| | - Sungwon Chang
- University of Technology Sydney, Improving Palliative, Aged and Chronic Care through Clinical Research and Translation (IMPACCT), Ultimo, NSW, 2007, Australia
| | - Michelle DiGiacomo
- University of Technology Sydney, Improving Palliative, Aged and Chronic Care through Clinical Research and Translation (IMPACCT), Ultimo, NSW, 2007, Australia
| | - Brian Draper
- School of Psychiatry, University of New South Wales Sydney, Sydney, NSW, 2052, Australia
| | - Meera R Agar
- University of Technology Sydney, Improving Palliative, Aged and Chronic Care through Clinical Research and Translation (IMPACCT), Ultimo, NSW, 2007, Australia
| | - David C Currow
- Faculty of Science, Medicine and Health, University of Wollongong, Wollongong, NSW, 2522, Australia
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23
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Zhang Q, Li XR, Zhang X, Ding JS, Liu T, Qian L, Song MM, Song CH, Barazzoni R, Tang M, Wang KH, Xu HX, Shi HP. PG-SGA SF in nutrition assessment and survival prediction for elderly patients with cancer. BMC Geriatr 2021; 21:687. [PMID: 34893024 PMCID: PMC8665602 DOI: 10.1186/s12877-021-02662-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Accepted: 11/24/2021] [Indexed: 12/25/2022] Open
Abstract
Background This study was sought to report the prevalence of malnutrition in elderly patients with cancer. Validate the predictive value of the nutritional assessment tool (Patient-Generated Subjective Global Assessment Short Form, PG-SGA SF) for clinical outcomes and assist the therapeutic decision. Methods This is a secondary analysis of a multicentric, observational cohort study. Elderly patients with cancer older than 65 years were enrolled after the first admission. Nutritional status was identified using the PG-SGA SF. Results Of the 2724 elderly patients included in the analysis, 65.27% of patients were male (n = 1778); the mean age was 71.00 ± 5.36 years. 31.5% of patients were considered malnourished according to PG-SGA SF. In multivariate analysis, malnutrition(PG-SGA SF > 5) was significantly associated with worse OS (HR: 1.47,95%CI:1.29–1.68), affects the quality of life, and was related to more frequent nutrition impact symptoms. During a median follow-up of 4.5 years, 1176 death occurred. The mortality risk was 41.10% for malnutrition during the first 12 months and led to a rate of 323.98 events per-1000-patient-years. All nutritional assessment tools were correlated with each other (PG-SGA SF vs. PG-SGA: r = 0.98; PG-SGA SF vs. GLIM[Global Leadership Initiative on Malnutrition]: r = 0.48, all P < 0.05). PG-SGA SF and PG-SGA performed similarly to predict mortality but better than GLIM. PG-SGA SF improves the predictive ability of the TNM classification system for mortality in elderly patients with cancer, including distinguishing patients’ prognoses and directing immunotherapy. Conclusions The nutritional status as measured by PG-SGA SF which is a prognostic factor for OS in elderly cancer patients and could improve the prognostic model of TNM. Supplementary Information The online version contains supplementary material available at 10.1186/s12877-021-02662-4.
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Affiliation(s)
- Qi Zhang
- Department of Gastrointestinal Surgery, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China.,Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China.,Beijing International Science and Technology Cooperation Base for Cancer Metabolism and Nutrition, Beijing, 100038, China.,Capital Medical University, Beijing, 100038, China
| | - Xiang-Rui Li
- Department of Gastrointestinal Surgery, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China.,Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China.,Beijing International Science and Technology Cooperation Base for Cancer Metabolism and Nutrition, Beijing, 100038, China
| | - Xi Zhang
- Department of Gastrointestinal Surgery, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China.,Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China.,Beijing International Science and Technology Cooperation Base for Cancer Metabolism and Nutrition, Beijing, 100038, China
| | - Jia-Shan Ding
- Department of Obstetrics and Gynecology, the First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi, China
| | - Tong Liu
- Department of Gastrointestinal Surgery, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China
| | - Liang Qian
- Department of Obstetrics and Gynecology, Hangzhou Women's hospital/ Hangzhou Maternal and Child Health Hospital/ Hangzhou First People's Hospital Qianjiang New City Campus, Hangzhou, 310008, China
| | - Meng-Meng Song
- Department of Gastrointestinal Surgery, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China.,Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China.,Beijing International Science and Technology Cooperation Base for Cancer Metabolism and Nutrition, Beijing, 100038, China
| | - Chun-Hua Song
- Department of Epidemiology, College of Public Health, Zhengzhou University, Zhengzhou, 450001, Henan, China
| | - Rocco Barazzoni
- Department of Medical, Surgical and Health Sciences - University of Trieste, Trieste, Italy
| | - Meng Tang
- Department of Gastrointestinal Surgery, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China.,Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China.,Beijing International Science and Technology Cooperation Base for Cancer Metabolism and Nutrition, Beijing, 100038, China
| | - Kun-Hua Wang
- Department of Gastrointestinal Surgery, Institute of Gastroenterology, the First Affiliated Hospital of Kunming Medical University, Kunming, 650032, Yunnan, China
| | - Hong-Xia Xu
- Department of Clinical Nutrition, Daping Hospital, Army Medical University, Chongqing, 400042, China
| | - Han-Ping Shi
- Department of Gastrointestinal Surgery, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China. .,Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China. .,Beijing International Science and Technology Cooperation Base for Cancer Metabolism and Nutrition, Beijing, 100038, China.
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Giuliani ME, Giannopoulos E, Gospodarowicz MK, Broadhurst M, O’Sullivan B, Tittenbrun Z, Johnson S, Brierley J. Examining the Landscape of Prognostic Factors and Clinical Outcomes for Cancer Control. Curr Oncol 2021; 28:5155-5166. [PMID: 34940071 PMCID: PMC8699872 DOI: 10.3390/curroncol28060432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 11/29/2021] [Accepted: 12/02/2021] [Indexed: 11/23/2022] Open
Abstract
Prognostic factors have important utility in various aspects of cancer surveillance, including research, patient care, and cancer control programmes. Nevertheless, there is heterogeneity in the collection of prognostic factors and outcomes data globally. This study aimed to investigate perspectives on the utility and application of prognostic factors and clinical outcomes in cancer control programmes. A qualitative phenomenology approach using expert interviews was taken to derive a rich description of the current state and future outlook of cancer prognostic factors and clinical outcomes. Individuals with expertise in this work and from various regions and institutions were invited to take part in one-on-one semi-structured interviews. Four areas related to infrastructure and funding challenges were identified by participants, including (1) data collection and access; (2) variability in data reporting, coding, and definitions; (3) limited coordination among databases; and (4) conceptualization and prioritization of meaningful prognostic factors and outcomes. Two areas were identified regarding important future priorities for cancer control: (1) global investment and intention in cancer surveillance and (2) data governance and exchange globally. Participants emphasized the need for better global collection of prognostic factors and clinical outcomes data and support for standardized data collection and data exchange practices by cancer registries.
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Affiliation(s)
- Meredith Elana Giuliani
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada; (M.K.G.); (B.O.); (J.B.)
- Department of Radiation Oncology, University of Toronto, Toronto, ON M5T 1P5, Canada
- Department of Cancer Education, Princess Margaret Cancer Centre, Toronto, ON M5G 2N2, Canada; (E.G.); (M.B.)
- Correspondence: ; Tel.: +1-416-946-2983
| | - Eleni Giannopoulos
- Department of Cancer Education, Princess Margaret Cancer Centre, Toronto, ON M5G 2N2, Canada; (E.G.); (M.B.)
| | - Mary Krystyna Gospodarowicz
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada; (M.K.G.); (B.O.); (J.B.)
- Department of Radiation Oncology, University of Toronto, Toronto, ON M5T 1P5, Canada
- Department of Cancer Education, Princess Margaret Cancer Centre, Toronto, ON M5G 2N2, Canada; (E.G.); (M.B.)
| | - Michaela Broadhurst
- Department of Cancer Education, Princess Margaret Cancer Centre, Toronto, ON M5G 2N2, Canada; (E.G.); (M.B.)
| | - Brian O’Sullivan
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada; (M.K.G.); (B.O.); (J.B.)
- Department of Radiation Oncology, University of Toronto, Toronto, ON M5T 1P5, Canada
| | - Zuzanna Tittenbrun
- Knowledge, Advocacy and Policy, Union for International Cancer Control, 1202 Geneva, Switzerland; (Z.T.); (S.J.)
| | - Sonali Johnson
- Knowledge, Advocacy and Policy, Union for International Cancer Control, 1202 Geneva, Switzerland; (Z.T.); (S.J.)
| | - James Brierley
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada; (M.K.G.); (B.O.); (J.B.)
- Department of Radiation Oncology, University of Toronto, Toronto, ON M5T 1P5, Canada
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Lee SH, Lee JG, Choi YJ, Seol YM, Kim H, Kim YJ, Yi YH, Tak YJ, Kim GL, Ra YJ, Lee SY, Cho YH, Park EJ, Lee Y, Choi J, Lee SR, Kwon RJ, Son SM. Prognosis palliative care study, palliative prognostic index, palliative prognostic score and objective prognostic score in advanced cancer: a prospective comparison. BMJ Support Palliat Care 2021:bmjspcare-2021-003077. [PMID: 34215569 DOI: 10.1136/bmjspcare-2021-003077] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 06/14/2021] [Indexed: 11/04/2022]
Abstract
BACKGROUND Predicting how long a patient with far advanced cancer has to live is a significant part of hospice and palliative care. Various prognostic models have been developed, but have not been fully compared in South Korea. OBJECTIVES We aimed to compare the accuracy of the Prognosis in Palliative Care Study (PiPS), Palliative Prognostic Index (PPI), Palliative Prognostic Score (PaP) and Objective Prognostic Score (OPS) for patients with far advanced cancer in a palliative care unit in South Korea. METHODS This prospective study included patients with far advanced cancer who were admitted to a single palliative care unit at the National University Hospital. Variables for calculating the prognostic models were recorded by a palliative care physician. The survival rate was estimated using the Kaplan-Meier method. The sensitivity, specificity, positive predictive value and negative predictive value of each model were calculated. RESULTS A total of 160 patients participated. There was a significant difference in survival rates across all groups, each categorised through the five prognostic models. The overall accuracy (OA) of the prognostic models ranged between 54.5% and 77.6%. The OA of clinicians' predictions of survival ranged between 61.9% and 81.3%. CONCLUSION The PiPS, PPI, PaP and OPS were successfully validated in a palliative care unit of South Korea. There was no difference in accuracy between the prognostic models, and OA tended to be lower than in previous studies.
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Affiliation(s)
- Seung Hun Lee
- Family Medicine, Pusan National University Hospital, Busan, Korea (the Republic of)
- Biomedical Research Institute, Pusan National University Hospital, Busan, Korea (the Republic of)
- Department of Family Medicine, Pusan National University School of Medicine, Busan, Korea (the Republic of)
| | - Jeong Gyu Lee
- Family Medicine, Pusan National University Hospital, Busan, Korea (the Republic of)
- Biomedical Research Institute, Pusan National University Hospital, Busan, Korea (the Republic of)
- Department of Family Medicine, Pusan National University School of Medicine, Busan, Korea (the Republic of)
| | - Young Jin Choi
- Division of Hemato-oncology, Department of Internal Medicine, Pusan National University School of Medicine, Busan, Korea (the Republic of)
| | - Young Mi Seol
- Division of Hemato-oncology, Department of Internal Medicine, Pusan National University School of Medicine, Busan, Korea (the Republic of)
| | - Hyojeong Kim
- Division of Hemato-oncology, Department of Internal Medicine, Pusan National University School of Medicine, Busan, Korea (the Republic of)
| | - Yun Jin Kim
- Family Medicine, Pusan National University Hospital, Busan, Korea (the Republic of)
- Department of Family Medicine, Pusan National University School of Medicine, Busan, Korea (the Republic of)
| | - Yu Hyeon Yi
- Family Medicine, Pusan National University Hospital, Busan, Korea (the Republic of)
- Department of Family Medicine, Pusan National University School of Medicine, Busan, Korea (the Republic of)
| | - Young Jin Tak
- Family Medicine, Pusan National University Hospital, Busan, Korea (the Republic of)
- Department of Family Medicine, Pusan National University School of Medicine, Busan, Korea (the Republic of)
| | - Gyu Lee Kim
- Family Medicine, Pusan National University Hospital, Busan, Korea (the Republic of)
| | - Young Jin Ra
- Family Medicine, Pusan National University Hospital, Busan, Korea (the Republic of)
| | - Sang Yeoup Lee
- Department of Family Medicine, Pusan National University School of Medicine, Busan, Korea (the Republic of)
- Department of Family Medicine, Pusan National University Yangsan Hospital, Yangsan, Korea (the Republic of)
- Department of Medical Education, Pusan National University School of Medicine, Yangsan, Korea (the Republic of)
| | - Young Hye Cho
- Department of Family Medicine, Pusan National University School of Medicine, Busan, Korea (the Republic of)
- Department of Family Medicine, Pusan National University Yangsan Hospital, Yangsan, Korea (the Republic of)
| | - Eun Ju Park
- Department of Family Medicine, Pusan National University School of Medicine, Busan, Korea (the Republic of)
- Department of Family Medicine, Pusan National University Yangsan Hospital, Yangsan, Korea (the Republic of)
| | - Youngin Lee
- Department of Family Medicine, Pusan National University Yangsan Hospital, Yangsan, Korea (the Republic of)
| | - Jungin Choi
- Department of Family Medicine, Pusan National University Yangsan Hospital, Yangsan, Korea (the Republic of)
| | - Sae Rom Lee
- Department of Family Medicine, Pusan National University Yangsan Hospital, Yangsan, Korea (the Republic of)
| | - Ryuk Jun Kwon
- Department of Family Medicine, Pusan National University Yangsan Hospital, Yangsan, Korea (the Republic of)
| | - Soo Min Son
- Department of Family Medicine, Pusan National University Yangsan Hospital, Yangsan, Korea (the Republic of)
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Al-Rashdan A, Sutradhar R, Nazeri-Rad N, Yao C, Barbera L. Comparing the Ability of Physician-Reported Versus Patient-Reported Performance Status to Predict Survival in a Population-Based Cohort of Newly Diagnosed Cancer Patients. Clin Oncol (R Coll Radiol) 2021; 33:476-482. [DOI: 10.1016/j.clon.2021.01.008] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 12/30/2020] [Accepted: 01/14/2021] [Indexed: 02/01/2023]
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Abstract
Predicting all-cause mortality risk is challenging and requires extensive medical data. Recently, large-scale proteomics datasets have proven useful for predicting health-related outcomes. Here, we use measurements of levels of 4,684 plasma proteins in 22,913 Icelanders to develop all-cause mortality predictors both for short- and long-term risk. The participants were 18-101 years old with a mean follow up of 13.7 (sd. 4.7) years. During the study period, 7,061 participants died. Our proposed predictor outperformed, in survival prediction, a predictor based on conventional mortality risk factors. We could identify the 5% at highest risk in a group of 60-80 years old, where 88% died within ten years and 5% at the lowest risk where only 1% died. Furthermore, the predicted risk of death correlates with measures of frailty in an independent dataset. Our results show that the plasma proteome can be used to assess general health and estimate the risk of death. Eiriksdottir et al. use a temporal proteomic dataset from over 22,000 Icelandic individuals to identify predictors and predict all-cause mortality. Their findings suggest that the plasma proteome may be of value in general health screening for risk of death.
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Lau C, Meaney C, Morgan M, Cook R, Zimmermann C, Wentlandt K. Disparities in access to palliative care facilities for patients with and without cancer: A retrospective review. Palliat Med 2021; 35:1191-1201. [PMID: 33855886 PMCID: PMC8189004 DOI: 10.1177/02692163211007387] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
BACKGROUND To date, little is known about the characteristics of patients who are admitted to a palliative care bed for end-of-life care. Previous data suggest that there are disparities in access to palliative care services based on age, sex, diagnosis, and socioeconomic status, but it is unclear whether these differences impact access to a palliative care bed. AIM To better identify patient factors associated with the likelihood/rate of admission to a palliative care bed. DESIGN A retrospective chart review of all initiated palliative care bed applications through an electronic referral program was conducted over a 24-month period. SETTING/PARTICIPANTS Patients who apply and are admitted to a palliative care bed in a Canadian metropolitan city. RESULTS A total of 2743 patients made a total of 5202 bed applications to 9 hospice/palliative care units in 2015-2016. Referred and admitted cancer patients were younger, male, and more functional than compared to non-cancer patients (all p < 0.001). Referred and admitted patients without cancer were more advanced in their illness trajectory, with an anticipated prognosis <1 month and Palliative Performance Status of 10%-20% (all p < 0.001). On multivariate analysis, a diagnosis of cancer and a prognosis of <3 months were associated with increased likelihood and/or rate of admission to a bed, whereas the presence of care needs, a longer prognosis and a PPS of 30%-40% were associated with decreased rates and/or likelihood of admission. CONCLUSION Patients without cancer have reduced access to palliative care facilities at end-of-life compared to patients with cancer; at the time of their application and admission, they are "sicker" with very low performance status and poorer prognoses. Further studies investigating disease-specific clinical variables and support requirements may provide more insights into these observed disparities.
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Affiliation(s)
- Christine Lau
- Division of Palliative Care, Sunnybrook Health Sciences, Toronto, ON, Canada.,Department of Family and Community Medicine, University of Toronto, Toronto, ON, Canada
| | - Christopher Meaney
- Department of Family and Community Medicine, University of Toronto, Toronto, ON, Canada
| | - Matthew Morgan
- Division of General Internal Medicine, Mount Sinai Hospital and University Health Network, Toronto, ON, Canada.,Department of Medicine, University of Toronto, Toronto, ON, Canada.,Ontario Health - Toronto Region, Toronto, ON, Canada
| | - Rose Cook
- Ontario Health - Toronto Region, Toronto, ON, Canada
| | - Camilla Zimmermann
- Department of Medicine, University of Toronto, Toronto, ON, Canada.,Department of Supportive Care, Division of Palliative Care, University Health Network, Toronto, ON, Canada
| | - Kirsten Wentlandt
- Department of Family and Community Medicine, University of Toronto, Toronto, ON, Canada.,Ontario Health - Toronto Region, Toronto, ON, Canada.,Department of Supportive Care, Division of Palliative Care, University Health Network, Toronto, ON, Canada
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Abstract
OBJECTIVE We aimed to develop prediction models for estimating the long-term survival in patients who have undergone surgery for esophageal cancer. BACKGROUND Few prediction models have been developed for the long-term survival in esophageal cancer patients. METHODS This nationwide Swedish population-based cohort study included 1542 patients who survived for ≥90 days after esophageal cancer surgery between 1987 and 2010, with follow-up until 2016. Risk prediction models for 1-, 3-, and 5-year all-cause mortality and 3- and 5-year disease-specific mortality were developed using logistic regression. Candidate predictors were established and readily identifiable prognostic factors. The performance of the models was assessed by the area under receiver-operating characteristic curve (AUC) with interquartile range (IQR) using bootstrap cross-validation and risk calibration. RESULTS Predictors included in all models were age, sex, pathological tumor stage, tumor histology, and resection margin status. The models also included various additional predictors depending on the outcome, that is, education level, neoadjuvant therapy, reoperation (within 30 d of primary surgery) and comorbidity (Charlson comorbidity index). The AUC statistics after cross-validation were 0.71 (IQR 0.69-0.74) for 1-year, 0.77 (IQR 0.75-0.80) for 3-year, and 0.78 (IQR 0.76-0.81) for 5-year all-cause mortality. The corresponding values were 0.76 (IQR 0.74-0.79) for 3-year and 0.77 (IQR 0.71-0.83) for 5-year disease-specific mortality. All models showed good agreement between the observed and predicted risks. CONCLUSIONS These models showed good performance for predicting long-term survival after esophageal cancer surgery and may thus be useful for patients in planning their lives and to guide the postoperative treatment and follow-up.
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Zhang Q, Zhang K, Li X, Zhang X, Song M, Liu T, Song C, Barazzoni R, Wang K, Xu H, Fu Z, Shi HP. A novel model with nutrition-related parameters for predicting overall survival of cancer patients. Support Care Cancer 2021; 29:6721-6730. [PMID: 33973079 DOI: 10.1007/s00520-021-06272-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 05/04/2021] [Indexed: 11/26/2022]
Abstract
BACKGROUND Increasing evidence indicates that nutritional status could influence the survival of cancer patients. This study aims to develop and validate a nomogram with nutrition-related parameters for predicting the overall survival of cancer patients. PATIENTS AND METHODS A total of 8749 patients from the multicentre cohort study in China were included as the primary cohort to develop the nomogram, and 696 of these patients were recruited as a validation cohort. Patients' nutritional status were assessed using the PG-SGA. LASSO regression models and Cox regression analysis were used for factor selection and nomogram development. The nomogram was then evaluated for its effectiveness in discrimination, calibration, and clinical usefulness by the C-index, calibration curves, and decision curve analysis. Kaplan-Meier survival curves were used to compare the survival rate. RESULTS Seven independent prognostic factors were identified and integrated into the nomogram. The C-index was 0.73 (95% CI, 0.72 to 0.74) and 0.77 (95% CI, 0.74 to 0.81) for the primary cohort and validation cohort, which were both higher than 0.59 (95% CI, 0.58 to 0.61) of the TNM staging system. DCA demonstrated that the nomogram was higher than the TNM staging system and the TNM staging system combined with PG-SGA. Significantly median overall survival differences were found by stratifying patients into different risk groups (score < 18.5 and ≥ 18.5) for each TNM category (all Ps < 0.001). CONCLUSION Our study screened out seven independent prognostic factors and successfully generated an easy-to-use nomogram, and validated and shown a better predictive validity for the overall survival of cancer patients.
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Affiliation(s)
- Qi Zhang
- Department of Gastrointestinal Surgery/Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China
- Beijing International Science and Technology Cooperation Base for Cancer Metabolism and Nutrition, Beijing, 100038, China
| | - Kangping Zhang
- Department of Gastrointestinal Surgery/Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China
- Beijing International Science and Technology Cooperation Base for Cancer Metabolism and Nutrition, Beijing, 100038, China
| | - Xiangrui Li
- Department of Gastrointestinal Surgery/Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China
- Beijing International Science and Technology Cooperation Base for Cancer Metabolism and Nutrition, Beijing, 100038, China
| | - Xi Zhang
- Department of Gastrointestinal Surgery/Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China
- Beijing International Science and Technology Cooperation Base for Cancer Metabolism and Nutrition, Beijing, 100038, China
| | - Mengmeng Song
- Department of Gastrointestinal Surgery/Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China
- Beijing International Science and Technology Cooperation Base for Cancer Metabolism and Nutrition, Beijing, 100038, China
| | - Tong Liu
- Department of Gastrointestinal Surgery/Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China
- Beijing International Science and Technology Cooperation Base for Cancer Metabolism and Nutrition, Beijing, 100038, China
| | - Chunhua Song
- Department of Epidemiology, College of Public Health, Zhengzhou University, Zhengzhou, 450001, Henan, China
| | - Rocco Barazzoni
- Department of Medical, Surgical and Health Sciences - University of Trieste, Trieste, Italy
| | - Kunhua Wang
- Department of Gastrointestinal Surgery, Institute of Gastroenterology, the First Affiliated Hospital of Kunming Medical University, Kunming, 650032, Yunnan, China
| | - Hongxia Xu
- Department of Clinical Nutrition, Daping Hospital, Army Medical University, Chongqing, 400042, China
| | - Zhenming Fu
- Cancer Center, Renmin Hospital of Wuhan University, Wuhan, 430060, China.
| | - Han-Ping Shi
- Department of Gastrointestinal Surgery/Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China.
- Beijing International Science and Technology Cooperation Base for Cancer Metabolism and Nutrition, Beijing, 100038, China.
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Pobar I, Job M, Holt T, Hargrave C, Hickey B. Prognostic tools for survival prediction in advanced cancer patients: A systematic review. J Med Imaging Radiat Oncol 2021; 65:806-816. [PMID: 33973382 DOI: 10.1111/1754-9485.13185] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 03/31/2021] [Indexed: 12/23/2022]
Abstract
Survival prediction for palliative cancer patients by physicians is often optimistic. Patients with a very short life expectancy (<4 weeks) may not benefit from radiation therapy (RT), as the time to maximal symptom relief after treatment can take 4-6 weeks. We aimed to identify a prognostic tool (or tools) to predict survival of less than 4 weeks and less than 3 months in patients with advanced cancer to guide the choice of radiation dose and fractionation. We searched Embase, Medline (EBSCOhost) and CINAHL (EBSCOhost) clinical databases for literature published between January 2008 and June 2018. Seventeen studies met the inclusion criteria and were included in the review. Prediction accuracy at less than 4 weeks and less than 3 months were compared across the prognostic tools. Reporting of prediction accuracy among the different studies was not consistent: the Palliative Prognostic Score (PaP), Palliative Prognostic Index (PPI) and Number of Risk Factors (NRF) best-predicted survival duration of less than 4 weeks. The PPI, performance status with Palliative Prognostic Index (PS-PPI), NRF and Survival Prediction Score (SPS) may predict 3-month survival. We recommend PPI and PaP tools to assess the likelihood of a patient surviving less than 4 weeks. If predicted to survive longer and RT is justified, the NRF tool could be used to determine survival probability less than 3 months which can then help clinicians select dose and fractionation. Future research is needed to verify the reliability of survival prediction using these prognostic tools in a radiation oncology setting.
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Affiliation(s)
- Isaiah Pobar
- Radiation Oncology Princess Alexandra Hospital Raymond Terrace, Brisbane, Queensland, Australia
| | - Mary Job
- Radiation Oncology Princess Alexandra Hospital Raymond Terrace, Brisbane, Queensland, Australia
| | - Tanya Holt
- Radiation Oncology Princess Alexandra Hospital Raymond Terrace, Brisbane, Queensland, Australia
| | - Catriona Hargrave
- Radiation Oncology Princess Alexandra Hospital Raymond Terrace, Brisbane, Queensland, Australia.,QUT, Faculty of Health, School of Clinical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Brigid Hickey
- Radiation Oncology Princess Alexandra Hospital Raymond Terrace, Brisbane, Queensland, Australia
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Maltoni M, Rossi R. Risk of detrimental recommendations for cancer pain management. J Transl Med 2021; 19:160. [PMID: 33879181 PMCID: PMC8056571 DOI: 10.1186/s12967-021-02831-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 04/12/2021] [Indexed: 11/10/2022] Open
Affiliation(s)
- Marco Maltoni
- Palliative Care Unit, Istituto Scientifico Romagnolo per lo Studio e la Cura Dei Tumori (IRST) IRCCS, via P. Maroncelli 40, Meldola, FC, Italy.
| | - Romina Rossi
- Palliative Care Unit, Istituto Scientifico Romagnolo per lo Studio e la Cura Dei Tumori (IRST) IRCCS, via P. Maroncelli 40, Meldola, FC, Italy
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Holub K, Louvel G. Poor performance status and brain metastases treatment: who may benefit from the stereotactic radiotherapy? J Neurooncol 2021; 152:383-393. [PMID: 33590401 DOI: 10.1007/s11060-021-03712-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 01/29/2021] [Indexed: 10/22/2022]
Abstract
BACKGROUND Poor Performance Status (PS) of cancer patients, defined as PS score 2-3, is an impediment for many drug- and irradiation-based treatments, supported by the trials that exclude subjects with PS < 1. Reports on the benefits of stereotactic radiotherapy (SRT) for brain metastases (BMs) in poor PS patients are scarce. We sought to review the characteristics and survival outcomes of this cohort, to assess who may benefit most from SRT. METHODS We retrospectively evaluated 73 patients with PS 2 or 3 (63 and 10 cases) treated with SRT for 150 BMs from 2012 to 2018. Patients' characteristics and post-SRT survival, stratified by concomitant systemic treatment (CST) were assessed using the Kaplan-Meier method (p-value < 0.05). RESULTS Non-small cell lung cancer was the most frequent primary tumor. Extracranial metastases were present in 86.3% of patients. The median overall survival (mOS) after SRT was estimated as 6.0 months (range 0.2-37.7), with 6- and 12-month survival rates of 51.0% and 21.0%, respectively. CST was administrated to 59.7% of patients (immunotherapy, target therapy or chemotherapy). Patients treated with CST presented larger mOS (6.7 vs. 4.4 months for patients treated with SRT alone, p = 0.3), and better 6- and 12-month survival rates (59% and 24% vs. 37% and 18% in patients not treated with CST). CONCLUSIONS Survival rate after SRT for BMs in poor performance patients, especially with PS 2, can justify SRT, in particular if an effective systemic treatment is available. Both SRT and CST should be more accessible for these patients in clinical practice.
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Affiliation(s)
- Katarzyna Holub
- Radiotherapy Department, Gustave Roussy Cancer Campus, Villejuif, France. .,Universitat de Barcelona, Barcelona, Spain.
| | - Guillaume Louvel
- Radiotherapy Department, Gustave Roussy Cancer Campus, Villejuif, France
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Wang RF, Lai CC, Fu PY, Huang YC, Huang SJ, Chu D, Lin SP, Chaou CH, Hsu CY, Chen HH. A-qCPR risk score screening model for predicting 1-year mortality associated with hospice and palliative care in the emergency department. Palliat Med 2021; 35:408-416. [PMID: 33198575 DOI: 10.1177/0269216320972041] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
BACKGROUND Evaluating the need for palliative care and predicting its mortality play important roles in the emergency department. AIM We developed a screening model for predicting 1-year mortality. DESIGN A retrospective cohort study was conducted to identify risk factors associated with 1-year mortality. Our risk scores based on these significant risk factors were then developed. Its predictive validity performance was evaluated using area under receiving operating characteristic analysis and leave-one-out cross-validation. SETTING AND PARTICIPANTS Patients aged 15 years or older were enrolled from June 2015 to May 2016 in the emergency department. RESULTS We identified five independent risk factors, each of which was assigned a number of points proportional to its estimated regression coefficient: age (0.05 points per year), qSOFA ⩾ 2 (1), Cancer (4), Eastern Cooperative Oncology Group Performance Status score ⩾ 2 (2), and Do-Not-Resuscitate status (3). The sensitivity, specificity, positive predictive value, and negative predictive value of our screening tool given the cutoff larger than 3 points were 0.99 (0.98-0.99), 0.31 (0.29-0.32), 0.26 (0.24-0.27), and 0.99 (0.98-1.00), respectively. Those with screening scores larger than 9 points corresponding to 64.0% (60.0-67.9%) of 1-year mortality were prioritized for consultation and communication. The area under the receiving operating characteristic curves for the point system was 0.84 (0.83-0.85) for the cross-validation model. CONCLUSIONS A-qCPR risk scores provide a good screening tool for assessing patient prognosis. Routine screening for end-of-life using this tool plays an important role in early and efficient physician-patient communications regarding hospice and palliative needs in the emergency department.
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Affiliation(s)
- Ruei-Fang Wang
- Department of Emergency Medicine, Taipei City Hospital, Taipei
| | - Chao-Chih Lai
- Department of Emergency Medicine, Taipei City Hospital, Taipei
- Master of Public Health Program, College of Public Health, National Taiwan University, Taipei
| | - Ping-Yeh Fu
- Department of Emergency Medicine, Taipei City Hospital, Taipei
| | | | | | - Dachen Chu
- Superintendent, Taipei City Hospital
- National Yang-Ming University, Taipei
| | - Shih-Pin Lin
- Department of Anesthesiology, Taipei Veterans General Hospital and School of Medicine, National Yang-Ming University, Taipei
| | - Chung-Hsien Chaou
- Department of Emergency Medicine, Chang Gung Memorial Hospital, Linkou Branch and Chang Gung University College of Medicine, Taoyuan City
| | - Chen-Yang Hsu
- Master of Public Health Program, College of Public Health, National Taiwan University, Taipei
- Da-Chung Hospital, Miaoli
| | - Hsiu-Hsi Chen
- Division Biostatistics, Graduate Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei
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Lee W, Pulbrook M, Sheehan C, Kochovska S, Chang S, Hosie A, Lobb E, Parker D, Draper B, Agar MR, Currow DC. Clinically Significant Depressive Symptoms Are Prevalent in People With Extremely Short Prognoses-A Systematic Review. J Pain Symptom Manage 2021; 61:143-166.e2. [PMID: 32688012 DOI: 10.1016/j.jpainsymman.2020.07.011] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Revised: 07/09/2020] [Accepted: 07/13/2020] [Indexed: 12/13/2022]
Abstract
CONTEXT Currently, systematic evidence of the prevalence of clinically significant depressive symptoms in people with extremely short prognoses is not available to inform its global burden, assessment, and management. OBJECTIVES To determine the prevalence of clinically significant depressive symptoms in people with advanced life-limiting illnesses and extremely short prognoses (range of days to weeks). METHODS A systematic review and meta-analysis (random-effects model) were performed (PROSPERO: CRD42019125119). MEDLINE, Embase, PsycINFO, CINAHL, and CareSearch were searched for studies (1994-2019). Data were screened for the prevalence of clinically significant depressive symptoms (assessed using validated depression-specific screening tools or diagnostic criteria) of adults with advanced life-limiting illnesses and extremely short prognoses (defined by survival or functional status). Quality assessment was performed using the Joanna Briggs Institute Systematic Reviews Checklist for Prevalence Studies for individual studies and Grading of Recommendations Assessment, Development and Evaluation (GRADE) across studies. RESULTS Thirteen studies were included. The overall pooled prevalence of clinically significant depressive symptoms in adults with extremely short prognoses (n = 10 studies; extremely short prognoses: N = 905) using depression-specific screening tools was 50% (95% CI: 29%-70%; I2 = 97.6%). Prevalence of major and minor depression was 10% (95% CI: 4%-16%) and 5% (95% CI: 2%-8%), respectively. Major limitations included high heterogeneity, selection bias, and small sample sizes in individual studies. CONCLUSIONS Clinically, significant depressive symptoms were prevalent in people with advanced life-limiting illnesses and extremely short prognoses. Clinicians need to be proactive in the recognition and assessment of these symptoms to allow for timely intervention.
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Affiliation(s)
- Wei Lee
- University of Technology Sydney, Ultimo, New South Wales, Australia; St Vincent Hospital, Darlinghurst, New South Wales, Australia.
| | - Marley Pulbrook
- St Vincent Hospital, Darlinghurst, New South Wales, Australia
| | | | | | - Sungwon Chang
- University of Technology Sydney, Ultimo, New South Wales, Australia
| | - Annmarie Hosie
- St Vincent Hospital, Darlinghurst, New South Wales, Australia; University of Notre Dame Australia, New South Wales, Australia
| | - Elizabeth Lobb
- Calvary Hospital, Kogarah, New South Wales, Australia; University of Notre Dame Australia, New South Wales, Australia
| | - Deborah Parker
- University of Technology Sydney, Ultimo, New South Wales, Australia
| | - Brian Draper
- University of New South Wales, Randwick, New South Wales, Australia
| | - Meera R Agar
- University of Technology Sydney, Ultimo, New South Wales, Australia
| | - David C Currow
- University of Technology Sydney, Ultimo, New South Wales, Australia
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Zhou J, Xu S, Cao Z, Tang J, Fang X, Qin L, Zhou F, He Y, Zhong X, Hu M, Wang Y, Lu F, Bao Y, Dai X, Wu Q. Validation of the Palliative Prognostic Index, Performance Status-Based Palliative Prognostic Index and Chinese Prognostic Scale in a home palliative care setting for patients with advanced cancer in China. BMC Palliat Care 2020; 19:167. [PMID: 33129305 PMCID: PMC7603699 DOI: 10.1186/s12904-020-00676-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2020] [Accepted: 10/22/2020] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND The predictive value of the prognostic tool for patients with advanced cancer is uncertain in mainland China, especially in the home-based palliative care (HPC) setting. This study aimed to compare the accuracy of the Palliative Prognostic Index (PPI), the Performance Status-Based Palliative Prognostic Index (PS-PPI), and the Chinese Prognosis Scale (ChPS) for patients with advanced cancer in the HPC setting in mainland China. METHODS Patients with advanced cancer admitted to the hospice center of Yuebei People's Hospital between January 2014 and December 2018 were retrospectively calculated the scores according to the three prognostic tools. The Kaplan-Meier method was used to compare survival times among different risk groups. Receiver operating characteristic curve analysis was used to assess the predictive value. The accuracy of 21-, 42- and 90-day survival was compared among the three prognostic tools. RESULTS A total of 1863 patients were included. Survival time among the risk groups of all prognostic tools was significantly different from each other except for the PPI. The AUROC of the ChPS was significantly higher than that of the PPI and PS-PPI for 7-, 14, 21-, 42-, 90-, 120-, 150- and 180-day survival (P < 0.05). The AUROC of the PPI and PS-PPI were not significantly different from each other (P > 0.05). CONCLUSIONS The ChPS is more suitable than the PPI and PS-PPI for advanced cancer patients in the HPC setting. More researches are needed to verify the predictive value of the ChPS, PPI, and PS-PPI in the HPC setting in the future.
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Affiliation(s)
- Jun Zhou
- Department of Spine Surgery, Yuebei People’s Hospital Affiliated to Shantou University Medical College, Shaoguan, Guangdong China
| | - Sitao Xu
- Department of Spine Surgery, Yuebei People’s Hospital Affiliated to Shantou University Medical College, Shaoguan, Guangdong China
| | - Ziye Cao
- Department of Spine Surgery, Yuebei People’s Hospital Affiliated to Shantou University Medical College, Shaoguan, Guangdong China
| | - Jing Tang
- Department of Spine Surgery, Yuebei People’s Hospital Affiliated to Shantou University Medical College, Shaoguan, Guangdong China
| | - Xiang Fang
- Department of Spine Surgery, Yuebei People’s Hospital Affiliated to Shantou University Medical College, Shaoguan, Guangdong China
| | - Ling Qin
- Department of Spine Surgery, Yuebei People’s Hospital Affiliated to Shantou University Medical College, Shaoguan, Guangdong China
| | - Fangping Zhou
- Department of Nursing, Yuebei People’s Hospital Affiliated to Shantou University Medical College, Shaoguan, Guangdong China
| | - Yuzhen He
- Department of Nursing, Yuebei People’s Hospital Affiliated to Shantou University Medical College, Shaoguan, Guangdong China
- Hospice center of Yuebei People’s Hospital Affiliated to Shantou University Medical College, Shaoguan, Guangdong China
| | - Xueren Zhong
- Department of Spine Surgery, Yuebei People’s Hospital Affiliated to Shantou University Medical College, Shaoguan, Guangdong China
| | - Mingcai Hu
- Hospice center of Yuebei People’s Hospital Affiliated to Shantou University Medical College, Shaoguan, Guangdong China
| | - Yan Wang
- Emergency rescue command center of Shaoguan city, Shaoguan, Guangdong China
| | - Fengjuan Lu
- Hospice center of Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou, Guangxi China
| | - Yongzheng Bao
- Department of Spine Surgery, Yuebei People’s Hospital Affiliated to Shantou University Medical College, Shaoguan, Guangdong China
| | - Xiangheng Dai
- Department of Spinal Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong China
| | - Qiang Wu
- Department of Spine Surgery, Yuebei People’s Hospital Affiliated to Shantou University Medical College, Shaoguan, Guangdong China
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Akram MJ, Khalid U, Ashraf MB, Bakar MA, Butt FM, Khan F. Predicting the survival in patients with malignant pleural effusion undergoing indwelling pleural catheter insertion. Ann Thorac Med 2020; 15:223-229. [PMID: 33381237 PMCID: PMC7720744 DOI: 10.4103/atm.atm_289_20] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/23/2020] [Accepted: 07/03/2020] [Indexed: 11/05/2022] Open
Abstract
CONTEXT Malignant pleural effusion (MPE) is a common comorbid condition in advanced malignancies with variable survival. AIMS The aim of this study was to predict the survival in patients with MPE undergoing indwelling pleural catheter (IPC) insertion. SETTINGS AND DESIGN This was a cross-sectional study conducted at Shaukat Khanum Memorial Cancer Hospital and Research Centre, Lahore, Pakistan. METHODS One hundred and ten patients with MPE who underwent IPC insertion from January 2011 to December 2019 were reviewed. Kaplan-Meier method was used to determine the overall survival (OS) of the patient's cohort with respect to LENT score. STATISTICAL ANALYSIS USED The IBM SPSS version 20 was used for statistical analysis. RESULTS We retrospectively reviewed 110 patients who underwent IPC insertion for MPE, with a mean age of 49 ± 15 years. 76 (69.1%) patients were females, of which majority 59 (53.6%) had a primary diagnosis of breast cancer. The LENT score was used for risk stratification, and Kaplan-Meier survival curves were used to predict the OS. The proportion of patients with low-risk LENT score had 91%, 58%, and 29% survival, the moderate-risk group had 76%, 52%, and 14% survival, and in the high-risk group, 61%, 15%, and 0% patients survived at 1, 3, and 6 months, respectively. In addition, there was a statistically significant survival difference (P = 0.05) in patients who received chemotherapy pre- and post-IPC insertion. CONCLUSIONS LENT score seems to be an easy and attainable tool, capable of predicting the survival of the patients with MPE quite accurately. It can be helpful in palliating the symptoms of patients with advanced malignancies by modifying the treatment strategies.
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Affiliation(s)
- Muhammad Junaid Akram
- Department of Internal Medicine, Shaukat Khanum Memorial Cancer Hospital and Research Center, Lahore, Pakistan
| | - Usman Khalid
- Department of Internal Medicine, Shaukat Khanum Memorial Cancer Hospital and Research Center, Lahore, Pakistan
| | | | - Muhammad Abu Bakar
- Department of Cancer Registry and Clinical Data Management, Shaukat Khanum Memorial Cancer Hospital and Research Center, Lahore, Pakistan
| | - Faheem Mahmood Butt
- Department of Internal Medicine, Shaukat Khanum Memorial Cancer Hospital and Research Center, Lahore, Pakistan
| | - Faheem Khan
- Royal Blackburn Teaching Hospital, East Lancashire Hospitals, NHS Trust, England, UK
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Afonso A, Silva J, Lopes AR, Coelho S, Patrão AS, Rosinha A, Carneiro F, Pinto AR, Maurício MJ, Medeiros R. YB-1 variant and androgen receptor axis-targeted agents in metastatic castration-resistant prostate cancer patients. Pharmacogenomics 2020; 21:919-928. [PMID: 32787509 DOI: 10.2217/pgs-2020-0008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Aim: To evaluate the influence of YB-1 rs10493112 variant as a genetic marker for response to second-generation androgen receptor axis-target agents. Methods: A hospital-based cohort study of 78 patients with metastatic castration-resistant prostate cancer was conducted. Genotyping was performed by TaqMan® allelic discrimination technology. Main results: In abiraterone-treated and high-risk patients, YB-1 rs10493112 AA genotype carriers showed lower progression-free survival than C allele genotype patients (4 vs 17 months; p = 0.009). For carriers of AA genotype, multivariate Cox regression analysis revealed a fivefold increased risk of progression (p = 0.035). Conclusion: The study findings suggest that, for metastatic and castration-resistant prostate cancer patients, this polymorphism might be a putative marker for the clinical outcome.
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Affiliation(s)
- Ana Afonso
- Department of Oncology, Portuguese Institute of Oncology, Rua Dr. António Bernardino Almeida, 4200-072, Porto, Portugal.,Molecular Oncology & Viral Pathology Group-Research Center, Portuguese Institute of Oncology, Rua Dr. António Bernardino Almeida, 4200-072, Porto, Portugal
| | - Jani Silva
- Molecular Oncology & Viral Pathology Group-Research Center, Portuguese Institute of Oncology, Rua Dr. António Bernardino Almeida, 4200-072, Porto, Portugal
| | - Ana Rita Lopes
- Department of Oncology, Portuguese Institute of Oncology, Rua Dr. António Bernardino Almeida, 4200-072, Porto, Portugal
| | - Sara Coelho
- Department of Oncology, Portuguese Institute of Oncology, Rua Dr. António Bernardino Almeida, 4200-072, Porto, Portugal
| | - Ana Sofia Patrão
- Department of Oncology, Portuguese Institute of Oncology, Rua Dr. António Bernardino Almeida, 4200-072, Porto, Portugal
| | - Alina Rosinha
- Department of Oncology, Portuguese Institute of Oncology, Rua Dr. António Bernardino Almeida, 4200-072, Porto, Portugal
| | - Filipa Carneiro
- Department of Oncology, Portuguese Institute of Oncology, Rua Dr. António Bernardino Almeida, 4200-072, Porto, Portugal
| | - Ana Rita Pinto
- Molecular Oncology & Viral Pathology Group-Research Center, Portuguese Institute of Oncology, Rua Dr. António Bernardino Almeida, 4200-072, Porto, Portugal
| | - Maria Joaquina Maurício
- Department of Oncology, Portuguese Institute of Oncology, Rua Dr. António Bernardino Almeida, 4200-072, Porto, Portugal
| | - Rui Medeiros
- Molecular Oncology & Viral Pathology Group-Research Center, Portuguese Institute of Oncology, Rua Dr. António Bernardino Almeida, 4200-072, Porto, Portugal.,Department of Research, Portuguese League Against Cancer (NRNorte), Estrada Interior da Circunvalação, no. 6657, 4200-172, Porto, Portugal.,CEBIMED, Faculty of Health Sciences, Fernando Pessoa University, Rua Delfim Maia, 334 4200-253, Porto, Portugal
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Nakamura ZM, Deal AM, Rosenstein DL, Quillen LJ, Chien SA, Wood WA, Shea TC, Park EM. Design of a randomized placebo controlled trial of high dose intravenous thiamine for the prevention of delirium in allogeneic hematopoietic stem cell transplantation. Contemp Clin Trials 2020; 95:106076. [PMID: 32619524 DOI: 10.1016/j.cct.2020.106076] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Revised: 06/15/2020] [Accepted: 06/18/2020] [Indexed: 01/28/2023]
Abstract
BACKGROUND Delirium is a highly prevalent and preventable neuropsychiatric condition with major health consequences. Thiamine deficiency is a well-established cause of delirium in those with chronic, severe alcoholism, but there remains an underappreciation of its significance in non-alcoholic populations, including patients with cancer. Treatment of suspected thiamine-related mental status changes with high dose intravenous (IV) thiamine has preliminary evidence for improving a variety of cognitive symptoms in oncology inpatient settings but has never been studied for the prevention of delirium in any population. OBJECTIVES The primary objective of this clinical trial is to determine if high dose IV thiamine can prevent delirium in patients receiving allogeneic hematopoietic stem cell transplantation (HSCT) for treatment of cancer. Secondary objectives are to determine if thiamine status is predictive of delirium onset and if high dose IV thiamine can attenuate the deleterious impact of delirium on health-related quality of life (HRQOL), functional status, and long-term neuropsychiatric outcomes. METHODS In this phase II study, we are recruiting 60 patients undergoing allogeneic HSCT, randomizing them to treatment with high dose IV thiamine (n = 30) versus placebo (n = 30), and systematically evaluating all participants for delirium and related comorbidities. We use the Delirium Rating Scale to measure the severity and duration of delirium during hospitalization for HSCT. We obtain thiamine levels weekly during the transplantation hospitalization. We assess HRQOL, functional status, depression, post-traumatic stress symptoms, and cognitive function prior to and at one, three, and six months after transplantation.
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Affiliation(s)
- Zev M Nakamura
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | - Allison M Deal
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Donald L Rosenstein
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Medicine, Division of Hematology/Oncology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Laura J Quillen
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Stephanie A Chien
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - William A Wood
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Medicine, Division of Hematology/Oncology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Thomas C Shea
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Medicine, Division of Hematology/Oncology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Eliza M Park
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Medicine, Division of Hematology/Oncology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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40
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Gómez-Batiste X, Turrillas P, Tebé C, Calsina-Berna A, Amblàs-Novellas J. NECPAL tool prognostication in advanced chronic illness: a rapid review and expert consensus. BMJ Support Palliat Care 2020; 12:e10-e20. [PMID: 32241958 DOI: 10.1136/bmjspcare-2019-002126] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Revised: 02/24/2020] [Accepted: 03/11/2020] [Indexed: 11/04/2022]
Abstract
OBJECTIVE To develop a proposal for a 2-year mortality prognostic approach for patients with advanced chronic conditions based on the palliative care need (PCN) items of the NECesidades PALiativas (NECPAL) CCOMS-ICO V.3.1 2017 tool. METHODS A phase 1 study using three components based on the NECPAL items: (1) a rapid review of systematic reviews (SRs) on prognostic factors of mortality in patients with advanced chronic diseases and PCNs; (2) a clinician and statistician experts' consensus based on the Delphi technique on the selection of mortality prognostic factors; and (3) a panel meeting to discuss the findings of components (1) and (2). RESULTS Twenty SRs were included in a rapid review, and 50% were considered of moderate quality. Despite methodological issues, nutritional and functional decline, severe and refractory dyspnoea, multimorbidity, use of resources and specific disease indicators were found to be potentially prognostic variables for mortality across four clinical groups and end-of-life (EoL) trajectories: cancer, dementia and neurologic diseases, chronic organ failure and frailty. Experts' consensus added 'needs' identified by health professionals. However, clinicians were less able to discriminate which NECPAL items were more reliable for a 'general' model. A retrospective cohort study was designed to evaluate this proposal in phase 2. CONCLUSIONS We identified several parameters with prognostic value and linked them to the tool's utility to timely identify PCNs of patients with advanced chronic conditions in all settings of care. Initial results show this is a clinical and feasible tool, that will help with clinical pragmatic decision-making and to define services.
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Affiliation(s)
- Xavier Gómez-Batiste
- The 'Qualy' Observatory/WHO Collaborating Centre for Public Health Palliative Care Programmes, Institut Catala d' Oncologia, Barcelona, Spain .,Chair of Palliative Care, Faculty of Medicine, University of Vic ‒ Central University of Catalonia, Vic, Barcelona, Spain
| | - Pamela Turrillas
- The 'Qualy' Observatory/WHO Collaborating Centre for Public Health Palliative Care Programmes, Institut Catala d' Oncologia, Barcelona, Spain.,Chair of Palliative Care, Faculty of Medicine, University of Vic ‒ Central University of Catalonia, Vic, Barcelona, Spain
| | - Cristian Tebé
- Department of Statistics, Biomedical Research Institute of Bellvitge (IDIBELL), Barcelona, Spain
| | - Agnès Calsina-Berna
- The 'Qualy' Observatory/WHO Collaborating Centre for Public Health Palliative Care Programmes, Institut Catala d' Oncologia, Barcelona, Spain.,Chair of Palliative Care, Faculty of Medicine, University of Vic ‒ Central University of Catalonia, Vic, Barcelona, Spain
| | - Jordi Amblàs-Novellas
- Chair of Palliative Care, Faculty of Medicine, University of Vic ‒ Central University of Catalonia, Vic, Barcelona, Spain.,Central Catalonia Chronicity Research Group (C3RG), Centre for Health and Social Care Research (CESS), University of Vic - Central University of Catalonia, Vic, Barcelona, Spain
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41
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Lee SF, Luk H, Wong A, Ng CK, Wong FCS, Luque-Fernandez MA. Prediction model for short-term mortality after palliative radiotherapy for patients having advanced cancer: a cohort study from routine electronic medical data. Sci Rep 2020; 10:5779. [PMID: 32238885 PMCID: PMC7113237 DOI: 10.1038/s41598-020-62826-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Accepted: 03/11/2020] [Indexed: 12/18/2022] Open
Abstract
We developed a predictive score system for 30-day mortality after palliative radiotherapy by using predictors from routine electronic medical record. Patients with metastatic cancer receiving first course palliative radiotherapy from 1 July, 2007 to 31 December, 2017 were identified. 30-day mortality odds ratios and probabilities of the death predictive score were obtained using multivariable logistic regression model. Overall, 5,795 patients participated. Median follow-up was 39.6 months (range, 24.5-69.3) for all surviving patients. 5,290 patients died over a median 110 days, of whom 995 (17.2%) died within 30 days of radiotherapy commencement. The most important mortality predictors were primary lung cancer (odds ratio: 1.73, 95% confidence interval: 1.47-2.04) and log peripheral blood neutrophil lymphocyte ratio (odds ratio: 1.71, 95% confidence interval: 1.52-1.92). The developed predictive scoring system had 10 predictor variables and 20 points. The cross-validated area under curve was 0.81 (95% confidence interval: 0.79-0.82). The calibration suggested a reasonably good fit for the model (likelihood-ratio statistic: 2.81, P = 0.094), providing an accurate prediction for almost all 30-day mortality probabilities. The predictive scoring system accurately predicted 30-day mortality among patients with stage IV cancer. Oncologists may use this to tailor palliative therapy for patients.
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Affiliation(s)
- Shing Fung Lee
- Department of Clinical Oncology, Tuen Mun Hospital, New Territories West Cluster, Hospital Authority, Hong Kong, Hong Kong
| | - Hollis Luk
- Department of Clinical Oncology, Tuen Mun Hospital, New Territories West Cluster, Hospital Authority, Hong Kong, Hong Kong
| | - Aray Wong
- Department of Clinical Oncology, Tuen Mun Hospital, New Territories West Cluster, Hospital Authority, Hong Kong, Hong Kong
| | - Chuk Kwan Ng
- Department of Clinical Oncology, Tuen Mun Hospital, New Territories West Cluster, Hospital Authority, Hong Kong, Hong Kong
| | - Frank Chi Sing Wong
- Department of Clinical Oncology, Tuen Mun Hospital, New Territories West Cluster, Hospital Authority, Hong Kong, Hong Kong
| | - Miguel Angel Luque-Fernandez
- Department of Non-Communicable Disease and Cancer Epidemiology, Institute de Investigacion Biosanitaria de Granada (ibs.GRANADA), University of Granada, Granada, Spain. .,Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.
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42
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Commentary: Failing to forecast. J Thorac Cardiovasc Surg 2020; 160:877. [PMID: 32199660 DOI: 10.1016/j.jtcvs.2020.02.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Accepted: 02/06/2020] [Indexed: 11/20/2022]
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43
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Verhoef MJ, de Nijs EJM, Fiocco M, Heringhaus C, Horeweg N, van der Linden YM. Surprise Question and Performance Status Indicate Urgency of Palliative Care Needs in Patients with Advanced Cancer at the Emergency Department: An Observational Cohort Study. J Palliat Med 2019; 23:801-808. [PMID: 31880489 DOI: 10.1089/jpm.2019.0413] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Background: The surprise question (SQ), "Would I be surprised if this patient died within one year?", is a simple instrument to identify patients with palliative care needs. The SQ-performance has not been evaluated in patients with advanced cancer visiting the emergency department (ED). Objective: To evaluate SQ's test characteristics and predictive value in patients with advanced cancer visiting the ED. Design: Observational cohort study. Setting: Patients >18 years with advanced cancer in the palliative phase visiting the ED of an academic medical center. Methods: Attending physicians answered the SQ (not surprised [NS] or surprised [S]) and estimated Eastern Cooperative Oncology Group (ECOG)-performance status. Disease, visit, and follow-up characteristics were retrospectively collected from charts. SQ's sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV), and Harrell's c-index were calculated. Prognostic values of SQ and other variables were assessed by using Cox proportional hazards models. Results: Two-hundred-and-forty-five patients were included (203 NS [83%] and 42 S [17%]), median age 62 years, 48% male. Follow-up on overall survival was updated until February 2019. At ED entry, NS-patients had worse ECOG-performance and more symptoms. At study closure, 233 patients had died (95%). Median survival was three months for NS-patients (interquartile [IQ]-range: 1-8); nine months for S-patients (IQ-range: 3-28) (p < 0.0001). SQ-performance for one-year mortality: sensitivity 89%, specificity 40%, PPV 85%, NPV 50%, c-index 0.56, and hazard ratio 2.1 for approaching death. ECOG 3-4 predicted death in NS-patients; addition to the SQ improved c-index (0.65); sensitivity (40%), specificity (92%), PPV (95%), and NPV (29%). Conclusions: At the ED, the SQ plus ECOG 3-4 helps identifying patients with advanced cancer and a limited life expectancy. Its use supports initiating appropriate care related to urgency of palliative care needs.
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Affiliation(s)
- Mary-Joanne Verhoef
- Center of Expertise Palliative Care, Leiden University Medical Center, Leiden, the Netherlands
| | - Ellen J M de Nijs
- Center of Expertise Palliative Care, Leiden University Medical Center, Leiden, the Netherlands
| | - Marta Fiocco
- Medical Statistics, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands.,Mathematical Institute, Leiden University, Leiden, the Netherlands
| | - Christian Heringhaus
- Department of Emergency Medicine and Leiden University Medical Center, Leiden, the Netherlands
| | - Nanda Horeweg
- Department of Radiation Oncology, Leiden University Medical Center, Leiden, the Netherlands
| | - Yvette M van der Linden
- Center of Expertise Palliative Care, Leiden University Medical Center, Leiden, the Netherlands.,Department of Radiation Oncology, Leiden University Medical Center, Leiden, the Netherlands
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Huang S, Yang J, Fong S, Zhao Q. Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges. Cancer Lett 2019; 471:61-71. [PMID: 31830558 DOI: 10.1016/j.canlet.2019.12.007] [Citation(s) in RCA: 225] [Impact Index Per Article: 45.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 12/04/2019] [Accepted: 12/06/2019] [Indexed: 02/06/2023]
Abstract
Cancer is an aggressive disease with a low median survival rate. Ironically, the treatment process is long and very costly due to its high recurrence and mortality rates. Accurate early diagnosis and prognosis prediction of cancer are essential to enhance the patient's survival rate. Developments in statistics and computer engineering over the years have encouraged many scientists to apply computational methods such as multivariate statistical analysis to analyze the prognosis of the disease, and the accuracy of such analyses is significantly higher than that of empirical predictions. Furthermore, as artificial intelligence (AI), especially machine learning and deep learning, has found popular applications in clinical cancer research in recent years, cancer prediction performance has reached new heights. This article reviews the literature on the application of AI to cancer diagnosis and prognosis, and summarizes its advantages. We explore how AI assists cancer diagnosis and prognosis, specifically with regard to its unprecedented accuracy, which is even higher than that of general statistical applications in oncology. We also demonstrate ways in which these methods are advancing the field. Finally, opportunities and challenges in the clinical implementation of AI are discussed. Hence, this article provides a new perspective on how AI technology can help improve cancer diagnosis and prognosis, and continue improving human health in the future.
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Affiliation(s)
- Shigao Huang
- Cancer Center, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Taipa, Macao, China
| | - Jie Yang
- Department of Computer and Information Science, University of Macau, Taipa, Macau, China; Chongqing Industry&Trade Polytechnic, Chongqing, China
| | - Simon Fong
- Department of Computer and Information Science, University of Macau, Taipa, Macau, China; Zhuhai Institute of Advanced Technology Chinese Academy of Sciences, Zhuhai, China.
| | - Qi Zhao
- Cancer Center, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Taipa, Macao, China.
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45
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Salsman JM, Pustejovsky JE, Schueller SM, Hernandez R, Berendsen M, McLouth LES, Moskowitz JT. Psychosocial interventions for cancer survivors: A meta-analysis of effects on positive affect. J Cancer Surviv 2019; 13:943-955. [PMID: 31741250 DOI: 10.1007/s11764-019-00811-8] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2019] [Accepted: 09/21/2019] [Indexed: 01/02/2023]
Abstract
PURPOSE Positive affect has demonstrated unique benefits in the context of health-related stress and is emerging as an important target for psychosocial interventions. The primary objective of this meta-analysis was to determine whether psychosocial interventions increase positive affect in cancer survivors. METHODS We coded 28 randomized controlled trials of psychosocial interventions assessing 2082 cancer survivors from six electronic databases. We calculated 76 effect sizes for positive affect and conducted synthesis using random effects models with robust variance estimation. Tests for moderation included demographic, clinical, and intervention characteristics. RESULTS Interventions had a modest effect on positive affect (g = 0.35, 95% CI [0.16, 0.54]) with substantial heterogeneity of effects across studies ([Formula: see text]; I2 = 78%). Three significant moderators were identified: in-person interventions outperformed remote interventions (P = .046), effects were larger when evaluated against standard of care or wait list control conditions versus attentional, educational, or component controls (P = .009), and trials with survivors of early-stage cancer diagnoses yielded larger effects than those with advanced-stage diagnoses (P = .046). We did not detect differential benefits of psychosocial interventions across samples varying in sex, age, on-treatment versus off-treatment status, or cancer type. Although no conclusive evidence suggested outcome reporting biases (P = .370), effects were smaller in studies with lower risk of bias. CONCLUSIONS In-person interventions with survivors of early-stage cancers hold promise for enhancing positive affect, but more methodological rigor is needed. IMPLICATIONS FOR CANCER SURVIVORS Positive affect strategies can be an explicit target in evidence-based medicine and have a role in patient-centered survivorship care, providing tools to uniquely mobilize human strengths.
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Affiliation(s)
- John M Salsman
- Department of Social Sciences and Health Policy, Wake Forest School of Medicine, Wake Forest Baptist Comprehensive Cancer Center, Winston Salem, NC, 27157, USA.
| | - James E Pustejovsky
- Department of Educational Psychology, University of Texas at Austin, Austin, TX, USA
| | - Stephen M Schueller
- Department of Psychological Science, University of California, Irvine, Irvine, CA, USA
| | - Rosalba Hernandez
- School of Social Work, University of Illinois at Urbana-Champaign, Urbana-Champaign, IL, USA
| | - Mark Berendsen
- Galter Health Sciences Library, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Laurie E Steffen McLouth
- Department of Social Sciences and Health Policy, Wake Forest School of Medicine, Wake Forest Baptist Comprehensive Cancer Center, Winston Salem, NC, 27157, USA
| | - Judith T Moskowitz
- Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
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Bertsimas D, Dunn J, Pawlowski C, Silberholz J, Weinstein A, Zhuo YD, Chen E, Elfiky AA. Applied Informatics Decision Support Tool for Mortality Predictions in Patients With Cancer. JCO Clin Cancer Inform 2019; 2:1-11. [PMID: 30652575 DOI: 10.1200/cci.18.00003] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
PURPOSE With rapidly evolving treatment options in cancer, the complexity in the clinical decision-making process for oncologists represents a growing challenge magnified by oncologists' disposition of intuition-based assessment of treatment risks and overall mortality. Given the unmet need for accurate prognostication with meaningful clinical rationale, we developed a highly interpretable prediction tool to identify patients with high mortality risk before the start of treatment regimens. METHODS We obtained electronic health record data between 2004 and 2014 from a large national cancer center and extracted 401 predictors, including demographics, diagnosis, gene mutations, treatment history, comorbidities, resource utilization, vital signs, and laboratory test results. We built an actionable tool using novel developments in modern machine learning to predict 60-, 90- and 180-day mortality from the start of an anticancer regimen. The model was validated in unseen data against benchmark models. RESULTS We identified 23,983 patients who initiated 46,646 anticancer treatment lines, with a median survival of 514 days. Our proposed prediction models achieved significantly higher estimation quality in unseen data (area under the curve, 0.83 to 0.86) compared with benchmark models. We identified key predictors of mortality, such as change in weight and albumin levels. The results are presented in an interactive and interpretable tool ( www.oncomortality.com ). CONCLUSION Our fully transparent prediction model was able to distinguish with high precision between highest- and lowest-risk patients. Given the rich data available in electronic health records and advances in machine learning methods, this tool can have significant implications for value-based shared decision making at the point of care and personalized goals-of-care management to catalyze practice reforms.
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Affiliation(s)
- Dimitris Bertsimas
- Dimitris Bertsimas, Jack Dunn, Colin Pawlowski, John Silberholz, Alexander Weinstein, and Ying Daisy Zhuo, Massachusetts Institute of Technology, Cambridge; Eddy Chen, Massachusetts General Hospital Cancer Center; Harvard Medical School; Aymen A. Elfiky, Dana-Farber Cancer Institute; Brigham and Women's Hospital; Harvard Medical School, Boston, MA
| | - Jack Dunn
- Dimitris Bertsimas, Jack Dunn, Colin Pawlowski, John Silberholz, Alexander Weinstein, and Ying Daisy Zhuo, Massachusetts Institute of Technology, Cambridge; Eddy Chen, Massachusetts General Hospital Cancer Center; Harvard Medical School; Aymen A. Elfiky, Dana-Farber Cancer Institute; Brigham and Women's Hospital; Harvard Medical School, Boston, MA
| | - Colin Pawlowski
- Dimitris Bertsimas, Jack Dunn, Colin Pawlowski, John Silberholz, Alexander Weinstein, and Ying Daisy Zhuo, Massachusetts Institute of Technology, Cambridge; Eddy Chen, Massachusetts General Hospital Cancer Center; Harvard Medical School; Aymen A. Elfiky, Dana-Farber Cancer Institute; Brigham and Women's Hospital; Harvard Medical School, Boston, MA
| | - John Silberholz
- Dimitris Bertsimas, Jack Dunn, Colin Pawlowski, John Silberholz, Alexander Weinstein, and Ying Daisy Zhuo, Massachusetts Institute of Technology, Cambridge; Eddy Chen, Massachusetts General Hospital Cancer Center; Harvard Medical School; Aymen A. Elfiky, Dana-Farber Cancer Institute; Brigham and Women's Hospital; Harvard Medical School, Boston, MA
| | - Alexander Weinstein
- Dimitris Bertsimas, Jack Dunn, Colin Pawlowski, John Silberholz, Alexander Weinstein, and Ying Daisy Zhuo, Massachusetts Institute of Technology, Cambridge; Eddy Chen, Massachusetts General Hospital Cancer Center; Harvard Medical School; Aymen A. Elfiky, Dana-Farber Cancer Institute; Brigham and Women's Hospital; Harvard Medical School, Boston, MA
| | - Ying Daisy Zhuo
- Dimitris Bertsimas, Jack Dunn, Colin Pawlowski, John Silberholz, Alexander Weinstein, and Ying Daisy Zhuo, Massachusetts Institute of Technology, Cambridge; Eddy Chen, Massachusetts General Hospital Cancer Center; Harvard Medical School; Aymen A. Elfiky, Dana-Farber Cancer Institute; Brigham and Women's Hospital; Harvard Medical School, Boston, MA
| | - Eddy Chen
- Dimitris Bertsimas, Jack Dunn, Colin Pawlowski, John Silberholz, Alexander Weinstein, and Ying Daisy Zhuo, Massachusetts Institute of Technology, Cambridge; Eddy Chen, Massachusetts General Hospital Cancer Center; Harvard Medical School; Aymen A. Elfiky, Dana-Farber Cancer Institute; Brigham and Women's Hospital; Harvard Medical School, Boston, MA
| | - Aymen A Elfiky
- Dimitris Bertsimas, Jack Dunn, Colin Pawlowski, John Silberholz, Alexander Weinstein, and Ying Daisy Zhuo, Massachusetts Institute of Technology, Cambridge; Eddy Chen, Massachusetts General Hospital Cancer Center; Harvard Medical School; Aymen A. Elfiky, Dana-Farber Cancer Institute; Brigham and Women's Hospital; Harvard Medical School, Boston, MA
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White N, Reid F, Harries P, Harris AJL, Minton O, McGowan C, Lodge P, Tookman A, Stone P. The (un)availability of prognostic information in the last days of life: a prospective observational study. BMJ Open 2019; 9:e030736. [PMID: 31292186 PMCID: PMC6624101 DOI: 10.1136/bmjopen-2019-030736] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
OBJECTIVES The aims of this study were (1) to document the clinical condition of patients considered to be in the last 2 weeks of life and (2) to compare patients who did or did not survive for 72 hours. DESIGN A prospective observational study. SETTING Two sites in London, UK (a hospice and a hospital palliative care team). PARTICIPANTS Any inpatient, over 18 years old, English speaking, who was identified by the palliative care team as at risk of dying within the next 2 weeks was eligible. OUTCOME MEASURES Prognostic signs and symptoms were documented at a one off assessment and patients were followed up 7 days later to determine whether or not they had died. RESULTS Fifty participants were recruited and 24/50 (48%) died within 72 hours of assessment. The most prevalent prognostic features observed were a decrease in oral food intake (60%) and a rapid decline of the participant's global health status (56%). Participants who died within 72 hours had a lower level of consciousness and had more care needs than those who lived longer. A large portion of data was unavailable, particularly that relating to the psychological and spiritual well-being of the patient, due to the decreased consciousness of the patient. CONCLUSIONS The prevalence of prognostic signs and symptoms in the final days of life has been documented between those predicted to die and those who did not. How doctors make decisions with missing information is an area for future research, in addition to understanding the best way to use the available information to make more accurate predictions.
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Affiliation(s)
- Nicola White
- Marie Curie Palliative Care Research Department, University College London, London, London, UK
| | - Fiona Reid
- Department of Primary Care & Public Health Sciences, King’s College London, London, London, UK
| | - Priscilla Harries
- Centre for Applied Health and Social Care Research (CAHSCR), Kingston University & St George’s, University of London, London, UK
- Department of Clinical Sciences, Brunel University London, London, UK
| | - Adam J L Harris
- Experimental Psychology, University College London, London, London, UK
| | - Ollie Minton
- Palliative Medicine, Brighton and Sussex University Hospitals NHS Trust, Brighton, Brighton and Hove, UK
| | - Catherine McGowan
- Palliative Medicine, St. Georges University Hospitals NHS Foundation Trust, London, UK
| | - Philip Lodge
- Palliative Medicine, Royal Free London NHS Foundation Trust, London, London, UK
- Marie Curie Hospice Hampstead, London, UK
| | - Adrian Tookman
- Palliative Medicine, Royal Free London NHS Foundation Trust, London, London, UK
- Marie Curie Hospice Hampstead, London, UK
| | - Patrick Stone
- Marie Curie Palliative Care Research Department, University College London, London, London, UK
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Zhao W, He Z, Li Y, Jia H, Chen M, Gu X, Liu M, Zhang Z, Wu Z, Cheng W. Nomogram-based parameters to predict overall survival in a real-world advanced cancer population undergoing palliative care. BMC Palliat Care 2019; 18:47. [PMID: 31167668 PMCID: PMC6551870 DOI: 10.1186/s12904-019-0432-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2019] [Accepted: 05/27/2019] [Indexed: 01/04/2023] Open
Abstract
Background Although palliative care has been accepted throughout the cancer trajectory, accurate survival prediction for advanced cancer patients is still a challenge. The aim of this study is to identify pre-palliative care predictors and develop a prognostic nomogram for overall survival (OS) in mixed advanced cancer patients. Methods A total of 378 consecutive advanced cancer patients were retrospectively recruited from July 2013 to October 2015 in one palliative care unit in China. Twenty-three clinical and laboratory characters were collected for analysis. Prognostic factors were identified to construct a nomogram in a training cohort (n = 247) and validated in a testing cohort (n = 131) from the setting. Results The median survival time was 48.0 (95% CI: 38.1–57.9) days for the training cohort and 52.0 (95% CI: 34.6–69.3) days for the validation cohort. Among pre-palliative care factors, sex, age, tumor stage, Karnofsky performance status, neutrophil count, hemoglobin, lactate dehydrogenase, albumin, uric acid, and cystatin-C were identified as independent prognostic factors for OS. Based on the 10 factors, an easily obtained nomogram predicting 90-day probability of mortality was developed. The predictive nomogram had good discrimination and calibration, with a high C-index of 0.76 (95% CI: 0.73–0.80) in the development set. The strong discriminative ability was externally conformed in the validation cohort with a C-index of 0.75. Conclusions A validated prognostic nomogram has been developed to quantify the risk of mortality for advanced cancer patients undergoing palliative care. This tool may be useful in optimizing therapeutic approaches and preparing for clinical courses individually. Electronic supplementary material The online version of this article (10.1186/s12904-019-0432-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Weiwei Zhao
- Department of Integrated Therapy, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Zhiyong He
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Department of Anesthesiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Yintao Li
- Department of Oncology, Shandong Cancer Hospital, Shandong Academy of Medical Sciences, Jinan, China
| | - Huixun Jia
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Menglei Chen
- Department of Integrated Therapy, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Xiaoli Gu
- Department of Integrated Therapy, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Minghui Liu
- Department of Integrated Therapy, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Zhe Zhang
- Department of Integrated Therapy, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Zhenyu Wu
- Department of Biostatistics, School of Public Health, Key Laboratory of Public Health Safety and Collaborative Innovation Center of Social Risks Governance in Health, Fudan University, Shanghai, China.
| | - Wenwu Cheng
- Department of Integrated Therapy, Fudan University Shanghai Cancer Center, Shanghai, China. .,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
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Gerlach C, Goebel S, Weber S, Weber M, Sleeman KE. Space for intuition - the 'Surprise'-Question in haemato-oncology: Qualitative analysis of experiences and perceptions of haemato-oncologists. Palliat Med 2019; 33:531-540. [PMID: 30688151 DOI: 10.1177/0269216318824271] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
BACKGROUND Early integration of palliative care can improve outcomes for people with cancer and non-cancer diagnoses. However, prediction of survival for individuals is challenging, in particular in patients with haematological malignancies who are known to have limited access to palliative care. The 'Surprise'-Question can be used to facilitate referral to palliative care. AIM To explore experiences, views and perceptions of haemato-oncologists on the use of the 'Surprise'-Question in the haemato-oncology outpatients clinics of a university hospital in Germany. DESIGN A qualitative study using individual semi-structured interviews transcribed verbatim and analysed thematically based on the framework approach. SETTING/PARTICIPANTS The study took place at the haemato-oncology outpatient clinic and the bone marrow transplantation outpatient clinic of a university hospital. Nine haemato-oncologists participated in qualitative interviews. RESULTS Thematic analysis identified 4 themes and 11 subthemes: (1) meaning and relevance of the 'Surprise'-Question; (2) feasibility; (3) the concept of 'surprise' and (4) personal aspects of prognostication. A key function of the 'Surprise'-Question was to stimulate intuition and promote patient-centred goals of care by initiating a process of pause → reflection → change of perspective. It was easy and quick to use, but required time and communication skills to act on. Participants' training in palliative care enhanced their willingness to use the 'Surprise'-Question. CONCLUSION Irrespective of its use in prognostication, the 'Surprise'-Question is a valuable tool to facilitate consideration of patient-centred goals and promote holistic care in haemato-oncology. However, prognostic uncertainty, lack of time and communication skills are barriers for integration into daily practice. Further research should involve haematology patients to integrate their needs and preferences.
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Affiliation(s)
- Christina Gerlach
- 1 III. Department of Medicine, Interdisciplinary Department of Palliative Care, University Medical Center, Johannes Gutenberg University of Mainz, Mainz, Germany
| | - Swantje Goebel
- 1 III. Department of Medicine, Interdisciplinary Department of Palliative Care, University Medical Center, Johannes Gutenberg University of Mainz, Mainz, Germany
| | - Sascha Weber
- 1 III. Department of Medicine, Interdisciplinary Department of Palliative Care, University Medical Center, Johannes Gutenberg University of Mainz, Mainz, Germany.,2 Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Germany
| | - Martin Weber
- 1 III. Department of Medicine, Interdisciplinary Department of Palliative Care, University Medical Center, Johannes Gutenberg University of Mainz, Mainz, Germany
| | - Katherine E Sleeman
- 3 King's College London, Cicely Saunders Institute, Florence Nightingale Faculty of Nursing, Midwifery & Palliative Care, London, UK
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Oostendorp L, White N, Harries P, Yardley S, Tomlinson C, Ricciardi F, Gokalp H, Stone P. Protocol for the ORaClES study: an online randomised controlled trial to improve clinical estimates of survival using a training resource for medical students. BMJ Open 2019; 9:e025265. [PMID: 30833321 PMCID: PMC6443051 DOI: 10.1136/bmjopen-2018-025265] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
INTRODUCTION Clinicians often struggle to recognise when palliative care patients are imminently dying (last 72 hours of life). A previous study identified the factors that expert palliative care doctors (with demonstrated prognostic skills) had used, to form a judgement about which patients were imminently dying. This protocol describes a study to evaluate whether an online training resource showing how experts weighted the importance of various symptoms and signs can teach medical students to formulate survival estimates for palliative care patients that are more similar to the experts' estimates. METHODS AND ANALYSIS This online double-blind randomised controlled trial will recruit at least 128 students in the penultimate or final year of medical school in the UK. Participants are asked to review three series of vignettes describing patients referred to palliative care and provide an estimate about the probability (0%-100%) that each patient will die within 72 hours. After the first series, students randomised to the intervention arm are given access to an online training resource. All participants are asked to complete a second series of vignettes. After 2 weeks, all participants are asked to complete a third series. The primary outcome will be the probability of death estimates (0%-100%) provided by students in the intervention and control arms for the second series of vignettes. Secondary outcomes include the maintenance effect at 2-week follow-up, weighting of individual symptoms and signs, and level of expertise (discrimination and consistency). ETHICS AND DISSEMINATION Approval has been obtained from the UCL Research Ethics Committee (8675/002) and local approvals will be obtained as appropriate. Results will be published in peer-reviewed journals using an open access format and presented at academic conferences. We will also publicise our findings on the Marie Curie website. TRIAL REGISTRATION NUMBER NCT03360812; Pre-results.
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Affiliation(s)
- Linda Oostendorp
- Marie Curie Palliative Care Research Department, University College London, London, UK
| | - Nicola White
- Marie Curie Palliative Care Research Department, University College London, London, UK
| | - Priscilla Harries
- Centre for Applied Health and Social Care Research (CAHSCR), Kingston University & St George’s, University of London, London, UK
| | - Sarah Yardley
- Marie Curie Palliative Care Research Department, University College London, London, UK
- Central and North West London NHS Foundation Trust, London, UK
| | - Christopher Tomlinson
- Marie Curie Palliative Care Research Department, University College London, London, UK
- Bioinformatics Data Science Group, Imperial College London, London, UK
| | - Federico Ricciardi
- Marie Curie Palliative Care Research Department, University College London, London, UK
- Department of Statistical Science, University College London, London, UK
| | - Hulya Gokalp
- Department of Clinical Sciences, Brunel University, Uxbridge, UK
- Department of Electrical and Electronic Engineering, Ondokuz Mayis Universitesi, Samsun, Turkey
| | - Patrick Stone
- Marie Curie Palliative Care Research Department, University College London, London, UK
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