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  • 1
    Online Resource
    Online Resource
    Development of a Clinical Prediction Model for 1-Year Mortality in Patients With Advanced Cancer
    American Medical Association (AMA) ; 2022
    In:  JAMA Network Open Vol. 5, No. 11 ( 2022-11-30), p. e2244350-
    Details
    In: JAMA Network Open, American Medical Association (AMA), Vol. 5, No. 11 ( 2022-11-30), p. e2244350-
    Abstract: To optimize palliative care in patients with cancer who are in their last year of life, timely and accurate prognostication is needed. However, available instruments for prognostication, such as the surprise question (“Would I be surprised if this patient died in the next year?”) and various prediction models using clinical variables, are not well validated or lack discriminative ability. Objective To develop and validate a prediction model to calculate the 1-year risk of death among patients with advanced cancer. Design, Setting, and Participants This multicenter prospective prognostic study was performed in the general oncology inpatient and outpatient clinics of 6 hospitals in the Netherlands. A total of 867 patients were enrolled between June 2 and November 22, 2017, and followed up for 1 year. The primary analyses were performed from October 9 to 25, 2019, with the most recent analyses performed from June 19 to 22, 2022. Cox proportional hazards regression analysis was used to develop a prediction model including 3 categories of candidate predictors: clinician responses to the surprise question, patient clinical characteristics, and patient laboratory values. Data on race and ethnicity were not collected because most patients were expected to be of White race and Dutch ethnicity, and race and ethnicity were not considered as prognostic factors. The models’ discriminative ability was assessed using internal-external validation by study hospital and measured using the C statistic. Patients 18 years and older with locally advanced or metastatic cancer were eligible. Patients with hematologic cancer were excluded. Main Outcomes and Measures The risk of death by 1 year. Results Among 867 patients, the median age was 66 years (IQR, 56-72 years), and 411 individuals (47.4%) were male. The 1-year mortality rate was 41.6% (361 patients). Three prediction models with increasing complexity were developed: (1) a simple model including the surprise question, (2) a clinical model including the surprise question and clinical characteristics (age, cancer type prognosis, visceral metastases, brain metastases, Eastern Cooperative Oncology Group performance status, weight loss, pain, and dyspnea), and (3) an extended model including the surprise question, clinical characteristics, and laboratory values (hemoglobin, C-reactive protein, and serum albumin). The pooled C statistic was 0.69 (95% CI, 0.67-0.71) for the simple model, 0.76 (95% CI, 0.73-0.78) for the clinical model, and 0.78 (95% CI, 0.76-0.80) for the extended model. A nomogram and web-based calculator were developed to support clinicians in adequately caring for patients with advanced cancer. Conclusions and Relevance In this study, a prediction model including the surprise question, clinical characteristics, and laboratory values had better discriminative ability in predicting death among patients with advanced cancer than models including the surprise question, clinical characteristics, or laboratory values alone. The nomogram and web-based calculator developed for this study can be used by clinicians to identify patients who may benefit from palliative care and advance care planning. Further exploration of the feasibility and external validity of the model is needed.
    Type of Medium: Online Resource
    ISSN: 2574-3805
    URL: Article
    DOI: 10.1001/jamanetworkopen.2022.44350
    Language: English
    Publisher: American Medical Association (AMA)
    Publication Date: 2022
    detail.hit.zdb_id: 2931249-8
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  • 2
    Online Resource
    Online Resource
    A clinical model to predict one-year mortality for patients with advanced cancer.
    American Society of Clinical Oncology (ASCO) ; 2019
    In:  Journal of Clinical Oncology Vol. 37, No. 31_suppl ( 2019-11-01), p. 132-132
    Details
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 37, No. 31_suppl ( 2019-11-01), p. 132-132
    Abstract: 132 Background: Advance care planning is necessary for cancer patients who are in their last year of life. We developed a clinical model to predict 1-year mortality for patients with advanced cancer. Methods: Patients with advanced cancer and no curatively aimed treatment options were included in a prospective multicenter observational study involving six hospitals in the Netherlands (June-November 2017). Using cox regression, a model was developed with candidate predictors as identified in literature: surprise question (SQ), clinical characteristics (age, sex, primary tumor, metastases, WHO performance status, food intake, weight loss, pain, comorbidity, dyspnea, and fatigue), and laboratory values (serum albumin, hemoglobin, and C-reactive protein). The primary outcome was all-cause 1-year mortality. Discriminative ability was measured using the c-statistic and assessed using internal-external validation by study hospital. Results: Of 867 patients (median age 66 years, male 47%), 362 (42%) died within 1-year follow-up. Three models were developed: model 1: SQ; model 2: SQ + clinical characteristics; model 3: SQ + clinical characteristics + laboratory values. Predictors included in the most expansive model 3 were: SQ ‘no’ (ref: ‘yes’; HR 3.43, 95% CI 2.57-4.58), age per 10 years (HR 1.07, 95% CI 0.97-1.18), primary tumor-site (ref: prostate, breast, or thyroid; HR 1.35, 95% CI 1.01-1.82), visceral metastases (HR 1.32, 95% CI 1.05-1.65), brain metastases (HR 1.54, 95% CI 1.07-2.22), WHO performance status 1 (ref: 0; HR 1.03, 95% CI 0.76-1.41), WHO performance status 2+ (ref: 0; HR 1.50, 95% CI 1.04-2.15), any weight loss (ref: none; HR 1.09, 95% CI 0.86-1.38), pain score (HR 1.03, 95% CI 0.98-1.09); dyspnea grade 1 (ref: 0; HR 1.20, 95% CI 0.94-1.54), dyspnea grade 2+ (ref: 0; HR 1.33, 95% CI 0.93-1.91), C-reactive protein (HR 1.15, 95% CI 1.04-1.27), and serum albumin (HR 0.97, 95% CI 0.95-1.00). The pooled c-statistic at internal-external validation was 0.69 for model 1, 0.76 for model 2, and 0.78 for model 3. Conclusions: A model that combines the SQ with easily available clinical characteristics (with or without laboratory values) is more accurate in predicting 1-year mortality in patients with advanced cancer than the SQ alone.
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
    URL: Article
    DOI: 10.1200/JCO.2019.37.31_suppl.132
    RVK:
    XA 52760
    RVK:
    XA 10000
    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2019
    detail.hit.zdb_id: 2005181-5
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  • 3
    Online Resource
    Online Resource
    Predictors of Mortality in Patients with Advanced Cancer—A Systematic Review and Meta-Analysis
    MDPI AG ; 2022
    In:  Cancers Vol. 14, No. 2 ( 2022-01-11), p. 328-
    Details
    In: Cancers, MDPI AG, Vol. 14, No. 2 ( 2022-01-11), p. 328-
    Abstract: To timely initiate advance care planning in patients with advanced cancer, physicians should identify patients with limited life expectancy. We aimed to identify predictors of mortality. To identify the relevant literature, we searched Embase, MEDLINE, Cochrane Central, Web of Science, and PubMed databases between January 2000–April 2020. Identified studies were assessed on risk-of-bias with a modified QUIPS tool. The main outcomes were predictors and prediction models of mortality within a period of 3–24 months. We included predictors that were studied in ≥2 cancer types in a meta-analysis using a fixed or random-effects model and summarized the discriminative ability of models. We included 68 studies (ranging from 42 to 66,112 patients), of which 24 were low risk-of-bias, and 39 were included in the meta-analysis. Using a fixed-effects model, the predictors of mortality were: the surprise question, performance status, cognitive impairment, (sub)cutaneous metastases, body mass index, comorbidity, serum albumin, and hemoglobin. Using a random-effects model, predictors were: disease stage IV (hazard ratio [HR] 7.58; 95% confidence interval [CI] 4.00–14.36), lung cancer (HR 2.51; 95% CI 1.24–5.06), ECOG performance status 1+ (HR 2.03; 95% CI 1.44–2.86) and 2+ (HR 4.06; 95% CI 2.36–6.98), age (HR 1.20; 95% CI 1.05–1.38), male sex (HR 1.24; 95% CI 1.14–1.36), and Charlson comorbidity score 3+ (HR 1.60; 95% CI 1.11–2.32). Thirteen studies reported on prediction models consisting of different sets of predictors with mostly moderate discriminative ability. To conclude, we identified reasonably accurate non-tumor specific predictors of mortality. Those predictors could guide in developing a more accurate prediction model and in selecting patients for advance care planning.
    Type of Medium: Online Resource
    ISSN: 2072-6694
    URL: Article
    DOI: 10.3390/cancers14020328
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2527080-1
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  • 4
    Online Resource
    Online Resource
    Predictors of mortality in chronic obstructive pulmonary disease: a systematic review and meta-analysis
    Springer Science and Business Media LLC ; 2022
    In:  BMC Pulmonary Medicine Vol. 22, No. 1 ( 2022-04-04)
    Details
    In: BMC Pulmonary Medicine, Springer Science and Business Media LLC, Vol. 22, No. 1 ( 2022-04-04)
    Abstract: Better insight in patients’ prognosis can help physicians to timely initiate advance care planning (ACP) discussions with patients with chronic obstructive pulmonary disease (COPD). We aimed to identify predictors of mortality. Methods We systematically searched databases Embase, PubMed, MEDLINE, Web of Science, and Cochrane Central in April 2020. Papers reporting on predictors or prognostic models for mortality at 3 months and up to 24 months were assessed on risk-of-bias. We performed a meta-analysis with a fixed or random-effects model, and evaluated the discriminative ability of multivariable prognostic models. Results We included 42 studies (49–418,251 patients); 18 studies were included in the meta-analysis. Significant predictors of mortality within 3–24 months in the random-effects model were: previous hospitalization for acute exacerbation (hazard ratio [HR] 1.97; 95% confidence interval [CI] 1.32–2.95), hospital readmission within 30 days (HR 5.01; 95% CI 2.16–11.63), cardiovascular comorbidity (HR 1.89; 95% CI 1.25–2.87), age (HR 1.48; 95% CI 1.38–1.59), male sex (HR 1.68; 95% CI 1.38–1.59), and long-term oxygen therapy (HR 1.74; 95% CI 1.10–2.73). Nineteen previously developed multicomponent prognostic models, as examined in 11 studies, mostly had moderate discriminate ability. Conclusion Identified predictors of mortality may aid physicians in selecting COPD patients who may benefit from ACP. However, better discriminative ability of prognostic models or development of a new prognostic model is needed for further large-scale implementation. Registration : PROSPERO (CRD42016038494), https://www.crd.york.ac.uk/prospero/ .
    Type of Medium: Online Resource
    ISSN: 1471-2466
    URL: Article
    DOI: 10.1186/s12890-022-01911-5
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2059871-3
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