Prognostic factors in patients in the terminal phase of haematological malignancies who are receiving home medical care.

Although there are many prognostic models for patients in the terminal phase of solid tumours, a reliable prognostic scoring system in patients in the terminal phase of haematological malignancies (HM) has not been established. We retrospectively evaluated 180 patients in the terminal phase of HM who were receiving home medical care (HMC). Multivariate analyses revealed that clinician's estimate, consciousness, loss of appetite, dyspnoea, neutrophil count, lymphocyte count, and lactate dehydrogenase were associated with overall survival (OS). Based on this result, we developed a novel prognostic scoring system, the Japan palliative haematological oncology prognostic estimates, in which four risk groups were shown to clearly differ in survival (p < 0.001): a low-risk group (n = 41, median OS of 434 days), an intermediate-low-risk group (n = 80, median OS of 112 days), an intermediate-high-risk group (n = 38, median OS of 31.5 days), and a high-risk group (n = 21, median OS of 10 days). This is the first investigation of prognostic factors that influence the OS of patients in the terminal phase of HM who are receiving HMC. Providing patients with reliable information about their prognosis is important for them to consider how to spend their remaining life.

Patient and caregiver agreement on prognosis estimates for older adults with advanced cancer.

BACKGROUND: Disagreements between patients and caregivers about treatment benefits, care decisions, and patients' health are associated with increased patient depression as well as increased caregiver anxiety, distress, depression, and burden. Understanding the factors associated with disagreement may inform interventions to improve the aforementioned outcomes. METHODS: For this analysis, baseline data were obtained from a cluster-randomized geriatric assessment trial that recruited patients aged ≥70 years who had incurable cancer from community oncology practices (University of Rochester Cancer Center 13070; Supriya G. Mohile, principal investigator). Patient and caregiver dyads were asked to estimate the patient's prognosis. Response options were 0 to 6 months, 7 to 12 months, 1 to 2 years, 2 to 5 years, and >5 years. The dependent variable was categorized as exact agreement (reference), patient-reported longer estimate, or caregiver-reported longer estimate. The authors used generalized estimating equations with multinomial distribution to examine the factors associated with patient-caregiver prognostic estimates. Independent variables were selected using the purposeful selection method. RESULTS: Among 354 dyads (89% of screened patients were enrolled), 26% and 22% of patients and caregivers, respectively, reported a longer estimate. Compared with dyads that were in agreement, patients were more likely to report a longer estimate when they screened positive for polypharmacy (ß = 0.81; P = .001), and caregivers reported greater distress (ß = 0.12; P = .03). Compared with dyads that were in agreement, caregivers were more likely to report a longer estimate when patients screened positive for polypharmacy (ß = 0.82; P = .005) and had lower perceived self-efficacy in interacting with physicians (ß = -0.10; P = .008). CONCLUSIONS: Several patient and caregiver factors were associated with patient-caregiver disagreement about prognostic estimates. Future studies should examine the effects of prognostic disagreement on patient and caregiver outcomes.

Comparison of Artificial Neural Networks and Logistic Regression for 30-days Survival Prediction of Cancer Patients.

INTRODUCTION: A machine learning technique that imitates neural system and brain can provide better than traditional methods like logistic regression for survival prediction and create an algorithm by determining influential factors. AIM: To determine the influential factors on survival time of palliative care cancer patients and to compare two statistical methods for better prediction of survival. METHODS: One-year data is gathered from the patients that we followed in the palliative care clinic of our hospital (2017-2018) (n = 189). All data were retrospectively evaluated. After descriptive statistics, we used Pearson and Spearman correlations for parametric and non-parametric variables. The Artificial Neural Networks (ANN) and logistic regression model were applied to parameters which have a significant correlation with short survival. RESULTS: Significantly correlated variables with short survival were Palliative Performance Scale (PPS), Edmonton Symptom Assessment System (ESAS), Karnofsky Performance Scale (KPS), brain, liver, and distant metastasis, hemogram parameters, cero-reactive protein (CRP) and albumin (ALB). ANN model showed 89.3% prediction accuracy while the logistic regression model showed 73.0%. ANN model achieved a better AUC value of 0.86 than logistic regression model (0.76). DISCUSSION: There are several prognostic evaluation tools such as PPS, KPS, CRP, albumin, leukocytes, neutrophil were reported several studies as survival-related parameters in logistic regression models, also. Many studies compare ANN with logistic regression. When we evaluated these parameters totally, we observed the same relations with survival then we used the same parameters in the ANN model. The effectivity of the survival prediction models can be improved with the use of ANN. CONCLUSION: ANN provides a more accurate estimation than logistic regression. ANN model is an important statistical method for survival prediction of cancer patients.

Predicting 1-Year Mortality in Older Hospitalized Patients: External Validation of the HOMR Model.

OBJECTIVES: Accurate prognostic information can enable patients and physicians to make better healthcare decisions. The Hospital-patient One-year Mortality Risk (HOMR) model accurately predicted mortality risk (concordance [C] statistic = .92) in adult hospitalized patients in a recent study in North America. We evaluated the performance of the HOMR model in a population of older inpatients in a large teaching hospital in Ireland. DESIGN: Retrospective cohort study. SETTING: Acute hospital. PARTICIPANTS: Patients aged 65 years or older cared for by inpatient geriatric medicine services from January 1, 2013, to March 6, 2015 (n = 1654). After excluding those who died during the index hospitalization (n = 206) and those with missing data (n = 39), the analytical sample included 1409 patients. MEASUREMENTS: Administrative data and information abstracted from hospital discharge reports were used to determine covariate values for each patient. One-year mortality was determined from the hospital information system, local registries, or by contacting the patient's general practitioner. The linear predictor for each patient was calculated, and performance of the model was evaluated in terms of its overall performance, discrimination, and calibration. Recalibrated and revised models were also estimated and evaluated. RESULTS: One-year mortality rate after hospital discharge in this patient cohort was 18.6%. The unadjusted HOMR model had good discrimination (C statistic = .78; 95% confidence interval = .76-.81) but was poorly calibrated and consistently overestimated mortality prediction. The model's performance was modestly improved by recalibration and revision (optimism corrected C statistic = .8). CONCLUSION: The superior discriminative performance of the HOMR model reported previously was substantially attenuated in its application to our cohort of older hospitalized patients, who represent a specific subset of the original derivation cohort. Updating methods improved its performance in our cohort, but further validation, refinement, and clinical impact studies are required before use in routine clinical practice. J Am Geriatr Soc 1-6, 2019.

Does Calculated Prognostic Estimation Lead to Different Outcomes Compared With Experience-Based Prognostication in the ICU? A Systematic Review.

Little is known about the impact of providing calculator/guideline based versus clinical experiential-based prognostic estimates to patients/caregivers in the ICU. We sought to determine whether studies have compared types of prognostic estimation in the ICU and associations with outcomes. DATA SOURCES: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, databases searched were PubMed, Embase, Web of Science, and Cochrane Library. The search was run on January 4, 2016, and April 12, 2017. References for included articles were searched. STUDY SELECTION: Studies meeting the following criteria were included in the analysis: communication of prognostic estimates, a comparator group, and in the adult ICU setting. DATA EXTRACTION: Titles/abstracts were reviewed by two researchers. We identified 10,704 articles of which 10 met inclusion criteria. Seven of the studies included estimates obtained from calculators/guidelines and three were based on subjective estimation wherein clinicians were asked to estimate prognosis based on experience. Only the seven using calculated/guideline based estimation were used for pooled analysis. Of these, one was a randomized trial, and six were nonrandomized before/after studies. All of the studies communicated the calculated/guideline-based estimates to the clinician. Two studies involved the communication of calculated prognostic estimates to the ICU physicians for all ICU patients. Four included identification of high-risk patients based on guidelines or review of historical local data which triggered a palliative care/ethics consultation, and one study included communication to physicians about guideline based likely outcomes for neurologic recovery for patients with out-of-hospital cardiac arrest survivors. The comparator arm in all studies was usual care without protocolized prognostication. DATA SYNTHESIS: Included studies were assessed for risk of bias. The most common outcomes measured were hospital mortality; do-not-resuscitate status; and medical ICU length of stay. In pooled analyses, there was an association between calculated/guideline based prognostic estimation and decreased medical ICU length of stay as well as increased do-not-resuscitate status, but no difference in hospital mortality. CONCLUSIONS: Protocolized assessment of calculator/guideline based prognosis in ICU patients is associated with decreased medical ICU length of stay and increased do-not-resuscitate status but does not have a significant effect on mortality. Future studies should explore how communicating these estimates to physicians changes behaviors including communication to patients/families and whether calculator/guideline based prognostication is associated with improved patient and family rated outcomes.