Prediction of survival for older hospitalized patients: the HELP survival model. Hospitalized Elderly Longitudinal Project

Predictors of 2-Year Post-Discharge Mortality in Hospitalized Older Patients

BACKGROUND

Understanding prognostic factors for adverse health outcomes is clinically relevant for improving treatment decision-making processes, potentially leading to enhanced patient prognosis. This secondary analysis of a prospective observational study aimed to identify independent factors associated with 2-year post-discharge mortality in acutely hospitalized older patients.

METHODS

All-cause mortality and date of death of 115 patients (83.3 ± 6.3 years, females: n = 75, 65.2%) admitted to acute geriatric wards were determined two years after hospital discharge through telephone interviews. Potential prognostic factors measured at hospital admission included demographic and clinical characteristics, nutritional, cognitive, and psychological status, Fried frailty phenotype, functioning in activities of daily living, locomotor capacity, and 24 h in-hospital mobility and objectively measured physical activity (PA) behaviors.

RESULTS

The 2-year mortality rate was 36.7% (n = 41). Univariate and multivariate Cox proportional hazards regression models revealed that mean daily PA level (hazards ratio (HR) = 0.59, 95% confidence interval (CI) 0.90-1.00; p = 0.042), frailty (HR = 3.39, 95% CI 1.20-9.51; p = 0.020), and underweight, in contrast to overweight (HR = 3.10, 95% CI 1.07-9.01; p = 0.038), at hospital admission were independently predictive of post-discharge mortality.

CONCLUSION

PA, frailty, and underweight at hospital admission should be considered when evaluating long-term survival prognosis, establishing risk profiles, and developing personalized care pathways in acute hospital care of older adults.

Validation and Comparison of Four Mortality Prediction Models in a Geriatric Ward in China

Purpose

The efficacy of mortality risk prediction models among older patients in China remains uncertain. We aimed to validate and compare the performances of the Walter Index, Geriatric Prognostic Index (GPI), Charlson Comorbidity Index (CCI), and FRAIL Scale in predicting 1-year all-cause mortality post-discharge in geriatric inpatients in China.

Patients and Methods

This study was conducted at a geriatric ward of a tertiary Hospital in Beijing, including patients aged 70 years or older with a documented comprehensive geriatric assessment, discharged between January 1, 2016, and December 31, 2021. Patients with a hospital stay ≤24 h or >60 days were excluded. All-cause mortality data within one year of discharge were collected from medical files and telephone interviews between August 2022 and February 2023. Multiple imputation, Logistic regression analysis, Brier scores, C-statistics, Hosmer-Lemeshow goodness-of-fit-test, and calibration plots were employed for statistical analysis.

Results

We included 832 patients with a median (interquartile range) age of 77 (74-82) years. One-hundred patients (12.0%) died within one year. After adjusting for covariates-marital status, social support, cigarette use, length of stay, number of medications, hemoglobin levels, handgrip strength, and Short Physical Performance Battery-CCI scores of 3-4 and >4, and increased Walter Index, GPI, and FRAIL Scale scores were significantly associated with 1-year mortality risk. The Brier scores varied from 0.07 (Walter Index) to 0.10 (FRAIL Scale). The C-statistic ranged from 0.74 (95% confidence interval, 0.69-0.78) for FRAIL Scale to 0.88 (95% confidence interval, 0.84-0.91) for the Walter Index. Calibration curves showed that the Walter Index, GPI, and FRAIL Scale were well calibrated, while the CCI was poor.

Conclusion

Combining the Brier score, discrimination and calibration, the Walter Index was confirmed for the first time to be the best model to predict the 1-year mortality risk of geriatric inpatients in China among the four models.

Atrial fibrillation as a new prognosis factor in chronic patients after hospitalization: the CHRONIBERIA index

A collaborative project in different areas of Spain and Portugal was designed to find out the variables that influence the mortality after discharge and develop a prognostic model adapted to the current healthcare needs of chronic patients in an internal medicine ward. Inclusion criteria were being admitted to an Internal Medicine department and at least one chronic disease. Patients' physical dependence was measured through Barthel index (BI). Pfeiffer test (PT) was used to establish cognitive status. We conducted logistic regression and Cox proportional hazard models to analyze the influence of those variables on one-year mortality. We also developed an external validation once decided the variables included in the index. We enrolled 1406 patients. Mean age was 79.5 (SD = 11.5) and females were 56.5%. After the follow-up period, 514 patients (36.6%) died. Five variables were identified as significantly associated with 1 year mortality: age, being male, lower BI punctuation, neoplasia and atrial fibrillation. A model with such variables was created to estimate one-year mortality risk, leading to the CHRONIBERIA. A ROC curve was made to determine the reliability of this index when applied to the global sample. An AUC of 0.72 (0.7-0.75) was obtained. The external validation of the index was successful and showed an AUC of 0.73 (0.67-0.79). Atrial fibrillation along with an advanced age, being male, low BI score, or an active neoplasia in chronic patients could be critical to identify high risk multiple chronic conditions patients. Together, these variables constitute the new CHRONIBERIA index.

Predicting mortality in acutely hospitalised older patients: the impact of model dimensionality

BACKGROUND

The prediction of long-term mortality following acute illness can be unreliable for older patients, inhibiting the delivery of targeted clinical interventions. The difficulty plausibly arises from the complex, multifactorial nature of the underlying biology in this population, which flexible, multimodal models based on machine learning may overcome. Here, we test this hypothesis by quantifying the comparative predictive fidelity of such models in a large consecutive sample of older patients acutely admitted to hospital and characterise their biological support.

METHODS

A set of 804 admission episodes involving 616 unique patients with a mean age of 84.5 years consecutively admitted to the Acute Geriatric service at University College Hospital were identified, in whom clinical diagnoses, blood tests, cognitive status, computed tomography of the head, and mortality within 600 days after admission were available. We trained and evaluated out-of-sample an array of extreme gradient boosted trees-based predictive models of incrementally greater numbers of investigational modalities and modelled features. Both linear and non-linear associations with investigational features were quantified.

RESULTS

Predictive models of mortality showed progressively increasing fidelity with greater numbers of modelled modalities and dimensions. The area under the receiver operating characteristic curve rose from 0.67 (sd = 0.078) for age and sex to 0.874 (sd = 0.046) for the most comprehensive model. Extracranial bone and soft tissue features contributed more than intracranial features towards long-term mortality prediction. The anterior cingulate and angular gyri, and serum albumin, were the greatest intracranial and biochemical model contributors respectively.

CONCLUSIONS

High-dimensional, multimodal predictive models of mortality based on routine clinical data offer higher predictive fidelity than simpler models, facilitating individual level prognostication and interventional targeting. The joint contributions of both extracranial and intracranial features highlight the potential importance of optimising somatic as well as neural functions in healthy ageing. Our findings suggest a promising path towards a high-fidelity, multimodal index of frailty.

A Comparison of Models Predicting One-Year Mortality at Time of Admission

CONTEXT

Hospitalization provides an opportunity to address end-of-life care (EoLC) preferences if patients at risk of death can be accurately identified while in the hospital. The modified Hospital One-Year Mortality Risk (mHOMR) uses demographic and admission data in a logistic regression algorithm to identify patients at risk of death one year from admission.

OBJECTIVES

This project sought to validate mHOMR and identify superior models.

METHODS

The mHOMR model was validated using historical data from an academic health system. Alternative logistic regression and random forest (RF) models were developed using the same variables. Receiver operating characteristic (ROC) and precision recall curves were developed, and sensitivity, specificity, and positive and negative predictive values were compared over a range of model thresholds.

RESULTS

The RF model demonstrated higher area under the ROC curve (0.950, 95% CI 0.947 - 0.954) as compared to the logistic regression models (0.818 [95% CI 0.812 - 0.825] and 0.841 [95% CI 0.836 - 0.847]). Area under the precision recall curve was higher with the random forest model compared to the logistic regression models (0.863 vs. 0.458 and 0.494, respectively). Across a range of thresholds, the RF model demonstrated superior sensitivity, equivalent specificity, and higher positive and negative predictive values.

CONCLUSION

A machine learning RF model, using common demographic and utilization data available on hospital admission, identified inpatients at risk of death more effectively than logistic regression models using the same variables. Machine learning models have promise for identifying admitted patients with elevated one-year mortality risk, increasing opportunities to prompt discussion of EoLC preferences.

Prediction value of the LACE index to identify older adults at high risk for all-cause mortality in South Korea: a nationwide population-based study

Background

As a tool to predict early hospital readmission, little is known about the association between LACE index and all-cause mortality in older adults. We aimed to validate the LACE index to predict all-cause mortality in older adults and also analyzed the LACE index outcome of all-cause mortality depending on the disease and age of the participants.

Methods

We used the National Health Insurance Service (NHIS) cohort, a nationwide claims database of Koreans. We enrolled 7491 patients who were hospitalized at least once between 2003 and 2004, aged ≥65 years as of the year of discharge, and subsequently followed-up until 2015. We estimated the LACE index using the NHI database. The Cox proportional hazards model was used to estimate the hazard ratio (HR) for all-cause mortality. Furthermore, we investigated all-cause mortality according to age and underlying disease when the LACE index was ≥10 and < 10, respectively.

Results

In populations over 65 years of age, patients with LACE index ≥10 had significantly higher risks of all-cause mortality than in those with LACE index < 10. (HR, 1.44; 95% confidence interval, 1.35–1.54). For those patients aged 65–74 years, the HR of all-cause mortality was found to be higher in patients with LACE index≥10 than in those with LACE index < 10 in almost all the diseases except CRF and mental illnesses. And those patients aged ≥75 years, the HR of all- cause mortality was found to be higher in patients with LACE index ≥10 than in those with LACE index < 10 in the diseases of pneumonia and MACE.

Conclusion

This is the first study to validate the predictive power of the LACE index to identify older adults at high risk for all-cause mortality using nationwide cohort data. Our findings have policy implications for selecting or managing patients who need post-discharge management.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-022-02848-4.

Mortality in hospitalised older patients: the WHALES short-term predictive score

OBJECTIVE

To develop and validate the WHALES screening tool predicting short-term mortality (3 months) in older patients hospitalised in an acute geriatric unit.

METHODS

Older patients transferred to an acute geriatric ward from June 2017 to December 2018 were included. The cohort was divided into two groups: derivation (n=664) and validation (n=332) cohorts. Cause for admission in emergency room, hospitalisation history within the previous year, ongoing medical conditions, cognitive impairment, frailty status, living conditions, presence of proteinuria on a urine strip or urine albumin-to-creatinine ratio and abnormalities on an ECG were collected at baseline. Multiple logistic regressions were performed to identify independent variables associated with mortality at 3 months in the derivation cohort. The prediction score was then validated in the validation cohort.

RESULTS

Five independent variables available from medical history and clinical data were strongly predictive of short-term mortality in older adults including age, sex, living in a nursing home, unintentional weight loss and self-reported exhaustion. The screening tool was discriminative (C-statistic=0.74 (95% CI: 0.67 to 0.82)) and had a good fit (Hosmer-Lemeshow goodness-of-fit test (X2 (3)=0.55, p=0.908)). The area under the curve value for the final model was 0.74 (95% CI: 0.67 to 0.82).

CONCLUSIONS AND IMPLICATIONS

The WHALES screening tool is a short and rapid tool predicting 3-month mortality among hospitalised older patients. Early identification of end of life may help appropriate timing and implementation of palliative care.

SARC-F Predicts Mortality Risk of Older Adults during Hospitalization

OBJECTIVES

To determine the association between SARC-F scores and the in-hospital mortality risk among older patients admitted to acute care hospitals.

DESIGN

Single-center retrospective study.

SETTING

A university hospital.

PARTICIPANTS

All consecutive patients aged older than 65 were admitted and discharged from the study hospital between July 2019 and September 2019.

MEASUREMENTS

Relevant patient data included age, sex, body mass index, nutritional status, fat-free mass, disease, activities of daily living (ADL), duration of hospital stay, SARC-F, and occurrence of death within 30 days of hospitalization. The diseases that caused hospitalization and comorbidities (Charlson Comorbidity Index; CCI) were obtained from medical records. The Eastern Cooperative Oncology Group-performance status (PS) was used to determine ADL, and the in-hospital mortality rate within 30 days of hospitalization as the outcome.

RESULTS

We analyzed 2,424 patients. The mean age was 75.9±6.9 and 55.5% were male. Fifty-three in-hospital mortalities occurred among the participants within the first 30 days of hospitalization. Patients who died in-hospital were older, had poorer nutritional status and severer PS scores, and more comorbidities than those who did not. A SARC-F score of ≥4 predicted a higher mortality risk within those 30 days with the following precision: sensitivity 0.792 and specificity 0.805. There were significantly more deaths in Kaplan-Meier curves regarding a score of SARC-F≥4 than a score of SARC-F<4 (p<0.001). Cox proportional hazard analysis was used to identify the clinical indicators most associated with in-hospital mortality. SARC-F≥4 (Hazard Ratio: HR 5.65, p<0.001), CCI scores (HR1.11, p=0.004), and infectious and parasitic diseases (HR3.13, p=0.031) were associated with in-hospital mortality. The SARC-F items with significant in-hospital mortality effects were assistance with walking (HR 2.55, p<0.001) and climbing stairs (HR 2.46, p=0.002).

CONCLUSION

The SARC-F questionnaire is a useful prognostic indicator for older adults because a SARC-F ≥4 score during admission to an acute care hospital predicts in-hospital mortality within 30 days of hospitalization.

Prognostication in Serious Illness

Prognostication is a vital aspect of decision making because it provides patients and families with information to establish realistic and achievable goals of care, is used in determining eligibility for certain benefits, and helps in targeting interventions to those likely to benefit. Prognostication consists of 3 components: clinicians use their clinical judgment or other tools to estimate the probability of an individual developing a particular outcome over a specific period of time; this prognostic estimate is communicated in accordance with the patient's information preferences; the prognostic estimate is interpreted by the patient or surrogate and used in clinical decision making.

Predicting in-hospital mortality of patients with febrile neutropenia using machine learning models

BACKGROUND

Febrile neutropenia (FN) has been associated with high mortality among adults with cancer. Current systems for early detection of inpatient FN mortality are based on scoring indexes that require intensive physicians' subjective evaluation.

OBJECTIVE

In this study, we leveraged machine learning techniques to build a FN mortality risk evaluation tool focused on FN admissions without physicians' subjective evaluation.

METHODS

We used the National Inpatient Sample and Nationwide Inpatient Sample (NIS) that included mortality data among adult inpatients who were diagnosed with FN during a hospital admission. Machine learning techniques that we compared included linear models (ridge logistic regression and linear support vector machine) and non-linear models (gradient boosting tree and neural network). The primary outcome for this study was death among individuals with a recorded FN admission. Model comparison was evaluated based on areas under the receiver operating characteristic curve (AUROC) and model performance was estimated using 30 % test set created via stratified split.

RESULTS

Our analysis detected 126,013 adult admissions within the NIS data that were diagnosed with FN, among which 5,856 were declared as deceased (4.6 %). Our machine learning results demonstrate linear models and non-linear models achieved areas under the receiver operating characteristic (AUROC) around 92 % in survival prediction.

CONCLUSIONS

We developed machine learning models that do not require physicians' subjective evaluation for FN mortality risk prediction.

Three-month mortality in permanently bedridden medical non-oncologic patients. The BECLAP study (permanently BEdridden, creatinine CLearance, albumin, previous hospital admissions study)

OBJECTIVE

To predict the 3-months mortality in permanently bedridden medical non-oncologic inpatients.

PATIENTS AND METHODS

2788 consecutive patients admitted in 5 Italian Internal Medicine units from January 2016 through January 2017 were prospectively screened; 644 oncologic patients were excluded; 2144 non-oncologic patients (1021 female) were followed-up for mortality for 6 months. Main outcome was 3-months mortality in permanently bedridden inpatients with at least 2 of: creatinine clearance <35 ml/min; albumin < 2.5 g/dl; at least 2 hospital admissions in the previous 6 months. Advanced dementia and dysphagia were also recorded.

RESULTS

Mean age of the 2144 patients was 73.9 (SD, 14.9) years; 374 (17%) were permanently bedridden, 435 (20%) had a creatinine clearance <35 ml/min, 217 (10%) albumin <2,5 g/dl, 112 (5%) at least 2 hospital admissions in the previous 6 months. Seventy-seven (4%) patients were permanently bedridden with at least 2 of the above mentioned items, and 48 of them died within 3 months (62%) (p < 0.001;95% CI 51-73%). Regression coefficients of the variables associated with 3-months mortality in multivariate analysis in 998 patients of unit 1 (training cohort) were used to create a simple score, which was validated in the 1146 patients of the other units (validation cohort) and performed well in predicting the 3-months mortality (https://www.ejcrim.com/beclap/).

CONCLUSIONS

Approximately two out of three non-oncologic medical patients permanently bedridden having 2 of the abovementioned items are dead 3 months after index admission; a simple score including bedridden status, creatinine clearance, albumin, dysphagia, age and sex may help discuss management priorities.

Health Insurance and Disparities in Mortality among Older Survivors of Critical Illness: A Population Study

OBJECTIVES

The 1.5 million Medicare beneficiaries who survive intensive care each year have a high post-hospitalization mortality rate. We aimed to determine whether mortality after critical illness is higher for Medicare beneficiaries with Medicaid compared with those with commercial insurance.

DESIGN

A retrospective cohort study from 2010 through 2014 with 1 year of follow-up using the New York Statewide Planning and Research Cooperative System database.

SETTING

A New York State population-based study of older (age ≥65 y) survivors of intensive care.

PARTICIPANTS

Adult Medicare beneficiaries age 65 years or older who were hospitalized with intensive care at a New York State hospital and survived to discharge.

INTERVENTION

None.

MEASUREMENT

Mortality in the first year after hospital discharge.

RESULTS

The study included 340 969 Medicare beneficiary survivors of intensive care with a mean (standard deviation) age of 77 (8) years; 20% died within 1 year. There were 152 869 (45%) with commercial insurance, 78 577 (23%) with Medicaid, and 109 523 (32%) with Medicare alone. Compared with those with commercial insurance, those with Medicare alone had a similar 1-year mortality rate (adjusted hazard ratio [aHR] = 1.01; 95% confidence interval [CI] = .99-1.04), and those with Medicaid had a 9% higher 1-year mortality rate (aHR = 1.09; 95% CI = 1.05-1.12). Among those discharged home, the 1-year mortality rate did not vary by insurance coverage, but among those discharged to skilled-care facilities (SCFs), the 1-year mortality rate was 16% higher for Medicaid recipients (aHR = 1.16; 95% CI = 1.12-1.21; P for interaction <.001).

CONCLUSIONS

Older adults with Medicaid insurance have a higher 1-year post-hospitalization mortality compared with those with commercial insurance, especially among those discharged to SCFs. Future studies should investigate care disparities at SCFs that may mediate these higher mortality rates. J Am Geriatr Soc 67:2497-2504, 2019.

Prognostic value for mortality of the new FADOI-COMPLIMED score(s) in patients hospitalized in medical wards

Background

Recently we defined a user-friendly tool (FADOI-COMPLIMED scores—FCS) to assess complexity of patients hospitalized in medical wards. FCS-1 is an average between the Barthel Index and the Exton-Smith score, while FCS-2 is obtained by using the Charlson score. The aim of this paper is to assess the ability of the FCS to predict mortality in-hospital and after 1-3-6-12-months. In this perspective, we performed comparisons with the validated Multidimensional Prognostic Index (MPI).

Methods

It is a multicenter, prospective observational study, enrolling patients aged over 40, suffering from at least two chronic diseases and consecutively admitted to Internal Medicine departments. For each patient, data from 13 questionnaires were collected. Survival follow-up was conducted at 1-3-6-12 months after discharge. The relationships between cumulative incidences of death with FCS were investigated with logistic regression analyses. ROC curve analyses were performed in order to compare the predictiveness of the logistic models based on FCS with respect to those with MPI taken as reference.

Results

A cohort of 541 patients was evaluated. A 10-point higher value for FCS-1 and FCS-2 leads to an increased risk of 1-year death equal to 25.0% and 27.1%, respectively. In case of in-hospital mortality, the relevant percentages were 63.1% and 15.3%. The logistic model based on FCS is significantly more predictive than the model based on MPI (which requires an almost doubled number of items) for all the time-points considered.

Conclusions

Assessment of prognosis of patients has the potential to guide clinical decision-making and lead to better care. We propose a new, efficient and easy-to-use instrument based on FCS, which demonstrated a good predictive power for mortality in patients hospitalized in medical wards. This tool may be of interest for clinical practice, since it well balances feasibility (requiring the compilation of 34 items, taking around 10 minutes) and performance.

mHOMR: a feasibility study of an automated system for identifying inpatients having an elevated risk of 1-year mortality

OBJECTIVE

The need for clinical staff to reliably identify patients with a shortened life expectancy is an obstacle to improving palliative and end-of-life care. We developed and evaluated the feasibility of an automated tool to identify patients with a high risk of death in the next year to prompt treating physicians to consider a palliative approach and reduce the identification burden faced by clinical staff.

METHODS

Two-phase feasibility study conducted at two quaternary healthcare facilities in Toronto, Canada. We modified the Hospitalised-patient One-year Mortality Risk (HOMR) score, which identifies patients having an elevated 1-year mortality risk, to use only data available at the time of admission. An application prompted the admitting team when patients had an elevated mortality risk and suggested a palliative approach. The incidences of goals of care discussions and/or palliative care consultation were abstracted from medical records.

RESULTS

Our model (C-statistic=0.89) was found to be similarly accurate to the original HOMR score and identified 15.8% and 12.2% of admitted patients at Sites 1 and 2, respectively. Of 400 patients included, the most common indications for admission included a frailty condition (219, 55%), chronic organ failure (91, 23%) and cancer (78, 20%). At Site 1 (integrated notification), patients with the notification were significantly more likely to have a discussion about goals of care and/or palliative care consultation (35% vs 20%, p = 0.016). At Site 2 (electronic mail), there was no significant difference (45% vs 53%, p = 0.322).

CONCLUSIONS

Our application is an accurate, feasible and timely identification tool for patients at elevated risk of death in the next year and may be effective for improving palliative and end-of-life care.

Hospital Discharge Decisions Concerning Older Patients: Understanding the Underlying Process

ABSTRACTWe aimed to understand clinical decision-making processes that influence the orientation of older patients after hospital discharge. We compared discharge decisions (i.e., discharge home, or nursing home stay) of the hospital team with those of an expert panel. Both panel and hospital team made their decisions independently. The blind study included 102 patients (mean age: 83.13 ± 6.74). There is a statistically significant difference between expert and hospital team decisions (p &lt; .001; kappa coefficient: 0.468). Panel decisions were more closely associated with isolation (p = .018), reliable caregivers (p = .004), social problems (p = .001), and behavioural symptoms perceived as aggressive (p = .001). Both decision processes considered refusal of care (p = 0,025 and 0.016 respectively) and social problems (p = .001 and &lt; 0.001 respectively). Discharge planning models differ depending on the country, team and patient's condition. Our study suggests more precise evaluation of patients' needs.

Prognostic Factors for the Survival of Elderly Patients Who Were Hospitalized in the Medical Ward of Our Hospital in Japan

It has been a long time since there were many elderly people in Japan. The medical care and costs for the elderly are enormous, and research to lower the mortality rate of the elderly is needed. We retrospectively investigated the prognostic factors for the survival of elderly patients who were hospitalized in the medical ward of our hospital. In total, 277 patients who were hospitalized between 1 January 2014 and 31 May 2017, were included in the retrospective study. Univariate and multivariate analyses of items (vital signs, laboratory data, and so on) were performed, and significant differences between the survival group and death group were subjected to receiver operating characteristic curve analysis. Serum urea nitrogen levels and serum albumin levels provided a relatively high area under the curve (AUC). However, there was no item for which AUC exceeded 0.70, and setting the cutoff value in this study was difficult. For treating the elderly, it is important to carefully evaluate each patient’s prognostic factors, including the demented state, renal function, and nutritional state; personalized treatment of each patient is also important.

NOD mice, susceptible to pancreatic autoimmunity, demonstrate delayed growth of pancreatic cancer

Pancreatic cancer is a high mortality form of cancer, with a median survival only six months. There are multiple associated risk factors associated, most importantly type 2 diabetes, obesity, pancreatitis and smoking. The relative rarity of the disease, however, has made it difficult to dissect causative risk factors, especially with related risk factors. A major unanswered question with important therapeutic implications is the effect of immunological responses on pancreatic cancer formation, with data from other cancers suggesting the potential for local immunological responses to either increase cancer development or increase cancer elimination. Due to the rarity and late diagnosis of pancreatic cancer direct epidemiological evidence is lacking, thus necessitating a reliance on animal models. Here we investigated the relationship between pancreatic autoimmunity and cancer by backcrossing the well characterised Ela1-Tag transgenic model of pancreatic cancer onto the pancreatic autoimmune susceptible NOD mouse strain. Through longitudinal magnetic resonance imaging we found that the NOD genetic background delayed the onset of pancreatic tumours and substantially slowed the growth rate of tumours after development. These results suggest that elevated autoimmune surveillance of the pancreas limits tumour formation and growth, identifying pancreatic cancer as a promising target for immune checkpoint blockade therapies that unleash latent autoimmunity.

Development and validation of a prognostic index for 6- and 12-month mortality in hospitalized older adults

BACKGROUND/AIM

Estimation of mortality in elderly patients is difficult yet very important when planning care. Previous tools are complicated or do no take into account some major determinants of mortality (i.e., frailty). We designed a simple, accurate, and non-disease-specific tool to predict individual mortality risk after hospital discharge in older adults.

METHODS

Patients admitted to the Acute Geriatric Unit were assessed at adission and at discharge and contacted 6 and 12 months later. Determinants of mortality were obtained. Using multivariable analysis, beta coeffcicients were calculated to build 2 scores able to predict mortality at 6 and 12 months after discharge. The scores were tested on a sample comprising 75% of the patients, who were randomly selected; they were validated using the remaining 25%. Discrimination was assessed using ROC curves. Scores were calculated for each patient and divided into tertiles. Survival analysis was performed.

RESULTS

Determinants of mortality at 6 months were dependent ambulation at baseline, full dependence at discharge, length of stay, pluripatology, pressure ulcers, low grip strength, malignacy, and male gender. At 12 months the determinants were: dependent amblation at baseline, full dependence at discharge, pluripatology, low BMI, low grip strength, heart failure, malignacy, and male gender. Discrimination and calibration were excellent. Survival analysis demonstrated different survival trajectories (p<0.001) for each tertile in both scores.

CONCLUSIONS

Our incices provide accurate prognostic information in elderly patients after discharge. They can be calculated easily, quickly and do not require technical or laboratory support, thus endorsing their value in dalily clinical practice.

Self-rated health as a predictor of mid-term and long-term mortality in older Afro-Caribbeans hospitalised via the emergency department

PURPOSE

To determine whether self-rated health (SRH) is an independent predictor for mortality in older Afro-Caribbean patients hospitalised for an acute condition.

METHODS

Prospective cohort of patients recruited from the University Hospitals of Martinique Acute Care for Elders unit. Patients aged 75 or older and hospitalised for an acute condition were eligible. The outcome was time to death within the 36-week follow-up. SRH was the explanatory variable of interest. Cox's Proportional Hazards model was used to estimate the relationship between SRH and mortality.

RESULTS

The 223 patients included in the study were aged 85.1 ± 5.5 years. In total, 123 patients reported "very good to good" health, and 100 "medium to very poor" health. Crude mortality rates at six months, 1, 2, and 3 years were 30.5, 34.8, 48.4, and 57.0%, respectively. By multivariate analysis, SRH reached significant relationship for all mortality timepoints. The adjusted hazard ratios for subjects who perceived their health as medium, poor or very poor was 1.6-2.7 times greater than that of subjects who reported good or very good health.

CONCLUSION

Assessment of SRH could have implications for clinical practice, particularly in helping practitioners to better estimate prognosis in the acute care settings.

Utility of the NECPAL CCOMS-ICO© tool and the Surprise Question as screening tools for early palliative care and to predict mortality in patients with advanced chronic conditions: A cohort study

BACKGROUND

The Surprise Question (SQ) identifies patients with palliative care needs. The NECPAL CCOMS-ICO^© (NECPAL) tool combines the Surprise Question with additional clinical parameters for a more comprehensive assessment. The capacity of these screening tools to predict mortality is still unknown.

AIM

To explore the predictive validity of the NECPAL and SQ to determine 12- to 24-month mortality.

DESIGN

Longitudinal, prospective and observational cohort study.

SETTING/PARTICIPANTS

Three primary care centres, one general hospital, one intermediate care centre, and four nursing homes. Population cohort with advanced chronic conditions and limited life prognosis. Patients were classified according to SQ and NECPAL criteria and followed for 24 months.

RESULTS

Data available to assess 1059 of 1064 recruited patients (99.6%) at 12 and 24 months: 837 patients were SQ+ and 780 were NECPAL+. Mortality rates at 24 months were as follows: 44.6% (SQ+) versus 15.8% (SQ-) and 45.8% (NECPAL+) versus 18.3% (NECPAL-) ( p = 0.000). SQ+ and NECPAL+ identification was significantly correlated with 24-month mortality risk (hazard ratios: 2.719 and 2.398, respectively). Both tools were highly sensitive (91.4, CI: 88.7-94.1 and 87.5, CI: 84.3-90.7) with high negative predictive values (84.2, CI: 79.4-89.0 and 81.7, CI: 77.2-86.2), with low specificity and positive predictive value. The prognostic accuracy of SQ and NECPAL was 52.9% and 55.2%, respectively. The predictive validity was slightly better for NECPAL.

CONCLUSION

SQ and NECPAL are valuable screening instruments to identify patients with limited life prognosis who may require palliative care. More research is needed to increase its prognostic utility in combination with other parameters.

Murine Pancreatic Acinar Cell Carcinoma Growth Kinetics Are Independent of Dietary Vitamin D Deficiency or Supplementation

Vitamin D has been proposed as a therapeutic strategy in pancreatic cancer, yet evidence for an effect of dietary vitamin D on pancreatic cancer is ambiguous, with conflicting data from human epidemiological and intervention studies. Here, we tested the role of dietary vitamin D in the in vivo context of the well-characterized Ela1-TAg transgenic mouse model of pancreatic acinar cell carcinoma. Through longitudinal magnetic resonance imaging of mice under conditions of either dietary vitamin D deficiency (<5 IU/kg vitamin D) or excess (76,500 IU/kg vitamin D), compared to control diet (1,500 IU/kg vitamin D), we measured the effect of variation of dietary vitamin D on tumor kinetics. No measurable impact of dietary vitamin D was found on pancreatic acinar cell carcinoma development, growth or mortality, casting further doubt on the already equivocal data supporting potential therapeutic use in humans. The lack of any detectable effect of vitamin D, within the physiological range of dietary deficiency or supplementation, in this model further erodes confidence in vitamin D as an effective antitumor therapeutic in pancreatic acinar cell carcinoma.

PREDICTing Mortality in the Emergency Department: External Validation and Derivation of a Clinical Prediction Tool

BACKGROUND

The Choosing Wisely campaign has called for better engagement of palliative and hospice care services for patients in the emergency department (ED). PREDICT is a clinical prediction tool that was derived in an Australian ED cohort. It assesses a patient's risk of mortality at 1 year to select those who would benefit from advanced care planning. Such goals-of-care discussion can improve patients' ability to communicate what they want out of their healthcare and, in cases of end of life, potentially reduce the number of futile interventions. Using a cutoff of 13 points, PREDICT had a reported 95.3% specificity and 53.9% sensitivity for 1-year mortality. We externally validated PREDICT and derived a simpler modified PREDICT tool to systematically identify high-risk patients eligible for goals-of-care discussions and palliative care consultation in the ED.

METHODS

This was an observational cohort study of a random sample of 927 patients aged 55+ seen in the ED in 2014. We identified advance healthcare directives (AHDs) on file. We summarized diagnostic accuracy of the clinical tool to predict 1-year mortality using sensitivity, specificity, and area under the curve (AUC). We refined PREDICT using multivariable modeling. We followed reporting guidelines including STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) for cohort studies and Standards for Reporting of Diagnostic Accuracy (STARD).

RESULTS

A total of 927 patients were included: 55.0% were male, 63 (7.0%) were nursing home residents, 389 (42.0%) patients had an AHD in their medical record at the time of ED visit, and 245 (26.4%) were deceased at 1 year. Of the 780 patients with PREDICT scores < 13, a total of 164 (21.0%; 95% confidence interval [CI] = 18.3-24.1) were deceased at 1 year, and of the 147 patients with PREDICT scores ≥ 13, a total of 81 (55.1%; 95% CI = 46.7-63.2) were deceased at 1 year. The AUC of the PREDICT score was 0.717 (95% CI = 0.680-0.754), sensitivity was 33.1% (95% CI = 27.3-39.4), and specificity was 90.3% (95% CI = 87.8-92.4) to predict 1-year mortality. The modified PREDICT tool resulted in an AUC of 0.709 (95% CI = 0.671-0.747). We decided to select this model as the preferred model, as the variable of intensive care unit (ICU) admission with multiorgan failure can be difficult to assess in the ED and may delay advanced care planning. Reweighting the score did not improve fit or the AUC, so points assigned to each variable were not adjusted.

CONCLUSION

PREDICT is an easy tool to administer to be able to identify patients who are at high risk of 1-year mortality and who could benefit from AHDs, goals-of-care discussion, and when appropriate in the context of an end-of-life setting, palliative medicine consultation. External validation of PREDICT was successful in our population. We simplified PREDICT and derived a new tool, the modified PREDICT minus ICU tool, without significantly altering the sensitivity, specificity, and AUC for death at 1 year. The next steps include external validation of the newly derived rule and prospective implementation.

Importance and added value of functional impairment to predict mortality: a cohort study in Swedish medical inpatients

BACKGROUND

Accurate estimation of prognosis in multimorbid hospital patients could improve quality of care. This study aims to determine the relative importance and added value of a performance-based activities of daily living (ADL) measure with regard to mortality prediction.

METHODS

200 inpatients, aged over 60 years, were recruited at the Department of General Internal Medicine at a tertiary university hospital. Two nested survival models were built, one with established risk factors (age, sex, Charlson comorbidity index, haemoglobin, albumin, body mass index and glomerular filtration rate), and one using the same covariates with the Gottfries-Bråne-Steen (GBS)-ADL measure added. The relative importance of GBS-ADL was evaluated in the full model. The added value of GBS-ADL was determined by comparing the nested models using four approaches: difference in overall χ², discrimination, continuous net reclassification index (NRI >0) and integrated discrimination improvement (IDI).

RESULTS

In the full model, GBS-ADL was the single most important predictor of mortality (χ²-df=30, p<0.001). The likelihood ratio χ² test showed significant added value of ADL (p<0.001). The C-statistic was 0.78 with ADL and 0.72 without (difference 0.058, 95% CI 0.022 to 0.094). The NRI >0 was 0.42 (95% CI 0.20 to 0.58) and IDI 0.15 (95% CI 0.07 to 0.22).

CONCLUSIONS

Compared with a set of available clinical risk factors, impairment in ADL was a stronger predictor of all-cause mortality, showing substantial added value. Implementing quantitative ADL measurements could enable more appropriate and individual care for the elderly.

No Functional Role for microRNA-342 in a Mouse Model of Pancreatic Acinar Carcinoma

The intronic microRNA (miR)-342 has been proposed as a potent tumor-suppressor gene. miR-342 is found to be downregulated or epigenetically silenced in multiple different tumor sites, and this loss of expression permits the upregulation of several key oncogenic pathways. In several different cell lines, lower miR-342 expression results in enhanced proliferation and metastasis potential, both in vitro and in xenogenic transplant conditions. Here, we sought to determine the function of miR-342 in an in vivo spontaneous cancer model, using the Ela1-TAg transgenic model of pancreatic acinar carcinoma. Through longitudinal magnetic resonance imaging monitoring of Ela1-TAg transgenic mice, either wild-type or knockout for miR-342, we found no role for miR-342 in the development, growth rate, or pathogenicity of pancreatic acinar carcinoma. These results indicate the importance of assessing miR function in the complex physiology of in vivo model systems and indicate that further functional testing of miR-342 is required before concluding it is a bona fide tumor-suppressor-miR.

miR-29a-deficiency does not modify the course of murine pancreatic acinar carcinoma

The development of cancers involves the complex dysregulation of multiple cellular processes. With key functions in simultaneous regulation of multiple pathways, microRNA (miR) are thought to have important roles in the oncogenic formation process. miR-29a is among the most abundantly expressed miR in the pancreas. Together with altered expression in pancreatic cancer cell lines and biopsies, and known oncogenic functions in leukemia, this expression data has identified miR-29a as a key candidate for miR involvement in pancreatic cancer biology. Here we used miR-29a-deficient mice and the TAg model of pancreatic acinar carcinoma to functionally test the role of miR-29a in vivo. We found no impact of miR-29a loss on the development or growth of pancreatic tumours, nor on the survival of tumour-bearing mice. These results suggest that, despite differential expression, miR-29a is oncogenically neutral in the pancreatic acinar carcinoma context. If these results are extended to other models of pancreatic cancer, they would reduce the attractiveness of miR-29a as a potential therapeutic target in pancreatic cancer.

Prognosticating with the Hospitalized Patient 1-year Mortality Risk Score Using Information Abstracted from the Medical Record

BACKGROUND

Predicting death risk in patients with diverse conditions is difficult. The Hospitalized-patient One-year Mortality Risk (HOMR) score accurately determines death risk in adults admitted to hospital using health administrative data unavailable to clinicians and most researchers.

OBJECTIVE

Determine if HOMR is valid when calculated using data abstracted directly from the medical record.

DESIGN

Medical record review linked to population-based administrative data.

PARTICIPANTS

4996 adults admitted in 2011 to a nonpsychiatric service at a tertiary hospital.

MAIN MEASURES

From the chart, we abstracted information required to calculate the HOMR score and linked to population-based mortality data to determine vital status within 1 year of admission date.

KEY RESULTS

Patients had a mean age of 55.6 (standard deviation [SD], 20.7) with 563 (11.3%) dying. The mean chart HOMR score was 22 (SD, 12) and significantly predicted death risk; a 1-point increase in HOMR increased death odds by 19% (odds ratio, 1.192;, 95% confidence interval [CI], 1.175-1.210;, P < 0.0001). Chart HOMR was strongly discriminative ( C statistic 0.888) and well calibrated (Hosmer-Lemeshow goodness-of-fit test, 12.9; P = 0.11). The observed death risk was strongly associated with expected death risk (calibration slope, 1.02; 95% CI, 0.89-1.16). Notation of delirium or falls on admitting notes or dependence for at least 1 activity of daily living were each associated with 1-year death risk independent of the HOMR score.

CONCLUSIONS

One-year mortality risk can be accurately determined in adults admitted to hospital with the HOMR score calculated using information abstracted from the medical record. Patient functional status was independently associated with death risk. Journal of Hospital Medicine 2017;12:224-230.

[Prognostic indices for older adults during the year following hospitalization in an acute medical ward: An update]

CONTEXT

As population grow older, chronic diseases are more prevalent. It leads to an increase of hospitalization for acute decompensation, sometimes iterative. Management of these patients is not always clear, and care provided is not always proportional to life expectancy. Making decisions in acute situations is not easy.

OBJECTIVE

This review aims to list and describe mortality scores within a year following hospitalization of patients of 65 years or older.

SOURCES

Following keywords were searched in title and abstract of articles via an advanced search in PudMed, and by searching Mesh terms: "aged", "aged, 80 and over", "mortality", "prognosis", "hospitalized", "models, statistical", "acute geriatric ward", "frailty", "outcome".

STUDIES SELECTION

Studies published in English between 1985 and 2015 were selected. Last article was published in June 2015. Articles that described prognostic factors of mortality without a scoring system were excluded. Articles that focus either on patients in the Emergency Department and in Intensive Care Unit, or living in institution were excluded.

RESULTS

Twenty-two scores are described in 17 articles. These scores use items that refer to functional status, comorbidities, cognitive status and frailty. Scores of mortality 3 or 6 months after hospitalization are not discriminative. Few of the 1-year mortality prognostic score are discriminative with AUC≥0.7.

LIMITS

This review is not systematic.

CONCLUSION

Practical use of these scores might help management of these patients, in order to initiate appropriate reflexion and palliative care if necessary.

Association of Hospital Admission Risk Profile Score with Mortality in Hospitalized Older Adults

OBJECTIVES

To evaluate the association of the Hospital Admission Risk Profile (HARP) score with mortality after discharge in a population of hospitalized older adults.

DESIGN

Retrospective cohort study.

PARTICIPANTS

Hospitalized patients aged 70 years or older.

MEASUREMENTS

Patient age at the time of admission, modified Folstein Mini-Mental Status Exam score, and self-reported instrumental activities of daily living 2 weeks prior to admission were used to calculate a HARP score. The primary outcome assessed was overall mortality up to 365 days after hospital discharge. Cox proportional hazard analyses evaluated the association between HARP score and mortality adjusting for age, sex, and comorbidities associated with increased mortality.

RESULTS

Of the 474 patients, 165 (34.8%) had a low HARP score, 177 (37.4%) had an intermediate, and 132 (27.8%) had a high score. HARP score was not associated with differences in 30-day readmission rates. High HARP score patients had higher mortality when compared to patients with low HARP scores at all time frames (30 days: 12.9% vs 1.8%, p < .05; 90 days: 19.7% vs 4.8%, p < .05; 365 days: 34.8% vs 16.9%, p < .05). In fully adjusted Cox proportional models, patients with high HARP scores had a 3.5 times higher odds of mortality when compared to low HARP score patients.

CONCLUSION

The HARP score is a simple and easy to use instrument that identifies patients at increased risk for mortality after hospital discharge. Early identification of patients at increased risk for mortality has the potential to help guide treatment decisions following hospital discharge and provides additional information to providers and patients for shared decision making and may help in clarifying and achieving patient and family goals of care.

No Effect of Dietary Aspartame or Stevia on Pancreatic Acinar Carcinoma Development, Growth, or Induced Mortality in a Murine Model

Pancreatic cancer has an extremely poor prognosis, largely due to a poor record for early detection. Known risk factors for pancreatic cancer include obesity, diet, and diabetes, implicating glucose consumption and regulation as a key player. The role of artificial sweeteners may therefore be pertinent to disease kinetics. The oncogenic impact of artificial sweeteners is a highly controversial area. Aspartame, one of the most studied food additives, is widely recognized as being generally safe, although there are still specific areas where research is incomplete due to study limitations. Stevia, by contrast, has been the subject of relatively few studies, and the potential health benefits are based on extrapolation rather than direct testing. Here, we used longitudinal tracking of pancreatic acinar carcinoma development, growth, and lethality in a sensitized mouse model. Despite exposure to aspartame and stevia from the in utero stage onward, we found no disease modification activity, in either direction. These results contribute to the data on aspartame and stevia safety, while also reducing confidence in several of the purported health benefits.

A Comprehensive Multimorbidity Index for Predicting Mortality in Intensive Care Unit Patients

BACKGROUND

Accurate prediction of mortality for patients admitted to the intensive care units (ICUs) is an important component of medical care. However, little is known about the role of multimorbidity in predicting end of life for high-risk and vulnerable patients.

OBJECTIVE

The aim of the study was to derive and validate a multimorbidity risk model in an attempt to predict all-cause mortality at 6 and 12 months posthospital discharge.

METHODS

This is a retrospective, observational, clinical cohort study. Data were collected on 442,692 ICU patients who received care through the Veterans Administration between January 2003 and December 2013. The primary outcome was all-cause mortality at 6 and 12 months posthospital discharge. We divided the data into derivation (80%) and validation (20%) sets. Using multivariable logistic regression models, we compared prognostic models based on age, principal diagnosis groups, physiological markers, immunosuppressants, comorbidity categories, and a newly developed multimorbidity index (MMI) based on 5695 comorbidities. The cross-validated area under the receiver operating characteristic curve (AUC) was used to report the accuracy of predicting all-cause mortality at 6 and 12 months of hospital discharge.

RESULTS

The average age of patients was 68.87 years (standard deviation = 12.1), 95.9% were males, 44.9% were widowed, divorced, or separated. The relative order of accuracy in predicting mortality was the MMI (AUC = 0.84, CI = 0.83-0.84), VA Inpatient Evaluation Center index (AUC = 0.80, CI = 0.79-0.81), principal diagnosis groups (AUC = 0.74, CI = 0.73-0.74), comorbidities (AUC = 0.69, CI = 0.68-0.70), physiological markers (AUC = 0.65, CI = 0.64-0.65), age (AUC = 0.60, CI = 0.60-0.61),and immunosuppressant use (AUC = 0.59, CI = 0.58-0.59).

CONCLUSIONS

The MMI improved the accuracy of predicting short- and long-term all-cause mortality for ICU patients. Further prospective studies are needed to validate the index in different clinical settings and test generalizability of results in patients outside the VA system of care.

CRONIGAL: Prognostic index for chronic patients after hospital admission

OBJECTIVE

We have followed patients admitted to a Polypathology and Advanced Age Unit for two years in order to identify the variables that best define the mortality prognosis at medium-term (1-2years) for chronic and polypathological patients requiring admission at an Internal Medicine Department.

METHODS

This is an observational, prospective study in clinical practice. Polypathological, chronic or multimorbidity patients were included. The classification of the Spanish Ministry for Health was used in order to classify patients as chronic or polypathological. The Charlson Index and Barthel Index were estimated and the Pfeiffer test was administered. The Spanish PROFUND Index was also used. Logistic regression models and Cox proportional hazard model were built in order to study the influence of prognostic factors on survival.

RESULTS

A total of 567 patients were included: 333 met polypathological (PPP) criteria and 234 chronic criteria (CC). Mean age was 84.8+7.3years. A total of 469 were followed up, most patients belonged to category E (282), 174 to category A and 118 to category C. The prognosis at one year of our patients can be estimated with 7 variables: age, neoplasia, delirium, Barthel, Pfeiffer, presence of atrial fibrillation, and creatinine. The area under the curve is 0.74.

CONCLUSION

The variables dementia, neoplasia, delirium at admission, Barthel Index under 60, or deceased spouse have mortality prognosis value at one or two years. An index with 7 variables applicable to chronic and polypathological patients after admission may serve as tool to better manage complex chronic patients and follow them up.

Improving Prognostic Web Calculators: Violation of Preferential Risk Independence

BACKGROUND

Web-based applications are available for prognostication of individual patients. These prognostic models were developed for groups of patients. No one is the average patient, and using these calculators to inform individual patients could provide misleading results.

OBJECTIVE

This article gives an example of paradoxical results that may emerge when indices used for prognosis of the average person are used for care of an individual patient.

METHODS

We calculated the expected mortality risks of stomach cancer and its associated comorbidities. Mortality risks were calculated using data from 140,699 Veterans Administration nursing home residents.

RESULTS

On average, a patient with hypertension has a higher risk of mortality than one without hypertension. Surprisingly, among patients with lung cancer, hypertension is protective and reduces risk of mortality. This paradoxical result is explained by how group-level, average prognosis could mislead individual patients. In particular, average prognosis of lung cancer patients reflects the impact of various comorbidities that co-occur in lung cancer patients. The presence of hypertension, a relatively mild comorbidity of lung cancer, indicates that more serious comorbidities have not occurred. It is not that hypertension is protective; it is the absence of more serious comorbidities that is protective. The article shows how the presence of these anomalies can be checked through the mathematical concept of preferential risk independence.

CONCLUSION

Instead of reporting average risk scores, web-based calculators may improve accuracy of predictions by reporting the unconfounded risks.

Wanted Dead or Alive

To determine who is dead or alive, many researchers, policy makers, and corporations have relied on the National Death Index (NDI). This study investigates the impact of using NDI information to establish mortality status upon longevity analyses. A community sample of 694 individuals, enrolled in the Ohio Longitudinal Study of Aging and Retirement in 1975, participated. The authors compared the survival of two groups of these participants: those for whom death information came from clear matches with NDI and those for whom death information was based on both NDI information and supplementary information, such as community informants. Those in the expanded group tended to have significantly shorter survival than those in the strict group. Findings suggest that using limited NDI information alone may falsely increase longevity. Use of NDI offers numerous benefits, but incomplete or inaccurate classification of mortality status may affect study results.

Factors Affecting Physicians' Intentions to Communicate Personalized Prognostic Information to Cancer Patients at the End of Life: An Experimental Vignette Study

PURPOSE

To explore the effects of personalized prognostic information on physicians' intentions to communicate prognosis to cancer patients at the end of life, and to identify factors that moderate these effects.

METHODS

A factorial experiment was conducted in which 93 family medicine physicians were presented with a hypothetical vignette depicting an end-stage gastric cancer patient seeking prognostic information. Physicians' intentions to communicate prognosis were assessed before and after provision of personalized prognostic information, while emotional distress of the patient and ambiguity (imprecision) of the prognostic estimate were varied between subjects. General linear models were used to test the effects of personalized prognostic information, patient distress, and ambiguity on prognostic communication intentions, and potential moderating effects of 1) perceived patient distress, 2) perceived credibility of prognostic models, 3) physician numeracy (objective and subjective), and 4) physician aversion to risk and ambiguity.

RESULTS

Provision of personalized prognostic information increased prognostic communication intentions (P < 0.001, η(2) = 0.38), although experimentally manipulated patient distress and prognostic ambiguity had no effects. Greater change in communication intentions was positively associated with higher perceived credibility of prognostic models (P = 0.007, η(2) = 0.10), higher objective numeracy (P = 0.01, η(2) = 0.09), female sex (P = 0.01, η(2) = 0.08), and lower perceived patient distress (P = 0.02, η(2) = 0.07). Intentions to communicate available personalized prognostic information were positively associated with higher perceived credibility of prognostic models (P = 0.02, η(2) = 0.09), higher subjective numeracy (P = 0.02, η(2) = 0.08), and lower ambiguity aversion (P = 0.06, η(2) = 0.04).

CONCLUSIONS

Provision of personalized prognostic information increases physicians' prognostic communication intentions to a hypothetical end-stage cancer patient, and situational and physician characteristics moderate this effect. More research is needed to confirm these findings and elucidate the determinants of prognostic communication at the end of life.

Is self-rated health an independent prognostic factor of six-week mortality in older patients hospitalized for an acute condition?

PURPOSE

To determine whether self-rated health is a prognostic factor of six-week mortality, independently of other known objective prognostic factors.

METHODS

The SAFMA study was a prospective cohort, which recruited patients from the University Hospital of Martinique Acute Care for Elders unit (French West Indies) from January to June 2012. Patients aged 75 or older and hospitalized for an acute condition were eligible. The outcome was time to death within the six-week follow-up. The main explanatory variable was self-rated health. Sociodemographic and clinical characteristics were considered as covariates. Cox's proportional hazards model was used.

RESULTS

The mean age of the 223 patients included was 85.1 ± 5.5 years. Six-week mortality rate was 14.8 %; none were lost to follow-up. In total, 123 claimed "very good to good" health, and 100 "medium to very poor" health. Self-rated health was the only independent prognostic factor associated with 6-week mortality (hazard ratio 2.61; 95 % confidence interval 1.18-5.77; p = .02), when adjusting for known prognostic factors such as age, dimensions of the comprehensive geriatric assessment and comorbidity burden.

CONCLUSION

The association between self-rated health and short-term mortality could have implications for clinical practice, particularly in helping in the estimation of prognosis in acute care setting.

Risk Prediction Tools in Oncology

Cancer prediction tools are widely available to clinicians, and the data retrieved from these tools can assist with patient counseling sessions on risk, prognosis, treatment, and recurrence. Current tools are able to synthesize data in a concise, unbiased, and evidence-based method, allowing patients to make better-informed decisions about their treatment options. As useful as these tools can be, clinicians must understand their limitations and evaluate the tools for quality and applicability.
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External validation of the Hospital-patient One-year Mortality Risk (HOMR) model for predicting death within 1 year after hospital admission

BACKGROUND

Predicting long-term survival after admission to hospital is helpful for clinical, administrative and research purposes. The Hospital-patient One-year Mortality Risk (HOMR) model was derived and internally validated to predict the risk of death within 1 year after admission. We conducted an external validation of the model in a large multicentre study.

METHODS

We used administrative data for all nonpsychiatric admissions of adult patients to hospitals in the provinces of Ontario (2003-2010) and Alberta (2011-2012), and to the Brigham and Women's Hospital in Boston (2010-2012) to calculate each patient's HOMR score at admission. The HOMR score is based on a set of parameters that captures patient demographics, health burden and severity of acute illness. We determined patient status (alive or dead) 1 year after admission using population-based registries.

RESULTS

The 3 validation cohorts (n = 2,862,996 in Ontario, 210 595 in Alberta and 66,683 in Boston) were distinct from each other and from the derivation cohort. The overall risk of death within 1 year after admission was 8.7% (95% confidence interval [CI] 8.7% to 8.8%). The HOMR score was strongly and significantly associated with risk of death in all populations and was highly discriminative, with a C statistic ranging from 0.89 (95% CI 0.87 to 0.91) to 0.92 (95% CI 0.91 to 0.92). Observed and expected outcome risks were similar (median absolute difference in percent dying in 1 yr 0.3%, interquartile range 0.05%-2.5%).

INTERPRETATION

The HOMR score, calculated using routinely collected administrative data, accurately predicted the risk of death among adult patients within 1 year after admission to hospital for nonpsychiatric indications. Similar performance was seen when the score was used in geographically and temporally diverse populations. The HOMR model can be used for risk adjustment in analyses of health administrative data to predict long-term survival among hospital patients.

Perspective: the challenge of clinical decision-making for drug treatment in older people. The role of multidimensional assessment and prognosis

A complex decision path with a careful evaluation of the risk-benefit ratio is mandatory for drug treatment in advanced age. Enrollment biases in randomized clinical trials (RCTs) cause an under-representation of older individuals. In high-risk frail older subjects, the lack of RCTs makes clinical decision-making particularly difficult. Frail individuals are markedly susceptible to adverse drug reactions, and frailty may result in reduced treatment efficacy. Life expectancy should be included in clinical decision-making paths to better assess the benefits and risks of different drug treatments in advanced age. We performed a scoping review of principal hospital- and community-based prognostic indices in older age. Mortality prognostic tools could help clinical decision-making in diagnostics and therapeutics, tailoring appropriate intervention for older patients. The effectiveness of drug treatments may be significantly different in older patients with different risk of mortality. Clinicians need to consider the prognostic information obtained through well-validated, accurate, and calibrated predictive tools to identify those patients who may benefit from drug treatments given with the aim of increasing survival.

Comprehensive versus consultative rehabilitation services postacute stroke: Outcomes differ

Comprehensive rehabilitation services postacute stroke have been shown efficacious in European trials; however, their effectiveness in everyday practices in the United States is unknown. We compared outcomes of veteran patients provided with comprehensive rehabilitation with those provided with consultative rehabilitation services postacute stroke using propensity scores. Outcomes included change in patients' physical and cognitive independence after rehabilitation, discharge to home as opposed to other settings, and 1-yr posthospital discharge survival. Of the 2,963 patients in the study, 683 (23.1%) received comprehensive rehabilitation while the remaining patients received consultative services. We found, after propensity adjustment, that those who received comprehensive rehabilitation compared with consultative gained on average 12.8 (95% confidence interval [CI]: 9.1 to 16.5) more points of physical independence on a 78-point scale and gained 1.5 (95% CI: 0.8 to 2.2) more points of cognitive independence on a 30-point scale. The likelihoods of discharge to home from the hospital (odds ratio [OR] = 1.61, 95% CI: 1.07 to 2.44) and 1-yr posthospital discharge survival (OR = 1.79, 95% CI: 1.25 to 2.56) were significantly higher among those who received comprehensive rehabilitation. Among patients hospitalized for acute stroke, comprehensive rehabilitation services are associated with greater recovery of physical and cognitive independence, improved home discharge likelihood, and improved 1-yr survival.

Predictors of six-month mortality among nursing home residents: diagnoses may be more predictive than functional disability

OBJECTIVE

Loss of daily living functions can be a marker for end of life and possible hospice eligibility. Unfortunately, data on patient's functional abilities is not available in all settings. In this study we compare predictive accuracy of two indices designed to predict 6-month mortality among nursing home residents. One is based on traditional measures of functional deterioration and the other on patients' diagnoses and demography.

METHODS

We created the Hospice ELigibility Prediction (HELP) Index by examining mortality of 140,699 Veterans Administration (VA) nursing home residents. For these nursing home residents, the available data on history of hospital admissions were divided into training (112,897 cases) and validation (27,832 cases) sets. The training data were used to estimate the parameters of the HELP Index based on (1) diagnoses, (2) age on admission, and (3) number of diagnoses at admission. The validation data were used to assess the accuracy of predictions of the HELP Index. The cross-validated accuracy of the HELP Index was compared with the Barthel Index (BI) of functional ability obtained from 296,052 VA nursing home residents. A receiver operating characteristic curve was used to examine sensitivity and specificity of the predicted odds of mortality.

RESULTS

The area under the curve (AUC) for the HELP Index was 0.838. This was significantly (α <0.01) higher than the AUC for the BI of 0.692.

CONCLUSIONS

For nursing home residents, comorbid diagnoses predict 6-month mortality more accurately than functional status. The HELP Index can be used to estimate 6-month mortality from hospital data and can guide prognostic discussions prior to and following nursing home admission.

Incidence of bullous pemphigoid and mortality of patients with bullous pemphigoid in Olmsted County, Minnesota, 1960 through 2009

BACKGROUND

Bullous pemphigoid (BP) is an autoimmune blistering disease that is associated with increased mortality.

OBJECTIVE

We sought to determine the incidence and mortality of patients with BP.

METHODS

A total of 87 residents of Olmsted County, Minnesota, were identified who had their first lifetime diagnosis of BP from January 1960 through December 2009. Incidence and mortality were compared with age- and sex-matched control patients from the same geographic area.

RESULTS

The adjusted incidence of BP was 2.4 per 100,000 person-years (95% confidence interval, 1.9-2.9). Incidence of BP increased significantly with age (P < .001) and over time (P = .034). Trend tests indicate increased diagnosis of localized disease (P = .006) may be a contributing factor. Survival observed in the incident BP cohort was significantly poorer than expected (P < .001). Survival was not different among patients with multisite versus localized disease (P = .90).

LIMITATIONS

Retrospective study design and study population from a small geographic area are limitations.

CONCLUSION

Incidence of BP in the United States is comparable with that found in Europe and Asia. The mortality of BP is lower in the United States than Europe, but higher than previous estimates.

Psychiatric and psychosocial challenges in patients undergoing haematopoietic stem cell transplants

Haematological malignancies are often treated with haematopoietic stem cell transplants (HSCT). The disease and its treatment are challenging and life threatening, as they not only affect the recipients, but also their families. This review highlights the available data on the psychological, psychiatric and social impact of these illnesses and their treatment on recipients and families. There are robust data that correlate HSCT with emotional distress, as emotional and physical functioning significantly affect quality of life. Psychiatric co-morbidity including anxiety, depression, adjustment and post-traumatic stress disorder, delirium and cognitive deficits have been reported at different stages in the transplant process. This review will highlight the psychosocial and clinical research findings relevant to HSCT patients and will summarize recommendations for future psychosocial research in this population.

Caring about prognosis: a validation study of the caring criteria to identify hospitalized patients at high risk for death at 1 year

BACKGROUND

Identifying patients, at the time of hospital admission, who are at high risk for 1-year mortality is an ideal opportunity to introduce palliative interventions into the hospital care plan. The CARING (C = primary diagnosis of cancer, A = ≥ 2 admissions to the hospital for a chronic illness within the last year; R = resident in a nursing home; I = intensive care unit admission with multiorgan failure, NG = noncancer hospice guidelines [meeting ≥ 2 of the National Hospice and Palliative Care Organization's guidelines] criteria is a practical prognostic index developed and validated in the Veteran's Administration hospital setting that identifies patients at high risk of death within 1 year, although its effectiveness in a broader patient population is unknown.

OBJECTIVE

To validate the CARING criteria in a university and safety-net hospital setting.

DESIGN

Retrospective observational cohort study.

SETTING

Inpatient.

PATIENTS

Adults admitted to medical and surgical inpatient services during the study period of July 2005 through August 2005.

MEASUREMENTS

Mortality at 1 year following the index hospitalization was the primary end point. The CARING criteria were abstracted from the chart using only medical data available at time of admission.

RESULTS

At total of 1064 patients were admitted during the study period. Primary diagnosis of cancer (odds ratio [OR) = 7.23 [4.45-11.75]), intensive care unit admission with multiple organ failure (OR = 6.97 [2.75-17.68]), >2 noncancer hospice guidelines (OR = 15.55 [7.28-33.23]), and age (OR = 1.60 [1.32-1.93]) were predictive of 1-year mortality (C statistic = 0.79). One-year survival was significantly lower for those who met ≥ 1 of the CARING criteria.

CONCLUSIONS

The CARING criteria are a practical prognostic tool validated in a broad inpatient population that can be utilized on hospital admission to estimate risk of death in 1 year, with the goal of identifying patients who may benefit most from incorporating palliative interventions into their hospital plan of care. Journal of Hospital Medicine 2013;8:696-701. © 2013 Society of Hospital Medicine.

One-year all-cause mortality after stroke: a prediction model

OBJECTIVE

By using data from Department of Veterans Affairs (VA) national databases, this article presents and internally validates a 1-year all-cause mortality prediction index after hospitalization for acute stroke.

DESIGN

An observational cohort.

SETTING

VA medical centers.

PARTICIPANTS

Veterans with a diagnosis of a new stroke who were discharged between October 1, 2006, and September 30, 2008.

MAIN OUTCOME MEASURE

Death due to any cause that occurred between the index hospital discharge date and the 1-year anniversary of that date.

RESULTS

Within 1-year after discharge, 1542 (12.3%) of the total 12,565 patients had died. Seventeen risk factors known at the point of hospital discharge remained in the predictive model of 1-year postdischarge mortality after backward selection, including advanced age, admission from extended care, type of stroke, 8 comorbid conditions, 4 types of procedures that occurred during the index hospitalization, hospital length of stay (longer than 3 weeks), and discharge location. We assigned a score to each variable in the final model and a risk score was determined for each patient by adding up the points for all risk factors present. According to these risk scores, the patients were divided into approximate quartiles that yielded low, moderate, high, and highest mortality likelihood strata. The risk of 1-year mortality ranged from 2.24% in the lowest quartile to 29.50% in the highest quartile in the derivation cohort and from 2.11%-30.77% in the validation cohort. Model discrimination demonstrated an area under the receiver operating characteristic curve of 0.785 in the derivation cohort and 0.787 in the validation cohort. The Hosmer-Lemeshow goodness of fit indicated that the model fit was adequate (P = .69).

CONCLUSION

When using readily available data, a simple index that stratifies stroke patients at hospital discharge according to low, moderate, high, and highest likelihood of all-cause 1-year mortality is feasible and can inform the postdischarge planning process, depending on level of risk.