Comparison of 6 Mortality Risk Scores for Prediction of 1-Year Mortality Risk in Older Adults With Multimorbidity

Combined nutritional status and activities of daily living disability is associated with one-year mortality after hip fracture surgery for geriatric patients: a retrospective cohort study

OBJECTIVE

We aimed to explore the association combined nutritional status and activities of daily living disability with all-cause mortality of older adults with hip fracture in the first year after hospitalization.

METHODS

This is a single-center retrospective cohort study in older adults with hip fracture patients. Clinical data and laboratory results were collected from electronic medical record system of our hospital (2014-2021). The endpoint of this study was all-cause mortality in the first year after hospitalization.

RESULTS

A total of 303 older adults were enrolled and all-cause mortality was 21.8%. The study population was categorized by CONUT score. Patients in CONUT score 5-12 had a higher age, ASA status, CRP and creatinine level, more patients with history of fracture, pneumonia and delirium, meanwhile, lower BMI and ADL score, lower hemoglobin, lymphocyte, total protein, albumin, triglyceride, total cholesterol and one year survival than those in CONUT score 0-4 (all P < 0.05). Multivariable Cox analysis showed that BMI, ADL score and CONUT score were independent risk factors for all-cause mortality of hip fracture in older adults (HR (95% CI):2.808(1.638, 4.814), P < 0.001; 2.862(1.637, 5.003), P < 0.001; 2.322(1.236, 4.359), P = 0.009, respectively). More importantly, the combined index of CONUT and ADL score had the best predictive performance based on ROC curve (AUC 0.785, 95% CI: 0.734-0.830, P < 0.0001). Kaplan-Meier survival curves for all-cause mortality showed that patients with CONUT score increase and ADL score impairment had a higher mortality rate at 1 year compared to CONUT score decrease and ADL score well (Log Rank χ2 = 45.717, P < 0.0001).

CONCLUSIONS

Combined CONUT and ADL score is associated with one-year mortality after hip fracture surgery for geriatric patients.

Incidence and Prediction of Unrelated Mortality After Successful Endoscopic Eradication Therapy for Barrett's Neoplasia

BACKGROUND & AIMS

Follow-up (FU) strategies after endoscopic eradication therapy (EET) for Barrett's neoplasia do not consider the risk of mortality from causes other than esophageal adenocarcinoma (EAC). We aimed to evaluate this risk during long-term FU, and to assess whether the Charlson Comorbidity Index (CCI) can predict mortality.

METHODS

We included all patients with successful EET from the nationwide Barrett registry in the Netherlands. Data were merged with National Statistics for accurate mortality data. We evaluated annual mortality rates (AMRs, per 1000 person-years) and standardized mortality ratio for other-cause mortality. Performance of the CCI was evaluated by discrimination and calibration.

RESULTS

We included 1154 patients with a mean age of 64 years (±9). During median 59 months (p25-p75 37-91; total 6375 person-years), 154 patients (13%) died from other causes than EAC (AMR, 24.1; 95% CI, 20.5-28.2), most commonly non-EAC cancers (n = 58), cardiovascular (n = 31), or pulmonary diseases (n = 26). Four patients died from recurrent EAC (AMR, 0.5; 95% CI, 0.1-1.4). Compared with the general Dutch population, mortality was significantly increased for patients in the lowest 3 age quartiles (ie, age <71 years). Validation of CCI in our population showed good discrimination (Concordance statistic, 0.78; 95% CI, 0.72-0.84) and fair calibration.

CONCLUSION

The other-cause mortality risk after successful EET was more than 40 times higher (48; 95% CI, 15-99) than the risk of EAC-related mortality. Our findings reveal that younger post-EET patients exhibit a significantly reduced life expectancy when compared with the general population. Furthermore, they emphasize the strong predictive ability of CCI for long-term mortality after EET. This straightforward scoring system can inform decisions regarding personalized FU, including appropriate cessation timing. (NL7039).

Proof-of-concept for an automatable mortality prediction scoring in hospitalised older adults

Introduction

It is challenging to prognosticate hospitalised older adults. Delayed recognition of end-of-life leads to failure in delivering appropriate palliative care and increases healthcare utilisation. Most mortality prediction tools specific for older adults require additional manual input, resulting in poor uptake. By leveraging on electronic health records, we aim to create an automatable mortality prediction tool for hospitalised older adults.

Methods

We retrospectively reviewed electronic records of general medicine patients ≥75 years at a tertiary hospital between April-September 2021. Demographics, comorbidities, ICD-codes, age-adjusted Charlson Comorbidity Index (CCI), Hospital Frailty Risk Score, mortality and resource utilization were collected. We defined early deaths, late deaths and survivors as patients who died within 30 days, 1 year, and lived beyond 1 year of admission, respectively. Multivariate logistic regression analyses were adjusted for age, gender, race, frailty, and CCI. The final prediction model was created using a stepwise logistic regression.

Results

Of 1,224 patients, 168 (13.7%) died early and 370 (30.2%) died late. From adjusted multivariate regression, risk of early death was significantly associated with ≥85 years, intermediate or high frail risk, CCI > 6, cardiovascular risk factors, AMI and pneumonia. For late death, risk factors included ≥85 years, intermediate frail risk, CCI >6, delirium, diabetes, AMI and pneumonia. Our mortality prediction tool which scores 1 point each for age, pneumonia and AMI had an AUC of 0.752 for early death and 0.691 for late death.

Conclusion

Our mortality prediction model is a proof-of-concept demonstrating the potential for automated medical alerts to guide physicians towards personalised care for hospitalised older adults.

Identifying Patterns of Primary Care In-Person and Telemedicine Use in the Veterans Health Administration: A Latent Class Analysis

BACKGROUND

The Veterans Health Administration increased synchronous telemedicine (video and telephone visits) in primary care in response to the COVID-19 pandemic.

OBJECTIVE

Our objective was to determine veteran use patterns of in-person and telemedicine primary care when all modalities were available.

DESIGN

A retrospective cohort analysis. We performed a latent class analysis of primary care visits over a 1-year period to identify veteran subgroup (i.e., class) membership based on amount of primary care use and modality used. Then, we used multinomial logistic regression with a categorical outcome to identify patient characteristics associated with class identification.

PARTICIPANTS

A random national sample consisting of 564,580 primary care empaneled veterans in June 2021.

MAIN MEASURES

Latent class membership.

KEY RESULTS

We identified three latent classes: those with few primary care visits that were predominantly telephone-based (45%), intermediate number of visits of all modalities (50%), and many visits of all modalities (5%). In an adjusted model, characteristics associated with the "few" visits class, compared to the intermediate class, were older age, male sex, White race, further driving distance to primary care, higher Gagne, optimal internet speed, and unmarried status (OR 1.002, 1.52, 1.13, 1.004, 1.04, 1.05, 1.06, respectively; p < .05). Characteristics associated with membership in the "many" visits class, compared to the intermediate class, were Hispanic race, higher JEN Frailty Index and Gagne (OR 1.12, 1.11, 1.02, respectively; p < .05), and higher comorbidity by Care Assessment Need score quartile (Q2 1.73, Q3 2.80, Q4 4.12; p < 0.05).

CONCLUSIONS

Veterans accessing primary care in-person or via telemedicine do so primarily in three ways: (1) few visits, predominantly telephone; (2) intermediate visits, all modalities, (3) many visits, all modalities. We found no groups of veterans receiving a majority of primary care through video.

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.

Frailty and uptake of angiotensin receptor neprilysin inhibitor for heart failure with reduced ejection fraction

BACKGROUND

Frail older adults may be less likely to receive guideline-directed medical therapy (GDMT)-renin-angiotensin blockers, beta-blockers, and mineralocorticoid receptor antagonists-for heart failure with reduced ejection fraction (HFrEF). We aimed to examine the uptake of angiotensin receptor neprilysin inhibitor (ARNI) and GDMT in frail older adults with HFrEF.

METHODS

Using 2015-2019 Medicare data, we estimated the proportion of beneficiaries with HFrEF receiving ARNI and GDMT each year by frailty status, defined by a claims-based frailty index. Logistic regression was used to identify clinical characteristics associated with ARNI initiation. Cox proportional hazards regression was used to examine the association of GDMT use in 2015 and death or heart failure hospitalization in 2016-2019.

RESULTS

Among 147,506-180,386 beneficiaries with HFrEF (mean age: 77 years; 27% women; 42.6-49.1% frail) in 2015-2019, the proportion of patients receiving ARNI increased in both non-frail (0.4%-16.4%) and frail (0.3%-13.7%) patients (p for yearly-trend-by-frailty = 0.970). Among those not receiving a renin-angiotensin system blocker, patients with age ≥ 85 years (odds ratio [95% CI], 0.89 [0.80-0.99]), dementia (0.88 [0.81-0.96]), and frailty (0.87 [0.81-0.94]) were less likely to initiate ARNI. The proportion of patients receiving all 3 GDMT classes increased in non-frail patients (22.0%-27.0%) but changed minimally in frail patients (19.6%-21.8%). Regardless of frailty status, treatment with at least 1 class of GDMT was associated with lower death or heart failure hospitalization than no GDMT medications (hazard ratio [95% CI], 0.94 [0.91-0.97], 0.92 [0.89-0.94], 0.94 [0.91-0.97] for 1, 2, and 3 classes, respectively).

CONCLUSIONS

Our results suggest an evidence-practice gap in the use of ARNI and GDMT in Medicare beneficiaries with HFrEF, particularly those with frailty. Efforts to narrow this gap are needed to reduce the burden of HFrEF in older adults.

Establishment of a prognostic risk prediction modelfor non-small cell lung cancer patients with brainmetastases: a retrospective study

Background

Patients with non-small cell lung cancer (NSCLC) who develop brain metastases (BM) have a poor prognosis. This study aimed to construct a clinical prediction model to determine the overall survival (OS) of NSCLC patients with BM.

Methods

A total of 300 NSCLC patients with BM at the Yunnan Cancer Centre were retrospectively analysed. The prediction model was constructed using the least absolute shrinkage and selection operator-Cox regression. The bootstrap sampling method was employed for internal validation. The performance of our prediction model was compared using recursive partitioning analysis (RPA), graded prognostic assessment (GPA), the update of the graded prognostic assessment for lung cancer using molecular markers (Lung-molGPA), the basic score for BM (BSBM), and tumour-lymph node-metastasis (TNM) staging.

Results

The prediction models comprising 15 predictors were constructed. The area under the curve (AUC) values for the 1-year, 3-year, and 5-year time-dependent receiver operating characteristic (curves) were 0.746 (0.678-0.814), 0.819 (0.761-0.877), and 0.865 (0.774-0.957), respectively. The bootstrap-corrected AUC values and Brier scores for the prediction model were 0.811 (0.638-0.950) and 0.123 (0.066-0.188), respectively. The time-dependent C-index indicated that our model exhibited significantly greater discrimination compared with RPA, GPA, Lung-molGPA, BSBM, and TNM staging. Similarly, the decision curve analysis demonstrated that our model displayed the widest range of thresholds and yielded the highest net benefit. Furthermore, the net reclassification improvement and integrated discrimination improvement analyses confirmed the enhanced predictive power of our prediction model. Finally, the risk subgroups identified by our prognostic model exhibited superior differentiation of patients' OS.

Conclusion

The clinical prediction model constructed by us shows promise in predicting OS for NSCLC patients with BM. Its predictability is superior compared with RPA, GPA, Lung-molGPA, BSBM, and TNM staging.

Stroke risk in older British men: Comparing performance of stroke-specific and composite-CVD risk prediction tools

Stroke risk is currently estimated as part of the composite risk of cardiovascular disease (CVD). We investigated if composite-CVD risk prediction tools QRISK3 and Pooled Cohort Equations-PCE, derived from middle-aged adults, are as good as stroke-specific Framingham Stroke Risk Profile-FSRP and QStroke for capturing the true risk of stroke in older adults. External validation for 10y stroke outcomes was performed in men (60-79y) of the British Regional Heart Study. Discrimination and calibration were assessed in separate validation samples (FSRP n = 3762, QStroke n = 3376, QRISK3 n = 2669 and PCE n = 3047) with/without adjustment for competing risks. Sensitivity/specificity were examined using observed and clinically recommended thresholds. Performance of FSRP, QStroke and QRISK3 was further compared head-to-head in 2441 men free of a range of CVD, including across age-groups. Observed 10y risk (/1000PY) ranged from 6.8 (hard strokes) to 11 (strokes/transient ischemic attacks). All tools discriminated weakly, C-indices 0.63-0.66. FSRP and QStroke overestimated risk at higher predicted probabilities. QRISK3 and PCE showed reasonable calibration overall with minor mis-estimations across the risk range. Performance worsened on adjusting for competing non-stroke deaths. However, in men without CVD, QRISK3 displayed relatively better calibration for stroke events, even after adjustment for competing deaths, including in oldest men. All tools displayed similar sensitivity (63-73 %) and specificity (52-54 %) using observed risks as cut-offs. When QRISK3 and PCE were evaluated using thresholds for CVD prevention, sensitivity for stroke events was 99 %, with false positive rate 97 % suggesting existing intervention thresholds may need to be re-examined to reflect age-related stroke burden.