Evidence map›Paper›PMID 38053653›Full record

ArticleClinical interventions in aging2023

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

Yuanyuan Li, Xiaohong Liu, Lin Kang, Jiaojiao Li

Abstract read
In one paragraph

Article in Clinical interventions in aging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Observational
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Yuanyuan LiDepartment of Geriatrics, Peking Union Medical College, Chinese Academy of Medical Sciences, Peking Union Medical College Hospital, Beijing, People's Republic of China.ORCID 0000-0003-0620-5958
Xiaohong Liu *Department of Geriatrics, Peking Union Medical College, Chinese Academy of Medical Sciences, Peking Union Medical College Hospital, Beijing, People's Republic of China.
Lin Kang *Department of Geriatrics, Peking Union Medical College, Chinese Academy of Medical Sciences, Peking Union Medical College Hospital, Beijing, People's Republic of China.
Jiaojiao LiDepartment of Geriatrics, Peking Union Medical College, Chinese Academy of Medical Sciences, Peking Union Medical College Hospital, Beijing, People's Republic of China.ORCID 0000-0001-5407-7431

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

AftercareHand StrengthAgedFrail ElderlyGeriatric AssessmentHumansLength of StayPatient DischargePrognosisagedcare for older adultscomprehensive geriatric assessmentfrailprediction models

Identifiers

PMID38053653
PMCPMC10695131

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.