Evidence map›Paper›PMID 38787554›Full record

ArticleJAMA network open2024

Adaptation of the Risk Analysis Index for Frailty Assessment Using Diagnostic Codes.

Alis J Dicpinigaitis, Yekaterina Khamzina, Daniel E Hall, Hasan Nassereldine, Jason Kennedy, Christopher W Seymour, Meic Schmidt, Katherine M Reitz, Christian A Bowers

Abstract read
In one paragraph

Article in JAMA network open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
–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

19 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Frailty Screening Using the Risk Analysis Index: A User Guide.Joint Commission journal on quality and patient safety · 2025
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  18. Assessment of Racial Bias within the Risk Analysis Index of Frailty.Annals of surgery open : perspectives of surgical history, education, and clinical approaches · 2024
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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

9 authors.

Alis J DicpinigaitisDepartment of Neurology, New York Presbyterian-Weill Cornell Medical Center, New York, New York.
Yekaterina KhamzinaDepartment of Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania.
Daniel E HallDepartment of Neurology, New York Presbyterian-Weill Cornell Medical Center, New York, New York.
Hasan NassereldineDepartment of Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania.
Jason KennedyClinical Research, Investigation, and Systems Modeling of Acute Illness (CRISMA) Center, Pittsburgh, Pennsylvania.
Christopher W SeymourClinical Research, Investigation, and Systems Modeling of Acute Illness (CRISMA) Center, Pittsburgh, Pennsylvania.
Meic SchmidtBowers Neurosurgical Frailty and Outcomes Data Science Lab, Albuquerque, New Mexico.
Katherine M ReitzDepartment of Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania.
Christian A BowersBowers Neurosurgical Frailty and Outcomes Data Science Lab, Albuquerque, New Mexico.

Funding

Sepsis subtypes: biology, treatment, and bedside applicationR35GM119519 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI Christopher W. Seymour · 2016 to 2026
$4.9M
NIA NIH HHS L30 AG064730NIGMS NIH HHS R35 GM119519
6 · The paper itself

Abstract

Importance: Frailty is associated with adverse outcomes after even minor physiologic stressors. The validated Risk Analysis Index (RAI) quantifies frailty; however, existing methods limit application to in-person interview (clinical RAI) and quality improvement datasets (administrative RAI). Objective: To expand the utility of the RAI utility to available International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) administrative data, using the National Inpatient Sample (NIS). Design, Setting, and Participants: RAI parameters were systematically adapted to ICD-10-CM codes (RAI-ICD) and were derived (NIS 2019) and validated (NIS 2020). The primary analysis included survey-weighed discharge data among adults undergoing major surgical procedures. Additional external validation occurred by including all operative and nonoperative hospitalizations in the NIS (2020) and in a multihospital health care system (UPMC, 2021-2022). Data analysis was conducted from January to May 2023. Exposures: RAI parameters and in-hospital mortality. Main Outcomes and Measures: The association of RAI parameters with in-hospital mortality was calculated and weighted using logistic regression, generating an integerized RAI-ICD score. After initial validation, thresholds defining categories of frailty were selected by a full complement of test statistics. Rates of elective admission, length of stay, hospital charges, and in-hospital mortality were compared across frailty categories. C statistics estimated model discrimination. Results: RAI-ICD parameters were weighted in the 9 548 206 patients who were hospitalized (mean [SE] age, 55.4 (0.1) years; 3 742 330 male [weighted percentage, 39.2%] and 5 804 431 female [weighted percentage, 60.8%]), modeling in-hospital mortality (2.1%; 95% CI, 2.1%-2.2%) with excellent derivation discrimination (C statistic, 0.810; 95% CI, 0.808-0.813). The 11 RAI-ICD parameters were adapted to 323 ICD-10-CM codes. The operative validation population of 8 113 950 patients (mean [SE] age, 54.4 (0.1) years; 3 148 273 male [weighted percentage, 38.8%] and 4 965 737 female [weighted percentage, 61.2%]; in-hospital mortality, 2.5% [95% CI, 2.4%-2.5%]) mirrored the derivation population. In validation, the weighted and integerized RAI-ICD yielded good to excellent discrimination in the NIS operative sample (C statistic, 0.784; 95% CI, 0.782-0.786), NIS operative and nonoperative sample (C statistic, 0.778; 95% CI, 0.777-0.779), and the UPMC operative and nonoperative sample (C statistic, 0.860; 95% CI, 0.857-0.862). Thresholds defining robust (RAI-ICD <27), normal (RAI-ICD, 27-35), frail (RAI-ICD, 36-45), and very frail (RAI-ICD >45) strata of frailty maximized precision (F1 = 0.33) and sensitivity and specificity (Matthews correlation coefficient = 0.26). Adverse outcomes increased with increasing frailty. Conclusion and Relevance: In this cohort study of hospitalized adults, the RAI-ICD was rigorously adapted, derived, and validated. These findings suggest that the RAI-ICD can extend the quantification of frailty to inpatient adult ICD-10-CM-coded patient care datasets.

Indexed as

FrailtyHospital MortalityInternational Classification of DiseasesAgedAged, 80 and overFemaleFrail ElderlyGeriatric AssessmentHospitalizationHumansMaleMiddle AgedRisk AssessmentUnited States

Identifiers

PMID38787554
PMCPMC11127118

What Socratic holds

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Registered trials

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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 Socratic graph.