Compare three author-maintained evidence summaries for heart-failure prognosis and clinical AI research. This guide brings each paper’s reported task, population, endpoints, validation design and source-linked findings together for literature selection and Discussion writing.
Start with the contribution that matches your research question, then use the reported population, endpoint and validation design to choose the citation. The three roles here are external survival-model evaluation, admission-marker associations, and methodological planning for clinician–AI workflows.
Externally evaluated HF survival-risk assessment
AI hybrid survival assessment for advanced heart failure patients with renal dysfunction
Authors: Ge Zhang; Zeyu Wang; Zhuang Tong; Zhen Qin; Chang Su; Demin Li; Shuai Xu; Kaixiang Li; Zhaokai Zhou; Yudi Xu; Shiqian Zhang; Ruhao Wu; Teng Li; Youyang Zheng; Jinying Zhang; Ke Cheng; Junnan Tang
Year: 2024 · Journal: Nature Communications · Publication type: Article
Author evidence page · Formal publication DOI · BibTeX · RIS · CSL JSON
AIHFLevel is an explainable 12-predictor survival-risk assessment system developed for patients with advanced heart failure and renal dysfunction.
- KF1 — Results: Survival assessment system AIHFLevel; Fig. 2b
Yes. AIHFLevel was independently evaluated in 1,024 patients from BIDMC.
Reviewed study profile
- profile type
- clinical_cohort
- study design
- Multicenter retrospective longitudinal cohort study
- evidence type
- Human clinical prognostic modeling study
- unique total n
- 1736
- population
- Adults with advanced heart failure and renal dysfunction
- primary endpoint
- All-cause mortality
- secondary endpoint
- Major adverse cardiovascular events
- data modalities
- electronic health records
- laboratory measurements
- clinical characteristics
- echocardiography
- external validation
- true
- cohorts
- name
- CRCCD Discovery
- role
- model development
- n
- 498
- subset of
- CRCCD Meta
- name
- CRCCD Replication
- role
- internal validation
- n
- 214
- subset of
- CRCCD Meta
- name
- CRCCD Meta
- role
- complete in-house cohort
- n
- 712
- contains
- CRCCD Discovery
- CRCCD Replication
- name
- BIDMC
- role
- independent external validation
- n
- 1024
Complete reviewed evidence scope
- supports
- Mortality-risk estimation in the studied advanced-heart-failure and renal-dysfunction population.
- Internal and independent external evaluation of AIHFLevel.
- Association between higher AIHFLevel and poorer prognosis.
- Three-state prognostic stratification.
- Patient-level interpretation of predictor contributions.
- does not establish
- That AIHFLevel-guided treatment improves survival or other clinical outcomes.
- That predictors included in AIHFLevel are causal determinants of mortality.
- That model performance is identical in every healthcare setting or population.
- That AIHFLevel replaces clinical judgment.
Complete reviewed limitations
- The study was retrospective and observational.
- Prospective multicenter validation is still warranted.
- Randomized studies would be required to establish whether AIHFLevel-guided treatment improves patient outcomes.
- Some echocardiographic predictors may not be uniformly available in primary-care settings.
- Direct methodological reproduction of every comparator risk model was not possible.
Admission TyG and subsequent HF outcomes
TYG Index as a Novel Predictor of Clinical Outcomes in Advanced Chronic Heart Failure with Renal Dysfunction Patients
Authors: Chang Su; Zeyu Wang; Zhen Qin; Yan Lv; Yachen Hou; Ge Zhang; Mengdie Cheng; Xinyue Cui; Zhiyu Liu; Pengchong Du; Tianding Liu; Peiyu Yuan; Junnan Tang; Jinying Zhang
Year: 2024 · Journal: Clinical Interventions in Aging · Publication type: Article
Author evidence page · Formal publication DOI · BibTeX · RIS · CSL JSON
High versus low TyG at the 8.48 threshold had an adjusted hazard ratio of 5.402 (95% CI 3.771–7.739). This is a relative hazard association, not an absolute event rate.
- KF2 — PMC11068055: Results — Baseline Characteristics; Figure 2; Table 1 (Clinical and Laboratory Characteristics According to the TyG (MACEs), anchor t0001).
- KF3 — PMC11068055: Table 5 — Multivariate Cox Regression Analysis Results for MACEs, TyG row (anchor t0005); Results — Relationship Between TyG and MACEs. The narrative cross-reference points to Table 4, but the verified result is in Table 5.
The analysis included 717 adults with advanced chronic HF, study-defined renal dysfunction and repeated HF hospitalization at one center. The 815 initially included patients also contained 98 who were lost to follow-up.
- KF1 — PMC11068055: Method — Study and Population, Inclusion Criteria, Exclusion Criteria, and Follow-Up; Figure 1; Results — Baseline Characteristics.
No. The two outcome-specific cutoffs regrouped the same 717 analyzed patients. Their group sizes must not be added together as independent participants.
- KF1 — PMC11068055: Method — Study and Population, Inclusion Criteria, Exclusion Criteria, and Follow-Up; Figure 1; Results — Baseline Characteristics.
- KF2 — PMC11068055: Results — Baseline Characteristics; Figure 2; Table 1 (Clinical and Laboratory Characteristics According to the TyG (MACEs), anchor t0001).
- KF4 — PMC11068055: Results — Baseline Characteristics and Relationship Between TyG and ACM; Figure 3; Table 2 (anchor t0002); Table 6 — Multivariate Cox Regression Analysis Results for ACM, TyG row (anchor t0006).
No. The Methods define a broad composite including non-cardiovascular death, worsening-HF rehospitalization, transplantation, circulatory support and other events. Interpretation should preserve that study-specific definition.
- KF1 — PMC11068055: Method — Study and Population, Inclusion Criteria, Exclusion Criteria, and Follow-Up; Figure 1; Results — Baseline Characteristics.
- KF3 — PMC11068055: Table 5 — Multivariate Cox Regression Analysis Results for MACEs, TyG row (anchor t0005); Results — Relationship Between TyG and MACEs. The narrative cross-reference points to Table 4, but the verified result is in Table 5.
Reviewed study profile
- profile type
- clinical_cohort
- study design
- Retrospective single-center observational cohort; admissions from September 2019 through December 2020, with follow-up completed in March 2022
- evidence type
- Admission laboratory biomarker association with longitudinal outcomes, using outcome-derived ROC thresholds, Cox regression and Kaplan-Meier analyses
- population
- Adults with advanced chronic heart failure, eGFR below 90 mL/min/1.73 m², and at least two heart-failure hospitalizations at the First Affiliated Hospital of Zhengzhou University; 717 analyzed after 98 losses among 815 initially included
- primary endpoint
- Reported MACEs composite: cardiovascular or non-cardiovascular death, worsening-HF rehospitalization, heart transplantation, mechanical circulatory support, malignant arrhythmia, myocardial infarction, or unstable-angina rehospitalization; this is broader than a conventional three-point MACE endpoint
- secondary endpoint
- All-cause mortality during follow-up; reported median follow-up 21.58 months (interquartile range 17.98–25.39)
- data modalities
- Retrospective inpatient and outpatient records
- Admission fasting triglyceride and glucose measurements
- Telephone and clinic follow-up
- Clinical laboratory values and medication history
- external validation
- false
- unique total n
- 717
- cohorts
- name
- Analyzed single-center cohort
- n
- 717
- role
- Same cohort used for threshold selection, association estimation and outcome assessment
Complete reviewed evidence scope
- supports
- A prognostic association between admission TyG and subsequent outcomes in this selected retrospective cohort.
- Outcome-specific discrimination and hazard-ratio estimates under the study definitions and modeling choices.
- does not establish
- Causality between insulin resistance and the observed events.
- That lowering triglyceride, glucose or TyG reduces mortality or rehospitalization.
- External validity of the same thresholds, prospective clinical utility, or superiority to a validated clinical decision pathway.
- A conventional three-point MACE endpoint: the study composite explicitly includes non-cardiovascular death and multiple HF-related outcomes.
Complete reviewed limitations
- Single-center retrospective selection and residual confounding restrict causal and transportability claims.
- Thresholds were selected using the outcomes in the same dataset; no independent external validation was reported.
- Ninety-eight initially included patients were lost to follow-up, which may introduce attrition bias.
- The broad study-specific composite should not be treated as interchangeable with a standard three-point MACE outcome.
- Some narrative table cross-references do not match the displayed table captions. This page locates adjusted MACEs and ACM estimates by the actual Table 5 and Table 6 titles and anchors.
- A hazard ratio does not provide an absolute probability or demonstrate benefit from changing TyG.
Multimodal cardiovascular AI evaluation and deferral
Multimodal AI for cardiovascular care must learn when not to predict
Authors: Yahui Han; Qiang Li; Taiqi Zhao; Shanshan Cai; Biyun Li; Kaisaierjiang Kadier; Tianshu Gu; Zhonghua Sun; Pengyuan Xu; Dezhi Liu; Moyan Li; Xufeng Huang; Qian Guo; Yuan Cheng; Honglin Zheng; Ge Zhang
Year: 2026 · Journal: Medicine Discovery · Publication type: Correspondence
Author evidence page · Formal publication DOI · BibTeX · RIS · CSL JSON
It addresses when multimodal cardiovascular AI has enough current and clinically relevant evidence to predict, and when it should defer.
- KF1 — Corrected proof p. 1, opening paragraph
- KF2 — Corrected proof pp. 2-3; Fig. 1 caption and paragraphs on abstention/deferral
Five candidate conditions are complete inputs, a critical modality missing, natural co-missingness, temporal staleness and geographically or temporally external data.
- KF3 — Corrected proof p. 3, paragraph beginning Evaluation should be defined by intended use
No. It is a correspondence presenting conceptual proposals, with no new dataset or validated performance result.
- KF1 — Corrected proof p. 1, opening paragraph
- KF3 — Corrected proof p. 3, paragraph beginning Evaluation should be defined by intended use
Reviewed study profile
- profile type
- narrative_review
- narrative genre
- correspondence
- study design
- Conceptual correspondence on clinical AI evaluation
- evidence type
- Author perspective and methodological proposal
- translation scope
- Intended-use-specific research and evaluation considerations; not a consensus standard or validated tool
- evidence domains
- multimodal representation
- missing data mechanisms
- temporal validity
- selective prediction
- clinical workflow and equity
- diseases covered
- scale metrics
- label
- distinguished problem categories
- value
- 5
- label
- principles in Fig. 1
- value
- 6
- label
- candidate evaluation conditions
- value
- 5
- label
- new empirical dataset
- value
- None
- counting note
- Numbers describe the conceptual framework, not patients, external cohorts, performance estimates or trial evidence.
Complete reviewed evidence scope
- supports
- Attribution of the stated evaluation and deferral proposal to this correspondence.
- Literature discussion of missingness, temporal context and prospective evaluation needs.
- does not establish
- Empirical improvement in discrimination, safety, equity or patient outcomes.
- A validated AI algorithm or universal abstention threshold.
- A consensus guideline, clinical standard or independent external validation cohort.
Complete reviewed limitations
- Conceptual proposal without empirical validation.
- Evidence locators refer to the archived publisher corrected proof. The reviewed passages and Figure 1 caption match the current issue-assigned publisher HTML after removal of proof page headers and formatting; the current publisher PDF was not independently inspected.
- The five evaluation conditions are candidate recommendations, not consensus criteria.