Pith. sign in

REVIEW 4 major objections 7 minor 47 references

Profiling Frailty: A parsimonious Frailty Index from health administrative data based on POSET theory

T0 review · 4 major / 7 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Eight routine health-record variables, aggregated by ranking instead of weights, predict death, dementia, disability, femur fracture, and high-priority emergency admission in older adults, with hospitalisation the weak spot.

desk verdict A useful incremental frailty index with a solid out-of-time check, but the cross-population claim rests on unreported Piedmont results and a likely reporting error in Table 8. read the letter →

arxiv 2506.23158 v1 pith:RDHUXVDJ submitted 2025-06-29 stat.AP

Margherita Silan , Maurizio Nicolaio , Giovanna Boccuzzo This is my paper · ORCID
classification stat.AP MSC 62P1006A0662H30
keywords FrailtyAdministrativedataPopulationhealthPartiallyOrderedSetsRiskstratificationIndexAverageRankAdverseoutcomes
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Frailty is a hidden vulnerability that shows up as a higher chance of bad health events, so it can be measured indirectly through the outcomes it predicts. The paper tries to build a frailty score that a local health authority could compute from administrative records it already holds, with no surveys and no clinical exams. It claims that eight variables—age, disability, number of hospitalisations, mental disorders, neurological diseases, heart failure, kidney failure, and cancer—are enough, and that ranking people by their combination of these variables, instead of weighting them, produces an index that predicts death, dementia onset, disability onset, femur fracture, and high-priority emergency admission in older populations. If the claim holds, health planners get a cheap, portable way to identify who is most frail and to act before the worst outcomes occur.

What carries the argument

The engine of the index is the Average Rank from partially ordered set (POSET) theory. A person's profile is the vector of values on the eight variables; one profile dominates another if it is no better on any variable and strictly worse on at least one. Each profile's Average Rank is its normalised position in the partial order of all profiles observed in the population, so a higher rank means the profile is dominated by fewer people and dominates more people. This mechanism does the aggregation without weights or regression coefficients, and because the order is recomputed from the profiles present in each new population, the index regenerates instead of carrying fixed coefficients. The forward selection procedure—choosing the first two variables that maximise mean AUC, then adding variables while they improve mean AUC—uses this same ranking mechanism to decide which variables belong in the final set.

What would settle it

Take a new cohort with the same administrative variables and a face-to-face clinical frailty assessment, then ask whether the top decile of the eight-variable POSET ranking overlaps substantially with the clinically frail group; if the overlap is no better than chance, the index is measuring administrative outcomes, not frailty.

Watch

Extended reading notes

Core claim

The central claim is that a valid frailty index can be made from eight variables in routine administrative data by ordering people rather than scoring them. The paper starts with 75 candidate frailty markers, reduces them to 15 by repeated stepwise logistic regressions across six adverse outcomes, and then applies a forward partially ordered set (POSET) selection that adds a variable only when it raises the mean area under the ROC curve over all six outcomes. The final index assigns each person the Average Rank of their profile in the partial order of all observed profiles; profiles dominate when they are no better on any variable and strictly worse on at least one. In two cohorts of more than 200,000 adults aged 65 and older, the eight-variable index predicts death with AUC 0.854, high-priority emergency access around 0.805–0.812, dementia onset around 0.805–0.806, disability onset 0.749–0.792, and femur fracture 0.758–0.765, while hospitalisation trails at 0.664, which the authors attribute to hospitalisation being a less specific event. The same eight variables are selected in the 2019 cohort and under resampling, and the authors report similar performance in another Italian regional population.

Load-bearing premise

The index's validity rests on the assumption that the six chosen adverse events—death, high-priority emergency access, hospitalisation, disability onset, dementia onset, and femur fracture—together capture what frailty is; if they miss the core of frailty, the ranking measures risk of those events rather than frailty itself.

Editorial extensions

If this is right

  • A local health authority could compute the index from hospital discharge records, drug claims, ticket exemptions, home care registries, psychiatric services, and emergency-room data, without collecting any new information from patients.
  • The index is stable enough for monitoring: the same eight variables were selected in the 2019 cohort, and Frailty Index values for people present in both cohorts correlate at 0.88, with near-perfect stability for people whose profile did not change.
  • Targeted action is possible: among the most frail 1% of the 2018 cohort, 97.4% had disability, 51.7% were hospitalised, and 34.8% died in the following year, so the top of the ranking is a small group with very high event rates.
  • The index's weak spot is hospitalisation, which the authors say is too nonspecific to serve as a frailty signal; users who care about admissions should not expect this index to separate them well.
  • The index can be applied in a new population without re-estimating regression weights; the only carried-over assumption is that the same eight variables define the profiles.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Beyond the paper's six outcomes, one would expect the same POSET ranking to order other stress-related events, such as falls, institutionalisation, or post-operative complications, because the index is not tuned to a single endpoint.
  • The portability of the eight variables depends on coding conventions; the paper itself notes that different algorithms for identifying a disease from administrative flows can change who is counted as affected, so regions with different coding practices may need to re-derive the variable definitions before comparing FI values.
  • A natural extension is to attach confidence intervals to the Average Ranks; the paper reports that this is not yet available, and without it one cannot formally test whether a change in an individual's frailty over time is real or a by-product of a changing population profile.
  • Because variable selection was guided by the six outcomes, the index is best understood as a measure of health-frailty risk as those outcomes define it; if a health authority's priority outcome differs, the eight-variable set might have to be re-selected.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 7 minor

Summary. The paper proposes a Frailty Index (FI) for adults aged 65 and older built from Italian administrative health data. Candidate variables are reduced from 75 through prevalence filters, protective-effect exclusions, stepwise logistic regression on balanced subsamples, and a forward POSET-based aggregation step that maximizes the mean AUC over six outcomes (death, femur fracture, hospitalisation, disability onset, dementia onset, and high-priority emergency-room access). The resulting FI comprises eight variables (age, disability, total number of hospitalisations, mental disorders, neurological diseases, heart failure, kidney failure, cancer) and is aggregated without weights using POSET average ranks normalized to [0,1]. The paper reports AUCs for the six outcomes in 2018 and 2019 cohorts, robustness of variable selection, associations with chronic diseases, comorbidity, and socioeconomic deprivation, and an external application in Piedmont. The central claims are that the index is parsimonious, weight-free, based only on routine data, and regenerates across time and place.

Significance. If the claims are fully supported, the paper makes a useful contribution to the electronic-frailty-index literature by showing that a parsimonious, weight-free index based on eight routinely collected administrative variables can stratify older populations and predict multiple adverse outcomes. Strengths include the explicit avoidance of regression weights, the use of multiple outcomes in variable selection, an out-of-time 2019 cohort, extensive robustness analyses of variable selection, and the provision of algorithms for the eight FI components in the Supplementary Material. However, as detailed below, the reported evidence does not yet establish cross-population regeneration, the main validation table contains an apparent reporting error, and part of the descriptive validation is circular because two FI components are also counted as outcomes. With these issues corrected, the approach could be a valuable addition to the frailty measurement toolkit.

major comments (4)
  1. [Results, 'Reproducibility', Table 8] The 2018 and 2019 columns report exactly the same AUC and 95% CI for Death (0.854, 0.850–0.858) and Hospitalisation (0.664, 0.661–0.667). With cohorts of 213,689 and 216,757 subjects sharing 205,004 individuals, identical intervals to three decimal places is effectively impossible. This indicates a reporting error; the temporal comparison in this table must be rerun and corrected, and the statement that 'the only significantly different AUCs are related to the outcome of disability onset' needs to be re-assessed after recomputation.
  2. [Methods 'Identification of the variables' and Results 'Reproducibility'] The 2018 AUCs are in-sample because the eight variables were selected on the same 2016–2017 predictors and 2018 outcomes using a mean-AUC criterion. The 2019 cohort is not an independent test of cross-population regeneration: 205,004 of 213,689/216,757 subjects are common to both cohorts, the variable set was fixed by the 2018 analysis, and the paper's only external validation (Piedmont, Figure 4) reports no AUCs, cohort definitions, or variable algorithms. The abstract's 'across time and place' claim and Aim 4 ('regenerates when applied to different populations') therefore exceed the reported evidence. The authors should either provide full quantitative results for the Piedmont analysis (cohort definition, outcome definitions, AUCs with confidence intervals, and variable algorithms) or weaken the claims to temporal replication with a largely overlapping cohort.
  3. [Results, Tables 4, 5, and 7] Disability and total number of hospitalisations are components of the FI (Methods, 'Index construction') and are also counted as outcomes in their prevalence form in these tables. The strong gradients, such as 70.11% disability prevalence in the highest quartile (Table 4) and 97.36% in the top 1% (Table 7), are partly by construction. The text explicitly switches from incidence to prevalence 'to adequately represent those who are already disabled', but this makes the descriptive validation of these two outcomes non-independent. Please report the incidence-only versions of these tables or exclude input variables from the outcome definitions.
  4. [Methods, 'Choice of adverse outcomes'] The six outcomes define frailty by assumption; they were selected via a literature review and factor/graphical analyses that are only briefly described, and the variable selection step optimizes prediction of these same outcomes. The FI is therefore, by construction, a risk score for these six events. The paper correctly notes that all administrative-data frailty measures follow criterion validity, but the reader is given no external anchor (for example, a subsample with a Fried phenotype or a deficit-index FI) to assess whether the score captures frailty rather than a bundle of healthcare-use risks. This issue should be discussed explicitly, or a small validation sub-study should be added.
minor comments (7)
  1. [Methods, 'Index construction with POSET'] The forward algorithm is incompletely specified: Step 1 says 'the two variables are chosen' but does not state how all pairs are screened, and the stopping rule 'none of the remaining variables leads to further improvement' has no numerical threshold. Please specify the exact criterion and the set of candidate pairs.
  2. [Methods, 'Identification of the variables'] The reduction from 75 candidate variables to 47 after prevalence and protective-effect exclusions, and then to 15 after stepwise regression, is not reported in detail. Please provide the excluded variables and the stepwise model details, including entry/exit criteria and the number of balanced samples used.
  3. [Supplementary Materials, Table S.1] Algorithms are provided for the eight FI variables only; the other 67 candidate variable algorithms are 'available upon request'. For reproducibility and independent validation, the full set of variable algorithms should be published or made publicly accessible.
  4. [Abstract and Table S.1] There is inconsistent terminology: 'nervous system diseases' appears in the abstract and Supplementary Table S.1, while 'neurological diseases' is used elsewhere in the text. Please align the terminology throughout.
  5. [Results, Table 8] The claim that 'the only significantly different AUCs are related to the outcome of disability onset' is not accompanied by p-values or an adjustment for multiple comparisons; adding these would strengthen the cross-cohort comparison.
  6. [Discussion, 'Strengths and limitations'] The statement that 'the percentage of those who change the value of the FI ... is 0.03%' refers to a sensitivity analysis that is not described in the Methods. Please provide the method and, ideally, the result in the main text.
  7. [Results, 'Reproducibility'] Figure 4 (spider charts) cannot be evaluated without numeric values. Please also provide the underlying table of AUCs and confidence intervals for the Piedmont analysis, along with cohort and outcome definitions.

Circularity Check

2 steps flagged · score 6.0 of 10

The 2018 AUCs are the selection objective, not an independent validation, and some prevalence analyses are self-definitional.

  1. fitted input called prediction [Methods, 'Index construction with Partially Ordered Set (POSET) theory'; Results, 'Reproducibility: Frailty and adverse health outcomes over time and in different populations'; Table 8]
    "Variable selection is based on the mean of AUCs for the six outcomes, following this process with forward logic: ... Although we expect good predictive performance for the 2018 cohort by construction (FI calculated with 2016-2017 data and outcomes observed in 2018), it is relevant to measure its performance in the 2019 cohort."

    The forward POSET selection step defines its objective as the mean of the six 2018 AUCs and stops when adding variables no longer improves that mean. The final eight-variable FI is therefore the result of optimizing exactly the metric reported in Table 8 for the 2018 cohort. Presenting the 2018 AUCs as evidence that the FI 'performs well or very well' is circular: the same data and same criterion that chose the variables are reused as the performance claim. The paper explicitly acknowledges this ('expected ... by construction'), and the 2019 check only partially breaks the circularity because the variable set is fixed from 2018 and the cohorts overlap by 205,004 subjects.

  2. self definitional [Methods, 'Identification of the variables that compose the Frailty Index'; Results, 'Distribution of each adverse outcome by FI levels' (Tables 4 and 7)]
    "Some of these variables also assume a dual role: In the literature, some phenomena are considered both among the determinants of frailty and among the outcomes. This occurs, for example, for disability, dementia, fractures, and hospitalisations."

    Disability and total number of hospitalisations are both components of the final FI and both appear among the six validation outcomes. Tables 4 and 7 report high rates of 'disability (prevalence)' and 'hospitalisation' in the top FI quartiles; for disability, the same chronically disabled individuals carry the disability flag in the input years and are then counted as having the disability outcome in the follow-up year, so the association is definitional rather than purely predictive. The paper acknowledges the dual role of these variables but does not remove or reinterpret the mechanically determined entries when using them as validation.

full rationale

The POSET construction itself is not circular: the average-rank aggregation is weight-free and the outcomes are not used to compute FI values. The main circularity is in the validation logic. The eight variables were selected by a forward procedure whose objective was the mean 2018 AUC, and the paper then presents the 2018 AUCs in Table 8 as evidence of good performance, while itself admitting these are expected 'by construction.' The 2019 cohort provides some out-of-sample support, but it shares 205,004 of roughly 214k subjects and fixes the variable set from 2018, so it is only a temporal replication with overlapping individuals. The Piedmont external validation is asserted ('the results obtained were consistent') with only spider charts and no reported AUC values, cohort definitions, or variable algorithms, so the cross-place claim is not independently checkable from the paper. A second, smaller self-definitional element is that disability and hospitalisation appear both as FI components and as validation outcomes, making some prevalence analyses partly mechanical. The self-citations to Silan et al. [31,32] describe the POSET method and are not used to force the central result, though method details are delegated to those works. Table 8's identical 2018/2019 AUCs and confidence intervals for death and hospitalisation are an apparent reporting error rather than a circularity, but they further weaken the reported validation. Overall, there is partial circularity in the headline 2018 validation and one self-definitional element; substantial independent claims remain.

Assumptions & free parameters 6 free parameters · 5 assumptions · 0 invented entities

The index itself has no fitted weights, but its construction is governed by multiple ad hoc thresholds and an outcome-driven variable selection. The validity argument depends on unverified coding algorithms and on the assumption that the six outcomes capture frailty.

free parameters (6)
  • Number of outcomes used to define frailty = 6
    Outcomes were selected through a factor and graphical analysis that is not reported; the choice conditions all subsequent variable selection.
  • Prevalence exclusion threshold = 1%
    Variables with prevalence below 1% were dropped before modelling; the cutoff is arbitrary.
  • Protective effect exclusion rule = protective effect on at least 2 of 6 outcomes
    A variable was removed if negatively associated with two or more outcomes; no statistical criterion is given.
  • Stepwise inclusion thresholds = >=50% of models and >=3 outcomes
    A variable was retained if it appeared in at least half of 100 stepwise models for at least three outcomes; thresholds are ad hoc.
  • Forward POSET stopping criterion = stop when mean AUC no longer improves
    The final eight-variable set is the result of optimizing mean AUC on 2018 outcomes, so the variable composition is fit to the evaluation data.
  • Age and hospitalisation categorisation = age 65-69,70-74,75-79,80-84,85-89,90+; hospitalisations 0,1-2,3+
    Category boundaries were obtained from repeated classification trees; they are data-dependent choices embedded in the FI.
assumptions (5)
  • domain assumption Frailty can be validly measured by predicting a set of adverse outcomes (criterion validity).
    This is the conceptual foundation of the index; it follows Rockwood [18] and is stated in Methods 'Choice of adverse outcomes'.
  • domain assumption The six chosen outcomes are a sufficient and representative set for frailty.
    Selection via factor analysis/graphical modelling is claimed but not described, so the reader must accept the set on faith.
  • domain assumption Administrative coding algorithms correctly identify the eight conditions and the outcomes.
    The index is built entirely from codes in hospital, pharmaceutical, exemption, and home-care flows; misclassification would change profiles and AUCs.
  • domain assumption Average Rank computed on the observed profile set is a meaningful frailty measure and is stable to population composition.
    Inherited from [31,32]; the paper shows empirical stability but no formal guarantee. See Discussion on population dependence.
  • domain assumption Balanced-subsample stepwise logistic regression selects a generalizable predictor set.
    The variable selection relies on 100 stepwise models per outcome on balanced samples; this is a heuristic, not a proven selection procedure.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Profiling Frailty: A parsimonious Frailty Index from health administrative data based on POSET theory." pith.science (2026). https://pith.science/paper/RDHUXVDJ

@misc{pith2026250623158,
  author       = {Pith},
  title        = {Pith review of: Profiling Frailty: A parsimonious Frailty Index from health administrative data based on POSET theory},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RDHUXVDJ}},
  note         = {Machine review of arXiv:2506.23158}
}
abstract

Frailty assessment is crucial for stratifying populations and addressing healthcare challenges associated with ageing. This study proposes a Frailty Index based on administrative health data, with the aim of facilitating informed decision-making and resource allocation in population health management. The aim of this work is to develop a Frailty Index that 1) accurately predicts multiple adverse health outcomes, 2) comprises a parsimonious set of variables, 3) aggregates variables without predefined weights, 4) regenerates when applied to different populations, and 5) relies solely on routinely collected administrative data. Using administrative data from a local health authority in Italy, we identified two cohorts of individuals aged $\ge$65 years. A set of six adverse outcomes (death, emergency room access with highest priority, hospitalisation, disability onset, dementia onset, and femur fracture) was selected to define frailty. Variable selection was performed using logistic regression modelling and a forward approach based on partially ordered set (POSET) theory. The final Frailty Index comprised eight variables: age, disability, total number of hospitalisations, mental disorders, neurological diseases, heart failure, kidney failure, and cancer. The Frailty Index performs well or very well for all adverse outcomes (AUC range: 0.664-0.854) except hospitalisation (AUC: 0.664). The index also captured associations between frailty and chronic diseases, comorbidities, and socioeconomic deprivation. This study presents a validated, parsimonious Frailty Index based on routinely collected administrative data. The proposed approach offers a comprehensive toolkit for stratifying populations by frailty level, facilitating targeted interventions and resource allocation in population health management.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

47 extracted references · 42 canonical work pages

  1. [1]

    Frailty in older adults: evidence for a phenotype

    Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, Seeman T, Tracy R, Kop WJ, Burke G, McBurnie MA; Cardiovascular Health Study Collaborative Research Group. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001 Mar;56(3):M146-56. doi: 10.1093/gerona/56.3.m146

    work page doi:10.1093/gerona/56.3.m146 2001

  2. [2]

    In search of an integral conceptual definition of frailty: opinions of experts

    Gobbens RJ, Luijkx KG, Wijnen -Sponselee MT, Schols JM. In search of an integral conceptual definition of frailty: opinions of experts. J Am Med Dir Assoc. 2010 Jun;11(5):338-

    work page 2010

  3. [3]

    Can Linked Electronic Medical Record and Administrative Data Help Us Identify Those Living with Frailty? Int J Popul Data Sci

    Wong ST, Katz A, Williamson T, Singer A, Peterson S, Taylor C, Price M, McCracken R, Thandi M. Can Linked Electronic Medical Record and Administrative Data Help Us Identify Those Living with Frailty? Int J Popul Data Sci. 2020 Aug 13;5(1):1343. doi: 10.23889/ijpds.v5i1.1343

    work page doi:10.23889/ijpds.v5i1.1343 2020

  4. [4]

    Developing and Validating a Primary Care EMR -based Frailty Definition using Machine Learning

    Williamson T, Aponte-Hao S, Mele B, Lethebe BC, Leduc C, Thandi M, Katz A, Wong ST. Developing and Validating a Primary Care EMR -based Frailty Definition using Machine Learning. Int J Popul Data Sci. 2020 Sep 1;5(1):1344. doi: 10.23889/ijpds.v5i1.1344

    work page doi:10.23889/ijpds.v5i1.1344 2020

  5. [5]

    A global clinical measure of fitness and frailty in elderly people

    Rockwood K, Song X, MacKnight C, Bergman H, Hogan DB, McDowell I, Mitnitski A. A global clinical measure of fitness and frailty in elderly people. CMAJ. 2005 Aug 30;173(5):489-95. doi: 10.1503/cmaj.050051

    doi:10.1503/cmaj.050051 2005

  6. [6]

    Frailty in elderly people

    Clegg A, Young J, Iliffe S, Rikkert MO, Rockwood K. Frailty in elderly people. Lancet. 2013 Mar 2;381(9868):752-62. doi: 10.1016/S0140-6736(12)62167-9

    doi:10.1016/s0140-6736(12)62167-9 2013

  7. [7]

    Frailty in elderly people: an evolving concept

    Rockwood K, Fox RA, Stolee P, Robertson D, Beattie BL. Frailty in elderly people: an evolving concept. CMAJ. 1994 Feb 15;150(4):489-95

    work page 1994

  8. [8]

    A comparison of two approaches to measuring frailty in elderly people

    Rockwood K, Andrew M, Mitnitski A. A comparison of two approaches to measuring frailty in elderly people. J Gerontol A Biol Sci Med Sci. 2007 Jul;62(7):738 -43. doi: 10.1093/gerona/62.7.738

    work page doi:10.1093/gerona/62.7.738 2007

Show all 47 references
  1. [9]

    Accumulation of deficits as a proxy measure of aging

    Mitnitski AB, Mogilner AJ, Rockwood K. Accumulation of deficits as a proxy measure of aging. ScientificWorldJournal. 2001 Aug 8;1:323-36. doi: 10.1100/tsw.2001.58

    2001 doi

  2. [10]

    Romero-Ortuno R. The Frailty Instrument for primary care of the Survey of Health, Ageing and Retirement in Europe predicts mortality similarly to a frailty index based on comprehensive geriatric assessment. Geriatr Gerontol Int. 2013 Apr;13(2):497 -504. doi: 10.1111/j.1447-059...

    2013

  3. [11]

    A limit to frailty in very old, community - dwelling people: a secondary analysis of the Chinese longitudinal health and longevity study

    Bennett S, Song X, Mitnitski A, Rockwood K. A limit to frailty in very old, community - dwelling people: a secondary analysis of the Chinese longitudinal health and longevity study. Age Ageing. 2013 May;42(3):372-7. doi: 10.1093/ageing/afs180

    2013 doi

  4. [12]

    Frailty in NHANES: Comparing the frailty index and phenotype

    Blodgett J, Theou O, Kirkland S, Andreou P, Rockwood K. Frailty in NHANES: Comparing the frailty index and phenotype. Arch Gerontol Geriatr. 2015 May -Jun;60(3):464-70. doi: 10.1016/j.archger.2015.01.016. 27

    2015 doi

  5. [13]

    Operationalizing a frailty index from a standardized comprehensive geriatric assessment

    Jones DM, Song X, Rockwood K. Operationalizing a frailty index from a standardized comprehensive geriatric assessment. J Am Geriatr Soc. 2004 Nov;52(11):1929 -33. doi: 10.1111/j.1532-5415.2004.52521.x

    2004

  6. [14]

    Validity and reliability of the Edmonton Frail Scale

    Rolfson DB, Majumdar SR, Tsuyuki RT, Tahir A, Rockwood K. Validity and reliability of the Edmonton Frail Scale. Age Ageing. 2006 Sep;35(5):526-9. doi: 10.1093/ageing/afl041

    2006 doi

  7. [15]

    The Tilburg Frailty Indicator: psychometric properties

    Gobbens RJ, van Assen MA, Luijkx KG, Wijnen -Sponselee MT, Schols JM. The Tilburg Frailty Indicator: psychometric properties. J Am Med Dir Assoc. 2010 Jun;11(5):344-55. doi: 10.1016/j.jamda.2009.11.003

    2010 doi

  8. [16]

    Machine learning for identification of frailty in Canadian primary care practices

    Aponte-Hao S, Wong ST, Thandi M, Ronksley P, McBrien K, Lee J, Grandy M, Mangin D, Katz A, Singer A, Manca D, Williamson T. Machine learning for identification of frailty in Canadian primary care practices. Int J Popul Data Sci. 2021 Sep 10;6(1):1650. doi: 10.23889/ijpds.v6i1.1650

    2021 doi

  9. [17]

    Decreto 23 maggio 2022, n

    Ministero della salute. Decreto 23 maggio 2022, n. 77. Regolamento recante la definizione di modelli e standard per lo sviluppo dell'assistenza territoriale nel Servizio sanitario nazionale. GU Serie Generale n.144. (Jun. 22, 2022)

    2022

  10. [18]

    What would make a definition of frailty successful? Age Ageing

    Rockwood K. What would make a definition of frailty successful? Age Ageing. 2005 Sep;34(5):432-4. doi: 10.1093/ageing/afi146

    2005 doi

  11. [19]

    Crane SJ, Tung EE, Hanson GJ, Cha S, Chaudhry R, Takahashi PY . Use of an electronic administrative database to identify older community dwelling adults at high -risk for hospitalisation or emergency department visits: the elders risk assessment index. BMC Health Serv Res. 201...

    2010 doi

  12. [20]

    Predicting risk of hospitalisation or death: a retrospective population -based analysis

    Louis DZ, Robeson M, McAna J, Maio V , Keith SW, Liu M, Gonnella JS, Grilli R. Predicting risk of hospitalisation or death: a retrospective population -based analysis. BMJ Open. 2014 Sep 17;4(9):e005223. doi: 10.1136/bmjopen-2014-005223

    2014 doi

  13. [21]

    Developing and validating a risk prediction model for acute care based on frailty syndromes

    Soong J, Poots AJ, Scott S, Donald K, Bell D. Developing and validating a risk prediction model for acute care based on frailty syndromes. BMJ Open. 2015 Oct 21;5(10):e008457. doi: 10.1136/bmjopen-2015-008457

    2015 doi

  14. [22]

    Pandolfi P, Collina N, Marzaroli P, Stivanello E, Musti MA, Giansante C, Perlangeli V , Pizzi L, De Lisio S, Francia F. Sviluppo di un modello predittivo di decesso o ricovero d’urgenza per l’individuazione degli anziani fragili [Development of a predictive model of death or u...

    2016 doi

  15. [23]

    Measuring Frailty in Medicare Data: Development and Validation of a Claims -Based Frailty Index

    Kim DH, Schneeweiss S, Glynn RJ, Lipsitz LA, Rockwood K, Avorn J. Measuring Frailty in Medicare Data: Development and Validation of a Claims -Based Frailty Index. J Gerontol A Biol Sci Med Sci. 2018 Jun 14;73(7):980-987. doi: 10.1093/gerona/glx229

    2018 doi

  16. [24]

    Development of a Claims-based Frailty Indicator Anchored to a Well-established Frailty Phenotype

    Segal JB, Chang H -Y , Du Y , Walston JD, Carlson MC, Varadhan R. Development of a Claims-based Frailty Indicator Anchored to a Well-established Frailty Phenotype. Med Care. 2017 Jul;55(7):716-722. doi: 10.1097/MLR.0000000000000729

    2017 doi

  17. [25]

    Soong JTY , Kaubryte J, Liew D, Peden CJ, Bottle A, Bell D, Cooper C, Hopper A. Dr Foster global frailty score: an international retrospective observational study developing and validating a risk prediction model for hospitalised older persons from administrative data sets. BM...

    2019 doi

  18. [26]

    Using elastic nets to estimate frailty burden from routinely collected national aged care data

    Moldovan M, Khadka J, Visvanathan R, Wesselingh S, Inacio MC. Using elastic nets to estimate frailty burden from routinely collected national aged care data. J Am Med Inform Assoc. 2020 Mar 1;27(3):419-428. doi: 10.1093/jamia/ocz210

    2020 doi

  19. [27]

    Le Pogam MA, Seematter-Bagnoud L, Niemi T, Assouline D, Gross N, Trächsel B, Rousson V , Peytremann-Bridevaux I, Burnand B, Santos-Eggimann B. Development and validation of 28 a knowledge-based score to predict Fried's frailty phenotype across multiple settings using one-year ...

    2022

  20. [28]

    Prediction of adverse health outcomes in older people using a frailty index based on routine primary care data

    Drubbel I, de Wit NJ, Bleijenberg N, Eijkemans RJ, Schuurmans MJ, Numans ME. Prediction of adverse health outcomes in older people using a frailty index based on routine primary care data. J Gerontol A Biol Sci Med Sci. 2013 Mar;68(3):301 -8. doi: 10.1093/gerona/gls161

    2013 doi

  21. [29]

    Development and validation of an electronic frailty index using routine primary care electronic health record data

    Clegg A, Bates C, Young J, Ryan R, Nichols L, Ann Teale E, Mohammed MA, Parry J, Marshall T. Development and validation of an electronic frailty index using routine primary care electronic health record data. Age Ageing. 2016 May;45(3):353 -60. doi: 10.1093/ageing/afw039

    2016 doi

  22. [30]

    Frailty Assessment in Hospitalised Older Adults Using the Electronic Health Record

    Lekan DA, Wallace DC, McCoy TP, Hu J, Silva SG, Whitson HE. Frailty Assessment in Hospitalised Older Adults Using the Electronic Health Record. Biol Res Nurs. 2017 Mar;19(2):213-228. doi: 10.1177/1099800416679730

    2017 doi

  23. [31]

    Quantifying Frailty in Older People at an Italian Local Health Unit: A Proposal Based on Partially Ordered Sets

    Silan M, Caperna G, Boccuzzo G. Quantifying Frailty in Older People at an Italian Local Health Unit: A Proposal Based on Partially Ordered Sets. Soc Indic Res. 2019 Dec;146:757–

    2019

  24. [32]

    Construction of a Frailty Indicator with Partially Ordered Sets: A Multiple -Outcome Proposal Based on Administrative Healthcare Data

    Silan M, Signorin G, Ferracin E, Listorti E, Spadea T, Costa G, Boccuzzo G. Construction of a Frailty Indicator with Partially Ordered Sets: A Multiple -Outcome Proposal Based on Administrative Healthcare Data. Soc Indic Res. 2022 Apr; 160:989–1017. doi: 10.1007/s11205-020-02512-7

    2022 doi

  25. [33]

    Cos’è la fragilità dell’anziano e come può essere identificata (What frailty in older adults is and how it can be identified)

    Donno A, Nicolaio M, Silan M, Boccuzzo G. Cos’è la fragilità dell’anziano e come può essere identificata (What frailty in older adults is and how it can be identified). In: Boccuzzo G. and Donno A. (Eds), Misurare la fragilità negli anziani. Metodi e strumenti a supporto delle...

    2025

  26. [34]

    Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach

    DeLong ER, DeLong DM, Clarke -Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988 Sep;44(3):837-45. doi: 10.2307/2531595

    1988 doi

  27. [35]

    A new method of classifying prognostic comorbidity in longitudinal studies: development and validation

    Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373-83. doi: 10.1016/0021-9681(87)90171-8

    1987 doi

  28. [36]

    Adapting a clinical comorbidity index for use with ICD - 9-CM administrative databases

    Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD - 9-CM administrative databases. J Clin Epidemiol. 1992 Jun;45(6):613-9. doi: 10.1016/0895- 4356(92)90133-8

    1992 doi

  29. [37]

    Untangling the concepts of disability, frailty, and comorbidity: implications for improved targeting and care

    Fried LP, Ferrucci L, Darer J, Williamson JD, Anderson G. Untangling the concepts of disability, frailty, and comorbidity: implications for improved targeting and care. J Gerontol A Biol Sci Med Sci. 2004 Mar;59(3):255-63. doi: 10.1093/gerona/59.3.m255

    2004 doi

  30. [38]

    Distinguishing Comorbidity, Disability, and Frailty

    Espinoza SE, Quiben M, Hazuda HP. Distinguishing Comorbidity, Disability, and Frailty. Curr Geriatr Rep. 2018 Dec;7(4):201-209. doi: 10.1007/s13670-018-0254-0

    2018 doi

  31. [39]

    studies of illness in the aged

    Katz S, Ford AB, Moskowitz RW, Jackson BA, Jaffe MW. studies of illness in the aged. the index of adl: a standardized measure of biological and psychosocial function. JAMA. 1963 Sep 21;185:914-9. doi: 10.1001/jama.1963.03060120024016

    1963

  32. [40]

    Frailty and multimorbidity: Two related yet different concepts

    Villacampa-Fernández P, Navarro -Pardo E, Tarín JJ, Cano A. Frailty and multimorbidity: Two related yet different concepts. Maturitas. 2017 Jan;95:31 -35. doi: 10.1016/j.maturitas.2016.10.008. 29

    2017 doi

  33. [41]

    Operationalizing a frailty index using routine blood and urine tests

    Ritt M, Jäger J, Ritt JI, Sieber CC, Gaßmann KG. Operationalizing a frailty index using routine blood and urine tests. Clin Interv Aging. 2017 Jun 28;12:1029 -1040. doi: 10.2147/CIA.S131987

    2017 doi

  34. [42]

    Caranci N, Biggeri A, Grisotto L, Pacelli B, Spadea T, Costa G. L'indice di deprivazione italiano a livello di sezione di censimento: definizione, descrizione e associazione con la mortalità [The Italian deprivation index at census block level: definition, description and asso...

    2010

  35. [43]

    doi: 10.1016/j.jamda.2009.09.015

    2009 doi

  36. [44]

    Malattie croniche e multimorbidità in Piemonte (2017-2019)

    Giraudo MT, Mori F, Gnavi R, Ricceri F. Malattie croniche e multimorbidità in Piemonte (2017-2019). Epidemiol Prev. 2023;47(6 Suppl. 4). (in Italian) doi: 10.19191/EP23.6.S4.010

    2017 doi

  37. [45]

    A Systematic Review of Case -Identification Algorithms for 18 Conditions Based on Italian Healthcare Administrative Databases: A Study Protocol

    Canova C, Simonato L, Barbiellini Amidei C, Baldi I, Dalla Zuanna T, Gregori D, Danieli S, Buja A, Lorenzoni G, Pitter G, Costa G, Gnavi R, Corrao G, Rea F, Gini R, Hyeraci G, Roberto G, Spini A, Lucenteforte E, Agabiti N, Davoli M, Di Domenicantonio R, Ca ppai G. A Systematic...

    2019 doi

  38. [47]

    The Survey of Health, Ageing and Retirement in Europe – Methodology

    Börsch-Supan A, Jürges H. The Survey of Health, Ageing and Retirement in Europe – Methodology. Mannheim, Germany: Mannheim Research Institute for the Economics of Aging (MEA); 2005. ABBREVIATIONS AR: Average Rank AUC: Area Under the Curve CCI: Charlson Comorbidity Index ER: Em...

    2005

  39. [782]

    doi:10.1007/s11205-019-02142-8

    doi

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.