A practical laboratory index to predict institutionalization and mortality - an 18-year population-based follow-up study

Relationship between the laboratory test-based frailty index and overall mortality in critically ill patients with acute pancreatitis: a retrospective study based on the MIMIC-IV database

Background and aims

The frailty index, based on laboratory assessments, helps identify individuals at risk for adverse health outcomes. However, its relationship with overall mortality in acute pancreatitis patients in ICUs remains unclear. This study aims to investigate the association between the frailty index and all-cause mortality and assess its prognostic value for these patients.

Methods

We carried out a retrospective observational investigation utilizing data from the Medical Information Mart for Intensive Care IV (MIMIC-IV 2.2) database. Extract data from the database for all ICU patients (first-time ICU admissions, age ≥ 18 years) who meet the diagnostic criteria for acute pancreatitis. The frailty index derived from laboratory tests (FI-lab) encompassed three vital sign indicators and 30 laboratory test indicators. Patients were categorized into four groups based on quartiles of the FI-lab score. To assess the differences in 28-day all-cause mortality among these groups, we employed Kaplan-Meier analysis, whereas the relationship between FI-lab scores and 28-day mortality was explored through Cox proportional hazards analysis. In addition, we applied Harrell's C statistic, Integrated Discrimination Improvement (IDI), and Net Reclassification Improvement (NRI) to assess the additional predictive capability of FI-lab scores compare to traditional disease severity metrics.

Results

The study included a total of 741 patients (all age ≥ 18 years, 19.84% age > 75 years, 41.16% Female). The Kaplan-Meier analysis demonstrated that individuals with elevated FI-lab scores exhibited a significantly heightened risk of all-cause mortality (log-rank p < 0.0001). The multivariate Cox regression analysis suggested that treating FI-lab as a continuous variable (per 0.01 increment) was linked to an increased risk of 28-day all-cause mortality [hazard ratio (HR) 1.072, 95% confidence interval (CI) (1.055-1.089), p < 0.001]. Moreover, when FI-lab was analyzed as a categorical variable, patients in the fourth quartile of FI-lab had a notably greater risk of 28-day all-cause mortality in comparison to those in the first quartile [HR 9.933, 95% CI (4.676-21.104), p < 0.001]. Additionally, the integration of FI-lab scores with conventional disease severity scores improved the predictive performance for 28-day mortality.

Conclusion

In patients in the ICU who have been diagnosed with acute pancreatitis, the FI-lab score functions as a reliable indicator of short-term mortality. Early detection of patients at high risk for acute pancreatitis through the implementation of the FI-lab score, along with prompt interventions, is essential for enhancing these individuals' prognoses.

Machine learning analyses identify multi-modal frailty factors that selectively discriminate four cohorts in the Alzheimer's disease spectrum: a COMPASS-ND study

BACKGROUND

Frailty indicators can operate in dynamic amalgamations of disease conditions, clinical symptoms, biomarkers, medical signals, cognitive characteristics, and even health beliefs and practices. This study is the first to evaluate which, among these multiple frailty-related indicators, are important and differential predictors of clinical cohorts that represent progression along an Alzheimer's disease (AD) spectrum. We applied machine-learning technology to such indicators in order to identify the leading predictors of three AD spectrum cohorts; viz., subjective cognitive impairment (SCI), mild cognitive impairment (MCI), and AD. The common benchmark was a cohort of cognitively unimpaired (CU) older adults.

METHODS

The four cohorts were from the cross-sectional Comprehensive Assessment of Neurodegeneration and Dementia dataset. We used random forest analysis (Python 3.7) to simultaneously test the relative importance of 83 multi-modal frailty indicators in discriminating the cohorts. We performed an explainable artificial intelligence method (Tree Shapley Additive exPlanation values) for deep interpretation of prediction effects.

RESULTS

We observed strong concurrent prediction results, with clusters varying across cohorts. The SCI model demonstrated excellent prediction accuracy (AUC = 0.89). Three leading predictors were poorer quality of life ([QoL]; memory), abnormal lymphocyte count, and abnormal neutrophil count. The MCI model demonstrated a similarly high AUC (0.88). Five leading predictors were poorer QoL (memory, leisure), male sex, abnormal lymphocyte count, and poorer self-rated eyesight. The AD model demonstrated outstanding prediction accuracy (AUC = 0.98). Ten leading predictors were poorer QoL (memory), reduced olfaction, male sex, increased dependence in activities of daily living (n = 6), and poorer visual contrast.

CONCLUSIONS

Both convergent and cohort-specific frailty factors discriminated the AD spectrum cohorts. Convergence was observed as all cohorts were marked by lower quality of life (memory), supporting recent research and clinical attention to subjective experiences of memory aging and their potentially broad ramifications. Diversity was displayed in that, of the 14 leading predictors extracted across models, 11 were selectively sensitive to one cohort. A morbidity intensity trend was indicated by an increasing number and diversity of predictors corresponding to clinical severity, especially in AD. Knowledge of differential deficit predictors across AD clinical cohorts may promote precision interventions.

Development, validation and performance of laboratory frailty indices: A scoping review

INTRODUCTION

Frailty is a syndrome characterised by decline in functional ability and increasing vulnerability to disease and associated with adverse outcomes. Several established methods exist for assessing frailty. This scoping review aims to characterise the development and validation of frailty indices based on laboratory test results (FI-Lab) and to assess their utility.

METHODS

Studies were included in the review if they included data concerning the development and/or testing an FI-Lab using the deficit accumulation method. Studies were identified using PubMed/MEDLINE, Embase (Elsevier), OpenGrey and Google Scholar from 2010 to 2021. Two reviewers independently screened all abstracts, and those that met the inclusion criteria were reviewed in detail. Data extracted included details about the study characteristics, number, type and coding of laboratory variables included, validation, and outcomes. A narrative synthesis of the available evidence was adopted.

RESULTS

The search yielded 915 articles, of which 29 studies were included. In general, 89% of studies were conducted after 2016 and 51% in a hospital-based setting. The number of variables included in FI-Labs ranged from 13 to 77, and 51% included some non-laboratory variables in their indices, with pulse and blood pressure being the most frequent. The validity of FI-Lab was demonstrated through change with age, correlation with established frailty indices and association with adverse health outcomes. The most frequent outcome studied was mortality (79% of the studies), with FI-Lab associated with increased mortality in all but one. Other outcomes studied included self-reported health, institutionalisation, and activities of daily living. The effect of combining the FI-Lab with a non-laboratory-based FI was assessed in 7 studies with a marginal increase in predictive ability.

CONCLUSION

Frailty indices constructed based on the assessment of laboratory variables, appear to be a valid measure of frailty and robust to the choice of variables included.

A novel easy-to-use index to predict institutionalization and death in older population - a 10-year population-based follow-up study

BACKGROUND

Various indexes have been developed to estimate the risk for mortality, institutionalization, and other adverse outcomes for older people. Most indexes are based on a large number of clinical or laboratory parameters. An index based on only a few parameters would be more practical to use in every-day clinical practice. Our aim was to create an index to predict the risk for mortality and institutionalization with as few parameters as possible without compromising their predictive ability.

METHODS

A prospective study with a 10-year follow-up period. Thirty-six clinical and fourteen laboratory parameters were combined to form an index. Cox regression model was used to analyze the association of the index with institutionalization and mortality. A backward statistical method was used to reduce the number of parameters to form an easy-to-use index for predicting institutionalization and mortality.

RESULTS

The mean age of the participants (n = 1172) was 73.1 (SD 6.6, range 64‒97) years. Altogether, 149 (14%) subjects were institutionalized, and 413 (35%) subjects deceased during the follow-up. Institutionalization and mortality rates increased as index scores increased both for the large 50-parameter combined index and for the reduced indexes. After a backward variable selection in the Cox regression model, three clinical parameters remained in the index to predict institutionalization and six clinical and three laboratory parameters in the index to predict mortality. The reduced indexes showed a slightly better predictive value for both institutionalization and mortality compared to the full index.

CONCLUSIONS

A large index with fifty parameters included many unimportant parameters that did not increase its predictive value, and therefore could be replaced with a reduced index with only a few carefully chosen parameters, that were individually associated with institutionalization or death.

The frailty index based on laboratory test data as a tool to investigate the impact of frailty on health outcomes: a systematic review and meta-analysis

The frailty index (FI) quantifies frailty as deficit accumulation. It has been adapted to employ laboratory test data (FI-Lab). Our objective was to systematically review and meta-analyse the FI-Lab's ability to predict mortality. Secondary objectives were to review the FI-Lab's association with adverse health outcomes and whether FI-Lab scores differed between the sexes. A systematic literature search was carried out using six online databases to identify studies that measured the FI-Lab in humans. Hazard ratios (HRs) were combined in a meta-analysis to create a pooled risk estimate for mortality. Of the 1,201 papers identified, spanning January 2010 until 11 July 2022, 38 were included. FI-Lab scores per 0.01 unit increase predicted mortality overall (HR = 1.04; 95% confidence interval (CI) = 1.03-1.05) and for studies with a mean age of 81+ years (HR = 1.04; 95% CI = 1.03-1.05). The quality of evidence for these meta-analyses are moderate and high, respectively. Further, higher FI-Lab scores were associated with more frequent adverse health outcomes. Sex differences in FI-Lab scores varied, with no consistent indication of a sex effect. The FI-Lab is associated with mortality and with a variety of adverse health outcomes. No consistent sex differences in FI-Lab scores were observed, with several studies in disagreement. Notably, these conclusions were most relevant to older (65+ years old) individuals; further evidence in younger people is needed in both clinical and population representative studies.

Association of a modified laboratory frailty index with adverse outcomes in geriatric rehabilitation inpatients: RESORT

A higher number of laboratory measurements is associated with mortality in patients admitted to hospital, but is not part of the frailty index based on laboratory tests (FILab). This study aimed to modify the FI-Lab (mFI-Lab) by accounting for the number of laboratory measurements and compare its validity to predict institutionalization and mortality at three-month post-discharge with the clinical frailty scale (CFS) in geriatric rehabilitation inpatients. In 1819 patients (median age 83.3 [77.6-88.3], 56.6% female), a higher FI-Lab was not associated with institutionalization but a higher risk of mortality. A higher mFI-Lab was associated with lower odds of institutionalization but a higher risk of mortality. A higher CFS was associated with institutionalization and higher mortality. The Akaike information criterion value was lowest for the CFS, followed by the mFI-Lab and the FI-Lab. The CFS is better than the mFI-Lab predicting short-term adverse outcomes in geriatric rehabilitation inpatients. When using laboratory data to estimate frailty, the mFI-Lab rather than the FI-Lab should be used.

Letter to editor: is laboratory index really a practical and valid tool to predict mortality?

We carefully studied the article titled “A practical laboratory index to predict institutionalization and mortality – an 18-year population-based follow-up study” written by Heikkilä et al. and published in BMC Geriatrics on 25 February 2021 with great interest. We would like to make some comments regarding this article and tool. Laboratory Index (LI) has been executed with the data of 728 patients who had followed-up in our center, however the LI score was not able to predict the 10-year and 18-year mortality. Therefore, a question mark has been aroused in our minds at some points. Neither frailty nor comorbidities were considered in this index. For a geriatric patient, it would be inadequate to evaluate laboratory results regardless of the clinical status. Similarly, it would not be appropriate to predict mortality only on the basis of laboratory results without considering the clinical status of the patient.

We think that although the recent study has a great impact, it can be improved by incorporating data on the comorbidities and frailty status of the patients into the analysis.