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The Simplified Comorbidity Index: a new tool for prediction of nonrelapse mortality in allo-HCT.

ABSTRACT: Individual comorbidities have distinct contributions to nonrelapse mortality (NRM) following allogeneic hematopoietic cell transplantation (allo-HCT). We studied the impact of comorbidities individually and in combination in a single-center cohort of 573 adult patients who underwent CD34-selected allo-HCT following myeloablative conditioning. Pulmonary disease, moderate to severe hepatic comorbidity, cardiac disease of any type, and renal dysfunction were associated with increased NRM in multivariable Cox regression models. A Simplified Comorbidity Index (SCI) composed of the 4 comorbidities predictive of NRM, as well as age >60 years, stratified patients into 5 groups with a stepwise increase in NRM. NRM rates ranged from 11.4% to 49.9% by stratum, with adjusted hazard ratios of 1.84, 2.59, 3.57, and 5.38. The SCI was also applicable in an external cohort of 230 patients who underwent allo-HCT with unmanipulated grafts following intermediate-intensity conditioning. The area under the receiver operating characteristic curve (AUC) of the SCI for 1-year NRM was 70.3 and 72.0 over the development and external-validation cohorts, respectively; corresponding AUCs of the Hematopoietic Cell Transplantation-specific Comorbidity Index (HCT-CI) were 61.7 and 65.7. In summary, a small set of comorbidities, aggregated into the SCI, is highly predictive of NRM. The new index stratifies patients into distinct risk groups, was validated in an external cohort, and provides higher discrimination than does the HCT-CI.

SUBMITTER: Shouval R

PROVIDER: S-EPMC8905694 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

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The Simplified Comorbidity Index: a new tool for prediction of nonrelapse mortality in allo-HCT.

Shouval Roni R Fein Joshua A JA Cho Christina C Avecilla Scott T ST Ruiz Josel J Tomas Ana Alarcon AA Sanchez-Escamilla Miriam M Flores Nerea Castillo NC Yáñez Lucrecia L Barker Juliet N JN Dahi Parastoo P Giralt Sergio A SA Geyer Alexander I AI Gyurkocza Boglarka B Jakubowski Ann A AA Lin Richard J RJ O'Reilly Richard J RJ Papadopoulos Esperanza B EB Politikos Ioannis I Ponce Doris M DM Sauter Craig S CS Scordo Michael M Shaffer Brian B Shah Gunjan L GL Sullivan James P JP Tamari Roni R van den Brink Marcel R M MRM Young James W JW Nagler Arnon A Devlin Sean S Shimoni Avichai A Perales Miguel-Angel MA

Blood advances 20220301 5

Individual comorbidities have distinct contributions to nonrelapse mortality (NRM) following allogeneic hematopoietic cell transplantation (allo-HCT). We studied the impact of comorbidities individually and in combination in a single-center cohort of 573 adult patients who underwent CD34-selected allo-HCT following myeloablative conditioning. Pulmonary disease, moderate to severe hepatic comorbidity, cardiac disease of any type, and renal dysfunction were associated with increased NRM in multiva ...[more]

PMID: 34507354

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Project description:BackgroundThe stratification of the general population according to health needs allows to provide better-tailored services. A simple score called Multisource Comorbidity Score (MCS) has been developed and validated for predicting several outcomes. The aim of this study was to evaluate whether the ability of MCS in predicting 1-year mortality improves by incorporating socioeconomic data (as measured by a deprivation index).MethodsBeneficiaries of the Italian National Health Service who in the index year (2018) were aged 50-85 years and were resident in the Sicily region for at least 2 years were identified. For each individual, the MCS was calculated according to his/her clinical profile, and the deprivation index of the census unit level of the individual's residence was collected. Frailty models were fitted to assess the relationship between the indexes (MCS and deprivation index) and 1-year mortality. Akaike information criterion and Bayesian information criterion statistics were used to compare the goodness of fit of the model that included only MCS and the model that also contained the deprivation index. The models were further compared by means of the area under the receiver operating characteristic curve (AUC).ResultsThe final cohort included 1,062,221 individuals, with a mortality rate of 15.6 deaths per 1,000 person-years. Both MCS and deprivation index were positively associated with mortality.The goodness of fit statistics of the two models were very similar. For MCS only and MCS plus deprivation index models, Akaike information criterion were 17,013 and 17,038, respectively, whereas Bayesian information criterion were 16,997 and 17,000, respectively. The AUC values were 0.78 for both models.ConclusionThe present study shows that socioeconomic features as measured by the deprivation index did not improve the capability of MCS in predicting 1-year risk of death. Future studies are needed to investigate other sources of data to enhance the risk stratification of populations.

Project description:BackgroundDuring the COVID-19 pandemic, nursing home (NH) residents faced the highest risk of severe COVID-19 disease and mortality. Due to their frailty status, comorbidity burden can serve as a useful predictive indicator of vulnerability in this frail population. However, the prognostic value of these cumulative comorbidity scores like the Charlson comorbidity index (CCI) remained unclear in this population. We evaluated the incremental predictive value of the CCI for predicting 28-day mortality in NH residents with COVID-19, compared to prediction using age and sex only.MethodsWe included older individuals of ≥ 70 years of age in a large retrospective observational cohort across NHs in the Netherlands. Individuals with PCR-confirmed COVID-19 diagnosis from 1 March 2020 to 31 December 2021 were included. The CCI score was computed by searching for the comorbidities recorded in the electronic patient records. All-cause mortality within 28 days was predicted using logistic regression based on age and sex only (base model) and by adding the CCI to the base model (CCI model). The predictive performance of the base model and the CCI model were compared visually by the distribution of predicted risks and area under the receiver operator characteristic curve (AUROC), scaled Brier score, and calibration slope.ResultsA total of 4318 older NH residents were included in this study with a median age of 88 years [IQR: 83-93] and a median CCI score of 6 [IQR: 5-7]. 1357 (31%) residents died within 28 days after COVID-19 diagnosis. The base model, with age and sex as predictors, had an AUROC of 0.61 (CI: 0.60 to 0.63), a scaled brier score of 0.03 (CI: 0.02 to 0.04), and a calibration slope of 0.97 (CI: 0.83 to 1.13). The addition of CCI did not improve these predictive performance measures.ConclusionThe addition of the CCI as a vulnerability indicator did not improve short-term mortality prediction in NH residents. Similar (high) age and number of comorbidities in the NH population could reduce the effectiveness of these predictors, emphasizing the need for other population-specific predictors that can be utilized in the frail NH residents.

Dataset Information

The Simplified Comorbidity Index: a new tool for prediction of nonrelapse mortality in allo-HCT.

Publications

The Simplified Comorbidity Index: a new tool for prediction of nonrelapse mortality in allo-HCT.

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