Development and validation of novel multimorbidity indices for older adults

Development of a predictive nomogram for in-hospital death risk in multimorbid patients with hepatocellular carcinoma undergoing Palliative Locoregional Therapy

Patients diagnosed with hepatocellular carcinoma (HCC) often present with multimorbidity, significantly contributing to adverse outcomes, particularly in-hospital mortality. This study aimed to develop a predictive nomogram to assess the impact of comorbidities on in-hospital mortality risk in HCC patients undergoing palliative locoregional therapy. We retrospectively analyzed data from 345 hospitalized HCC patients who underwent palliative locoregional therapy between January 2015 and December 2022. The nomogram was constructed using independent risk factors such as length of stay (LOS), hepatitis B virus (HBV) infection, hypertension, chronic obstructive pulmonary disease (COPD), anemia, thrombocytopenia, liver cirrhosis, hepatic encephalopathy (HE), N stage, and microvascular invasion. The model demonstrated high predictive accuracy with an AUC of 0.908 (95% CI: 0.859-0.956) for the overall dataset, 0.926 (95% CI: 0.883-0.968) for the training set, and 0.862 (95% CI: 0.728-0.994) for the validation set. Calibration curves indicated a strong correlation between predicted and observed outcomes, validated by statistical tests. Decision curve analysis (DCA) and clinical impact curves (CIC) confirmed the model's clinical utility in predicting in-hospital mortality. This nomogram offers a practical tool for personalized risk assessment in HCC patients undergoing palliative locoregional therapy, facilitating informed clinical decision-making and improving patient management.

10-Year Multimorbidity Trajectories in Older People Have Limited Benefit in Predicting Short-Term Health Outcomes in Comparison to Standard Multimorbidity Thresholds: A Population-Based Study

Purpose

To identify multimorbidity trajectories among older adults and to compare their health outcome predictive performance with that of cross-sectional multimorbidity thresholds (eg, ≥2 chronic conditions (CCs)).

Patients and Methods

We performed a population-based longitudinal study with a random sample of 99,411 individuals aged >65 years on April 1, 2019. Using health administrative data, we calculated for each individual the yearly CCs number from 2010 to 2019 and constructed the trajectories with latent class growth analysis. We used logistic regression to determine the increase in predictive capacity (c-statistic) of multimorbidity trajectories and traditional cross-sectional indicators (≥2, ≥3, or ≥4 CCs, assessed in April 2019) over that of a baseline model (including age, sex, and deprivation). We predicted 1-year mortality, hospitalization, polypharmacy, and frequent general practitioner, specialist, or emergency department visits.

Results

We identified eight multimorbidity trajectories, each representing between 3% and 25% of the population. These trajectories exhibited trends of increasing, stable, or decreasing number of CCs. When predicting mortality, the 95% CI for the increase in the c-statistic for multimorbidity trajectories [0.032-0.044] overlapped with that of the ≥3 indicator [0.037-0.050]. Similar results were observed when predicting other health outcomes and with other cross-sectional indicators.

Conclusion

Multimorbidity trajectories displayed comparable health outcome predictive capacity to those of traditional cross-sectional multimorbidity indicators. Given its ease of calculation, continued use of traditional multimorbidity thresholds remains relevant for population-based multimorbidity surveillance and clinical practice.

Comparison of Comorbidity Models Within a Population-Based Cohort of Older Adults With Non-Hodgkin Lymphoma

PURPOSE

Compare the association of individual comorbidities, comorbidity indices, and survival in older adults with non-Hodgkin lymphoma (NHL), including in specific NHL subtypes.

METHODS

Data source was SEER-Medicare, a population-based registry of adults age 65 years and older with cancer. We included all incident cases of NHL diagnosed during 2008-2017 who met study inclusion criteria. Comorbidities were classified using the three-factor risk estimate scale (TRES), Charlson comorbidity index (CCI), and National Cancer Institute (NCI) comorbidity index categories and weights. Overall survival (OS) and lymphoma-specific survival, with death from other causes treated as a competing risk, were estimated using the Kaplan-Meier method from time of diagnosis. Multivariable Cox models were constructed, and Harrel C-statistics were used to compare comorbidity models. A two-sided P value of <.05 was considered significant.

RESULTS

A total of 40,486 patients with newly diagnosed NHL were included. Patients with aggressive NHL had higher rates of baseline comorbidity. Despite differences in baseline comorbidity between NHL subtypes, cardiovascular, pulmonary, diabetes, and renal comorbidities were frequent and consistently associated with OS in most NHL subtypes. These categories were used to construct a candidate comorbidity score, the non-Hodgkin lymphoma 5 (NHL-5). Comparing three validated comorbidity scores, TRES, CCI, NCI, and the novel NHL-5 score, we found similar associations with OS and lymphoma-specific survival, which was confirmed in sensitivity analyses by NHL subtypes.

CONCLUSION

The optimal measure of comorbidity in NHL is unknown. Here, we demonstrate that the three-category TRES and five-category NHL-5 scores perform as well as the 14-16 category CCI and NCI scores in terms of association with OS and lymphoma-specific survival. These simple scores could be more easily used in clinical practice without prognostic loss.

Can markers of disease severity improve the predictive power of claims-based multimorbidity indices?

BACKGROUND

Claims-based measures of multimorbidity, which evaluate the presence of a defined list of diseases, are limited in their ability to predict future outcomes. We evaluated whether claims-based markers of disease severity could improve assessments of multimorbid burden.

METHODS

We developed 7 dichotomous markers of disease severity which could be applied to a range of diseases using claims data. These markers were based on the number of disease-associated outpatient visits, emergency department visits, and hospitalizations made by an individual over a defined interval; whether an individual with a given disease had outpatient visits to a specialist who typically treats that disease; and ICD-9 codes which connote more versus less advanced or symptomatic manifestations of a disease. Using Medicare claims linked with Health and Retirement Study data, we tested whether including these markers improved ability to predict ADL decline, IADL decline, hospitalization, and death compared to equivalent models which only included the presence or absence of diseases.

RESULTS

Of 5012 subjects, median age was 76 years and 58% were female. For a majority of diseases tested individually, adding each of the 7 severity markers yielded minimal increase in c-statistic (≤0.002) for outcomes of ADL decline and mortality compared to models considering only the presence versus absence of disease. Gains in predictive power were more substantial for a small number of individual diseases. Inclusion of the most promising marker in multi-disease multimorbidity indices yielded minimal gains in c-statistics (<0.001-0.007) for predicting ADL decline, IADL decline, hospitalization, and death compared to indices without these markers.

CONCLUSIONS

Claims-based markers of disease severity did not contribute meaningfully to the ability of multimorbidity indices to predict ADL decline, mortality, and other important outcomes.