We developed a clinical prediction model to assess the risk of death within the year immediately following discharge from geriatric wards. Our nomogram identified patients who were at increased risk of death within this time frame. Our internal validation of the model confirmed its reliability and performance. Decision curve analysis revealed the clinical applicability of the model across various threshold probabilities. These findings indicate that our model is particularly relevant for identifying patients in geriatric wards with a high mortality risk. We identified three risk groups based on predicted probability and clinical applicability. The robustness of our regression model was determined using sensitivity analyses.
An objectively measured and readily accessible set of variables enabled us to construct this model for integration into routine practice. The included variables were the Barthel index score for ADLs at discharge, serum albumin level, CCI, FRAIL scale scores, and Mini-Nutritional Assessment-Short Form (MNA-SF) scores. These respectively address function, biological indicators, disease burden, overall status, and nutritional status. The predictive significance of individual variables in assessing mortality risk among older patients has been established. A close inverse correlation has been identified between ADL measured using the Barthel index and mortality.26 Furthermore, when combined with other risk factors, ADLs have potential as predictors of short-term mortality among institutionalised older adults.26 A low serum albumin level that represents malnutrition to some extent, is a prognostic factor for death among older adults10 The ability of the disease burden to predict death has been evaluated and the CCI is an important predictor in this context.27 The results of a systematic review and meta-analysis have indicated that frailty is a significant predictor of mortality,28 and that malnutrition leads to poor survival.29
We thoroughly examined several variables in previous models and subsequently incorporated some of them into our final model. For instance, albumin and ADL were incorporated in a study on 1-year post-hospitalisation mortality among medical patients aged ≥ 70 years.9 Inclusion of the CCI and MNA-SF has previously been limited. We incorporated the Comorbidity Index and Mini Nutritional Assessment within the MPI with comparable clinical significance. Although various tools are available to assess disease burden and nutritional status, we selected this version for our model based on its simplicity and widespread accessibility.
Frailty has rarely been incorporated as a variable in predictive models, and a lack of consensus on a standardised frailty assessment tool hinders its clinical applicability as a unified indicator for mortality prediction. We, therefore, opted for the concise and versatile FRAIL scale tool that comprises five questions that can be self- or caregiver-assessed. This is practical and suitable for clinical implementation. As the predictive value of frailty for mortality gains recognition, clinicians have increasingly advocated for incorporating patient frailty status into clinical decision-making.30 Therefore, frailty is an important potential variable. The finally selected predictors were further validated via expert opinions regarding clinical plausibility, feasibility, and applicability.
Our model offers several advantages. It builds upon data modelling of hospitalised older patients with multisystem clinical manifestations and is thus suitable for assessing such patients. A specialised medical model has historically prioritised disease-focused care. However, the significance of functional status among older patients is essential to recognise alongside disease management. Prognostic information derived from the systematic evaluation of patients aligns more closely with real-world clinical practice. We evaluated ADLs, frailty, and nutritional status, all of which are crucial components of a comprehensive evaluation of older individuals and relevant to their needs. These indicators have often been overlooked because of the predominant reliance on data obtained from electronic medical record systems in constructing existing prediction models.4 We selected potential variables considering the comprehensive assessment and clinical data of older patients as potential predictors. The potential value of combining clinical and CGA data to build predictive models supports the foundation of the model.10, 11 However, although a combination of clinical data and CGA updated the MPI, the model performance was insufficient, with a maximum C-index of 0.76.12 The C-index of our model at 1 year was 0·917, which was superior to that of previous models validated in this cohort (PI, 0·885; MPI, 0·828). Consequently, CGA data typically lacks disease coding and is often excluded from data platforms. Our model was constructed using readily available variables and a specific formula, enabling its direct application in the clinical setting and potential for external validation.
The development and validation of our model strictly adhered to established guidelines. We applied LASSO regression to reduce dimensionality. This surpassed the conventional approach of selecting predictors solely based on the strength of their univariate association with an outcome. The results of sensitivity analyses of the complete and imputed data confirmed the clinical usefulness of our model.
The collective analysis discerned 1-year mortality risk among discharged older patients and confirmed the effectiveness of our model. Earlier models have high predictive accuracy but are more suitable for big data platforms than for everyday medical decision-making and palliative care. Personalised and comprehensive assessment data can serve as impartial criteria that offer an objective foundation for the provision of palliative care. This is important in the context of geriatric wards, particularly with respect to collaborative decision-making between doctors and older patients regarding care interventions. In turn, this can facilitate deliberations regarding end-of-life care alternatives, including hospice palliative care. Engaging in these dialogues might be challenging but are essential to ensure that older patients receive care aligned with their goals and preferences. This would improve their QOL and help to alleviate stress for patients and their families.
Our model has several limitations. The standardisation of geriatric comprehensive assessments poses a challenge, especially when implemented across diverse healthcare institutions. The complexity of comprehensive assessments further confuses this issue. For instance, numerous tools are available to assess frailty; however, we used common and relatively simple tools with enhanced applicability in the clinical setting. This study was conducted at a single centre, and the verification process was limited to internal validation, which indicated favourable model performance. Therefore, future external validation is imperative.