Medicine (RMH) - Theses
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ItemBiological assessment of geriatric rehabilitation inpatients( 2022)Chronological age is a major risk factor for the development of chronic diseases and frailty. The growing ageing population has imposed a heavy burden on healthcare systems which are being inundated with geriatric patients. In clinical practice, older adults are assessed and managed by the Comprehensive Geriatric Assessment (CGA), a multidimensional and interdisciplinary clinical tool that evaluates medical conditions and functional capacity in multiple domains. While the CGA contains several detailed clinical tools, it currently does not involve any biological assessment. A biological assessment could identify individuals with an accelerated ageing process, provide additional information about their health status and ultimately help early diagnosis, prevention and recovery of age-related diseases. This PhD project investigated the biological determinants of adverse health outcomes in geriatric rehabilitation inpatients using clinical pathology data. The unresolved inflammation characterized by high C-reactive protein and low albumin, vitamin D deficiency and higher biological age determined by combined blood biochemistry markers were associated with frailty, institutionalization and mortality. In addition, a literature review that encompasses cell cycle regulators as cellular senescence markers in human peripheral blood cells was conducted, showing the potential as a biological assessment clinically. This thesis highlighted the predictive value of pathology parameters for adverse health outcomes and the importance of ensuring the resolution of inflammation and adequate levels of vitamin D during geriatric rehabilitation. Future studies are required to investigate the association of senescence burden in blood samples with clinical phenotype and rehabilitation outcomes, and evaluate the utility of CGA integrated with biological assessments in care planning.
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ItemPredicting poor clinical outcomes among older inpatients( 2021)Predicting poor clinical outcomes among inpatients has significant impacts on clinical decision making and treatment plan. A valid prognostic tool allows early interventions and patient-specific therapies by identifying patients who are at a higher risk of poor clinical outcomes. The Comprehensive Geriatric Assessment (CGA), a multidisciplinary diagnostic and treatment process, is implemented in geriatric rehabilitation inpatients and it includes multiple validated clinical assessment tools to evaluate older patients’ medical, psychosocial and functional limitations. The primary objective of this PhD thesis is to evaluate the performance of morbidity measures and frailty assessment tools in predicting poor clinical outcomes among geriatric rehabilitation inpatients. The systematic review included in this thesis shows that morbidity measures, predominantly the Charlson Comorbidity Index (CCI) and the Cumulative Illness Rating Scale (CIRS) and Geriatric Index of Comorbidity (GIC), are predictive for post-discharge mortality among older inpatients and the predictive performances are better in longer follow-up period. However, they are not predictive for functional decline among older inpatients. A total of 1890 geriatric rehabilitation inpatients (median age 83.4 [IQR 77.6-88.4] years, 56% female) were included in this thesis. They were mostly independent two-week prior to hospitalization (median Activities of Daily Living (ADL) score: 6 [IQR 4-6]; median Instrumental (I)ADL score: 5 [IQR 2-7] respectively). At admission to the geriatric rehabilitation wards, they had a median CCI score of 2 [IQR 1-4] and were mostly frail (median Clinical Frailty Scale (CFS) score: 6 [IQR 5-7]). Among these geriatric rehabilitation inpatients, three distinct functional trajectories were identified from two-week prior to hospitalization to three-month post-discharge: remained poor, deteriorated and recovered. Cognitive impairment and greater frailty status, assessed using the CFS, were each associated with deterioration and remaining poor in patients’ functional performance. Greater frailty status, assessed using the CFS, was also shown to be associated with in-hospital, three-month and one-year post-discharge mortality. An increased frailty severity from admission to discharge from geriatric rehabilitation was also associated with three-month post-discharge mortality. This thesis shows that frailty assessment tools, based on either clinical judgment (CFS) or laboratory tests (frailty index-laboratory test (FI-lab)), were poor predictors for one-year mortality among geriatric rehabilitation inpatients. Nonetheless, the clinical judgement-based frailty assessment tool (CFS) was slightly better than the laboratory test-based assessment tools (FI-lab and modified FI-lab) in predicting mortality and that they were all significantly associated with one-year mortality. Furthermore, this thesis includes a study applying the machine learning approach and the characteristics included in the CGA were shown to be poor predictors for geriatric rehabilitation length of stay. Lastly, greater frailty status, assessed using the CFS, was associated with the transitions from premorbid functional performance to institutionalisation and mortality from two-week preadmission to three-month post-discharge. This thesis emphasises the importance of assessing patients’ frailty status at geriatric rehabilitation admission as it reflects those with a higher risk of poor clinical outcomes. Future studies should focus on evaluating the treatment and intervention for frailty and investigate the importance of other CGA components.
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ItemFrailty: risk stratification, measurement and outcomes in surgical and critically ill patients( 2020)Surgical and intensive care populations are ageing worldwide. Frailty, a state of increased vulnerability to stress, is increasingly common in older surgical and critically ill patients. In these cohorts, frailty predisposes to increased complications, mortality, and longer lengths of stay. Implementation of frailty screening can aid case finding, to select patients for more in-depth frailty measurement. This can assist identification of at-risk surgical and critically ill patients, and help identify which areas of health are more affected by frailty in these populations. Using routinely collected hospital data may allow construction of frailty indices, which can accurately and reproducibly measure frailty in granular detail, and help clarify the interplay between age, medical comorbidities, and frailty in individual patients. Aims of this PhD were to: 1. Review the role of frailty indices in the measurement of frailty in critically ill and surgical populations. 2. Determine which areas of health are adversely affected by frailty in surgical and intensive care patients. 3. Investigate the correlation between the screening Clinical Frailty Scale and multi-dimensional frailty measurement tools. 4. Explore whether frailty can be measured retrospectively from the clinical record. 5. Examine the prevalence and impact of frailty in intensive care unit populations in Australia and New Zealand. 6. To develop and validate a frailty index from medical admission data to measure frailty in surgical and intensive care unit patients. Phases of research and results The phases of research to accomplish these aims were: Phase 1: A systematic literature review was undertaken, assessing the role of frailty indices in the measurement of frailty in intensive care and surgery. Studies were identified through systematic review of MEDLINE, EMBASE, CINAHL databases, including studies which utilised a frailty index containing at least 30 health deficits. Outcomes assessed included mortality, complications, length of stay and discharge location. Study and frailty index quality were assessed, with findings narratively described. Phase 1 Results (Chapter 2): Thirteen studies utilising frailty indices were identified, nine prospective and four retrospective. Frailty index quality was high in 11 of the 13 studies. Frailty indices identified patients at risk of increased death, surgical complications, increased length of stay, and discharge to residential care. The term “frailty index” was found to be misapplied to a number of measurement tools not fulfilling criteria to be true frailty index scales. Phase 2: Two concurrent prospective cohort studies were conducted in a tertiary metropolitan hospital, in an intensive care unit and peri-operative department. Adult patients aged >/= 50 years (ICU) or >/= 65 years (surgery) admitted between February and June 2017 were eligible for inclusion. Frailty (Clinical Frailty Scale, Edmonton Frail Scale, Frailty Index) were measured at baseline. Outcomes included mortality, discharge to residential care, ICU and peri-operative complications, six-month mortality and residential location. Phase 2 Results (Chapters 4, 5, 8) : Three-hundred and thirty-six patients were enrolled in a four-month period, 160 ICU patients, 218 surgical patients, with 42 patients in both cohorts. Frailty prevalence (measured via the Edmonton Frail scale) was 36% in ICU patients, and 24% in surgical patients. Mortality in frail patients was higher in both ICU and surgical cohorts (ICU: 24% vs. 9%, p = 0.010; surgery = 10% vs. 2%, p = 0.019). Patients with frailty were less likely to be discharged home and more likely to be discharged to in-patient rehabilitation, and to be residing in assisted living facilities at six month follow up. Phase 3: Based on the results from the cohorts enrolled in Phase 2 above, the Clinical Frailty Scale was compared to the multi-dimensional Edmonton Frail Scale, with agreement measured via Kappa co-efficient, and correlation via Spearman’s correlation coefficient. The affected health domains of patients with frailty were compared with those of patients without frailty. Phase 3 Results (Chapter 4): Clinical Frailty Scale and Edmonton Frail Scales were highly correlated in ICU patients (Spearman correlation coefficient = 0.85; 95% CI, 0.81 to 0.88), with high agreement (kappa coefficient = 0.78; 95% CI, 0.68 to 0.88), and in surgical patients (Spearman correlation coefficient, 0.81; 95% CI, 0.77 to 0.86; kappa coefficient, 0.76; 95% CI, 0.70 to 0.81). Frail patients had worse health status across the full spectrum of frailty domains, in particular functional dependence, malnutrition, and prior hospital admissions. Phase 4: A secondary analysis of an existing dataset was conducted to examine the feasibility and inter-rater reliability of retrospectively determining a Clinical Frailty Scale from the medical record of critically ill patients. One-hundred and forty-four ICU patients had CFS scores independently assigned by four blinded investigators, with inter-rater agreement between CFS scores examined via quadratic weighted Cohen’s kappa coefficients. Phase 4 Results (Chapter 6): Of 144 enrolled patients, 137 (95%) were able to have a CFS score assigned retrospectively from the medical record. Cohen’s kappa coefficient for inter-rater reliability between frailty assessors was 0.67, confirming substantial agreement. Frailty measurement was thus deemed feasible from the ICU clinical record. Phase 5: A retrospective population-based cohort study was conducted, analysing data from the Australian and New Zealand Intensive Care Society Adult Patient Database. All patients aged >/= 80 years on admission to ICU between 1 January 2017 and 31 December 2018 were included in the study. The database was interrogated for the Clinical Frailty Scale on admission, with a mixed effects logistic regression fitted to the primary outcome of in-hospital mortality. Secondary outcomes were length of stay (hospital and ICU), re-admission to ICU, and discharge destination (including new chronic care or nursing home admission). Phase 5 Results (Chapter 7): 15,613 patients aged >/= 80 years were included from 131 ICUs; 6,203 patients (40%) were frail. Patients with frailty had higher illness severity, and were more likely to be admitted emergently to ICU with sepsis or respiratory failure. Mortality was higher in patients with frailty (17.6% vs. 8.2%, p < 0.001; adjusted mortality OR [95% CI] = 1.87 [1.65 – 2.11], p < 0.001). Patients with frailty had longer lengths of stay in-ICU and in-hospital, and were more likely to be newly discharged to nursing home/chronic care (4.9% vs. 2.8%, p < 0.001). Phase 6: Based on the results from the cohorts enrolled in Phase 2 above, I developed a frailty index from routine data collected on hospital admission, and tested in both surgical and ICU cohorts. The diagnostic performance of the frailty index against existing frailty tools for both screening (the Clinical Frailty Scale) and measurement (the Edmonton Frail Scale) was assessed. The discriminative ability of the frailty index for mortality was compared to existing risk predictions tools, including the Acute Physiology and Chronic Health Evaluation (APACHE) III score (ICU) and the P-POSSUM score (surgical patients). Phase 6 Results (Chapter 8): A 36-item frailty index was constructed, able to be completed for all patients. Correlation between Edmonton and Clinical Frailty scales was strong for both ICU and surgical patients. The frailty index had good discriminative ability for prediction of mortality, comparable with the performance of the APACHE-III illness severity score in ICU (AUC-ROC [95% CI] = 0.75 [0.64 – 0.85] vs. 0.80 [0.72 – 0.88]) and the P-POSSUM score in surgery (AUC-ROC = 0.76 [0.61 – 0.91] vs. 0.81 [0.71 – 0.92]). Conclusions Frailty is common in critically ill and surgical patients, affecting the full spectrum of health domains, and predisposing to poor outcomes. The Clinical Frailty Scale accurately screens for frailty in these cohorts, is able to be measured retrospectively from the clinical record, and can be used to determine frailty in critically ill patients at a population registry level. Frailty indices derived from routine hospital data are able to measure frailty in the peri-operative and ICU setting; electronic medical records show promise in automating such measurement.