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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.