Development and Validation of an Electronic Postoperative Morbidity Score.

BACKGROUND: Electronic health records are being adopted due to numerous potential benefits. This requires the development of objective metrics to characterize morbidity, comparable to studies performed in centers without an electronic health record. We outline the development of an electronic version of the postoperative morbidity score for integration into our electronic health record. METHODS: Twohundred and three frail patients who underwent elective surgery were reviewed. We retrospectively defined postoperative morbidity score on postoperative day 3. We also recorded potential electronic surrogates for morbidities that could not be easily extracted in an objective format. We compared discriminative capability (area under the receiver operator curve) for patients having prolonged length of stay or complex discharge requirements. RESULTS: One hundred thirty-nine patients (68%) had morbidity in ≥1 postoperative morbidity score domain. Initial electronic surrogates were overly sensitive, identifying 173 patients (84%) as having morbidity. We refined our definitions using backward logistic regression against "gold-standard" postoperative morbidity score. The final electronic postoperative morbidity score differed from the initial version in its definition of cardiac and neurological morbidity. There was no significant difference in the discriminative capability between electronic postoperative morbidity score and postoperative morbidity score for either outcome (area under the receiver operator curve: 0.66 vs 0.66 for complex discharge requirement, area under the receiver operator curve: 0.66 vs 0.67 for a prolonged length of stay; P> .05 for both). Patients with postoperative morbidity score or electronic postoperative morbidity score-defined morbidity on day 3 had increased risk of prolonged length of stay (P < .001 for both). CONCLUSIONS: We present a variant of postoperative morbidity score based on objective electronic metrics. Discriminative performance appeared comparable to gold-standard definitions for discharge outcomes. Electronic postoperative morbidity score may allow characterization of morbidity within our electronic health record, but further study is required to assess external validity.

Predicting readmissions: poor performance of the LACE index in an older UK population.

INTRODUCTION: interventions to prevent hospital readmission depend on the identification of patients at risk. The LACE index predicts readmission (and death) and is in clinical use internationally. The LACE index was investigated in an older UK population. METHODS: randomly selected alive-discharge episodes were reviewed. A LACE score was calculated for each patient and assessed using receiver operator characteristic (ROC) curves. A logistic regression model was constructed, compared with the LACE and validated in a separate population. RESULTS: a total of 507 patients were included with a mean (SD) age of 85 (6.5) years; 17.8% were readmitted and 4.5% died within 30 days. The median LACE score of those readmitted compared with those who were not was 12.5 versus 12 (P = 0.13). The Lace index was only a fair predictor of both 30-day readmission and death with c-statistics of 0.55 and 0.70, respectively. Only the emergency department visit was an independent predictor of readmission, with a c-statistic of 0.61 for readmission. In a validation cohort of 507 cases, the c-statistic of the regression model was 0.57. CONCLUSION: the LACE index is a poor tool for predicting 30-day readmission in older UK inpatients. The absence of a simple predictive model may limit the benefit of readmission avoidance strategies.

Frailty Assessment in Clinical Practice: Opportunity in the Midst of a Pandemic.

Emerging evidence from studies of older adults hospitalised with COVID-19 suggests that there is a high prevalence of frailty in this patient group. We reflect on the measurement of frailty in older patients hospitalized as an emergency and the translation of frailty from a research to a clinical concept. We consider whether, despite the contemporary challenges in the care of older adults as a result of COVID-19, there are opportunities for care quality improvement during a pandemic.

The Association of Geriatric Syndromes with Hospital Outcomes.

BACKGROUND: Frailty, history of dementia (HoD), and acute confusional states (ACS) are common in older patients admitted to hospital. OBJECTIVE: To study the association of frailty (≥6 points in the Clinical Frailty Scale [CFS]), HoD, and ACS with hospital outcomes, controlling for age, gender, acute illness severity (measured by a Modified Early Warning Score in the emergency department), comorbidity (Charlson Comorbidity Index), and discharging specialty (general medicine, geriatric medicine, surgery). DESIGN: Retrospective observational study. SETTING: Large university hospital in England. PATIENTS: We analyzed 8202 first nonelective inpatient episodes of people aged 75 years and older between October 2014 and October 2015. MEASUREMENTS: The outcomes studied were prolonged length of stay (LOS ≥10 days), inpatient mortality, delayed discharge, institutionalization, and 30-day readmission. Statistical analyses were based on multivariate regression models. RESULTS: Independently of controlling variables, prolonged LOS was predicted by CFS ≥6: odds ratio (OR) =1.55; 95% confidence interval [CI], 1.36-1.77; P ⟨ 0.001; HoD: OR = 2.16; 95% CI, 1.79-2.61; P ⟨ 0.001; and ACS: OR = 3.31; 95% CI, 2.64-4.15; P ⟨ 0.001. Inpatient mortality was predicted by CFS ≥6: OR = 2.29; 95% CI, 1.79-2.94; P ⟨ 0.001. Delayed discharge was predicted by CFS ≥6: OR = 1.46; 95% CI, 1.27-1.67; P ⟨ 0.001; HoD: OR = 2.17; 95% CI, 1.80-2.62; P ⟨ 0.001; and ACS: OR = 2.29; 95% CI: 1.83-2.85; P ⟨ 0.001. Institutionalization was predicted by CFS ≥6: OR = 2.56; 95% CI, 2.09-3.14; P ⟨ 0.001; HoD: OR = 2.51; 95% CI, 2.00-3.14; P ⟨ 0.001; and ACS: OR 1.93; 95% CI, 1.46-2.56; P ⟨ 0.001. Readmission was predicted by ACS: OR = 1.36; 95% CI, 1.09-1.71; P = 0.006. CONCLUSIONS: Routine screening for frailty, HoD, and ACS in hospitals may aid the development of acute care pathways for older adults. Journal of Hospital Medicine 2017;12:83-89.

Clinical frailty adds to acute illness severity in predicting mortality in hospitalized older adults: An observational study.

AIM: Frail individuals may be at higher risk of death from a given acute illness severity (AIS), but this relationship has not been studied in an English National Health Service (NHS) acute hospital setting. METHODS: This was a retrospective observational study in a large university NHS hospital in England. We analyzed all first non-elective inpatient episodes of people aged ≥75years (all specialties) between October 2014 and October 2015. Pre-admission frailty was assessed with the Clinical Frailty Scale (CFS) of the Canadian Study on Health & Aging, and AIS in the Emergency Department was measured with a Modified Early Warning Score (ED-MEWS<4 was considered as low acuity, and ED-MEWS≥4 as high acuity). A survival analysis compared times to 30-day inpatient death between CFS categories (1-4: very fit to vulnerable, 5: mildly frail, 6: moderately frail, and 7-8: severely or very severely frail). RESULTS: There were 12,282 non-elective patient episodes (8202 first episodes, of which complete data was available for 5505). In a Cox proportional hazards model controlling for age, gender, Charlson Comorbidity Index, history of dementia, current cognitive concern, and discharging specialty (medical versus surgical), ED-MEWS≥4 (HR=2.87, 95% CI: 2.27-3.62, p<0.001), and CFS 7-8 (compared to CFS 1-4, HR=2.10, 95% CI: 1.52-2.92, p<0.001) were independent predictors of survival time. CONCLUSIONS: We found frailty and AIS independently associated with inpatient mortality after adjustment for confounders. Hospitals may find it informative to undertake large scale assessment of frailty (vulnerability), as well as AIS (stressor), in older patients admitted to hospital as emergencies.