Clinical decision-making in older adults following emergency admission to hospital. Derivation and validation of a risk stratification score: OPERA.

OBJECTIVES OF THE STUDY: Demographic changes alongside medical advances have resulted in older adults accounting for an increasing proportion of emergency hospital admissions. Current measures of illness severity, limited to physiological parameters, have shortcomings in this cohort, partly due to patient complexity. This study aimed to derive and validate a risk score for acutely unwell older adults which may enhance risk stratification and support clinical decision-making. METHODS: Data was collected from emergency admissions in patients ≥65 years from two UK general hospitals (April 2017- April 2018). Variables underwent regression analysis for in-hospital mortality and independent predictors were used to create a risk score. Performance was assessed on external validation. Secondary outcomes included seven-day mortality and extended hospital stay. RESULTS: Derivation (n = 8,974) and validation (n = 8,391) cohorts were analysed. The model included the National Early Warning Score 2 (NEWS2), clinical frailty scale (CFS), acute kidney injury, age, sex, and Malnutrition Universal Screening Tool. For mortality, area under the curve for the model was 0.79 (95% CI 0.78-0.80), superior to NEWS2 0.65 (0.62-0.67) and CFS 0.76 (0.74-0.77) (P<0.0001). Risk groups predicted prolonged hospital stay: the highest risk group had an odds ratio of 9.7 (5.8-16.1) to stay >30 days. CONCLUSIONS: Our simple validated model (Older Persons' Emergency Risk Assessment [OPERA] score) predicts in-hospital mortality and prolonged length of stay and could be easily integrated into electronic hospital systems, enabling automatic digital generation of risk stratification within hours of admission. Future studies may validate the OPERA score in external populations and consider an impact analysis.

Improving clinical prediction rules in acute kidney injury with the use of biomarkers of cell cycle arrest: a pilot study.

INTRODUCTION: Early recognition of patients developing acute kidney injury (AKI) is of considerable interest, we report the first use of a combination of a clinical prediction rule with a biomarker in emergent adult medical patients to improve AKI recognition. METHODS: Single-centre prospective pilot study of medical admissions without AKI identified as high risk by a clinical prediction rule. Urine samples were obtained and tissue inhibitor of metalloproteinases-2 (TIMP-2) and insulin-like growth factor binding protein 7 (IGFBP7) - biomarkers associated with cell cycle arrest, were measured. OUTCOME: Creatinine-based KDIGO hospital-acquired AKI (HA-AKI). RESULTS: Of 69 patients recruited, HA-AKI developed in 13% (n = 9), in whom biomarker values were higher (median 0.43 (interquartile range (IQR) 0.21-1.25) vs. 0.07 (0.03-0.16) in cases without (p = 0.008). Peak rise in creatinine was higher in biomarker positive cases (median 30 µmol/L (7-72) vs. 1 µmol/L (0-16), p = 0.002). AUROC was 0.78 (95% CI 0.57-0.98). At the suggested cut-off (0.3) sensitivity for predicting AKI was 78% (95% CI 40-97%), specificity 89% (78-95%), positive predictive value 50% (31-69%) and negative predictive value 96% (89-99%). DISCUSSION: Addition of a urinary biomarker allows exclusion of a significant number of patients identified to be at higher risk of AKI by a clinical prediction rule.

Correction: The ICE-AKI study: Impact analysis of a Clinical prediction rule and Electronic AKI alert in general medical patients.

[This corrects the article DOI: 10.1371/journal.pone.0200584.].

The ICE-AKI study: Impact analysis of a Clinical prediction rule and Electronic AKI alert in general medical patients.

BACKGROUND: Acute kidney injury (AKI) is assoicated with high mortality and measures to improve risk stratification and early identification have been urgently called for. This study investigated whether an electronic clinical prediction rule (CPR) combined with an AKI e-alert could reduce hospital-acquired AKI (HA-AKI) and improve associated outcomes. METHODS AND FINDINGS: A controlled before-and-after study included 30,295 acute medical admissions to two adult non-specialist hospital sites in the South of England (two ten-month time periods, 2014-16); all included patients stayed at least one night and had at least two serum creatinine tests. In the second period at the intervention site a CPR flagged those at risk of AKI and an alert was generated for those with AKI; both alerts incorporated care bundles. Patients were followed-up until death or hospital discharge. Primary outcome was change in incident HA-AKI. Secondary outcomes in those developing HA-AKI included: in-hospital mortality, AKI progression and escalation of care. On difference-in-differences analysis incidence of HA-AKI reduced (odds ratio [OR] 0.990, 95% CI 0.981-1.000, P = 0.049). In-hospital mortality in HA-AKI cases reduced on difference-in-differences analysis (OR 0.924, 95% CI 0.858-0.996, P = 0.038) and unadjusted analysis (27.46% pre vs 21.67% post, OR 0.731, 95% CI 0.560-0.954, P = 0.021). Mortality in those flagged by the CPR significantly reduced (14% pre vs 11% post intervention, P = 0.008). Outcomes for community-acquired AKI (CA-AKI) cases did not change. A number of process measures significantly improved at the intervention site. Limitations include lack of randomization, and generalizability will require future investigation. CONCLUSIONS: In acute medical admissions a multi-modal intervention, including an electronically integrated CPR alongside an e-alert for those developing HA-AKI improved in-hospital outcomes. CA-AKI outcomes were not affected. The study provides a template for investigations utilising electronically generated prediction modelling. Further studies should assess generalisability and cost effectiveness. TRIAL REGISTRATION: Clinicaltrials.org NCT03047382.

Systematic review of prognostic prediction models for acute kidney injury (AKI) in general hospital populations.

OBJECTIVE: Critically appraise prediction models for hospital-acquired acute kidney injury (HA-AKI) in general populations. DESIGN: Systematic review. DATA SOURCES: Medline, Embase and Web of Science until November 2016. ELIGIBILITY: Studies describing development of a multivariable model for predicting HA-AKI in non-specialised adult hospital populations. Published guidance followed for data extraction reporting and appraisal. RESULTS: 14 046 references were screened. Of 53 HA-AKI prediction models, 11 met inclusion criteria (general medicine and/or surgery populations, 474 478 patient episodes) and five externally validated. The most common predictors were age (n=9 models), diabetes (5), admission serum creatinine (SCr) (5), chronic kidney disease (CKD) (4), drugs (diuretics (4) and/or ACE inhibitors/angiotensin-receptor blockers (3)), bicarbonate and heart failure (4 models each). Heterogeneity was identified for outcome definition. Deficiencies in reporting included handling of predictors, missing data and sample size. Admission SCr was frequently taken to represent baseline renal function. Most models were considered at high risk of bias. Area under the receiver operating characteristic curves to predict HA-AKI ranged 0.71-0.80 in derivation (reported in 8/11 studies), 0.66-0.80 for internal validation studies (n=7) and 0.65-0.71 in five external validations. For calibration, the Hosmer-Lemeshow test or a calibration plot was provided in 4/11 derivations, 3/11 internal and 3/5 external validations. A minority of the models allow easy bedside calculation and potential electronic automation. No impact analysis studies were found. CONCLUSIONS: AKI prediction models may help address shortcomings in risk assessment; however, in general hospital populations, few have external validation. Similar predictors reflect an elderly demographic with chronic comorbidities. Reporting deficiencies mirrors prediction research more broadly, with handling of SCr (baseline function and use as a predictor) a concern. Future research should focus on validation, exploration of electronic linkage and impact analysis. The latter could combine a prediction model with AKI alerting to address prevention and early recognition of evolving AKI.

Anesthesia and electroconvulsive therapy: a retrospective study comparing etomidate and propofol.

BACKGROUND: The choice of anesthetic can influence the efficacy of electroconvulsive therapy (ECT). In the UK, propofol is a popular induction agent for ECT, but is associated with higher stimulus charge, shorter seizures, and known to affect seizure threshold. Etomidate is an alternative induction agent but there are concerns over its adverse events and safety. OBJECTIVES: We examined the differences between propofol and etomidate in the real life situation of an ECT clinic by assessing their effect on (i) length of course of ECT (ie, number of treatments required to remission), (ii) adverse effects of each induction agent, (iii) the number of 'missed seizures,' and (iv) stimulus dose (charge in mC), which relates to seizure threshold. METHOD: Using a retrospective naturalistic study design, 94 patients were identified over a 36-month period in our ECT clinic, of which, 65 met the inclusion criteria. Of these, 36 had received etomidate and 29 had received propofol as induction agents throughout their course of ECT. RESULTS: Patients who received propofol had a significantly longer course of ECT, higher seizure thresholds, and increased amounts of electrical charge (mC) over their course. There were no significant differences in adverse events with either of the induction agents. CONCLUSIONS: When used for acute courses of ECT, propofol and etomidate are equally well tolerated as induction agents. Patients who received propofol had longer acute courses of ECT and, consequently, longer and costlier inpatient stays. Etomidate could be a better alternative induction agent in ECT.