Background:Older patients are vulnerable to developing new or worsening disability after surgery. Despite this, patient or surgical characteristics predisposing to postoperative disability are poorly defined. The aim of the study was to develop and validate a model, subsequently transformed to point-score form, to predict 6-month death or disability in older patients after surgery.Methods:The authors built a prospective, single-center registry to develop and validate the prediction model. The registry included patients 70 yr of age or older undergoing elective and nonelective, cardiac and noncardiac surgery between May 25, 2017, and February 11, 2021, and combined clinical data from the electronic medical record, hospital administrative data (International Classification of Diseases, Tenth Revision, Australian Modification codes) and World Health Organization (Geneva, Switzerland) Disability Assessment Schedule data collected directly from the patients. Death or disability was defined as being dead or having a World Health Organization Disability Assessment Schedule score 16% or greater. Included patients were randomly divided into model development (70%) and internal validation (30%) cohorts. Once constructed, the logistic regression and point-score models were assessed using the internal validation cohort and an external validation cohort comprising data from a separate randomized trial.Results:Of 2,176 patients who completed the World Health Organization Disability Assessment Schedule immediately before surgery, 927 (43%) patients were disabled, and 413 (19%) had significant disability. By 6 months after surgery, 1,640 patients (75%) had data available for the primary outcome analysis. Of these patients, 195 (12%) patients had died, and 691 (42%) were dead or disabled. The developed point-score model included the preoperative World Health Organization Disability Assessment Schedule score, patient age, dementia, and chronic kidney disease. The point score model retained good discrimination in the internal (area under the curve, 0.74; 95% CI, 0.69 to 0.79) and external (area under the curve, 0.77; 95% CI, 0.74 to 0.80) validation data sets.Conclusions:The authors developed and validated a point score model to predict death or disability in older patients after surgery. In a prospective single-center analysis, among 2,176 patients 70 yr of age or older undergoing surgery with preoperative World Health Organization Disability Assessment Schedule data, 1,640 (75%) had 6-month postoperative disability assessment data available. Six months after surgery, 12% (195 of 1,640) of patients had died, 23% (372) had significant disability, and 42% (691) died or had disability. A point-score model including the preoperative World Health Organization Disability Assessment Schedule score, patient age, dementia, and chronic kidney disease demonstrated good discrimination in the internal and external validation datasets.

Accurately measuring the incidence of major postoperative complications is essential for funding and reimbursement of healthcare providers, for internal and external benchmarking of hospital performance and for valid and reliable public reporting of outcomes. Actual or surrogate outcomes data are typically obtained by one of three methods: clinical quality registries, clinical audit, or administrative data. In 2017 a perioperative registry was developed at the Alfred Hospital and mapped to administrative and clinical data. This study investigated the statistical agreement between administrative data (International Statistical Classification of Diseases and Related Health Problems (10th edition) Australian Modification codes) and clinical audit by anaesthetists in identifying major postoperative complications. The study population included 482 high-risk surgical patients referred to the Alfred Hospital anaesthesia postoperative service over two years. Clinical audit was conducted to determine the presence of major complications and these data were compared to administrative data. The main outcome was statistical agreement between the two methods, as defined by Cohen's kappa statistic. Substantial agreement was observed for five major complications, moderate agreement for three, fair agreement for six and poor agreement for two. Sensitivity and positive predictive value ranged from 0 to 100%. Specificity was above 90% for all complications. There was important variation in inter-rater agreement. For four of the five complications with substantial agreement between administrative data and clinical audit, sensitivity was only moderate (61.5%-75%). Using International Statistical Classification of Diseases and Related Health Problems (10th edition) Australian Modification codes to identify postoperative complications at our hospital has high specificity but is likely to underestimate the incidence compared to clinical audit. Further, retrospective clinical audit itself is not a highly reliable method of identifying complications. We believe a perioperative clinical quality registry is necessary to validly and reliably measure major postoperative complications in Australia for benchmarking of hospital performance and before public reporting of outcomes should be considered.

Severe asthma is complex and heterogeneous; ad hoc outpatient assessment can be suboptimal. Systematic evaluation improves outcomes and is recommended by international guidelines. Electronic templates improve physician performance and clinical processes, and may be useful in severe asthma systematic evaluation. We developed the Severe Asthma Global Evaluation (SAGE) electronic platform to streamline this process, via Research Electronic Data Capture (REDCap). It incorporates: a questionnaire battery for patient completion before clinical consultation; asthma and comorbidity modules; a clinical summary page in an asthma management module; a nurse educator module; a structured panel discussion record; and an automatically generated report incorporating all key data. SAGE incorporates 282 clinician input fields, with a typical consultation requiring completion of 169. To streamline the process SAGE contains 34 autocalculations and 20 decision support tools. It incorporates all 95 core variables of the International Severe Asthma Registry, with which it is directly compatible. SAGE improves symptom control and exacerbations in patients with difficult asthma. In conclusion, we developed and validated an electronic platform that facilitates a comprehensive but streamlined systematic evaluation of severe asthma that is available for free download via REDCap. Its use enhances management of patients with severe asthma and facilitates audit and international research collaboration.

The field of Information Systems is about bridging the digital and information divide. Advances in the digital world enable information to be stored and structured in a manner that facilitates effective use of the information for future modelling purposes. Elderly inpatient falls are a common global phenomenon, and an inpatient fall incident can have severe consequences for the patient, caregivers and the healthcare provider. An inpatient fall can result from many causes and its risk can be increased through the combination of these causes. Many risk factors of elderly inpatient falls have been reported in various papers in the literature. However, a logical comprehensive categorisation of all these factors does not currently exist. The objective of this research in progress is to come up with a generic categorisation of the risk factors for elderly inpatient falls alongside the usage of a contextual model to illustrate the inherent interactions amongst these various factors. In addition, we found that the effect of the interaction amongst some risk factors is time dependent which also needs to be incorporated in the contextual model. Such comprehensive categorisation and contextual risk model will help health providers in the process of profiling of an elderly inpatient with respect to his/her fall risk. It is useful to experts in health informatics in formulating models to automate this process.

This paper outlines the journey of a large Australian academic health service in relation to the acquisition, installation and roll out of the REDCap platform (RCP) for the betterment of clinical review (clinical audit) and research data collection. The main aims of the acquisition of the platform were to facilitate data collection and management for audit and research across the organization in a more sustainable way than had previously been possible. We found the platform to be easily installed and maintained. There was rapid uptake of the platform by a range of health service stakeholders across the audit, research and operational domains. We were also able to successfully integrate data from our corporate clinical data environment, The REASON Discovery Platform R (REASON) into selected REDCap “applications” using the Dynamic Data Pull (DDP) functionality it provides. In summary the acquisition and installation of REDCap at our health service has been hugely successful and has provided a great facility for use by a large number of organizational stakeholders going forwards into the future.