Development and validation of an interpretable clinical score for early identification of acute kidney injury at the emergency department.

Acute kidney injury (AKI) in hospitalised patients is a common syndrome associated with poorer patient outcomes. Clinical risk scores can be used for the early identification of patients at risk of AKI. We conducted a retrospective study using electronic health records of Singapore General Hospital emergency department patients who were admitted from 2008 to 2016. The primary outcome was inpatient AKI of any stage within 7 days of admission based on the Kidney Disease Improving Global Outcome (KDIGO) 2012 guidelines. A machine learning-based framework AutoScore was used to generate clinical scores from the study sample which was randomly divided into training, validation and testing cohorts. Model performance was evaluated using area under the curve (AUC). Among the 119,468 admissions, 10,693 (9.0%) developed AKI. 8491 were stage 1 (79.4%), 906 stage 2 (8.5%) and 1296 stage 3 (12.1%). The AKI Risk Score (AKI-RiSc) was a summation of the integer scores of 6 variables: serum creatinine, serum bicarbonate, pulse, systolic blood pressure, diastolic blood pressure, and age. AUC of AKI-RiSc was 0.730 (95% CI 0.714-0.747), outperforming an existing AKI Prediction Score model which achieved AUC of 0.665 (95% CI 0.646-0.679) on the testing cohort. At a cut-off of 4 points, AKI-RiSc had a sensitivity of 82.6% and specificity of 46.7%. AKI-RiSc is a simple clinical score that can be easily implemented on the ground for early identification of AKI and potentially be applied in international settings.

Characteristics of Prehospital Heat Illness Cases During the Annual Heat Wave Period in Telangana, India.

OBJECTIVES: Global warming and more intense heat wave periods impact health. Heat illness during heat waves has not been studied in the prehospital setting of a low- and middle-income country (LMIC). Early intervention in the community and in the prehospital setting can improve outcomes. Hence, this paper aims to describe the characteristics of heat illness patients utilizing the ambulance service in Telangana state, India with the aim of optimizing public prevention and first aid strategies and prehospital response to this growing problem. METHODS: This retrospective observational study reviewed patients presenting to Telangana's prehospital emergency care system with heat illness symptoms during the heat wave period from March through June in 2018 and 2019. Descriptive analysis was done on the prehospital, dispatch, and environmental data looking at the patients' characteristics and prehospital intervention. RESULTS: There were 295 cases in 2018 and 230 cases in 2019 from March-June. The overall incidence of calls with heat illness symptoms was 1.5 cases per 100,000 people. The Scheduled Tribes (ST) had the highest incidence of 4.5 per 100,000 people. Over 96% were from the white income group (below poverty line) while two percent were from the pink income group (above poverty line). From geospatial mapping of the cases, the highest incidence of calls came from the rural, tribal areas. However, the time to response in rural areas was longer than that in an urban area. Males with an average age of 47 were more likely to be affected. The three most common symptoms recorded by the first responders were vomiting (44.4%), general weakness (28.7%), and diarrhea (15.9%). The three most common medical interventions on scene were oxygen therapy (35.1%), oral rehydration salt (ORS) solution administration (26.9%), and intravenous fluid administration (27.0%), with cold sponging infrequently mentioned. CONCLUSION: This descriptive study provides a snapshot of the regions and groups of people most affected by heat illness during heat waves and the heterogeneous symptom presentation and challenges with management in the prehospital setting. These data may aid planning of prehospital resources and preparation of community first responders during heat wave periods.

Novel model for predicting inpatient mortality after emergency admission to hospital in Singapore: retrospective observational study.

OBJECTIVES: To identify risk factors for inpatient mortality after patients' emergency admission and to create a novel model predicting inpatient mortality risk. DESIGN: This was a retrospective observational study using data extracted from electronic health records (EHRs). The data were randomly split into a derivation set and a validation set. The stepwise model selection was employed. We compared our model with one of the current clinical scores, Cardiac Arrest Risk Triage (CART) score. SETTING: A single tertiary hospital in Singapore. PARTICIPANTS: All adult hospitalised patients, admitted via emergency department (ED) from 1 January 2008 to 31 October 2017 (n=433 187 by admission episodes). MAIN OUTCOME MEASURE: The primary outcome of interest was inpatient mortality following this admission episode. The area under the curve (AUC) of the receiver operating characteristic curve of the predictive model with sensitivity and specificity for optimised cut-offs. RESULTS: 15 758 (3.64%) of the episodes were observed inpatient mortality. 19 variables were observed as significant predictors and were included in our final regression model. Our predictive model outperformed the CART score in terms of predictive power. The AUC of CART score and our final model was 0.705 (95% CI 0.697 to 0.714) and 0.817 (95% CI 0.810 to 0.824), respectively. CONCLUSION: We developed and validated a model for inpatient mortality using EHR data collected in the ED. The performance of our model was more accurate than the CART score. Implementation of our model in the hospital can potentially predict imminent adverse events and institute appropriate clinical management.