Objective To develop a risk score for the real-time prediction of readmissions for patients using patient specific information captured in electronic medical records (EMR) in Singapore to enable the prospective identification of high-risk patients for enrolment in timely interventions. Methods Machine-learning models were built to estimate the probability of a patient being readmitted within 30 days of discharge. EMR of 25,472 patients discharged from the medicine department at Ng Teng Fong General Hospital between January 2016 and December 2016 were extracted retrospectively for training and internal validation of the models. We developed and implemented a real-time 30-day readmission risk score generation in the EMR system, which enabled the flagging of high-risk patients to care providers in the hospital. Based on the daily high-risk patient list, the various interfaces and flow sheets in the EMR were configured according to the information needs of the various stakeholders such as the inpatient medical, nursing, case management, emergency department, and postdischarge care teams. Results Overall, the machine-learning models achieved good performance with area under the receiver operating characteristic ranging from 0.77 to 0.81. The models were used to proactively identify and attend to patients who are at risk of readmission before an actual readmission occurs. This approach successfully reduced the 30-day readmission rate for patients admitted to the medicine department from 11.7% in 2017 to 10.1% in 2019 (p < 0.01) after risk adjustment. Conclusion Machine-learning models can be deployed in the EMR system to provide real-time forecasts for a more comprehensive outlook in the aspects of decision-making and care provision.

An extensive range of diseases or reactions can cause pustular eruptions of the skin. Drug-provoked cutaneous eruptions includes acute generalized exanthematous pustulosis (AGEP) and acneiform eruption. We report a 50-year-old man who developed fever and a sudden eruption of widespread pustules 12 days after ingestion of celecoxib prescribed for a prolapsed intervertebral disc. AGEP was diagnosed based on the typical history, characteristic features, and laboratory findings. However, histopathological findings were consistent with folliculitic drug reaction pattern without features of AGEP. We present, the first known reported case of folliculitic drug reaction pattern caused by celecoxib.

Introduction: Cryotherapy with liquid nitrogen is an effective, safe and convenient form of treatment for plantar warts. EMLA® cream (eutectic mixture of lidocaine 2.5% and prilocaine 2.5%) is a topical local anaesthetic agent that has proven to be effective and well tolerated in the relief of pain associated with various minor interventions in numerous clinical settings. Materials and Methods: In a single-centre, double-blind, randomised placebo-controlled study, 64 subjects were randomised into 2 groups. The subjects had a thick layer of EMLA® cream or placebo cream applied to pared plantar wart(s) and onto the surrounding margin of 1 mm to 2 mm under occlusion for 60 minutes prior to receiving cryotherapy. The pain of cryotherapy was evaluated by the subjects using a self-administered Visual Analogue Scale (VAS) immediately after the cryotherapy. Results: There was no statistical difference between the mean VAS score for EMLA® cream (47.0 ± 21.4 mm) and placebo (48.9 ± 22.0 mm). Those with more than 1 wart had a significantly higher VAS score than those with only 1 wart (59.1 ± 21.8 vs. 44.3 ± 20.4, P <0.05) but this did not affect the therapeutic effect of EMLA® cream prior to cryotherapy. Conclusion: We conclude that the application of EMLA® cream prior to cryotherapy does not reduce the pain associated with cryotherapy. Key words: Anaesthetic, Pain, Wart