Development and Validation of an Electronic Postoperative Morbidity Score

Abstract

We present a variant of POMS based on objective electronic metrics. Discriminative performance appeared comparable to gold-standard definitions for discharge outcomes. E-POMS may allow characterization of morbidity within our EHR but further work is required to assess external validity.

Identification of factors associated with morbidity and postoperative length of stay in surgically managed chronic subdural haematoma using electronic health records: a retrospective cohort study

“…New scoring systems are required to generate computable phenotypes from such complex, routinely collected data. 22 One such score, an electronic variant of the widely used Postoperative Morbidity Score (POMS), 10 has equivalent discriminative performance in identifying discharge complexity or prolonged LOS in a heterogeneous population of elderly surgical patients. 22 LOS is often critiqued as an outcome measure in improvement efforts due to the impact of non-clinical events.…”

“…22 One such score, an electronic variant of the widely used Postoperative Morbidity Score (POMS), 10 has equivalent discriminative performance in identifying discharge complexity or prolonged LOS in a heterogeneous population of elderly surgical patients. 22 LOS is often critiqued as an outcome measure in improvement efforts due to the impact of non-clinical events. 23 The complex relationship between clinical and non-clinical events in determining LOS is likely to vary between patients with differing disease processes.…”

“…Perioperative medical complications A variant of the electronic POMS (ePOMS) 22 was used to determine the presence of morbidity across nine distinct domains, following key organ systems (respiratory, cardiovascular, neurological, renal, haematological, gastrointestinal, infectious, reoperation/wound and pain) (table 1). Recognising that ePOMS had not previously been employed in a neurosurgical population, the score was modified to capture a greater number of dimensions of neurological morbidity.…”

Exploratory Analysis of Preoperative and Postoperative Risk Stratification Tools to Identify Acute Kidney and Myocardial Injury in Patients Undergoing Surgery for Chronic Subdural Haematoma

“…If multiple potential criteria are listed then an individual scores if any of these are met % In the original EPOMS score this would correspond to the 'wound' category. * Indicates additional criterion included in this variant from previously published [1]. ** indicates that all drugs below this level of ATC code were included created in B1 were use d to perform cross validation forming sequential training (green rows) and test (grey row) datasets.…”

“…Logistic regression models were built using variables available prior to (age, American society of Anesthesiologists (ASA) score, creatinine, antithrombotic use, inter-hospital transfer, pre-operative physiological state, and comorbidities), and end of (opioid dose, length of wait, time with mean arterial pressure, (MAP) <80mmHg, time with end tidal carbon dioxide (ETCO2) outside of 3-5kPa, and volatile v intravenous anaesthetic maintenance), surgery. Physiological state was encapsulated on each admission day using the electronic postoperative morbidity score (ePOMS) 5 (details in supplemental digital content). Full details of variable generation are published elsewhere 2 .…”