Early Postoperative Death in Patients Undergoing Emergency High-Risk Surgery: Towards a Better Understanding of Patients for Whom Surgery May Not Be Beneficial.
Geeta AggarwalKatherine J BroughtonLinda J WilliamsCarol Jane PedenNial QuineyPublished in: Journal of clinical medicine (2020)
The timing, causes, and quality of care for patients who die after emergency laparotomy have not been extensively reported. A large database of 13,953 patients undergoing emergency laparotomy, between July 2014 and March 2017, from 28 hospitals in England was studied. Anonymized data was extracted on day of death, patient demographics, operative details, compliance with standards of care, and 30-day and in-patient mortality. Thirty-day mortality was 8.9%, and overall inpatient mortality was 9.8%. Almost 40% of postoperative deaths occurred within three days of surgery, and 70% of these early deaths occurred on the day of surgery or the first postoperative day. Such early deaths could be considered nonbeneficial surgery. Patients who died within three days of surgery had a significantly higher preoperative lactate, American Society of Anesthesiologists Physical Status (ASA-PS) grade, and Physiological and Operative Severity Score for the enumeration of Mortality and morbidity (P-POSSUM). Compliance with perioperative standards of care based on the Emergency Laparotomy Collaborative care bundle was high overall and better for those patients who died within three days of surgery. Multidisciplinary team involvement from intensive care, care of the elderly physicians, and palliative care may help both the communication and the burden of responsibility in deciding on the risk-benefit of operative versus nonoperative approaches to care.
Keyphrases
- palliative care
- patients undergoing
- minimally invasive
- healthcare
- coronary artery bypass
- quality improvement
- public health
- emergency department
- surgical site infection
- advanced cancer
- cardiovascular events
- risk factors
- cardiovascular disease
- percutaneous coronary intervention
- pain management
- acute coronary syndrome
- machine learning
- prognostic factors
- deep learning
- chronic kidney disease
- atrial fibrillation
- cardiac surgery