Anaesthesiologists might be able to mitigate risk if they know which patients are at greatest risk for postoperative complications. This trial examined the impact of machine learning models on clinician risk assessment. 

Effect of machine learning models on clinician prediction of postoperative complications: the Perioperative ORACLE randomised clinical trial

Objectives Although inguinal hernia (IH) repair is low-risk surgery, older patients are occasionally offered watchful waiting because of their functional status and comorbidities. This study reviewed the surgical outcomes of IH repair in older patients in comparison with outcomes in younger patients. Methods This retrospective study included all patients who had IH repair from 2010 to 2020. The primary outcomes of interest were postoperative complications and recurrence. Results A total of 262 patients underwent IH repair during the study period; 40% were ≥60 years old. One patient had a recurrence. Among the 8% of patients who had postoperative complications, groin pain was the most common one (1.9%). Female patients had a significantly higher rate of complications than male patients did (38.5% female versus 6.4% male, p<0.001). The rate of complications was also higher for emergency surgery than for elective surgery (22.6% emergency versus 6.1% elective, p<0.001), as well for patients who needed concomitant bowel resection compared with those who did not. Patients who had emergency surgery or postoperative complications had a prolonged hospital stay.  Conclusions IH repair in older patients is low-risk surgery, comparable to that in younger patients. In this study, emergency surgery was more common in older than in younger patients and posed a higher risk of complications. We recommend offering elective hernia repair to older patients to avoid the higher complication rate associated with emergency repair.

Short-Term Outcomes of Inguinal Hernia Repair in Older Patients: A Retrospective Review at a Tertiary Center.

Background: Anaesthesiology clinicians can implement risk mitigation strategies if they know which patients are at greatest risk for postoperative complications. Although machine learning models predicting complications exist, their impact on clinician risk assessment is unknown. Methods: This single-centre randomised clinical trial enrolled patients age [≥]18 undergoing surgery with anaesthesiology services. Anaesthesiology clinicians providing remote intraoperative telemedicine support reviewed electronic health records with (assisted group) or without (unassisted group) also reviewing machine learning predictions. Clinicians predicted the likelihood of postoperative 30-day all-cause mortality and postoperative acute kidney injury within 7 days. Area under the receiver operating characteristic curve (AUROC) for the clinician predictions was determined. Results: Among 5,071 patient cases reviewed by 89 clinicians, the observed incidence was 2% for postoperative death and 11% for acute kidney injury. Clinician predictions agreed with the models more strongly in the assisted versus unassisted group (weighted kappa 0.75 versus 0.62 for death [difference 0.13, 95%CI 0.10-0.17] and 0.79 versus 0.54 for kidney injury [difference 0.25, 95%CI 0.21-0.29]). Clinicians predicted death with AUROC of 0.793 in the assisted group and 0.780 in the unassisted group (difference 0.013, 95%CI -0.070 to 0.097). Clinicians predicted kidney injury with AUROC of 0.734 in the assisted group and 0.688 in the unassisted group (difference 0.046, 95%CI -0.003 to 0.091). Conclusions: Although there was evidence that the models influenced clinician predictions, clinician performance was not statistically significantly different with and without machine learning assistance. Further work is needed to clarify the role of machine learning in real-time perioperative risk stratification. Trial Registration: ClinicalTrials.gov NCT05042804

Effect of Machine Learning on Anaesthesiology Clinician Prediction of Postoperative Complications: The Perioperative ORACLE Randomised Clinical Trial

Incorporating Evidence-based Ultrasound-guided Vascular Access (USGVA) Standards Into the Nurse Anesthetist Armamentarium: A Quality Improvement Project.

Due to the global trend of the population ageing, an important task of modern medicine is to improve the provision of medical care for elderly patients. These people have reduced compensatory reserves of the body and numerous chronic pathologies, necessitating a more meticulous approach to anesthesia induction and maintenance by anesthesiologists. According to the authors, the use of propofol and fentanyl leads to decreased myocardial contractility and reduction in overall peripheral vascular resistance. However, the resulting decrease in perfusion pressure in vital organs and tissues, as a consequence of the aforementioned effects, is unacceptable in elderly patients with coronary heart disease (CHD). 
The aim 
. To analyze changes in hemodynamics in elderly patients undergoing coronary artery bypass grafting (CABG) during the induction of anesthesia. 
  
Materials and methods. A prospective study was conducted which included the results of examination of 20 patients with CHD over 60 years old who underwent CABG without the use of cardiopulmonary bypass at the National Amosov Institute of Cardiovascular Surgery (Kyiv, Ukraine). The mean age of the patients was 67.5±6.8 years. There were 16 (80%) men and 4 (20%) women. Typical manifestations of heart failure were noted (there were 1 [5%], 10 [50%] and 9 [45%] NYHA functional class I, II and III patients, respectively). Also, the patients were classified according to the Canadian classification of angina: 1 (5%), 9 (45%) and 10 (50%) patients fell within class I, II and III, respectively. Stage 2-3 hypertension was reported in 18 patients (90%). 
For induction of anesthesia, the following medications were used: propofol 1.5 mg/kg, fentanyl 2.0 μg/kg, and rocuronium bromide 0.1 mg/kg. Anesthesia maintenance included: propofol 4 mg/kg/hour, fentanyl 2.0 μg/kg/hour. Hemodynamic parameters saturation indices (SpO2), and transthoracic echocardiography parameters were recorded at the following stages: 1) upon the patient’s arrival in the operating room; 2) prior to tracheal intubation; 3) immediately after tracheal intubation; 4) 25 minutes after intubation. 
Results. Upon arrival in the operating room, the hemodynamic parameters reflected stable general condition of the examined patients. After administration of induction agents according to the study design, it was recorded that mean blood pressure (BP) was 72.7±4.76% (p<0.001), heart rate was 89.8±5.95% (p=0.0004), cardiac index was 81.47±6.6% (p<0.001), and systemic vascular resistance index (SVRI) was 89.9±7.02% of the baseline values (p=0.063). After intubation, an increase in most hemodynamic indicators was observed, although only mean BP, which constituted 86.7±8.8% of the baseline data (p<0.001), and SVRI (89.8±11.04% of the baseline data, p=0.0315), significantly differed from the baseline values. Within 25 minutes after the intubation, the parameters decreased again: with mean BP 74.9±7.9% (p<0.001), heart rate 91.3±9.5% (p=0.008), cardiac index 79.6±10.1% (p<0.001), and SVRI 91.1±9.34% (p=0.062) of the baseline values. 
Conclusions. The main changes in hemodynamics were reported during induction of anesthesia, where a significant decrease in most indicators compared to the baseline state was recorded. After intubation, the parameters slightly increased but did not return to the baseline values. During further anesthesia maintenance, a significant decrease in most indicators relative to the baseline data were observed. The instability of hemodynamic parameters during the induction stage of anesthesia in elderly patients with CHD can be an obstacle to the normal course of the perioperative period, necessitating the search for methods to stabilize them.

Features of Hemodynamics in Elderly Patients Undergoing CABG Surgery at the Stage of Induction Anesthesia

The portion of the population comprised by those aged 65 years and older is the fastest growing age group. With the aging of Americans, so goes the age of a surgeon’s patients. It is also well established that with aging, the incidence of various diseases also increases. Thus, more surgical care will be consumed by the elderly. The cost of care however will be greater for geriatric patients when compared to younger patients due to the greater incidence of comorbidities in the elderly. The surgical community will need to develop new protocols for the perioperative care of these frail patients in order to provide safe care with acceptable morbidity and mortality rates. Over the past 5 years, there have been notable changes in the surgical management of geriatric patients. Increased evidence in several surgical specialties supports that minimally invasive techniques for traditional open procedures improve outcomes. Additional knowledge regarding the effect of aging on organ function has led to improved stratification and preoperative preparation of patients prior to surgery. An example of this is the establishment of specific triage criteria for geriatric trauma patients. These changes have resulted in reduced morbidity and mortality rates. The elderly form a large portion of surgical patients, and this portion will continue to grow as life expectancy improves. Surgeons must adapt to this changing environment to provide optimal care to geriatric patients.

The Impact of the Aging Population on Surgical Diseases

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The Impact of the Aging Population on Surgical Diseases