Use of the American College of Surgeons National Surgical Quality Improvement Program Surgical Risk Calculator During Preoperative Risk Discussion: The Patient Perspective

Aids to improve understanding of statistical risk in patients consenting for surgery and interventional procedures: A systematic review

BACKGROUND

Informed consent is an essential process in clinical decision-making, through which healthcare providers educate patients about benefits, risks, and alternatives of a procedure. Statistical risk information is difficult to communicate and the effectiveness of aids aimed at supporting this type of communication is uncertain. This systematic review aims to study the impact of risk communication adjuncts on patients' understanding of statistical risk in surgery and interventional procedures.

METHODS

A systematic search was performed across Medline, Embase, PsycINFO, Scopus, and Web of Science until July 2021 with a repeated search in September 2022. RCTs and observational studies examining risk communication tools (e.g., information leaflets and audio-video) in adult (age >16) patients undergoing a surgical or interventional procedure were included. Primary outcomes included the objective assessment of statistical risk recall. Secondary outcomes included patient attitudes with respect to statistical information. Due to the study heterogeneity, a narrative synthesis was performed.

RESULTS

A total of 4348 articles were identified, and following abstract and full-text screening 14 articles, including 9 RCTs, were included. The total number of adult patients was 1513. The most common risk communication tool used was written information (n = 7). Most RCTs (7/9, 77.8%) showed statistically significant improvements in patient understanding of statistical risk in the intervention group. Quality assessment found some concerns with all RCTs.

CONCLUSION

Risk communication tools appear to improve recall of statistical risk. Additional prospective trials comparing various aids simultaneously are warranted to determine the most effective method of improving understanding.

Artificial Intelligence in Surgical Research: Accomplishments and Future Directions

MINI-ABSTRACT

The study introduces various methods of performing conventional ML and their implementation in surgical areas, and the need to move beyond these traditional approaches given the advent of big data.

OBJECTIVE

Investigate current understanding and future directions of machine learning applications, such as risk stratification, clinical data analytics, and decision support, in surgical practice.

SUMMARY BACKGROUND DATA

The advent of the electronic health record, near unlimited computing, and open-source computational packages have created an environment for applying artificial intelligence, machine learning, and predictive analytic techniques to healthcare. The "hype" phase has passed, and algorithmic approaches are being developed for surgery patients through all stages of care, involving preoperative, intraoperative, and postoperative components. Surgeons must understand and critically evaluate the strengths and weaknesses of these methodologies.

METHODS

The current body of AI literature was reviewed, emphasizing on contemporary approaches important in the surgical realm.

RESULTS AND CONCLUSIONS

The unrealized impacts of AI on clinical surgery and its subspecialties are immense. As this technology continues to pervade surgical literature and clinical applications, knowledge of its inner workings and shortcomings is paramount in determining its appropriate implementation.

Improving risk communication: a proof-of-concept randomised control trial assessing the impact of visual aids for neurosurgical consent

Introduction

Informed consent is a fundamental component in the work-up for surgical procedures. Statistical risk information pertaining to a procedure is by nature probabilistic and challenging to communicate, especially to those with poor numerical literacy. Visual aids and audio/video tools have previously been shown to improve patients' understanding of statistical information. In this study, we aimed to explore the impact of different methods of risk communication in healthy participants randomized to either undergo the consent process with visual aids or the standard consent process for lumbar puncture.

Material and methods

Healthy individuals above 18 years old were eligible. The exclusion criteria were prior experience of the procedure or relevant medical knowledge, lack of capacity to consent, underlying cognitive impairment and hospitalised individuals. After randomisation, both groups received identical medical information about the procedure of a lumbar puncture in a hypothetical clinical scenario via different means of consent. The control group underwent the standard consent process in current clinical practice (Consent Form 1 without any illustrative examples), whereas the intervention group received additional anatomy diagrams, the Paling Palette and the Paling perspective scale. Anonymised questionnaires were received to evaluate their perception of the procedure and its associated risks.

Results

Fifty-two individuals were eligible without statistically significant differences in age, sex, professional status and the familiarity of the procedure. Visual aids were noted to improve the confidence of participants to describe the risks by themselves (p = 0.009) and participants in the intervention group felt significantly less overwhelmed with medical information (p = 0.028). The enhanced consent process was found to be significantly more acceptable by participants (p = 0.03). There was a trend towards greater appropriateness (p = 0.06) and it appeared to have "good" usability (median SUS = 76.4), although this also did not reach statistical significance (p = 0.06).

Conclusion

Visual aids could be an appropriate alternative method for medical consent without being inferior regarding the understanding of the procedure, its risks and its benefits. Future studies could possibly compare or incorporate multiple interventions to determine the most effective tools in a larger scale of population including patients as well as healthy individuals.

An Individualized Postoperative Pain Risk Communication Tool for Use in Pediatric Surgery: Co-Design and Usability Evaluation

BACKGROUND

Risk identification and communication tools have the potential to improve health care by supporting clinician-patient or family discussion of treatment risks and benefits and helping patients make more informed decisions; however, they have yet to be tailored to pediatric surgery. User-centered design principles can help to ensure the successful development and uptake of health care tools.

OBJECTIVE

We aimed to develop and evaluate the usability of an easy-to-use tool to communicate a child's risk of postoperative pain to improve informed and collaborative preoperative decision-making between clinicians and families.

METHODS

With research ethics board approval, we conducted web-based co-design sessions with clinicians and family participants (people with lived surgical experience and parents of children who had recently undergone a surgical or medical procedure) at a tertiary pediatric hospital. Qualitative data from these sessions were analyzed thematically using NVivo (Lumivero) to identify design requirements to inform the iterative redesign of an existing prototype. We then evaluated the usability of our final prototype in one-to-one sessions with a new group of participants, in which we measured mental workload with the National Aeronautics and Space Administration (NASA) Task Load Index (TLX) and user satisfaction with the Post-Study System Usability Questionnaire (PSSUQ).

RESULTS

A total of 12 participants (8 clinicians and 4 family participants) attended 5 co-design sessions. The 5 requirements were identified: (A) present risk severity descriptively and visually; (B) ensure appearance and navigation are user-friendly; (C) frame risk identification and mitigation strategies in positive terms; (D) categorize and describe risks clearly; and (E) emphasize collaboration and effective communication. A total of 12 new participants (7 clinicians and 5 family participants) completed a usability evaluation. Tasks were completed quickly (range 5-17 s) and accurately (range 11/12, 92% to 12/12, 100%), needing only 2 requests for assistance. The median (IQR) NASA TLX performance score of 78 (66-89) indicated that participants felt able to perform the required tasks, and an overall PSSUQ score of 2.1 (IQR 1.5-2.7) suggested acceptable user satisfaction with the tool.

CONCLUSIONS

The key design requirements were identified, and that guided the prototype redesign, which was positively evaluated during usability testing. Implementing a personalized risk communication tool into pediatric surgery can enhance the care process and improve informed and collaborative presurgical preparation and decision-making between clinicians and families of pediatric patients.

MBSAQIP risk calculator use in bariatric surgery is associated with a reduction in serious complications: a retrospective analysis of 210,710 patients

BACKGROUND

The Metabolic and Bariatric Accreditation and Quality Improvement Program (MBSAQIP) Bariatric Surgical Risk/Benefit Calculator was developed to provide patient-specific information to assist surgical decision-making. To date, no study has characterized which patients are being evaluated with this tool.

OBJECTIVE

We sought to characterize the use and impact of the MBSAQIP calculator.

SETTING

MBSAQIP collects data from 955 centers in North America.

METHODS

The 2021 MBSAQIP database was evaluated for the use of the calculator on preoperative counseling for patients undergoing bariatric surgery. Patient characteristics, operative techniques, and outcomes were compared with bivariate analysis. Multivariable modeling evaluated factors including use of the calculator independently associated with serious complications and mortality.

RESULTS

Our study included 210,710 patients, 35,158 (16.7%) of whom were evaluated using the calculator. Patients with whom the calculator was used preoperatively were older (43.8 ± 11.6 yr versus 43.6 ± 11.7 yr; P < .001) and were more likely to have insulin-dependent diabetes, hypertension, gastroesophageal reflux disease, renal insufficiency, and sleep apnea. More patients underwent Roux-en-Y gastric bypass in the calculator cohort compared with the cohort that did not use the calculator (29.6% versus 28.6%; P < .003). The rate of serious complication was significantly less in the calculator cohort (3.1% versus 3.4%; P < .030). Multivariable modeling evaluating serious complications showed that use of the calculator was independently associated with reduced risk of serious complications (odds ratio .87, CI .82-.93, P < .001) but was not associated with mortality.

CONCLUSION

The use of the risk calculator may help to reduce the incidence of complications by opening a dialogue between healthcare professionals and patients, setting realistic expectations, and identifying modifiable risk factors.

The Effect on Theatre Nurses for Rendering Perioperative Care to Patients Living with HIV in a South African Tertiary Hospital

Purpose

The study aimed to gain an in-depth understanding of how theatre nurses are being affected when they render perioperative care to patients living with HIV in a South African tertiary hospital.

Background

There is a scarcity of studies that focus solely on the wellbeing of theatre nurses who render perioperative care to HIV patient due to the ramifications of the nurses' fear of contracting HIV. Patients living with HIV often receive substandard care.

Objectives

To establish how theatre nurses are being impacted when rendering perioperative care to patient living with HIV, the study followed a qualitative approach using an interpretative phenomenological analysis design. Data were collected through in-depth individual interviews from ten theatre nurses who were purposively selected according to specific criteria. They voluntarily agreed to participate. An interpretive phenomenological analysis framework was used to analyse the data. Two main themes emerged from the data analysis, namely, the negative effect on nurses' wellbeing and the impact that it had on them professionally.

Results

The study revealed that the perioperative care of patients living with HIV had a negative impact on physical, mental, and social wellbeing of theatre nurses. Their compromised wellbeing in turn led to poor patient care, which put nurses at risk of losing their jobs and even potentially having to face litigation. The study further indicated that nurses did not receive psychological support from the management which further affected their health and professional performance.

Conclusion

The study proposes that theatre nurses rendering perioperative care to people living with HIV should receive proper training and support; staff shortages should also be addressed. There is also an urgent need for appropriate and sufficient protective equipment. Such changes will be essential in order to mitigate the negative impact that their jobs have on their wellbeing and on them in their professional capacity.

Personalized risk communication and opioid prescribing in association with nonprescribed opioid use: A secondary analysis of a randomized controlled trial

BACKGROUND

To determine the impact of personalized risk communication and opioid prescribing on nonprescribed opioid use, we conducted a secondary analysis of randomized controlled trial participants followed prospectively for 90 days after an emergency department (ED) visit for acute back or kidney stone pain.

METHODS

A total of 1301 individuals were randomized during an encounter at four academic EDs into a probabilistic risk tool (PRT) arm, a narrative-enhanced PRT arm, or a general risk information arm (control). In this secondary analysis, both risk tool arms were combined and compared with the control arm. We used logistic regressions to determine associations between receiving personalized risk information, receiving an opioid prescription in the ED, and nonprescribed opioid use in general and by race.

RESULTS

Complete follow-up data were available for 851 participants; 23.3% (n = 198) were prescribed opioids (34.2% of White vs. 11.6% of Black participants, p < 0.001). Fifty-six (6.6%) participants used nonprescribed opioids. Participants in the personalized risk communication arms had lower nonprescribed opioid use odds (adjusted odds ratio [aOR] 0.58, 95% confidence interval [CI] 0.4-0.83). Black versus White participants had greater nonprescribed opioid use odds (aOR 3.47, 95% CI 2.05-5.87, p < 0.001). Black participants who were prescribed opioids had a lower marginal probability of using nonprescribed opioids versus those who were not (0.06, 95% CI 0.04-0.08, p < 0.001 vs. 0.10, 95% CI 0.08-0.11, p < 0.001). The absolute risk difference in nonprescribed opioid use for Black and White participants, respectively, in the risk communication versus the control arm, was 9.7% and 0.1% (relative risk ratio 0.43 vs. 0.95).

CONCLUSIONS

Among Black but not White participants, personalized opioid risk communication and opioid prescribing were associated with lower odds of nonprescribed opioid use. Our findings suggest that racial disparities in opioid prescribing-which have been previously described within the context of this trial-may paradoxically increase nonprescribed opioid use. Personalized risk communication may effectively reduce nonprescribed opioid use, and future research should be designed specifically to explore this possibility in a larger cohort.

Bilateral renal artery stenosis impacts postoperative complications after major vascular surgery

Background

Incidental atherosclerotic renal artery stenosis (RAS) is common in patients undergoing vascular surgery and has been shown to be associated with postoperative AKI among patients undergoing major non-vascular surgeries. We hypothesized that patients with RAS undergoing major vascular procedures would have a higher incidence of AKI and postoperative complications than those without RAS.

Methods

A single-center retrospective cohort study of 200 patients who underwent elective open aortic or visceral bypass surgery (100 with postoperative AKI; 100 without AKI) were identified. RAS was then evaluated by review of pre-surgery CTAs with readers blinded to AKI status. RAS was defined as ≥50 % stenosis. Univariate and multivariable logistic regression was used to assess association of unilateral and bilateral RAS with postoperative outcomes.

Results

17.4 % (n = 28) of patients had unilateral RAS while 6.2 % (n = 10) of patients had bilateral RAS. Patients with bilateral RAS had similar preadmission creatinine and GFR as compared to unilateral RAS or no RAS. 100 % (n = 10) of patients with bilateral RAS had postoperative AKI compared with 45 % (n = 68) of patients with unilateral or no RAS (p < 0.05). In adjusted logistic regression models, bilateral RAS predicted severe AKI (OR 5.82; CI 1.33, 25.53; p = 0.02), in-hospital mortality (OR 5.71; CI 1.03, 31.53; p = 0.05), 30-day mortality (OR 10.56; CI 2.03, 54.05; p = 0.005) and 90-day mortality (OR 6.88; CI 1.40, 33.87; p = 0.02).

Conclusions

Bilateral RAS is associated with increased incidence of AKI as well as in-hospital, 30-day, and 90-day mortality suggesting it is a marker of poor outcomes and should be considered in preoperative risk stratification.

Dynamic predictions of postoperative complications from explainable, uncertainty-aware, and multi-task deep neural networks

Accurate prediction of postoperative complications can inform shared decisions regarding prognosis, preoperative risk-reduction, and postoperative resource use. We hypothesized that multi-task deep learning models would outperform conventional machine learning models in predicting postoperative complications, and that integrating high-resolution intraoperative physiological time series would result in more granular and personalized health representations that would improve prognostication compared to preoperative predictions. In a longitudinal cohort study of 56,242 patients undergoing 67,481 inpatient surgical procedures at a university medical center, we compared deep learning models with random forests and XGBoost for predicting nine common postoperative complications using preoperative, intraoperative, and perioperative patient data. Our study indicated several significant results across experimental settings that suggest the utility of deep learning for capturing more precise representations of patient health for augmented surgical decision support. Multi-task learning improved efficiency by reducing computational resources without compromising predictive performance. Integrated gradients interpretability mechanisms identified potentially modifiable risk factors for each complication. Monte Carlo dropout methods provided a quantitative measure of prediction uncertainty that has the potential to enhance clinical trust. Multi-task learning, interpretability mechanisms, and uncertainty metrics demonstrated potential to facilitate effective clinical implementation.

Mortality Risk Stratification in Emergency Surgery for Obstructive Colon Cancer-External Validation of International Scores, American College of Surgeons National Surgical Quality Improvement Program Surgical Risk Calculator (SRC), and the Dedicated Score of French Surgical Association (AFC/OCC Score)

BACKGROUND

The increased rates of postoperative mortality after emergency surgery for obstructive colon cancer (OCC) require the use of risk-stratification scores. The study purpose is to external validate the surgical risk calculator (SRC) and the AFC/OCC score and to create a score for risk stratification.

PATIENTS AND METHODS

Overall, 435 patients with emergency surgery for OCC were included in this retrospective study. We used statistical methods suitable for the aimed purpose.

RESULTS

Postoperative mortality was 11.72%. SRC performance: strong discrimination (AUC = 0.864) and excellent calibration (11.80% predicted versus 11.72% observed); AFC/OCC score performance: adequate discrimination (AUC = 0.787) and underestimated mortality (6.93% predicted versus 11.72% observed). We identified nine predictors of postoperative mortality: age > 70 years, CHF, ECOG > 2, sepsis, obesity or cachexia, creatinine (aN) or platelets (aN), and proximal tumors (AUC = 0.947). Based on the score, we obtained four risk groups of mortality rate: low risk (0.7%)-0-2 factors, medium risk (12.5%)-3 factors, high risk (40.0%)-4 factors, very high risk (84.4%)-5-6 factors.

CONCLUSIONS

The two scores were externally validated. The easy identification of predictors and its performance recommend the mortality score of the Clinic County Emergency Hospital of Galați/OCC for clinical use.

Association between Preoperative Medication Lists and Postoperative Hospital Length of Stay after Endoscopic Transsphenoidal Pituitary Surgery

Background: Endoscopic transsphenoidal surgery is the most common technique for the resection of pituitary adenoma. Data on factors associated with extended hospital stay after this surgery are limited. We aimed to characterize the relationship between preoperative medications and the risk of prolonged postoperative length of stay after this procedure. Methods: This single-center, retrospective cohort study included all adult patients scheduled for transsphenoidal pituitary surgery from 1 July 2016 to 31 December 2019. Anatomical Therapeutic Chemical codes were used to identify patients’ preoperative medications. The primary outcome was a prolonged postoperative hospital length of stay. Secondary outcomes included unplanned admission to the Intensive Care Unit, and in-hospital and one-year mortality. We developed a descriptive logistic model that included preoperative medications, obesity and age. Results: Median postoperative length of stay was 3 days for the 704 analyzed patients. Patients taking ATC-H drugs were at an increased risk of prolonged length of stay (OR 1.56, 95% CI 1.26−1.95, p < 0.001). No association was found between preoperative ATC-H medication and unplanned ICU admission or in-hospital mortality. Patients with multiple preoperative ATC-H medications had a significantly higher mean LOS (5.4 ± 7.6 days) and one-year mortality (p < 0.02). Conclusions: Clinicians should be aware of the possible vulnerability of patients taking systemic hormones preoperatively. Future studies should test this medication-based approach on endoscopic transsphenoidal pituitary surgery populations from different hospitals and countries.

Perioperative mortality in pediatric patients: a systematic review of risk assessment tools for use in the preoperative setting

BACKGROUND

There are multiple preoperative risk scores for pediatric mortality. The aim of this study was to systematically describe and compare the existing studies of patient-specific multispecialty risk prediction scores for perioperative mortality in pediatric populations, with the goal of guiding clinicians on which may be most appropriate for use in the preoperative setting.

METHODS

We conducted a systematic literature review of published journal articles that presented the development, extension/updating, and/or validation of a risk score which predicted all-cause mortality (up to 30 days postoperatively) in pediatric patients undergoing a procedure in which anesthesia was used. Scores needed to be applicable to surgeries in more than one non-cardiac surgical specialty and had to be able to be calculated by the anesthesiologist at the time of the pre-anesthetic assessment. Two investigators independently screened studies for inclusion and assessed study quality in the domains of clinical applicability, feasibility/ease of use in the clinical setting, and risk of bias.

RESULTS

A total of 1,681 titles were retrieved. Of these, 10 studies met inclusion criteria: nine reported the development and validation of scores, and one was an external validation of an existing score. Seven studies used varying years of multicenter data from the National Surgical Quality Improvement Program - Pediatric Participant Use File for development and/or validation. The unadjusted rate of mortality in the studies ranged from 0.3% to 3.6%. The preoperative predictors of mortality used in score development included patient demographics, preoperative therapies, and chronic conditions, among others. All models showed good discrimination upon validation (AUC > 0.8). Most risk scores had high or unclear risks of bias.

CONCLUSION

There are numerous scores available for the prediction of mortality in pediatric populations, all of which exhibited good performance. However, many have high or unclear risks of bias, and most have not undergone external validation.

Evaluation of the American College of Surgeons National Surgical Quality Improvement Program Risk Calculator to predict outcomes after hysterectomies

OBJECTIVE

To evaluate the American College of Surgeons (ACS) surgical risk calculator's reliability in predicting outcomes in hysterectomies.

METHODS

This is a prospective cohort study at a large community-based hospital. Twenty-one preoperative and postoperative criteria were abstracted from the electronic medical record and entered into the online ACS calculator to determine a risk score. Logistical regression was used to determine the association between risk score and actual outcome. The prediction capability was analyzed with c-statistic, Hosmer-Lemeshow, and Brier score.

RESULTS

A total of 634 hysterectomies were performed during the study period from January to April 2019. Patients were predominantly 55 years old, white (53%) and overweight (body mass index 30). Predicted perioperative adverse events were significantly higher than actual adverse events across all domains. In all, 54/634 (8.5%) patients experienced postoperative urinary tract infection. C-statistics for return to operating room, renal failure, and readmission were 0.607 (95% C Statistic index [CI] 0.370-0.845), 0.882 (95% CI 0.802-0.962), 0.637 (95% CI 0.524-0.750), respectively. Brier scores approached one in all categorical domains.

CONCLUSION

The ACS surgical risk calculator holds the promise of predicting postoperative complications or length of stay for patients undergoing hysterectomy. Further adjustment to this tool is required before it can be advocated for use in the clinical setting.

Performance of a Machine Learning Algorithm Using Electronic Health Record Data to Predict Postoperative Complications and Report on a Mobile Platform

IMPORTANCE

Predicting postoperative complications has the potential to inform shared decisions regarding the appropriateness of surgical procedures, targeted risk-reduction strategies, and postoperative resource use. Realizing these advantages requires that accurate real-time predictions be integrated with clinical and digital workflows; artificial intelligence predictive analytic platforms using automated electronic health record (EHR) data inputs offer an intriguing possibility for achieving this, but there is a lack of high-level evidence from prospective studies supporting their use.

OBJECTIVE

To examine whether the MySurgeryRisk artificial intelligence system has stable predictive performance between development and prospective validation phases and whether it is feasible to provide automated outputs directly to surgeons' mobile devices.

DESIGN, SETTING, AND PARTICIPANTS

In this prognostic study, the platform used automated EHR data inputs and machine learning algorithms to predict postoperative complications and provide predictions to surgeons, previously through a web portal and currently through a mobile device application. All patients 18 years or older who were admitted for any type of inpatient surgical procedure (74 417 total procedures involving 58 236 patients) between June 1, 2014, and September 20, 2020, were included. Models were developed using retrospective data from 52 117 inpatient surgical procedures performed between June 1, 2014, and November 27, 2018. Validation was performed using data from 22 300 inpatient surgical procedures collected prospectively from November 28, 2018, to September 20, 2020.

MAIN OUTCOMES AND MEASURES

Algorithms for generalized additive models and random forest models were developed and validated using real-time EHR data. Model predictive performance was evaluated primarily using area under the receiver operating characteristic curve (AUROC) values.

RESULTS

Among 58 236 total adult patients who received 74 417 major inpatient surgical procedures, the mean (SD) age was 57 (17) years; 29 226 patients (50.2%) were male. Results reported in this article focus primarily on the validation cohort. The validation cohort included 22 300 inpatient surgical procedures involving 19 132 patients (mean [SD] age, 58 [17] years; 9672 [50.6%] male). A total of 2765 patients (14.5%) were Black or African American, 14 777 (77.2%) were White, 1235 (6.5%) were of other races (including American Indian or Alaska Native, Asian, Native Hawaiian or Pacific Islander, and multiracial), and 355 (1.9%) were of unknown race because of missing data; 979 patients (5.1%) were Hispanic, 17 663 (92.3%) were non-Hispanic, and 490 (2.6%) were of unknown ethnicity because of missing data. A greater number of input features was associated with stable or improved model performance. For example, the random forest model trained with 135 input features had the highest AUROC values for predicting acute kidney injury (0.82; 95% CI, 0.82-0.83); cardiovascular complications (0.81; 95% CI, 0.81-0.82); neurological complications, including delirium (0.87; 95% CI, 0.87-0.88); prolonged intensive care unit stay (0.89; 95% CI, 0.88-0.89); prolonged mechanical ventilation (0.91; 95% CI, 0.90-0.91); sepsis (0.86; 95% CI, 0.85-0.87); venous thromboembolism (0.82; 95% CI, 0.81-0.83); wound complications (0.78; 95% CI, 0.78-0.79); 30-day mortality (0.84; 95% CI, 0.82-0.86); and 90-day mortality (0.84; 95% CI, 0.82-0.85), with accuracy similar to surgeons' predictions. Compared with the original web portal, the mobile device application allowed efficient fingerprint login access and loaded data approximately 10 times faster. The application output displayed patient information, risk of postoperative complications, top 3 risk factors for each complication, and patterns of complications for individual surgeons compared with their colleagues.

CONCLUSIONS AND RELEVANCE

In this study, automated real-time predictions of postoperative complications with mobile device outputs had good performance in clinical settings with prospective validation, matching surgeons' predictive accuracy.

Effective Communication of Personalized Risks and Patient Preferences During Surgical Informed Consent Using Data Visualization: Qualitative Semistructured Interview Study With Patients After Surgery

Background

There is no consensus on which risks to communicate to a prospective surgical patient during informed consent or how. Complicating the process, patient preferences may diverge from clinical assumptions and are often not considered for discussion. Such discrepancies can lead to confusion and resentment, raising the potential for legal action. To overcome these issues, we propose a visual consent tool that incorporates patient preferences and communicates personalized risks to patients using data visualization. We used this platform to identify key effective visual elements to communicate personalized surgical risks.

Objective

Our main focus is to understand how to best communicate personalized risks using data visualization. To contextualize patient responses to the main question, we examine how patients perceive risks before surgery (research question 1), how suitably the visual consent tool is able to present personalized surgical risks (research question 2), how well our visualizations convey those personalized surgical risks (research question 3), and how the visual consent tool could improve the informed consent process and how it can be used (research question 4).

Methods

We designed a visual consent tool to meet the objectives of our study. To calculate and list personalized surgical risks, we used the American College of Surgeons risk calculator. We created multiple visualization mock-ups using visual elements previously determined to be well-received for risk communication. Semistructured interviews were conducted with patients after surgery, and each of the mock-ups was presented and evaluated independently and in the context of our visual consent tool design. The interviews were transcribed, and thematic analysis was performed to identify major themes. We also applied a quantitative approach to the analysis to assess the prevalence of different perceptions of the visualizations presented in our tool.

Results

In total, 20 patients were interviewed, with a median age of 59 (range 29-87) years. Thematic analysis revealed factors that influenced the perception of risk (the surgical procedure, the cognitive capacity of the patient, and the timing of consent; research question 1); factors that influenced the perceived value of risk visualizations (preference for rare event communication, preference for risk visualization, and usefulness of comparison with the average; research question 3); and perceived usefulness and use cases of the visual consent tool (research questions 2 and 4). Most importantly, we found that patients preferred the visual consent tool to current text-based documents and had no unified preferences for risk visualization. Furthermore, our findings suggest that patient concerns were not often represented in existing risk calculators.

Conclusions

We identified key elements that influence effective visual risk communication in the perioperative setting and pointed out the limitations of the existing calculators in addressing patient concerns. Patient preference is highly variable and should influence choices regarding risk presentation and visualization.

The false dichotomy of surgical futility in the emergency laparotomy setting: scoping review

BACKGROUND

Futile is defined as 'the fact of having no effect or of achieving nothing'. Futility in medicine has been defined through seven guiding principles, which in the context of emergency surgery, have been relatively unexplored. This scoping review aimed to identify key concepts around surgical futility as it relates to emergency laparotomy.

METHODS

Using the Arksey and O'Malley framework, a scoping review was conducted. A search of the Cochrane Library, Google Scholar, MEDLINE, and Embase was performed up until 1 November 2021 to identify literature relevant to the topic of futility in emergency laparotomy.

RESULTS

Three cohort studies were included in the analysis. A total of 105 157 patients were included, with 1114 patients reported as futile. All studies were recent (2019 to 2020) and focused on the principle of quantitative futility (assessment of the probability of death after surgery) within a timeline after surgery: two defining futility as death within 48 hours of surgery and one as death within 72 hours. In all cases this was derived from a survival histogram. Predictors of defined futile procedures included age, level of independence prior to admission, surgical pathology, serum creatinine, arterial lactate, and pH.

CONCLUSION

There remains a paucity of research defining, exploring, and analysing futile surgery in patients undergoing emergency laparotomy. With limited published work focusing on quantitative futility and the binary outcome of death, research is urgently needed to explore all principles of futility, including the wishes of patients and their families.

Prediction of intensive care unit admission (>24h) after surgery in elective noncardiac surgical patients using machine learning algorithms

Background

To develop a highly discriminative machine learning model for the prediction of intensive care unit admission (>24h) using the easily available preoperative information from electronic health records. An accurate prediction model for ICU admission after surgery is of great importance for surgical risk assessment and appropriate utilization of ICU resources.

Method

Data were collected retrospectively from a large hospital, comprising 135,442 adult patients who underwent surgery except for cardiac surgery between 1 January 2014, and 31 July 2018 in China. Multiple existing predictive machine learning algorithms were explored to construct the prediction model, including logistic regression, random forest, adaptive boosting, and gradient boosting machine. Four secondary analyses were conducted to improve the interpretability of the results.

Results

A total of 2702 (2.0%) patients were admitted to the intensive care unit postoperatively. The gradient boosting machine model attained the highest area under the receiver operating characteristic curve of 0.90. The machine learning models predicted intensive care unit admission better than the American Society of Anesthesiologists Physical Status (area under the receiver operating characteristic curve: 0.68). The gradient boosting machine recognized several features as highly significant predictors for postoperatively intensive care unit admission. By applying subgroup analysis and secondary analysis, we found that patients with operations on the digestive, respiratory, and vascular systems had higher probabilities for intensive care unit admission.

Conclusion

Compared with conventional American Society of Anesthesiologists Physical Status and logistic regression model, the gradient boosting machine could improve the performance in the prediction of intensive care unit admission. Machine learning models could be used to improve the discrimination and identify the need for intensive care unit admission after surgery in elective noncardiac surgical patients, which could help manage the surgical risk.

Ideal algorithms in healthcare: Explainable, dynamic, precise, autonomous, fair, and reproducible

Established guidelines describe minimum requirements for reporting algorithms in healthcare; it is equally important to objectify the characteristics of ideal algorithms that confer maximum potential benefits to patients, clinicians, and investigators. We propose a framework for ideal algorithms, including 6 desiderata: explainable (convey the relative importance of features in determining outputs), dynamic (capture temporal changes in physiologic signals and clinical events), precise (use high-resolution, multimodal data and aptly complex architecture), autonomous (learn with minimal supervision and execute without human input), fair (evaluate and mitigate implicit bias and social inequity), and reproducible (validated externally and prospectively and shared with academic communities). We present an ideal algorithms checklist and apply it to highly cited algorithms. Strategies and tools such as the predictive, descriptive, relevant (PDR) framework, the Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence (SPIRIT-AI) extension, sparse regression methods, and minimizing concept drift can help healthcare algorithms achieve these objectives, toward ideal algorithms in healthcare.

Optimizing predictive strategies for acute kidney injury after major vascular surgery

BACKGROUND

Postoperative acute kidney injury is common after major vascular surgery and is associated with increased morbidity, mortality, and cost. High-performance risk stratification using a machine learning model can inform strategies that mitigate harm and optimize resource use. It is hypothesized that incorporating intraoperative data would improve machine learning model accuracy, discrimination, and precision in predicting acute kidney injury among patients undergoing major vascular surgery.

METHODS

A single-center retrospective cohort of 1,531 adult patients who underwent nonemergency major vascular surgery, including open aortic, endovascular aortic, and lower extremity bypass procedures, was evaluated. The validated, automated MySurgeryRisk analytics platform used electronic health record data to forecast patient-level probabilistic risk scores for postoperative acute kidney injury using random forest models with preoperative data alone and perioperative data (preoperative plus intraoperative). The MySurgeryRisk predictions were compared with each other as well as with the American Society of Anesthesiologists physical status classification.

RESULTS

Machine learning models using perioperative data had greater accuracy, discrimination, and precision than models using either preoperative data alone or the American Society of Anesthesiologists physical status classification (accuracy: 0.70 vs 0.64 vs 0.62, area under the receiver operating characteristics curve: 0.77 vs 0.68 vs 0.61, area under the precision-recall curve: 0.70 vs 0.58 vs 0.48).

CONCLUSION

In predicting acute kidney injury after major vascular surgery, machine learning approaches that incorporate dynamic intraoperative data had greater accuracy, discrimination, and precision than models using either preoperative data alone or the American Society of Anesthesiologists physical status classification. Machine learning methods have the potential for real-time identification of high-risk patients who may benefit from personalized risk-reduction strategies.

Inadequate prediction of postoperative complications in breast cancer surgery: An evaluation of the ACS Surgical Risk Calculator

BACKGROUND

The American College of Surgeon (ACS) Surgical Risk Calculator is an online tool that helps surgeons estimate the risk of postoperative complications for numerous surgical procedures across several surgical specialties.

METHODS

We evaluated the predictive performance of the calculator in 385 cancer patients undergoing breast surgery. Calculator-predicted complication rates were compared with observed complication rates; calculator performance was evaluated using calibration and discrimination analyses.

RESULTS

The mean calculator-predicted rates for any complication (4.1%) and serious complication (3.2%) were significantly lower than the observed rates (11.2% and 5.2%, respectively). The area under the curve was 0.617 for any complication and 0.682 for serious complications. p Values for the Hosmer-Lemeshow test were significant (<.05) for both outcomes. Brier scores were 0.102 for any complication and 0.048 for serious complication.

CONCLUSIONS

The ACS risk calculator is not an ideal tool for predicting individual risk of complications following breast surgery in a Mexican cohort. The most valuable use of the calculator may reside in its role as an aid for patient-led surgery planning. The possibility of introducing breast surgery-specific data could improve the performance of the calculator. Furthermore, a disease-specific calculator could provide more accurate predictions and include complications more frequently found in breast cancer surgery.

Palliative Care in Emergency General Surgery Patients: Reduced Inpatient Mortality And Increased Discharge to Hospice

BACKGROUND

Admissions due to emergency general surgery (EGS) are on the rise, and patients who undergo emergency surgery are at increased risk of mortality. We hypothesized that utilization of palliative care and discharge to hospice in the EGS population have increased over time and that this is associated with a decrease in inpatient mortality.

METHODS

Using the 2002-2011 nationwide inpatient sample and American Association for the Surgery of Trauma-defined EGS diagnosis codes, we identified patients ≥18 years old with an EGS admission. Demographics, hospitalization characteristics, mortality, use of palliative care services, and discharge to hospice were queried. All Patient Refined-Diagnosis Related Group risk of mortality was used to categorize those with an extreme likelihood of dying (ELD). Multivariable logistic regression was used to investigate the association between palliative care consult and discharge to hospice.

RESULTS

Of the included patients, 0.3% received palliative care and 0.2% were discharged to hospice. Over time, rates of palliative care and hospice discharge increased while inpatient mortality decreased. In the 4% of patients with ELD, 3% received palliative care, 5% were transitioned to hospice care, and 22% suffered inpatient mortality. Controlling for patient characteristics, utilization of palliative care services was associated with increased odds of discharge to hospice compared to inpatient mortality (OR = 1.78 all patients and OR = 2.04 for ELD).

CONCLUSIONS

Despite the known increased risks associated with emergency surgical diagnoses, palliative care services remain infrequently utilized in the EGS population. This may be an opportunity for lessening suffering, improving patient-concordant care and outcomes, and reducing nonbeneficial and unwanted care.

The role of artificial intelligence in surgical patient perioperative management

Perioperative medicine is a patient-centered, multidisciplinary and integrated clinical practice that starts from the moment of contemplation of surgery until full recovery. Every perioperative phase (preoperative, intraoperative and postoperative) must be studied and planned in order to optimize the entire patient management. Perioperative optimization does not only concern a short-term outcome improvement, but it has also a strong impact on long term survival. Clinical cases variability leads to the collection and analysis of a huge amount of different data, coming from multiple sources, making perioperative management standardization very difficult. Artificial Intelligence (AI) can play a primary role in this challenge, helping human mind in perioperative practice planning and decision-making process. AI refers to the ability of a computer system to perform functions and reasoning typical of the human mind; Machine Learning (ML) could play a fundamental role in presurgical planning, during intraoperative phase and postoperative management. Perioperative medicine is the cornerstone of surgical patient management and the tools deriving from the application of AI seem very promising as a support in optimizing the management of each individual patient. Despite the increasing help that will derive from the use of AI tools, the uniqueness of the patient and the particularity of each individual clinical case will always keep the role of the human mind central in clinical and perioperative management. The role of the physician, who must analyze the outputs provided by AI by following his own experience and knowledge, remains and will always be essential.

Use of Common Carotid Intima-Media Thickness Measured by Ultrasound Echo-Tracking in Cardiovascular Risk Stratification Before Noncardiac Surgery in Low-Risk Category: A Research Idea

Preoperative cardiac risk stratification algorithms typically use a Bayesian approach to identify a low-risk category group for which the outcome is unlikely to be improved by further testing. This report suggests evaluating common carotid intima-media thickness (CCIMT) as measured by ultrasound to determine whether it strengthens and optimizes perioperative Bayesian risk indices. The idea proposes to use CCIMT Z score and vascular age to quantify atherosclerotic burden. CCIMT may be useful as part of shared decision-making for perioperative care. A website (www.suhitam.com/vascularage) designed by one of the authors (S.M.) is a useful resource.).

Added Value of Intraoperative Data for Predicting Postoperative Complications: The MySurgeryRisk PostOp Extension

BACKGROUND

Models that predict postoperative complications often ignore important intraoperative events and physiological changes. This study tested the hypothesis that accuracy, discrimination, and precision in predicting postoperative complications would improve when using both preoperative and intraoperative data input data compared with preoperative data alone.

METHODS

This retrospective cohort analysis included 43,943 adults undergoing 52,529 inpatient surgeries at a single institution during a 5-y period. Random forest machine learning models in the validated MySurgeryRisk platform made patient-level predictions for seven postoperative complications and mortality occurring during hospital admission using electronic health record data and patient neighborhood characteristics. For each outcome, one model trained with preoperative data alone; one model trained with both preoperative and intraoperative data. Models were compared by accuracy, discrimination (expressed as area under the receiver operating characteristic curve), precision (expressed as area under the precision-recall curve), and reclassification indices.

RESULTS

Machine learning models incorporating both preoperative and intraoperative data had greater accuracy, discrimination, and precision than models using preoperative data alone for predicting all seven postoperative complications (intensive care unit length of stay >48 h, mechanical ventilation >48 h, neurologic complications including delirium, cardiovascular complications, acute kidney injury, venous thromboembolism, and wound complications), and in-hospital mortality (accuracy: 88% versus 77%; area under the receiver operating characteristic curve: 0.93 versus 0.87; area under the precision-recall curve: 0.21 versus 0.15). Overall reclassification improvement was 2.4%-10.0% for complications and 11.2% for in-hospital mortality.

CONCLUSIONS

Incorporating both preoperative and intraoperative data significantly increased the accuracy, discrimination, and precision of machine learning models predicting postoperative complications and mortality.

Artificial Intelligence and Surgical Decision-making

Importance

Surgeons make complex, high-stakes decisions under time constraints and uncertainty, with significant effect on patient outcomes. This review describes the weaknesses of traditional clinical decision-support systems and proposes that artificial intelligence should be used to augment surgical decision-making.

Observations

Surgical decision-making is dominated by hypothetical-deductive reasoning, individual judgment, and heuristics. These factors can lead to bias, error, and preventable harm. Traditional predictive analytics and clinical decision-support systems are intended to augment surgical decision-making, but their clinical utility is compromised by time-consuming manual data management and suboptimal accuracy. These challenges can be overcome by automated artificial intelligence models fed by livestreaming electronic health record data with mobile device outputs. This approach would require data standardization, advances in model interpretability, careful implementation and monitoring, attention to ethical challenges involving algorithm bias and accountability for errors, and preservation of bedside assessment and human intuition in the decision-making process.

Conclusions and Relevance

Integration of artificial intelligence with surgical decision-making has the potential to transform care by augmenting the decision to operate, informed consent process, identification and mitigation of modifiable risk factors, decisions regarding postoperative management, and shared decisions regarding resource use.

Evaluation of a preoperative personalized risk communication tool: a prospective before-and-after study

PURPOSE

Patients want personalized information before surgery; most do not receive personalized risk estimates. Inadequate information contributes to poor experience and medicolegal complaints. We hypothesized that exposure to the Personalized Risk Evaluation and Decision Making in Preoperative Clinical Assessment (PREDICT) app, a personalized risk communication tool, would improve patient knowledge and satisfaction after anesthesiology consultations compared with standard care.

METHODS

We conducted a prospective clinical study (before-after design) and used patient-reported data to calculate personalized risks of morbidity, mortality, and expected length of stay using a locally calibrated National Surgical Quality Improvement Program risk calculator embedded in the PREDICT app. In the standard care (before) phase, the application's materials and output were not available to participants; in the PREDICT app (after) phase, personalized risks were communicated. Our primary outcome was knowledge score after the anesthesiology consultation. Secondary outcomes included patient satisfaction, anxiety, feasibility, and acceptability.

RESULTS

We included 183 participants (90 before; 93 after). Compared with standard care phase, the PREDICT app phase had higher post-consultation: knowledge of risks (14.3% higher; 95% confidence interval [CI], 6.5 to 22.0; P < 0.001) and satisfaction (0.8 points; 95% CI, 0.1 to 1.4; P = 0.03). Anxiety was unchanged (- 1.9%; 95% CI, - 4.2 to 0.5; P = 0.13). Acceptability was high for patients and anesthesiologists.

CONCLUSION

Exposure to a patient-facing, personalized risk communication app improved knowledge of personalized risk and increased satisfaction for adults before elective inpatient surgery.

TRIAL REGISTRATION

www.clinicaltrials.gov (NCT03422133); registered 5 February 2018.

Non-cardiac surgery in patients with coronary artery disease: risk evaluation and periprocedural management

Perioperative cardiovascular complications are important causes of morbidity and mortality associated with non-cardiac surgery, particularly in patients with coronary artery disease (CAD). Although preoperative cardiac risk assessment can facilitate the identification of vulnerable patients and implementation of adequate preventive measures, excessive evaluation might lead to undue resource utilization and surgical delay. Owing to conflicting data, there remains some uncertainty regarding the most beneficial perioperative strategy for patients with CAD. Antithrombotic agents are the cornerstone of secondary prevention of ischaemic events but substantially increase the risk of bleeding. Given that 5-25% of patients undergoing coronary stent implantation require non-cardiac surgery within 2 years, surgery is the most common reason for premature cessation of dual antiplatelet therapy. Perioperative management of antiplatelet therapy, which necessitates concomitant evaluation of the individual thrombotic and bleeding risks related to both clinical and procedural factors, poses a recurring dilemma in clinical practice. Current guidelines do not provide detailed recommendations on this topic, and the optimal approach in these patients is yet to be determined. This Review summarizes the current data guiding preoperative risk stratification as well as periprocedural management of patients with CAD undergoing non-cardiac surgery, including those treated with stents.

Intelligent, Autonomous Machines in Surgery

Surgeons perform two primary tasks: operating and engaging patients and caregivers in shared decision-making. Human dexterity and decision-making are biologically limited. Intelligent, autonomous machines have the potential to augment or replace surgeons. Rather than regarding this possibility with denial, ire, or indifference, surgeons should understand and steer these technologies. Closer examination of surgical innovations and lessons learned from the automotive industry can inform this process. Innovations in minimally invasive surgery and surgical decision-making follow classic S-shaped curves with three phases: (1) introduction of a new technology, (2) achievement of a performance advantage relative to existing standards, and (3) arrival at a performance plateau, followed by replacement with an innovation featuring greater machine autonomy and less human influence. There is currently no level I evidence demonstrating improved patient outcomes using intelligent, autonomous machines for performing operations or surgical decision-making tasks. History suggests that if such evidence emerges and if the machines are cost effective, then they will augment or replace humans, initially for simple, common, rote tasks under close human supervision and later for complex tasks with minimal human supervision. This process poses ethical challenges in assigning liability for errors, matching decisions to patient values, and displacing human workers, but may allow surgeons to spend less time gathering and analyzing data and more time interacting with patients and tending to urgent, critical-and potentially more valuable-aspects of patient care. Surgeons should steer these technologies toward optimal patient care and net social benefit using the uniquely human traits of creativity, altruism, and moral deliberation.

Older patients' attitudes towards, and perceptions of, preoperative physical activity and exercise prior to colorectal cancer surgery-a gap between awareness and action

Purpose

Time for preoperative optimisation prior to colorectal cancer surgery is limited and older people tend to decline exercise interventions. This study sought to describe attitudes towards, and perceptions of, preoperative physical activity and exercise in older people prior to colorectal cancer surgery.

Methods

This is a qualitative interview study, analysed with inductive content analysis. Seventeen participants scheduled for colorectal surgery were recruited as a purposeful sample from two hospitals in Stockholm, Sweden. Individual semi-structured interviews were conducted, face-to-face (n = 8) or by telephone (n = 9).

Results

Nine participants were male, median age was 75 years (range 70–91). The theme, ‘a gap between awareness and action’, was identified based on two main categories: ‘Attitudes towards preoperative physical exercise have a multifactorial base’ and ‘Preoperative physical exercise is possible with a push in the right direction’. The material described a gap between awareness of the benefits of physical activity and reports of performing physical activity. The reasons for the gap between thoughts and action in this respect seem to be multifactorial. Support from others emerged as an important possibility for overcoming the gap.

Conclusions

A gap between the patients’ awareness and action appeared in our material. Understanding this can guide healthcare professionals (HCPs) as to the support needed preoperatively. Advice on physical exercise before surgery should be specific, and individually tailored support for action should be offered. This support should also consider the individual’s current physical activity and preoperative attitude towards physical exercise.