Can We Improve Prediction of Adverse Surgical Outcomes? J Am Coll Surg. 2020;230:43-52.e1. [PMID: 31672674 DOI: 10.1016/j.jamcollsurg.2019.09.015]

Optimal inputs for machine learning models in predicting total joint arthroplasty outcomes: a systematic review

INTRODUCTION

Machine learning (ML) models may offer a novel solution to reducing postoperative complication rates and improving post-surgical outcomes after total joint arthroplasty (TJA). However, the variety of different ML models that exist paired with the increasing number of potential inputs can make the implementation of this tool challenging. Therefore, we conducted a systematic review to assess the most optimal inputs of different ML models in predicting postoperative (1) medical outcomes, (2) orthopedic outcomes, and (3) patient-reported outcome measures (PROMs) after total joint arthroplasty.

METHODS

The PubMed, MEDLINE, EBSCOhost, and Google Scholar databases were utilized to identify all studies evaluating ML models predicting outcomes following TJA between January 1, 2000, and June 23, 2023 (PROSPERO study protocol registration: CRD42023437586). The mean risk of bias in non-randomized studies-of interventions score, was 13.8 ± 0.5. Our initial query yielded 656 articles, of which 25 articles aligned with our aims, examining over 20 machine learning models and 1,555,300 surgeries. The area under the curve (AUC), accuracy, inputs, and the importance of each input were reported.

RESULTS

Twelve studies evaluating medical complications with 13 ML models reported AUCs ranging from 0.57 to 0.997 and accuracy between 88% and 99.98%. Key predictors included age, hyper-coagulopathy, total number of diagnoses, admission month, and malnutrition. Five studies evaluating orthopedic complications with 10 ML models reported AUCs from 0.49 to 0.93 and accuracy ranging from 92 to 97%, with age, BMI, CCI, AKSS scores, and height identified as key predictors. Ten studies evaluating PROMs comprising of 12 different ML models had an AUC ranging from 0.453 to 0.97 ranked preoperative PROMs as the post-predictive input. Overall, age was the most predictive risk factor for complications post-total joint arthroplasty (TJA).

CONCLUSION

These studies demonstrate the predictive capabilities of these models for anticipating complications and outcomes. Furthermore, these studies also highlight ML models' ability to identify non-classical variables not commonly considered in addition to confirming variables known to be crucial. To advance the field, forthcoming research should adhere to established guidelines for model development and training, employ industry-standard input parameters, and subject their models to external validity assessments.

Artificial intelligence in total and unicompartmental knee arthroplasty

The application of Artificial intelligence (AI) and machine learning (ML) tools in total (TKA) and unicompartmental knee arthroplasty (UKA) emerges with the potential to improve patient-centered decision-making and outcome prediction in orthopedics, as ML algorithms can generate patient-specific risk models. This review aims to evaluate the potential of the application of AI/ML models in the prediction of TKA outcomes and the identification of populations at risk.An extensive search in the following databases: MEDLINE, Scopus, Cinahl, Google Scholar, and EMBASE was conducted using the PIOS approach to formulate the research question. The PRISMA guideline was used for reporting the evidence of the data extracted. A modified eight-item MINORS checklist was employed for the quality assessment. The databases were screened from the inception to June 2022.Forty-four out of the 542 initially selected articles were eligible for the data analysis; 5 further articles were identified and added to the review from the PUBMED database, for a total of 49 articles included. A total of 2,595,780 patients were identified, with an overall average age of the patients of 70.2 years ± 7.9 years old. The five most common AI/ML models identified in the selected articles were: RF, in 38.77% of studies; GBM, in 36.73% of studies; ANN in 34.7% of articles; LR, in 32.65%; SVM in 26.53% of articles.This systematic review evaluated the possible uses of AI/ML models in TKA, highlighting their potential to lead to more accurate predictions, less time-consuming data processing, and improved decision-making, all while minimizing user input bias to provide risk-based patient-specific care.

Predicting 30-day unplanned hospital readmission after revision total knee arthroplasty: machine learning model analysis of a national patient cohort

Revision total knee arthroplasty (TKA) is associated with a higher risk of readmission than primary TKA. Identifying individual patients predisposed to readmission can facilitate proactive optimization and increase care efficiency. This study developed machine learning (ML) models to predict unplanned readmission following revision TKA using a national-scale patient dataset. A total of 17,443 revision TKA cases (2013-2020) were acquired from the ACS NSQIP database. Four ML models (artificial neural networks, random forest, histogram-based gradient boosting, and k-nearest neighbor) were developed on relevant patient variables to predict readmission following revision TKA. The length of stay, operation time, body mass index (BMI), and laboratory test results were the strongest predictors of readmission. Histogram-based gradient boosting was the best performer in distinguishing readmission (AUC: 0.95) and estimating the readmission probability for individual patients (calibration slope: 1.13; calibration intercept: -0.00; Brier score: 0.064). All models produced higher net benefit than the default strategies of treating all or no patients, supporting the clinical utility of the models. ML demonstrated excellent performance for the prediction of readmission following revision TKA. Optimization of important predictors highlighted by our model may decrease preventable hospital readmission following surgery, thereby leading to reduced financial burden and improved patient satisfaction.

Development and preliminary assessment of a machine learning model to predict myocardial infarction and cardiac arrest after major operations

INTRODUCTION

Accurate prediction of complications often informs shared decision-making. Derived over 10 years ago to enhance prediction of intra/post-operative myocardial infarction and cardiac arrest (MI/CA), the Gupta score has been criticized for unreliable calibration and inclusion of a wide spectrum of unrelated operations. In the present study, we developed a novel machine learning (ML) model to estimate perioperative risk of MI/CA and compared it to the Gupta score.

METHODS

Patients undergoing major operations were identified from the 2016-2020 ACS-NSQIP. The Gupta score was calculated for each patient, and a novel ML model was developed to predict MI/CA using ACS NSQIP-provided data fields as covariates. Discrimination (C-statistic) and calibration (Brier score) of the ML model were compared to the existing Gupta score within the entire cohort and across operative subgroups.

RESULTS

Of 2,473,487 patients included for analysis, 25,177 (1.0%) experienced MI/CA (55.2% MI, 39.1% CA, 5.6% MI and CA). The ML model, which was fit using a randomly selected training cohort, exhibited higher discrimination within the testing dataset compared to the Gupta score (C-statistic 0.84 vs 0.80, p < 0.001). Furthermore, the ML model had significantly better calibration in the entire cohort (Brier score 0.0097 vs 0.0100). Model performance was markedly improved among patients undergoing thoracic, aortic, peripheral vascular and foregut surgery.

CONCLUSIONS

The present ML model outperformed the Gupta score in the prognostication of MI/CA across a heterogenous range of operations. Given the growing integration of ML into healthcare, such models may be readily incorporated into clinical practice and guide benchmarking efforts.

Overview of Artificial Intelligence Research Within Hip and Knee Arthroplasty

Hip and knee arthroplasty are high-volume procedures undergoing rapid growth. The large volume of procedures generates a vast amount of data available for next-generation analytics. Techniques in the field of artificial intelligence (AI) can assist in large-scale pattern recognition and lead to clinical insights. AI methodologies have become more prevalent in orthopaedic research. This review will first describe an overview of AI in the medical field, followed by a description of the 3 arthroplasty research areas in which AI is commonly used (risk modeling, automated radiographic measurements, arthroplasty registry construction). Finally, we will discuss the next frontier of AI research focusing on model deployment and uncertainty quantification.

Prediction of gap balancing based on 2-D radiography in total knee arthroplasty for knee osteoarthritis patients

BACKGROUND

To investigate the influence of osteophytes on postoperative gap balancing, and to work out a predictive model of the relationship between osteophyte size and gap gaining in primary total knee replacement.

METHODS

One hundred and ten patients were enrolled in the study. Pre- and postoperative radiographs were collected and analyzed. They were assigned to the training dataset and test dataset randomly at a ratio of 9:1 by using the statistical package R (version 4.0.5). Size and marginal distances of osteophytes, planned bone cut planes, predicted bone cuts and joint gaps were labeled on the preoperative standing anteroposterior and lateral views, while actual bone cuts and joint gaps were recorded on the postoperative plain films, respectively. Statistical analysis was performed.

RESULTS

Actual joint gaps were significantly related to the distances of medial and lateral predictive bone cutting lines, bone cut thickness on tibial side and posterior condylar, as well as size and marginal distances of osteophytes (P < 0.05). A predictive equation was generated, with a root mean square error (RMSE) of 3.4761 in validation. A 2-D planning system with adjustable input parameters and dim predictive outputs on joint gap was developed. The equation is [Formula: see text] CONCLUSION: Postoperative joint gap can be predicted on the basis of preoperative measurements on 2-D plain films. Larger sample size may help improve the effectiveness and accuracy of the predictive equation.

Acute Biological Changes in Gynecologic Surgeons during Surgery: A Prospective Study

STUDY OBJECTIVE

To assess changes in biological measures of acute stress in surgeons during surgery in real-world settings DESIGN: A prospective cohort study.

SETTING

A tertiary teaching hospital.

PATIENTS

8 consultant and 9 training gynecologists.

INTERVENTION

A total of, 161 elective gynecologic surgeries of 3 procedures: laparoscopic hysterectomy, laparoscopic excision of endometriosis, or hysteroscopic myomectomy.

MEASUREMENTS AND MAIN RESULTS

Changes in surgeons' biological measures of acute stress while undertaking elective surgery. Salivary cortisol, mean and maximum heart rate (HR), and indices of the HR variability were recorded before and during surgery. From baseline to during surgery over the cohort, salivary cortisol decreased from 4.1 nmol/L to 3.6 nmol/L (p = .03), maximum HR increased from 101.8 beats per min (bpm) to 106.5 bpm (p <.01), root mean square of standard deviation decreased from 51.1 ms to 39.0 ms (p <.01), and standard deviation of beat-to-beat variability decreased from 73.7 to 59.8 ms (p <.01). Analysis of individual changes in stress by participant-surgery event by paired data graphs reveal inconsistent direction of change in all measures of biological stress despite stratification by surgical experience, role in surgery, level of training, or type of surgery performed.

CONCLUSION

This study measured biometric stress changes at both a group and individual level in real-world, live surgical settings. Individual changes have not previously been reported and the variable direction of stress change by participant-surgery episode identified in this study demonstrates a problematic interpretation of mean cohort findings previously reported. Results from this study suggest that either live surgery with tight environment control or surgical simulation studies may identify what, if any, biological measures of stress can predict acute stress reactions during surgery.

Artificial intelligence in vascular surgical decision making

Despite advances in prevention, detection, and treatment, cardiovascular disease is a leading cause of mortality and represents a major health problem worldwide. Artificial intelligence and machine learning have brought new insights to the management of vascular diseases by allowing analysis of huge and complex datasets and by offering new techniques to develop advanced imaging analysis. Artificial intelligence-based applications have the potential to improve prognostic evaluation and evidence-based decision making and contribute to vascular therapeutic decision making. In this scoping review, we provide an overview on how artificial intelligence could help in vascular surgical clinical decision making, highlighting potential benefits, current limitations, and future challenges.

A role for artificial intelligence applications inside and outside of the operating theatre: a review of contemporary use associated with total knee arthroplasty

BACKGROUND

Artificial intelligence (AI) has become involved in many aspects of everyday life, from voice-activated virtual assistants built into smartphones to global online search engines. Similarly, many areas of modern medicine have found ways to incorporate such technologies into mainstream practice. Despite the enthusiasm, robust evidence to support the utility of AI in contemporary total knee arthroplasty (TKA) remains limited. The purpose of this review was to provide an up-to-date summary of the use of AI in TKA and to explore its current and future value.

METHODS

Initially, a structured systematic review of the literature was carried out, following PRISMA search principles, with the aim of summarising the understanding of the field and identifying clinical and knowledge gaps.

RESULTS

A limited body of published work exists in this area. Much of the available literature is of poor methodological quality and many published studies could be best described as "demonstration of concepts" rather than "proof of concepts". There exists almost no independent validation of reported findings away from designer/host sites, and the extrapolation of key results to general orthopaedic sites is limited.

CONCLUSION

While AI has certainly shown value in a small number of specific TKA-associated applications, the majority to date have focused on risk, cost and outcome prediction, rather than surgical care, per se. Extensive future work is needed to demonstrate external validity and reliability in non-designer settings. Well-performed studies are warranted to ensure that the scientific evidence base supporting the use of AI in knee arthroplasty matches the global hype.

Drivers of Individual and Regional Variation in CMS Hierarchical Condition Categories Among Florida Beneficiaries

Objective

To explore hierarchical condition categories (HCC) risk score variation among Florida Fee for Service (FFS) Medicare beneficiaries between 2016 and 2018.

Data Sources

This study analyzed HCC risk score variation using Medicare claims data for Florida beneficiaries enrolled in Parts A & B between 2016 and 2018.

Study Design

The CMS methodology analyzed HCC risk score variation using annual mean county- and beneficiary-level risk score changes. The association between variation and beneficiary characteristics, diagnoses, and geographic location was characterized using mixed-effects negative binomial regression models.

Data Collection

Not applicable.

Principal Findings

Counties in the Northeast [marginal effect (ME)=-0.003], Central (ME=-0.021), and Southwest (ME=-0.009) Florida have relatively lower mean risk scores. A higher number of lifetime (ME=0.246) and treatable (ME=0.288) conditions were associated with higher county-level risk scores, while more preventable conditions (ME=-0.249) were associated with lower risk scores. Counties with older beneficiaries (ME=0.015) and more Blacks (ME=0.070) have higher risk scores, while having female beneficiaries reduced risk scores (ME=-0.005). Individual risk scores did not vary by age (ME=0.000), but Blacks (ME=0.001) had higher rates of variation relative to Whites, while other races had comparatively lower variation (ME=-0.003). In addition, individuals diagnosed with more lifetime (ME=0.129), treatable (ME=0.235), and preventable (ME=0.001) conditions had higher risk score variation. Most condition-specific indicators showed small associations with risk score changes; however, metastatic cancer/acute leukemia, respirator dependence/tracheostomy, and pressure ulcers of the skin were significantly associated with both types of HCC risk score variation.

Conclusion

Results showed demographics, HCC condition classifications (ie, lifetime, preventable, and treatable), and some specific conditions were associated with higher variation in mean county-level and individual risk scores. Results suggest consistent coding and reductions in the prevalence of certain treatable or preventable conditions could reduce the county and individual HCC risk score year-to-year change.

Machine Learning Can Accurately Predict Overnight Stay, Readmission, and 30-Day Complications Following Anterior Cruciate Ligament Reconstruction

PURPOSE

This study aimed to develop machine learning (ML) models to predict hospital admission (overnight stay) as well as short-term complications and readmission rates following anterior cruciate ligament reconstruction (ACLR). Furthermore, we sought to compare the ML models with logistic regression models in predicting ACLR outcomes.

METHODS

The American College of Surgeons National Surgical Quality Improvement Program database was queried for patients who underwent elective ACLR from 2012 to 2018. Artificial neural network ML and logistic regression models were developed to predict overnight stay, 30-day postoperative complications, and ACL-related readmission, and model performance was compared using the area under the receiver operating characteristic curve. Regression analyses were used to identify variables that were significantly associated with the predicted outcomes.

RESULTS

A total of 21,636 elective ACLR cases met inclusion criteria. Variables associated with hospital admission included White race, obesity, hypertension, and American Society of Anesthesiologists classification 3 and greater, anesthesia other than general, prolonged operative time, and inpatient setting. The incidence of hospital admission (overnight stay) was 10.2%, 30-day complications was 1.3%, and 30-day readmission for ACLR-related causes was 0.9%. Compared with logistic regression models, artificial neural network models reported superior area under the receiver operating characteristic curve values in predicting overnight stay (0.835 vs 0.589), 30-day complications (0.742 vs 0.590), reoperation (0.842 vs 0.601), ACLR-related readmission (0.872 vs 0.606), deep-vein thrombosis (0.804 vs 0.608), and surgical-site infection (0.818 vs 0.596).

CONCLUSIONS

The ML models developed in this study demonstrate an application of ML in which data from a national surgical patient registry was used to predict hospital admission and 30-day postoperative complications after elective ACLR. ML models developed performed well, outperforming regression models in predicting hospital admission and short-term complications following elective ACLR. ML models performed best when predicting ACLR-related readmissions and reoperations, followed by overnight stay.

LEVEL OF EVIDENCE

IV, retrospective comparative prognostic trial.

Comparative performance of three comorbidity scores in predicting survival after the elective repair of abdominal aortic aneurysms

BACKGROUND

We aimed to study the discriminative power of 3 comorbidity scores for predicting 5-year survival after the elective repair of aorto-iliac aneurysms (AAA).

METHODS

444 patients with AAA undergoing elective repair (33% open and 67% endovascular) between 2000 and 2020 were reviewed. The Charlson Comorbidity Index (CCI) and subsequent adjustments by Schneeweiss, Quan and Armitage, the Modified Frailty Index (MFI) and the American Society of Anesthesiologists Score (ASA) were calculated from preoperative data. Their association with 5-year survival was analyzed using Cox regression models and their discriminative power and its changes with C statistics and Net Reclassification Index (NRI).

RESULTS

All comorbidity scores were associated with survival after adjusting by age, sex and type of surgical repair: original CCI HR=1.24, P<0.001; Schneeweiss CCI HR=1.23, P<0.001; Quan CCI HR=1.27, P<0.001, Armitage CCI HR=1.46, P<0.001, MFI HR=1.39, P<0.001 and ASA HR=1.68 (P=0.04) and 2.86 (P=0.01) for classes III and IV, respectively. Associated C statistics were of 0.64, 0.65, 0.65, 0.64, 0.61 and 0.59, respectively. Compared with the original CCI, models based on Schneeweiss CCI and Armitage CCI provided minor improvements in NRI (0.32 and 0.23), and the model based on ASA showed lower C statistics (P=0.014) and NRI (-0.30).

CONCLUSIONS

Established comorbidity scores, such as CCI, MFI or ASA, are all associated with 5-year survival after the elective repair of AAAs, being ASA the worst of them. However, their predictive power is in no case sufficient to identify, by themselves, those patients who may not be eligible for intervention on the basis of life expectancy.

Pancreatogenic Diabetes after Partial Pancreatectomy: A Common and Understudied Cause of Morbidity

BACKGROUND

Partial pancreatic resection is a known risk factor for new-onset pancreatogenic diabetes mellitus (P-DM). The long-term incidence of P-DM and its clinical impact after partial pancreatic resection remains unknown. The primary objective of this study is to determine the long-term incidence of P-DM and its clinical impact after partial pancreatic resection.

STUDY DESIGN

The Medicare 100% Standard Analytic File (2013 to 2017) was queried for all patients who underwent partial pancreatic resection (pancreaticoduodenectomy, distal pancreatectomy). The primary outcome was the development of postoperative P-DM after surgery.

RESULTS

Among 4,255 patients who underwent a pancreaticoduodenectomy or distal pancreatectomy, with a median follow-up of 10.8 months, the incidence of P-DM was 20.3% (n=863) and occurred at a median of 3.6 months after surgery. For patients with at least a 3-year follow-up, 32.2% of patients developed P-DM. Risk factors for developing P-DM included male sex (odds ratio [OR] 1.32, 95% CI 1.13 to 1.54), undergoing a distal pancreatectomy (OR 1.98, 95% CI 1.68 to 2.35), having a malignant diagnosis (OR 1.65, 95% CI 1.34 to 2.04), a family history of diabetes (OR 2.06, 95% CI 1.43 to 2.97; all p < 0.001), and being classified as prediabetic in the preoperative setting (OR 1.57, 95% CI 1.18 to 2.08; p = 0.002). Patients who developed P-DM were more commonly readmitted within 90 days of surgery and had higher postoperative healthcare expenditures in the year after surgery ($24,440 US dollars vs $16,130 US dollars; both p < 0.001) vs patients without P-DM.

CONCLUSIONS

Approximately 1 in 5 Medicare beneficiaries who undergo a pancreatic resection develop P-DM after pancreatic resection. Appropriate screening and improved patient education should be conducted for these patients, in particular, for those with identified risk factors.

Development and Validation of Machine Learning Models to Predict Readmission After Colorectal Surgery

BACKGROUND

Readmission after colorectal surgery is common and often implies complications for patients and costs for hospitals. Previous works have created predictive models using logistic regression for this outcome but have shown limited accuracy. Machine learning has shown promise in improving predictions by identifying non-linear patterns in data. We sought to create a more accurate predictive model for readmission after colorectal surgery using machine learning.

METHODS

Patients who underwent colorectal surgery were identified in the National Quality Improvement Program (NSQIP) database including years 2012-2019 and split into training, validation, and test sets. The primary outcome was readmission within 30 days of surgery. Three types of machine learning models were created, including random forest (RF), gradient boosting (XGB), and neural network (NN). A logistic regression (LR) model was also created for comparison. Model performance was evaluated using area under the receiver operating characteristic curve (AUROC).

RESULTS

The dataset included 213,827 patients after application of exclusion criteria. A total of 23,083 (10.8%) of patients experienced readmission. NN obtained an AUROC of 0.751 (95% CI 0.743-0.759), compared with 0.684 (95% CI 0.676-0.693) for LR. RF and XGB performed similarly with AUROCs of 0.749 (95% CI 0.741-0.757) and 0.745 (95% CI 0.737-0.753) respectively. Ileus, index admission length of stay, organ-space surgical site infection present at time of surgery, and ostomy placement were identified as the most contributory variables.

CONCLUSIONS

Machine learning approaches outperformed traditional statistical methods in the prediction of readmission after colorectal surgery. After external validation, this improved prediction model could be used to target interventions to reduce readmission rate.

Accuracy of Risk Estimation for Surgeons Versus Risk Calculators in Emergency General Surgery

INTRODUCTION

Surgical risk calculators have expanded in both number and sophistication of their predictive approach. These calculators are gaining popularity as validated tools to help surgeons estimate mortality and complications following emergency general surgery (EGS). However, the accuracy of risk estimates generated by these calculators compared to risk estimation by practicing surgeons has not been explored.

METHODS

Acute care surgeons at a quaternary care center prospectively estimated 30-d mortality and complications for adult EGS patients (2019-2021). Surgeon predictions were compared to Predictive OpTimal Trees in Emergency Surgery Risk (POTTER) and NSQIP estimates. Observed-to-expected (O:E) ratios of median aggregate estimates were calculated. C-statistics for surgeon and calculator estimations were utilized to quantify predictive accuracy.

RESULTS

Among 150 patients (median 61 y, 45% male), 30-d mortality was 15% (n = 23). Observed rates of prolonged mechanical ventilation and acute renal failures were 30% and 10%, respectively. Overall, surgeon predictions were similar to risk calculator estimates for mortality (c-statistics 0.843 [surgeon] versus 0.848 [POTTER] and 0.815 [NSQIP]) and need for prolonged ventilation (c-statistics 0.801 versus 0.722 and 0.689, respectively). Surgeons tended to overestimate complication risks. Surgeon experience was not significantly associated with mortality prediction in an adjusted model.

CONCLUSIONS

Acute care surgeons at a quaternary care center predicted postoperative mortality and complications with similar discrimination when compared to surgical risk calculators. Surgeon expertise should be utilized in conjunction with risk calculators when counseling EGS patients regarding anticipated postoperative outcomes. Surgeons should be cognizant of patterns in overestimation or underestimation of complications.

A multicenter prospective study on postoperative pulmonary complications prediction in geriatric patients with deep neural network model

Aim

Postoperative pulmonary complications (PPCs) can increase the risk of postoperative mortality, and the geriatric population has high incidence of PPCs. Early identification of high-risk geriatric patients is of great value for clinical decision making and prognosis improvement. Existing prediction models are based purely on structured data, and they lack predictive accuracy in geriatric patients. We aimed to develop and validate a deep neural network model based on combined natural language data and structured data for improving the prediction of PPCs in geriatric patients.

Methods

We consecutively enrolled patients aged ≥65 years who underwent surgery under general anesthesia at seven hospitals in China. Data from the West China Hospital of Sichuan University were used as the derivation dataset, and a deep neural network model was developed based on combined natural language data and structured data. Data from the six other hospitals were combined for external validation.

Results

The derivation dataset included 12,240 geriatric patients, and 1949(15.9%) patients developed PPCs. Our deep neural network model outperformed other machine learning models with an area under the precision-recall curve (AUPRC) of 0.657(95% confidence interval [CI], 0.655–0.658) and an area under the receiver operating characteristic curve (AUROC) of 0.884(95% CI, 0.883–0.885). The external dataset included 7579 patients, and 776(10.2%) patients developed PPCs. In external validation, the AUPRC was 0.632(95%CI, 0.632–0.633) and the AUROC was 0.889(95%CI, 0.888–0.889).

Conclusions

This study indicated that the deep neural network model based on combined natural language data and structured data could improve the prediction of PPCs in geriatric patients.

Effect of the Age-Adjusted Charlson Comorbidity Index on All-Cause Mortality and Readmission in Older Surgical Patients: A National Multicenter, Prospective Cohort Study

Background

Identifying a high-risk group of older people before surgical procedures is very important. The study aimed to explore the association between the age-adjusted Charlson comorbidity index (ACCI) and all-cause mortality and readmission among older Chinese surgical patients (age ≥65 years).

Methods

A large-scale cohort study was performed in 25 general public hospitals from six different geographic regions of China. Trained registered nurses gathered data on clinical and sociodemographic characteristics. All-cause mortality was recorded when patients died during hospitalization or during the 90-day follow-up period. Readmission was also tracked from hospital discharge to the 90-day follow-up. The ACCI, in assessing comorbidities, was categorized into two groups (≥5 vs. &lt;5). A multiple regression model was used to examine the association between the ACCI and all-cause mortality and readmission.

Results

There were 3,911 older surgical patients (mean = 72.46, SD = 6.22) in our study, with 1,934 (49.45%) males. The average ACCI score was 4.77 (SD = 1.99), and all-cause mortality was 2.51% (high ACCI = 5.06% vs. low ACCI = 0.66%, P &lt; 0.001). After controlling for all potential confounders, the ACCI score was an independent risk factor for 90-day hospital readmission (OR = 1.18, 95% CI: 1.14, 1.23) and 90-day all-cause mortality (OR = 1.26, 95% CI: 1.16–1.36). Furthermore, older surgical patients with a high ACCI (≥5) had an increased risk of all-cause mortality (OR = 6.13, 95% CI: 3.17, 11.85) and readmission (OR = 2.13, 95% CI: 1.78, 2.56) compared to those with a low ACCI (&lt;5). The discrimination performance of the ACCI was moderate for mortality (AUC:0.758, 95% CI: 0.715–0.80; specificity = 0.591, sensitivity = 0.846) but poor for readmission (AUC: 0.627, 95% CI: 0.605–0.648; specificity = 0.620; sensitivity = 0.590).

Conclusions

The ACCI is an independent risk factor for all-cause mortality and hospital readmission among older Chinese surgical patients and could be a potential risk assessment tool to stratify high-risk older patients for surgical procedures.

A novel machine learning approach to identify social risk factors associated with textbook outcomes after surgery

BACKGROUND

Identifying social determinants of health has become a priority for many researchers, health care providers, and payers. The vast amount of patient and population-level data available on social determinants creates, however, both an opportunity and a challenge as these data can be difficult to synthesize and analyze.

METHODS

Medicare beneficiaries who underwent 1 of 4 common operations between 2013 and 2017 were identified. Using a machine learning algorithm, the primary independent variable, surgery social determinants of health index, was derived from 15 common, publicly available social determents of health measures. After development of a surgery social determinants of health index, multivariable logistic regression was used to estimate the association of this index with textbook outcomes, as well as the component metrics of textbook outcomes.

RESULTS

A novel surgery social determinants of health index was developed with factor component weights that varied relative to their impact on postoperative outcomes. Factors with the highest weight in the algorithm relative to postoperative outcomes were the proportion of noninstitutionalized civilians with a disability and persons without high school diploma, while components with the lowest weights were the proportion of households with more people than rooms and persons below poverty. Overall, an increase in surgery social determinants of health index was associated with 6% decreased odds (95% confidence interval: 0.93-0.94) of achieving a textbook outcome. In addition, an increase in surgery social determinants of health index was associated with increased odds of each of the individual components of textbook outcome; ranging from 3% increased odds (95% confidence interval: 1.03-1.04) for 90-day readmission to 10% increased odds (95% confidence interval: 1.09-1.11) for 90-day mortality. Further, there was 6% increased odds (95% confidence interval: 1.05-1.07) of experiencing a complication and 7% increased odds (95% confidence interval: 1.06-1.07) of having an extended length of stay. Minority patients from a high surgery social determinants of health index had 38% lower odds (95% confidence interval: 0.60-0.65) of achieving a textbook outcome compared with White/non-Hispanic patients from a low surgery social determinants of health index area.

CONCLUSION

Using a machine learning approach, we developed a novel social determents of health index to predict the probability of achieving a textbook outcome after surgery.

Development of a Machine Learning Algorithm for Prediction of Complications and Unplanned Readmission Following Primary Anatomic Total Shoulder Replacements

Background

The demand and incidence of anatomic total shoulder arthroplasty (aTSA) procedures is projected to increase substantially over the next decade. There is a paucity of accurate risk prediction models which would be of great utility in minimizing morbidity and costs associated with major post-operative complications. Machine learning is a powerful predictive modeling tool and has become increasingly popular, especially in orthopedics. We aimed to build a ML model for prediction of major complications and readmission following primary aTSA.

Methods

A large California administrative database was retrospectively reviewed for all adults undergoing primary aTSA between 2015 to 2017. The primary outcome was any major complication or readmission following aTSA. A wide scope of standard ML benchmarks, including Logistic regression (LR), XGBoost, Gradient boosting, AdaBoost and Random Forest were employed to determine their power to predict outcomes. Additionally, important patient features to the prediction models were indentified.

Results

There were a total of 10,302 aTSAs with 598 (5.8%) having at least one major post-operative complication or readmission. XGBoost had the highest discriminative power (area under receiver operating curve AUROC of 0.689) of the 5 ML benchmarks with an area under precision recall curve AURPC of 0.207. History of implant complication, severe chronic kidney disease, teaching hospital status, coronary artery disease and male sex were the most important features for the performance of XGBoost. In addition, XGBoost identified teaching hospital status and male sex as markedly more important predictors of outcomes compared to LR models.

Conclusion

We report a well calibrated XGBoost ML algorithm for predicting major complications and 30-day readmission following aTSA. History of prior implant complication was the most important patient feature for XGBoost performance, a novel patient feature that surgeons should consider when counseling patients.

Understanding Artificial Intelligence and Predictive Analytics: A Clinically Focused Review of Machine Learning Techniques

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Machine learning and artificial intelligence have seen tremendous growth in recent years and have been applied in numerous studies in the field of orthopaedics.

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Machine learning will soon become critical in the day-to-day operations of orthopaedic practice; therefore, it is imperative that providers become accustomed to and familiar with not only the terminology but also the fundamental techniques behind the technology.

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A foundation of knowledge regarding machine learning is critical for physicians so they can begin to understand the details in the algorithms that are being developed, which provide improved accuracy compared with clinicians, decreased time required, and a heightened ability to triage patients.

Artificial intelligence in diagnosis of knee osteoarthritis and prediction of arthroplasty outcomes: a review

Background

Artificial intelligence is an emerging technology with rapid growth and increasing applications in orthopaedics. This study aimed to summarize the existing evidence and recent developments of artificial intelligence in diagnosing knee osteoarthritis and predicting outcomes of total knee arthroplasty.

Methods

PubMed and EMBASE databases were searched for articles published in peer-reviewed journals between January 1, 2010 and May 31, 2021. The terms included: ‘artificial intelligence’, ‘machine learning’, ‘knee’, ‘osteoarthritis’, and ‘arthroplasty’. We selected studies focusing on the use of AI in diagnosis of knee osteoarthritis, prediction of the need for total knee arthroplasty, and prediction of outcomes of total knee arthroplasty. Non-English language articles and articles with no English translation were excluded. A reviewer screened the articles for the relevance to the research questions and strength of evidence.

Results

Machine learning models demonstrated promising results for automatic grading of knee radiographs and predicting the need for total knee arthroplasty. The artificial intelligence algorithms could predict postoperative outcomes regarding patient-reported outcome measures, patient satisfaction and short-term complications. Important weaknesses of current artificial intelligence algorithms included the lack of external validation, the limitations of inherent biases in clinical data, the requirement of large datasets in training, and significant research gaps in the literature.

Conclusions

Artificial intelligence offers a promising solution to improve detection and management of knee osteoarthritis. Further research to overcome the weaknesses of machine learning models may enhance reliability and allow for future use in routine healthcare settings.

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

BACKGROUND

The term "data science" encompasses several methods, many of which are considered cutting edge and are being used to influence care processes across the world. Nursing is an applied science and a key discipline in health care systems in both clinical and administrative areas, making the profession increasingly influenced by the latest advances in data science. The greater informatics community should be aware of current trends regarding the intersection of nursing and data science, as developments in nursing practice have cross-professional implications.

OBJECTIVES

This study aimed to summarize the latest (calendar year 2020) research and applications of nursing-relevant patient outcomes and clinical processes in the data science literature.

METHODS

We conducted a rapid review of the literature to identify relevant research published during the year 2020. We explored the following 16 topics: (1) artificial intelligence/machine learning credibility and acceptance, (2) burnout, (3) complex care (outpatient), (4) emergency department visits, (5) falls, (6) health care-acquired infections, (7) health care utilization and costs, (8) hospitalization, (9) in-hospital mortality, (10) length of stay, (11) pain, (12) patient safety, (13) pressure injuries, (14) readmissions, (15) staffing, and (16) unit culture.

RESULTS

Of 16,589 articles, 244 were included in the review. All topics were represented by literature published in 2020, ranging from 1 article to 59 articles. Numerous contemporary data science methods were represented in the literature including the use of machine learning, neural networks, and natural language processing.

CONCLUSION

This review provides an overview of the data science trends that were relevant to nursing practice in 2020. Examinations of such literature are important to monitor the status of data science's influence in nursing practice.

Machine learning to guide clinical decision-making in abdominal surgery-a systematic literature review

PURPOSE

An indication for surgical therapy includes balancing benefits against risk, which remains a key task in all surgical disciplines. Decisions are oftentimes based on clinical experience while guidelines lack evidence-based background. Various medical fields capitalized the application of machine learning (ML), and preliminary research suggests promising implications in surgeons' workflow. Hence, we evaluated ML's contemporary and possible future role in clinical decision-making (CDM) focusing on abdominal surgery.

METHODS

Using the PICO framework, relevant keywords and research questions were identified. Following the PRISMA guidelines, a systemic search strategy in the PubMed database was conducted. Results were filtered by distinct criteria and selected articles were manually full text reviewed.

RESULTS

Literature review revealed 4,396 articles, of which 47 matched the search criteria. The mean number of patients included was 55,843. A total of eight distinct ML techniques were evaluated whereas AUROC was applied by most authors for comparing ML predictions vs. conventional CDM routines. Most authors (N = 30/47, 63.8%) stated ML's superiority in the prediction of benefits and risks of surgery. The identification of highly relevant parameters to be integrated into algorithms allowing a more precise prognosis was emphasized as the main advantage of ML in CDM.

CONCLUSIONS

A potential value of ML for surgical decision-making was demonstrated in several scientific articles. However, the low number of publications with only few collaborative studies between surgeons and computer scientists underpins the early phase of this highly promising field. Interdisciplinary research initiatives combining existing clinical datasets and emerging techniques of data processing may likely improve CDM in abdominal surgery in the future.

Machine learning in vascular surgery: a systematic review and critical appraisal

Machine learning (ML) is a rapidly advancing field with increasing utility in health care. We conducted a systematic review and critical appraisal of ML applications in vascular surgery. MEDLINE, Embase, and Cochrane CENTRAL were searched from inception to March 1, 2021. Study screening, data extraction, and quality assessment were performed by two independent reviewers, with a third author resolving discrepancies. All original studies reporting ML applications in vascular surgery were included. Publication trends, disease conditions, methodologies, and outcomes were summarized. Critical appraisal was conducted using the PROBAST risk-of-bias and TRIPOD reporting adherence tools. We included 212 studies from a pool of 2235 unique articles. ML techniques were used for diagnosis, prognosis, and image segmentation in carotid stenosis, aortic aneurysm/dissection, peripheral artery disease, diabetic foot ulcer, venous disease, and renal artery stenosis. The number of publications on ML in vascular surgery increased from 1 (1991-1996) to 118 (2016-2021). Most studies were retrospective and single center, with no randomized controlled trials. The median area under the receiver operating characteristic curve (AUROC) was 0.88 (range 0.61-1.00), with 79.5% [62/78] studies reporting AUROC ≥ 0.80. Out of 22 studies comparing ML techniques to existing prediction tools, clinicians, or traditional regression models, 20 performed better and 2 performed similarly. Overall, 94.8% (201/212) studies had high risk-of-bias and adherence to reporting standards was poor with a rate of 41.4%. Despite improvements over time, study quality and reporting remain inadequate. Future studies should consider standardized tools such as PROBAST and TRIPOD to improve study quality and clinical applicability.

Artificial Learning and Machine Learning Decision Guidance Applications in Total Hip and Knee Arthroplasty: A Systematic Review

BACKGROUND

Artificial intelligence (AI) and machine learning (ML) modeling in hip and knee arthroplasty (total joint arthroplasty [TJA]) is becoming more commonplace. This systematic review aims to quantify the accuracy of current AI- and ML-based application for cognitive support and decision-making in TJA.

METHODS

A comprehensive search of publications was conducted through the EMBASE, Medline, and PubMed databases using relevant keywords to maximize the sensitivity of the search. No limits were placed on level of evidence or timing of the study. Findings were reported according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Analysis of variance testing with post-hoc Tukey test was applied to compare the area under the curve (AUC) of the models.

RESULTS

After application of inclusion and exclusion criteria, 49 studies were included in this review. The application of AI/ML-based models and average AUC is as follows: cost prediction-0.77, LOS and discharges-0.78, readmissions and reoperations-0.66, preoperative patient selection/planning-0.79, adverse events and other postoperative complications-0.84, postoperative pain-0.83, postoperative patient-reported outcomes measures and functional outcome-0.81. Significant variability in model AUC across the different decision support applications was found (P < .001) with the AUC for readmission and reoperation models being significantly lower than that of the other decision support categories.

CONCLUSIONS

AI/ML-based applications in TJA continue to expand and have the potential to optimize patient selection and accurately predict postoperative outcomes, complications, and associated costs. On average, the AI/ML models performed best in predicting postoperative complications, pain, and patient-reported outcomes and were less accurate in predicting hospital readmissions and reoperations.

Machine learning in gastrointestinal surgery

Machine learning (ML) is a collection of algorithms allowing computers to learn directly from data without predetermined equations. It is used widely to analyze "big data". In gastrointestinal surgery, surgeons deal with various data such as clinical parameters, surgical videos, and pathological images, to stratify surgical risk, perform safe surgery and predict patient prognosis. In the current "big data" era, the accelerating accumulation of a large amount of data drives studies using ML algorithms. Three subfields of ML are supervised learning, unsupervised learning, and reinforcement learning. In this review, we summarize applications of ML to surgical practice in the preoperative, intraoperative, and postoperative phases of care. Prediction and stratification using ML is promising; however, the current overarching concern is the availability of ML models. Information systems that can manage "big data" and integrate ML models into electronic health records are essential to incorporate ML into daily practice. ML is fundamental technology to meaningfully process data that exceeds the capacity of the human mind to comprehend. The accelerating accumulation of a large amount of data is changing the nature of surgical practice fundamentally. Artificial intelligence (AI), represented by ML, is being incorporated into daily surgical practice.

A Novel, Potentially Universal Machine Learning Algorithm to Predict Complications in Total Knee Arthroplasty

BACKGROUND

There remains a lack of accurate and validated outcome-prediction models in total knee arthroplasty (TKA). While machine learning (ML) is a powerful predictive tool, determining the proper algorithm to apply across diverse data sets is challenging. AutoPrognosis (AP) is a novel method that uses automated ML framework to incorporate the best performing stages of prognostic modeling into a single well-calibrated algorithm. We aimed to compare various ML methods to AP in predictive performance of complications after TKA.

METHODS

Thirty-eight preoperative patient demographics and clinical features from all primary TKAs performed at California-licensed hospitals between 2015 and 2017 were evaluated as predictors of major complications after TKA. Traditional logistic regression (LR), various other ML methods (XGBoost, Gradient Boosting, AdaBoost, and Random Forest), and AP were used for model building to determine discriminative power (area under receiver operating curve), calibration (Brier score), and feature importance.

RESULTS

Between 2015 and 2017, there were a total of 156,750 TKAs with 1109 (0.7%) total major complications. AP had the highest discriminative performance with area under receiver operating curve 0.679 compared with LR, XGBoost, Gradient Boosting, AdaBoost, and Random Forest (0.617, 0.601, 0.662, 0.657, and 0.545, respectively). AP (Brier score 0.007) had similar calibration as the other ML methods (0.006, 0.006, 0.022, 0.007, and 0.008, respectively). The variables that are most important for AP differ from those that are most important for LR.

CONCLUSION

Compared to conventional ML algorithms, AP has superior discriminative ability with similar calibration and suggests nonlinear relationships between variables in outcomes of TKA.

The Prediction Model of Medical Expenditure Appling Machine Learning Algorithm in CABG Patients

Most patients face expensive healthcare management after coronary artery bypass grafting (CABG) surgery, which brings a substantial financial burden to the government. The National Health Insurance Research Database (NHIRD) is a complete database containing over 99% of individuals' medical information in Taiwan. Our research used the latest data that selected patients who accepted their first CABG surgery between January 2014 and December 2017 (n = 12,945) to predict which factors will affect medical expenses, and built the prediction model using different machine learning algorithms. After analysis, our result showed that the surgical expenditure (X4) and 1-year medical expenditure before the CABG operation (X14), and the number of hemodialysis (X15), were the key factors affecting the 1-year medical expenses of CABG patients after discharge. Furthermore, the XGBoost and SVR methods are both the best predictive models. Thus, our research suggests enhancing the healthcare management for patients with kidney-related diseases to avoid costly complications. We provide helpful information for medical management, which may decrease health insurance burdens in the future.

A higher hospital case mix index increases the odds of achieving a textbook outcome after hepatopancreatic surgery in the Medicare population

BACKGROUND

The objective of the current study was to assess the impact of case mix index at the hospital level on postoperative outcomes among Medicare beneficiaries who underwent hepatopancreatic surgery.

METHODS

Medicare beneficiaries who underwent hepatopancreatic surgery between 2013 and 2017 were identified and analyzed. The primary independent variable, Case Mix Index, is a freely available metric; the primary outcome was textbook outcome defined as the absence of complications, extended length of stay, readmission, and mortality.

RESULTS

Among 37,412 Medicare beneficiaries, 64.9% (n = 24,299) underwent a pancreatectomy and 35.1% (n = 13,113) underwent hepatectomy. The overall incidence of textbook outcome was 47.2%, which varied by case mix index (low case mix index: 41.6% vs high case mix index: 51.3%), as did extended length of stay (low case mix index: 27.9% versus high case mix index: 19.3%), complications (low case mix index: 33.3% vs high case mix index: 24.7%), and 90-day mortality (low case mix index: 12.5% vs high case mix index: 6.3%). After controlling for hepatopancreatic-specific surgical volume and hospital teaching status, multivariable analyses revealed that patients who underwent surgery at a low case mix index hospital had 28% decreased odds (95% confidence interval 0.66-0.79) of achieving a textbook outcome versus patients from a high case mix index hospital. Moreover, patients at a low case mix index hospital had 39% increased odds of extended length of stay (95% confidence interval 1.23-1.59), 48% increased odds of experiencing a complication (95% confidence interval 1.32-1.65), and 56% increased odds of 90-day mortality (95% confidence interval 1.31-1.87).

CONCLUSION

Case mix index was strongly associated with the probability of achieving a textbook outcome after hepatopancreatic surgery. Hospitals with a higher case mix index were more likely to perform hepatopancreatic surgeries with no adverse postoperative outcomes.

Clinical value of artificial intelligence in hepatocellular carcinoma: Current status and prospect

Hepatocellular carcinoma (HCC) is the most commonly diagnosed type of liver cancer and the fourth leading cause of cancer-related mortality worldwide. The early identification of HCC and effective treatments for it have been challenging. Due to the sufficient compensatory ability of early patients and its nonspecific symptoms, HCC is more likely to escape diagnosis in the incipient stage, during which patients can achieve a more satisfying overall survival if they undergo resection or liver transplantation. Patients at advanced stages can profit from radical therapies in a limited way. In order to improve the unfavorable prognosis of HCC, diagnostic ability and treatment efficiency must be improved. The past decade has seen rapid advancements in artificial intelligence, underlying its unique usefulness in almost every field, including that of medicine. Herein, we sought and reviewed studies that put emphasis on artificial intelligence and HCC.

Adverse Outcomes Prediction for Congenital Heart Surgery: A Machine Learning Approach

OBJECTIVE

Risk assessment tools typically used in congenital heart surgery (CHS) assume that various possible risk factors interact in a linear and additive fashion, an assumption that may not reflect reality. Using artificial intelligence techniques, we sought to develop nonlinear models for predicting outcomes in CHS.

METHODS

We built machine learning (ML) models to predict mortality, postoperative mechanical ventilatory support time (MVST), and hospital length of stay (LOS) for patients who underwent CHS, based on data of more than 235,000 patients and 295,000 operations provided by the European Congenital Heart Surgeons Association Congenital Database. We used optimal classification trees (OCTs) methodology for its interpretability and accuracy, and compared to logistic regression and state-of-the-art ML methods (Random Forests, Gradient Boosting), reporting their area under the curve (AUC or c-statistic) for both training and testing data sets.

RESULTS

Optimal classification trees achieve outstanding performance across all three models (mortality AUC = 0.86, prolonged MVST AUC = 0.85, prolonged LOS AUC = 0.82), while being intuitively interpretable. The most significant predictors of mortality are procedure, age, and weight, followed by days since previous admission and any general preoperative patient risk factors.

CONCLUSIONS

The nonlinear ML-based models of OCTs are intuitively interpretable and provide superior predictive power. The associated risk calculator allows easy, accurate, and understandable estimation of individual patient risks, in the theoretical framework of the average performance of all centers represented in the database. This methodology has the potential to facilitate decision-making and resource optimization in CHS, enabling total quality management and precise benchmarking initiatives.

Prediction of Postoperative Complications for Patients of End Stage Renal Disease

End stage renal disease (ESRD) is the last stage of chronic kidney disease that requires dialysis or a kidney transplant to survive. Many studies reported a higher risk of mortality in ESRD patients compared with patients without ESRD. In this paper, we develop a model to predict postoperative complications, major cardiac event, for patients who underwent any type of surgery. We compare several widely-used machine learning models through experiments with our collected data yellow of size 3220, and achieved F1 score of 0.797 with the random forest model. Based on experimental results, we found that features related to operation (e.g., anesthesia time, operation time, crystal, and colloid) have the biggest impact on model performance, and also found the best combination of features. We believe that this study will allow physicians to provide more appropriate therapy to the ESRD patients by providing information on potential postoperative complications.

Preoperative assessment of patient comorbidities before left colectomy: Comparison between ASA performance status scale and a new computed tomography physical status score

PURPOSE

To compare a newly developed preoperative computed tomography physical status (CT-PS) score with the American Society of Anesthesiology performance status (ASA-PS) scale in the assessment of patient preoperative health status and stratification of perioperative risk before left colectomy.

MATERIALS AND METHODS

Preoperative chest-abdomen-pelvis CT examinations of patients who were scheduled to undergo elective laparoscopic left colonic resection for cancer in two centers were reviewed by two radiologists blinded to clinical data for the presence of several key imaging features in order to assess general, cardiac, pulmonary, abdominal, renal, vascular and musculoskeletal status. CT examinations of patients from center 1 were used to build a CT-PS score to predict ASA-PS≥III. CT-PS score was further validated using an external cohort of patients from center 2.

RESULTS

During a 2-year period, 117 consecutive patients (63 men, 54 women; mean age, 65±13 [SD] years; age range: 53-90 years) who underwent laparoscopic left colectomy for cancer in center 1 (66 patients, building cohort) and center 2 (51 patients, validation cohort) were retrospectively included. Ninety-one percent of patients were ASA-PS 1-2. Overall postoperative morbidity was 23% and severe morbidity 12%. The area under the receiver operating characteristic curve of CT-PS score was 0.968 (95% CI: 0.901-1.000) in the building cohort and 0.828 (95% CI: 0.693-0.963) in the validation cohort. The optimal thresholds yielded 87% (95% CI: 83-91%) sensitivity and 100% (95% CI: 91-100%) specificity in the building cohort and 75% (95% CI: 69-81%) sensitivity and 83% (95% CI: 77-88%) specificity in the validation cohort for the prediction of ASA-PS.

CONCLUSION

Preoperative chest-abdomen-pelvis CT thoroughly and wisely read is highly accurate to differentiate patients with ASA-PS I/II from those with ASA-PS III/IV before left colectomy.

Part 1: Artificial intelligence technology in surgery

Artificial intelligence (AI) is one of the disruptive technologies of the fourth Industrial Revolution that is changing our work practices. This technology is in use in highly diverse industries including health care, defence, insurance and e-commerce. This review focuses on the relevance of AI to surgery. AI will aid surgeons with diagnostic decision-making, patient selection for surgery as well as improve patient pre- and post-operative care and management. Ethical considerations of AI with respect to patient rights and data privacy are highlighted. A further challenge is how best to present to national regulators a pragmatic way to assess AI as 'software as a medical device'. This relates to the ramifications for the adoption of AI technology in clinical practice, and its subsequent public funding support and reimbursement. It is evident that AI technology has important applications in surgery in the 21st century. The establishment of a key work programme in this area will be important if surgeons are to fully utilize AI in surgery.

Patient generated health data and electronic health record integration in oncologic surgery: A call for artificial intelligence and machine learning

In this review, we aim to assess the current state of science in relation to the integration of patient-generated health data (PGHD) and patient-reported outcomes (PROs) into routine clinical care with a focus on surgical oncology populations. We will also describe the critical role of artificial intelligence and machine-learning methodology in the efficient translation of PGHD, PROs, and traditional outcome measures into meaningful patient care models.

Assessment of utilization efficiency using machine learning techniques: A study of heterogeneity in preoperative healthcare utilization among super-utilizers

INTRODUCTION

In the United States, 5% of patients represent up to 55% of all health care costs. This study sought to define healthcare utilization patterns among super-utilizers, as well as assess possible variation in patient outcomes.

METHODS

Medicare super-utilizers undergoing either a total hip or knee arthroplasty were identified and entered into a cluster analysis using annual preoperative charges to identify distinct patterns of utilization.

RESULTS

Among 19,522 super-utilizers who underwent THA or TKA, there was a marked heterogeneity in overall utilization with 5 distinct clusters of utilization patterns. Of note, comorbidity burden was similar among the 5 clusters. Patient outcomes also varied by Cluster type, ranging from 6.9% to 16.5% experiencing complications and 1.0%-3.2% experiencing 90-day mortality.

CONCLUSION

While previous studies have suggested that super-utilizers are a homogenous group of patients, the current study demonstrated a large degree of heterogeneity within super-utilizers. Variations in utilization patterns were associated with postoperative outcomes and subsequent health care costs.