Development of a Risk Stratification Scoring System to Predict General Surgical Complications in Hand Surgery Patients

The Modified Five-Item Frailty Index to Predict Hand and Wrist Surgical Repair Postoperative Outcomes: An ACS-NSQIP Analysis of 11 369 Patients

BACKGROUND

Hand and wrist injuries can cause painful, everyday obstacles for patients. Carefully indexing preoperative patient health conditions may better inform surgical care, leading to improved postoperative outcomes. The purpose of the present study is to evaluate if the Modified Five-Item Frailty Index (mFI-5) can accurately predict postoperative complications for hand and wrist surgical repair.

METHODS

A retrospective review of the American College of Surgeons' National Surgical Quality Improvement Program database was conducted to investigate patients who underwent hand and wrist surgical repair from January 2013 to December 2019. Patient demographics, comorbidities, surgical logistics, and 30-day readmission due to postoperative complications were extracted. Surgical risk proxies including the mFI-5, age, body mass index (BMI), smoking status within 1 year, the Modified Charlson Comorbidity Index (mCCI), comorbidities, and American Society of Anaesthesiologists Physical Status Classification (ASA class) were calculated.

RESULTS

A total of 11 369 patients were included. Thirty-day readmission for total postoperative complications (n = 258) was significantly associated with all surgical risk proxies. However, age, mFI-5 > 2, mCCI > 2, comorbidities > 1, and ASA class 2/3 had the highest statistical significance (P = <.001). Thirty-day readmission rates for surgical site infections (n = 118) had the highest statistical significance with age, BMI, mFI-5 > 2, and ASA class 2/3 (P = <.001). A Clavien-Dindo score > 1 (n = 224) had the highest statistical significance with age, mCCI > 2, comorbidity of 1, and an ASA class 3 (P = <.001).

CONCLUSIONS

The mFI-5 may have value in predicting 30-day readmission due to postoperative complications after surgical repair of hand and wrist injuries.

The Role of Perioperative Nutritional Status and Supplementation in Orthopaedic Surgery: A Review of Postoperative Outcomes

» Identification of malnourished and at-risk patients should be a standardized part of the preoperative evaluation process for every patient.» Malnourishment is defined as a disorder of energy, protein, and nutrients based on the presence of insufficient energy intake, weight loss, muscle atrophy, loss of subcutaneous fat, localized or generalized fluid accumulation, or diminished functional status.» Malnutrition has been associated with worse outcomes postoperatively across a variety of orthopaedic procedures because malnourished patients do not have a robust metabolic reserve available for recovery after surgery.» Screening assessment and basic laboratory studies may indicate patients' nutritional risk; however, laboratory values are often not specific for malnutrition, necessitating the use of prognostic screening tools.» Nutrition consultation and perioperative supplementation with amino acids and micronutrients are 2 readily available interventions that orthopaedic surgeons can select for malnourished patients.

Complications in Hand Surgery During Early Independent Practice: A Single Surgeon's 5-Year Experience

BACKGROUND

The objective of this study was to understand the frequency and types of complications, and the associated postoperative outcomes within the first 5 years of practice after hand and upper extremity surgery fellowship.

METHODS

This was a retrospective observational study of all patients seen and surgically treated by a single surgeon at a single institution from August 2014 to September 2019. This corresponded to the first 5 years of practice after fellowship. Data collected included patient demographics, perioperative data, complication type, and outcome of the complication (better/same/worse than preoperative status). Complications were classified using the Clavien-Dindo system and a unique, self-derived system.

RESULTS

In total, 3301 surgeries were performed during the first 5 years of practice. The overall complication rate was 7.9% (261 complications from 239 patients). The 30-day complication rate was 5.2% (171/3301). Eleven (4.2%) of the 261 complications occurred intraoperatively. The total number of complications significantly declined during the first 5 years of practice as follows: 74, 71, 46, 37, and 33 (P = .010, R2 = .92). Hand and wrist were the most frequent anatomic locations involved and bone pathology was the predominant indication.

CONCLUSION

The overall surgical complication rate for hand and upper extremity surgery was 7.9%, with a 30-day complication rate of 5.2% (171/3301). The rate of complications after fellowship declined over the first 5 years of independent practice. Superficial infections were the most common complication. More than 90% of patients ultimately improved after addressing the complication.

LEVEL OF EVIDENCE

IV.

A Nationally Validated Novel Risk Assessment Calculator for Prediction of Unplanned Reoperations and Readmissions in Hand Surgery

BACKGROUND

Risk predictors are an emerging tool as the need for individualized risk estimation in clinical decision-making persists. Existing risk indices have had limited success in accurately predicting hand surgery risks. This study provides a novel risk calculator for reliably predicting reoperations and readmissions in hand surgery.

METHODS

Hand surgeries from the National Surgical Quality Improvement Program (NSQIP) 2012-2019 database were identified. Independent predictors of 30-day unplanned reoperation and readmission were identified in the modeling sample (2012-2019) and subsequently weighted to generate a Novel Risk Score (NRS). The NRS was validated on a 2020 NSQIP hand surgery cohort and compared to the modified frailty index (mFI-5) and the modified Charlson Comorbidity Index (mCCI) with receiver operating characteristics (ROC) analysis.

RESULTS

Eighty-three thousand four hundred nine hand surgeries were identified for modeling. Reoperations and readmissions rates were 1.1% and 1.3%, respectively. Independent risk factors included male gender, inpatient status, smoking, dialysis dependence, transfusion within 72 h of surgery, wound classification, ASA class, diabetes mellitus, CHF, sepsis or septic shock, emergent case, and operative time longer than 75 min (all P < 0.05). ROC analysis of the 2020 cohort rendered an area under the curve (AUC) of 0.730, which demonstrates the accuracy of this prediction model. The mFI-5 and mCCI rendered AUCs of 0.580 and 0.585, respectively.

CONCLUSION

We present a validated risk prediction tool for unplanned reoperations and readmissions following hand surgery that outperforms the mFI-5 and mCCI that are available online. Future studies should evaluate clinical efficacy.

Patient-Related Risk Factors Associated With Surgical Site Complications After Elective Hand Surgery

BACKGROUND

The purpose of this study was to identify independent risk factors associated with an increased rate of surgical site complications after elective hand surgery.

METHODS

This study is a retrospective review of all patients who underwent elective hand, wrist, forearm, and elbow surgery over a 10-year period at a single institution. Electronic medical records were reviewed, and information regarding patient demographics, past medical and social history, perioperative laboratory values, procedures performed, and surgical complications was collected. Surgical site complications included surgical site infections, seromas or hematomas, and delayed wound healing or wound dehiscence. A univariate analysis was then performed to identify potential risk factors, which were then included in a multivariate regression analysis.

RESULTS

A total of 3261 patients who underwent elective hand surgery and met the above inclusion and exclusion criteria were included in this study. The mean age was 57 years, with 65% female and 35% male patients. The overall surgical complication rate was 2.2%. Univariate analysis of patient factors identified male sex; number of procedures >1; history of drug, alcohol, or smoking use; American Society of Anesthesiologists (ASA) class III and IV; and serum albumin <3.5 mg/dL to be significantly associated with complications. However, multivariate regression analysis identified that only ASA class III and IV (odds ratio = 3.27) was significantly associated with surgical complications.

CONCLUSIONS

Patients classified as ASA class III or IV were identified to be at a significantly increased risk of complications following elective hand surgery. Health factors which triage patients into these 2 groups may represent potentially modifiable factors to mitigate perioperative risk in the elective hand surgery population.

Development of a Risk Stratification Scoring System to Predict General Surgical Complications for Patients Undergoing Foot and Ankle Surgery

Preventing postoperative complications is crucial for patients, surgeons, and health care facilities. We developed a risk stratification scoring system to optimize postoperative outcomes for patients undergoing foot and ankle surgery. A total of 35,580 patients who underwent foot and ankle procedures from 2005 to 2017 were identified as part of the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP). To assess the risk of a postoperative complication, we identified several independent risk factors associated with 30-day postoperative complications, then proceeded to develop a point-based risk scoring system. To validate our scoring system, we used it on a cohort of patients from the database who underwent foot and ankle surgery. Risk factors that correlated with postoperative complications included tobacco abuse, age (≥65 years), diabetes mellitus, hypertension, elevated creatinine level (≥1.3 mg/dL), hypoalbuminemia (<3.5 g/dL), chronic obstructive pulmonary disease (COPD), congestive heart failure (CHF), hyponatremia (<135 mEq/L), and anemia (hematocrit value, men <42%; women <38%). Point scores for each factor were: anemia, +10; hypoalbuminemia, +9; elevated creatinine level, +6; CHF, +4; diabetes mellitus, +3; hyponatremia, +3; COPD, +2; hypertension, +2; age, +1; and tobacco abuse, +1. For the validation cohort, we stratified patients according to risk as low (0-20 points), medium (21-30 points), and high (≥31 points) risk. In terms of having a postoperative complication, compared with low-risk patients, patients who were at medium risk had an odds ratio of 4.7 (95% CI, 2.8-7.9) and those at high risk had an odds ratio of 8.3 (95% CI, 4.8-14.5). [Orthopedics. 2022;45(3):139-144.].

Effect of Smoking on Short-term Postoperative Complications After Elective Upper Extremity Surgery

Background: There is a paucity of literature exploring the impact of smoking on short-term complications, readmissions, and reoperations after elective upper extremity surgery using a large multicenter national database. We hypothesized that smokers will have an increased rate of complications, readmissions, and reoperations compared with a cohort of nonsmokers undergoing elective upper extremity surgery. Methods: Patient data were collected from the American College of Surgeons National Surgical Quality Improvement Program database between the years 2012 and 2017. Patients were included if they underwent elective surgery of the upper extremity using 338 predetermined Current Procedural Terminology codes. The data collected were divided into patient demographics, comorbidities, perioperative variables, and 30-day complications. Current smoking status was defined as smoking within 1 year prior to surgery. The incidence of surgical complications, reoperations, and readmissions was compared between the 2 cohorts using multivariable regression analysis. Results: Of the 107 943 patients undergoing elective surgeries of the upper extremity, 73 806 met the inclusion criteria. Of these, 57 986 (78.6%) were nonsmokers in the year prior to surgery, and 15 820 (21.4%) were current smokers. Between these groups, current smokers were younger (P < .001), more often men (P < .001), had lower body mass index (P < .001), and more often underwent procedures that involved bone manipulation (P < .001). Multivariate regression analysis defined current smoking as significantly associated with overall surgical site complications, superficial surgical site infections, deep surgical site infections, reoperation, and readmission. Conclusion: Current smoking was significantly associated with an increase in all surgical site complications, readmissions, and reoperations after elective upper extremity surgery. Surgeons should consider smoking a modifiable risk factor for postoperative complications and appropriately counsel patients on outcomes and complications given the elective nature of upper extremity surgery.

Unplanned Return to the Operating Room in Upper-Extremity Surgery: Incidence and Reason for Return

PURPOSE

Complications after upper-extremity surgery are generally infrequent. The purpose of this study was to assess the rate of early unplanned return to the operating room (URTO) within 3 months after surgery) in upper-extremity surgical procedures. Our hypotheses were that the rate of URTO in upper-extremity surgery would be low and that surgically treated fractures would be at greatest risk for complications.

METHODS

We performed a retrospective review of all upper-extremity surgical procedures performed at a large academic practice of fellowship-trained hand surgeons over a 5-year period. A chart review was conducted of all patients who underwent a second surgery within 3 months of the initial surgery. The surgical billing database was queried to determine the incidence of URTO per Current Procedural Terminology code.

RESULTS

There were 422 Current Procedural Terminology codes with URTO out of a total of 62,608, for an incidence of 0.6%. The most frequently performed procedures were carpal tunnel release (10,674; 0.1% URTO), trigger finger release (4,549; 0.5% URTO), and open reduction internal fixation (ORIF) for distal radius fracture (2,728; 1.2% URTO). Procedures with the highest incidences of URTO were open reduction and internal fixation of the ulna (4.9%) and excision of the olecranon bursa (4.1%). Traumatic injuries were more commonly associated with URTO compared with elective procedures. Bony trauma and soft tissue trauma had URTO incidences of 1.4% and 1.1%, respectively, whereas bony elective and soft tissue elective cases were 0.6% and 0.4%, respectively.

CONCLUSIONS

The 90-day URTO rate after upper-extremity surgery was low but higher than previously reported 30-day reoperation rates. Elbow procedures were most likely to result in URTO, as were procedures relating to bony and soft tissue trauma. Based on these results, we are able to counsel patients that the most common procedures we perform have low URTO rates, but surgically treated fractures are at greatest risk.

TYPE OF STUDY/LEVEL OF EVIDENCE

Prognostic II.

Using Predictive Modeling and Supervised Machine Learning to Identify Patients at Risk for Venous Thromboembolism Following Posterior Lumbar Fusion

STUDY DESIGN

Retrospective review.

OBJECTIVE

To use predictive modeling and machine learning to identify patients at risk for venous thromboembolism (VTE) following posterior lumbar fusion (PLF) for degenerative spinal pathology.

METHODS

Patients undergoing single-level PLF in the inpatient setting were identified in the National Surgical Quality Improvement Program database. Our outcome measure of VTE included all patients who experienced a pulmonary embolism and/or deep venous thrombosis within 30-days of surgery. Two different methodologies were used to identify VTE risk: 1) a novel predictive model derived from multivariable logistic regression of significant risk factors, and 2) a tree-based extreme gradient boosting (XGBoost) algorithm using preoperative variables. The methods were compared against legacy risk-stratification measures: ASA and Charlson Comorbidity Index (CCI) using area-under-the-curve (AUC) statistic.

RESULTS

13, 500 patients who underwent single-level PLF met the study criteria. Of these, 0.95% had a VTE within 30-days of surgery. The 5 clinical variables found to be significant in the multivariable predictive model were: age > 65, obesity grade II or above, coronary artery disease, functional status, and prolonged operative time. The predictive model exhibited an AUC of 0.716, which was significantly higher than the AUCs of ASA and CCI (all, P < 0.001), and comparable to that of the XGBoost algorithm (P > 0.05).

CONCLUSION

Predictive analytics and machine learning can be leveraged to aid in identification of patients at risk of VTE following PLF. Surgeons and perioperative teams may find these tools useful to augment clinical decision making risk stratification tool.

Using Predictive Modeling and Machine Learning to Identify Patients Appropriate for Outpatient Anterior Cervical Fusion and Discectomy

STUDY DESIGN

Retrospective, case-control.

OBJECTIVE

The aim of this study was to use predictive modeling and machine learning to develop novel tools for identifying patients who may be appropriate for single-level outpatient anterior cervical fusion and discectomy (ACDF), and to compare these to legacy metrics.

SUMMARY OF BACKGROUND DATA

ACDF performed in an ambulatory surgical setting has started to gain popularity in recent years. Currently there are no standardized risk-stratification tools for determining which patients may be safe candidates for outpatient ACDF.

METHODS

Adult patients with American Society of Anesthesiologists (ASA) Class 1, 2, or 3 undergoing one-level ACDF in inpatient or outpatient settings were identified in the National Surgical Quality Improvement Program database. Patients were deemed as "unsafe" for outpatient surgery if they suffered any complication within a week of the index operation. Two different methodologies were used to identify unsafe candidates: a novel predictive model derived from multivariable logistic regression of significant risk factors, and an artificial neural network (ANN) using preoperative variables. Both methods were trained using randomly split 70% of the dataset and validated on the remaining 30%. The methods were compared against legacy risk-stratification measures: ASA and Charlson Comorbidity Index (CCI) using area under the curve (AUC) statistic.

RESULTS

A total of 12,492 patients who underwent single-level ACDF met the study criteria. Of these, 9.79% (1223) were deemed unsafe for outpatient ACDF given development of a complication within 1 week of the index operation. The five clinical variables that were found to be significant in the multivariable predictive model were: advanced age, low hemoglobin, high international normalized ratio, low albumin, and poor functional status. The predictive model had an AUC of 0.757, which was significantly higher than the AUC of both ASA (0.66; P < 0.001) and CCI (0.60; P < 0.001). The ANN exhibited an AUC of 0.740, which was significantly higher than the AUCs of ASA and CCI (all, P < 0.05), and comparable to that of the predictive model (P > 0.05).

CONCLUSION

Predictive analytics and machine learning can be leveraged to aid in identification of patients who may be safe candidates for single-level outpatient ACDF. Surgeons and perioperative teams may find these tools useful to augment clinical decision-making.Level of Evidence: 3.

Complications After Immediate 2-Stage Tissue Expander/Implant Breast Reconstruction: A Deeper Look at the Second Stage

BACKGROUND

Complications after 2-stage tissue expander/implant breast reconstruction have been studied as outcomes of a single procedure. We separately evaluated complications after the second stage and assessed factors associated with the outcomes of the second stage of breast reconstruction.

METHODS

Patients who underwent immediate 2-stage breast reconstruction between February 2010 and April 2017 were retrospectively reviewed. Patient demographics, surgical factors of the first stage of breast reconstruction, and complications and number of revision surgeries after the second stage were recorded. Factors associated with postoperative complications were analyzed, and a risk-scoring system was devised.

RESULTS

We analyzed 619 patients who underwent 653 immediate 2-stage breast reconstructions. Multivariate analysis showed that complications were associated independently with smoking history, radiotherapy, and a final inflation volume of 450 mL or greater. Each factor contributed 1 point in the creation of a risk-scoring system. The overall complication rate was increased as the risk score increased (1.2%, 4.7%, and 16.0% for 0, 1, and 2 risk scores, respectively, P < 0.001). Revision operation rate was also significantly different across the 3 groups (0.2%, 1.6%, and 12.0% for 0, 1, and 2 risk scores, respectively, P < 0.001). The area under the receiver operating characteristic curve was 0.732 and 0.731 for the logistic regression model and risk-scoring system, respectively (P = 0.975).

CONCLUSIONS

In the second stage of immediate 2-stage tissue expander/implant breast reconstruction, the rate of complication and revision surgery can be predicted by a novel risk-scoring system. Greater attention and preventive measures for complications are needed for high-risk patients.

Locoregional Options for Acute Volar Pulp Fingertip Defects

The volar fingertip is a unique anatomic structure, delicate yet durable, that allows us to navigate the world, acquire information from our surroundings, and express ourselves. Injuries to the volar finger can cause permanent dysfunction and should be taken seriously. In treating injuries of the volar fingertip, the surgeon has an opportunity to choose from a host of reconstructive options and provide the patient with an outcome suitable to their needs. In doing so, the hand surgeon is well-positioned to aim for the reconstructive ideal of restoring both structure and function.

Development of a risk-stratification scoring system for predicting lymphovascular invasion in breast cancer

Background

Lymphovascular invasion (LVI) is a vital risk factor for prognosis across cancers. We aimed to develop a scoring system for stratifying LVI risk in patients with breast cancer.

Methods

A total of 301 consecutive patients (mean age, 49.8 ± 11.0 years; range, 29–86 years) with breast cancer confirmed by pathological reports were retrospectively evaluated at the authors’ institution between June 2015 and October 2018. All patients underwent contrast-enhanced Magnetic Resonance Imaging (MRI) examinations before surgery. MRI findings and histopathologic characteristics of tumors were collected for analysis. Breast LVI was confirmed by postoperative pathology. We used a stepwise logistic regression to select variables and two cut-points were determined to create a three-tier risk-stratification scoring system. The patients were classified as having low, moderate and high probability of LVI. The area under the receiver operating characteristic curve (AUC) was used to evaluate the discrimination ability of the scoring system.

Results

Tumor margins, lobulation sign, diffusion-weighted imaging appearance, MRI-reported axillary lymph node metastasis, time to signal intensity curve pattern, and HER-2 were selected as predictors for LVI in the point-based scoring system. Patients were considered at low risk if the score was < 3.5, moderate risk if the score was 3.5 to 6.0, and high risk if the score was ≥6.0. LVI risk was segmented from 0 to 100.0% and was positively associated with an increase in risk scores. The AUC of the scoring system was 0.824 (95% confidence interval [CI]: 0.776--0.872).

Conclusion

This study shows that a simple and reliable score-based risk-stratification system can be practically used in stratifying the risk of LVI in breast cancer.