Synergistic Effects of Perioperative Complications on 30-Day Mortality Following Hepatopancreatic Surgery

Distal Pancreatectomy: Extent of Resection Determines Surgical Risk Categories

BACKGROUND

Recently, subclassification of pancreatoduodenectomy in 4 differing types has been reported, because additional major vascular and multivisceral resections have been shown to be associated with an increased risk of postoperative morbidity and mortality.

OBJECTIVE

To classify distal pancreatectomy (DP) based on the extent of resection and technical difficulty and to evaluate postoperative outcomes with regards to this classification system.

METHODS

All consecutive patients who had undergone DP between 2001 and 2020 in a high-volume pancreatic surgery center were included in this study. DPs were subclassified into 4 distinct categories reflecting the extent of resection and technical difficulty, including standard DP (type 1), DP with venous (type 2), multivisceral (type 3), or arterial resection (type 4). Patient characteristics, perioperative data, and postoperative outcomes were analyzed and compared among the 4 groups.

RESULTS

A total of 2135 patients underwent DP. Standard DP was the most frequently performed procedure (64.8%). The overall 90-day mortality rate was 1.6%. Morbidity rates were higher in patients with additional vascular or multivisceral resections, and 90-day mortality gradually increased with the extent of resection from standard DP to DP with arterial resection (type 1: 0.7%; type 2: 1.3%; type 3: 3%; type 4: 8.7%; P <0.0001). Multivariable analysis confirmed the type of DP as an independent risk factor for 90-day mortality.

CONCLUSIONS

Postoperative outcomes after DP depend on the extent of resection and correlate with the type of DP. The implementation of the 4-type classification system allows standardized reporting of surgical outcomes after DP improving comparability of future studies.

Improving hospital quality risk-adjustment models using interactions identified by hierarchical group lasso regularisation

BACKGROUND

Risk-adjustment (RA) models are used to account for severity of illness in comparing patient outcomes across hospitals. Researchers specify covariates as main effects, but they often ignore interactions or use stratification to account for effect modification, despite limitations due to rare events and sparse data. Three Agency for Healthcare Research and Quality (AHRQ) hospital-level Quality Indicators currently use stratified models, but their variable performance and limited interpretability motivated the design of better models.

METHODS

We analysed patient discharge de-identified data from 14 State Inpatient Databases, AHRQ Healthcare Cost and Utilization Project, California Department of Health Care Access and Information, and New York State Department of Health. We used hierarchical group lasso regularisation (HGLR) to identify first-order interactions in several AHRQ inpatient quality indicators (IQI) - IQI 09 (Pancreatic Resection Mortality Rate), IQI 11 (Abdominal Aortic Aneurysm Repair Mortality Rate), and Patient Safety Indicator 14 (Postoperative Wound Dehiscence Rate). These models were compared with stratum-specific and composite main effects models with covariates selected by least absolute shrinkage and selection operator (LASSO).

RESULTS

HGLR identified clinically meaningful interactions for all models. Synergistic IQI 11 interactions, such as between hypertension and respiratory failure, suggest patients who merit special attention in perioperative care. Antagonistic IQI 11 interactions, such as between shock and chronic comorbidities, illustrate that naïve main effects models overestimate risk in key subpopulations. Interactions for PSI 14 suggest key subpopulations for whom the risk of wound dehiscence is similar between open and laparoscopic approaches, whereas laparoscopic approach is safer for other groups. Model performance was similar or superior for composite models with HGLR-selected features, compared to those with LASSO-selected features.

CONCLUSIONS

In this application to high-profile, high-stakes risk-adjustment models, HGLR selected interactions that maintained or improved model performance in populations with heterogeneous risk, while identifying clinically important interactions. The HGLR package is scalable to handle a large number of covariates and their interactions and is customisable to use multiple CPU cores to reduce analysis time. The HGLR method will allow scholars to avoid creating stratified models on sparse data, improve model calibration, and reduce bias. Future work involves testing using other combinations of risk factors, such as vital signs and laboratory values. Our study focuses on a real-world problem of considerable importance to hospitals and policy-makers who must use RA models for statutorily mandated public reporting and payment programmes.

Construction and evaluation of networks among multiple postoperative complications

BACKGROUND AND OBJECTIVE

Postoperative complications confer an increased risk of reoperation, prolonged length of hospital stay, and increased mortality. Many studies have attempted to identify the complex associations among complications to preemptively interrupt their progression, but few studies have looked at complications as a whole to reveal and quantify their possible trajectories of progression. The main objective of this study was to construct and quantify the association network among multiple postoperative complications from a comprehensive perspective to elucidate the possible evolution trajectories.

METHODS

In this study, a Bayesian network model was proposed to analyze the associations among 15 complications. Prior evidence and score-based hill-climbing algorithms were used to build the structure. The severity of complications was graded according to their connection to death, with the association between them quantified using conditional probabilities. The data of surgical inpatients used in this study were collected from four regionally representative academic/teaching hospitals in a prospective cohort study in China.

RESULTS

In the network obtained, 15 nodes represented complications or death, and 35 arcs with arrows represented the directly dependent relationship between them. With three grades classified on that basis, the correlation coefficients of complications within grades increased with increased grade, ranging from -0.11 to -0.06, 0.16, and 0.21 to 0.4 in grade 1 to grade 3, respectively. Moreover, the probability of each complication in the network increased with the occurrence of any other complication, even mild complications. Most seriously, once cardiac arrest requiring cardiopulmonary resuscitation occurs, the probability of death will be up to 88.1%.

CONCLUSIONS

The present evolving network can facilitate the identification of strong associations among specific complications and provides a basis for the development of targeted measures to prevent further deterioration in high-risk patients.

Real-time mortality risk calculator following pancreatoduodenectomy: quantifying the impact of perioperative events

BACKGROUND

Estimation of mortality risk traditionally has only included preoperative factors. We sought to develop "real-time" mortality risk-calculator for patients who undergo pancreatoduodenectomy (PD) based on preoperative factors, as well as events that occurred during the course of patient's surgery and hospitalization.

METHODS

Patients who underwent PD from 2014 to 2018 were identified in the ACS-NSQIP dataset. Training and validation cohorts were created. Pre-, intra-, and post-operative models to predict 30-day mortality were developed based on perioperative variables selected by stepwise cox regression analyses; model performance was assessed using AUC.

RESULTS

Among 17,683 patients who underwent PD, 1.6% died within 30-days. Patient factors and events associated with 30-day mortality were incorporated into a risk calculator (https://ktsahara.shinyapps.io/Real-timePD/). The accuracy of the risk-calculator increased relative to hospital time-course in both the training (AUC, pre-:0.696, intra-:0.724, post-operative:0.871) and validation (AUC, pre-:0.681, intra-:0.702, post-operative:0.850) cohorts. One in 3 patients had a concordant calculated risk of mortality using pre-versus postoperative variables to inform the risk model (kappa = 0.474).

CONCLUSION

Risk of mortality fluctuated over the hospital course following PD and preoperative risk assessment was often discordant with risk assessed at other periods. The proposed "real-time" calculator may help better stratify patients with increased risk of 30-day mortality.

Timing and Severity of Postoperative Complications and Associated 30-Day Mortality Following Hepatic Resection: a National Surgical Quality Improvement Project Study

BACKGROUND

The effect of varying severity and timing of complications after hepatic resection on 30-day mortality has not been thoroughly examined.

METHODS

National Surgical Quality Improvement Program Patient User Files (NSQIP-PUF) were used to identify patients who underwent elective hepatic resection between 2014 and 2019. The impact of number, timing, and severity of complications on 30-day mortality was examined.

RESULTS

Among 25,084 patients who underwent hepatic resection, 7436 (29.9%) patients developed at least one NSQIP complication, while 2688 (10.7%) had multiple (≥2) complications. Overall, 30-day mortality was 1.7% (n=424), among whom 81.4% (n=345) patients had ≥2 complications. The 30-day mortality was highest among patients with three consecutive severe complications (47.8%), as well as patients with one non-severe and two subsequent severe complications (47.6%). The adjusted probability of 30-day mortality was 35.5% (95%CI: 29.5-41.4%) when multiple severe complications occurred within the first postoperative week and 16.2% (95%CI: 7.2-25.1%) when the second severe complication occurred at least one week apart. The adjusted risk of 30-day mortality after even two non-severe complications was as high as 5.3% (95%CI: 3.7-6.9%) when the second complication occurred within a week postoperatively.

CONCLUSION

Approximately 1 in 10 patients developed multiple complications following hepatectomy. Timing and severity of complications were independently associated with 30-day mortality.

Novel Personalized Score Predicts Risk for Postoperative Biliary Leak in Liver Surgery-a Retrospective Database Analysis

BACKGROUND

The number of liver resections is constantly rising over the last decades. Despite the reduction of overall mortality and morbidity in liver surgery, biliary leakage is still a relevant postoperative complication that can lead to a fatal postoperative course. Aim of this analysis is the identification of specific risk factors for postoperative biliary complications after liver resections and the development of a predictive biliary leakage risk score.

METHODS

A single-center, retrospective analysis of 844 liver resections performed in the Department of Visceral, Thoracic and Vascular Surgery, Technische Universität Dresden, between 1/2013 and 12/2019 is conducted to identify risk factors for postoperative biliary leakage and a risk score for biliary leakage after hepatectomy is established based on multivariate regression. The score has been validated by an independent validation cohort consisting of 142 patients.

RESULTS

Overall morbidity is 43.1% with 36% surgical complications and an overall mortality of 4.3%. Biliary leakage occurred in 15.8% of patients. A predictive score for postoperative biliary leakage based on age, major resection, pretreatment with FOLFOX/cetuximab and operating time is created. Patients are stratified to low (< 15%) and high (> 15%) risk with a sensitivity of 67.4% and a specificity of 70.7% in development cohort and a specificity of 68.2% and sensitivity of 75.8% in validation cohort.

CONCLUSIONS

The presented score is robust and has been validated in an independent patient cohort. Depending on the calculated risk, prevention or early treatment can be initiated to avoid bile leakage and to improve postoperative course.

Emergency Department Utilization Following Hepatopancreatic Surgery Among Medicare Beneficiaries

BACKGROUND

Care delivered in hospital-based emergency departments (ED) is a target for cost savings. ED utilization following hepatopancreatic surgery remains poorly defined. We sought to define the rate of ED utilization following liver and pancreatic resection, as well as to identify factors associated with ED visits post-discharge.

METHODS

The Medicare 100% Standard Analytic Files were used to identify Medicare beneficiaries who underwent hepatectomy or pancreatectomy between 2013 and 2017. Claims associated with ED services were identified using the relevant Revenue Center Codes. Patient characteristics and postoperative outcomes associated with ED care within 30 days of discharge were investigated.

RESULTS

Among 37,707 patients who underwent hepatopancreatic surgery, 10,323 (27.4%) had at least one ED visit within 30 days of discharge. Patients presenting to the ED were more likely to be male (OR 1.13, 95%CI 1.07-1.18). Patients undergoing a pancreatectomy (OR 1.39, 95%CI 1.32-1.47), as well as patients who had a perioperative complication (OR 1.16, 95%CI 1.10-1.23) and patients not discharged home (OR 1.41, 95%CI 1.33-1.49), were more likely to require ED care. In contrast, patients undergoing resection for cancer or surgery for an elective basis were less likely to present to the ED postoperatively (OR 0.92, 95%CI 0.87-0.97 and OR 0.22, 95%CI 0.20-0.23, respectively). Patients often had multiple ED visits within 30 days of discharge as 37.2% of patients presented to the ED with at least 2 visits. Visits were also most common in the immediate postoperative period, with 30.9% of ED visits taking place in the first 2 days from discharge. Among patients requiring postoperative ED care, 53.9% were readmitted within 30 days.

CONCLUSION

More than 1 in 4 patients undergoing hepatopancreatic surgery presented to the ED within 30 days of discharge, with most patients returning to the ED within the first week of discharge. A subset of patients had multiple ED visits. Future efforts should target patients most likely to be high ED utilizers to avoid the need for early post-discharge ED use.

Social vulnerability and fragmentation of postoperative surgical care among patients undergoing hepatopancreatic surgery

BACKGROUND

Regionalization of hepatopancreatic surgery to high-volume hospitals has been associated with fragmentation of postoperative care and, in turn, inferior outcomes after surgery. The objective of this study was to examine the association of social vulnerability with the likelihood of experiencing fragmentation of postoperative care (FPC) after hepatopancreatic surgery.

METHODS

Patients who underwent hepatopancreatic surgery and had at least 1 readmission within 90 days were identified using Medicare 100% Standard Analytical Files between 2013 and 2017. Fragmentation of postoperative care was defined as readmission at a hospital other than the index institution where the initial surgery was performed. The association of social vulnerability index and its components with fragmentation of postoperative care was examined.

RESULTS

Among 11,142 patients, 8,053 (72.3%) underwent pancreatectomy, and 3,089 (27.7%) underwent hepatectomy. The overall incidence of fragmentation of postoperative care was 32.9% (n = 3,667). Patients who experienced fragmentation of postoperative care were older (73 years [interquartile range: 69-77]_FPC vs 72 years [interquartile range: 68-77]_non-FPC) and had a higher Charlson comorbidity score (4 [interquartile range: 2-8]_FPC vs 3 [interquartile range: 2-8]_non-FPC) (both P < .001). Median overall social vulnerability index was higher among patients who experienced fragmentation of postoperative care (52.5 [interquartile range: 29.3-70.4]_FPC vs 51.3 [interquartile range: 27.9-69.4]_non-FPC, P = .02). On multivariable analysis, the odds of experiencing fragmentation of postoperative care was higher with increasing overall social vulnerability index (odds ratio: 1.14; 95% confidence interval 1.01-1.30). Additionally, the odds of experiencing fragmentation of postoperative care were higher among patients with high vulnerability owing to their socioeconomic status (odds ratio: 1.28; 95% confidence interval 1.12-1.45) or their household composition and disability (odds ratio: 1.35; 95% confidence interval 1.19-1.54), whereas high vulnerability owing to minority status and language was inversely associated with fragmentation of postoperative care (odds ratio: 0.73; 95% confidence interval 0.64-0.84).

CONCLUSION

Social vulnerability was strongly associated with the odds of experiencing fragmented postoperative care after hepatopancreatic surgery.

Complications After Complex Gastrointestinal Cancer Surgery: Benefits and Costs Associated with Inter-hospital Transfer Among Medicare Beneficiaries

BACKGROUND

Inter-hospital transfer (IHT) may help reduce failure-to-rescue (FTR) by transferring patients to centers with a higher level of expertise than the index hospital. We sought to identify factors associated with an IHT and examine if IHT was associated with improved outcomes after complex gastrointestinal cancer surgery.

METHODS

Medicare Inpatient Standard Analytic Files were utilized to identify patients with >1 postoperative complication following resection for esophageal, pancreatic, liver, or colorectal cancer between 2013 and 2017. Multivariable logistic regression was used to examine the association of different factors with the chance of IHT, as well as the impact of IHT on failure-to-rescue (FTR) and expenditures.

RESULTS

Among 39,973 patients with >1 postoperative complications, 3090 (7.7%) patients were transferred to a secondary hospital. The median LOS at the index hospital prior to IHT was 10 days (IQR, 6-17 days). Patients who underwent IHT more often had experienced multiple complications at the index hospital compared with non-IHT patients (57.7% vs. 38.9%) (p<0.001). Transferred patients more commonly had undergone surgery at a low-volume index hospital (n=218, 60.2%) compared with non-IHT (n=10,351, 25.9%) patients (p<0.001). On multivariate analysis, hospital volume remained strongly associated with transfer to an acute care hospital (ACH) (OR 5.53; 95% CI 3.91-7.84; p<0.001), as did multiple complications (OR 2.01, 95% CI 1.56-2.57). The incidence of FTR was much higher among IHT-ACH patients (20.2%) versus non-IHT patients (11.5%) (OR 1.51, 95% CI 1.11-2.05) (p<0.001). Medicare expenditures were higher among patients who had IHT-ACH ($72.1k USD; IQR, $48.1k-$116.7k) versus non-IHT ($38.5k USD; IQR, $28.1k-$59.2k USD) (p<0.001).

CONCLUSION

Approximately 1 in 13 patients had an IHT after complex gastrointestinal cancer surgery. IHT was associated with high rates of FTR, which was more pronounced among patients who underwent surgery at an index low-volume hospital. IHT was associated with higher overall CMS expenditures.

Preoperative Medical Referral Prior to Hepatopancreatic Surgery-Is It Worth it?

INTRODUCTION

Many patients who present for complex surgery have underlying medical comorbidities. While surgeons often refer these patients to medical appointments for preoperative "optimization" or "clearance," the actual impact of these visits remains poorly examined. The objective of the current study was to define the potential benefit of preoperative medical appointments on outcomes and costs associated with hepatopancreatic (HP) surgery.

METHODS

Patients with modifiable comorbidities undergoing HP surgery were identified in the Medicare claims data. The association of preoperative non-surgical visit and postoperative outcomes and expenditures was assessed using inverse propensity treatment weighting analysis and multivariable logistic regression.

RESULTS

Among the 5574 Medicare beneficiaries who underwent a hepatopancreatic surgery, one in seven patients (n = 830, 14.9%) was "optimized" preoperatively. On multivariable logistic regression analysis, age (OR 1.02; 95% CI 1.01-1.03; p = 0.006) and higher comorbidity burden (OR 1.03; 95% CI 1.01-1.05; p = 0.007) were associated with modest increased odds of being referred in the preoperative period for a non-surgical evaluation; the factor most associated with preoperative non-surgical visit was male patient sex (OR 1.33; 95% CI 1.14-1.56; p < 0.001). After adjustment for competing risk factors and random site effect, patients with an "optimization" visit had 28% lower odds (OR 0.72; 95% CI 0.59-0.86; p < 0.001) of experiencing an operative complication. Additionally, patients who had a non-surgical visit had 13% higher median total expenditures compared with individuals who did not undergo an "optimization" visit (p < 0.05).

CONCLUSION

In conclusion, roughly one in seven Medicare beneficiaries who underwent HP surgery may have been risk stratified by a non-surgical provider prior to surgery. Preoperative evaluation was associated with modestly lower odds of complications following HP surgery and higher Medicare expenditures. Further research is needed to determine its routine utility as a means to decrease the morbidity surrounding HP surgery.

Machine learning predicts unpredicted deaths with high accuracy following hepatopancreatic surgery

Background

Machine learning to predict morbidity and mortality-especially in a population traditionally considered low risk-has not been previously examined. We sought to characterize the incidence of death among patients with a low estimated morbidity and mortality risk based on the National Surgical Quality Improvement Program (NSQIP) estimated probability (EP), as well as develop a machine learning model to identify individuals at risk for "unpredicted death" (UD) among patients undergoing hepatopancreatic (HP) procedures.

Methods

The NSQIP database was used to identify patients who underwent elective HP surgery between 2012-2017. The risk of morbidity and mortality was stratified into three tiers (low, intermediate, or high estimated) using a k-means clustering method with bin sorting. A machine learning classification tree and multivariable regression analyses were used to predict 30-day mortality with a 10-fold cross validation. C statistics were used to compare model performance.

Results

Among 63,507 patients who underwent an HP procedure, median patient age was 63 (IQR: 54-71) years. Patients underwent either pancreatectomy (n=38,209, 60.2%) or hepatic resection (n=25,298, 39.8%). Patients were stratified into three tiers of predicted morbidity and mortality risk based on the NSQIP EP: low (n=36,923, 58.1%), intermediate (n=23,609, 37.2%) and high risk (n=2,975, 4.7%). Among 36,923 patients with low estimated risk of morbidity and mortality, 237 patients (0.6%) experienced a UD. According to the classification tree analysis, age was the most important factor to predict UD (importance 16.9) followed by preoperative albumin level (importance: 10.8), disseminated cancer (importance: 6.5), preoperative platelet count (importance: 6.5), and sex (importance 5.9). Among patients deemed to be low risk, the c-statistic for the machine learning derived prediction model was 0.807 compared with an AUC of only 0.662 for the NSQIP EP.

Conclusions

A prognostic model derived using machine learning methodology performed better than the NSQIP EP in predicting 30-day UD among low risk patients undergoing HP surgery.

Hepatopancreatic Surgery in the Rural United States: Variation in Outcomes at Critical Access Hospitals

BACKGROUND

Sixty million Americans live in rural America, with roughly 17.5% of the rural population being 65 y or older. Outcomes and costs of Medicare beneficiaries undergoing hepatopancreatic surgery at critical access hospitals (CAHs) are not known.

MATERIALS AND METHODS

Medicare files were used to identify patients who underwent hepatopancreatic resection. Outcomes were compared (CAHs versus non-CAHs).

RESULTS

Patients undergoing hepatopancreatic surgery at non-CAHs versus CAHs had a similar comorbidity score (4 versus 5, P = 0.53). After adjusting for patient-level factors and procedure-specific volume, there was no difference in complication rate (adjusted odds ratio (aOR) 0.80, 95% confidence interval (CI) 0.52-1.24). The median cost of hospitalization was roughly $4000 less at CAHs than that at non-CAHs (P < 0.001). However, compared with patients undergoing surgery at non-CAHs, beneficiaries operated at CAHs had more than two times the odds of dying within 30 (aOR 2.45, 95% CI 1.42-4.2) and 90 d (aOR 2.28, 95% CI 1.4-3.71).

CONCLUSIONS

Only a small subset of Medicare beneficiaries underwent hepatic or pancreatic resection at a CAH. Despite similar complication rate, Medicare beneficiaries undergoing surgery at a CAH had more than two times the odds of dying within 30 and 90 d after surgery.

Development and validation of a real-time mortality risk calculator before, during and after hepatectomy: an analysis of the ACS NSQIP database

BACKGROUND

Although most conventional risk prediction models have been based on preoperative information, intra- and post-operative events may be more relevant to mortality after surgery. We sought to develop a mortality risk calculator based on real time characteristics associated with hepatectomy.

METHODS

Patients who underwent hepatectomy between 2014 and 2017 were identified in the ACS-NSQIP dataset. Three prediction models (pre-, intra-, post-operative) were developed and validated using perioperative data.

RESULTS

Among 14,720 patients, 197 (1.3%) experienced 30-day mortality. The predictive ability of the real-time mortality risk calculator was very good based on only preoperative factors (AUC; training cohort: 0.813, validation cohort: 0.731). Incorporating intra-operative variables into the model increased the AUC (training: 0.838, validation: 0.777), while the post-operative model achieved an AUC of 0.922 in the training and 0.885 in the validation cohorts, respectively. While patients with low preoperative risk had only very small fluctuations in the estimated 30-day mortality risk during the intraoperative (Δ0.4%) and postoperative (Δ0.6%) phases, patients who were already deemed high risk preoperatively had additional increased mortality risk based on factors that occurred in the intraoperative (Δ5.4%) and postoperative (Δ9.3%) periods.

CONCLUSION

A real-time mortality risk calculator may better help clinicians identify patients at risk of death at the different stages of the surgical episode.

Use of Machine Learning for Prediction of Patient Risk of Postoperative Complications After Liver, Pancreatic, and Colorectal Surgery

BACKGROUND

Surgical resection is the only potentially curative treatment for patients with colorectal, liver, and pancreatic cancers. Although these procedures are performed with low mortality, rates of complications remain relatively high following hepatopancreatic and colorectal surgery.

METHODS

The American College of Surgeons (ACS) National Surgical Quality Improvement Program was utilized to identify patients undergoing liver, pancreatic and colorectal surgery from 2014 to 2016. Decision tree models were utilized to predict the occurrence of any complication, as well as specific complications. To assess the variability of the performance of the classification trees, bootstrapping was performed on 50% of the sample.

RESULTS

Algorithms were derived from a total of 15,657 patients who met inclusion criteria. The algorithm had a good predictive ability for the occurrence of any complication, with a C-statistic of 0.74, outperforming the ASA (C-statistic 0.58) and ACS-Surgical Risk Calculator (C-statistic 0.71). The algorithm was able to predict with high accuracy thirteen out of the seventeen complications analyzed. The best performance was in the prediction of stroke (C-statistic 0.98), followed by wound dehiscence, cardiac arrest, and progressive renal failure (all C-statistic 0.96). The algorithm had a good predictive ability for superficial SSI (C-statistic 0.76), organ space SSI (C-statistic 0.76), sepsis (C-statistic 0.79), and bleeding requiring transfusion (C-statistic 0.79).

CONCLUSION

Machine learning was used to develop an algorithm that accurately predicted patient risk of developing complications following liver, pancreatic, or colorectal surgery. The algorithm had very good predictive ability to predict specific complications and demonstrated superiority over other established methods.

The Effect of Prone Positioning as Postoperative Physiotherapy to Prevent Atelectasis After Hepatectomy

BACKGROUND

The incidences of postoperative pulmonary complications (PPCs) such as atelectasis, pneumonia and pleural effusion after major surgery range from <1 to 23%. Atelectasis after abdominal surgery increases the duration of hospitalization and short-term mortality rate, but there are few reports about atelectasis after hepatectomy. The effectiveness of prone position drainage as physiotherapy has been reported, but it remains unclarified whether prone positioning prevents atelectasis after hepatectomy. This study aimed to evaluate the effect of the prone position on the incidence of atelectasis after hepatectomy.

METHODS

We retrospectively analyzed the incidence of PPCs after hepatectomy at a single center. Patients were divided into two cohorts. The earlier cohort (n = 165) underwent hepatectomy between January 2016 and March 2018 and was analyzed to identify the risk factors for atelectasis and short-term outcomes; the later cohort (n = 51) underwent hepatectomy between April 2018 and March 2019 and underwent prone position drainage in addition to regular mobilization postoperatively. The incidences of PPCs were compared between the two cohorts.

RESULTS

Independent risk factors for atelectasis were anesthetic duration (P = 0.016), operation time (P = 0.046) and open surgery (P = 0.011). The incidence of atelectasis was significantly lower in the later cohort (9.8%) than the earlier cohort (34.5%, P < 0.001). Moreover, the later cohort had a significantly shorter duration of oxygen support (P < 0.001) and postoperative hospitalization (P < 0.001). After propensity score-matching, the incidence of atelectasis remained significantly lower in the later cohort (P = 0.027).

CONCLUSION

Prone position drainage may decrease the incidence of atelectasis after hepatectomy and improve the short-term outcomes.

Complications after liver surgery: a benchmark analysis

BACKGROUND

The best achievable short-term outcomes after liver surgery have not been identified. Several factors may influence the post-operative course of patients undergoing hepatectomy increasing the risk of post-operative complications. We sought to identify risk-adjusted benchmark values [BMV] for liver surgery.

METHODS

The National Surgery Quality Improvement Program (NSQIP) database was used to develop Bayesian models to estimate risk-adjusted BMVs for overall and liver related (post-hepatectomy liver failure [PHLF], biliary leakage [BL]) complications. A separate international multi-institutional database was used to validate the risk-adjusted BMVs.

RESULTS

Among the 11,243 patients included in the NSQIP database, the incidence of complications, PHLF, and BL was 36%, 5%, and 8%, respectively. The risk-adjusted BMVs for complication (range, 16-72%), PHLF (range, 1%-20%), and BL (range, 4%-22%) demonstrated a high variability based on patients characteristics. When tested using an international database including nine institutes, the risk-adjusted BMVs for complications ranged from 26% (Institute-4) to 43% (Institute-1), BMVs for PHLF between 3% (Institute-3) and 12% (Institute-5), while BMVs for BL ranged between 5% (Institute-4) and 9% (Institute-7).

CONCLUSIONS

Multiple factors influence the risk of complications following hepatectomy. Risk-adjusted BMVs are likely much more applicable and appropriate in assessing "acceptable" benchmark outcomes following liver surgery.