‘Textbook Outcome’ (TO) represents an effort to define a standardized, composite quality benchmark based on intraoperative and postoperative endpoints. This study aimed to assess the applicability of TO as an outcome measure following liver resection for hepatic neoplasms from a low- to middle-income economy and determine its impact on long-term survival. Based on identified perioperative predictors, we developed and validated a nomogram-based scoring and risk stratification system.
Methods
We retrospectively analyzed patients undergoing curative resections for hepatic neoplasms between 2012 and 2023. Rates of TO were assessed over time and factors associated with achieving a TO were evaluated. Using stepwise regression, a prediction nomogram for achieving TO was established based on perioperative risk factors.
Results
Of the 1018 consecutive patients who underwent liver resections, a TO was achieved in 64.9% (661/1018). The factor most responsible for not achieving TO was significant post-hepatectomy liver failure (22%). Realization of TO was independently associated with improved overall and disease-free survival. On logistic regression, American Society of Anesthesiologists score of 2 (p = 0.0002), perihilar cholangiocarcinoma (p = 0.011), major hepatectomy (p = 0.0006), blood loss >1500 mL (p = 0.007), and presence of lymphovascular emboli on pathology (p = 0.026) were associated with the non-realization of TO. These independent risk factors were integrated into a nomogram prediction model with the predictive efficiency for TO (area under the curve 75.21%, 95% confidence interval 70.69–79.72%).
Conclusion
TO is a realizable outcome measure and should be adopted. We recommend the use of the nomogram proposed as a convenient tool for patient selection and prognosticating outcomes following hepatectomy.
Hinweise
Kaival K. Gundavda and Shraddha Patkar have contributed equally to this work as first authors.
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Liver resection is an established standard treatment for malignant and some benign liver tumors. Despite technological advances and the use of intraoperative adjuncts, liver resection remains technically challenging and is associated with significant morbidity and mortality. Optimizing outcomes following liver resection requires meticulous patient selection, a thorough understanding of the anatomical tenets, and early identification of postoperative complications.1
Traditional outcome measures such as the Clavien–Dindo classification (CDC) system, although universally acceptable, are not tailored to the application in the post-hepatectomy setting.2,3 The Comprehensive Complication Index (CCI) was proposed to standardize the reporting of surgical complications; however, it was calculated as a sum of all complications weighted for their severity based on the CDC system.4 Over a decade ago, the International Study Group of Liver Surgery (ISGLS) proposed a classification of complications unique to hepatobiliary surgery.5‐7 Despite this, there was an unmet need for a comprehensive and unified system to define the ideal postoperative course after liver resection and to enable standardization and comparison on a global scale. ‘Textbook Outcomes’ (TO) were determined with the objective of hospital quality of care with special attention to patient-centered care, and may represent a more holistic approach to assess and standardize postoperative outcomes.8
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Although the concept of TO has been used previously in the field of gastrointestinal cancer surgery, it was not until 2023 that an international expert consensus-based definition was proposed, using the formal Delphi approach. Regrettably, only 4.6% of the expert panel members that represented 80 countries classified as lower- to middle-income and low-income economies.8
This study aimed to assess the feasibility of including TO as a standard outcome measure following liver resection in a lower- to middle-income country (LMIC). We identified our rates of TO and their temporal trends over a decade, identified factors predicting TO, and determined the impact of TO on overall survival (OS) and disease-free survival (DFS). Based on identified perioperative predictors, we proposed and validated a nomogram-based scoring and risk stratification system to predict the achievement of TO following liver resections.
Materials and Methods
Study Population
Data were collected from a prospectively maintained database in the Gastrointestinal and Hepatobiliary Division of the Department of Surgical Oncology. Consecutive patients with suspected hepatic neoplasms who underwent curative resection from January 2011 to June 2023 were included. Analysis for postoperative outcome measures as per the Textbook Outcome in Liver Surgery (TOLS) criteria were analyzed.8
The demographic, clinicopathological and operative parameters analyzed were age, sex, body mass index (BMI), American Society of Anesthesiologists (ASA) class, histopathological diagnosis, size and location of the lesion (i.e., unilobar or bilobar), extent of liver resection, surgical approach, intraoperative blood loss, and micro- and macrovascular invasion.
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Inclusion Criteria
All patients with suspected primary hepatic neoplasms, as well as liver secondaries, who underwent resection with curative intent were included. Patients found to have inoperable disease intraoperatively were excluded from the study.
Preoperative Assessment
The preoperative evaluation included physical examination; contrast-enhanced cross-sectional imaging and relevant tumor markers were assessed. In suspected primary hepatic neoplasms, serum α-fetoprotein (AFP) and protein induced by vitamin K absence-II (PIVKA-II) were routinely performed, whereas in the case of secondaries from the gastrointestinal tract, carcinoembryonic antigen (CEA)/carbohydrate antigen 19.9 (CA-19.9) levels were estimated. The preferred initial imaging modality was a triphasic contrast-enhanced computed tomography (CECT) scan of the thorax, abdomen and pelvis. Magnetic resonance imaging (MRI) was used as a troubleshooting modality, especially in cirrhotic livers. A percutaneous biopsy was performed selectively, and in patients who had undergone a biopsy elsewhere, a pathology review was obtained.
Treatment
All patients were discussed in a dedicated hepatobiliary multidisciplinary tumor board, comprising a hepatobiliary surgical oncologist, intervention radiologist, medical oncologist, radiation oncologist, and pathologist. Patients deemed suitable for surgery underwent a thorough preoperative anesthesia assessment, nutritional rehabilitation, and chest physiotherapy evaluation.
In patients planned for a major hepatectomy, defined as the resection of three or more Couinaud segments,9 the future functional liver remnant (FLR) was confirmed by volumetric assessment on radiology and an indocyanine green retention (ICG -R15) test.
Liver resections were performed using a combination of transection devices, such as a cavitron ultrasonic surgical aspirator (CUSA), waterjet system, procoagulators, and coagulating devices, such as bipolar forceps and vessel sealant, without routine use of vascular occlusion. Enhanced recovery after surgery (ERAS) protocols were practised during the perioperative period.10 Avoidance of volume overload was ensured to reduce blood loss, by central venous pressure monitoring, goal-directed fluid therapy, use of epidural analgesia and acute normovolemic intraoperative hemodilution. Early oral intake and mobilization were encouraged in the postoperative period, supplemented with the use of multimodal analgesia.
Pathology Review
Pathology review included confirmation of the primary diagnosis, tumor location, tumor size, histological differentiation, lymphovascular invasion (LVI), perineural invasion (PNI), and resection margins. By the TOLS definition, a 1 mm parenchymal margin was sufficient to categorize as R0 resection.8
Post-Treatment Surveillance
Follow-up was performed on an outpatient basis with liver function tests, disease-specific tumor markers, and transabdominal ultrasonography every three months for 2 years. After 2 years, patients were followed up twice yearly, and then annually after the 5th year. A CECT of the thorax and abdomen was performed alternately with an ultrasound at intervals of 3–6 months for the first 2 years, then 6-monthly for 3 years, and then annually up to 5 years or whenever there was a suspicion of relapse.
Definitions and Data Collection
Textbook outcomes (TO) were defined as per the consensus, after consideration of the following parameters:8 (1) absence of intraoperative grade ≥2 incidents scored according to the Oslo classification;11 (2) absence of postoperative complications graded according to the CDC system, with CD grade IIIa or higher being considered a major complication;3 (3) absence of postoperative bile leaks and post-hepatectomy liver failure, defined by the ISGLS;5,6 (4) negative resection margins, defined as R0 (1 mm or more tumor-free margin), R1 (<1 mm tumor-free margin), or R2 (macroscopic tumor involvement at the margin); (5) readmission, defined as unplanned rehospitalization within 90 days after discharge; and (6) mortality, defined as death due to any cause in-hospital or within 90 days of the surgical procedure.
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Statistical Analysis
Continuous variables were described as median (interquartile range [IQR]) or mean (standard deviation [SD]), while categorical variables were described as number (%). Differences in baseline characteristics were assessed using the Kruskal–Wallis test for continuous variables and Chi-square or Fisher’s exact tests for categorical variables.
A univariate logistic regression model was used to test the ability of potential baseline risk factors to predict the risk of TOLS. Significant variables were further entered into the multivariate logistic regression analysis to identify independent prognostic factors. The whole cohort was randomly assigned to define development and validation cohorts. The reliability of the predictive model was assessed with respect to discrimination and calibration. Predictive model discrimination was analyzed by the area under the receiver operating characteristic (ROC) curve. The model’s calibration was tested using a calibration plot and performance was evaluated using the concordance index.
The training and test cohorts were divided into high- and low-risk scores based on the median of the total nomogram score. Factors differentiating the risk groups were compared using the Chi-square test for the training and test cohorts. Kaplan–Meier curves were used to compare the survival probabilities between the high- and low-risk groups.
All statistical analyses were performed using Statistical Product and Service Solutions (SPSS) version 26 (IBM Corporation, Armonk, NY, USA), and a p-value <0.05 was considered statistically significant.
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Results
Clinicopathologic Demographics
A total of 1018 consecutive patients of suspected hepatic neoplasms undergoing curative resections between January 2011 and June 2023 were included in the cohort. The median patient age was 54 years (IQR 43–63), with almost two-thirds of patients being male (n = 668, 65.6%). The most common indication for surgery was primary hepatic neoplasms (n = 519, 51%), followed by colorectal liver metastasis (CRLM) in one-quarter of the patients (n = 252, 24.8%). The majority of patients were ASA class 1 (n = 523, 51.4%) and underwent a major liver resection (n = 531, 52.2%), predominantly via an open surgical approach (n = 972, 95.5%).
The demographic, clinicopathologic, and treatment characteristics of the analytic cohort and training and test cohorts are summarized in Table 1.
Table 1
Comparison of demographic, tumor, and treatment characteristics and immediate postoperative outcomes
Variable
All patients [n = 1018]
Training cohort
[n = 455]
Test cohort
[n = 194]
p-Value
Demographics
Age at diagnosis, years [median (IQR)]
54 (43, 63)
56 (45, 64)
53.5 (44, 62)
0.393
Sex
Male
668 (65.6)
305 (67)
130 (67)
1
Female
350 (34.4)
150 (33)
64 (33)
ASA
0.117
1
523 (51.4)
200 (44)
92 (47.4)
2
442 (43.4)
227 (49.9)
83 (42.8)
3 and 4
53 (5.2)
28 (6.2)
19 (9.8)
Tumor characteristics
Diagnosis
0.2896
Primary hepatic neoplasm
519 (51)
237 (52.1)
97 (50)
CRLM
252 (24.8)
116 (25.5)
45 (23.2)
NET mets
61 (6)
35 (7.7)
12 (6.2)
pHCC
55 (5.4)
27 (5.9)
12 (6.2)
Others
131 (12.9)
40 (8.8)
28 (14.4)
Tmax, cm [median (IQR)]
5.5 (3.2–9)
Lymphovascular invasion
0.52
No
682 (66.9)
338 (74.4)
139 (71.6)
Yes
212 (20.85)
116 (25.6)
55 (28.4)
NK
124 (12.1)
1
0
Margins
Free (R0)
970 (95.3)
431 (94.7)
187 (96.4)
0.4775
Positive
48 (4.7)
24 (5.3)
7 (3.6)
Treatment characteristics
Extent of resection
0.2052
Major
531 (52.2)
234 (51.4)
111 (57.2)
Minor
487 (47.8)
221 (48.6)
83 (42.8)
Surgical approach
0.6291
Open
972 (95.5)
429 (94.3)
182 (93.8)
Laparoscopic
23 (2.3)
11 (2.4)
7 (3.6)
Robotic
23 (2.3)
15 (3.3)
5 (2.6)
Surgery duration, min [median (IQR)]
240 (180–320)
240 (170–320)
240 (180–300)
0.1453
Immediate postoperative outcomes
Significant PHLF
0.4553
No
794 (78)
345 (75.8)
141 (72.7)
Yes
224 (22)
110 (24.2)
53 (27.3)
Significant PHBL
0.3505
No
944 (92.7)
422 (92.7)
175 (90.2)
Yes
74 (7.3)
33 (7.3)
19 (9.8)
Significant complication
1
No
815 (80.1)
360 (79.1)
154 (79.4)
Yes
203 (19.9)
95 (20.9)
40 (20.6)
Mortality
0.3011
No
970 (95.3)
437 (96)
182 (93.8)
Yes
48 (4.7)
18 (4)
12 (6.3)
Readmission
0.1559
No
974 (95.7)
436 (95.8)
180 (92.8)
Yes
44 (4.3)
19 (4.2)
14 (7.2)
Data are expressed as n (%) unless otherwise specified
ASA American Society of Anesthesiologists, NK not known, IQR interquartile range, CRLM colorectal liver metastasis, NET neuroendocrine tumor, pHCC perihilar cholangiocarcinoma, Tmax maximum tumor dimension, PHLF post-hepatectomy liver failure, PHBL post-hepatectomy bile leak, mets metastasis
Textbook Outcome Rates and Trends Over Time
TO were achieved in 64.9% (661/1018) of cases. Post-hepatectomy liver failure (n = 224, 22%), followed by significant complications (n = 203, 19.9%), had the greatest negative impact on the ability to obtain a TO. Absence of readmission (n = 974, 95.7%) and no mortality (n = 970, 95.3%) were the outcomes most commonly realized, followed by a margin-negative resection (n = 970, 95.3%).
When stratified by extent of liver resection, TO was seen in 51% of major resections (271/531) and 80.1% of minor resections (390/487).
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The incidence of TO ranged from 54% to 70% across the study period, with no significant increase in trends over time (ptrend = 0.09).
Impact of Textbook Outcomes (TO) on Long-Term Survivals
TO was significantly associated with improved OS, even after adjustment on multivariate analysis (p < 0.001). Estimated survival for patients with TO at 3 years was 74% (69.8–78.5%) versus 55.2% (49.1–62%) for patients not achieving TO (p = 0.001); at 5 years, estimated survival was 64.8% (59.7–70.4%) and 52% (45.7–59.2%), respectively (Fig. 1a)
Fig. 1
Kaplan–Meier curves depicting a overall survival and b disease-free survival between patients achieving a TO versus no TO. TO Textbook Outcome
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The estimated DFS for TO at 3 years was 64.5% (60–69.4%) versus 53.9% (47.4–61.2%) for patients not achieving TO; at 5 years, the estimated DFS was 55.9% (50.5–61.8%) and 49.7% (42.7–57.8%), respectively (p = 0.0028) (Fig. 1b)
Development and Validation of a Prediction Model for Predicting TO
Several factors were identified on univariate analysis that were associated with TO. On multivariable analysis, patients with perihilar cholangiocarcinoma (pHCC; hazard ratio [HR] 2.9, 95% confidence interval [CI] 1.274–6.642; p = 0.011) and presence of lymphovascular emboli (LVE) on pathology (HR 1.587, 95% CI 1.058–2.381; p = 0.026) were associated with lesser TO. Major resections (HR 2.454, 95% CI 1.638–3.676; p = 0.0006) and blood loss >1500 mL (HR 2.101, 95% CI 1.227–3.597; p = 0.007) were also significantly associated with lower TO rates (Table 2)
Table 2
Univariate and multivariable analysis of factors predicting realization of Textbook Outcomes following liver resection
Variable
N
Univariable
Multivariable
OR (95% CI)
p-Value
OR (95% CI)
p-Value
Age
1.017 (1.007–1.026)
0.0001
1.008 (0.994–1.022)
0.293
Sex
Male
668
1
Female
350
0.693 (0.525–0.915)
0.010
0.978 (0.669–1.43)
0.910
ASA
1
523
1
0.0002
2
442
2.022 (1.545–2.646)
0.0004
1.998 (1.362–2.931)
0.0002
3–4
53
2.037 (1.145–3.625)
0.015
1.989 (0.962–4.112)
0.063
Histological diagnosis
Primary hepatic neoplasms
519
1
CRLM
252
0.752 (0.544–1.04)
0.085
1.64 (1.027–2.621)
0.038
NET mets
61
0.803 (0.454–1.421)
0.452
0.896 (0.433–1.853)
0.767
pHCC
55
4.734 (2.547–8.8)
0.0008
2.909 (1.274–6.642)
0.011
Others
131
0.673 (0.441–1.027)
0.067
1.082 (0.597–1.961)
0.795
No. of lesions
1
788
0.597 (0.294–1.215)
0.155
2
119
0.472 (0.213–1.049)
0.066
3
46
0.872 (0.352–2.158)
0.767
4
12
0.567 (0.142–2.267)
0.422
≥5
18
1.133 (0.357–3.6)
0.832
Size of largest lesion, cm
<3
239
1
3–5
226
1.198 (0.815–1.763)
0.359
5–10
321
1.221 (0.855–1.743)
0.272
10–20
186
1.12 (0.744–1.685)
0.587
≥20
15
4.373 (1.445–13.24)
0.009
Distribution of lesions
Unilobar
908
1
Bilobar
75
0.825 (0.495–1.372)
0.458
LVE
No
682
1
Yes
212
1.9 (1.388–2.602)
0.0005
1.587 (1.058–2.381)
0.026
Surgical approach
Open
972
1
Laparoscopic
23
0.08 (0.011–0.599)
0.014
0 (0–0)
0.999
Robotic
23
0.491 (0.181–1.335)
0.164
0.661 (0.164–2.666)
0.561
Extent of resection
Minor hepatectomy
487
1
Major hepatectomy
531
3.857 (2.915–5.104)
0.0002
2.454 (1.638–3.676)
0.0006
Blood loss, mL
<800
219
1
800–1500
215
2.422 (1.551–3.784)
0.0004
1.389 (0.824–2.34)
0.217
1500–2550
216
4.448 (2.873–6.885)
0.0003
2.101 (1.227–3.597)
0.007
>2550
97
7.166 (4.198–12.233)
0.0006
2.677 (1.375–5.209)
0.004
Duration of surgery
1.006 (1.004–1.007)
0.0007
Additional organ surgery
No
215
1
Yes
803
1.01 (0.737–1.385)
0.949
ASA American Society of Anesthesiologists, CRLM colorectal liver metastasis, NET neuroendocrine tumor, pHCC perihilar cholangiocarcinoma, LVE lymphovascular emboli, OR odds ratio, CI confidence interval, mets metastasis
Bold indicate significant p values
These independent predictors were integrated into a nomogram prediction model to show the probability of achieving TO, and the points of each factor were calculated by their weight of odds ratios (ORs). Based on the nomogram, the ROC curve analysis was applied to evaluate the accuracy of the prediction. The area under the curve (AUC) of the nomogram model in predicting TO was 75.21% (95% CI 73.14–80.41%). The dataset was divided into a training set and a test set to internally validate the predictive performance of the nomogram model. The AUC of the nomogram model in predicting TO was 75.21% (95% CI 70.69–79.72%) and 75.21% (95% CI 72.73–85.36%) for the training and test sets, respectively (Fig. 2).
Fig. 2
A nomogram model for preoperative estimation of the probability of achieving a TO in the a training dataset and c test dataset, and performance of the nomogram in predicting TO in the b training cohort and d test cohort. ASA American Society of Anesthesiologists, AUC area under the curve, CI confidence interval, CRC colorectal cancer, Lap laparoscopic, LVE lymphovascular emboli, NET neuroendocrine tumor, PHN primary hepatic neoplasms, pHCC perihilar cholangiocarcinoma, TO Textbook Outcome, TPR true positive rate
×
Calibration curves of the nomogram by bootstrapping showed good agreement between the predicted probability of TO and the actual observed incidence of TO for the training and test cohorts (Fig. 3)
Fig. 3
Calibration curve of the nomogram for the estimation of the risk of realizing a Textbook Outcome in the a training data and b test data. CI confidence interval
×
Performance of the Nomogram
An optimal cut-off value of the nomogram for discriminating TO was identified as 247, based on the median of the total nomogram score. According to this optimal cut-off value of the nomogram score, patients in the test cohort were divided into the following groups: the high-probability TO group (n = 328) and the low-probability TO group (n = 321).
Patients in the high-probability TO group were confirmed to have a better OS and DFS compared with the low-probability TO group (Fig. 4). The characteristics of patients based on nomogram scores and their postoperative outcomes are summarized in Table 3.
Fig. 4
Kaplan–Meier curves comparing a overall survival and b disease-free survival between patients in the ‘low-probability’ and ‘high-probability’ TO groups. TO Textbook Outcome
Table 3
Comparison of characteristics of patients with nomogram scores: high probability (score <247) and low probability (score >247) of TO in the training set
Variable
Nomogram score
High probability of TO (below median) [n = 328]
Low probability of TO (above median) [n = 321]
p-Value
Treatment characteristics
Extent of resection
Major
62 (18.9)
283 (88.2)
< 0.001
Minor
266 (81.1)
38 (11.8)
Blood loss [median (IQR)]
900 (500, 1500)
2400 (1500, 3525)
< 0.001
Surgery duration [median (IQR)]
180 (120, 260)
300 (210, 360)
< 0.001
R0 margins
Yes
315 (96)
303 (94.4)
0.42
No
13 (4)
18 (5.6)
Immediate postoperative outcomes
Significant PHLF
Yes
23 (7)
140 (43.6)
< 0.001
No
305 (93)
181 (56.4)
Significant PHBL
Yes
11 (3.4)
41 (12.8)
< 0.001
No
317 (96.6)
280 (87.2)
Significant complications
Yes
33 (10.1)
102 (31.8)
< 0.001
No
295 (89.9)
219 (68.2)
Mortality
Yes
4 (1.2)
26 (8.1)
< 0.001
No
324 (98.8)
295 (91.9)
Long-term outcomes
Median OS (IQR)
18.7 (7.9, 36.7)
13.2 (5.0, 27.3)
0.0014
Median DFS (IQR)
13.4 (6.1, 31.2)
10.1 (3.9, 22.7)
0.0008
Data are expressed as n (%) unless otherwise specified
IQR interquartile range, PHLF post-hepatectomy liver failure, PHBL post-hepatectomy bile leak, OS overall survival, DFS disease-free survival, TO Textbook Outcome
Bold indicate significant p values
×
Discussion
Liver resection remains a technically challenging surgical procedure, with morbidity ranging from 20 to 40%, with a mortality rate of <5%.12,13 Previous efforts to standardize liver resections in India have reported comparable morbidity and mortality rates.14 In recent years, advancements in techniques such as parenchyma preservation,15 intraoperative image guidance,16 minimal access surgery,17 and the implementation of ERAS protocol have improved the safety of liver resections.10,18 Despite this, there remained an unmet need for a standardized outcome measure encompassing complex surgical processes’ multidimensional aspects into a single indicator.8,19 Although objective and reproducible, the CDC and CCI scoring systems are generally more suitable for heterogeneous populations.3,4 The ISGLS subsequently defined and classified intervention-specific complications in patients undergoing hepatobiliary resections.5‐7
The ‘Textbook Outcome’ was developed as a measure to unify these systems into a single comprehensive indicator of multidimensional and holistic postoperative care and has been used to compare the overall quality of cancer care across hospitals.20 Over a span of 12 years, in our experience of 1018 hepatic resections, Textbook Outcomes were achieved in 64.9% (661/1018). These TO rates are comparable with the available literature, with TO rates ranging from as low as 11.2% to upwards of 80% in specialized centers.19,21‐29
Based on the surgical approach, TO was achieved in 74.8% of patients undergoing laparoscopic liver resection, compared with 61.9% for open surgery.24 When categorized by histology, the TO rates for benign, CRLM, and other malignancies were 82%, 73%, and 74%, respectively.25,29 However, TO rates for hepatic resection for cholangiocarcinoma are significantly less promising, with a multi-institutional study reporting a TO in only 26% of patients with intrahepatic cholangiocarcinoma.21,24 We found that hepatic resections for pHCC were associated with worse outcomes, with TO rates as low as 27.27%. Another study of over 200 resections for pHCC corroborates our poor outcomes.30
The analysis of temporal trends of TO across the study period showed that TO ranged from 54% to 70%, and there was no significant increase in trends over time (ptrend = 0.09). These consistent rates reflect our efforts to standardize liver resections over the past decade.14 In another study mirroring our results, no increasing trend in TO rates was found in either major or minor liver resections over a 12-year period.23
TO was significantly associated with improved OS and DFS. Multiple studies have similarly reported a significant improvement in long-term OS and DFS associated with TO fulfilment.23,28‐32 On the other hand, a pairwise comparison demonstrated that the long-term survival following hepatocellular carcinoma (HCC) resection was influenced more by hospital case volume rather than by TO.33 In our study, post-hepatectomy liver failure (n = 224, 22%), followed by significant complications (n = 203, 19.9%), had the greatest negative impact on the ability to obtain a TO, whereas in other studies, the major deterrents to achieving a TO were prolonged length of hospital stay19 and major morbidity (Clavien–Dindo ≥3).29
Numerous studies have analyzed factors predictive of TO. For open resections, only histological diagnosis of cholangiocarcinoma and a tumor size ≥30 mm were associated with a worse TO rate.24 Among patients with pHCC, preoperative biliary drainage, high prognostic nutritional index, and minimally invasive approach were identified as independent predictors of TO, whereas a high ASA score decreased the odds of TO.30 Advanced age, concomitant surgery, prolonged operative time, and increased blood loss are associated with lower TO rates.26,29 A high tumor burden,27 high aspartate transaminase-platelet ratio index (APRI),27 Charlson Comorbidity Index >3, major liver resection, intermediate HCC, and a Model for End-Stage Liver Disease (MELD) score >10 are independently associated with not achieving a TO.27,29 In another study, the absence of diabetes mellitus (DM) and thrombocytopenia were found to adversely predict TO.28 Higher ASA grade was also found to be detrimental to achieving a TO.29 Patients undergoing liver surgery at dedicated cancer centers had 31% and 36% higher odds of achieving TO compared with National Cancer Institute-affiliated cancer centers and other US hospitals, respectively.34 Extremes of BMI were independently associated with higher incidences of non-TO compared with normal BMI individuals.35 Obesity was a factor in failing to achieve TO even after laparoscopic resections.36
We noticed that while there are various studies that have developed predictive nomograms for post-hepatectomy liver failure,37‐43 there is a dearth of predictive models to assess the likelihood of achieving TO. Therefore, we developed a nomogram and used an internal cohort for its validation with good accuracy in predicting TO. A previous study of 687 patients also showed similar predictive probability in both the training set (AUC 0.755) and validation set (AUC 0.763).21
The proposed nomogram model enables us to identify patients at high risk of non-realization of TO. This information can guide surgeons to make personalized decisions, thereby enabling an ‘ideal’ postoperative course and ultimately improving long-term survival.44 Risk scoring based on perioperative factors may enhance case selection, facilitate close monitoring, and potentially enable timely intervention.
Our study represents the largest experience of assessment of TO in hepatic resections from a single center in South Asia. It allows us to compare immediate postoperative and long-term outcomes among patients who realize a TO against those who do not. Being a high-volume center for liver resections, all treatments were standardized and administered following a multidisciplinary tumor board discussion. Despite operating in resource-constrained settings, from an LMIC, we report outcomes comparable with global standards. This encourages the adoption of TO as a standard outcome measure even in resource-limited settings. Our nomogram utilizes accessible perioperative variables and proposes an effortless scoring system, thereby enabling its widespread implementation in clinical practice. However, the study is limited by its retrospective design and shortcomings of data maintenance in terms of missing data and a 6% loss of follow-up.
Conclusion
Textbook Outcome after liver resection is a realizable outcome measure and should be adopted even in resource-constrained settings. By incorporating essential perioperative parameters, we developed and validated a simplified nomogram for the prediction of TO that showed good performance in predicting TO after liver resection. We recommend use of the nomogram proposed as a convenient tool for patient selection and prognosticating outcomes following hepatectomy.
Acknowledgment
None.
Disclosure
Kaival K. Gundavda, Shraddha Patkar, Sadhana Kannan, Gurudutt P. Varty, Kunal Nandy, Tanvi Shah, Kaushik Polusany, Sohan Lal Solanki, Suyash Kulkarni, Nitin Shetty, Kunal Gala, Vikas Ostwal, Anant Ramaswamy, Prabhat Bhargava, and Mahesh Goel have no conflicts of interest to declare that may be relevant to the contents of this article.
Ethical Approval
The data of the present study were collected in the course of common clinical practice and, accordingly, the signed informed consent was obtained from each patient for any surgical and clinical procedures. The study protocol was in accordance with the ethical standards of the institutional research committee and the 1964 Helsinki Declaration and its later amendments.
Consent to Participate
As this was a retrospective study, formal consent was not required and Institutional Research Committee approval was not needed.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Publisher's Note
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