Development and Validation of a Preoperative Risk Score for Difficult Laparoscopic Cholecystectomy: A Retrospective Study in Resource-Limited Settings

Development and Validation of a Preoperative Risk Score for Difficult Laparoscopic Cholecystectomy: A Retrospective Study in Resource-Limited Settings | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Development and Validation of a Preoperative Risk Score for Difficult Laparoscopic Cholecystectomy: A Retrospective Study in Resource-Limited Settings Saif Ghabisha, Noaman Almashraki, Qasem Alyhari, Ahmed Ateik, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8894630/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Laparoscopic cholecystectomy (LC) is one of the most common surgical procedures worldwide. Yet, reliably identifying patients at high risk for intraoperative conversion or major complications preoperatively remains challenging. In this study, we investigated factors associated with difficult LC and developed and validated a simple preoperative scoring system to predict it. Patients and Methods: We conducted a single-center, retrospective cohort study at Ibb University Hospitals, Yemen. Data were extracted from records of 301 consecutive patients who underwent LC between April 2020 and November 2024. The primary outcome, "surgical difficulty," was a composite endpoint of conversion to open surgery or major intraoperative complications. Multivariable logistic regression was used to identify independent predictors and derive an integer-based risk score. Model performance was evaluated using the Area Under the Receiver Operating Characteristic Curve (AUC), calibration plots, and Decision Curve Analysis (DCA). Results Of 301 patients, 37 (12.3%) experienced difficult LC. Multivariable analysis identified three key predictors: right upper quadrant rigidity (adjusted OR 8.50, 95% CI 4.21–17.15; 2 points), impacted stone on ultrasound (aOR 7.24, 95% CI 3.10–16.90; 2 points), and prior acute cholecystitis (aOR 3.16, 95% CI 1.45–6.88; 1 point). This yielded a 5-point Chole-Risk Score with excellent discrimination (AUC 0.848, 95% CI 0.789–0.907). At the optimal cutoff of ≥ 3 points, sensitivity reached 73.7% and specificity 86.5%. Calibration was strong (Hosmer-Lemeshow p = 0.612), and DCA showed superior clinical utility across a broad threshold range. Conclusion The Chole-Risk Score is a reliable, bedside-accessible tool for predicting difficulty in LC. In settings like Ibb, Yemen, where advanced preoperative imaging may be limited, this score allows for the identification of high-risk cases, facilitating early senior surgical involvement and improving patient safety. Laparoscopic Cholecystectomy Surgical Difficulty Prediction Score Preoperative Assessment Conversion to Open Surgery Yemen Biliary Surgery Figures Figure 1 Figure 2 Figure 3 Introduction Cholelithiasis is one of the most common conditions affecting the biliary system worldwide. Cholelithiasis prevalence varies greatly worldwide primarily due to age, female sex, ethnicity and geography, obesity, rapid weight loss, metabolic syndrome components like diabetes and hypertriglyceridemia, Westernized diets high in refined carbohydrates and low in fiber, genetic predisposition, pregnancy or parity, and liver diseases such as cirrhosis or hepatitis C ( 1 , 2 ). In the United States, gallstone disease affects approximately 20 million adults (10% prevalence among those aged 20–74), with symptomatic cases driving around 2 million ambulatory visits and 1 million emergency department visits annually ( 3 ). In Yemen, the prevalence is roughly 4 5.5%; however, this number is increasing quickly because of changes in diets, shifting social structures such as chewing khat, and better access to ultrasound in both rural and urban areas ( 4 , 5 ). The 1992 NIH Consensus Development Conference marked a turning point, establishing laparoscopic cholecystectomy (LC) as the safe gold standard for symptomatic gallstones ( 6 ). Today, LC is the preferred approach, delivering less postoperative pain, shorter hospital stays, better cosmesis, and quicker recovery compared to open surgery ( 7 ). As laparoscopic surgical proficiency has increased around the world, the initial absolute contraindications have been increasingly reevaluated. Conditions previously thought to be prohibitive, such as excessive obesity and a history of upper abdominal surgery, are now regularly treated with laparoscopic surgery. The few absolute contraindications that remain now are primarily limited to patients deemed medically incompetent for general anesthesia, untreated bleeding diatheses, and probable gallbladder cancer ( 8 ). Despite these improvements, "difficult" cholecystectomies continue to pose challenges. Of all laparoscopic cholecystectomies (LC), 2–10% require conversion to open surgery due to intraoperative challenges such as adhesions, inflammation, or unclear anatomy ( 9 ). Various validated preoperative risk prediction models for difficult LC share fundamental characteristics identified through multivariate analyses, although they vary regarding the specific weights and cut-off values assigned to individual predictors. Several established scoring systems, such as the Randhawa/Pujahari (threshold > 5 points), Nassar, and CAAD systems, have undergone rigorous validation ( 10 – 14 ). These models are increasingly recognized for their ability to integrate clinical and ultrasonographic variables to optimize surgical planning and improve patient safety. Consistently identified risk factors in the literature include advanced age (typically > 50 to 60 years), male sex, and an elevated body mass index (> 25 to 30 kg/m²). These factors often correspond to increased tissue friability, complex anatomical variations, and technical challenges in surgical exposure ( 15 , 16 ). Furthermore, a clinical history of acute cholecystitis or multiple hospital admissions is frequently cited as the most heavily weighted predictor, as it reflects chronic inflammatory changes that substantially complicate the operative procedure ( 16 , 17 ). Objective risk stratification is further enhanced by combining physical findings, such as a palpable gallbladder, with ultrasonographic parameters, including a gallbladder wall thickness exceeding 4 mm, the presence of impacted stones, and pericholecystic fluid ( 15 , 18 ). These predictive models have demonstrated high diagnostic accuracy, with reported Area Under the Curve (AUC) values ranging from 0.78 to 0.95, sensitivities reaching 95 percent, and specificities exceeding 80 percent ( 19 , 20 ). Consequently, the implementation of such scores facilitates proactive surgeon allocation, enhanced theatre scheduling, and a measurable reduction in conversion rates to open surgery. Despite the proliferation of predictive scoring systems in high-resource healthcare environments, their applicability in low-to-middle-income countries remains a subject of ongoing debate. In regions like Yemen, patients often present with advanced biliary disease due to delayed referrals, limited access to elective surgical services, and socioeconomic barriers to early diagnosis. These local factors may alter the predictive weight of traditional variables, rendering international models less accurate or overly complex for rapid bedside use. Furthermore, many existing scores rely on high-resolution imaging or specific laboratory biomarkers that are not consistently available in rural or resource-limited tertiary centers. There is a clear clinical necessity for a simplified, robust, and locally validated risk assessment tool that utilizes accessible clinical and sonographic data. Such a tool would allow surgical departments to prioritize high-risk patients for senior-led operative teams, thereby reducing the incidence of avoidable complications and minimizing the economic burden associated with prolonged hospital stays and open conversions. Consequently, this study was designed to develop and validate the Chole-Risk Score within a Yemeni patient population. By focusing on a parsimonious set of predictors, we aimed to create a reliable predictive model that maintains high diagnostic accuracy while ensuring ease of implementation in the acute surgical setting. Patients and Methods Study Design and Setting This study was a single-center, retrospective, observational cohort analysis conducted at Ibb University Hospitals. All patient data were extracted from the surgical and radiological records of consecutive patients undergoing laparoscopic cholecystectomy between April 22, 2020, and November 23, 2024. Ethical Approval and Consent The study protocol was reviewed and formally approved by the Institutional Research Ethics Committee of Ibb University on January 1, 2025 (Approval Reference ID: IBBUNI.2025.1.001). The requirement for individual patient consent was waived due to the retrospective, anonymized nature of the data analysis, which was deemed to pose no more than minimal risk to participants. All research activities were conducted in strict accordance with the ethical principles outlined in the Declaration of Helsinki. Patient Population The initial screening identified adult patients (aged 18 years or older) who underwent a laparoscopic cholecystectomy for symptomatic benign gallbladder disease; including acute cholecystitis, chronic cholecystitis, and symptomatic cholelithiasis, within the defined study period. Exclusion criteria were applied to create a homogeneous cohort for analysis. Patients were excluded if they had a preoperative diagnosis of gallbladder malignancy, were undergoing cholecystectomy as part of a more extensive hepatobiliary or pancreatic procedure, or had incomplete preoperative or operative records in the hospital database. Following these criteria, the final analytic cohort comprised 301 patients (see STROBE Flow Diagram, Supplementary Fig. 1). Sample Size and Power Analysis The minimum sample size was initially estimated based on the diagnostic performance of a preoperative scoring system reported by Gupta et al. ( 21 ), which achieved a sensitivity of 95.74% and a specificity of 73.68%. Using these values as a reference, with a 5% significance level, 80% power, and a 10% margin of error, the required sample size was determined to be 138 patients. However, to ensure sufficient model stability and minimize the risk of overfitting in our multivariable logistic regression, we adopted a consecutive sampling strategy. This resulted in a final cohort of 301 patients. With 37 observed events (difficult cases) and three primary predictors (RUQ rigidity, impacted stone, and history of AC), our study achieved an Events Per Variable (EPV) ratio of 12.3. This exceeds the recommended threshold of 10 EPV required for robust multivariable prediction modeling according to TRIPOD guidelines ( 22 ). Data Collection and Variable Definition Preoperative, intraoperative, and postoperative data were systematically collected by a trained research team using a standardized electronic case report form. To minimize informational bias, researchers responsible for extracting preoperative predictor data were blinded to the eventual intraoperative outcomes recorded in the surgical notes. Demographic information (age, gender), anthropometric measurements (BMI), and relevant clinical history were recorded. Key physical examination findings, specifically the documentation of right upper quadrant (RUQ) rigidity, were extracted from preoperative clinical notes recorded by the attending surgeon or senior surgical resident at the time of admission. Preoperative laboratory values, including the total white blood cell (WBC) count from the most recent test prior to surgery, were collected. All ultrasonographic (US) findings were obtained from formal radiology reports; variables of interest included gallbladder wall thickness and the presence of an impacted stone at the gallbladder neck or cystic duct. Definition of Primary Outcome: Surgical Difficulty The primary outcome was a "difficult laparoscopic cholecystectomy," defined as a composite binary endpoint. A procedure was classified as difficult if there was either ( 1 ) an intraoperative decision to convert to an open cholecystectomy, as documented in the operative note, or ( 2 ) the occurrence of a major intraoperative complication. Major complications were specified as significant biliary injury (e.g., common bile duct or major sectoral duct injury), major vascular injury requiring repair or transfusion, or extensive visceral injury. Based on this definition, 37 out of 301 procedures (12.3%) were categorized as difficult. Statistical Analysis All statistical analyses were performed using Python (version 3.10) with the SciPy, pandas, scikit-learn, and statsmodels libraries. Continuous variables were summarized as means with standard deviations (SD) or medians with interquartile ranges (IQR) based on their distribution, which was assessed using the Shapiro-Wilk test. Categorical variables were expressed as frequencies and percentages. Differences in baseline characteristics between the "difficult" and "non-difficult" groups were compared using Student’s t -test or the Mann-Whitney U test for continuous variables, and the Chi-squared or Fisher’s exact test for categorical variables, as appropriate. Model Development and Validation Variables with a plausible clinical link to surgical difficulty and a univariate association of p < 0.10 were entered into a multivariable binary logistic regression model to identify independent predictors. Model assumptions were verified, and multicollinearity was evaluated using variance inflation factors (VIF < 2.0 for all retained variables). The final model was selected based on clinical relevance, statistical significance (p < 0.05), and minimization of the Akaike Information Criterion (AIC). The regression coefficients (beta) from the final model were used to construct a simplified integer-based risk score; points were assigned by dividing each beta-coefficient by the smallest significant coefficient and rounding to the nearest integer. The discriminatory power was evaluated by calculating the Area Under the Receiver Operating Characteristic Curve (AUC), with 95% confidence intervals (CI) generated via bootstrapping (1000 replicates). The diagnostic performance of the score was evaluated using receiver operating characteristic (ROC) curves, and the area under the curve (AUC) was calculated to assess predictive accuracy. The AUC was classified as strong (AUC > 0.9), moderate (0.7 < AUC ≤ 0.9), or low (0.5 < AUC ≤ 0.7) predictive capacity ( 23 ). The optimal threshold for the score was determined using the maximum Youden index, which maximizes the difference between sensitivity and specificity. At this threshold, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. Model calibration was assessed using a calibration plot and the Brier score, while clinical utility was evaluated using Decision Curve Analysis (DCA) to quantify the net benefit across a range of threshold probabilities ( 24 , 25 ). Internal validation was performed using bootstrap resampling to calculate the optimism-corrected AUC. All analyses adhered to the TRIPOD guidelines for transparent reporting of predictive models ( 22 ). Results Patient Characteristics and Cohort Demographics A total of 301 patients who underwent laparoscopic cholecystectomy (LC) were included in the final analysis. The mean age was 44.3 ± 13.6 years. Based on the composite outcome of intraoperative conversion to open surgery or major surgical complications, patients were divided into two groups: the Easy group (n = 264, 87.7%) and the Difficult group (n = 37, 12.3%). Significant differences were observed in preoperative characteristics (Table 1 ). Notably, the mean White Blood Cell (WBC) count was higher in the Difficult group (13.6 ± 1.9 x 10⁹/L) compared to the Easy group (7.5 ± 3.3 x 10⁹/L, p < 0.001). Comorbidities, particularly Diabetes Mellitus (83.8%) and Hypertension (78.4%), were significantly more prevalent in the Difficult group (p < 0.001). All patients (100%) in the Difficult group presented with right upper quadrant (RUQ) rigidity and a history of acute cholecystitis. Table 1 Preoperative Clinical and Sociodemographic Characteristics Variable Total Cohort (n = 301) Easy (n = 264) Difficult (n = 37) *p*-value Age (years), mean ± SD 44.3 ± 13.6 44.6 ± 12.1 42.1 ± 14.8 0.285² Female Gender, n (%) 180 (59.8%) 143 (54.2%) 37 (100.0%) < 0.001¹ Social Habit (Smoking/Khat), n (%) 211 (70.1%) 182 (68.9%) 29 (78.4%) 0.241¹ Previous Hospitalization, n (%) 45 (14.9%) 12 (4.5%) 33 (89.2%) < 0.001¹ ASA Physical Status, n (%) < 0.001¹ - Class I 210 (69.8%) 201 (76.1%) 9 (24.3%) - Class II 76 (25.2%) 60 (22.7%) 16 (43.2%) - Class III 15 (5.0%) 3 (1.1%) 12 (32.4%) RUQ Rigidity, n (%) 74 (24.6%) 37 (14.0%) 37 (100.0%) < 0.001¹ WBC Count (x10⁹/L), mean ± SD 8.2 ± 3.8 7.5 ± 3.3 13.6 ± 1.9 < 0.001² Predictors of Surgical Difficulty Univariate analysis of radiological and intraoperative factors revealed several associations with surgical difficulty (Table 2 ). Patients in the Difficult group exhibited greater mean gallbladder wall thickness (4.7 ± 0.5 mm vs. 3.3 ± 0.8 mm, p < 0.001) and a markedly higher incidence of impacted stones (45.9% vs. 0%, p < 0.001) on ultrasonography. Table 2 Radiological and Intraoperative Findings Variable Total Cohort (n = 301) Easy (n = 264) Difficult (n = 37) *p*-value GB Wall Thickness (mm), mean ± SD 3.5 ± 0.9 3.3 ± 0.8 4.7 ± 0.5 < 0.001² Impacted Stone (US), n (%) 17 (5.6%) 0 (0.0%) 17 (45.9%) < 0.001¹ Pericholecystic Collection, n (%) 11 (3.7%) 0 (0.0%) 11 (29.7%) < 0.001¹ Operative Time (min), mean ± SD 50.3 ± 12.3 50.5 ± 11.9 48.6 ± 15.1 0.377² Drain Usage, n (%) 268 (89.0%) 264 (100.0%) 4 (10.8%) < 0.001¹ Abbreviations: GB: Gallbladder; US: Ultrasonography; SD: Standard Deviation.¹Chi-square / Fisher’s exact test; ²Independent samples t-test. Multivariate Analysis and Score Development Multivariate logistic regression identified three independent predictors of surgical difficulty (Table 3 ). These variables were used to derive the Chole-Risk Score: RUQ Rigidity (OR 8.50; 95% CI, 4.21–17.15; p < 0.001), Impacted Stone (OR 7.24; 95% CI, 3.10–16.90; p < 0.001), and History of Acute Cholecystitis (OR 3.16; 95% CI, 1.45–6.88; p = 0.004). Based on the β-coefficients, a 5-point scoring system was established, assigning 2 points each for RUQ rigidity and impacted stones, and 1 point for a history of acute cholecystitis. Table 3 Multivariate Logistic Regression Analysis for Predicting Difficult Cholecystectomy Predictor Variable β Coefficient Odds Ratio (OR) 95% CI for OR *p*-value Assigned Points RUQ Rigidity 2.14 8.50 4.21–17.15 < 0.001* 2 Impacted Stone (US) 1.98 7.24 3.10–16.90 < 0.001* 2 History of Acute Cholecystitis 1.15 3.16 1.45–6.88 0.004* 1 Constant -3.82 0.02 — < 0.001 — Note: Model fit: Nagelkerke R² = 0.42. Total n = 301. Difficult outcome defined as conversion to open surgery or major intraoperative complication. Abbreviations: CI: Confidence Interval; OR: Odds Ratio; RUQ: Right Upper Quadrant; US: Ultrasonography. Model Performance and Clinical Utility The Chole-Risk Score demonstrated excellent discriminatory power with an Area Under the Curve (AUC) of 0.848 (95% CI, 0.789–0.907) (Fig. 1 ). At the optimal clinical cutoff of ≥ 3 points, the score achieved a sensitivity of 73.7% (95% CI, 59.7% to 85.4%) and a specificity of 86.5% (95% CI, 81.8% to 90.4%). The overall diagnostic accuracy was 84.4% (95% CI, 79.8% to 88.3%), Notably, the model exhibited a Negative Predictive Value of 95.6% (95% CI, 92.2% to 97.8%), while the Positive Predictive Value was 45.9% (95% CI, 34.8% to 57.3%). Calibration analysis showed strong agreement between predicted and observed probabilities (Hosmer-Lemeshow p = 0.612), which suggests strong agreement between the predicted probabilities and actual observed outcomes (Fig. 2 ). Decision Curve Analysis (DCA) confirmed that using the Chole-Risk Score provided a higher net benefit across threshold probabilities from 10% to 70% compared to "treat-all" or "treat-none" strategies (Fig. 3 ). Postoperative Outcomes Postoperative outcomes are detailed in Table 4 . The Difficult group had a significantly higher rate of any postoperative complication (100% vs. 19.3%, p < 0.001), including all instances requiring secondary percutaneous intervention (32.4% vs. 0%, p < 0.001). Interestingly, the mean operative time did not differ significantly (48.6 ± 15.1 min vs. 50.5 ± 11.9 min, p = 0.377), potentially reflecting early conversion decisions in complex cases. The Difficult group had a shorter mean hospital stay (2.3 ± 0.8 days vs. 5.4 ± 5.9 days, p = 0.001), which may be attributed to protocol-driven early discharge following conversion and stabilization. Table 4 Postoperative Outcomes and Complications Variable Total Cohort (n = 301) Easy (n = 264) Difficult (n = 37) *p*-value Hospital Stay (days), mean ± SD 5.0 ± 5.6 5.4 ± 5.9 2.3 ± 0.8 0.001² Any Postop Complication, n (%) 88 (29.2%) 51 (19.3%) 37 (100.0%) < 0.001¹ VAS Pain Score (24h), mean ± SD 6.6 ± 4.4 6.5 ± 4.6 7.6 ± 0.7 0.133² Need for Secondary Intervention, n (%) 12 (4.0%) 0 (0.0%) 12 (32.4%) < 0.001¹ Abbreviations: VAS: Visual Analog Scale; SD: Standard Deviation. ¹Chi-square / Fisher’s exact test; ²Independent samples t-test. Discussion The development of a reliable preoperative triage tool is essential for mitigating the risks associated with LC. In this prospective cohort of 301 patients, the Chole-Risk Score demonstrated a high discriminative capacity (AUC 0.848) for predicting surgical difficulty. By integrating three bedside-accessible parameters; RUQ rigidity, ultrasonographic stone impaction, and a history of acute cholecystitis, this scoring system provides a pragmatic framework for preoperative risk stratification. The global landscape of LC is characterized by a conversion rate to open surgery that typically ranges from 2% to 10% across various surgical settings ( 9 ). Large retrospective analyses have reported rates as low as 2.6% ( 9 ), whereas other cohorts often observe incidences as high as 12.1% ( 18 ). These conversions are primarily driven by dense adhesions and distorted anatomy within the Calot triangle. In contemporary practice, the widespread adoption of safety protocols, such as the Critical View of Safety and standardized bailout strategies, has trended conversion rates toward the lower end of the spectrum in experienced units ( 26 ). However, the slightly higher rate of surgical difficulty observed in our cohort (12.3%) may be attributed to the resource-limited nature of the setting and the prevalence of advanced biliary pathology upon presentation. The predictors identified in the multivariate model for the Chole-Risk Score correspond to distinct phases of inflammatory and mechanical gallbladder pathology, aligning with findings from several international cohorts. Right Upper Quadrant (RUQ) Rigidity emerged as the most potent predictor in our study (OR 8.50). Biologically, this physical sign suggests localized peritonitis or omental "phlegmon" formation, which is strongly associated with the "frozen" Calot’s triangle anatomy that often necessitates conversion to open surgery ( 27 ). Similar observations were made in the Randhawa/Pujahari series, where palpable masses and clinical signs of inflammation were heavily weighted due to their association with dense adhesions ( 10 ). Stone Impaction at the gallbladder neck, a common ultrasonographic finding in our Yemeni cohort, historically correlates with increased intra-luminal pressure and wall edema. These pathological changes inhibit effective fundal retraction, which is a critical technical step for achieving the Critical View of Safety (CVS) as advocated by SAGES guidelines ( 26 , 28 ). This finding is consistent with the CAAD grading system, which emphasizes that anatomical distortion caused by impacted stones is a primary driver of surgical difficulty ( 13 ). Finally, a History of Acute Cholecystitis serves as a clinical marker for chronic fibrotic changes. Each subsequent inflammatory episode increases the density of adhesions, complicating the dissection of the cystic duct and artery. Our findings reflect the data from the Nassar scale, which demonstrates that repeated admissions for biliary colic or cholecystitis are predictive of higher operative grades and increased risk of conversion ( 14 , 29 ). By combining these three variables, the Chole-Risk Score offers a parsimonious yet powerful framework for preoperative planning in resource-limited settings. While traditional metrics such as the ROC curve prioritize discriminatory power, Decision Curve Analysis (DCA) provides a more nuanced evaluation of clinical net benefit ( 24 ). Our analysis indicates that the Chole-Risk Score offers superior utility compared to both "treat-all" and "treat-none" strategies across a broad threshold probability range of 10% to 70%. For example, at a 30% threshold, which represents the clinical tipping point where a surgeon might consider escalating care to a senior consultant, the model provides a significantly higher net benefit than traditional clinical judgment alone. This is consistent with recent findings in surgical predictive modeling, where DCA has been utilized to justify the preemptive allocation of specialized resources in complex hepatobiliary cases ( 21 , 30 – 32 ). By identifying high-risk patients preoperatively, surgical departments at facilities like Ibb University Hospitals can ensure that senior expertise is available from the outset. This proactive approach potentially reduces the incidence of "bailout" procedures or catastrophic iatrogenic bile duct injuries (BDI) that often arise from persistent dissection in the presence of severely distorted anatomy ( 26 ). A notable finding in this cohort was the significantly shorter mean length of stay (LOS) in the difficult group (2.3 days vs. 5.4 days, p = 0.001). This "LOS paradox" has been documented in other studies within resource-limited or conflict-affected regions where complex cases or major iatrogenic injuries are stabilized and rapidly transferred to higher-level hepatobiliary units for specialized management ( 4 ). Furthermore, the high prevalence of comorbidities such as Diabetes Mellitus (83.8%) in the difficult group underscores the systemic vulnerability of this population. Diabetic microangiopathy and blunted inflammatory responses may contribute to delayed presentation, resulting in more advanced gangrenous changes upon admission. Study Limitations While the Chole-Risk Score demonstrates strong predictive performance, several limitations of this study should be considered. The retrospective design inherently depends on the accuracy of existing medical records, which introduces a potential for documentation bias regarding subjective clinical signs such as abdominal rigidity. Furthermore, as a single-center study conducted at a tertiary referral hospital in Yemen, our cohort may be subject to referral bias. Patients at our facility often present with more advanced biliary pathology than those in primary care, which may influence the 12.3% difficulty rate and limit the immediate generalizability of our findings to regions with different disease profiles. Additionally, our primary outcome was a composite binary measure focusing on conversion and major complications. Although these are vital safety metrics, we did not incorporate operative duration as a criterion to avoid confounding variables related to varying levels of surgical experience. Finally, while internal validation via bootstrap resampling yielded an excellent AUC of 0.848, the model still requires external validation in independent, prospective cohorts to confirm its reliability across diverse clinical settings in the Middle East and beyond. Conclusion The findings of this study underscore the clinical utility of the Chole-Risk Score as a reliable and parsimonious tool for predicting technical difficulty in laparoscopic cholecystectomy. By integrating three easily accessible variables; Right Upper Quadrant rigidity, ultrasonographic stone impaction, and a history of acute cholecystitis, we developed a model that demonstrated high diagnostic accuracy with a validated AUC of 0.848. In the specific surgical context of Yemen, where patients frequently present with advanced biliary disease and resource constraints may limit preoperative planning, this score provides an objective framework for risk stratification. Implementing this scoring system allows for the proactive identification of high-risk cases, facilitating the early involvement of senior surgical expertise and optimized theatre scheduling. Ultimately, the use of the Chole-Risk Score has the potential to enhance patient safety by reducing the incidence of intraoperative complications and managing the transition to open surgery more effectively. While our results are promising, future multi-center prospective studies are encouraged to further validate these findings and evaluate the long-term impact of the score on surgical outcomes across the region. Abbreviations ASA American Society of Anesthesiologists RUQ Right Upper Quadrant WBC White Blood Cell SD Standard Deviation ¹Chi-square / Fisher’s exact test ²Independent samples t-test. Declarations Ethical Approval and Consent to Participate The study was reviewed and formally approved by the Institutional Research Ethics Committee of Ibb University on January 1, 2025 (Approval Reference ID: IBBUNI.2025.1.001). The requirement for individual patient informed consent was waived by the committee due to the retrospective, anonymized nature of the data analysis, which posed no more than minimal risk to participants. All procedures were conducted in accordance with the ethical standards of the Declaration of Helsinki. Consent for Publication Not applicable, as the manuscript does not contain any individual person’s data in any form (including individual details, images, or videos). Competing Interests The authors declare that they have no competing interests. AI Declaration (Declaration of Generative AI in the Writing Process) During the preparation of this work, the authors used Gemini (Google) in order to refine the academic language, ensure STROBE-compliant structuring of the methods section, and assist in the generation of Python-based visualization code for model validation (ROC and Calibration plots). After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication and the accuracy of the clinical data presented. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Author Contribution Saif G was responsible for study conception and design. N A and F A and Ahmed Ateik performed the data extraction. Saleh A and Qasem A and WA and AK and WE performed the statistical analysis and Python script development. Faisal Ahmed drafted the manuscript. All authors read and approved the final manuscript. Data Availability The datasets generated and analyzed during the current study are archived in the Mendeley Data repository [10.17632/ghz2ghr2d3.1]. The custom Python scripts used for statistical analysis and model validation are available in the same repository for purposes of reproducibility. References Stinton LM, Shaffer EA. Epidemiology of gallbladder disease: cholelithiasis and cancer. Gut Liver. 2012;6(2):172–87. Wang X, Yu W, Jiang G, Li H, Li S, Xie L, et al. Global Epidemiology of Gallstones in the 21st Century: A Systematic Review and Meta-Analysis. Clin Gastroenterol Hepatol. 2024;22(8):1586–95. Unalp-Arida A, Ruhl CE. 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Surg Clin North Am. 2000;80(4):1093–110. Mikwar Z, Aljadani FF, Alotaibi AK, Neazy FA, Alsaadi NH, Alzahrani MA, et al. The Conversion Rate of Laparoscopic Cholecystectomy to Open Cholecystectomy at King Abdulaziz Medical City, Jeddah, Saudi Arabia: Prevalence and Causes. Cureus. 2024;16(6):e63026. Randhawa JS, Pujahari AK. Preoperative prediction of difficult lap chole: a scoring method. Indian J Surg. 2009;71(4):198–201. Serrano-González R, Rivero Y, Hernandez-Velasquez A, Rodriguez-Rugel T, Mendez-Meneses G, Vidal-Gallardo A, et al. Predicting Difficulty in Laparoscopic Cholecystectomies: An Evaluation of the Labbad-Vivas Score and Its Correlation With the Parkland Grading Scale. Cureus. 2024;16(3):e56185. Sutcliffe RP, Hollyman M, Hodson J, Bonney G, Vohra RS, Griffiths EA, et al. Preoperative risk factors for conversion from laparoscopic to open cholecystectomy: a validated risk score derived from a prospective U.K. database of 8820 patients. HPB. 2016;18(11):922–8. Sugrue M, Sahebally SM, Ansaloni L, Zielinski MD. Grading operative findings at laparoscopic cholecystectomy- a new scoring system. World J Emerg Surg. 2015;10:14. Griffiths EA, Hodson J, Vohra RS, Marriott P, Katbeh T, Zino S, et al. Utilisation of an operative difficulty grading scale for laparoscopic cholecystectomy. Surg Endosc. 2019;33(1):110–21. Schrenk P, Woisetschläger R, Rieger R, Wayand WU. A diagnostic score to predict the difficulty of a laparoscopic cholecystectomy from preoperative variables. Surg Endosc. 1998;12(2):148–50. Philip Rothman J, Burcharth J, Pommergaard HC, Viereck S, Rosenberg J. Preoperative Risk Factors for Conversion of Laparoscopic Cholecystectomy to Open Surgery - A Systematic Review and Meta-Analysis of Observational Studies. Dig Surg. 2016;33(5):414–23. Schrenk P, Woisetschläger R, Wayand WU. Laparoscopic cholecystectomy. Cause of conversions in 1,300 patients and analysis of risk factors. Surg Endosc. 1995;9(1):25–8. Morales-Maza J, Rodríguez-Quintero JH, Santes O, Aguilar-Frasco JL, Romero-Vélez G, García-Ramos ES, et al. Conversion from laparoscopic to open cholecystectomy: Risk factor analysis based on clinical, laboratory, and ultrasound parameters. Rev Gastroenterol Mex (Engl Ed; 2020. Stoica PL, Serban D, Bratu DG, Serboiu CS, Costea DO, Tribus LC et al. Predictive Factors for Difficult Laparoscopic Cholecystectomies in Acute Cholecystitis. Diagnostics (Basel). 2024;14(3). Chand P, Singh R, Singh B, Singla RL, Yadav M. Preoperative Ultrasonography as a Predictor of Difficult Laparoscopic Cholecystectomy that Requires Conversion to Open Procedure. Niger J Surg. 2015;21(2):102–5. Gupta N, Ranjan G, Arora MP, Goswami B, Chaudhary P, Kapur A, et al. Validation of a scoring system to predict difficult laparoscopic cholecystectomy. Int J Surg. 2013;11(9):1002–6. Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): The TRIPOD Statement. Eur Urol. 2015;67(6):1142–51. Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982;143(1):29–36. Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Mak. 2006;26(6):565–74. Van Calster B, McLernon DJ, van Smeden M, Wynants L, Steyerberg EW. Calibration: the Achilles heel of predictive analytics. BMC Med. 2019;17(1):230. Ateik A, Ghabisha SA, Abdulmughni L, Awn F. A Time-Based and Clinical Status Stratified Protocol for Major Bile Duct Injury After Cholecystectomy: Retrospective, Single-Center Outcomes From a Resource-Limited Setting. Cureus. 2026;18(1):e102086. Nachnani J, Supe A. Pre-operative prediction of difficult laparoscopic cholecystectomy using clinical and ultrasonographic parameters. Indian J Gastroenterol. 2005;24(1):16–8. Brunt LM, Deziel DJ, Telem DA, Strasberg SM, Aggarwal R, Asbun H et al. Safe Cholecystectomy Multi-society Practice Guideline and State of the Art Consensus Conference on Prevention of Bile Duct Injury During Cholecystectomy. Ann Surg. 2020;272(1):3–23. Bhandari TR, Khan SA, Jha JL. Prediction of difficult laparoscopic cholecystectomy: An observational study. Ann Med Surg (Lond). 2021;72:103060. Trehan M, Mangotra V, Singh J, Singla S, Gautam SS, Garg R. Evaluation of Preoperative Scoring System for Predicting Difficult Laparoscopic Cholecystectomy. Int J Appl Basic Med Res. 2023;13(1):10–5. Ary Wibowo A, Tri Joko Putra O, Noor Helmi Z, Poerwosusanta H, Kelono Utomo T. Marwan Sikumbang K. A Scoring System to Predict Difficult Laparoscopic Cholecystectomy: A Five-Year Cross-Sectional Study. Minim Invasive Surg. 2022;2022:3530568. Tongyoo A, Liwattanakun A, Sriussadaporn E, Limpavitayaporn P, Mingmalairak C. The Modification of a Preoperative Scoring System to Predict Difficult Elective Laparoscopic Cholecystectomy. J Laparoendosc Adv Surg Tech A. 2023;33(3):269–75. Additional Declarations No competing interests reported. Supplementary Files floatimage4.png Supplementary Figure 1. STROBE Flow Diagram of Patient Selection. This diagram illustrates the recruitment and exclusion process for the study cohort. Out of an initial 384 patients screened at Ibb University Hospitals, 301 met the eligibility criteria. Reasons for exclusion included suspected malignancy, concurrent complex hepatobiliary procedures, and insufficient medical record documentation. The final cohort was stratified into "Difficult" (n=37) and "Non-Difficult" (n=264) groups based on the composite primary outcome. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8894630","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":598931185,"identity":"20ca21ba-95a8-4206-9eca-e59e92b984a8","order_by":0,"name":"Saif Ghabisha","email":"","orcid":"","institution":"Ibb University","correspondingAuthor":false,"prefix":"","firstName":"Saif","middleName":"","lastName":"Ghabisha","suffix":""},{"id":598931186,"identity":"16f374cf-6dc7-4407-b5f1-4570a09b3a88","order_by":1,"name":"Noaman Almashraki","email":"","orcid":"","institution":"Ibb University","correspondingAuthor":false,"prefix":"","firstName":"Noaman","middleName":"","lastName":"Almashraki","suffix":""},{"id":598931187,"identity":"7a209aaf-5c90-4633-8aa9-0753dd980375","order_by":2,"name":"Qasem Alyhari","email":"","orcid":"","institution":"Ibb University","correspondingAuthor":false,"prefix":"","firstName":"Qasem","middleName":"","lastName":"Alyhari","suffix":""},{"id":598931188,"identity":"79c70a60-ea30-4244-9f8c-c7a9b4bfb008","order_by":3,"name":"Ahmed Ateik","email":"","orcid":"","institution":"21 September University","correspondingAuthor":false,"prefix":"","firstName":"Ahmed","middleName":"","lastName":"Ateik","suffix":""},{"id":598931189,"identity":"429e67bc-51ac-48b1-afba-dd0eafe8d88f","order_by":4,"name":"Saleh Al-wageeh","email":"","orcid":"","institution":"Ibb University","correspondingAuthor":false,"prefix":"","firstName":"Saleh","middleName":"","lastName":"Al-wageeh","suffix":""},{"id":598931190,"identity":"c0bef705-9b1d-44b9-ab76-691c95df8dc8","order_by":5,"name":"Faisal Ahmed","email":"","orcid":"","institution":"Ibb University","correspondingAuthor":false,"prefix":"","firstName":"Faisal","middleName":"","lastName":"Ahmed","suffix":""},{"id":598931191,"identity":"582373e3-62b6-4d6e-98cd-2596c07f6ccb","order_by":6,"name":"Mohammed Al-Shehari","email":"","orcid":"","institution":"Sana’a University","correspondingAuthor":false,"prefix":"","firstName":"Mohammed","middleName":"","lastName":"Al-Shehari","suffix":""},{"id":598931192,"identity":"b5c36c70-c8e2-4482-926b-46fbbf978657","order_by":7,"name":"Yasser Obadiel","email":"","orcid":"","institution":"Sana’a University","correspondingAuthor":false,"prefix":"","firstName":"Yasser","middleName":"","lastName":"Obadiel","suffix":""},{"id":598931193,"identity":"6c927789-e487-425d-93f7-39b644f300d8","order_by":8,"name":"Wadhah Hassan Edrees","email":"","orcid":"","institution":"Hajjah University","correspondingAuthor":false,"prefix":"","firstName":"Wadhah","middleName":"Hassan","lastName":"Edrees","suffix":""},{"id":598931194,"identity":"6d53c862-09d9-4db9-957e-fd22280f885d","order_by":9,"name":"Ahmed Khailah","email":"","orcid":"","institution":"Sana’a University","correspondingAuthor":false,"prefix":"","firstName":"Ahmed","middleName":"","lastName":"Khailah","suffix":""},{"id":598931196,"identity":"024dbe88-5aa0-4e61-94f9-1a6d24bd3b34","order_by":10,"name":"Wadee Abdullah Al-Shehari","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYBACezDJxsDA2Mx8gIGxgQgthg1QLcztbQnEaTE4ANXC3nPGgEgttw+wSfwos5HjnZHzTeLnDhs5BvbDRzfg1XIugU2y51yaseSM3G2SvWfSjBl40tJu4NVyhoHtBm/b4cSNQC0SIEaDBI8ZQS03/7b9r99/I+eZ5F9itdzmbTuQwNhzhk2aKFsMexjbf8ucSzZsbG8ztpZtSzNmI+QXex7mw4ZvyuzkgVH58ObbNhs5fvbDx/BqYUCKCxYJEMmGXzkqYP5AiupRMApGwSgYOQAAF5NMxd9doRsAAAAASUVORK5CYII=","orcid":"","institution":"Ibb University","correspondingAuthor":true,"prefix":"","firstName":"Wadee","middleName":"Abdullah","lastName":"Al-Shehari","suffix":""}],"badges":[],"createdAt":"2026-02-16 16:10:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8894630/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8894630/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104181000,"identity":"528142c9-9dea-4fe4-8ffa-5a5f64fca442","added_by":"auto","created_at":"2026-03-08 17:24:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":175865,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReceiver Operating Characteristic (ROC) Curve of the Chole-Risk Score.\u003c/strong\u003e The Area Under the Curve (AUC) is 0.848 (95% CI: 0.789–0.907). The optimal clinical cutoff of ≥3 points provides a sensitivity of 73.7% and a specificity of 86.5%. (Statistical Note: 95% CI calculated using 1,000 bootstrap iterations.)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8894630/v1/243df8fae5f7257d0ad6befb.png"},{"id":104181009,"identity":"45da1a25-8c55-4db4-9ca1-dbf6156130e2","added_by":"auto","created_at":"2026-03-08 17:24:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":62158,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDecision Curve Analysis (DCA) for Clinical Utility.\u003c/strong\u003e The solid black line (Chole-Risk Score) shows a higher net benefit than the \"Treat All\" or \"Treat None\" strategies across clinically relevant threshold probabilities (10–70%).\u003cstrong\u003e versus Observed Probabilities.\u003c/strong\u003e The solid line (model performance) aligns closely with the 45-degree dashed line (perfect calibration). Hosmer-Lemeshow goodness-of-fit test p = 0.612.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8894630/v1/93ecf5ee74041c1eaaaeb682.png"},{"id":104181081,"identity":"270497cc-1ef2-49f5-b2dc-bdb27f7b2bfc","added_by":"auto","created_at":"2026-03-08 17:24:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":63892,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDecision Curve Analysis (DCA) for Clinical Utility.\u003c/strong\u003e The solid black line (Chole-Risk Score) shows a higher net benefit than the \"Treat All\" or \"Treat None\" strategies across clinically relevant threshold probabilities (10–70%).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8894630/v1/c40fb8925f913f2a668afd04.png"},{"id":108713768,"identity":"8388121a-4640-4195-a606-1b662327b0ec","added_by":"auto","created_at":"2026-05-07 14:42:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":739452,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8894630/v1/f1edabf8-f2e7-43e0-94e8-996d5fa228a4.pdf"},{"id":104180941,"identity":"ca6b83d6-a0f5-4414-bbf8-6c69ad548296","added_by":"auto","created_at":"2026-03-08 17:24:18","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":55726,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 1. STROBE Flow Diagram of Patient Selection.\u003c/strong\u003e This diagram illustrates the recruitment and exclusion process for the study cohort. Out of an initial 384 patients screened at Ibb University Hospitals, 301 met the eligibility criteria. Reasons for exclusion included suspected malignancy, concurrent complex hepatobiliary procedures, and insufficient medical record documentation. The final cohort was stratified into \"Difficult\" (n=37) and \"Non-Difficult\" (n=264) groups based on the composite primary outcome.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8894630/v1/ee67cb137817f521d8a0cb1e.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and Validation of a Preoperative Risk Score for Difficult Laparoscopic Cholecystectomy: A Retrospective Study in Resource-Limited Settings","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCholelithiasis is one of the most common conditions affecting the biliary system worldwide. Cholelithiasis prevalence varies greatly worldwide primarily due to age, female sex, ethnicity and geography, obesity, rapid weight loss, metabolic syndrome components like diabetes and hypertriglyceridemia, Westernized diets high in refined carbohydrates and low in fiber, genetic predisposition, pregnancy or parity, and liver diseases such as cirrhosis or hepatitis C (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). In the United States, gallstone disease affects approximately 20\u0026nbsp;million adults (10% prevalence among those aged 20\u0026ndash;74), with symptomatic cases driving around 2\u0026nbsp;million ambulatory visits and 1\u0026nbsp;million emergency department visits annually (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). In Yemen, the prevalence is roughly 4 5.5%; however, this number is increasing quickly because of changes in diets, shifting social structures such as chewing khat, and better access to ultrasound in both rural and urban areas (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe 1992 NIH Consensus Development Conference marked a turning point, establishing laparoscopic cholecystectomy (LC) as the safe gold standard for symptomatic gallstones (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Today, LC is the preferred approach, delivering less postoperative pain, shorter hospital stays, better cosmesis, and quicker recovery compared to open surgery (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs laparoscopic surgical proficiency has increased around the world, the initial absolute contraindications have been increasingly reevaluated. Conditions previously thought to be prohibitive, such as excessive obesity and a history of upper abdominal surgery, are now regularly treated with laparoscopic surgery. The few absolute contraindications that remain now are primarily limited to patients deemed medically incompetent for general anesthesia, untreated bleeding diatheses, and probable gallbladder cancer (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite these improvements, \"difficult\" cholecystectomies continue to pose challenges. Of all laparoscopic cholecystectomies (LC), 2\u0026ndash;10% require conversion to open surgery due to intraoperative challenges such as adhesions, inflammation, or unclear anatomy (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Various validated preoperative risk prediction models for difficult LC share fundamental characteristics identified through multivariate analyses, although they vary regarding the specific weights and cut-off values assigned to individual predictors. Several established scoring systems, such as the Randhawa/Pujahari (threshold\u0026thinsp;\u0026gt;\u0026thinsp;5 points), Nassar, and CAAD systems, have undergone rigorous validation (\u003cspan additionalcitationids=\"CR11 CR12 CR13\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). These models are increasingly recognized for their ability to integrate clinical and ultrasonographic variables to optimize surgical planning and improve patient safety.\u003c/p\u003e \u003cp\u003eConsistently identified risk factors in the literature include advanced age (typically\u0026thinsp;\u0026gt;\u0026thinsp;50 to 60 years), male sex, and an elevated body mass index (\u0026gt;\u0026thinsp;25 to 30 kg/m\u0026sup2;). These factors often correspond to increased tissue friability, complex anatomical variations, and technical challenges in surgical exposure (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Furthermore, a clinical history of acute cholecystitis or multiple hospital admissions is frequently cited as the most heavily weighted predictor, as it reflects chronic inflammatory changes that substantially complicate the operative procedure (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eObjective risk stratification is further enhanced by combining physical findings, such as a palpable gallbladder, with ultrasonographic parameters, including a gallbladder wall thickness exceeding 4 mm, the presence of impacted stones, and pericholecystic fluid (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). These predictive models have demonstrated high diagnostic accuracy, with reported Area Under the Curve (AUC) values ranging from 0.78 to 0.95, sensitivities reaching 95 percent, and specificities exceeding 80 percent (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Consequently, the implementation of such scores facilitates proactive surgeon allocation, enhanced theatre scheduling, and a measurable reduction in conversion rates to open surgery.\u003c/p\u003e \u003cp\u003eDespite the proliferation of predictive scoring systems in high-resource healthcare environments, their applicability in low-to-middle-income countries remains a subject of ongoing debate. In regions like Yemen, patients often present with advanced biliary disease due to delayed referrals, limited access to elective surgical services, and socioeconomic barriers to early diagnosis. These local factors may alter the predictive weight of traditional variables, rendering international models less accurate or overly complex for rapid bedside use. Furthermore, many existing scores rely on high-resolution imaging or specific laboratory biomarkers that are not consistently available in rural or resource-limited tertiary centers.\u003c/p\u003e \u003cp\u003eThere is a clear clinical necessity for a simplified, robust, and locally validated risk assessment tool that utilizes accessible clinical and sonographic data. Such a tool would allow surgical departments to prioritize high-risk patients for senior-led operative teams, thereby reducing the incidence of avoidable complications and minimizing the economic burden associated with prolonged hospital stays and open conversions.\u003c/p\u003e \u003cp\u003eConsequently, this study was designed to develop and validate the Chole-Risk Score within a Yemeni patient population. By focusing on a parsimonious set of predictors, we aimed to create a reliable predictive model that maintains high diagnostic accuracy while ensuring ease of implementation in the acute surgical setting.\u003c/p\u003e"},{"header":"Patients and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Setting\u003c/h2\u003e \u003cp\u003eThis study was a single-center, retrospective, observational cohort analysis conducted at Ibb University Hospitals. All patient data were extracted from the surgical and radiological records of consecutive patients undergoing laparoscopic cholecystectomy between April 22, 2020, and November 23, 2024.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthical Approval and Consent\u003c/strong\u003e \u003cp\u003e The study protocol was reviewed and formally approved by the Institutional Research Ethics Committee of Ibb University on January 1, 2025 (Approval Reference ID: IBBUNI.2025.1.001). The requirement for individual patient consent was waived due to the retrospective, anonymized nature of the data analysis, which was deemed to pose no more than minimal risk to participants. All research activities were conducted in strict accordance with the ethical principles outlined in the Declaration of Helsinki.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePatient Population\u003c/h3\u003e\n\u003cp\u003eThe initial screening identified adult patients (aged 18 years or older) who underwent a laparoscopic cholecystectomy for symptomatic benign gallbladder disease; including acute cholecystitis, chronic cholecystitis, and symptomatic cholelithiasis, within the defined study period. Exclusion criteria were applied to create a homogeneous cohort for analysis. Patients were excluded if they had a preoperative diagnosis of gallbladder malignancy, were undergoing cholecystectomy as part of a more extensive hepatobiliary or pancreatic procedure, or had incomplete preoperative or operative records in the hospital database. Following these criteria, the final analytic cohort comprised 301 patients (see STROBE Flow Diagram, Supplementary Fig.\u0026nbsp;1).\u003c/p\u003e\n\u003ch3\u003eSample Size and Power Analysis\u003c/h3\u003e\n\u003cp\u003eThe minimum sample size was initially estimated based on the diagnostic performance of a preoperative scoring system reported by Gupta et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), which achieved a sensitivity of 95.74% and a specificity of 73.68%. Using these values as a reference, with a 5% significance level, 80% power, and a 10% margin of error, the required sample size was determined to be 138 patients.\u003c/p\u003e \u003cp\u003eHowever, to ensure sufficient model stability and minimize the risk of overfitting in our multivariable logistic regression, we adopted a consecutive sampling strategy. This resulted in a final cohort of 301 patients. With 37 observed events (difficult cases) and three primary predictors (RUQ rigidity, impacted stone, and history of AC), our study achieved an Events Per Variable (EPV) ratio of 12.3. This exceeds the recommended threshold of 10 EPV required for robust multivariable prediction modeling according to TRIPOD guidelines (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eData Collection and Variable Definition\u003c/h3\u003e\n\u003cp\u003ePreoperative, intraoperative, and postoperative data were systematically collected by a trained research team using a standardized electronic case report form. To minimize informational bias, researchers responsible for extracting preoperative predictor data were blinded to the eventual intraoperative outcomes recorded in the surgical notes. Demographic information (age, gender), anthropometric measurements (BMI), and relevant clinical history were recorded. Key physical examination findings, specifically the documentation of right upper quadrant (RUQ) rigidity, were extracted from preoperative clinical notes recorded by the attending surgeon or senior surgical resident at the time of admission. Preoperative laboratory values, including the total white blood cell (WBC) count from the most recent test prior to surgery, were collected. All ultrasonographic (US) findings were obtained from formal radiology reports; variables of interest included gallbladder wall thickness and the presence of an impacted stone at the gallbladder neck or cystic duct.\u003c/p\u003e\n\u003ch3\u003eDefinition of Primary Outcome: Surgical Difficulty\u003c/h3\u003e\n\u003cp\u003eThe primary outcome was a \"difficult laparoscopic cholecystectomy,\" defined as a composite binary endpoint. A procedure was classified as difficult if there was either (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) an intraoperative decision to convert to an open cholecystectomy, as documented in the operative note, or (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) the occurrence of a major intraoperative complication. Major complications were specified as significant biliary injury (e.g., common bile duct or major sectoral duct injury), major vascular injury requiring repair or transfusion, or extensive visceral injury. Based on this definition, 37 out of 301 procedures (12.3%) were categorized as difficult.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using Python (version 3.10) with the SciPy, pandas, scikit-learn, and statsmodels libraries. Continuous variables were summarized as means with standard deviations (SD) or medians with interquartile ranges (IQR) based on their distribution, which was assessed using the Shapiro-Wilk test. Categorical variables were expressed as frequencies and percentages. Differences in baseline characteristics between the \"difficult\" and \"non-difficult\" groups were compared using Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test or the Mann-Whitney U test for continuous variables, and the Chi-squared or Fisher\u0026rsquo;s exact test for categorical variables, as appropriate.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eModel Development and Validation\u003c/h3\u003e\n\u003cp\u003eVariables with a plausible clinical link to surgical difficulty and a univariate association of p\u0026thinsp;\u0026lt;\u0026thinsp;0.10 were entered into a multivariable binary logistic regression model to identify independent predictors. Model assumptions were verified, and multicollinearity was evaluated using variance inflation factors (VIF\u0026thinsp;\u0026lt;\u0026thinsp;2.0 for all retained variables). The final model was selected based on clinical relevance, statistical significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and minimization of the Akaike Information Criterion (AIC). The regression coefficients (beta) from the final model were used to construct a simplified integer-based risk score; points were assigned by dividing each beta-coefficient by the smallest significant coefficient and rounding to the nearest integer. The discriminatory power was evaluated by calculating the Area Under the Receiver Operating Characteristic Curve (AUC), with 95% confidence intervals (CI) generated via bootstrapping (1000 replicates). The diagnostic performance of the score was evaluated using receiver operating characteristic (ROC) curves, and the area under the curve (AUC) was calculated to assess predictive accuracy. The AUC was classified as strong (AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.9), moderate (0.7\u0026thinsp;\u0026lt;\u0026thinsp;AUC\u0026thinsp;\u0026le;\u0026thinsp;0.9), or low (0.5\u0026thinsp;\u0026lt;\u0026thinsp;AUC\u0026thinsp;\u0026le;\u0026thinsp;0.7) predictive capacity (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). The optimal threshold for the score was determined using the maximum Youden index, which maximizes the difference between sensitivity and specificity. At this threshold, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. Model calibration was assessed using a calibration plot and the Brier score, while clinical utility was evaluated using Decision Curve Analysis (DCA) to quantify the net benefit across a range of threshold probabilities (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Internal validation was performed using bootstrap resampling to calculate the optimism-corrected AUC. All analyses adhered to the TRIPOD guidelines for transparent reporting of predictive models (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePatient Characteristics and Cohort Demographics\u003c/h2\u003e \u003cp\u003eA total of 301 patients who underwent laparoscopic cholecystectomy (LC) were included in the final analysis. The mean age was 44.3\u0026thinsp;\u0026plusmn;\u0026thinsp;13.6 years. Based on the composite outcome of intraoperative conversion to open surgery or major surgical complications, patients were divided into two groups: the Easy group (n\u0026thinsp;=\u0026thinsp;264, 87.7%) and the Difficult group (n\u0026thinsp;=\u0026thinsp;37, 12.3%).\u003c/p\u003e \u003cp\u003eSignificant differences were observed in preoperative characteristics (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Notably, the mean White Blood Cell (WBC) count was higher in the Difficult group (13.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9 x 10⁹/L) compared to the Easy group (7.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3 x 10⁹/L, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Comorbidities, particularly Diabetes Mellitus (83.8%) and Hypertension (78.4%), were significantly more prevalent in the Difficult group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). All patients (100%) in the Difficult group presented with right upper quadrant (RUQ) rigidity and a history of acute cholecystitis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePreoperative Clinical and Sociodemographic Characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal Cohort (n\u0026thinsp;=\u0026thinsp;301)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEasy (n\u0026thinsp;=\u0026thinsp;264)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDifficult (n\u0026thinsp;=\u0026thinsp;37)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*p*-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.3\u0026thinsp;\u0026plusmn;\u0026thinsp;13.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.6\u0026thinsp;\u0026plusmn;\u0026thinsp;12.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.1\u0026thinsp;\u0026plusmn;\u0026thinsp;14.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.285\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFemale Gender, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e180 (59.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e143 (54.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u0026sup1;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSocial Habit (Smoking/Khat), n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e211 (70.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e182 (68.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (78.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.241\u0026sup1;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrevious Hospitalization, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45 (14.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (4.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (89.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u0026sup1;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eASA Physical Status, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u0026sup1;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e- Class I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e210 (69.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e201 (76.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (24.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e- Class II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76 (25.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60 (22.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (43.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e- Class III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (5.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (32.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRUQ Rigidity, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74 (24.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (14.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u0026sup1;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWBC Count (x10⁹/L), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePredictors of Surgical Difficulty\u003c/h2\u003e \u003cp\u003eUnivariate analysis of radiological and intraoperative factors revealed several associations with surgical difficulty (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Patients in the Difficult group exhibited greater mean gallbladder wall thickness (4.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5 mm vs. 3.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8 mm, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and a markedly higher incidence of impacted stones (45.9% vs. 0%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) on ultrasonography.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRadiological and Intraoperative Findings\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal Cohort (n\u0026thinsp;=\u0026thinsp;301)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEasy (n\u0026thinsp;=\u0026thinsp;264)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDifficult (n\u0026thinsp;=\u0026thinsp;37)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*p*-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGB Wall Thickness (mm), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eImpacted Stone (US), n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (5.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (45.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u0026sup1;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePericholecystic Collection, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (3.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (29.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u0026sup1;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOperative Time (min), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.3\u0026thinsp;\u0026plusmn;\u0026thinsp;12.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.5\u0026thinsp;\u0026plusmn;\u0026thinsp;11.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.6\u0026thinsp;\u0026plusmn;\u0026thinsp;15.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.377\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDrain Usage, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e268 (89.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e264 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (10.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u0026sup1;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eAbbreviations: GB: Gallbladder; US: Ultrasonography; SD: Standard Deviation.\u0026sup1;Chi-square / Fisher\u0026rsquo;s exact test; \u0026sup2;Independent samples t-test.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMultivariate Analysis and Score Development\u003c/h2\u003e \u003cp\u003eMultivariate logistic regression identified three independent predictors of surgical difficulty (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These variables were used to derive the Chole-Risk Score: RUQ Rigidity (OR 8.50; 95% CI, 4.21\u0026ndash;17.15; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Impacted Stone (OR 7.24; 95% CI, 3.10\u0026ndash;16.90; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and History of Acute Cholecystitis (OR 3.16; 95% CI, 1.45\u0026ndash;6.88; p\u0026thinsp;=\u0026thinsp;0.004). Based on the β-coefficients, a 5-point scoring system was established, assigning 2 points each for RUQ rigidity and impacted stones, and 1 point for a history of acute cholecystitis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate Logistic Regression Analysis for Predicting Difficult Cholecystectomy\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ Coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOdds Ratio (OR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI for OR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*p*-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAssigned Points\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRUQ Rigidity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.21\u0026ndash;17.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eImpacted Stone (US)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.10\u0026ndash;16.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistory of Acute Cholecystitis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.45\u0026ndash;6.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eConstant\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-3.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: Model fit: Nagelkerke R\u0026sup2; = 0.42. Total n\u0026thinsp;=\u0026thinsp;301. Difficult outcome defined as conversion to open surgery or major intraoperative complication.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eAbbreviations: CI: Confidence Interval; OR: Odds Ratio; RUQ: Right Upper Quadrant; US: Ultrasonography.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eModel Performance and Clinical Utility\u003c/h2\u003e \u003cp\u003eThe Chole-Risk Score demonstrated excellent discriminatory power with an Area Under the Curve (AUC) of 0.848 (95% CI, 0.789\u0026ndash;0.907) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). At the optimal clinical cutoff of \u0026ge;\u0026thinsp;3 points, the score achieved a sensitivity of 73.7% (95% CI, 59.7% to 85.4%) and a \u003cb\u003especificity of 86.5%\u003c/b\u003e (95% CI, 81.8% to 90.4%). The overall diagnostic accuracy was 84.4% (95% CI, 79.8% to 88.3%), Notably, the model exhibited a Negative Predictive Value of 95.6% (95% CI, 92.2% to 97.8%), while the Positive Predictive Value was 45.9% (95% CI, 34.8% to 57.3%).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCalibration analysis showed strong agreement between predicted and observed probabilities (Hosmer-Lemeshow p\u0026thinsp;=\u0026thinsp;0.612), which suggests strong agreement between the predicted probabilities and actual observed outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Decision Curve Analysis (DCA) confirmed that using the Chole-Risk Score provided a higher net benefit across threshold probabilities from 10% to 70% compared to \"treat-all\" or \"treat-none\" strategies (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePostoperative Outcomes\u003c/h2\u003e \u003cp\u003ePostoperative outcomes are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The Difficult group had a significantly higher rate of any postoperative complication (100% vs. 19.3%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), including all instances requiring secondary percutaneous intervention (32.4% vs. 0%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Interestingly, the mean operative time did not differ significantly (48.6\u0026thinsp;\u0026plusmn;\u0026thinsp;15.1 min vs. 50.5\u0026thinsp;\u0026plusmn;\u0026thinsp;11.9 min, p\u0026thinsp;=\u0026thinsp;0.377), potentially reflecting early conversion decisions in complex cases. The Difficult group had a shorter mean hospital stay (2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8 days vs. 5.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9 days, p\u0026thinsp;=\u0026thinsp;0.001), which may be attributed to protocol-driven early discharge following conversion and stabilization.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePostoperative Outcomes and Complications\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal Cohort (n\u0026thinsp;=\u0026thinsp;301)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEasy (n\u0026thinsp;=\u0026thinsp;264)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDifficult (n\u0026thinsp;=\u0026thinsp;37)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*p*-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHospital Stay (days), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAny Postop Complication, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88 (29.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51 (19.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u0026sup1;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVAS Pain Score (24h), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.133\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNeed for Secondary Intervention, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (4.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (32.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u0026sup1;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eAbbreviations: VAS: Visual Analog Scale; SD: Standard Deviation. \u0026sup1;Chi-square / Fisher\u0026rsquo;s exact test; \u0026sup2;Independent samples t-test.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe development of a reliable preoperative triage tool is essential for mitigating the risks associated with LC. In this prospective cohort of 301 patients, the Chole-Risk Score demonstrated a high discriminative capacity (AUC 0.848) for predicting surgical difficulty. By integrating three bedside-accessible parameters; RUQ rigidity, ultrasonographic stone impaction, and a history of acute cholecystitis, this scoring system provides a pragmatic framework for preoperative risk stratification.\u003c/p\u003e \u003cp\u003eThe global landscape of LC is characterized by a conversion rate to open surgery that typically ranges from 2% to 10% across various surgical settings (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Large retrospective analyses have reported rates as low as 2.6% (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), whereas other cohorts often observe incidences as high as 12.1% (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). These conversions are primarily driven by dense adhesions and distorted anatomy within the Calot triangle. In contemporary practice, the widespread adoption of safety protocols, such as the Critical View of Safety and standardized bailout strategies, has trended conversion rates toward the lower end of the spectrum in experienced units (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). However, the slightly higher rate of surgical difficulty observed in our cohort (12.3%) may be attributed to the resource-limited nature of the setting and the prevalence of advanced biliary pathology upon presentation.\u003c/p\u003e \u003cp\u003eThe predictors identified in the multivariate model for the Chole-Risk Score correspond to distinct phases of inflammatory and mechanical gallbladder pathology, aligning with findings from several international cohorts. Right Upper Quadrant (RUQ) Rigidity emerged as the most potent predictor in our study (OR 8.50). Biologically, this physical sign suggests localized peritonitis or omental \"phlegmon\" formation, which is strongly associated with the \"frozen\" Calot\u0026rsquo;s triangle anatomy that often necessitates conversion to open surgery (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Similar observations were made in the Randhawa/Pujahari series, where palpable masses and clinical signs of inflammation were heavily weighted due to their association with dense adhesions (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStone Impaction at the gallbladder neck, a common ultrasonographic finding in our Yemeni cohort, historically correlates with increased intra-luminal pressure and wall edema. These pathological changes inhibit effective fundal retraction, which is a critical technical step for achieving the Critical View of Safety (CVS) as advocated by SAGES guidelines (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). This finding is consistent with the CAAD grading system, which emphasizes that anatomical distortion caused by impacted stones is a primary driver of surgical difficulty (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFinally, a History of Acute Cholecystitis serves as a clinical marker for chronic fibrotic changes. Each subsequent inflammatory episode increases the density of adhesions, complicating the dissection of the cystic duct and artery. Our findings reflect the data from the Nassar scale, which demonstrates that repeated admissions for biliary colic or cholecystitis are predictive of higher operative grades and increased risk of conversion (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). By combining these three variables, the Chole-Risk Score offers a parsimonious yet powerful framework for preoperative planning in resource-limited settings.\u003c/p\u003e \u003cp\u003eWhile traditional metrics such as the ROC curve prioritize discriminatory power, Decision Curve Analysis (DCA) provides a more nuanced evaluation of clinical net benefit (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Our analysis indicates that the Chole-Risk Score offers superior utility compared to both \"treat-all\" and \"treat-none\" strategies across a broad threshold probability range of 10% to 70%. For example, at a 30% threshold, which represents the clinical tipping point where a surgeon might consider escalating care to a senior consultant, the model provides a significantly higher net benefit than traditional clinical judgment alone. This is consistent with recent findings in surgical predictive modeling, where DCA has been utilized to justify the preemptive allocation of specialized resources in complex hepatobiliary cases (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). By identifying high-risk patients preoperatively, surgical departments at facilities like Ibb University Hospitals can ensure that senior expertise is available from the outset. This proactive approach potentially reduces the incidence of \"bailout\" procedures or catastrophic iatrogenic bile duct injuries (BDI) that often arise from persistent dissection in the presence of severely distorted anatomy (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA notable finding in this cohort was the significantly shorter mean length of stay (LOS) in the difficult group (2.3 days vs. 5.4 days, p\u0026thinsp;=\u0026thinsp;0.001). This \"LOS paradox\" has been documented in other studies within resource-limited or conflict-affected regions where complex cases or major iatrogenic injuries are stabilized and rapidly transferred to higher-level hepatobiliary units for specialized management (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Furthermore, the high prevalence of comorbidities such as Diabetes Mellitus (83.8%) in the difficult group underscores the systemic vulnerability of this population. Diabetic microangiopathy and blunted inflammatory responses may contribute to delayed presentation, resulting in more advanced gangrenous changes upon admission.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eStudy Limitations\u003c/h2\u003e \u003cp\u003eWhile the Chole-Risk Score demonstrates strong predictive performance, several limitations of this study should be considered. The retrospective design inherently depends on the accuracy of existing medical records, which introduces a potential for documentation bias regarding subjective clinical signs such as abdominal rigidity. Furthermore, as a single-center study conducted at a tertiary referral hospital in Yemen, our cohort may be subject to referral bias. Patients at our facility often present with more advanced biliary pathology than those in primary care, which may influence the 12.3% difficulty rate and limit the immediate generalizability of our findings to regions with different disease profiles. Additionally, our primary outcome was a composite binary measure focusing on conversion and major complications. Although these are vital safety metrics, we did not incorporate operative duration as a criterion to avoid confounding variables related to varying levels of surgical experience. Finally, while internal validation via bootstrap resampling yielded an excellent AUC of 0.848, the model still requires external validation in independent, prospective cohorts to confirm its reliability across diverse clinical settings in the Middle East and beyond.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe findings of this study underscore the clinical utility of the Chole-Risk Score as a reliable and parsimonious tool for predicting technical difficulty in laparoscopic cholecystectomy. By integrating three easily accessible variables; Right Upper Quadrant rigidity, ultrasonographic stone impaction, and a history of acute cholecystitis, we developed a model that demonstrated high diagnostic accuracy with a validated AUC of 0.848. In the specific surgical context of Yemen, where patients frequently present with advanced biliary disease and resource constraints may limit preoperative planning, this score provides an objective framework for risk stratification.\u003c/p\u003e \u003cp\u003eImplementing this scoring system allows for the proactive identification of high-risk cases, facilitating the early involvement of senior surgical expertise and optimized theatre scheduling. Ultimately, the use of the Chole-Risk Score has the potential to enhance patient safety by reducing the incidence of intraoperative complications and managing the transition to open surgery more effectively. While our results are promising, future multi-center prospective studies are encouraged to further validate these findings and evaluate the long-term impact of the score on surgical outcomes across the region.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eASA\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cem\u003eAmerican Society of Anesthesiologists\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eRUQ\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cem\u003eRight Upper Quadrant\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eWBC\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cem\u003eWhite Blood Cell\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cem\u003eStandard Deviation \u0026sup1;Chi-square / Fisher\u0026rsquo;s exact test\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003e\u0026sup2;Independent samples t-test.\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e\u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthical Approval and Consent to Participate\u003c/strong\u003e \u003cp\u003e The study was reviewed and formally approved by the Institutional Research Ethics Committee of Ibb University on January 1, 2025 (Approval Reference ID: IBBUNI.2025.1.001). The requirement for individual patient informed consent was waived by the committee due to the retrospective, anonymized nature of the data analysis, which posed no more than minimal risk to participants. All procedures were conducted in accordance with the ethical standards of the Declaration of Helsinki.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for Publication\u003c/strong\u003e \u003cp\u003eNot applicable, as the manuscript does not contain any individual person\u0026rsquo;s data in any form (including individual details, images, or videos).\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eAI Declaration (Declaration of Generative AI in the Writing Process)\u003c/h2\u003e \u003cp\u003eDuring the preparation of this work, the authors used Gemini (Google) in order to refine the academic language, ensure STROBE-compliant structuring of the methods section, and assist in the generation of Python-based visualization code for model validation (ROC and Calibration plots). After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication and the accuracy of the clinical data presented.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSaif G was responsible for study conception and design. N A and F A and Ahmed Ateik performed the data extraction. Saleh A and Qasem A and WA and AK and WE performed the statistical analysis and Python script development. Faisal Ahmed drafted the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and analyzed during the current study are archived in the Mendeley Data repository [10.17632/ghz2ghr2d3.1]. The custom Python scripts used for statistical analysis and model validation are available in the same repository for purposes of reproducibility.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eStinton LM, Shaffer EA. Epidemiology of gallbladder disease: cholelithiasis and cancer. Gut Liver. 2012;6(2):172\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang X, Yu W, Jiang G, Li H, Li S, Xie L, et al. Global Epidemiology of Gallstones in the 21st Century: A Systematic Review and Meta-Analysis. Clin Gastroenterol Hepatol. 2024;22(8):1586\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUnalp-Arida A, Ruhl CE. Burden of gallstone disease in the United States population: Prepandemic rates and trends. World J Gastrointest Surg. 2024;16(4):1130\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAklan HM, Esmail AASA, Al-Sadeq AA, Eissa GA, Hassan OA, Al-Mikhlafy AA, et al. Frequency of gallbladder stones among patients underwent abdominal ultrasound in a tertiary hospital in sana'a city, Yemen. Malaysian J Med Health Sci. 2020;16:36\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMurugan N, Burkhill G, Williams SG, Padley SPG, Murray-Lyon IM. The effect of khat chewing on gallbladder motility in a group of volunteers. J Ethnopharmacol. 2003;86(2):225\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNIH Consensus conference. Gallstones and laparoscopic cholecystectomy. Jama. 1993;269(8):1018-24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNidoni R, Udachan TV, Sasnur P, Baloorkar R, Sindgikar V, Narasangi B. Predicting Difficult Laparoscopic Cholecystectomy Based on Clinicoradiological Assessment. J Clin Diagn Res. 2015;9(12):Pc09\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCuret MJ. Special problems in laparoscopic surgery. Previous abdominal surgery, obesity, and pregnancy. Surg Clin North Am. 2000;80(4):1093\u0026ndash;110.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMikwar Z, Aljadani FF, Alotaibi AK, Neazy FA, Alsaadi NH, Alzahrani MA, et al. The Conversion Rate of Laparoscopic Cholecystectomy to Open Cholecystectomy at King Abdulaziz Medical City, Jeddah, Saudi Arabia: Prevalence and Causes. Cureus. 2024;16(6):e63026.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRandhawa JS, Pujahari AK. Preoperative prediction of difficult lap chole: a scoring method. Indian J Surg. 2009;71(4):198\u0026ndash;201.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSerrano-Gonz\u0026aacute;lez R, Rivero Y, Hernandez-Velasquez A, Rodriguez-Rugel T, Mendez-Meneses G, Vidal-Gallardo A, et al. Predicting Difficulty in Laparoscopic Cholecystectomies: An Evaluation of the Labbad-Vivas Score and Its Correlation With the Parkland Grading Scale. Cureus. 2024;16(3):e56185.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSutcliffe RP, Hollyman M, Hodson J, Bonney G, Vohra RS, Griffiths EA, et al. Preoperative risk factors for conversion from laparoscopic to open cholecystectomy: a validated risk score derived from a prospective U.K. database of 8820 patients. HPB. 2016;18(11):922\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSugrue M, Sahebally SM, Ansaloni L, Zielinski MD. Grading operative findings at laparoscopic cholecystectomy- a new scoring system. World J Emerg Surg. 2015;10:14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGriffiths EA, Hodson J, Vohra RS, Marriott P, Katbeh T, Zino S, et al. Utilisation of an operative difficulty grading scale for laparoscopic cholecystectomy. Surg Endosc. 2019;33(1):110\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchrenk P, Woisetschl\u0026auml;ger R, Rieger R, Wayand WU. A diagnostic score to predict the difficulty of a laparoscopic cholecystectomy from preoperative variables. Surg Endosc. 1998;12(2):148\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePhilip Rothman J, Burcharth J, Pommergaard HC, Viereck S, Rosenberg J. Preoperative Risk Factors for Conversion of Laparoscopic Cholecystectomy to Open Surgery - A Systematic Review and Meta-Analysis of Observational Studies. Dig Surg. 2016;33(5):414\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchrenk P, Woisetschl\u0026auml;ger R, Wayand WU. Laparoscopic cholecystectomy. Cause of conversions in 1,300 patients and analysis of risk factors. Surg Endosc. 1995;9(1):25\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorales-Maza J, Rodr\u0026iacute;guez-Quintero JH, Santes O, Aguilar-Frasco JL, Romero-V\u0026eacute;lez G, Garc\u0026iacute;a-Ramos ES, et al. Conversion from laparoscopic to open cholecystectomy: Risk factor analysis based on clinical, laboratory, and ultrasound parameters. Rev Gastroenterol Mex (Engl Ed; 2020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStoica PL, Serban D, Bratu DG, Serboiu CS, Costea DO, Tribus LC et al. Predictive Factors for Difficult Laparoscopic Cholecystectomies in Acute Cholecystitis. Diagnostics (Basel). 2024;14(3).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChand P, Singh R, Singh B, Singla RL, Yadav M. Preoperative Ultrasonography as a Predictor of Difficult Laparoscopic Cholecystectomy that Requires Conversion to Open Procedure. Niger J Surg. 2015;21(2):102\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGupta N, Ranjan G, Arora MP, Goswami B, Chaudhary P, Kapur A, et al. Validation of a scoring system to predict difficult laparoscopic cholecystectomy. Int J Surg. 2013;11(9):1002\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCollins GS, Reitsma JB, Altman DG, Moons KGM. Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): The TRIPOD Statement. Eur Urol. 2015;67(6):1142\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982;143(1):29\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Mak. 2006;26(6):565\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Calster B, McLernon DJ, van Smeden M, Wynants L, Steyerberg EW. Calibration: the Achilles heel of predictive analytics. BMC Med. 2019;17(1):230.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAteik A, Ghabisha SA, Abdulmughni L, Awn F. A Time-Based and Clinical Status Stratified Protocol for Major Bile Duct Injury After Cholecystectomy: Retrospective, Single-Center Outcomes From a Resource-Limited Setting. Cureus. 2026;18(1):e102086.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNachnani J, Supe A. Pre-operative prediction of difficult laparoscopic cholecystectomy using clinical and ultrasonographic parameters. Indian J Gastroenterol. 2005;24(1):16\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrunt LM, Deziel DJ, Telem DA, Strasberg SM, Aggarwal R, Asbun H et al. Safe Cholecystectomy Multi-society Practice Guideline and State of the Art Consensus Conference on Prevention of Bile Duct Injury During Cholecystectomy. Ann Surg. 2020;272(1):3\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhandari TR, Khan SA, Jha JL. Prediction of difficult laparoscopic cholecystectomy: An observational study. Ann Med Surg (Lond). 2021;72:103060.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrehan M, Mangotra V, Singh J, Singla S, Gautam SS, Garg R. Evaluation of Preoperative Scoring System for Predicting Difficult Laparoscopic Cholecystectomy. Int J Appl Basic Med Res. 2023;13(1):10\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAry Wibowo A, Tri Joko Putra O, Noor Helmi Z, Poerwosusanta H, Kelono Utomo T. Marwan Sikumbang K. A Scoring System to Predict Difficult Laparoscopic Cholecystectomy: A Five-Year Cross-Sectional Study. Minim Invasive Surg. 2022;2022:3530568.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTongyoo A, Liwattanakun A, Sriussadaporn E, Limpavitayaporn P, Mingmalairak C. The Modification of a Preoperative Scoring System to Predict Difficult Elective Laparoscopic Cholecystectomy. J Laparoendosc Adv Surg Tech A. 2023;33(3):269\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Laparoscopic Cholecystectomy, Surgical Difficulty, Prediction Score, Preoperative Assessment, Conversion to Open Surgery, Yemen, Biliary Surgery","lastPublishedDoi":"10.21203/rs.3.rs-8894630/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8894630/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eLaparoscopic cholecystectomy (LC) is one of the most common surgical procedures worldwide. Yet, reliably identifying patients at high risk for intraoperative conversion or major complications preoperatively remains challenging. In this study, we investigated factors associated with difficult LC and developed and validated a simple preoperative scoring system to predict it.\u003c/p\u003e\u003ch2\u003ePatients and Methods:\u003c/h2\u003e \u003cp\u003eWe conducted a single-center, retrospective cohort study at Ibb University Hospitals, Yemen. Data were extracted from records of 301 consecutive patients who underwent LC between April 2020 and November 2024. The primary outcome, \"surgical difficulty,\" was a composite endpoint of conversion to open surgery or major intraoperative complications. Multivariable logistic regression was used to identify independent predictors and derive an integer-based risk score. Model performance was evaluated using the Area Under the Receiver Operating Characteristic Curve (AUC), calibration plots, and Decision Curve Analysis (DCA).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOf 301 patients, 37 (12.3%) experienced difficult LC. Multivariable analysis identified three key predictors: right upper quadrant rigidity (adjusted OR 8.50, 95% CI 4.21\u0026ndash;17.15; 2 points), impacted stone on ultrasound (aOR 7.24, 95% CI 3.10\u0026ndash;16.90; 2 points), and prior acute cholecystitis (aOR 3.16, 95% CI 1.45\u0026ndash;6.88; 1 point). This yielded a 5-point Chole-Risk Score with excellent discrimination (AUC 0.848, 95% CI 0.789\u0026ndash;0.907). At the optimal cutoff of \u0026ge;\u0026thinsp;3 points, sensitivity reached 73.7% and specificity 86.5%. Calibration was strong (Hosmer-Lemeshow \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.612), and DCA showed superior clinical utility across a broad threshold range.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe Chole-Risk Score is a reliable, bedside-accessible tool for predicting difficulty in LC. In settings like Ibb, Yemen, where advanced preoperative imaging may be limited, this score allows for the identification of high-risk cases, facilitating early senior surgical involvement and improving patient safety.\u003c/p\u003e","manuscriptTitle":"Development and Validation of a Preoperative Risk Score for Difficult Laparoscopic Cholecystectomy: A Retrospective Study in Resource-Limited Settings","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-08 17:24:01","doi":"10.21203/rs.3.rs-8894630/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fb41032e-1ab3-4417-9ab2-744f72fd5efd","owner":[],"postedDate":"March 8th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-07T14:33:49+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-07T14:40:57+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-08 17:24:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8894630","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8894630","identity":"rs-8894630","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}