Machine Learning Based Algorithm for the Early Prediction of Postoperative Hypocalcemia Risk After Thyroidectomy.

OBJECTIVE: We used machine learning to develop and validate a multivariable algorithm allowing the accurate and early prediction of postoperative hypocalcemia risk. SUMMARY BACKGROUND: Post-operative hypocalcemia is frequent after total thyroidectomy. An early and accurate individualized prediction of the risk of hypocalcemia could guide the selective prescription of calcium supplementation only to patients most likely to present with hypocalcemia after total thyroidectomy. METHODS: This retrospective study enrolled all patients undergoing total thyroidectomy in a single referral center between November 2019 and march 2022 (derivation cohort) and april 2022 and September 2022 (validation cohort) . The primary study outcome was post-operative hypocalcemia (serum calcium under 80 mg/L). Exposures were multiple clinical and biological variables prospectively collected and analyzed with various machine learning methods, to develop and validate a multi variable prediction algorithm. RESULTS: Among 610 / 118 participants in the derivation / validation cohorts, 100 (16.4%) / 26 (22%) presented post-operative hypocalcemia. The most accurate prediction algorithm was obtained with random forest, and combined intraoperative parathyroid hormone measurements with three clinical variables (age, sex and body mass index), to calculate a postoperative hypocalcemia risk for each patient. After multiple cross validation, the area under the receiver operative characteristic curve was 0.902 (0.829-0.970) in the derivation cohort, and 0.928 (95% CI : 0.86; 0.97) in the validation cohort. Postoperative hypocalcemia risk values of 7% (low threshold) and 20% ( high threshold) had respectively a sensitivity of 92% and a negative likelihood ratio of 0.11, and a specificity of 90% and a positive of 7.6 for the prediction of postoperative hypocalcemia. CONCLUSION: Using machine learning, we developed and validated a simple multivariable model which allowed the accurate prediction of postoperative hypocalcemia. The resulting algorithm could be used at the point of care to guide clinical management after total thyroidectomy.

Development and validation of an interpretable machine learning-based calculator for predicting 5-year weight trajectories after bariatric surgery: a multinational retrospective cohort SOPHIA study.

BACKGROUND: Weight loss trajectories after bariatric surgery vary widely between individuals, and predicting weight loss before the operation remains challenging. We aimed to develop a model using machine learning to provide individual preoperative prediction of 5-year weight loss trajectories after surgery. METHODS: In this multinational retrospective observational study we enrolled adult participants (aged ≥18 years) from ten prospective cohorts (including ABOS [NCT01129297], BAREVAL [NCT02310178], the Swedish Obese Subjects study, and a large cohort from the Dutch Obesity Clinic [Nederlandse Obesitas Kliniek]) and two randomised trials (SleevePass [NCT00793143] and SM-BOSS [NCT00356213]) in Europe, the Americas, and Asia, with a 5 year follow-up after Roux-en-Y gastric bypass, sleeve gastrectomy, or gastric band. Patients with a previous history of bariatric surgery or large delays between scheduled and actual visits were excluded. The training cohort comprised patients from two centres in France (ABOS and BAREVAL). The primary outcome was BMI at 5 years. A model was developed using least absolute shrinkage and selection operator to select variables and the classification and regression trees algorithm to build interpretable regression trees. The performances of the model were assessed through the median absolute deviation (MAD) and root mean squared error (RMSE) of BMI. FINDINGS: 10 231 patients from 12 centres in ten countries were included in the analysis, corresponding to 30 602 patient-years. Among participants in all 12 cohorts, 7701 (75·3%) were female, 2530 (24·7%) were male. Among 434 baseline attributes available in the training cohort, seven variables were selected: height, weight, intervention type, age, diabetes status, diabetes duration, and smoking status. At 5 years, across external testing cohorts the overall mean MAD BMI was 2·8 kg/m2 (95% CI 2·6-3·0) and mean RMSE BMI was 4·7 kg/m2 (4·4-5·0), and the mean difference between predicted and observed BMI was -0·3 kg/m2 (SD 4·7). This model is incorporated in an easy to use and interpretable web-based prediction tool to help inform clinical decision before surgery. INTERPRETATION: We developed a machine learning-based model, which is internationally validated, for predicting individual 5-year weight loss trajectories after three common bariatric interventions. FUNDING: SOPHIA Innovative Medicines Initiative 2 Joint Undertaking, supported by the EU's Horizon 2020 research and innovation programme, the European Federation of Pharmaceutical Industries and Associations, Type 1 Diabetes Exchange, and the Juvenile Diabetes Research Foundation and Obesity Action Coalition; Métropole Européenne de Lille; Agence Nationale de la Recherche; Institut national de recherche en sciences et technologies du numérique through the Artificial Intelligence chair Apprenf; Université de Lille Nord Europe's I-SITE EXPAND as part of the Bandits For Health project; Laboratoire d'excellence European Genomic Institute for Diabetes; Soutien aux Travaux Interdisciplinaires, Multi-établissements et Exploratoires programme by Conseil Régional Hauts-de-France (volet partenarial phase 2, project PERSO-SURG).

Impact of Robotic Assistance on Complications in Bariatric Surgery at Expert Laparoscopic Surgery Centers: A Retrospective Comparative Study With Propensity Score.

OBJECTIVE: To investigate the way robotic assistance affected rate of complications in bariatric surgery at expert robotic and laparoscopic surgery facilities. BACKGROUND: While the benefits of robotic assistance were established at the beginning of surgical training, there is limited data on the robot's influence on experienced bariatric laparoscopic surgeons. METHODS: We conducted a retrospective study using the BRO clinical database (2008-2022) collecting data of patients operated on in expert centers. We compared the serious complication rate (defined as a Clavien score≥3) in patients undergoing metabolic bariatric surgery with or without robotic assistance. We used a directed acyclic graph to identify the variables adjustment set used in a multivariable linear regression, and a propensity score matching to calculate the average treatment effect (ATE) of robotic assistance. RESULTS: The study included 35,043 patients [24,428 sleeve gastrectomy (SG); 10,452 Roux-en-Y gastric bypass (RYGB); 163 single anastomosis duodenal-ileal bypass with sleeve gastrectomy (SADI-S)], with 938 operated on with robotic assistance (801 SG; 134 RYGB; 3 SADI-S), among 142 centers. Overall, we found no benefit of robotic assistance regarding the risk of complications (average treatment effect=-0.05, P =0.794), with no difference in the RYGB+SADI group ( P =0.322) but a negative trend in the SG group (more complications, P =0.060). Length of hospital stay was decreased in the robot group (3.7±11.1 vs 4.0±9.0 days, P <0.001). CONCLUSIONS: Robotic assistance reduced the length of stay but did not statistically significantly reduce postoperative complications (Clavien score≥3) following either GBP or SG. A tendency toward an elevated risk of complications following SG requires more supporting studies.

Data-driven subgroups of type 2 diabetes, metabolic response, and renal risk profile after bariatric surgery: a retrospective cohort study.

BACKGROUND: A novel data-driven classification of type 2 diabetes has been proposed to personalise anti-diabetic treatment according to phenotype. One subgroup, severe insulin-resistant diabetes (SIRD), is characterised by mild hyperglycaemia but marked hyperinsulinaemia, and presents an increased risk of diabetic nephropathy. We hypothesised that patients with SIRD could particularly benefit from metabolic surgery. METHODS: We retrospectively related the newly defined clusters with the response to metabolic surgery in participants with type 2 diabetes from independent cohorts in France (the Atlas Biologique de l'Obésite Sévère [ABOS] cohort, n=368; participants underwent Roux-en-Y gastric bypass or sleeve gastrectomy between Jan 1, 2006, and Dec 12, 2017) and Brazil (the metabolic surgery cohort of the German Hospital of San Paulo, n=121; participants underwent Roux-en-Y gastric bypass between April 1, 2008, and March 20, 2016). The study outcomes were type 2 diabetes remission and improvement of estimated glomerular filtration rate (eGFR). FINDINGS: At baseline, 34 (9%) of 368 patients, 314 (85%) of 368 patients, and 17 (5%) of 368 patients were classified as having SIRD, mild obesity-related diabetes (MOD), and severe insulin deficient diabetes (SIDD) in the ABOS cohort, respectively, and in the São Paulo cohort, ten (8%) of 121 patients, 83 (69%) of 121 patients, and 25 (21%) of 121 patients were classified as having SIRD, MOD, and SIDD, respectively. At 1 year, type 2 diabetes remission was reported in 26 (81%) of 32 and nine (90%) of ten patients with SIRD, 167 (55%) of 306 and 42 (51%) of 83 patients with MOD, and two (13%) of 16 and nine (36%) of 25 patients with SIDD, in the ABOS and São Paulo cohorts, respectively. The mean eGFR was lower in patients with SIRD at baseline and increased postoperatively in these patients in both cohorts. In multivariable analysis, SIRD was associated with more frequent type 2 diabetes remission (odds ratio 4·3, 95% CI 1·8-11·2; p=0·0015), and an increase in eGFR (mean effect size 13·1 ml/min per 1·73 m2, 95% CI 3·6-22·7; p=0·0070). INTERPRETATION: Patients in the SIRD subgroup had better outcomes after metabolic surgery, both in terms of type 2 diabetes remission and renal function, with no additional surgical risk. Data-driven classification might help to refine the indications for metabolic surgery. FUNDING: Agence Nationale de la Recherche, Investissement d'Avenir, Innovative Medecines Initiative, Fondation Cœur et Artères, and Fondation Francophone pour la Recherche sur le Diabète.

A tool to predict progression of non-alcoholic fatty liver disease in severely obese patients.

BACKGROUND & AIMS: Severely obese patients are a growing population at risk of non-alcoholic fatty liver disease (NAFLD). Considering the increasing burden, a predictive tool of NAFLD progression would be of interest. Our objective was to provide a tool allowing general practitioners to identify and refer the patients most at risk, and specialists to estimate disease progression and adapt the therapeutic strategy. METHODS: This predictive tool is based on a Markov model simulating steatosis, fibrosis and non-alcoholic steatohepatitis (NASH) evolution. This model was developped from data of 1801 severely obese, bariatric surgery candidates, with histological assessment, integrating duration of exposure to risk factors. It is then able to predict current disease severity in the absence of assessment, and future cirrhosis risk based on current stage. RESULTS: The model quantifies the impact of sex, body-mass index at 20, diabetes, age of overweight onset, on progression. For example, for 40-year-old severely obese patients seen by the general practitioners: (a) non-diabetic woman overweight at 20, and (b) diabetic man overweight at 10, without disease assessment, the model predicts their current risk to have NASH or F3-F4: for (a) 5.7% and 0.6%, for (b) 16.1% and 10.0% respectively. If those patients have been diagnosed F2 by the specialist, the model predicts the 5-year cirrhosis risk: 1.8% in the absence of NASH and 6.0% in its presence for (a), 10.3% and 26.7% respectively, for (b). CONCLUSIONS: This model provides a decision-making tool to predict the risk of liver disease that could help manage severely obese patients.

A Model to Identify Heavy Drinkers at High Risk for Liver Disease Progression.

BACKGROUND & AIMS: Alcohol-related liver disease (ALD) causes chronic liver disease. We investigated how information on patients' drinking history and amount, stage of liver disease, and demographic feature can be used to determine risk of disease progression. METHODS: We collected data from 2334 heavy drinkers (50 g/day or more) with persistently abnormal results from liver tests who had been admitted to a hepato-gastroenterology unit in France from January 1982 through December 1997; patients with a recorded duration of alcohol abuse were assigned to the development cohort (n=1599; 75% men) or the validation cohort (n=735; 75% men), based on presence of a liver biopsy. We collected data from both cohorts on patient history and disease stage at the time of hospitalization. For the development cohort, severity of the disease was scored by the METAVIR (due to the availability of liver histology reports); in the validation cohort only the presence of liver complications was assessed. We developed a model of ALD progression and occurrence of liver complications (hepatocellular carcinoma and/or liver decompensation) in association with exposure to alcohol, age at the onset of heavy drinking, amount of alcohol intake, sex and body mass index. The model was fitted to the development cohort and then evaluated in the validation cohort. We then tested the ability of the model to predict disease progression for any patient profile (baseline evaluation). Patients with a 5-y weighted risk of liver complications greater than 5% were considered at high risk for disease progression. RESULTS: Model results are given for the following patient profiles: men and women, 40 y old, who started drinking at an age of 25 y, drank 150 g/day, and had a body mass index of 22 kg/m2 according to the disease severity at baseline evaluation. For men with baseline F0-F2 fibrosis, the model estimated the probabilities of normal liver, steatosis, or steatohepatitis at baseline to be 31.8%, 61.5% and 6.7%, respectively. The 5-y weighted risk of liver complications was 1.9%, ranging from 0.2% for men with normal liver at baseline evaluation to 10.3% for patients with steatohepatitis at baseline. For women with baseline F0-F2 fibrosis, probabilities of normal liver, steatosis, or steatohepatitis at baseline were 25.1%, 66.5% and 8.4%, respectively; the 5-y weighted risk of liver complications was 3.2%, ranging from 0.5% for women with normal liver at baseline to 14.7% for patients with steatohepatitis at baseline. Based on the model, men with F3-F4 fibrosis at baseline have a 24.5% 5-y weighted risk of complications (ranging from 20.2% to 34.5%) and women have a 30.1% 5-y weighted risk of complications (ranging from 24.7% to 41.0%). CONCLUSIONS: We developed a Markov model that integrates data on level and duration of alcohol use to identify patients at high risk of liver disease progression. This model might be used to adapt patient care pathways.