BACKGROUND: In the hospital setting, frailty is a significant risk factor, but difficult to measure in clinical practice. We propose a reweighting of an existing diagnoses-based frailty score using routine data from a tertiary care teaching hospital in southern Germany.
METHODS: The dataset includes patient characteristics such as sex, age, primary and secondary diagnoses and in-hospital mortality. Based on this information, we recalculate the existing Hospital Frailty Risk Score. The cohort includes patients aged ≥ 75 and was divided into a development cohort (admission year 2011 to 2013, N = 30,525) and a validation cohort (2014, N = 11,202). A limited external validation is also conducted in a second validation cohort containing inpatient cases aged ≥ 75 in 2022 throughout Germany (N = 491,251). In the development cohort, LASSO regression analysis was used to select the most relevant variables and to generate a reweighted Frailty Score for the German setting. Discrimination is assessed using the area under the receiver operating characteristic curve (AUC). Visualization of calibration curves and decision curve analysis were carried out. Applicability of the reweighted Frailty Score in a non-elderly population was assessed using logistic regression models.
RESULTS: Reweighting of the Frailty Score included only 53 out of the 109 frailty-related diagnoses and resulted in substantially better discrimination than the initial weighting of the score (AUC = 0.89 vs. AUC = 0.80, p < 0.001 in the validation cohort). Calibration curves show a good agreement between score-based predictions and actual observed mortality. Additional external validation using inpatient cases aged ≥ 75 in 2022 throughout Germany (N = 491,251) confirms the results regarding discrimination and calibration and underlines the geographic and temporal validity of the reweighted Frailty Score. Decision curve analysis indicates that the clinical usefulness of the reweighted score as a general decision support tool is superior to the initial version of the score. Assessment of the applicability of the reweighted Frailty Score in a non-elderly population (N = 198,819) shows that discrimination is superior to the initial version of the score (AUC = 0.92 vs. AUC = 0.87, p < 0.001). In addition, we observe a fairly age-stable influence of the reweighted Frailty Score on in-hospital mortality, which does not differ substantially for women and men.
CONCLUSIONS: Our data indicate that the reweighted Frailty Score is superior to the original Frailty Score for identification of older, frail patients at risk for in-hospital mortality. Hence, we recommend using the reweighted Frailty Score in the German in-hospital setting.

OBJECTIVE: This study aimed to investigate whether a significant trend regarding inpatient falls in Swiss acute care hospitals between 2011 and 2019 could be confirmed on a national level, and whether the trend persists after risk adjustment for patient-related fall risk factors.
METHODS: A secondary data analysis was conducted based on annual multicentre cross-sectional studies carried out between 2011 and 2019.
METHODS: All Swiss acute care hospitals were obliged to participate in the surveys. Except for emergency departments, outpatient wards and recovery rooms, all wards were included.
METHODS: All inpatients aged 18 or older who had given their informed consent and whose data were complete and available were included.
METHODS: Whether a patient had fallen in the hospital was retrospectively determined on the survey day by asking patients the following question: Have you fallen in this institution in the last 30 days?
RESULTS: Based on data from 110 892 patients from 222 Swiss hospitals, a national inpatient fall rate of 3.7% was determined over the 9 survey years. A significant linear decreasing trend (p=0.004) was observed using the Cochran-Armitage trend test. After adjusting for patient-related fall risk factors in a two-level random intercept logistic regression model, a significant non-linear decreasing trend was found at the national level.
CONCLUSIONS: A significant decrease in fall rates in Swiss hospitals, indicating an improvement in the quality of care provided, could be confirmed both descriptively and after risk adjustment. However, the non-linear trend, that is, an initial decrease in inpatient falls that flattens out over time, also indicates a possible future increase in fall rates. Monitoring of falls in hospitals should be maintained at the national level. Risk adjustment accounts for the observed increase in patient-related fall risk factors in hospitals, thus promoting a fairer comparison of the quality of care provided over time.

BACKGROUND: Few objective, real-time measurements of surgeon performance exist. The risk-adjusted cumulative sum is a novel method that can track surgeon-level outcomes on a continuous basis. The objective of this study was to demonstrate the feasibility of using risk-adjusted cumulative sum to monitor outcomes after colorectal operations and identify clinically relevant performance variations.
METHODS: The National Surgical Quality Improvement Program was queried to obtain patient-level data for 1,603 colorectal operations at a high-volume center from 2011 to 2020. For each case, expected risks of morbidity, mortality, reoperation, readmission, and prolonged length of stay were estimated using the National Surgical Quality Improvement Program risk calculator. Risk-adjusted cumulative sum curves were generated to signal observed-to-expected odds ratios of 1.5 (poor performance) and 0.5 (exceptional performance). Control limits were set based on a false positive rate of 5% (α = 0.05).
RESULTS: The cohort included data on 7 surgeons (those with more than 20 cases in the study period). Institutional observed versus expected outcomes were the following: morbidity 12.5% (vs 15.0%), mortality 2.5% (vs 2.0%), prolonged length of stay 19.7% (vs 19.1%), reoperation 11.1% (vs 11.3%), and 30-day readmission 6.1% (vs 4.8%). Risk-adjusted cumulative sum accurately demonstrated within- and between-surgeon performance variations across these metrics and proved effective when considering division-level data.
CONCLUSIONS: Risk-adjusted cumulative sum adjusts for patient-level risk factors to provide real-time data on surgeon-specific outcomes. This approach enables prompt identification of performance outliers and can contribute to quality assurance, root-cause analysis, and incentivization not only at the surgeon level but at divisional and institutional levels as well.

OBJECTIVE: To evaluate the additional advantages of integrating contrast-enhanced ultrasound (CEUS) into the Ovarian-Adnexal Reporting and Data System (O-RADS) ultrasound (US) for the characterization of adnexal lesions with solid components.
METHODS: This prospective multicenter study recruited women suspected of having adnexal lesions with solid components between September 2021 and December 2022. All patients scheduled for surgery underwent preoperative CEUS and US examinations. The lesions were categorized according to the O-RADS US system, and quantitative CEUS indexes were recorded. Pathological results served as the reference standard. Univariable and multivariable analyses were performed to identify risk factors for malignancy in adnexal lesions with solid components. Receiver operating characteristic (ROC) curve analysis was employed to assess diagnostic performance.
RESULTS: A total of 180 lesions in 175 women were included in the study. Among these masses, 80 were malignant and 100 were benign. Multivariable analysis revealed that serum CA-125, the presence of acoustic shadowing, and peak intensity (PI) ratio (PImass/PIuterus) of solid components on CEUS were independently associated with adnexal malignancy. The modified CEUS risk stratification model demonstrated superior diagnostic value in assessing adnexal lesions with solid components compared to O-RADS US (AUC: 0.91 vs 0.78, p < 0.001) and exhibited comparable performance to the Assessment of Different NEoplasias in the adnexa (ADNEX) model (AUC 0.91 vs 0.86, p = 0.07).
CONCLUSIONS: Our findings underscore the potential value of CEUS as an adjunctive tool for enhancing the precision of diagnostic evaluations of O-RADS US.
CONCLUSIONS: The promising performance of the modified CEUS risk stratification model suggests its potential to mitigate unnecessary surgeries in the characterization of adnexal lesions with solid components.
CONCLUSIONS: • The additional value of CEUS to O-RADS US in distinguishing between benign and malignant adnexal lesions with solid components requires further evaluation. • The modified CEUS risk stratification model displayed superior diagnostic value and specificity in characterizing adnexal lesions with solid components when compared to O-RADS US. • The inclusion of CEUS demonstrated potential in reducing the need for unnecessary surgeries in the characterization of adnexal lesions with solid components.

We experiment with recent ensemble machine learning methods in estimating healthcare costs, utilizing Finnish data containing rich individual-level information on healthcare costs, socioeconomic status and diagnostic data from multiple registries. Our data are a random 10% sample (553,675 observations) from the Finnish population in 2017. Using annual healthcare cost in 2017 as a response variable, we compare the performance of Random forest, Gradient Boosting Machine (GBM) and eXtreme Gradient Boosting (XGBoost) to linear regression. As machine learning methods are often seen as unsuitable in risk adjustment applications because of their relative opaqueness, we also introduce visualizations from the machine learning literature to help interpret the contribution of individual variables to the prediction. Our results show that ensemble machine learning methods can improve predictive performance, with all of them significantly outperforming linear regression, and that a certain level of interpretation can be provided for them. We also find individual-level socioeconomic variables to improve prediction accuracy and that their effect is larger for machine learning methods. However, we find that the predictions used for funding allocations are sensitive to model selection, highlighting the need for comprehensive robustness testing when estimating risk adjustment models used in applications.

OBJECTIVE: Presence of venous vascular invasion is a criterion of intermediate risk of recurrence in papillary thyroid carcinoma (PTC). However, the presence and type of vascular invasion (lymphatic or venous) is often underreported and its impact on PTCs without other risk features remains unknown. The aim of this study was to evaluate the impact of both lymphatic and venous invasion on the risk of recurrence/persistence on otherwise low-risk PTCs.
METHODS: Retrospective study including patients with otherwise low-risk PTCs but with vascular invasion, diagnosed between 2013 and 2019. The persistence/recurrence during the follow-up was evaluated. Pathology was reviewed to confirm the presence of lymphovascular invasion and determine the type of invasion.
RESULTS: A total of 141 patients were included. Lymphovascular invasion was confirmed in 20.6%. After surgery, 48.9% (N = 69) of the patients received radioactive iodine (RAI). The median follow-up time was 4 [3-6] years. Overall, 6 (4.2%) patients experienced persistent/recurrent disease in the neck, including 3 with lymphovascular invasion, confirmed as \"only lymphatic\". Overall, patients with tumors harboring lymphovascular invasion had sensibly more persistent/recurrence disease compared with those without lymphovascular invasion (10.3% vs 2.7%, p = 0.1), especially in the subgroup of patients not treated with RAI (20% vs 1.6%, p = 0.049) [OR 15.25, 95% CI 1.24-187.85, p = 0.033].
CONCLUSIONS: Lymphovascular invasion, including lymphatic invasion only, is associated with a sensibly higher risk of persistent/recurrent disease in otherwise low-risk PTCs, namely in patients not treated with RAI. Lymphatic invasion could have a role in risk-stratification systems for decision making.

OBJECTIVE: Intradialytic hypotension (IDH) is closely associated with adverse clinical outcomes in HD-patients. An IDH predictor model is important for IDH risk screening and clinical decision-making. In this study, we used Machine learning (ML) to develop IDH model for risk prediction in HD patients.
METHODS: 62,227 dialysis sessions were randomly partitioned into training data (70%), test data (20%), and validation data (10%). IDH-A model based on twenty-seven variables was constructed for risk prediction for the next HD treatment. IDH-B model based on ten variables from 64,870 dialysis sessions was developed for risk assessment before each HD treatment. Light Gradient Boosting Machine (LightGBM), Linear Discriminant Analysis, support vector machines, XGBoost, TabNet, and multilayer perceptron were used to develop the predictor model.
RESULTS: In IDH-A model, we identified the LightGBM method as the best-performing and interpretable model with C- statistics of 0.82 in Fall30Nadir90 definitions, which was higher than those obtained using the other models (P<0.01). In other IDH standards of Nadir90, Nadir100, Fall20, Fall30, and Fall20Nadir90, the LightGBM method had a performance with C- statistics ranged 0.77 to 0.89. As a complementary application, the LightGBM model in IDH-B model achieved C- statistics of 0.68 in Fall30Nadir90 definitions and 0.69 to 0.78 in the other five IDH standards, which were also higher than the other methods, respectively.
CONCLUSIONS: Use ML, we identified the LightGBM method as the good-performing and interpretable model. We identified the top variables as the high-risk factors for IDH incident in HD-patient. IDH-A and IDH-B model can usefully complement each other for risk prediction and further facilitate timely intervention through applied into different clinical setting.

BACKGROUND: Methods to accurately predict the risk of in-hospital mortality are important for applications including quality assessment of healthcare institutions and research.
OBJECTIVE: To update and validate the Kaiser Permanente inpatient risk adjustment methodology (KP method) to predict in-hospital mortality, using open-source tools to measure comorbidity and diagnosis groups, and removing troponin which is difficult to standardize across modern clinical assays.
METHODS: Retrospective cohort study using electronic health record data from GEMINI. GEMINI is a research collaborative that collects administrative and clinical data from hospital information systems.
METHODS: Adult general medicine inpatients at 28 hospitals in Ontario, Canada, between April 2010 and December 2022.
METHODS: The outcome was in-hospital mortality, modeled by diagnosis group using 56 logistic regressions. We compared models with and without troponin as an input to the laboratory-based acute physiology score. We fit and validated the updated method using internal-external cross-validation at 28 hospitals from April 2015 to December 2022.
RESULTS: In 938,103 hospitalizations with 7.2% in-hospital mortality, the updated KP method accurately predicted the risk of mortality. The c-statistic at the median hospital was 0.866 (see Fig. 3) (25th-75th 0.848-0.876, range 0.816-0.927) and calibration was strong for nearly all patients at all hospitals. The 95th percentile absolute difference between predicted and observed probabilities was 0.038 at the median hospital (25th-75th 0.024-0.057, range 0.006-0.118). Model performance was very similar with and without troponin in a subset of 7 hospitals, and performance was similar with and without troponin for patients hospitalized for heart failure and acute myocardial infarction.
CONCLUSIONS: An update to the KP method accurately predicted in-hospital mortality for general medicine inpatients in 28 hospitals in Ontario, Canada. This updated method can be implemented in a wider range of settings using common open-source tools.

BACKGROUND: We investigated the short- and long-term risks of pancreatic cancer after the diagnosis of acute pancreatitis.
METHODS: This population-based matched-cohort study used data from the Korean National Health Insurance Service database. Patients with acute pancreatitis (n = 25,488) were matched with the control group (n = 127,440) based on age, sex, body mass index, smoking status, and diabetes. We estimated the hazard ratios for developing pancreatic cancer in both groups using Cox regression analysis.
RESULTS: During a median follow-up of 5.4 years, pancreatic cancer developed in 479 patients (1.9%) in the acute pancreatitis group and 317 patients (0.2%) in the control group. Compared with the control group, the risk of pancreatic cancer in the acute pancreatitis group was very high within the first 2 years, which gradually decreased over time. The hazard ratio for the risk of developing pancreatitis was 8.46 (95% confidence interval, 5.57-12.84) at 1-2 years, and then decreased to 3.62 (95% confidence interval, 2.26-4.91) at 2-4 years. However, even after 8-10 years, the hazard ratio was still statistically significantly increased to 2.80 (95% confidence interval, 1.42-5.53). After 10 years, there was no significant difference in the risk of pancreatic cancer between the two groups.
CONCLUSIONS: The risk of pancreatic cancer increases rapidly after acute pancreatitis diagnosis, gradually declines after 2 years, and remains elevated for up to 10 years. Further studies are needed to determine the long-term effects of acute pancreatitis on the risk of pancreatic cancer.

BACKGROUND: Pressure injuries are a major public health problem because of their impact on morbidity and mortality, quality of life, and increased healthcare costs. The Centros Comprometidos con la Excelencia en Cuidados/Best Practice Spotlight Organization (CCEC/BPSO®) program provides guidelines that can improve these outcomes.
OBJECTIVE: This study aimed to assess the effectiveness of the CCEC/BPSO® program in improving the care of patients at risk of pressure injury (PI) at an acute care hospital in Spain.
METHODS: A quasi-experimental regression discontinuity design in three periods was used: (1) baseline (2014), (2) implementation (2015-2017), and (3) sustainability (2018-2019). The study population was comprised of 6377 patients discharged from 22 units of an acute care hospital. The performance of the PI risk assessment and reassessment, the application of special pressure management surfaces, and the presence of PIs were all monitored.
RESULTS: Forty-four percent of patients (n = 2086) met the inclusion criteria. After implementing the program, the number of patients assessed (53.9%-79.5%), reassessed (4.9%-37.5%), the application of preventive measures (19.6%-79.7%), and the number of people identified with a PI in implementation (1.47%-8.44%) and sustainability (1.47%-8.8%) all increased.
CONCLUSIONS: The implementation of the CCEC/BPSO® program achieved improved patient safety. Risk assessment monitoring, risk reassessment, and special pressure management surfaces were practices that increased during the study period and were incorporated by professionals to prevent PIs. The training of professionals was instrumental to this process. Incorporating these programs is a strategic line to improve clinical safety and the quality of care. The implementation of the program has been effective in terms of improving the identification of patients at risk and the application of surfaces.