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Background The Charlson and Elixhauser Comorbidity Indices are the most widely used comorbidity assessment methods in medical research. Both methods are adapted for use with the International Classification of Diseases, which 10th revision (ICD-10) is used by over a hundred countries in the world. Available Charlson and Elixhauser Comorbidity Index calculating methods are limited to a few applications with command-line user interfaces, all requiring specific programming language skills. This study aims to use Microsoft Excel to develop a non-programming and ICD-10 based dataset calculator for Charlson and Elixhauser Comorbidity Index and to validate its results with R- and SAS-based methods. Methods The Excel-based dataset calculator was developed using the program’s formulae, ICD-10 coding algorithms, and different weights of the Charlson and Elixhauser Comorbidity Index. Real, population-wide, nine-year spanning, index hip fracture data from the Estonian Health Insurance Fund was used for validating the calculator. The Excel-based calculator’s output values and processing speed were compared to R- and SAS-based methods. Results A total of 11,491 hip fracture patients’ comorbidities were used for validating the Excel-based calculator. The Excel-based calculator’s results were consistent, revealing no discrepancies, with R- and SAS-based methods while comparing 192,690 and 353,265 output values of Charlson and Elixhauser Comorbidity Index, respectively. The Excel-based calculator’s processing speed was slower but differing only from a few seconds up to four minutes with datasets including 6250–200,000 patients. Conclusions This study proposes a novel, validated, and non-programming-based method for calculating Charlson and Elixhauser Comorbidity Index scores. As the comorbidity calculations can be conducted in Microsoft Excel’s simple graphical point-and-click interface, the new method lowers the threshold for calculating these two widely used indices. Trial registration retrospectively registered.

The ACS-NSQIP Surgical Risk Calculator (SRC) is used to predict surgical outcomes, but its accuracy in gastrectomy has been questioned.1,2 We investigated if adding hypoalbuminemia enhances its predictive ability in gastrectomy. We identified gastrectomy patients from the ACS-NSQIP database from 2005 to 2019. We constructed pairs of logistic regression models: one with the existing 21 preoperative risk factors from the SRC and another with the addition of hypoalbuminemia. We evaluated improvement using Likelihood Ratio Test (LRT), Brier scores, and c-statistics. Of 18,070 gastrectomy patients, 34.5 ​% had hypoalbuminemia. Hypoalbuminemia patients had 2.34 higher odds of mortality and 1.79 higher odds of morbidity. Adding hypoalbuminemia to the RC model statistically improved predictions for mortality, cumulative morbidity, pulmonary, renal, and wound complications (LRT p ​< ​0.001). It did not improve predictions for cardiac complications (LRT p ​= ​0.11) Hypoalbuminemia should be considered as an additional variable to the ACS-NSQIP SRC for gastrectomy. •Hypoalbuminemia linked to higher morbidity, mortality in gastrectomy.•Hypoalbuminemia improves NSQIP risk calculator predictions in gastrectomy.•Improved mortality, morbidity, wound, renal, pulmonary complications predictions.•Most measures did not show improvements in predicting cardiac complications.

Background Fracture-related infection (FRI) and subsequent persistent infections significantly affect fracture surgeries. We aimed to develop a precise, personalized risk calculator to assist orthopedic surgeons in evaluating perioperative FRI risk. Methods Data from 36,087 patients across two medical centers and four regional hospitals were analyzed. We assessed 29 risk factors, including patient characteristics, comorbidities, fracture location, and surgical variables using multivariable logistic regression. Each factor was weighted based on its regression coefficient. Discrimination and calibration were assessed with optimism-corrected AUC and Brier scores (1 000-fold bootstrap) and calibration plots. Results FRI occurred in 2396 patients (6.64%), and 453 patients (1.26%) experienced persistent infections. The top 10 risk factors included male sex, open fractures, tibiofibular fractures, ankle/foot fractures, operative time, hospital stay, peripheral vascular disease, diabetes, chronic kidney disease, and psychotic disorders. Optimism-corrected AUCs for predicting FRI and persistent infections were 0.781 [ p  < 0.001, 95% confidence interval (CI) 0.772–0.791] and 0.801 ( p  < 0.001, 95% CI 0.779–0.823), respectively. Optimal cutoff scores for predicting FRI and persistent infections were 213 (sensitivity 0.638, specificity 0.796) and 232 (sensitivity 0.658, specificity 0.821). Calibration plots demonstrated good predictive performance (mean absolute errors: FRI 0.006, persistent infection 0.006). Brier scores were 0.055 (FRI) and 0.012 (persistent infections), indicating good accuracy. Conclusions The FRI risk calculator showed good predictive abilities, with optimized cutoff points aiding perioperative planning and preventive measures. Patient engagement in understanding of infection risk can improve treatment outcomes. Limitations include participant biases and retrospective design; prospective external validation is recommended.

Background Intensive Care Unit (ICU) readmissions in patients with heart failure (HF) result in a significant risk of death and financial burden for patients and healthcare systems. Prediction of at-risk patients for readmission allows for targeted interventions that reduce morbidity and mortality. Methods and results We presented a process mining/deep learning approach for the prediction of unplanned 30-day readmission of ICU patients with HF. A patient’s health records can be understood as a sequence of observations called event logs; used to discover a process model. Time information was extracted using the DREAM (Decay Replay Mining) algorithm. Demographic information and severity scores upon admission were then combined with the time information and fed to a neural network (NN) model to further enhance the prediction efficiency. Additionally, several machine learning (ML) algorithms were developed to be used as the baseline models for the comparison of the results. Results By using the Medical Information Mart for Intensive Care III (MIMIC-III) dataset of 3411 ICU patients with HF, our proposed model yielded an area under the receiver operating characteristics (AUROC) of 0.930, 95% confidence interval of [0.898–0.960], the precision of 0.886, sensitivity of 0.805, accuracy of 0.841, and F-score of 0.800 which were far better than the results of the best baseline model and the existing literature. Conclusions The proposed approach was capable of modeling the time-related variables and incorporating the medical history of patients from prior hospital visits for prediction. Thus, our approach significantly improved the outcome prediction compared to that of other ML-based models and health calculators.

The increasing burden of atrial fibrillation (AF) emphasizes the need to identify high-risk individuals for enrolment in clinical trials of AF screening and primary prevention. We aimed to develop prediction models to identify individuals at high-risk of AF across prediction horizons from 6-months to 10-years. We used secondary-care linked primary care electronic health record data from individuals aged ≥30 years without known AF in the UK Clinical Practice Research Datalink-GOLD dataset between January 2, 1998 and November 30, 2018; randomly divided into derivation (80%) and validation (20%) datasets. Models were derived using logistic regression from known AF risk factors for incident AF in prediction periods of 6 months, 1-year, 2-years, 5-years, and 10-years. Performance was evaluated using in the validation dataset with bootstrap validation with 200 samples, and compared against the CHA2DS2-VASc and C2HEST scores. Of 2,081,139 individuals in the cohort (1,664,911 in the development dataset, 416,228 in the validation dataset), the mean age was 49.9 (SD 15.4), 50.7% were women, and 86.7% were white. New cases of AF were 7,386 (0.4%) within 6 months, 15,349 (0.7%) in 1 year, 38,487 (1.8%) in 5 years, and 79,997 (3.8%) by 10 years. Valvular heart disease and heart failure were the strongest predictors, and association of hypertension with AF increased at longer prediction horizons. The optimal risk models incorporated age, sex, ethnicity, and 8 comorbidities. The models demonstrated good-to-excellent discrimination and strong calibration across prediction horizons (AUROC, 95%CI, calibration slope: 6-months, 0.803, 0.789-0.821, 0.952; 1-year, 0.807, 0.794-0.819, 0.962; 2-years, 0.815, 0.807-0.823, 0.973; 5-years, 0.807, 0.803-0.812, 1.000; 10-years 0.780, 0.777-0.784, 1.010), and superior to the CHA2DS2-VASc and C2HEST scores. The models are available as a web-based FIND-AF calculator. The FIND-AF models demonstrate high discrimination and calibration across short- and long-term prediction horizons in 2 million individuals. Their utility to inform trial enrolment and clinical decisions for AF screening and primary prevention requires further study. [Display omitted]

Psoriatic arthritis (PsA) affects approximately one in five individuals with psoriasis. Early identification of patients with psoriasis at risk of developing PsA is crucial to prevent poor prognosis. We established a derivation cohort comprising 1,661 patients with psoriasis from 49 hospitals. Clinical and demographic variables ascertained at hospital admission were screened using the Least Absolute Shrinkage and Selection Operator and logistic regression to construct a prediction model and a new web-based calculator. Ultimately, six significant independent predictors were identified: history of unexplained swollen joints (odds ratio [OR]: 5.814, 95% confidence interval [95% CI]: 3.304–10.117; p< 0.001), history of arthritis (OR: 3.543, 95% CI: 1.982–6.246; p< 0.001), history of unexplained swollen and painful fingers or toes (OR: 2.707, 95% CI: 1.463–4.915; p = 0.001), nail involvement (OR: 1.907, 95% CI: 1.235–2.912; p = 0.003), hyperlipidemia (OR: 4.265, 95% CI: 0.921–15.493; p = 0.042), and prolonged topical use of glucocorticosteroids (OR: 1.581, 95% CI: 1.052–2.384, p = 0.028). The web-based calculator derived from this model can assist clinicians in promptly determining the probability of developing PsA in patients with psoriasis, thereby facilitating improved clinical decision-making.

Purpose Accurate prediction of hernia occurrence is vital for surgical decision-making and patient management, particularly in colorectal surgery patients. While a hernia prediction model has been developed, its performance in external populations remain to be investigated. This study aims to validate the existing model on an external dataset of patients who underwent colorectal surgery. Methods The “Penn Hernia Calculator” model was externally validated using the Hughes Abdominal Repair Trial (HART) data, a randomized trial comparing colorectal cancer surgery closure techniques. The data encompassed demographics, comorbidities, and surgical specifics. Patients without complete follow-up were omitted. Model performance was assessed using key metrics, including area under the curve (AUC-ROC and AUC-PR) and Brier score. Reporting followed the TRIPOD consensus. Results An external international dataset consisting of 802 colorectal surgery patients were identified, of which 674 patients with up to 2 years follow-up were included. Average patient age was 68 years, with 63.8% male. The average BMI was 28.1. Prevalence of diabetes, hypertension, and smoking were 15.7%, 16.3%, and 36.5%, respectively. Additionally, 7.9% of patients had a previous hernia. The most common operation types were low anterior resection (35.3%) and right hemicolectomy (34.4%). Hernia were observed in 24% of cases by 2-year follow-up. The external validation model revealed an AUC-ROC of 0.66, AUC-PR of 0.72, and a Brier score of 0.2. Conclusion The hernia prediction model demonstrated moderate performance in the external validation. Its potential generalizability, specifically in those undergoing colorectal surgery, may suggest utility in identifying and managing high-risk hernia candidates.

Background An increasing number of people are using multiple medications each day, named polypharmacy. This is driven by an ageing population, increasing multimorbidity, and single disease-focussed guidelines. Medications carry obvious benefits, yet polypharmacy is also linked to adverse consequences including adverse drug events, drug-drug and drug-disease interactions, poor patient experience and wasted resources. Problematic polypharmacy is ‘the prescribing of multiple medicines inappropriately, or where the intended benefits are not realised’. Identifying people with problematic polypharmacy is complex, as multiple medicines can be suitable for people with several chronic conditions requiring more treatment. Hence, polypharmacy is often potentially problematic, rather than always inappropriate, dependent on clinical context and individual benefit vs risk. There is a need to improve how we identify and evaluate these patients by extending beyond simple counts of medicines to include individual factors and long-term conditions. Aim To produce a Polypharmacy Assessment Score to identify a population with unusual levels of prescribing who may be at risk of potentially problematic polypharmacy. Methods Analyses will be performed in three parts: 1. A prediction model will be constructed using observed medications count as the dependent variable, with age, gender and long-term conditions as independent variables. A ‘ Polypharmacy Assessment Score ’ will then be constructed through calculating the differences between the observed and expected count of prescribed medications, thereby highlighting people that have unexpected levels of prescribing. Parts 2 and 3 will examine different aspects of validity of the Polypharmacy Assessment Score : 2. To assess ‘construct validity’, cross-sectional analyses will evaluate high-risk prescribing within populations defined by a range of Polypharmacy Assessment Scores , using both explicit (STOPP/START criteria) and implicit (Medication Appropriateness Index) measures of inappropriate prescribing . 3. To assess ‘predictive validity’, a retrospective cohort study will explore differences in clinical outcomes (adverse drug reactions, unplanned hospitalisation and all-cause mortality) between differing scores . Discussion Developing a cross-cutting measure of polypharmacy may allow healthcare professionals to prioritise and risk stratify patients with polypharmacy using unusual levels of prescribing. This would be an improvement from current approaches of either using simple cutoffs or narrow prescribing criteria.

: The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) and SpineSage risk calculators are automated online tools to predict short-term complications for surgical procedures. The objective of this study was to assess the validity of ACS-NSQIP and SpineSage risk calculators to predict short-term complications after adult spinal deformity (ASD) surgery. : We included ASD patients who had surgery between 2017 and 2020 (≥5 levels, single-stage, posterior-only). Patient factors were entered into the risk calculators to generate probabilities for 30-day outcomes. Calibration and discrimination were assessed using Brier scores and C-statistics, respectively. : A total of 198 patients were included (67 male, 131 female) who underwent posterior spinal fusion for ASD surgery. The ACS-NSQIP risk calculator had strong calibration for all complications (Brier score < 0.09) except non-home discharge (Brier score 0.2). Discrimination was poor for all complications except surgical site infection (C-statistic 0.86), venous thromboembolism (C-statistic 0.84), and readmission (C-statistic 0.7). The SpineSage risk calculator had strong calibration for all complications (Brier score < 0.09) aside from the \"any complications\" subset (Brier score 0.36). The discrimination capacity was poor for all complications (C-statistic < 0.7). : The ACS-NSQIP calculator had strong calibration and poor discrimination for most complications. The SpineSage calculator had strong calibration for most complications and a poor discrimination capacity for all complications. NSQIP calculation deficits may be due to the reliance on a single CPT code to calculate risk. The deficient discriminatory capacity of the SpineSage calculator may be due to the inclusion of common perioperative occurrences as complications.