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It is widely acknowledged that the performance of diagnostic and prognostic prediction models should be assessed in external validation studies with independent data from “different but related” samples as compared with that of the development sample. We developed a framework of methodological steps and statistical methods for analyzing and enhancing the interpretation of results from external validation studies of prediction models. We propose to quantify the degree of relatedness between development and validation samples on a scale ranging from reproducibility to transportability by evaluating their corresponding case-mix differences. We subsequently assess the models' performance in the validation sample and interpret the performance in view of the case-mix differences. Finally, we may adjust the model to the validation setting. We illustrate this three-step framework with a prediction model for diagnosing deep venous thrombosis using three validation samples with varying case mix. While one external validation sample merely assessed the model's reproducibility, two other samples rather assessed model transportability. The performance in all validation samples was adequate, and the model did not require extensive updating to correct for miscalibration or poor fit to the validation settings. The proposed framework enhances the interpretation of findings at external validation of prediction models.

Aims: To obtain information on organizational aspects, case mix and practices in Indian Intensive Care Units (ICUs). Patients and Methods: An observational, 4-day point prevalence study was performed between 2010 and 2011 in 4209 patients from 124 ICUs. ICU and patient characteristics, and interventions were recorded for 24 h of the study day, and outcomes till 30 days after the study day. Data were analyzed for 4038 adult patients from 120 ICUs. Results: On the study day, mean age, Acute Physiology and Chronic Health Evaluation (APACHE II) and sequential organ failure assessment (SOFA) scores were 54.1 ± 17.1 years, 17.4 ± 9.2 and 3.8 ± 3.6, respectively. About 46.4% patients had ≥1 organ failure. Nearly, 37% and 22.2% patients received mechanical ventilation (MV) and vasopressors or inotropes, respectively. Nearly, 12.2% patients developed an infection in the ICU. About 28.3% patients had severe sepsis or septic shock (SvSpSS) during their ICU stay. About 60.7% patients without infection received antibiotics. There were 546 deaths and 183 terminal discharges (TDs) from ICU (including left against medical advice or discharged on request), with ICU mortality 729/4038 (18.1%). In 1627 patients admitted within 24 h of the study day, the standardized mortality ratio was 0.67. The APACHE II and SOFA scores, public hospital ICUs, medical ICUs, inadequately equipped ICUs, medical admission, self-paying patient, presence of SvSpSS, acute respiratory failure or cancer, need for a fluid bolus, and MV were independent predictors of mortality. Conclusions: The high proportion of TDs and the association of public hospitals, self-paying patients, and inadequately equipped hospitals with mortality has important implications for critical care in India.

The All Patient Refined Diagnosis Related Group (APR-DRG) is an inpatient visit classification system that assigns a diagnostic related group, a Risk of Mortality (ROM) subclass and a Severity of Illness (SOI) subclass. While extensively used for cost adjustment, no study has compared the APR-DRG subclass modifiers to the popular Charlson Comorbidity Index as a measure of comorbidity severity in models for perioperative in-hospital mortality. In this study we attempt to validate the use of these subclasses to predict mortality in a cohort of surgical patients. We analyzed all adult (age over 18 years) inpatient non-cardiac surgery at our institution between December 2005 and July 2013. After exclusions, we split the cohort into training and validation sets. We created prediction models of inpatient mortality using the Charlson Comorbidity Index, ROM only, SOI only, and ROM with SOI. Models were compared by receiver-operator characteristic (ROC) curve, area under the ROC curve (AUC), and Brier score. After exclusions, we analyzed 63,681 patient-visits. Overall in-hospital mortality was 1.3%. The median number of ICD-9-CM diagnosis codes was 6 (Q1-Q3 4–10). The median Charlson Comorbidity Index was 0 (Q1-Q3 0–2). When the model was applied to the validation set, the c-statistic for Charlson was 0.865, c-statistic for ROM was 0.975, and for ROM and SOI combined the c-statistic was 0.977. The scaled Brier score for Charlson was 0.044, Brier for ROM only was 0.230, and Brier for ROM and SOI was 0.257. The APR-DRG ROM or SOI subclasses are better predictors than the Charlson Comorbidity Index of in-hospital mortality among surgical patients.

This study compared the Charlson comorbidity index (CCI) information derived from chart review and administrative systems to assess the completeness and agreement between scores, evaluate the capacity to predict 30-day and 1-year mortality in intensive care unit (ICU) patients, and compare the predictive capacity with that of the Simplified Acute Physiology Score (SAPS) II model. Using data from 959 patients admitted to a general ICU in a Norwegian university hospital from 2007 to 2009, we compared the CCI score derived from chart review and administrative systems. Agreement was assessed using % agreement, kappa, and weighted kappa. The capacity to predict 30-day and 1-year mortality was assessed using logistic regression, model discrimination with the -statistic, and calibration with a goodness-of-fit statistic. The CCI was complete (n=959) when calculated from chart review, but less complete from administrative data (n=839). Agreement was good, with a weighted kappa of 0.667 (95% confidence interval: 0.596-0.714). The -statistics for categorized CCI scores from charts and administrative data were similar in the model that included age, sex, and type of admission: 0.755 and 0.743 for 30-day mortality, respectively, and 0.783 and 0.775, respectively, for 1-year mortality. Goodness-of-fit statistics supported the model fit. The CCI scores from chart review and administrative data showed good agreement and predicted 30-day and 1-year mortality in ICU patients. CCI combined with age, sex, and type of admission predicted mortality almost as well as the physiology-based SAPS II.

Analyzing general surgeons’ operative case mix can provide an update on contemporary practice patterns and inform pragmatic residency training. We performed a retrospective cohort study of general surgeons in Florida, Iowa, and Maryland, 2016–2020. Cases were identified using billing codes. The Cochran-Armitage test of trends was used to evaluate the proportion of practice devoted to specific case types and operative setting over time. General surgeons (n ​= ​1300) performed 1,287,745 cases. The mean (±SD) annual volume per surgeon for all procedures was 356 (±250), with 198 (±152) general surgery operations, 57 (±142) endoscopic procedures, and 101 (±109) other cases. On average, surgeons operated on 7.1 (±2.6) different organ systems. Trends toward a lower proportion of general surgery operations, and a greater proportion of subspecialty procedures and surgery in the outpatient setting over time were demonstrated (p ​< ​0.001). The practice pattern of the general surgeon continues to be heterogeneous, reflecting the persistent need for a broad training paradigm that permits specialization. [Display omitted] •Contemporary general surgery case mix remains heterogeneous with a continuing trend toward the outpatient setting.•Surgical setting selected by the surgeon, whether inpatient vs outpatient, differs by hospital characteristics.•Graduate surgical education should use data on independently practicing surgeons' practices to inform surgical training.

The growth of bariatric surgery in the USA followed the adoption of gastric bypass (RYGB). The recent introduction of sleeve gastrectomy (SG) has been met with wide adoption. A single state report suggests that the popularity of SG has surpassed that of RYGB. Our study aimed to assess the nationwide changes in trend of bariatric procedures performed, using data from the National Surgical Quality Improvement Program from 2010 to 2013. In this cohort of 74,790 bariatric patients, there was a significant difference in trend between laparoscopic RYGB and SG. By 2013, SG was the most common bariatric procedure performed (49.4 %). This report underlines the exponential adoption of SG and aims to alert patients, physicians, and funding agencies of the need for longitudinal prospective long-term data.

Background The coronavirus disease-19 (COVID-19) pandemic had a relatively minimal direct impact on critical illness in children compared to adults. However, children and paediatric intensive care units (PICUs) were affected indirectly. We analysed the impact of the pandemic on PICU admission patterns and patient characteristics in the UK and Ireland. Methods We performed a retrospective cohort study of all admissions to PICUs in children < 18 years during Jan–Dec 2020, using data collected from 32 PICUs via a central database (PICANet). Admission patterns, case-mix, resource use, and outcomes were compared with the four preceding years (2016–2019) based on the date of admission. Results There were 16,941 admissions in 2020 compared to an annual average of 20,643 (range 20,340–20,868) from 2016 to 2019. During 2020, there was a reduction in all PICU admissions (18%), unplanned admissions (20%), planned admissions (15%), and bed days (25%). There was a 41% reduction in respiratory admissions, and a 60% reduction in children admitted with bronchiolitis but an 84% increase in admissions for diabetic ketoacidosis during 2020 compared to the previous years. There were 420 admissions (2.4%) with either PIMS-TS or COVID-19 during 2020. Age and sex adjusted prevalence of unplanned PICU admission reduced from 79.7 (2016–2019) to 63.1 per 100,000 in 2020. Median probability of death [1.2 (0.5–3.4) vs. 1.2 (0.5–3.4) %], length of stay [2.3 (1.0–5.5) vs. 2.4 (1.0–5.7) days] and mortality rates [3.4 vs. 3.6%, (risk-adjusted OR 1.00 [0.91–1.11, p  = 0.93])] were similar between 2016–2019 and 2020. There were 106 fewer in-PICU deaths in 2020 ( n  = 605) compared with 2016–2019 ( n  = 711). Conclusions The use of a high-quality international database allowed robust comparisons between admission data prior to and during the COVID-19 pandemic. A significant reduction in prevalence of unplanned admissions, respiratory diseases, and fewer child deaths in PICU observed may be related to the targeted COVID-19 public health interventions during the pandemic. However, analysis of wider and longer-term societal impact of the pandemic and public health interventions on physical and mental health of children is required.

Background Risk stratification scores are used to predict outcomes among patients with pneumonia. We have developed a novel model that predicts the risk of death or escalation of care in internal medicine. We compared our model with a widely used model for predicting clinical outcomes in patients admitted for pneumonia, using information available at the time of admission. Methods We examined 3,856 pneumonia admissions to the internal medicine service of a tertiary medical center. We compared the ability of two scores to predict in-hospital mortality and escalation of care (ICU, mechanical ventilation, or vasopressors (among patients admitted for pneumonia. One was the CURB-65 score, which is currently in use at this hospital. The other score was one we developed, based on the Elixhauser case mix adjustment model with additional data such as vital signs and laboratory values. Results 12% of patients died in-hospital and 18% required an escalation of care. The most common CURB-65 score was 2 (44%), the lowest score ordinarily requiring admission. Our risk prediction score was better than CURB-65 at predicting mortality (c-statistic 0.846 vs. 0.724) and escalation (0.757 vs. 0.633). Our score was able to meaningfully discriminate among patients classified as similar-risk by CURB-65. For example, of the 1681 patients with a (medium-risk) CURB-65 score of 2, our model placed 180 (11%) into the lowest-risk quintile, and 309 (18%) into the highest-risk quintile. Conclusions Our risk stratification tool is calculatable with information available in the electronic medical record of most hospitals. The new score was much better able to predict the outcomes of in-hospital mortality and escalation of care among patients admitted for pneumonia, compared to CURB-65. Displaying this score in real time would be possible using modern hospital computers and would help physicians determine which patients require admission, and to which hospital unit. Key messages • Traditional risk adjustment models, which were developed in an age of paper charts, can be improved by a considerable margin by adding data found in modern hospital computer systems. • Displaying enhanced risk prediction information to clinicians in real time could meaningfully improve risk assessment and admission decisions.

In this paper, we propose a general class of indices that can be used for comparing the performances of organizations providing a given public service to citizens, such as universities, hospitals, nursing homes, employment agencies or other institutions. In particular, we handle the case where evaluation is performed by assessing the probability that a given event has happened as a result of the service provided to users requiring it. Indices are designed for settings where users can be divided into groups with similar characteristics in order to account for case-mix, that is, for the different composition of users within each organization with respect to personal features influencing the probability of the event at hand. For the proposed class, we build a taxonomy leading to nine index types. These different types constitute a useful toolbox to satisfy specific needs and/or criteria set by the evaluator in applied contexts. A general inferential framework is also discussed to deal with settings where, whatever the index chosen, its value has to be estimated from sample data. A simulation study based on a real-world dataset is presented to assess the behavior of indices’ estimators.

Background When developing a clinical prediction model (CPM), a case-mix shift could occur in the development dataset where the distribution of individual predictors changes, potentially affecting model performance. This study exploits the case-mix shift that is already observed in the development dataset to address the case-mix shift between the development and deployment phase of a CPM. Methods We propose a Membership-based method to correct for case-mix shift in the development phase of CPMs. This method uses a probabilistic similarity metric to re-weight data samples in the source set (before the case-mix shift) to more closely match the target set (after the case-mix shift), assuming the target set reflects the target population. We apply the proposed method in a real-world dataset of myocardial infarction patients with out-of-hospital cardiac arrest within 90 days as the outcome. We design nine scenarios (including case-mix shift and no case-mix shift with a range of target/source sets sample sizes) to explore the impact on predictive performance of CPM developed with the proposed method in comparison to CPMs developed by either using all data samples but ignore the shift, or only using the most recent data. We report calibration and discrimination on development and 200 bootstrap samples. Results and Conclusions The proposed method shows promise in accounting for case-mix shift when developing a CPM, particularly when the target set sample size is insufficient. In a partial case-mix shift scenario with an insufficient target sample size, the Membership-based model achieved an optimism-adjusted calibration slope (c-slope) of 0.98, outperforming other models. Conversely, when the target set sample size is sufficient, the Unweighted model on target data only had an optimism-adjusted c-slope of 0.95, compared to 0.92 for the Membership-based model. In complete case-mix shift cases, the Membership-based and Unweighted on target data only models performed similarly. Both achieved an optimism-adjusted c-slope of 0.77 with insufficient target sample size, and optimism-adjusted c-slope of 0.94 with a sufficient target sample size. Further investigation and testing are needed, as well as accounting for other types of data distribution shift to improve model fit for the latest distribution shift in the development dataset.