C-statistic: A brief explanation of its construction, interpretation and limitations

Short-acting opioid prescriptions and Workers' Compensation using the National Ambulatory Medical Care Survey

BACKGROUND

Short-acting opioids have been utilized for pain management with little known about their use in patients on Workers' Compensation (WC) insurance. Our goal was to investigate this association in the ambulatory care setting.

METHODS

Using the National Ambulatory Medical Care Survey, visits from patients aged 18-64 during the years 2010 until 2018 were evaluated (excluding 2017 due to data availability). Demographic and co-morbidity data from each visit was obtained along with the visit year. The first short-acting opioid medication prescribed in the database was considered. Survey-weighted frequencies were evaluated. Logistic regression estimated the crude and adjusted odds ratios (OR) with 95% confidence intervals for the use of short-acting opioid prescription.

RESULTS

There were 155,947 included visits with 62.5% for female patients. Most patients were White with 11.7% identifying as Black, and 6% identifying as another race. Over 13% of the sample was of Hispanic descent. WC was the identified insurance type in 1.6% of the sample population. Of these patients, 25.6% were prescribed a short-acting opioid, compared with 10.1% of those with another identified insurance. On multivariable regression, Black patients had increased odds of being prescribed a short-acting opioid compared to white patients (OR: 1.22, 95% CI: 1.11-1.34). Those on WC had 1.7-fold higher odds of being prescribed short-acting opioids (95% CI: 1.46-2.06).

CONCLUSION

Certain patient characteristics, including having WC insurance, increased the odds of a short-acting opioid prescription. Further work is needed to identify prescribing patterns in specific high-risk occupational groups, as well as to elicit potential associated health outcomes.

Should we use nomograms for risk predictions in diffuse large B cell lymphoma patients? A systematic review

Models based on risk stratification are increasingly reported for Diffuse large B cell lymphoma (DLBCL). Due to a rising interest in nomograms for cancer patients, we aimed to review and critically appraise prognostic models based on nomograms in DLBCL patients. A literature search in PubMed/Embase identified 59 articles that proposed prognostic models for DLBCL by combining parameters of interest (e.g., clinical, laboratory, immunohistochemical, and genetic) between January 2000 and 2024. Of them, 40 studies proposed different gene expression signatures and incorporated them into nomogram-based prognostic models. Although most studies assessed discrimination and calibration when developing the model, many lacked external validation. Current nomogram-based models for DLBCL are mainly developed from publicly available databases, lack external validation, and have no applicability in clinical practice. However, they may be helpful in individual patient counseling, although careful considerations should be made regarding model development due to possible limitations when choosing nomograms for prognostication.

External Validation of the OAC3-PAD Bleeding Score in a Nationwide Population of Patients Undergoing Invasive Treatment for Peripheral Arterial Disease

OBJECTIVE

The OAC3-PAD score was developed to predict bleeding risk in patients with lower extremity peripheral arterial disease (PAD), but its performance in concomitant international cohorts is largely unknown. This study aimed to validate the OAC3-PAD score in an unselected nationwide population of patients undergoing invasive treatment for symptomatic PAD.

METHODS

This was a nationwide cohort study including all patients who underwent a first revascularisation procedure or major amputation for symptomatic PAD in Denmark from 2000 - 2021. The study population was stratified based on OAC3-PAD score, and the one year risk of major bleeding was assessed, accounting for the competing risk of death. The score performance was evaluated using calibration plots, C statistic, Brier score, and the index of prediction accuracy (IPA).

RESULTS

A total of 52 016 patients were included (mean age 71 years, 43.8% female). The one year risk of major bleeding increased with higher OAC3-PAD score, ranging from 1.6% (95% confidence interval [CI] 1.4 - 1.8%) to 2.3% (95% CI 2.0 - 2.5%), 3.5% (95% CI 3.2 - 3.8%), and 5.2% (95% CI 4.8 - 5.6%) for patients with low, low moderate, moderate high, and high score, respectively. Using patients with low risk as reference, the OAC3-PAD score effectively categorised patients, demonstrating statistically significant differences in bleeding risk across strata. However, the score showed modest discriminative performance, with a C statistic of 65% (95% CI 63 - 66%) and a Brier score of 2.6% (95% CI 2.5 - 2.7%). Nevertheless, it performed significantly better than the null model, as indicated by an IPA of 3.1%.

CONCLUSION

Among patients who underwent invasive treatment for symptomatic PAD in routine care, the OAC3-PAD score was associated with greater risk of major bleeding with increasing score level. However, its discriminatory ability was modest, and the clinical utility remains to be determined.

Mortality index is more accurate than volume in predicting outcome and failure to rescue in Medicare beneficiaries undergoing robotic right upper lobectomy

Background

Surgical volume is known to influence failure to rescue (FTR), defined as death following a complication. Robotic lung surgery continues to expand and there is variability in outcomes among hospitals. We sought to estimate the contribution of hospital-based factors on outcomes and FTR following robotic right upper lobectomy (RRUL).

Methods

Using the Centers for Medicare and Medicaid Services inpatient claims database, we evaluated all patients age ≥65 years with a diagnosis of lung cancer who underwent RRUL between January 2018 and December 2020. We excluded patients who had undergone segmentectomy, sublobar, wedge, or bronchoplastic resection; had metastatic or nonmalignant disease; or had a history of neoadjuvant chemotherapy. Primary outcomes included FTR rate, length of stay (LOS), readmissions, conversion to open surgery, complications, and costs. We analyzed hospitals by tertiles of volume and Medicare Mortality Index (MMI). Defined as the institutional number of deaths per number of survivors, MMI is a marker of overall hospital performance and quality. Propensity score models were adjusted for confounding using goodness of fit.

Results

Data for 4317 patients who underwent robotic right upper lobectomy were analyzed. Hospitals were categorized by volume of cases (low, <9; medium, 9-20; high, >20) and MMI (low, <0.04; medium, 0.04-0.13; high, >0.13). After propensity score balancing, patients from tertiles of lowest volume and highest MMI had higher costs ($34,222 vs $30,316; P = .006), as well as higher mortality (odds ratio, 7.46; 95% confidence interval, 2.67-28.2; P < .001). Compared to high-volume centers, low-volume centers had higher rates of conversion to open surgery, respiratory failure, hemorrhagic anemia, and death; longer LOS; and greater cost (P < .001 for all). The C-statistic for volume as a predictor of overall mortality was 0.6, and the FTR was 0.8. Hospitals in the highest tertile of MMI had the highest rates of conversion to open surgery (P = .01), pneumothorax (P = .02), and respiratory failure (P < .001). They also had the highest mortality and rate of readmission, longest LOS, and greatest costs (P < .001 for all) and the shortest survival (P < .001). The C-statistic for MMI as a predictor of overall mortality was 0.8, and FTR was 0.9.

Conclusions

The MMI incorporates hospital-based factors in the adjudication of outcomes and is a more sensitive predictor of FTR rates than volume alone. Combining MMI and volume may provide a metric that can guide quality improvement and cost-effectiveness measures in hospitals seeking to implement robotic lung surgery programs.

Improving risk models for patients having emergency bowel cancer surgery using linked electronic health records: a national cohort study

BACKGROUND

Life-saving emergency major resection of colorectal cancer (CRC) is a high-risk procedure. Accurate prediction of postoperative mortality for patients undergoing this procedure is essential for both healthcare performance monitoring and preoperative risk assessment. Risk-adjustment models for CRC patients often include patient and tumour characteristics, widely available in cancer registries and audits. The authors investigated to what extent inclusion of additional physiological and surgical measures, available through linkage or additional data collection, improves accuracy of risk models.

METHODS

Linked, routinely-collected data on patients undergoing emergency CRC surgery in England between December 2016 and November 2019 were used to develop a risk model for 90-day mortality. Backwards selection identified a 'selected model' of physiological and surgical measures in addition to patient and tumour characteristics. Model performance was assessed compared to a 'basic model' including only patient and tumour characteristics. Missing data was multiply imputed.

RESULTS

Eight hundred forty-six of 10 578 (8.0%) patients died within 90 days of surgery. The selected model included seven preoperative physiological and surgical measures (pulse rate, systolic blood pressure, breathlessness, sodium, urea, albumin, and predicted peritoneal soiling), in addition to the 10 patient and tumour characteristics in the basic model (calendar year of surgery, age, sex, ASA grade, TNM T stage, TNM N stage, TNM M stage, cancer site, number of comorbidities, and emergency admission). The selected model had considerably better discrimination compared to the basic model (C-statistic: 0.824 versus 0.783, respectively).

CONCLUSION

Linkage of disease-specific and treatment-specific datasets allowed the inclusion of physiological and surgical measures in a risk model alongside patient and tumour characteristics, which improves the accuracy of the prediction of the mortality risk for CRC patients having emergency surgery. This improvement will allow more accurate performance monitoring of healthcare providers and enhance clinical care planning.

Multicentric development and evaluation of [18F]FDG PET/CT and CT radiomic models to predict regional and/or distant recurrence in early-stage non-small cell lung cancer treated by stereotactic body radiation therapy

PURPOSE

To develop machine learning models to predict regional and/or distant recurrence in patients with early-stage non-small cell lung cancer (ES-NSCLC) after stereotactic body radiation therapy (SBRT) using [18F]FDG PET/CT and CT radiomics combined with clinical and dosimetric parameters.

METHODS

We retrospectively collected 464 patients (60% for training and 40% for testing) from University Hospital of Liège and 63 patients from University Hospital of Brest (external testing set) with ES-NSCLC treated with SBRT between 2010 and 2020 and who had undergone pretreatment [18F]FDG PET/CT and planning CT. Radiomic features were extracted using the PyRadiomics toolbox®. The ComBat harmonization method was applied to reduce the batch effect between centers. Clinical, radiomic, and combined models were trained and tested using a neural network approach to predict regional and/or distant recurrence.

RESULTS

In the training (n = 273) and testing sets (n = 191 and n = 63), the clinical model achieved moderate performances to predict regional and/or distant recurrence with C-statistics from 0.53 to 0.59 (95% CI, 0.41, 0.67). The radiomic (original_firstorder_Entropy, original_gldm_LowGrayLevelEmphasis and original_glcm_DifferenceAverage) model achieved higher predictive ability in the training set and kept the same performance in the testing sets, with C-statistics from 0.70 to 0.78 (95% CI, 0.63, 0.88) while the combined model performs moderately well with C-statistics from 0.50 to 0.62 (95% CI, 0.37, 0.69).

CONCLUSION

Radiomic features extracted from pre-SBRT analog and digital [18F]FDG PET/CT outperform clinical parameters in the prediction of regional and/or distant recurrence and to discuss an adjuvant systemic treatment in ES-NSCLC. Prospective validation of our models should now be carried out.

Diagnostic accuracy of serum protein induced by vitamin K absence (PIVKA-II), serum a-fetoprotein and their combination for hepatocellular carcinoma among Caucasian cirrhotic patients with diagnostic or non-diagnostic serum a-fetoprotein levels

AIM

The aim of our study was to evaluate the accuracy of serum biomarkers (AFP/PIVKA-II) and their combination in HCC diagnosis among Caucasian cirrhotic patients.

METHODS

Serum AFP/PIVKA-II levels were evaluated in 218 cirrhotics (163 males, 118 CTP-A, 66 ALBI-I, 111 with varices, 63 with diabetes) with (n = 90) or without (n = 128) HCC. Patients with HCC were categorized to BCLC Stage 0/A (n = 12), B (n = 21), C (n = 48), and D (n = 9).

RESULTS

The two groups were comparable for all baseline parameters except for age, platelets, and diabetes presence. Median levels of AFP (239.1 vs. 4.0 ng/mL) and PIVKA-II (4082.7 vs. 45.8 mAU/mL) were both significantly higher in HCC group compared to controls (p < 0.001). AUROC and cutoff value for HCC diagnosis were 88%/12.35 ng/mL (AFP) and 84.4%/677.13 mAU/mL (PIVKA-II), whereas their combination showed better diagnostic accuracy (AUROC = 90.2%). The diagnostic accuracy of each biomarker separately was moderate or good in BCLC-0/A/B and was excellent only for BCLC-C patients (AFP: AUROC = 94.3%, cutoff = 12.35 ng/mL and PIVKA-II: 91.3%, 253.51 mAU/mL) whereas their combination presented quite acceptable results in BCLC-B (AUROC = 92.4%) and BCLC-C (AUROC = 95.7%). Excluding HCC patients with high AFP (above 400 ng/mL), the diagnostic accuracy of each biomarker separately and their combination was moderate/good in all groups, except for their combination in BCLC-C (AUROC = 90.5%).

CONCLUSIONS

Each biomarker separately showed acceptable accuracy for detecting HCC in cirrhotic patients and excellent for those in BCLC-C stage. The combination of the biomarkers presented excellent results in BCLC-B/C patients. The diagnostic accuracy of PIVKA-II and the combination of the two biomarkers in patients expressing low/non-diagnostic AFP levels was good in BCLC-B and excellent in BCLC-C patients.

Development and validation of a prognostic nomogram for predicting in-hospital mortality of patients with acute paraquat poisoning

This study aimed to develop and validate a predictive model to determine the risk of in-hospital mortality in patients with acute paraquat poisoning. This retrospective observational cohort study included 724 patients with acute paraquat poisoning whose clinical data were collected within 24 h of admission. The primary outcome was in-hospital mortality. Patients were randomly divided into training and validation cohorts (7/3 ratio). In the training cohort, the least absolute shrinkage and selection operator regression models were used for data dimension reduction and feature selection. Multivariate logistic regression was used to generate a predictive nomogram for in-hospital mortality. The prediction model was assessed for both the training and validation cohorts. In the training cohort, decreased level of consciousness (Glasgow Coma Scale score < 15), neutrophil-to-lymphocyte ratio, alanine aminotransferase, creatinine, carbon dioxide combining power, and paraquat plasma concentrations at admission were identified as independent predictors of in-hospital mortality in patients with acute paraquat poisoning. The calibration curves, decision curve analysis, and clinical impact curves indicated that the model had a good predictive performance. It can be used on admission to the emergency department to predict mortality and facilitate early risk stratification and actionable measures in clinical practice after further external validation.

The role of family support in the self-rated health of older adults in eastern Nepal: findings from a cross-sectional study

BACKGROUND

Nepal's low fertility rate and increasing life expectancy have resulted in a burgeoning older population. For millennia, filial piety shaped family cohesion and helped Nepali older adults achieve positive outcomes, but recently, it has been eroding. Furthermore, there are not enough institutional support options or alternatives to family-based care to deal with the biosocial needs of older adults. This study explored the association between family support and self-rated health among Nepali older adults.

METHODS

A community-based cross-sectional survey in eastern Nepal's two districts, Sunsari and Morang, interviewed 847 older adults (≥ 60 years). The final analytical sample was 844. Participants were asked whether they received assistance with various aspects of daily life and activities of daily living from their families. Multivariable logistic regression examined the association between family support and self-rated health.

RESULTS

Participants who received support with various aspects of daily life had 43% higher odds of good health, but after adjusting for control variables, the result only approached statistical significance (p = 0.087). Those who received family assistance with activities of daily living had nearly four times higher odds (OR: 3.93; 95% CI: 2.58 - 5.98) of reporting good health than participants who lacked this support.

CONCLUSIONS

Given the important role of family support in Nepali older adults' health, government programs and policies should create a conducive environment to foster family-based care until more comprehensive policies for older adults' care can be put into effect. The results of this study can also help shape the global aging environment by highlighting the need for family support in older care, particularly in low-income nations with declining traditional care systems and weak social security policies.

Development and validation of a prognostic nomogram for patients with laryngeal cancer with synchronous or metachronous lung cancer

BACKGROUND

The objective of this study was to examine the independent prognostic factors of laryngeal cancer with synchronous or metachronous lung cancer (LCSMLC), and to generate and verify a clinical prediction model.

METHODS

In this study, laryngeal cancer alone and LCSMLC were defined using the Surveillance, Epidemiology, and End Results (SEER) database. Risk factors of patients with LCSMLC were analyzed through univariate and multivariate logistic regression analysis. Independent prognostic factors were selected by Cox regression analyses, on the basis of which a nomogram was constructed using R code. Kaplan-Meier survival analyses were applied to test the application of a risk stratification system. Finally, we conducted a comparison of the American Joint Committee on Cancer (AJCC) staging system of laryngeal cancer with the new model of nomogram and risk stratification. For further validation of the nomogram, data from patients at two Chinese independent institutions were also analyzed.

RESULTS

According to the eligibility criteria, 32 429 patients with laryngeal cancer alone and 641 patients with LCSMLC from the SEER database (the training cohort) and additional 61 patients from two Chinese independent institutions (the external validation cohort) were included for final analyses. Compared with patients with laryngeal cancer who did not have synchronous or metachronous lung cancer, age, sex, race, primary site of laryngeal cancer, grade, and stage were risk factors for LCSMLC, while marriage, surgery, radiation therapy, and chemotherapy are not their risk factors. Age, two cancers' interval, pathological type, stage, surgery, radiation, primary lung site, and primary throat site were independent prognostic predictors of LCSMLC. The risk stratification system of high-, medium-, and low-risk groups significantly distinguished the prognosis in different patients with LCSMLC, regardless of the training cohort or the validation cohort. Compared with the 6th AJCC TNM stage of laryngeal cancer, the new model of nomogram and risk stratification showed an improved net benefit.

CONCLUSIONS

Age, sex, race, primary site of laryngeal cancer, grade, and stage were risk factors for LCSMLC. An individualized clinical prognostic predictive model by nomogram was generated and validated, which showed superior prediction ability for LCSMLC.

Prognostic indices in diffuse large B-cell lymphoma: a population-based comparison and validation study of multiple models

Currently, the International Prognostic Index (IPI) is the most used and reported model for prognostication in patients with newly diagnosed diffuse large B-cell lymphoma (DLBCL). IPI-like variations have been proposed, but only a few have been validated in different populations (e.g., revised IPI (R-IPI), National Comprehensive Cancer Network IPI (NCCN-IPI)). We aimed to validate and compare different IPI-like variations to identify the model with the highest predictive accuracy for survival in newly diagnosed DLBCL patients. We included 5126 DLBCL patients treated with immunochemotherapy with available data required by 13 different prognostic models. All models could predict survival, but NCCN-IPI consistently provided high levels of accuracy. Moreover, we found similar 5-year overall survivals in the high-risk group (33.4%) compared to the original validation study of NCCN-IPI. Additionally, only one model incorporating albumin performed similarly well but did not outperform NCCN-IPI regarding discrimination (c-index 0.693). Poor fit, discrimination, and calibration were observed in models with only three risk groups and without age as a risk factor. In this extensive retrospective registry-based study comparing 13 prognostic models, we suggest that NCCN-IPI should be reported as the reference model along with IPI in newly diagnosed DLBCL patients until more accurate validated prognostic models for DLBCL become available.

A systematic review of predictive accuracy via c-statistic of preoperative frailty tests for extended length of stay, post-operative complications, and mortality

Frailty, as an age-related syndrome of reduced physiological reserve, contributes significantly to post-operative outcomes. With the aging population, frailty poses a significant threat to patients and health systems. Since 2012, preoperative frailty assessment has been recommended, yet its implementation has been inhibited by the vast number of frailty tests and lack of consensus. Since the anesthesiologist is the best placed for perioperative care, an anesthesia-tailored preoperative frailty test must be simple, quick, universally applicable to all surgeries, accurate, and ideally available in an app or online form. This systematic review attempted to rank frailty tests by predictive accuracy using the c-statistic in the outcomes of extended length of stay, 3-month post-operative complications, and 3-month mortality, as well as feasibility outcomes including time to completion, equipment and training requirements, cost, and database compatibility. Presenting findings of all frailty tests as a future reference for anesthesiologists, Clinical Frailty Scale was found to have the best combination of accuracy and feasibility for mortality with speed of completion and phone app availability; Edmonton Frailty Scale had the best accuracy for post-operative complications with opportunity for self-reporting. Finally, extended length of stay had too little data for recommendation of a frailty test. This review also demonstrated the need for changing research emphasis from odds ratios to metrics that measure the accuracy of a test itself, such as the c-statistic.

The Social, Demographic, and Clinical Predictors of COVID-19 Severity: a Model-based Analysis of United States Veterans

PURPOSE

This study aims to identify the contributions of individual and community social determinants of health (SDOH), demographic, and clinical factors in COVID-19 disease severity through a model-based analysis.

METHODS

This national cross-sectional study focused on hospitalization among those tested for COVID-19 and use of intensive care, analyzing data on 220,848 Veterans tested between February 20, 2020 and October 20, 2021. Multiple logistic regression models were constructed using backwards elimination. The predictive value of each model was assessed with a c-statistic.

RESULTS

Those hospitalized were older, more likely to be male, of Black or Asian race, have an income less than $39,999, live in an urban residence, and have medical comorbidities. The strongest predictors for hospitalization included Gini inequality index, race, income, heart failure, chronic kidney disease (CKD), and chronic obstructive pulmonary disease (COPD). For intensive care, Asian race, rural residence, COPD, and CKD were the strongest predictors. C-statistics were c = 0.749 for hospitalization and c = 0.582 for ICU admission.

CONCLUSIONS

A combination of clinical, demographic, individual and community SDOH factors predict COVID-19 hospitalization with good predictive ability and can inform risk stratification, discharge planning, and public health interventions. Racial disparities were not explained by social or clinical factors. Intensive care models had low discriminative power and may be better explained by other characteristics.

Cancer Informatics for Cancer Centers: Sharing Ideas on How to Build an Artificial Intelligence-Ready Informatics Ecosystem for Radiation Oncology

In August 2022, the Cancer Informatics for Cancer Centers brought together cancer informatics leaders for its biannual symposium, Precision Medicine Applications in Radiation Oncology, co-chaired by Quynh-Thu Le, MD (Stanford University), and Walter J. Curran, MD (GenesisCare). Over the course of 3 days, presenters discussed a range of topics relevant to radiation oncology and the cancer informatics community more broadly, including biomarker development, decision support algorithms, novel imaging tools, theranostics, and artificial intelligence (AI) for the radiotherapy workflow. Since the symposium, there has been an impressive shift in the promise and potential for integration of AI in clinical care, accelerated in large part by major advances in generative AI. AI is now poised more than ever to revolutionize cancer care. Radiation oncology is a field that uses and generates a large amount of digital data and is therefore likely to be one of the first fields to be transformed by AI. As experts in the collection, management, and analysis of these data, the informatics community will take a leading role in ensuring that radiation oncology is prepared to take full advantage of these technological advances. In this report, we provide highlights from the symposium, which took place in Santa Barbara, California, from August 29 to 31, 2022. We discuss lessons learned from the symposium for data acquisition, management, representation, and sharing, and put these themes into context to prepare radiation oncology for the successful and safe integration of AI and informatics technologies.

Developing and validating a nomogram for cognitive impairment in the older people based on the NHANES

Objective

To use the United States National Health and Nutrition Examination Study (NHANES) to develop and validate a risk-prediction nomogram for cognitive impairment in people aged over 60 years.

Methods

A total of 2,802 participants (aged ≥ 60 years) from NHANES were analyzed. The least absolute shrinkage and selection operator (LASSO) regression model and multivariable logistic regression analysis were used for variable selection and model development. ROC-AUC, calibration curve, and decision curve analysis (DCA) were used to evaluate the nomogram's performance.

Results

The nomogram included five predictors, namely sex, moderate activity, taste problem, age, and education. It demonstrated satisfying discrimination with a AUC of 0.744 (95% confidence interval, 0.696-0.791). The nomogram was well-calibrated according to the calibration curve. The DCA demonstrated that the nomogram was clinically useful.

Conclusion

The risk-prediction nomogram for cognitive impairment in people aged over 60 years was effective. All predictors included in this nomogram can be easily accessed from its' user.

Development of a new prognostic index PNPI for prognosis prediction of CKD patients with pneumonia at hospital admission

Background

The aim of this study was to investigate the relationship between pneumonia and chronic kidney disease (CKD), to elucidate potential risk factors, and to develop a new predictive model for the poor prognosis of pneumonia in CKD patients.

Method

We conducted a retrospective observational study of CKD patients admitted to Tongji Hospital between June 2012 and June 2022. Demographic information, comorbidities or laboratory tests were collected. Applying univariate and multivariate logistic regression analyses, independent risk factors associated with a poor prognosis (i.e., respiratory failure, shock, combined other organ failure, and/or death during hospitalization) for pneumonia in CKD patients were discovered, with nomogram model subsequently developed. Predictive model was compared with other commonly used pneumonia severity scores.

Result

Of 3,193 CKD patients with pneumonia, 1,013 (31.7%) met the primary endpoint during hospitalization. Risk factors predicting poor prognosis of pneumonia in CKD patients were selected on the result of multivariate logistic regression models, including chronic cardiac disease; CKD stage; elevated neutrophil to lymphocyte ratio (NLR) and D-dimer; decreased platelets, PTA, and chloride iron; and significant symptom presence and GGO presentation on CT. The nomogram model outperformed other pneumonia severity indices with AUC of 0.82 (95% CI: 0.80, 0.84) in training set and 0.83 (95% CI: 0.80, 0.86) in testing set. In addition, calibration curve and decision curve analysis (DCA) proved its efficiency and adaptability.

Conclusion

We designed a clinical prediction model PNPI (pneumonia in nephropathy patients prognostic index) to assess the risk of poor prognosis in CKD patients with pneumonia, which may be generalized after more external validation.

A prognostic nomogram for the cancer-specific survival of white patients with invasive melanoma at BANS sites based on the Surveillance, Epidemiology, and End Results database

Objective

The purpose of this study was to develop a comprehensive nomogram for the cancer-specific survival (CSS) of white patients with invasive melanoma at back, posterior arm, posterior neck, and posterior scalp (BANS) sites and to determine the validity of the nomogram by comparing it with the conventional American Joint Committee on Cancer (AJCC) staging system.

Methods

This study analyzed the patients with invasive melanoma in the Surveillance, Epidemiology, and End Results (SEER) database. R software was used to randomly divide the patients into training and validation cohorts at a ratio of 7:3. Multivariable Cox regression was used to identify predictive variables. The new survival nomogram was compared with the AJCC prognosis model using the concordance index (C-index), area under the receiver operating characteristic (ROC) curve (AUC), net reclassification index (NRI), integrated discrimination index (IDI), calibration plotting, and decision-curve analysis (DCA).

Results

A novel nomogram was established to determine the 3-, 5-, and 8-year CSS probabilities of patients with invasive melanoma. According to the nomogram, the Age at Diagnosis had the greatest influence on CSS in invasive melanoma, followed by Bone Metastasis, AJCC, Stage, Liver Metastasis, Histologic Subtype, Brain Metastasis, Ulceration, and Primary Site. The nomogram had a higher C-index than the AJCC staging system in both the training (0.850 versus 0.799) and validation (0.829 versus 0.783) cohorts. Calibration plotting demonstrated that the model had good calibration ability. The nomogram outperformed the AJCC staging system in terms of AUC, NRI, IDI, and DCA.

Conclusion

This was the first study to develop and evaluate a comprehensive nomogram for the CSS of white patients with invasive melanoma at BANS sites using the SEER database. The novel nomogram can assist clinical staff in predicting the 3-, 5-, and 8-year CSS probabilities of patients with invasive melanoma more accurately than can the AJCC staging system.

Development of a nomogram for the prediction of complicated appendicitis during pregnancy

BACKGROUND

Complicated appendicitis during pregnancy directly affects the clinical prognosis of both mother and fetus. However, accurate identification of complicated appendicitis in pregnancy is fraught with various challenges. The purpose of this study was to identify the risk factors and to develop a useful nomogram to predict complicated appendicitis during pregnancy.

METHODS

This retrospective study involved pregnant women who underwent appendectomy at the Maternal and Child Health Hospital of Hubei Provincial from May 2016 to May 2022 and who ultimately had histopathological confirmed acute appendicitis. Univariate and multivariate logistic regression were applied to analyze clinical parameters and imaging features as a way to identify risk factors. Then, nomogram and scoring systems predicting complicated appendicitis in pregnancy were constructed and evaluated. Finally, the potential non-linear association between risk factors and complicated appendicitis was analyzed using restricted cubic splines.

RESULTS

Three indicators were finally identified for the construction of the nomogram: gestational weeks, C-reactive protein (CRP), and neutrophil percentage (NEUT%). To improve the clinical utility, the gestational weeks were divided into three periods (first trimesters, second trimesters, and third trimesters), while the optimal cut-offs for CRP level and NEUT% were found to be 34.82 mg/L and 85.35%, respectively. Multivariate regression analysis showed that third trimesters (P = 0.013, OR = 16.81), CRP level ≥ 34.82 mg/L (P = 0.007, OR = 6.24) and NEUT% ≥85.35% (P = 0.011, OR = 18.05) were independent risk factors for complicated appendicitis. The area under the ROC curve (AUC) of the nomogram predicting complicated appendicitis in pregnancy was 0.872 (95% CI: 0.803-0.942). In addition, the model was shown to have excellent predictive performance by plotting calibration plots, Decision Curve Analysis (DCA), and clinical impact curves. When the optimal cut-off point of the scoring system was set at 12, the corresponding AUC, sensitivity, specificity, Positive Likelihood Ratio (PLR), Negative Likelihood Ratio (NLR), Positive Predictive Value (PPV), and Negative Predictive Value (NPV) values were AUC: 0.869(95% CI: 0.799-0.939),100%, 58.60%, 2.41, 0, 42%, and 100%, respectively. The restricted cubic splines revealed a linear relationship between these predictors and complicated appendicitis during pregnancy.

CONCLUSIONS

The nomogram utilizes a minimum number of variables to develop an optimal predictive model. Using this model, the risk of developing complicated appendicitis in individual patients can be determined so that reasonable treatment choices can be made.

Performance of subgrouped proportional reporting ratios in the US Food and Drug Administration (FDA) adverse event reporting system

BACKGROUND

Many signal detection algorithms give the same weight to information from all products and patients, which may result in signals being masked or false positives being flagged as potential signals. Subgrouped analysis can be used to help correct for this.

RESEARCH DESIGN AND METHODS

The publicly available US Food and Drug Administration Adverse Event Reporting System quarterly data extract files from 1 January 2015 through 30 September 2017 were utilized. A proportional reporting ratio (PRR) analysis subgrouped by either age, sex, ADE report type, seriousness of ADE, or reporter was compared to the crude PRR analysis using sensitivity, specificity, precision, and c-statistic.

RESULTS

Subgrouping by age (n = 78, 34.5% increase), sex (n = 67, 15.5% increase), and reporter (n = 64, 10.3% increase) identified more signals than the crude analysis. Subgrouping by either age or sex increased both the sensitivity and precision. Subgrouping by report type or seriousness resulted in fewer signals (n = 50, -13.8% for both). Subgrouped analyses had higher c-statistic values, with age having the highest (0.468).

CONCLUSIONS

Subgrouping by either age or sex produced more signals with higher sensitivity and precision than the crude PRR analysis. Subgrouping by these variables can unmask potentially important associations.

Cardiovascular risk indicators among depressed persons: A special case?

BACKGROUND

Traditional cardiovascular risk indicators only partially explain cardiovascular risks in depressed persons. Depressed persons may exhibit a profile of cardiovascular risk indicators that goes beyond traditional cardiovascular risk indicators, such as symptom severity, insomnia, loneliness and neuroticism, yet research on the added value of these depression-related characteristics in predicting cardiovascular risks of depressed persons is scarce.

METHODS

Data from N = 1028 depressed Dutch adults without prevalent CVD were derived from two longitudinal depression cohort studies. The outcome was medication-confirmed self-reported CVD. Fifteen depression-related clinical and psychological characteristics were included and tested against traditional cardiovascular risk indicators. Data were analysed using Cox regression models. Incremental values of these characteristics were calculated using c-statistics.

RESULTS

After a median follow-up of 65.3 months, 12.7% of the participants developed CVD. Only anxiety and depressive symptom severity were associated with incident CVD beyond traditional cardiovascular risk indicators. The c-statistic of the model with traditional cardiovascular risk indicators was 85.47%. This increased with 0.56 or 0.33 percentage points after inclusion of anxiety or depression severity, respectively.

LIMITATIONS

Other relevant depression-related characteristics were not available in the datasets used.

CONCLUSION

Anxiety and depressive symptom severity were indicative of an increased cardiovascular risk. Including these as additional risk indicators barely improved the ability to assess cardiovascular risks in depressed persons. Although traditional cardiovascular risk indicators performed well in depressed persons, existing risk prediction algorithms need to be validated in depressed persons.

Impaired cerebral autoregulation in Fabry disease: A case-control study

BACKGROUND AND PURPOSE

Cerebral small vessel disease is a common manifestation among patients with Fabry disease (FD). As a biomarker of cerebral small vessel disease, the prevalence of impaired cerebral autoregulation as assessed by transcranial Doppler (TCD) ultrasonography was evaluated in FD patients and healthy controls.

METHODS

TCD was performed to assess pulsatility index (PI) and vasomotor reactivity expressed by breath-holding index (BHI) for the middle cerebral arteries of included FD patients and healthy controls. Prevalence of increased PI (>1.2) and decreased BHI (<0.69) and ultrasound indices of cerebral autoregulation were compared in FD patients and controls. The potential association of ultrasound indices of impaired cerebral autoregulation with white matter lesions and leukoencephalopathy on brain MRI in FD patients was also evaluated.

RESULTS

Demographics and vascular risk factors were similar in 23 FD patients (43% women, mean age: 51 ± 13 years) and 46 healthy controls (43% women, mean age: 51 ± 13 years). The prevalence of increased PI (39%; 95% confidence interval [CI]: 20%-61%), decreased BHI (39%; 95% CI: 20%-61%), and the combination of increased PI and/or decreased BHI (61%; 95% CI: 39%-80%) was significantly (p < .001) higher in FD patients compared to healthy controls (2% [95% CI: 0.1%-12%], 2% [95% CI: 0.1%-12%], and 4% [95% CI: 0.1%-15%], respectively). However, indices of abnormal cerebral autoregulation were not associated independently with white matter hyperintensities and presented a low-to-moderate predictive ability for the discrimination of FD patients with and without white matter hyperintensities.

CONCLUSIONS

Impaired cerebral autoregulation as assessed by TCD appears to be highly more prevalent among FD patients compared to healthy controls.

Identification of hub cuproptosis related genes and immune cell infiltration characteristics in periodontitis

Introduction

Periodontitis is an inflammatory disease and its molecular mechanisms is not clear. A recently discovered cell death pathway called cuproptosis, may related to the disease.

Methods

The datasets GSE10334 of human periodontitis and control were retrieved from the Gene Expression Omnibus database (GEO) for analysis.Following the use of two machine learning algorithms, least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature removal (SVM-RFE) were used to find CRG-based signature. Then the Receiver operating characteristic (ROC) curves was used to evaluate the gene signature's discriminatory ability. The CIBERSORT deconvolution algorithm was used to study the link between hub genes and distinct types of immune cells. Next, the association of the CRGs with immune cells in periodontitis and relevant clusters of cuproptosis were found. The link between various clusters was ascertained by the GSVA and CIBERSORT deconvolution algorithm. Finally, An external dataset (GSE16134) was used to confirm the diagnosis capacity of the identified biomarkers. In addition, clinical samples were examined using qRT-PCR and immunohistochemistry to verifiy the expression of genes related to cuprotosis in periodontitis and the signature may better predict the periodontitis.

Results

15 periodontitis-related DE-CRGs were found,then 11-CRG-based signature was found by using of LASSO and SVM-RFE. ROC curves also supported the value of signature. CIBERSORT results of immune cell signature in periodontitis showed that signature genes is a crucial component of the immune response.The relevant clusters of cuproptosis found that the NFE2L2, SLC31A1, FDX1,LIAS, DLD, DLAT, and DBT showed a highest expression levels in Cluster2 ,while the NLRP3, MTF1, and DLST displayed the lowest level in Cluster 2 but the highest level in Cluster1. The GSVA results also showed that the 11 cuproptosis diagnostic gene may regulate the periodontitis by affecting immune cells. The external dataset (GSE16134) confirm the diagnosis capacity of the identified biomarkers, and clinical samples examined by qRT-PCR and immunohistochemistry also verified that these cuprotosis related signiture genes in periodontitis may better predict the periodontitis.

Conclusion

These findings have important implications for the cuproptosis and periodontitis, and highlight further research is needed to better understand the mechanisms underlying this relationship between the cuproptosis and periodontitis.

Development and validation of a novel nomogram for postoperative overall survival of patients with primary gastric signet-ring cell carcinoma: a population study based on SEER database

BACKGROUND

Gastric signet ring cell carcinoma (GSRCC) is a highly malignant subtype of gastric cancer. We tried to establish and validate a nomogram using common clinical variables to achieve more personalized management.

METHODS

We analyzed patients with GSRCC in the Surveillance, Epidemiology, and End Results database between 2004 and 2017. The survival curve was calculated by the Kaplan-Meier method, and the difference in survival curve was tested by log-rank test. We used the cox proportional hazard model to evaluate independent factors of prognosis, and established a nomogram to predict 1-, 3- and 5- overall survival (OS). Harrell's consistency index and calibration curve were used to measure the discrimination and calibration of the nomogram. In addition, we used decision curve analysis (DCA) to compare the net clinical benefits of the nomogram and American Joint Committee on Cancer (AJCC) staging system.

RESULTS

The prognosis nomogram predicting 1-, 3- and 5-years OS for patients with GSRCC is established for the first time. The C-index and AUC of nomogram were higher than that of the American Joint Committee on Cancer (AJCC) staging system in the training set. Our model also shows better performance than the AJCC staging system in the validation set, and importantly, DCA shows that our model has a better net benefit than the AJCC stage.

CONCLUSIONS

We have developed and validated a new nomogram and risk classification system, which is better than the AJCC staging system. It will help clinicians manage postoperative patients with GSRCC more accurately.

Can extant comorbidity indices identify patients who experience poor outcomes following total joint arthroplasty?

INTRODUCTION

It is uncertain if generic comorbidity indices commonly used in orthopedics accurately predict outcomes after total hip (THA) or knee arthroplasty (TKA). The purpose of this study was to determine the predictive ability of such comorbidity indices for: (1) 30-day mortality; (2) 30-day rate of major and minor complications; (3) discharge disposition; and (4) extended length of stay (LOS).

METHODS

The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database was retrospectively reviewed for all patients who underwent elective THA (n = 202,488) or TKA (n = 230,823) from 2011 to 2019. The American Society of Anesthesiologists (ASA) physical status classification system score, modified Charlson Comorbidity Index (mCCI), Elixhauser Comorbidity Measure (ECM), and 5-Factor Modified Frailty Index (mFI-5) were calculated for each patient. Logistic regression models predicting 30-day mortality, discharge disposition, LOS greater than 1 day, and 30-day major and minor complications were fit for each index.

RESULTS

The ASA classification (C-statistic = 0.773 for THA and TKA) and mCCI (THA: c-statistic = 0.781; TKA: C-statistic = 0.771) were good models for predicting 30-day mortality. However, ASA and mCCI were not predictive of major and minor complications, discharge disposition, or LOS. The ECM and mFI-5 did not reliably predict any outcomes of interest.

CONCLUSION

ASA and mCCI are good models for predicting 30-day mortality after THA and TKA. However, similar to ECM and mFI-5, these generic comorbidity risk-assessment tools do not adequately predict 30-day postoperative outcomes or in-hospital metrics. This highlights the need for an updated, data-driven approach for standardized comorbidity reporting and risk assessment in arthroplasty.

Comparison of Black and white individuals who report diagnoses of schizophrenia in a national sample of US adults: Discrimination and service use

BACKGROUND

While there is increasing recognition of disparities in healthcare for Black Americans, there have been no comparisons in a nationally representative U.S. sample of Black and White adults with clinical diagnoses of schizophrenia.

METHODS

Using nationally representative survey data from the National Epidemiologic Survey on Alcohol and Related Conditions-III, we compared Black (n = 240, 36.2%) and White (n = 423, 63.8%) adults who report having been told by a physician that they have schizophrenia. Due to the large sample size, effect sizes (risk ratios and Cohen's d), rather than p-values, were used to identify the magnitude of differences in sociodemographic and clinical characteristics, including experiences of discrimination and service use. Multivariate analyses were used to identify independent factors.

RESULTS

Black individuals with diagnoses of schizophrenia reported multiple sociodemographic disadvantages, including lower rates of employment, educational attainment, income, marriage, and social support, with little difference in incarceration, violent behavior, and quality of life. They reported much higher scores on a general lifetime discrimination scale (Cohen's d = 0.75) and subscales representing job discrimination (d = 0.85), health system discrimination (d = 0.70), and public race-based abuse (d = 0.55) along with higher rates of past year alcohol and drug use disorders, but lower rates of co-morbid psychiatric disorders. Multivariable-adjusted regression analyses highlighted the independent association of Black race with measures of discrimination and religious service attendance; less likelihood of receiving psychiatric treatment (p = 0.02) but no difference in substance use treatment.

CONCLUSION

Black adults with schizophrenia report numerous social disadvantages, especially discrimination, but religious service attendance may be an important social asset.

Assessment of a serum calcification propensity test for the prediction of all-cause mortality among hemodialysis patients

BACKGROUND

Vascular calcification is a major contributor to the high cardiac burden among hemodialysis patients. A novel in vitro T50-test, which determines calcification propensity of human serum, may identify patients at high risk for cardiovascular (CV) disease and mortality. We evaluated whether T50 predicts mortality and hospitalizations among an unselected cohort of hemodialysis patients.

METHODS

This prospective clinical study included 776 incident and prevalent hemodialysis patients from 8 dialysis centers in Spain. T50 and fetuin-A were determined at Calciscon AG, all other clinical data were retrieved from the European Clinical Database. After their baseline T50 measurement, patients were followed for two years for the occurrence of all-cause mortality, CV-related mortality, all-cause and CV-related hospitalizations. Outcome assessment was performed with proportional subdistribution hazards regression modelling.

RESULTS

Patients who died during follow-up had a significantly lower T50 at baseline as compared to those who survived (269.6 vs. 287.7 min, p = 0.001). A cross-validated model (mean c statistic: 0.5767) identified T50 as a linear predictor of all-cause-mortality (subdistribution hazard ratio (per min): 0.9957, 95% CI [0.9933;0.9981]). T50 remained significant after inclusion of known predictors. There was no evidence for prediction of CV-related outcomes, but for all-cause hospitalizations (mean c statistic: 0.5284).

CONCLUSION

T50 was identified as an independent predictor of all-cause mortality among an unselected cohort of hemodialysis patients. However, the additional predictive value of T50 added to known mortality predictors was limited. Future studies are needed to assess the predictive value of T50 for CV-related events in unselected hemodialysis patients.

[Determining factors of the accident rate and occupational diseases of the shellfish divers of the Coquimbo Region, Chile]

OBJECTIVES

To identify the causes of accidents and occupational diseases of shell-fish divers in the small coves de Coquimbo, Los Vilos, and Tongoy, in the Coquimbo Region, Chile, in the period 2008-2018.

METHODS

A structured survey of 44 questions was applied to determine the causes or factors that influence accidents and occupational diseases of shellfish divers in the region; collecting information, experiences and observations of the protagonists. The double entry table, the X2 and C contingency association statistics were used for the data analysis.

RESULTS

Of the 52 surveys carried out, 28 shellfish divers have had accidents and occupational diseases (53%). The segment that does not complete basic education concentrates 46% of accidents. It also shows incidence, the depth at which diving is done, the consumption of alcohol and tobacco, the non-use of decompression tables and the lack of training.

CONCLUSIONS

The type of accident or associated occupational disease with the highest incidence is decompression syndrome and suffocation, both of which have a high association with alcohol consumption and non-use of decompression tables.

Machine learning applied to electronic health record data in home healthcare: A scoping review

OBJECTIVE

Despite recent calls for home healthcare (HHC) to integrate informatics, the application of machine learning in HHC is relatively unknown. Thus, this study aimed to synthesize and appraise the literature describing the application of machine learning to predict adverse outcomes (e.g., hospitalization, mortality) using electronic health record (EHR) data in the HHC setting. Our secondary aim was to evaluate the comprehensiveness of predictors used in the machine learning algorithms guided by the Biopsychosocial Model.

METHODS

During March 2022 we conducted a literature search in four databases: PubMed, Embase, CINAHL, and Scopus. Inclusion criteria were 1) describing services provided in the HHC setting, 2) applying machine learning algorithms to predict adverse outcomes, defined as outcomes related to patient deterioration, 3) using EHR data and 4) focusing on the adult population. Predictors were mapped to the Biopsychosocial Model. A risk of bias analysis was conducted using the Prediction Model Risk Of Bias Assessment Tool.

RESULTS

The final sample included 20 studies. Eighteen studies used predictors from standardized assessments integrated in the EHR. The most common outcome of interest was hospitalization (55%), followed by mortality (25%). Psychological predictors were frequently excluded (35%). Tree based algorithms were most frequently applied (75%). Most studies demonstrated high or unclear risk of bias (75%).

CONCLUSION

Future studies in HHC should consider incorporating machine learning algorithms into clinical decision support systems to identify patients at risk. Based on the Biopsychosocial model, psychological and interpersonal characteristics should be used along with biological characteristics to enhance risk prediction. To facilitate the widespread adoption of machine learning, stakeholders should encourage standardization in the HHC setting.

The performance of EuroSCORE II in CABG patients in relation to sex, age, and surgical risk: a nationwide study in 14,118 patients

BACKGROUND

To determine the discriminative accuracy and calibration of EuroSCORE II in relation to age, sex, and surgical risk in a large nationwide coronary artery bypass grafting (CABG) cohort.

METHODS

All 14,118 patients undergoing isolated CABG in Sweden during 2012-2017 were included. Individual patient data were taken from the SWEDEHEART registry. Patients were divided by age (< 60, 60-69, 70-79, ≥ 80 years), sex, and surgical risk (low: EuroSCORE < 4%, intermediate: 4-8%, high: > 8%). Discriminative accuracy was determined by the area under the receiver operating characteristic curve (AUC) and calibration by the observed/estimated (O/E) mortality ratio at 30 days.

RESULTS

AUC and O/E ratio were 0.82 (95% CI 0.79-0.85) and 0.58 (0.50-0.66) overall, 0.82 (0.79-0.86) and 0.57 (0.48-0.66) in men, and 0.79 (0.73-0.85) and 0.60 (0.47-0.75) in women. Regarding age, discriminative accuracy was highest in patients aged 60-69 years (AUC: 0.86 [0.80-0.93]) but was satisfactory in all groups (AUC: 0.74-0.80). O/E ratio varied from 0.26 for patients > 60 years to 0.90 for patients > 80 years. Regarding surgical risk, AUC and O/E ratio were 0.63 (0.44-0.83) and 0.18 (0.09-0.30) in low-risk patients, 0.60 (0.55-0.66) and 0.57 (0.46-0.68) in intermediate-risk patients, and 0.78 (0.73-0.83) and 0.78 (0.64-0.92) in high-risk patients.

CONCLUSIONS

EuroSCORE II had good discriminative accuracy independently of sex and age, but markedly overestimated mortality risk, especially in younger patients. Accuracy and calibration were better in high-risk patients than in low-risk and intermediate-risk patients.

Evaluation of Risk Scores as Predictive Tools for Stroke in Patients with Retinal Artery Occlusion: A Danish Nationwide Cohort Study

Purpose  We investigated the 1-year risk of stroke in patients with retinal artery occlusion and evaluated the predictive and discriminating abilities of contemporary risk stratification models for embolic stroke. Methods  This register-based cohort study included 7,906 patients with retinal artery occlusion from Danish nationwide patient registries between 1995 and 2018. The study population was stratified according to the number of points obtained in the stroke risk scores: the CHA ₂ DS ₂ -VASc score and the ESSEN Stroke Risk score. The 1-year risk of stroke within strata was evaluated and compared using the cox proportional hazards model. Furthermore, the discrimination of the risk scores as predictive tools for stroke risk assessment was investigated using C-statistics, Brier score, and the index of prediction accuracy. Results  The stroke event rate in patients with retinal artery occlusion increased as the score increased for both risk scores, ranging from 3.62 (95% confidence interval [CI]: 2.46-5.31) per 100 person-years to 13.25 (95% CI: 11.78--14.89) per 100-person-years for increasing levels of the CHA ₂ DS ₂ -VASc score and from 3.97 (95% CI: 2.97-5.32) per 100 person-years to 16.43 (95% CI: 14.01-19.27) per 100 person-years for increasing levels of the ESSEN Stroke Risk score. Using a risk score of 0 as a reference, the difference was statistically significant for retinal artery occlusion patients with a CHA ₂ DS ₂ -VASc score of 2 or above and for all levels of the ESSEN Stroke Risk score. The C-statistics for the risk scores was 61% (95% CI: 58%-63%) and 62% (95% CI: 59-64%) for the CHA ₂ DS ₂ -VASc score and ESSEN Stroke Risk score, respectively. Conclusion  The results suggested that the use of the CHA ₂ DS ₂ -VASc score and the ESSEN Stroke Risk score was applicable for risk stratification of stroke in patients with retinal artery occlusion, but discrimination was poor due to low specificity.

Validation of a novel risk score to predict early and late recurrence in solitary fibrous tumour

BACKGROUND

Current risk models in solitary fibrous tumour (SFT) were developed using cohorts with short follow-up and cannot reliably identify low-risk patients. We recently developed a novel risk model (G-score) to account for both early and late recurrences. Here, we aimed to validate the G-score in a large international cohort with long-term follow-up.

METHODS

Data were collected from nine sarcoma referral centres worldwide. Recurrence-free interval (RFi) was the primary endpoint.

RESULTS

The cohort comprised 318 patients with localised extrameningeal SFTs. Disease recurrence occurred in 96 patients (33%). The estimated 5-year RFi rate was 72%, and the 10-year RFi rate was 52%. G-score precisely predicted recurrence risk with estimated 10-year RFi rate of 84% in low risk, 54% in intermediate risk and 36% in high risk (p < 0.001; C-index 0.691). The mDemicco (p < 0.001; C-index 0.749) and SalasOS (p < 0.001; C-index 0.674) models also predicted RFi but identified low-risk patients less accurate with 10-year RFi rates of 72% and 70%, respectively.

CONCLUSIONS

G-score is a highly significant predictor of early and late recurrence in SFT and is superior to other models to predict patients at low risk of relapse. A less intensive follow-up schedule could be considered for patients at low recurrence risk according to G-score.

A prediction model for hospital mortality in patients with severe community-acquired pneumonia and chronic obstructive pulmonary disease

Background

No personalized prediction model or standardized algorithm exists to identify those at high risk of death among severe community-acquired pneumonia (SCAP) patients with chronic obstructive pulmonary disease (COPD). The aim of this study was to investigate the risk factors and to develop a useful nomogram for prediction of mortality in those patients.

Methods

We performed a retrospective, observational, cohort study in the intensive care unit (ICU) of West China Hospital, Sichuan University with all consecutive SCAP patients with COPD between December 2011 and December 2018. The clinical data within 24 h of admission to ICU were collected. The primary outcome was hospital mortality. We divided the patients into training and testing cohorts (70% versus 30%) randomly. In the training cohort, univariate and multivariate logistic regression analysis were used to identify independent risk factors applied to develop a nomogram. The prediction model was assessed in both training and testing cohorts.

Results

Finally, 873 SCAP patients with COPD were included, among which the hospital mortality was 41.4%. In training cohort, the independent risk factors for hospital mortality were increased age, diabetes, chronic renal diseases, decreased systolic blood pressure (SBP), and elevated fibrinogen, interleukin 6 (IL-6) and blood urea nitrogen (BUN). The C index was 0.840 (95% CI 0.809–0.872) in training cohort and 0.830 (95% CI 0.781–0.878) in testing cohort. Furthermore, the time-dependent AUC, calibration plots, DCA and clinical impact curves indicated the model had good predictive performance. Significant association of risk stratification based on nomogram with mortality was also found (P for trend < 0.001). The restricted cubic splines suggested that estimated associations between these predictors and hospital mortality were all linear relationships.

Conclusion

We developed a prediction model including seven risk factors for hospital mortality in patients with SCAP and COPD. It can be used for early risk stratification in clinical practice after more external validation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12931-022-02181-9.

A prediction model for major adverse cardiovascular events (MACE) in patients with coronavirus disease 2019 (COVID-19)

Background

Emerging evidence shows that cardiovascular injuries and events in coronavirus disease 2019 (COVID-19) should be considered. The current study was conducted to develop an early prediction model for major adverse cardiovascular events (MACE) during hospitalizations of COVID-19 patients.

Methods

This was a retrospective, multicenter, observational study. Hospitalized COVID-19 patients from Wuhan city, Hubei Province and Sichuan Province, China, between January 14 and March 9, 2020, were randomly divided into a training set (70% of patients) and a testing set (30%). All baseline data were recorded at admission or within 24 h after admission to hospitals. The primary outcome was MACE during hospitalization, including nonfatal myocardial infarction, nonfatal stroke and cardiovascular death. The risk factors were selected by LASSO regression and multivariate logistic regression analysis. The nomogram was assessed by calibration curve and decision curve analysis (DCA).

Results

Ultimately, 1206 adult COVID-19 patients were included. In the training set, 48 (5.7%) patients eventually developed MACE. Six factors associated with MACE were included in the nomogram: age, PaO₂/FiO₂ under 300, unconsciousness, lymphocyte counts, neutrophil counts and blood urea nitrogen. The C indices were 0.93 (95% CI 0.90, 0.97) in the training set and 0.81 (95% CI 0.70, 0.93) in the testing set. The calibration curve and DCA demonstrated the good performance of the nomogram.

Conclusions

We developed and validated a nomogram to predict the development of MACE in hospitalized COVID-19 patients. More prospective multicenter studies are needed to confirm our results.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-022-02143-3.

Review of potential biomarkers of inflammation and kidney injury in diabetic kidney disease

Diabetic kidney disease is expected to increase rapidly over the coming decades with rising prevalence of diabetes worldwide. Current measures of kidney function based on albuminuria and estimated glomerular filtration rate do not accurately stratify and predict individuals at risk of declining kidney function in diabetes. As a result, recent attention has turned towards identifying and assessing the utility of biomarkers in diabetic kidney disease. This review explores the current literature on biomarkers of inflammation and kidney injury focussing on studies of single or multiple biomarkers between January 2014 and February 2020. Multiple serum and urine biomarkers of inflammation and kidney injury have demonstrated significant association with the development and progression of diabetic kidney disease. Of the inflammatory biomarkers, tumour necrosis factor receptor-1 and -2 were frequently studied and appear to hold most promise as markers of diabetic kidney disease. With regards to kidney injury biomarkers, studies have largely targeted markers of tubular injury of which kidney injury molecule-1, beta-2-microglobulin and neutrophil gelatinase-associated lipocalin emerged as potential candidates. Finally, the use of a small panel of selective biomarkers appears to perform just as well as a panel of multiple biomarkers for predicting kidney function decline.

Adiponectin, leptin, and leptin/adiponectin ratio with risk of gestational diabetes mellitus: A prospective nested case-control study among Chinese women

AIMS

To examine the associations of serum concentrations of adiponectin and leptin and leptin/adiponectin ratio (LAR) in early pregnancy with risk of gestational diabetes mellitus (GDM) in Chinese women. The predictive ability of those biomarkers for GDM was also assessed.

METHODS

Within the Tongji-Shuangliu Birth Cohort, a nested case-control study was established with 332 GDM cases and 664 matched controls at 1:2 ratio on age (±3 years) and gestational age (±4 weeks). Serum adiponectin and leptin levels were measured in early pregnancy (median gestational week, 11; range, 6-15). Conditional logistic regression models with adjustment for potential covariates were used to evaluate the associations.

RESULTS

Multivariable-adjusted odds ratios (ORs) comparing extreme quartiles of adiponectin, leptin and LAR were 0.55 (95 % CI, 0.35, 0.85), 1.96 (95 % CI, 1.19, 3.24), and 2.72 (95 % CI, 1.63, 4.54) for GDM, respectively (All P-trend < 0.02). Adding adiponectin and leptin to a conventional prediction model (including traditional risk factors and fasting glucose) increased the C-statistics from 0.708 (95 % CI, 0.674, 0.741) to 0.728 (95 % CI, 0.695, 0.760), and achieved a net reclassification improvement of 0.292.

CONCLUSIONS

Our findings indicate that adiponectin is inversely associated with GDM, while leptin and LAR are positively associated with GDM in Chinese pregnant women.

Infection Polygenic Factors Account for a Small Proportion of the Relationship Between Infections and Mental Disorders

BACKGROUND

Several recent studies have suggested a role for infections in the development of mental disorders; however, the genetic contribution to this association is understudied.

METHODS

We use the iPSYCH case-cohort genotyped sample (n = 65,534) and Danish health care registry data to study the genetic association between infections and mental disorders. To test the hypothesis that these associations are due to genetic pleiotropy, we estimated the genetic correlation between infection and mental disorders. Polygenic risk scores (PRSs) were used to assess whether genetic pleiotropy of infections and mental disorders was mediated by actual infection diagnoses.

RESULTS

We observed that schizophrenia, attention-deficit/hyperactivity disorder, major depressive disorder, bipolar disorder, and posttraumatic stress disorder (r_g ranging between 0.18 and 0.83), but not autism spectrum disorder and anorexia nervosa, were significantly genetically correlated with infection diagnoses. PRSs for infections were associated with modest increase in risk of attention-deficit/hyperactivity disorder, major depressive disorder, and schizophrenia in the iPSYCH case-cohort (hazard ratios = 1.04 to 1.10) but was not associated with risk of anorexia, autism, or bipolar disorder. Using mediation analysis, we show that infection diagnoses account for only a small proportion (6%-14%) of the risk for mental disorders conferred by infection PRSs.

CONCLUSIONS

Infections and mental disorders share a modest genetic architecture. Infection PRSs can predict risk of certain mental disorders; however, this effect is moderate. Finally, recorded infections partially explain the relationship between infection PRSs and mental disorders.

Prognostic models to predict survival in patients with pancreatic cancer: a systematic review

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) has poor survival. Current treatments offer little likelihood of cure or long-term survival. This systematic review evaluates prognostic models predicting overall survival in patients diagnosed with PDAC.

METHODS

We conducted a comprehensive search of eight electronic databases from their date of inception through to December 2019. Studies that published models predicting survival in patients with PDAC were identified.

RESULTS

3297 studies were identified; 187 full-text articles were retrieved and 54 studies of 49 unique prognostic models were included. Of these, 28 (57.1%) were conducted in patients with advanced disease, 17 (34.7%) with resectable disease, and four (8.2%) in all patients. 34 (69.4%) models were validated, and 35 (71.4%) reported model discrimination, with only five models reporting values >0.70 in both derivation and validation cohorts. Many (n = 27) had a moderate to high risk of bias and most (n = 33) were developed using retrospective data. No variables were unanimously found to be predictive of survival when included in more than one study.

CONCLUSION

Most prognostic models were developed using retrospective data and performed poorly. Future research should validate instruments performing well locally in international cohorts and investigate other potential predictors of survival.

Development and Validation of a Risk Score for Post-Transplant Lymphoproliferative Disorders among Solid Organ Transplant Recipients

Post-transplant lymphoproliferative disease (PTLD) is a well-recognized complication after transplant. This study aimed to develop and validate a risk score to predict PTLD among solid organ transplant (SOT) recipients. Poisson regression identified predictors of PTLD with the best fitting model selected for the risk score. The derivation cohort consisted of 2546 SOT recipients transpanted at Rigshospitalet, Copenhagen between 2004 and 2019; 57 developed PTLD. Predictors of PTLD were high-risk pre-transplant Epstein–Barr Virus (EBV), IgG donor/recipient serostatus, and current positive plasma EBV DNA, abnormal hemoglobin and C-reactive protein levels. Individuals in the high-risk group had almost 7 times higher incidence of PTLD (incidence rate ratio (IRR) 6.75; 95% CI: 4.00–11.41) compared to the low-risk group. In the validation cohort of 1611 SOT recipients from the University Hospital of Zürich, 24 developed PTLD. A similar 7 times higher risk of PTLD was observed in the high-risk group compared to the low-risk group (IRR 7.17, 95% CI: 3.05–16.82). The discriminatory ability was also similar in derivation (Harrell’s C-statistic of 0.82 95% CI (0.76–0.88) and validation (0.82, 95% CI:0.72–0.92) cohorts. The risk score had a good discriminatory ability in both cohorts and helped to identify patients with higher risk of developing PTLD.

C-index regression for recurrent event data

Recurrent event data analysis plays an important role in many fields, e.g., medicine, social science, and economics. While the existing approaches under the proportional rates or mean model yield poor performance when the underlying model is misspecified, we propose a novel model-free approach by introducing a lower bound on the concordance index (C-Index). We develop an estimation method through deriving a continuous lower bound on the C-Index based on the log-sigmoid function and also provide a variable selection procedure in high dimensional settings. Under both low and high dimensional settings, simulation results show that the proposed methods outperform the gamma frailty recurrent event model when the proportional mean assumption is violated. Moreover, an application to the hospital readmission dataset shows results in line with previous studies and a higher C-Index value further assures model decency.

Using real-world data to monitor and improve quality of care in coronary artery disease: results from the Netherlands Heart Registration

Worldwide, quality registries for cardiovascular diseases enable the use of real-world data to monitor and improve the quality of cardiac care. In the Netherlands Heart Registration (NHR), cardiologists and cardiothoracic surgeons register baseline, procedural and outcome data across all invasive cardiac interventional, electrophysiological and surgical procedures. This paper provides insight into the governance and processes as organised by the NHR in collaboration with the hospitals. To clarify the processes, examples are given from the percutaneous coronary intervention and coronary artery bypass grafting registries. Physicians who are mandated by their hospital to instruct the NHR to process their data are united in registration committees. The committees determine standard sets of variables and periodically discuss the completeness and quality of data and patient-relevant outcomes. In the case of significant variation in outcomes, processes of healthcare delivery are discussed and good practices are shared in a non-competitive and safe setting. To create new insights for further improvement in patient-relevant outcomes, quality projects are initiated on, for example, multivessel disease treatment, cardiogenic shock and diagnostic intracoronary procedures. Moreover, possibilities are explored to expand the quality registries through additional relevant indicators, such as resource use before and after the procedure, by enriching NHR data with other existing data resources.

Unexpected malignancy at the time of hysterectomy performed for a benign indication: A retrospective review

Objective

To determine the proportion of patients undergoing hysterectomy for a benign indication who have unexpected malignancy (UM) on postoperative pathology and characterize the nature of UMs.

Methods

This was a multi-center, retrospective study of patients undergoing hysterectomy for a benign indication from July 2016 to December 2019 at 7 Ontario, Canada hospitals (4 academic, 3 community). Hysterectomies for invasive placentation, malignant, and premalignant indications were excluded. Primary outcome was rate of unexpected malignancy as defined by the number of patients with malignancy on final pathology divided by the total number of hysterectomy cases. Data was extracted from health records and electronic charts. Patient, surgical, and surgeon characteristics were compared between benign and UM groups using bivariate methods. Associations between UM status and perioperative variables were assessed using bivariate logistic regression.

Results

In the study period, 2779 hysterectomies were performed. UM incidence was 1.8% (51 malignancies/2779 cases), with one patient having two malignancies (total UMs = 52). The most common UM types were endometrial (27/52, 51.9%) and sarcoma (13/52, 25%). Patients with UM were older (57.2 ± 11.4 years vs. 52.8 ± 12.5 years, p = .015), had more previous laparotomies (2 (1.25, 2.0) vs. 1 (1.0, 1.0), p < .001), and higher BMI (29.7 ± 7.2 kg/m² vs. 28.0 ± 5.9 kg/m², p = .049) and ASA class (p < .028). Regarding surgical factors, patients with UM had more adhesions (p = .001), transfusions (p = .020), and blood loss (p = .006) compared to those with benign pathology. Patient characteristics most strongly associated with UM were age (OR 2.57, 95% CI 1.78–3.72, p < .001) and preoperative diagnosis of pelvic mass (OR 2.76, 95% CI 1.11–6.20, p = .019).

Conclusion

Incidence of UM at hysterectomy for benign indication was 1.8%. Several perioperative variables are associated with an increased chance of UM.

Socioeconomic Disparities in Gastric Cancer and Identification of a Single SES Variable for Predicting Risk

INTRODUCTION

Socioeconomic status (SES) is a known risk factor for gastric cancer (GC). This study seeks to examine education, income, and occupation variables separately to identify the single variable that can be best used to assess SES risk for GC.

METHODS

Data from a case-control survey study were used. Logistic regression models were created for education, income, and occupation adjusted for age, sex, and race. Models were compared using AIC, c-statistics, and pseudo-R square to determine the model that had the highest risk predictive ability.

RESULTS

GC cases had lower education levels and more commonly held jobs in unskilled labor. Annual household income was lower in cases compared to controls. Age, gender, race, education, and occupation were associated with increased risk of GC. The education model adjusted for age, gender, and race found < high school (HS) education to have an OR of 3.18 (95% CI 1.09-9.25) for GC compared to > HS education. The occupation model demonstrated that employment in unskilled labor had OR of 4.32 (95% CI 1.05-17.76) for GC compared to professional occupation. Model fit was best for the education model (AIC: 113.583, lower AIC is better) compared to income (117.562) or occupation (117.032). Education contributed the most to model variability (% delta pseudo-R square (4.7%)) compared to occupation (4.0%) or income (3.8%).

CONCLUSION

Education level was the single most reliable measure of GC risk among 3 SES variables and can be employed as an ideal single indicator of SES-related GC risk when multiple SES factors cannot be obtained.

Scoring systems for the management of oncological hepato-pancreato-biliary patients

Oncological scoring systems in surgery are used as evidence-based decision aids to best support management through assessing prognosis, effectiveness and recurrence. Currently, the use of scoring systems in the hepato-pancreato-biliary (HPB) field is limited as concerns over precision and applicability prevent their widespread clinical implementation. The aim of this review was to discuss clinically useful oncological scoring systems for surgical management of HPB patients. A narrative review was conducted to appraise oncological HPB scoring systems. Original research articles of established and novel scoring systems were searched using Google Scholar, PubMed, Cochrane, and Ovid Medline. Selected models were determined by authors. This review discusses nine scoring systems in cancers of the liver (CLIP, BCLC, ALBI Grade, RETREAT, Fong's score), pancreas (Genç's score, mGPS), and biliary tract (TMHSS, MEGNA). Eight models used exclusively objective measurements to compute their scores while one used a mixture of both subjective and objective inputs. Seven models evaluated their scoring performance in external populations, with reported discriminatory c-statistic ranging from 0.58 to 0.82. Selection of model variables was most frequently determined using a combination of univariate and multivariate analysis. Calibration, another determinant of model accuracy, was poorly reported amongst nine scoring systems. A diverse range of HPB surgical scoring systems may facilitate evidence-based decisions on patient management and treatment. Future scoring systems need to be developed using heterogenous patient cohorts with improved stratification, with future trends integrating machine learning and genetics to improve outcome prediction.

Association of red blood cell distribution width with postoperative new-onset atrial fibrillation following cardiac valve replacement surgery: A retrospective study

PURPOSE

The aim of this study was to evaluate the impact of preoperative red blood cell distribution width (RDW) values on the risk of postoperative new-onset atrial fibrillation (PNAF) during hospitalization following cardiac valve replacement surgery.

MATERIALS AND METHODS

The clinical data of 148 patients with preoperative sinus rhythm who underwent cardiac valve replacement surgery at The First Affiliated Hospital of Anhui Medical University from September 2017 to June 2018 were retrospectively analyzed. Univariate and multivariate logistic regression analyses were used to determine the relationship between preoperative RDW values and the development of PNAF.

RESULTS

Forty-nine of the 148 patients (33.1%) developed PNAF. The median preoperative RDW was 13.1 (12.6-17.2), while the median RDW value was significantly higher in patients with PNAF than in those without PNAF [14.1 (13.2-15.0) vs. 12.9 (12.4-13.5), P < 0.001]. Multivariate logistic regression analysis showed that preoperative RDW values were significantly correlated with the occurrence of PNAF (odds ratio: 1.940, 95% confidence interval: 1.377 to 2.731, P < 0.001).

CONCLUSIONS

Preoperative RDW is an independent risk factor for PNAF during hospitalization following cardiac valve replacement surgery. This finding suggests that preoperative RDW measurement may be used to stratify the risk for PNAF development in patients undergoing cardiac surgery.

Clinical characteristics and risk factors associated with mortality in patients with severe community-acquired pneumonia and type 2 diabetes mellitus

BACKGROUND

The present study was performed to investigate the impacts of type 2 diabetes mellitus (T2DM) on severe community-acquired pneumonia (SCAP) and to develop a novel prediction model for mortality in SCAP patients with T2DM.

METHODS

This was a retrospective observational study conducted in consecutive adult patients with SCAP admitted to the intensive care unit (ICU) of West China Hospital, Sichuan University, China, between September 2011 and September 2019. The primary outcome was hospital mortality. A propensity score matching (PSM) analysis model with a 1:2 ratio was used for the comparisons of clinical characteristics and outcomes between T2DM and nondiabetic patients. The independent risk factors were identified via univariate and then multivariable logistic regression analysis and were then used to establish a nomogram.

RESULTS

In total, 1262 SCAP patients with T2DM and 2524 matched patients without T2DM were included after PSM. Patients with T2DM had longer ICU length of stay (LOS) (13 vs. 12 days, P = 0.016) and higher 14-day mortality (15% vs. 10.8%, P < 0.001), 30-day mortality (25.7% vs. 22.7%, P = 0.046), ICU mortality (30.8% vs. 26.5%, P = 0.005), and hospital mortality (35.2% vs. 31.0%, P = 0.009) than those without T2DM. In SCAP patients with T2DM, the independent risk factors for hospital mortality were increased numbers of comorbidities and diabetes-related complications; elevated C-reactive protein (CRP), neutrophil to lymphocyte ratio (NLR), brain natriuretic peptide (BNP) and blood lactate; as well as decreased blood pressure on admission. The nomogram had a C index of 0.907 (95% CI: 0.888, 0.927) in the training set and 0.873 (95% CI: 0.836, 0.911) in the testing set, which was superior to the pneumonia severity index (PSI, AUC: 0.809, 95% CI: 0.785, 0.833). The calibration curve and decision curve analysis (DCA) also demonstrated its accuracy and applicability.

CONCLUSIONS

SCAP patients with T2DM had worse clinical outcomes than nondiabetic patients. The nomogram has good predictive performance for hospital mortality and might be generally applied after more external validations.

Predicting Risk for Early Breastfeeding Cessation in Israel

OBJECTIVES

This study aimed to 1) Examine factors associated with cessation of exclusive breastfeeding in Israel and 2) Develop predictive models to identify women at risk for early exclusive breastfeeding cessation.

METHODS

The study used data from longitudinal national representative infant nutrition survey in Israel (n = 2119 participants). Logistic regression was used to identify risk factors and build predictive models.

RESULTS

The rate of exclusive breastfeeding cessation increased from 45.4% at 2 months to 85.7% at 6 months. Five factors were significantly associated with higher odds of exclusive breastfeeding cessation at 2 months: being a primapara, low educational level, lack of previous breastfeeding experience, negative attitude towards birth, and lack of intention to breastfeed. Six factors were significantly associated with higher odds of exclusive breastfeeding cessation at 6 months: younger age, being in a relationship with a partner, lower religiosity level, cesarean delivery, not taking folic acid during pregnancy, and negative attitude towards birth. Both 2 and 6-months models had good predictive performance (C-statistic of .72 and .68, accordingly).

CONCLUSIONS FOR PRACTICE

This nationwide study successfully identified several predictors of exclusive breastfeeding cessation and created breastfeeding cessation prediction tools for two time periods (2 and 6 months). The resulting tools can be applied to identify women at risk for stopping exclusive breastfeeding in hospitals or at community clinics. Further studies should examine practical aspects of applying these tools in practice and explore whether applying those tools can lead to higher exclusive breastfeeding rates.

Diagnostic remission of substance use disorders: Racial differences and correlates of remission in a nationally representative sample

INTRODUCTION

Research has shown racial/ethnic minorities to have similar risk of developing substance use disorders (SUDs) as Whites. However, few studies have compared the likelihood of diagnostic remission (i.e., no longer meeting criteria for current SUDs).

METHODS

Using nationally representative survey data from the National Epidemiologic Survey on Alcohol and Related Conditions-III (NESARC-III), we examined all adults with lifetime SUDs; compared the proportions experiencing diagnostic remission; and used logistic regression analyses to compare Black, Hispanic, and other racial/ethnic minorities to Whites. The research team initially used bivariate comparisons to identify potentially confounding factors also associated with remission. The study used multivariable-adjusted logistic regression analyses to adjust for these potentially confounding covariates. The team conducted separate analyses for alcohol use disorder (AUD) and drug use disorders (DUDs).

RESULTS

Of 10,916 individuals with lifetime SUDs, 5120 no longer met criteria for an SUD in the past year (55.2% of White, 34.0% of Black, 38.5% Hispanic, and 40.1% of other individuals). In unadjusted analyses, Black, Hispanic, and others were significantly and about half as likely as Whites to have remitted with odds ratios (ORs) of 0.42 (95% CI 0.36-0.48), 0.51 (0.45-0.58), and 0.55 (0.45-0.65), respectively. The study found similar results for both AUD and DUDs. Adjusting for potentially confounding factors only modestly improved the likelihood of remission among racial/ethnic minorities compared to White individuals.

CONCLUSION

Minority race/ethnicity is robustly associated with reduced likelihood of diagnostic remission from SUDs even after adjusting for other factors. This study could identify only partial moderators of these disparities; these moderators deserve further study.

High concentration of serum FGF19 at ICU admission is associated with 28-day mortality in sepsis patients

BACKGROUND

Sepsis remains associated with a high mortality rate despite recent advances in treatment. Traditional biomarkers are inadequate for stratification of patients by sepsis severity. We examined use of the baseline concentration of fibroblast growth factor 19 (FGF19) in predicting 28-day mortality from sepsis.

METHODS

A total of 220 consecutive adult patients with sepsis who were admitted to our intensive care unit (ICU) during 2020 were prospectively recruited. Patients were categorized as survivors or non-survivors according to status at 28 days. Baseline concentrations of FGF19 and other parameters were measured. Receiver operating characteristic (ROC) analysis was used to determine the sensitivity, specificity, predictive value, and optimal cutoff of FGF19 in prediction of survival. Prognostic factors were identified using Cox regression analysis.

RESULTS

The serum FGF19 concentration was much higher in non-survivors than in survivors (355.0 pg/ml [range: 37.2, 2315.6] vs. 127.3 pg/ml [5.7, 944.1]; P < 0.05]. ROC analysis indicated an FGF19 concentration of 180 pg/ml was the optimal cutoff value. Multivariable Cox regression analysis showed that FGF19 concentration and the change in sequential organ failure assessment (ΔSOFA) score at baseline were independently and significantly associated with 28-day mortality. ROC analysis indicated that FGF19 had a better predictive value than PCT or CRP. Although ΔSOFA had a better predictive value than FGF19, ΔSOFA and FGF19 together had a significantly better predictive value than ΔSOFA alone.

CONCLUSION

Sepsis patients with high serum concentrations of FGF19 at ICU admission were associated with an increased risk of 28-day mortality in our ICU.

Expected individual benefit of prophylactic platelet transfusions in hemato-oncology patients based on bleeding risks

BACKGROUND

Prophylactic platelet transfusions prevent bleeding in hemato-oncology patients, but it is unclear how any benefit varies between patients. Our aim was to assess if patients with different baseline risks for bleeding benefit differently from a prophylactic platelet transfusion strategy.

STUDY DESIGN AND METHODS

Using the data from the randomized controlled TOPPS trial (Trial of Platelet Prophylaxis), we developed a prediction model for World Health Organization grades 2, 3, and 4 bleeding risk (defined as at least one bleeding episode in a 30 days period) and grouped patients in four risk-quartiles based on this predicted baseline risk. Predictors in the model were baseline platelet count, age, diagnosis, disease modifying treatment, disease status, previous stem cell transplantation, and the randomization arm.

RESULTS

The model had a c-statistic of 0.58 (95% confidence interval [CI] 0.54-0.64). There was little variation in predicted risks (quartiles 46%, 47%, and 51%), but prophylactic platelet transfusions gave a risk reduction in all risk quartiles. The absolute risk difference (ARD) was 3.4% (CI -12.2 to 18.9) in the lowest risk quartile (quartile 1), 7.4% (95% CI -8.4 to 23.3) in quartile 2, 6.8% (95% CI -9.1 to 22.9) in quartile 3, and 12.8% (CI -3.1 to 28.7) in the highest risk quartile (quartile 4).

CONCLUSION

In our study, generally accepted bleeding risk predictors had limited predictive power (expressed by the low c-statistic), and, given the wide confidence intervals of predicted ARD, could not aid in identifying subgroups of patients who might benefit more (or less) from prophylactic platelet transfusion.