Hosmer-Lemeshow Goodness Of Fit Research Articles

Pathogenic bacteria and treatment resistance in older cardiovascular disease patients with lung infection and risk prediction model.

This study analyzes the distribution of pathogenic bacteria and their antimicrobial susceptibilities in elderly patients with cardiovascular diseases to identify risk factors for pulmonary infections. A risk prediction model is established, aiming to serve as a clinical tool for early prevention and management of pulmonary infections in this vulnerable population. A total of 600 patients were categorized into infected and uninfected groups. Independent risk factors such as older age, diabetes history, hypoproteinemia, invasive procedures, high cardiac function grade, and a hospital stay of ≥10 days were identified through logistic regression. A predictive model was constructed, with a Hosmer-Lemeshow goodness of fit (P = 0.236) and an area under the receiver operating characteristic curve of 0.795, demonstrating good discriminative ability. The model had 63.40% sensitivity and 82.80% specificity, with a cut-off value of 0.13. Our findings indicate that the risk score model is valid for identifying high-risk groups for pulmonary infection among elderly cardiovascular patients. The study contributes to the early prevention and control of pulmonary infections, potentially reducing infection rates in this vulnerable population.

Predictors of Urgent Dialysis Following Ambulance Transport to the Emergency Department in Patients Treated With Maintenance Hemodialysis

Patients receiving maintenance hemodialysis frequently require ambulance transport to the emergency department (ambulance-ED transport). Identifying predictors of outcomes after ambulance-ED transport, especially the need for timely dialysis, is important to health care providers. The purpose of this study was to derive a risk-prediction model for urgent dialysis after ambulance-ED transport. Observational cohort study. All ambulance-ED transports among incident and prevalent patients receiving maintenance hemodialysis affiliated with a regional dialysis program (catchment area of approximately 750 000 individuals) from 2014 to 2018. Patients' vital signs (systolic blood pressure, oxygen saturation, respiratory rate, and heart rate) at the time of paramedic transport and time since last dialysis were utilized as predictors for the outcome of interest. The primary outcome was urgent dialysis (defined as dialysis in a monitored setting within 24 hours of ED arrival or dialysis within 24 hours with the first ED patient blood potassium level >6.5 mmol/L) for an unscheduled indication. Secondary outcomes included, hospitalization, hospital length of stay, and in-hospital mortality. A logistic regression model to predict outcomes of urgent dialysis. Discrimination and calibration were assessed using the C-statistic and Hosmer-Lemeshow test. Among 878 ED visits, 63 (7.2%) required urgent dialysis. Hypoxemia (odds ratio [OR]: 4.04, 95% confidence interval [CI]: 1.75-9.33) and time from last dialysis of 24 to 48 hours (OR: 3.43, 95% CI: 1.05-11.9) and >48 hours (OR: 9.22, 95% CI: 3.37-25.23) were strongly associated with urgent dialysis. A risk-prediction model incorporating patients' vital signs and time from last dialysis had good discrimination (C-statistic 0.8217) and calibration (Hosmer-Lemeshow goodness of fit P value .8899). Urgent dialysis patients were more likely to be hospitalized (63% vs 34%), but there were no differences in inpatient mortality or length of stay. Missing data, requires external validation. We derived a risk-prediction model for urgent dialysis that may better guide appropriate transport and care for patients requiring ambulance-ED transport.

Establishment of a Nomogram for Predicting the Suboptimal Angiographic Outcomes of Coronary De Novo Lesions Treated with Drug-Coated Balloons.

Factors affecting the angiographic outcomes of coronary de novo lesions treated with drug-coated balloons (DCBs) have not been well illustrated. The aim of the study is to establish a nomogram for predicting the risk of suboptimal diameter stenosis (DS) at angiographic follow-up. A retrospective analysis was performed on a cohort of patients who underwent DCB intervention for coronary de novo lesions with angiographic follow-up data. Multivariable logistic regression analysis was applied to determine the independent predictors of DS ≥ 30% at follow-up, and then a nomogram model was established and validated. A total of 196 patients (313 lesions) were divided into the suboptimal (DS ≥ 30%) and optimal (DS < 30%) DS groups according to quantitative coronary angiography (QCA) measurements of the target lesions at follow-up. Seven independent factors including calcified lesions, true bifurcation lesions, immediate lumen gain rate (iLG%) < 20%, immediate diameter stenosis (iDS) ≥ 30%, DCB diameter/reference vessel diameter ratio (DCB/RVD) < 1.0, DCB length and mild dissection wereidentified. The area under the curve (AUC) (95% CI) of the receiver-operating characteristic (ROC) curve of the nomogram was 0.738 (0.683, 0.794). After the internal validation, the AUC (95% CI) was 0.740 (0.685, 0.795). The Hosmer-Lemeshow goodness of fit (GOF) test (χ2 = 6.57, P = 0.766) and the calibration curve suggested a good predictive consistency of the nomogram. The well-calibrated nomogram could efficiently predict the suboptimal angiographic outcomes at follow-up.This model may be helpful to optimize lesion preparation to achieve optimal outcomes.

Factors for selective ultrasound screening in newborns with developmental dysplasia of the hip (DDH).

Hip ultrasound screening for DDH provides better sensitivity compared to physical examination. Due to a lower prevalence and limited resources, selective hip ultrasound in newborns at risk could be considered a proper screening protocol in Thailand and Asian countries. This study was aimed to evaluate risk factors and define criteria for selective screening. A case-control study was conducted in 2020. All newborns with hip ultrasound screening were included. Cases were defined as newborns with abnormal hip ultrasounds, while controls were those with normal studies. Inter and intra-rater reliability were evaluated. All factors were analyzed using univariate and multivariate logistic regression. The model performance was tested by Hosmer-Lemeshow goodness of fit. Internal validity was performed by the split data method. Area under the receiver operating characteristic (ROC) curve was estimated. Ninety-five newborns (29 cases and 66 controls) were included. Eighty percent of cases and 58% of controls were female. The gestational age was 36.6 and 37.7 weeks in case and control, respectively. Female, breech presentation, positive Ortolani test, positive Barlow test, and limited hip abduction were significant factors with odds ratio of 2.82, 5.12, 34.21, 69.64, and 5.48, respectively. The final model included breech presentation, positive Ortolani test, and positive Barlow test. The model cut-off value 15.02 provided sensitivity (93.10%) and specificity were (80.30%). The area under the ROC curve was 0.9308. The split data remained significant internal validity for all factors with p-value < 0.05. Careful history taking and physical examination are essential to identify the risk factors for DDH. Newborns with breech presentation, positive Ortolani test and positive Barlow test should be screened by hip ultrasound.

Development and validation of a predictive model for the risk of 30-day death in emergency department patients

To explore the risk factors for 30-day death in emergency department patients, and then construct a prediction model and validate it using nomogram. A retrospective cohort study was conducted. The clinical data of 1 091 patients admitted to the emergency department of the First People's Hospital of Changde from January 1 to June 30, 2021 was collected, including 741 patients from January 1 to March 31 in the development group and 350 patients from April 1 to June 30 in the validation group. General information, first vital signs admitted to the emergency department, and laboratory results were collected, the modified early warning score (MEWS) was calculated, and 30-day outcomes were recorded. Univariate and multivariate Logistic regression analysis was used to screen out the risk factors of 30-day death. According to the results of multivariate analysis, the nomogram was used to construct a 30-day death prediction model. The receiver operator characteristic curve (ROC curve) was used to evaluate the consistency of the prediction model, the calibration of the prediction model was evaluated by the Hosmer-Lemeshow goodness of fit test. A total of 1 091 patients were enrolled. There were 741 patients in the development group, including 356 males and 385 females, aged (51.42±17.33) years old, and the 30-day mortality was 28.88%. There were 350 patients in the validation group, including 188 males and 162 females, aged (52.88±16.11) years old, and the 30-day mortality was 24.00%. The results of the univariate analysis showed that age, primary diagnosis on admission, consciousness, respiratory rate (RR), systolic blood pressure (SBP), heart rate (HR), pulse oxygen saturation (SpO2), MEWS score, erythrocyte sedimentation rate (ESR), procalcitonin (PCT) and body mass index (BMI) might be the risk factors for 30-day death in patients in the emergency department. The results of the multivariate analysis showed that the MEWS score [odds ratio (OR) = 14.22, 95% confidence interval (95%CI) was 1.46-138.12], ESR (OR = 46.71, 95%CI was 20.48-106.53), PCT (OR = 4.97, 95%CI was 2.46-10.02), BMI (24.0-27.9 kg/m2: OR = 37.82, 95%CI was 14.69-97.36; ≥ 28.0 kg/m2: OR = 62.11, 95%CI was 25.77-149.72) were independent risk factors for 30-day death in the emergency department (all P < 0.05). Using the four variables with the results of multivariate analysis to construct a nomogram prediction model, the area under the ROC curve (AUC) was 0.974 (95%CI was 0.753-0.983) for the development group, and the AUC was 0.963 (95%CI was 0.740-0.975) for the validation group. The Hosmer-Lemeshow test showed no statistically significant difference between the predicted outcome of the nomogram prediction model and the actual occurrence (χ2 = 1.216, P = 1.270). The prediction model developed by the MEWS score combined with BMI, ESR and PCT can scientifically and effectively predict the 30-day outcome of emergency department patients.

Abstract WP216: Breast Cancer Susceptibility Genes And Cerebral Small Vessel Disease

Introduction: Aside from being implicated in breast and ovarian cancer, breast cancer susceptibility genes (BRCA) growing evidence suggests an additional intersection between BRCA mutations and hypercholesterolemia, endothelial damage, hyperglycemia and an increased non-neoplastic mortality. With the influence of BRCA on typical cerebrovascular risk factors, we aimed to identify if BRCA mutations increased the prevalence of cerebral small vessel disease (CSVD). Methods: We performed a retrospective cross-sectional chart review of adults with confirmed BRCA1 or BRCA2 mutations compared against BRCA wild-type individuals with breast and/or ovarian cancer. Demographic and medical history were recorded. Brain MRI was interpreted for markers of CSVD. Statistical significance (p &lt; 0.05) was tested via the Chi-squared or Fisher’s, Wilcoxon rank-sum and the Student’s t-test analyses for binary, ordinal or interval variables, respectively. Adjusted logistic regression models were fit to calculate odds ratio. All models were tested for multicollinearity by variance inflation factor calculation and for goodness-of-fit (GOF) via the Hosmer-Lemeshow GOF test. Results: One hundred forty individuals were included, with 47% (66/140) being BRCA mutant. Demographic and cerebrovascular risk factors were similar amongst the cohort. Estrogen antagonist therapy was more common in the BRCA wild-type group [25% (16/64) vs 44.6% (33/74), p = 0.016]. Lacunar infarcts, Fazekas grading and perivascular spaces were not significantly different across the cohort. Cerebral microbleeds (CMBs) were more prevalent in those with BRCA mutation: [36.4% (24/66) vs 18.9% (14/74), p = 0.021]. This finding persisted despite adjustment for cerebrovascular risk factors (OR 2.71, 95%CI 1.20 - 6.14, p = 0.017) yielding a predicted probability of 18.2% (95%CI 3.7 - 32.8). Conclusion: For those with BRCA mutation, we identified a nearly 3 fold increase in CMB prevalence despite adjustment for cerebrovascular risk factors. The mechanism underlying is presently unknown, though likely related to endothelial dysregulation. Further studies are needed to confirm our findings on a larger scale and investigate intraparenchymal hemorrhage risk amongst those with BRCA mutations.

Performance of Pediatric Index of Mortality in a Tertiary Care PICU in India

AbstractPediatric index of mortality-3 (PIM-3) is the latest update of one of the commonly used scoring systems in pediatric intensive care. It has free accessibility and is easy to use. However, there are some skepticisms regarding its practical usefulness in resource-limited settings. Hence, there is a need to generate region-specific data to evaluate its performance in different case mixes and resource constraints. The aim of the study is to evaluate the performance of the PIM-3 score in predicting mortality in a tertiary care PICU of a developing country. This was a retrospective cohort study. All children aged 1 month to 18 years admitted to the PICU during the study period from July 2016 to December 2018 were included. We reviewed the patient admission details and the case records of the enrolled. patients. Patient demographics, disease profile, co-morbidities, and PIM-3 scores were recorded along with the outcome. Area under receiver operating characteristics (AUROC) curves was used to determine discrimination. Standardized mortality ratio (SMR) and Hosmer Lemeshow goodness of fit were used to assess the calibration. Out of 282 children enrolled, 62 (21.9%) died. 58.5% of the patients were males, and 60% were less than 5 years of age. The principal diagnoses included respiratory and neurological conditions. The AUROC for PIM-3 was 0.961 (95% CI [0.93, 0.98]) and overall SMR was 1.28 (95% CI [0.96, 1.59]). Hosmer-Lemeshow goodness-of-fit was suggestive of poor calibration (χ 2 = 11.7, p &lt; 0.05). We concluded that PIM-3 had good discrimination but poor calibration in our PICU setting.

Predicting Adverse Events Among Patients With COPD Exacerbations in the Emergency Department.

COPD exacerbations lead to excessive health care utilization, morbidity, and mortality. The Ottawa COPD Risk Scale (OCRS) was developed to predict short-term serious adverse events (SAEs) among patients in the emergency department (ED) with COPD exacerbations. We assessed the utility of the OCRS, its component elements, and other clinical variables for ED disposition decisions in a United States population. We compared the OCRS and other factors in predicting SAEs among a retrospective cohort of ED patients with COPD exacerbations. We followed subjects for 30 d, and the primary outcome, SAE, was defined as any death, admission to monitored unit, intubation, noninvasive ventilation, major procedure, myocardial infarction, or revisit with hospital admission. A total of 246 subjects (median 61-y old, 46% male, total admission rate to ward 52%) were included, with 46 (18.7%) experiencing SAEs. Median OCRS scores did not differ significantly between those with and without an SAE (difference: 0 [interquartile range 0-1)]. The OCRS predicted SAEs poorly (Hosmer-Lemeshow goodness of fit [H-L GOF] P ≤ .001, area under the receiver operating characteristic [ROC] curve 0.519). Three variables were significantly related to SAEs in our final model (H-L GOF P = .14, area under the ROC curve 0.808): Charlson comorbidity index (odds ratio [OR] 1.3 [1.1-1.5] per 1-point increase); triage venous PCO2 (OR 1.7 [1.2-2.4] per 10 mm Hg increase); and hospitalization within previous year (OR 9.1 [3.3-24.8]). The OCRS did not reliably predict SAEs in our population. We found 3 risk factors that were significantly associated with 30-d SAE in our United States ED population: triage [Formula: see text] level, Charlson comorbidity index, and hospitalization within the previous year. Further studies are needed to develop generalizable decision tools to improve safety and resource utilization for this patient population.

Development and external validation of a prediction risk model for short-term mortality among hospitalized U.S. COVID-19 patients: A proposal for the COVID-AID risk tool.

BackgroundThe 2019 novel coronavirus disease (COVID-19) has created unprecedented medical challenges. There remains a need for validated risk prediction models to assess short-term mortality risk among hospitalized patients with COVID-19. The objective of this study was to develop and validate a 7-day and 14-day mortality risk prediction model for patients hospitalized with COVID-19.MethodsWe performed a multicenter retrospective cohort study with a separate multicenter cohort for external validation using two hospitals in New York, NY, and 9 hospitals in Massachusetts, respectively. A total of 664 patients in NY and 265 patients with COVID-19 in Massachusetts, hospitalized from March to April 2020.ResultsWe developed a risk model consisting of patient age, hypoxia severity, mean arterial pressure and presence of kidney dysfunction at hospital presentation. Multivariable regression model was based on risk factors selected from univariable and Chi-squared automatic interaction detection analyses. Validation was by receiver operating characteristic curve (discrimination) and Hosmer-Lemeshow goodness of fit (GOF) test (calibration). In internal cross-validation, prediction of 7-day mortality had an AUC of 0.86 (95%CI 0.74–0.98; GOF p = 0.744); while 14-day had an AUC of 0.83 (95%CI 0.69–0.97; GOF p = 0.588). External validation was achieved using 265 patients from an outside cohort and confirmed 7- and 14-day mortality prediction performance with an AUC of 0.85 (95%CI 0.78–0.92; GOF p = 0.340) and 0.83 (95%CI 0.76–0.89; GOF p = 0.471) respectively, along with excellent calibration. Retrospective data collection, short follow-up time, and development in COVID-19 epicenter may limit model generalizability.ConclusionsThe COVID-AID risk tool is a well-calibrated model that demonstrates accuracy in the prediction of both 7-day and 14-day mortality risk among patients hospitalized with COVID-19. This prediction score could assist with resource utilization, patient and caregiver education, and provide a risk stratification instrument for future research trials.

Development of a Risk Model for Pediatric Hospital-Acquired Thrombosis: A Report from the Children's Hospital-Acquired Thrombosis Consortium

To identify pertinent clinical variables discernible on the day of hospital admission that can be used to assess risk for hospital-acquired venous thromboembolism (HA-VTE) in children. The Children's Hospital-Acquired Thrombosis Registry is a multi-institutional registry for all hospitalized participants aged 0-21years diagnosed with a HA-VTE and non-VTE controls. A risk assessment model (RAM) for the development of HA-VTE using demographic and clinical VTE risk factors present at hospital admission was derived using weighted logistic regression and the least absolute shrinkage and selection (Lasso) procedure. The models were internally validated using 5-fold cross-validation. Discrimination and calibration were assessed using area under the receiver operating characteristic curve and Hosmer-Lemeshow goodness of fit, respectively. Clinical data from 728 cases with HA-VTE and 839 non-VTE controls, admitted between January 2012 and December 2016, were abstracted. Statistically significant RAM elements included age <1year and 10-22years, cancer, congenital heart disease, other high-risk conditions (inflammatory/autoimmune disease, blood-related disorder, protein-losing state, total parental nutrition dependence, thrombophilia/personal history of VTE), recent hospitalization, immobility, platelet count >350K/μL, central venous catheter, recent surgery, steroids, and mechanical ventilation. The area under the receiver operating characteristic curve was 0.78 (95% CI 0.76-0.80). Once externally validated, this RAM will identify those who are at low-risk as well as the greatest-risk groups of hospitalized children for investigation of prophylactic strategies in future clinical trials.

Artificial Intelligence-related Literature in Transplantation: A Practical Guide.

Artificial Intelligence-related Literature in Transplantation: A Practical Guide.

A Recipient Risk Prediction Tool for Short-term Mortality After Pediatric Heart Transplantation.

The first year after heart transplantation (HT) has the highest risk of mortality. We aim to derive and validate a recipient risk prediction tool for early mortality after pediatric HT. The International Society for Heart and Lung Transplantation (ISHLT) registry was used to identify patients (≤18 y) who underwent primary HT during January 2000-December 2014. Independent predictors of 1-year mortality were identified based on recipient characteristics at HT. Risk scores were assigned based on the magnitude of relative odds of 1-year mortality. The predictive capability of the ISHLT registry derived recipient risk score was externally validated using the Scientific Registry of Transplant Recipients registry data from 2015 to 2017 to ensure a cohort of patients completely exclusive from the derivation cohort. A total of 5045 eligible patients were included in the analysis. The 20-point risk scoring system incorporated 8 recipient variables, including age at HT, diagnosis, pre-HT ventilator use, extracorporeal membrane oxygenation, inhaled nitric oxide use, infection, estimated glomerular filtration rate, and serum bilirubin. Compared with low-risk score group, high-risk group had 7-fold increased risk of 1-year mortality (hazard ratio 7.4; 95% CI [5.2-9.1]; P < 0.001). The C-statistics (0.77) and Hosmer-Lemeshow goodness of fit (0.9) for recipient risk score using derivation cohort from ISHLT registry performed well and was similar to the internal and external validation cohort (C-statistics 0.75, 0.78 and Hosmer-Lemeshow goodness of fit P = 0.4, 0.3, respectively). This study derived and externally validated a simple risk predictive model based on recipient characteristics at HT that has good prediction characteristics for 1-year post-HT mortality. This model may help clinicians identify candidates who are at a higher risk for post-HT mortality and may optimize organ sharing.

Development and validation of a postoperative delirium prediction model for patients admitted to an intensive care unit in China: a prospective study

ObjectivesWe aimed to develop and validate a postoperative delirium (POD) prediction model for patients admitted to the intensive care unit (ICU).DesignA prospective study was conducted.SettingThe study was conducted in the...

A risk assessment model for esophageal varices occurrence based on endoscopic ultrasonography

Objective To identify the independent risk factors of esophageal varices (EV) in cirrhosis by endoscopic ultrasonography (EUS), and further to establish a risk assessment model for predicting EV occurrence and evaluate the clinical predictive value of the model. Methods A retrospective cohort study was used in this study. Data of patients with cirrhosis without varicosity, who were hospitalized in Tianjin Second People's Hospital from September 2014 to March 2017 were collected. The location, diameter, and number of esophageal collateral circulation were measured by EUS. The non-selective beta blocker (NSBB) medication history and antiviral therapy were recorded. The time of the first EUS examination was taken as the starting point and the follow-up period was set up as 18 months. The end point was the occurrence of EV or the end of follow-up. The independent risk factors of EV occurrence were determined by univariate and multivariate logistic regression analysis, and the risk assessment model of EV occurrence was constructed. The predictive value of evaluation model for disease was studied by ROC analysis. Hosmer-Lemeshow goodness of fit was used to test the fitting efficiency of the evaluation model. Results A total of 638 subjects were recruited initially, 13 of them were lost in the course of the study. Finally, 625 cases were included in the study. Among them, 369 cases did not develop EV (the non-progress group) and 256 cases developed EV (the progress group). (1) Multivariate logistic regression analysis showed that 7 independent risk factors were selected into the risk assessment model of EV occurrence, and were assigned corresponding scores: no NSBB (3 points), no antiviral treatment (2 points), Child-Pugh stage B (1 point), the diameter of peri-ECV>2 mm (1 point), the number of peri-ECV≥5 (3 points), the diameter of para-ECV≥5 mm (4 points), and the number of para-ECV≥5 (4 points). (2) In the risk assessment model, the risk factor scores ranged from 1 to 4 with a total score of 0-18. The predicted incidence of EV increased from 0.003 to 1.000 with the increase of the score. (3) In the risk assessment model, the total risk score ≤2 was assigned into low-risk group, 3-5 into medium-risk group, and ≥6 into high-risk group. The actual EV incidence of each risk stratification was 2.78% in the low-risk group, 36.36% in the medium-risk group and 93.91% in the high-risk group, respectively. (4) The ROC analysis showed that area under curve (AUC) was 0.947 (P<0.05), suggesting that the risk assessment model had a good effect on predicting disease progression. Hosmer-Lemeshow test showed that P was 0.450, suggesting that the model fitted well. Conclusion The risk assessment model based on EUS can accurately predict the occurrence of EV, and is simple and easy to use. The model can provide scientific basis for the prevention and rational treatment of EV in liver cirrhosis. Key words: Liver cirrhosis; Varicose veins; Endosonography; Logistic models

Intraoperative Perfusion Strategies during Heart Transplantation: Are We Optimizing Renal Protection?

Purpose Acute kidney injury (AKI) is a an important complication following heart transplant (HTX). In this study, we evaluated incidence of AKI after HTX and whether nadir hemoglobin (Hgb), nadir oxygen delivery (DO2) and average DO2 on cardiopulmonary bypass (CPB) were risk factors for AKI. Methods A retrospective, single-institution study of 233 adults undergoing HTX between August 2012 and February 2018 was conducted. AKI was defined as serum creatinine increase ≥0.3mg/dL within 48 hours or ≥50% within 7 days according to Kidney Disease: Improving Global Outcome criteria. Perfusion and laboratory data were examined to calculate nadir values. Chi-square for categorical variables and unpaired t-test for continuous variable were used to detect significant differences between those who did and did not develop AKI. Clinically or statistically significant variables were entered into a multivariable logistic regression model. Hosmer-Lemeshow goodness of fit was used to assess model strength. Results There were no significant differences in age, gender, race, baseline creatinine or baseline GFR between those who did and did not develop AKI (See Table 1). Overall 30-day and 60-day mortality were 2.6% (6/233) and 3.4% (8/233), respectively. Nadir DO2 and average DO2 were significantly different between those with and without AKI (218.86 ml/m2 versus 232.21 ml/m2, p=0.04, and 267.20 ml/m2 versus 281.36 ml/m2, p=0.01, respectively) while nadir Hgb approached significance (8.10 g/dl versus 8.46 g/dl, p=0.057). On multiple regression analysis, higher average DO2 decreased AKI risk (OR 0.991, CI95%=0.983-0.999, and OR 0.987, CI95%=0.975-1.000, respectively). Conclusion This is the largest modern study to examine the relationship between intraoperative perfusion strategy and post op AKI in HTX. Higher oxygen deliver on CPB is associated with a decrease in AKI.

Development and Validation of a Pediatric Heart Failure Risk-Prediction Model for Children Listed for Heart Transplantion in the Current Era

Purpose Previous studies have developed validated risk-prediction models for pediatric heart failure (HF). However, ventricular assist device (VAD) use and organ allocation practices have shifted markedly over the last decade for pediatric heart transplant (HT) candidates. We sought to develop and validate a risk-prediction model in a contemporary cohort of pediatric HF patients. Methods Children Results The selected predictive model had 5 categorical variables: mechanical support [ECMO (Odds ratio, OR 6.3), VAD support (OR 1.3), vs no support], ventilator support (OR 2.5), cardiac diagnosis [congenital heart disease (CHD) (OR 2.3), vs. cardiomyopathy], renal dysfunction [dysfunction (OR 2.3), vs. normal], and weight categories at listing [25-50 kg (OR 2.0), 10-25 kg (OR 3.2), 5-10 kg (OR 3.7), and 0-5 kg (OR 5.6), vs. ≥50 kg]. The final model developed using the derivation cohort after internal cross-validation (N=2029; C-statistic 0.83, Hosmer-Lemeshow Goodness of Fit P=0.81) performed well in the independent validation cohort (N=1194, C-statistic 0.81, HL P=0.68), suggesting excellent prediction in a contemporary cohort of pediatric HF patients as shown in the calibration plot in Figure 1. Conclusion This heart failure risk-prediction model using 5 baseline clinical factors at the time of listing has excellent prediction characteristics for 30-day mortality in a contemporary cohort of children. The model may be useful to support decision-making surrounding referral for advanced cardiac therapies including HT and VAD support.

An Updated Nomogram for Predicting Invasiveness in Preoperative Ductal Carcinoma In Situ of the Breast.

PurposeTo validate and update a nomogram for predicting ductal carcinoma in situ (DCIS) upstaging in preoperative biopsy.Materials and MethodsMedical records of 444 preoperative DCIS patients were evaluated and used to validate a previous version of the Severance nomogram for predicting DCIS upstaging in preoperative biopsy. Patients were divided into two groups according to the final postoperative pathology. Univariate and multivariate analyses with the chi-square test, Student's t-test, and binary logistic regression method identified new significant variables. The updated nomogram was evaluated with the C-index and Hosmer—Lemeshow goodness of fit test.ResultsThe area under a receiver operating characteristic curve for comparison with the previous nomogram was 0.48. In postoperative pathology, the pure DCIS and invasive cancer groups comprised 345 and 99 cases, respectively. Approximately 22.3% of patients preoperatively diagnosed with DCIS were upstaged to invasive cancer. Significant variables in the univariate analysis were operation type, human epidermal growth factor receptor 2 overexpression, comedo necrosis, sonographic mass, mammographic mass, preoperative biopsy method, and suspicious microinvasion in preoperative biopsy. In multivariate analysis, operation type, sonographic mass, mammographic mass, and suspicious microinvasion were risk factors for upstaging. The updated model with these variables showed moderate discrimination and was appropriate in the calibration test.ConclusionThe previous nomogram did not effectively discriminate upstaging of preoperative DCIS in an independent cohort. An updated version of the nomogram appears to provide more accurate information for predicting preoperative DCIS upstaging.

CR-Possum-Can It Be Used to Predict Morbidity? A Single-Centre Retrospective Study.

Preoperative prediction of morbidity in colorectal cancer (CRC) surgery helps to optimize the surgical outcome. In this study, we aim to develop a dedicated equation for predicting operative morbidity using colorectal possum scoring system and also to validate the predictive accuracy of CR-POSSUM scoring system in prognosticating actual complications. We did a retrospective analysis of 322 patients undergoing colorectal cancer surgery from a single centre in South India from 2004 to 2016. Mortality and morbidity risk factors as defined by CR POSSUM were collected from 322 patients who underwent CRC surgery and were used to derive equations to predict morbidity, and the results were compared with the observed morbidity. Logistic regression analysis was used to derive the equation. The model fit and model discrimination were analysed using the Hosmer-Lemeshow statistical test for goodness of fit, the Nagelkerke R 2 and area under the receiver operating characteristic (ROC) curve respectively. Out of 322 patients, 103 (32%) patients developed complications and 10 (3%) died due to complications. The regression equation we derived has an overall correct classification of about 70% (P < 0.01) with positive and negative predictive value of 60% and 73% respectively. The Hosmer-Lemeshow goodness of fit was 3.147 (P = 0.829), and the Nagelkerke R 2 was 17% and area under ROC as model discrimination was 71.6%. Hence, CR-POSSUM scoring which was originally used for predicting mortality risk can also be extrapolated to predict morbidity.

The everlasting issue of prolonged air leaks after lobectomy for non-small cell lung cancer: A data-driven prevention planning model in the era of minimally invasive approaches.

Prolonged air leaks (PAL) are the most frequent complication after lobectomy for non-small cell lung cancer, even in case of minimally invasive approaches. We developed a novel score to identify high-risk patients for PAL during minimally invasive lobectomy. A dedicated database was created. We investigated preoperative candidate features and specific intraoperative variables. Univariate and subsequent logistic regression analysis with bootstrap resampling have been used. Model performance has been assessed by reckoning the area under the receiver operating characteristics curve and the Hosmer-Lemeshow goodness of fit. PAL (>5 days) occurred in 72 (15.69%) patients. Five variables emerged from the model. Each one was assigned a score to provide a cumulative scoring system: forced expiratory volume in 1 second below 86% (P = 0.004, 1.5 points), body mass index <24 ( P = 0.002, 1 point), active smoking ( P = 0.001, 1.5 points), incomplete fissures ( P = 0.004, 1.5 points), and adhesions ( P = 0.0001, 1 point). The new score provided a stratification into four risk classes. The risk score incorporates either general or more specific variables, providing a risk stratification that could be readily applied intra- and postoperatively. Henceforth, specific technical and management measures could be properly allocated to curb PAL.

The PARIS score can reliably predict 7-day all-cause mortality for both acute medical and surgical patients: an international validation study.

We believe errors in the risk assessment of acutely ill patients occur because only vital signs without concurrent functional capacity are considered. We, therefore, developed the PARIS risk score based on blood pressure, age, respiratory rate, loss of independence and oxygen saturation. Validation of the PARIS score in four independent cohorts from three countries. Retrospective cohort study of acutely ill patients admitted to hospitals in Denmark, Ireland and Uganda. Vital signs and functional capacity (registered as ability to stand or walk or get into bed unaided) was recorded upon arrival. Patients were followed up for 7 days (Denmark and Ireland) or until discharge (Uganda) and mortality recorded. The discriminatory power (ability to identify patients at increased risk) was determined using area under the receiver operating characteristics curve (AUROC) and calibration (precision) using Hosmer-Lemeshow goodness of fit test. Out of 14447 patients, 327 (2.3%) died within 7 days: median age was 59 (39-75) years and 7458 (51.8%) were female. Seven-day mortality increased from 0.3% with a score of 0-26.7% with a score of 5. The score's AUROC as 0.833 [95% confidence interval (95% CI) 0.811-0.856], 0.817 (95% CI 0.792-0.842) and 0.894 (95% CI 0.813-0.974) for all patients, medical patients and surgical patients, respectively. However, except for surgical patients, calibration of the score was poor. The PARIS score can identify both high and low risk acutely admitted medical and surgical patients, but calibration was poor for medical patients.