Dataset Information

Development and Validation of a Clinical Prediction Model for Complicated Appendicitis in the Elderly.

ABSTRACT:

Background

For elderly patients with mild clinical symptoms of uncomplicated appendicitis(UA), non-surgical treatment has been shown to be feasible, whereas emergency surgical treatment is recommended in elderly patients with complicated appendicitis(CA), but it is still challenging to accurately distinguish CA and UA before treatment. This study aimed to develop a predictive model to assist clinicians to quickly determine the type of acute appendicitis.

Methods

We retrospectively studied the clinical data of elderly patients with acute appendicitis who visited the First Affiliated Hospital of Anhui University of Traditional Chinese Medicine from January 2012 to January 2022. The patients were divided into UA group and CA group, and the general conditions, medical history, physical examination, laboratory examination and imaging examination were compared between the two groups, and SPSS 26.0 and R 4.0.2 software were used to establish CA clinic. Predict the model, and validate it internally.

Results

The clinical data of 441 elderly patients with acute appendicitis were collected, 119 patients were excluded due to incomplete clinical data or other diseases. Finally, 332 patients were included in the study and divided into UA group (n = 229) and CA group (n = 103). By analyzing the clinical data of the two groups of patients, the duration of abdominal pain [OR = 1.094, 95% CI (1.056-1.134)], peritonitis [OR = 8.486, 95% CI (2.017-35.703))] and total bilirubin [OR = 1.987, 95% CI (1.627-2.426)] were independent predictors of CA (all p < 0.01). The model's Area Under Curve(AUC) = 0.985 (95% CI, 0.975-0.994). After internal verification by Bootstrap method, the model still has high discriminative ability (AUC = 0.983), and its predicted CA curve is still in good agreement with the actual clinical CA curve.

Conclusion

We found that a clinical prediction model based on abdominal pain duration, peritonitis, and total bilirubin can help clinicians quickly and effectively identify UA or CA before treatment of acute appendicitis in the elderly, so as to make more scientific clinical decisions.

SUBMITTER: Feng H

PROVIDER: S-EPMC9218209 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BACKGROUND:No reliably specific method for complicated appendicitis has been identified in children younger than five years of age. This study aimed to analyze the independent factors for complicated appendicitis in children younger than five years of age, develop and validate a prediction model for the differentiation of simple and complicated appendicitis. METHODS:A retrospective study of 382 children younger than five years of age with acute appendicitis from January 2007 to December 2016 was conducted with assessments of demographic data, clinical symptoms and signs, and pre-operative laboratory results. According to intraoperative findings and postoperative pathological results, acute appendicitis was divided into simple and complicated appendicitis. Univariate and multivariate analyses were used to screen out the independent factors of complicated appendicitis, and develop a prediction model for complicated appendicitis. Then 156 such patients from January 2017 to December 2019 were collected as validation sample to validate the prediction model. Test performance of the prediction model was compared with the ALVARADO score and Pediatric Appendicitis Score (PAS). RESULTS:Of the 382 patients, 244 (63.9%) had complicated appendicitis. Age, white blood cell count, and duration of symptoms were the independent factors for complicated appendicitis in children younger than five years of age. The final predication model for complicated appendicitis included factors above. In validation sample, the prediction model exhibited a high degree of discrimination (area under the curve [AUC]: 0.830; 95% confidence interval [CI]: 0.762-0.885) corresponding to a optimal cutoff value of 0.62, and outperformed the PAS (AUC: 0.735; 95% CI: 0.658-0.802), ALVARADO score (AUC: 0.733; 95% CI: 0.657-0.801). CONCLUSION:Age, white blood cell count, and duration of symptoms could be used to predict complicated appendicitis in children younger than five years of age with acute appendicitis. The prediction model is a novel but promising method that aids in the differentiation of acute simple and complicated appendicitis.

Project description:ImportanceComplicated appendicitis is a common condition in children that causes substantial morbidity. Significant variation in practice exists within and between centers. We observed highly variable practices within our hospital and hypothesized that a clinical practice guideline (CPG) would standardize care and be associated with improved patient outcomes.ObjectiveTo determine whether a CPG for complicated appendicitis could be associated with improved clinical outcomes.Design, setting, and participantsA comprehensive CPG was developed for all children with complicated appendicitis at Monroe Carell Jr Children's Hospital at Vanderbilt, a freestanding children's hospital in Nashville, Tennessee, and was implemented in July 2013. All patients with complicated appendicitis who were treated with early appendectomy during the study period were included in the study. Patients were divided into 2 cohorts, based on whether they were treated before or after CPG implementation. Clinical characteristics and outcomes were recorded for 30 months prior to and 16 months following CPG implementation.ExposureClinical practice guideline developed for all children with complicated appendicitis at Monroe Carell Jr Children's Hospital at Vanderbilt.Main outcomes and measuresThe primary outcome measure was the occurrence of any adverse event such as readmission or surgical site infection. In addition, resource use, practice variation, and CPG adherence were assessed.ResultsOf the 313 patients included in the study, 183 were boys (58.5%) and 234 were white (74.8%). Complete CPG adherence occurred in 78.7% of cases (n = 96). The pre-CPG group included 191 patients with a mean (SD) age of 8.8 (4.0) years, and the post-CPG group included 122 patients with a mean (SD) age of 8.7 (4.1) years. Compared with the pre-CPG group, patients in the post-CPG group were less likely to receive a peripherally inserted central catheter (2.5%, n = 3 vs 30.4%, n = 58; P < .001) or require a postoperative computed tomographic scan (13.1%, n = 16 vs 29.3%, n = 56; P = .001), and length of hospital stay was significantly reduced (4.6 days post-CPG vs 5.1 days pre-CPG, P < .05). Patients in the post-CPG group were less likely to have a surgical site infection (relative risk [RR], 0.41; 95% CI, 0.27-0.74) or require a second operation (RR, 0.35; 95% CI, 0.12-1.00). In the pre-CPG group, 30.9% of patients (n = 59) experienced any adverse event, while 22.1% of post-CPG patients (n = 27) experienced any adverse event (RR, 0.72; 95% CI, 0.48-1.06).Conclusions and relevanceSignificant practice variation exists among surgeons in the management of pediatric complicated appendicitis. In our institution, a CPG that standardized practice patterns was associated with reduced resource use and improved patient outcomes. Most surgeons had very high compliance with the CPG.

Project description:BackgroundThe COVID-19 pandemic has affected millions of individuals and caused hundreds of thousands of deaths worldwide. Predicting mortality among patients with COVID-19 who present with a spectrum of complications is very difficult, hindering the prognostication and management of the disease. We aimed to develop an accurate prediction model of COVID-19 mortality using unbiased computational methods, and identify the clinical features most predictive of this outcome.MethodsIn this prediction model development and validation study, we applied machine learning techniques to clinical data from a large cohort of patients with COVID-19 treated at the Mount Sinai Health System in New York City, NY, USA, to predict mortality. We analysed patient-level data captured in the Mount Sinai Data Warehouse database for individuals with a confirmed diagnosis of COVID-19 who had a health system encounter between March 9 and April 6, 2020. For initial analyses, we used patient data from March 9 to April 5, and randomly assigned (80:20) the patients to the development dataset or test dataset 1 (retrospective). Patient data for those with encounters on April 6, 2020, were used in test dataset 2 (prospective). We designed prediction models based on clinical features and patient characteristics during health system encounters to predict mortality using the development dataset. We assessed the resultant models in terms of the area under the receiver operating characteristic curve (AUC) score in the test datasets.FindingsUsing the development dataset (n=3841) and a systematic machine learning framework, we developed a COVID-19 mortality prediction model that showed high accuracy (AUC=0·91) when applied to test datasets of retrospective (n=961) and prospective (n=249) patients. This model was based on three clinical features: patient's age, minimum oxygen saturation over the course of their medical encounter, and type of patient encounter (inpatient vs outpatient and telehealth visits).InterpretationAn accurate and parsimonious COVID-19 mortality prediction model based on three features might have utility in clinical settings to guide the management and prognostication of patients affected by this disease. External validation of this prediction model in other populations is needed.FundingNational Institutes of Health.

Project description:ObjectiveTo develop and validate a multivariate prediction model to estimate the risk of coronary heart disease (CHD) in middle-aged and elderly people and to provide a feasible method for early screening and diagnosis in middle-aged and elderly CHD patients.MethodsThis study was a single-center, retrospective, case-control study. Admission data of 932 consecutive patients with suspected CHD were retrospectively assessed from September 1, 2020 to December 31, 2021 in the Department of Integrative Cardiology at China-Japan Friendship Hospital. A total of 839 eligible patients were included in this study, and 588 patients were assigned to the derivation set and 251 as the validation set at a 7:3 ratio. Clinical characteristics of included patients were compared between derivation set and validation set by univariate analysis. The least absolute shrinkage and selection operator (Lasso) regression analysis method was performed to avoid collinearity and identify key potential predictors. Multivariate logistic regression analysis was used to construct a clinical prediction model with identified predictors for clinical practice. Bootstrap validation was used to test performance and eventually we obtained the actual model. And the Hosmer-Lemeshow test was carried out to evaluate the goodness-fit of the constructed model. The area under curve (AUC) of receiver operating characteristic (ROC), calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC) were plotted and utilized with validation set to comprehensively evaluate the predictive accuracy and clinical value of the model.ResultsA total of eight indicators were identified as risk factors for the development of CHD in middle-aged and elderly people by univariate analysis. Of these candidate predictors, four key parameters were defined to be significantly related to CHD by Lasso regression analysis, including age (OR 1.034, 95% CI 1.002 ~ 1.067, P = 0.040), hemoglobin A1c (OR 1.380, 95% CI 1.078 ~ 1.768, P = 0.011), ankle-brachial index (OR 0.078, 95% CI 0.012 ~ 0.522, P = 0.009), and brachial artery flow-mediated vasodilatation (OR 0.848, 95% CI 0.726 ~ 0.990, P = 0.037). The Hosmer-Lemeshow test showed a good calibration performance of the clinical prediction model (derivation set, χ2 = 7.865, P = 0.447; validation set, χ2 = 11.132, P = 0.194). The ROCs of the nomogram in the derivation set and validation set were 0.722 and 0.783, respectively, suggesting excellent predictive power and suitable performance. The clinical prediction model presented a greater net benefit and clinical impact based on DCA and CIC analysis.ConclusionOverall, the development and validation of the multivariate model combined the laboratory and clinical parameters of patients with CHD, which could be beneficial to the individualized prediction of middle-aged and elderly people, and helped to facilitate clinical assessments and decisions during treatment and management of CHD.