Nomogram-Derived Prediction of Postoperative Ileus after Colectomy: An Assessment from Nationwide Procedure-Targeted Cohort

2017 ◽  
Vol 83 (6) ◽  
pp. 564-572 ◽  
Author(s):  
Ahmet Rencuzogullari ◽  
Cigdem Benlice ◽  
Meagan Costedio ◽  
Feza H. Remzi ◽  
Emre Gorgun
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Postoperative Ileus ◽  
Logistic Regression Model ◽  
Operative Time ◽  
Operating Time ◽  
Stepwise Logistic Regression ◽  
Oral Antibiotic ◽  
Open Approach ◽  
Improvement Program

Postoperative ileus (POI) is a clinical burden to health-care system. This study aims to evaluate the incidence and predictors of POI in patients undergoing colectomy and create a nomogram by using recently released procedure-targeted nationwide database. Patients who underwent elective colectomy in 2012 and 2013 were identified from American College of Surgeons National Surgical Quality Improvement Program using the new procedure-targeted database. Demographics, comorbidities, and 30-day postoperative outcomes were evaluated. Variables in the final step-wise multiple logistic regression model for each outcome were selected in a stepwise fashion using Akaike's information criterion. A nomogram was created to aid in the calculation of POI risk for individual patients. A total of 29,201 patients met the inclusion criteria; 3834 (13.1%) developed POI with a male predominance (55.9%). Patients who developed ileus had longer length of hospital stay (11 vs 5 days; P < 0.001) and operative time (200 vs 174 minutes; P < 0.001). In the stepwise logistic regression model, the following variables were found to be independent risk factors for POI: older age (P < 0.001), male gender (P < 0.001), American Society of Anesthesiologists class III/IV (P < 0.001), open approach (P < 0.001), preoperative septic conditions (P < 0.001), omission of oral antibiotic before surgery (P < 0.001), right colectomy or total colectomy vs other procedures (P < 0.001), smoking (P = 0.001), decreased preoperative serum albumin level (P < 0.001), and prolonged operating time (P < 0.001). All postoperative complications were more frequently occurred in patients with POI. The nomogram accurately predicted POI with a concordant index for this model of 0.69. The use of minimal invasive techniques, control of preoperative septic conditions, oral antibiotic bowel preparation and shorter operative time are associated with a decreased rate of POI. External validation is essential for the confirmation and further evaluation of our logistic regression model and nomogram.

A STUDY ON PREVALENCE OF POSTPARTUM DEPRESSION AT TERTIARY HOSPITAL, WEST BENGAL.

2021 ◽  
pp. 33-36
Author(s):  
Chandrima Maity ◽  
Debasish Sanyal ◽  
Arati Biswas ◽  
Sudarsan Saha
Keyword(s):  
Logistic Regression ◽  
Postpartum Depression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Depression Scale ◽  
Sampling Technique ◽  
Simple Random Sampling ◽  
Background Information ◽  
Stepwise Logistic Regression ◽  
Postpartum Mothers

The investigators assessed the prevalence of Postpartum Depression (PPD), its clinical features and relationship of PPD with socio-demographical and obstetrical factors. The samples were selected from the OPD and IPD, of a Medical college in Kolkata.. Observational study was performed on 500(N=500) postpartum mothers who were selected by using Simple Random Sampling Technique within the six weeks of postpartum period. Data were collected by using the Structured Questionnaire for background information, Edinburgh Postnatal Depression Scale (Bengali Version of EPDS) for postpartum depression. Data analysis was performed using Descriptive Statistics, Chi-square, Logistic Regression and Decision Tree. A total of 112 (Prevalence Rate 22.4%) postpartum mothers had PPD. Stepwise logistic regression model correctly classied 92.2% of women who developed PPD. Using logistic regression model, postpartum depression is best predicted by: No. of Postpartum days p< 0.001***, Age of the mother p<0.024**, Religion p<0.003**, Type of family p<0.020**, Education of the mother p<0.001***, Monthly Income of the family p<0.001***, No of other living children p<0.001***, Pregnancy outcome p<0.033**, Any complication during pregnancy / delivery/ postpartum p< 0.001*** and Problems with family members p< 0.001***. The study recommends that evaluation should be carried out for Postpartum Depression and its risk factors to prevent and treat PPD in a timely manner.

scholarly journals A machine learning-based prediction of hospital mortality in patients with postoperative sepsis

2020 ◽  
Author(s):  
Ren-qi Yao ◽  
Xin Jin ◽  
Guo-wei Wang ◽  
Yue Yu ◽  
Guo-sheng Wu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Hospital Mortality ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Gradient Boosting ◽  
Stepwise Logistic Regression ◽  
Postoperative Sepsis ◽  
Extreme Gradient Boosting ◽  
Stepwise Logistic Regression Model

Abstract Background: The incidence of postoperative sepsis is continually increased, while few studies have specifically focused on the risk factors and clinical outcomes associated with the development of sepsis after surgical procedures. The present study aimed to develop a mathematical model for predicting the in-hospital mortality among patients with postoperative sepsis.Methods: Surgical patients in Medical Information Mart for Intensive Care (MIMIC-III) database who simultaneously fulfilled Sepsis 3.0 as well as Agency for Healthcare Research and Quality (AHRQ) criteria during ICU admission were incorporated. We employed both extreme gradient boosting (XGBoost) and stepwise logistic regression model to predict in-hospital mortality among included patients with postoperative sepsis. Consequently, model performance was assessed from the angles of discrimination and calibration.Results: We included 3713 patients who fulfilled our inclusion criteria, in which 397 (10.7%) patients died during hospitalization, while 3316 (89.3%) of them survived through discharge. Fluid-electrolyte disturbance, coagulopathy, renal replacement therapy (RRT), urine output, and cardiovascular surgery were important features related to the in-hospital mortality. The XGBoost model had a better performance in both discriminatory ability (c-statistics, 0.835 [95% CI, 0.786 to 0.877] vs. c-statistics, 0.737 [95% CI, 0.688 to 0.786]) and goodness of fit (visualized by calibration curve) compared to the stepwise logistic regression model. Conclusion: XGBoost model appears to be a better performance in predicting hospital mortality among postoperative septic patients compared to the conventional stepwise logistic regression model. Machine learning-based algorithm might have significant application in the development of early warning system for septic patients following major operations.

scholarly journals Ghost cell glaucoma after intravitreous injection of ranibizumab in proliferative diabetic retinopathy

2020 ◽  
Author(s):  
Jun XU ◽  
Meng ZHAO ◽  
Jipeng LI ◽  
Ningpu LIU
Keyword(s):  
Logistic Regression ◽  
Retinal Detachment ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Case Series ◽  
Stepwise Logistic Regression ◽  
Ghost Cell ◽  
Tractional Retinal Detachment ◽  
Intravitreous Injection ◽  
Case Series Study

Abstract BACKGROUND: The development of ghost cell glaucoma in patients with PDR after intravitreous injection was rare. Here we reported a series of patients with PDR who received Intravitreous Ranibizumab (IVR) and developed ghost cell glaucoma and analyzed the potential factors that might be related to the development of ghost cell glaucoma.METHODS: Retrospective case series study. The medical records of 71 consecutive eyes of 68 PDR patients who received vitrectomy after IVR from January 2015 to January 2017 were reviewed. The development of ghost cell glaucoma after IVR was recorded. Characteristics of enrolled patients were retrieved from their medical charts. Factors associated with ghost cell glaucoma were compared between eyes with the development of ghost cell glaucoma and eyes without the development of ghost cell glaucoma. Variables were further enrolled in a binary backward stepwise logistic regression model, and the model that had the lowest AIC was chosen. RESULTS: There were 8 out of 71 eyes of the PDR patients developed ghost cell glaucoma after they received IVR. The interval between detection of elevation of intraocular pressure ( IOP ) and intravitreous injection ranged from 0 to 2 days. Among them, After IVR, there were two eyes had IOP greater than 30mmHg within 30 minutes, four eyes showed normal IOP at 30 min, and then developed ghost cell glaucoma within 1 day, two eyes developed ghost cell glaucoma between 24 and 48 hours. The mean maximum IOP was 46.5±8.0 mmHg. All patients gained normal IOP after vitrectomy without medicine for lowering IOP. The presence of ghost cell glaucoma was associated with tractional retinal detachment (RR= 4.60 [2.02~8.48], p= 0.004) and fibrovascular membrane involving disk (RR=-3.57 [-7.59~-0.92], p=0.03) (AIC= 39.23，AUC=0.88) in a logistic regression model.CONCLUSION: Attention to postoperative IOP should be paid to patients with PDR undergoing vitrectomy who receive a preoperative intravitreous injection of anti-VEGF agents. PDR patients with tractional retinal detachment or fibrovasucular membrane involving optic disc are more likely to develop ghost cell glaucoma after IVR.

Unplanned, Postoperative Intubation in Pediatric Surgical Patients

2016 ◽  
Vol 125 (5) ◽  
pp. 914-928 ◽  
Author(s):  
Eric C. Cheon ◽  
Hannah L. Palac ◽  
Kristine H. Paik ◽  
John Hajduk ◽  
Gildasio S. De Oliveira ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Pediatric Patients ◽  
Postoperative Mortality ◽  
Operation Time ◽  
Noncardiac Surgery ◽  
Improvement Program ◽  
Physical Status Classification ◽  
Increased Risk

Abstract Background To date, the independent predictors and outcomes of unplanned postoperative intubation (UPI) in pediatric patients after noncardiac surgery are yet to be characterized. The authors aimed to identify the incidence and predictors of this event and evaluated the effect of this event on postoperative mortality. Methods Data of 87,920 patients from the American College of Surgeons National Surgical Quality Improvement Program Pediatric database were analyzed and assigned to derivation (n = 58,614; 66.7%) or validation (n = 29,306; 33.3%) cohorts. The derivation cohort was analyzed for the incidence and independent predictors of early UPI. The final multivariable logistic regression model was validated using the validation cohort. Results Early UPI occurred with an incidence of 0.2% in both cohorts. Among the 540 patients who experienced a UPI, 178 (33.0%) were intubated within the first 72 h after surgery. The final logistic regression model indicated operation time, severe cardiac risk factors, American Society of Anesthesiologists physical status classification more than or equal to 2, tumor involving the central nervous system, developmental delay/impaired cognitive function, past or current malignancy, and neonate status as independent predictors of early UPI. Having an early UPI was associated with an increased risk of unadjusted, all-cause 30-day mortality, demonstrating an odds ratio of 11.4 (95% CI, 5.8 to 22.4). Conclusions Pediatric patients who experienced an early UPI after noncardiac surgery had an increased likelihood of unadjusted 30-day mortality by more than 11-fold. Identification of high-risk patients can allow for targeted intervention and potential prevention of such outcomes.

scholarly journals Ghost cell glaucoma after intravitreous injection of ranibizumab in proliferative diabetic retinopathy

2020 ◽  
Author(s):  
Jun XU ◽  
Meng ZHAO ◽  
Jipeng LI ◽  
Ningpu LIU
Keyword(s):  
Logistic Regression ◽  
Retinal Detachment ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Case Series ◽  
Stepwise Logistic Regression ◽  
Ghost Cell ◽  
Tractional Retinal Detachment ◽  
Intravitreous Injection ◽  
Case Series Study

Abstract BACKGROUND: The development of ghost cell glaucoma in patients with PDR after intravitreous injection (IV) was rare. Here we reported a series of patients with PDR who received Intravitreous Ranibizumab (IVR) and developed ghost cell glaucoma and analyzed the potential factors that might be related to the development of ghost cell glaucoma. METHODS: Retrospective case series study. The medical records of 71 consecutive eyes of 68 PDR patients who received vitrectomy after IVR from January 2015 to January 2017 were reviewed. The development of ghost cell glaucoma after IVR was recorded. Characteristics of enrolled patients were retrieved from their medical charts. Factors associated with ghost cell glaucoma were compared between eyes with the development of ghost cell glaucoma and eyes without the development of ghost cell glaucoma. Variables were further enrolled in a binary backward stepwise logistic regression model, and the model that had the lowest AIC was chosen. RESULTS: There were 8 out of 71 eyes of the PDR patients developed ghost cell glaucoma after they received IVR. The interval between detection of elevation of intraocular pressure ( IOP ) and IV ranged from 0 to 2 days. Among them, after IVR, there were two eyes had IOP greater than 30mmHg within 30 minutes, four eyes showed normal IOP at 30 min, and then developed ghost cell glaucoma within 1 day, two eyes developed ghost cell glaucoma between 24 and 48 hours. The mean IOP was 46.5±8.0 mmHg. All patients gained normal IOP after vitrectomy without medicine for lowering IOP. The presence of ghost cell glaucoma was associated with tractional retinal detachment (RR=4.60 [2.02~8.48], p=0.004) and fibrovascular membrane involving disk (RR=-3.57 [-7.59~-0.92], p=0.03) (AIC= 39.23，AUC=0.88) in a logistic regression model. CONCLUSION: Attention to postoperative IOP should be paid to patients with PDR undergoing vitrectomy who receive a preoperative IV of anti-VEGF agents. PDR patients with tractional retinal detachment or fibrovasucular membrane involving optic disc are more likely to develop ghost cell glaucoma after IV.

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