Establishment of a prediction model for early and mid-term complications for patients undergoing catheter insertion for peritoneal dialysis

Objective To investigate the factors involved in early and mid-term complications after catheter insertion for peritoneal dialysis and to establish prediction models. Methods A total of 158 patients with peritoneal dialysis in the Department of Nephrology of our hospital were retrospectively analyzed. General information, laboratory indices, early complications (within 1 month after the operation), mid-term complications (1–6 months after the operation), and other relevant data were recorded. Multivariate logistic regression analysis was performed to establish a prediction model of complications and generate a nomogram. Receiver operating characteristic (ROC) curve analysis was used to evaluate the efficacy of the model. Results Among the patients, 48 (30.8%) had early complications, which were mainly catheter-related complications, and 29 (18.4%) had mid-term complications, which were mainly abdominal infection and catheter migration. We constructed a prediction model for early complications (area under the curve = 0.697, 95% confidence interval: 0.609–0.785) and mid-term complications (area under the curve = 0.730, 95% confidence interval: 0.622–0.839). The sensitivity was 0.750 and 0.607, and the specificity was 0.589 and 0.765, respectively. Conclusions Our prediction model has clinical significance for risk assessment of early and mid-term complications and prevention of complications after catheterization for peritoneal dialysis.

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Maintaining the Uptake of Peritoneal Dialysis During the COVID-19 Pandemic: A Research Letter

Canadian Journal of Kidney Health and Disease ◽

10.1177/2054358120986265 ◽

2021 ◽

Vol 8 ◽

pp. 205435812098626

Author(s):

Mark Canney ◽

Lee Er ◽

John Antonsen ◽

Michael Copland ◽

Rajinder Suneet Singh ◽

...

Keyword(s):

British Columbia ◽

Peritoneal Dialysis ◽

Confidence Interval ◽

Clinical Information System ◽

Clinical Information ◽

Catheter Insertion ◽

Peritoneal Dialysis Catheter ◽

Time Period ◽

Hospital Patients ◽

Maintenance Dialysis

Background: Due to inherent challenges in maintaining physical distancing in hemodialysis units, the Canadian Society of Nephrology has recommended peritoneal dialysis as the preferred modality for patients requiring maintenance dialysis during the coronavirus disease 19 (COVID-19) pandemic. However, pursuing peritoneal dialysis is not without risk due to the requirement for in-person contact during catheter insertion and training, and there is a paucity of data regarding the experience of peritoneal dialysis during the early phases of the pandemic. Objective: To examine the incidence and outcomes of peritoneal dialysis between March 17 and June 01, 2020 compared to the same time period in preceding years. Design: Retrospective observational study. Setting: British Columbia, Canada. After the pandemic was declared on March 17, 2020, patients continued to be trained in peritoneal dialysis. In an effort to limit time spent in hospital, patients were preferentially trained in continuous ambulatory peritoneal dialysis, training times were truncated for some patients, and peritoneal dialysis catheters were inserted by a physician at the bedside whenever feasible. Patients: All patients aged >18 years who started chronic maintenance dialysis during the period March 17 to June 01 in the years 2018 to 2020 inclusive. The time period was extended to include the years 2010 to 2020 inclusive to evaluate longer term trends in dialysis incidence. Measurements: A provincial clinical information system was used to capture the date of commencing dialysis, dialysis modality, and complications including peritonitis. Overall uptake of peritoneal dialysis included new starts and transitions to peritoneal dialysis from in-center hemodialysis during the observation period. Methods: The incidence of dialysis during the specified time period, overall and by modality, was calculated per million population using census figures for the population at risk. Patients were followed for a minimum of 30 days from the start of peritoneal dialysis to capture episodes of peritonitis and COVID-19. Results: A total of 211 patients started maintenance dialysis between March 17 and June 01, 2020. The incidence dialysis rate (41.3 per million population) was lower than that expected based on the 10-year trend from 2010 to 2019 inclusive (expected rate 45.7 per million population, 95% confidence interval 41.7 to 50.1). A total of 93 patients started peritoneal dialysis, including 32 patients who transitioned from in-center hemodialysis, contributing to a higher overall uptake of peritoneal dialysis compared to preceding years. The incidence rate for peritoneal dialysis of 18.2 per million population was higher than that expected (16.3 per million population, 95% confidence interval 14.0 to 19.0). Half of patients (48%) underwent a bedside peritoneal dialysis catheter insertion by a physician. During 30 days of follow-up, 2 (2.2%) patients experienced peritonitis and no patients were diagnosed with COVID-19. Limitations: Results are short term and generalizable only to regions with similarly low community rates of transmission of severe acute respiratory syndrome coronavirus 2. Conclusions: These preliminary findings indicate that peritoneal dialysis can be safely started and perhaps expanded as a means of mitigating the anticipated surge in in-center hemodialysis during the COVID-19 pandemic. Important contributors to the uptake of peritoneal dialysis in British Columbia were bedside catheter insertions and expediting transitions from in-center hemodialysis to peritoneal dialysis.

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Predictive Value of The Combined Detection of Platelets, D-Dimer, and Procalcitonin On Severe Heat Stroke

10.21203/rs.3.rs-618567/v1 ◽

2021 ◽

Author(s):

wang lei ◽

jiang dai shan ◽

zhang Yi ◽

jia han yu ◽

shen jun hua

Keyword(s):

Body Temperature ◽

Characteristic Curve ◽

Heat Stroke ◽

Area Under The Curve ◽

General Information ◽

The Body ◽

Control Group ◽

Clinical Test ◽

Roc Curve Analysis ◽

Combined Detection

Abstract BackgroundTo explore the clinical characteristics of patients with severe heat stroke, we explored the early sensitive indicators of heat stroke (HS) patients, with a view to early intervention for HS patients. MethodsFrom July 30, 2015 to October 5, 2020, 70 inpatients with severe heat stroke admitted to the Second Affiliated Hospital of Nantong University, Jiangsu Province were selected as the research objects. The general information and clinical test indicators of the patients were recorded, and all patients were assessed for acute physiology (APACH Ⅱ) upon admission. According to the severity of heatstroke, they were divided into three groups: control group (heat cramps and heat exhaustion), EHS, and CHS to compare the differences in indicators of each group. Further draw the receiver operating characteristic curve (ROC).Results1. According to the severity of heat stroke, 28 cases were divided into the control group, 24 cases in the EHS group, and 18 cases in the CHS group. The body temperature of the EHS group and the CHS group was significantly higher than that of the control group (both P<0.05), but there was no statistical difference in the body temperature of the EHS group and the CHS group; the DD, PCT, and APACH of the EHS group were significantly higher than those of the control group and the CHS group (both P＜0.05); PLT, CRP, Na, GLU of EHS group were lower than those of control group and CHS group (all P＜0.05), and the decrease of PLT was more significant; CHS group HbA1C was significantly higher than that of control group and EHS group (all P <0.05). 2. ROC curve analysis the areas under the curves of DD, PCT, and PLT are 0.670, 0.705, 0.791, respectively, the sensitivity is 40.48%, 100%, 73.81%, and the specificity is 96.43%, 32.14%, 78.57%, respectively. Using the combined analysis of the three series tests, the area under the curve was 0.838, the sensitivity was 71.43%, and the specificity was 85.71%. ConclusionsEHS patients have higher DD, PCT, APACH, but PLT, CRP, Na, and blood sugar are lower. At the same time, the significant decrease of PLT and the increase of PCT and DD may be early sensitive indicators of HS. The combined detection of the three can be used as a reference basis for early diagnosis of HS and critical illness.

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Clinical-Radiological Parameters Improve the Prediction of the Thrombolysis Time Window by Both MRI Signal Intensities and DWI-FLAIR Mismatch

Cerebrovascular Diseases ◽

10.1159/000444887 ◽

2016 ◽

Vol 42 (1-2) ◽

pp. 57-65 ◽

Cited By ~ 7

Author(s):

Vince Istvan Madai ◽

Carla N. Wood ◽

Ivana Galinovic ◽

Ulrike Grittner ◽

Sophie K. Piper ◽

...

Keyword(s):

Acute Stroke ◽

Prediction Models ◽

Time Window ◽

Area Under The Curve ◽

Lesion Size ◽

Mean Value ◽

Roc Curve Analysis ◽

Vessel Occlusion ◽

Relative Signal Intensity ◽

Radiological Parameters

Background: With regard to acute stroke, patients with unknown time from stroke onset are not eligible for thrombolysis. Quantitative diffusion weighted imaging (DWI) and fluid attenuated inversion recovery (FLAIR) MRI relative signal intensity (rSI) biomarkers have been introduced to predict eligibility for thrombolysis, but have shown heterogeneous results in the past. In the present work, we investigated whether the inclusion of easily obtainable clinical-radiological parameters would improve the prediction of the thrombolysis time window by rSIs and compared their performance to the visual DWI-FLAIR mismatch. Methods: In a retrospective study, patients from 2 centers with proven stroke with onset <12 h were included. The DWI lesion was segmented and overlaid on ADC and FLAIR images. rSI mean and SD, were calculated as follows: (mean ROI value/mean value of the unaffected hemisphere). Additionally, the visual DWI-FLAIR mismatch was evaluated. Prediction of the thrombolysis time window was evaluated by the area-under-the-curve (AUC) derived from receiver operating characteristic (ROC) curve analysis. Factors such as the association of age, National Institutes of Health Stroke Scale, MRI field strength, lesion size, vessel occlusion and Wahlund-Score with rSI were investigated and the models were adjusted and stratified accordingly. Results: In 82 patients, the unadjusted rSI measures DWI-mean and -SD showed the highest AUCs (AUC 0.86-0.87). Adjustment for clinical-radiological covariates significantly improved the performance of FLAIR-mean (0.91) and DWI-SD (0.91). The best prediction results based on the AUC were found for the final stratified and adjusted models of DWI-SD (0.94) and FLAIR-mean (0.96) and a multivariable DWI-FLAIR model (0.95). The adjusted visual DWI-FLAIR mismatch did not perform in a significantly worse manner (0.89). ADC-rSIs showed fair performance in all models. Conclusions: Quantitative DWI and FLAIR MRI biomarkers as well as the visual DWI-FLAIR mismatch provide excellent prediction of eligibility for thrombolysis in acute stroke, when easily obtainable clinical-radiological parameters are included in the prediction models.

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Predicting poor outcome in patients with suspected COVID-19 presenting to the Emergency Department (COVERED) – Development, internal and external validation of a prediction model

Acute Medicine Journal ◽

10.52964/amja.0836 ◽

2021 ◽

Vol 20 (1) ◽

pp. 4-14

Author(s):

K. Azijli ◽

◽

A.W.E. Lieveld ◽

S.F.B. van der Horst ◽

N. de Graaf ◽

...

Keyword(s):

Emergency Department ◽

Prediction Model ◽

Clinical Decision Making ◽

Prediction Models ◽

External Validation ◽

Model Performance ◽

Area Under The Curve ◽

Brier Score ◽

Multi Centre Study ◽

Poor Outcome

Background: A recent systematic review recommends against the use of any of the current COVID-19 prediction models in clinical practice. To enable clinicians to appropriately profile and treat suspected COVID-19 patients at the emergency department (ED), externally validated models that predict poor outcome are desperately needed. Objective: Our aims were to identify predictors of poor outcome, defined as mortality or ICU admission within 30 days, in patients presenting to the ED with a clinical suspicion of COVID-19, and to develop and externally validate a prediction model for poor outcome. Methods: In this prospective, multi-centre study, we enrolled suspected COVID-19 patients presenting at the EDs of two hospitals in the Netherlands. We used backward logistic regression to develop a prediction model. We used the area under the curve (AUC), Brier score and pseudo-R2 to assess model performance. The model was externally validated in an Italian cohort. Results: We included 1193 patients between March 12 and May 27 2020, of whom 196 (16.4%) had a poor outcome. We identified 10 predictors of poor outcome: current malignancy (OR 2.774; 95%CI 1.682-4.576), systolic blood pressure (OR 0.981; 95%CI 0.964-0.998), heart rate (OR 1.001; 95%CI 0.97-1.028), respiratory rate (OR 1.078; 95%CI 1.046-1.111), oxygen saturation (OR 0.899; 95%CI 0.850-0.952), body temperature (OR 0.505; 95%CI 0.359-0.710), serum urea (OR 1.404; 95%CI 1.198-1.645), C-reactive protein (OR 1.013; 95%CI 1.001-1.024), lactate dehydrogenase (OR 1.007; 95%CI 1.002-1.013) and SARS-CoV-2 PCR result (OR 2.456; 95%CI 1.526-3.953). The AUC was 0.86 (95%CI 0.83-0.89), with a Brier score of 0.32 and, and R2 of 0.41. The AUC in the external validation in 500 patients was 0.70 (95%CI 0.65-0.75). Conclusion: The COVERED risk score showed excellent discriminatory ability, also in external validation. It may aid clinical decision making, and improve triage at the ED in health care environments with high patient throughputs.

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Individual participant data validation of the PICNICC prediction model for febrile neutropenia

Archives of Disease in Childhood ◽

10.1136/archdischild-2019-317308 ◽

2019 ◽

Vol 105 (5) ◽

pp. 439-445 ◽

Cited By ~ 1

Author(s):

Bob Phillips ◽

Jessica Elizabeth Morgan ◽

Gabrielle M Haeusler ◽

Richard D Riley

Keyword(s):

Prediction Model ◽

Prediction Models ◽

Meta Analysis ◽

Characteristic Curve ◽

Area Under The Curve ◽

Infectious Complications ◽

Cancer Therapies ◽

Individual Participant Data ◽

Documented Infection ◽

Individual Participant

BackgroundRisk-stratified approaches to managing cancer therapies and their consequent complications rely on accurate predictions to work effectively. The risk-stratified management of fever with neutropenia is one such very common area of management in paediatric practice. Such rules are frequently produced and promoted without adequate confirmation of their accuracy.MethodsAn individual participant data meta-analytic validation of the ‘Predicting Infectious ComplicatioNs In Children with Cancer’ (PICNICC) prediction model for microbiologically documented infection in paediatric fever with neutropenia was undertaken. Pooled estimates were produced using random-effects meta-analysis of the area under the curve-receiver operating characteristic curve (AUC-ROC), calibration slope and ratios of expected versus observed cases (E/O).ResultsThe PICNICC model was poorly predictive of microbiologically documented infection (MDI) in these validation cohorts. The pooled AUC-ROC was 0.59, 95% CI 0.41 to 0.78, tau2=0, compared with derivation value of 0.72, 95% CI 0.71 to 0.76. There was poor discrimination (pooled slope estimate 0.03, 95% CI −0.19 to 0.26) and calibration in the large (pooled E/O ratio 1.48, 95% CI 0.87 to 2.1). Three different simple recalibration approaches failed to improve performance meaningfully.ConclusionThis meta-analysis shows the PICNICC model should not be used at admission to predict MDI. Further work should focus on validating alternative prediction models. Validation across multiple cohorts from diverse locations is essential before widespread clinical adoption of such rules to avoid overtreating or undertreating children with fever with neutropenia.

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Outcome of Peritoneal Catheter Placement in the High-Risk Patient

Peritoneal Dialysis International ◽

10.1177/089686088400402s12 ◽

1984 ◽

Vol 4 (2_suppl) ◽

pp. 112-114 ◽

Cited By ~ 5

Author(s):

A.S. Levey ◽

G.M. Simon ◽

J. McCauley ◽

T.J. Smith ◽

S. I. Cho ◽

...

Keyword(s):

Peritoneal Dialysis ◽

High Risk ◽

Abdominal Surgery ◽

High Risk Patient ◽

Catheter Insertion ◽

Catheter Placement ◽

Major Abdominal Surgery ◽

Early Complications ◽

Peritoneal Catheter ◽

History Of

Serious early complications of catheter insertion and catheter outcomes are compared in patients with or without a history of major abdominal surgery or peritonitis. Previous major surgery and peritonitis are important risk factors for early serious complications of catheter placement. Despite the higher risk of early complications, subsequent attempts at catheter placement were successful in all but one of the high risk patients. Patients with previous major abdominal surgery or peritonitis generally are considered to be at high risk for complications of permanent peritoneal catheter placement. However, few physicians would refuse to attempt catheter insertion in patients highly motivated to undergo peritoneal dialysis. In this setting, the patient and the physician must decide whether the motivation and potential benefit justify the risk. Unfortunately, extensive quantitative data on the success rate and the likelihood of complications in the high-risk patient are not available. In order to determine the success rate and relative risk, we reviewed the outcome of catheter placement for continuous ambulatory peritoneal dialysis (CAPD) in our patients. In this report, we identify the serious early complications of catheter insertion and compare the outcomes in patients with and without a history of major abdominal surgery or peritonitis.

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Predicting Prolonged Length of Hospital Stay for Peritoneal Dialysis–Treated Patients Using Stacked Generalization: Model Development and Validation Study

JMIR Medical Informatics ◽

10.2196/17886 ◽

2021 ◽

Vol 9 (5) ◽

pp. e17886

Author(s):

Guilan Kong ◽

Jingyi Wu ◽

Hong Chu ◽

Chao Yang ◽

Yu Lin ◽

...

Keyword(s):

Peritoneal Dialysis ◽

High Risk ◽

Prediction Model ◽

Performance Measures ◽

Prediction Models ◽

Data Driven ◽

Brier Score ◽

Prolonged Length ◽

Admission Data ◽

Overall Performance

Background The increasing number of patients treated with peritoneal dialysis (PD) and their consistently high rate of hospital admissions have placed a large burden on the health care system. Early clinical interventions and optimal management of patients at a high risk of prolonged length of stay (pLOS) may help improve the medical efficiency and prognosis of PD-treated patients. If timely clinical interventions are not provided, patients at a high risk of pLOS may face a poor prognosis and high medical expenses, which will also be a burden on hospitals. Therefore, physicians need an effective pLOS prediction model for PD-treated patients. Objective This study aimed to develop an optimal data-driven model for predicting the pLOS risk of PD-treated patients using basic admission data. Methods Patient data collected using the Hospital Quality Monitoring System (HQMS) in China were used to develop pLOS prediction models. A stacking model was constructed with support vector machine, random forest (RF), and K-nearest neighbor algorithms as its base models and traditional logistic regression (LR) as its meta-model. The meta-model used the outputs of all 3 base models as input and generated the output of the stacking model. Another LR-based pLOS prediction model was built as the benchmark model. The prediction performance of the stacking model was compared with that of its base models and the benchmark model. Five-fold cross-validation was employed to develop and validate the models. Performance measures included the Brier score, area under the receiver operating characteristic curve (AUROC), estimated calibration index (ECI), accuracy, sensitivity, specificity, and geometric mean (Gm). In addition, a calibration plot was employed to visually demonstrate the calibration power of each model. Results The final cohort extracted from the HQMS database consisted of 23,992 eligible PD-treated patients, among whom 30.3% had a pLOS (ie, longer than the average LOS, which was 16 days in our study). Among the models, the stacking model achieved the best calibration (ECI 8.691), balanced accuracy (Gm 0.690), accuracy (0.695), and specificity (0.701). Meanwhile, the stacking and RF models had the best overall performance (Brier score 0.174 for both) and discrimination (AUROC 0.757 for the stacking model and 0.756 for the RF model). Compared with the benchmark LR model, the stacking model was superior in all performance measures except sensitivity, but there was no significant difference in sensitivity between the 2 models. The 2-sided t tests revealed significant performance differences between the stacking and LR models in overall performance, discrimination, calibration, balanced accuracy, and accuracy. Conclusions This study is the first to develop data-driven pLOS prediction models for PD-treated patients using basic admission data from a national database. The results indicate the feasibility of utilizing a stacking-based pLOS prediction model for PD-treated patients. The pLOS prediction tools developed in this study have the potential to assist clinicians in identifying patients at a high risk of pLOS and to allocate resources optimally for PD-treated patients.

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Percutaneous Versus Surgical Insertion of Peritoneal Dialysis Catheters: A Systematic Review and Meta-Analysis

Canadian Journal of Kidney Health and Disease ◽

10.1177/20543581211052731 ◽

2021 ◽

Vol 8 ◽

pp. 205435812110527

Author(s):

Anirudh Agarwal ◽

Reid H. Whitlock ◽

Ryan J. Bamforth ◽

Thomas W. Ferguson ◽

Jenna M. Sabourin ◽

...

Keyword(s):

Systematic Review ◽

Peritoneal Dialysis ◽

Kidney Failure ◽

Meta Analysis ◽

Infectious Complications ◽

Risk Of Bias ◽

Catheter Insertion ◽

Early Complications ◽

Exit Site ◽

Percutaneous Insertion

Background: Home-based peritoneal dialysis (PD) is an alternative to facility-based hemodialysis and has lower costs and greater freedom for patients with kidney failure. For a patient to undergo PD, a safe and reliable method of accessing the peritoneum is needed. However, different catheter insertion techniques may affect patient health outcomes. Objective: To compare the risk of infectious and mechanical complications between surgical (open and laparoscopic) PD catheter insertion and percutaneous catheter insertion. Design: Systematic review and meta-analysis. Setting: We searched for observational studies and randomized controlled trials (RCTs) in CENTRAL, EMBASE, MEDLINE, PubMed, and SCOPUS from inception until June 2018. Data were extracted by 2 independent reviewers based on a preformed template. Patients: Adult (aged 18+) patients with kidney failure who underwent a PD catheter insertion procedure. Measurements: We analyzed leak, malfunction, and bleed as early complications (occurring within 1 month of catheter insertion). Infectious complications (exit-site infections, tunnel infections, and peritonitis) were presented as both early complications and with the longest duration of follow-up. Methods: Random effects meta-analyses with the generic inverse variance method to estimate pooled rate ratios and 95% confidence intervals. We quantified heterogeneity by using the I2 statistic for inconsistency and assessed heterogeneity using the χ2 test. Sensitivity analysis was performed by removing studies at high risk of bias as measured with the Newcastle-Ottawa Scale and the Cochrane Risk of Bias tool. Results: Twenty-four studies (22 observational, 2 RCTs) with 3108 patients and 3777 catheter insertions were selected. Data from 2 studies were unable to be extracted and were qualitatively assessed. In the remaining 22 studies, percutaneous insertion was associated with a lower risk of both exit-site infections (risk ratio [RR] = 0.36, 95% confidence interval [CI] = 0.24-0.53, I2 = 0%) and peritonitis (RR = 0.52, 95% CI = 0.36-0.77, I2 = 3%) within 1 month of the procedure. There was no difference in mechanical complication rates between the 2 techniques. Limitations: Lack of consistency in the time periods for the various outcomes reported, risk of bias concerns with respect to population comparability, and the inability to analyze individual component causes of primary nonfunction (catheter obstruction, catheter migration, and leak). Conclusions: Our meta-analysis suggests differences in early infectious complications in favor of percutaneous insertion and no significant differences in mechanical complications compared with surgical insertion. These findings have implications on the direction of PD programs in terms of maximizing operating room resources.

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Prediction Scores for Acute Kidney Injury following Adult Cardiac Surgery

10.21203/rs.3.rs-88293/v1 ◽

2020 ◽

Author(s):

Yu Tian ◽

Wei Zhao ◽

Yuefu Wang ◽

Chunrong Wang ◽

Xiaolin Diao ◽

...

Keyword(s):

Acute Kidney Injury ◽

Cardiac Surgery ◽

Prediction Model ◽

Prediction Models ◽

Area Under The Curve ◽

Kidney Injury ◽

Scoring Systems ◽

Multivariable Logistic Regression Analysis ◽

Derivation Cohort ◽

Associated Risk Factors

Abstract Background In the development of scoring systems for acute kidney injury (AKI) following cardiac surgery, previous investigations have primarily and solely attached importance to preoperative associated risk factors without any consideration for surgery-derived physiopathology. We sought to internally derive and then validate risk score systems using pre- and intraoperative variables to predict the occurrence of any-stage (stage 1-3) and stage-3 AKI within 7 days.Methods Patients undergoing cardiac surgery from Jan 1, 2012, to Jan 1, 2019, were enrolled in our retrospective study. The clinical data were divided into a derivation cohort (n= 43799) and a validation cohort (n= 14600). Multivariable logistic regression analysis was used to develop the prediction models.Results The overall prevalence of any-stage and stage-3 AKI after cardiac surgery was 34.3% and 1.7%, respectively. Any-stage AKI prediction-model discrimination measured by the area under the curve (AUC) was acceptable (AUC = 0.69, 95% CI: 0.68, 0.69), and the prediction model calibration measured by the Hosmer-Lemshow test was good (P = 0.95). The stage-3 AKI prediction model had an AUC of 0.84 (95% CI 0.83, 0.85) and good calibration according to the Hosmer-Lemshow test (P = 0.73).Conclusions Using pre- and intraoperative data, we developed two scoring systems for any-stage AKI and stage-3 AKI in a cardiac surgery population. These scoring systems can potentially be adopted clinically in the field of AKI recognition and therapeutic intervention.

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A Prediction Model for Recognizing Strangulated Small Bowel Obstruction

Gastroenterology Research and Practice ◽

10.1155/2018/7164648 ◽

2018 ◽

Vol 2018 ◽

pp. 1-7 ◽

Cited By ~ 6

Author(s):

Xiaming Huang ◽

Guan Fang ◽

Jie Lin ◽

Keyu Xu ◽

Hongqi Shi ◽

...

Keyword(s):

Logistic Regression ◽

Small Bowel ◽

Prediction Model ◽

Small Bowel Obstruction ◽

Bowel Obstruction ◽

Clinical Symptoms ◽

Multivariate Logistic Regression Analysis ◽

Area Under The Curve ◽

Clinical Score ◽

Radiologic Findings

Introduction. Early and accurate diagnosis of strangulated small bowel obstruction (SSBO) is difficult. This study aimed to devise a prediction model for predicting the risk of SSBO.Materials and Methods.A database of 417 patients who had clinical symptoms of intestinal obstruction confirmed by computed tomography (CT) were evaluated for inclusion in this study. Symptoms and laboratory and radiologic findings of these patients were collected after admission. These clinical factors were analyzed using logistic regression. A logistic regression model was applied to identify determinant variables and construct a clinical score that would predict SSBO.Results. Seventy-six patients were confirmed to have SSBO, 169 patients required surgery but had no evidence of intestinal ischemia, and 172 patients were successfully managed conservatively. In multivariate logistic regression analysis, body temperature ≥ 38.0°C, positive peritoneal irritation sign, white blood cell (WBC) count > 10.0 × 10^9/L, thick-walled small bowel ≥3 mm, and ascites were significantly associated with SSBO. A new prediction model with total scores ranging from 0 to 481 was developed with these five variables. The area under the curve (AUC) of the new prediction model was 0.935.Conclusions. Our prediction model is a good predictive model to evaluate the severity of SBO.

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