Using Nomograms to Predict the PPCs of Patients With Diffuse Peritonitis Undergoing Emergency Gastrointestinal Surgery

Objective: To develop and validate a nomogram model for predicting postoperative pulmonary complications (PPCs) in patients with diffuse peritonitis undergoing emergency gastrointestinal surgery.Methods: We used the least absolute shrinkage and selection operator (LASSO) regression model to analyze the independent risk factors for PPCs in patients with diffuse peritonitis who underwent emergency gastrointestinal surgery. Using R, we developed and validated a nomogram model for predicting PPCs in patients with diffuse peritonitis undergoing emergency gastrointestinal surgery.Results: The LASSO regression analysis showed that AGE, American Society of Anesthesiologists physical status classification (ASA), DIAGNOSIS, platelets (on the 3rd day after surgery), cholesterol (on the 3rd day after surgery), ALBUMIN (on the first day after surgery), and preoperative ALBUMIN were independent risk factors for PPCs in patients with diffuse peritonitis undergoing emergency gastrointestinal surgery. The area under the curve (AUC) value of the nomogram model in the training group was 0.8240; its accuracy was 0.7000, and its sensitivity was 0.8658. This demonstrates that the nomogram has a high prediction value. Also in the test group, the AUC value of the model established by the variables AGE, ASA, and platelets (on the 3rd day after surgery), cholesterol (on the 3rd day after surgery), ALBUMIN (on the first day after surgery), and preoperative ALBUMIN was 0.8240; its accuracy was 0.8000; and its specificity was 0.8986. In the validation group, the same results were obtained. The results of the clinical decision curve show that the benefit rate was also high.Conclusion: Based on the risk factors AGE, ASA, DIAGNOSIS, platelets (on the 3rd day after surgery), cholesterol (on the 3rd day after surgery), ALBUMIN (on the first day after surgery), and preoperative ALBUMIN, the nomogram model established in this study for predicting PPCs in patients with diffuse peritonitis undergoing emergency gastrointestinal surgery has high accuracy and discrimination.

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Developing Machine Learning Algorithms to Predict Pulmonary Complications After Emergency Gastrointestinal Surgery

Frontiers in Medicine ◽

10.3389/fmed.2021.655686 ◽

2021 ◽

Vol 8 ◽

Author(s):

Qiong Xue ◽

Duan Wen ◽

Mu-Huo Ji ◽

Jianhua Tong ◽

Jian-Jun Yang ◽

...

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Learning Algorithms ◽

Gastrointestinal Surgery ◽

Test Group ◽

Secondary Data ◽

Pulmonary Complications ◽

Machine Learning Algorithms ◽

Diffuse Peritonitis ◽

Preoperative Albumin

Objective: Investigate whether machine learning can predict pulmonary complications (PPCs) after emergency gastrointestinal surgery in patients with acute diffuse peritonitis.Methods: This is a secondary data analysis study. We use five machine learning algorithms (Logistic regression, DecisionTree, GradientBoosting, Xgbc, and gbm) to predict postoperative pulmonary complications.Results: Nine hundred and twenty-six cases were included in this study; 187 cases (20.19%) had PPCs. The five most important variables for the postoperative weight were preoperative albumin, cholesterol on the 3rd day after surgery, albumin on the day of surgery, platelet count on the 1st day after surgery and cholesterol count on the 1st day after surgery for pulmonary complications. In the test group: the logistic regression model shows AUC = 0.808, accuracy = 0.824 and precision = 0.621; Decision tree shows AUC = 0.702, accuracy = 0.795 and precision = 0.486; The GradientBoosting model shows AUC = 0.788, accuracy = 0.827 and precision = 1.000; The Xgbc model shows AUC = 0.784, accuracy = 0.806 and precision = 0.583. The Gbm model shows AUC = 0.814, accuracy = 0.806 and precision = 0.750.Conclusion: Machine learning algorithms can predict patients' PPCs with acute diffuse peritonitis. Moreover, the results of the importance matrix for the Gbdt algorithm model show that albumin, cholesterol, age, and platelets are the main variables that account for the highest pulmonary complication weights.

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Nomogram for Predicting COVID-19 Disease Progression Based on Single-Center Data: Observational Study and Model Development (Preprint)

10.2196/preprints.19588 ◽

2020 ◽

Author(s):

Tao Fan ◽

Bo Hao ◽

Shuo Yang ◽

Bo Shen ◽

Zhixin Huang ◽

...

Keyword(s):

Risk Factors ◽

Regression Analysis ◽

Clinical Characteristics ◽

Cox Regression ◽

Disease Onset ◽

Lasso Regression ◽

Prediction Ability ◽

Calibration Curves ◽

Independent Risk Factors ◽

Critical Patients

BACKGROUND In late December 2019, a pneumonia caused by SARS-CoV-2 was first reported in Wuhan and spread worldwide rapidly. Currently, no specific medicine is available to treat infection with COVID-19. OBJECTIVE The aims of this study were to summarize the epidemiological and clinical characteristics of 175 patients with SARS-CoV-2 infection who were hospitalized in Renmin Hospital of Wuhan University from January 1 to January 31, 2020, and to establish a tool to identify potential critical patients with COVID-19 and help clinical physicians prevent progression of this disease. METHODS In this retrospective study, clinical characteristics of 175 confirmed COVID-19 cases were collected and analyzed. Univariate analysis and least absolute shrinkage and selection operator (LASSO) regression were used to select variables. Multivariate analysis was applied to identify independent risk factors in COVID-19 progression. We established a nomogram to evaluate the probability of progression of the condition of a patient with COVID-19 to severe within three weeks of disease onset. The nomogram was verified using calibration curves and receiver operating characteristic curves. RESULTS A total of 18 variables were considered to be risk factors after the univariate regression analysis of the laboratory parameters (P<.05), and LASSO regression analysis screened out 10 risk factors for further study. The six independent risk factors revealed by multivariate Cox regression were age (OR 1.035, 95% CI 1.017-1.054; P<.001), CK level (OR 1.002, 95% CI 1.0003-1.0039; P=.02), CD4 count (OR 0.995, 95% CI 0.992-0.998; P=.002), CD8 % (OR 1.007, 95% CI 1.004-1.012, P<.001), CD8 count (OR 0.881, 95% CI 0.835-0.931; P<.001), and C3 count (OR 6.93, 95% CI 1.945-24.691; P=.003). The areas under the curve of the prediction model for 0.5-week, 1-week, 2-week and 3-week nonsevere probability were 0.721, 0.742, 0.87, and 0.832, respectively. The calibration curves showed that the model had good prediction ability within three weeks of disease onset. CONCLUSIONS This study presents a predictive nomogram of critical patients with COVID-19 based on LASSO and Cox regression analysis. Clinical use of the nomogram may enable timely detection of potential critical patients with COVID-19 and instruct clinicians to administer early intervention to these patients to prevent the disease from worsening.

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Safety of Biologic Agents in Elderly Patients with Rheumatoid Arthritis

The Journal of Rheumatology ◽

10.3899/jrheum.160012 ◽

2016 ◽

Vol 43 (11) ◽

pp. 1984-1988 ◽

Cited By ~ 11

Author(s):

Atsuko Murota ◽

Yuko Kaneko ◽

Kunihiro Yamaoka ◽

Tsutomu Takeuchi

Keyword(s):

Rheumatoid Arthritis ◽

Risk Factors ◽

Adverse Events ◽

Elderly Patients ◽

Young Patients ◽

Pulmonary Complications ◽

Biologic Agents ◽

Treatment Withdrawal ◽

Old Patients ◽

Independent Risk Factors

Objective.To clarify the safety of biologics in elderly patients with rheumatoid arthritis.Methods.Biologics were analyzed for safety in relation to age in 309 patients.Results.Young (< 65 yrs old, n = 174), elderly (65–74 yrs old, n = 86), and older elderly patients (≥ 75 yrs old, n = 49) were enrolled. Although the incidence of adverse events causing treatment withdrawal was significantly higher in elderly and old elderly compared with young patients, no difference was found between elderly and older elderly patients. Pulmonary complications were independent risk factors.Conclusion.Old patients require special attention, although the safety of biologics in those ≥ 75 years old and 65–74 was comparable.

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Analysis of clinico-demographic risk factors for postoperative pulmonary complications following gastrointestinal surgery

International Surgery Journal ◽

10.18203/2349-2902.isj20195950 ◽

2019 ◽

Vol 7 (1) ◽

pp. 93

Author(s):

Vijay Sinouvassan ◽

Hemalatha Dayalane ◽

Subalakshmi Balagurunathan ◽

Ashok Kumar Sahoo ◽

Vishnu Kanth ◽

...

Keyword(s):

Risk Factors ◽

Serum Albumin ◽

Gastrointestinal Surgery ◽

Pulmonary Complications ◽

Lung Pathology ◽

Postoperative Pulmonary Complications ◽

Increased Risk ◽

Education Status ◽

Demographic Risk ◽

Demographic Risk Factors

Background: Postoperative pulmonary complications (PPC) are one of the commonest complications following gastrointestinal surgery. They lead to increased mortality, increased length of intensive care unit (ICU) stay, and higher cost of treatment. Identifying the risk factors of PPC helps in predicting its occurrence and to develop preventive measures. The objectives of the present study were to study the clinical and demographic risk factors for PPC following gastrointestinal surgery.Methods: The study was designed as an observational descriptive analytic study. All the patients ≥18 years of age undergoing gastrointestinal surgery were included. The patients with preoperative lung pathology requiring ICU care or ventilatory support and patients with lung metastasis were excluded. The demographic and clinical parameters at admission were recorded. The details of pulmonary complications like the time of occurrence after surgery and the mode of treatment for pulmonary complications were noted. The risk association was assessed for statistical significance.Results: A total of 100 patients were underwent various gastrointestinal surgeries during the study period. The incidence of PPC was 34% in our study. Age, education status, smoking, and presence of comorbidities were found to be positively associated with an increased incidence of PPCs. The serum albumin of less than 3.5gm and the haemoglobin of less than 8 gm were also associated with an increased incidence of PPC. Pleural effusion was the commonest PPC seen in 15 (44.1%) patients followed by pneumonia in 9 (26.5%).Conclusions: Age, smoking, education status, serum albumin, haemoglobin, emergency surgery, elective postoperative ventilation, nasogastric intubation and blood loss in the intraoperative period were found to associated with increased risk of PPCs.

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Development of a prediction model for 1-year poor prognosis in patients with acute ischemic stroke

Journal of Investigative Medicine ◽

10.1136/jim-2018-000883 ◽

2019 ◽

Vol 67 (6) ◽

pp. 957-963 ◽

Cited By ~ 1

Author(s):

Xia Ling ◽

Bo Shen ◽

Kangzhi Li ◽

Lihong Si ◽

Xu Yang

Keyword(s):

Risk Factors ◽

Logistic Regression ◽

Ischemic Stroke ◽

Prediction Model ◽

Acute Ischemic Stroke ◽

Poor Prognosis ◽

Multivariate Logistic Regression ◽

Lemeshow Test ◽

Independent Risk Factors ◽

Auc Value

The goals of this study were to develop a new prediction model to predict 1-year poor prognosis (death or modified Rankin scale score of ≥3) in patients with acute ischemic stroke (AIS) and to compare the performance of the new prediction model with other prediction scales. Baseline data of 772 patients with AIS were collected, and univariate and multivariate logistic regression analyses were performed to identify independent risk factors for 1-year poor prognosis in patients with AIS. The area under the receiver operating characteristics curve (AUC) value of the new prediction model and the THRIVE, iScore and ASTRAL scores was compared. The Hosmer-Lemeshow test was used to assess the goodness of fit of the model. We identified 196 (25.4%) patients with poor prognosis at 1-year follow-up, and of these 68 (68/196, 34.7%) had died. Multivariate logistic regression and receiver operating characteristic curve analyses showed that age ≥70 years, consciousness (lethargy or coma), history of stroke or transient ischemic attack, cancer, abnormal fasting blood glucose levels ≥7.0 mmol/L, and National Institutes of Health Stroke Scale score were independent risk factors for 1-year poor prognosis in patients with AIS. Scores were assigned for each variable by rounding off β coefficient to the integer score, and a new prediction model with a maximum total score of 9 points was developed. The AUC value of the new prediction model was higher than the THRIVE score (p<0.05). The χ2 value for the Hosmer-Lemeshow test was 7.337 (p>0.05), suggesting that the prediction model had a good fit. The new prediction model can accurately predict 1-year poor prognosis in Chinese patients with AIS.

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Identification and Validation of lncRNA-Related Prognostic Signatures in Cholangiocarcinoma

10.21203/rs.3.rs-705273/v1 ◽

2021 ◽

Author(s):

JiaNing Zhang ◽

Fengwei Li ◽

Yihua Huang ◽

Hui Xue ◽

Qifei Tao ◽

...

Keyword(s):

Risk Factors ◽

Vascular Invasion ◽

Disease Free Survival ◽

Lasso Regression ◽

Cox Models ◽

Hepatobiliary Cancer ◽

Tumorigenesis And Progression ◽

Independent Risk Factors

Abstract Background and Aims: Cholangiocarcinoma (CCA), the second most common hepatobiliary cancer, is associated with poor prognosis. Therefore, there is a need to elucidate on the pathogenic mechanisms of CCA. In this study, we aimed at identifying lncRNA-related prognostic signatures for CCA through bioinformatics analysis and further validated their functions in CCA tumorigenesis and progression.Methods: The RNA-seq data of CCA were downloaded from public databases. Differentially expressed lncRNAs (DElncRNAs) were screened using R packages. Then, candidate OS- and DFS-related DElncRNAs were selected through Kaplan–Meier survival analysis. Furthermore, LASSO regression analyses were performed to establish two prognostic signatures, termed the OS and DFS signatures, respectively. Multivariate COX models and nomograms for overall survival (OS) and disease-free survival (DFS) were established based on OS/DFS signature and clinical data. Hub lncRNAs were identified and enrichment analyses performed to explore their potential functions. Finally, in vitro and in vivo models were used to validate the effects of the hub lncRNAs in CCA tumorigenesis and progression.Results: A total of 925 DElncRNAs were selected, from which six candidate OS-related lncRNAs and 15 candidate DFS-related lncRNAs were identified. The OS and DFS signatures were then established using four lncRNAs, respectively. We found that the OS signature and vascular invasion were significant independent risk factors for OS outcomes of CCA, while the DFS signature, vascular invasion and CA19-9 were significant independent risk factors for DFS outcomes of CCA. MiR4435-2HG and GAPLINC were selected as hub lncRNAs because they were included in both OS and DFS signatures. GO and KEGG enrichment analyses revealed that the two hub lncRNAs were involved in CCA tumorigenesis and progression. Finally, we constructed in vitro and in vivo models and revealed that the lncRNAs, MiR4435-2HG and GAPLINC can prompt CCA proliferation and migration in vitro and in vivo.Conclusions: The established OS and DFS signatures, which were based on DElncRNAs, are independent risk factors for OS and DFS of CCA patients, respectively. MIR4435-2HG and GAPLINC were identified as hub lncRNAs. In vitro and in vivo models revealed that MiR4435-2HG and GAPLINC can prompt CCA progression, which might be novel prognostic biomarkers and therapeutic targets for CCA.

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Prognostic Factors of Complications After Thoracoscopic Lobectomy for Non-Small Cell Lung Cancer-A Retrospective Study

10.21203/rs.3.rs-515147/v1 ◽

2021 ◽

Author(s):

Kai Wang ◽

Derun Xia ◽

Yi Yin ◽

Yu Wang ◽

Sibo Sun ◽

...

Keyword(s):

Risk Factors ◽

Lung Cancer ◽

Thoracoscopic Surgery ◽

Univariate Analysis ◽

Pulmonary Complications ◽

Smoking History ◽

Cardiopulmonary Complications ◽

Independent Risk Factors ◽

Postoperative Cardiopulmonary Complications

Abstract Background: Video-assisted thoracoscopic surgery, safe and minimally invasive, is the first strategy recommended for non-small cell lung cancer. The purpose of this study was to determine the risk factors for postoperative cardiopulmonary complications (including cardiac and pulmonary complications) in patients with NSCLC who underwent video-assisted thoracoscopic surgery (VATS).Methods: We retrospectively collected information of 3142 lung cancer patients undergoing VATS tumor resection at Jiangsu Provincial People's Hospital from January 2017 to June 2018, and established a clinical prediction model using the factors selected by univariate analysis.Results: A total of 305 in 3142 patients developed postoperative cardiopulmonary complications. In univariate analysis, age, PNI, CCI, long-term smoking history before surgery, conversion to thoracotomy, albumin before surgery, pre-albumin, Δalbumin and pleura adhesion were all associated with cardiac and pulmonary complications. Multivariate analysis showed that age, PNI, CCI, long-term smoking history before surgery, conversion to thoracotomy, pre-albumin, Δalbumin were important independent risk factors for complications. Finally, age, PNI, CCI, long-term smoking history before surgery, conversion to thoracotomy, Δalbumin variables were included in the model (AUC=0.743).Conclusion: Nutritional status (PNI, pre-albumin), CCI, age, long-term smoking history before surgery, conversion to thoracotomy were all independent risk factors for postoperative complications and poor prognosis. Experienced surgeons should instruct the operation of high-risk patients to avoid long-term hospitalization and possible poor prognosis after surgery.

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Retrospective Study of Postoperative Pulmonary Complications in Patients with Cervical Spine Pathology

Journal of Neuroanaesthesiology and Critical Care ◽

10.1055/s-0038-1660876 ◽

2018 ◽

Vol 05 (02) ◽

pp. 98-104 ◽

Cited By ~ 1

Author(s):

Surya Kumar Dube ◽

Mihir Prakash Pandia ◽

Arvind Chaturvedi ◽

Shailender Kumar

Keyword(s):

Risk Factors ◽

Mechanical Ventilation ◽

Cervical Spine ◽

Retrospective Study ◽

Pulmonary Complications ◽

Postoperative Pulmonary Complication ◽

Care Hospital ◽

Cervical Laminectomy ◽

Independent Risk Factors ◽

Postoperative Mechanical Ventilation

Abstract Background Postoperative pulmonary complication (PPC) after cervical spine surgery is known to increase patient morbidity and mortality as well as the hospital and intensive care unit (ICU) stay. However, studies addressing this issue are scarce in the current literature. The aim of this study was to find out the incidence and various factors associated with PPC in patients undergoing cervical spine surgeries. Materials and Methods It is a retrospective study in a tertiary care hospital. Two hundred and seven patients who underwent different cervical spinal surgeries were included in this study. Various perioperative data including demography, history of smoking, associated systemic illness, type and site of lesion, preoperative respiratory status, and signs of involvement of lower cranial nerves were collected. The incidence and the risk factors for PPC were found out. Statistical analysis was done using chi-square/Fisher's exact test/Student's t-test, followed by univariate and multiple logistic regression analysis. Results The incidence of PPC in our study was 39.6%. Various pulmonary complications observed were difficulty in breathing requiring some intervention (19.3%), pneumonia (5.3%), tracheobronchitis (3.9%), arterial desaturation (3.4%), reintubation (3.4%), atelectasis (1.3%), pleural effusion (0.97%), pneumothorax (0.97%), and acute respiratory distress syndrome (ARDS) (0.97%). Preoperative respiratory abnormality, cervical laminectomy and instrumentation surgery and postoperative mechanical ventilation of > 24 hours duration were found to be independent risk factors for occurrence of PPC. Conclusions The patients with cervical spinal cord pathology are at increased risk for PPC. Preoperative respiratory abnormality, postoperative mechanical ventilation of > 24-hour duration, and cervical laminectomy and instrumentation surgery are independent risk factors for PPC.

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Nomogram for Predicting COVID-19 Disease Progression Based on Single-Center Data: Observational Study and Model Development

JMIR Medical Informatics ◽

10.2196/19588 ◽

2020 ◽

Vol 8 (9) ◽

pp. e19588

Author(s):

Tao Fan ◽

Bo Hao ◽

Shuo Yang ◽

Bo Shen ◽

Zhixin Huang ◽

...

Keyword(s):

Risk Factors ◽

Regression Analysis ◽

Clinical Characteristics ◽

Cox Regression ◽

Disease Onset ◽

Lasso Regression ◽

Prediction Ability ◽

Calibration Curves ◽

Independent Risk Factors ◽

Critical Patients

Background In late December 2019, a pneumonia caused by SARS-CoV-2 was first reported in Wuhan and spread worldwide rapidly. Currently, no specific medicine is available to treat infection with COVID-19. Objective The aims of this study were to summarize the epidemiological and clinical characteristics of 175 patients with SARS-CoV-2 infection who were hospitalized in Renmin Hospital of Wuhan University from January 1 to January 31, 2020, and to establish a tool to identify potential critical patients with COVID-19 and help clinical physicians prevent progression of this disease. Methods In this retrospective study, clinical characteristics of 175 confirmed COVID-19 cases were collected and analyzed. Univariate analysis and least absolute shrinkage and selection operator (LASSO) regression were used to select variables. Multivariate analysis was applied to identify independent risk factors in COVID-19 progression. We established a nomogram to evaluate the probability of progression of the condition of a patient with COVID-19 to severe within three weeks of disease onset. The nomogram was verified using calibration curves and receiver operating characteristic curves. Results A total of 18 variables were considered to be risk factors after the univariate regression analysis of the laboratory parameters (P<.05), and LASSO regression analysis screened out 10 risk factors for further study. The six independent risk factors revealed by multivariate Cox regression were age (OR 1.035, 95% CI 1.017-1.054; P<.001), CK level (OR 1.002, 95% CI 1.0003-1.0039; P=.02), CD4 count (OR 0.995, 95% CI 0.992-0.998; P=.002), CD8 % (OR 1.007, 95% CI 1.004-1.012, P<.001), CD8 count (OR 0.881, 95% CI 0.835-0.931; P<.001), and C3 count (OR 6.93, 95% CI 1.945-24.691; P=.003). The areas under the curve of the prediction model for 0.5-week, 1-week, 2-week and 3-week nonsevere probability were 0.721, 0.742, 0.87, and 0.832, respectively. The calibration curves showed that the model had good prediction ability within three weeks of disease onset. Conclusions This study presents a predictive nomogram of critical patients with COVID-19 based on LASSO and Cox regression analysis. Clinical use of the nomogram may enable timely detection of potential critical patients with COVID-19 and instruct clinicians to administer early intervention to these patients to prevent the disease from worsening.

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Preoperative Albumin Level Serves as a Predictor for Postoperative Pulmonary Complications Following Elective Laparoscopic Gastrectomy

Current Pharmaceutical Design ◽

10.2174/1381612824666180713104307 ◽

2018 ◽

Vol 24 (27) ◽

pp. 3250-3255 ◽

Cited By ~ 2

Author(s):

Yun Chen ◽

Guorong Wu ◽

Ruichun Wang ◽

Junping Chen

Keyword(s):

Risk Factors ◽

Laparoscopic Gastrectomy ◽

Independent Risk Factor ◽

Multiple Logistic Regression Analysis ◽

Albumin Level ◽

Pulmonary Complications ◽

Multiple Logistic Regression ◽

Postoperative Pulmonary Complications ◽

Potential Risk Factors ◽

Preoperative Albumin

Objective: Postoperative Pulmonary Complications (PPCs) can contribute to increased mortality and prolonged hospital stay in surgical patients with Gastric Cancer (GC). This study aimed to investigate potential risk factors for PPCs in elderly GC patients following elective laparoscopic gastrectomy. Methods: Eligible consecutive elderly GC patients (aged over 65 years) who were scheduled to undergo elective laparoscopic gastrectomy were enrolled in this study. The demographic, clinicopathological characteristics and laboratory variables were compared in patients with or without PPCs within postoperative 30 days. Risk factors for PPCs were analyzed by multiple logistic regression analysis and receiver operating characteristic (ROC) curve analysis. Results: 35 of all the 262 enrolled patients have developed PPCs with an incidence of 13.4%. Age, chronic obstructive pulmonary disease (COPD), congestive heart failure (CHF), forced expiratory volume in one second/ forced vital capacity (FEV1/FVC) ratio, duration of operation, hemoglobin, albumin and C-reactive protein (CRP) were potential risk factors for PPCs by univariate analysis. The preoperative albumin level was the only independent risk factor for PPCs (OR: 1.15, 95%CI: 1.06-1.28, P=0.011) by multiple logistic regression analysis. Preoperative albumin level was a predictor for PPCs with an area under the curve (AUC) of 0.728 and a cut-off value of 33.8 mg/dl (specificity: 54.19%, sensitivity: 77.14%, P<0.001). Conclusions: Preoperative albumin level was an independent risk factor for PPCs in elderly GC patients after elective laparoscopic gastrectomy.

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