Comparisons of Prediction Models of Quality of Life after Laparoscopic Cholecystectomy: A Longitudinal Prospective Study

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Abstract

Background

Few studies of laparoscopic cholecystectomy (LC) outcome have used longitudinal data for more than two years. Moreover, no studies have considered group differences in factors other than outcome such as age and nonsurgical treatment. Additionally, almost all published articles agree that the essential issue of the internal validity (reproducibility) of the artificial neural network (ANN), support vector machine (SVM), Gaussian process regression (GPR) and multiple linear regression (MLR) models has not been adequately addressed. This study proposed to validate the use of these models for predicting quality of life (QOL) after LC and to compare the predictive capability of ANNs with that of SVM, GPR and MLR.

Methodology/Principal Findings

A total of 400 LC patients completed the SF-36 and the Gastrointestinal Quality of Life Index at baseline and at 2 years postoperatively. The criteria for evaluating the accuracy of the system models were mean square error (MSE) and mean absolute percentage error (MAPE). A global sensitivity analysis was also performed to assess the relative significance of input parameters in the system model and to rank the variables in order of importance. Compared to SVM, GPR and MLR models, the ANN model generally had smaller MSE and MAPE values in the training data set and test data set. Most ANN models had MAPE values ranging from 4.20% to 8.60%, and most had high prediction accuracy. The global sensitivity analysis also showed that preoperative functional status was the best parameter for predicting QOL after LC.

Conclusions/Significance

Compared with SVM, GPR and MLR models, the ANN model in this study was more accurate in predicting patient-reported QOL and had higher overall performance indices. Further studies of this model may consider the effect of a more detailed database that includes complications and clinical examination findings as well as more detailed outcome data.

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Most cited references 23

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Finding Statistically Significant Communities in Networks

Andrea Lancichinetti, Filippo Radicchi, José Ramasco … (2011)

Community structure is one of the main structural features of networks, revealing both their internal organization and the similarity of their elementary units. Despite the large variety of methods proposed to detect communities in graphs, there is a big need for multi-purpose techniques, able to handle different types of datasets and the subtleties of community structure. In this paper we present OSLOM (Order Statistics Local Optimization Method), the first method capable to detect clusters in networks accounting for edge directions, edge weights, overlapping communities, hierarchies and community dynamics. It is based on the local optimization of a fitness function expressing the statistical significance of clusters with respect to random fluctuations, which is estimated with tools of Extreme and Order Statistics. OSLOM can be used alone or as a refinement procedure of partitions/covers delivered by other techniques. We have also implemented sequential algorithms combining OSLOM with other fast techniques, so that the community structure of very large networks can be uncovered. Our method has a comparable performance as the best existing algorithms on artificial benchmark graphs. Several applications on real networks are shown as well. OSLOM is implemented in a freely available software (http://www.oslom.org), and we believe it will be a valuable tool in the analysis of networks.

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Current Guidelines Have Limited Applicability to Patients with Comorbid Conditions: A Systematic Analysis of Evidence-Based Guidelines

Marjolein Lugtenberg, Jako Burgers, Carolyn Clancy … (2011)

Background Guidelines traditionally focus on the diagnosis and treatment of single diseases. As almost half of the patients with a chronic disease have more than one disease, the applicability of guidelines may be limited. The aim of this study was to assess the extent that guidelines address comorbidity and to assess the supporting evidence of recommendations related to comorbidity. Methodology/Principal Findings We conducted a systematic analysis of evidence-based guidelines focusing on four highly prevalent chronic conditions with a high impact on quality of life: chronic obstructive pulmonary disease, depressive disorder, diabetes mellitus type 2, and osteoarthritis. Data were abstracted from each guideline on the extent that comorbidity was addressed (general comments, specific recommendations), the type of comorbidity discussed (concordant, discordant), and the supporting evidence of the comorbidity-related recommendations (level of evidence, translation of evidence). Of the 20 guidelines, 17 (85%) addressed the issue of comorbidity and 14 (70%) provided specific recommendations on comorbidity. In general, the guidelines included few recommendations on patients with comorbidity (mean 3 recommendations per guideline, range 0 to 26). Of the 59 comorbidity-related recommendations provided, 46 (78%) addressed concordant comorbidities, 8 (14%) discordant comorbidities, and for 5 (8%) the type of comorbidity was not specified. The strength of the supporting evidence was moderate for 25% (15/59) and low for 37% (22/59) of the recommendations. In addition, for 73% (43/59) of the recommendations the evidence was not adequately translated into the guidelines. Conclusions/Significance Our study showed that the applicability of current evidence-based guidelines to patients with comorbid conditions is limited. Most guidelines do not provide explicit guidance on treatment of patients with comorbidity, particularly for discordant combinations. Guidelines should be more explicit about the applicability of their recommendations to patients with comorbidity. Future clinical trials should also include patients with the most prevalent combinations of chronic conditions.

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Prediction of outcome in acute lower-gastrointestinal haemorrhage based on an artificial neural network: internal and external validation of a predictive model.

Ananya Das, Tamir Ben-Menachem, Gregory Cooper … (2003)

Models based on artificial neural networks (ANN) are useful in predicting outcome of various disorders. There is currently no useful predictive model for risk assessment in acute lower-gastrointestinal haemorrhage. We investigated whether ANN models using information available during triage could predict clinical outcome in patients with this disorder. ANN and multiple-logistic-regression (MLR) models were constructed from non-endoscopic data of patients admitted with acute lower-gastrointestinal haemorrhage. The performance of ANN in classifying patients into high-risk and low-risk groups was compared with that of another validated scoring system (BLEED), with the outcome variables recurrent bleeding, death, and therapeutic interventions for control of haemorrhage. The ANN models were trained with data from patients admitted to the primary institution during the first 12 months (n=120) and then internally validated with data from patients admitted to the same institution during the next 6 months (n=70). The ANN models were then externally validated and direct comparison made with MLR in patients admitted to an independent institution in another US state (n=142). Clinical features were similar for training and validation groups. The predictive accuracy of ANN was significantly better than that of BLEED (predictive accuracy in internal validation group for death 87% vs 21%; for recurrent bleeding 89% vs 41%; and for intervention 96% vs 46%) and similar to MLR. During external validation, ANN performed well in predicting death (97%), recurrent bleeding (93%), and need for intervention (94%), and it was superior to MLR (70%, 73%, and 70%, respectively). ANN can accurately predict the outcome for patients presenting with acute lower-gastrointestinal haemorrhage and may be generally useful for the risk stratification of these patients.

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Author and article information

Contributors

Frank Emmert-Streib: Role: Editor

Journal

Journal ID (nlm-ta): PLoS One

Journal ID (iso-abbrev): PLoS ONE

Journal ID (publisher-id): plos

Journal ID (pmc): plosone

Title: PLoS ONE

Publisher: Public Library of Science (San Francisco, USA )

ISSN (Electronic): 1932-6203

Publication date Collection: 2012

Publication date (Electronic): 28 December 2012

Volume: 7

Issue: 12

Electronic Location Identifier: e51285

Affiliations

[1 ]Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung, Taiwan

[2 ]Department of Surgery, Chi Mei Medical Center, Liouying, Taiwan

[3 ]Department of Computer Science, National Pingtung University of Education, Pingtung, Taiwan

[4 ]Emergency Department, Kaohsiung Municipal United Hospital, Kaohsiung, Taiwan

[5 ]Department of Health Business Administration, Meiho University, Pigntung, Taiwan

[6 ]Division of Hepatobiliary Surgery, Department of Surgery, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan

[7 ]Department of General Surgery, Chi Mei Medical Center, Tainan, Taiwan

[8 ]Taipei Medical University, Taipei, Taiwan

[9 ]Chia Nan University of Pharmacy and Science, Tainan, Taiwan

Queen's University Belfast, United Kingdom

Author notes

* E-mail: chiuchongchi@ 123456yahoo.com.tw

Competing Interests: The authors have declared that no competing interests exist.

Conceived and designed the experiments: HYS CCC. Performed the experiments: HYS HHL JTT WHH CFC KTL CCC. Analyzed the data: HYS. Contributed reagents/materials/analysis tools: HYS HHL JTT WHH CFC KTL CCC. Wrote the paper: HYS HHL JTT WHH CFC KTL CCC. Administrative, technical, and material support: HYS HHL JTT WHH CFC KTL CCC. Study supervision: HHL KTL CCC.

Article

Publisher ID: PONE-D-12-15202

DOI: 10.1371/journal.pone.0051285

PMC ID: 3532431

PubMed ID: 23284677

SO-VID: fb38fb23-84ea-442e-8a92-d23e6d8a3c72

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

History

Date received : 28 May 2012

Date accepted : 31 October 2012

Page count

Pages: 8

Funding

This work was in part supported by the National Science Council, Taiwan, Republic of China, under grant numbers NSC99-2314-B-037-069-MY3. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. No additional external funding received for this study.

Comparisons of Prediction Models of Quality of Life after Laparoscopic Cholecystectomy: A Longitudinal Prospective Study

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Abstract

Background

Methodology/Principal Findings

Conclusions/Significance

Related collections

PLOS Climate

Most cited references 23

Finding Statistically Significant Communities in Networks

Current Guidelines Have Limited Applicability to Patients with Comorbid Conditions: A Systematic Analysis of Evidence-Based Guidelines

Prediction of outcome in acute lower-gastrointestinal haemorrhage based on an artificial neural network: internal and external validation of a predictive model.

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