Dataset Information

Factors predicting hospital length-of-stay after radical prostatectomy: a population-based study.

ABSTRACT:

Background

Radical prostatectomy (RP) is a leading treatment option for localised prostate cancer. Although hospital in-patient stays accounts for much of the costs of treatment, little is known about population-level trends in length-of-stay (LOS). We investigated factors predicting hospital LOS and readmissions in men who had RP following prostate cancer.

Methods

Incident prostate cancers (ICD-O3: C61), diagnosed January 2002-December 2008 in men < 70 years, were identified from the Irish Cancer Registry, and linked to public hospital episodes. For those who had RP (ICD-9 CM procedure codes 60.3, 60.4, 60.5, 60.62) the associated hospital episode was identified. LOS was calculated as the number of days from date of admission to date of discharge. Patient-, tumour-, and health service-related factors predicting longer LOS (upper quartile, >9 days) were investigated using logistic regression. Patterns in day-case and in-patient readmissions within 28 days of discharge following RP were explored.

Results

Over the study period 9096 prostate cancers were diagnosed in men under 70, 26.5% of whom had RP by end of follow-up 31/12/2009. Two of eight public hospitals and eight of forty surgeons carried out 50% of all public-service RPs. Median LOS was 8 days (10th-90th percentile = 6-13 days) and fell significantly over time (2002, 9 days; 2008, 7 days; p < 0.001). In adjusted analyses men who were not married (OR = 1.71, 95% CI 1.25-2.34), had co-morbidities (OR = 1.64, 95% CI 1.25-2.16) or stage III-IV cancer (OR = 2.19, 95% CI 1.44-3.34) were significantly more likely to have prolonged LOS. Those treated in higher volume hospitals (annual median >49 RPs) or by higher volume surgeons (annual median >17 RPs) were significantly less likely to have prolonged LOS (OR = 0.34, 95% CI 0.26-0.45; OR = 0.55, 95% CI 0.42-0.71 respectively).

Conclusion

Median LOS after RP decreased between 2002 and 2008 in Ireland but it remains higher than in both England and the US. Although volumes of RPs conducted in Ireland are low, there is considerable variation between hospitals and surgeons. Hospital and surgeon volume were strong predictors of shorter LOS, after adjusting for other variables. These factors point to a need for a comprehensive review of prostate cancer service provision.

SUBMITTER: Kelly M

PROVIDER: S-EPMC3750445 | biostudies-literature | 2013 Jul

REPOSITORIES: biostudies-literature

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Publications

Factors predicting hospital length-of-stay after radical prostatectomy: a population-based study.

Kelly Maria M Sharp Linda L Dwane Fiona F Kelleher Tracy T Drummond Frances J FJ Comber Harry H

BMC health services research 20130702

<h4>Background</h4>Radical prostatectomy (RP) is a leading treatment option for localised prostate cancer. Although hospital in-patient stays accounts for much of the costs of treatment, little is known about population-level trends in length-of-stay (LOS). We investigated factors predicting hospital LOS and readmissions in men who had RP following prostate cancer.<h4>Methods</h4>Incident prostate cancers (ICD-O3: C61), diagnosed January 2002-December 2008 in men < 70 years, were identified from ...[more]

PMID: 23816338

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Project description:Background The incidence of stroke is a challenge in China, as stroke imposes a heavy burden on families, national health services, social services, and the economy. The length of hospital stay (LOS) is an essential indicator of utilization of medical services and is usually used to assess the efficiency of hospital management and patient quality of care. This study established a prediction model based on a machine learning algorithm to predict ischemic stroke patients’ LOS. Methods A total of 18,195 ischemic stroke patients’ electronic medical records and 28 attributes were extracted from electronic medical records in a large comprehensive hospital in China. The prediction of LOS was regarded as a multi classification problem, and LOS was divided into three categories: 1–7 days, 8–14 days and more than 14 days. After preprocessing the data and feature selection, the XGBoost algorithm was used to build a machine learning model. Ten fold cross-validation was used for model validation. The accuracy (ACC), recall rate (RE) and F1 measure were used to evaluate the performance of the prediction model of LOS of ischemic stroke patients. Finally, the XGBoost algorithm was used to identify and remove irrelevant features by ranking all attributes based on feature importance. Results Compared with the naive Bayesian algorithm, logistic region algorithm, decision tree classifier algorithm and ADaBoost classifier algorithm, the XGBoot algorithm has higher ACC, RE and F1 measure. The average ACC, RE and F1 measure were 0.89, 0.89 and 0.89 under the 10-fold cross-validation. According to the analysis of the importance of features, the LOS of ischemic stroke patients was affected by demographic characteristics, past medical history, admission examination features, and operation characteristics. Finally, the features in terms of hemiplegia aphasia, MRS, NIHSS, TIA, Operation or not, coma index etc. were found to be the top features in importance in predicting the LOS of ischemic stroke patients. Conclusions The XGBoost algorithm was an appropriate machine learning method for predicting the LOS of patients with ischemic stroke. Based on the prediction model, an intelligent medical management prediction system could be developed to predict the LOS based on ischemic stroke patients’ electronic medical records. Supplementary Information The online version contains supplementary material available at 10.1186/s12911-023-02140-4

Project description:AimThis study aimed to explore factors may affect the length of hospital stay after laparoscopic appendectomy.MethodsThe data of 636 patients undergoing laparoscopic appendectomy between July 2016 and July 2019 in Beijing Tsinghua Changgung Hospital were retrospectively analyzed. The patients were divided into group A (hospital stay ≤3 days, 348 patients) and group B (hospital stay >3 days, 288 patients) according to their hospital stay.Sex, age, disease onset time(time from onset to admission), nausea, vomiting, diarrhea, peritonitis, comorbidities, and history of appendicitis; preoperative body temperature (T), white blood cell (WBC) count, percentage of neutrophilic granulocytes, and preoperative C-reactive protein (CRP) level; time from diagnosis to surgery. appendix diameter, appendicolith, and ascites in ultrasound or CT; surgical time(the surgery start time was the time of skin incision, and the end time was the time the anesthesia intubation was removed), intraoperative blood loss (the volume of blood infiltrating into a gauze was calculated by weighing the gauze infiltrated with water and calculating the volume of water), intraoperative adhesions or effusions, and stump closure methods, convert to open appendectomy, appendix pathology(perforated or gangrenous appendicitis were defined as complicated appendicitis and simple or suppurative appendicitis were defined as uncomplicated appendicitis) and antibiotic treatment schemes were analyzed.ResultsSignificant differences were detected between group A and group B in age (37.10 ± 13.52y vs 42.94 ± 15.57y, P<0.01), disease onset time (21.36 ± 16.56 h vs 32.52 ± 27.99 h, P <0.01), time from diagnosis to surgery (8.63 ± 7.29 h vs 10.70 ± 8.47 h, P<0.01); surgical time(64.09 ± 17.24 min vs 86.19 ± 39.96 min, P < 0.01); peritonitis(52.9% vs 74%, P < 0.01), comorbidities (12.4% vs 20.5%, P < 0.01), appendicolith (27.6% vs 41.7%, P < 0.01), ascites before the surgery(13.8% vs 22.9%, P < 0.01), intraoperative adhesions or effusions(56% vs 80.2%, P < 0.01); preoperative temperature (37.11 ± 0.64°C vs 37.54 ± 0.90°C, P < 0.01); preoperative WBC count (13.06 ± 3.39 × 109/L vs 14.21 ± 4.54 × 109/L, P = 0.04);preoperative CRP level(18.99 ± 31.72 mg/L vs 32.46 ± 46.68 mg/L, P < 0.01); appendix diameter(10.22 ± 2.59 mm vs 11.26 ± 3.23 mm, P < 0.01); intraoperative blood loss (9.36 ± 7.29 mL vs 13.74 ± 13.49 mL, P < 0.01); using Hem-o-lok for stump closure(30.7% vs 38.5%, P = 0.04); complicated appendicitis (9.5% vs 45.8%, P < 0.01); and using ertapenem for antibiotic treatment after the surgery(4.3% vs 21.5%, P < 0.01). Multivariate analysis demonstrated that age (OR = 1.021; 95%CI = 1.007-1.036), peritonitis (OR = 1.603; 95% CI = 1.062-2.419), preoperative WBC count (OR = 1.084; 95% CI = 1.025-1.046), preoperative CRP level (OR = 1.010; 95% CI = 1.005-1.015), time from diagnosis to surgery (OR = 1.043; 95% CI = 1.015-1.072), appendicolith (OR = 1.852; 95% CI = 1.222-2.807), complicated appendicitis (OR = 3.536; 95% CI = 2.132-5.863), surgical time (OR = 1.025; 95% CI = 1.016-1.034), use of Hem-o-lok for stump closure (OR = 1.894; 95% CI = 1.257-2.852), and use of ertapenem for antibiotic treatment (OR = 3.076; 95% CI = 1.483-6.378) were the risk factors for a prolonged hospital stay.ConclusionsThe patient with appendicitis was older and had peritonitis, higher preoperative WBC count or CRP level, longer time from diagnosis to surgery, appendicolith, and complicated appendicitis, predicting a prolonged hospital stay. Shorter surgical time and the use of silk ligation for stump closure and cephalosporins + metronidazole for antibiotic treatment might be better choices to obtain a shorter hospital stay.

Project description:STUDY DESIGN: Retrospective cohort study. BACKGROUND: Several studies focus on the long-term results of anterior cervical discectomy and fusion (ACDF) surgeries, but little information exists regarding how various patient-related, procedure-related, and payer-related variables may affect postoperative hospital length of stay (LOS). OBJECTIVE: To determine what factors, if any, contribute to increased hospital LOS in patients who have had an ACDF. METHODS: Retrospective cohort study of 108 consecutive patients who underwent elective ACDF at a Midwest academic medical center. Extensive preoperative, intraoperative, and postoperative data were abstracted and analyzed to identify prognostic factors for an increased LOS. Multivariate analysis was performed to analyze the effects of patient and hospital characteristics on hospital LOS. RESULTS: 103 patients met inclusion and exclusion criteria. The mean LOS for patients undergoing ACDF was 1.98 (±1.6) days. Only 29% of patients had one level fused. The mean blood loss during surgery was 87.4 ± 99.6 mL. One subject lost 700 mL of blood. Complications, though rare, included uncontrolled postoperative pain (13%), cardiac (6%), pulmonary (4%), and urinary (3%). Covariates included in the final model were age, sex, cardiac complication, urinary complication, and pulmonary complication. Factors that contributed to increased LOS and their associated adjusted mean days were: ≥50 years of age (2.5 ± 1.2 days), female gender (2.3 ± 1.2 days), and three particular types of complications. The complications that had the largest effect on increased LOS from least to most severe were cardiac (3.5 ± 1.3 days), urinary (4.7 ± 1.3 days), and pulmonary (5.3 ± 1.3 days). CONCLUSIONS: The information presented in this study may be useful for patients, clinicians, and insurance companies, including precertification and case-management services. Our results can be instrumental in designing future prospective studies using more detailed analyses with more patients, more surgeons, and multiple institutions. [Table: see text].

Project description:PurposeOne of the most frequent side effects of radical prostatectomy (RP) is urinary incontinence. The primary cause of urine incontinence is usually thought to be impaired urethral sphincter function; nevertheless, the pathophysiology and recovery process of urine incontinence remains unclear. This study aimed to identify potential risk variables, build a risk prediction tool that considers preoperative urodynamic findings, and direct doctors to take necessary action to reduce the likelihood of developing early urinary incontinence.MethodsWe retrospectively screened patients who underwent radical prostatectomy between January 1, 2020 and December 31, 2023 at the First People 's Hospital of Nantong, China. According to nomogram results, patients who developed incontinence within three months were classified as having early incontinence. The training group's general characteristics were first screened using univariate logistic analysis, and the LASSO method was applied for the best prediction. Multivariate logistic regression analysis was carried out to determine independent risk factors for early postoperative urine incontinence in the training group and to create nomograms that predict the likelihood of developing early urinary incontinence. The model was internally validated by computing the performance of the validation cohort. The nomogram discrimination, correction, and clinical usefulness were assessed using the c-index, receiver operating characteristic curve, correction plot, and clinical decision curve.ResultsThe study involved 142 patients in all. Multivariate logistic regression analysis following RP found seven independent risk variables for early urinary incontinence. A nomogram was constructed based on these independent risk factors. The training and validation groups' c-indices showed that the model had high accuracy and stability. The calibration curve demonstrates that the corrective effect of the training and verification groups is perfect, and the area under the receiver operating characteristic curve indicates great identification capacity. Using a nomogram, the clinical net benefit was maximised within a probability threshold of 0.01-1, according to decision curve analysis (DCA).ConclusionThe nomogram model created in this study can offer a clear, personalised analysis of the risk of early urine incontinence following RP. It is highly discriminatory and accurate, and it can help create efficient preventative measures and identify high-risk populations.

Project description:BackgroundThe 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.ObjectiveThis study aimed to develop an optimal data-driven model for predicting the pLOS risk of PD-treated patients using basic admission data.MethodsPatient 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.ResultsThe 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.ConclusionsThis 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.

Project description:Accurate estimation of recurrence risk is needed for optimal treatment of clinically localized prostate cancer (PCa) patients. We combined classical clinicopathological predictors of biochemical recurrence (BCR) and molecular markers into a new risk score for predicting BCR after radical prostatectomy (RP). A retrospective study which included 122 PCa patients who attended our department between 2000 and 2007 was conducted. Total RNA was isolated from formalin-fixed paraffin embedded PCa tissue. Gene expression patterns were analyzed in 21 selected samples from 7 localized, 6 locally advanced, and 8 metastatic PCa patients using Illumina microarrays. Expression levels of 41 genes were validated by quantitative PCR in an independent retrospective series of 101 patients with ≥ 10 years follow-up who underwent RP for clinically localized PCa. Univariate and multivariate logistic regression analysis were used to identify individual predictors of BCR. A risk score (RS) for predicting BCR including clinicopathological and gene expression data was developed and Kaplan-Meier curves were generated. Interaction networks between the genes of the model were built by GeneMANIA Cytoscape plugin. In a median follow-up of 120 months (range 3-190), 37 patients developed BCR (36.6%). Expression levels of 7,930 transcripts differed between clinically localized and locally advanced-metastatic PCa groups (FDR<0.1). A set of 41 genes were validated by quantitative PCR. Regression analysis showed that expression of ASF1B and MCL1 as well as Gleason score, extracapsular extension, seminal vesicle invasion and positive margins were independent prognostic factors of BCR. A RS generated using these gene expression and clinicopathological variables was able to discriminate between two groups of patients with a significantly different probability of BCR (HR 6.24; CI 3.23-12.4, p<0.01), improving the individual discriminative performance of each of these variables on their own. Direct interactions between the two genes of the model were not found. In conclusion, identification of new gene expression patterns associated with a high probability of BCR in clinically localized PCa patients treated with RP, and their combination with clinicopathological variables in a robust, easy-to-use and reliable classifier may contribute to improve PCa risk stratification and, consequently, to tailor treatment and surveillance strategies in these patients.

Dataset Information

Factors predicting hospital length-of-stay after radical prostatectomy: a population-based study.

Background

Methods

Results

Conclusion

Publications

Factors predicting hospital length-of-stay after radical prostatectomy: a population-based study.

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