Health technology assessment through Six Sigma Methodology to assess cemented and uncemented protheses in total hip arthroplasty

Efficacy of Hip Strengthening on Pain Intensity, Disability, and Strength in Musculoskeletal Conditions of the Trunk and Lower Limbs: A Systematic Review with Meta-Analysis and Grade Recommendations

To investigate the efficacy of hip strengthening on pain, disability, and hip abductor strength in musculoskeletal conditions of the trunk and lower limbs, we searched eight databases for randomized controlled trials up to 8 March 2022 with no date or language restrictions. Random-effect models estimated mean differences (MDs) with 95% confidence intervals (CIs), and the quality of evidence was assessed using the GRADE approach. Very low quality evidence suggested short-term effects (≤3 months) of hip strengthening on pain intensity (MD of 4.1, 95% CI: 2.1 to 6.2; two trials, n = 48 participants) and on hip strength (MD = 3.9 N, 95% CI: 2.8 to 5.1; two trials, n = 48 participants) in patellofemoral pain when compared with no intervention. Uncertain evidence suggested that hip strengthening enhances the short-term effect of the other active interventions on pain intensity and disability in low back pain (MD = -0.6 points, 95% CI: 0.1 to 1.2; five trials, n = 349 participants; MD = 6.2 points, 95% CI: 2.6 to 9.8; six trials, n = 389 participants, respectively). Scarce evidence does not provide reliable evidence of the efficacy of hip strengthening in musculoskeletal conditions of the trunk and lower limbs.

Length of Stay, Hospital Costs and Mortality Associated With Comorbidity According to the Charlson Comorbidity Index in Immobile Patients After Ischemic Stroke in China: A National Study

BACKGROUND

In this study, we examined the length of stay (LoS)-predictive comorbidities, hospital costs-predictive comorbidities, and mortality-predictive comorbidities in immobile ischemic stroke (IS) patients; second, we used the Charlson Comorbidity Index (CCI) to assess the association between comorbidity and the LoS and hospitalization costs of stroke; third, we assessed the magnitude of excess IS mortality related to comorbidities.

METHODS

Between November 2015 and July 2017, 5114 patients hospitalized for IS in 25 general hospitals from six provinces in eastern, western, and central China were evaluated. LoS was the period from the date of admission to the date of discharge or date of death. Costs were collected from the hospital information system (HIS) after the enrolled patients were discharged or died in hospital. The HIS belongs to the hospital's financial system, which records all the expenses of the patient during the hospital stay. Cause of death was recorded in the HIS for 90 days after admission regardless of whether death occurred before or after discharge. Using the CCI, a comorbidity index was categorized as zero, one, two, and three or more CCI diseases. A generalized linear model with a gamma distribution and a log link was used to assess the association of LoS and hospital costs with the comorbidity index. Kaplan-Meier survival curves was used to examine overall survival rates.

RESULTS

We found that 55.2% of IS patients had a comorbidity. Prevalence of peripheral vascular disease (21.7%) and diabetes without end-organ damage (18.8%) were the major comorbidities. A high CCI=3+ score was an effective predictor of a high risk of longer LoS and death compared with a low CCI score; and CCI=2 score and CCI=3+ score were efficient predictors of a high risk of elevated hospital costs. Specifically, the most notable LoS-specific comorbidities, and cost-specific comorbidities was dementia, while the most notable mortality-specific comorbidities was moderate or severe renal disease.

CONCLUSION

CCI has significant predictive value for clinical outcomes in IS. Due to population aging, the CCI should be used to identify, monitor and manage chronic comorbidities among immobile IS populations.

An Experimental and Virtual Approach to Hip Revision Prostheses

(1) Introduction: The changes in the joint morphology inevitably lead to prosthesis, but the hip pathology is complex. The hip arthroplasty is a therapeutic solution and can be caused, most frequently, by primary and secondary coxarthrosis due to or followed by traumatic conditions. The main aim of this study was to find the method of revision hip prosthesis that preserves as much bone material as possible and has sufficiently good mechanical strength. (2) Materials and Methods: In this study, in a first step, the two revision prostheses were performed on bone components taken from an animal (cow), and then, they were tested on a mechanical testing machine until the prostheses physically failed, and the force causing their failure was determined. (3) Results: These prostheses were then modelled in a virtual environment and tested using the finite element method (FEM) in order to determine their behaviour under loading from normal human gait. Displacement, strain, and stress maps were obtained. (4) Discussion: Discussions on hip revision prostheses, method, and theory analysis are presented at the end of the paper. (5) Conclusions: Important conclusions are drawn based on comparative analyses. The main conclusion shows that the both orthopaedic prostheses provide a very good resistance.

The role of bone mineral density and cartilage volume to predict knee cartilage degeneration

Knee Osteoarthritis (OA) is a highly prevalent condition affecting knee joint that causes loss of physical function and pain. Clinical treatments are mainly focused on pain relief and limitation of disabilities; therefore, it is crucial to find new paradigms assessing cartilage conditions for detecting and monitoring the progression of OA. The goal of this paper is to highlight the predictive power of several features, such as cartilage density, volume and surface. These features were extracted from the 3D reconstruction of knee joint of forty-seven different patients, subdivided into two categories: degenerative and non-degenerative. The most influent parameters for the degeneration of the knee cartilage were determined using two machine learning classification algorithms (logistic regression and support vector machine); later, box plots, which depicted differences between the classes by gender, were presented to analyze several of the key features’ trend. This work is part of a strategy that aims to find a new solution to assess cartilage condition based on new-investigated features.

Is It Possible to Predict the Length of Stay of Patients Undergoing Hip-Replacement Surgery?

The proximal fracture of the femur and hip is the most common reason for hospitalization in orthopedic departments. In Italy, 115,989 hip-replacement surgeries were performed in 2019, showing the economic relevance of studying this type of procedure. This study analyzed the data relating to patients who underwent hip-replacement surgery in the years 2010-2020 at the "San Giovanni di Dio e Ruggi d'Aragona" University Hospital of Salerno. The multiple linear regression (MLR) model and regression and classification algorithms were implemented in order to predict the total length of stay (LOS). Lastly, using a statistical analysis, the impact of COVID-19 was evaluated. The results obtained from the regression analysis showed that the best model was MLR, with an R² value of 0.616, compared with XGBoost, Gradient-Boosted Tree, and Random Forest, with R² values of 0.552, 0.543, and 0.448, respectively. The t-test showed that the variables that most influenced the LOS, with the exception of pre-operative LOS, were gender, age, anemia, fracture/dislocation, and urinary disorders. Among the classification algorithms, the best result was obtained with Random Forest, with a sensitivity of the longest LOS of over 89%. In terms of the overall accuracy, Random Forest and Gradient-Boosted Tree achieved a value of 71.76% and an error of 28.24%, followed by Decision Tree, with an accuracy of 71.13% and an error of 28.87%, and, finally, Support Vector Machine, with an accuracy of 65.06% and an error of 34.94%. A significant difference in cardiovascular disease, fracture/dislocation, and post-operative LOS variables was shown by the chi-squared test and Mann-Whitney test in the comparison between 2019 (before COVID-19) and 2020 (in full pandemic emergency conditions).

Comparing Two Approaches for Thyroidectomy: A Health Technology Assessment through DMAIC Cycle

Total thyroidectomy is very common in endocrine surgery and the haemostasis can be obtained in different ways across surgery; recently, some devices have been developed to support this surgical phase. In this paper, a health technology assessment is conducted through the define, measure, analyse, improve, and control cycle of the Six Sigma methodology to compare traditional total thyroidectomy with the surgical operation performed through a new device in an overall population of 104 patients. Length of hospital stay, drain output, and time for surgery were considered the critical to qualities in order to compare the surgical approaches which can be considered equal regarding the organizational, ethical, and security impact. Statistical tests (Kolmogorov–Smirnov, t test, ANOVA, Mann–Whitney, and Kruskal–Wallis tests) and visual management diagrams were employed to compare the approaches, but no statistically significant difference was found between them. Considering these results, this study shows that the introduction of the device to perform total thyroidectomy does not guarantee appreciable clinical advantages. A cost analysis to quantify the economic impact of the device into the practice could be a future development. Healthy policy leaders and clinicians who are requested to make decisions regarding the supply of biomedical technologies could benefit from this research.

Reducing the Healthcare-Associated Infections in a Rehabilitation Hospital under the Guidance of Lean Six Sigma and DMAIC

The reduction of healthcare-associated infections (HAIs) is one of the most important issues in the healthcare context for every type of hospital. In three operational units of the Scientific Clinical Institutes Maugeri SpA SB, a rehabilitation hospital in Cassano delle Murge (Italy), some corrective measures were introduced in 2017 to reduce the occurrence of HAIs. Lean Six Sigma was used together with the Define, Measure, Analyze, Improve, Control (DMAIC) roadmap to analyze both the impact of such measures on HAIs and the length of hospital stay (LOS) in the Rehabilitative Cardiology, Rehabilitative Neurology, Functional Recovery and Rehabilitation units in the Medical Center for Intensive Rehabilitation. The data of 2415 patients were analyzed, considering the phases both before and after the introduction of the measures. The hospital experienced a LOS reduction in both patients with and without HAIs; in particular, Cardiology had the greatest reduction for patients with infections (-7 days). The overall decrease in HAIs in the hospital was 3.44%, going from 169 to 121 cases of infections. The noteworthy decrease in LOS implies an increase in admissions and in the turnover indicator of the hospital, which has a positive impact on the hospital management as well as on costs.

A Health Technology Assessment in Maxillofacial Cancer Surgery by Using the Six Sigma Methodology

Squamous cell carcinoma represents the most common cancer affecting the oral cavity. At the University of Naples "Federico II", two different antibiotic protocols were used in patients undergoing oral mucosa cancer surgery from 2006 to 2018. From 2011, there was a shift; the combination of Cefazolin plus Clindamycin as a postoperative prophylactic protocol was chosen. In this paper, a health technology assessment (HTA) is performed by using the Six Sigma and DMAIC (Define, Measure, Analyse, Improve, Control) cycle in order to compare the performance of the antibiotic protocols according to the length of hospital stay (LOS). The data (13 variables) of two groups were collected and analysed; overall, 136 patients were involved. The American Society of Anaesthesiologist score, use of lymphadenectomy or tracheotomy and the presence of infections influenced LOS significantly (p-value < 0.05) in both groups. Then, the groups were compared: the overall difference between LOS of the groups was not statistically significant, but some insights were provided by comparing the LOS of the groups according to each variable. In conclusion, in light of the insights provided by this study regarding the comparison of two antibiotic protocols, the utilization of DMAIC cycle and Six Sigma tools to perform HTA studies could be considered in future research.

Using machine learning to reduce unnecessary rehospitalization of cardiovascular patients in Saudi Arabia

OBJECTIVES

Patient readmission is a costly and preventable burden on healthcare systems. The main objective of this study was to develop a machine-learning classification model to identify cardiovascular patients with a high risk of readmission.

METHODS

Inpatient data were collected from 48 Ministry of Health hospitals (MOH) in Saudi Arabia from 2016 to 2019. Cardiovascular disease (CVD)-related diagnoses were defined as congestive heart failure (HF), ischemic heart disease (IHD), cardiac arrhythmias (CA), and valvular diseases (VD). Hospitalization days, daily hospitalization price, and the price of each basic and medical service provided were used to calculate the healthcare utilization cost. We employed a Python machine-learning model to identify all-cause 30-day CVD-related readmissions using the International Classification of Diseases, Revision 10 classification system (ICD10) as the gold standard. Demographics, comorbidities, and healthcare utilization were used as the independent variables.

RESULTS

From 2016 to 2019, we identified 403,032 hospitalized patients from 48 hospitals in 13 administrative regions of Saudi Arabia. Out of these patients, 17,461 had a history of hospital admission for cardiovascular reasons. The total direct cost of overall hospitalizations was 1.6 B international dollars (I$) with an average of I$ 3,156 per hospitalization, whereas CVD-related readmission costs were estimated to be I$ 14.9 M, with an average of I$ 7,600 per readmission. Finally, an empirical approach was followed to test several algorithms to identify patients at high risk of readmission. The comparison indicated that the decision-tree algorithm correctly classified 2,336 instances (926 readmitted and 1,410 not readmitted) and showed a higher F1 score than other models (64%), with a recall of 71% and precision of 57%.

CONCLUSION

This study identified IHD as the most prevalent CVD, and hypertension and diabetes were found to be the most common comorbidities among hospitalized CVD patients. Compared to general encounters, readmission encounters were nearly two times higher on average among the study population. Furthermore, we concluded that a machine-learning model can be used to identify CVD patients at a high risk of readmission. Further research is required to develop more accurate models based on clinical notes and laboratory results.