脑卒中患者住院费用影响因素的通径分析

目的研究脑卒中患者住院费用的影响因素,探索控制医疗费用的有效方法,为控制医疗费用、提高医疗质量提供科学依据。方法对我院2010年1470例脑卒中患者的住院费用及有关数据进行通径分析。结果手术和住院日是影响脑卒中住院费用的最主要因素,其他因素的影响由大到小依次为:医院感染、入院情况、疾病类型、其他诊断个数、付款方式、三日确诊。结论控制脑卒中患者住院费用应采取合理的措施,加强可控因素的控制。     

老年人脑卒中复发的危险因素分析

目的 探讨老年人脑卒中复发相关的危险因素。方法 对天津市第一医院收治的198例老年脑卒中的临床资料进行回顾性分析,所有患者均符合1995年全国第四届脑血管学术会议制定的各类脑血管病诊断要点,并以CT确诊。首次卒中与第1次复发的病程间隔最短为1个月,最长为10a,对复发组不同类型卒中危险因素进行比较。结果 高血压因素对脑梗死的复发危险性显著低于腔隙性梗死与脑出血;高血脂因素对脑梗死的复发危险性显著高于腔隙性梗死与脑出血,高血糖、冠心病因素对不同类型卒中的复发危险性差异均无显著性。结论 4种危险因素中仅高血压与高血脂是明显增加第2次卒中的危险因素。合理控制血压与血脂是从病因上预防脑卒中较实际的有效措施。     

脑卒中患者住院费用影响因素的通径分析

要:目的 探讨影响脑卒中患者住院费用的主要因素,为合理控制医疗费用的过快增长、减轻患者的经济负担提供参考。方法 回顾性分析2007年1月至2014年12月出院的安徽省某三甲医院8 585例主要诊断为脑卒中患者的病案资料,采用通径分析研究影响其住院费用的直接因素和间接因素。结果 住院日是直接影响脑卒中患者住院费用的首要因素;疾病类型、患者来源、手术情况、年龄以及出院转归在直接影响住院费用的同时,还通过住院日对住院费用存在间接影响,而住院日、性别、入院病情以及出院年份对住院费用仅存在直接作用。结论 控制脑卒中患者住院费用要以缩短平均住院日为突破口,采取综合措施,控制可控因素,有效缓解医疗费用的不合理增长,减轻患者和社会的经济负担。     

影响缺血性脑卒中发病相关因素的研究进展

脑血管相关疾病已经成为威胁人类生命的主要疾病。缺血性脑卒中占脑卒中患者的绝大多数,严重影响人类的生活质量,并对社会医疗及经济造成巨大负担。为有效降低IS发病率,尽早开始脑血管疾病的预防工作,本文从影响缺血性脑卒中发病的代谢因素、行为因素、心理因素及环境因素等方面作一综述。     

脑血管性疾病患者认知障碍发生情况及影响因素研究

目的 探讨脑血管性疾病患者认知障碍发生情况及影响因素。方法 选择2017年1月1日至2020年12月31日期间河南省某医院诊治存活的脑血管性疾病患者作为研究对象在患者出院前1 d进行认知功能情况评估,同时收集患者实验室及相关诊断结果数据,采用描述流行病学方法分析该类患者出现认知障碍的情况。使用单、多因素分析方法对脑血管性疾病患者认知障碍发生的影响因素进行分析。结果 1 537例脑血管性疾病患者中男性927例,女性610例,年龄分布在55～83岁,平均（69.10±12.11）岁。疾病类型以缺血性脑卒中为主,占45.22%。检出认知障碍532例,占34.61%。多因素Logistic回归分析结果显示年龄（OR=2.060,2.413）、文化程度（OR=0.588,0.515,0.474）、饮茶习惯（OR=0.408）、高血压（OR=2.619）、同型半胱氨酸（OR=1.023）、超敏C反应蛋白（OR=1.018）、血管内皮生长因子（OR=0.985）等情况不同的脑血管疾病患者认知障碍发生风险不同。结论 脑血管疾病的患者有较高的认知功能障碍发生风险,部分因素对该种类患者的认知功能障碍发病具...     

中西医综合康复方案对脑卒中后抑郁的疗效观察——评《脑卒中(第三版)》

脑血管疾病最常见的并发症之一就是卒中后抑郁.调查发现,脑卒中急性期(发病＜30 d)及恢复期(发病1个月～6个月)发生卒中后抑郁的比率约为66％;发生卒中后的5年中合并卒中后抑郁的比率超过30％.脑卒中发生后,肢体或/和言语的残疾或障碍,以及神经功能缺损都影响患者重新融入社会;而合并抑郁是另一个影响患者康复的重要因素....     

中风的“识”与“防”

中风,也叫脑卒中。分为两种类型：缺血性脑卒中和出血性脑卒中。 中风是中医学对急性脑血管疾病的统称。它是以猝然昏倒,不省人事,伴发口角歪斜、语言不利而出现半身不遂为主要症状的一类疾病。由于本病发病率高、死亡率高、致残率高、复发率高以及并发症多的特点,是一种危害中老年人的严重的病症,给病人及家人都会带来很大的痛苦。要预防中风,就要了解中风发作的早期或先兆期的症状有哪些,这样才能根据症状来识别,帮助中风患者早诊断、早治疗。     

基于不同模型的脑卒中住院费用影响因素的对比研究

目的：通过基于多重线性回归分析和BP神经网络模型的分析方法,对比两模型性能,探讨脑卒中患者住院费用的影响因素。方法：资料来源于广西某医院2021年出院主要诊断为脑卒中的病例,比较两种模型的预测结果,再利用R、R2、调整R2、标准误差评价其优劣,并对住院费用的影响因素进行分析。结果：多重线性回归分析的调整R2和标准误差分别是0.403、290.90,影响程度从大到小依次为是否有手术、住院天数、入院时情况、卒中类型和医疗付费方式。BP神经网络模型的调整R2和标准误差分别是0.612、224.76;前五位影响因素为是否有手术、住院天数、卒中类型、入院时情况和入院途径。结论：BP神经网络模型的预测性能优于多重线性回归分析,是否有手术操作、住院天数、卒中类型和入院时情况是共同的影响因素,可通过开通脑卒中救治绿色通道,形成多学科联合救治新模式,加强宣传教育和三级预防来达到控费目的。     

结直肠癌患者术后非计划性再入院影响因素分析

目的探讨结直肠癌患者术后非计划性再入院的发生率及其影响因素,并确定预测结直肠癌患者术后非计划性再入院的预测标准,为结直肠癌患者的顺利康复提供临床指导意见。方法选取2016年3月-2019年6月行结直肠癌术的患者92例作为研究对象。回顾性分析结直肠癌患者术后非计划性再入院的影响因素。结果再入院患者组中患有基础疾病的患者占大多数,比例为78.05%;而非再入院患者组中患有基础疾病的患者所占比例为25.13%。再入院患者组中患有术前合并症的比例为87.80%;而非再入院患者组中患有术前合并症的比例为72.45%。82例患者再入院的原因中,肠梗阻及切口疝这两种原因最普遍,有56例,占68.29%。基础疾病对患者再入院的影响差异显著(P=0.034; OR=1.95);术前合并症对患者再入院的影响差异显著(P=0.006; OR=3.09)。结论对术前有合并症及基础疾病的结直肠癌患者,在治疗过程中应更加的注意,出院之后对患者要多加教育及指导,减少患者非计划性再入院的风险,促进患者健康的恢复。     

脑出血护理小常识

近些年,人们的生活水平显著提高,各种疾病的发病率越来越高,许多疾病看似问题不大,患者可以通过药物控制病情,但随着病情加重,并发症对患者的生命产生严重的威胁,比如脑出血疾病就是其中之一.近几年我们听到的脑溢血指的就是脑出血疾病,脑出血是属于脑卒中的一种类型,其病因与高血压、脑血管畸形、患者自身因素等有密切的关系,大部分脑...     

Factors associated with inpatient readmission among managed care enrollees with COPD

Objective. To assess factors associated with inpatient readmission among a US managed care population with chronic obstructive pulmonary disease (COPD). Background. COPD is often accompanied by intermittent acute exacerbations, which may result in hospitalizations. These exacerbations are often associated with an increased frequency of subsequent exacerbations, which may lead to inpatient readmissions. Methods. We assessed US managed care claims data for enrollees ≥ 40 years old with an inpatient admission with a primary diagnosis of COPD (ICD-9-CM codes 491.xx, 492.xx or 496.xx) between 1 January 2010 and 31 December 2013 (discharge date of first observed inpatient admission defined the “index date”). Patients were required to be continuously enrolled for ≥ 12 months before the index date. Two non-mutually exclusive cohorts were analyzed: (1) patients with ≥ 30 days of post-index date continuous enrollment (to evaluate 30-day readmission) and (2) patients with ≥ 90 days of post-index date continuous enrollment (to evaluate 90-day readmission). Logistic regression evaluated the association between patient characteristics and risk of 30- and 90-day COPD-related and all-cause readmission. Results. After applying selection criteria, 140,981 patients had ≥ 30 days of enrollment post-index date, and 123,545 patients had ≥ 90 days of enrollment post-index date. Within 30 days, nearly 20% of patients had an all-cause readmission and 7% had a COPD-related readmission. Within 90 days, 28% had an all-cause readmission and 12% had a COPD-related readmission. Logistic regression indicated that longer length of stay, older age, greater comorbidity burden, specific comorbidities and COPD complexity were associated with significantly greater odds of COPD-related 30- and 90-day readmission. Results for all-cause readmission were generally similar. Conclusions. Many of the factors associated with inpatient readmission documented here can be ascertained at discharge and may be used to inform discharge plans, with the end goal of improving patient outcomes, including reducing the risk of readmission.     

Variation in 30-Day Readmission Rates from Inpatient Rehabilitation Facilities to Acute Care Hospitals

ObjectivesTo quantify the rate of readmission from inpatient rehabilitation facilities (IRFs) to acute care hospitals (ACHs) during the first 30 days of rehabilitation stay. To measure variation in 30-day readmission rate across IRFs, and the extent that patient and facility characteristics contribute to this variation.DesignRetrospective analysis of an administrative database.Setting and ParticipantsAdult IRF discharges from 944 US IRFs captured in the Uniform Data System for Medical Rehabilitation database between October 1, 2015 and December 31, 2017.MethodsMultilevel logistic regression was used to calculate adjusted rates of readmission within 30 days of IRF admission and examine variation in IRF readmission rates, using patient and facility-level variables as predictors.ResultsThere were a total of 104,303 ACH readmissions out of a total of 1,102,785 IRFs discharges. The range of 30-day readmission rates to ACHs was 0.0%‒28.9% (mean = 8.7%, standard deviation = 4.4%). The adjusted readmission rate variation narrowed to 2.8%‒17.5% (mean = 8.7%, standard deviation = 1.8%). Twelve patient-level and 3 facility-level factors were significantly associated with 30-day readmission from IRF to ACH. A total of 82.4% of the variance in 30-day readmission rate was attributable to the model predictors.Conclusions and ImplicationsFifteen patient and facility factors were significantly associated with 30-day readmission from IRF to ACH and explained the majority of readmission variance. Most of these factors are nonmodifiable from the IRF perspective. These findings highlight that adjusting for these factors is important when comparing readmission rates between IRFs.     

2002年－2013年某市常见慢性病患者住院指标分析

目的分析某市冠心病、糖尿病、脑卒中、高血压等4种常见慢性病的住院情况,以此了解慢性病治疗的发展趋势。方法对2002年-2013年某市所有二级及以上医院的4种慢性病患者的住院病案首页资料进行整理,计算各种慢性病患者的住院人数、30天再住院率、住院天数、住院费用等。结果12年间,4种慢性病患者的住院比例均上升（P <0.001）,其中冠心病的住院比例上升最显著,平均每年提高0.18个百分点。4种慢性病的住院天数都呈逐年显著递减（P <0.001）。与之相反的是,患者次均住院费用呈显著逐年上升（P <0.001）。高血压患者30天再住院率平均每年增长0.25个百分点（P <0.05）,其他三种疾病30天再住院率增长较缓慢。结论冠心病、脑卒中、糖尿病、高血压4种慢性病患者的住院比例、住院天数及费用、30天再住院率等呈现出不同的变化趋势,为有关部门制定慢性病管理相关政策提供了参考依据。     

The use of an automated notification system to identify and address early readmissions by a university hospitalist service

Objectives. To examine the association of an automated physician feedback system prompting physician review of early readmissions with a change in overall readmission rates. Methods. The University of Utah Internal Medicine Hospitalist Service created an automated system to alert discharging providers to any patient readmitted within 30 days. For any 7-day readmission, the physician was asked to complete a survey to describe the admission and discharge and to identify contributing factors to the readmission. Using the University HealthSystem Consortium database, readmission rates were compared prior to and following this intervention. Results. Following the intervention, 30-day readmission rates significantly decreased from 13.93% to 11.99% (p = 0.0298). The 7-day readmission rates decreased as well but the findings were not statistically significant. The discharging physician deemed 45% of the readmissions preventable or probably preventable. Conclusion. Readmissions are common, costly and potentially preventable. This tool for physician audit and feedback and identification of defects contributing to readmissions was associated with a statistically significant decrease in 30-day readmissions. Further investigation is needed to verify these results and evaluate the best mechanism of application.     

Do Older Rural and Urban Veterans Experience Different Rates of Unplanned Readmission to VA and Non‐VA Hospitals?

ABSTRACT:  Context: Unplanned readmission within 30 days of discharge is an indicator of hospital quality. Purpose: We wanted to determine whether older rural veterans who were enrolled in the VA had different rates of unplanned readmission to VA or non-VA hospitals than their urban counterparts. Methods: We used the combined VA/Medicare dataset to examine 3,513,912 hospital admissions for older veterans that occurred in VA or non-VA hospitals between 1997 and 2004. We calculated 30-day readmission rates and odds ratios for rural and urban veterans, and we performed a logistic regression analysis to determine whether living in a rural setting or initially using the VA for hospitalization were independent risk factors for unplanned 30-day readmission, after adjusting for age, sex, length of stay of the index admission, and morbidity. Findings: Overall, rural veterans had slightly higher 30-day readmission rates than their urban counterparts (17.96% vs 17.86%; OR 1.006, 95% CI: 1.0004, 1.013). For both rural- and urban-dwelling veterans, readmission after using a VA hospital was more common than after using a non-VA hospital (20.7% vs 16.8% for rural veterans, 21.2% vs 16.1% for urban veterans). After adjusting for other variables, readmission was more likely for rural veterans and following admission to a VA hospital. Conclusions: Our findings suggest that VA should consider using the unplanned readmission rate as a performance metric, using the non-VA experience of veterans as a performance benchmark, and helping rural veterans select higher performing non-VA hospitals.     

Associations between published quality ratings of skilled nursing facilities and outcomes of medicare beneficiaries with heart failure

IntroductionNursing Home Compare quality ratings are designed to allow patients, families, and clinicians to compare facilities based on quality, but associations of the current measures with important clinical outcomes are not known. Our study examined associations between ratings and readmission and mortality among Medicare beneficiaries admitted to a skilled nursing facility with a primary diagnosis of heart failure.MethodsWe conducted a retrospective cohort study of 164,672 Medicare beneficiaries discharged to skilled nursing facilities after hospitalization for heart failure in 2006–2007. The main outcome measures were readmission and mortality within 90 days.ResultsOne-fifth of the 13,619 skilled nursing facilities received a 1-star rating and 11% received a 5-star rating. Nearly half of the patients discharged to a skilled nursing facility were readmitted to a hospital within 90 days after discharge, and 30% died within 90 days. Compared with patients in 5-star skilled nursing facilities, patients in 1-star facilities had higher risks of 90-day readmission (hazard ratio, 1.08) and mortality (1.15). After adjustment for facility size and ownership type, the associations between the quality rating and readmission were not statistically significant, but the associations with mortality were significant.ConclusionPublicly reported Nursing Home Compare quality ratings of Medicare-certified skilled nursing facilities were modestly associated with 90-day readmission and mortality among Medicare beneficiaries discharged to these facilities after hospitalization for heart failure.     

Risk factors associated with the timing of hospital readmission in an underserved low socioeconomic population

Objective: Compare risk factors of hospital readmission between 30-, 60- and 90-day readmission groups in a low socioeconomic population. Methods: Secondary data obtained from the Epic Systems database management system for patients who experienced a 30-, 60- or 90-day hospital readmission between 2006 and 2013. Risk factors analyzed included sex, race/ethnicity, follow-up status, age, BMI, systolic blood pressure, body temperature and pulse rate. Records for 2191 low-income patients (µ age = 44.5 years; 72.5% female; 10.1% African American, 26.2% Hispanic, 63.7% White) from a central Texas acute health and primary care facility. Results: The amount of time that passed between a patent’s initial hospital encounter and a follow-up visit had an effect in predicting both 60-day (OR = 1.055) and 90-day (OR = 1.088) hospital readmission. Patient race/ethnicity had an effect in predicting 90-day readmission. Hispanic patients had a lower likelihood of being readmitted after 90 days than being readmitted after 30 days as compared with White, non-Hispanic patients (OR = 0.688). Conclusions: Our study suggests that risk factors identified at 30 days are similar to those at 60 and 90 days, with the exception of follow-up status and race/ethnicity.     

Derivation and Validation of a Model to Predict 30-Day Readmission in Surgical Patients Discharged to Skilled Nursing Facility

ObjectivesTo identify factors associated with 30-day all-cause readmission rates in surgical patients discharged to skilled nursing facilities (SNFs), and derive and validate a risk score.DesignRetrospective cohort.Setting and participantsPatients admitted to 1 tertiary hospital's surgical services between January 1, 2011, and December 31, 2014 and subsequently discharged to 110 SNFs within a 25-mile radius of the hospital. The first 2 years were used for the derivation set and the last 2 for validation.MethodsData were collected on 30-day all cause readmissions, patient demographics, procedure and surgical service, comorbidities, laboratory tests, and prior health care utilization. Multivariate regression was used to identify risk factors for readmission.ResultsDuring the study period, 2405 surgical patients were discharged to 110 SNFs, and 519 (21.6%) of these patients experienced readmission within 30 days. In a multivariable regression model, hospital length of stay [odds ratio (OR) per day: 1.03, 95% confidence interval (CI) 1.02-1.04], number of hospitalizations in past year (OR 1.24 per hospitalization, 95% CI 1.18-1.31), nonelective surgery (OR 1.33, 95% CI 1.18-1.65), low-risk service (orthopedic/spine service) (OR 0.32, 95% CI 0.25-0.42), and intermediate-risk service (cardiothoracic surgery/urology/gynecology/ear, nose, throat) (OR 0.69, 95% CI 0.53-0.88) were associated with all-cause readmissions. The model had a C index of 0.71 in the validation set. Using the following risk score [0.8 × (hospital length of stay) + 7 × (number of hospitalizations in past year) +10 for nonelective surgery, +36 for high-risk surgery, and +20 for intermediate-risk surgery], a score of >40 identified patients at high risk of 30-day readmission (35.8% vs 12.6%, P < .001).Conclusions/ImplicationsAmong surgical patients discharged to an SNF, a simple risk score with 4 parameters can accurately predict the risk of 30-day readmission.     

Clinical and sociodemographic risk factors for readmission of Medicare beneficiaries.

In a random sample of Medicare beneficiaries, multiple logistic regression was used to examine clinical, sociodemographic, and insurance coverage risk factors for readmission within 60 days of discharge. The patients most likely to be readmitted were those with poor health status or with chronic diseases and those who had not had surgery. Age, marital status, living situation, and having insurance to supplement Medicare were not independently predictive of readmission risk. The dominant roles of health status, diagnosis, and surgery as predictors of readmission provide evidence that risk-adjusted readmission rates can be equitably used for quality of care studies.     

恶性血液病合并血流感染患者病原菌分布及其预后影响因素

 目的 分析恶性血液病合并血流感染患者的病原学特征及其预后影响因素。方法 选取2016年1月-2022年5月遵义医科大学附属医院血液内科的恶性血液病合并血流感染的住院患者为研究对象。根据患者发生血流感染30 d内的治疗结局分为生存组和死亡组。分析患者的病原学特征及预后情况,并采用单因素及logistic回归分析影响恶性血液病合并血流感染预后的危险因素。结果 共纳入185例患者,基础疾病以急性白血病为主(125例,67.6%)。共分离197株病原菌,革兰阴性菌109株(55.3%),其中大肠埃希菌55株(27.9%);革兰阳性菌86株(43.7%),其中人葡萄球菌24株(12.2%);真菌2株(1.0%)。大肠埃希菌中产超广谱β-内酰胺酶(EBSLs)菌株28株(50.9%);肺炎克雷伯菌中产EBSLs菌株2株(10.0%);耐甲氧西林的人葡萄球菌、表皮葡萄球菌、金黄色葡萄球菌的检出率分别为70.8%、71.4%、36.4%。单因素分析表明,年龄≥70岁、粒细胞缺乏持续时间≥7 d、未合理使用抗菌药物、合并心功能不全、合并急性肾功能不全、感染性休克、肺部感染患者的30天病死率较高,差异均有统计学意义(均P<0.05)。多因素logistic回归分析表明,粒细胞缺乏持续时间≥7 d[OR=3.306,95%CI(1.224~8.927)]、合并心功能不全[OR=6.291,95%CI(1.930~20.508)]、合并急性肾功能不全[OR=8.419,95%CI(2.198~32.241)]、感染性休克[OR=22.150,95%CI(3.639~134.806)]均为恶性血液病合并血流感染患者30天内死亡的独立危险因素(均P<0.05)。结论 恶性血液病合并血流感染最常见的病原菌中,革兰阴性菌以大肠埃希菌为主,革兰阳性菌以人葡萄球菌为主。影响恶性血液病合并血流感染患者预后的危险因素较多,缩短粒细胞缺乏持续时间,改善心功能不全、肾功能不全,积极控制感染性休克是减少恶性血液病合并血流感染患者30天内死亡的有效措施。