scholarly journals Readmissions After Complex Cancer Surgery: Analysis of the Nationwide Readmissions Database

2018 ◽  
Vol 14 (6) ◽  
pp. e335-e345 ◽  
Author(s):  
Syed Nabeel Zafar ◽  
Adil A. Shah ◽  
Christine Nembhard ◽  
Lori L. Wilson ◽  
Elizabeth B. Habermann ◽  
...  
Keyword(s):  
Private Insurance ◽  
Lung Resection ◽  
Hospital Readmissions ◽  
International Classification Of Diseases ◽  
Primary Diagnosis ◽  
Cancer Surgery ◽  
Prolonged Length ◽  
Factors Associated ◽  
Diagnosis Codes ◽  
Classification Of Diseases

Purpose: Hospital readmissions after surgery are a focus of quality improvement efforts. Although some reflect appropriate care, others are potentially preventable readmissions (PPRs). We aim to describe the burden, timing, and factors associated with readmissions after complex cancer surgery. Methods: The Nationwide Readmissions Database (2013) was used to select patients undergoing a complex oncologic resection, which was defined as esophagectomy/gastrectomy, hepatectomy, pancreatectomy, colorectal resection, lung resection, or cystectomy. Readmissions within 30 days from discharge were analyzed. International Classification of Diseases (9th revision) primary diagnosis codes were reviewed to identify PPRs. Multivariable logistic regression analyses identified demographic, clinical, and hospital factors associated with readmissions. Results: Of the 59,493 eligible patients, 14% experienced a 30-day readmission, and 82% of these were deemed PPRs. Half of the readmissions occurred within the first 8 days of discharge. Infections (26%), GI complications (17%), and respiratory conditions (10%) accounted for most readmissions. Factors independently associated with an increased likelihood of readmission included Medicaid versus private insurance (odds ratio [OR], 1.32; 95% CI, 1.17 to 1.48), higher comorbidity score (OR, 1.5; 95% CI, 1.33 to 1.63), discharge to a facility (OR, 1.39; 95% CI, 1.29 to 1.51), prolonged length of stay (OR, 1.42; 95% CI, 1.32 to 1.52), and occurrence of a major in-hospital complication (OR, 1.24; 95% CI, 1.16 to 1.34). Conclusion: One in seven patients undergoing complex cancer surgery suffered a readmission within 30 days. We identified common causes of these and identified patients at high risk for such an event. These data can be used by physicians, administrators, and policymakers to develop strategies to decrease readmissions.

Potentially preventable readmissions after complex cancer surgery: Analysis of the national readmissions dataset.

2017 ◽  
Vol 35 (4_suppl) ◽  
pp. 109-109 ◽  
Author(s):  
Syed Nabeel Zafar ◽  
Adil A Shah ◽  
Mustafa Raoof ◽  
Lori L Wilson ◽  
Nabil Wasif
Keyword(s):  
Private Insurance ◽  
Lung Resection ◽  
Hospital Readmissions ◽  
Major Complication ◽  
Primary Diagnosis ◽  
Cancer Surgery ◽  
Wound Complications ◽  
Gastrointestinal Complications ◽  
Factors Associated ◽  
Common Causes

109 Background: Hospital readmissions following surgery are a focus of quality improvement efforts nationwide. Although some reflect appropriate care, others are potentially preventable readmissions (PPRs). We aim to describe the burden, timing, and factors associated with PPRs after complex cancer surgery. Methods: The National Readmissions Dataset (2013) was used to select patients undergoing a complex oncologic resection (defined as esophagectomy/gastrectomy, hepatectomy, pancreatectomy, colorectal resection, lung resection, and cystectomy). All Readmissions occurring within 30 days from discharge were analyzed. Causes for readmissions were categorized in eleven diagnostic groups and tabulated. ICD-9 primary diagnosis codes were reviewed to identify PPRs. Logistic regression analyses were used to identify demographic, clinical and hospital factors associated with PPRs. Results: We analyzed 60,970 patients with 92,260 admissions. A 30 day readmission occurred in 14% of patients, and was highest following cystectomy (25%) and lowest for lung resections (9%). Of all readmissions 82% were deemed to be PPRs. Half of PPRs occurred within the first 10 days of discharge. Infections, gastrointestinal complications and respiratory conditions accounted for 59% of PPRs. Other common causes included dehydration/electrolyte deficiencies (11%), exacerbation of comorbidity (9.2%), bleeding (5%), thromboembolism (3.5%), and wound complications (3.2%). Factors associated with an increased likelihood of PPRs include Medicaid compared with private insurance (OR 1.24, 95%CI 0.8-1.7), higher comorbid conditions (OR 1.44, 95%CI 1.32, 1.59), and discharge to a facility (OR 1.9, 95%CI 1.7-2.1). Patients with a prolonged hospital stay or a major complication during the index admission had a 34% (OR 1.34, 95%CI 1.3-1.4), and 64% (OR 1.64, 95%CI 1.5-1.7) higher likelihood of a PPR respectively. Conclusions: Most 30 day readmissions after complex cancer surgery are potentially preventable and occur within 10 days of discharge. We identify common causes of readmission and high risk populations to help physicians, administrators and policy makers develop strategies to decrease PPRs.

OPIOID PRESCRIBING AMONG OUTPATIENTS WITH RHEUMATOID ARTHRITIS

Pain Medicine ◽  
2021 ◽  
Author(s):  
Yinan Huang ◽  
Sanika Rege ◽  
Satabdi Chatterjee ◽  
Rajender R Aparasu
Keyword(s):  
Rheumatoid Arthritis ◽  
International Classification Of Diseases ◽  
Cross Sectional Study ◽  
Primary Diagnosis ◽  
Office Visits ◽  
Prescribing Practices ◽  
Cross Sectional ◽  
Factors Associated ◽  
Opioid Prescribing ◽  
Classification Of Diseases

Abstract Study Objectives To examine the outpatient opioid prescribing practices and the factors associated with opioid prescriptions in patients with Rheumatoid Arthritis (RA). Design This cross-sectional study used the 2011-2016 National Ambulatory Medical Care Survey. Descriptive weighted analyses were used to examine the trends in opioid prescribing practices for RA. Multivariable logistic regression was used to examine the factors associated with opioid prescriptions among RA visits. Subjects Adult patients with a primary diagnosis of RA based on the International Classification of Diseases Results According to the national surveys, an average of 4.45 (95% Confidence Interval [CI], 2.30-6.60) million office visits were made annually for RA. Approximately 24.28% of these visits involved opioid prescriptions. The RA visits involving opioid prescriptions increased from 1.43 million in 2011-2012 to 3.69 million in 2015-2016 (P < 0.0001). Being in the age group of 50-64 years (odds ratio [OR] = 3.40; 95% CI, 1.29-9.00), being Hispanic or Latino (OR = 2.92, 95% CI, 1.10-7.74), visiting primary physician (OR = 4.67; 95% CI, 1.86-11.75), muscle relaxants (OR = 64.32; 95% CI, 9.71-426.09), acetaminophen (OR = 93.40; 95% CI, 26.19-333.04), antidepressants (OR = 6.10; 95% CI, 2.63-14.14), and glucocorticoids (OR = 3.20; 95% CI, 1.61-6.38) were associated with an increased likelihood of receiving opioid prescriptions in RA. Conclusions One in four adult RA visits resulted in opioid prescriptions, and the opioid visits rate more than doubled during the study period. Several patient and provider factors were associated with the opioid prescribing among RA visits. Understanding these prescribing practices can help to devise strategies for safe opioid prescribing practices in RA.

Prevalence and risk factors for chalazion in an older veteran population

2021 ◽  
pp. bjophthalmol-2020-318420
Author(s):  
Sneh Patel ◽  
Natalia Tohme ◽  
Emmanuel Gorrin ◽  
Naresh Kumar ◽  
Brian Goldhagen ◽  
...  
Keyword(s):  
Risk Factors ◽  
Tear Film ◽  
International Classification Of Diseases ◽  
Retrospective Case ◽  
Eyelid Margin ◽  
Factors Associated ◽  
Classification Of Diseases ◽  
Health Database ◽  
Irritable Bowel ◽  
The Impact

BackgroundChalazia are common inflammatory eyelid lesions, but their epidemiology remains understudied. This retrospective case–control study examined the prevalence, risk factors and geographic distribution of chalazia in a large veteran population.MethodsData on all individuals seen at a Veterans Affairs (VA) clinic between October 2010 and October 2015 were extracted from the VA health database. Subjects were grouped based on International Classification of Diseases, Ninth Revision (ICD-9) code for chalazion. Univariable logistic regression modelling was used to identify clinical and demographic factors associated with chalazion presence, followed by multivariable modelling to examine which factors predicted risk concomitantly. All cases were mapped across the continental US using geographic information systems modelling to examine how prevalence rates varied geographically.ResultsOverall, 208 720 of 3 453 944 (6.04%) subjects were diagnosed with chalazion during the study period. Prevalence was highest in coastal regions. The mean age of the population was 69.32±13.9 years and most patients were male (93.47%), white (77.13%) and non-Hispanic (93.72%). Factors associated with chalazion risk included smoking (OR=1.12, p<0.0005), conditions of the tear film (blepharitis (OR=4.84, p<0.0005), conjunctivitis (OR=2.78, p<0.0005), dry eye (OR=3.0, p<0.0005)), conditions affecting periocular skin (eyelid dermatitis (OR=2.95, p<0.0005), rosacea (OR=2.50, p<0.0005)), allergic conditions (history of allergies (OR=1.56, p<0.0005)) and systemic disorders (gastritis (OR=1.54, p<0.0005), irritable bowel syndrome (OR=1.45, p<0.0005), depression (OR=1.35, p<0.0005), anxiety (OR=1.31, p<0.0005)). These factors remained associated with chalazion risk when examined concomitantly.ConclusionPeriocular skin, eyelid margin and tear film abnormalities were most strongly associated with risk for chalazion. The impact of environmental conditions on risk for chalazion represents an area in need of further study.

Abstract 16199: Trend of Hypertensive Emergencies in Atrial Fibrillation Hospitalization; National Estimate

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Mae'n Al-Dabbas ◽  
Jay Shah ◽  
devina adalja ◽  
Archana Gundabolu ◽  
Ashish Kumar ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
International Classification Of Diseases ◽  
Primary Diagnosis ◽  
Hypertensive Emergency ◽  
P Value ◽  
National Inpatient Sample ◽  
Common Risk Factor ◽  
The Past ◽  
Classification Of Diseases

Introduction: Hypertension has been reported as one of the most common risk factor causing atrial fibrillation (AF). Also, hypertension is associated with more persistent AF. However the epidemiology for incidence of hypertensive emergency in AF hospitalization is not well documented. Hypothesis: We hypothesize that frequency of hypertensive emergency has been increasingly associated with Atrial fibrillation. Methods: National inpatient sample from 2005 -2015 was used for the present analysis. Patients over 18 years, with AF as the primary diagnosis were identified using International Classification of Diseases, Ninth Revision-Clinical Modification (ICD-9-CM) code, 427.31. Patients with hypertensive emergency were identified using ICD-9-CM code. 401.0, 402.0, 403.0, 404.0, 405.0. Results: A total of 4,988,269 AF patients over 18 years of age were included in the present analysis. Of these hospitalization 49,423 had hypertensive emergency during hospitalizations and the rest 4,938,846 had no hypertensive emergency. There was a monotonic increase in the trend of rates of hypertensive emergency per 1000 AF hospitalizations, from 2005 - 2015. (P value <0.001) [Figure 1]. Conclusion: There has been a significant increase in the rates of hypertensive emergency among AF hospitalization, over the past decade. Further research is needed to investigate the reason for this increased trend.

Readmission after Adult Uvulopalatopharyngoplasty: A Population-Based Inpatient Cohort Study in Taiwan

2018 ◽  
Vol 160 (3) ◽  
pp. 559-566 ◽  
Author(s):  
Ying-Shuo Hsu ◽  
Wei-Chung Hsu ◽  
Jenq-Yuh Ko ◽  
Te-Huei Yeh ◽  
Chia-Hsuan Lee ◽  
...  
Keyword(s):  
Odds Ratio ◽  
International Classification Of Diseases ◽  
Population Based ◽  
Factors Associated ◽  
Risk Of Bleeding ◽  
Increased Risk ◽  
Classification Of Diseases ◽  
Insurance Database ◽  
Population Based Survey

Objective To investigate readmissions among adult inpatients who underwent uvulopalatopharyngoplasty (UPPP) in Taiwan. Design Population-based survey. Setting Retrospective study with the National Health Insurance Database. Methods All cases of inpatient adult UPPP (age >20 years) from 1997 to 2012 were identified through International Classification of Diseases, Ninth Revision, Clinical Modification. Factors associated with readmission within 30 days after surgery were analyzed. Results A total of 38,839 adults with UPPP were identified (mean age, 39.3 years; men, 73.7%). The incidence of UPPP was 14.6 per 100 000 adults, which increased from 1997 to 2012 (6.7 to 16.7 per 100,000, Ptrend < .001). The rates of readmission for any reason, readmission for bleeding, reoperation for bleeding, and 30-day mortality were 4.2%, 1.7%, 1.0%, and 0.14%, respectively. Young age increased the risk of reoperation for bleeding, and old age increased the risk of readmission for any reason and mortality. Men had an increased risk of readmission and reoperation. Hypertension was associated with an increased risk of readmission for any reason (odds ratio [OR], 1.29; 95% CI, 1.10-1.51), bleeding-related readmission (OR, 1.89; 95% CI, 1.52-2.36), and reoperation (OR, 2.47; 95% CI, 1.84-3.30). Concurrent hypopharyngeal surgery was associated with an increased risk of readmission for any reason (OR, 1.34; 95% CI, 1.07-1.66) and bleeding-related readmission (OR, 1.69; 95% CI, 1.25-2.27). Finally, the use of steroids was associated with an increased risk of bleeding-related readmission and reoperation. Conclusions The incidence of adult UPPP increased from 1997 to 2012 in Taiwan. Age, sex, comorbidity, concurrent hypopharyngeal surgery, and drug administration were associated with readmission after inpatient UPPP.

scholarly journals Association Between Diagnosis Code Expansion and Changes in 30‐Day Risk‐Adjusted Outcomes for Cardiovascular Diseases

2021 ◽  
Author(s):  
Lauren Gilstrap ◽  
Rishi K. Wadhera ◽  
Andrea M. Austin ◽  
Stephen Kearing ◽  
Karen E. Joynt Maddox ◽  
...  
Keyword(s):  
Logistic Regression ◽  
International Classification Of Diseases ◽  
Fee For Service ◽  
Readmission Rates ◽  
Difference In Differences ◽  
Diagnosis Codes ◽  
Classification Of Diseases ◽  
Changes In Risk ◽  
The Impact

BACKGROUND In January 2011, Centers for Medicare and Medicaid Services expanded the number of inpatient diagnosis codes from 9 to 25, which may influence comorbidity counts and risk‐adjusted outcome rates for studies spanning January 2011. This study examines the association between (1) limiting versus not limiting diagnosis codes after 2011, (2) using inpatient‐only versus inpatient and outpatient data, and (3) using logistic regression versus the Centers for Medicare and Medicaid Services risk‐standardized methodology and changes in risk‐adjusted outcomes. METHODS AND RESULTS Using 100% Medicare inpatient and outpatient files between January 2009 and December 2013, we created 2 cohorts of fee‐for‐service beneficiaries aged ≥65 years. The acute myocardial infarction cohort and the heart failure cohort had 578 728 and 1 595 069 hospitalizations, respectively. We calculate comorbidities using (1) inpatient‐only limited diagnoses, (2) inpatient‐only unlimited diagnoses, (3) inpatient and outpatient limited diagnoses, and (4) inpatient and outpatient unlimited diagnoses. Across both cohorts, International Classification of Diseases, Ninth Revision ( ICD‐9 ) diagnoses and hierarchical condition categories increased after 2011. When outpatient data were included, there were no significant differences in risk‐adjusted readmission rates using logistic regression or the Centers for Medicare and Medicaid Services risk standardization. A difference‐in‐differences analysis of risk‐adjusted readmission trends before versus after 2011 found that no significant differences between limited and unlimited models for either cohort. CONCLUSIONS For studies that span 2011, researchers should consider limiting the number of inpatient diagnosis codes to 9 and/or including outpatient data to minimize the impact of the code expansion on comorbidity counts. However, the 2011 code expansion does not appear to significantly affect risk‐adjusted readmission rate estimates using either logistic or risk‐standardization models or when using or excluding outpatient data.

scholarly journals Use of Diagnosis Codes and/or Wound Culture Results for Surveillance of Surgical Site Infection after Mastectomy and Breast Reconstruction

2010 ◽  
Vol 31 (05) ◽  
pp. 544-547 ◽  
Author(s):  
Margaret A. Olsen ◽  
Victoria J. Fraser
Keyword(s):  
Surgical Site Infection ◽  
Claims Data ◽  
Breast Surgery ◽  
International Classification Of Diseases ◽  
Site Infection ◽  
Wound Culture ◽  
Diagnosis Codes ◽  
Classification Of Diseases ◽  
Major Breast

We compared surveillance of surgical site infection (SSI) after major breast surgery by using a combination of International Classification of Diseases, Ninth Revision, Clinical Modification diagnosis codes and microbiology-based surveillance. The sensitivity of the coding algorithm for identification of SSI was 87.5%, and the sensitivity of wound culture for identification of SSI was 78.1%. Our results suggest that SSI surveillance can be reliably performed using claims data.

The prevalence of localised scleroderma in childhood assessed in the administrative claims data from the United States

2018 ◽  
Vol 4 (1) ◽  
pp. 77-78
Author(s):  
Timothy Beukelman ◽  
Fenglong Xie ◽  
Ivan Foeldvari
Keyword(s):  
United States ◽  
Systemic Sclerosis ◽  
Claims Data ◽  
International Classification Of Diseases ◽  
The United States ◽  
Administrative Claims ◽  
Administrative Claims Data ◽  
Diagnosis Codes ◽  
Classification Of Diseases

Juvenile localised scleroderma is believed an orphan autoimmune disease, which occurs 10 times more often than systemic sclerosis in childhood and is believed to have a prevalence of 1 per 100,000 children. To gain data regarding the prevalence of juvenile localised scleroderma, we assessed the administrative claims data in the United States using the International Classification of Diseases, Ninth Revision diagnosis codes. We found an estimated prevalence in each year ranging from 3.2 to 3.6 per 10,000 children. This estimate is significantly higher as found in previous studies.

scholarly journals Validating Use of Electronic Health Data to Identify Patients with Urinary Tract Infections in Outpatient Settings

Antibiotics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 536
Author(s):  
George Germanos ◽  
Patrick Light ◽  
Roger Zoorob ◽  
Jason Salemi ◽  
Fareed Khan ◽  
...  
Keyword(s):  
Urinary Tract ◽  
Urinary Tract Infections ◽  
Chart Review ◽  
International Classification Of Diseases ◽  
Primary Care Settings ◽  
Diagnosis Codes ◽  
Classification Of Diseases ◽  
Icd 10 ◽  
Tract Infections ◽  
Outpatient Settings

Objective: To validate the use of electronic algorithms based on International Classification of Diseases (ICD)-10 codes to identify outpatient visits for urinary tract infections (UTI), one of the most common reasons for antibiotic prescriptions. Methods: ICD-10 symptom codes (e.g., dysuria) alone or in addition to UTI diagnosis codes plus prescription of a UTI-relevant antibiotic were used to identify outpatient UTI visits. Chart review (gold standard) was performed by two reviewers to confirm diagnosis of UTI. The positive predictive value (PPV) that the visit was for UTI (based on chart review) was calculated for three different ICD-10 code algorithms using (1) symptoms only, (2) diagnosis only, or (3) both. Results: Of the 1087 visits analyzed, symptom codes only had the lowest PPV for UTI (PPV = 55.4%; 95%CI: 49.3–61.5%). Diagnosis codes alone resulted in a PPV of 85% (PPV = 84.9%; 95%CI: 81.1–88.2%). The highest PPV was obtained by using both symptom and diagnosis codes together to identify visits with UTI (PPV = 96.3%; 95%CI: 94.5–97.9%). Conclusions: ICD-10 diagnosis codes with or without symptom codes reliably identify UTI visits; symptom codes alone are not reliable. ICD-10 based algorithms are a valid method to study UTIs in primary care settings.

Sign in / Sign up

Export Citation Format

Share Document