This study (1) developed a 30-day postoperative mortality risk calculator for patients with necrotizing soft tissue infections (NSTIs) using data from the National Surgical Quality Improvement Program (NSQIP), (2) found that older age, dependence level, dialysis, higher ASA class, emergent surgery, septic shock, and low platelet count were significant predictors of mortality in the risk model, and (3) validated the model with a receiver operating characteristic (ROC) area of 0.85, indicating a strong predictive performance.
Nomogram based estimate of axillary nodal involvement in acosog z0011Matthew Katz
Nomograms can outperform experts in predicting additional axillary nodal metastases in clinical N0 breast cancer patients with a positive sentinel node biopsy.
In ACOSOG Z0011, prior analysis showed radiation (RT) fields showed that half of all patients with confirmed RT fields used high tangents and 19% include regional nodal irradiation. We sought to evaluate two hypotheses in this secondary analysis:
1. Nomograms are valid in Z0011 and confirm similar distribution of nodal risk in two treatment arms;
2. Radiation fields including lymph nodes were not in the highest risk patients despite best clinical judgment.
I presented this research October 24, 2018 at the American Society for Radiation Oncology (ASTRO) Annual Meeting in San Antonio, Texas.
diagnosis and outline of management of localized prostate cancer for non-urol...Dr Mayank Mohan Agarwal
a brief introduction of anatomy of prostate, screening of prostate cancer, measures to improve specificity of PSA screening, risk stratification of prostate cancer, treatment options - active surveillance, radical prostatectomy, radical radiotherapy
External validation of prognostic model of tbiDhaval Shukla
Develop and describe prognostic model incorporating clinical and radiological variables for the prediction of mortality in severe traumatic brain injury. Externally validation of CRASH model with the dataset from our neurosurgical intensive care unit.
Achieving target refraction after cataract surgeryRiyad Banayot
This study evaluates the difference between target and actual refraction after phacoemulsification and intraocular lens implantation at St John Eye Hospital – Hebron, Palestine.
Automated hematology analyzer as a cost effective aid to screen and monitor s...nisaiims
Praveen Kumar, Parul Arora, Subhadra Sharma, Arti Kapil$, A.K.Mukhopadhyay
Departments of Lab Medicine & Microbiology$
All India Institute of Medical Sciences, New Delhi
Nomogram based estimate of axillary nodal involvement in acosog z0011Matthew Katz
Nomograms can outperform experts in predicting additional axillary nodal metastases in clinical N0 breast cancer patients with a positive sentinel node biopsy.
In ACOSOG Z0011, prior analysis showed radiation (RT) fields showed that half of all patients with confirmed RT fields used high tangents and 19% include regional nodal irradiation. We sought to evaluate two hypotheses in this secondary analysis:
1. Nomograms are valid in Z0011 and confirm similar distribution of nodal risk in two treatment arms;
2. Radiation fields including lymph nodes were not in the highest risk patients despite best clinical judgment.
I presented this research October 24, 2018 at the American Society for Radiation Oncology (ASTRO) Annual Meeting in San Antonio, Texas.
diagnosis and outline of management of localized prostate cancer for non-urol...Dr Mayank Mohan Agarwal
a brief introduction of anatomy of prostate, screening of prostate cancer, measures to improve specificity of PSA screening, risk stratification of prostate cancer, treatment options - active surveillance, radical prostatectomy, radical radiotherapy
External validation of prognostic model of tbiDhaval Shukla
Develop and describe prognostic model incorporating clinical and radiological variables for the prediction of mortality in severe traumatic brain injury. Externally validation of CRASH model with the dataset from our neurosurgical intensive care unit.
Achieving target refraction after cataract surgeryRiyad Banayot
This study evaluates the difference between target and actual refraction after phacoemulsification and intraocular lens implantation at St John Eye Hospital – Hebron, Palestine.
Automated hematology analyzer as a cost effective aid to screen and monitor s...nisaiims
Praveen Kumar, Parul Arora, Subhadra Sharma, Arti Kapil$, A.K.Mukhopadhyay
Departments of Lab Medicine & Microbiology$
All India Institute of Medical Sciences, New Delhi
Basavarajeeyam is a Sreshta Sangraha grantha (Compiled book ), written by Neelkanta kotturu Basavaraja Virachita. It contains 25 Prakaranas, First 24 Chapters related to Rogas& 25th to Rasadravyas.
Basavarajeeyam is an important text for ayurvedic physician belonging to andhra pradehs. It is a popular compendium in various parts of our country as well as in andhra pradesh. The content of the text was presented in sanskrit and telugu language (Bilingual). One of the most famous book in ayurvedic pharmaceutics and therapeutics. This book contains 25 chapters called as prakaranas. Many rasaoushadis were explained, pioneer of dhatu druti, nadi pareeksha, mutra pareeksha etc. Belongs to the period of 15-16 century. New diseases like upadamsha, phiranga rogas are explained.
Knee anatomy and clinical tests 2024.pdfvimalpl1234
This includes all relevant anatomy and clinical tests compiled from standard textbooks, Campbell,netter etc..It is comprehensive and best suited for orthopaedicians and orthopaedic residents.
NVBDCP.pptx Nation vector borne disease control programSapna Thakur
NVBDCP was launched in 2003-2004 . Vector-Borne Disease: Disease that results from an infection transmitted to humans and other animals by blood-feeding arthropods, such as mosquitoes, ticks, and fleas. Examples of vector-borne diseases include Dengue fever, West Nile Virus, Lyme disease, and malaria.
Recomendações da OMS sobre cuidados maternos e neonatais para uma experiência pós-natal positiva.
Em consonância com os ODS – Objetivos do Desenvolvimento Sustentável e a Estratégia Global para a Saúde das Mulheres, Crianças e Adolescentes, e aplicando uma abordagem baseada nos direitos humanos, os esforços de cuidados pós-natais devem expandir-se para além da cobertura e da simples sobrevivência, de modo a incluir cuidados de qualidade.
Estas diretrizes visam melhorar a qualidade dos cuidados pós-natais essenciais e de rotina prestados às mulheres e aos recém-nascidos, com o objetivo final de melhorar a saúde e o bem-estar materno e neonatal.
Uma “experiência pós-natal positiva” é um resultado importante para todas as mulheres que dão à luz e para os seus recém-nascidos, estabelecendo as bases para a melhoria da saúde e do bem-estar a curto e longo prazo. Uma experiência pós-natal positiva é definida como aquela em que as mulheres, pessoas que gestam, os recém-nascidos, os casais, os pais, os cuidadores e as famílias recebem informação consistente, garantia e apoio de profissionais de saúde motivados; e onde um sistema de saúde flexível e com recursos reconheça as necessidades das mulheres e dos bebês e respeite o seu contexto cultural.
Estas diretrizes consolidadas apresentam algumas recomendações novas e já bem fundamentadas sobre cuidados pós-natais de rotina para mulheres e neonatos que recebem cuidados no pós-parto em unidades de saúde ou na comunidade, independentemente dos recursos disponíveis.
É fornecido um conjunto abrangente de recomendações para cuidados durante o período puerperal, com ênfase nos cuidados essenciais que todas as mulheres e recém-nascidos devem receber, e com a devida atenção à qualidade dos cuidados; isto é, a entrega e a experiência do cuidado recebido. Estas diretrizes atualizam e ampliam as recomendações da OMS de 2014 sobre cuidados pós-natais da mãe e do recém-nascido e complementam as atuais diretrizes da OMS sobre a gestão de complicações pós-natais.
O estabelecimento da amamentação e o manejo das principais intercorrências é contemplada.
Recomendamos muito.
Vamos discutir essas recomendações no nosso curso de pós-graduação em Aleitamento no Instituto Ciclos.
Esta publicação só está disponível em inglês até o momento.
Prof. Marcus Renato de Carvalho
www.agostodourado.com
These lecture slides, by Dr Sidra Arshad, offer a quick overview of the physiological basis of a normal electrocardiogram.
Learning objectives:
1. Define an electrocardiogram (ECG) and electrocardiography
2. Describe how dipoles generated by the heart produce the waveforms of the ECG
3. Describe the components of a normal electrocardiogram of a typical bipolar lead (limb II)
4. Differentiate between intervals and segments
5. Enlist some common indications for obtaining an ECG
6. Describe the flow of current around the heart during the cardiac cycle
7. Discuss the placement and polarity of the leads of electrocardiograph
8. Describe the normal electrocardiograms recorded from the limb leads and explain the physiological basis of the different records that are obtained
9. Define mean electrical vector (axis) of the heart and give the normal range
10. Define the mean QRS vector
11. Describe the axes of leads (hexagonal reference system)
12. Comprehend the vectorial analysis of the normal ECG
13. Determine the mean electrical axis of the ventricular QRS and appreciate the mean axis deviation
14. Explain the concepts of current of injury, J point, and their significance
Study Resources:
1. Chapter 11, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 9, Human Physiology - From Cells to Systems, Lauralee Sherwood, 9th edition
3. Chapter 29, Ganong’s Review of Medical Physiology, 26th edition
4. Electrocardiogram, StatPearls - https://www.ncbi.nlm.nih.gov/books/NBK549803/
5. ECG in Medical Practice by ABM Abdullah, 4th edition
6. Chapter 3, Cardiology Explained, https://www.ncbi.nlm.nih.gov/books/NBK2214/
7. ECG Basics, http://www.nataliescasebook.com/tag/e-c-g-basics
Flu Vaccine Alert in Bangalore Karnatakaaddon Scans
As flu season approaches, health officials in Bangalore, Karnataka, are urging residents to get their flu vaccinations. The seasonal flu, while common, can lead to severe health complications, particularly for vulnerable populations such as young children, the elderly, and those with underlying health conditions.
Dr. Vidisha Kumari, a leading epidemiologist in Bangalore, emphasizes the importance of getting vaccinated. "The flu vaccine is our best defense against the influenza virus. It not only protects individuals but also helps prevent the spread of the virus in our communities," he says.
This year, the flu season is expected to coincide with a potential increase in other respiratory illnesses. The Karnataka Health Department has launched an awareness campaign highlighting the significance of flu vaccinations. They have set up multiple vaccination centers across Bangalore, making it convenient for residents to receive their shots.
To encourage widespread vaccination, the government is also collaborating with local schools, workplaces, and community centers to facilitate vaccination drives. Special attention is being given to ensuring that the vaccine is accessible to all, including marginalized communities who may have limited access to healthcare.
Residents are reminded that the flu vaccine is safe and effective. Common side effects are mild and may include soreness at the injection site, mild fever, or muscle aches. These side effects are generally short-lived and far less severe than the flu itself.
Healthcare providers are also stressing the importance of continuing COVID-19 precautions. Wearing masks, practicing good hand hygiene, and maintaining social distancing are still crucial, especially in crowded places.
Protect yourself and your loved ones by getting vaccinated. Together, we can help keep Bangalore healthy and safe this flu season. For more information on vaccination centers and schedules, residents can visit the Karnataka Health Department’s official website or follow their social media pages.
Stay informed, stay safe, and get your flu shot today!
Local Advanced Lung Cancer: Artificial Intelligence, Synergetics, Complex Sys...Oleg Kshivets
Overall life span (LS) was 1671.7±1721.6 days and cumulative 5YS reached 62.4%, 10 years – 50.4%, 20 years – 44.6%. 94 LCP lived more than 5 years without cancer (LS=2958.6±1723.6 days), 22 – more than 10 years (LS=5571±1841.8 days). 67 LCP died because of LC (LS=471.9±344 days). AT significantly improved 5YS (68% vs. 53.7%) (P=0.028 by log-rank test). Cox modeling displayed that 5YS of LCP significantly depended on: N0-N12, T3-4, blood cell circuit, cell ratio factors (ratio between cancer cells-CC and blood cells subpopulations), LC cell dynamics, recalcification time, heparin tolerance, prothrombin index, protein, AT, procedure type (P=0.000-0.031). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and N0-12 (rank=1), thrombocytes/CC (rank=2), segmented neutrophils/CC (3), eosinophils/CC (4), erythrocytes/CC (5), healthy cells/CC (6), lymphocytes/CC (7), stick neutrophils/CC (8), leucocytes/CC (9), monocytes/CC (10). Correct prediction of 5YS was 100% by neural networks computing (error=0.000; area under ROC curve=1.0).
Adv. biopharm. APPLICATION OF PHARMACOKINETICS : TARGETED DRUG DELIVERY SYSTEMSAkankshaAshtankar
MIP 201T & MPH 202T
ADVANCED BIOPHARMACEUTICS & PHARMACOKINETICS : UNIT 5
APPLICATION OF PHARMACOKINETICS : TARGETED DRUG DELIVERY SYSTEMS By - AKANKSHA ASHTANKAR
Adv. biopharm. APPLICATION OF PHARMACOKINETICS : TARGETED DRUG DELIVERY SYSTEMS
Using NSQIP to calculate mortality risk from NSTIs
1. Development and Validation of a
Necrotizing Soft-Tissue Infection
Mortality Risk Calculator Using NSQIP
Iris Faraklas, RN, BSN, Gregory J.
Stoddard, MPH, Leigh Neumayer MD, FACS, Jeffrey
Saffle, MD, FACS, Amalia Cochran, MD, FACS
University of Utah
3. Necrotizing Soft Tissue Infections
(NSTI)
• Aggressive infections requiring prompt surgical
debridement and systemic support
– May be mono- or polymicrobial
• Incidence in US: 500-1,500 cases per year
• Previous work from our group showed a
mortality rate of 12%
4. Identifying Risk Factors
• Focus on correctly
identifying NSTI
• Anaya et al (2009)
created a mortality
risk calculator
– 2 centers
– 350 patients in
9 years
5. National Surgical Quality Improvement
Program (NSQIP)
• Created by the VA
– Managed by ACS
• Prospective, multicenter database
>500 hospitals
• Patient data
– Pre-, intra-, and postoperative variables
– 30-day postoperative mortality and morbidity
outcomes
6. Receiver Operating Characteristic (ROC)
• Risk estimate to 1
discriminate cases
from non-cases .8
– Non-survivors from
Sensitivity
.6
survivors
• ROC=1.0 if .4
discrimination is
perfect .2
• ROC=0.50 if 0.50
1.0
ROC area = 0.85
discrimination is no 0
0 .2 .4 .6 .8 1
better than chance 1 - Specificity
Low…..False Positives….High
10. Methods: Dataset
• Pre- and intraoperative variables included:
– Demographics & lifestyle variables
– Comorbidities & previous surgeries
– ASA classification
– Presence of septic shock per NSQIP criteria
• Laboratory variables within 2 days
preoperative
– Missing laboratory values were excluded
11. Methods: Dataset
• Primary outcome variable: Mortality
• NSQIP tracks outcomes for 30 days
postoperatively
12. Methods: Statistical Analysis
• Univariate exploratory analysis on each variable
compared to mortality
• All variables showing significance (p<0.05) were
included in stepwise multiple logistic regression
• Preoperative laboratory values >400
observations were included:
– Hct, BUN, Crt, Plt
• The bootstrap method was used to validate the
model
13. Results
• 1,392 patients identified
– 82% (1,142) Necrotizing fasciitis
– 8.6% (119) Gas gangrene
– 9.4% (131) Fournier’s gangrene
Number of NSTI cases vs. Total NSQIP Cases per Year
Year NSTI Cases Total NSQIP Cases
2005 47 34,099
2006 140 118,391
2007 212 211,407
2008 281 271,368
2009 352 336,190
2010 360 363,431
14. Results: Demographics n=1,392
• Median Age: 55 (IQR: 46-63)
• 58% Male
• Median BMI: 32 (IQR: 26-40)
• 51% Were either partially or totally dependent
• 49% Diabetic
• 54% Hypertension requiring medication
15. Results: Demographics n=1,392
• 71% Admitted from home
• 43% ASA Class ≥4
• 25% Septic shock
• 31% OR in past 30 days
• 62% Surgery considered emergent
16. Results: Outcomes n=1,392
• Median length of stay:
– 16 days (IQR: 9-30)
– 9 patients locked (>120 days)
• 30-day mortality: 13% (n=181)
17. Multivariable Logistic Regression Model for 30-Day Postoperative Mortality (n=1,329)
Variables Odds 95% Confidence P value
Ratio Interval
>60 years old 2.47 1.72-3.55 <0.001
Dependence Level
Partial Dependent 1.61 0.95-2.69 0.072
Completely Dependent 2.33 1.43-3.80 0.001
Dialysis Prior to OR 1.89 1.15-3.10 0.012
ASA Class ≥ 4 3.55 2.25-5.59 <0.001
Emergent OR 1.56 1.03-2.34 0.035
Preoperative Septic Shock 2.35 1.55-3.56 <0.001
Platelet Count
Platelet count 3.48 1.65-7.37 0.001
<50,000/mm3
Platelet count 1.86 1.21-2.87 0.005
<150,000/mm3 but >50,000/mm3
18. Results: Outcomes
• ROC of 0.85 (CI:0.82- 1
0.87)
.8
– Strong predictive model
• Bootstrap validation
Sensitivity
.6
showed a ROC of 0.83
.4
(CI:0.81-0.86)
– Represents the model .2
in future patients ROC area = 0.85
0
0 .2 .4 .6 .8 1
1 - Specificity
19.
20. Limitations
• Relatively small number of patients
• Lack of microbial picture and surgical
management
• Variability of NSTI diagnosis
• Selection bias for tertiary facilities
21. Conclusions
• Strong predictive model
• Variables are available early in hospital course
• Risk models should not dictate management
• Helpful communication tool
Necrotizing soft-tissue infections (NSTI) are a group of uncommon, aggressive infections, requiring prompt surgical debridement and systemic support ; these infections may be monomicrobial or polymicrobial. Varying studies have reported the incidence in the US ranges from 500-1,500 cases per year. A previous large patient series from our group showed a mortality rate of 12%.
In the past decade investigators have focused on correctly identifying NSTI cases. Some studies have tried to delineate specific risk mortality factors . To date Anaya (An I yah) et al created the only mortality risk scoring system. Shown in this table are their 6 variables. It should be noted that this scoring system was created from only 2 centers with a total of 350 patients over 9 years.
Large national databases with validated data obtained by trained collectors have emerged as valuable sources of high-quality data, creating a unique opportunity to evaluate mortality risk factors for NSTI. The National Surgical Quality Improvement Program (NSQIP) is perhaps the best of these databases. The VA had the foresight to create NSQIP- currently it is managed by the American College of Surgeons. nsqip is a prospective, multicenter database with greater than 500 hospitals participating. Over a 130 data points are collected, including pre, intra and post operative variables, 30-day mortality and morbidity outcomes are also collected for patients undergoing surgical procedures. With this large program we were able to develop and validate a mortality risk calculator for NSTI patients.
Before diving into our study…a little statistical back story A Receiver Operating Characteristic (or the ROC) is a plot of the true positive against the false positive rate at different points in a diagnostic test. It is the ability of the risk estimate to discriminate cases from noncases (or in our study: nonsurvivors from survivors).15, 16 The closer the curve follows the left-hand border and then the top border, the more accurate the test. The ROC area equals 1 if discrimination is perfect <<click>>and .5 if it is no better than chance.
Among statisticians a rule of thumb is that a ROC of >.7 shows acceptable discrimination in clinical studies , while a ROC >.8 is considered excellent discrimination.
The objective of this study was to develop and validate a 30-day postoperative mortality risk calculator for NSTI patients using the NSQIP database
After receiving IRB approval, data were extracted from 2005 through 2010. The Participant Use Data Files do not identify hospitals, health care providers or patients. The files are HIPAA compliant. Patients discharged with an ICD-9 diagnosis of necrotizing fasciitis; Fournier’s gangrene, or gas gangrene were included in the analysis.
Due to our time constraint The full definition of all nsqip variables are delineated elsewhere. 23 Some of the pre- and intraoperative variables included were: Demographic and lifestyle variables, comorbidities and previous surgeries Other factors considered were the patient’s ASA classification and presence of septic shock. If available within 2 days prior to surgery, preoperative laboratory variables were reported as number of abnormal per reported. Missing laboratory variables were not assumed to be normal and missing values were excluded from regression.
The primary outcome variable was surgical mortality. Outcomes were tracked for 30 days postoperatively. If the patient was still inpatient at 30 days they were tracked until discharge or until NSQIP locked the data file at 120 days after their first surgery.
Univariate exploratory analysis was performed on each preoperative variable compared to mortality. All variables showing significance were included in the stepwise multiple logistic regression analysis. Only the following preoperative laboratory values had > 400 observations and were included in the regression: hematocrit, BUN, creatinine and platelet count. Bootstrap method was used to validate this model
A total of 1,392 patients were identified from 2005 through 2010, 82% had a diagnosis of necrotizing fasciitis , 8.6% were diagnosed with gas gangrene and the remaining 9.4% were diagnosed with Fournier’s gangrene. As shown in this Table , the number of NSTI admissions reported to NSQIP increased progressively from 47 patients in 2005 to 360 in 2010. Please note that the total number NSQIP cases reported increased more than 10X over this same period as centers were added to the program
This is a much abbreviated general demographics slide. Patients were mostly male in their 50s, and obese. More than half were considered partially or totally dependent, and required medication to treat their diabetes and or hypertension.
The majority were admitted from home. Over 40% were ASA classified as a 4 or greater; a quarter were in septic shock, while a third had had previous surgery in the past month and the majority of the surgeries were considered emergent in nature meaning they were within 12 hours of admission
Median length of stay was 16 days; however there were 9 patients that were still in the hospital at time that NSQIP locked their data file (>120 days from first surgery). And the Thirty-day mortality was 13%.
Multivariable logistic regression identified the following 7 independent variables that affected mortality: patients older than 60, totally dependent functional status, requiring dialysis prior to surgery, having a ASA class 4 or greater, considered as an emergent surgery (meaning <12 hours post admission), septic shock and low platelet count stratified between those <50,000 and those (<150,000/mm3 but more than 50,000
The ROC for our model was .85 which indicates a strong predictive model. Using boot-strap validation the ROC curve area was .83, which represents how the model will behave in future patients.
The model was used to develop an interactive risk calculator. Once the model was validated, an interactive spreadsheet was created that uses the demonstrated risk factors from a specific patient to return a probability of mortality expressed as a percent. This calculator is available upon request. It calculates a patient’s estimated risk of mortality, after clinicians enter patient data based on the variable definitions that are listed on spreadsheet. Definitions are the same as NSQIP definitions and if the user hovers over the variable the specific definition pops up.
As with any study, limitations exist: due to the rarity of disease the number of patients included in this analysis is relatively small. Although NSQIP is probably one of the best databases available- it doesn’t include the microbial picture or which patients required serial debridements. Nor are we able to control for diagnosis variablity that might occur at different facilities which is ultimately only as good as the ICD-9 coding. Also tertiary facilities might participate in NSQIP more frequently thus a selection bias might exist.
As stated earlier an ROC greater than .8 is considered an excellent predictive model this calculator is based on a strong predictive model (with an ROC of .85). The variables included in the model are available early in the hospital course. However risk models should not dictate management but are just one added tool in the clinican’s toolbox. This simply provides extra information or a helpful communication tool to help patients and their families make informed decisions.