The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) Statement includes a 22-item checklist, which aims to improve the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. This explanation and elaboration document describes the rationale; clarifies the meaning of each item; and discusses why transparent reporting is important, with a view to assessing risk of bias and clinical usefulness of the prediction model. Each checklist item of the TRIPOD Statement is explained in detail and accompanied by published examples of good reporting. The document also provides a valuable reference of issues to consider when designing, conducting, and analyzing prediction model studies. To aid the editorial process and help peer reviewers and, ultimately, readers and systematic reviewers of prediction model studies, it is recommended that authors include a completed checklist in their submission. The TRIPOD checklist can also be downloaded from www.tripod-statement.org.

/pdf/transparent-reporting-of-a-multivariable-prediction-model-3n8wy14hp9.pdf

Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration.

AAA
: abdominal aortic aneurysm
ACEI
: angiotensin converting enzyme inhibitor
ACS
: acute coronary syndromes
AF
: atrial fibrillation
AKI
: acute kidney injury
AKIN
: Acute Kidney Injury Network
ARB
: angiotensin receptor blocker
ASA
: American Society of Anesthesiologists
b.i.d.
: bis in diem (twice daily)
BBSA
: Beta-Blocker in Spinal Anesthesia
BMS
: bare-metal stent
BNP
: B-type natriuretic peptide
bpm
: beats per minute
CABG
: coronary artery bypass graft
CAD
: coronary artery disease
CARP
: Coronary Artery Revascularization Prophylaxis
CAS
: carotid artery stenting
CASS
: Coronary Artery Surgery Study
CEA
: carotid endarterectomy
CHA2DS2-VASc
: cardiac failure, hypertension, age ≥75 (doubled), diabetes, stroke (doubled)-vascular disease, age 65–74 and sex category (female)
CI
: confidence interval
CI-AKI
: contrast-induced acute kidney injury
CKD
: chronic kidney disease
CKD-EPI
: Chronic Kidney Disease Epidemiology Collaboration
Cmax
: maximum concentration
CMR
: cardiovascular magnetic resonance
COPD
: chronic obstructive pulmonary disease
CPG
: Committee for Practice Guidelines
CPX/CPET
: cardiopulmonary exercise test
CRP
: C-reactive protein
CRT
: cardiac resynchronization therapy
CRT-D
: cardiac resynchronization therapy defibrillator
CT
: computed tomography
cTnI
: cardiac troponin I
cTnT
: cardiac troponin T
CVD
: cardiovascular disease
CYP3a4
: cytochrome P3a4 enzyme
DAPT
: dual anti-platelet therapy
DECREASE
: Dutch Echocardiographic Cardiac Risk Evaluation Applying Stress Echocardiography
DES
: drug-eluting stent
DIPOM
: DIabetic Post-Operative Mortality and Morbidity
DSE
: dobutamine stress echocardiography
ECG
: electrocardiography/electrocardiographically/electrocardiogram
eGFR
: estimated glomerular filtration rate
ESA
: European Society of Anaesthesiology
ESC
: European Society of Cardiology
EVAR
: endovascular abdominal aortic aneurysm repair
FEV1
: Forced expiratory volume in 1 second
HbA1c
: glycosylated haemoglobin
HF-PEF
: heart failure with preserved left ventricular ejection fraction
HF-REF
: heart failure with reduced left ventricular ejection fraction
ICD
: implantable cardioverter defibrillator
ICU
: intensive care unit
IHD
: ischaemic heart disease
INR
: international normalized ratio
IOCM
: iso-osmolar contrast medium
KDIGO
: Kidney Disease: Improving Global Outcomes
LMWH
: low molecular weight heparin
LOCM
: low-osmolar contrast medium
LV
: left ventricular
LVEF
: left ventricular ejection fraction
MaVS
: Metoprolol after Vascular Surgery
MDRD
: Modification of Diet in Renal Disease
MET
: metabolic equivalent
MRI
: magnetic resonance imaging
NHS
: National Health Service
NOAC
: non-vitamin K oral anticoagulant
NSQIP
: National Surgical Quality Improvement Program
NSTE-ACS
: non-ST-elevation acute coronary syndromes
NT-proBNP
: N-terminal pro-BNP
O2
: oxygen
OHS
: obesity hypoventilation syndrome
OR
: odds ratio
P gp
: platelet glycoprotein
PAC
: pulmonary artery catheter
PAD
: peripheral artery disease
PAH
: pulmonary artery hypertension
PCC
: prothrombin complex concentrate
PCI
: percutaneous coronary intervention
POBBLE
: Peri-Operative Beta-BLockadE
POISE
: Peri-Operative ISchemic Evaluation
POISE-2
: Peri-Operative ISchemic Evaluation 2
q.d.
: quaque die (once daily)
RIFLE
: Risk, Injury, Failure, Loss, End-stage renal disease
SPECT
: single photon emission computed tomography
SVT
: supraventricular tachycardia
SYNTAX
: Synergy between Percutaneous Coronary Intervention with TAXUS and Cardiac Surgery
TAVI
: transcatheter aortic valve implantation
TdP
: torsades de pointes
TIA
: transient ischaemic attack
TOE
: transoesophageal echocardiography
TOD
: transoesophageal doppler
TTE
: transthoracic echocardiography
UFH
: unfractionated heparin
VATS
: video-assisted thoracic surgery
VHD
: valvular heart disease
VISION
: Vascular Events In Noncardiac Surgery Patients Cohort Evaluation
VKA
: vitamin K antagonist
VPB
: ventricular premature beat
VT
: ventricular tachycardia

Guidelines summarize and evaluate all available evidence, at the time of the writing process, on a particular issue with the aim of assisting health professionals in selecting the best management strategies for an individual patient with a given condition, taking into account the impact on outcome, as well as the risk–benefit ratio of particular diagnostic …

/pdf/2014-esc-esa-guidelines-on-non-cardiac-surgery-bbdr0c4n87.pdf

2014 ESC/ESA Guidelines on non-cardiac surgery: cardiovascular assessment and management: The Joint Task Force on non-cardiac surgery: cardiovascular assessment and management of the European Society of Cardiology (ESC) and the European Society of Anaesthesiology (ESA).

Institutional Affiliations Co-chairs Feldman D: Minneapolis Heart Institute, Minneapolis, Minnesota, Georgia Institute of Technology and Morehouse School of Medicine; Pamboukian SV: University of Alabama at Birmingham, Birmingham, Alabama; Teuteberg JJ: University of Pittsburgh, Pittsburgh, Pennsylvania Task force chairs Birks E: University of Louisville, Louisville, Kentucky; Lietz K: Loyola University, Chicago, Maywood, Illinois; Moore SA: Massachusetts General Hospital, Boston, Massachusetts; Morgan JA: Henry Ford Hospital, Detroit, Michigan Contributing writers Arabia F: Mayo Clinic Arizona, Phoenix, Arizona; Bauman ME: University of Alberta, Alberta, Canada; Buchholz HW: University of Alberta, Stollery Children’s Hospital and Mazankowski Alberta Heart Institute, Edmonton, Alberta, Canada; Deng M: University of California at Los Angeles, Los Angeles, California; Dickstein ML: Columbia University, New York, New York; El-Banayosy A: Penn State Milton S. Hershey Medical Center, Hershey, Pennsylvania; Elliot T: Inova Fairfax, Falls Church, Virginia; Goldstein DJ: Montefiore Medical Center, New York, New York; Grady KL: Northwestern University, Chicago, Illinois; Jones K: Alfred Hospital, Melbourne, Australia; Hryniewicz K: Minneapolis Heart Institute, Minneapolis, Minnesota; John R: University of Minnesota, Minneapolis, Minnesota; Kaan A: St. Paul’s Hospital, Vancouver, British Columbia, Canada; Kusne S: Mayo Clinic Arizona, Phoenix, Arizona; Loebe M: Methodist Hospital, Houston, Texas; Massicotte P: University of Alberta, Stollery Children’s Hospital, Edmonton, Alberta, Canada; Moazami N: Minneapolis Heart Institute, Minneapolis, Minnesota; Mohacsi P: University Hospital, Bern, Switzerland; Mooney M: Sentara Norfolk, Virginia Beach, Virginia; Nelson T: Mayo Clinic Arizona, Phoenix, Arizona; Pagani F: University of Michigan, Ann Arbor, Michigan; Perry W: Integris Baptist Health Care, Oklahoma City, Oklahoma; Potapov EV: Deutsches Herzzentrum Berlin, Berlin, Germany; Rame JE: University of Pennsylvania, Philadelphia, Pennsylvania; Russell SD: Johns Hopkins, Baltimore, Maryland; Sorensen EN: University of Maryland, Baltimore, Maryland; Sun B: Minneapolis Heart Institute, Minneapolis, Minnesota; Strueber M: Hannover Medical School, Hanover, Germany Independent reviewers Mangi AA: Yale University School of Medicine, New Haven, Connecticut; Petty MG: University of Minnesota Medical Center, Fairview, Minneapolis, Minnesota; Rogers J: Duke University Medical Center, Durham, North Carolina

The 2013 International Society for Heart and Lung Transplantation Guidelines for mechanical circulatory support: executive summary.

https://www.thelancet.com/pdfs/journals/lancet/PIIS0140-6736%2812%2961148-9.pdf

Mortality after surgery in Europe: a 7 day cohort study

Clinical prediction models combine multiple predictors to estimate risk for the presence of a particular condition (diagnostic models) or the occurrence of a certain event in the future (prognostic models). PROBAST (Prediction model Risk Of Bias ASsessment Tool), a tool for assessing the risk of bias (ROB) and applicability of diagnostic and prognostic prediction model studies, was developed by a steering group that considered existing ROB tools and reporting guidelines. The tool was informed by a Delphi procedure involving 38 experts and was refined through piloting. PROBAST is organized into the following 4 domains: participants, predictors, outcome, and analysis. These domains contain a total of 20 signaling questions to facilitate structured judgment of ROB, which was defined to occur when shortcomings in study design, conduct, or analysis lead to systematically distorted estimates of model predictive performance. PROBAST enables a focused and transparent approach to assessing the ROB and applicability of studies that develop, validate, or update prediction models for individualized predictions. Although PROBAST was designed for systematic reviews, it can be used more generally in critical appraisal of prediction model studies. Potential users include organizations supporting decision making, researchers and clinicians who are interested in evidence-based medicine or involved in guideline development, journal editors, and manuscript reviewers.

/pdf/probast-a-tool-to-assess-the-risk-of-bias-and-applicability-250aflps2y.pdf

PROBAST: A tool to assess the risk of bias and applicability of prediction model studies

Background: Current knowledge of the risk for postoperative pulmonary complications (PPCs) rests on studies that narrowly selected patients and procedures. Hypothesizing thatPPCoccurrencecouldbepredictedfromareducedsetof perioperative variables, we aimed to develop a predictive index for a broad surgical population. Methods: Patients undergoing surgical procedures given general, neuraxial, or regional anesthesia in 59 hospitals were randomly selected for this prospective, multicenter study. The main outcome was the development of at least one of the following:respiratoryinfection,respiratoryfailure,bronchospasm, atelectasis, pleural effusion, pneumothorax, or aspiration pneumonitis. The cohort was randomly divided into a development subsample to construct a logistic regression model and a validation subsample. A PPC predictive index was constructed. Results: Of 2,464 patients studied, 252 events were observed in 123 (5%). Thirty-day mortality was higher in patients with a PPC (19.5%; 95% [CI], 12.5–26.5%) than in those without a PPC (0.5%; 95% CI, 0.2–0.8%). Regression modeling identified seven independent risk factors: low preoperative arterial oxygen saturation, acute respiratory infectionduringthepreviousmonth,age,preoperativeanemia, upper abdominal or intrathoracic surgery, surgical duration of at least 2 h, and emergency surgery. The area under the receiver operating characteristic curve was 90% (95% CI, 85–94%) for the development subsample and 88% (95% CI, 84–93%) for the validation subsample. Conclusion: The risk index based on seven objective, easily assessed factors has excellent discriminative ability. The index can be used to assess individual risk of PPC and focus further research on measures to improve patient care.

/pdf/prediction-of-postoperative-pulmonary-complications-in-a-24x3asnhsf.pdf

Prediction of Postoperative Pulmonary Complications in a Population-based Surgical Cohort

Background: Digital health interventions and mobile technologies can help to reduce the rates of obesity and overweight conditions. Although weight management apps are widely used, they usually lack professional content and evaluation, so the quality of these apps cannot be guaranteed. The EVALAPPS project aims to design and validate a tool to assess the safety and effectiveness of health-related apps whose main goal is to manage and prevent obesity and overweight conditions. Objective: The aim of this paper is two-fold: (a) to co-create and codesign the EVALAPPS assessment tool and (b) to pilot its feasibility among overweight and obese individuals that use weight control apps. Methods: A mixed-methods approach was used. A multidisciplinary team (n = 12) participated in a co-creation workshop to provide proposals and inputs about the look and feel of the content, usability aspects, appearance, sections, and main features of the EVALAPPS tool. The tool was tested for its feasibility among 31 overweight and obese individuals, attending the CP Endocrinologia i Nutrició SL Clinic for the first time. Participants were asked to use a specific weight control app [Yazio (YAZIO GmbH, Erfurt, Germany), My FitnessPal (MyFitnessPal, Austin, TX, USA) or MyPlate (MyPlate, Santa Monica, CA, USA)] for two weeks and then evaluate them by using the EVALAPPS (EVALAPPS, David Ganyan, Barcelona, Spain) (June 2020, David Ganyan, Barcelona, Spain) tool. Seven participants were phone interviewed to gain more insight into the use of the EVALAPPS tool. Results: The co-creation workshop allowed conceptualizing the EVALAPPS tool. The feasibility study showed that all criteria from the Usability and Functionality dimensions had valid answers, while Reliability, Security, Privacy, and Health indicators were the dimensions with less valid answers. In all three apps, the dimension with the highest score was Usability/functionality, followed by app purpose. Clinical effectiveness and Development were the dimensions with the lowest scores in all three tested weight control apps. Conclusions: The participation of the multidisciplinary team and end-users in the conceptualization and testing of a tool to assess health apps was feasible and relevant for the usability of the tool.

/pdf/codesign-and-feasibility-testing-of-a-tool-to-evaluate-2iqdybn4.pdf

Guillem Paluzie

Papers

Prediction of Postoperative Pulmonary Complications in a Population-based Surgical Cohort

Codesign and Feasibility Testing of a Tool to Evaluate Overweight and Obesity Apps