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Observational Study
. 2019 Jul 15:14:34.
doi: 10.1186/s13017-019-0253-2. eCollection 2019.

Physiological parameters for Prognosis in Abdominal Sepsis (PIPAS) Study: a WSES observational study

Massimo Sartelli  1 , Fikri M Abu-Zidan  2 , Francesco M Labricciosa  3 , Yoram Kluger  4 , Federico Coccolini  5 , Luca Ansaloni  5 , Ari Leppäniemi  6 , Andrew W Kirkpatrick  7 , Matti Tolonen  6 , Cristian Tranà  1 , Jean-Marc Regimbeau  8 , Timothy Hardcastle  9 , Renol M Koshy  10 , Ashraf Abbas  11 , Ulaş Aday  12 , A R K Adesunkanmi  13 , Adesina Ajibade  14 , Lali Akhmeteli  15 , Emrah Akın  16 , Nezih Akkapulu  17 , Alhenouf Alotaibi  18 , Fatih Altintoprak  19 , Dimitrios Anyfantakis  20 , Boyko Atanasov  21 , Goran Augustin  22 , Constança Azevedo  23 , Miklosh Bala  24 , Dimitrios Balalis  25 , Oussama Baraket  26 , Suman Baral  27 , Or Barkai  4 , Marcelo Beltran  28 , Roberto Bini  29 , Konstantinos Bouliaris  30 , Ana B Caballero  31 , Valentin Calu  32 , Marco Catani  33 , Marco Ceresoli  34 , Vasileios Charalampakis  35 , Asri Che Jusoh  36 , Massimo Chiarugi  37 , Nicola Cillara  38 , Raquel Cobos Cuesta  39 , Luigi Cobuccio  37 , Gianfranco Cocorullo  40 , Elif Colak  41 , Luigi Conti  42 , Yunfeng Cui  43 , Belinda De Simone  44 , Samir Delibegovic  45 , Zaza Demetrashvili  46 , Demetrios Demetriades  47 , Ana Dimova  22 , Agron Dogjani  48 , Mushira Enani  49 , Federica Farina  50 , Francesco Ferrara  51 , Domitilla Foghetti  52 , Tommaso Fontana  40 , Gustavo P Fraga  53 , Mahir Gachabayov  54 , Grelpois Gérard  55 , Wagih Ghnnam  56 , Teresa Giménez Maurel  57 , Georgios Gkiokas  58 , Carlos A Gomes  59 , Ali Guner  60 , Sanjay Gupta  61 , Andreas Hecker  62 , Elcio S Hirano  53 , Adrien Hodonou  63 , Martin Hutan  64 , Igor Ilaschuk  65 , Orestis Ioannidis  66 , Arda Isik  67 , Georgy Ivakhov  68 , Sumita Jain  69 , Mantas Jokubauskas  70 , Aleksandar Karamarkovic  71 , Robin Kaushik  61 , Jakub Kenig  72 , Vladimir Khokha  73 , Denis Khokha  74 , Jae Il Kim  75 , Victor Kong  76 , Dimitris Korkolis  25 , Vitor F Kruger  53 , Ashok Kshirsagar  77 , Romeo Lages Simões  78 , Andrea Lanaia  79 , Konstantinos Lasithiotakis  80 , Pedro Leão  81 , Miguel León Arellano  82 , Holger Listle  83 , Andrey Litvin  84 , Aintzane Lizarazu Pérez  85 , Eudaldo Lopez-Tomassetti Fernandez  86 , Eftychios Lostoridis  87 , Davide Luppi  88 , Gustavo M Machain V  89 , Piotr Major  90 , Dimitrios Manatakis  91 , Marianne Marchini Reitz  47 , Athanasios Marinis  92 , Daniele Marrelli  93 , Aleix Martínez-Pérez  94 , Sanjay Marwah  95 , Michael McFarlane  96 , Mirza Mesic  45 , Cristian Mesina  97 , Nickos Michalopoulos  98 , Evangelos Misiakos  99 , Felipe Gonçalves Moreira  78 , Ouadii Mouaqit  100 , Ali Muhtaroglu  16 , Noel Naidoo  101 , Ionut Negoi  102 , Zane Nikitina  103 , Ioannis Nikolopoulos  104 , Gabriela-Elisa Nita  105 , Savino Occhionorelli  106 , Iyiade Olaoye  107 , Carlos A Ordoñez  108 , Zeynep Ozkan  109 , Ajay Pal  110 , Gian M Palini  111 , Kyriaki Papageorgiou  112 , Dimitris Papagoras  113 , Francesco Pata  114 , Michał Pędziwiatr  115 , Jorge Pereira  116 , Gerson A Pereira Junior  117 , Gennaro Perrone  118 , Tadeja Pintar  119 , Magdalena Pisarska  120 , Oleksandr Plehutsa  121 , Mauro Podda  122 , Gaetano Poillucci  123 , Martha Quiodettis  124 , Tuba Rahim  9 , Daniel Rios-Cruz  125 , Gabriel Rodrigues  126 , Dmytry Rozov  4 , Boris Sakakushev  127 , Ibrahima Sall  128 , Alexander Sazhin  68 , Miguel Semião  23 , Taanya Sharda  61 , Vishal Shelat  129 , Giovanni Sinibaldi  130 , Dmitrijs Skicko  131 , Matej Skrovina  132 , Dimitrios Stamatiou  133 , Marco Stella  51 , Marcin Strzałka  134 , Ruslan Sydorchuk  135 , Ricardo A Teixeira Gonsaga  136 , Joel Noutakdie Tochie  137 , Gia Tomadze  138 , Lara Ugoletti  139 , Jan Ulrych  140 , Toomas Ümarik  141 , Mustafa Y Uzunoglu  142 , Alin Vasilescu  143 , Osborne Vaz  144 , Andras Vereczkei  145 , Nutu Vlad  143 , Maciej Walędziak  146 , Ali I Yahya  147 , Omer Yalkin  148 , Tonguç U Yilmaz  149 , Ali Ekrem Ünal  148 , Kuo-Ching Yuan  150 , Sanoop K Zachariah  151 , Justas Žilinskas  71 , Maurizio Zizzo  152 , Vittoria Pattonieri  153 , Gian Luca Baiocchi  154 , Fausto Catena  153
Affiliations
Observational Study

Physiological parameters for Prognosis in Abdominal Sepsis (PIPAS) Study: a WSES observational study

Massimo Sartelli et al. World J Emerg Surg. .

Abstract

Background: Timing and adequacy of peritoneal source control are the most important pillars in the management of patients with acute peritonitis. Therefore, early prognostic evaluation of acute peritonitis is paramount to assess the severity and establish a prompt and appropriate treatment. The objectives of this study were to identify clinical and laboratory predictors for in-hospital mortality in patients with acute peritonitis and to develop a warning score system, based on easily recognizable and assessable variables, globally accepted.

Methods: This worldwide multicentre observational study included 153 surgical departments across 56 countries over a 4-month study period between February 1, 2018, and May 31, 2018.

Results: A total of 3137 patients were included, with 1815 (57.9%) men and 1322 (42.1%) women, with a median age of 47 years (interquartile range [IQR] 28-66). The overall in-hospital mortality rate was 8.9%, with a median length of stay of 6 days (IQR 4-10). Using multivariable logistic regression, independent variables associated with in-hospital mortality were identified: age > 80 years, malignancy, severe cardiovascular disease, severe chronic kidney disease, respiratory rate ≥ 22 breaths/min, systolic blood pressure < 100 mmHg, AVPU responsiveness scale (voice and unresponsive), blood oxygen saturation level (SpO2) < 90% in air, platelet count < 50,000 cells/mm3, and lactate > 4 mmol/l. These variables were used to create the PIPAS Severity Score, a bedside early warning score for patients with acute peritonitis. The overall mortality was 2.9% for patients who had scores of 0-1, 22.7% for those who had scores of 2-3, 46.8% for those who had scores of 4-5, and 86.7% for those who have scores of 7-8.

Conclusions: The simple PIPAS Severity Score can be used on a global level and can help clinicians to identify patients at high risk for treatment failure and mortality.

Keywords: Acute peritonitis; Early warning score; Emergency surgery; Source control.

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Conflict of interest statement

Competing interestsThe authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Distribution of the percentile PIPAS Severity Score of hospitalized peritonitis patients for those who survived (continuous line) (n = 2832) and those who died (interrupted line) (n = 268). Global data from 153 worldwide surgical departments in 56 countries, over a 4-month study period (February 1, 2018–May 31, 2018). Thirty-seven patients (1.2%) had missing data in whom the score could not be computed
Fig. 2
Fig. 2
Receiver operating characteristic (ROC) curve for the best PIPAS Severity Score (1.5, black circle) that predicted mortality in peritonitis patients. Global data from 153 worldwide surgical departments in 56 countries, over a 4-month study period (February 1, 2018–May 31, 2018)
See this image and copyright information in PMC

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